inspection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.120292 M parameters\n",
      "Output shape: torch.Size([1, 4])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class TinyConvNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(TinyConvNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
    "        self.fc = nn.Linear(32 * 30 * 30, 4)  # Assuming input image size is 480x480 after pooling\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "        x = x.view(x.size(0), -1)  # Flatten the output of conv layers\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# Example usage\n",
    "# Initialize the network\n",
    "net = TinyConvNet()\n",
    "print(sum(p.numel() for p in net.parameters())/1e6, 'M parameters')\n",
    "\n",
    "# Example input tensor (batch_size, channels, height, width)\n",
    "input_tensor = torch.randn(1, 3, 120, 120)\n",
    "\n",
    "# Forward pass\n",
    "output = net(input_tensor)\n",
    "print(\"Output shape:\", output.shape)  # Output shape should be (batch_size, 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t torch.Size([10])\n",
      "torch.Size([10, 4])\n",
      "torch.Size([10, 4])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import math\n",
    "\n",
    "class CustomLoss(nn.Module):\n",
    "    def __init__(self, T, C, D, E):\n",
    "        super(CustomLoss, self).__init__()\n",
    "        self.criterion = nn.MSELoss()\n",
    "        self.T = T\n",
    "        self.C = C\n",
    "        self.D = D\n",
    "        self.E = E\n",
    "\n",
    "    def forward(self, action_predicted, action_ground_truth, x_des, x_t, t):\n",
    "        t = t / self.T\n",
    "        loss_torques = self.criterion(action_predicted, action_ground_truth)\n",
    "        \n",
    "        # loss_latent = self.C * self.criterion(x_des, x_t)\n",
    "        # exp = math.exp(self.D * (t - (self.T / self.T)))\n",
    "        # loss_latent *= exp\n",
    "        # x_des = x_des[:, :2] # (batch_size, 2)\n",
    "        # x_t = x_t[:, :2] # (batch_size, 2)\n",
    "\n",
    "        rescale_vector = torch.tensor([1, 1, 0.0, 0.0], dtype=torch.float64)\n",
    "        # print(x_des.shape)\n",
    "        x_des *= rescale_vector\n",
    "        x_t *= rescale_vector\n",
    "        # print(x_des.shape)\n",
    "\n",
    "        mse_latent = torch.mean((x_des - x_t).pow(2), dim=1) #(batch_size, 1)\n",
    "        exp = torch.exp(self.D * (t - self.E)) #(batch_size, 1)\n",
    "        scalar = self.C * exp\n",
    "        scaled_mse_latent = scalar * mse_latent\n",
    "        average_scaled_mse_latent = torch.mean(scaled_mse_latent) #(1, 1)\n",
    "\n",
    "        return loss_torques + average_scaled_mse_latent, loss_torques\n",
    "    \n",
    "    \n",
    "T = 49 \n",
    "C = 1\n",
    "D = 5\n",
    "E = 1\n",
    "\n",
    "criterion = CustomLoss(T, C, D, E)\n",
    "\n",
    "batch_size = 10\n",
    "act_pred = torch.randn(batch_size, 2)\n",
    "act_truth = torch.randn(batch_size, 2)\n",
    "x_des = torch.randn(batch_size, 4)\n",
    "x_t = torch.randn(batch_size, 4)\n",
    "t = torch.randint(low=0, high=50, size=(batch_size, ))\n",
    "print(\"t\", t.shape)\n",
    "loss_custom, loss_torques = criterion(act_pred, act_truth, x_des, x_t, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01250504"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.0244 * 0.0345\n",
    "0.8227 * 0.0152"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_des torch.Size([10, 4])\n",
      "x_des torch.Size([10, 4])\n",
      "diff torch.Size([10, 4])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import math\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, inp_dim, enc_dim, out_dim):\n",
    "        super().__init__()\n",
    "        # super(MLP, self).__init__()\n",
    "\n",
    "        self.linear = nn.Sequential(\n",
    "            nn.Linear(inp_dim, enc_dim),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(enc_dim, enc_dim),\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(enc_dim, out_dim),\n",
    "            # nn.Tanh()\n",
    "            # nn.ReLU(True),\n",
    "            # nn.Linear(128, encoded_space_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class GeneralModel(nn.Module):\n",
    "    def __init__(self, encoded_space_dim):\n",
    "        super().__init__()\n",
    "        # super(GeneralModel, self).__init__()\n",
    "\n",
    "        # self.alexnet = AlexNetPT(encoded_space_dim)\n",
    "        # self.enc = Encoder(encoded_space_dim)\n",
    "        # self.enc = TinyConvNet(encoded_space_dim)\n",
    "        self.enc = MLP(2, 64, encoded_space_dim)\n",
    "\n",
    "        # self.mlp_state = MLP(4, 128, encoded_space_dim)\n",
    "        self.mlp_controller = MLP(encoded_space_dim, 128, 2)\n",
    "        # Set the bias of the last linear layer to 0\n",
    "        self.mlp_controller.linear[2].bias.data.fill_(0.0)\n",
    "\n",
    "    def forward(self, target_pos, state):\n",
    "        # x = self.mlp_state(state)\n",
    "        x = state\n",
    "\n",
    "        # x_des = self.alexnet(img_tensor)\n",
    "        x_des = self.enc(target_pos)\n",
    "        \n",
    "        print(\"x_des\", x_des.shape)\n",
    "        x_des = torch.squeeze(x_des, 0) # (1, encoded_space_dim)\n",
    "        print(\"x_des\", x_des.shape)\n",
    "        diff = x_des - x\n",
    "        print(\"diff\", diff.shape)\n",
    "\n",
    "        acts_pred = F.tanh(self.mlp_controller(diff))\n",
    "\n",
    "        return acts_pred, x_des, diff\n",
    "\n",
    "\n",
    "batch_size = 10\n",
    "target_pos = torch.randn(batch_size, 2)\n",
    "state = torch.randn(batch_size, 4)\n",
    "\n",
    "model = GeneralModel(4)\n",
    "act_pred, x_des, diff = model(target_pos, state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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mPR/jx48HULODxM1WrFgBAM1+TdoDY17X69ev46mnnkJQUBCWLFmCzz//HAcPHsSSJUsMzquqqtK7U1XtnatcXV0RGBgIAPD29oaPjw8OHDhgcG5ds7X79+/H77//3mbvoAQGBkKhUBjEA9T8+Wnqn53Q0FC9r7/P1LbUwYMHMXLkSIP2zMxMyGQyBAcHt+p4RK2BM7NE7ZBcLsc777yDGTNmYMyYMXj00UdRVFSE999/H35+fnjxxRd1fWv/8X7hhRcQEREBa2vrOrd0Amo+rBIbG4v4+Hjcc889uO+++5CdnY2PP/4Yt99+u96Hn4xhTAz1SUxMxOjRozFw4EDMnDkTPXr0QFFREfbu3YsLFy7g8OHDRsfVv39/jBgxArGxsbh69SqcnZ2xfv16VFVVGX2tWtOmTcPatWsRExOD/fv344477oBKpcL27dvx7LPPYtKkSbCzs0O/fv2wYcMG9O7dG87OzhgwYAAGDBhgcL2AgABERUXh008/xfXr1zFmzBjs378fX3zxBSZPnqxb19kadu7cqSvsL126BJVKhcWLFwMA7rzzTtx5552tNlatpr6us2fPxpUrV7B9+3ZYW1vjnnvuwVNPPYXFixdj0qRJCAgI0F3Ty8sL77zzDs6dO4fevXtjw4YNyMrKwqeffqo3Wz5p0iRs2rQJQgjd7GdZWRl8fHwwZcoU9O/fHw4ODvj999+xZs0adOzYsd61vS1la2uL8PBwbN++HYsWLdJ7LDAwENu3b8eKFSvg5eWF7t27Y/jw4a0yrjGveWZmJq5evVrnh+7S0tIwatSoBrcTJDIb82yiQHRrqO+mCX9X300TNmzYIIYMGSKUSqVwdnY2uGmCEDW3FH3++eeFq6urkMlkTdqm66OPPhL+/v5CLpcLd3d38cwzz+jdNEEI47bmqi+Ghm5nizq2rjpz5oyYNm2a8PDwEHK5XHh7e4t7771XfPPNN43GUNfWXLXXDA0NFUqlUri7u4vXXntNpKWl1XvThL+LiorS3b61Vnl5uXj99ddF9+7dhVwuFx4eHuKhhx4SZ86c0fXZs2ePCAwMFAqFokk3TYiPj9ddz8fHp8GbJvzdmDFjmrTNVu3YdX3Vd1OM1tDY61q7VdvN220JIURJSYnw9fUVAQEBui236rppgq+vr/joo48Mxj148KAAoHdzB7VaLWbPni0GDRoknJychFwuF76+vuLJJ5802MKttW3cuFHIZDKRm5ur137ixAlx5513Cjs7u1a/aYIxr/mrr74qunXrZrDV2vXr14VCoRCff/55q8VF1JpkQrTT+xASERH9TUhICC5fvowjR440qf/dd98NLy+vRm8mYgrV1dXo168fHn74Ybz55pvmDkePWq2Gn58f5s2bh9mzZ+s9lpCQgHfffRdnzpxp1Z0TiFoL18wSEZHFWrJkCTZs2NBm284Zw9raGosWLUJiYiLKysrMHY6eNWvWQC6XY9asWXrtGo0GK1aswBtvvMFCltotzswSEZFkGDszS0SWjzOzRERERCRZnJklIiIiIsnizCwRERERSRaLWSIiIiKSrFvupglarRb5+fno0KFDi2/BSUREREStTwiB0tJSeHl5wcqq4bnXW66Yzc/Ph4+Pj7nDICIiIqJG/PHHH+jatWuDfW65YrZDhw4Aap4cJycnk4yp0Wjw008/ITw8XO9Wi5aC+UmfpefI/KTP0nNkftJn6TmaOr+SkhL4+Pjo6raG3HLFbO3SAicnJ5MWs/b29nBycrLYX3DmJ22WniPzkz5Lz5H5SZ+l52iu/JqyJJQfACMiIiIiyWIxS0RERESSxWKWiIiIiCTrllszS0REZKmEEKiqqkJ1dbW5Q9Gj0WhgY2ODioqKdhdba7H0HNsiP7lcDmtr6xZfh8UsERGRBaisrERBQQHKy8vNHYoBIQQ8PDzwxx9/WOwe75aeY1vkJ5PJ0LVrVzg6OrboOixmiYiIJE6r1SInJwfW1tbw8vKCQqFoVwWVVqtFWVkZHB0dG90AX6osPcfWzk8IgUuXLuHChQu47bbbWjRDy2KWiIhI4iorK6HVauHj4wN7e3tzh2NAq9WisrIStra2FlnoAZafY1vk5+rqinPnzkGj0bSomLW8Z5uIiOgWZYlFFFmu1nr3gL/1RERERCRZLGaJiIiISLJYzBIRERG1gqtXr8LDwwPnzp0z6bhXrlyBm5ubycetdezYMXTt2hUqlUqvfcSIEfj222/bfHwWs0RERGQ2O3fuxMSJE+Hl5QWZTIbNmzc3+dzp06ebrYCry/Lly3HffffBz88PQE2Rec8998DLywtKpRI+Pj6Ijo5GSUlJo9fKyMhAXFxck8Z96623MGnSJN24GRkZkMlkuH79evMSqeP6o0ePhpeXF5ydnQ0e79evH0aMGIEVK1botb/xxhuYN28etFptq8RRHxazREREZDYqlQoBAQFITExsUv+rV68iMTERQghd25kzZ/Dll1+2VYhNUl5ejnXr1uGJJ57QtVlZWWHSpEn4z3/+g5MnTyIlJQXbt2/HrFmz6r1OUlISLl68qDuurKzE8uXLodFo6h139erVePLJJ1svmb+prKzEQw89pJfb382YMQOrVq1CVVWVrm3cuHEoLS3FDz/80GaxASxmiYiILJIQAuWVVWb5urnQbMy4ceOwePFi3H///U3qb2tri7y8PNxzzz24cOECkpKSMH36dHTv3r3ec/744w88/PDD6NSpE5ydnTFp0iTdjO6JEydgb2+P1NRUXf+vvvoKdnZ2OHbsGICaGeDJkycjPj4erq6ucHJywqxZs1BZWak7Z+vWrVAqlRgxYoSurXPnznjmmWcwbNgw+Pr64u6778azzz6LXbt21Rurj48P7rvvPmzatAlHjx7FXXfdBaD+T/7/fdxz585h7NixuvFlMhmmT5/ewDPauPj4eMyZMwf9+vWrt09YWBiuXr2KHTt26Nqsra0xfvx4rF+/vkXjN4b7zBIREVmgG5pq9Fvwo1nGPrYoAvaKtikx7O3tsWTJEmzduhX33Xcfqqqq8PPPP0Mul9fZX6PRICIiAsHBwdi1axdsbGywePFi3HPPPfjtt9/g7++PZcuW4dlnn8Xo0aNhZWWFWbNm4Z133tEr3tLT02Fra4uMjAycO3cOM2bMQJcuXfDWW28BAHbv3o2AgIAGY8/Pz8fGjRsxZsyYevtMmDABo0ePxogRI5Cbm4vdu3djyJAh9fbftWsXAgMDdcc+Pj749ttv8eCDDyI7OxtOTk6ws7NrMK7WoFAoMHjwYOzatQt33323rj0oKAhvv/12m47NmVkiIiKSjIqKCixYsADvv/8+QkJCMGLECISGhmL//v119t+wYQO0Wi0+//xzDBw4EH379sWaNWuQm5uLjIwMANAVso8//jimT5+O22+/Hc8//7zedRQKBZKTk9G/f39MmDABixYtwgcffKBbD3r+/Hl4enrWGcOjjz4Ke3t7eHt7w8nJCZ9//nm9+W3btg333HMPwsPDMX78eLzwwgt4//33UV1dXWf/8+fPw8vLS3dsbW2tW9fq5uYGDw8PdOzYsd7xWpOXlxfOnz9v0PbHH3+06bpZzswSERFZIDu5NY4tijDb2G2lvLwc7u7u2LZtG2bMmIFZs2Zh5syZ2Lt3L4KCggz6Hz58GKdPn0aHDh302isqKnDmzBndcXJyMnr37g0rKyscPXrU4G39gIAAvburBQcHo6ysDH/88Qd8fX1x48YNuLq61hnzypUrsXDhQpw8eRKxsbGIiYnBxx9/XGffnJwcfPfddzh27BgyMjLw5Zdf6ormuu6SdePGDdja2tb/hDXA0dFR9/Pjjz+OpKSkZl2nlp2dHcrLyw3atFot1Gp1m80Qs5glIiKyQDKZrM3e6jcnZ2dnPPfcc3ptPXv2RM+ePevsX1ZWhsDAwDo/IHZz8Xn48GGoVCpYWVmhoKCg3lnW+ri4uNS7e4CHhwc8PDzg7+8PZ2dn3HHHHZg/f36dYzzzzDMAoFuvq1Ao8PLLLzc47rVr14yKtVZWVpbuZycnp2Zd42ZXr141eB2uXr0KBweHNl3qYHm/5URERHRLSElJabTP0KFDsWHDBri5udVbsF29ehXTp0/H66+/joKCAjz22GM4ePCgXgF2+PBh3LhxQ9f266+/wtHRET4+PgCAIUOGYO3atY3GU/t2u1qtbrBfSEgIQkJCGr3ekCFD8K9//UuvTaFQAEC9SxNq9erVq9HrG+PIkSN46KGHDNoaWvPbGrhmloiIiMymrKwMWVlZulnCnJwcZGVlITc3t1Wu/9hjj8HFxQWTJk3Crl27kJOTg4yMDLzwwgu4cOECAGDWrFnw8fHBG2+8gRUrVqC6utpgNrSyshJPPvkkjh07hq1bt2LhwoWIjo6GlVVNKRUeHo4TJ07ozZJu3boVa9aswZEjR3Du3Dls2bIFs2bNwqhRo3R7wrZUREQEjh49qjeur68vZDIZvv/+e1y6dAllZWUtGiM3NxdZWVm4cOECqqurda/Xzdc9d+4c8vLyEBoaqnfurl27EB4e3qLxG8NiloiIiMzmwIEDGDJkiG72LiYmBkOGDMGCBQta5fr29vbYuXMnunXrhgceeAB9+/bFk08+iYqKCjg5OWHt2rXYunUr1q1bBxsbGzg4OOBf//oXPvvsM739Ue+++27cdtttuPPOOzFlyhTcd999ejc1GDhwIAICAvDVV1/p2uzs7PDZZ59h9OjR6Nu3L1588UXcd999+P7771slt9pxhw4dqjeut7c34uPjMW/ePLi7uyM6OrpFYyxYsACBgYFYunQpysrKdK/XgQMHdH3+/e9/Izw8HL6+vrq2vLw87NmzBzNmzGjR+I3hMgMiIiIym5CQEKP2pW0ODw8PfPHFF3U+Nm3aNEybNk2vLSgoSG8P2Vrx8fGIj4+vd5xXXnkF8fHxePrpp2FlZYWxY8diz549LQu+CRYsWIBXXnkFM2fO1M0Uz58/H/Pnz2+V66ekpCA5ORklJSVwcnLSjVGrsrISSUlJenv1AsAHH3yA6dOno2vXrq0SR31YzBIRERG1goiICOTn5yMvL0+3ltYUJkyYgFOnTpl83Fq5ubl47bXXMGrUKL12Nzc3xMTEtPn4LGaJiIiIWsns2bMNZi5NYc6cOSYfs1avXr3q/DDZSy+9ZJLxWcwSERERNaApuyaQ+fADYEREREQkWSxmiYiILERbf5CKqDW11u8ri1kiIiKJk8vlAGBwK1Gi9qx2x4i6btNrDK6ZJSIikjhra2t06tQJFy9eBFCzt6pMJjNzVH/RarWorKxERUWFWT4cZQqWnmNr56fVanHp0iXY29vDxqZl5SiLWSIiIgvg4eEBALqCtj0RQuhuBdueiuzWZOk5tkV+VlZW6NatW4uvx2KWiIjIAshkMnh6esLNzQ0ajcbc4ejRaDTYuXMn7rzzTt2SCEtj6Tm2RX4KhaJVZnlZzBIREVkQa2vrFq9BbG3W1taoqqqCra2tRRZ6gOXn2J7zs7xFHURERER0y2AxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJLFYpaIiIiIJIvFLBERERFJFotZIiIiIpIsFrNEREREJFksZomIiIhIsljMEhEREZFksZglIiIiIsliMUtEREREksViloiIiIgki8UsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJLFYpaIiIiIJIvFLBERERFJFotZIiIiIpIsFrNEREREJFksZomIiIhIsljMEhEREZFksZglIiIiIsliMUtEREREksViloiIiIgki8UsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJLFYpaIiIiIJIvFLBERERFJFotZIiIiIpIsFrNEREREJFksZomIiIhIsljMEhEREZFksZglIiIiIsliMUtEREREksViloiIiIgki8UsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJLFYpaIiIiIJIvFLBERERFJFotZIiIiIpIsFrNEREREJFksZomIiIhIsljMEhEREZFksZglIiIiIsliMUtEREREksViloiIiIgki8UsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJLFYpaIiIiIJIvFLBERERFJFotZIiIiIpIsFrNEREREJFksZomIiIhIsljMEhEREZFksZglIiIiIsliMUtEREREksViloiIiIgki8UsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSIiIiLJYjFLRERERJJl1mJ2586dmDhxIry8vCCTybB58+ZGz0lMTETfvn1hZ2eHPn36YO3atW0fKBERERG1SzbmHFylUiEgIABPPPEEHnjggUb7r1q1CrGxsfjss89w++23Y//+/Zg5cyY6d+6MiRMnmiBiIiIiImpPzFrMjhs3DuPGjWty/3Xr1uHpp5/GlClTAAA9evTA//73P7zzzjssZomIiIhuQWYtZo2lVqtha2ur12ZnZ4f9+/dDo9FALpfXeY5ardYdl5SUAAA0Gg00Gk3bBvyn2nFMNZ6pMT/ps/QcmZ/0WXqOzE/6LD1HU+dnzDgyIYRow1iaTCaTYdOmTZg8eXK9fV577TWsWbMG33//PYYOHYrMzEzce++9KCoqQn5+Pjw9PQ3OiYuLQ3x8vEF7amoq7O3tWzMFIiIiImoF5eXliIyMRHFxMZycnBrsK6li9saNG3juueewbt06CCHg7u6Oxx9/HO+++y4KCwvh7u5ucE5dM7M+Pj64fPlyo09Oa9FoNEhLS0NYWFids8dSx/ykz9JzZH7SZ+k5Mj/ps/QcTZ1fSUkJXFxcmlTMSmqZgZ2dHZKTk/HJJ5+gqKgInp6e+PTTT9GhQwe4urrWeY5SqYRSqTRol8vlJv9lM8eYpsT8pM/Sc2R+0mfpOTI/6bP0HE2VnzFjSKqYrSWXy9G1a1cAwPr163HvvffCyopb5hIRERHdasxazJaVleH06dO645ycHGRlZcHZ2RndunVDbGws8vLydHvJnjx5Evv378fw4cNx7do1rFixAkeOHMEXX3xhrhSIiIiIyIzMWsweOHAAY8eO1R3HxMQAAKKiopCSkoKCggLk5ubqHq+ursby5cuRnZ0NuVyOsWPHYs+ePfDz8zN16ERERETUDpi1mA0JCUFDnz9LSUnRO+7bty8OHTrUxlERERERkVRwoSkRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZJm1mN25cycmTpwILy8vyGQybN68udFzvvzySwQEBMDe3h6enp544okncOXKlbYPloiIiIjanWYVs7t27cLjjz+O4OBg5OXlAQDWrVuH3bt3G3UdlUqFgIAAJCYmNqn/f//7X0ybNg1PPvkkjh49iq+//hr79+/HzJkzjc6BiIiIiKTP6GL222+/RUREBOzs7HDo0CGo1WoAQHFxMZYsWWLUtcaNG4fFixfj/vvvb1L/vXv3ws/PDy+88AK6d++O0aNH4+mnn8b+/fuNTYOIiIiILIDRN01YvHgxkpKSMG3aNKxfv17XPmrUKCxevLhVg/u74OBgvPbaa9i6dSvGjRuHixcv4ptvvsH48ePrPUetVusKbgAoKSkBAGg0Gmg0mjaNt1btOKYaz9SYn/RZeo7MT/osPUfmJ32WnqOp8zNmHJlo6BZcdbC3t8exY8fg5+eHDh064PDhw+jRowfOnj2Lfv36oaKiwuiAAUAmk2HTpk2YPHlyg/2+/vprPPHEE6ioqEBVVRUmTpyIb7/9FnK5vM7+cXFxiI+PN2hPTU2Fvb19s2IlIiIiorZTXl6OyMhIFBcXw8nJqcG+Rs/Menh44PTp0/Dz89Nr3717N3r06GHs5Yxy7NgxzJ49GwsWLEBERAQKCgrwyiuvYNasWVi9enWd58TGxiImJkZ3XFJSAh8fH4SHhzf65LQWjUaDtLQ0hIWF1Vt0Sxnzkz5Lz5H5SZ+l58j8pM/SczR1frXvpDeF0cXszJkzMXv2bCQnJ0MmkyE/Px979+7Fyy+/jPnz5xt7OaMsXboUo0aNwiuvvAIAGDRoEBwcHHDHHXdg8eLF8PT0NDhHqVRCqVQatMvlcpP/spljTFNiftJn6TkyP+mz9ByZn/RZeo6mys+YMYwuZufNmwetVou7774b5eXluPPOO6FUKvHyyy/j+eefN/ZyRikvL4eNjX7I1tbWAAAjV0sQERERkQUwupiVyWR4/fXX8corr+D06dMoKytDv3794OjoaPTgZWVlOH36tO44JycHWVlZcHZ2Rrdu3RAbG4u8vDysXbsWADBx4kTMnDkTq1at0i0zmDNnDoKCguDl5WX0+EREREQkbUYXs7UUCgX69evXosEPHDiAsWPH6o5r17ZGRUUhJSUFBQUFyM3N1T0+ffp0lJaW4qOPPsJLL72ETp064a677sI777zTojiIiIiISJqMLmbHjh0LmUxW7+M///xzk68VEhLS4PKAlJQUg7bnn3++zZczEBEREZE0GF3MDh48WO9Yo9EgKysLR44cQVRUVGvFRURERETUKKOL2ZUrV9bZHhcXh7KyshYHRERERETUVEbfzrY+jz/+OJKTk1vrckREREREjWq1Ynbv3r2wtbVtrcsRERERETXK6GUGDzzwgN6xEAIFBQU4cOCA0TdN2LlzJ9577z1kZmaioKCg0dvZTp8+HV988YVBe79+/XD06FGjxiYiIiIi6TN6ZrZjx456X87OzggJCcHWrVuxcOFCo66lUqkQEBCAxMTEJvV///33UVBQoPv6448/4OzsjH/84x/GpkFEREREFsDomdk1a9a02uDjxo3DuHHjmty/toCutXnzZly7dg0zZsxotZiIiIiISDqafdOE9mD16tUIDQ2Fr69vvX3UajXUarXuuKSkBEDNlmIajabNY6wd6+bvlob5SZ+l58j8pM/Sc2R+0mfpOZo6P2PGkYmG7lrwp86dOzd4o4SbXb16tcmD6wUikzW6ZvZm+fn56NatG1JTU/Hwww/X2y8uLg7x8fEG7ampqbC3t29WrERERETUdsrLyxEZGYni4mI4OTk12LdJM7MJCQmtEVer+uKLL9CpU6dGi9/Y2FjdbXKBmplZHx8fhIeHN/rktBaNRoO0tDSEhYVBLpebZExTYn7SZ+k5Mj/ps/QcmZ/0WXqOps6v9p30pmhSMdve7uwlhEBycjKmTp0KhULRYF+lUgmlUmnQLpfLTf7LZo4xTYn5SZ+l58j8pM/Sc2R+0mfpOZoqP2PGaNGa2YqKClRWVuq1mWK2c8eOHTh9+jSefPLJNh+LiIiIiNovo7fmUqlUiI6OhpubGxwcHNC5c2e9L2OUlZUhKysLWVlZAICcnBxkZWUhNzcXQM0SgWnTphmct3r1agwfPhwDBgwwNnwiIiIisiBGF7Nz587Fzz//jFWrVkGpVOLzzz9HfHw8vLy8sHbtWqOudeDAAQwZMgRDhgwBAMTExGDIkCFYsGABAKCgoEBX2NYqLi7Gt99+y1lZIiIiIjJ+mcH//d//Ye3atQgJCcGMGTNwxx13oFevXvD19cWXX36Jxx57rMnXCgkJQUObKaSkpBi0dezYEeXl5caGTUREREQWyOiZ2atXr6JHjx4AatbH1m7FNXr0aOzcubN1oyMiIiIiaoDRxWyPHj2Qk5MDAPD398dXX30FoGbGtlOnTq0aHBERERFRQ4wuZmfMmIHDhw8DAObNm4fExETY2trixRdfxCuvvNLqARIRERER1afJa2ZffvllPPXUU3jxxRd1baGhoThx4gQyMzPRq1cvDBo0qE2CJCIiIiKqS5NnZr/77jv0798fI0eORHJyMlQqFQDA19cXDzzwQLMK2Z07d2LixInw8vKCTCbD5s2bGz1HrVbj9ddfh6+vL5RKJfz8/JCcnGz02EREREQkfU0uZk+dOoVffvkFvXv3xuzZs+Hh4YEnnngCe/bsafbgKpUKAQEBSExMbPI5Dz/8MNLT07F69WpkZ2fj3//+N/r06dPsGIiIiIhIuozamuvOO+/EnXfeicTERGzYsAFr1qzB6NGj0adPHzz55JOYOnUq3N3dm3y9cePGYdy4cU3uv23bNuzYsQNnz56Fs7MzAMDPz8+YFIiIiIjIgjTrdrYODg544okn8MQTT+D06dNYs2YNli5ditdffx1qtbq1Y9T5z3/+g2HDhuHdd9/FunXr4ODggPvuuw9vvvkm7Ozs6jxHrVbrxVRSUgIA0Gg00Gg0bRbrzWrHMdV4psb8pM/Sc2R+0mfpOTI/6bP0HE2dnzHjyERDdy1ohEqlwldffYXVq1djz5496NOnD44fP96sa8lkMmzatAmTJ0+ut88999yDjIwMhIaGYsGCBbh8+TKeffZZjB07FmvWrKnznLi4OMTHxxu0p6amwt7evlmxEhEREVHbKS8vR2RkJIqLi+Hk5NRg32YVs7t370ZycjK++eYbCCHwj3/8A08++SRGjRrV7KCbUsyGh4dj165dKCwsRMeOHQEAGzduxEMPPQSVSlXn7GxdM7M+Pj64fPlyo09Oa9FoNEhLS0NYWBjkcrlJxjQl5id9lp4j85M+S8+R+Umfpedo6vxKSkrg4uLSpGK2ycsMCgoK8MUXXyAlJQUnT57EiBEjsGLFCjzyyCNwdHRscdBN4enpCW9vb10hCwB9+/aFEAIXLlzAbbfdZnCOUqmEUqk0aJfL5Sb/ZTPHmKbE/KTP0nNkftJn6TkyP+mz9BxNlZ8xYzS5mPXx8UGXLl0wdepUPPnkk+jbt2+zgmuJUaNG4euvv0ZZWZmugD558iSsrKzQtWtXk8dDRERERObV5K25vvrqK+Tl5WHZsmWtVsiWlZUhKysLWVlZAICcnBxkZWUhNzcXABAbG4tp06bp+kdGRqJLly6YMWMGjh07hp07d+KVV17BE088Ue8HwIiIiIjIcjW5mH3ggQdgY9OszQ/qdeDAAQwZMgRDhgwBAMTExGDIkCFYsGABgJqlDbWFLQA4OjoiLS0N169fx7Bhw/DYY49h4sSJ+OCDD1o1LiIiIiKShtatTo0UEhKChj5/lpKSYtDm7++PtLS0NoyKiIiIiKSiyTOzRERERETtDYtZIiIiIpIsFrNEREREJFlGr5lVqVR4++23kZ6ejosXL0Kr1eo9fvbs2VYLjoiIiIioIUYXs0899RR27NiBqVOnwtPTEzKZrC3iIiIiIiJqlNHF7A8//IAtW7a06Na1tXbu3In33nsPmZmZKCgoaPR2thkZGRg7dqxBe0FBATw8PFocDxERERFJi9FrZjt37gxnZ+dWGVylUiEgIACJiYlGnZednY2CggLdl5ubW6vEQ0RERETSYvTM7JtvvokFCxbgiy++gL29fYsGHzduHMaNG2f0eW5ubujUqVOLxiYiIiIi6TO6mF2+fDnOnDkDd3d3+Pn5QS6X6z1+8ODBVguuPoMHD4ZarcaAAQMQFxfX4JIHtVoNtVqtOy4pKQEAaDQaaDSaNo+1dqybv1sa5id9lp4j85M+S8+R+Umfpedo6vyMGUcmGroFVx3i4+MbfHzhwoXGXO6vQGSyRtfMZmdnIyMjA8OGDYNarcbnn3+OdevWYd++fRg6dGid58TFxdUZc2pqaotnlomIiIio9ZWXlyMyMhLFxcVwcnJqsK/RxWxbaUoxW5cxY8agW7duWLduXZ2P1zUz6+Pjg8uXLzf65LQWjUaDtLQ0hIWFGcxkWwLmJ32WniPzkz5Lz5H5SZ+l52jq/EpKSuDi4tKkYtboZQa1MjMzcfz4cQBA//79MWTIkOZeqkWCgoKwe/fueh9XKpVQKpUG7XK53OS/bOYY05SYn/RZeo7MT/osPUfmJ32WnqOp8jNmDKOL2YsXL+KRRx5BRkaG7kNY169fx9ixY7F+/Xq4uroae8kWycrKgqenp0nHJCIiIqL2weituZ5//nmUlpbi6NGjuHr1Kq5evYojR46gpKQEL7zwglHXKisrQ1ZWFrKysgAAOTk5yMrKQm5uLgAgNjYW06ZN0/VPSEjAd999h9OnT+PIkSOYM2cOfv75Zzz33HPGpkFEREREFsDomdlt27Zh+/bt6Nu3r66tX79+SExMRHh4uFHXOnDggN5NEGJiYgAAUVFRSElJQUFBga6wBYDKykq89NJLyMvLg729PQYNGoTt27fXeSMFIiIiIrJ8RhezWq22znUMcrkcWq3WqGuFhISgoc+fpaSk6B3PnTsXc+fONWoMIiIiIrJcRi8zuOuuuzB79mzk5+fr2vLy8vDiiy/i7rvvbtXgiIiIiIgaYnQx+9FHH6GkpAR+fn7o2bMnevbsie7du6OkpAQffvhhW8RIRERERFQno5cZ+Pj44ODBg9i+fTtOnDgBAOjbty9CQ0NbPTgiIiIiooY0a59ZmUyGsLAwhIWFtXY8RERERERN1qRi9oMPPsA///lP2Nra4oMPPmiwrzHbc+3cuRPvvfceMjMzUVBQYNQdwP773/9izJgxGDBggG5rLyIiIiK6tTSpmF25ciUee+wx2NraYuXKlfX2k8lkRhWzKpUKAQEBeOKJJ/DAAw80+bzr169j2rRpuPvuu1FUVNTk84iIiIjIsjSpmM3Jyanz55YaN24cxo0bZ/R5s2bNQmRkJKytrbF58+ZWi4eIiIiIpMXoNbOLFi3Cyy+/DHt7e732Gzdu4L333sOCBQtaLbi6rFmzBmfPnsW//vUvLF68uNH+arUaarVad1xSUgIA0Gg00Gg0bRbnzWrHMdV4psb8pM/Sc2R+0mfpOTI/6bP0HE2dnzHjyERDdy2og7W1NQoKCuDm5qbXfuXKFbi5uaG6utqYy/0ViEzW6JrZU6dOYfTo0di1axd69+6NuLg4bN68ucE1s3FxcYiPjzdoT01NNSjIiYiIiMj8ysvLERkZieLiYjg5OTXY1+iZWSEEZDKZQfvhw4fh7Oxs7OWarLq6GpGRkYiPj0fv3r2bfF5sbKzuNrlAzcysj48PwsPDG31yWotGo0FaWhrCwsLqvHua1DE/6bP0HJmf9Fl6jsxP+iw9R1PnV/tOelM0uZjt3LkzZDIZZDIZevfurVfQVldXo6ysDLNmzTIuUiOUlpbiwIEDOHToEKKjowHU3FpXCAEbGxv89NNPuOuuuwzOUyqVUCqVBu1yudzkv2zmGNOUmJ/0WXqOzE/6LD1H5id9lp6jqfIzZowmF7MJCQkQQuCJJ55AfHw8OnbsqHtMoVDAz88PwcHBxkVqBCcnJ/z+++96bR9//DF+/vlnfPPNN+jevXubjU1ERERE7VOTi9moqCgAQPfu3TFq1CjY2DTrfgt6ysrKcPr0ad1xTk4OsrKy4OzsjG7duiE2NhZ5eXlYu3YtrKysMGDAAL3z3dzcYGtra9BORERERLcGK2NPUKlUSE9PN2j/8ccf8cMPPxh1rQMHDmDIkCEYMmQIACAmJgZDhgzR7YhQUFCA3NxcY0MkIiIioluE0cXsvHnz6tyxQAiBefPmGXWtkJAQCCEMvlJSUgAAKSkpyMjIqPf8uLg43v2LiIiI6BZmdDF76tQp9OvXz6Dd399fb8kAEREREVFbM7qY7dixI86ePWvQfvr0aTg4OLRKUERERERETWF0MTtp0iTMmTMHZ86c0bWdPn0aL730Eu67775WDY6IiIiIqCFGF7PvvvsuHBwc4O/vj+7du6N79+7o27cvunTpgmXLlrVFjEREREREdTJ6f62OHTtiz549SEtLw+HDh2FnZ4dBgwbhzjvvbIv4iIiIiIjqZfTMLADIZDKEh4fjlVdeQXR0dLML2Z07d2LixInw8vKCTCbD5s2bG+y/e/dujBo1Cl26dIGdnR38/f2xcuXKZo1NRERERNLXrDsfpKenIz09HRcvXoRWq9V7LDk5ucnXUalUCAgIwBNPPIEHHnig0f4ODg6Ijo7GoEGD4ODggN27d+Ppp5+Gg4MD/vnPfxqdBxERERFJm9HFbHx8PBYtWoRhw4bB09MTMpms2YOPGzcO48aNa3L/m2+wAAB+fn7YuHEjdu3axWKWiIiI6BZkdDGblJSElJQUTJ06tS3iMcqhQ4ewZ88eLF68uN4+arUaarVad1xSUgIA0Gg00Gg0bR5j7Vg3f7c0zE/6LD1H5id9lp4j85M+S8/R1PkZM45MCCGMuXiXLl2wf/9+9OzZ0+jAGgxEJsOmTZswefLkRvt27doVly5dQlVVFeLi4jB//vx6+8bFxSE+Pt6gPTU1Ffb29i0JmYiIiIjaQHl5OSIjI1FcXAwnJ6cG+xpdzL766qtwdHRssIBsDmOK2ZycHJSVleHXX3/FvHnz8NFHH+HRRx+ts29dM7M+Pj64fPlyo09Oa9FoNEhLS0NYWBjkcrlJxjQl5id9lp4j85M+S8+R+Umfpedo6vxKSkrg4uLSpGLW6GUGFRUV+PTTT7F9+3YMGjTIIKEVK1YYe0mjde/eHQAwcOBAFBUVIS4urt5iVqlUQqlUGrTL5XKT/7KZY0xTYn7SZ+k5Mj/ps/QcmZ/0WXqOpsrPmDGMLmZ/++03DB48GABw5MgRvcda8mGw5tJqtXozr0RERER06zC6mP3ll19abfCysjKcPn1ad5yTk4OsrCw4OzujW7duiI2NRV5eHtauXQsASExMRLdu3eDv7w+gZp/aZcuW4YUXXmi1mIiIiIhIOpq1z2xrOXDgAMaOHas7jomJAQBERUUhJSUFBQUFyM3N1T2u1WoRGxuLnJwc2NjYoGfPnnjnnXfw9NNPmzx2IiIiIjI/o4vZsWPHNric4Oeff27ytUJCQtDQ589SUlL0jp9//nk8//zzTb4+EREREVk2o4vZ2vWytTQaDbKysnDkyBFERUW1VlxERERERI0yuphduXJlne1xcXEoKytrcUBERERERE1l1VoXevzxx5GcnNxalyMiIiIialSrFbN79+6Fra1ta12OiIiIiKhRRi8zeOCBB/SOhRAoKCjAgQMHjL4r2M6dO/Hee+8hMzMTBQUFjd4BbOPGjVi1ahWysrKgVqvRv39/xMXFISIiwtg0iIiIiMgCGD0z27FjR70vZ2dnhISEYOvWrVi4cKFR11KpVAgICEBiYmKT+u/cuRNhYWHYunUrMjMzMXbsWEycOBGHDh0yNg0iIiIisgBNnpk9e/YsunfvjjVr1rTa4OPGjcO4ceOa3D8hIUHveMmSJfjuu+/wf//3fxgyZEirxUVERERE0tDkYva2225DQUEB3NzcAABTpkzBBx98AHd39zYLrjFarRalpaVwdnaut49arda73W1JSQmAmi3FNBpNm8dYO9bN3y0N85M+S8+R+UmfpefI/KTP0nM0dX7GjCMTDd214CZWVlYoLCzUFbMdOnTA4cOH0aNHj+ZF+fdAZLJG18z+3bvvvou3334bJ06c0MX1d3FxcYiPjzdoT01Nhb29fXPDJSIiIqI2Ul5ejsjISBQXF8PJyanBvma9nW1LpKamIj4+Ht999129hSwAxMbG6m6TC9TMzPr4+CA8PLzRJ6e1aDQapKWlISwsDHK53CRjmhLzkz5Lz5H5SZ+l58j8pM/SczR1frXvpDdFk4tZmUxmcBvbhm5r25bWr1+Pp556Cl9//TVCQ0Mb7KtUKqFUKg3a5XK5yX/ZzDGmKTE/6bP0HJmf9Fl6jsxP+iw9R1PlZ8wYTS5mhRCYPn26rjCsqKjArFmz4ODgoNdv48aNTR68Of7973/jiSeewPr16zFhwoQ2HYuIiIiI2rcmF7NRUVF6x48//niLBy8rK8Pp06d1xzk5OcjKyoKzszO6deuG2NhY5OXlYe3atQBqlhZERUXh/fffx/Dhw1FYWAgAsLOzQ8eOHVscDxERERFJS5OL2dbckqvWgQMHMHbsWN1x7drWqKgopKSkoKCgALm5ubrHP/30U1RVVeG5557Dc889p2uv7U9EREREtxazfgAsJCQEDW2m8PcCNSMjo20DIiIiIiJJMfoOYERERERE7QWLWSIiIiKSLBazRERERCRZLGaJiIiISLLMWszu3LkTEydOhJeXF2QyGTZv3txg/4KCAkRGRqJ3796wsrLCnDlzTBInEREREbVPZi1mVSoVAgICkJiY2KT+arUarq6ueOONNxAQENDG0RERERFRe2fWrbnGjRuHcePGNbm/n58f3n//fQBAcnJyW4VFRERERBJh1mLWFNRqNdRqte64pKQEAKDRaKDRaEwSQ+04phrP1Jif9Fl6jsxP+iw9R+YnfZaeo6nzM2YcmWjorgUmJJPJsGnTJkyePLlJ/UNCQjB48GAkJCQ02C8uLg7x8fEG7ampqbC3t29GpERERETUlsrLyxEZGYni4mI4OTk12NfiZ2ZjY2N1t8kFamZmfXx8EB4e3uiT01o0Gg3S0tIQFhYGuVxukjFNiflJn6XnyPykz9JzZH7SZ+k5mjq/2nfSm8Lii1mlUgmlUmnQLpfLTf7LZo4xTYn5SZ+l58j8pM/Sc2R+0mfpOZoqP2PG4D6zRERERCRZZp2ZLSsrw+nTp3XHOTk5yMrKgrOzM7p164bY2Fjk5eVh7dq1uj5ZWVm6cy9duoSsrCwoFAr069fP1OETERERkZmZtZg9cOAAxo4dqzuuXdsaFRWFlJQUFBQUIDc3V++cIUOG6H7OzMxEamoqfH19ce7cOZPETERERETth1mL2ZCQEDS0mUJKSopBWzvZfIGIiIiI2gGumSUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLLMWszt37sTEiRPh5eUFmUyGzZs3N3pORkYGhg4dCqVSiV69etW54wERERER3RrMWsyqVCoEBAQgMTGxSf1zcnIwYcIEjB07FllZWZgzZw6eeuop/Pjjj20cKRERERG1R2bdZ3bcuHEYN25ck/snJSWhe/fuWL58OQCgb9++2L17N1auXImIiIi2CpOIiIjolpZ3/QZ+yZdhvLkDqYNZi1lj7d27F6GhoXptERERmDNnTr3nqNVqqNVq3XFJSQkAQKPRQKPRtEmcf1c7jqnGMzXmJ32WniPzkz5Lz5H5SZ8l57j/3FVE//swrpVbI+hALv4xrFubj2nM8ygT7eSWWjKZDJs2bcLkyZPr7dO7d2/MmDEDsbGxuratW7diwoQJKC8vh52dncE5cXFxiI+PN2hPTU2Fvb19q8ROREREZIl2F8rw7TkraIUMXR0EnupTjc7Kth+3vLwckZGRKC4uhpOTU4N9JTUz2xyxsbGIiYnRHZeUlMDHxwfh4eGNPjmtRaPRIC0tDWFhYZDL5SYZ05SYn/RZeo7MT/osPUfmJ32WlqO6Sos3txzH1zl5AIAJA9wQYp+PCfeYJr/ad9KbQlLFrIeHB4qKivTaioqK4OTkVOesLAAolUoolYb/hZDL5Sb/ZTPHmKbE/KTP0nNkftJn6TkyP+mzhBzzr9/AM18exOE/rkMmA+ZG+OPJkT744Yd8k+VnzBiSKmaDg4OxdetWvba0tDQEBwebKSIiIiIiy7H3zBVEpx7EFVUlOtnL8f4jQzCmt2u7Xgts1q25ysrKkJWVhaysLAA1W29lZWUhNzcXQM0SgWnTpun6z5o1C2fPnsXcuXNx4sQJfPzxx/jqq6/w4osvmiN8IiIiIosghMDnu87i8dX7cEVViX6eTvi/6NEY09vV3KE1yqwzswcOHMDYsWN1x7VrW6OiopCSkoKCggJdYQsA3bt3x5YtW/Diiy/i/fffR9euXfH5559zWy4iIiKiZiqvrMLcb37D978VAAAeGOKNt+4fCDuFtZkjaxqzFrMhISFoaDOFuu7uFRISgkOHDrVhVERERES3hnOXVXh6XSayi0phYyXD/Hv7YVqwL2QymblDazJJrZklIiIiotbx84kizF6fhdKKKrh2UGLVY0MxzM/Z3GEZjcUsERER0S2kqlqL5WknsSrjDAAg0LczPn5sKNydbM0cWfOwmCUiIiK6RRSVVOD5fx/C/pyrAICoYF+8PqEfFDZm3ROgRVjMEhEREd0Cdp26hDnrs3BFVQlHpQ3efnAg7h3kZe6wWqxdlOGJiYnw8/ODra0thg8fjv3799fbV6PRYNGiRejZsydsbW0REBCAbdu2mTBaIiIiIumo1gqs+Ckb05L3/7Xt1vOjLaKQBdpBMbthwwbExMRg4cKFOHjwIAICAhAREYGLFy/W2f+NN97AJ598gg8//BDHjh3DrFmzcP/993OHAyIiIqK/uVhagcc/34cPfj4NIYDI4d2w8dmR6O7iYO7QWo3Zi9kVK1Zg5syZmDFjBvr164ekpCTY29sjOTm5zv7r1q3Da6+9hvHjx6NHjx545plnMH78eCxfvtzEkRMRERG1X3vOXMb493dj79krsFdY4/1HBmPJ/QNhK5fG/rFNZdY1s5WVlcjMzERsbKyuzcrKCqGhodi7d2+d56jVatja6n/azs7ODrt37663v1qt1h2XlJQAqFmuYKpbs9WO055vBdcSzE/6LD1H5id9lp4j85O+9pRjVbUWq3bk4KOMM9AKoLebIz54JAA9XR2aHZ+p8zNmHJlo6K4FbSw/Px/e3t7Ys2cPgoODde1z587Fjh07sG/fPoNzIiMjcfjwYWzevBk9e/ZEeno6Jk2ahOrqar2itVZcXBzi4+MN2lNTU2Fvb9+6CRERERGZ0VU1sO6UNc6W1tz0YLirFg9110IiN/PSKS8vR2RkJIqLi+Hk5NRgX8ntZvD+++9j5syZ8Pf3h0wmQ8+ePTFjxox6lyXExsbqbpML1MzM+vj4IDw8vNEnp7VoNBqkpaUhLCwMcrncJGOaEvOTPkvPkflJn6XnyPykrz3k+MORQqz47hhKK6rgoLTGoon9cF+AZ6tc29T51b6T3hRmLWZdXFxgbW2NoqIivfaioiJ4eHjUeY6rqys2b96MiooKXLlyBV5eXpg3bx569OhRZ3+lUgmlUmnQLpfLTf7LZo4xTYn5SZ+l58j8pM/Sc2R+0meOHMsrqxD3n6P46sAFAMBgn0744JEh6Nal9d+BNlV+xoxh1g+AKRQKBAYGIj09Xdem1WqRnp6ut+ygLra2tvD29kZVVRW+/fZbTJo0qa3DJSIiImpXjuQV494PduOrAxcgkwHRY3vh61nBbVLItldmX2YQExODqKgoDBs2DEFBQUhISIBKpcKMGTMAANOmTYO3tzeWLl0KANi3bx/y8vIwePBg5OXlIS4uDlqtFnPnzjVnGkREREQmo9UKrN6dg3d/PAFNtYBnR1usnDIYI3p0MXdoJmf2YnbKlCm4dOkSFixYgMLCQgwePBjbtm2Du7s7ACA3NxdWVn9NIFdUVOCNN97A2bNn4ejoiPHjx2PdunXo1KmTmTIgIiIiMp3C4gq88s1h7Dp1GQAQ0d8d7zw4CJ3sFWaOzDzMXswCQHR0NKKjo+t8LCMjQ+94zJgxOHbsmAmiIiIiImpf/nM4H/M3H0HxDQ1s5VZYcG9/PBrkA5lMZu7QzKZdFLNEREREVL/r5ZV4Y/MRfP9bAQBgoHdHrJwSgF5uHcwcmfmxmCUiIiJqxzKyL2LuN7/hYqka1lYyRI/thei7ekFubfYbubYLLGaJiIiI2qHyyiq8teU4vtyXCwDo4eqAlQ8PRoBPJ/MG1s6wmCUiIiJqZzLPX8NLX2Xh3JVyAMD0kX549R5/2EntVl4mwGKWiIiIqJ2o0FQjYfspfLrzDLQC8Oxoi2X/CMCoXi7mDq3daheLLRITE+Hn5wdbW1sMHz4c+/fvb7B/QkIC+vTpAzs7O/j4+ODFF19ERUWFiaIlIiIian2Z569hwge7kLSjppC9f4g3ts25k4VsI8w+M7thwwbExMQgKSkJw4cPR0JCAiIiIpCdnQ03NzeD/qmpqZg3bx6Sk5MxcuRInDx5EtOnT4dMJsOKFSvMkAERERFR892orMayn7KR/N8cCAG4dlDirckDEN7fw9yhSYLZi9kVK1Zg5syZujt+JSUlYcuWLUhOTsa8efMM+u/ZswejRo1CZGQkAMDPzw+PPvoo9u3bZ9K4iYiIiFpq39krePXb33RrYx8c2hXz7+17y94AoTnMWsxWVlYiMzMTsbGxujYrKyuEhoZi7969dZ4zcuRI/Otf/8L+/fsRFBSEs2fPYuvWrZg6dWqd/dVqNdRqte64pKQEAKDRaKDRaFoxm/rVjmOq8UyN+UmfpefI/KTP0nNkftJnbI4qdRWWp53Cun1/AADcnZRYPKkfQnq7GnUdUzH1a2jMODIhhGjDWBqUn58Pb29v7NmzB8HBwbr2uXPnYseOHfXOtn7wwQd4+eWXIYRAVVUVZs2ahVWrVtXZNy4uDvHx8QbtqampsLe3b51EiIiIiJoou1iG9WescFVdc9euYDctJvlqYWf298vbj/LyckRGRqK4uBhOTk4N9pXc05aRkYElS5bg448/xvDhw3H69GnMnj0bb775JubPn2/QPzY2FjExMbrjkpIS+Pj4IDw8vNEnp7VoNBqkpaUhLCwMcrncJGOaEvOTPkvPkflJn6XnyPykryk5XlVV4u0fT2LTsXwAgHcnW7w1uT9G9exiylCbxdSvYe076U1h1mLWxcUF1tbWKCoq0msvKiqCh0fdi57nz5+PqVOn4qmnngIADBw4ECqVCv/85z/x+uuvw8pKf4MGpVIJpVJpcB25XG7yP1DmGNOUmJ/0WXqOzE/6LD1H5id9deUohMCmQ3lYvOU4rqoqIZMBU0f4Yu49/nBUSmte0VSvoTFjmHVrLoVCgcDAQKSnp+vatFot0tPT9ZYd3Ky8vNygYLW2rtlA2IwrJoiIiIgMnLuswuOr9yHmq8O4qqpEH/cO+GbWSCyaNEByhWx7ZfZnMSYmBlFRURg2bBiCgoKQkJAAlUql291g2rRp8Pb2xtKlSwEAEydOxIoVKzBkyBDdMoP58+dj4sSJuqKWiIiIyJwqq7T4bNdZfJB+CuoqLZQ2Vpgdehtm3tEDcut2sc2/xTB7MTtlyhRcunQJCxYsQGFhIQYPHoxt27bB3d0dAJCbm6s3E/vGG29AJpPhjTfeQF5eHlxdXTFx4kS89dZb5kqBiIiISCfz/FW8tvEIsotKAQCje7ngrfsHwLeLg5kjs0xmL2YBIDo6GtHR0XU+lpGRoXdsY2ODhQsXYuHChSaIjIiIiKhpVBpg/n+OYcOBCxACcHZQYP69fTF5sDdkMpm5w7NY7aKYJSIiIpIqrVbgqwMXsCTLGqqqCwCAhwK74vXxfdHZgTc/aGssZomIiIia6fcLxXjjuyM4/Md1ADL0dnPEm5MHYHiP9r/dlqVgMUtERERkpOvllVj2Uza+3JcLIQAHpTXCPSvx1vQRsLc13BKU2g6LWSIiIqIm0moFvsm8gLe3ncBVVSUAYPJgL7wSfhsO7ErnTgVm0C6e8cTERPj5+cHW1hbDhw/H/v376+0bEhICmUxm8DVhwgQTRkxERES3msN/XMeDSXsw99vfcFVVid7ujlj/zxFIeGQI3DpwNtZczD4zu2HDBsTExCApKQnDhw9HQkICIiIikJ2dDTc3N4P+GzduRGVlpe74ypUrCAgIwD/+8Q9Thk1ERES3iKKSCryz7QQ2HswDADgorDEntDemj/LjTGw7YPZidsWKFZg5c6buJglJSUnYsmULkpOTMW/ePIP+zs7Oesfr16+Hvb09i1kiIiJqVRWaaqzenYPEX06jvLIaAPDAUG+8eo8/3J1szRwd1TJrMVtZWYnMzEzExsbq2qysrBAaGoq9e/c26RqrV6/GI488AgeHujciVqvVUKvVuuOSkhIAgEajgUajaUH0TVc7jqnGMzXmJ32WniPzkz5Lz5H5tS9CCGw7WoR3fzyJC9crAABDfDri9fH+COjaEYBhLlLL0Vimzs+YcWRCCNGGsTQoPz8f3t7e2LNnD4KDg3Xtc+fOxY4dO7Bv374Gz9+/fz+GDx+Offv2ISgoqM4+cXFxiI+PN2hPTU2Fvb19yxIgIiIii3JBBWw6Z43TJTU3OeikEJjYTYtAFwHe98B0ysvLERkZieLiYjg5OTXY1+zLDFpi9erVGDhwYL2FLADExsYiJiZGd1xSUgIfHx+Eh4c3+uS0Fo1Gg7S0NISFhUEul5tkTFNiftJn6TkyP+mz9ByZn/kVllTgg5/P4Jvf8yAEoLSxwszRfph5hx/sFY2XS1LIsSVMnV/tO+lNYdZi1sXFBdbW1igqKtJrLyoqgoeHR4PnqlQqrF+/HosWLWqwn1KphFJp+AlDuVxu8l82c4xpSsxP+iw9R+YnfZaeI/MzvTJ1FT7ZcQaf7TqLCo0WADAxwAvzxvnDu5Od0ddrjzm2JlPlZ8wYZi1mFQoFAgMDkZ6ejsmTJwMAtFot0tPTER0d3eC5X3/9NdRqNR5//HETREpERESWRFOtxfr9uUjYfgpX/twvNtC3M14b3xeBvp3NHB0Zw+zLDGJiYhAVFYVhw4YhKCgICQkJUKlUut0Npk2bBm9vbyxdulTvvNWrV2Py5Mno0oW3iyMiIqKmEULgp2NFeOeHEzh7WQUA6O7igFfv8UdEf3fIuDBWcsxezE6ZMgWXLl3CggULUFhYiMGDB2Pbtm1wd3cHAOTm5sLKSn8Pt+zsbOzevRs//fSTOUImIiIiCTqYew1Ltx7H/85dAwB0cVBgduhteDSoG/eLlTCzF7MAEB0dXe+ygoyMDIO2Pn36wIybMBAREZGEZBeWYtlP2Ug7VvMZHaWNFZ66oztmjemJDraWu771VtEuilkiIiKi1pZ7pRwrt5/E5qyaHQqsZMCDQ7siJrw3PDsa/+Euap9YzBIREZFFKSqpwIc/n8L6/X+gSlvzTu74gR6ICeuNXm4dzBwdtTYWs0RERGQRrpdXYtWOM/hizzndNlt39nbFK+F9MPDPO3eR5WExS0RERJJWUqHBmt3n8PnusyitqAJQs83WKxF9MKIHdz2ydO3io3uJiYnw8/ODra0thg8fjv379zfY//r163juuefg6ekJpVKJ3r17Y+vWrSaKloiIiNqD0goNPkg/hdFv/4yV20+itKIK/h4dkDx9GL6ZFcxC9hZh9pnZDRs2ICYmBklJSRg+fDgSEhIQERGB7OxsuLm5GfSvrKxEWFgY3Nzc8M0338Db2xvnz59Hp06dTB88ERERmVxphQYp/z2Hz3fnoPiGBgDQy80Rs+++DRMGesLKinvF3krMXsyuWLECM2fO1N0kISkpCVu2bEFycjLmzZtn0D85ORlXr17Fnj17dLc68/PzM2XIREREZAalFRp8seccPtulX8S+8GcRa80i9pZk1mK2srISmZmZiI2N1bVZWVkhNDQUe/furfOc//znPwgODsZzzz2H7777Dq6uroiMjMSrr74Ka2trg/5qtRpqtVp3XFJSAgDQaDTQaDStnFHdascx1Ximxvykz9JzZH7SZ+k5Mr+GlVZo8K99fyD5v+dx/c8itoeLA6LH9sD4AR6wtpJBW10FbXWrhWw0voZtM15TyIQZ7z6Qn58Pb29v7NmzB8HBwbr2uXPnYseOHdi3b5/BOf7+/jh37hwee+wxPPvsszh9+jSeffZZvPDCC1i4cKFB/7i4OMTHxxu0p6amwt7evnUTIiIiolZTqgF2FFhhV6EMFdU1s65utgIRXbUY6iLAiVjLVV5ejsjISBQXF8PJyanBvmZfZmAsrVYLNzc3fPrpp7C2tkZgYCDy8vLw3nvv1VnMxsbGIiYmRndcUlICHx8fhIeHN/rktBaNRoO0tDSEhYXplkZYEuYnfZaeI/OTPkvPkfnpKyiuwOe7z+Grwxd0W2z1cnXArDE9cO9Aj3a5nICvYeuqfSe9KcxazLq4uMDa2hpFRUV67UVFRfDw8KjzHE9PT8jlcr0lBX379kVhYSEqKyuhUCj0+iuVSiiVSoPryOVyk/+ymWNMU2J+0mfpOTI/6bP0HG/1/M5eKkPSjjPYeDBPd7ODgK4d8ezYXgjr6y6JD3bd6q9ha47TVGYtZhUKBQIDA5Geno7JkycDqJl5TU9PR3R0dJ3njBo1CqmpqdBqtbCyqtlZ7OTJk/D09DQoZImIiKj9O5JXjFUZZ7D1SAFqFz+O7NkFz43thZE9u0Ama/9FLJmP2ZcZxMTEICoqCsOGDUNQUBASEhKgUql0uxtMmzYN3t7eWLp0KQDgmWeewUcffYTZs2fj+eefx6lTp7BkyRK88MIL5kyDiIiIjPS/c1eR+MtpZGRf0rWF9nXHs2N7Ymi3zmaMjKTE7MXslClTcOnSJSxYsACFhYUYPHgwtm3bBnd3dwBAbm6ubgYWAHx8fPDjjz/ixRdfxKBBg+Dt7Y3Zs2fj1VdfNVcKRERE1ETVWoEfjxbis11ncSj3OgDASgZMDPDCMyE94e9hms+zkOUwezELANHR0fUuK8jIyDBoCw4Oxq+//trGUREREVFrUVcDX+w9jy9+zcUfV28AABTWVngw0BuzxvSEbxcHM0dIUtUuilkiIiKyTIXFFUjefQbrMq1xozobANDZXo6pI3wxNdgPrh0MP6RNZAwWs0RERNTqjuYX4/NdOfi/w/l/7kwgg18Xezx1Rw88OLQr7BSGNzoiag4Ws0RERNQqtFqBHScv4bNdZ7HnzBVd++1+nRGgvIxXIkdBqeTOQ9S6WMwSERFRi5RWaPBt5gWs/fU8zl5SAQCsrWQYP9ATM+/ojr7uDti6dask9okl6WExS0RERM1yqqgUa/eex8aDF6CqrAYAOCpt8MjtPpgxuju8O9kBqLl7FFFbsWq8S9tLTEyEn58fbG1tMXz4cOzfv7/evikpKZDJZHpftra2JoyWiIjo1lVVrcW2I4WI/OxXhK3ciXW/noeqshq93Bzx5qT++PW1u/HGvf10hSxRWzP7zOyGDRsQExODpKQkDB8+HAkJCYiIiEB2djbc3NzqPMfJyQnZ2dm6Y94ZhIiIqG1dKVNj/f/+wJe/nkd+cQWAmv1hw/q5IyrYD8G8UxeZidmL2RUrVmDmzJm6O34lJSVhy5YtSE5Oxrx58+o8RyaTwcPDw5RhEhER3XKEEPjtQjG+2HsO3x8uQGW1FgDg7KDAI7f74LERvpyBJbMzazFbWVmJzMxMxMbG6tqsrKwQGhqKvXv31nteWVkZfH19odVqMXToUCxZsgT9+/evs69arYZardYdl5SUAKhZv2OqNTy141jqmiHmJ32WniPzkz5Lz7G95VdaocF/fivEhv9dwPHCUl37AC8nTB3hgwkDPKCU12yt1ZSY21t+bcHSczR1fsaMIxNCiDaMpUH5+fnw9vbGnj17EBwcrGufO3cuduzYgX379hmcs3fvXpw6dQqDBg1CcXExli1bhp07d+Lo0aPo2rWrQf+4uDjEx8cbtKempsLe3r51EyIiIpIoIYDzZcCeIiscuiJDpbZmyYCNTGBwF4E7PLTwdQS4koBMoby8HJGRkSguLoaTU8O3ODb7MgNjBQcH6xW+I0eORN++ffHJJ5/gzTffNOgfGxuLmJgY3XFJSQl8fHwQHh7e6JPTWjQaDdLS0hAWFga5XG6SMU2J+UmfpefI/KTP0nM0Z34lNzT47nABNhy4gOyiMl17T1cHPHJ7V0wO8EIn+5bFZOmvH2D5OZo6v9p30pvCrMWsi4sLrK2tUVRUpNdeVFTU5DWxcrkcQ4YMwenTp+t8XKlUQqk0vFWeXC43+S+bOcY0JeYnfZaeI/OTPkvP0VT5CSFwMPcaUvf9gS2/56NCU7MWVmljhQmDPBEZ1A2Bvp1b/QNdlv76AZafo6nyM2YMsxazCoUCgYGBSE9Px+TJkwEAWq0W6enpiI6ObtI1qqur8fvvv2P8+PFtGCkREZH0XSytwHeH8vF15h84edMsbB/3Dng0yAf3D+mKji2chSUyNbMvM4iJiUFUVBSGDRuGoKAgJCQkQKVS6XY3mDZtGry9vbF06VIAwKJFizBixAj06tUL169fx3vvvYfz58/jqaeeMmcaRERE7ZK6qhrpxy/i28wLyDh5CdXamo/K2MqtMHGQFx4d3g1DfDpxWy2SLLMXs1OmTMGlS5ewYMECFBYWYvDgwdi2bRvc3d0BALm5ubCy+uveDteuXcPMmTNRWFiIzp07IzAwEHv27EG/fv3MlQIREVG7Urul1rcHL+C7rHwU3/jrk+FDu3XCg4FdMTHAC062nIUl6TN7MQsA0dHR9S4ryMjI0DteuXIlVq5caYKoiIiIpOViSQU2HcrDN5kXcOriX8sIPJxs8cBQbzwY2BU9XR3NGCFR62sXxSwRERE1T3llFbYfv4hNBy9gx8lL+HMVAZQ2VrhngAceHNoVo3q5wNqKywjIMrGYJSIikhhNtRa7T1/Gd4fy8NOxIpRXVuseC/TtjIcCu2LCIE8uI6BbAotZIiIiCajdTmvzoXxs+b0AV1WVusd8nO0wKcAbDwz1Rg8uI6BbDItZIiKidiy7sBTfZeXhP4fzceHaDV17FwcF7h3kifsGe2NoN+5GQLeudlHMJiYm4r333kNhYSECAgLw4YcfIigoqNHz1q9fj0cffRSTJk3C5s2b2z5QIiIiEzh/RYUtvxfgP1n5OFFYqmt3UFgjYoAHJg32xqieXWBjbdXAVYhuDWYvZjds2ICYmBgkJSVh+PDhSEhIQEREBLKzs+Hm5lbveefOncPLL7+MO+64w4TREhERtY1LN4CkHWex7dhFHM3/61aecmsZQvq4YdJgL9zt7w47hbUZoyRqf8xezK5YsQIzZ87U3SQhKSkJW7ZsQXJyMubNm1fnOdXV1XjssccQHx+PXbt24fr16yaMmIiIqHXkXFZh6+8F+P5wPo4X2gCouTW7tZUMwT26YMIgT4wb4IFO9grzBkrUjpm1mK2srERmZiZiY2N1bVZWVggNDcXevXvrPW/RokVwc3PDk08+iV27djU4hlqthlqt1h2XlNT8b1ej0UCj0dR3WquqHcdU45ka85M+S8+R+UmfJeWYc1mFH44U4YejRXpLCKwgMKKHM8YP9ERYXzc4O/xVwEo9b0t6/epj6TmaOj9jxpEJIUQbxtKg/Px8eHt7Y8+ePQgODta1z507Fzt27MC+ffsMztm9ezceeeQRZGVlwcXFBdOnT8f169frXTMbFxeH+Ph4g/bU1FTY29u3Wi5ERER1EQLIKwd+v2qF367KkF/+1we1rGQCvZ0EBncRGOgs4MidtIgAAOXl5YiMjERxcTGcnJwa7Gv2ZQbGKC0txdSpU/HZZ5/BxcWlSefExsYiJiZGd1xSUgIfHx+Eh4c3+uS0Fo1Gg7S0NISFhUEut7y/qZif9Fl6jsxP+qSWY1W1Fpm515F2/CK2n7iIvOsVusdsrGQY2dMZ9/T3QGhfV3S2V0guP2NZen6A5edo6vxq30lvCrMWsy4uLrC2tkZRUZFee1FRETw8PAz6nzlzBufOncPEiRN1bVqtFgBgY2OD7Oxs9OzZU+8cpVIJpVJpcC25XG7yXzZzjGlKzE/6LD1H5id97TnH8soq7Dx5GT8dK8TPJy7ievlfb5Payq0wprcrwvp5ILSvW71rYNtzfq3B0vMDLD9HU+VnzBhmLWYVCgUCAwORnp6OyZMnA6gpTtPT0xEdHW3Q39/fH7///rte2xtvvIHS0lK8//778PHxMUXYREREAIArZWqkn7iIn44WYdepS1BXaXWPdbaXI7SvO8L7e2B0LxfuQkDURsy+zCAmJgZRUVEYNmwYgoKCkJCQAJVKpdvdYNq0afD29sbSpUtha2uLAQMG6J3fqVMnADBoJyIiam1CCBzNL8HPJy7i5xMXcfjCddz8yZOune0Q0d8D4f3cEejbmfvAEpmA2YvZKVOm4NKlS1iwYAEKCwsxePBgbNu2De7u7gCA3NxcWFnxLwMiIjIPlboKu09fxi8nLuKX7IsoKlHrPd7P0wnh/d0R3s8DfT078E5cRCZm9mIWAKKjo+tcVgAAGRkZDZ6bkpLS+gEREdEt7fwVlW72dd/Zq6is/mv5gL3CGqN6ueAufzeM7eMGj462ZoyUiNpFMUtERGRO5ZVV2Hf2KnacvISdpy7h7CWV3uPdnO1rild/Nwzv7gxbOde/ErUXLGaJiOiWI4TA8YJS7DpVU7z+L+ea3uyrjZUMw/w64y5/N9zl746erg5cPkDUTrGYJSKiW8KVMjV2n76MHScvYdepy7hUqr/21buTHe7s7YoxvV0Q3NMFHe0sd3slIkvCYpaIiCxSeWUVDpy7hv+euYw9p6/gSH6x3s4DdnJrjOjhjDt7u+LO3q7o4cLZVyIpYjFLREQWobJKi0O517DnzBXsPXMFh/64Bk21/h3b+3o64c7eLrjzNlcM8+sMpQ3XvhJJXbsoZhMTE/Hee++hsLAQAQEB+PDDDxEUFFRn340bN2LJkiU4ffo0NBoNbrvtNrz00kuYOnWqiaMmIiJzqtYKHMkrxp4zV7DnzGX879xVVGi0en28O9lhZM8uCO7ZBaN7ucDNiTsPEFkasxezGzZsQExMDJKSkjB8+HAkJCQgIiIC2dnZcHNzM+jv7OyM119/Hf7+/lAoFPj+++8xY8YMuLm5ISIiwgwZEBGRKQgBnCwqxf7zNQXsr2evoLSiSq+Pi6MCwT1dMLJnF4zs2QXdnO25dIDIwpm9mF2xYgVmzpypu+NXUlIStmzZguTkZMybN8+gf0hIiN7x7Nmz8cUXX2D37t0sZomILEhVtRZH80vwv3NX8euZy9h72hqqX/fq9elga4MRPWoK11G9XHCbmyOLV6JbjFmL2crKSmRmZiI2NlbXZmVlhdDQUOzdu7eBM2sIIfDzzz8jOzsb77zzTp191Go11Oq/PrFaUlICANBoNNBoNC3MoGlqxzHVeKbG/KTP0nNkftJQoanG4QvFOHD+Ov537hqy/rgOVWX1TT1ksLWxwjC/zhjR3Rkjezqjn6cTrK3+Kl6rqqoMLywBlvIa1sfS8wMsP0dT52fMODIhhGi8W9vIz8+Ht7c39uzZg+DgYF373LlzsWPHDuzbt6/O84qLi+Ht7Q21Wg1ra2t8/PHHeOKJJ+rsGxcXh/j4eIP21NRU2Nvbt04iRERktBtVQE6pDGdKZDhTKkNuGVAt9GdV7awFejgJ9Owg0NNJwMcBsOYdzoksXnl5OSIjI1FcXAwnJ6cG+5p9mUFzdOjQAVlZWSgrK0N6ejpiYmLQo0cPgyUIABAbG4uYmBjdcUlJCXx8fBAeHt7ok9NaNBoN0tLSEBYWBrnc8vYtZH7SZ+k5Mj/zE0Lgj2s3cOiPYhz+4zoyc6/jRGEptH+bTnHroMQw30643a8zhvl2Rm83R1hZySSRY0swP+mz9BxNnV/tO+lNYdZi1sXFBdbW1igqKtJrLyoqgoeHR73nWVlZoVevXgCAwYMH4/jx41i6dGmdxaxSqYRSqTRol8vlJv9lM8eYpsT8pM/Sc2R+pnOjshq/XbiOg7nXcTD3Gg7lXsflMrVBP98u9rjdzxlB3Z0R5OcM3y4Nf2CrPeXYFpif9Fl6jqbKz5gxzFrMKhQKBAYGIj09HZMnTwYAaLVapKenIzo6usnX0Wq1eutiiYjIdIQQuHDtBg7mXsPB89dwMPc6jheUoOpv065yaxn6eXXE0G6dMLRbZwR1d4Y7t8oiohYy+zKDmJgYREVFYdiwYQgKCkJCQgJUKpVud4Np06bB29sbS5cuBQAsXboUw4YNQ8+ePaFWq7F161asW7cOq1atMmcaRES3jJIKDX6/UIzfLhTjUG5N8VrXrKtbByWGduuMob41xesA746wlfMmBUTUusxezE6ZMgWXLl3CggULUFhYiMGDB2Pbtm1wd3cHAOTm5sLK6q/V/iqVCs8++ywuXLgAOzs7+Pv741//+hemTJlirhSIiCzWjcpqHM2vKVx/u3Adv10oxtnLKoN+f591HerbGV4dbblNFhG1ObMXswAQHR1d77KCjIwMvePFixdj8eLFJoiKiOjWUlmlRXZhKQ5fuI7fLxTj8IXrOHWxDNV//5QWAB9nOwzq2gkBXTty1pWIzKpdFLNERGRaFZpqnCoqw9H8YhzNL8FvecU4XlCCyiqtQV+3DkoM6toJg7p2/POrE5wdFGaImojIEItZIiILV1KhwbH8EhzNL8HR/GIcyy/B6YtlBh/QAoCOdnK9ojWgayd4dOSHtIio/WIxS0RkQS6WVOiK1qN/FrC5V8vr7NvZXo7+Xh3R38sJ/b07IqBrR3RzbnhrLCKi9obFLBGRBN2orMapi6U4UViK4/nF+O8xK7z5ewYul1XW2d+roy36/Vm4DvCu+e7JD2gRkQVoF8VsYmIi3nvvPRQWFiIgIAAffvghgoKC6uz72WefYe3atThy5AgAIDAwEEuWLKm3PxGRlFVrBXKvliO7sAQnCkuR/efXuSuqv909ywpAJWQyoIeLw18zrn9+78w1rkRkocxezG7YsAExMTFISkrC8OHDkZCQgIiICGRnZ8PNzc2gf0ZGBh599FGMHDkStra2eOeddxAeHo6jR4/C29vbDBkQEbWOy2VqZBeW4nhBSU3RWlSKk0WlqNAYfigLAJwdFOjj3gG3uTmg8mIOHgwNRv+unWGvMPtf7UREJmP2v/FWrFiBmTNn6m6SkJSUhC1btiA5ORnz5s0z6P/ll1/qHX/++ef49ttvkZ6ejmnTppkkZiKi5hJCoLCkAqcvlhl8XVHVvURAaWOF29wd4e/hBH+PDujz55eroxIymQwajQZbt57FYJ9OkMvN/tc6EZFJmfVvvcrKSmRmZiI2NlbXZmVlhdDQUOzdu7dJ1ygvL4dGo4Gzs3Odj6vVar1b3ZaUlAAANBoNNBpNC6JvutpxTDWeqTE/6bP0HM2RX7W25havpy+V4fRFFc5cVuHMpTKcuaSCSl1d5zkyGdCtsz16uzuij7vjn987wLeLPaytDNe2VlVVAbD81w+w/ByZn/RZeo6mzs+YcWRCCMO9WUwkPz8f3t7e2LNnD4KDg3Xtc+fOxY4dO7Bv375Gr/Hss8/ixx9/xNGjR2Fra7h9TFxcHOLj4w3aU1NTYW9v37IEiOiWV1kNXK4Aim7IUHij9rsMl24AVaLuD1dZQcDFFnC3E3C3BzzsRM3PdoCS9x0gIkJ5eTkiIyNRXFwMJyenBvtK+v2ot99+G+vXr0dGRkadhSwAxMbGIiYmRndcUlICHx8fhIeHN/rktBaNRoO0tDSEhYVBLpebZExTYn7SZ+k5tjQ/TbUWF67dwLkr5Th3pRw5l1U4f6UcOVfKUVBcUe95Shsr9HBxQE/Xv756uTrCt4s9FDZW9Z5ndHwW/voBlp8j85M+S8/R1PnVvpPeFGYtZl1cXGBtbY2ioiK99qKiInh4eDR47rJly/D2229j+/btGDRoUL39lEollEqlQbtcLjf5L5s5xjQl5id9lp5jQ/lptQIFJRXIuaRCzuUy5Fwu//O7Cn9cu1HnLV1rOdnaoKebI3q5OuI2d0f0cnNEL9cO8O5sV+fygLZi6a8fYPk5Mj/ps/QcTZWfMWOYtZhVKBQIDAxEeno6Jk+eDADQarVIT09HdHR0vee9++67eOutt/Djjz9i2LBhJoqWiKSuskqLC8Uq5F4tr/m6UvPzucvlOHdFBXUdt3KtZSe3hp+LA3q4OMDPxR7dXRzR3cUB3V0c0Nlezv1aiYjMxOzLDGJiYhAVFYVhw4YhKCgICQkJUKlUut0Npk2bBm9vbyxduhQA8M4772DBggVITU2Fn58fCgsLAQCOjo5wdHQ0Wx5E1D4Ul2tw/mpNkXr+Sjn+uFqO81dUyM6zxou/bkcDE6yQW8vQzdleV6R2d3GEn4s9erg4wt1JyYKViKgdMnsxO2XKFFy6dAkLFixAYWEhBg8ejG3btsHd3R0AkJubCyurv9aWrVq1CpWVlXjooYf0rrNw4ULExcWZMnQiMoMbldXIu36j5uvaDeRe/bNgvapC7pVylFRU1XNmTSFqJ7dGN2d7+Djbo5uzPbo52/054+oIr062sLFuvbWsRETU9sxezAJAdHR0vcsKMjIy9I7PnTvX9gERkdmUVGiQd+0GLly7gbxr5XqF64VrN+rdi/Vmrh2U8K0tVrvYw7ujEhdOZGHKhLvh2dmBM6xERBakXRSzRHRrEELgiqoSedf+KlDzrt/AhWvlNcXr9RsorXdm9S+OSht4d7KDd2c7+HS2Q7cuDujmbA/fLvbo2tnO4A5YGo0GW/Oz4NqBSwWIiCwNi1kiahVabU2hWlhcgYLiGygsqUBBccVfx8U1xw19yKqWs4Oiplj9s2Ct/d61sx26drKHk50Ni1IiIgLAYpaImqBaK3C5TI2C4goUXL9RU6TqitWa46KSCmiqG78Hi0wGuHew1StSdcXqn9//PrNKRERUH/6LQXQLq9YKXFGpkX9VhaPXZFBlXsBlVRUullbgYokaF0vVuFhSgaJSdYP7rNaSyQC3Dkp4dLSDp5MtPDrawrNjzXevTnbwcLKFu5Ntq94wgIiIbm3tophNTEzEe++9h8LCQgQEBODDDz9EUFBQnX2PHj2KBQsWIDMzE+fPn8fKlSsxZ84c0wZM1M5VVWtxuawSRSUVNQXp34rT2rbLZZU3FanWwIlj9V7T2koG9w7KmgK1083Fqp2uaHXtoIScuwEQEZEJmb2Y3bBhA2JiYpCUlIThw4cjISEBERERyM7Ohpubm0H/8vJy9OjRA//4xz/w4osvmiFiIvNQV1XjSlklLpepcaWsEpf+/F5zrMblP3++XKbGFVUlROMTqQBqZlO7OChgK9To6e0KDyc7uDkp4eZkC7cOSrh1UMKrkx1cHJUmvZsVERFRU5i9mF2xYgVmzpypu0lCUlIStmzZguTkZMybN8+g/+23347bb78dAOp8nEgqhBAoU1fhclnln8WoGpdu+vnvhWtTPuV/M2srGVwdlTWFaYebi9Oa7+5OtnBzUqKLgwJCW42tW7di/PihFn0bRiIisjxmLWYrKyuRmZmJ2NhYXZuVlRVCQ0Oxd+/eVhlDrVZDrVbrjktKSgDUbNWj0WhaZYzG1I5jqvFMjfnVUFdpcb28EtfKNbhWXonr5RpcLdfgmuqvttrvV1UaXFVVNumT/TeTW8vg7KBAFwcFXBxrvndxVN70c813tw5KONsrYNWEmVShreZrKHGWnh9g+TkyP+mz9BxNnZ8x48iEaOqbka0vPz8f3t7e2LNnD4KDg3Xtc+fOxY4dO7Bv374Gz/fz88OcOXMaXDMbFxeH+Ph4g/bU1FTY29s3O3aybFVaoLyq5qusClBpZFBVAWUaQFUlg0rzt/YqQF3dvLfglVYCHeSAoxzoIBfooAA62AAdFOKvNjnQQQ7YWdcsCyAiIrJk5eXliIyMRHFxMZycnBrsa/ZlBm0tNjYWMTExuuOSkhL4+PggPDy80SentWg0GqSlpSEsLMwi38Jtr/nVvo1//YYGJTdqvheXa1BcUfu9CsU3NLherkHJn23Xb2hQUlGF8srqZo1pbSVDJzs5OtvL0dlBUfPdXgFnvWM5Otn/OZPqoICdwrqVMzdee30NWwvzkz5Lz5H5SZ+l52jq/GrfSW8KsxazLi4usLa2RlFRkV57UVERPDw8WmUMpVIJpVJp0C6Xy03+y2aOMU2pLfKrqtaiTF2FkhtVKKnQoLTir+/XyytRcqOmAK0tSmsKVw2ul1ei+IYGTdhNql4yGeBkK4ezvRyoVKGHtxu6OCrR2UEBZ3uF/vc/f+5ga9Okt/bbK/6OSpul5wdYfo7MT/osPUdT5WfMGGYtZhUKBQIDA5Geno7JkycDALRaLdLT0xEdHW3O0KgVCCGgqqxGyY2bi9A/f/5zBvTm4rS0QqPrW9ve3BnSm9nKrdDRTo5Odgp0tJf/+bMcnf78uaO9wqCtk91fhalGo/nzw1FDLPovKCIiIiky+zKDmJgYREVFYdiwYQgKCkJCQgJUKpVud4Np06bB29sbS5cuBVDzobFjx47pfs7Ly0NWVhYcHR3Rq1cvs+VhKYQQUFdpUVpRBZW6CmV/fhn+XI2yP/uUVlQi54IV1ubth6qy+s+ZVA3K1FUtmhm9mZ3cGh1sbeBkJ0cHWxt0sK0pPjveXJTa1bx9X9vWyU4OJzs5bOXmfxufiIiI2obZi9kpU6bg0qVLWLBgAQoLCzF48GBs27YN7u7uAIDc3FxYWf21CXt+fj6GDBmiO162bBmWLVuGMWPGICMjw9Thm121VuCGphrllVUoV1ejvPLPnyv/+rm2+GyoOFWpq1FaoYGqsrpJd3oyZAVcv17nI3JrGZxs/ypCnexs0EEpNyhOnW76fnN7B1sbbsRPREREdTJ7MQsA0dHR9S4r+HuB6ufnBzNuwNAsQghotMC18kpoRBXK1frFZl0FaHllNW5UVkNVWY0bfx7X/qxSV+sK2AqNcVs7NZVMBjgobOCgtIaj0gaOShs4/PnV4eafbW1gayPDmRNHMfL2oejooISj8q+i1clWDqWNFWT8CD4RERG1gXZRzFqypB1n8N6P2ajW2gD7MtpsHCsZYK+wgZ3CGg4Ka9gpbP78bq0rRG8uSh2V1oZttn/9bC+3bvIHmTQaDbZePYKI/u5cU0pEREQmxWK2jdlYyfTetlfaWMFeYQ17hc2f363/LEBt9L7bK2qKTTt5zc/2fxaY9kr9c2t/5uwnERER3YpYzLaxKbf7YFx/N+z+JR33TbgHdraG24QRERERUfOwmG1jHWzlsLUGbG0AG36IiYiIiKhVsboiIiIiIslqF8VsYmIi/Pz8YGtri+HDh2P//v0N9v/666/h7+8PW1tbDBw4EFu3bjVRpERERETUnpi9mN2wYQNiYmKwcOFCHDx4EAEBAYiIiMDFixfr7L9nzx48+uijePLJJ3Ho0CFMnjwZkydPxpEjR0wcORERERGZm9mL2RUrVmDmzJmYMWMG+vXrh6SkJNjb2yM5ObnO/u+//z7uuecevPLKK+jbty/efPNNDB06FB999JGJIyciIiIiczPrB8AqKyuRmZmJ2NhYXZuVlRVCQ0Oxd+/eOs/Zu3cvYmJi9NoiIiKwefPmOvur1Wqo1WrdcXFxMQDg6tWr0Gg0LcygaTQaDcrLy3HlyhWL3IeV+UmfpefI/KTP0nNkftJn6TmaOr/S0lIAaNKNssxazF6+fBnV1dW6W9fWcnd3x4kTJ+o8p7CwsM7+hYWFdfZfunQp4uPjDdq7d+/ezKiJiIiIyBRKS0vRsWPHBvtY/NZcsbGxejO5Wq0WV69eRZcuXUx2k4GSkhL4+Pjgjz/+gJOTk0nGNCXmJ32WniPzkz5Lz5H5SZ+l52jq/IQQKC0thZeXV6N9zVrMuri4wNraGkVFRXrtRUVF8PDwqPMcDw8Po/orlUoolfo3KujUqVPzg24BJycni/wFr8X8pM/Sc2R+0mfpOTI/6bP0HE2ZX2MzsrXM+gEwhUKBwMBApKen69q0Wi3S09MRHBxc5znBwcF6/QEgLS2t3v5EREREZLnMvswgJiYGUVFRGDZsGIKCgpCQkACVSoUZM2YAAKZNmwZvb28sXboUADB79myMGTMGy5cvx4QJE7B+/XocOHAAn376qTnTICIiIiIzMHsxO2XKFFy6dAkLFixAYWEhBg8ejG3btuk+5JWbmwsrq78mkEeOHInU1FS88cYbeO2113Dbbbdh8+bNGDBggLlSaJRSqcTChQsNljtYCuYnfZaeI/OTPkvPkflJn6Xn2J7zk4mm7HlARERERNQOmf2mCUREREREzcViloiIiIgki8UsEREREUkWi1kiIiIikiwWs60kMTERfn5+sLW1xfDhw7F///4G+3/99dfw9/eHra0tBg4ciK1bt5oo0uYxJr+jR4/iwQcfhJ+fH2QyGRISEkwXaDMZk99nn32GO+64A507d0bnzp0RGhra6OvdHhiT48aNGzFs2DB06tQJDg4OGDx4MNatW2fCaI1n7J/BWuvXr4dMJsPkyZPbNsAWMia/lJQUyGQyvS9bW1sTRts8xr6G169fx3PPPQdPT08olUr07t27Xf9dakx+ISEhBq+hTCbDhAkTTBixcYx9/RISEtCnTx/Y2dnBx8cHL774IioqKkwUbfMYk6NGo8GiRYvQs2dP2NraIiAgANu2bTNhtMbZuXMnJk6cCC8vL8hkMmzevLnRczIyMjB06FAolUr06tULKSkpbR5nnQS12Pr164VCoRDJycni6NGjYubMmaJTp06iqKiozv7//e9/hbW1tXj33XfFsWPHxBtvvCHkcrn4/fffTRx50xib3/79+8XLL78s/v3vfwsPDw+xcuVK0wZsJGPzi4yMFImJieLQoUPi+PHjYvr06aJjx47iwoULJo686YzN8ZdffhEbN24Ux44dE6dPnxYJCQnC2tpabNu2zcSRN42x+dXKyckR3t7e4o477hCTJk0yTbDNYGx+a9asEU5OTqKgoED3VVhYaOKojWNsjmq1WgwbNkyMHz9e7N69W+Tk5IiMjAyRlZVl4sibxtj8rly5ovf6HTlyRFhbW4s1a9aYNvAmMja/L7/8UiiVSvHll1+KnJwc8eOPPwpPT0/x4osvmjjypjM2x7lz5wovLy+xZcsWcebMGfHxxx8LW1tbcfDgQRNH3jRbt24Vr7/+uti4caMAIDZt2tRg/7Nnzwp7e3sRExMjjh07Jj788EOz/TvBYrYVBAUFieeee053XF1dLby8vMTSpUvr7P/www+LCRMm6LUNHz5cPP30020aZ3MZm9/NfH19230x25L8hBCiqqpKdOjQQXzxxRdtFWKLtTRHIYQYMmSIeOONN9oivBZrTn5VVVVi5MiR4vPPPxdRUVHtupg1Nr81a9aIjh07mii61mFsjqtWrRI9evQQlZWVpgqxRVr6Z3DlypWiQ4cOoqysrK1CbBFj83vuuefEXXfdpdcWExMjRo0a1aZxtoSxOXp6eoqPPvpIr+2BBx4Qjz32WJvG2RqaUszOnTtX9O/fX69typQpIiIiog0jqxuXGbRQZWUlMjMzERoaqmuzsrJCaGgo9u7dW+c5e/fu1esPABEREfX2N6fm5CclrZFfeXk5NBoNnJ2d2yrMFmlpjkIIpKenIzs7G3feeWdbhtoszc1v0aJFcHNzw5NPPmmKMJutufmVlZXB19cXPj4+mDRpEo4ePWqKcJulOTn+5z//QXBwMJ577jm4u7tjwIABWLJkCaqrq00VdpO1xt8zq1evxiOPPAIHB4e2CrPZmpPfyJEjkZmZqXub/uzZs9i6dSvGjx9vkpiN1Zwc1Wq1wfIeOzs77N69u01jNZX2VMuwmG2hy5cvo7q6WnfHslru7u4oLCys85zCwkKj+ptTc/KTktbI79VXX4WXl5fBH+r2ork5FhcXw9HREQqFAhMmTMCHH36IsLCwtg7XaM3Jb/fu3Vi9ejU+++wzU4TYIs3Jr0+fPkhOTsZ3332Hf/3rX9BqtRg5ciQuXLhgipCN1pwcz549i2+++QbV1dXYunUr5s+fj+XLl2Px4sWmCNkoLf17Zv/+/Thy5AieeuqptgqxRZqTX2RkJBYtWoTRo0dDLpejZ8+eCAkJwWuvvWaKkI3WnBwjIiKwYsUKnDp1ClqtFmlpadi4cSMKCgpMEXKbq6+WKSkpwY0bN0waC4tZohZ4++23sX79emzatEkSH7AxRocOHZCVlYX//e9/eOuttxATE4OMjAxzh9VipaWlmDp1Kj777DO4uLiYO5w2ERwcjGnTpmHw4MEYM2YMNm7cCFdXV3zyySfmDq3VaLVauLm54dNPP0VgYCCmTJmC119/HUlJSeYOrdWtXr0aAwcORFBQkLlDaTUZGRlYsmQJPv74Yxw8eBAbN27Eli1b8Oabb5o7tFbz/vvv47bbboO/vz8UCgWio6MxY8YMWFmx9GptNuYOQOpcXFxgbW2NoqIivfaioiJ4eHjUeY6Hh4dR/c2pOflJSUvyW7ZsGd5++21s374dgwYNasswW6S5OVpZWaFXr14AgMGDB+P48eNYunQpQkJC2jJcoxmb35kzZ3Du3DlMnDhR16bVagEANjY2yM7ORs+ePds2aCO0xp9BuVyOIUOG4PTp020RYos1J0dPT0/I5XJYW1vr2vr27YvCwkJUVlZCoVC0aczGaMlrqFKpsH79eixatKgtQ2yR5uQ3f/58TJ06VTfbPHDgQKhUKvzzn//E66+/3u4Kvubk6Orqis2bN6OiogJXrlyBl5cX5s2bhx49epgi5DZXXy3j5OQEOzs7k8bSvn5bJEihUCAwMBDp6em6Nq1Wi/T0dAQHB9d5TnBwsF5/AEhLS6u3vzk1Jz8paW5+7777Lt58801s27YNw4YNM0WozdZar6FWq4VarW6LEFvE2Pz8/f3x+++/IysrS/d13333YezYscjKyoKPj48pw29Ua7x+1dXV+P333+Hp6dlWYbZIc3IcNWoUTp8+rfuPCACcPHkSnp6e7aqQBVr2Gn799ddQq9V4/PHH2zrMZmtOfuXl5QYFa+1/TIQQbRdsM7XkNbS1tYW3tzeqqqrw7bffYtKkSW0drkm0q1rG5B85s0Dr168XSqVSpKSkiGPHjol//vOfolOnTrqtcKZOnSrmzZun6//f//5X2NjYiGXLlonjx4+LhQsXtvutuYzJT61Wi0OHDolDhw4JT09P8fLLL4tDhw6JU6dOmSuFBhmb39tvvy0UCoX45ptv9LbOKS0tNVcKjTI2xyVLloiffvpJnDlzRhw7dkwsW7ZM2NjYiM8++8xcKTTI2Pz+rr3vZmBsfvHx8eLHH38UZ86cEZmZmeKRRx4Rtra24ujRo+ZKoVHG5pibmys6dOggoqOjRXZ2tvj++++Fm5ubWLx4sblSaFBzf0dHjx4tpkyZYupwjWZsfgsXLhQdOnQQ//73v8XZs2fFTz/9JHr27Ckefvhhc6XQKGNz/PXXX8W3334rzpw5I3bu3Cnuuusu0b17d3Ht2jUzZdCw0tJS3b/dAMSKFSvEoUOHxPnz54UQQsybN09MnTpV1792a65XXnlFHD9+XCQmJnJrLqn78MMPRbdu3YRCoRBBQUHi119/1T02ZswYERUVpdf/q6++Er179xYKhUL0799fbNmyxcQRG8eY/HJycgQAg68xY8aYPvAmMiY/X1/fOvNbuHCh6QM3gjE5vv7666JXr17C1tZWdO7cWQQHB4v169ebIeqmM/bP4M3aezErhHH5zZkzR9fX3d1djB8/vt3ubXkzY1/DPXv2iOHDhwulUil69Ogh3nrrLVFVVWXiqJvO2PxOnDghAIiffvrJxJE2jzH5aTQaERcXJ3r27ClsbW2Fj4+PePbZZ9ttoVfLmBwzMjJE3759hVKpFF26dBFTp04VeXl5Zoi6aX755Zc6/22rzSkqKsrg3/FffvlFDB48WCgUCtGjRw+z7YMsE6IdzucTERERETUB18wSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZLGaJiIiISLJYzBIRERGRZLGYJSKSuJCQEMyZM8fcYRARmQWLWSIiIiKSLJkQQpg7CCIiap7p06fjiy++0GvLycmBn5+feQIiIjIxFrNERBJWXFyMcePGYcCAAVi0aBEAwNXVFdbW1maOjIjINGzMHQARETVfx44doVAoYG9vDw8PD3OHQ0RkclwzS0RERESSxWKWiIiIiCSLxSwRkcQpFApUV1ebOwwiIrNgMUtEJHF+fn7Yt28fzp07h8uXL0Or1Zo7JCIik2ExS0QkcS+//DKsra3Rr18/uLq6Ijc319whERGZDLfmIiIiIiLJ4swsEREREUkWi1kiIiIikiwWs0REREQkWSxmiYiIiEiyWMwSERERkWSxmCUiIiIiyWIxS0RERESSxWKWiIiIiCSLxSwRERERSRaLWSIiIiKSLBazRERERCRZ/w8mEx0IaRJ3fQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# Constants\n",
    "C = 1 #10 #5  # You can change the value of C as per your requirement\n",
    "D = 3 #5 #10 # You can change the value of D as per your requirement\n",
    "E = 1\n",
    "\n",
    "# Define the function\n",
    "def custom_function(t):\n",
    "    return C * np.exp(D * (t - E))\n",
    "\n",
    "# Generate values for t in the range [0, 1]\n",
    "t_values = np.linspace(0, 1, 200)  # 1000 points between 0 and 1\n",
    "\n",
    "# Calculate corresponding function values\n",
    "function_values = custom_function(t_values)\n",
    "\n",
    "# Plot the function\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(t_values, function_values, label=f'{C} * exp({D} * (t - {E}))')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('Function Value')\n",
    "plt.title(f'Plot of the function {C} * exp({D} * (t - {E}))')\n",
    "plt.legend()\n",
    "\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))  # Set x-axis grid at every integer from 0 to 1\n",
    "plt.yticks(np.trajsarange(0, 2, 0.1))  # Set y-axis grid at every integer from 0 to 10\n",
    "plt.ylim(0, 3)\n",
    "\n",
    "plt.grid(True)\n",
    "plt.savefig(os.path.join(os.getcwd(), \"tmp_result/loss.png\"))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.011108996538242306\n"
     ]
    }
   ],
   "source": [
    "print(custom_function(49/49))\n",
    "print(custom_function(20/200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 50, 9)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torchvision.io import read_image\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "class TrajectoryDataset(Dataset):\n",
    "    def __init__(self, data_root_dir, file_adr, transform=None):\n",
    "        self.trajectories = np.load(data_root_dir + file_adr)\n",
    "        print(self.trajectories.shape)\n",
    "        self.data_root_dir = data_root_dir\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return 1000 * 50\n",
    "        # return 70*49\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        traj_no = idx // 50\n",
    "        step_no = idx % 50\n",
    "\n",
    "        step = self.trajectories[traj_no, step_no, 0]\n",
    "        state = self.trajectories[traj_no, step_no, 1:5]\n",
    "        target_pos = self.trajectories[traj_no, step_no, 5:7]\n",
    "        action = self.trajectories[traj_no, step_no, 7:]\n",
    "\n",
    "        # image = Image.open(self.data_root_dir + f\"/scene_images/goal_{traj_no}.png\")\n",
    "        # if self.transform:\n",
    "        #     image = self.transform(image)\n",
    "\n",
    "        return step, state, target_pos, action\n",
    "    \n",
    "\n",
    "# transform = transforms.Compose([\n",
    "#                 transforms.Resize([120, 120]),\n",
    "#                 # transforms.RandomHorizontalFlip(), # Flip the data horizontally\n",
    "#                 transforms.ToTensor(),\n",
    "#                 # transforms.Normalize(mean=(0.5,0.5,0.5), std=(0.5,0.5,0.5))\n",
    "#             ])\n",
    "dir_name = \".\"#os.path.dirname(__file__)\n",
    "dataset_name = \"1K_cartesian_w_step\"\n",
    "batch_size = 32\n",
    "dataset = TrajectoryDataset(dir_name + f'/data/{dataset_name}/0', '/trajectories_normalized.npy')\n",
    "train_set, val_set = torch.utils.data.random_split(dataset, [0.9, 0.1])\n",
    "train_dataloader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
    "val_dataloader = DataLoader(val_set, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.3480, -0.5244,  1.5999,  0.9089],\n",
      "        [-1.8859,  0.5702,  1.4260,  0.7666],\n",
      "        [-0.2379,  0.3068, -1.3214, -1.3079]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[-1.3480, -0.5244,  0.1600,  0.0909],\n",
       "        [-1.8859,  0.5702,  0.1426,  0.0767],\n",
       "        [-0.2379,  0.3068, -0.1321, -0.1308]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "batch_size = 3\n",
    "act_pred = torch.randn(batch_size, 4)\n",
    "rescale_vector = torch.tensor([1, 1, 0.1, 0.1], dtype=torch.float32)\n",
    "print(act_pred)\n",
    "act_pred*rescale_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "num_params = [\"0.27\", \"0.57\", \"1.846\"]\n",
    "num_params_base = [\"0.269\", \"0.575\", \"1.806\"]\n",
    "\n",
    "for i in range(len(num_params)):\n",
    "    n_p = num_params[i]\n",
    "    n_p_b = num_params_base[i]\n",
    "\n",
    "    results_root_path = \"./test_results/466_man_tra|cus_los_5_10_1|st_vel_norm\"\n",
    "    results_root_path += f\"|tar_cart|{n_p}K_params|epoch_1200\"\n",
    "\n",
    "    loss_torques_value_trains = np.load(results_root_path + \\\n",
    "                                                    \"/loss_torques_value_trains.npy\")\n",
    "    \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.75161045, 1.7237823 , 1.68088485, 1.69851039, 2.00452566],\n",
       "       [1.36258173, 1.39338349, 1.28268452, 1.33953193, 1.62887984],\n",
       "       [1.16691254, 1.37057928, 1.1099989 , 1.22319595, 1.24395918],\n",
       "       [0.66343274, 0.63163631, 0.56455241, 0.69700024, 0.79249429],\n",
       "       [0.61742273, 0.71223477, 0.43436044, 0.81787861, 0.79718367]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_torques_value_trains/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import math\n",
    "\n",
    "def get_line_equation(x1, y1, x2, y2):\n",
    "    # Calculate the slope\n",
    "    slope = (y2 - y1) / ((x2 - x1)+1e-10) \n",
    "    # Calculate the y-intercept (b) using one of the points\n",
    "    b = y1 - slope * x1\n",
    "\n",
    "    return slope, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "arctan2() takes from 2 to 3 positional arguments but 1 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m s, b \u001b[38;5;241m=\u001b[39m get_line_equation(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marctan2\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.001\u001b[39m\n\u001b[1;32m      4\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.10\u001b[39m\n",
      "\u001b[0;31mTypeError\u001b[0m: arctan2() takes from 2 to 3 positional arguments but 1 were given"
     ]
    }
   ],
   "source": [
    "s, b = get_line_equation(1, 1, 2, 2)\n",
    "alpha = np.arctan(s)\n",
    "alpha = np.arctan()\n",
    "d = 0.001\n",
    "x = 0.10\n",
    "y = 0.10\n",
    "print(y+d*np.sin(alpha))\n",
    "print(x+d*np.cos(alpha))\n",
    "print(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.101\n",
      "0.10000000000010001\n",
      "1.5707963266948965\n"
     ]
    }
   ],
   "source": [
    "s, b = get_line_equation(0, 1, 0, 2)\n",
    "alpha = np.arctan(s)\n",
    "d = 0.001\n",
    "x = 0.10\n",
    "y = 0.10\n",
    "print(y+d*np.sin(alpha))\n",
    "print(x+d*np.cos(alpha))\n",
    "print(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09929289321884881\n",
      "0.10070710678122191\n",
      "-0.7853981633474483\n"
     ]
    }
   ],
   "source": [
    "s, b = get_line_equation(-2, 2, -1, 1)\n",
    "alpha = np.arctan(s)\n",
    "d = 0.001\n",
    "x = 0.10\n",
    "y = 0.10\n",
    "print(y+d*np.sin(alpha))\n",
    "print(x+d*np.cos(alpha))\n",
    "print(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s (4.148937818729735, -7.5152554883358675) e (4.911527792338497, 8.819361184962315)\n",
      "(0.04663472049842385, 0.9989120095604185) 0.9999999999999997\n",
      "(0.046634720498423834, 0.9989120095604186) 0.9999999999999999\n",
      "(0.046634720498423814, 0.9989120095604187) 1.0\n",
      "(0.046634720498423786, 0.9989120095604186) 0.9999999999999999\n",
      "(0.046634720498423765, 0.9989120095604187) 1.0\n",
      "(0.04663472049842374, 0.9989120095604186) 0.9999999999999999\n",
      "(0.04663472049842371, 0.9989120095604187) 1.0\n",
      "(0.04663472049842367, 0.9989120095604186) 0.9999999999999999\n",
      "(0.04663472049842364, 0.9989120095604187) 1.0\n",
      "(0.04663472049842368, 0.9989120095604187) 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to calculate distance between two points\n",
    "def distance(point1, point2):\n",
    "    return math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2)\n",
    "\n",
    "# Function to calculate unit vector\n",
    "def unit_vector(point1, point2):\n",
    "    dx = point2[0] - point1[0]\n",
    "    dy = point2[1] - point1[1]\n",
    "    length = distance(point1, point2)\n",
    "    return (dx/length, dy/length)\n",
    "\n",
    "# Function to displace along the line between two points\n",
    "def displace_along_line(start_point, end_point, epsilon):\n",
    "    unit_vec = unit_vector(start_point, end_point)\n",
    "    print(unit_vec, unit_vec[0]**2 + unit_vec[1]**2)\n",
    "    displacement = (unit_vec[0] * epsilon, unit_vec[1] * epsilon)\n",
    "    return (start_point[0] + displacement[0], start_point[1] + displacement[1])\n",
    "\n",
    "# Generate random start and end points\n",
    "start_point = (random.uniform(-10, 10), random.uniform(-10, 10))\n",
    "end_point = (random.uniform(-10, 10), random.uniform(-10, 10))\n",
    "\n",
    "# Set epsilon (displacement amount)\n",
    "epsilon = 0.5\n",
    "\n",
    "# Number of iterations\n",
    "iterations = 10\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot([start_point[0], end_point[0]], [start_point[1], end_point[1]], 'b-', label='Line')\n",
    "# plt.plot([start_point[0], start_point[1]], [end_point[0], end_point[1]], 'b-', label='Line')\n",
    "plt.scatter(start_point[0], start_point[1], color='green', marker='x', label='Start Point')\n",
    "plt.scatter(end_point[0], end_point[1], color='black', marker='x', label='End Point')\n",
    "\n",
    "# Iterate and displace along the line\n",
    "print(\"s\", start_point, \"e\", end_point)\n",
    "for i in range(iterations):\n",
    "    new_point = displace_along_line(start_point, end_point, epsilon)\n",
    "    plt.scatter(new_point[0], new_point[1], color='purple', label='Displacement' if i == 0 else None)\n",
    "    plt.plot([start_point[0], new_point[0]], [start_point[1], new_point[1]], \n",
    "             color='yellow', linestyle='--')\n",
    "    # print(\"Iteration:\", i+1, \"New point:\", new_point)\n",
    "    # Update start_point for next iteration\n",
    "    start_point = new_point\n",
    "\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Line and Displacement')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### [0.5547002  0.83205029]\n",
      "(1.2773500981126147, 1.4160251471689218)\n",
      "(1.5547001962252294, 1.8320502943378436)\n",
      "(1.832050294337844, 2.2480754415067654)\n",
      "(2.1094003924504587, 2.664100588675687)\n",
      "(2.3867504905630734, 3.080125735844609)\n",
      "(2.664100588675688, 3.496150883013531)\n",
      "(2.941450686788303, 3.9121760301824526)\n",
      "(3.2188007849009175, 4.328201177351374)\n",
      "(3.496150883013532, 4.744226324520296)\n",
      "(3.773500981126147, 5.160251471689218)\n",
      "(4.050851079238761, 5.57627661885814)\n",
      "(4.328201177351375, 5.992301766027062)\n",
      "(4.60555127546399, 6.4083269131959835)\n",
      "(4.882901373576604, 6.824352060364905)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def displacement(start_point, end_point, epsilon):\n",
    "    # Convert points to numpy arrays for easier computation\n",
    "    start_point = np.array(start_point)\n",
    "    end_point = np.array(end_point)\n",
    "    \n",
    "    # Calculate the vector representing the direction from start_point to end_point\n",
    "    direction_vector = end_point - start_point\n",
    "    \n",
    "    # Calculate the unit vector in the direction of the line\n",
    "    unit_vector = direction_vector / np.linalg.norm(direction_vector)\n",
    "    print(\"###\", unit_vector)\n",
    "    \n",
    "    # Initialize current position as start_point\n",
    "    current_position = start_point.copy()\n",
    "    \n",
    "    # Iterate and displace epsilon amount towards end_point\n",
    "    while np.linalg.norm(current_position - end_point) >= epsilon:\n",
    "        # Move epsilon distance towards end_point along the direction vector\n",
    "        current_position += epsilon * unit_vector\n",
    "        yield tuple(current_position)\n",
    "\n",
    "# Example usage:\n",
    "start_point = (1.0, 1.0)\n",
    "end_point = (5, 7)\n",
    "epsilon = 0.5\n",
    "\n",
    "# Iterate over the displaced points\n",
    "for point in displacement(start_point, end_point, epsilon):\n",
    "    print(point)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(815, 50, 3, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = np.load('./data/obs_final.npy', allow_pickle=True, encoding='latin1')\n",
    "array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [array([0.95378724, 0.24798383, 0.99937049, 1.35779412, 0.79928581,\n",
       "               0.13905992, 0.12295033])                                   ],\n",
       "       [array([ 0.        ,  0.        ,  0.34413712,  0.39380216,  1.3167559 ,\n",
       "                1.71655127, -1.5071147 , -1.30665576,  1.13387109,  0.        ])]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 7])\n",
      "torch.Size([7])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Sample tensors tensor_a and tensor_b of size 32x7\n",
    "tensor_a = torch.randn(32, 7)\n",
    "tensor_b = torch.randn(32, 7)\n",
    "\n",
    "# Compute the absolute difference\n",
    "abs_diff = torch.abs(tensor_a - tensor_b)\n",
    "print(abs_diff.shape)\n",
    "\n",
    "# Compute the mean of the absolute differences along dimension 0 (rows)\n",
    "result = torch.mean(abs_diff, dim=0)\n",
    "\n",
    "print(result.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([7])\n"
     ]
    }
   ],
   "source": [
    "print(result.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0217, 1.0456, 1.1665, 1.2228, 1.0123, 1.1400, 1.2556])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.045613408088684"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[1].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "with open('./weights/815_trajs_static|cus_los_5_10_1|tar_cart|45.333K_params/train_no_0/info.pickle', 'rb') as handle:\n",
    "    train_info = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae_joint_1_val': [0.26852163672447205,\n",
       "  0.033704765141010284,\n",
       "  0.016566550359129906,\n",
       "  0.01693328656256199,\n",
       "  0.014870665036141872,\n",
       "  0.008178610354661942,\n",
       "  0.011552088893949986,\n",
       "  0.016923733055591583,\n",
       "  0.00878257118165493,\n",
       "  0.012893233448266983,\n",
       "  0.012214136309921741,\n",
       "  0.010287226177752018,\n",
       "  0.01214129664003849,\n",
       "  0.01658087782561779,\n",
       "  0.010347940027713776,\n",
       "  0.01154912170022726,\n",
       "  0.01319499034434557,\n",
       "  0.01335305068641901,\n",
       "  0.015974802896380424,\n",
       "  0.016094140708446503,\n",
       "  0.014884638600051403,\n",
       "  0.015091034583747387,\n",
       "  0.011653183959424496,\n",
       "  0.014001808129251003,\n",
       "  0.013740289025008678,\n",
       "  0.009688280522823334,\n",
       "  0.01099972054362297,\n",
       "  0.010429061017930508,\n",
       "  0.007617153227329254,\n",
       "  0.012672141194343567,\n",
       "  0.00930751021951437,\n",
       "  0.009840307757258415,\n",
       "  0.018065501004457474,\n",
       "  0.009812978096306324,\n",
       "  0.011294678784906864,\n",
       "  0.008895433507859707,\n",
       "  0.011843254789710045,\n",
       "  0.008137794211506844,\n",
       "  0.014961645007133484,\n",
       "  0.007366584148257971,\n",
       "  0.010310426354408264,\n",
       "  0.01431648712605238,\n",
       "  0.007811837363988161,\n",
       "  0.012580934911966324,\n",
       "  0.018819278106093407,\n",
       "  0.00870366021990776],\n",
       " 'mae_joint_1_train': [0.04520486667752266,\n",
       "  0.03014514595270157,\n",
       "  0.03480316326022148,\n",
       "  0.013098414056003094,\n",
       "  0.00887333881109953,\n",
       "  0.04766358435153961,\n",
       "  0.03590289503335953,\n",
       "  0.020201874896883965,\n",
       "  0.0644911378622055,\n",
       "  0.0564875602722168,\n",
       "  0.024536987766623497,\n",
       "  0.03852669522166252,\n",
       "  0.004271318670362234,\n",
       "  0.0031070206314325333,\n",
       "  0.012278812937438488,\n",
       "  0.013670064508914948,\n",
       "  0.03701190650463104,\n",
       "  0.008365360088646412,\n",
       "  0.019653180614113808,\n",
       "  0.004721268080174923,\n",
       "  0.012597615830600262,\n",
       "  0.01662738062441349,\n",
       "  0.047133367508649826,\n",
       "  0.015604537911713123,\n",
       "  0.004085874650627375,\n",
       "  0.0073201521299779415,\n",
       "  0.03343529626727104,\n",
       "  0.008552937768399715,\n",
       "  0.01374360453337431,\n",
       "  0.005566457286477089,\n",
       "  0.020360425114631653,\n",
       "  0.039084743708372116,\n",
       "  0.041155993938446045,\n",
       "  0.00993334036320448,\n",
       "  0.017515024170279503,\n",
       "  0.008515520952641964,\n",
       "  0.019977951422333717,\n",
       "  0.022274112328886986,\n",
       "  0.03182877227663994,\n",
       "  0.009196062572300434,\n",
       "  0.007282985839992762,\n",
       "  0.002003511181101203,\n",
       "  0.010539203882217407,\n",
       "  0.005386886652559042,\n",
       "  0.017131490632891655],\n",
       " 'mae_joint_2_val': [0.09743509441614151,\n",
       "  0.017725780606269836,\n",
       "  0.00970487017184496,\n",
       "  0.00348572782240808,\n",
       "  0.005916404537856579,\n",
       "  0.005548151209950447,\n",
       "  0.004105004481971264,\n",
       "  0.008792523294687271,\n",
       "  0.005556838121265173,\n",
       "  0.0025194110348820686,\n",
       "  0.0035880364011973143,\n",
       "  0.002807424170896411,\n",
       "  0.0028921805787831545,\n",
       "  0.006933677010238171,\n",
       "  0.004267652053385973,\n",
       "  0.006839809939265251,\n",
       "  0.005330527201294899,\n",
       "  0.0043455990962684155,\n",
       "  0.005456022918224335,\n",
       "  0.00619400292634964,\n",
       "  0.004520261660218239,\n",
       "  0.009245016612112522,\n",
       "  0.004004713613539934,\n",
       "  0.009001098573207855,\n",
       "  0.006170451641082764,\n",
       "  0.004426795523613691,\n",
       "  0.00561278872191906,\n",
       "  0.008250846527516842,\n",
       "  0.006207691039890051,\n",
       "  0.005287693347781897,\n",
       "  0.004211463034152985,\n",
       "  0.0047025117091834545,\n",
       "  0.00804899912327528,\n",
       "  0.005963972304016352,\n",
       "  0.005217946600168943,\n",
       "  0.006019472610205412,\n",
       "  0.004502678290009499,\n",
       "  0.00734262028709054,\n",
       "  0.008296821266412735,\n",
       "  0.0034356191754341125,\n",
       "  0.00614079087972641,\n",
       "  0.005591327324509621,\n",
       "  0.006595282349735498,\n",
       "  0.006262228358536959,\n",
       "  0.005667233373969793,\n",
       "  0.005762581247836351],\n",
       " 'mae_joint_2_train': [0.0061242058873176575,\n",
       "  0.00854059960693121,\n",
       "  0.010486983694136143,\n",
       "  0.02046946994960308,\n",
       "  0.001997901825234294,\n",
       "  0.0153242452070117,\n",
       "  0.008179585449397564,\n",
       "  0.014534535817801952,\n",
       "  0.02038789726793766,\n",
       "  0.01487922016531229,\n",
       "  0.0075907050631940365,\n",
       "  0.014802462421357632,\n",
       "  0.0040611568838357925,\n",
       "  0.0013216935331001878,\n",
       "  0.003355545923113823,\n",
       "  0.0037947401870042086,\n",
       "  0.03453132510185242,\n",
       "  0.01509338989853859,\n",
       "  0.007093297317624092,\n",
       "  0.003714219434186816,\n",
       "  0.004688870161771774,\n",
       "  0.007323853671550751,\n",
       "  0.02738785557448864,\n",
       "  0.0032076023053377867,\n",
       "  0.007874819450080395,\n",
       "  0.00374078587628901,\n",
       "  0.018625376746058464,\n",
       "  0.006797484587877989,\n",
       "  0.0031328359618782997,\n",
       "  0.002467949176207185,\n",
       "  0.001545356004498899,\n",
       "  0.007947507314383984,\n",
       "  0.03224445506930351,\n",
       "  0.0036404530983418226,\n",
       "  0.010302428156137466,\n",
       "  0.007135495077818632,\n",
       "  0.0009729603189043701,\n",
       "  0.007588079199194908,\n",
       "  0.0031781746074557304,\n",
       "  0.023642277345061302,\n",
       "  0.0010599179659038782,\n",
       "  0.0018494323594495654,\n",
       "  0.008060059510171413,\n",
       "  0.0020726583898067474,\n",
       "  0.011226675473153591],\n",
       " 'mae_joint_3_val': [0.2722736597061157,\n",
       "  0.05118211731314659,\n",
       "  0.009985101409256458,\n",
       "  0.015591046772897243,\n",
       "  0.009029670618474483,\n",
       "  0.006345951929688454,\n",
       "  0.011058219708502293,\n",
       "  0.009489341638982296,\n",
       "  0.0071252393536269665,\n",
       "  0.011705136857926846,\n",
       "  0.005025284364819527,\n",
       "  0.011092845350503922,\n",
       "  0.010749624110758305,\n",
       "  0.005518410820513964,\n",
       "  0.0045486935414373875,\n",
       "  0.007518787868320942,\n",
       "  0.007199177518486977,\n",
       "  0.011440262198448181,\n",
       "  0.01028014812618494,\n",
       "  0.013679721392691135,\n",
       "  0.010001078248023987,\n",
       "  0.013665764592587948,\n",
       "  0.012639496475458145,\n",
       "  0.008378715254366398,\n",
       "  0.010394842363893986,\n",
       "  0.0075659542344510555,\n",
       "  0.010753639973700047,\n",
       "  0.0113129997625947,\n",
       "  0.010003291070461273,\n",
       "  0.00964309647679329,\n",
       "  0.012426575645804405,\n",
       "  0.004206615500152111,\n",
       "  0.014256111346185207,\n",
       "  0.008819685317575932,\n",
       "  0.007666795048862696,\n",
       "  0.008895493112504482,\n",
       "  0.006494040600955486,\n",
       "  0.005846147425472736,\n",
       "  0.012188402004539967,\n",
       "  0.012233936227858067,\n",
       "  0.010522915981709957,\n",
       "  0.008001738227903843,\n",
       "  0.0053900484926998615,\n",
       "  0.008832214400172234,\n",
       "  0.011543565429747105,\n",
       "  0.009015819989144802],\n",
       " 'mae_joint_3_train': [0.007609484251588583,\n",
       "  0.004318589810281992,\n",
       "  0.0023063458502292633,\n",
       "  0.053722456097602844,\n",
       "  0.0006919759325683117,\n",
       "  0.01094538439065218,\n",
       "  0.03927824646234512,\n",
       "  0.019734663888812065,\n",
       "  0.028078816831111908,\n",
       "  0.017310498282313347,\n",
       "  0.0030491568613797426,\n",
       "  0.003024345962330699,\n",
       "  0.0013723148731514812,\n",
       "  0.0011230305535718799,\n",
       "  0.006396828684955835,\n",
       "  0.006365419831126928,\n",
       "  0.0250240508466959,\n",
       "  0.022962572053074837,\n",
       "  0.004467107821255922,\n",
       "  0.009103693999350071,\n",
       "  0.011034291237592697,\n",
       "  0.009645278565585613,\n",
       "  0.04311210289597511,\n",
       "  0.019053392112255096,\n",
       "  0.014185181818902493,\n",
       "  0.002126994775608182,\n",
       "  0.04222536087036133,\n",
       "  0.0058346581645309925,\n",
       "  0.0018456334946677089,\n",
       "  0.0006856404361315072,\n",
       "  0.009902856312692165,\n",
       "  0.024099238216876984,\n",
       "  0.03407292440533638,\n",
       "  0.0014439249644055963,\n",
       "  0.023252489045262337,\n",
       "  0.014386773109436035,\n",
       "  0.0125435097143054,\n",
       "  0.007367928046733141,\n",
       "  0.0168744083493948,\n",
       "  0.004097009543329477,\n",
       "  0.011368364095687866,\n",
       "  0.0024584734346717596,\n",
       "  0.005016515962779522,\n",
       "  0.005220388527959585,\n",
       "  0.03494761511683464],\n",
       " 'mae_joint_4_val': [0.0979306772351265,\n",
       "  0.027074597775936127,\n",
       "  0.008721055462956429,\n",
       "  0.00718353409320116,\n",
       "  0.00843835063278675,\n",
       "  0.00693570077419281,\n",
       "  0.008228913880884647,\n",
       "  0.012434526346623898,\n",
       "  0.006802132353186607,\n",
       "  0.005548541434109211,\n",
       "  0.0038045102264732122,\n",
       "  0.007326862309128046,\n",
       "  0.008741036988794804,\n",
       "  0.005956767126917839,\n",
       "  0.007042634300887585,\n",
       "  0.010973638854920864,\n",
       "  0.006222598720341921,\n",
       "  0.008125617168843746,\n",
       "  0.005389831960201263,\n",
       "  0.008982963860034943,\n",
       "  0.005751108750700951,\n",
       "  0.007864312268793583,\n",
       "  0.009821990504860878,\n",
       "  0.005535787437111139,\n",
       "  0.005845754873007536,\n",
       "  0.0037654265761375427,\n",
       "  0.00551608158275485,\n",
       "  0.006612719502300024,\n",
       "  0.0038140215910971165,\n",
       "  0.00895287748426199,\n",
       "  0.006444297730922699,\n",
       "  0.004352482035756111,\n",
       "  0.008388403803110123,\n",
       "  0.004945102147758007,\n",
       "  0.003847631625831127,\n",
       "  0.0035304739139974117,\n",
       "  0.003996283281594515,\n",
       "  0.005431825295090675,\n",
       "  0.007803818676620722,\n",
       "  0.004546604584902525,\n",
       "  0.005596201401203871,\n",
       "  0.003109851386398077,\n",
       "  0.004160081967711449,\n",
       "  0.002946537220850587,\n",
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       " 'mae_joint_5_val': [0.33261623978614807,\n",
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       " 'mae_joint_6_train': [0.018544979393482208,\n",
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       "  0.0033479928970336914,\n",
       "  0.00443571200594306,\n",
       "  0.008434596471488476],\n",
       " 'mae_joint_7_val': [0.44526407122612,\n",
       "  0.04200105369091034,\n",
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       " 'mae_joint_7_train': [0.037649307399988174,\n",
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       "  0.028475679457187653,\n",
       "  0.01243013609200716,\n",
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       "  0.037737153470516205,\n",
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       "  0.012574359774589539,\n",
       "  0.021180694922804832,\n",
       "  0.07164934277534485]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mae_joint_1_val', 'mae_joint_1_train', 'mae_joint_2_val', 'mae_joint_2_train', 'mae_joint_3_val', 'mae_joint_3_train', 'mae_joint_4_val', 'mae_joint_4_train', 'mae_joint_5_val', 'mae_joint_5_train', 'mae_joint_6_val', 'mae_joint_6_train', 'mae_joint_7_val', 'mae_joint_7_train'])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        0.00870366]),\n",
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       "        0.00728299, 0.00200351, 0.0105392 , 0.00538689, 0.01713149]),\n",
       " 'mae_joint_2_val': array([0.09743509, 0.01772578, 0.00970487, 0.00348573, 0.0059164 ,\n",
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       "        0.00576258]),\n",
       " 'mae_joint_2_train': array([0.00612421, 0.0085406 , 0.01048698, 0.02046947, 0.0019979 ,\n",
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       "        0.00105992, 0.00184943, 0.00806006, 0.00207266, 0.01122668]),\n",
       " 'mae_joint_3_val': array([0.27227366, 0.05118212, 0.0099851 , 0.01559105, 0.00902967,\n",
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       "        0.01052292, 0.00800174, 0.00539005, 0.00883221, 0.01154357,\n",
       "        0.00901582]),\n",
       " 'mae_joint_3_train': array([0.00760948, 0.00431859, 0.00230635, 0.05372246, 0.00069198,\n",
       "        0.01094538, 0.03927825, 0.01973466, 0.02807882, 0.0173105 ,\n",
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       "        0.00212699, 0.04222536, 0.00583466, 0.00184563, 0.00068564,\n",
       "        0.00990286, 0.02409924, 0.03407292, 0.00144392, 0.02325249,\n",
       "        0.01438677, 0.01254351, 0.00736793, 0.01687441, 0.00409701,\n",
       "        0.01136836, 0.00245847, 0.00501652, 0.00522039, 0.03494762]),\n",
       " 'mae_joint_4_val': array([0.09793068, 0.0270746 , 0.00872106, 0.00718353, 0.00843835,\n",
       "        0.0069357 , 0.00822891, 0.01243453, 0.00680213, 0.00554854,\n",
       "        0.00380451, 0.00732686, 0.00874104, 0.00595677, 0.00704263,\n",
       "        0.01097364, 0.0062226 , 0.00812562, 0.00538983, 0.00898296,\n",
       "        0.00575111, 0.00786431, 0.00982199, 0.00553579, 0.00584575,\n",
       "        0.00376543, 0.00551608, 0.00661272, 0.00381402, 0.00895288,\n",
       "        0.0064443 , 0.00435248, 0.0083884 , 0.0049451 , 0.00384763,\n",
       "        0.00353047, 0.00399628, 0.00543183, 0.00780382, 0.0045466 ,\n",
       "        0.0055962 , 0.00310985, 0.00416008, 0.00294654, 0.00369711,\n",
       "        0.0040417 ]),\n",
       " 'mae_joint_4_train': array([0.00956231, 0.00683465, 0.00481194, 0.02336408, 0.0016727 ,\n",
       "        0.00953067, 0.01163967, 0.0129499 , 0.00239051, 0.02516639,\n",
       "        0.00847265, 0.00575305, 0.00650335, 0.002448  , 0.0079265 ,\n",
       "        0.01210348, 0.04693062, 0.02096789, 0.00714881, 0.0013575 ,\n",
       "        0.01038647, 0.00826034, 0.03463051, 0.00447171, 0.00361321,\n",
       "        0.00252945, 0.01811116, 0.00589209, 0.00892482, 0.00154091,\n",
       "        0.0320939 , 0.01828762, 0.04020109, 0.00259888, 0.01190066,\n",
       "        0.00690698, 0.0028391 , 0.00220096, 0.02789212, 0.01411395,\n",
       "        0.01084147, 0.00257305, 0.0077302 , 0.00591402, 0.03243493]),\n",
       " 'mae_joint_5_val': array([0.33261624, 0.04298801, 0.02507284, 0.02376352, 0.02166561,\n",
       "        0.01982016, 0.01790719, 0.02159043, 0.0134235 , 0.02117593,\n",
       "        0.01498004, 0.02347081, 0.02103922, 0.01516664, 0.00969063,\n",
       "        0.01587162, 0.01583329, 0.02178031, 0.02365713, 0.02523824,\n",
       "        0.01827309, 0.02448503, 0.01700647, 0.01763576, 0.01742137,\n",
       "        0.01426448, 0.01711853, 0.01737098, 0.01548544, 0.02309947,\n",
       "        0.02150643, 0.0175849 , 0.02426329, 0.01646722, 0.01642772,\n",
       "        0.01070298, 0.00895365, 0.01311341, 0.02125536, 0.01670825,\n",
       "        0.01559885, 0.01653695, 0.00978694, 0.01658303, 0.01997676,\n",
       "        0.00979434]),\n",
       " 'mae_joint_5_train': array([0.04594256, 0.03543859, 0.02917838, 0.09216826, 0.00494915,\n",
       "        0.03719058, 0.09976852, 0.04147497, 0.08205217, 0.08816078,\n",
       "        0.02489178, 0.03873798, 0.01175681, 0.00646285, 0.02508276,\n",
       "        0.02351328, 0.04428533, 0.03674258, 0.02326652, 0.02118814,\n",
       "        0.00725615, 0.01968748, 0.06490529, 0.04853098, 0.01328433,\n",
       "        0.00580056, 0.06119196, 0.02013138, 0.00965533, 0.0066552 ,\n",
       "        0.03924307, 0.04254536, 0.05783043, 0.01099615, 0.05377765,\n",
       "        0.03402638, 0.02911663, 0.02229612, 0.04757988, 0.01180919,\n",
       "        0.03511175, 0.00174495, 0.01310853, 0.01324994, 0.06948371]),\n",
       " 'mae_joint_6_val': array([0.27037433, 0.02859351, 0.01267522, 0.01274686, 0.00736613,\n",
       "        0.00737522, 0.00688414, 0.00730178, 0.00666268, 0.00864344,\n",
       "        0.00633861, 0.00453462, 0.00848761, 0.00764508, 0.00613018,\n",
       "        0.00802839, 0.0090315 , 0.00842051, 0.00914596, 0.00888836,\n",
       "        0.0068862 , 0.01475089, 0.00608927, 0.00887867, 0.01015619,\n",
       "        0.00839933, 0.00567214, 0.00950966, 0.00801262, 0.00855862,\n",
       "        0.00912039, 0.00689682, 0.01192808, 0.00612557, 0.00749847,\n",
       "        0.00722658, 0.00888882, 0.00602868, 0.01045839, 0.00863635,\n",
       "        0.0064603 , 0.01250492, 0.00685738, 0.01156892, 0.01107272,\n",
       "        0.00882391]),\n",
       " 'mae_joint_6_train': array([0.01854498, 0.01616458, 0.01707894, 0.01429871, 0.00186455,\n",
       "        0.01538392, 0.02162712, 0.01117588, 0.0417155 , 0.04169735,\n",
       "        0.01471099, 0.02226221, 0.01076357, 0.00273289, 0.00955553,\n",
       "        0.01121975, 0.03792629, 0.01479324, 0.01593544, 0.00389962,\n",
       "        0.00912736, 0.01175618, 0.0402845 , 0.00883118, 0.00624116,\n",
       "        0.00614316, 0.03832687, 0.00655118, 0.00912812, 0.00505095,\n",
       "        0.02352192, 0.00578235, 0.04428734, 0.00651323, 0.00877962,\n",
       "        0.00899286, 0.00773767, 0.01178152, 0.01871841, 0.00322716,\n",
       "        0.0087437 , 0.00318035, 0.00334799, 0.00443571, 0.0084346 ]),\n",
       " 'mae_joint_7_val': array([0.44526407, 0.04200105, 0.0239167 , 0.02323533, 0.02200676,\n",
       "        0.0220218 , 0.01917705, 0.01974437, 0.0151647 , 0.01875779,\n",
       "        0.01347474, 0.02539526, 0.02216223, 0.01778126, 0.0129071 ,\n",
       "        0.02006763, 0.01884337, 0.02584163, 0.02654708, 0.02900418,\n",
       "        0.02180766, 0.03034359, 0.02010041, 0.02121211, 0.01923451,\n",
       "        0.01693241, 0.01997235, 0.01951693, 0.01844407, 0.02664708,\n",
       "        0.02235729, 0.02076296, 0.02789651, 0.01864092, 0.01825962,\n",
       "        0.01154052, 0.00881557, 0.01435103, 0.0243306 , 0.01718516,\n",
       "        0.01925631, 0.01357099, 0.0129695 , 0.01640602, 0.02035289,\n",
       "        0.01261738]),\n",
       " 'mae_joint_7_train': array([0.03764931, 0.03614476, 0.02743112, 0.08763871, 0.00357705,\n",
       "        0.04138713, 0.10127208, 0.03568023, 0.08567574, 0.08546805,\n",
       "        0.02214143, 0.03691605, 0.01151644, 0.00481753, 0.02126333,\n",
       "        0.02175175, 0.04341989, 0.03481613, 0.02223878, 0.01966418,\n",
       "        0.00841144, 0.02056565, 0.06182841, 0.04485471, 0.01231547,\n",
       "        0.00468609, 0.05730289, 0.01983187, 0.01006083, 0.00461015,\n",
       "        0.04469892, 0.06542411, 0.05912294, 0.01285052, 0.05035257,\n",
       "        0.03094727, 0.02847568, 0.01243014, 0.04850706, 0.01412257,\n",
       "        0.03773715, 0.00358467, 0.01257436, 0.02118069, 0.07164934])}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info_arr = dict()\n",
    "\n",
    "for key in train_info.keys():\n",
    "    train_info_arr[key] = np.array(train_info[key])\n",
    "\n",
    "train_info_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae_joint_1_val': 0.018337181445373142,\n",
       " 'mae_joint_1_train': 0.021108174349905716,\n",
       " 'mae_joint_2_val': 0.007955079199746251,\n",
       " 'mae_joint_2_train': 0.009422145423013718,\n",
       " 'mae_joint_3_val': 0.01612037877835657,\n",
       " 'mae_joint_3_train': 0.013859687477815897,\n",
       " 'mae_joint_4_val': 0.008743108336246856,\n",
       " 'mae_joint_4_train': 0.012098317412245605,\n",
       " 'mae_joint_5_val': 0.025308088561438995,\n",
       " 'mae_joint_5_train': 0.03447267569394575,\n",
       " 'mae_joint_6_val': 0.014614870265612135,\n",
       " 'mae_joint_6_train': 0.014495025970973074,\n",
       " 'mae_joint_7_val': 0.029496488950984633,\n",
       " 'mae_joint_7_train': 0.03419100446626544}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info_mean = dict()\n",
    "\n",
    "for key in train_info_arr.keys():\n",
    "    train_info_mean[key] = np.mean(train_info_arr[key])\n",
    "\n",
    "train_info_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae_joint_1_val': 0.03754395507536457,\n",
       " 'mae_joint_1_train': 0.01560763831058418,\n",
       " 'mae_joint_2_val': 0.013561544752963431,\n",
       " 'mae_joint_2_train': 0.008202291331875416,\n",
       " 'mae_joint_3_val': 0.0387554047604528,\n",
       " 'mae_joint_3_train': 0.013164492728275195,\n",
       " 'mae_joint_4_val': 0.01380285869595092,\n",
       " 'mae_joint_4_train': 0.011066740775993855,\n",
       " 'mae_joint_5_val': 0.04615559483225237,\n",
       " 'mae_joint_5_train': 0.024556944211123515,\n",
       " 'mae_joint_6_val': 0.03829716383987713,\n",
       " 'mae_joint_6_train': 0.011598280019879476,\n",
       " 'mae_joint_7_val': 0.06223933058506731,\n",
       " 'mae_joint_7_train': 0.024851619075394075}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info_std = dict()\n",
    "\n",
    "for key in train_info_arr.keys():\n",
    "    train_info_std[key] = np.std(train_info_arr[key])\n",
    "\n",
    "train_info_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae_joint_1_val': 0.007366584148257971,\n",
       " 'mae_joint_1_train': 0.002003511181101203,\n",
       " 'mae_joint_2_val': 0.0025194110348820686,\n",
       " 'mae_joint_2_train': 0.0009729603189043701,\n",
       " 'mae_joint_3_val': 0.004206615500152111,\n",
       " 'mae_joint_3_train': 0.0006856404361315072,\n",
       " 'mae_joint_4_val': 0.002946537220850587,\n",
       " 'mae_joint_4_train': 0.0013575031189247966,\n",
       " 'mae_joint_5_val': 0.008953646756708622,\n",
       " 'mae_joint_5_train': 0.0017449511215090752,\n",
       " 'mae_joint_6_val': 0.004534616135060787,\n",
       " 'mae_joint_6_train': 0.0018645523814484477,\n",
       " 'mae_joint_7_val': 0.00881557073444128,\n",
       " 'mae_joint_7_train': 0.003577054711058736}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info_min = dict()\n",
    "\n",
    "for key in train_info_arr.keys():\n",
    "    train_info_min[key] = np.min(train_info_arr[key])\n",
    "\n",
    "train_info_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mae_joint_1_val': 0.26852163672447205,\n",
       " 'mae_joint_1_train': 0.0644911378622055,\n",
       " 'mae_joint_2_val': 0.09743509441614151,\n",
       " 'mae_joint_2_train': 0.03453132510185242,\n",
       " 'mae_joint_3_val': 0.2722736597061157,\n",
       " 'mae_joint_3_train': 0.053722456097602844,\n",
       " 'mae_joint_4_val': 0.0979306772351265,\n",
       " 'mae_joint_4_train': 0.046930622309446335,\n",
       " 'mae_joint_5_val': 0.33261623978614807,\n",
       " 'mae_joint_5_train': 0.09976851940155029,\n",
       " 'mae_joint_6_val': 0.2703743278980255,\n",
       " 'mae_joint_6_train': 0.044287342578172684,\n",
       " 'mae_joint_7_val': 0.44526407122612,\n",
       " 'mae_joint_7_train': 0.1012720838189125}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_info_max = dict()\n",
    "\n",
    "for key in train_info_arr.keys():\n",
    "    train_info_max[key] = np.max(train_info_arr[key])\n",
    "\n",
    "train_info_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "fbc",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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