Data_retrieval_and_model_classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Tuple, Optional\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms as transforms\n",
    "from torch.autograd import Variable\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Steinmetz dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data retrieval\n",
    "import os, requests\n",
    "\n",
    "fname = []\n",
    "for j in range(3):\n",
    "    fname.append('steinmetz_part%d.npz'%j)\n",
    "url = [\"https://osf.io/agvxh/download\"]\n",
    "url.append(\"https://osf.io/uv3mw/download\")\n",
    "url.append(\"https://osf.io/ehmw2/download\")\n",
    "\n",
    "for j in range(len(url)):\n",
    "    if not os.path.isfile(fname[j]):\n",
    "        try:\n",
    "            r = requests.get(url[j])\n",
    "        except requests.ConnectionError:\n",
    "            print(\"!!! Failed to download data !!!\")\n",
    "        else:\n",
    "            if r.status_code != requests.codes.ok:\n",
    "                print(\"!!! Failed to download data !!!\")\n",
    "            else:\n",
    "                with open(fname[j], \"wb\") as fid:\n",
    "                    fid.write(r.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a8f550a753d4ecb9ae7d3cc8efd2aba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Data loading\n",
    "\n",
    "all_data = []\n",
    "for j in trange(len(fname)):\n",
    "    all_data.extend(np.load('steinmetz_part%d.npz'%j, allow_pickle=True)['dat'])\n",
    "\n",
    "# select just one of the recordings here. 11 is nice because it has some neurons in vis ctx. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`alldat` contains 39 sessions from 10 mice, data from Steinmetz et al, 2019. Time bins for all measurements are 10ms, starting 500ms before stimulus onset. The mouse had to determine which side has the highest contrast. For each `dat = alldat[k]`, you have the fields below. For extra variables, check out the extra notebook and extra data files (lfp, waveforms and exact spike times, non-binned). \n",
    "\n",
    "* `dat['mouse_name']`: mouse name\n",
    "* `dat['date_exp']`: when a session was performed\n",
    "* `dat['spks']`: neurons by trials by time bins.    \n",
    "* `dat['brain_area']`: brain area for each neuron recorded. \n",
    "* `dat['ccf']`: Allen Institute brain atlas coordinates for each neuron. \n",
    "* `dat['ccf_axes']`: axes names for the Allen CCF. \n",
    "* `dat['contrast_right']`: contrast level for the right stimulus, which is always contralateral to the recorded brain areas.\n",
    "* `dat['contrast_left']`: contrast level for left stimulus. \n",
    "* `dat['gocue']`: when the go cue sound was played. \n",
    "* `dat['response_times']`: when the response was registered, which has to be after the go cue. The mouse can turn the wheel before the go cue (and nearly always does!), but the stimulus on the screen won't move before the go cue.  \n",
    "* `dat['response']`: which side the response was (`-1`, `0`, `1`). When the right-side stimulus had higher contrast, the correct choice was `-1`. `0` is a no go response. \n",
    "* `dat['feedback_time']`: when feedback was provided. \n",
    "* `dat['feedback_type']`: if the feedback was positive (`+1`, reward) or negative (`-1`, white noise burst).  \n",
    "* `dat['wheel']`: turning speed of the wheel that the mice uses to make a response, sampled at `10ms`. \n",
    "* `dat['pupil']`: pupil area  (noisy, because pupil is very small) + pupil horizontal and vertical position.\n",
    "* `dat['face']`: average face motion energy from a video camera. \n",
    "* `dat['licks']`: lick detections, 0 or 1.   \n",
    "* `dat['trough_to_peak']`: measures the width of the action potential waveform for each neuron. Widths `<=10` samples are \"putative fast spiking neurons\". \n",
    "* `dat['%X%_passive']`: same as above for `X` = {`spks`, `pupil`, `wheel`, `contrast_left`, `contrast_right`} but for  passive trials at the end of the recording when the mouse was no longer engaged and stopped making responses. \n",
    "* `dat['prev_reward']`: time of the feedback (reward/white noise) on the previous trial in relation to the current stimulus time. \n",
    "* `dat['reaction_time']`: ntrials by 2. First column: reaction time computed from the wheel movement as the first sample above `5` ticks/10ms bin. Second column: direction of the wheel movement (`0` = no move detected).  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupings of brain regions\n",
    "regions = [\"vis ctx\", \"thal\", \"hipp\", \"other ctx\", \"midbrain\", \"basal ganglia\", \"cortical subplate\", \"other\"]\n",
    "brain_groups = [[\"VISa\", \"VISam\", \"VISl\", \"VISp\", \"VISpm\", \"VISrl\"], # visual cortex\n",
    "                [\"CL\", \"LD\", \"LGd\", \"LH\", \"LP\", \"MD\", \"MG\", \"PO\", \"POL\", \"PT\", \"RT\", \"SPF\", \"TH\", \"VAL\", \"VPL\", \"VPM\"], # thalamus\n",
    "                [\"CA\", \"CA1\", \"CA2\", \"CA3\", \"DG\", \"SUB\", \"POST\"], # hippocampal\n",
    "                [\"ACA\", \"AUD\", \"COA\", \"DP\", \"ILA\", \"MOp\", \"MOs\", \"OLF\", \"ORB\", \"ORBm\", \"PIR\", \"PL\", \"SSp\", \"SSs\", \"RSP\",\" TT\"], # non-visual cortex\n",
    "                [\"APN\", \"IC\", \"MB\", \"MRN\", \"NB\", \"PAG\", \"RN\", \"SCs\", \"SCm\", \"SCig\", \"SCsg\", \"ZI\"], # midbrain\n",
    "                [\"ACB\", \"CP\", \"GPe\", \"LS\", \"LSc\", \"LSr\", \"MS\", \"OT\", \"SNr\", \"SI\"], # basal ganglia \n",
    "                [\"BLA\", \"BMA\", \"EP\", \"EPd\", \"MEA\"] # cortical subplate\n",
    "                ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of sessions in all_data\n",
    "len(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['spks', 'wheel', 'pupil', 'response', 'response_time', 'bin_size', 'stim_onset', 'contrast_right', 'contrast_left', 'brain_area', 'feedback_time', 'feedback_type', 'gocue', 'mouse_name', 'date_exp', 'trough_to_peak', 'active_trials', 'contrast_left_passive', 'contrast_right_passive', 'spks_passive', 'pupil_passive', 'wheel_passive', 'prev_reward', 'ccf', 'ccf_axes', 'cellid_orig', 'reaction_time', 'face', 'face_passive', 'licks', 'licks_passive'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keys in each session\n",
    "all_data[0].keys()\n",
    "\n",
    "# what I need for now are 'mouse_name', 'date_exp', 'spks', 'brain_area', 'spks_passive, and (maybe) 'stim_onset'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1156, 250, 250)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of 'spks' : neurons x trials x time bins.\n",
    "all_data[7]['spks'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1156, 110, 250)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of 'spks_passive' : neurons x trials x time bins.\n",
    "all_data[7]['spks_passive'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data we are going to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 'Forssmann', '2017-11-01', ['LGd', 'VISp', 'VISa']],\n",
       " [7, 'Hench', '2017-06-15', ['DG', 'CA3', 'CA1']],\n",
       " [7, 'Hench', '2017-06-15', ['CA3', 'CA1', 'SUB']],\n",
       " [11, 'Lederberg', '2017-12-05', ['LGd', 'VISp', 'VISam']],\n",
       " [19, 'Moniz', '2017-05-16', ['LGd', 'VISp', 'VISa']]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# triplet brain projections we can use: out of 39 sessions, we can make use of 4.\n",
    "\n",
    "triple_nets = [['LGd', 'VISp', 'VISa'], ['LGd', 'VISp', 'VISam'], ['LGd', 'VISp', 'VISl'], #Dorsal, Dorsal, Ventral Stream\n",
    "               ['DG', 'CA3', 'CA1'], ['CA3', 'CA1', 'SUB'], ['CA2', 'CA1', 'SUB'], #HPC\n",
    "               ['CP', 'GPe', 'SNr'], ['CP', 'SNr', 'VAL'], ['SNr', 'VAL', 'MOp'], # feedback loop projections \n",
    "               ['VAL', 'MOp', 'CP'], ['VAL', 'MOs', 'CP'],['VAL', 'MOs', 'SNr']] #Corticostriatal projections\n",
    "\n",
    "usable_triplets = []\n",
    "\n",
    "for i in range(len(all_data)):\n",
    "    data = all_data[i]\n",
    "    session = i\n",
    "    name = data['mouse_name']\n",
    "    date = data['date_exp']\n",
    "    unique_area = np.unique(data['brain_area'])\n",
    "    num_neurons = data['spks'].shape[0]\n",
    "    num_trials = data['spks'].shape[1]\n",
    "\n",
    "    for j in range(len(triple_nets)):\n",
    "        if all([triple_nets[j][x] in unique_area for x in range(3)]):\n",
    "            usable_triplets.append([session, name, date, triple_nets[j]]) # session number, mouse_name, date_exp, triple_nets (brain regions)\n",
    "\n",
    "usable_triplets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate three brain area dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_datasets(session, area1_name, area2_name, area3_name, cut_40=False, add_passive=False, smoothing=False, sigma=3):\n",
    "\n",
    "    '''\n",
    "    make a dataset triplet.\n",
    "    session: session number.\n",
    "    area1_name, area2_name, area3_name: three brain areas from low to high hierarchy.\n",
    "    cut_40: True if you want to include time bins 50-90 only.\n",
    "    add_passive: True if you want to include the passive data.\n",
    "    smoothing: True if you want to apply gaussian filter to your data.\n",
    "    sigma: gaussian filter sd. 3 to 5 is recommended. \n",
    "    '''\n",
    "\n",
    "    area1 = []\n",
    "    area2 = []\n",
    "    area3 = []\n",
    "\n",
    "    mini_data = all_data[session]\n",
    "\n",
    "    for i in range(len(mini_data['brain_area'])):\n",
    "    \n",
    "        list_mapping = {area1_name: area1, area2_name: area2, area3_name: area3}\n",
    "        area_name = mini_data['brain_area'][i]\n",
    "\n",
    "        if area_name in list_mapping.keys():\n",
    "            area_list = list_mapping[area_name]\n",
    "            data_keys = ['spks']\n",
    "\n",
    "            if add_passive:\n",
    "                data_keys.append('spks_passive')\n",
    "\n",
    "            if np.vstack([mini_data[data_key][i] for data_key in data_keys]).mean() < 0.01:\n",
    "                continue\n",
    "            \n",
    "            items = []    \n",
    "            for data_key in data_keys:\n",
    "                item = mini_data[data_key][i].astype(np.float32)\n",
    "\n",
    "                if smoothing:\n",
    "                    item = gaussian_filter1d(item, sigma)\n",
    "                    \n",
    "                if cut_40:\n",
    "                    item = item[:, 50:90]\n",
    "\n",
    "                items.append(item)\n",
    "            \n",
    "            items = np.vstack(items)\n",
    "            area_list.append(items)\n",
    "\n",
    "    return np.array(area1), np.array(area2), np.array(area3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area1 (8, 340, 250) area2: (21, 340, 250) area3: (42, 340, 250)\n"
     ]
    }
   ],
   "source": [
    "# run this to set up three brain areas you are interested in\n",
    "area1, area2, area3 = make_datasets(11, 'LGd', 'VISp', 'VISam', smoothing=True, sigma=3)\n",
    "print('area1', area1.shape, 'area2:', area2.shape, 'area3:', area3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape the data in order to use PyTorch.\n",
    "area1_ = area1.transpose(1, 0, 2)\n",
    "area2_ = area2.transpose(1, 0, 2)\n",
    "area3_ = area3.transpose(1, 0, 2)\n",
    "\n",
    "# make sure shapes of the data are the same. Here, shape[0] is the number of trials. \n",
    "assert area1_.shape[0] == area2_.shape[0] == area3_.shape[0]\n",
    "\n",
    "indices = np.arange(area1_.shape[0])\n",
    "\n",
    "np.random.seed(42)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "area1_ = area1_[indices, :, :]\n",
    "area2_ = area2_[indices, :, :]\n",
    "area3_ = area3_[indices, :, :]\n",
    "\n",
    "train_len = area1_.shape[0] * 4 // 5\n",
    "\n",
    "X = torch.from_numpy(area1_)\n",
    "Y = torch.from_numpy(area2_)\n",
    "Z = torch.from_numpy(area3_)\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "X = X.to(device)\n",
    "Y = Y.to(device)\n",
    "Z = Z.to(device)\n",
    "\n",
    "# separate train and validation set \n",
    "X_train = X[:train_len]\n",
    "Y_train = Y[:train_len]\n",
    "Z_train = Z[:train_len]\n",
    "\n",
    "X_valid = X[train_len:]\n",
    "Y_valid = Y[train_len:]\n",
    "Z_valid = Z[train_len:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Convolutional FF model (we ended up not using this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvModel(nn.Module):\n",
    "    def __init__(self, input_neurons, output_neurons, num_layers=8, kernel_size=64, hidden_channels=128):\n",
    "        super().__init__()\n",
    "        self.in_conv = nn.Conv1d(input_neurons, hidden_channels, 1)\n",
    "        \n",
    "        self.convolutions = nn.ModuleList([nn.Sequential(\n",
    "            nn.ConstantPad1d(padding=(kernel_size - 1, 0), value=0),\n",
    "            nn.Conv1d(hidden_channels, hidden_channels, kernel_size)\n",
    "        ) for _ in range(num_layers)])\n",
    "        \n",
    "        self.out_conv = nn.Conv1d(hidden_channels, output_neurons, 1)\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.in_conv(x).relu()\n",
    "        \n",
    "        for layer in self.convolutions:\n",
    "            x = x + layer(x).relu()\n",
    "            \n",
    "        x = self.out_conv(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Unet model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UNetModel(nn.Module):\n",
    "    def __init__(self, input_neurons, output_neurons, layers_per_block=3, num_blocks=4, kernel_size=16, hidden_channels=4):\n",
    "        super().__init__()\n",
    "        self.in_padding = nn.ConstantPad1d(padding=(3, 3), value=0)  # 250 -> 256\n",
    "        self.in_conv = nn.Conv1d(input_neurons, hidden_channels, 1)\n",
    "        \n",
    "        self.downsamplers = nn.ModuleList([\n",
    "            nn.Sequential(\n",
    "                *[\n",
    "                    nn.Conv1d(hidden_channels * (2 ** (i - 1)), hidden_channels * (2 ** i), 1)\n",
    "                    if i > 0 \n",
    "                    else nn.Identity()\n",
    "                ],\n",
    "                *[\n",
    "                    nn.Sequential(\n",
    "                        nn.ConstantPad1d(padding=(kernel_size - 1, 0), value=0),\n",
    "                        nn.Conv1d(hidden_channels * (2 ** i), hidden_channels * (2 ** i), kernel_size),\n",
    "                        nn.ReLU()\n",
    "                    )\n",
    "                    for j in range(layers_per_block)\n",
    "                ]\n",
    "            )\n",
    "            for i in range(num_blocks)\n",
    "        ])\n",
    "        \n",
    "        self.residual_downsamplers = nn.ModuleList([\n",
    "            nn.Sequential(\n",
    "                nn.Conv1d(hidden_channels * (2 ** (i - 1)), hidden_channels * (2 ** i), 1),\n",
    "                nn.AvgPool1d(2)\n",
    "            )\n",
    "            if i > 0\n",
    "            else nn.Identity()\n",
    "            for i in range(num_blocks)\n",
    "        ])\n",
    "        \n",
    "        self.upsamplers = nn.ModuleList([\n",
    "            nn.Sequential(\n",
    "                *[\n",
    "                    nn.Sequential(\n",
    "                        nn.ConstantPad1d(padding=(kernel_size - 1, 0), value=0),\n",
    "                        nn.Conv1d(hidden_channels * (2 ** i), hidden_channels * (2 ** i), kernel_size),\n",
    "                        nn.ReLU()\n",
    "                    )\n",
    "                    for j in range(layers_per_block)\n",
    "                ],\n",
    "                *[\n",
    "                    nn.Conv1d(hidden_channels * (2 ** i), hidden_channels * (2 ** (i - 1)), 1)\n",
    "                    if i > 0\n",
    "                    else nn.Identity()\n",
    "                ]\n",
    "            )\n",
    "            for i in reversed(range(num_blocks))\n",
    "        ])\n",
    "        \n",
    "        self.residual_upsamplers = nn.ModuleList([\n",
    "            nn.Conv1d(hidden_channels * (2 ** i), hidden_channels * (2 ** (i - 1)), 1)\n",
    "            if i > 0\n",
    "            else nn.Identity()\n",
    "            for i in reversed(range(num_blocks))\n",
    "        ])\n",
    "        \n",
    "        self.out_conv = nn.Conv1d(hidden_channels, output_neurons, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.forward_features(x)\n",
    "\n",
    "        for upsampler, residual_upsampler in zip(self.upsamplers, self.residual_upsamplers):\n",
    "            r = x\n",
    "            x = x.repeat_interleave(2, dim=-1)\n",
    "            x = upsampler(x)\n",
    "            \n",
    "            if r.shape[1] != r.shape[0]:\n",
    "                r = residual_upsampler(r)      \n",
    "                \n",
    "            if r.shape[-1] * 2 == x.shape[-1]:\n",
    "                r = r.repeat_interleave(2, dim=-1)\n",
    "            \n",
    "            x = x + r\n",
    "\n",
    "        x = self.out_conv(x).relu()\n",
    "        return x[:, :, 3:-3]  # 256 -> 250\n",
    "    \n",
    "    \n",
    "    def forward_features(self, x):\n",
    "        x = self.in_padding(x)\n",
    "        x = self.in_conv(x).relu()\n",
    "        \n",
    "        for downsampler, residual_downsampler in zip(self.downsamplers, self.residual_downsamplers):\n",
    "            r = x\n",
    "            x = F.avg_pool1d(x, 2)\n",
    "            x = downsampler(x)\n",
    "            \n",
    "            if r.shape[1] != x.shape[1]:\n",
    "                assert r.shape[1] * 2 == x.shape[1]\n",
    "                r = residual_downsampler(r)\n",
    "                \n",
    "            if r.shape[-1] == x.shape[-1] * 2:\n",
    "                r = F.avg_pool1d(r, 2)\n",
    "            \n",
    "            x = x + r\n",
    "            \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Fulaly connected FF model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedforwardNeuralNetModel(nn.Module):\n",
    "    def __init__(self, timebins, in_neurons, out_neurons, hidden_dim):\n",
    "        super(FeedforwardNeuralNetModel, self).__init__()\n",
    "        \n",
    "        self.timebins = timebins\n",
    "\n",
    "        # Linear function 1: X_dim = [time bins x neurons] --> hidden_dim\n",
    "        self.fc1 = nn.Linear(timebins * in_neurons, hidden_dim)\n",
    "        \n",
    "        # Linear function 2: hidden_dim --> Y_dim = [time bins x neurons] \n",
    "        self.fc2 = nn.Linear(hidden_dim, timebins * out_neurons)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        n_batch = x.shape[0]\n",
    "        assert x.shape[-1] == self.timebins\n",
    "        \n",
    "        # Linear function 1\n",
    "        out = self.fc1(x.reshape(n_batch, -1)).relu()\n",
    "        # Linear function 2\n",
    "        out = self.fc2(out).relu()\n",
    "\n",
    "        return out.reshape(n_batch, -1, self.timebins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model (use GPU for this!) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = UNetModel(X.shape[1], Y.shape[1], hidden_channels=4).to(device)\n",
    "model2 = UNetModel(Y.shape[1], Z.shape[1], hidden_channels=4).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model1 has 133,917 parameters\n",
      "model2 has 134,074 parameters\n"
     ]
    }
   ],
   "source": [
    "print(f\"model1 has {sum([np.prod(p.shape) for p in model1.parameters()]):,} parameters\")\n",
    "print(f\"model2 has {sum([np.prod(p.shape) for p in model2.parameters()]):,} parameters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# quick plotting and check\n",
    "state = torch.load('./sessionshuffle2/session11-noshuffle/models-100000.pt', map_location=torch.device('cpu'))\n",
    "weights_1 = model1.load_state_dict(state[0])\n",
    "weights_2 = model2.load_state_dict(state[1])\n",
    "out1 = model1(X_train)\n",
    "out2 = model2(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter = 0\n",
    "optimizer = torch.optim.Adam([*model1.parameters(), *model2.parameters()], lr=1e-4)\n",
    "\n",
    "X = X.to(device)\n",
    "Y = Y.to(device)\n",
    "Z = Z.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "with trange(20000) as loop:\n",
    "    for epoch in loop:\n",
    "        # Clear gradients w.r.t. parameters\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass to get output\n",
    "        out1 = model1(X)\n",
    "        out2 = model2(Y)\n",
    "\n",
    "        # compute the loss\n",
    "        loss1 = F.mse_loss(out1, Y)\n",
    "        loss2 = F.mse_loss(out2, Z)\n",
    "                \n",
    "        loss = loss1 + loss2\n",
    "\n",
    "        # getting gradients wrt parameters\n",
    "        loss.backward()\n",
    "\n",
    "        loop.set_postfix(loss=loss.item(), loss1=loss1.item(), loss2=loss2.item())\n",
    "\n",
    "        # updating parameters\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forward pass to get output\n",
    "features1 = model1.forward_features(X)\n",
    "features2 = model2.forward_features(Y)\n",
    "\n",
    "torch.save([features1, features2], f\"unet{model1.in_conv.out_channels * 8}-features.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot some stuff to inspect model outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y mean: 0.035151258 Z mean: 0.06388488\n"
     ]
    }
   ],
   "source": [
    "print('Y mean:', Y.cpu().detach().numpy().mean(), 'Z mean:', Z.cpu().detach().numpy().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out1 mean: 0.022127798 out2 mean: 0.04464619\n"
     ]
    }
   ],
   "source": [
    "print('out1 mean:', (out1.cpu().detach().numpy()).mean(), 'out2 mean:', (out2.cpu().detach().numpy()).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "out1 sd: 0.056155637 out2 sd: 0.070529886\n"
     ]
    }
   ],
   "source": [
    "print('out1 sd:', (out1.cpu().detach().numpy()).std(), 'out2 sd:', (out2.cpu().detach().numpy()).std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Trial 18')"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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fppCjPH7WE0oou6x9riqd65LUonBjoEv6Wr33QaMMIhcxVP87Ri6qznZdLHJRvbZ9OblojsK5g9/H34V3JvZhrRoTkYt61qWRc9FfFM5pfVeh8VaqgVAhjYnOknS6wnmx1kR99kv7G3JKvfdBowzqY7mo7hu0vw8KdztcoXARYUf8A/wNJVqRMVR8m6f+uC2RtCX+/P4s8xKQKrfdp+Y7qCT2Tc475EtY98JY7lpJ348/fzzLvLPi9Kck/TjT/pc0I/FZFuRZd49eltRAjYkkDVLXP2I9gp+kg9QV5k4qofxUi+efZ5iWvCM+/a6S4ZIOzFHuDIXW5i7pCxmmpy72/SnL8j9QV+jlLrLC9uWixPc227AkMf8/KTQGW6nQ08J2hV42fippbp51LVQIuFvid+R2SW+t9zZgqMn3jFxU+21OLqrPdn974m9nj5MICidKU41OKnpRLC2HXBBfL84y7wiFO1k2K/xT2u1vfZxnmLruSDonz7oHKu3EpBqvMVFJ2aXAsq+Ly/5FUnPatKHq6sGzx4UpSe9Q1wWxHnf+JfbPc4on3nLUI9Xo+ueJ4/vKem/73jaQjRhqMZCNqrJNyT712/bpx76FKvyiWcWOgyXUe2Us+z8Vzo88l/5ZEvP+b5z3S4ljQUfaPNfE8UuzlZOYN9M5pSWN8r2UdGasyyZJe2aYnmq8fG8JZZe8z1XFc12JermkA+q9DxplELmIoQaDyEXV2KaLRS6q17YvJxc1Kd7AlRiX+ltbq8ZE5KKedWnIXJTl+5b63c/bmKiAuv0hlnVLvfdBowx9LRd1e74oas/dV7v7B919grsPdPcp7n6mu79c77r1A2slXR5/XmRmA0opxMz2N7MfmNnf4nMiX4nPOD3dzFoyzD8/PotyZY4yF8Z5lmSY1vlsUTObaWZXxmeHvmZm16XNe1B8lu3q+IzRl8zsJjM7Oce6U888nW9mI83s6/GzbTezZ8zsu2Y2obitJEkal/j5/izzpJ7L21ZC+YX6YXz9QJbpqfGpcJzJ6xI//yrXytx9W+FVq4s3ShqreOEqfaK736/Qwl2S3ldMwWY2W9KB8e3FGcpeI+nqTGW7+wZ3X5qtbHd/RF3PwT4kbb0TFFrnS+Efyky+Hl8nS5qXbT3o4u6L3N3yDPMT83/f3d/q7h3uPtTdB7n7ZHd/t7v/Mc+6Frv7Ye7e5u7t7j7P3XP+rqFvIBfVFbko87qrlYtSz65e4e43p0909ycl/TK+zZZZKuFHChngH82sOcP0dytcjPm5wj+lmUxXaCQk5c9FO9x9d4l1rbpysksBZe+h0OuRFC5q7Uore7Okb8e37zUzSysitb7fu/sDGVaRusttvKTjc9RjqMLv+haFE10oEdkItUA2qiqyT+Z1Vyv7KP3YV6SKHAfLtErh5Px4SSekT4zH+pMUHl17bY5yUueUfpNvm/SCc0qp/fITd38mw/RUnjrYzKaXWHYp+7xq57oU7jSXpPvd/cEil+2zyEWoBXJRVZGLMq+7IXORu+9292zXrmqFXNRTo+aicnN4Pn+NrxOruI5epa/lIhoTob/7isJjqqZJ+mCxC5vZGQotZj+ocFf1awp3FR8l6VuSbjazIZWqbJq5Cn+kP6DQi8vOtLqdFqe/T+Eu6q0KjxM7QdK1ZvajLBeNUiYpdMV3lrr+AZ8o6cOS/mRmI4qs78rEzwdlmSfVKOS+Issuxh8U7lo4PP2AbWZN6joo/6jA8vasYN3q4bj4+nCWgCOFR7pJxZ+QS5W9QdLdeco+PF7cKsa6+Jr+PZ6c+PnRLMs+rtByXpLeVOR6AaCvIhdlV+lcNCW+ZjtOSdIj8fWoKm63lZLuUPgsb8wwPdWQqb/lompkl2MUHoshhUea5Sp7gqSZWep2kzKIOW5ZfJsrs/2Hwh29X3T3VbkqDAD9ANknu0pnn3JV6jhYrlQmytTY+x8UHjVxncIdxfn06txkZu3qOo+Xcb8o3AS2If78hiJXUc4+r8q5LjMbLekt8e2VhS4HAL0EuSi7RstFjYJcFDV4LqqaeCPcEfHt32q1XtQWjYnQr7n7OoXu3yTpPDMbVOiyZvYOSZcp3NX7b5LGuHu7pCGSTlRorDA/UX6l/bdC12Wvc/dhcb1nx7qlAlqTQqvfvdx9hEJA+rxid5WSPpuj/MsUnrt6lLu3KQS/t0t6RSEM5lq2B3dfq6671S8xswVmNjDWdw8zO0/h7p6NCl3AVUVssX1VfJseco5XCIZ/dvfHcxRzb+Ln/zKzMRWsYq3Niq/LcsyzPL6OiSdOii17hWfvhSBVtik8uqwg8e6Io+Pbh9MmJ1vlZ/snoCmuUwrPegWAfo9cVLtcpK5jVa6TVak7AZvUs2FJJWXstdHM9pF0pMIzu2/JsfwyhUePStLFZtZR4frVUtWyS6LstfF3LVfZyfllZmMljYpvC8lsszJNNLODJH1S0gp19dIIAP0W2aem2adklToOVsj/KeSet5vZsLRpqSz1Q+WWunv73Wb2zkpWrsZmquu8Ssb9EvNUqvF8wfulAvu8Wue63qvQOPw1ST8pcBkA6BXIRb0jFzUYclGXRs5FFWdmw83scIXHcB0ZR1+eYxH0YjQmAsKJ9PUKd+ieXsgCsZVy6vFJ73L3i939Janz8Q03KTy+YqukD5Xa1WEeL0h6s7s/HNfrHh6JIUlfVPj9vlPSezw8kkHuvtndL5D05TjfpzMc5FO2S3qju98Vl93p7r9UeL6pJC0ooc4fkvRHhQPf/0l61cxeUQhi/67QSvkId19RQtnFSAWY98WWsykFBRx3fyoxz99JWmNmvzezL5nZ24tsXPQXM1ubY/hI+gKJ7juLHjKsP/XdfDZHHZPTivkuV7Psjyt02bhbPe8GS95lny04JcNdNX4/AaC3IhdlVulc9HR8zdVIKHkMq+ax6v8kbZP0jrSedlK56Mc5GtbI3beqq6vmgyQ9aaEb84vN7F1mtlcRdfl5nlz0H+kLJLodL2XoSCuurrnI3V9VOBmZXnby50Lq1qNesQfO/1FowPbP7v5avgoDQD9B9smsGueESlX2cbBS3H2TwrmrwUpsAzPbW+GGp+fU9fisbL6i8N1okfSzmGWuMLOPmdkheXpGSHp3ntzUowdMM1tURm5alFZcNfdLuWVXK9MtjK83uvuLBS4DAL0JuSizRspFDYNc1E0j56KKMLNjEtcZX1Ho0ftdCh1EfNjdb6zGelF/NCZCv+fuGxUOWJL0WTNrK2Cx+QqPp3g4hqFM5T6p0G3dgDh/pV0eLzh0Y2Yj1dXl3YWe+VmYFylcNBoq6e+zlP8/We6aTj1rdmqB26pT/Ef7rerqGahJodtJKVzYGKquFrZV4+6PKRzopkiaJ0nxs7xT0g5J1xRQzEcUwvUOSQMVuiU8V2H7vGBm95hZemOlTEZLGpdjyLSNny9jSJcqv8d3KWFr4udiHudRlbLN7ABJF8a3l7t78i7+VC9YqefWnx0vnqX7dOLn9kLWCwD9AbmoZrko9Zirfczs/6VPNLP90+pStWOVu2+QdL3CnXsL4vpN4c48Kf9dZFLoVfJzCl1XNyl0Y36OpP+VtMrMlpnZGWbWkr0ISdII5c5FmU7svajSc1H696HeuShZfrLs5HerkLplqtfHJR2q0DhsSZ46AEC/Qfap3TmhMlTiOFhJmXp1TDbCzrTNO7n7MoXHy6buLJ+i0EjlvxXuzl9nZt8uoEF2q3LnpnEZltms0nNT+iNKqrlfyi274pku5vOD41secQagTyIX9Ypc1GjIRUEj56JK2aGe59O2KlwXvbpK60QDoDEREFym8AdwnELX//kcFV/3zdXaNTFfMXdkF+quLOMPUuhxxSXdnmmGeNEo9aiugzPNo9AtZCbJZ43vkaeO3ZjZEQpdWp6s0PXjvgoHtgMVQscbJd1iZicVU26J0kPOyQoH5V+5+8v5Fo6t6s9WVyv9qxU+W6r3n8MUGk39NEtjlpSp7m45hm+kL+Du40sdCtoyDSzeuZBq7X6vujcKSvpifJ2j0KJ9tpm1mNkUM7tU4Zm9qTvys/a2AAD9FLmop0rnol9KWhp//oGZnWrhsa+Dzewtkm5Q9+NTtY9V6blorqSpku6LJ3Zyinf9XajwjPtTJV2hcCIodXJhlsL36lYzG5KjqOPy5KIzM6z7sDKy0eqCtk4vZ2YTFe6Y3KDQyAsA0B3Zp6eKnxPqQ36ncKf9sWY2JY47Jb4W0ghbsWeD1ylcUL1I0h8U7uqWwk13H5X0kJnNzVHMlXlyU4/94+5fLSM3fbWQz9aHnRpf10n6dT0rAgBVRi7qiVyUHbmon3D3exLXGVsVfr9+r/A34y4z27OuFUTV0JgIUOfjGf4zvv1XMxuea351dRM3SLlburbG+XJdNClVtu50U4/Y2uDu6a1jk9akzZ9uU6aR7r4t8Tbf3eWdYheRN0gaK+k0d/+yuz/h7lvc/UF3XyjpBwq9/FxuRTyTt0TXKLSkPdnMBqvwZ7h24+4vuPt33P0f3X0/he/GRySlLk69S9InKlTnatgSXwfnmCf5/c31napq2fFOgpsVLm4+Luktad/HTu5+rUIvCZL0DkkPK+zvlQr747fqOvnzigAAnchFGVU0F3m4M+udkp5UOOG0WOGxr1sl/UohL/1bYpFqH6tuUjhZOD/e7VVqLtro7j909w+5+/4KPTC+V113mB0j6YIK1bka6p2LkuUny96S+LmQuqXX6zKFXp3O89CDIwAggeyTUUWzT5nKPQ5WVMxxP1G4OPl+MztG0t6SHnD3h4oox939dnf/jLvPkzRSIStdqXDRc7jCDWr5skO9VHO/lFt2pc9HNUt6X3z7E3ffkWt+AOjNyEUZNVIuaijkok6NnIsqzsOj/h5w97dL+pmkAxR6k0IfRGMioMt3FBqAjJB0dp55U7871+dp7ZoaFlWhvjm7B1QIb43k/QoXlF5y96uyzHNJfJ2s0Kq1atx9vUJjkmGSzlDo7nKdpLKe6+nuz7v79xRasaceK/ahcsqsstRzVCfmmCc57bl6lB3/ablJ0v6SVik8ozjTY9s6ufv5Cnc8/FDS8rjcHxR6knqLwu+6FBomAQC6IxdVmbs/pdCD3r8pHJ+elrRC0vclHSLpgcTsVT1WpZ38+bDC4852qsxuit39FXe/RuHxWqkGRafm6bWxnuqai+JJsdTdcsmyn038XEjdOpc1s+MUGq4tk/RDMxuaHBQa8sdZO8cPyP9xAKDPIfs0rpKPg1WUanB9ikpshJ3O3Xe5+53xZrt/j6MnSDqxnHKrqJr7pdyyK53pTlDXxXIecQagPyAXoRjkosbORdV2eXx9m5mNqvG6UQONehIXqDl3366uRyOdaWajc8yeasQwuYRV7YyvrTnmydfaO5dUK+zBZpatJbUkTUqbv9pmxte/5ZjnqcTPHdWrSqcfxdcLFP4eXuPur+WYv2Du/pKk6+Pb/SpRZkqu7kLzDRmKWx5fZ+dY5az4+mL8XIVKlT0zx0XDVNmucAG1h/i84RsVLkSuVWhItKqQCrj7Xe5+qrvPdvcp7j7P3b+jcLH0gDhbtm5QAaDfIhfVhrtvdveL4/Gpw91nufuH3f0RdXWv/UJseFRtqVz0WYVtfpO7v1CJguPdej+Ob0co+91+RTOzv5SRjdK7V69IdskiVfb4HCdXZiV+Ts0vd39RUiqDFZLZlifGpboZn63Qw9WmtOGzcfrkxLj351gHAPRJZJ/GVeZxsCrc/UFJD0qaLmmhwkXMn1RwFd9P/Fyxc0pmdk4ZuSn9UamPKOQhKct+iXlqenxb8H6pwD6v9LmuhfH1YXe/N9eMANAXkItQDHKRpMbORdWWfNzftBqvGzVAYyKguysUHjfRLukzOeZLNT44oITnQKYeUzHWzAZmmeewIstMul9dB63jMs0Qe3k5JL69r4x1FWN3fM0VKqckfs7YdWSF/VqhN6JUF5RltZbOINX9YKW7P87VXWi+Id1t8XW2mU3IMF0Kd2BJ0i1F1jNV9nBl/06nyr7b3bekT4x36N+g0MPQOoWGRJXoneFEhYuZOyRdW4HyAKAvIhfV13viayVPwGTl7vcrPBa02rlIqmw2GqPSc1FzWlllZ5cc7pCUarT+xjxlP6ueDZVSdXtTpgXj717qpFKxmQ0AEJB9GlcjHgdTWalF0s35ek8uUrVy01CVnpuGJgty902S/hrfZtwvkl6vrovApZ5TKmWfV+xcl5ntIelt8S29EgHoT8hFKAa5qHFzUbVNTfxc9UesofZoTAQkuPtOSYvi239WVxe26W5R6OaxWdLFuco0sxFpox6TtF2hZ5STMsy/j6STC650mvj4rtSB5dNZ7qr+tEJr780q87FeRVgaX8eZWY/PHX0kvrqkv1S7QvEZ52dK+pqk/3D3ewpZzsymmlnOFrZmNkTSO+LbB3LNW6wCuwvNOGQo7hZJLygcD3p0WWpmB6rrgteP06fnqedyde33f81Q9kRJ781WdvwH4ucKQf8VSSe4+7L0+YoV/0FI/d5+P7bsBgCkIRfVj5mdpnDCbKukb9Zw1Z9RyEVflfTLQhYws9FmNifPPE2S3h3fPu3uL5dVy4TYo1Op2WhlWlllZZc89dygru/Xp9K/i7EnxtPj26vd3dVdqlHZCTGfpfuUwu/Rc+r6zsvdF+fJhufHWZ9OjF9czGcDgL6C7NPQSjoOVtmPFHLT1xR6vC6Imc03s/QGzen+MfFzxc4pufuiMnLTogxFpvbL+7I02kndtX+vuz9aZHXL2eeVPNf1boXfl10FzAsAfQa5CEUiFzVuLiqZmQ3IM70prlsKPXs9Uql1o3HQmAjo6ScK3cANlnR8phk8PArrDIVGL+81s+uSF1HMrMXMDjWzryjtsV6xAUvq8VeXmNkxZtYUhxMk/U7Sq2V+hvMUegI6WNI1ZjYp1muomX1OXS3Jv+zuG8tcV6GuVVdXfIvNbKGZDY31GmtmF0r6lzj9mvTHasRQ4XGYX6lKuftV7n6Ou3+hiMVmS3rUzH5uZv+QDAZm1hYbS/1RXS1ya3kRsCixy9JF8e1ZZna2mQ2SJDM7UtIvFI4Vd7r7r9KXN7NFqf2SZRWfi68nm9lXzKw9LjdLocehdoXH2303rdxmhd/FExV6qXqzuxd8Z4CZjTOzi8zsEDNrjeNazOzvJd2p8Ni9J5T7rgoAALmoaszsNDM7xczGJcZNNrOLJH0rjjonvcFLnK8jkYsWVqpO7v7rmIv+1cOjyQoxXtL9Zva7mO86e5o0s9aY225W6GVQki6tVH2rpKTsEudZmNgvHRnK/oJC70SHK+Th0XG5yQoNqCcrNKC+KMOy10u6WyGX/cLMjojLDjKzsxUayEvSF+LvFQCgNGSfKonbZXRqUNcd3ZYcn+FCo1TmcdDMlsTj85JKfR53fyHmpnPc/c4iFv2qpCfi+ZTDzKwl1rHJws1rF6orLz0g6Q+VqnMVfEfS0wr56FcxL8nM2uP3/51xvs9lWjiRmxZlmFzyPi/3XFeaU+Prze7+XJ55AaCvIRdVSZm5SGY2Im35lGFpy7dkWJZcVB0NmYvifEPSvi+D4qSBad+XoWmLHmNmt5rZu81sbKK8AWZ2lEIDvL+Lo7/k7ruFvsfdGRj61SBpsUKwuSbHPCfHeVLD4izzfVCh5XRqvq0Kj2LamVw+w3J7KzSsSc2zRSEUuULXi5+MPy/JsGxqmY48n/OjCnfNuEJYWp9Wr6skNWdYbmWcPj9H2QXVIcNy8xQukCS37ca093dLGp5h2fmJebLWLct6F8bl1ha53FWZ9r/CwdHThq0ZPttOSZ/L81lelLQ211Cj34v/SdRph0IDntT7JyVNzLLcomzf88Q8n0/bJhvSPv/+GZY5NjHPq3m20V8yLN+RWD71/X8tMe4BSXvWYtsyMDAwNPIgclE9c9Fidd9WyUy0Q9JZOZZNHucWFrne+YllW4tY7kuZ9oOkGXGbJr8j2+I2Ts9Ll0tqyvFZ1uc55q+VtFcNfi+Kzi5xuYX5vg8KF6RSmWS3uufHzZKOz1GvSQoNmVLzb1L3fPOtEj7rorjsympvVwYGBoZGGET2qWf2mZ8hG2QaVmZZvuTjoKQl2bZpAfVObZPTi1hmn2zbSeFxMMnPuyvunx1p45dLmpLjs+Q7V7JWoWF6tX+nDkz7Pm9I++59poDv0qJK7/O4fEnnuhLL75eY/x+qvS0ZGBgYaj2IXNSbc9HKApfvUXeRi6r5O9WQuUiJ63h5hsVpy6V/TzcrnBdL7p9dki6o9rZlqN9Az0RAZj9XAc9HdfcrJE2X9A1JyxT+aA5TCElLFO4+np5huacUno95tcIf3mZJaxS6/zta4WJSWdz9OwqPx/iJQtd2QxUOXL+T9C53f7+77yp3PUXW6XaFXn0uUmjMsUmhVfs6ha73Tpd0jIfHQKRL9f6zVSE41I2736SwX8+RdJ1CDzdS2MavKHx3viHpQHf/zzzFjVb+569WnbufptB1820KgWCAQpeEF0ia4+7PllH2lxSe5fprSS8rtHp+SqFF+f7u/nCGxZLHp1bl3j5jMiz/osLv3+0KQXFoXPetCv88HOruz5T6mQCgnyEXVceVcXhE4SRWs6THJf23Qoa4JMeyqVzkku6tZiXzcfdHJE2R9HFJP5W0QuFkxjCFrPeQwoWcI939DM99l9II5c9F+bq/LluJ2aXQsq+UdKSk/5X0vEIWXi3pBwqZ69Ycy66RNEfSfyp8bwYobOPbFC5wfazUegEAuiH7NKAyj4Op7PSXqlayMMdJeoekyyT9WeGCWbvC92e1pF9J+ieFXPB0jnLynSsZp65eDqrG3ZdK2l8hJz2lkJvWKeSoN7n7l8sou6zsU4FzXR+Ir6+owEcAA0AfRC7qe8hFVdLIuahE9yrcFPdDSQ8rNNraQ+E67QMK++0gdz+3CutGgzAPLcsAoKGZ2bcVGoF8zd3PyTc/AABAX2Vmn5F0oaSfufuCetcHAACgUZnZeIULh1slTXX3F+pcJQAAgLogFwEoFj0TAegt5im0er243hUBAACos3kKvRL9R70rAgAA0ODmxddvc8EMAAD0c+QiAEWhMRGAhmdmYyTNkPQdd3++3vUBAACoFzNrlnSUpOvd/cF61wcAAKDBHStuTgMAAJDIRQCKxGPOAAAAAAAAAAAAAAAAAEiiZyIAAAAAAAAAAAAAAAAAEY2JAAAAAAAAAAAAAAAAAEhqkMZEZrbAzC4zsz+a2UYzczO7qoRyVsZlMw1rq1F3AACASiMbAQAABOQiAACAgFwEAABqaUC9KxB9XtKBkjZLWiNpRhllbZD0jQzjN5dRJgAAQC2RjQAAAAJyEQAAQEAuAgAANdMojYnOUgg+T0iaJ+m2Msp6xd0XVaJSAAAAdUI2AgAACMhFAAAAAbkIAADUTEM0JnL3zsBjZvWsCgAAQN2RjQAAAAJyEQAAQEAuAgAAtdQQjYkqbJCZvV/SZElbJD0o6Q/uvqu+1QIAAKgLshEAAEBALgIAAAjIRQAAIKe+2JhovKQfpY37m5l90N1vL7QQM7s3y6T9FZ4Zu7K06gEAgArpkLTR3afWuyINruxsRC4CAKBX6BDZKB/OGQEA0D90iFyUD7kIAID+oUMl5qK+1pjoCkl/lLRM0iZJe0s6Q9Jpkn5jZke6+9Iy19E8aNCgkZMnTx5ZZjnd7N69W5LU1NRUyWIBAGgYlT7WrVq1Stu3b69IWX1YtbMRuQgAgBJU41hHNsqLc0YAADQgclFdkIsAAGhQjXQtrU81JnL389NGPSzpdDPbLOlsSYsk/b8Cyzok03gzu3f27NkH33tvtsbWpVmyZIkkaf78+RUtFwCARlHpY90hhxyi++67b2VFCuujKpWNyEUAAFRWNY51ZKPcOGcEAEBjIhfVHrkIAIDG1UjX0vpL091vx9dj61oLAACAxkA2AgAACMhFAAAAAbkIAAB06i+NiV6Mr211rQUAAEBjIBsBAAAE5CIAAICAXAQAADr1l8ZER8TXp+p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wAWGi+MJCXfaXv+joZZep00YXqBUrVigjI2PE86mpqdq8ebOKi4tVU1Ojnp4eRUZGKikpSRkZGZo3b17AHYkGysnJ0dGjR22Nraqq0tJxbEfY3d1tu4OQ/3vZ7jZndCYCAAAAAAAz3enTp3Xy5Mkxx7ndbu3du1cf+MAHxv0LY+GqublZx48fV0tLS1+YKDc3VxdddNGEXjsDAADTl93noklJScPutDKayMhIzZo1y9Y9mpqaCBOFKcJEwCQbcT9Ir1dpJ04o6exZZRUWSpJMh0O7nnxShter9GPHFO10ynQ4lPS5zykjgDBJMNTU1AxpbWdGRenI7bfrsu9+Vw6vV435+Sr7yEfU8KEP6cM//an02mvWwDfeGDtM9N570g9/KJWXW12Mvv1t6cMfts594QvSr3/dP/bdd6WlS6X0dOmGGyT//p2trdLdd0s33iiN8kPGbuu9zs5OdXd3B/wPaJ/Pp/379wcUJPI7c+SIln7rWxqteV/llVcG3Omprq5Oubm5Y44zTVPV1dW25szOzg64WJAkLVggJSZKvd9P2du2KeOVV7TnO99R0wjhm6ioKOXn52vBggVjTh8dHa0VK1ZoRQDdm+yKjY1Venq6re5EFRUVWrJkScB/Rna7EkVGRioxMVGS/TBRV1cX7SEBAAAAAMCM1dbWZitI5NfT06ODBw9q48aN43sdLAydPn1aRUVFg4719PTo9OnTOnfunFavXq3Zs2dP0eoAAMBU8e9OM5ZZs2aNa/60tDTbYSKEJ8JEwCQbuAd35v79yt69Ww6PR7MPH1bceQGFyg98QE29gYjGVav6jufk5Gjk3iyTo7i4eNjjdZddpr/9/OeSpK7eLZfet3y5HP/0T/1hov/9XyvkM5Lf/MbqNDRwa6qDB6XSUunkycFBIkny+aQNG6SFC/uDRH49PdLhw9LmzSPeLi4uTtHR0bbCPq2traN2whnO6dOnbf/AHcjwerX4z39WRG+nJ196uqpWrFBXaqpcWVnyRUaqKzVVdQUFAc9tN0zU0tIit9tta86cnJyA1yHJCkJ94hPSSy/1HYpobdXG++6TNzFRTUuWaP83viFfQoKSk5M1f/58zZ07N2zaLc+dO9dWmKijo0MNDQ2jfv/42zkOfDGqubnZ1jr8LSElK2xl93u6o6Nj3IUeAMwkXV1dqqmpUWtrqzwejwzD6Ot0F+ie4AAAAADCw5EjR2xt3zFQU1OTKioqbP2SW7g7evSoSktLRzzvdru1e/durVq1ytZriQAQLtxut2pqauR0OuXxeORwOPpex+H1cMAeu52JxvvaaFpa2qh1iF9TU5NM07xggtwXEsJEwCTq6urqC5nE1dRYHX08nmHHmg6Him+8cdhzdXV1Ie0u0tDQMOoPEH+ISLJa2+Xm5krXXts/YPt2ye2WYmP7j/X0SL/4hfTCC9LOnUMnbWmRvv99aceO/mNXXSXt3y+1tVmfl5VZ72NipHnzpJIS6/MxwkSS9YNuxC5Rg5bRElCYqK2tbcTg1SCmqeQzZxRXV6eY1lbN+/vflXbeb0VV33mnDqxda/veo2loaJDP55PD4Rh1nN0tziIjIyf2G0r/+Z9WoKiyUnrySamhQUZPjyKbm5W5b5+uqayU7r5bkZHh92MpJyfH9gtPZWVlQ75/fD6fqqqqdO7cOTU1Ncnn8ykmJkZpaWnKzs62nbhOHfD3TrK6E9kJE7W3t/OPJwAYQ2VlpY4cOaKenp4h54qKipSbm6uVK1eO+XMVAAAAQPioqqqy9Qtiwzl+/Liys7On9RZgJSUlth7gSdLhw4eVmJio9PT0SV4VAExceXm5jh07Js8wz9sMw9CiRYuUn59PMAEYQyg6E9nR09Oj1tZWpaSkjOs+mDzh99QWuIAM7EqU09uRaKCu5GQ1L18uX2SkKq+4Qu3z5w87j9frVVNTU8Adc8arzB/asSEvL88qyBYtsrY2O3VK6uyUnnpKuvdeKS7O6lj0wAPSee10lZcnrVsn/epX1udPPNF/LiJC2rZNeucd6Ytf7D/ucEg/+5m1xdk991jHDh8ec50pKSm2wkR2U7iS1WXm0KFD8vl8Y46d/9e/6tJnnx3xfPvcuTqwerXte4/F4/Goubl5zBcA7G5xlpWVNbEHqHFx1hZ1krRli/SZz0iHDvWdjjx5UgrDIJFkbaOWkZGhut4OUqOpra1VZ2en4uLiJFkvWh07dmxI9ye3262qqipVVVXZXsf5RVRiYqKtLdLa/GE8AMCwmpqadODAgVFDo2fPnlVkZKTy8/NDuDIAAABg+mlqalJdXZ3a2trk8/kUERGhWbNmac6cOX3bt4eCaZo6ceLEuK/v6elRaWmp8vLygriq0Glvbx+ytdlYioqKdPnll0/Sioe0SzQAACAASURBVAAgOOrq6nRowLOF85mmqTNnzigqKkrLli0L4cqA6cXlcg0byBvOeMNEsbGxio+Pl8vlGnNsfX09YaIwFJ5PboELxMAwUfbevX0fN+bn6/QNN6h+1SqZNrdyqqurC0mYyN8a0o7ExERlZ2f3H9iyxQoTSdJjj0mPPy4lJ1tdhwaKiJD++Z+l556zzr/3nnTs2OAxX/qStHSptGSJFRzauVNavVr6+Mel971Peuut/rE2wkR2W/AFEiYqKyuzvUVV7ptvDjlmGobMiAh1paTo0F13yQxy56m6urpRw0ROp1MdHR225hr3FmfDWb5cOnBA+uUvpVtvtY7Z6e40hXJzc22FiUzT1MmTJ3XJJZfo2LFjtn/7y47zOxMlJSXZuq69vT1oawCAC43P5xszSORXUlKiOXPm8I9aAAAAYBher1dHjhxRRUXFkHPV1dUqLi7WkiVLtGzZspB0imhoaLD9utdIysvLtXTp0mnZobSoqCjg7d2am5vldDrpcA0gbPl8vlGDRAOdOnVKOTk5tl9HB2Yau12JEhISJrSrSHp6uq0wUV1dnZYuXTru+2ByECYCJpG/a0i006m03t8EMQ1D+//1X9V9XjBgLHV1dVqxYkXQ13i+iooK2//QXLJkyeB//N95p/Sb30j+bjemOThIlJQk3X+/FRQaGIx64glp69b+z6+6Svrud62PDcPqQOTvQuT3vvf1f3z0qOT1WiGlEdgNE7lcLnV3d4/Zwtjlctn+7Z4op1MpA8Iy9atWqW7NGp374AfVHeA+o5GRkcrJyRn2hZnz1dXV6eKLLx7x/Llz52zd0+FwKDMz0/YabTEMacOG/s/DPEyUlZWluLg4dXZ2jjm2oqJC1dXVthPddiQkJCgmJmbQMbu/zUdnIgAYWVVVla1/zPoVFxfrsssum8QVAQAAANNPT0+P9u7dO+pW7j6fT6dOnVJHR4cuvfTSSQ/o2HntbCxut1u1tbXB/SW7EGhqarL9y6LnKysr06pVq4K8IgAIjvLy8iG7AIzENE2dPn1aq4O4IwRwIbHbXGGiIeOMjAxbdVlzc7M8Hs+EgksIvukXqQemCa/X2/cQP3P/fhm9W2E15+UFHCSSrO4idoIME2Gaps6ePWtrbGxsrObOnTv44LJl0unT0k9+Il1yibUlmWQFh+67zwqM3H//4CCRJF1/vfTf/y099JC0d6/VdWisfTQzMiR/V6TOTqmkZNTh8fHxirLZBcrOD9DDhw/L6/Xami/j0CEZvQGtprw87XniCZ3ZujXgIJEkzZkzx/YLGE6nc8TC2jRNVVZW2ponMzNTEUHumiTJ2hrPP++5c1IAD3NDzTAM5ebm2h4fzCCRpKF/12S/M1FHR0fAv4kGADOBaZoqDjDMWlNTY/u3dgAAAICZwOPxaNeuXaMGiQaqrKxUYWHhpL5W0dPTo2r/LztOkN3XSsPJRLZ3q6ysDPrrWv+fvTOPb+Ss0/xTpcOyZcm2LPmQb/lu92H3fdLdEEK4AwMDDAMzkFmym5nZMBAIYQkQZhN2AiHsDLDLFWBYjvDhHJJASBhI+r7sttttW7J8y7Zs2bIsW7LOqv3jbdmyrePV5bbd7/fz0cdSqVRVsqQ6fu/zex4Gg8FIB4IgwGw2J/Sa8fHxlF3qGIztCm2NMx1iIhpEUVyV+MPYHDAxEYORIebn55Hf04PDjz6Ktq9+dXm69dChdfPSWvvSxCylwszMDLVgqbKyMnIHUU4O8MADwPXrgNMJGI1EKPLFLwLFxdEX+L73kWi0RLr9w7tkQlFnLhe5//LLwOzsqtlpY0niiYnGxsZgs9moN7PKaFy+b0tRBV9RUQGtVkvdvRVtO2dnZ6kV/BnrvpLJgOrqlceDg5lZT5qI+p3PMBzHobKyct30nJwcqu0RBCGq60YwGITNZsPExATsdjsTHTEYjDuKycnJpKIgExUgMRgMBoPBYDAY25murq6EBfdWqzXhAeFEmJiYgHCrsTNVbDbblhqInpmZweyammgiBAIB6gZEBoPB2EgsFkvCDfchdyIGg7Ee2vM32uSXaMjlcurx2UyPgzMSh4mJGIx0IwiA2Qz/T3+KI48+Ct2a/NapNWKigoICalVmpneitPa/1C4tSiVxK4oTGZY04WKi730PuP9+oLCQuCK9/vVAaysQZulLe8CLJSZyu924efMm9SYqc3JQeO3a8mPb3r3Ur123LKUSGo0GEokEhYWFVK+J9p2hLQpIJBKUhBygMkFd3cr9TT44m5WVhfLy8g1fb1FREbKzs9dN5zgu6agzl8uFGzdu4MUXX8TFixdx7do1nDt3Dn/84x8xNDSUtoIbg8FgbGaGh4eTet3k5CS8Xm96N4bBYDAYDAaDwdiCWK3WpIUnJpMpY9HsFotlUy8vk5hMppSXkS5XJwaDwUgnyTrFWSwW5rjGYKzB7/dHbUJfS6rORAC9O1E6BeGM9MDERAxGOvF4iMClvh7Ff//3kPh8q56eOHYMi2viigoKClBUVES1eJvNRh2tlSiBQIA6S7u4uBgKhSIj25EQ4WKiF14AvvUtIHxwz2IB7ruPCLx+8xvUPPAAXvPRj2L/E09Af+ZM1MU6HI6I0wVBwNWrV+H3+6O+lvd6IXc4oOnpwYHHH8dr77oL3K2iil+phKO+PrH3GEZFRcXyfdoDr81mW+c24/V6qQs9JSUlmc0nDf9/bHIxEQA0NjZueF5rLOFeMmIis9mMP//5zxgeHl63P1laWkJ3dzf+/Oc/Y25uLrkNZjAYjC2A3++njmFYiyiKrEuGwWAwGAwGg3HH4/P50BVyCk8CQRBw/fr1tLske73epM/1o7FVnHpSdSUKMTs7m7EadDQcDgdzzGYwGFHxeDxRx23iIQhCQkkTDMadAK0rkUwmi9jsnii04+B+v596rJqxMWzsiCiDsd156SVgjWuNu6gI7Q89BI9Gg6UIO8v8/Hxqe7dQJFEmnGKsViv1RSKVK9FGEC0yzGBYicx64QWgoABwOqEAoACQNziIkkuX4KyqwmKE+Ci32w2/3w+ZTLZq+o0bN2K6FunPnEHr009DEkXlHjx1CqJEQvPO1sHz/CoxUVFREXp6euK+zu/3w+FwoKCgYHnawMAA9Wddtkb8lna2mJhIoVCgvr4evb29G7I+pVIZ8yRLpVJRLScU42M2m6m23eVy4eLFizh27FhaVOcMBoOx2Ygktk2E6enpVcdlBoPBYDAYDAbjTqO7uztlx06Hw4GBgQHUhTtXp8jU1BT1vFVVVVROFy6XC/Pz8ynHfGQSURQTclOPhSAIsNvt1M2MqWCz2dDV1YXFxUUcOHAg87VIBoOxJUm1qWt6ehqlpaVp2hoGY+tDKyZK1/hQfn4+ZDJZTLOGEKOjo9Dr9WlZLyN1mDMRg5FOQgKWW4yfOIHzX/wi5pqaIgqJALIDVSqVUCqVVKvIlCKT1q43KytrQy4kqdi5E/jIR0icWl0d8O53A3/+M2A2Ax/72Mp8EQ6KnChCG6N7aq0ry+joKEZHR6POL1laws5vfSuqkAiFhVB8/vNJ/+8MBsMqNyiVSkWtBg63Jvb5fNSxLnK5PPOfdXixaItkFxsMBmpHoFTZtWsXOI6L+nwizkQ2my0hEVQgEMC1a9c2vBONwWAwNoJUi1CpipEYDAaDwWAwGIytTCrxZmsxGo1pjTujFRMVFBSgubkZPE83RLLZ3YmGh4epBwZpyLSLh91ux/nz53Hx4sXlJjij0ciiTRgMRkTSISZiMBgr0J4zpEtIzfM8taDPZrNRR7AxMg8TEzEY6WRoaPluz9/8Ddo/8QksxRBjyGSyZRERrdvQ1NRURux/Z2ZmqOYtKyuLKW7YcL75TWBhgbja/OxnwMmTAMcBjz8OHDmyMp9aDTz0EGZPnVqeVGA0Rl1s+P9jfn4eN27ciLkZNb/9LbJuuRYFpVIES0qAv/1boKsLmJkBrFZg/37s3LkTkgTdiXJyctDQ0LBuOq3QZ3x8fPk709nZmZArEW1BJWm2mDMRQE569u3bl/DnGP76eP9XiUSCffv2xf2MaZ2J5ufnceXKFeptDLG4uEjlgMVgMBhbiXTElPn9fhYHyWAwGAwGg8G4I1laWkop3mwtgiCgvb09Lc1MIVd3GkpLSyGTyZKqr2023G43jDHqnMmQKTGR0+nEpUuXcO7cuXWRbC6Xi7rhlcFg3DmIokg9fhUNj8eTVuEqg7HViZXCEk46kysqIyTFRMNkMqVtvYzUYGIiBiOdhImJ3MXFcWcPjzejFRP5fL60535brVbqi+FNGekRSdykUBCXops3gclJYG4O+NKX4Lz//uVZaMREfr8fV69ejdgVI1tcRO0vfoF9//IvqPvlL5enWx55BJLJSeB73wN27QIKCwEpSZXMzc3Fnj17Enp7u3fvjihcoc0Y9Xg8mJmZQX9/P7WzFcdxqKmpSWg7k6K6Ggi9t/Fx4OGHgTQXPzKBWq3G4cOHIZXSp4VKpVIcOnQIb37zm/Ha1742qm1zWVkZTp8+TWXjqFQqqcV9yRblhoeH1xV3GAwGYyvjdDpTjmMAWFcbg8FgMBgMBuPOw+fz4fLly2k5nw7H6XSivb09ZVeamZkZ6vpH8a3aLW2slsfj2ZQNBcFgEFevXqWKDUmEdF03hTM2NoYzZ87EvJYymUzMnYjBYKxibm4uLfs4VsdhMAiCIFCL69IZ8VpQUECdtjE2NpZWx0VG8jAxEYORTsJizmjERAUFBavuy+VyqtXEittKhomJCar5VCpVWlWoGUcuB3bsAEpKgFtOMLnHjyN4SwCitFohj6K+nZ+fh8/nQ0dHxyo7PW1nJ45//OM49cADuOvDH8aOH/wA+nPnILs1j6usDKWf+lTMzSorK0NrayvVW2hqaoraIaXT6aidgy5evIi+vj6qeQFAr9dTR++lhExGBEUhnnwSaG0FnnmGiPPSXLRIJxqNBkeOHFkVPxeN/Px8nDhxYlkAlp2djb179+Kuu+5CS0sL6uvr0dLSgrvuugt79+6ljrDjeX5DfpOJfHcYDAZjs5Ou4hErQjEYDAaDwWAw7iSsViteeeWVjA3sWK1WnDlzJiXBDm3EmVKpXB7MKikpoXaf3oxRZ11dXdTuAgASinZL1QkknLGxMVy/fj2uUGhpaQkjIyNpWy+Dwdj6sDoOg5FeXC4XlXCX53lq8Q8tibgTsdSMzQETEzEY6UIUU3Im4jiO2p1oYmICHo8n8W2MgM/no3YdKS8vT8s6byea0lI4a2uXH+fHsMq7cuXKqiJEXn8/DvzP/4mC/n6oLBZII3wG4pNPQp6TE3c7KioqcODAAchksojPKxQK7Ny5E/XhMWBrkEql1BmjiRIpVi1jvOlNqx97PMB99wEGA1BeDvzqVxu3LQmSn5+PkydPoqGhIeJJlVarxb59+3D8+PGIz2dnZ8NgMKCpqQkGg4FaRLR2HZnGbrentYDEYDAYt5N07c/m5+fT3v3LYDAYDAaDwWBsNtxuN86fP48rV64kVI+USqXYt29fQutyOp04e/Ys2tvbkzrXph0oLg6r20okkoRqspsp6sxkMiUUC6ZSqWAwGKDRaKjmT9e10/T0NDo7O6nn7+/vT0vsHYPB2B6ka19kt9vZvoXBAH3EWW5uLrUAmZby8nLqZdpsNhZ/ugmgz2dhbAzT00B2NqBS3e4tYSTKzAzgcgEA/NnZ8FN8huFiIoB0wtC4DgmCgP7+fuzatSu5bQ1jcnKS+iI4U8KVjUQikWBpz57liLMCoxHTe/ei9MIF5Fos8Go08BQWYkmjgddiQdHkJMpeeQW5ExNQTk5CusYpx1lZieE3vxk+lQr6U6egf+MbqbelpKQEp06dwsTEBOx2O3ieR05ODjQaDbRaLdUBtby8PO1dUWVlZWlXG8fkX/4FOHKExJw98wzQ27vy3MwM8O53A48+SoRFcvnKrakJaG7euO2MglwuR2NjIxobG+H1euFyuRAMBpGXl0ftNpYKWq0WAwMDGV9Pf3//hgiXGAwGI5MIgpDWuNjZ2VnqgQcGg8FgMBgMBmOrYbPZcO3ataSEPS0tLdDr9Zibm8NgmJs7DePj43A4HDhw4ABUlHXyhYUFLC0tUc279hxer9dT1dd8Ph9mZmaiuohvJBMTEzDeqm/SsmvXLvA8D51ORzU4n47Ye4/Hg/b29oREWF6vF4ODgzGbLBkMxp1BIBCAw+FIy7IEQYDD4UBhYWFalsdgbFVoXSYzkYqRlZWFiooKahfCmzdvoqioaEPG2hiRYWKiTYT2lVeAu+4CpFLgHe8AioqAQAB4y1uAe+4BOO52byIjFmtdieJ8XtnZ2cjKylo1TafTQSaTUV2gDw8PY3h4GHK5HCqVClqtFmVlZQlHU01OTlLNl5eXtzGxVxvBkSPAz34GAGj42c9Q2N2NwgTs8ny5ubjy6U9jSavF0q3PuqKiAnrK6LJwFAoFDAYDDAZDwq8FyHdGoVCkzalKJpNhx44daVkWNdnZwPveR+7ffz/wyCPAiy8CNhswPw8Eg8DnPx/5tT/5CfDe927YpsYjKytr3e8602g0GnAcl/HOuJmZGczNza2KZwzH5/PBYrHA4XDA4/FAFEVkZ2dDrVZDr9cjh8Kxi8FgMDKNw+GgsvGlhYmJGAwGg8FgMBjblfHxcXR0dCRVb9DpdMsxFk1NTZiamoLrVhMmLS6XC2fPnsXhw4ej1iLCsdlsVMuVyWTrnHmKioqoa7Lj4+O3XUy0uLiYkNMPQJoHQwPotM1iLpcLXq83pVpXT09PUmK0gYEBVFdXR3V1ZzAYdwZzc3NprXvPzMwwMRHjjodWTJSXl5eR9Tc0NMBisVA5hfl8Pty8eRNtbW0Z2RZGfJiYaBNR/otfkEHzYBD46U9XnvjGN4CdO4EnnwQScD1hbDBhYqIlioizSBfBPM+jqqoKZrOZerWhmLLZ2VkYjUbU1NRgx44dVK42oW4aGraDK1GInNOnVz1OREgUUChw9dOfhn3nzuVparU6LS5RycBxHMrKytLmTNPa2gqFQpGWZSWFSgV87Wvk/tQUEVh2d0ef/6c/zayYqLsb+Pa3AbsdyM0lYqckRGOZRCqVIj8/H3Nzcxlf1+DgYESL8tHRUdy8eROBQGDdc+Pj4+jt7UVBQQEmJyfZxRqDwUgZv9+PpaWlpHK709FZm+ryPB4PfD4fpFJpWoWWgiDAZrNhaWkJWVlZ0Gg0CRX9/X4/5ubm4HK5oFAooNPpIJVu/OWiKIpYXFxcFqVmevDAbrdjdHR0eYBDpVKhoKAAWq0WEokk4vY5HA7MzMxgYWEBwWAQPM+juLgYer0+7fbPW52QY+PtEFwnyvz8PEZGRuC95USqVCpRUFAQ87fgdDphs9ngdDrh9/shkUig1WpRUVHBvguMOx6v1wuv1wuJRHJbG5NC+yGFQrEtu1kFQcDi4iKAzMQQbAcy9V10Op0YHh5eddzIz89HUVFR3OPGwsICfD4fJBIJCgsLUVFREfG8IxkCgQBcLhc4joNKpQKXZHOq3W5PWkiUlZWF1rDaiUQiwd69e3Hu3LmEhf2BQACXLl3CyZMn40bD00ac6XS6df8XnudRWlpK5Rg/MTGBHTt2QC6Xw263Y3JyEk6nE4FAADzPo6ioCFVVVQntcwRBwOTkJCwWCwKBACQSCdRqNTQaDYqKilb9toPBIK5duxaxBhON7Oxs7AyrZebl5UEqlVItY3Z2Fnq9nnpd4djt9qQd1WUyGdxud8YGMhkMxtZgM9RxGIztBm3MWSaciQBislBbWwuTyUQ1v8ViQU1Nzbq0H8bGwMREmwTpwgLybt6MPkN3N/CmNwFvfzvw1a8C1dUbtm0MStY6E8Uh2k6vpqYGAwMDSauth4aG4Ha7sX///rhFJKvVSr2eZC8aNyPqnTvhrKmBOuwzE3geY697HfhgEIrZWSjsdnCCgKBcDltbG6YOHkRQJoO7uBj+sAOoVCrF/v3701b0SYaqqioMDg6mrNA3GAyby12huBg4cwb4zneAgQHA5yO32Vngd78j81CebCTEmTPA+fOA1Qp8/etAePfUH/4AmM2bzilOq9VuiJhocnISS0tLywU8QRDQ1dWFsbGxuK+dm5vD0NAQhoeHl92KSktLWYcZg8Ggxul0wmg0Ynp6enkQIicnBzU1NaiurqYaPKMVUavVaqounfn5efj9fqp9mc1mQ39//6rClVqtRl1dHfR6fdIDPaIoYmRkBCaTaXkwCyCCY51Oh5qaGhQVFUV9vcPhwMDAAKxW66rBHalUCoPBgPr6+g0ZmAwGg8vHiVA8Bc/zKCkpQUNDA3W8BS0ejwfXr19f18FutVoBkEGw3bt3rzo3WlxcxPXr1yMec0MxE21tbes63e80RFHE2NgYBgcHsbCwAIB8H7VaLRoaGjbd/8fv96OrqwsTExMRnw85d4bcFQBgaWkJnZ2dER0QQt+F1tbWmL89BmO7Mjc3B6PRiJmZmeVr1NzcXBgMBlRWViZ9vEuE0LFxaGhoWWgTOi42NjZuiyK01+tFf38/LBbLsiBWJpOhvLwc9fX1m17AuRHY7XaYTKZV30WVSgWDwYCKioqkv4t+vx83btyIKpCQyWRobm5GVVXV8jSPx4POzs6IYpeJiQmYTCbs2bMHxRT1xGi4XC6YTCZMTk4ud3lnZWWhsrIStbW1CV17+/3+hGOpQkgkEhw8eHBdo1p+fj7a2tpw7dq1hJcZ2p4jR45EPS8NBoPUA8TRXIX0ej2VmCgYDKK9vR0ej2f5XCccu90Os9mMlpaWVecP0bDb7bh+/fo65yabzYaBgQEoFArs2bNn+bzixo0b1I4CADmn3r9//ypxE8dxKCgooHJzstvtSdWFRVHEzVjjHVFQKBRoaGhg4mwGgwGAvo6Tm5u7fN4Xi7m5OQiCwPYvjDuWUJMjDZkSEwFAbW0tRkdHqVNXbt68iWPHjmVsexjRYWKiTULB1avgQsV7uRx47DHyd3QU+O53gdBB8De/IfE/d98NCALQ3k6igOrricBIpSIuRn/xF0Bz8217P3ckaRITKRQK6pzuaExNTeHKlSs4cOBAzJOiaAXztajV6u0TcQaAl0gw8LWvQfHss8gdG4MokWDk7rvhaGpKeFltbW23/X+jVCpRVVWF4eHhpJdRUlKy8fFmNOTnAw89tHqa0wmEupLMZhIHmS7nhHPngFOnyP41EoODRNhUV5ee9aUJrVaL/v7+pF6bl5eH8vJyqgKPKIoYHh5Gc3MzRFHE1atXMTU1ldD6RFGEzWaDzWbDjRs30NrairKysqS2ncFg3BkEg0H09PREPM653W7cvHkTQ0ND2LNnT0y7fkEQqIWXdXV1uH79OlXndLyoM6/Xi87Ozoj7S6fTifb2dgwODqK1tTVhwYzX68XVq1dht9vXPSeKIqanpzE9PY2ysjK0trauOy/s6+uLevwIBAIwmUwYHx/HwYMHE3aBSgSbzYbOzs5lEVEIQRAwMTGByclJ1NTUoLm5OS0Fv7m5OVy5cmWV+GotXq8XV65cQV1d3XI0R0dHR8zubbfbjfPnz6OlpQU1NTUpb+dWZH5+HtevX183wBV+/Nfr9di9e/emEBQvLCzg0qVL67574fj9fnR2dmJubg67du3C3Nwcrl69GrP45vV6cenSJTQ0NKCxsTETm85gbDpiCSwWFxfR1dWFoaEhtLW1ZdRlwuFwoKOjY91gUvhxsby8HDt37twU+6FkGB4eRm9v77pjkt/vx9DQEMbGxtDU1HTHHotiiUQXFhbQ2dmJoaEhtLa2JvxdXFxcxKVLl+B2u+Ouf25uDrt374bD4cDVq1fjnndcvnwZdXV1aE6wnisIAkwmEwYGBtadu4ZEZyMjI9i5cyf1tXek8zIaOI7D3r17o9Y89Xo93G43ent7E1623W5Hf39/1OPqzMwMtetRNLGvVqtFVlZWzM8qRDwRTiAQQGdnJ2ZmZtDW1hZVvDY8PIwbN27EXJbH48GlS5fQ2NiI7OxsqoaucHbt2hXxMyksLKQSEyXr4jE1NQWHw0E9P8/zaGlpoW4UYTAY259gMEi9H2loaEB7e3vc+QRBgMPh2HRNLgzGRkH7m8q0s6tUKsXOnTtx9epVqvntdjsmJia2lfHFVoGJiTYJmsuXVx488gjwqU+tPP70p8m0Z54hjz0e4D/+Y/UCenrIDQCefRZ49FHgPe8BfvhDYIsWR7YcCYiJOI6LWbCora1NSUwEEGvfvr6+qAIRv99Prerejjvn0r17cYUijzMWNTU1m8bJp7m5GdPT0zELWtEIDTBuRIdoWlCrgdJSYHKSuAaNjAC1telZ9v/6X+uFRIcOAZcurTy+eHHTiYk0Gg0UCgW1ijucXbt2Qa1Wo7+/n0qRPjIygoaGBvT39ycsJFqLIAjboiuYwWBkDkEQcPHixYhimXDcbjcuXryI/fv3Rz02OxwOqixugHQrFxQUUBXOY4mJvF4vzpw5E3cwxuFw4OzZszh27Bh114/X68WFCxcidkOvZXx8HKIoYu/evcvHe6PRSCVEdblcOH/+PI4cOZJ2dyCAiNvjdaiLorjscnPw4MGUBhecTicuXbq07OIQD7PZnFAEsSiK6O7uhkQioepE307Mzc3h/PnzcQfyJiYm4HQ6cfz48ds6kB/ab9CeP42OjlK5FYRjMpnA8zzq6+uT2UQGY8sQCARw7ty5uMekhYUFnD17FkeOHMnIAM7s7CwuXrwYdz9ksVjgdDpx7Nix2xLpmQq9vb1xj0uBQADd3d1YWlrCjpoaoLMT6O0l19F33ZW+ZpxNiN/vx9mzZ+M6EzidTpw9exZHjx5FQUEB1bKXlpZw4cIF6uPG2NhYwqIPs9kMnuephaiiKKK9vR2Tk5Mx5/P5fGhvb4fP54srMrPZbHGXFwm5XI62tra4rnx1dXWQyWTo7u5OOPLMbDajoqIiYlQwbX1CrVavc00KRsYsHgAAIABJREFUwXEc9Ho9hsJqvKkyPj4OjuPQ1ta27rnR0dG4QqJwjEZjwuuvra2Nek5KG0MfinNN9LxtcHCQet6cnBy0tLTAYDAktA4Gg7G9sdvtVC55HMehuLiY2mV6ZmaGiYkYdyzxaqwhNiJmtLS0FKWlpdTnniaTCaWlpVtnLHObwCTemwFBWC0meuMbVz9fVETcic6fByJceETl2WeBj3wESDH66I7h/HngQx8C3vc+4P77iQMJDTYb8P3vAy+9tDwpnphIpVLFLFjl5eWlZfBhcHAwavbl5OQktV1xaWlpytuy2SguLo5YfKAlNzc34W6xTCKVSiMWJuJhMBjQ1ta29Tp+GhpW7idRTIlIXx/w3HMrj++9F/jRj4hb0WOPrUy/eDE960sjPM8nVXCpqalBQUEBJBIJqinjM/1+Py5fvpy0E1I4eXl5t93Zi8FgbG76+vqoL3JFUcT169ejDvDQiqhVKhXkcjl1cT3Wcjs6Oqi7ugOBAK5du0YteOro6KASEoWYmJhAz63mg1AsGi0hlx7abaPF7Xbj+vXr1PPbbLaEhD1rEQQB7e3t1EKiVLhx4waVxfp2IfT9pR0UXFxcTGjwLN2IooiOjo6khNiJksh+jMHYqty4cYP6mCQIAq5du5b2fbHf709oP+R0OpOK37mdTE9PrzsOcn4/6n/2M7Q+/TRan34aDT/+MXTXrqGgrw/8449DKCkBjhwBPvxhUm+sqACOHQNOngQ+8QngG98A/ut/JQ7nb3gDsGMHYDAADz8MxHJnoYxG2GgSOf4m+l2MdZ6ZTkLRbDQMDw8nJPy5efNm1DohQI6PybgGFRYW4uTJk9TxnlVVVThx4kTCzpeCIEQV1NCKieJtYyYcvSwWyzph2cLCQsbPhUpKSmLWLvPz86nrgYm6EzkcDurXyGQytLS0bFm3OAaDkTlo9yN5eXmQSqXUdZxkHdcYjO0ArWv7RjWB79y5k7rBY2FhIWJsMCOzbN9WlK1EZyeyQsXFwkJg//7I8x05Aly5QhwyrFYS7bNjBykEmExkms0G/OIXwAsvkNd8//skAu3Tn96Qt7JlGRkB7rkHCC9+/e53RGBUXg5MTQEvv0xcoRYXiXjBZgOWlsj0NQUWd5wLU5qdcEtLC5xOZ0J2sGsRRRE3btzAsWPH1ik1E4k4y2Ssxe2C4zgYDAZ0d3cn/Fqe59HW1gaJRJKBLUsejUaD3bt3o6urK+68UqkUra2tW1co1tgIvPIKuW8yAW96U/zXBAJAMEi6MMM/u8lJ4Ac/IA5wId76VuBXv1p5fPjwyv1NKCYCSMFrfHw8ZmEwnOzsbDSFRftVVVXBbDZTFd9pC5vxYPFmDAYjFnNzcwl1swIrcUSHDh1a9xyNhT+w0qGbSKeux+NZ1+E8OjpKvc4Qi4uLMBqNcaNHk1k2QITmPp8vKQdMl8sFk8mUNjG1KIro7OxMWKBkMplQUlKSVG77wMBAQgKsVBAEAZ2dnTh69Ogd0THV09OTcBzK+Pg49Hr9bXH6HBsb21CBT2dnJ06ePLn1BPwMBgVTU1OwWCwJvcbj8aCnpwd79uxJ23Z0d3dTRROFMzo6Cr1eD51Ol7btyBShc5y17Pz2t1H9+9/TL8hqJTcAePXV6PM9+SSpLe7bB2i15G9eHmnC+da3SF2sogI4ehT4h38Ajh9P8B2lH6vVmvA5ztLSEtV3cWxsLG3XwTR0dXXh5MmTMetOLpcrpvAne2oKRR0dkHi9EHkenoIC+NRqDA4MYE9bG3iZDOB5QC4HmpqA7GxMTk5S1xQAEn+xY8cO6PX6hM931Go1Tp48ieHhYRiNxphRsuFYLBbU1tauOhecn5+nFnrF+70rlUqUlJTAGvqdpInu7m5oNBoolcplgXuizkyJoFQqY8arAaS+SevGarPZEjpnS+Q6rqGhIWH3RwaDcWeQTB2Hxl3ObrcjGAxuuvEdBiPTCIJAfa5H696ZKgqFAvX19dSC9v7+fhTHMfRgpBcmJtoMhIQ/AOkCinUAk0jIhfpaDhxYuf+hDwH33Qd873vk8WOPAe99LxlIV6mIrTGDIIpECHTffauFRAAwNkY6tRoagD/+kcQpUTBfU4NgdnbMeWgGp6RSKQ4ePIhz587B5XJRrTsSc3NzGB8fR3l5+fI0r9dLXQTZsmITCioqKmA0GhPuiGxubt600UxVVVXIycmB0WiMqDCWSCSoqKhAQ0MDsrKybsMWpolEnIk8HuBjHyP7RI+HiDYffRR48EFgdhY4cQIYGFj9mo9/fPXjgwdX7nd2Am43kIKzVSbgeR579+7Fq6++GndgNiSIC1d8KxQK6PX6hAcCUoGJiRgMRjQEQcD169epXRTDmZ6extjYGCoqKpan+f1+6s4brVYLgFw08zxPLbIMP9fyeDxJux0MDAygtLQ06kV7aAA2WVLZzw8MDECv16fF6nh0dDSpQbmQo8xrXvOahAatQmKojcRut2NsbGzbx53NzMxgZGQkqdd2dXWhsLBwQzvRvV5vSr+hZFhcXITZbEZD+Dksg7EN8Pv9VM0skUinkCcZQVOIzs5OnDp1anPHnVkscDzyCHaazQDHwVVairnGRuRMTVEJiXylpZAfOUIa5hIRSHR3k1s0xsaIK/qzzwKtraSG9ld/tfr6eYPI5HfR5/NtuIuVy+VCf3//qgagtXR2dkL/u9+h7pe/hMTng8jz8OfkIKhQQOLzIS8RUb5MBrGpCSq3Gyf9fvDBICAI4EQRC1VVsDc3I5iVBW9eHqb370dQoUB+fj4OHToEuVye9PsMuSwXFxfj/Pnz1IIgo9GIA2G1cFpXIplMRhVrU1tbm3YxUSAQWBaam0wmqhieZOF5Hvv376farxUWFlKLiWjxeDzUTaw5OTmorq5mYiIGg7EOn89H3WifaFOYIAiYnZ2ldtRjMLYLTqeTuqlvo8REAElQGRkZgdvtjjvv3NwcZmdnqX/vjNTZxFfKdxBSKbwaDXEnonHXiAfHAd/8Jrngv3KFWA/X1pLnsrOBX/8auPvu1Nez1TGbgde/HhgeXpnG88BnPgM88QQRXw0O0sWdHTgAX2srRubmMHrXXXFnp93JZWVl4ejRo7hy5UpKDkVGoxF6vX65E3ZiYuKOjjgLIZVKUV9fn9BgQmlp6abP79bpdNDpdHC5XJienobL5YIgCCgoKEBJScn2sA1ubFy5H2lgMBAAvvY18ju/eBG4dm3ludlZ4KMfJR2VN2+uFxIdPQq85jWrp+XnA83NQG8vWXZ7++rOS7cb+MIXiDg0EABqasjjfftSf68JkJubi7a2Nly7di3qb1yhUKC1tTXifqimpmbDxESFhYXrXDwYDAYjxPDwcEoRUb29vdDr9ctdZjMzM1TnPhzHLYuJJBIJCgsLqQrn09PTq8REfX191N3Vkejp6cGxY8ciPmcymTYkpisSoiiip6cHR44cSWk5gUAAfX19Sb/e6XQmLNLp6+vLaPd3rPWWlZVt245HURRTGmD1er0wm80bGh/c399/W35DZrMZVVVVW1vQz2CsYWBgIKXYp5s3b+LkyZMpObiluh9aWlrC4ODg7Rf7eb3AM88Q51xBIK66wSCwtATxBz+ALo6znvXAAUwdOgT14CDUw8PgAwEEcnJgOXUK4ydP4sTp08hTKoGODlInnJ0F/vQnwG4HWlpItFl2NqDXA2fPAg89lFiU2fXr5Pa//zfwd38HVFWRetvICFnu0aPAu94F1NWl9n+KQn9/f8LOVOHE+i7eruPGwMAAqqurI143T05Ognv5Zez5xjfApeP8xu8Hd+MGVBGeUlqtKLl0aWVWpRJzLS0oLCqCpK2N1LJVKvJ98fvJ30CAOFcZDKROff48aXa9eZM81mrJcwoFoFBA+d//O/bt24fz589TnbNbrVYsLCxApVItP6ahqKiIyiVQo9FAo9Gk3cVwdnYWRqMRA2vrUGlm165d1C6eOp2OSnDvcrmwtLSE7DgNtACJVKatO9fW1jLnRgaDERHa5iOO45br3HK5HGq1mkqwabPZmJiIccdB22ipVqs3tNmC53k0NTWhvb2dav6BgQEmJtpAmJhoM/Dww7hw4AByBwaw/y1vSc8yZTLgX/+VRKOFs7QEvPvd5DlRBN7+dmAD1YWbiieeWC0kAkhe/GOPEfHV3/4t+R+FOHyYxMrJZKT4UVFBxEf19UBrK8aHhtBHEZmVk5NDdeEVQqFQ4Pjx45iamsLExATm5uao1JnhuN1uWCyW5QEXWttntVq9fGG+XTEYDJibm6PKmC8qKkJbW9sGbFV6UCqVGcl63xSEi4kiORN96lPAU0+tny6RkIIsAPzf/7v6uY99jPyu/+qvSHFrLYcPEzERQIRCu3ev7CN+/3sgXJTW2wu89BLwwQ+Sfey73gVEiNzJBKWlpThw4ABu3ry5ytUsKysLBoMBNTU1UQc08/PzodPpkorOSRTmSsRgMKIhCELKBXav14uhoSHU3Rqwot2v5efnrxLd0u4TbTYbRFEEx3FYXFxMWZhpt9ths9nWdci73e7b3rU7MzMDu91O1dUdjeHhYfgSGaCMQH9/P8rLy6kGHxYXF6m7o9ON1+vF6Ojotj0nm5qaSrmzfmhoCLW1tSk5G9Di8XiSdlFKlWAwCLPZjJaWltuyfgYj3fj9fqoYiVgsLCxgcnISer0+6WVMTEyk5OYMkDiempqa29d4I4rkOvSXv4z4dDyplaukBB0f/zgCMdxzTSYTcXIJdzZ/29siz9zWBtx7L4kW9/mAoSEiQgoESNTZ614H/OVfksacb3wD+OEPV7t5f+c765f5298Cn/scmf++++K8o8Tw+XwYXlvfS5Bo30WPx5PyshNGFJFts0HqcmF8fh61VisRagkCwPMQeR6YmsK+c+diColEnodtzx4sVlSACwSgsNshX1gARBGcICBLJoMyOxtwOCI3aUVB5nKh6PJl8uC554B//ufoMxcUALm5xMUqHIuFvKcQP/85NC+9hOrqaur9itlsRltbG5aWlqjjOhKJxNi5cyfOnDmTlFNqLDLtlFleXp6Q4D4/Px9SqZSqEcJms8VdtiAI1OdaMplslZssg8FghENbxykoKFgletDpdFTXqBsZX8pgbBZoxUS3I5lFr9fDaDRSXdtNTU2tEpYzMgsTE20WeB6L9fXkojxdHD4MvP/9wI9+tHq600mEMgDJxL5wgbhu3EkEAsB//MfqaffeS4REABEAtLUR5xKAdGnt2BFzkTSWsAC9K1E4HMehpKRkOZtaEAQEg0H09fVRFzVCAy5LS0vUB4w7YbCf4zjs27cPg4ODMBqNES3+OI5DfX096uvrWbfMZqG6GpBKyW95fBxYXCQFKlEEXn0V+MpXVs/P82TaRz4CvOc9pJAZzhNPAI88Enudhw+vxEe+9BK5xcLvB777XXL/m98kLme33C5i4nYToSPHEbFiEgrw4uJiFBUVweFwwOfzQSaToaCggKrjt6GhIW1iooKCgoj7G47jtrXrGYPBSI2RkZGUXA5ChLq5pVIp9X5trXiHNnrF5/PB6XQiLy8PJpMpLYMOfX1969bf39+f9gGNZDCZTDh8+HBSrw0EAmnpxna73RgfH6cagNjoeLO1hBxptuN5pDFe3CwFwWAQAwMDG+JONDAwcFscqkKMjIygrq6OuRMxtgUDAwMpufCFMJlMKC0tTcqdSBTFtOzjQ8KoDXcnmpwkgpzf/S6qkCjEQnk5zO9+NwSeR/7AAFQjI+AEAX6lEn0f+EBMIRFAnFucTie1WwkqKoC//uvY8+zbR655n3qKOM/8n/9DxCXR8PmIa9GPfwy85S3APfeQuiTHkaYfoxEYHSXCE4sFmJ8n1/K7d5P6pkxGBDV/+ANw7hyJbFOrYW1qQt7sLNRDQyhqb4f6Vp1sSavF9QcfhIuitiVbWMD4b3+L0nvuAVdWtnwdbjab03bckLpcUA8PgxMEqEZGoO3qgtzpBCcI4IJB8MEguGAQitlZyGM4dHIAwq+mPRoNLj72GIJZWZC5XOBvOTS5S0vhjdNAeurUKahUKswYjeh5/nmIPA9RIlm+8V4vNL29UI2NgQsGoe3qQm4iAu25OXKLh9MJnD6NHVVVqPB6IUgkEKVSBLKzMfTWt2I6guvz+Pg4GhsbqRsmOY5LyIEiLy8PLS0t6I7ROCpxu6GcmgJEES69HsHb7L6cl5eH3bt3J/QanudRWFhIFRU3MzMTV0xktVqpXcKqq6u3rXsng8FInVTqODQ1B6fTCY/Hw5zzGXcUtK6LqTQQJgvHcaitraWOLh4cHMSePXsyvFUMgImJtj9f/jKJO7NagX/4B+CLXyQD1SH6+kg30XPPAeGdoLe6XZb51a+IE4fVCmg05DWPPLL6NeH4fCQGaHISKC0ljkiDg6TQ0NqamfeaCGfPEktngFg4j42tfr8AsGsXuVEgCAK1kjkdO2Ge58HzPFpaWmCz2aiUmiF3ooU41tjhpNIduJUIHaTKysowMDCA8fFxeL1eSCQS6PV61NbWMoXrZkMmIw5ioQGse+4hgqKurtWOYidOEPHQ615HipQA8ItfAN//Pok/A4hj0H/5L/HXec89xII72gB3Tg4RLO3ZAzz4IBDq1AOAhQUiPnrf+6IvXxRJ8fWTnwRCv+nqatKtl4TQlOO4pHJtNRoNtFptSt0ZRUVFOHjwIDiOg9vtxsTExKoCn06n2xD3AQaDsfUQBAFmszkty/L5fBgaGkJJSQm1q+PaIpRarUZWVhZVQdxqtYLneeoBjXg4HA5MTU0td1C7XC6Mre3qvk3YbDbMzc0ldZwZGRlJ2ZUoREgsH2sA2uVy3TZXohAejwejo6Oorq6+rduRbkID0+lgeHg44+5EXq/3trkShWDuRIztQjpciUIsLCzAarUm1WwwMTGRUixqOIODgzAYDJm18+/oAP7pn0g9amGBRICt5d57Se2M5wGJBJaJCUzJZLAePQrhlnPSxMmTSa3eZDJh//79qbyDyOTnk6irN74R+M1viHNvXh65nq2qAqamgK9+lVyvA8B//ie5fexjgE5H6nKDg+R/Eo0vf5nEkf/xj+tcdCpv3daSPTODfV/6Es58+csQpVIgGETe0BAK+vqQa7FAYbdDMTuL7NlZKMIHVgoLgX/9V3je+c7kjxuiiOypKaiHhyHx+aAeHkbN889DurSU3PKiIEiluPbJT2Khqiqp1/f19eHAgQMwzc5ivr4+4jwL4e6KgoDDPA+dIJAazIsvAleukO+rTEbqxDIZEYj19KwWEr373cBnPwuo1cDEBGmicrmIs/TMDOB2g+/txdrqR2F3N/78b/8G95p9hCiKMJvN1LWLwsLChN3Hampq4Ha7MTg4CADImp2F/tw5SN1uKCcnoT93DpJb57V+pRLX//EfYT16dP2CBAHKyUlIl5aIYCvs5svPhz83N6HtioRSqcShQ4eSEudotVoqMVG4G2s0aI8NHMdtu3NjBoORPhYXF7FEecxcW8fRaDTgeZ5KDGyz2ZhDGuOOwel0Uv+ukqn3pYOKigqYTCaqJlOLxYKmpibWrLUBMDHRdqekhAxEiyK5kDt2jIiCJBJyAQ6QAe6aGuC97yWD8z//OXH2yMsjjzUacnEYwmoFPv95IjA6fZoUBdrbySD7vn2kAHDpEhEQrUUuJ0KecFvl20F4x9e9964XEiXIzMwMdX56OnMceZ7Hjh07cOXKFar5e3p6IjrvREKj0SAnTmfbdkOhUKClpQU7brlQJdMZydhAGhpWxETnzq1/Pj8f+MlPgLVdiDIZEQ/RCIjCqawkAqSXXyaCS4DsVzmO7P/e8hZSKAXIfu6550jXZago+Yc/RBYT/du/kU7OhYWVeUMMDxPx04c/nNi2pkhjY2PSYqLs7Gy0tbUt/35ycnJQV1eHuro6uFwuzMzMsIIRg8GIyujoaFpciUIMDAxQC4mkUmnEi2WdTkcVW2axWNImrAhhNBqXxUSbxZUohMlkwqEEIzxDDjTpwuVywWKxxCz+JfJ/UyqV8Hq91E4beXl51LEaZrMZlZWV28qdKJ2OT4FAAIODg2gKib8zwODgIPW1kEKhgCAI1MK3aG6MkQgJpxiMRFGazcC1a0BxMalhGI1AVhZxUpZKiTPq3r3kuiXDDA4OpsWVKITJZEJJSUlC1+CiKKK/vz9t2xASSNVHEVSkTCBAosxCDtiROHYMePbZ5ca9hYUFdPz5z2nbhMnJyczGAXAcqbHde+/65/7yL4H/9t9IJFo4Nhu5xePmTXJLkLzBQex45hks6XSofuEF4iITj9lZ4IMfhNXhgHDrHIMLBkm8mChCYbdDtrAADoBidhY5VivkALLsdmgvXEBuGoTlvtxceAoLIXIcXBUV0L7znZBrtbDPzmJkaIjEm4ki5pqasJjCb95qtaK/v5/abV2VlwftyZMr0fDvf3/0mUWRiIYCASIgCj/Prqwk7s8AqRG/853EpSsCEp8Pu7/+dVz8whdWLZsTBFg7OiBzuxGS4oTO+ASZDEtFRasi7BOJOIPHQ7bbasWOV19F6fXrcI6NoeKPf1wWD61F5nJh35NPov8974EgkyHfZIJycnI5tk4WZfBO5Hl0/NM/YTxMJMhxHOrq6qjPXXJzc3Ho0KGkB9MScWOdmZmJOr/D4aB2PCgtLWVuIAwGIyq0DUEymWxdHJNEIkFhYSGVs9HExAQTEzHuGGiEwwD5XSmVygxvTWR4nkdNTQ16e3vjzisIAoaGhjJaR2IQmJjoTiF08XT6NLkBRBAUivWamFgfC2S3rx/YDqezk9zWTotFyNL46lUyoL/RWCzAr39NBu9DvOMdKS92cnKSaj6FQpH2nXBJSQny8/PhcDjizksreALujIizaDAR0RbhnnvWx5WF0GiAf//39UKiVNmzh9ziIZORfUtpKXDkCJn20ksrws4QJhPw0Y+uiJMicenShouJNBoNampqEu42DsUGRnMVyMnJQWVlZWJFPAaDcccgCEJaBwUBcu4zOjpKNa9Wq414DlBUVEQlJnK73dTCJVrm5+dhtVqhUqmotiGEXC5Pyv1HKpVSDw5PT0/D4XAklKM+MjJCHXtASyx3opAzJw0SiQTHjx+H3+9Hd3c3pqeno86rVCrR0tKC4uJinD9/nmoAbmlpCRaLJW40xFZhamqKWkhFy9DQEGpraxN2DaDB5/NRx0NzHIejR49CIpHg5s2bMQvZCoUCO3bsQFlZGTo6Oqi+b4IgpFVUx7gzKHn+eTR89atkcDseZWXENdXrBRwOcm2Sm0sGx0URaGwkjWDveQ9xaU3w+jedrkQhnE4npqamlqPdaQgJY9LJwMAAampqMuNO9MMfrhcSKRTEETsrizSmfOUrqxzA031eBBDh1r4IkVEZR6kk1+iPP06ci373O9KoE/4ZlpYCzc0kYq28nDgETU+TGl64I7daDfzN3yDY0ICxl19GQWcnRKkUrtJSzLa0YHb3bpSeP4+m//f/AACGWNFrtwjKZHCXlCDLbofc5QKCQVQ++CA0ZWWQuVzInpkh0Vs8D0kCtbW1uPV6SCoqkFVWBktTE0aVSrLcW7FeAs8joFRiSadb9dusrq7Gzp07cf1Pf4LLYEh6/ZHoiyVwW0NdXR19zYzj6Goyu3cTceTgIKkb+3wYNBph7+jAvi9/GZwgQNfVhbdGEqnFwN7YiKuPPAKvRgOO41bXOW024o7l9RLxWGcncUcKxe2F7eM4AJpbt0gEqqogcbvB2WzgBQGNP/lJQtvJCQJ2f/3rmKuvh/uWQ7zBYEBTUxPKysrQ09MT9byU53lUVVWhubk5pbgwlUoFhUJB1dQxPj4eVUyUyPkNazJjMBixoHV8LiwsjHhc0ul0VGIim80Gn8/HHPQZdwRWq5Vqvmj10Y2iqqoK/f39VPXJ4eFh1NfXs9jUDMPERHcyn/scKSh9+cvEXSgeb34z8PTTwPPPEwvaeAMBBgOJFbJaSfGqq4u4FXV1EUFTbi65OHO5yP3Tp0kMWwIDEgnx3HPEFSTcArugAEjSHjqEKIrUO+FMDaA3NDTgcnikUorwPH/HRJwxtjAf+QhQVASYzcRtbccOYnsesofeDKKw/fvJPs3hAMbHgd5esp0hHn98tZAoKwt44AHg9a8nNvHAShzbBrNjxw44nU7qDkUA2LVr122zwGQwGFufsbExalciWsvqRIh2nqbT6cBxXFpdgWQyGbXI22g0QqVSUa+/pqYGNTU1OHv2bEKCIp7ncfDgQTgcDvT09FC9xmQy4eDBg1TzhuKd0o3L5cL4+DjKy8vXPZeIK1FNTQ3kcjnkcjkOHToEh8OB8fFxzMzMYHFxEQqFAgUFBdDr9SgqKlp2GGpoaMCFCxeo1hESPm0Hd6J0uhKFCLkTNTY2pn3ZibiolJWVLTeA7Nu3Dw0NDbBYLLDZbFhcXIRcLkd+fj70ej2Ki4uXi1YNDQ0YHx+n+s4NDw9DJpOxojUjPoEA8IlPoOmrX6V/TaTBl/Bz+ulp4MwZEju1Ywfw5JOkfjM1RRon4pzPDw0NRT6GCUJk12dRBO/zQZDJIFtcRIHJhAKjEerhYXjz8mBvbobU44Hz1VdR8vDDgFYb9y2KopiR/ZDf78fw8DDq6urIBIeD1L4uXyZR2cPD5DqzrQ34xCeIW27IfSVSM97SEtDfT/7nTz+9Mv2hh4APfQioq1slHgpncXExbdGp4UxMTKChoeH2RblXVKw4BQcC5H9ntZJr+6qqyNfxDzxAHHtFkTionz4NqFQwG40wRYn36v+Lv0DRlSvQhNyMb+FTqWBrbYWjrg5LOh08hYXwFBTAo9NBlEiQNTeHEw8/jGyrFXwgAHVYzBkfDBKxCSV+lQrzBgMCBQWQ5uSAf8tbkPd3fwfJLdGsxu3G9f/8T6rjxujoKLKysuAKF1XFQC6XIxAIpPV8OTs7O3O1QokECHMF0zc3o1elwqDJhNpf/zq8Eb78AAAgAElEQVSpRWqMRhz9H/8DI/fcA1VuLrL6+sh+ymIBvvtdINkmgNZWUiNXKIA3vAHS/fvJd/jUqXURfOF48/LgKSwEBAHcrZtibg4ylwtSjwcHH38c9qYmIDcX5bt3AzIZVAMDOPTiixBDTawcBxFE+CyoVBA/8AFIP/OZlf1vCnUwnU5HFak8OTmJ3bt3rzufdbvd1M22KpUqrc79DAZjezE/P08dY1tUVBRxOq3jmiiKmJiYYAJHxrbH4/FQGVIASKjBIxPIZDJUVlYux8zGItREWhMey8tIO0xMdCfDcaSA8eCDpCPo8mViKdvYCNx3H7kIGRwkt7w84G1vI9M++lFiWfzii0QIlJ1NrLRdLiIU0uuB48fJ33CeeoqsD4gcSWQ0An/6E/CZz5ACgd2+0nm0Zw+5UKOhv5/kdQeD5O9zz5Hukkgda+99b8oOSTMzM9QDNaVr8r3TRXFxcUIxC/EoLy9nhW3G5kcqBd71rtu9FbGRSoHXvnYlWvGXvyRdwn19wOQk8KMfrcz7+98DJ06Q5xcWyD5aFIHubiKCDImkNgie57F//36cPXs2bsGS53ns2rVr27gtMBiMjScQCCQ0KHjo0CEMDAzEdI9JBIlEEnVwRC6Xo7i4mFo8Hg+pVIpTp07h3LlzVE5GTqeTOj6N53nU1dVBoVDgyJEjuHTpEpVASyqVYt++fSgsLER+fj7MZjPV+e3U1BTsdjs0mmi92isMDQ1RuxLpdDpUV1dTR/kajUbo9fpVgxqLi4tUAyIA+fwNa7r88/PzqVyXtFotNBoNVaSD2+3G2NgYqqIMfG4VrFYrdRFKqVRi//79eOWVV6jmHxwcRHV1dVoz771eL7WLCsdx6yKOVCoVmpub0dzcHPO1SqUSZWVl1O5EQ0NDGRFOMbYZIdFPiJYWIgDy+0ntxu0m1xYSCXH0OH+eOBDR0tND4ppD8DxQXQ3MzxNxx5vfDOzcSZoe7HYEpqaQ1dGBvQ4H5E4nwHEQJBKoR0aIc4tUimBWFrnJ5VDk5UGwWCCLcT1R9Yc/LN8Pfu97kHzxi0BJCRmct1hIbYfnV24ch0WrFTUDA4AgQJRI4Kivh725GYJMhmBWFnxqNZlXFJEvkWB3RQWuXL0KkeMgchzA8+Rv2GPp4iJyx8excPEiAqWlkP7gB8RVOxLXrgHPPEOEV243EQ3RotUCn/0sEEfMk4hbTH19PTiOoz6X6uvrw4EDB6iXnzGkUhJ3Fe86sqKC1CLD8Hq9sQcYJBJc++Qn0fLd70LmciG/vh6DVVUYOHwYwRjHGG9BAS589rPY+9RTyI/isuJTqeC5de7jU6uRv38/pPn5RBh2/DjwutcBCgVkHIdY0ricnBxUVFRQuWgKggDjGmFULHbt2gWn05lWd6va2toNE0QrFApUVFTA+P73QzUygqKOjuXnxFuCGZHj4M/NhT83d52IJmdyErwgIHd8HC3f/W7iGyCRkP1eVhaJHzx4kHxfW1rIPnPt/6G0FLhwAfjOd+AfHIRlchLO8nLM19ZCkMngVyrh0WrXbWfewACOP/QQ+GAQqrExqELnrr/61ar5ll8lisv3JQ4Hce0Kue8rFMQJSiYj+81AgPxde5NIiCvdXXeR76zfDwQCqJubg7KvDwVGI3ItFkAUIUql8ObnI5CdDZHj4DQYMH7iBGb0ehTp9WRdUikgk8FkMlGL+NmgPYPBiAWtmJrjuKjjbWq1Grm5uVSipPHxcbZfYmx7aCPOOI6LKtLbSAwGA4aGhqjOLQYGBlBZWcnciTIIExMxyIn/295GbmspKyOD22uprgbuv3/99Ne+Nvp6HnyQXAxFEhKFMBqBD3wg8nM//jFxFlrL1BTwhS+QTq+JCVI8i0V1Ncni1uuJs0mK0Bam5XI5tBQddsnS1NSES5cupWVZTMXJYKSRu+9eERM9+ii5reWuu4A3vGHlsUpFivY3bpAOs6tXSZdbLDo6SFFbFEmnXBqKw3K5HMePH0dHR0fUAXulUom2tjbmSMRgMFKiv7+f2pVIo9FAq9VCJpOlTUxUUlISM9KkoqIibWKimpoaKBQK1NfXozNeRHCCVFdXQ6FQACDFsxMnTuDKlSsxhR85OTk4cOAA1Go1ACKsqa2tpconB4AbN27gNa95TUwLZI/Hk5BYrKGhARqNhlos73a7YTab0dDQsDytu7ubekCjqqoqJfFKQ0MDLlI6Cfb29qK0tHTLCveDwSC6u7up56+vr4darYZer48ZFxYiEAigp6cHbW1tqWzmKnp6eqhdifR6PXJTEHAn4k40OzubEdcRxjbj9GkiPPnCF2A7cQK6F16I3WTg9RJnIr+f1Hry88mA8uIiaQTz+cg1xq9/DTz77GrnZoBce4QEGrOzxFk1DCmAWHJIPhAAHwisiIesViRS0pXMzlLVaVS3bsv8/vernhckEghyObhgEJJb4ti7EtgOKgRhteMTLY8+GldIZLPZqB0+QoJYjuOoXdisVitsNht11/5mhGbf7tHpcO1Tn4Jer4du3z6oJycRjCYQC8NVXo4zX/kK5AsLyJ6aQjA7G66SEnCiCN7vRyAnZ3neyspKaGmi0KNQX1+PsbGxtDpgqlQqlJaWQqfTYXh4mNoNMxYKhWLDm4dC/5tLjz22PjI+DiUXLmDfl74EPtZ3ZNcuUjsJxQ1WV69Es7W0RHUNi4pGA3zyk5ABkI+PY7S9Pe5L5mtr0feBD2DH97+f2Loi4fGQJmEannuO3MLIBVAfYdbsmZnl+7qurqhOUc1qNcorKxFUKCDyPPy5uQgoFOBEEcqJCeRaLOTz4HnIy8tJXOGtWDv4fDg0P48bTzxB+WYZDMZ2JRAIUDcF6XS6mNfV5eXlVOJsu92O+fl55OXlUW8ng7GVEEWRyuUHIDXXzVCvCjli0tRMlpaWYDabWbNWBmFiIsbGIZUCf/gD8MorpGtLJiMWxgUFxJHo/vtjd9A9/DBxRMrOXj39Ax8AXnqJbhve+EZiTZ0mZaXD4aBWdJaUlGQ0Z7KoqAiFhYUJRRJFQqvVLg8mMRiMNHD33fHn+dzn1k87fJgU+gESdbZWTDQ8DPzkJ6Rr+No14OWXVz//939PupjXDo77fMALLxBnJKmUCEabmqJuWijuxeVyYWpqCgsLCwgEApBIJNBqtSgrK7utGboMBmPrs7CwQH1RC2BZMJKXl4eSkpK0iHwqKipiPl9UVAS5XJ5QbFgkZDIZamtrAZDCltlspo6riIdEIlmJZbmFQqHA8ePHMTo6irGxMSwsLCAYDILneeTm5qKkpAS1tbXruneqq6thNpupBp+cTieGhobWOfuE093djSBlLEjI6QcgnzWtO1F/f/+yEGR8fBw2m43qdTzPL38myaLT6ZCfn0/l1uP3+9Hb24s9KQw83k5MJhOWKB04cnJyUFZWBoAMCNKIiQDAYrGgsrIyLfEXs7OzVE5BIda6EiWKUqlEeXk5dQF8ZGQEvb29aGpqYudTjOh87nPoEQRMnz6NU/HEbllZJLJsLeE1kKoq4qzxxBNE2PLznxN31MJC4jadRlFDCEEqBR8IQJBKMW8wYK6xEfO1tcixWqEeHoZPpUJRezuyU6xnhOCDQfCJuAVFIyTIUquJK+7b305cZJ9+erWASaMhziRrf8cSCVBTQ5x3eJ4IFO67L+YqExVthmI6Q/dpnWhu3LiBkydPbskO3kT37aFzx5KSEqjVajrHR46DT60mLle3EAEIYe7mkdzsEiUnJweVlZUYCYtTS5XGxkZwHAeZTIb6+nrq+NpYNDQ0bPh3JTs7G1VVVaSJM8FjpPXIEbz61FMof+UVFOTkoFCnW4kBk0iAo0cjOwylibKyMszOzlJ9rgPveAfma2pQuLiIOoMB/OIiiVgURSIePX0a2L9/xb06dHv+eeCf/5nUjUQxoQi+TJDldCKLdt81N7duUjYASSLOegwGY1syODhIXXcJXWtGQ6/XUzs99vX14dChQ1TzMhhbDavVSh0dWFxcnOGtocdgMFA3YJnNZpSXly9H1jPSCxMTMTaWnBwi6FnLBz9Iosy++U0SbyaREJGRSkVyrG02YGyMFLre+U4Sq6ZQEBHRWiGRREJcPrRaUgx729vIRZdUCqRxRyKKInXHNoDM5YqH0dzcjLNnz6a0jFQHUxgMxhpqakg38de/TjqFc3JIJEFxMdlfvfWtxAp9LYcOAd/+Nrl//jwwOkpEQMPDZD/57/9OlheNr3+ddBsrlWRZd99NCmVf+tK6DmO84Q1kUCErCwh1iPE80NBARE0KBZRKZcyBYgaDwUgGn8+Hy5cvQxCEVdO5QAC5Y2OkezUUYaBUIk+rhU6lIm4API/GxsbVYiJRhMTjgXxhAbLFRRJnIpFA4vWSQaFbnWbSxUXIXS5IFxeR4/NB63CQQn15+fqNnJsD/+KL2HPlCmYWFuDRaLBQWQlPQQHpTE9gEMJgMEB2axCK53k0NDSgIyy2IRWiuetwHIeqqqqEorWkUilqa2upC289PT1Qq9URXTj7+/up3RUArOokKi4uph70EwQBFy5cQFVVVUKRHlVVVctuTqnQ0NCAy5cvU807OjqKvLy8LWelPjExAbPZvDJBEMCJ4vJfThDIfUGA1OPBjooK8NevA34/1H4/GsbHYZ+ehkenw2JZWcyBwatXr+LEiRPICXN/SBS3242rFA4UIUpLS6GK4xZCQ319PSwWC7XLhNlsxvz8PPbu3bspOgAZmxCex/TrXpf+5RYVkRrMN7+5Ms1uJ/Fi+fnE+fTll4HZWQRcLkx6PPAolfCq1fDl5cF36/ci8fngKinBYkUFOFGExOtFiVqN1oYG0khWXIxxrxfXOzrIgHcUQYJ0cRG1v/411ENDyFeroSgtXblGEQRAFOF0ODA1MUGizHJzIUqlkDud0HZ2Qjk5CYgipEtLkIcXy7OzSW2I4+DzeCAEgyQq6NYyQ/sxQS6HS6+HNy8PIs/D1dSEys9/HopIA1V3301cnTweIjhKUye7KIro6OigLvaH3ARDGAwGDA4OUgl4XS4X2tvbsX///i0lZkxl385xHBobG6mFyvGoqKhI6TgVIuTAs/Z8OBnUajVKSkqWH1dXV2NwcJDaATQSoTi220F9fT1GR0epRenhLNTUwFRXh9e+9rWkhrzB7Ny5E263O77AnePgOnoUrceOgV/bQBuLd7yD3EI4ncSxXxDIfjbSTSol+/nnnyd1odC0W7elYBA9EgnmGhogZGVB4vUiy+EA7/NB5nKh9MIFaHp6wPt8kAgCpADEW85CfBq+v1yKjRsMBmNrs7S0hAFKhzWJRLLqeBcJpVJJ3fQzPT2N6enpTRHvxGCkE0EQEnIKj/e72kjy8/OpDTQEQcCVK1dw7Nix5borI31sGjERx3HlAL4A4B4AhQAmAfwawGOiKK6Xq0dfjgbAZwHcC6AUwCyA3wP4rCiK9G0rjI1nzx7gG99YP72yEnjgAXL/qafIrbQU+Md/BH7605X53vlO4K//mgx8R8lKTQeCIMDtduPGjRuYCbN6/f/s3XmcHFd57//v09uMRrNqGe2bNaNdsmXZBssLNiG+DsGQH5BAAiTmJiQkYV8TAjcmhiRAgBAggYRcnF8CgQQIWyCQYIxtjPFuy4tkWbI0tqWRZGubfT33j1M109Pq7umZ3ns+79erXjVdy6nTNdVdT1c9dU42iUSiIE/WTqetrS3nLgTSaW9vJ2ACiuGDH/TDTDz/+ZN/f+c7fsgmTEw6e1a6+WY/7dln/XD4sPRv/5Z53R/8IPO8WMxfwG9r8zcaXvzi3N8D8jJnY6PhYdUfPar42bP+Bs3AwJQLnIrF/I2o557zCXWLF/vk42jUJ3VkGjc0+Bs9jY0ze7J1bMzfLOrtlfr6/OvkJ0Klqa9Th9On/U25o0f9hdvmZl+HgQFfp02bfL3Gx33Zwc149fb6LlwHBvz0sTHfTUn4d/IQ3AyTc/6G2YUXSlu2+H2VevE4HvdDLDbjJ3xnJdwHQfKPIhE5Mw2dOKHhT39a9t3v6pLhYblIxA/RqJyZmp56SrHpbnpEImqOx/ViM580FIn4C9v5dOUQtiqQvE9PnpRGR7VUUupP6vFIRP1Ll2oguZsQ5zTa0KDh5mb1t7erb/ly9S9bptGVK3VeSleyK1as0P79+3O+aZhJulaJJoyN+fPAY49Jjz/uW1QYHPSfn/Fxn3S6YoVvAa+jY+LYWLdunQ4cOJC5daIwiUSSxsd19223qWP9ei1asEDxaFRDPT06tnevjnd1qck5xQYHFevrkzmn8XhcZ1ev1nBKF5kLFy6caJVI8jf9NmzYMHHTMDowoJW33KLW/fsnurtwYTJXcBzITFuCcfg6+e/ksZlpTdhixObNfl8krTPxBHvy6+TvoqRtL5HUPjionp4e33JBIqGRxka5aHTiWApvWEdGR7Xv1ls1+NhjWt7SogbnFB0e9jdz6+p8wnHYEsLp0/7p7fFxf47fu3eyW53kZJVo1K8X3DCfMoyM+Acz0v12CecdOzb1OyUY3NiYxkdHNTI0pJaeHv3S6dOKDg35m/AzlNzgdO+yZepfunTis6/wOyAS0XjwvX3q4x/X6OrVmtferlg8Ljtzxh/LYWsjLS0+4SGRmPifuGhUo5L6RkZ04tQprZRvRcJFoxqPxeRiMWl8XNHhYUWGhxUZHfX7fmxMa1av9l0/Je/TlSv9fg2/D5L2TabX84eGdMkdd2jkyScVGR2VjY/Lxsb8ECRb2diYtoyM6KvXXy/Jd2l02223TelyEFPN2bio1BYs8IMkt2yZBq6+Ws8++6z27t2roWwPMgTCllvWXX75lASblc7p8f371d/fn3Hd0cZG7XvtayX57/9169ZNdAs5PDyso0ePZmzJ8IlXvnLK68jwsJrq63XFlVfKkmK/vlOnZvQA1uFHH9Wm8XEtXLhQ9fX1iiQnEDc2Zu9ybgZGRkZ0+vRpPfHEEzlfZ5J8okhyEmIikdC6deumJn5m0d3drZ/97Gfq7OxUS0uL4vF4RSYWOec0MDCgEydOaN++fTNqKTK5C1TJ3yDJ9cZiNpFIJO9WiULz5s3T6tWrdejQobzLClslCkWjUW3atEkPPPDArMvcvHnz1GO/hOrq6tTR0aF9+/bNav3Ozs6CJI3PRiQS0SWXXKKDBw9mbPHTzLR69Wpt3rw5/xtfzc2T8WM2ixb5B8fSqHdOp2++WQNJ39X9STcVuy+9NH2Z4+NqOH5c848ckY2N+e4ue3omupkcamvT2XXrtGTDBm3t7PQPDff0+Lg3kZASCf38gQc0VMVdL5YacRFqyejoqE6ePKn7778/5+6plyxZkrWr+tCKFStyPuf//Oc/144dO6q6W3IgND4+rr6+Pt1///25tcopn7xTaS37dHR05NwbT09Pj26//XZt3Lhxoru2csWwtaYikonMbL2kOyS1S/qWpL2SLpH0VknXmtllzrlpjxYzWxiUs0HSzZK+ImmTpNdL+mUzu9Q5l3sfCkX0k5/8ZMqPiAcffFCSJk6Wheore7blzGa9YtXZVq7U5StXqjG5CeOjR6X3vW/i5VhdnW552cs0VFfnn57L4Qnv2dZ3Nk8KrVu3rmRfWtu3b9fJkydn/NRRLBbTjh07ilQrADMWJhicOZN5mUsu8d0/NjT4JrrXr/c3sz74QemjH83cdWRjo/Rrv+YTHL73vez1GB31Q1+f9KpXSXfd5W+4phob8zciJWnp0qI1Fz5XzLXYaM+ePRPdhrY88oie/7a3FW1bLhLRaEODRufP11giMdmaRnKrGs4pMjKi6OCgb9GmRo3HYhM318ejUT9Ofh0OQZLPeHCjPnmai0anLJucrBEZGVHbo4+qIaUbMpNUHwz5vYFxaWhIBe1wYQYt6EhSZHxcjUeOqDHXRO76ep+wtGSJNDoq6+vTlT09Gunr8/stSLZKl/wyIc1NvkRdnRLxuG9lorPTX5Q/dconnuzbl70r4VRBQkssHtc1Y2Nyo6OTLdAECTGWQxzdKH9Ve0uWZUbmzZtMJDFTPJGYTAAM9sfSSES/ODysMedUd/asYoXotqZI0jWK7sxy2l+VyiRFg6GQGo8eVeMMP2+5MElxSa3BUC65PJ4xP6Ulsf7+ft1+++3avXu3WlvLWfvKM9fioqefflr79u2bcs3iwQcflHMuY4Jn6vWNdNc7sl0DSbf+bFoCkfxNm5aUlnrMTFu2bMm5RRnnnA4ePDijblCTjScS2rBrlyyltbG2tjYtW7Ys5xbzBgcHpyRhRCIRRaPRGXf3FCZ3OOfOGcbHx2d1nSmRSKRNZuno6FBXV1fOCTfPPffclAv00WhUsVhsRklFycvmcuwlv55u+XyOxVWrVqVN0Ny+fbtuv/32vK5jdnR0FKRVotDGjRt19OjRnJL2MlmyZEnap8lXrVql7u7uWXUNvGLFipK0tJ5NR0eHTpw4oZMnT85ovYaGhrK3vh6JRNTR0aE1a9aoq6tLR44c0eDgoGKxmJYsWaK1a9cW9DjKV5jI+cgjj8xsxeABi/4srRnEYjGtv/RS/3soTWuwAzl2UYy5Fxf19/frjjvumDIt9V6aVLh7U/mWVcr7cZVUh3zWne39tlysWrVK+/btyzlJ6aGHHtJDDz2kSCSiSCQiCx5CCu/tZYqPCvn/y1Uxtlms91Hq/VNN76NYdZ3N56rccVM67e3tObdOJEm9vb269957J15HIhHFYrEZ35+vhAcsks91kUjEt7ZZJhWRTCTpb+WDn7c45z4dTjSzT0h6u6QPS3pjDuX8uXzw8wnn3DuTynmLpE8F27m2gPWetYGBgSkXgcIfiwMVfHG8nO7+oz/Spi99SfHeXjU+/bTqU/pWPvjSl6q/ra3s/UOnE4/HS9o1UCKR0EUXXaQ77rhjRieMrVu3at5MmtMFUFzRqO+S7D3v8QlF9fXSC14g7d7tWxXZvt13G5kaCEUiPpnoPe/xN5KffdY3Yb1nj39ifs0a6a1v9a1QSL6Vittu8wlDPT3SM89I/f3+xvOdd/r5od5e6dprfdeRF1wgveENPnHoa1+T3vKWyZvwa9b4LiqL0R3D3DGnYqPh4eGJGChS5Kd/bHxc8d5exfNsCaYWREZHfTdiFWpg0SINtbbKxsYU7+3VvOFh2ciIb0klS+tDY/G4hltaNBI8TWNjYxpPJKTxcSWCp3FGGhs10tioxhUrVLdkif/+uvXWzOVefLG0e7fGhoZ06p571NDVpXhPj+Izjd0HB6Unn/RDoKCJGvv2+e/0fDjn6zk4qGKnheay/8Lks2pVzYlEuXBBIpgLEsDC5LBY2GpQXd1ki2jxuMad0/jdd1d0UlippOsOpKWlhZaJ0ptTcdHo6Og5LfiE14zy6aqoFGKxmLZsSZ9GumzZMi1evHj6Ln8KoL29PWMT/Vu3btXx48dnlaASJv5kbLWvhDK1ZBKPx7V58+aJi88zNTY2NuvknUoSi8W0Od1DMPJPXa9fvz7nFpxSNTc3F6xVolAikdD27dtn1IVbsng8nvUBwQsuuEC33Xab+vr6ci6zsbGxIh46jEQi2rVrl2699dYZJVvt2LGjYp5Gj8fjWr9+fUXepEu1Zs0aHThwoODnm46OjrK1ElWD5lRcFLZOl4x7aXPbkiVLprRonE08HldHR0fOXbiHZpvsDVSrxsZGLStijz/52LZtm2699dZZJV6Nj4/PqFXTSpJ8rit3TFv2ZKIgk/oaSYckfTZl9p9K+l1JrzOzdzrnMv7iMbNGSa+T1CfphpTZn5H0Dkn/y8zOq4SMasxM7+rVuueP/1iSb6565c03q+3xxyXn1LdsmQ68/OVlrmFm69evz6nJxUJqa2vT9u3bc754tGbNGq1evbrItQIwY294gx9mY/58P6xc6RN/MtmwIWMT15J8VyKPPCJdeaX/u6vLD9/4hnTjjb47ldQnHA8flq65xnfV5txEtynndXTo4BtzuZ4xt8312Gi4pUVnWls1MG+eou3tGquv990CJXURI+c00tio8XhciTNnfJc7QctCya0LJb+ODQ0p1ten2Cye9h2dN88PdXVTWo4JJb+eMs9Mo/X1GlywQENtbRpualJsYECxwUGNJRI+SfqZZxQZHZ1shSbs5ice11Brq0aDlltSuwFKHcLtJc6eVdu+fao/edLvh+RhbMzvx9HRtDexi2UskdBYXd2U/4uc09l16/Tkddfp7Jo1vl5hV0Dj4xpcsEADSV2vrl+/furNyfC7ZWRkolumhx98UF3d3X5bOTxBsnLlSu3cuXNyQn+/7z4qtWurhoaJLgOikka7u/Wju+/2r4eGNP/pp1V35szk/945xQcGVHfqlBq6u9V8/LgWnTole+op38pbOSxZ4luV27TJd21QX+8HM58oev/90u23+y7dZnBsuLDlpEhETppIcA27qhppbNR4IiFnprH6eo02NGg8GlW8r0/NTz45q89j7/Ll6rrmGg03NU12HZY0ljTRBVfYrZhJU7sac04rly9Xc1OTP4YOHpSeeML/ndxNYXJ3d+HrsMvB5GXD8uUvVAwNDio6PKx4b+9EMtGUfRWJaLS+XmP19Rqrq9Nofb3viisSUXRoSPWnTvlW0cIu85qaJloH61u+XIMLF/qypIljPTI6qrpTpxTv65vcD0H9XCQy8T3kUj8bwbzBhQt962NJLWQlJwg5M43H4xpO+l5K9zkzM73gBS9QU0prIBObk3T4scd05NvfVnRgYOp3VOowOqp4b+9E4tFYXZ0G2ts10tAgc06xvj7F+/pkyeeIYJj4rksawtcuGtV4PK6xRELj8bgWrlihtsWLz30/g4O+C7iTJ6e0ljXt39GoT6w+7zwdOHJEJ8+enWzhLalLxycOH56yufr6el100UVlv0hUaeZiXFQJT0HO1oYNG7LeLN62bZtuueWWoj6dHIlEtG3btozz582bp87OzhnfVKokra2tWrVqVcGp44wAACAASURBVMb5q1atUldXl06dyrmnm5qzceNG1aW0AJc6/9ixY+rp6ZlRuZFIRDt37izKd/WyZcu0YsUKPfPMMzNed9u2bVk/e/F4fOLBw1yS4RKJhC6++OKSX8/MJDxH3nnnndMmu5mZtm/frsV0mTUr0WhUGzZs0EMPPVSwMiuhlahaMRfjIiDVpk2bZrT8unXrdPDgwapNKABKoaOjo2J/hzY3N2vNmjUF6RIYs1MJvwiuDsY/dM5NuXLtnOsxs5/KB0jPl/SjLOU8X9K8oJwpvwSdc+Nm9gP5YOpqSQRAVWw8kVDXtdeq69qyJ8ZPK+yvvhxWr16tRCKhhx9+OGOWfiKR0ObNm0kkApDZvHm+JaIvfEF67Wsnb5pK/kZqciJRY6O/0drf78cpzRDT+lnO5nRsNNLUpM+++92SfBcEhWajo4r19yve36/I8PBkgk7KjX4XjWp03jyNJRK12W1fePM95SZ7ZGzMv069MR8m+iQldaXevJ9IBgrKl6TelSt1atMmuTxuRMyfP18bUpMezaRYzA+BrZdfrrGHHlJXV9e0Zba2tp57fDU0+GEaS5cuVWdnp/bv36+xujqdzXJhvLGxUZdeeqksvLnT0yMdOSIdP+67IgsTPxMJ9fX26p677tLI8PA53YpNSPo7Eolo+7ZtWrhwoZ8wNuYTHw4enEyC6ujwSUQ5PrUnySfKDA357/hIRIpGtefRR3X4qaemJtPle5FhbEyxgQGZpB3btmn50qWTCTvhkPR6ZGhId913n07On5/3Z3Lp0qVqvvji/OqfQURS95NP6uGHH55MzCrE/qoSnZ2dGROJQms3bdIzzz2nExVwk7utrU3n7d5dtO/5NaOjOvKzn+n06dPnzHsm5fN80UUXZb3xPYfN6biomrS2tk57/aOxsVEbNmzQvn37ilaPjRs3an7QOmEm69ev19GjR3UmW5fSFSoSiej888/PerHfzLRjxw7ddtttc/Kp+ra2tmmPxUgkouc973m6/fbbc26Bxcx04YUXFrUFufPPP18DAwMz6tKrs7NTK9N0G5WqublZl112mR588MGsiWYLFizQ+eefr8bGxpzrUAoLFizQZZddpocffjjj/mlubtb5559Pd6F5WrVqlQ4cODCjlqyyqaRWomoAcRHmtBUrVsz4PByLxbRhwwb/Gx3AOebPn68VYS8WFWrTpk06duwYLdKVSSUkE20Mxo9nmL9fPgDaoOwBUC7lKChnWmZ2b4ZZm3p6enTLLbfkUkxGDz300JR+OsMPwJ49e/IqF5Vl06ZNuv3228tah0gkosHBQZ04cUJnzpyRc0719fVqb2/XsmXLdPDgQR08yG8CANNYvlzzv/AFzXv6acV6e7XsP/9TLY8+Ksm3QNF9zTV64g//UNGhIW258Ua1pmkZbWxoSIU4h4Zm+hRpFam42KjYcdG+ffum9H1MXFTBolE/5Mo56bHHZr25WCym7du3zyie6u/v1+HDh6fE2iEz09KlSzVv3ry8Y7SBgQF1dXVlfLot3M6dd96ZqQA/PPvs5KRFi7Rv375zurdJFY/HtXHjRu1JTeqsr5eSW3AaGZEK8FTx+Pi4nnzmmaJ87y5btkz1zz2nx3Po/7y/tVWPPfxw2v9trhoaGtTQ0FCwc1Emx44d0/Hjx4u6jUqzYMECNTY26mjY7WkWw8PD2rdvX1mfDk0kEtqxY4duvfXWom5nbGxMR44cmXKek6ae69avXz/rLomS1WhsVHFxkVTc2Ki7u/uc3+iVHhvN5PPknEv7mSiEhQsXqqmpSU8//fS0yw4NDWnv3r0V0WXZTHR2duq+++7LadmBgQE9/nimQ7421dXVafv27frJT36S0/LOOT355JPqnaYL5PBG5L59+4qaDCf588bRo0f1bFKMmI6Zae3ateru7lZ3amvBOWyju7tbZ8+e1ejoqGKxmJqbm7Vs2TKNjIzMuru1UjEznThxYqL+dXV1am9v1/z58/XAAw+Uu3o1oa+vTw8//HDeLcmtWbNGjzzyyLTLhTFMIWN04qLaiIsGBgbOiX/e9yd/MuvyMDetCwYA1Sku6UXlrkSJXSfpA+9/v/bs2SMzm/aBmenkExdVQjJRSzDO9DhQOH26RwoKVQ5QEGvWrMm579ZiikQiWrx4sRYvXjzxA7BSm6sDUNn6zjtPfeedJ0nqfvGLFTtzRpHhYY01NGgsCGbGGhv1wCc/qfkHDyrW2+u7QYlG5SIRnea7J1fERoB8vLJx48YZt2q2ZMkSLViwQMeOHdOpU6c0MjKi+vp6tba2avHixYrH4wWpX3t7uxYtWqTTp0/r2WefVV9fn8xsop/x2fzImzdvnrZv366uri51d3envXi/cOFCrVu3TolEohBvIyeRSESbNm3SQw89NNFndyG0tbVp7dq1OS/f0NCgjRs36tFHH53VjY1EIqEtW7YoOpOEuFk677zzNDg4qLNnzxZ9W5WgoaFBnZ2dOf/OCFtI3bNnT1lazQiP6VJ8jqLRqDZu3Kinn346bctpS5Ys0ZIlS4pejypGXFThZvp5MjN1dHRocHCwYK1eSFJTU5M6OjpyXr6urk6bNm0qyM3yUlm5cuWMum5atGiRBgYG9NRTTxWxVpUjGo3O+Ls9kUho27Zteuqpp3TkyJG0x0LY0lG2bsQKKexmqrm5WV1dXWmTqOfPn69169bNupWk1tbWidZ7hoeHSxpXFkJjY2PFtZxUa8Lv1P3790+/cAbt7e0V39JBFSIuAgAAJVUJyUQVyTm3K910M7u3qanpwquuuiqv8oeGhqY8hRlmVxejOw+U3rp167Rt27ZyVwMAyufqq8+ZdMstt6hJUr7n0NB03aigcIodFzU1NenIkSMTr4mLEIvFtGvXLrW3t5e7KmUzODio06dPT7RGUVdXp5aWlryfRMnH5Zdfrp///OcFecp32bJluvDCC2fV5cHll1+ue+65Z0at2jQ3N+uSSy4paZebV155pe69996ab6Gora1Nl1xyyaxuRO7evVt33XVXSVsoSiQSuuSSS9TW1laybYaOHz+u++67TyMjI9qzZ4+ampr0+te/vmBdfxAblU4xY6PDhw+f811fqbFRIpHQxRdfPKsHqa688krdddddM+rOKZOFCxfq4osvnlXC8O7du3X33XdXfAtFGzZs0MaNG6dfMI39+/dr7969Ba5RZSnEd/vQ0JBOnz490UJkIpFQS0tLWZNWRkdHdfr0afX29so5p1gspqamJrW0tPCgIEri0KFDs2oVb926ddq6dWvOx2nYck2hrhdJxEWlVMy4qK+vL6+WaQEAqFbhQ6eRSCTvGCmfuKgSkonCLOeWDPPD6adLVA4wa5FIRJs3b9Z5QcsdAADMArER5rTW1lZdeOGFZU2aqQT19fVaunRpuasxxbx583TFFVfo0Ucf1aFDh2ZVRiQS0datW2fUIlGqhQsX6qqrrtIjjzyiZ555ZtrtdXZ2qqOjo2AJG7mKxWK65JJLdODAAe3du7dqWr6Yic7OTm3YsGHW+7atrU1XXXWV7r//fp04caLAtTvX4sWLdcEFF5SsdYlU7e3tuuKKK3T33XcrkUho48aNJT8uq9Cci4uqJUFg0aJF2rlz56w/T/F4XLt379bjjz+u/fv3z+o70sy0YcOGGbWMlio8p9x3331F6XotX3V1ddq5c+eMWiRK1dnZqdbWVt1///0FbWGwUixevFg7d+5UXV1dXuXU1dVVXEtxsVhMixYt0qJFi8pdFcxRa9euVWNjo+6//34NDg5Ou3wikdD5559fcb9jasici4vS+fMPf1hS5SVZo/AaGhq0a9euidb0CmFwcLBi4z6gFGKxmLZu3arVq1eXuyqz1tPTo/vuu6+mWwOvpC7OKyGZKOxoOlP/q53BeLpOvgtVDjBjZqbly5dr06ZNamhoKHd1AADVjdgIc1JTU5POO+88rVq1qmpupM5F0WhU27dv18qVK7V//34dP348pxvAsVhMy5cv14YNGwrSOlBdXZ0uvPBCbdiwQYcPH9bJkyfV09Mj55yi0ahaWlq0ePFirVq1Ku+bi/kIu/NZunSp9u/fryNHjpSlW69CMjMtXbp0oguWfNXV1en5z3++jhw5ogMHDuj06cLfs2htbdX69eu1fPnygpc9U/Pnz9fll1+u/v7+qutWpkyIiypM+HlatmxZ3ufrsEvT5cuX6/HHH1d3d3dO35GRSETLli1TZ2dnQVqeqK+v16WXXjrxPXTmTKZeX0qnrq5Oq1ev1vr16wvSRevixYt19dVX6+DBgzp8+HBNJBVV0nc7UMsWLVqkF77wherq6lJXV1faG3ctLS1asWKF1qxZo1isEm451SziIswJ8XhcHR0dOu+88wr+8EV9fb12796tZ555Rvv27Stot7tAJTMzrVixQps3by7bA1aF0tTUpCuuuEJdXV06ePAgn+Miq4TI7sfB+BozizjnJq4amFmTpMsk9Uu6c5py7pQ0IOkyM2tyzk20vW9mEUnXpGwPmLV4PK76+no1NTVp0aJFWrJkSdV/+QIAKgaxEWpeNBqdiKVaWlrU3t5e0CfNUHxh11aDg4M6fvz4RBccQ0NDcs4pEolM/I/b2tq0ZMkSRaPRgtejsbFRW7duLXi5hdbY2KidO3dq27Zt6u7u1smTJ3X27FkNDAxoZGSkohOM4vG4Ghoa1NzcrLa2Ni1durQoCVrLly/X8uXL1dPTo+7ubp05c0Y9PT0aGhrS6OhoTklrZqZYLKa6urqJ75elS5dWXDcXsViMh1ByR1xURuHnqbm5WS0tLVqyZElBkgh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or8XFxbAvfO18dKKvr8/xSZt46O/vd1yEkJubq/r6enk8nrA6PLS3t8f19RUMBnX37l3H40+fPi2PxxO3eJw6ceKEcnNzHY2dmZmh6E9SV1eX44vF5eXl28UDTot5nJzo9Pv96uzsdDSftFmAZ4xxPH5LTk6O4xO01lq1t7eHfQwg05w5cybifQsKCraLQF0ul5566qmw30tyc3N15coVGWPkcrnCKnbs7Ox03NUnGXV0dDjOBY4fP67CwkJVVFQ47k60vr6ue/fuRRMiAABAxgk3h2pubt6+OcrpzS9jY2Nps2wvAAAAACA8FBMlqby8PFVUVDgau7S0lPAOOcnG7/frxo0bstY6Gl9ZWbl9wau0tFRVVVWO9rPW6ubNmwm5QOjz+cI6adTU1LR9sujMmTOOuxMtLCyEVXwRrq6uLscFUVlZWVFdTI4ll8sVVizt7e1aWlqKY0TJbWRkJKxlbHYW9YVT4Dc2NqaBgYF9n7PW6vbt2467leXn5zteZm0/4XQnmp6e1sOHDyM+FpAJ6urqIu4odPHixUd+H/Pz87cLg5xwu916+umnlZ2dvb2tvr7ecXeitbU13b5923Fekkz6+/s1Pj7ueHykf7/7+/s1NjYWVmwAAACZylqrW7duOb5hp7CwcPv/t7m5uTp58qTjY92+fdvxcuUAAAAAgPSRNMVExpg6Y8wfGWNGjDFrxph+Y8xvGWPKwpznWGi//tA8I6F5nbf6SRKNjY2Ox3Z3d2tgYCAlL1LF2urqqt5///2wOgbtvtgVzsWvhYUFvffee0d6YmV6elrvvfeeAoGAo/G7TxRlZ2eH1Z2or69P9+7di2mHomAwqHv37qm3t9fxPsnSlWjLqVOnHMezsbGhd999N6GdrBLF6/Xq1q1bjscfO3bskWLK4uJi1dTUON6/vb19z99Dv9+vO3fuOF5OT1JYd2vuJy8vL6wuc11dXert7eXvOLaRGz3KGKMrV644Lobd0tDQsG+BdlVVlZ555plDu8zl5+frueeeU1nZo992l8sV1pKXo6OjunPnTsp0KLLWamBgIKzugbW1tSoqKtr+vLKycs/37XFu3rypkZGRsOIEAGQG8iLgezY2NnTr1i1NTEw43md3191w/r+7urqqd955R4uLi2HHCgAAAABIXeFdjYkTY0yjpLclVUn6S0n3JD0j6eclfcIYc9VaO+1gnvLQPM2SXpP0NUktkn5C0t83xjxvrXVeuZBgVVVVKi0tlc/nczT+zp076u7uVnFxsTwej7KyspSVleXornsnY5xc4H7cmEifc7r/xsaGlpeX5fP5wroYv7Mr0ZaysjJVV1c7vhN/enpaf/u3f6uSkhLl5+dvf+9dLlfE339rray12wU8gUBA6+vrWlhYCPsEzs6uRFvOnDmj/v5+x3exdXd3q7+/X6WlpcrOzn7ka3QiGAzK7/fL7/drfX1dPp/P8bElyePxJE1Xoi1ZWVlqbGx03CFqaWlJ169fV3FxsQoKCra/h263Oya/p4e97g96PtztTsYEAgGtra1pbm4u7EK7lpaWPdvOnTun8fFxxzHduXNHXV1dKigoUDAY1OLiYljFcPn5+aqvrw8r7v00NTVpeHjY8bE7OjrU3d2t0tLSR/6OO/09i2RJtqO01Z3q/v37kjaLPHJychIZUtIiN9pfQUGBLl26pBs3bjgaX1NTo9bW1gOfr6qq0ssvv6yBgQH19PQ88veqoKBAjY2Nqq+vP/B38OTJk+rp6dHy8rKjeAYHB+X1elVaWqrc3Fy53e7t9wEnDvod3/23MZrPA4GAVldXNTc357iT21Zs586d27O9paVF3/3udx3NEQgEdOPGDXV2dsYtnwWAZLE7L9rv/6XYlGl5kc/n23MuYvfrZT9HfVNCvI4Xj3nT4XtjrZXf79fKyop8Pp/jm8skqaioSCdOnHhkW15enk6dOqW+vj5HcywsLOj111+P2/kMAJlt9/tcYWHhnr9bAAAAOHpJUUwk6b9o86TQp621X97aaIz5kqTPSPqipJ92MM9vaPOk0JestZ/dMc+nJf126DifiGHccdfU1KT333/f8fiVlRWtrKzEMaL04nK51NbWtu9zra2tmpycdFwEYK2Vz+dzXPx1VIqLi3Xq1Kk927Ozs9XS0qL29nbHc21sbGhycjKW4TnW0tLyyPIyyaKxsVGDg4OOLyRL0vz8fEZ2KHLixIkTKi8v37N963Xc39/veK6NjY2Ifx/b2tqi6kq0JT8/X2fPng1rKcr19fWw7jBNJcPDw5K03amktraWYqKDkRsd4Pjx49vdxh53oaahoUFtbW2HXrhwu906c+aMTp8+rfn5ea2vrysnJ0fFxcWHxuJyudTa2hpWrhYIBDQ9fej1zpRz+vTpR7oSbamoqFBtbW1YneHIZwFkgt15UVZWFsVEB8uovGhubm7P/x92v16AcByUE587d05er1fr6+uO5+J8BoB42P0+V1VVRTERAABAEkj4MmehO8xeldQv6Xd3Pf2rkpYkfcoYU3DIPIWSPhUa/4VdT39F0oCkjxtjkqu1ySGqq6sdXcxCZM6cOaPCwsJ9n9vqSJDqnnjiiQMvpJ46dSolXl8HFUQlg60LyYie2+3WhQsXDnz+qArKqqqqVF1dHbP5zp49q7y8vJjNh/RHbnS4kydP6oUXXtDJkyf3FKQVFBTo6aeffuz7336MMSopKVFlZWVIiQNBAAAXpUlEQVRY7401NTWqqqpyPD4d5eTkPHaJ2NbWVsfdlwAA2Im8CIjO8ePH913yV9rsAH3+/PkjjggAAAAAkCoSXkwk6aXQ47estY+0gLHWLkj6jqR8Sc8dMs9zkvIkfSe03855gpK+uet4KcEYo4sXL9IOOA7y8vLU1NT02DFNTU0qKHjsOcmkdvLkycfe3ZoKry9jTNgXhI9aTU1NTItPMtW5c+eUm5t74PMej+exxUax4Ha7D+xWlkxzIu2RGzlQUlKiS5cu6dVXX9XHP/5xXbt2Ta+88opefvll1dbWHmksbW1tGV0s09raKo/Hc+DzeXl5jy02AgDgMciLgAh5PJ5Db36qr6+nKxoAAAAAYF/JsMzZudDjQWvAdGvzLrRmSd+Och6F5jmUMebGAU+1LCws6I033nAyjWPXXjr4fFWZpB+I6dHglFvSy4kOIs54fcXGM4kOIEPUhz5STY2kTyY6iCTwSUmf/5Vf2V5e0eVyRVWwubCwcPig1JR0uVEy5UX7yQ59JEqBpL+XwOOngrOhDwDApt150fz8vIaGhqKaM01zo6TLi6T45kZjY2Pq7e19ZNsvf+5zEc8HPI6RdDXRQQDIeFvnzH7ji1+UJJWWlka99HWa5kUAAABHKhk6E5WEHucOeH5re+kRzQMAAJBI5EYAAACbyIsAAAAAAAASIBk6EyUla+1T+203xtwoKiq6cu3atSOOCACA1JeXl6cnnnhCkvTCCy+ouLg44rmKiopiFRYOQV4EAEDs7cyLLly4oMbGxqjmIzc6OvHMjQYGBlJ6uXUAACK1lRdVVVXp2WefjWou8iIAAIDoJUMx0dbdXyUHPL+13XdE8yTEG6+/Lkk67IRTIBBQe3t71O3PM1F2drYuX76s6urqiOeYnJzUrVu3tLa2FsPIYqesrExXrlxRfn5+RPtba9Xd3a0HDx7IWhvj6JwxxqipqUnNzc0yxiQkhmgtLCzoxo0btNM9RG1trS5duiSPxxPR/oFAQHfv3tXg4GDEMbhcLl24cEGnT5+OeI5wBYNBdXZ2qq+v78iOmSy2lvHAoTI+N3KaFyWr/v5+dXR0KBgMJjqUuDh58qTa2trkdrsj2n9jY0O3b9/W2NhYjCMDgNRBXuRYxudF0veWfdm6yArsx+12q62tTSdPnox4Dp/Pp5s3b2ppaSmGkQHA423lRbzLAQAAJJdkKCa6H3o8aF36ptDjQevax3qepOZ2u3X58mVVVlaqq6sr6rWDM0VdXZ3Onz+v3NzcqOaprKzUxz72MXV1dSVVQZfH49HZs2fV2NgYVQGOMUbNzc2qrKzU3bt35fMd7XnU0tJStba26tixY0d63FgrKirSCy+8oO7ubvX09KTtxeRI5ebm6vz586qrq4tqHrfbrUuXLqm6ulqdnZ1hn+ysrKxUa2vrkd+p5HK51NbWpqqqKnV0dGhxcfFIj4+UQG6U4hoaGlReXq6Ojg5NTk4mOpyYKSgo0IULF1RTUxPVPB6PRx/5yEc0NDSke/fuaXV1NUYRAgDSEHkR4EB1dbVaW1uj7mpVWlqqF198Uffv31d/fz/nMwAAAAAggyVDMdHrocdXjTEua+32/1KNMUWSrkpalvTOIfO8I2lF0lVjTJG1drsliDHGJenVXcdLaSdOnNDx48c1Pj6uoaEhTU9Pa2NjI9FhJZXi4mLV1NSovr4+4k49+8nJydHly5d17tw5DQ0NaXR0VAsLC0feycftduvYsWM6ceKEamtrlZUVu1/nsrIyvfDCC5qdndXg4KAmJibidqEvNzdXVVVVqq+vT/kiop3cbrdaWlrU2Ngor9crr9crn8+XsSfiPB6PysvLVV9fr6qqKrlcrpjNXVNTo+rqak1NTWl4eFhTU1P7vl6NMSooKFB1dbXq6uqiWl4rFqqqqlRZWanp6WkNDQ1pcnIyabue4ciRG6WBoqIiPffcc5qfn9fQ0JDGx8dT8g7v7OxsVVZWqq6uTpWVlTHtGlhfX68TJ05ofHx8+++33++P2fwAgLRAXgQcoKCgYPu8VyxvksnKylJra6uampo0PDyskZERzc3NZez5DAAAAADIVAkvJrLW9hhjvqXNEzc/I+nLO57+NUkFkv7AWrt99cUY0xLa996OeRaNMX8i6V9I+oKkz+6Y52clNUj6prW2Nz5fydEzxqimpmb77vDl5WUtLS1pY2NDfr9ffr9fgUDgwP0fV/xirX3sxSInF5Ii3d/pRard49xut7Kzs5Wbm6uioqKIl95wKi8vT83NzWpublYgENDi4qJWVlY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      "text/plain": [
       "<Figure size 1440x2160 with 19 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 983,
       "width": 1161
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "trial = 18\n",
    "plt.figure(figsize=(20, 30))\n",
    "for i in range(19):\n",
    "    plt.subplot(10, 4, i+1)\n",
    "    data = Z_valid[trial, i, :]\n",
    "    prediction = out2[trial, i, :]\n",
    "    plt.plot(data.detach().cpu().numpy(), label=\"data\", linewidth=5, alpha=0.3, color='k')\n",
    "    plt.plot(prediction.detach().cpu().numpy(), label=\"prediction\", color='r')\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.ylim([-0.5, 1.5])\n",
    "    plt.title(f\"Neuron {i}, MSE={F.mse_loss(data, prediction).item():.3f}\")\n",
    "    plt.xticks([0, 250])\n",
    "    plt.grid()\n",
    "    \n",
    "plt.suptitle(f\"Trial {trial}\", y=0.95)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}