S2Moon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using device: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Using device: \u001b[1;36m0\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "from scipy.stats import norm\n",
    "import plotly.graph_objects as go\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, StepLR, OneCycleLR\n",
    "from pathlib import Path\n",
    "import matplotlib.animation as animation\n",
    "from sklearn.datasets import *\n",
    "\n",
    "from rich import print\n",
    "import imageio\n",
    "\n",
    "\n",
    "from utils.Models import MLP\n",
    "from utils.Datasets import BBdataset\n",
    "from utils.utils import *\n",
    "from utils.two_dim import data_distrib\n",
    "\n",
    "seed = 233\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device = 0\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "\n",
    "experiment_name = \"S2Moon\"  # 你可以根据需要动态设置这个变量\n",
    "# log_dir = Path('experiments') / experiment_name / 'test' / time.strftime(\"%Y-%m-%d/%H_%M_%S/\")\n",
    "log_dir = Path('experiments') / experiment_name\n",
    "log_dir.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">910</span><span style=\"font-weight: bold\">])</span>\n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">901</span><span style=\"font-weight: bold\">])</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m910\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m901\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成初始和目标密度样本\n",
    "def gen_data(num_samples=1000):\n",
    "    source_dist = data_distrib(num_samples, 'scurve')\n",
    "    target_dist = data_distrib(num_samples, 'moon')\n",
    "    return source_dist, target_dist\n",
    "\n",
    "# 生成二维Brownian bridge\n",
    "def gen_bridge_2d(x, y, ts, T, num_samples):\n",
    "    sigma = 1\n",
    "    bridge = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    drift = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    bridge[0] = x\n",
    "    for i in range(len(ts) - 1):\n",
    "        dt = ts[i+1] - ts[i]\n",
    "        dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        drift[i, :] = dydt\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        bridge[i+1] = bridge[i] + dydt * dt\n",
    "        bridge[i+1, :] += diffusion\n",
    "    return bridge, drift\n",
    "\n",
    "# 主函数\n",
    "def gen_2d_data(num_samples=1000, epsilon=0.001, T=1):\n",
    "    if not isinstance(num_samples, int):\n",
    "        num_samples = int(num_samples)\n",
    "    source_dist, target_dist = gen_data(num_samples)\n",
    "        \n",
    "    epsilon1 = 0.001\n",
    "    epsilon2 = 0.0001\n",
    "\n",
    "    t1 = torch.arange(0, 0.91, epsilon1)\n",
    "    t2 = torch.arange(0.91, T+epsilon2, epsilon2)\n",
    "    print(t1.shape, t2.shape)\n",
    "    ts = torch.concatenate((t1, t2))\n",
    "    \n",
    "    source_dist = torch.Tensor(source_dist)\n",
    "    target_dist = torch.Tensor(target_dist)\n",
    "    bridge, drift = gen_bridge_2d(source_dist, target_dist, ts, T=T, num_samples=num_samples)\n",
    "    return ts, bridge, drift, source_dist, target_dist\n",
    "\n",
    "ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(source_sample, target_sample, left_title=\"Source Sample\", right_title=\"Target Sample\", bound=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">910</span><span style=\"font-weight: bold\">])</span>\n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">901</span><span style=\"font-weight: bold\">])</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m910\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m901\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([21720000, 1, 7])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples = 12000\n",
    "ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=num_samples)\n",
    "# print_debug(ts, bridge, drift, source_sample, target_sample)\n",
    "start = torch.cat(torch.split(source_sample.repeat(len(ts)-1, 1, 1), 1, dim=1), dim=0)\n",
    "times = ts[:len(ts)-1].repeat(num_samples, 1).reshape(-1, 1, 1)\n",
    "positions = torch.cat(torch.split(bridge[:-1, :], 1, dim=1), dim=0)\n",
    "\n",
    "scores = torch.cat(torch.split(drift[:-1, :], 1, dim=1), dim=0)\n",
    "# print_debug(start, times, positions, scores)\n",
    "raw_data = torch.concat([start, times, positions, scores], dim=-1)\n",
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MLP(input_dim=5, output_dim=2, hidden_layers=2, hidden_dim=128).to(device)\n",
    "model.load_state_dict(torch.load(log_dir / 'model.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 50\n",
    "batch_size = 100000\n",
    "lr = 1e-3\n",
    "epochs = 1000\n",
    "\n",
    "# 组合成data\n",
    "train_ds = BBdataset(raw_data)\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=20)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "# scheduler = OneCycleLR(optimizer, max_lr=1e-3, total_steps=epochs*len(train_dl))\n",
    "scheduler = None\n",
    "loss_fn = nn.MSELoss()\n",
    "loss_list = []\n",
    "# print('='*10+'model'+'='*10)\n",
    "# print(\"batch_szie:{}, channel:{}, length:{}\".format(*next(train_dl.__iter__()).shape)) # batch_szie, channel, length\n",
    "# print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_dl, optimizer, scheduler, loss_fn):\n",
    "    losses = 0\n",
    "    for training_data in train_dl:\n",
    "        training_data = training_data.to(device)\n",
    "        # x: (batch_size, channel, length-2), y: (batch_size, channel, 2)\n",
    "        x, y = training_data[:, 0, :-2], training_data[:, 0, -2:] # 不同模型要改\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "\n",
    "        pred = model(x)\n",
    "        loss = loss_fn(pred, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        losses += loss.item() / len(train_dl)\n",
    "        \n",
    "    return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc798a7150f541c0bd8e929ef2459e0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training (lr: X)  (loss= X):   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1559961/2865432567.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mepoch_iterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mnow_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mloss_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnow_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mcur_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1559961/1684657653.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, train_dl, optimizer, scheduler, loss_fn)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mtraining_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_dl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mtraining_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;31m# x: (batch_size, channel, length-2), y: (batch_size, channel, 2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_worker_number_rationality\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_MultiProcessingDataLoaderIter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignal_handling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_SIGCHLD_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_worker_pids_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_reset\u001b[0;34m(self, loader, first_iter)\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# prime the prefetch loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prefetch_factor\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMP_STATUS_CHECK_INTERVAL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_put_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1340\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1341\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0midx_in_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m                 \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx_in_batch\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                 \u001b[0midx_in_batch\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "epoch_iterator = tqdm(range(epochs), desc=\"Training (lr: X)  (loss= X)\", dynamic_ncols=True)\n",
    "model.train()\n",
    "model.to(device)\n",
    "for e in epoch_iterator:\n",
    "    now_loss = train(model ,train_dl, optimizer, scheduler, loss_fn)\n",
    "    loss_list.append(now_loss)\n",
    "    cur_lr = optimizer.param_groups[-1]['lr']\n",
    "    epoch_iterator.set_description(\"Training (lr: %2.5f)  (loss=%2.5f)\" % (cur_lr, now_loss))\n",
    "epoch_iterator.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), log_dir / 'model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OqKOsiIiIy3HLgAIaySMiIuLK3D6gHChQQBEREXE1bhtQTg41zlILioiIiMtx24ByaiSPAoqIiIircduAEh8aAKgFRURExBW5bUA52Qclt6RCiwaKiIi4GLcNKG38vQjy8QTg4DG1ooiIiLgStw0oJpPJ0Q9FI3lERERci9sGFNBIHhEREVfl1gFFc6GIiIi4JrcOKB3Cakfy7M4vdXIlIiIi8mNuHVB6tLMCsOVgMXa7VjUWERFxFW4dUBIjg/DxNFNaUcP+gjJnlyMiIiJ13DqgeHmYSYq2ALWtKCIiIuIa3DqgAPSKCQbgh4NFTq1DRERETnH7gJIcc6ofioiIiLgGBZS6FpSMw8XU2OzOLUZEREQABRQ6hgUQ5ONJRbWdXXnHnV2OiIiIoICC2Wz60XDjIucWIyIiIoACCgC9YoMBdZQVERFxFQooQK+6jrI/ZKujrIiIiCtQQAGS61pQduaVUlJR7dxiRERERAEFoF2wH53aBmKzGyzenu/sckRERNyeAkqdsT0iAfg2PcfJlYiIiIgCSp0r6gLKsl1HKKuscXI1IiIi7k0BpU5SlIW4EH8qa+ws3XnE2eWIiIi4tQYFlGnTppGcnIzFYsFisZCSksK8efMA2L9/PyaT6YyPjz/++KIU35RMJhNje9a2oszbqts8IiIiztSggBITE8PUqVNJTU1l48aNDB8+nPHjx5ORkUFsbCw5OTn1Hi+99BKBgYGMHTv2YtXfpMb2iAJg8Y58KqptTq5GRETEfXk2ZOdx48bVe/7KK68wbdo01q5dS/fu3YmMjKy3fe7cudx0000EBgY2vtJm0CvGSrTVl8PFFazac5QR3SKcXZKIiIhbuuA+KDabjTlz5lBWVkZKSspp21NTU0lLS+Oee+4553EqKyspKSmp93AWk8nE4M5hAPyg1Y1FREScpsEBJT09ncDAQHx8fJg0aRJz584lKSnptP2mT59Ot27dGDRo0DmPN2XKFKxWq+MRGxvb0JKaVNdICwA7cpwXlERERNxdgwNKYmIiaWlprFu3jgceeICJEyeybdu2evucOHGC2bNn/2zrCcDkyZMpLi52PLKzsxtaUpPqGhUEwI7cUqfWISIi4s4a1AcFwNvbm06dOgHQt29fNmzYwFtvvcV7773n2OeTTz6hvLycCRMm/OzxfHx88PHxaWgZF83JFpSswnKOV9YQ6NPg/0QiIiLSSI2eB8Vut1NZWVnvtenTp3P11VcTHh7e2MM3u5AAbyIstYFpp1pRREREnKJBzQOTJ09m7NixxMXFUVpayuzZs1m6dCnz58937LNnzx6WL1/Ot99+2+TFNpeukRbySo6wI7eEvvFtnF2OiIiI22lQQMnPz2fChAnk5ORgtVpJTk5m/vz5jBo1yrHPjBkziImJYfTo0U1ebHPpGhXEsl1H2JGjFhQRERFnMBmGYTi7iB8rKSnBarVSXFyMxWJxSg2fbz7Eox+l0a99Gz6edO5RSCIiItL0399ai+cMHCN5ckpxsfwmIiLiFhRQzqBjWCBeHiZKK2s4VHTC2eWIiIi4HQWUM/D2NJMQXjs9v/qhiIiIND8FlLPoFlU3o2yuZpQVERFpbgooZ9E1srYfyjZNeS8iItLsFFDO4pLYYADWZBZQY7M7txgRERE3o4ByFn3j22D18+JYeTWbsoqcXY6IiIhbUUA5C08PM8O7tgVgwbZcJ1cjIiLiXhRQzmFUUgQAC7blaT4UERGRZqSAcg5Du4Tj7WFmf0E5mUfKnF2OiIiI21BAOYdAH08GJoQCsHB7npOrERERcR8KKD/j5G2ehdsUUERERJqLAsrPGNmttqNsatYx8ksrnFyNiIiIe1BA+RlRVj8uiQ3GMOC7rRrNIyIi0hwUUM7DL5OjAPjqh8NOrkRERMQ9KKCch18mR2MywYb9x8gp1urGIiIiF5sCynmItPrSLz4EgG+25Di5GhERkdZPAeU8jeul2zwiIiLNRQHlPI3tGYXZBD8cLOZAgSZtExERuZgUUM5TWKAPgxLCALj3g41szylxckUiIiKtlwJKAzwztittg3zYnX+c8e+s4ou0Q84uSUREpFVSQGmAHu2szHtkCMO7tqWqxs4LX2ZQVWN3dlkiIiKtjgJKA4UG+vDPCZfSNsiHovJqluzMd3ZJIiIirY4CygXwMJu4tnc7AD7bdNDJ1YiIiLQ+CigX6Lo+MQAs3pHPsbIqJ1cjIiLSuiigXKDEyCC6R1uothl8vUVzo4iIiDQlBZRGONmK8skmjeYRERFpSgoojXB1r2g8zCZ+yC5SXxQREZEmpIDSCOFBPtw5qD0AT3z8g0KKiIhIE1FAaaTfX9mN2wfEYRi1IeX7jFxnlyQiItLiKaA0ktls4g/je3Br/1gMA178MoOKapuzyxIREWnRFFCagNls4oVx3Ym2+nK4uIJ/Lt/r7JJERERaNAWUJuLr5cHTY7sCMG1ZJnklFU6uSEREpOVSQGlCV/eKpndcMOVVNm7951runrWBF7Vej4iISIMpoDQhk8nE879MwmyCvUfKWLwjn1mr9/Nteo6zSxMREWlRFFCaWO+4Nnz50GDevrU3VyVHATBfI3tEREQaxNPZBbRGPdpZ6dHOSofQAL7ZksPSnUeoqLbh6+Xh7NJERERaBLWgXEQ92lloF+zHiWoby3cdcXY5IiIiLYYCykVkMpkY3T0CgPkZeU6uRkREpOVQQLnIxnSPBGDRjjxqbHYqqm0a1SMiIvIz1AflIuvXPoSQAG8Ky6qYMGM9qQeOERfiz6e/GYTF18vZ5YmIiLgktaBcZB5mEyO7tQVgdWYBlTV2ducf59m5WzEMw8nViYiIuCYFlGZw/+UJ9G8fwoSUeP58Yy88zCa+/OEwn2465OzSREREXJJu8TSDhPBA/jcpxfE8r6SC1+bv5PkvttK/fQhxof5OrE5ERMT1qAXFCSZdnsCADiGUV9n4y4Kdzi5HRETE5TQooEybNo3k5GQsFgsWi4WUlBTmzZtXb581a9YwfPhwAgICsFgsDB06lBMnTjRp0S2dh9nEc79MAuDLHw6zM7fUyRWJiIi4lgYFlJiYGKZOnUpqaiobN25k+PDhjB8/noyMDKA2nFxxxRWMHj2a9evXs2HDBh566CHMZjXU/FSPdlbG9ojEMOB1taKIiIjUYzIaOZQkJCSE1157jXvuuYeBAwcyatQo/vCHP1zw8UpKSrBarRQXF2OxWBpTmsvbnVfK6DeXYxjw1UOD6RljdXZJIiIiF6Spv78vuGnDZrMxZ84cysrKSElJIT8/n3Xr1tG2bVsGDRpEREQEl19+OStXrjzncSorKykpKan3cBedI4K45pJ2APzpux0adiwiIlKnwQElPT2dwMBAfHx8mDRpEnPnziUpKYm9e/cC8OKLL3Lffffx3Xff0adPH0aMGMHu3bvPerwpU6ZgtVodj9jY2As/mxbosZFd8PYws3LPURZtz3d2OSIiIi6hwQElMTGRtLQ01q1bxwMPPMDEiRPZtm0bdnvt9O33338/d911F7179+aNN94gMTGRGTNmnPV4kydPpri42PHIzs6+8LNpgeJC/bl7cAcAXvl2u6bBFxER4QICire3N506daJv375MmTKFXr168dZbbxEVFQVAUlJSvf27detGVlbWWY/n4+PjGBV08uFuHvxFAmGBPuw7Wsa/1ux3djkiIiJO1+jhNXa7ncrKStq3b090dDQ7d9YfkbJr1y7i4+Mb+zGtWpCvF0+O6QLAW4t2U3C8EoB9R8v4xZ+X8qfvdjizPBERkWbXoIAyefJkli9fzv79+0lPT2fy5MksXbqU22+/HZPJxJNPPsnbb7/NJ598wp49e3juuefYsWMH99xzz8Wqv9W4oW8s3aMtlFbU8JcFuzAMg2c/T2ff0TLeXZbJtsPu03lYRESkQVPd5+fnM2HCBHJycrBarSQnJzN//nxGjRoFwKOPPkpFRQWPPfYYhYWF9OrViwULFpCQkHBRim9NPMwmXhjXnZveW8Oc9VmEBnizak8BAIZRO8rng7v7O7lKERGR5tHoeVCamjvNg3ImD/53E9+k5zie39A3hi/SDlFtM/jvvQO4rFOYE6sTERE5M5eZB0UujmfGdsXbs/ayxIX483/X9OD2AbV9eKbM247d7lJ5UkRE5KJQQHExsSH+PDUmEYuvJ1Ov64mvlwcPD+9EgLcHWw+VsCnrmLNLFBERuegUUFzQvUM6suXFMQyqu50TGujDyKQIABZsz3NmaSIiIs1CAaWFGNmtNqAs3KaAIiIirZ8CSgtxeWI4Xh4mMo+UsffIcWeXIyIiclE1aJixOI/F14uBHUNZsfsoC7fncWcbf95ZsoeKahsdwwPoHdeGLhFBzi5TRESkSSigtCAju0WwYvdRvs/IY0dOKZ9tPuTYZjLB948OpbNCioiItAK6xdOCnOwou/HAMT7bfAgPs4lb+sUS08YPw4Dv1T9FRERaCQWUFqRdsB9JUacmv5lybU+mXp/M/UM7ArBs1xFnlSYiItKkFFBamFv6xwLw+Kgu3NSv9uehXcIB2HTgGKUV1U6rTUREpKmoD0oLMyGlPdf0bofF18vxWnxoAO1D/dlfUM6azAJGd490YoUiIiKNpxaUFujH4eSkk60ous0jIiKtgQJKKzG0c21AWb77CC62/qOIiEiDKaC0EikJoXh5mMguPMH+gnJnlyMiItIoCiitRICPJ5fGhwDwyjfb+G5rDuVVNU6uSkRE5MIooLQio7vXrdezPZ9J/9nEze+txWbX7R4REWl5FFBakYkp7Xl/wqVMTIknwNuD9EPFLNmR7+yyREREGkwBpRUxm02MTIrgpfE9uG1AHAD/WXfAyVWJiIg0nAJKK3X7gHigdthxVkE5G/YXctXbK3j+i63kl1Y4uToREZFz00RtrVT7sACGdA5jxe6jvPx1BmsyCyirspFxuISPNx5kQMcQCsuqMAx465ZL6Bge6OySRUREHNSC0ordMbC2FWXh9nzKqmz0a9+GS2KDOVFtY+nOI2w5WEz6oWL+smDXOY+zes9Rsgs1dFlERJqPWlBaseFd2xJt9eVwcQWXxrdh1l398ff2YMXuoxwqOoHZBE9/ms636TnsPXKcjuGBVNvsVNbYCfSp/afxfUYuv/53Kj3aWfj64SFOPiMREXEXCiitmKeHmTdv6c2Snfk8MCyBgLrQcXJafID5GXks3pHPe8v28uvLOzJh+npOVNv4ZFIK8aEB/Pn7nQBsPVRCUXkVwf7eTjkXERFxLwoorVz/DiH07xBy1u2/GZbA4h35fLb5IIt25HP0eCUAj8xJ446B8ezKO+7Yd3NWEb/o2vai1ywiIqI+KG7u0vYh9G8fQrXN4OjxSrpGBhHs70X6oWImz00HIMDbA4BNWccAOFFl438bsjleqZlqRUTk4lBAER4d2RmTCZJjrMz59UBevT4ZAJvdIDzIh8dGdQFOBZQ/f7+Tpz7dwp/n73RazSIi0ropoAiDOoWx8unhfPrAIIL9vRndPZK7LmsPwJOjE7msUxgAaVlFVNbY+CLtEADzM3K1crKIiFwU6oMiALQL9qv3/PlfJjHp8gQiLL7Y7AaBPp4cr6xhxsr9HD1eBUBOcQXbckroHm11RskiItKKqQVFzshkMhFh8QXAw2zikthgAP62eHe9/RZv11o/IiLS9BRQ5Lz0iW8DQFmVDYCbL40FYNFPFiM0DIMVu49Qpg60IiLSCAoocl76xAU7fm4f6s/jo2s7zv5wsIgjpZWObR+uz+aO6euZOm9Hc5coIiKtiAKKnJfesW0cP4+/pB0RFl96trNiGLBk56lWlG/TcwBYsftIs9coIiKthwKKnBervxeDO4UR5OPJDX1jABjRrXbStkXb8wAoq6xh3b4CAPYXlFNwvPLMBxMREfkZCihy3t6feCkrnx5ObIg/ACO7RQCwdOcRCsuqWLnnKNW2U8OON2cVOaNMERFpBRRQ5Lz5enlg9fdyPO8ebaFHOwuVNXZmrzvAkp90mD05sZuIiEhDKaDIBTOZTNw7uCMAH6w54OiLcmXPSEABRURELpwCijTKlT2jiLD4cKS0krySSvy8PHjg8k4A/JBdTI3NfsHHPnisnBe/zCC3uKKpyhURkRZCAUUaxdvTzMRB7R3PL+sUSvdoC0G+npyotrEjt5QPVu/n6r+tZOP+wgYd+/kvMpi1ej9vLNjVxFWLiIirU0CRRrutfxx+XrUrHg9LbIu53syze3jxqwy2HCzmjunrWb3naL33Zh45zu/npnPH9HVc8eZy3luWCcC+o2UsruvTsmhHPna71vwREXEnCijSaMH+3rw0vjtje0Qy/pJoAPrE1c6b8l1GLoYBIQHenKi2cdesDXyfkQvU3sK55R9r+e+6LFbsPsqO3FKmfreD1APH+GD1fsfxjx6v5IeDRc19WiIi4kQKKNIkbro0lmm/6kuQb+0on5NT4wMkRVlY+uQwRnZrS2WNnV//O5WXvsrgrpkbOFJaSWJEEK/ekMzYHpEYBjz1yQ98knoQOLWI4SKt+SMi4lYUUOSi6B0XTJCvJ0G+nvz99j5YfL2Y9qu+3FnXX2Xmqv3szj9OpMWXWXf346ZLY5lyXU/CAr3JPFLG8coaOrUN5Im6KfUX1k0GJyIi7kEBRS4Ki68X8x4ZwoLHLqd9WAAAXh5mXry6O/+4oy9WPy+sfl7MvKsfUdbaVpJgf29eurqH4xh3DmrPLxLbYjbBjtxSDh4rx2Y3OFG3YKGIiLRens4uQFqvmDb+Z3x9dPdIhnQOp9pux+LrVW/blT0juW9IB/YdLeP6PjH4eXtwafsQ1u8rZMq3O0jLLqLkRDWfP3QZCeGBzXEaIiLiBA1qQZk2bRrJyclYLBYsFgspKSnMmzfPsX3YsGGYTKZ6j0mTJjV50dLy+Xl7nBZOoHbyt99flcT7E/vh5107Mmhk3Zo/36TncKjoBKWVNfxn7YFmrVdERJpXgwJKTEwMU6dOJTU1lY0bNzJ8+HDGjx9PRkaGY5/77ruPnJwcx+PVV19t8qLFvYztEUWAtweBPp5cUzdKaO7mQ1TW6FaPiEhr1aBbPOPGjav3/JVXXmHatGmsXbuW7t27A+Dv709kZGTTVShuLzbEnyVPDsPPywN/b0/W7i0kt6SCBdvy+GVytLPLExGRi+CCO8nabDbmzJlDWVkZKSkpjtf/+9//EhYWRo8ePZg8eTLl5eXnPE5lZSUlJSX1HiI/1TbIlyBfLzzMJq7v2w6A/2086OSqRETkYmlwJ9n09HRSUlKoqKggMDCQuXPnkpSUBMBtt91GfHw80dHRbNmyhaeffpqdO3fy2WefnfV4U6ZM4aWXXrrwMxC3c9OlsbyzJJMVu4+w72gZkRZffDzNmM0mZ5cmIiJNxGQYRoPmEK+qqiIrK4vi4mI++eQT3n//fZYtW+YIKT+2ePFiRowYwZ49e0hISDjj8SorK6msrHQ8LykpITY2luLiYiwWSwNPR9zFLf9Yw9q9p9b28TCbCA/0IS7Unxv6xnB1r2h866bfFxGRi6+kpASr1dpk398NDig/NXLkSBISEnjvvfdO21ZWVkZgYCDfffcdY8aMOa/jNfUJSuu0ZGc+9/8rlaqzrJYcEuDNlOt6MqZ7bX+onOITLNyWx8394vD21PQ/IiJNram/vxs9D4rdbq/XAvJjaWlpAERFRTX2Y0Tq+UViW7a+NIYaux3DgNKKGvJKKlidWcB/1h7gUNEJnvjfD/R8zEobf29+9f46Mo+UUVpZw2+GdQLgvWWZZB8r54Vx3fHyUGgREXElDQookydPZuzYscTFxVFaWsrs2bNZunQp8+fPJzMzk9mzZ3PllVcSGhrKli1beOyxxxg6dCjJyckXq35xY96eZrzr+nkH+HgSafWlV2ww9w3pwE3vrWFTVhHPfr6VmDZ+ZB4pA+B/G7J54PIE9h4tY8q8HQAkRVm5bUCc085DRERO16CAkp+fz4QJE8jJycFqtZKcnMz8+fMZNWoU2dnZLFy4kDfffJOysjJiY2O5/vrrefbZZy9W7SJn5Olh5k/XJ3PV2ytZvOPUIoPenmb2F5Szfl8hX2/Jcbz+1qJdXNennfqsiIi4kEb3QWlq6oMiTeWvi3bzlwW7gNp1fU5U2fhoYzajkiJYteco5VU2Arw9KKuy8fsru3Hf0I5OrlhEpOVyuT4oIq5q0rAEfjhYRJXN4JmxXck4XMJHG7NZsK12ZeSukUHcfVkHnvp0C39fuoc+8W0IDfBmc/Yx5m/Nw24YvH7zJQT66NdERKS56S+vtFpeHmben9jP8bxPXDAJ4QGO/ih3Xdae6/q0473lmWQeKeP6aatPO8ac9VncO0QtKyIizU1DF8RtmEwmbro0FoA2/l6Mv6Qdnh5mplyXzCWxwURbayd86xIRyMhuEQDMWr0fm92l7oKKiLgFtaCIW/nVwHgOFJYzomtbR6fY/h1C+PzBy+rtd6LKRsrURRw8doKF2/Mc86mIiEjzUAuKuJUAH0/+eG1PRtS1kJyNn7cHt/avHXo8c9W+5ihNRER+RAFF5CzuGBiPh9nE2r2FZBwudnY5IiJuRQFF5Cyig/24okftrZ1b/7GWmav28eUPh7n9/bVc/toSVmcedXKFIiKtl+ZBETmHg8fKue9fqWzPKTltm6fZxCvX9uDmfpqFVkSkqb+/1YIicg4xbfz5+uHB/N81PYi0+NIu2I/fjujMuF7R1NgNnv40nX8sz3R2mSIirY5G8Yj8DA+ziV8NjOdXA+MdrxmGQYdQf95evIc/fbeTvvEh9I1v48QqRURaF7WgiFwAk8nEY6O6MP6SaGx2g0fmbKakopq9R47zfUYuG/cXkl1Yzo/voJZX1bAp6xgudldVRMQlqQVF5AKZTCb+cE0PNmUdI7vwBIOmLOZ4ZU29ffrEBfPqDb2oqrHzm/+msr+gnOd/mcTdgzs4qWoRkZZBnWRFGmlT1jFufHcNNruBl4eJLhFBlFRUk1tcQbXNwNvTjAmorLEDEB/qz5InhmE2m5xbuIhIE9JigSIupk9cG+b+ZhCFZVX07xCCv3ftr9WhohNM/iyd5buOAHB5l3A2HTjGgYJy1uwt4LJOYZRUVFNUVk1cqL8zT0FExOUooIg0geSY4NNeaxfsxwd39ePb9FzKKmu4oW8Mz3+5lf+szeLD9Vl0jgjkmr+tIqekgkdGdOa3wzurVUVEpI5u8Yg0o4zDxVz19kq8PEx0jbSQfujUDLWXdwnn7Vt6Y/X3cmKFIiIXRvOgiLRg3aOt9IqxUm0zSD9UjNXPi2fGdsXXy8yyXUd46MNN2LV6soiIAopIczu5CKGH2cQ7t/Vh0uUJfHz/IHy9zKzYfZS/L93j2LfGZmfF7iO8sWAX2YXlzipZRKTZqQ+KSDO7rk8Me/KP0ze+DYM7hwHQM8bKH8b34MlPtvD6gl1U1tg5dOwEK/Yc5UhpJQB78o/zzu19AKissZFdeIK4EH+8PU///wzDMDCZ1J9FRFouBRSRZubtaebZXyad9vqNl8aydm8hn246yF8Xn2pF8ff2oLzKxtq9BY7g8ezcrXycehAfTzM921l5fFQXBnWqDTv/XXeA17/fxeQru3FD35hmOy8RkaakgCLiQv5wTXfshkFZZQ092lnpFRtMv/Zt6P3yAgrKqsg8cpy4kAC+Tc8BaudW2XjgGC9/vY3vHh0KwIyV+ygoq+J3H//A0eOV3D+0I5U1dkorajAMA19vDyy+6ogrIq5NAUXEhfh7e/LGzZec9nqfuDas2VvAun2F5JdWUlZlIzTAm//cO4Bf/nUlO3JLOVBQhs1ukHmkzPG+qfN28NdFuymrsjle8/Iw8exVSUwc1L4ZzkhE5MKok6xIC9C/QwgA6/YWsuxHE791i7KQ0jEUgPkZuSzYlgfAkM5h/P7KbphM1AsnJhNU2wxe+iqD1ZlHm/ksRETOn1pQRFqAAR1DYBGs31dIcN08KZcnhgMwpnsEK/cc5butuXjUTfQ2slsEEwe15+pLojleWUN4kA9BPrW/7k98/AOfbTrEw7M389XDg4kO9nPOSYmInINaUERagN6xbfDyMJFbUsGO3FJMJhjSuTagjO4eCcCmrCJSDxwDYGRSBAARFl8SwgOx+HphMpkwmUz88dqeJEVZKCir4sZ31/DRhiyqbfZ6n5ddWO4YPSQi4gwKKCItgJ+3R73p9HvFBBMS4A3UhpA+cbXb7AYkRVlod45WEV8vD967oy9RVl8OFZ3g6U/TueLN5RSWVQFw8Fg5Y95czg3vrtakcSLiNAooIi3EgLp+KFDb/+THrugR6fh5VF3rybnEhviz+IlhPHtVN0IDvMk8Usb0lXsB+PeaA5RX2ThQUM6O3NImql5EpGEUUERaiP4/CijDEusHlDHdGxZQoLZV5t4hHXnl2p5AbTA5UlrJh+uzHPus2Vtw1vcXllWxYvcRXGw5LxFpJRRQRFqIfu1DiLL60iUi8LTVk+NDA3hyTCIP/iKB7tENW6RrVFIEHcMCKKmo4e5ZGyipqHFsW5NZG1BsdoPPNx9i2+ESALYcLOKKN5dzx/T1TF+5r3EnJiJyBlrNWKQFKa+qwYQJP2+PJj3unPVZPPNZuuP5+Eui+SLtMEG+nqQ9P5rpK/fyx293ANCvfRvSDxVTUV3bsTYkwJsVT/2CAB9P8ksqKK+y0T4soEnrExHXp9WMRdyYv7dnk4cTgGt6tyM8yKfuMzx46eruBPl4UlpRw5aDRXyw+oBj3w37j1FRbWdYYjjtQ/0pLKviX2sOsCO3hJGvL+OKt5ZrBJCINJoCiojg6+XBA5cnAPCrgfEE+3s7+ry88s12DhWdICTAmyW/G8Zvh3fi6Su6Mn1iPx4e3hmAfyzPZML09ZRU1FBRbWfVHk0CJyKNo4naRASAuy5rz8COoSRGBgGQkhDKoh35bKybW+W2/nF0CAvg8dGJjveMvySavy3Zw76jtdPrm0xgGLBqz1Gu6d3uvD+7qsZOaUU1oYE+TXhGItKSqQVFRAAwmUwkRVscs9EOrJtCH8DTbOJXA+NPe4+nh5lHR9a2orQL9uNP1yUDsDqz4Kyje5bsyOfy15awaHue47WHP9xEypTFrD3HqCERcS8KKCJyRklRFqx+tdPqj+0ZRaTV94z7Xd0rmn/f058vHrqMX/aKwtNs4lDRCbIKy0/b93DRCR6Zs5kDBeW88s127HaDnbmlzM/Io8pm5/dz06mqsZ/hU0TE3SigiMgZmc0mbuwbQ6CPp6N/ypmYTCaGdA4nLNAHf29PetfNars6s35riM1u8NhHaY5hzHuPlrFgex4zfjRMOfNIGf9csbfpT0ZEWhz1QRGRs3r2l0n8vyu7Ya677XM+BiWEsWH/MVZnFnBr/zjH6+8uy2TdvkICvD0YmRTBF2mHeWPBLvbW9V+5Y2A8/157gL8u3k2U1RcvDzMlFdXkFVdgMpl4YFgCvl5NP4JJRFyTAoqInFNDwgnAoIRQ3lq0mzWZRzEMA5PJxA/ZRbyxYBcAL17dncsTw5mXnuuYSr9XjJWXx3dnT/5x1uwt4PH//XDacQN9PLlvaMfGn5CItAi6xSMiTap3XBt8vcwcPV7FrrzjHK+s4ZE5m6mxG1yVHMUNfWNoG+TLdX1OjfK5e3AHTCYTf7o+mV8khtO/QwgDOoQwsltbhnQOA+CLHw4565RExAnUgiIiTcrb00y/9iGs2H2Uu2auJ9Lqy/6CcqKtvvzxmp6YTLUtMvcN7cjczYdoa/FhbI8oAOJC/Zl5V/96xyssq6L/KwvZeqiEPfnH6dQ2sME15ZdUEODjSYCP/uSJtBRqQRGRJnfXZe0J8vHkcHEFm7KKMJvgzVt6Y/X3cuyTEB7IgscuZ+5vLsPb8+x/ikICvBlat3rzl2kNa0VJPVDIPbM20P+Pi5gwY/2FnYyIOIX+d0JEmtzwrhFseHYkC7fn8X1GHoM7hdVbjfmkuFD/8zre+EuiWbwjn8/TDvPYqC6OVphz+WzTwXp9WVIPHOPgsXJi2pzfZ4qIc6kFRUQuCl8vD36ZHM3bt/bmpn6xjTrWqKQI/Lw8yCosJy276Lze8/6K2uHLY3tE0rVudtzVe06fCO6H7CK2HipuVH0i0vTUgiIiLs/f25PR3WuHJj/6URqXxofg7Wkmq7CM4xU1vDy+B71igx3778k/zracEjzNJv54bU9mrtrHjtxSVuw5Wi8s7TtaxvXTVlNjN7ilXyyTr+zmmJxORJxLLSgi0iLc0q92TpUDBeV8uukgH67PYtWeAn44WMz9/07l6PFTKyh/9cNhAIZ0DqNNgDeXdaodCbR6z1Hs9lNT8M9ed4CauudzNmQz+o1l7Mk/3lynJCLn0KCAMm3aNJKTk7FYLFgsFlJSUpg3b95p+xmGwdixYzGZTHz++edNVauIuLGUhFCWPTmMv9/ehydGdeG3Izrz2g3JJIQHkFtSwUOzN1Fjs2MYhiOgXH1JNFA79Nnf24OCsirH3CsV1TY+Tj0IwG9HdKZDWAB5JZX85r+plFfVnLGGAwVlXPX2Cv62eHcznLGIe2tQQImJiWHq1KmkpqayceNGhg8fzvjx48nIyKi335tvvnlendhERBoiPjSAK3tG8fCIzjw+qgs3XhrLe3f0JcDbg7V7C3n603TW7ytk79EyfDzNjEqKBGqHPg+o66S7cs8RAOZtzaGovJpoqy+PjOjMR/cPJDzIh115x3n2862nLXZYY7Pz6EdpZBwu4fUFu9h2uKR5T17EzTQooIwbN44rr7ySzp0706VLF1555RUCAwNZu3atY5+0tDT+8pe/MGPGjCYvVkTkpzq1DeK1G3sB8Ommg9z2/joARnaLIPBH854M7lw7VHllXUfZ/67NAuDW/nF4mE20DfLlr7f2xmyCzzYd4pO61pWT3l2WyeasIgDsBrz4VcZZV2wWkca74D4oNpuNOXPmUFZWRkpKCgDl5eXcdtttvPPOO0RGRp7XcSorKykpKan3EBFpiCt7RvGvu/sT08YPW12fknG9ourtM7iuH8r6fQX839fb2HjgGJ5mEzf/qNPswI6hPD6qCwCvL9hFta12ZeWth4p5c2HtbZ0nxyTi62Vm/b5Cvt6Sc9HPTcRdNTigpKenExgYiI+PD5MmTWLu3LkkJSUB8NhjjzFo0CDGjx9/3sebMmUKVqvV8YiNbdxwRBFxT0O7hDP/0aH8dngn7rqsPSO6RdTb3iUikPAgHyqq7bxft4LymO6RtLX41tvvvqEdCQv0Iae4gm/Tc7DbDSZ/lk6N3WBsj0h+MyyBSXWrO0/5djsV1bbmOUERN9PgYcaJiYmkpaVRXFzMJ598wsSJE1m2bBl79uxh8eLFbN68uUHHmzx5Mo8//rjjeUlJiUKKiFyQAB9PHh+deMZtJlPtkOPP0w4RFuBNVLAfN/SNOW0/H08P7hgYzxsLdzFj5T4qqm2kHyomyMeTl8f3wGQyMenyBD7eeJBDRSf4z9oD3DtEixiKNDWT0cibqCNHjiQhIQE/Pz/efvttzOZTjTI2mw2z2cyQIUNYunTpeR2vpKQEq9VKcXExFoulMaWJiFyQo8crGTR1MVU1dvy9PSivsvHsVd3qBZE567N45rN0wgK9Wf7UL6i2GUz5djtdIoK467L2Th8o8Pai3Xy/LZd/3T2AkABvp9Yi7qGpv78bPQ+K3W6nsrKSZ555hi1btpCWluZ4ALzxxhvMnDmzsR8jItJswgJ9uK537WrL5VU2OoYHMCGlfb19ru8bQ2yIH0ePV/HP5fu494MNzNmQzctfb+PZz7disxvY7Qb5pRVn7ExbWFbFH77edt6z2Fbb7Gw7XHJeHXNzik/w9qLdbD1UwsLteed1fBFX06BbPJMnT2bs2LHExcVRWlrK7NmzWbp0KfPnzycyMvKMHWPj4uLo0KFDkxUsItIc7h7cgTkbsgF4/pdJpy1o6OVh5uHhnXnqky28sXAXAP7eHpyotvHfdVlsyioit/gEx8qrmZASz0tXd3e0qhiGwVOf/MDC7fls3F/IFw8N/tl6Xpu/k38s38vDwzvxxFluY500a9V+xwR0Gg4tLVWDAkp+fj4TJkwgJycHq9VKcnIy8+fPZ9SoURerPhERp+gSEcRrNyRTWWNnWGLbM+5zXe92vLNkDwcKyvH1MvOvu/uTW1LBYx+lsT3nVDD415oDRFh8efAXnQD4Iu0wC7fnA/DDwWJ255XSOSLorLWcqLLx4fraYdF/W7KHQQlhpCSEnnHf0opqZq/LcjzXOkPSUjUooEyfPr1BB9ccASLSkt146bk77Ht6mPm/a3rw5/k7+d2YRC5tXzsZXHSwH2lZRfSOCyb1wDH+75vtvDZ/JzU2g95xwbzwZe3kloE+nhyvrOGT1INMvrLbWT/nm/QcSitqZ7c1DHj8f2nMe2QIwf6n9y35aEM2pZU1WP28KD5RzbacEux2A7NZk2dKy6K1eEREGmFI53C+eGgwQ+omggPoE9eGuwd3oHdcG+4d0pFfD63tXPvGwl1MmLGe4hPVdI+28KfrkwH4bPMhaurmXDmTOXWtJ78ZlkCHsAByiit46attp+1XY7Mzc9V+AH5XN19LeZWNfQVlTXW6Is1GAUVE5CJ75oquPHtVN4Z0DiPK6kt4kA9/uakXo5IiCAnw5khpJSt2Hz3je/fkl7LxwDE8zCYmDmrPX26qnTX36y2HKamorrfvvK25HCo6QWiANzf2jaFrZO1Iigz1Q5EWSAFFROQiM5tN3DukI/++ZwBrJo9gw+9H0jXSgrenmfF1CxrO2ZB1xtvic9bXdtT9RWJbIiy+9IlrQ0J4ANU2gyU78h37GYbBP1fsBeCOlHh8vTzo0a4uoFzEfii78kr5eGM2ZZVnXmBR5EIpoIiIONGNfWv7uczPyGP8O6v4Nj3HEVT2HS3jo421AeXW/qf6w4ztUTuN/7z0XMdr6/cVsuVgMT6eZu4YGA9A92gr0PgWlILjlWQVlJ/2eo3Nzp0z1vPkJ1u4/LWl/HvN/nPeqhJpCAUUEREnSoq28PioLvh4mtlysJjf/HcT9/87layCcu6etYHSihouiQ3m8i6n+rhc0aN2Soelu/Ipr6ptufjnitrp+6/vG0NooA8A3aNrW1C2Hi6+4EELNrvBje+uYeTry9iRWz/oLNt1hMPFFUDt5HbPfZHBe8v3XtDniPyUAoqIiJP9dkRnVj8znIeHd8Lbw8z32/IY9ucl7DtaRrtgP/454VI8PU79ue4ebSE2xI+KajvLdh4h88hxFu2onZDtnsGn5p3qEhGEp9lEUXm1I0g01LJd+ew9WkaVzc6f5++st+3DuttPE1Piuf/y2o7AC7ZpYjhpGgooIiIuIDTQhydGJ/LJAynEtPHDbkCQjycz7uxHeJBPvX1NJpPjNs/7K/fxq/fXYRgwsltbEsIDHfv5ennQqW3t8wudD2X2umzHzwu355N6oBCAvJIKluys7QNzR0o8t/evva207XAJlTXnv4Bitc3O24t2sznr2AXVJ62XAoqIiAtJjgnmm4eH8OSYRD789UASI888gduY7rW3eVIPHCOnuIKOYQE898uk0/br0a62H8pXPxzmWFkV2YXlvPhlBje9u4ZFPzMN/o9DSErH2onhXv1uJ4Zh8PHGbGx2g37t29CpbRCxIX6EBHhTZbM3qM/LxxsP8vqCXbxYNzeMyEkNXs1YREQuLqu/l2PW2bPpHRtMTBs/Dh47wbhe0Uy5rieBPqf/Sb80vg2fpB7k6y05fL8tjxqbnbpZ8Fm/v5CR3SL4wzXdibL6nfbeH4eQP9/Ui1+8tpR1+wq5c+YGRwi5pV8cUNuq0zs2mEU78knLKqJPXJvzOtdv03MA2JFbis1u4KEJ5aSOAoqISAtkNpv4zz0DyD5WzuBOYWddPfnGS2Mxm0x8sGa/I1QM7RJOx7AA/rP2AAu357Ezr4RPJw2ircWXlbuPMnfzIcKCvPkq7TAAt/aPo12wH3de1p5/LN/Lsl1HALD4enJlzyjHZ/WOqw0om7OLzlr324t2U22z8/ioLhwrr2bN3gIAKmvsZBeW0z4soCn+80groIAiItJCtQ8L+NkvdA+ziZv6xXLjpTHsyjuOl4eJjnX9VG4fEMe9/9rIgYJyJsxYzxU9Inlr0W5+PODnxyHkyTGJDOkcRlZhObnFFQzuFIaft4dj30tia1tN0rLP3J9ke04Jry+oXVixe7SV4hNV2OynPmxXXqkCijgooIiIuAGTyXRaf5bOEUH8++4BXP/uanbklrIjtxSAq3tFE+zvRVZhOdf2boevV20I8fIw15vS/6eSY62YTJBdeIKjxysJC6zfuffztEOOn//03Q6ig32B2hBlsxvszj/O6O7nPo/KGhvzM/IY2a0t/t4//xX29ZbDfLB6P2/f2vuMt7HEdamTrIiIG4sL9eeDu/oT5OuJl4eJP17bk7dv7c3L43sw667+jL+k3Xkfy+LrRae61pm0rKJ62+x2gy/rbhl5mE3sO1rGqj21t3eu6137GbvySn/2M177bie//XAzL59hLaIz+euiPWzYf4xPUw+e72mIi1BAERFxc0nRFpb8bhgrnhrObQPiGnWs3nHBAGz+yW2edfsKySmuIMjXk//3o5Wbu0YGMbpuRNKuvOPnPHZxeTWz6xZOnLv5EMXl9dcimp+RS98/LODfaw8AUFhWxc660LP5J4FJXJ8CioiIEBboQ6TVt9HHOdkPZeP+Y/Vmr/2i7vbOlT2imJAST8fw2r4mV/aMoktEbatL5pHj9fqk/NR/1h2gvKp2jpXKGjsfp56ao2VNZgEPz95MQVkVM1buwzAM1u8rcGzfnF10wbPpinMooIiISJM52YKybl8hg/+0hFe+2cbczQf5pm448TW92+HlYeYfd/Tlt8M7ce+QDsS28cfXy0xVjZ0DBWX8kF3EvR9sZMXuI47jVtbYmLlqPwCDEmrnZPnvuizsdoOMw8X8+l8bqapbB2jf0TIyjxxn7d5Cx/sLy6rIKjx9PSFxXQooIiLSZLpGBnHnoPYEeHtwqOgE/1yxj8c++oHSihqirL4M6BACQKe2QTw+OhF/b0/MZpNjxttdecd55rN0Fm7PY+KM9Uxbmkl5VQ3/XnOAo8cribL6Mu32vgT5eLLvaBnPfbGVG6atobSyhv4dQhwTyi3Yls+6fbUB5eTcKidv89jthlpTWgAFFBERaTImk4kXr+5O6nOj+Pvtfbh9QBwDOoQQ08aPx0Z2wXyWidi6RNSOMJq1eh/bc0owmcBu1I72SXp+Pv/3zXYA7r6sA1Z/L67vGwPUtqKcqLZxWadQ/jnhUq5Krh0S/dmmg47FDU8Ok96UdYwam50b3l1Nv1cW8dGG2hYYcU0aZiwiIk3O18uDK3tG1ZvI7VxOBpSTt2UmDIynS2QQL325jSqbHaufF73jgh2deH81MJ7Z67IwMHhqTFfuGdwBs9nEyG4RPPv5Vnbn13a4TQgPYHRSBF/9cJjNWUV8k57DprqWlKc/TefjjQd5946+pw2JFudTQBEREac72VEWwGyCewZ3JC7Un2suaUe1zU6wv3e9/Tu1DeTr3w7Gz8uD2BB/x+uRVl+SY6xsOVi7OOKAjqH0ia/tuLs9p4S3F+0G4LJOoWzOKmLjgWO8tXA3f7imx8U+RWkg3eIRERGn69z21CRyY3tEERdaGzoCfDxPCycndYkIqhdOThrVLcLx88COoURbfWkb5EON3SDzSBmBPp78/fa+vPurvgB8vvkQ5VU1Z63NMNRnxRkUUERExOnaBdeuhgxw39COjTrWqO6nAsqADiG1CxnWjS4CuCMlHqufF4M7hREf6k9pZQ1f/XD4tOOUVlTz5sJd9Hrpe+6atUEhpZnpFo+IiDid2Wxi5p39OFZexSWxwY06VmJEEE+OScTXy4MIS+3cLr3j2jA/Iw8fTzN3X9bB8Zm39o9j6rwdzF6Xxc394ig4XsniHfms3VvI4h15HKubDG7pziMs3pHPiB+1zvyYYRiUVNRg9fNyvFZUXkWN3VD/lgtkMlwsEpaUlGC1WikuLsZisTi7HBERaQUOFZ1gwvR13No/jnuHnGqhOXq8kpQpi6i2Gdw/tCOz12VRWnnqdk/H8AA6hgWwcHs+PdtZ+fKhy05bObrGZuf+f6eybNcRpl6fzA19Y8g8cpwb311D8YlqruoZxf2Xd6R7tLXZztcZmvr7WwFFRETc2kOzN/H1lhzH866RQQzv2paBHUMZlBBK8Ylqhry6hPIqG9MnXnpaK8oLX2zlgzW10+t7mE386fpk3lq0i+zCE459TCZ4+5bejOsV3Twn5QQKKCIiIk0o9UAhN767Bn9vT54ck8ivBsY7Jnc7aeq8Hby7LJOE8AB6xQaz72gZMW38CfTx5MO69YH6tW/Dhv2n1iCKD/VnyrU9mbl6Pwu25dHG34tFTwxz9LX5qYpqG94e5rPOFePqFFBERESa2J78UkICfM4aHgqOVzpaUc7kyTGJ/HpoR+6etYEVu48SEuDNpw8MokNYANU2O+P+upIduaVc17sdf7yuJx+nHqSiyuaYv2VXXik3vruGPnHBzLyr/1nrzC2uYFPWMYZ2CSfQ5+e7kVZU2/Awm/DyuPhjYhRQREREnOCbLTl8uzWHzm0DSQgPJKuwnK2HiukcEcRjIztjMpkoq6zh443ZDOkSTkL4qbldNmcd47ppqzEMCAv05ujxKgAeHdmZB3/RiWv/voqth2pnvv3+saGOiet+zDAMrv7bKtIPFRPk68ltA+J48BedsPh6nbbfou35zN18iEU78gj282bhE5efV6BpDAUUERGRFujFLzOYtXo/cCqkmEwwomtbFm7Pd+x335AO/P6qpNPev3F/ITe8u6bea1d0j+TdO/rWe235riNMmLG+3muz7urHsMS2TXQmZ9bU39+aB0VERKQZPHVFIvdf3pE/XNODVc8M51cD4zAMHOFk/CW1HWjnbj5Edd3KzD/2r7qOuDf0jeGd2/oA8P22XA4eq79K88lVoAd2DGFI5zAAx/T+LYkCioiISDPw9/Zk8thu3DEwHh9PD57/ZXfHBHJjukfw5xt7OVpWauddyeOm99bwRdoh8ksrmLe1dqTRnYPac1VyFIMSQrEbODrpnrS+bhXnW/rFMbp7JFB7i6ml0URtIiIiTuDtaWbmnf2YtzWXq3tF4+Vh5ppL2vH+yn28+GUGh4tPYBi1gaNHOwvVNoM+ccH0aFc7n8odA+NZnVnARxuyeWREF7w9zRyvrGHr4dq+LP07hHCsvLavS1pWEXa70aJGCKkFRURExEmC/b25tX8cAXUdWG+8NBaonVjOMHC0sJzsQDtxUHvHe0cmRRBh8eHo8Sq+y8gFIPXAMWx2g9gQP6KD/UiMCMLf24PSyhrHCs8thQKKiIiIi0iMDCKlYygeZhMvXd2dzx4YxJTreuJhNhFt9eWKHpGOfb08zNzSLw6Af6/ZD8D6fQUA9G8fCoCnh5leMcFAbXhpSXSLR0RExIXMursfpRU1jjV8bu0fx6CEUPy8PfDx9Ki3763943hnyR427D/G8l1HHP1PBnQIcezTN74Na/YWsCnrGLcNiGu+E2kktaCIiIi4EB9Pj9MWGIwPDaBtkO9p+0ZafbkjJR6AF7/K4IfsYqC2/8lJfeKDAdjUwjrKKqCIiIi0YI+O7EJIgDd7j5RRZbPTNsiH+FB/x/besW0A2HukjO05Jbz+/U7eX7HXWeWeNwUUERGRFszq58VTYxIdzwd0DK234nKbAG86hgUAMPatFby9eA/vLsukqub0uVZciQKKiIhIC3fjpbH0rBt+PLhT6GnbB3Q89dqADiG8eHV3XH3EsTrJioiItHAeZhMz7uzH8l1HuKZ3u9O2Pzkmka6RQaQkhJ5xnR9XpLV4REREpNG0Fo+IiIi0egooIiIi4nIUUERERMTlNCigTJs2jeTkZCwWCxaLhZSUFObNm+fYfv/995OQkICfnx/h4eGMHz+eHTt2NHnRIiIi0ro1KKDExMQwdepUUlNT2bhxI8OHD2f8+PFkZGQA0LdvX2bOnMn27duZP38+hmEwevRobDbbRSleREREWqdGj+IJCQnhtdde45577jlt25YtW+jVqxd79uwhISHhvI6nUTwiIiItT1N/f1/wPCg2m42PP/6YsrIyUlJSTtteVlbGzJkz6dChA7GxsWc9TmVlJZWVlY7nJSUlF1qSiIiItBIN7iSbnp5OYGAgPj4+TJo0iblz55KUlOTY/ve//53AwEACAwOZN28eCxYswNvb+6zHmzJlClar1fE4V5gRERER99DgWzxVVVVkZWVRXFzMJ598wvvvv8+yZcscIaW4uJj8/HxycnL485//zKFDh1i1ahW+vqevwghnbkGJjY3VLR4REZEWpKlv8TS6D8rIkSNJSEjgvffeO21bVVUVbdq04f333+fWW289r+OpD4qIiEjL43Izydrt9notID9mGAaGYZx1u4iIiMiZNKiT7OTJkxk7dixxcXGUlpYye/Zsli5dyvz589m7dy8fffQRo0ePJjw8nIMHDzJ16lT8/Py48sorL1b9IiIi0go1KKDk5+czYcIEcnJysFqtJCcnM3/+fEaNGsXhw4dZsWIFb775JseOHSMiIoKhQ4eyevVq2rZte7HqFxERkVbI5VYzLi4uJjg4mOzsbPVBERERaSFODnIpKirCarU2+ngXPA/KxVJaWgqg4cYiIiItUGlpaZMEFJdrQbHb7Rw+fJigoCBMJlOTHPNkqnOHVhmda+ukc22ddK6tk7uea1BQEKWlpURHR2M2N34tYpdrQTGbzcTExFyUY59c5NAd6FxbJ51r66RzbZ3c8VybouXkpMZHHBEREZEmpoAiIiIiLsctAoqPjw8vvPACPj4+zi7lotO5tk4619ZJ59o66Vybhst1khURERFxixYUERERaVkUUERERMTlKKCIiIiIy1FAEREREZejgCIiIiIup9UHlHfeeYf27dvj6+vLgAEDWL9+vbNLarQpU6bQr18/goKCaNu2Lddccw07d+6st8+wYcMwmUz1HpMmTXJSxRfuxRdfPO08unbt6theUVHBgw8+SGhoKIGBgVx//fXk5eU5seIL1759+9PO1WQy8eCDDwIt+5ouX76ccePGER0djclk4vPPP6+33TAMnn/+eaKiovDz82PkyJHs3r273j6FhYXcfvvtWCwWgoODueeeezh+/HgznsX5Ode5VldX8/TTT9OzZ08CAgKIjo5mwoQJHD58uN4xzvRvYerUqc18Jj/v567rnXfeedp5XHHFFfX2aQ3XFTjj767JZOK1115z7NNSruv5fMecz9/erKwsrrrqKvz9/Wnbti1PPvkkNTU1511Hqw4oH330EY8//jgvvPACmzZtolevXowZM4b8/Hxnl9Yoy5Yt48EHH2Tt2rUsWLCA6upqRo8eTVlZWb397rvvPnJychyPV1991UkVN0737t3rncfKlSsd2x577DG++uorPv74Y5YtW8bhw4e57rrrnFjthduwYUO981ywYAEAN954o2OflnpNy8rK6NWrF++8884Zt7/66qu8/fbbvPvuu6xbt46AgADGjBlDRUWFY5/bb7+djIwMFixYwNdff83y5cv59a9/3VyncN7Oda7l5eVs2rSJ5557jk2bNvHZZ5+xc+dOrr766tP2ffnll+td64cffrg5ym+Qn7uuAFdccUW98/jwww/rbW8N1xWod445OTnMmDEDk8nE9ddfX2+/lnBdz+c75uf+9tpsNq666iqqqqpYvXo1H3zwAbNmzeL5558//0KMVqx///7Ggw8+6Hhus9mM6OhoY8qUKU6squnl5+cbgLFs2TLHa5dffrnxyCOPOK+oJvLCCy8YvXr1OuO2oqIiw8vLy/j4448dr23fvt0AjDVr1jRThRfPI488YiQkJBh2u90wjNZzTQFj7ty5jud2u92IjIw0XnvtNcdrRUVFho+Pj/Hhhx8ahmEY27ZtMwBjw4YNjn3mzZtnmEwm49ChQ81We0P99FzPZP369QZgHDhwwPFafHy88cYbb1zc4prYmc514sSJxvjx48/6ntZ8XcePH28MHz683mst8boaxunfMefzt/fbb781zGazkZub69hn2rRphsViMSorK8/rc1ttC0pVVRWpqamMHDnS8ZrZbGbkyJGsWbPGiZU1veLiYgBCQkLqvf7f//6XsLAwevToweTJkykvL3dGeY22e/duoqOj6dixI7fffjtZWVkApKamUl1dXe8ad+3albi4uBZ/jauqqvjPf/7D3XffXW9V79ZyTX9s37595Obm1ruOVquVAQMGOK7jmjVrCA4O5tJLL3XsM3LkSMxmM+vWrWv2mptScXExJpOJ4ODgeq9PnTqV0NBQevfuzWuvvdagpnFXsnTpUtq2bUtiYiIPPPAABQUFjm2t9brm5eXxzTffcM8995y2rSVe159+x5zP3941a9bQs2dPIiIiHPuMGTOGkpISMjIyzutzXW4146Zy9OhRbDZbvf84ABEREezYscNJVTU9u93Oo48+ymWXXUaPHj0cr992223Ex8cTHR3Nli1bePrpp9m5cyefffaZE6ttuAEDBjBr1iwSExPJycnhpZdeYsiQIWzdupXc3Fy8vb1P+8MeERFBbm6ucwpuIp9//jlFRUXceeedjtdayzX9qZPX6ky/qye35ebm0rZt23rbPT09CQkJadHXuqKigqeffppbb7213qq3v/3tb+nTpw8hISGsXr2ayZMnk5OTw+uvv+7Eahvuiiuu4LrrrqNDhw5kZmby//7f/2Ps2LGsWbMGDw+PVntdP/jgA4KCgk673dwSr+uZvmPO529vbm7uGX+nT247H602oLiLBx98kK1bt9brlwHUu4fbs2dPoqKiGDFiBJmZmSQkJDR3mRds7Nixjp+Tk5MZMGAA8fHx/O9//8PPz8+JlV1c06dPZ+zYsURHRzteay3XVGpVV1dz0003YRgG06ZNq7ft8ccfd/ycnJyMt7c3999/P1OmTGlR67vccsstjp979uxJcnIyCQkJLF26lBEjRjixsotrxowZ3H777fj6+tZ7vSVe17N9xzSHVnuLJywsDA8Pj9N6Fefl5REZGemkqprWQw89xNdff82SJUuIiYk5574DBgwAYM+ePc1R2kUTHBxMly5d2LNnD5GRkVRVVVFUVFRvn5Z+jQ8cOMDChQu59957z7lfa7mmJ6/VuX5XIyMjT+vcXlNTQ2FhYYu81ifDyYEDB1iwYEG91pMzGTBgADU1Nezfv795CrxIOnbsSFhYmOPfbGu7rgArVqxg586dP/v7C65/Xc/2HXM+f3sjIyPP+Dt9ctv5aLUBxdvbm759+7Jo0SLHa3a7nUWLFpGSkuLEyhrPMAweeugh5s6dy+LFi+nQocPPvictLQ2AqKioi1zdxXX8+HEyMzOJioqib9++eHl51bvGO3fuJCsrq0Vf45kzZ9K2bVuuuuqqc+7XWq5phw4diIyMrHcdS0pKWLduneM6pqSkUFRURGpqqmOfxYsXY7fbHUGtpTgZTnbv3s3ChQsJDQ392fekpaVhNptPux3S0hw8eJCCggLHv9nWdF1Pmj59On379qVXr14/u6+rXtef+445n7+9KSkppKen1wugJ8N4UlLSeRfSas2ZM8fw8fExZs2aZWzbts349a9/bQQHB9frVdwSPfDAA4bVajWWLl1q5OTkOB7l5eWGYRjGnj17jJdfftnYuHGjsW/fPuOLL74wOnbsaAwdOtTJlTfcE088YSxdutTYt2+fsWrVKmPkyJFGWFiYkZ+fbxiGYUyaNMmIi4szFi9ebGzcuNFISUkxUlJSnFz1hbPZbEZcXJzx9NNP13u9pV/T0tJSY/PmzcbmzZsNwHj99deNzZs3O0auTJ061QgODja++OILY8uWLcb48eONDh06GCdOnHAc44orrjB69+5trFu3zli5cqXRuXNn49Zbb3XWKZ3Vuc61qqrKuPrqq42YmBgjLS2t3u/vyZENq1evNt544w0jLS3NyMzMNP7zn/8Y4eHhxoQJE5x8Zqc717mWlpYav/vd74w1a9YY+/btMxYuXGj06dPH6Ny5s1FRUeE4Rmu4ricVFxcb/v7+xrRp0057f0u6rj/3HWMYP/+3t6amxujRo4cxevRoIy0tzfjuu++M8PBwY/LkyeddR6sOKIZhGH/961+NuLg4w9vb2+jfv7+xdu1aZ5fUaMAZHzNnzjQMwzCysrKMoUOHGiEhIYaPj4/RqVMn48knnzSKi4udW/gFuPnmm42oqCjD29vbaNeunXHzzTcbe/bscWw/ceKE8Zvf/MZo06aN4e/vb1x77bVGTk6OEytunPnz5xuAsXPnznqvt/RrumTJkjP+m504caJhGLVDjZ977jkjIiLC8PHxMUaMGHHaf4OCggLj1ltvNQIDAw2LxWLcddddRmlpqRPO5tzOda779u076+/vkiVLDMMwjNTUVGPAgAGG1Wo1fH19jW7duhl//OMf632pu4pznWt5ebkxevRoIzw83PDy8jLi4+ON++6777T/QWwN1/Wk9957z/Dz8zOKiopOe39Luq4/9x1jGOf3t3f//v3G2LFjDT8/PyMsLMx44oknjOrq6vOuw1RXjIiIiIjLaLV9UERERKTlUkARERERl6OAIiIiIi5HAUVERERcjgKKiIiIuBwFFBEREXE5CigiIiLichRQRERExOUooIiIiIjLUUARERERl6OAIiIiIi7n/wNzfz3SStAxMAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(loss_list)+1), loss_list)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">910</span><span style=\"font-weight: bold\">])</span>\n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">901</span><span style=\"font-weight: bold\">])</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m910\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m901\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load(log_dir / 'model.pth'))\n",
    "test_num_samples = 4000\n",
    "test_ts, test_bridge, test_drift, test_source_sample, test_target_sample = gen_2d_data(num_samples=test_num_samples)\n",
    "# print_debug(test_ts, test_bridge, test_drift, test_source_sample, test_target_sample)\n",
    "pred_bridge = torch.zeros_like(test_bridge)\n",
    "pred_bridge[0, :] = test_source_sample\n",
    "model.eval()\n",
    "model.to(device)\n",
    "sigma=1\n",
    "with torch.no_grad():\n",
    "    for i in range(len(test_ts)-1):\n",
    "        dt = (test_ts[i+1] - test_ts[i])\n",
    "        test_source_sample_reshaped = test_source_sample.reshape(-1, 1, test_source_sample.shape[-1])\n",
    "        test_ts_reshaped = test_ts[i].reshape(-1, 1, 1).repeat(test_num_samples, 1, 1)\n",
    "        pred_bridge_reshaped = pred_bridge[i].reshape(-1, 1, pred_bridge.shape[-1])\n",
    "        x = torch.concat([test_source_sample_reshaped, test_ts_reshaped, pred_bridge_reshaped], axis=-1)\n",
    "        dydt = model(x[:,0,:].to(device)).cpu()\n",
    "        # print_debug(dydt)\n",
    "        # dydt = dydt[:, 0, :]\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(test_num_samples, 2)\n",
    "        pred_bridge[i+1, :] = pred_bridge[i, :] + dydt * dt + diffusion\n",
    "        \n",
    "        \n",
    "        \n",
    "        # dt = ts[i+1] - ts[i]\n",
    "        # dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        # drift[i, :] = dydt\n",
    "        # diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        # bridge[i+1] = bridge[i] + dydt * dt + diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.9998)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ts[list(range(len(test_ts)-1))[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(pred_bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(pred_bridge[0, :, :], pred_bridge[-1, :, :], left_title=\"Source Sample\", right_title=\"Predict Sample\", save_path=log_dir / 'pred_bridge.jpg', bound=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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txuHDh/Hdd9+hfv36eOGFFwAALVu2xMiRI/Hpp58iMzMTnTt3xvHjx7FhwwYMGDCg1BGQ0aNH45VXXsGgQYPQs2dPnDlzBjt37oSPj49Rmdzd3bF8+XKMHj0abdu2xTPPPAMvLy+cOXMGeXl52LBhA5RKJT777DP07t0bTZs2xQsvvIA6deogKSkJe/fuhbu7O7Zu3Sr6Nb766it88803eOqppxAREQGVSoULFy7giy++gJOTE2bNmgUACAsLQ4MGDTBt2jQkJSXB3d0dP/74o1H3NaostVqN7t27Y+jQobh06RJWrVqFjh07lrrYWNfzzz+PzZs345VXXsHevXvx+OOPo7i4GBcvXsTmzZu19w4hIrIVvXr1gkqlQr9+/fDyyy8jJycHa9euhZ+fn9FnMLz88sv4+OOPMXz4cLz22msICAjAN998o70m6MHRlIrMOREREQCAN998E08//TQcHBzQr18/vTdTz8vLQ4cOHfDYY48hKioKgYGByMzMxJYtW/DHH39gwIABCA8PBwD07dsXP/30E5566in06dMHiYmJWLNmDZo0aaK3Kamqxo0b46WXXsKJEydQq1YtfPHFF7h9+7bBRrSqczMRlwcni/Lbb78JL774ohAWFia4ubkJKpVKaNiwoTBp0iTh9u3bpcYWFhYK8+bNE4KDgwUHBwchMDBQiI6OLrWkqCAIQnFxsTBjxgzBx8dHcHFxESIjI4XLly+LLg9+4sQJvdl+/fVXoUOHDoKzs7Pg7u4uPProo8LGjRtLjTl9+rQwcOBAoWbNmoKjo6MQFBQkDB06VNizZ4/B7/uvv/4Spk+fLrRu3Vrw9vYW7O3thYCAAGHIkCHCn3/+WWrs+fPnhR49eghubm6Cj4+PMGbMGOHMmTMCAGHdunXacSNHjhRcXV3LfK3OnTsLTZs2LbM9KChI6NOnT5n9sX//fmHs2LGCl5eX4ObmJjz77LPCnTt3yrzmw8uDC4IgqNVqYdGiRULTpk0FR0dHwcvLS4iIiBDmzZsnZGVlGdwfRESWTt/y4L/++qvQokULwcnJSahfv76waNEi4YsvviizPLfu79uHXb16VejTp4/g7Ows+Pr6ClOnThV+/PFHAYBw9OjRUmONnXPmz58v1KlTR1AqlQaXCi8sLBTWrl0rDBgwQAgKChIcHR0FFxcXITw8XFiyZEmp5cA1Go2wYMEC7bjw8HBh27ZtwsiRI4WgoCDtuAfLgy9ZsqTU13qwlPf3339faru+ufjB/tq5c6fQokULwdHRUQgLCyvzXN3lwSu6n4h0KQTBRFdvE5FNWb9+PV544QWcOHGCR3+IiMzogw8+wOTJk3Hz5k3UqVPH3HFkV79+fTRr1gzbtm0zdxSqZniNEhEREZGFuH//fqnH+fn5+OSTT9CoUaNq2SQRmROvUSIiIiKyEAMHDkS9evXQqlUrZGVl4euvv8bFixfxzTffmDsaUbXDRomIiIjIQkRGRuKzzz7DN998g+LiYjRp0gSbNm3CsGHDzB2NqNqR9NS7mJgYtG3bFjVq1ICfnx8GDBiAS5culRqTn5+PCRMmoGbNmnBzc8OgQYNw+/ZtKWMRkRFGjRoFQRB4fRLZFM5LZOlef/11nD17Fjk5Obh//z5OnTpV7Zuka9eu8fokMgtJG6X9+/djwoQJOHr0KGJjY1FYWIhevXqVuofK5MmTsXXrVnz//ffYv38/bt26hYEDB0oZi4iIqinOS0REZCxZV71LS0uDn58f9u/fj06dOiErKwu+vr749ttvMXjwYADAxYsX8cgjj+DIkSN47LHH5IpGRETVEOclIiISI+s1SllZWQAAb29vAMCpU6dQWFiIHj16aMeEhYWhXr16ohNSQUEBCgoKtI81Gg3u3r2LmjVram/ERkRE0hMEAffu3UPt2rWhVFrnIqqmmJcAzk1ERJbClHOTbI2SRqPB66+/jscffxzNmjUDAKSkpEClUsHT07PU2Fq1aiElJUXv68TExGDevHlSxyUiIiP9888/qFu3rrljVJip5iWAcxMRkaUxxdwkW6M0YcIEnD17FgcPHqzS60RHR2PKlCnax1lZWahXrx4SExPLTGyWSKPRID09HT4+PlbxCSzzSot5pcW80srMzERwcDBq1Khh7iiVYqp5CeDcJDfmlRbzSot5pWXKuUmWRmnixInYtm0bDhw4UKqz8/f3h1qtRmZmZqmJ5Pbt2/D399f7Wo6OjnB0dCyz3dPT02omI7VaDU9PT6t4szGvtJhXWswrD2s8tcyU8xLAuUluzCst5pUW88rDFHOTpN+tIAiYOHEifv75Z/z+++8IDg4uVY+IiICDgwP27Nmj3Xbp0iXcuHED7du3lzIaERFVQ5yXiIjIWJIeUZowYQK+/fZb/PLLL6hRo4b2/G4PDw84OzvDw8MDL730EqZMmQJvb2+4u7tj0qRJaN++PVcWIiIik+O8RERExpK0UVq9ejUAoEuXLqW2r1u3DqNGjQIALF++HEqlEoMGDUJBQQEiIyOxatUqKWMREVE1xXmJiIiMJWmjZMwtmpycnLBy5UqsXLlSyihEREScl4iIyGjWc0UWERERERGRTNgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOiRtlA4cOIB+/fqhdu3aUCgU2LJlS6n6qFGjoFAoSv2LioqSMhIREVVznJuIiMgYkjZKubm5aNmyJVauXCk6JioqCsnJydp/GzdulDISERFVc5ybiIjIGPZSvnjv3r3Ru3dvg2McHR3h7+8vZQwiIiItzk1ERGQMSRslY+zbtw9+fn7w8vJCt27d8O6776JmzZqi4wsKClBQUKB9nJ2dDQDQaDTQaDSS560qjUYDQRCsIivAvFJjXmkxr7SsJWdlcG6ybMwrLeaVFvNKy5Q5zdooRUVFYeDAgQgODsaVK1cwa9Ys9O7dG0eOHIGdnZ3e58TExGDevHlltqelpUGtVksduco0Gg2ysrIgCAKUSstfS4N5pcW80mJeaWVlZZk7giQ4N1n+e495pcW80mJeaZlyblIIgiCY7NUMfSGFAj///DMGDBggOubq1ato0KABdu/eje7du+sdo+9Tu8DAQNy5cweenp4mTm16Go0GaWlp8PX1tYo3G/NKi3mlxbzSyszMRM2aNZGVlQV3d3dzx6kUzk0lrO29x7zSYl5pMa+0TDk3mf3Uu4eFhITAx8cHly9fFp2MHB0d4ejoWGa7Uqm0ih8eUDIxM690mFdazCsta8prDRlNgXOTZWJeaTGvtJhXOqbMaFHf7c2bN3Hnzh0EBASYOwoREREAzk1ERNWVpEeUcnJycPnyZe3jxMRExMXFwdvbG97e3pg3bx4GDRoEf39/XLlyBW+88QYaNmyIyMhIKWMREVE1xrmJiIiMIWmjdPLkSXTt2lX7eMqUKQCAkSNHYvXq1fjrr7+wYcMGZGZmonbt2ujVqxfmz5+v9/QFIiIiU+DcRERExpC0UerSpQsMrRWxc+dOKb88ERFRGZybiIjIGBZ1jRIREREREZElYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ67M0dgKg6uP7dVuRcvAxN3v1S2wUAed5uSM3JhwJKQAGguBhKZ2d4P9oKAb06mSUvERERUXXHRonIhNIOnkDq/qMlDZGDPVCsAdRqw08SAKiLSm3S3MtB+p6DSN9zsNR2hbMjanZoywaKiIiISGJslIiqIHnXAWT++ReKsnNKmqKHFRXpf1IVCPcL/mug7OxQo0kj1P6/blB5e5r8axERERFVZ2yUiCoo5+oN3PzpNxSm3TFvkOJi3Iu/iEvxF6FwdYZLYG34dW4Pt5B65s1FREREZAPYKBEZ6fp3W5EddxbQCOaOUoaQex+5F68g8eIVQKmAf58e8O3Y1tyxiIiIiKwWGyWiclz/biuy/4w3dwzjaQSkbI1FytZY+HTvyOuZiIiIiCqBjRKRiLSDJ5CyNVb6L6QAoLIHoAQKCwHBdEesHlzPpKrjj9BXXzTZ6xIRERHZOjZKRDqufLYReQmJpnkxlaqk+XFwgEeLMPh371hq4QWNRoPU1FT4+flBqSy5rVnO1RtI/u135N9MATQakReuGHVSCuJnLEDojPFc+IGIiIjICGyUiP6VEXceNzduqdqLKBWw93CHZ+vmlT7lzS2kHhpNGFVm+81fdiHr9Flo7udXOt6lRasAB3sEv/g0F30gIiIiMoCNEhGAC4tXoehOZuWe7KiCe9NQBA3rZ9JMuur274W6/XtpH9/8ZRdyLl5G4d3Mir1QYRESP/kaDr41ETbtZdOGJCIiIrIRbJSoWlPfzSw5ylJR9naoO6QfvFo1MX0oI9Xt3wv4t3GqzIIThWl3ED9jAeoOH2DW74OIiIjIErFRomrr8pqvcD/xnwo9R+HqhDpPRllcYxE0rB8wrB8y4s4jbf9hFNxKNfq5Nzduwe09BxE2dayECYmIiIisCxslqpbiZyyo0HiFsxPqjxhs8df1eLVqom3iKnKUqTA1HfEzY9B8YbSU8YiIiIishtLcAYjkVpEmyd7bE6EzxqPZ3CkW3yTpChrWD80XzYKDb03jniAIFW4giYiIiGwVGyWqVirSCITOGI9HbGA57bBpLyP45eeMHh8/YwHUFV0ggoiIiMjGsFGiaiEj7rzRTZJ9TU80XzTL6hukh7mF1KvQ0aVLi1Yh/cgpiVMRERERWS5eo0Q279KHn0Ft5OIGtr4CXNi0l5Fz9QYSP/m63LHJW3YCAHzaR0gdi4iIiMji8IgS2TRjmyQ7dzc0XzTLppukBx4cXVK4upQ7NnnLTuRcvSFDKiIiIiLLwkaJbNb177Ya1ST59+uJJm++KkMiy9Js9utwDW1Q7rjET77maXhERERU7fDUO7JJCSvWQX3rdrnjmi+aJUMayxXy4jCjTsV7cBqed7twOWIRERERmR2PKJHNyTqfgAI2SUZ7cCpeeZK37IQ6I0uGRERERETmx0aJbE7G8dPljmGTVJYx++TvxatlSEJERERkfmyUyKacjV5Y7hg2SeKM2TfX1m+WIQkRERGRebFRIpthzH2S2CSVz5h9dJFHloiIiMjGsVEim3B5zVfljmGTZLzQGeMN1osysnBp2acypSEiIiKSn6SN0oEDB9CvXz/Url0bCoUCW7ZsKVUXBAGzZ89GQEAAnJ2d0aNHDyQkJEgZiWyQ+m4m7if+Y3BMeX/4U2kqb0/49nzC4Bj17XSkHTwhUyIi0+HcRERExpC0UcrNzUXLli2xcuVKvfXFixfjo48+wpo1a3Ds2DG4uroiMjIS+fn5UsYiG3Np0SqD9RrNw6Dy9pQnjA3x7/EEVAF+BsekbI2VKQ2R6XBuIiIiY0jaKPXu3RvvvvsunnrqqTI1QRDwwQcf4K233kL//v3RokULfPnll7h161aZT/eIxJx79yPDA+yUqP/cQHnC2KDQ10cDKsO3W4t/a7FMaYhMg3MTEREZw2zXKCUmJiIlJQU9evTQbvPw8EC7du1w5MgRc8UiK5J28AQ093IMjmm+YKZMaWxX8/lvGB5QWMRT8MhmcG4iIqIHDH9ULKGUlBQAQK1atUptr1WrlramT0FBAQoKCrSPs7OzAQAajQYajUaCpKal0WggCIJVZAUsO2+yntO+hIf+1R/7rEXmfpgl79+HBY19Ftc+/abU/n1Y8rZY1OwQYYZkhlnL/n3AGvPaGs5Nlp8VYF6pMa+0mFdapsxptkapsmJiYjBv3rwy29PS0qBWq82QqGI0Gg2ysrIgCAKUSstfdNBS8949fRZ5Xm56a2o3Z9h7uCPP1RF5qakyJ6sYS92/Zbg6Ql2/NoqysqF2c4ZCz5CEfYfg0aSR7NEMsZr9+y9ry5uVlWXuCBaDc5O8mFdazCst5pWWKecmszVK/v7+AIDbt28jICBAu/327dto1aqV6POio6MxZcoU7ePs7GwEBgbC19cXnp6eUsU1GY1GA4VCAV9fX6t4s1lq3tR9x+GiZ/uDox3h016wqLxiLHX/6uP38gjERy+EAMA5I6dMs1Sw8w/4dXncHNFEWdP+Bawvr0qlMncEk+PcZB3vPeaVFvNKi3mlZcq5yWyNUnBwMPz9/bFnzx7t5JOdnY1jx45h3Lhxos9zdHSEo6Njme1KpdIqfngAoFAomLcKEtZ8pfeIxgO+nR6zqLzlsbT9a0jg0/3x9849UAB6fwaXlq3FI9NeljuWQda0fwHrymsNGSuKcxPzSoV5pcW80rKmvKbMKGmjlJOTg8uXL2sfJyYmIi4uDt7e3qhXrx5ef/11vPvuu2jUqBGCg4Px9ttvo3bt2hgwYICUsciKqe9mIt/APZPsPGvALaSejImqF8+WjwC7fhetF6XdQc7VG/wZkEXj3ERERMaQtFE6efIkunbtqn384LSEkSNHYv369XjjjTeQm5uLsWPHIjMzEx07dsSOHTvg5OQkZSyyYolf/WCw/siMCUi18OuSrF39kUOQuvwL0frNH/+HsOnin7wTmRvnJiIiMoakjVKXLl0gCLrrY/1HoVDgnXfewTvvvCNlDLIROVdvQH1LvAny6d5RxjTVm1vTUOSeu6S3VpiewaNKZNE4NxERkTEs/0RDon/d/Ok38aICCOjVSb4w1Vz958reqPNhSb/ulCkJERERkTTYKJHVKEy7I1rz79tTxiQElBxVEqNOTpMxCREREZHpsVEiq3Dls40G674d28qUhB4IHjHIYP3mL7tkSkJERERkemyUyCrkJSSK1gwd2SBpqQL8RGsZR07JmISIiIjItNgokcXLiDtvsF7ekQ2STp0ne4kXBaHcnx0RERGRpWKjRBYvZdc+0Zqdu5t8QagMt5B6gEL89r/JO8TvuURERERkydgokcUrupMpWgvo00O+IKSXW5PGorXijGwZkxARERGZDhslsmjlLQjg1aqJTElITHmnPibvOiBTEiIiIiLTYaNEFi3r9FnRmmPdABmTkCEqf1/R2t2jJ2VMQkRERGQabJTIomnu54vW6j9r+KanJJ/gkUNEa5pc8Z8hERERkaVio0QWK+3gCdGawskRKm9P+cKQQfxZEBERka1ho0QW6+7xONGaW3CgfEHIKHbeHqK18m4YTERERGRp2CiRxVKnpovWPFo1kzEJGaPhmGdFa3lXr8uYhIiIiKjq2CiR5RIE0RJXu7M8Bk+/K9bIloOIiIjIFNgokUXKiDtv7ghkYuq7meaOQERERGQ0NkpkkdL2HxEvOqrkC0IV42AvWko/JL44BxEREZGlYaNEFkl9J0O05hXRQsYkVBHuzR8Rrd1LvCFjEiIiIqKqYaNEFkkwcE1L3f69ZExCFRE0rJ9oTchXy5iEiIiIqGrYKJFl0hTp3853rMVTuDjr3V5k4ObBRERERJaGf3aSZRI7oMTF0yyekK+/IRLbTkRERGSJ2CgRkWmJrequEV/unYiIiMjSsFEii5N20MDqaA4O8gWhSlE4O4rWLi77VMYkRERERJXHRokszt3jcaI1l5B68gWhSqnRsL5orfB2unxBiIiIiKqAjRJZHHWa+B/TgQMiZUxClVGzfRtzRyAiIiKqMjZKZHkMXMui8vaULwdVipuho35KhXxBiIiIiKqAjRIRyYcLOhAREZGVYKNERERERESkg40SEZmeg33FthMRERFZGP7VQkQmp3Cwh1BYpHc7EVF1tmzhZmzffgK5OfcBAAoFIAgl/6AAFACUdkpAEKDRCCXb/6Us2Qw7OyVcXJzg6eWKxqF1MWhoJ4RHNDLL90Nky/hXCxGZnL2rCwrz8vVuJyKydbE7TuKT1duQcisDxcXFUCgVCGlQE1cu34Ggc63mw40QhJJ7dhcXafS+rubfzUVFGmRn5yE7Ow83rqdh967T2jF2dgp4ebmh/1OPY8z4vib+zoiqFzZKRGRyHi2aIH3PQb3biYhszbKFm7Fzx0lk38uDoK/HkXEdm+JiAenp9/D52h34fO0OKJUKeNesgYGDOyKqz6OoXcdHvjBEVo6NEhGZXECvTsg89ReKMrO12+y9PBDQq5MZUxERmcbaVdvwy8+HkZGRg+Ji/Ud/LIVGIyA9LRufrt6OT1dvh529AkFBtfDC6Cj0jOJ974gMYaNERJJ4JHoikncdQP4/t+AUWJtNEhFZtdgdJ7Hyo1+Qkpxh7ihVUlwk4OqVFLwdvR5vR69HQIAXxr/an00TkR5slIhIMmyOiMia3UpKx9IF3+Hw4QvmjiKZ5OQMbdMUHhGCt+eNgH+At7ljEVkENkpERERED4ndcRIrlv+M1NQsyb/Wg5XsjF31TiPhmX6nT13FwL5z4entgtem9ENkbz/pvhiRFWCjRFZFfTcTKm9Pc8cgIiIbFLvjJN6b9y3y89WSvL4CgLOLI0IaBGDCa/0rvaR37I6T+PXnI7h69RZycwpQVFSI4uLSTVVVZGXexzdf7sE7szejc+fmWLhsrGlemMjKsFEii6N0doLmftmlpQHg6uebEDb9FZkTERGRLdv87V58tPwnFBWZptOws1PCz88DfQd0QO8+beEf4I3U1FT4+flBqVRW+fV7RrURvaYodsdJfLF2B5JupkOtLns/u4rat/cvPBY+Ea1bN8Bb7zzPVfOoWjF7ozR37lzMmzev1LbQ0FBcvHjRTInI3Px6PIGUrbF6a4Xpd2VOQ0TVDeel6mPtqm34fO2OKr+OSmWH0NBAjBc5SqSR8nw5HbpNVOyOk/hk1TakJGegqKi40q/7559XMLDvXLTv8AiWr5xgiqhEFs/sjRIANG3aFLt379Y+tre3iFhkJr4d24o2SkREcuC8ZNuWLdyMzd8dqNJrBIf448Uxlr/Etm7jtPnbvfh6w+5KX3915PAFPBY+ES+NieINbcnmWcRvfnt7e/j7+5s7BhEREQDOS7bqVlI6Bj05V/9NYY3g4qrC8Ge6WXWDMPSZrhj6TFcAJUfUtvx8CHfS71X4dT5fuwPrPt+JlZ++WulrrYgsnUU0SgkJCahduzacnJzQvn17xMTEoF69euaOReakgKx3MiciehjnJdvz4nOLcf7cjUo997HHH8Eb0cNs7vqcMeP7apu+yhxl02gEjBv9IWrXrYmfts4r/wlEVsbsjVK7du2wfv16hIaGIjk5GfPmzcMTTzyBs2fPokaNGmXGFxQUoKCgQPs4OzsbQMn5v3KeA1xZGo0GgiBYRVbAfHkNrdyTfycDKi8PvTXuX2kxr7SsMa8tqui8BHBukltF8sb9eRkTXl4BAFAoFRX6OkOGPIHX3xhc6utWhjXs39ffGIzX3xiMz9f8D99v3g+FAlAoFIARa08k37qL9hGTMHnqUxj8dBfJs+qyhv37MOaVlilzKgTBVItJmkZmZiaCgoKwbNkyvPTSS2Xq+i6yBYCLFy/Cw0P/H8+WRKPRICsrCx4eHiZZ+UZq5sp7bcNm0SNKdm6uCBzcR2+N+1dazCsta8ublZWFsLAwZGVlwd3d3dxxJFPevARwbpKbsXkXL9iE1NuZFX79npERiPy/tlVIWJo17t+bN5Ox7ZcTuHo5pULPdXR0wNQZQ+BdU77fCda4f5lXOqacm8x+REmXp6cnGjdujMuXL+utR0dHY8qUKdrH2dnZCAwMhK+vLzw9PWVKWXkajQYKhQK+vr5W8WYzV95sd0/kX7upv5iRAz8//TfB4/6VFvNKy9ryqlQqc0eQRXnzEsC5SW7l5d298xTmvPVlhV7TyckB4yb0leSIiLXu39nzRkOpVGLMyKU4f/4fo5//0ogV6NP3Ucya86yEKf9jrfuXeaVhyrnJ4hqlnJwcXLlyBc8//7zeuqOjIxwdHctsVyqVVvHDA0oOZTOvYY3HjUD8jAWidUNZuH+lxbzSsqa81pDRFMqblwDOTeYglndAn7eQcivT6Ndxq+GEJctflnxBAmvev59/9QZuJaXj+adjkJtTUP6TAWz79Rh+3xOH3w++L3HSEta8f62BNeU1ZUazf7fTpk3D/v37ce3aNRw+fBhPPfUU7OzsMHz4cHNHIyKiaojzkvV6LHxihZqk1Z+9ht0HlnLVNiPUruODPX+8j/kxo4x+Tl5uAR4LnyhdKCKJmb1RunnzJoYPH47Q0FAMHToUNWvWxNGjR+Hr62vuaGRmClcX0VpG3HkZkxBRdcJ5yfqcPpVQoT/I6wfXwtHTH7NBqoSeUW1w9PTHaN/hEaOf81j4RJw+lSBhKiJpmP3Uu02bNpk7Almomo+1Rvqeg3prt3ftg1erJjInIqLqgPOSdXlpxBKci79u9PjVn73GBskElq+cgFtJ6RjSfx6Ki8tfF2zc6A/RtHkQPv9yugzpiEzD7EeUiMQE9OokWiu8kylfECIiskhdO0wxuklqFdGAR5FMrHYdHxw6uQLdurc0avy5+Ot44tHXJE5FZDpslMiyOYmvXHLzl10yBiEiIksy7bU1UBcWGzX2p21zseazyRInqr4WLB2Dn7bNhb1D+X9WFhYW87olshpslMiiebVuIVrLOHFGxiRERGQJbiWl4/G2xh2V8K5ZA0dPf4zadXwkTkW16/jg4PGPEBxSy6jxbJbIGrBRIotWt38v8WJhoXxBiIjI7PbuPo2BfecaNXbK9EHYvjtG2kBUxsYf3zZ6ZTw2S2Tp2CiRVbu47FNzRyAiIhncSkpH9PTPjRp79PTHGPpMV4kTkZgHK+MZg80SWTI2SmTxVAF+orXC2+kyJiEiInMx5kiSvYPS6D/QSXpHT38MJ2eHcsc9Fj4RsTtOypCIqGLYKJHFq/OkgdPvAKQdPCFTEiIiMgdjjjr4+nng4PGPZEhDFbHv8HJ4eIrfF/GBt6PXY+JY/vzIsrBRIovnFlLPYD1lxz55ghARkeyMaZIi2jbC1p3vyZCGKmPn3sVGLfJw8sTf+GDJDzIkIjIOGyWyCj7dO4oXCwuRc/WGfGGIiEgWxjRJTz/bBSs/5b15LN3GH9/GlOmDyh236dt9uJXE0+rJMrBRIqtg6OazAJC4YbNMSYiISA7GNEl9+j2K16cNliENmcLQZ7pievTQcscZu7IhkdTYKJHVcG/dXLyYr+ZRJSIiG2FMk9SocV3Mmv2sDGnIlAYN7WRUs8TV8MgSsFEiqxE0rJ/B+q2tsTIlISIiqXTtMLncMZOnPoWXJ/SVIQ1JYdDQTlj9WfmnSz7eZpIMaYjEsVEiq+Lfr6doreDWbagzsmRMQ0REpjT4ybm4f9/wzcSnRw/D4Ke7yBOIJBMe0Qhjxv2fwTHFxQL69YqWKRFRWWyUyKr4dmwL2NuJ1v/+YK2MaYiIyFSWL/0BN/8xfBF/3wHtMGjoEzIlIqm9NPb/8OSA9gbHpKXdw5RJq2RKRFQaGyWyOnWHGDgFT12ErPMJ8oUhIqIqu5WUju++2WdwTECAF96a87w8gUg2s+Y8i6HDDC/YdPjgeZw+xbmd5MdGiayOV6smgEK8nnH8tHxhiIioyspb5czeXomft8+XJwzJbsrMofCu6WZwzLjRH8qUhug/bJTIKnm1b2OwfjZ6oUxJiIioKnp2eaPcMQdPfCRDEjKn7bsXQmHgQ1AA6Pgo75dF8mKjRFapbv9e5Y65/t1WGZIQEVFlLV/6A+5l5Rkc89O2ufKEIbM78ufHButFhcWYNY3XIpN82CiR1Wq+aJbBevaf8TIlISKiijLmuqSYpaNRu46PPIHIIsyPGWWw/vueM7iVZHjRDyJTYaNEVs3QcuEAED9jgUxJiIioIgY/Oddg/aUxUejavZUsWchy9Ixqg8ZhdQyOKe+aNiJTYaNEVs23Y1so3FwMjmGzRERkWV4asQQajXjd0ckBY8bzhrLV1Zcbo+Hu4WxwzEsjlsiUhqozNkpk9Zq9/Xq5Y84tWCF9ECIiKtfpUwk4F3/d4Jj9R5bLlIYs1a59hhuhc/HXuWQ4SY6NEtmE4JefM1jXZN3D+fe4ahIRkblNfMXwB1erP+PKZlSivIU8uGQ4SY2NEtkEt5B6qNEizOCY4uwc3Pxll0yJiIhI19pV21BcJH7OXXCwH8IjGsmYiCxZ7To+aNI00OCY1ycaXimPqCrYKJHNqP/sQChruBock3H4JFJ2/yFTIiIietjna3cYrG/8abZMSchafPH1DIP1o4cuIvnWHZnSUHXDRolsSpNZk8odkxb7B66u2yRDGiIiemDcS8sM1qdMHyRTErI25Z2OOXI4bzJP0mCjRDan/qih5Y7JvXgVCR+vlz4MERHhVlI6Tv95VbSucrTH0Ge6ypiIrEl4RCP4B3iJ1nPz1Di4/y8ZE1F1wUaJbFKzmJnljsn/5xYuLF4lQxoiourtmcHvGqx/uHKCTEks362kdPy4+Q9s+GInV3V7yKq1ho8qbfnpsExJSFfsjpNY9dEviN1x0txRTM7e3AGIpNJ80axy76FUdCcT8dExaB4TLVMqIqLqZfO3e5GfXyRabxxahws4/Gvv7tNY+N4mZGXmAgAcHOwx5OlOeHXKQDMnM7/adXzw2ONhOHroouiYz9f8D2PG95MxFU17bQ0OHjirfbzzt5NY+uErZkxkWjyiRDat+aJZ5Q/SCIifsQDJuw5IH4iIqJpZtuRHg/UvN/GDKqDkSNJ7877RNkkAUFhYhG+//p1Hlv71wccTDda/+Jwr28opdsfJUk0SABw8cNamjiyxUSKbZ1SzBCB9z0HEz3lf4jRERNXH2lXbDNa5gMN/fvvfCeTk5JctCMCPm7la6wPzY0YZrC9buFmeIIQvRFaxFNtujdgoUbVgbLOE/ALEz4yB+m6mpHmIiKoDQ8uB29sruYDDQy6evy5aO3/umnxBLFzPqDZwdXMUrW/+jmeHyCXpZnqFtlsjNkpUbRjdLAkCLi1ahRubDX8SSkRE4sr7ZH/FmvJv51CdaIoF0VrOPT1HmqqxpR8YvgamvCOZZBpqtf5rDwsLxa9JtDZslKhaab5oFlQBfkaNzTr1F+KjFyIj7rzEqYiIbM+Wn8VXIXOt4cQFHHQ4uapEaxpNsYxJLF94RCP4+rqL1su7sTFV3a0k8aNGSqVCxiTSYqNE1U7o66MR/PJzxg3WaHBz4xbEv7kIaQdPSBuMiMhGnD6VIPppMwDMnPW0jGmsg38tb9FaUTEbJV2frJtisM6jStK6e+eeeNF2+iQ2SlQ9uYXUKzkVT+Vg3BOKipGyNRbxMxbg0vK1vIaJiMiAVR/+IlpzUNmhZ1QbGdNYh46dmonWiovYKOmqXccHPn48qmQuybfuiNbs7exkTCItNkpUrTWfPx0OfjUr9Bx1ShouLVqF+FmLcPMXLkVKRKQrPv6aaG3EyJ7yBbEihk5F1GhkDGJF5i94wWCdK+BJZ/++M6I1VzdnGZNIyyIapZUrV6J+/fpwcnJCu3btcPz4cXNHomokbOrLqDt8QMUPFRcXI+PwScTPWICz7yxHztUbUsQjIjPh3FQ5M6d8arA+ZnxfmZJYH3t7/X+WKRQ2dC6TCYVHNIKbm5NoPTb2TxnTVC+nT14WrTVoUFvGJNKyN3eA7777DlOmTMGaNWvQrl07fPDBB4iMjMSlS5fg52fcRfdEVeXVqgm8WjVB8q4DSN9zsMLPF3LvI/GTr0ttU7q5otGEkVB5e5ooJRHJhXNT5e3b+5doLbx1AxmTWB9B0H/oSGw7ATPefBqz39ygt5ZxN0fmNNVH3v0C0VqDRgEyJpGW2RulZcuWYcyYMXjhhZLDp2vWrMH//vc/fPHFF5g5c6aZ01F1E9CrEwJ6dcLNX3Yh43DV7iytycnFpUWr9BftlECxBgKAPC83pGbkwLlebQT07ga3kHpV+rpEVHWcmyqnvAvo337neZmSWCexU+x46p24nlFtMPct/Y0SALzz1gbMfnekjImqB02x+JuyZXhDGZNIy6yNklqtxqlTpxAdHa3dplQq0aNHDxw5csSMyai6q9u/F+r274WrX2xC7qWrpv8Cen7B5N+49d9RKaUCgAKwswOKiwClEi7B9dBg9HDTZyGiUjg3Vd4vBpYED6jthdp1fGRMY32USiWK9cwP4ndYIgCIjGqDvxMS9da2bz/BRkkCGgPde2hYXRmTSMusjVJ6ejqKi4tRq1atUttr1aqFixcv6n1OQUEBCgr+O9yXnZ0NoOQHZuiHZik0Gg0EQbCKrADz1h81FOqMLFz78geoU9JM8poPEx76V4rm360Pvg9NMXITEvHXjAUlj5UK2LvXgE/HR+HzuHyrR1X394PUrDGvLeLcVHl37t6DQuQeKuMmPmmyfWGN/60Yk9fe3h4aoVBvLelmGgJqV2zxocqytv07a+6zeOG5d0uu5dJzmdeu306gR2SE/MFEWNv+1ZdXIwii/637B3ib9Xsz5dc2+6l3FRUTE4N58+aV2Z6Wlga1Wm2GRBWj0WiQlZUFQRCgVFrEWhoGMW8Jz+H9AACpfxxH3pVrJntdAFC7OVfylgMaZB86iquHjgJ2dnBvFgrvcPHlZU2B7wdpWVverKwsc0ewGJybgJ3bT6BBQ/E/5FuEByI1NbWyEUuxtv9WjM3boJG36P2nzp1LgJ29PMuEW+P+bdDQD1AoIGjKHn/bvu0PtAgPNEMy/axx/+rmDWnoo3dfK5QKk/13XlmmnJvM2ij5+PjAzs4Ot2/fLrX99u3b8Pf31/uc6OhoTJny303GsrOzERgYCF9fX3h6ekoZ1yQ0Gg0UCgV8fX2t5j8O5v2P36CS1ZpyEv/BjW9+gib3fpVe78HRJOeMnCrfn61o33Gk7jsO99bNUG+INKtK8f0gLWvLq1KpzB1BEpybKvfeW7t6N4pFPskNC61j0kUwrO2/FWPzJt3MRna2/nnl+JEr6NHzMakilmKN+7dZ8wbYteMiBKHsH+9XLt+xqEVYrHH/6ua9mnAHGj37WqlQmH1fm3JuMmujpFKpEBERgT179mDAgAEASn4Ye/bswcSJE/U+x9HREY6OjmW2K5VKq3izASXLfDKvdOTI694gCM1mTwYAqO9m4uaWHchNuFapK24VD/0zhXt/nsW5P8/CqX5dNBo3wkSv+h++H6RlTXmtIWNlcG6qeN7YHSdRZOCmqM+M6GHy/WBN/60AxuUNCqqFv87ov9bm8KHzsn6v1rZ/O3ZuifcXb9N7lAMA9uz606JudGxt+1c3r75r6QAAdgqzf0+m/PpmP/VuypQpGDlyJNq0aYNHH30UH3zwAXJzc7UrDRFZOpW3J0JefLrUtuRdB3D36J/Q5Obpf9K/q95JKf/aTcTPWACvDm1Qt38vSb8Wka3h3FQx32zYLVqzd7CzqD9QLdmQpzuLNkrZmSLzCWkF1vPFjWv6T/v6ZPU2vg9NxNDqlipHs7cWJmX272bYsGFIS0vD7NmzkZKSglatWmHHjh1lLqIlsiYPlhkvj0ajQWpqKvJ2HULu35dLmieFEhA0KLXqncgnZMbIOHwSGYdPwt7LAw3GPsv7OhEZgXNTxVy+fEu09urrA+QLYuV6RrXB29Hr9dY0GnmuT7Jmg4d0xLIlP+mt3byRLnMa2/Xbb+K3T3kkzHKuBTMFszdKADBx4kTR0xmIqoP6zz1V7qHi8ws/RnFGdqW/RlFGFi4tWgWPiBaoN1Saa5iIbAnnJuPcSkpHUZH4EfKhz3SVMY0NUEDveuBV+Lys2hj8dBfRRgkoOUWUR5WqLj01Q7Q2xMb+e7eIRomIytdkZuk/2C4sWY2idPFfVmKyTv2F+D//QvDY53hzWyKqsqULvhOtubqWvW6LDLNTKlBcrKcrYqNkFB8fd6Sn6/9Q8esNu9komYBaLX5005buoQToXW2eiKzBI9PHofmiWfDv1xMKF+eKPVkAEj/5GheXfiJNOCKqNuLOiN+Uu137MBmT2AY7O/HPsE+fSpAxiXXq/1QH0dq1xNuiNTLOrSTDpzDa2k2l2SgRWTnfjm3RbM5kNF80C3WHD6jQcwvT7iB+xgJkxJ2XJhwR2by83ALR2sTXn5IxiW1w9xD/4Ovjj7bIF8RKjRkvfmp5QYH+m/mS8X7733HRmqOT7Z2oxkaJyIZ4tWqC5otmwa1paIWed3PjFlx8n0eXiKhiDB7hUNjep8ty6NatlWjt8iXxRTPoPw4OdqK1zd/ulTGJ7dm960/Rmq+fp3xBZMJGicgGBY8YhOaLZsGlUbDRzylMvYP4NxdJmIqIbM3X68WXBXfj9UmVMmXmUNFaYWGRjEmsV91A8QZ94zdslKoiOemOaK13n0dlTCIPNkpENqzB6OFovmgWnIONXLShqBjxMxZIG4qIbEZ8vP57/gBAp84tZExiW5R2+m9BruHSd0Z5cUxv0dodkYUeyDj5+eKnL7JRIiKr1PCV5xD88nOAg4NR49ksEZExsrPEb4I6+92RMiaxLc5O4r+rly3cLGMS62RoZTu1mkflKivuz8uiNYWNnmrLRomomnALqYfm706Hb88njBrPZomIDDF0fZKdyBERMk7nLi1Fa79tPyFjEutlby9+nRJXD6ycz9duF615eLrJmEQ+bJSIqhn/Hk8gdMZ4o8ayWSIiMV9tEL8+ydmZ1ydVhaGjcffu3ZcxifXy9q4hWlvJ1QMr5eL5G6K1jp2ayphEPmyUiKohlbcnmi+aBajKPxWPzRIR6fP3BfE/mlqGh8iYxDYZOirHIyLle25kd9HalYQUGZPYjvt54tcnGbouzJqxUSKqxprPnw5lDddyx7FZIiJd2dniRzaeG9lTxiS2ydvHXbS2fMkPMiaxTkOf6Spa4/2UKu7unWyILSWiUNrm9UkAGyWiaq/pW6/BI6L81anYLBHRw4qLi0Vr4RGNZExim54f0UO0djmB91MyhlLkMiWNRiNvEBuwycCy6jXcXGRMIi82SkSEekP7lqyKV46z0QtlSENE1qC4WP/ny0olF3IwBUNHRDQaAbeS0mVMY53sDCzoELvjpIxJrN/VK8mitaje4qsMWjs2SkQEoGRVvMDnBpY77tpXP8qQhogsmaE/0hVslEzG3d1ZtLbig59lTGKdnFQq0dquHadkTGLddu80vK8M3STZ2rFRIiItz+Zh5a+IV1yMc/M/kCUPEVmmz9f+JlpzcjTufm1UvhYtxBfFOHTgnIxJrFMND/FrcBMSbsqYxLqt/2KnaC0w0DavTXqAjRIRlaJdEc8AIS8fybsOyJSIiCzN4QNnRWuNw+rImMS2PTtK/DoltbqIp9+Vo3VEA9FaZkaOjEmsW+LV26K1seP7yphEfmyUiEiv8o4spe85KFMSIrI0ubn5orWhw7vJmMS2hUc0gkolfp3N/NlfyZjG+hhasrpQLb4YCf2nvGu5ekbZ7vVJABslIhKh8vaEY90Ag2PiZ3FxB6LqyNCKd6FhdWVMYvueN7DU+uk/r8iYxPoYXrKaK98Z4xsDN5b29feUL4iZsFEiIlGNJ71geECxBmkHT8gThogshiCIL9hgq/dTMZcx5ZzatHbVNpmSWCc7e5E/dRX8E9gYFy+KX8vVrXsr+YKYCd8lRGRQedcrpWyNlSkJEVkOsaXBZY5RTTRtESRa22jg/jYECIL+96rYdvrPsoWbDdaHDe8iTxAz4q80IiqXT/eOBuu8GS1R9aIQ+TRebDtVzcRXB4jW8vIKuKiDAWLHPgUNG6Xy/PjDH6K12rW9qsXRY/5GI6JyBfTqVO6Ym7/skiEJEVkChchfn2LbqWrCIxrBzk585y6O2SRjGmujf78JguH7gVV3sTtOit5UGgDefmeEjGnMh40SERmlvFPwMg7zLudE1UVxsdiF8PyUXiqdurYUrR09dFHGJNbF3kF81cAjh87LmMS6vL/oe9Gai4sK4RGNZExjPmyUiMho/n3F7+kBAIlf/ihTEiIyl1tJ6RC/vIN/Vkhl0usDDNbLu56kuvL2chOt/R57WsYk1uNWUjoyM3NF669M6CdjGvPibzQiMprP420Ae3vRes65SzKmISJz+O1/x0VrSjv+WSGV2nV8UDdQ/JqQzd/xJuD6+Ph4iNZu3kyTMYn1eGfOl6I1hQIY+kxXGdOYF3+jEVGF1OrxhMF68i5O1kS27NihC6I1D08XGZNUP2/OedZgnUeVynJ2dRKtFeQXypjEesSduipae3F0lIxJzI+NEhFViLO/L+w8a4jW0/cclDENEcktJ69AtOZq4I9SqrrwiEbw9HQVrfOoUlnNm9cXranVRfIFsRLjXlpusF7efb1sDRslIqqwBmOfM1hPWC1+2J6IrJtQLP7HZWFBsYxJqqeYpaMN1nlUqbSGoXVEawVqHlF62K2kdJz+84po/bEOYTKmsQxslIiowlReHlAF+InW86+J38mbiKxbgVq8GarpI360mUwjPKIR/AO8ROs8qlTa3Ts5ojV7A9fcVkcvv7DMYH3ajKEyJbEcbJSIqFKCRww2WOe1SkS2qaaPu2htyNOdZUxSfa1a+5rB+usTP5YpieXzrukmen+vho0D5A1jwU6fSkBaWrZoPaRhAAJq15QxkWVgo0RElaLy9oTSXXzZVV6rRGSbhoo0Q2FhddEzqo3Maaqn2nV84G3g6N3RQxd5M9V/hYYFom6gr97axFcHyBvGgr0x+ROD9fGT+suUxLKwUSKiSqvdx/B9lXhUicj29Ixqg46dmpX6lL5xWF2s3zjTfKGqoclTBxmsjx/zoUxJLFvtOj4YP+lJ+NXy1G5TKhV4dkT3anPT1PKcPpWAe/fyRetDhhhe7daW8eRMIqo0r1ZNcPO7XwGNRm/9ztFTCOjVSeZURCS1pR++gtgdJ5HwdxIaNa7DI0lm0DOqDVZ+9AtSkjP01lOSM3D6VAKbAQBde4Qj9JFAHDl0Hjn37qNFqxDul4dMGGu4qX79jcFITU2VKY1lYaNERFXi36c7UrbG6q0JufdlTkNEcukZ1YYNkplt2T4fj4VPFK1PeXU19h4yfIF+dVG7jg8GDeUHd7qip68V+6wTADBluuEjl7aOp94RUZX4dmxrsJ4Rd16mJERE1c//9RH/HXw/T43YHSdlTEPW5FZSOvbuPiNat7NTYugzXWVMZHnYKBFR1akcREup+w/LGISIqHqZ/e5Ig/W3o9fLE4SsztMD5xusz313hExJLBcbJSKqMv/ILqI19Z278gUhIqqGXhoTZbDer9csmZKQtdj87V6oDdwTzbumG0+thZkbpfr160OhUJT6t3DhQnNGIqJKMHj6XUGRfEGITIBzE1mbMeP7QmngL7q0tGyegkelLFvyo8H69t38nQdYwBGld955B8nJydp/kyZNMnckIqoMR5Vo6cpnG2UMQlR1nJvI2qz81PBNaHkKHj3weBvDv8+q+wIODzN7o1SjRg34+/tr/7m6upo7EhFVgnvTUNFa3uVr8gUhMgHOTWRtwiMaoWmLIINjOrZlw1/djRi2AMXFgmjd1U1V7RdweJjZG6WFCxeiZs2aCA8Px5IlS1BUxNN0iKxR0LB+4kVB/JcykSXi3ETW6PMN06Ew8JddUZGAAf/3tnyByKJ8/ul2/P33LYNj9vzB5eQfZtb7KL366qto3bo1vL29cfjwYURHRyM5ORnLlon/kAoKClBQUKB9nJ2dDQDQaDTQGFoI3kJoNBoIgmAVWQHmlZqt5TXUDpnje7S1/WtprCVnRXFusnzMK+6HX+ZgcP93ROu3b2di+dIf8NqUgaJjuH+lZY68ybfu4LNPfoNCqRAdM3nqU3ozWeP+NRWTN0ozZ87EokWLDI65cOECwsLCMGXKFO22Fi1aQKVS4eWXX0ZMTAwcHR31PjcmJgbz5s0rsz0tLQ1qtbpq4WWg0WiQlZUFQRCgNHTlpYVgXmnZWt78AG9o8vX/d3hx5+/wDm8mdcRSbG3/WpqsrCxzRzAa5ybDrO29x7zi7OyBYc92wMljl0THnDpxFhcvtIB3TXe9de5faZkj74ypa9CgYU3RuqurIzp1a4LU1NQyNWvbv6acmxSCYNpzYtLS0nDnzh2DY0JCQqBSlb3w+9y5c2jWrBkuXryI0FD91zvo+9QuMDAQd+7cgaenZ5Wyy0Gj0SAtLQ2+vr5W8WZjXmnZWt70QyeRsm233ucqXJzR9G3DFxubmq3tX0uTmZmJmjVrIisrC+7u+v/gshScmwyztvce85YvstsbyLlXYHDMoRMf6t3O/SstufP2eGIa7ucXGhwj9l4ArG//mnJuMvkRJV9fX/j6+lbquXFxcVAqlfDz8xMd4+joqPcTPaVSaRU/PABQKBTMKyHmlZahvH5PPIrbIo0S8u6b5Xu0pf1raawh4wOcm8pnTe89gHnLE7tvKR4Ln2hwzBOPvopDJz/WW+P+lZZceV8asQR5eYaPah89rf898DBr2r+mzGi2a5SOHDmCY8eOoWvXrqhRowaOHDmCyZMn47nnnoOXl5e5YhERUTXGuYlsyU/b5mJg37mi9eJioHf3mfhtD++ZY4s+/3Q7zsVfNzhm9WfynulhbczWFjo6OmLTpk3o3LkzmjZtivfeew+TJ0/Gp59+aq5IRERUzXFuIltSu44Phj3bxeCYjLs5GD5ovjyBSDa3ktKxdvV2g2Mah9ZBeEQjmRJZJ7MdUWrdujWOHj1qri9PRERUBucmsjWTpw3G8SMXkHj1tuiYxKu3MXnCx1i+0vCpemQ9DB1JBAA7OwW+3BQtTxgrZvknGhKRzbj5yy5zRyAiqnY2/vg23D2cDY45cvgiJo79SKZEJKXyrk0DgEMnV8iQxPqxUSIik1KILJ8MABl/xsuYhIiIHti1b0m5Y06e+BtrV22TIQ1JxZgm6adtc6UPYiPYKBGRSXlGNBcv5hteqpaIiKRjzOpmn6/dgYXvbpQhDZnSraR0o5qkseP6oHYdHxkS2QY2SkRkUnX79zJ3BCIiEmFMs7T1l6PYsG6nDGnIFPbuPl3uNUkA0P+pDnhxbG/pA9kQNkpERERE1YgxzVJ8XCKeG/qeDGmoKk6fSkD09M/LHbf6s9cQPfsZGRLZFjZKZLFyrt7A3WNxyLl6w9xRiIiIbIoxzVJiYip6dXlDhjRUGZ9/uh3jRn9Y7rifts3lMuCVZLblwYkMubVtN+4cPgkUawA7JWp2aIPafXuYOxYREZHNOHr643Kva8nOysNjrSfi6J/lN1Ykn+efXoCES7fKHTc9ehivSaoCHlEii5Nz9Qbu/HG8pEkCgGIN7vxxnEeWiIiITMyYI0sQjFtNjeTRpf3rRjVJY8f1waChT8iQyHaxUSKLk/zb7xXaTkRERJVnVLOEkmYpdsdJidOQmAcr2+XnF5U7dsy4Ply4wQTYKJHFyb+h/1MSse1ERERUNcY2S29Hr8fg/nOlDUNlLF/6g1Er2wHAG7OG4iU2SSbBRomITE8lcvmj2HYiIjK7o6c/htKu/D8Nb95IR/vWE3ErKV2GVNSj01R8980+o8b+tG0uBg7pJG2gaoSNElkepcjbUmw7WRw7J6cKbSciIstw+ORH8PZyLXecIAAD+87FrGlrZUhVPZ0+lYDHwici555xN2s/evpjLtxgYvzLkyyOwtmxQtvJ8hTfv1+h7UREZDm27lqAOnVrGjX29z1n0L41r10ytacHvWPU0t8A4OBgZ/Spk1QxbJTI4gi5+v+YFttOFqiwuGLbiYjIokyePgTz3h1h1FhB+PfapSfnShuqGli7ahseC5+Ia1dTjRrv6+eBP44b11BRxbFRIiKTUt/NFC/a2cmWg4iIqqZHZESFjlTc/CedK+NV0uZv9+Kx8In4fO0Oo58zZfogbN35noSpiI0SWRy7Gm6itbSDJ2RMQpVxe89B0ZrKx0vGJEREZApHT3+MGjWMv8b07ej16NL+dZw+lSBhKttwKykd3Z+YhmVLfjT6Ofb2Shw9/TGGPtNVwmQEsFEiCxTQt4doLXXvIRmTUGVknrskWvN+NFzGJEREZCqxB5ZiyvRBRo/Pzy/CuNEfom+vWVwdT8SYkUsxsO9c5ObkG/2cocM64eCJjyRMRQ/jWr1kcbxaNcHNjVv01jS5efKGoYq7L746j2/HtjIGISIiUxr6TFcMfaYrunWcirxc41ZiS0/LxsC+c1E30AdvznkW4RGNJE5p2W4lpWPl8i24/s9NXLl8x+jnKZXA4VNcsEFuPKJE1kUwdwAiIqLq7feD71fo6BJQcv3SuNEfolfnN6rlNUynTyVg+KD5GNh3Ln7fe6ZCz23WIohNkpmwUSIiIiKiChn6TFccPf2x0cuIP5CdnYe3o9ejQ8RELFu4WaJ0luOdtzbg8TaTMG70h0i8ertCz3VycsDqz17DZxumS5SOysNT74hIHgpzByAiIlP7ces8nD6VgEmvrEBRkcbo52k0wObvDmDzdwfg6e2GQYM6Ysz4vhImlc+tpHS8NfNznD/7T6VfY37MKPSMamPCVFQZbJTI6qQdPMFrXSxU8q4D4kUHB/mCEBGRbMIjGuHgiY+w+du9FVq97YHMuzn4fO0OfL52Bzw8XBAZ1QZTZg6VIKl0Tp9KwPKl3+PvS7eqdJnAY48/gg8+nmC6YFQlbJTIItl51EBx1j29tewLCWyULFTGCfHzrl2DA2VMQkREcnuw2MOyhZux+TsDH5wZkJWVpz3SZO9gh5AQf0yePtgiF4FYtnAztm8/jpx7xq9aJ6ZxaB0sfH8MatfxMUEyMhU2SmSR6j3dH4mffK23dj+5Yuf4knyK798XrdUdECVjEiIiMpcpM4diysyhePG5xTh/7kalX6eosBh/X0rCuNEfAgBUKjs0CgvExFf7y944bf52L7b8fBi3bt6BWq2GxvizDA16pFk9vLfwRTZIFoqNElkkt5B6ojUhV/yPcTKzwiL92+0UUHl7yhqFiIjM64uv38CtpHSs+OBn7N1dsZXe9FGri3Hur2vaxkmhAAQBcHCwQ7sOIegV2QG9elftjJPTpxLw8Ue/4MrfSVCriyAIAgQJVtxtHFoXX26cDaWS66pZMjZKZLEULk4Q8vQfzr7+3VYEDesncyIy5OYvu0RrSkeVjEmIiMhS1K7jg5glYwAAa1dtw5frd6NQ7EO1CnrQwBQVa5CSnIE5b32J2bM2lCweJABQlPzfBw1VCQWgKGl+FA+9hlIJkx0lEuPo6ID+A9rj9TcGIzU1VdovRibBRoksllfr5rh78ITeWs6lKzKnofJknIgTrTn515IvCBERWaQx4/tizPi+OH0qAas+/AXx8dek+ULCf/8rADpHhARt/eHNUjZJrSJCMHveCO3pdRqpOzIyGTZKZLF8H28r2ihpDFwLQ2Zi4BPCWj07yRiEiIgsWXhEI6z9chqAkmt/vvpyN9JuZ5k5lWm5u7tgyLBONrPkeXXFRokslsrbE3ae7ijOzC5b1AjIiDsPr1ZN5A9GZajvZhqsG7rmjIiIqq8HK+UB/y6xveQHJF5NQWFhsZmTVZyPjzv6P9WBzZENYaNEFs3JpyZy9TVKAFL3/MFGyULc3nNQtKZ0d5MxCRERWavwiEb4clO09vHaVdvw0w9/IDMzV5IFFarKwcEOzZrXx9vvPM9V62wUGyWyaDUeaYjcy4l6a+rUOzKnITGZJ/8SrdXu00PGJEREZCseXNP0QMl9i04gLy8fCiig0Whka6Ds7ZVw93RFeEQjDB7yhEXe14lMj40SWTTfjm2RsjVWtM7T78wvTeQ6sgf48yEiIlN4cH8mXZu//R0H9p+Cvd1dFAoPrdRQwVXvAEChUMDZxRGPtgvFxNef4pGiao6NElk85+BA3E/8R2/tdux+/iFuZulHTorW7Lw85QtCRETV0uCnu6BTtybw8/PjfYnIpPhuIotXb6j4/ZIK0zNkTEL6FBn4GTQc+4yMSYiIiIhMh40SWTyVtyfgIH7w8/p3W+ULQ6UYPO1O5VDysyMiIiKyQmyUyCp4tW0lWsv+M16+IFSKoevHvNq0lDEJERERkWmxUSKrULd/L4P1m7/skikJPZC864DBenk/MyIiIiJLxkaJrIZb01DRWsZh8QUFSBrpBu6d5FS/roxJiIiIiExPskbpvffeQ4cOHeDi4gJPT0+9Y27cuIE+ffrAxcUFfn5+mD59OoqKiqSKRFYueMQgg/XyjnCQ6aQfMtyYNho3QqYkRBXDuYmIiIwlWaOkVqsxZMgQjBs3Tm+9uLgYffr0gVqtxuHDh7FhwwasX78es2fPlioS2QDHugGiNUNHOMi0UrbtFq3Ze3nImISoYjg3ERGRsSRrlObNm4fJkyejefPmeuu7du3C+fPn8fXXX6NVq1bo3bs35s+fj5UrV0KtVksVi6xc/WefMlg/O3eZTEmqr/KuB2sw9lmZkhBVHOcmIiIyltluOHvkyBE0b94ctWrV0m6LjIzEuHHjcO7cOYSHh+t9XkFBAQoKCrSPs7OzAQAajQYajUba0Cag0WggCIJVZAUsL6+9pztcQkOQe+mq3rpQoMa9K9fh42Mdd9K2tP1bnuyrN1CYkQkHkbr3421g7+luMd+Pte1fa8xrazg3WX5WgHmlxrzSYl5pmTKn2RqllJSUUhMRAO3jlJQU0efFxMRg3rx5ZbanpaVZxad9Go0GWVlZEATBKu4ebYl5Xf+vC9JSU0Xrmaf/gmtwoMXkNcQS968h1374FWo3ZyhE6n6PtkCqgZ+N3Kxt/1pb3qysLHNHMDnOTdbx3mNeaTGvtJhXWqacmyrUKM2cOROLFi0yOObChQsICwurUihDoqOjMWXKFO3j7OxsBAYGwtfXV/TCXEui0WigUCjg6+trFW82S82bH1wf2X+eLbNd+Pdf2iffovmcKWXqlsZS968+F5esgXNGDgQAzhk5ZZolex8v+Pn5mSOaKGvav4D15VWpVOaOAIBzkylY23uPeaXFvNJiXmmZcm6qUKM0depUjBo1yuCYkJAQo17L398fx48fL7Xt9u3b2poYR0dHODo6ltmuVCqt4ocHAAqFgnmrqP6wJxGvp1ECAAUARb4adw6fgm/HtvIGqwRL3L+6cq7eQPHdzJJ9+9C/hzWZrv/ieHOzhv37MGvKaykZOTeZhjW99wDmlRrzSot5pWPKjBVqlHx9feHr62uSL9y+fXu89957SE1N1X4KHRsbC3d3dzRp0sQkX4NsW+iM8bi0aJVoPWVrrFU0StYg8ZOvDdZDZ4yXKQlRWZybiIhICpK1hTdu3EBcXBxu3LiB4uJixMXFIS4uDjk5OQCAXr16oUmTJnj++edx5swZ7Ny5E2+99RYmTJig91M5Il0qb084BwcaHBM/a6FMaWxX/FuLDdZdQ0Og8vaUJwxRFXFuIiIiY0nWKM2ePRvh4eGYM2cOcnJyEB4ejvDwcJw8WXKjSjs7O2zbtg12dnZo3749nnvuOYwYMQLvvPOOVJHIBjV85XnDA4o1OPfuh/KEsUGXPvgMKDR8o82QF5+WKQ1R1XFuIiIiY0m26t369euxfv16g2OCgoKwfft2qSJQNVHeKXiae7m4uGQ1wiz0GhpLlbL7D6iTDa9gx1PuyNpwbiIiImNZ/hVZROVQeXuiRnPDq1kVpmcg7eAJmRJZP/XdTKTF/mFwDE+5IyIiIlvGRolsQv3nBgIKsbv7lEjZGitTGutn6AgdAEBlz1PuiIiIyKaxUSKb0XxhdLlj4mcskCGJdStvHymcHNFs3jSZ0hARERGZBxslsinNYmaWO4bNkrhy942dAkFP95cnDBEREZEZsVEim1N/1NByx7BZKk19N9OofdLs3RkypCEiIiIyPzZKZJPqj3223DHxMxYg5+oNGdJYtqtfbCr/miQAzRfNkiENERERkWVgo0Q2yS04EJ5tW5Y7LvGTr0vuFVRNxb+5CLmXrpY7Lvjl52RIQ0RERGQ52CiRzQoc3Ae+PZ8od5w6ObVanooXP2MBUFRc7jjPti3hFlJPhkREREREloONEtk0/x5PGH00JH7Ggmpxr6WMuPNGN4Z+PZ9A4OA+EiciIiIisjxslMjmuYXUQ+BzA40am7I1FvFvL5E4kflcWLwKNzduMWps6IzxqNWj/CNyRERERLaIjRJVC57NwxA6Y7xxg9WFNrfQQ9rBE4ifsQBFdzKNGt980SyovD0lzURERERkyezNHYBILipvTzRfNMvo084SP/kacFSh7sD/g1erJhKnk0bO1RtI/HyjUdciPcDV7YiIiIh4RImqoeaLZgH2dsYNLlDj5sYtiJ+z1OqOMMW/vbSk2TOySVK4urBJIiIiIvoXjyhRtdT8vRm49tVPuHf2onFPyFeXNB1KBfz79IBvx7bSBqyk5F0HkHXmHArTMyr0vLrDB1jtUTMiIiIiKbBRomqr/vMDob6bicQvf4A6OdW4J2kEpGyNRcrWWDjVr4tG40ZIG9JIybsOIH3PwUo9l0eRiIiIiMpio0TVmsrbE6Gvj8Y/P/wPmSfOVOi5+ddullzvZGcH95ZNEDSsn0Qpy1LfzcSNzdtwPykZUBdW6jVcGgWjwejhJk5GREREZBvYKBGh5Oa0Xq2b4/q3W6C5l1OxJxcXI/vPeMT/GQ8AULq7oXafHiY/le3mL7uQeToewv2CKr2OW9NQBI8YZKJURERERLaJjRLRv9xC6qHpW69CfTcTlxatqvTraLJzcHPjlrL3K1IqYOdeAwG9u4k2UWkHTyD19z+gycsH7P5dcEKjATRCpfM8oHBxQrM5U6r8OkRERETVARslIh0PlhG/8tlG5CUkmu6FNQKKM7NLNVECgDwvN6Rm5EChO74CS3qXx79fT4tdgIKIiIjIErFRIhLx4Pqdm7/sQsbhk2ZOU3EOPt7waNkEAb06mTsKERERkdVho0RUjrr9e6Fu/15I3nUAmSfOoCj7nrkjGeTeurmsC0sQERER2SI2SkRGCujVSXt05spnG5F3ObHk3DlLYKeEU2Bti1munIiIiMjasVEiqoSHl9W+/t1WZP91DijSyBtCoYCqdi2EvvqivF+XiIiIqBpgo0RURUHD+gEPnep2/butyD5zDig2QeNkb1ey6h0UUDqp4BxYG3UHREHl7Vn11yYiIiIiUWyUiExMt3F6IO3gCdyO3Q8hX23w+XbuNeDb+TGuUkdERERkRmyUiGTi27FtmeZHo9EgNTUVfn5+UCqVZkpGRERERLr4lxkREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREemQrFF677330KFDB7i4uMDT01PvGIVCUebfpk2bpIpERETVHOcmIiIylr1UL6xWqzFkyBC0b98en3/+uei4devWISoqSvtYbOIiIiKqKs5NRERkLMkapXnz5gEA1q9fb3Ccp6cn/P39pYpBRESkxbmJiIiMJVmjZKwJEyZg9OjRCAkJwSuvvIIXXngBCoVCdHxBQQEKCgq0j7OzswEAGo0GGo1G8rxVpdFoIAiCVWQFmFdqzCst5pWWteSsDM5Nlo15pcW80mJeaZkyp1kbpXfeeQfdunWDi4sLdu3ahfHjxyMnJwevvvqq6HNiYmK0nwg+LC0tDWq1Wsq4JqHRaJCVlQVBEKBUWv5aGswrLeaVFvNKKysry9wRJMG5yfLfe8wrLeaVFvNKy5RzU4UapZkzZ2LRokUGx1y4cAFhYWFGvd7bb7+t/f/h4eHIzc3FkiVLDE5G0dHRmDJlivZxdnY2AgMD4evraxXnkGs0GigUCvj6+lrFm415pcW80mJeaalUKnNHAMC5yRSs7b3HvNJiXmkxr7RMOTdVqFGaOnUqRo0aZXBMSEhIpcO0a9cO8+fPR0FBARwdHfWOcXR01FtTKpVW8cMDSlZUYl7pMK+0mFda1pTXUjJybjINa3rvAcwrNeaVFvNKx5QZK9Qo+fr6wtfX12RfXFdcXBy8vLxEJyIiIiJdnJuIiEgKkl2jdOPGDdy9exc3btxAcXEx4uLiAAANGzaEm5sbtm7ditu3b+Oxxx6Dk5MTYmNjsWDBAkybNk2qSEREVM1xbiIiImNJ1ijNnj0bGzZs0D4ODw8HAOzduxddunSBg4MDVq5cicmTJ0MQBDRs2BDLli3DmDFjpIpERETVHOcmIiIylmSN0vr16w3epyIqKqrUzfyIiIikxrmJiIiMZflXZBEREREREcmMjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRDjZKREREREREOtgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKRDskbp2rVreOmllxAcHAxnZ2c0aNAAc+bMgVqtLjXur7/+whNPPAEnJycEBgZi8eLFUkUiIqJqjPMSERFVhL1UL3zx4kVoNBp88sknaNiwIc6ePYsxY8YgNzcXS5cuBQBkZ2ejV69e6NGjB9asWYP4+Hi8+OKL8PT0xNixY6WKRkRE1RDnJSIiqgjJGqWoqChERUVpH4eEhODSpUtYvXq1dkL65ptvoFar8cUXX0ClUqFp06aIi4vDsmXLOCEREZFJcV4iIqKKkKxR0icrKwve3t7ax0eOHEGnTp2gUqm02yIjI7Fo0SJkZGTAy8urzGsUFBSgoKCg1GsCQGZmpnTBTUij0SA7OxsqlQpKpeVfIsa80mJeaTGvtB783hUEwbxBqsAU8xLAuUluzCst5pUW80rLlHOTbI3S5cuXsWLFCu2ndgCQkpKC4ODgUuNq1aqlrembkGJiYjBv3rwy23Vfh4iI5HHnzh14eHiYO0aFmWpeAjg3ERFZGlPMTRVulGbOnIlFixYZHHPhwgWEhYVpHyclJSEqKgpDhgzBmDFjKp7yIdHR0ZgyZYr2cWZmJoKCgnDjxg2rmKizs7MRGBiIf/75B+7u7uaOUy7mlRbzSot5pZWVlYV69eqVOiJjDuaelwDOTXJjXmkxr7SYV1qmnJsq3ChNnToVo0aNMjgmJCRE+/9v3bqFrl27okOHDvj0009LjfP398ft27dLbXvw2N/fX+9rOzo6wtHRscx2Dw8Pq/jhPeDu7s68EmJeaTGvtKwtr7lPxTD3vARwbjIX5pUW80qLeaVlirmpwo2Sr68vfH19jRqblJSErl27IiIiAuvWrSsTuH379njzzTdRWFgIBwcHAEBsbCxCQ0NFT28gIiJ6GOclIiKSgmQfAyYlJaFLly6oV68eli5dirS0NKSkpCAlJUU75plnnoFKpcJLL72Ec+fO4bvvvsOHH35Y6vQFIiIiU+C8REREFSHZYg6xsbG4fPkyLl++jLp165aqPViFwsPDA7t27cKECRMQEREBHx8fzJ49u0JLsDo6OmLOnDl6T3mwRMwrLeaVFvNKi3mlJde8BFjfvmFeaTGvtJhXWtU5r0Kw5nVdiYiIiIiIJGD5i6ETERERERHJjI0SERERERGRDjZKREREREREOtgoERERERER6bDaRunatWt46aWXEBwcDGdnZzRo0ABz5syBWq0uNe6vv/7CE088AScnJwQGBmLx4sVmSgy899576NChA1xcXODp6al3jEKhKPNv06ZN8gb9lzF5b9y4gT59+sDFxQV+fn6YPn06ioqK5A0qon79+mX25cKFC80dq5SVK1eifv36cHJyQrt27XD8+HFzR9Jr7ty5ZfZlWFiYuWNpHThwAP369UPt2rWhUCiwZcuWUnVBEDB79mwEBATA2dkZPXr0QEJCgnnCovy8o0aNKrO/o6KizJI1JiYGbdu2RY0aNeDn54cBAwbg0qVLpcbk5+djwoQJqFmzJtzc3DBo0KAyN22tLjg3SY9zk7SsZV4CODeZGuemsqy2Ubp48SI0Gg0++eQTnDt3DsuXL8eaNWswa9Ys7Zjs7Gz06tULQUFBOHXqFJYsWYK5c+eWuRO7XNRqNYYMGYJx48YZHLdu3TokJydr/w0YMECegDrKy1tcXIw+ffpArVbj8OHD2LBhA9avX4/Zs2fLnFTcO++8U2pfTpo0ydyRtL777jtMmTIFc+bMwZ9//omWLVsiMjISqamp5o6mV9OmTUvty4MHD5o7klZubi5atmyJlStX6q0vXrwYH330EdasWYNjx47B1dUVkZGRyM/PlzlpifLyAkBUVFSp/b1x40YZE/5n//79mDBhAo4ePYrY2FgUFhaiV69eyM3N1Y6ZPHkytm7diu+//x779+/HrVu3MHDgQLPkNTfOTdLj3CQda5uXAM5NpsS5SQ/BhixevFgIDg7WPl61apXg5eUlFBQUaLfNmDFDCA0NNUc8rXXr1gkeHh56awCEn3/+WdY85RHLu337dkGpVAopKSnabatXrxbc3d1L7XNzCQoKEpYvX27uGKIeffRRYcKECdrHxcXFQu3atYWYmBgzptJvzpw5QsuWLc0dwyi6/w1pNBrB399fWLJkiXZbZmam4OjoKGzcuNEMCUvT99/8yJEjhf79+5slT3lSU1MFAML+/fsFQSjZlw4ODsL333+vHXPhwgUBgHDkyBFzxbQonJukwbnJ9KxpXhIEzk1S4txUwmqPKOmTlZUFb29v7eMjR46gU6dOUKlU2m2RkZG4dOkSMjIyzBHRKBMmTICPjw8effRRfPHFF9obIVqaI0eOoHnz5qhVq5Z2W2RkJLKzs3Hu3DkzJvvPwoULUbNmTYSHh2PJkiUWc+qFWq3GqVOn0KNHD+02pVKJHj164MiRI2ZMJi4hIQG1a9dGSEgInn32Wdy4ccPckYySmJiIlJSUUvvaw8MD7dq1s9h9DQD79u2Dn58fQkNDMW7cONy5c8fckQCU/J4FoP1de+rUKRQWFpbav2FhYahXr55F7185cW6SF+emyrHGeQng3CS36jY32Zs2pvlcvnwZK1aswNKlS7XbUlJSEBwcXGrcg1+cKSkp8PLykjWjMd555x1069YNLi4u2LVrF8aPH4+cnBy8+uqr5o5WRkpKSqmJCCi9f83t1VdfRevWreHt7Y3Dhw8jOjoaycnJWLZsmbmjIT09HcXFxXr338WLF82USly7du2wfv16hIaGIjk5GfPmzcMTTzyBs2fPokaNGuaOZ9CD96K+fW0J71N9oqKiMHDgQAQHB+PKlSuYNWsWevfujSNHjsDOzs5suTQaDV5//XU8/vjjaNasGYCS/atSqcpcK2LJ+1dOnJvkx7mpcqxtXgI4N8mtOs5NFndEaebMmXovGn34n+5/sElJSYiKisKQIUMwZswYi89ryNtvv43HH38c4eHhmDFjBt544w0sWbLEYvPKrSL5p0yZgi5duqBFixZ45ZVX8P7772PFihUoKCgw83dhfXr37o0hQ4agRYsWiIyMxPbt25GZmYnNmzebO5pNevrpp/Hkk0+iefPmGDBgALZt24YTJ05g3759Zs01YcIEnD171mwX8ZsT5ybOTYZwbjIPzk3yqo5zk8UdUZo6dSpGjRplcExISIj2/9+6dQtdu3ZFhw4dylwI6+/vX2Z1iweP/f39zZK3otq1a4f58+ejoKAAjo6OlX6dB0yZ19/fv8xqOKbev7qqkr9du3YoKirCtWvXEBoaKkE64/n4+MDOzk7v+1OqfWdKnp6eaNy4MS5fvmzuKOV6sD9v376NgIAA7fbbt2+jVatWZkpVMSEhIfDx8cHly5fRvXt3s2SYOHEitm3bhgMHDqBu3bra7f7+/lCr1cjMzCz1yZ21vJeNxbmpNM5NpdnC3GTt8xLAuUlu1WFusrhGydfXF76+vkaNTUpKQteuXREREYF169ZBqSx9gKx9+/Z48803UVhYCAcHBwBAbGwsQkNDTXZqQ0XyVkZcXBy8vLxMMhEBps3bvn17vPfee0hNTYWfnx+Akv3r7u6OJk2amORr6KpK/ri4OCiVSm1Wc1KpVIiIiMCePXu0K0dpNBrs2bMHEydONG84I+Tk5ODKlSt4/vnnzR2lXMHBwfD398eePXu0k092djaOHTtW7ipfluLmzZu4c+dOqclULoIgYNKkSfj555+xb9++MqeMRUREwMHBAXv27MGgQYMAAJcuXcKNGzfQvn172fNKhXNTaZybSrOFucna5yWAc5PcqsXcZNIlJ2R08+ZNoWHDhkL37t2FmzdvCsnJydp/D2RmZgq1atUSnn/+eeHs2bPCpk2bBBcXF+GTTz4xS+br168Lp0+fFubNmye4ubkJp0+fFk6fPi3cu3dPEARB+PXXX4W1a9cK8fHxQkJCgrBq1SrBxcVFmD17tkXmLSoqEpo1ayb06tVLiIuLE3bs2CH4+voK0dHRZsn7sMOHDwvLly8X4uLihCtXrghff/214OvrK4wYMcLc0bQ2bdokODo6CuvXrxfOnz8vjB07VvD09Cy1UpOlmDp1qrBv3z4hMTFROHTokNCjRw/Bx8dHSE1NNXc0QRAE4d69e9r3JwBh2bJlwunTp4Xr168LgiAICxcuFDw9PYVffvlF+Ouvv4T+/fsLwcHBwv379y0u771794Rp06YJR44cERITE4Xdu3cLrVu3Fho1aiTk5+fLnnXcuHGCh4eHsG/fvlK/Z/Py8rRjXnnlFaFevXrC77//Lpw8eVJo37690L59e9mzWgLOTebPy7mp8qxpXhIEzk1y5q2uc5PVNkrr1q0TAOj997AzZ84IHTt2FBwdHYU6deoICxcuNFPikmUV9eXdu3evIAiC8NtvvwmtWrUS3NzcBFdXV6Fly5bCmjVrhOLiYovMKwiCcO3aNaF3796Cs7Oz4OPjI0ydOlUoLCw0S96HnTp1SmjXrp3g4eEhODk5CY888oiwYMECs/zHbMiKFSuEevXqCSqVSnj00UeFo0ePmjuSXsOGDRMCAgIElUol1KlTRxg2bJhw+fJlc8fS2rt3r9736siRIwVBKFmG9e233xZq1aolODo6Ct27dxcuXbpkkXnz8vKEXr16Cb6+voKDg4MQFBQkjBkzxmx/qIj9nl23bp12zP3794Xx48cLXl5egouLi/DUU0+VagyqE85N5s8rCJybqsJa5iVB4NwkZ97qOjcp/v1iRERERERE9C+LW/WOiIiIiIjI3NgoERERERER6WCjREREREREpIONEhERERERkQ42SkRERERERDrYKBEREREREelgo0RERERERKSDjRIREREREZEONkpEREREREQ62CgRERERERHpYKNERERERESkg40SERERERGRjv8Hxm0uXcyVhf4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(test_bridge[0, :, :], test_bridge[-1, :, :], save_path=log_dir / 'gt_test_bridge.jpg', bound=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7889a5bfef644ea2a42bda4251bc9bfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Exception ignored in: &lt;function _ConnectionBase.__del__ at 0x7f00e2be2f80&gt;\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Exception ignored in: <function _ConnectionBase.__del__ at 0x7f00e2be2f80>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Traceback (most recent call last):\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Traceback (most recent call last):\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 132, in __del__\n",
       "</pre>\n"
      ],
      "text/plain": [
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 132, in __del__\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    self._close()\n",
       "</pre>\n"
      ],
      "text/plain": [
       "    self._close()\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 361, in _close\n",
       "</pre>\n"
      ],
      "text/plain": [
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 361, in _close\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    _close(self._handle)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "    _close(self._handle)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OSError: [Errno 9] Bad file descriptor\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OSError: [Errno 9] Bad file descriptor\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Exception in thread QueueFeederThread:\n",
       "Traceback (most recent call last):\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/queues.py\", line 232, in _feed\n",
       "    close()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 177, in close\n",
       "    self._close()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 361, in _close\n",
       "    _close(self._handle)\n",
       "OSError: [Errno 9] Bad file descriptor\n",
       "\n",
       "During handling of the above exception, another exception occurred:\n",
       "\n",
       "Traceback (most recent call last):\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
       "    self.run()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/threading.py\", line 870, in run\n",
       "    self._target(*self._args, **self._kwargs)\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/queues.py\", line 263, in _feed\n",
       "    queue_sem.release()\n",
       "ValueError: semaphore or lock released too many times\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Exception in thread QueueFeederThread:\n",
       "Traceback (most recent call last):\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/queues.py\", line 232, in _feed\n",
       "    close()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 177, in close\n",
       "    self._close()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/connection.py\", line 361, in _close\n",
       "    _close(self._handle)\n",
       "OSError: [Errno 9] Bad file descriptor\n",
       "\n",
       "During handling of the above exception, another exception occurred:\n",
       "\n",
       "Traceback (most recent call last):\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/threading.py\", line 926, in _bootstrap_inner\n",
       "    self.run()\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/threading.py\", line 870, in run\n",
       "    self._target(*self._args, **self._kwargs)\n",
       "  File \"/home/ljb/miniconda3/envs/ljb/lib/python3.7/multiprocessing/queues.py\", line 263, in _feed\n",
       "    queue_sem.release()\n",
       "ValueError: semaphore or lock released too many times\n"
      ]
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     "ename": "KeyboardInterrupt",
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     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1559961/254695229.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msave_gif_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_bridge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/sde/utils/utils.py\u001b[0m in \u001b[0;36msave_gif_frame\u001b[0;34m(bridge, save_path, bound)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m'temp'\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34mf'{frame:03d}.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m         \u001b[0mframe\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2957\u001b[0m                 \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2959\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2961\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2259\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2260\u001b[0m                         \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2261\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   2262\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2263\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \"\"\"\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    510\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    404\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    405\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2731\u001b[0m                     \u001b[0;31m# ValueError can occur when resizing a window.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2733\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2734\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m   2735\u001b[0m                 renderer, self, artists, self.suppressComposite)\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    612\u001b[0m                       \u001b[0;31m# do not draw the hatches if the facecolor is fully\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m                       \u001b[0;31m# transparent, but do if it is None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m                       self._facecolor if self._facecolor[3] else None)\n\u001b[0m\u001b[1;32m    615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/ljb/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw_path\u001b[0;34m(self, gc, path, transform, rgbFace)\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrgbFace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[0mnmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'agg.path.chunksize'\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# here at least for testing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m         \u001b[0mnpts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_gif_frame(pred_bridge, log_dir, bound=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}