{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2a2f94a-1a70-43b2-9322-ecd4df6b236e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xdchen/Softwares/miniforge3/envs/py312-E2S/lib/python3.12/site-packages/pyproj/network.py:59: UserWarning: pyproj unable to set PROJ database path.\n",
      "  _set_context_ca_bundle_path(ca_bundle_path)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import PlotFuncsXC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "794292c4-b5a4-4138-ba39-b9d708b4675f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f7928067d10>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seed = 42\n",
    "seed = 100\n",
    "random.seed(seed)\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f07396e5-15f7-42f2-bef6-e70a250048e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def manual_train_test_split(X, y, test_size=0.2, random_state=None):\n",
    "    \"\"\"\n",
    "    A lightweight alternative to sklearn's train_test_split.\n",
    "    \"\"\"\n",
    "    if random_state:\n",
    "        np.random.seed(random_state)\n",
    "    \n",
    "    # Ensure we are working with numpy arrays\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    # Create an array of indices and shuffle them\n",
    "    indices = np.arange(X.shape[0])\n",
    "    np.random.shuffle(indices)\n",
    "    \n",
    "    # Calculate the split point\n",
    "    split_idx = int(len(indices) * (1 - test_size))\n",
    "    \n",
    "    # Slice the indices\n",
    "    train_idx, test_idx = indices[:split_idx], indices[split_idx:]\n",
    "    \n",
    "    # Return the split data\n",
    "    return X[train_idx], X[test_idx], y[train_idx], y[test_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe516d4c-cae0-4cd3-802c-927d263b34ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "rootdir = '/raid1/xdchen/collaborations/Pei/NN_type/'\n",
    "datadir = rootdir + 'data/'\n",
    "plotdir = rootdir + 'plots/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c716c0e4-bc83-4621-9cb4-6a04f1472deb",
   "metadata": {},
   "source": [
    "## 1. load data, shuffle it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cde6cc9c-15bb-4299-b991-1e1ab23dc8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = datadir + 'fulldata_8type.mat'\n",
    "Xdata_full = sio.loadmat(infile)['X_full']\n",
    "ydata_full_raw = sio.loadmat(infile)['y_full'][0]-1\n",
    "\n",
    "ydata_full = np.zeros(ydata_full_raw.shape[0])\n",
    "ydata_full[ydata_full_raw==0] = 0\n",
    "ydata_full[ydata_full_raw==1] = 1\n",
    "ydata_full[ydata_full_raw==2] = 2\n",
    "ydata_full[ydata_full_raw==3] = 4\n",
    "ydata_full[ydata_full_raw==4] = 5\n",
    "ydata_full[ydata_full_raw==5] = 6\n",
    "ydata_full[ydata_full_raw==6] = 7\n",
    "ydata_full[ydata_full_raw==7] = 3\n",
    "#sio.savemat(outfile, {'X_full':X_full, 'y_full':y_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4176c1d5-1f96-45f5-b868-0930ea56d881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1385\n"
     ]
    }
   ],
   "source": [
    "nrec = Xdata_full.shape[0]\n",
    "print(nrec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9d4612f-d831-4d83-948a-af26c09efc73",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xdata_rnd = np.zeros((nrec, 201))\n",
    "ydata_rnd = np.zeros(nrec)\n",
    "\n",
    "nums = random.sample(range(nrec), nrec)\n",
    "Xdata_rnd = Xdata_full[nums]\n",
    "ydata_rnd = ydata_full[nums]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f59843ff-b956-46ee-9ce6-3fc11de33c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_ratio = 0.8\n",
    "Xdata_dev, Xdata_eval, ydata_dev, ydata_eval = manual_train_test_split(Xdata_rnd, ydata_rnd, test_size=1-tt_ratio, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77207021-42e5-4e1f-9999-a4b90ed727dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.tensor(Xdata_dev, dtype=torch.float32)\n",
    "y = torch.tensor(ydata_dev, dtype=torch.long)\n",
    "\n",
    "N = X.size(0)\n",
    "perm = torch.randperm(N)\n",
    "split = int(tt_ratio * N)\n",
    "idx_train, idx_val = perm[:split], perm[split:]\n",
    "X_train, y_train = X[idx_train], y[idx_train]\n",
    "X_val,   y_val   = X[idx_val],   y[idx_val]\n",
    "\n",
    "train_ds = TensorDataset(X_train, y_train)\n",
    "val_ds   = TensorDataset(X_val,   y_val)\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True)\n",
    "val_loader   = DataLoader(val_ds,   batch_size=16, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84627d24-5837-4ddb-8fbd-40ee02856279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "170\n",
      "264\n",
      "91\n",
      "190\n",
      "190\n",
      "190\n",
      "190\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(8):\n",
    "    print((ydata_rnd==i).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8954fc4a-938d-40c2-9a06-ecea9053eec5",
   "metadata": {},
   "source": [
    "## 2. get the NN model up and running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eca0e818-3595-4a2d-b58b-916549ca76e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81d88fe6-05a6-4852-81dc-fbc35d79595b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseNet(nn.Module):\n",
    "    def __init__(self, in_dim=201, num_classes=8):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(in_dim, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64, num_classes)  # raw logits\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "model = DenseNet().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a05dd565-c13f-45a2-ae40-cc76c5fe96c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   0 | Val acc: 0.428\n",
      "Epoch  20 | Val acc: 0.842\n",
      "Epoch  40 | Val acc: 0.914\n",
      "Epoch  60 | Val acc: 0.932\n",
      "Epoch  80 | Val acc: 0.955\n",
      "Epoch 100 | Val acc: 0.986\n",
      "Epoch 120 | Val acc: 0.986\n",
      "Epoch 140 | Val acc: 1.000\n",
      "\n",
      "Validation accuracy: 1.0\n",
      "Confusion matrix (rows=true, cols=pred):\n",
      " [[14  0  0  0  0  0  0  0]\n",
      " [ 0 28  0  0  0  0  0  0]\n",
      " [ 0  0 47  0  0  0  0  0]\n",
      " [ 0  0  0 12  0  0  0  0]\n",
      " [ 0  0  0  0 27  0  0  0]\n",
      " [ 0  0  0  0  0 31  0  0]\n",
      " [ 0  0  0  0  0  0 26  0]\n",
      " [ 0  0  0  0  0  0  0 37]]\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# Loss & optimizer\n",
    "# -----------------------------\n",
    "criterion = nn.CrossEntropyLoss()  # expects (N,C) logits and (N,) long labels\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "epochs = 150\n",
    "\n",
    "# -----------------------------\n",
    "# Training loop\n",
    "# -----------------------------\n",
    "def evaluate(loader):\n",
    "    model.eval()\n",
    "    correct, total = 0, 0\n",
    "    # Confusion matrix 4x4\n",
    "    cm = torch.zeros(8, 8, dtype=torch.int64)\n",
    "    with torch.no_grad():\n",
    "        for xb, yb in loader:\n",
    "            xb, yb = xb.to(device), yb.to(device)\n",
    "            logits = model(xb)\n",
    "            preds = torch.argmax(logits, dim=1)\n",
    "            correct += (preds == yb).sum().item()\n",
    "            total += yb.size(0)\n",
    "            for t, p in zip(yb.view(-1), preds.view(-1)):\n",
    "                cm[t.long(), p.long()] += 1\n",
    "    acc = correct / max(total, 1)\n",
    "    return acc, cm\n",
    "\n",
    "train_progress_matrix = np.zeros((epochs, 2))\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    for xb, yb in train_loader:\n",
    "        xb, yb = xb.to(device), yb.to(device)\n",
    "        logits = model(xb)\n",
    "        loss = criterion(logits, yb)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    \n",
    "    if epoch % 5 == 0 or epoch == 1:\n",
    "        train_acc, _ = evaluate(train_loader)\n",
    "        train_progress_matrix[epoch,0] = train_acc\n",
    "        val_acc, _ = evaluate(val_loader)\n",
    "        train_progress_matrix[epoch,1] = val_acc\n",
    "        if epoch % 20 == 0:\n",
    "            print(f\"Epoch {epoch:3d} | Val acc: {val_acc:.3f}\")\n",
    "\n",
    "# -----------------------------\n",
    "# Final evaluation\n",
    "# -----------------------------\n",
    "val_acc, cm = evaluate(val_loader)\n",
    "print(\"\\nValidation accuracy:\", round(val_acc, 3))\n",
    "print(\"Confusion matrix (rows=true, cols=pred):\\n\", cm.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5e709f6-a56b-4c4c-b691-e17bf1acb3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f77880f31a0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rloelr8BwEUoAJI0Nf23W9x6utnUiZqrk2/88Xvk5o9/Z0xS+mjoTc+jMnsGjr5bvwUhE33xri35bts/evmb6LH1z/kJ2iwVGiFACIOHMm//Pn6jXQ+sb7O1lZ6Rp9XX58g9yWma09BVI6oNB/fPmTXql+ohdsvvNBWfoupLZCXl+wMmGUAIg4W3av/uLaj27ObZN/FXLs3Xj3+X0uRIm0fY1N+kLf31Re5ub7A6yZrrG7PMCID4IJQASxizt/eaPj2j73qBcTtm+HyvOi19fjtZQSLXtQU30p8k5wM67g90Mb+Xrm1TXHtQkf5ruO/cDmpedG7fnCoBQAiBBTCHpN+6pVEV1WJlpDt3+j+N7NCIbrrZwWM8dLrcbxD17uNyuikl3uTSrYxO42R2bwpmVMias9DVF09ujZfu0+s3Nao9GtTA3Tz89+3yN94/drrxAqmCkBMCY2/xuq27/aZXtxjppvNuusJlqikyHKRAOa+ORw3riUJn+XHFIzeGjHdVM3UdLOKy36mrt0V2W29O1YVxnUJmTnaMCr8+GlUg0qu+/947u2f2e/fzLiqbYpmh+1+gX3wKpiFACYEz96cVG/eCRWkUi0mmzfLrjHwts35Ghao9E9PKRw3ZE5JmKQ2oMHW2GNikt3XZTNYdp817a3KQdjQ3a1VivnY0N2tlYb+tCzGO21Fbbo7txHq8NJ53TNsbNs07RV05ZMOJpIAD9I5QAJ3mb9lffbtWfX2+2IeCc+WlassCvgtyx/9MPR6K2O+rv/tJob3/4nHR95R/y5fUM/k0+FInYVS9mRMQ0LTP1HZ0K/X59tKhYl08u1hm5eT2mZWZmZdtDmtJjdMUEExNUTGDZ2VCvXU0N2t/cZOtQOsOIx+HQmoWL7S69AEYXoQQ4Ses1ntzUpD+/1qzaxkjX/WYfGWN2sUdLT0uzx+xi76ivdGkNRPSfP6vWy2/Fvr/pjvr5j2QPqp7D7Lr7evUROyLyVPlBVQePtp0v8Pm0omiKHRFZnFcwpFEMn8tlR1HMcUX35xoK6f2mRjuicqC1WRdNKNJpueOG+BMDGA5H1DQISHINDQ3KyclRfX29srPNv3YA9NbUGtHzW1psGHl339ERhHHZTl16bobS/E47avLe/qDdObdTXrZTSxbEAspZp/iV5otfbxDzv5dDR0K6/f4q7S5rt5vhff2afF20+Pht2E04eHj/Ht23Z4cOt7Ud/Xk8Xn3EBJHJxTo3f7xcTKcAJ837N6EEOIGZvVzMZnEmiGzc2qpAeyxtmOW1S05L04qlGXbKxu06OoJQ0xDWa9ta9eo7rXp9e5taA0cTigkNZ87x28eaoNK7tXpfocO0gzf7yZiW7BXVsaO8OqTD9nq46zmZPWr+feV4zZ/hG/BrmmmVX+3fo3W739ORQCyMZHs8unTiZH10UrHOK5ggj9mqF0DSI5QAKaCyJqSn/9qsp15ptmGg07SJbtvn40PnZCgv2zWoxmVv7W6zIyivvN2q8upwj4/PmOSxAcWMoLS2RWzYMEGjM3yYo6Vt4MFWM5BxaolX37iuwO45M1AY+V3ZPt29611VtMWmeSanpetLc07VxyZPtdMtAE4shBLgJGUCxKa3WvTkK83a/G5b1xRMut+hixal2zAyb7p3UHUa/Y167K8IdQWUbXsCigxyctdMAZlRFXMU5btVaC4LzG2XJoxzy+N2DLiKxvQB+dGud3WoNdbVtcifpi/OnqdPTy2Rl1ERIGVCCYWuQBJqC0ZUWhHSvkPBrh1rTdfTxpajRasLZ/vs9MyyM9PjUgdiwsz0Io89TKv3+qawnd555Z1Wbd8TsMt2TcjoETzspWtYe9SYlTT/c7BUP9y5XaUtzfa+CT6/bp49T1dNLWFkBEhBhBIgwaMfByrbY8GjI3zsLW+3UzJ9laAX5Lp06ZIMfWRJhiZPGH6zscGMmjS7AvJMb1Rhfr3az262vT9iHVHT7bTKcEdkzGqaPx0s1Q92brd7yRj5Xp9unn2KPjdtJo3JgBRGKAEG4f0DQT3+QqPe3h2Qx+WQ3+eQ32sunR2X5nbH9W6307p9ns/jUHV9OBY8DgW1v7xdZZUh2z+kL9kZTpVM8mi6OYo8mjnZq3klXrniuHzXhI+qQEA7Guu1q6OpmFkKa3p3NIaO1qr0Zjaji3VBjXVCNdfnZufYkY7+worpjrq+/IB+sGObdjc1dq2k+adZp+jq6TOV7uZ/R0Cq4/8CQD/C4ajt6/HY87EwMloy/A4bPEomeWPTJx0hZFyWc9ijEX2pDQa0o6FBu5rqtbPhaADp3oCsO7PUtiQj04aO4vQMHWxtsY/Z09Ro27i/UVdjj+7MKpmudu0dlyawbKmp1n/v2GbDj5Hj8eimmXN1bcksZbpHb8QHwImFUAL0UtcY1v++1KQ/vdikI3Wx1Sim1nLZGem234dZBBIIRtXWcZjGYPZ6IKpAMHa9teO2qQ2J3R+7Lzvd2RFAYsHDXJopmXiGj4b29h7t1M0IiAkDZkSkL+Y7T7Pho3uQyLGBpK8VL8FIRPuaY83FbBdU+30a7H3me2+uqbJHX7Lcbt04c66uK5ltAwwAdEcoATrs2B/Q48836bktzWrvmLkwvTUuX5apK5ZlanwCWrMPpCUUsm3RTTDoHkDKO5bT9mVKWnoseGRna3ZmjuZmZ2tmZvaQ6jjMaphYeMmRJhX3WNIb64R6NAiZS1PEaqZ7rp8xWzfOmKMcr3fEPzuAk1Ny/V8WGGPtoag2bm3R48832tUtneZO8+oTF2bpwrPSh7Q3S391GyYomDfo5gHqNI6nNRxrf975Zl/WsWKlLxP9aV0jHnM7LmdlZY3qVIkZVTk1J9cevcOT2+lkaS+A4yKUICWZglOzW62ZpqlpiFWaul2yIcSEkXklA3cdjWfR6EiYvV+6T7nEAki2sj3JMxpBASuAwSKUICWYwGDCh1nxsn5Tk174W4vCHate8nNc+rtlmfro+ZnKyxncNEZNIBALHIMsGnXbotEsjRvB1IVprW6+RufUiwkieb6hhycASFaEEpwUOvdgie27ElZF731YasK2J0h3C2b69IkPZtrmY933hulLaXOTnjh0QC9XHR6waNS0EJuakdnRzyO767IkM4vpCwA4DkIJTihVdSG9ty/YteGbDSAd4aP7xnJ9Me09xo9z6cy5fjtFM7t44FEL0/L8iUNl+t9DZXqrrrbvotHsnstfh1o0CgA4ilCCpN8Fd1dZ0O7FYo5dZe0Dfr6Ziulshd7ZDn2i3YPFrQnjXMcdETnc1qr1hw7YIPK32uoeIyBLCyboI0VTtCBnnB39yKDZFwDEFaEEScf0/fjbe202hLz6TmtXIWqnmVM8mjLBo4l5Lhs4bPAwe7DkueQbxh4sRwJteqr8gP73YJler6lS53iLiS9n5xXo8snFNoyM9/nj9BMCAPpCKEFSMNMvJoCYHWq37mzr6hNimFbti+f5teS0NJ07P0152SOfHjGFqk9XHLTTM69UVap77DlrXL4un1SsyyZNUaE/bcTfCwAwOIQSJEQ4EtW7e4NdQWTPoZ7TMmYKZulpaVqyIE0LZ/tH3CvEaGgP6unyg3Zq5uWqSrsxXKfTc8fFgkhRsSanp4/4ewEAho5QglEPH9V14R6FqWWH27XlvTbVN0V6FKHOn+GzoyEmjEyb6I5L6/XG9nb9+fAhG0RerKxQe7cgcmp2rg0iH500xa6YAQAkFqEEI16KW9vYsRS32zLciupYEKmsCSkU2z7mGBlpDp0zP01LF6Tp7FP9ysmMz6oV00H02cPlNog8V1lu92rpZJqLXTap2IaRGZlZcfl+AID4IJRgyJ7d3KwNf23uCh+BXv0/enM5pQl5piD16KoY0yPEHMdbDTNYbeGwnq+MBRETSFrDR5PQjIwsW6xqRkTsfi0AgKREKMGQ/OX1Zv3Hz44ulTXMLMv4XJcKO5fgdoSPzuW4BTkuueIUProzG8C9eOSwDSJ/rjik5vDR6tip6RkdUzPFmpedE9ddeAEAo4NQgkF7c1ebvvuLakUdUS07z6Ulp6drfI5T+bluedx9vemHFVRYh/pufjpse5oabRAxRauNoaMFspPS0u1oiAkjp+WMI4gAQCqEknvuuUf/9//+X5WXl2v+/Pm68847tWzZsn4//+6779Zdd92lffv2aerUqbrtttt0zTXXjOR5Y4yVVrTrX39SpWAoquZL3tMvvA36xb7E/xoK/X67YsYEkTPG5cnJiAgApE4oeeSRR7Rq1SobTM4//3z95Cc/0YoVK7R9+3YbOHpbt26dVq9erZ/+9Kc6++yz9dprr+mmm27SuHHjdMUVV8Tr58AoqmkI6+t3V6qxJaKJp7bqVW+DbSyWlqB26mYH3A9NnGSDiGluRhABgJODI2qWTwzBueeeq7POOsuGjU7z5s3Txz/+ca1Zs+aYzz/vvPNseDEjK51MqNm8ebNeeumlQX3PhoYG5eTkqL6+XtnZ2UN5uhihtmBEt95ZafebmTTerfQV+7ThyEF9duoM/efCRZxfAEDc3r+H1JM7GAxqy5YtWr58eY/7ze1Nmzb1+ZhAICC/v2d77rS0NDti0t7e3u9jzA/S/UBieoz8xwPVNpBkZzj1lZsy9WzVIfuxq0tm8isBAMTVkEJJVVWVwuGwCgsLe9xvbldUVPT5mEsvvVT33XefDTNmUMaMkDzwwAM2kJiv1xcz4mKSVedRXFw8lKeJOFn3+1q9/FarPG7p2yvH67mmUtsF9Zy8As3LzuU8AwDiaui7l9kloD1XWpiw0d+Sy29+85u25mTJkiXyeDz62Mc+puuuu85+zNVPTYKpQTFDPZ1HWVnZcJ4mRuD3zzboseebYr+P6wo0p8SjX5fusbevLpnFuQUAJDaUFBQU2CDRe1SksrLymNGT7lM1ZmSkpaXFrr4pLS3V9OnTlZWVZb9eX3w+n5176n5g7Gzc2qJ1j9bZ6//4iVxdeFa63UW3KhDQBJ9fl06czK8DAJDYUOL1erVo0SJt2LChx/3mtiloHYgZJZkyZYoNNb/5zW90+eWXy+kc1kANRtH2vQH954PVMuXPf7csU1d+KNaK/aG9u+3lP0yfKQ+/NwBAMiwJvvXWW3X11Vdr8eLFWrp0qe699147+rFy5cquqZeDBw/qoYcesrd37txpi1rNqp3a2lqtXbtW77zzjn7+85/H/6fBiBw80q7b1h1RsD2qJQv8uuUzsQZk2+prtaW2Wh6Hw666AQAgKULJlVdeqerqat1xxx22edqCBQu0fv16TZs2zX7c3GdCSidTGPv9739fO3bssKMlF110kV2pY6ZwkDzqm8JaffcRu3Pv7GKPvvkFM1UXqxN6aN/79vIjRVM0vtdKKgAAEtanJBHoUzK6zMjIV39YqXfeD2hCnkt3f22i8nNiRch1waCWbPiTApGIfnf+RVqc13cdEAAAY9qnBCefSCSq7/y82gaSjDSH1tw8viuQGL8t22sDyanZuVo0Lj+hzxUAcHIjlKS4+/6nTs//rUVul3THP45XySRv18dMT5KHO6Zurp4+kw3uAACjilCSwv64sVG/2dBor3/18/k6c27PepEXKitU2tKsbNNfZvKx+xoBABBPhJIU9eo7rfrhI7X2+vWX52j5uRnHfE7nMuDPFJcozT2sDaUBABg03mlS0Ctvt+rfH6hSJCpduiRDn19xbPHRvuYmvXCkwu4G/Pnp7HMDABh9hJIUUtMQ1l2/rbU1JMaiU/z6yj/k9Vkr8st9sVGSCydM1LSMzDF/rgCA1EMoSQFm1fdTrzTrx4/VqbElIqdD+vQlWbru8hy5O3qRdNcSCum3pfvs9Wums88NAGBsEEpOcgcq27X2VzV6Y2fA3p5V7NFX/yFfc6YeXWXT2x8Olqox1K5p6Rm6YMLEMXy2AIBURig5SYXCUf32zw16aH2DbY7m8zh07eU5+vuLs7o6tfY3qvKLjqmbz0+fJWc/uz8DABBvhJKT0Hv7Avr+r2r0/oH2rtqRL38uT5MKjv/rfr2mSu811MvvdOnvi9kKAAAwdgglJ5HWQEQ/+1O9Hnuu0a6syc5w6uZP5erD52YMuvHZQx2jJB+fMlU53v6neAAAiDdCyUnitW2t+u9f1+hwTdjevuTsdN386XEal3W0ZfzxHG5r1dPlB+31qylwBQCMMULJCa6uMax7fl+rP78eW+ZbmOfSqs/m6dz5aUP+Wr/ev0ehaNRuundqTu4oPFsAAPpHKDlBmYLUDa+12EDS0Bxb5vuJi7L0hctzlOYfeqPeYCSiX+3fY69fyygJACABCCUn6M6+d9xfpY1bW+3tGZPNMt88nTLdN+yv+Uz5QR0JtGmCz6/lRZPj+GwBABgcQskJyEzVmEDicUvXXpajz3w4u88maEPx844C189OmyGvky2RAABjj1Bygmlujejex2Mb6V13ea4+u/zYfWuGant9nTbXVMntcNhQAgBAIvBP4hPML5+sV01DRJMnuPTxC+OzJ03nMuCPFE1RoX/oBbIAAMQDoeQEUnq4XY8+16iwt117l7ylpc/+UXftfFdNoViTtOGoDwb1PwdK7fWr2Q0YAJBAhJITaLXN3b+rVXs0opYP7NbB9ibVt7fr+zve0QV/Xq8f737PbqQ3VL8r26e2SFinZOfo7LyCUXnuAAAMBqHkBLHp7Va9tr1V1aftVZWvQVluj/51/hmanpGp2vag/uvdt3XBX9br/vd3qi0ca6B2PJFoVL/smLoxuwEPtusrAACjgVByAjAb6t3z+zrVzzikxklVcjkcumvREl0/Y7Y2XHipvnfG2ZqanqHqYEDf3v6mPviX9fr53t0KHCecbKys0P6WZmV7PPrY5Klj9vMAANAXQskJwOz2u9tVqdq5B+ztby04QxdMmGivu51Ofap4uv580Ue05vRFmpSWrspAm/7tna266Nkn9at979vGaAMVuJqN99LdLMQCACQWoSTJVdaEdP+mQzqy8H17+9qSWX3uS+NxOnXVtBl67uIV+vfTztJEf5rK21p129t/08XPPqlHSveqvVs42dfcpOcrK+z1z9PBFQCQBAglSW7t/xxW2ek7FHVFdMH4Qv3/py4c8PNN47PPT5+p5y9eYWtOxvv8Otjaoq+/uVkffu4pPVq2T6FIRL/c976iki6cMNHWpQAAkGiOqFnWkeQaGhqUk5Oj+vp6ZWePvFnYieKv7zXpms0vKJjTomn+LP3xwkts/cdQtIZC+uX+9/WT3TtszYlRkpGpqkBAjaF23X/OB3RxYdEo/QQAgFTWMMT3b0ZKklR7KKJbNr9mA0la1KNfnL9syIHESHO7ddPMuXrhksv0/807Tbker/Y2N9lAYopjP9hRmwIAQKJR3Zikbv7LVh3JqZYj4tA9i5aqOD1jRF8vw+3Wylmn6B+mzdSDe3dpffkBfXnufLuSBwCAZEAoSUK/3L1Hfw7usdc/l7FAF04pjNvXzvJ4dMucU+0BAEAyYfomybxeXaVvbf+bvT69copuv3huop8SAABjglCSREqbm3TTX19WxBFVesU4/fcFZ8jlZHoFAJAaCCVJoqG9XTe89pLqw0F56zP0addpOmM2O/YCAFIHoSQJmL4ht2x5RbubGuVq82jq23P1xU/kJ/ppAQAwpgglSeDft72pjUcOyxl2qnDLXF1/UYHGj6MGGQCQWgglCfbQ3t1de9AUvDlTJb4cffqS1GkQBwBAJ0JJApldeu/Y9oa9nr+zWBmH8/Qvnx4nr4fiVgBA6mGOIEHeb2rUF7e8onA0qulNhdL7RTpnvl9LFvgT9ZQAAEgoRkoS5P73d6oxFNJc3zjp5anyuBx2lMRBh1UAQIoilCTI6zVHYr+AdwrliDj16YuzVFw49L1tAAA4WRBKEqAmELDLf43m/enKz3Hp8ytyEvFUAABIGoSSBNhSW20vvU1pcrV79I8fz1W6n18FACC18U6YwKkbX02W5s/w6kPnpCfiaQAAkFQIJQmwuabKXvpqM3XZ+ZkUtwIAQCgZe62hkN6pq7XX/bXZmlPs5YUIAAChZOy9WVej9mjU7nGT1u7VtCJW3AAAQChJgNc7pm78NVmaNcUnt4vurQAAGNSUJKiexF+bxdQNAADdEErGkGkp/7eO5cC+2izNnko9CQAAIwol99xzj0pKSuT3+7Vo0SK9+OKLA37+ww8/rIULFyo9PV1FRUW6/vrrVV0de3NOJe811KkpFJIz5JK3MV1zCCUAAAw/lDzyyCNatWqVbrvtNm3dulXLli3TihUrVFpa2ufnv/TSS7rmmmt0ww03aNu2bfrd736n119/XTfeeKNStZ7EV5Mpr9uh6RS5AgAw/FCydu1aGzBMqJg3b57uvPNOFRcXa926dX1+/quvvqrp06frS1/6kh1d+cAHPqB/+qd/0ubNm5VqXq8+Wk9SMskrj5siVwAAhhVKgsGgtmzZouXLl/e439zetGlTn48577zzdODAAa1fv17RaFSHDx/W73//e330ox/t9/sEAgE1NDT0OE505mc/2jQtS7OLWQoMAMCwQ0lVVZXC4bAKCwt73G9uV1RU9BtKTE3JlVdeKa/Xq4kTJyo3N1c/+tGP+v0+a9asUU5OTtdhRmJOdGUtzaoMtMkRdchXn0k9CQAA8Sh0dTgcx4wC9L6v0/bt2+3Uzb/+67/aUZannnpKe/fu1cqVK/v9+qtXr1Z9fX3XUVZWppOlniStIVPOiJNQAgBAL24NQUFBgVwu1zGjIpWVlceMnnQf9Tj//PP1ta99zd4+/fTTlZGRYQtkv/3tb9vVOL35fD57nEw6Q4mnOlMup2xNCQAAGOZIiZl+MUuAN2zY0ON+c9tM0/SlpaVFTmfPb2OCTecISyo2TSuZ5JHXQ5ErAAAjmr659dZbdd999+mBBx7Qu+++qy9/+ct2OXDndIyZejFLgDtdccUVeuyxx+zqnD179ujll1+20znnnHOOJk2apFRQHQjo/abGbkWujJIAADCi6RvDFKyaxmd33HGHysvLtWDBAruyZtq0afbj5r7uPUuuu+46NTY26q677tJXvvIVW+R68cUX67/+67+UKrZ0jJJkB9PlandTTwIAQB8c0RNgDsUsCTarcEzRa3Z2tk40/7HtTd23Z6cKyguV9cZ03fW1Qp1acnLVzAAAMNL3b/a+GcMiV0dlpkx5zczJ9CgBAKA3QskoawmFtK2+1l731WRp+kSPfF5OOwAAvfHuOMrerKtRKBpVtnxyt3nZGRgAgH4QSsZo6ia3OVsOOVh5AwBAPwglY7QJnw5n2os5U1kODABAXwgloygUiWhrbbW9Hj6UKdOJf+YUilwBAOgLoWQUvdtQr+ZwSOlOtzyNaZpa6Faaj1MOAEBfeIccg9bykyO5sXoSpm4AAOgXoWQMilwz6rPsJfUkAAD0j1AySkyj3NdrjtjrgQMZ9pI9bwAA6B+hZJTsb2lWVSAgj8OplgNp9j5CCQAA/SOUjHI9yQxfjpwRp6ZMcCvdz+kGAKA/vEuOkterY1M3E4I59pJ6EgAABkYoGeUiV09VrGkaUzcAAAyMUDIKqgJt2tvcZK837k23l4yUAAAwMELJKNaTzMrIVlWlw15npAQAgIERSkazyNU9zl5OGu9WZjqnGgCAgfBOOco7AxtzitmEDwCA4yGUxFlzKKRt9XX2erS8o8iV9vIAABwXoSTO3qitVjga1SR/mg7tj51eilwBADg+Qsko1ZOckVOgQ0dC9vqsKZ54fxsAAE46hJJRqieZEs21lxPzXcrJdMX72wAAcNIhlMRReySirbXV9np6XWxnYJYCAwAwOISSOHq3oU4t4bCyPR41HIytuKGeBACAwSGUjEI9yeJxBdpV2m6vE0oAABgcQkkcvV4dCyWnZ+frQGWsyJXpGwAABodQEifRaLSryHVCMNY0bcI4l3KzKHIFAGAwCCVxsq+5SdXBgLxOp3Qkw97HKAkAAINHKImTzlGS03PztPdAbOqGehIAAAaPUBLnItez80yRa9BeZ6QEAIDBI5TEe6QkK0+lhxkpAQBgqAglcXCkrc3WlDjMzsAt2YpGpfwcl/JyKHIFAGCwCCVxnLqZk5WjQwdj980uZr8bAACGglASz3qS/ALtKovVk1DkCgDA0BBK4lhPYopcd3YWuU6NtZkHAACDQygZoaZQu7bV19rrp2XmaX857eUBABgOQskIvVFbo4ikSWnpaq3xKBKVxmU5VUCRKwAAQ0IoiePUTVd/kqleORxmLQ4AABgsQkkcm6Z11pNQ5AoAwNARSkagPRLR1tpqe32xCSUdK2/o5AoAwNARSkZgW32dWsNh5Xg8mubP0r5DFLkCADBchJI4TN0syiuwgSQckbIznJowjk6uAAAMFaEkXpvwdWuaRpErAABDRygZpmg02meRK/UkAAAMD6FkmMrbWlUdDMjtcGhBzjhW3gAAMEKEkmGqbGuzl+N9fjmjTu2lyBUAgBEhlAzTkcDRUGICSSgsZaU7NTGfIlcAAMYslNxzzz0qKSmR3+/XokWL9OKLL/b7udddd50t/Ox9zJ8/XydDKCnw+7uKXGcXeyhyBQBgrELJI488olWrVum2227T1q1btWzZMq1YsUKlpaV9fv4PfvADlZeXdx1lZWXKy8vT3//93+tEVtVtpIQiVwAAEhBK1q5dqxtuuEE33nij5s2bpzvvvFPFxcVat25dn5+fk5OjiRMndh2bN29WbW2trr/+ep0s0zfdlwMDAIAxCCXBYFBbtmzR8uXLe9xvbm/atGlQX+P+++/Xhz70IU2bNq3fzwkEAmpoaOhxJJuqjkLXfI9P7x84uhEfAAAYg1BSVVWlcDiswsLCHveb2xUVFcd9vJm+efLJJ+0oy0DWrFljR1g6DzMSk6wjJdFWj9pDUobfoUkF7kQ/LQAAUqvQtXfHUtNIbDBdTB988EHl5ubq4x//+ICft3r1atXX13cdpg4lWUNJc01stc2sYq+czuOfAwAA0Lch/dO+oKBALpfrmFGRysrKY0ZPejPB5YEHHtDVV18tr3fgaQ6fz2ePZGV+ls5QUl1hgkiUehIAAMZypMSECbMEeMOGDT3uN7fPO++8AR/7wgsvaPfu3bZI9kTXHA7Z3YGN8tLY6AhFrgAAjMyQiyBuvfVWO9qxePFiLV26VPfee69dDrxy5cquqZeDBw/qoYceOqbA9dxzz9WCBQt0ojvSUeSa4XJrf1nEXmfPGwAAxjiUXHnllaqurtYdd9xhC1dNyFi/fn3XahpzX++eJaYu5NFHH7U9S04GnVM3uW6fAu1RpfkcmjKBIlcAAEZiWO+kN998sz36K2btzaygaWlp0cmiM5Skhb0ykzizplDkCgDASLH3zQhCiSMQy3T0JwEAYOQIJSOoKWlv9NjLOcWxSwAAMHyEkhHse9NcEzt9jJQAADByhJKRdHNt9sjjlqZOZKQEAICRIpSMIJS4Ah4V5LjkopMrAAAjRigZhqpAoCuU5OeyFBgAgHgglAxRJBrtqilxBWMjJQAAYOQIJUNUFwwqFI0enb7JJZQAABAPhJJh1pP4Ih45ok7lM1ICAEBcEEqGGUq87bEVN4yUAAAQH4SSEay8MagpAQAgPgglwwwlkRZGSgAAiCdCyTBbzKslthQ4n0JXAADiglAyzJESd9CrzDSH/F5OIQAA8cA76hB19SihcRoAAHFFKBlhi3kAABAfhJJh1pTQOA0AgPgilAxBMBJRbXvQXqfFPAAA8UUoGYLqjqkbR1RyBt2svAEAII4IJcPp5hryyiEHNSUAAMQRoWQIqgKBHt1c6VECAED8EEqGMVKiVrq5AgAQb4SSYay8cbZ55HRIeVksCQYAIF4IJcPsUTIu2yWXyxG3XwQAAKmOUDKcUBL0KJ/GaQAAxBWhZDj73phurmzEBwBAXBFKhrvvDSMlAADEFaFkWC3mvYyUAAAQZ4SSQWoOhdQcDtnrbMYHAED8EUqGOHXjjDjlCDsZKQEAIM4IJcMocjUt5qkpAQAgvgglQ6wncbTRzRUAgNFAKBlG4zSPW8pK59QBABBPvLMOI5QU5LrlcNDNFQCAeCKUDKObawE9SgAAiDtCyZBHSuhRAgDAaCCUDBLdXAEAGF2EkkGq6lh9w743AACMDkLJIESj0R6FrvQoAQAg/gglg1Df3q72aPRooSs7BAMAEHeEkkHoHCVxtrvkiDhZfQMAwCgglAxC96kbI5+REgAA4o5QMoQW82Y5cGaaQ34vpw0AgHjj3XUY3VwBAED8EUqGHEpco/BrAAAAhJJBYDkwAACjj1AypJoSRkoAABgthJJBYDM+AACSNJTcc889Kikpkd/v16JFi/Tiiy8O+PmBQEC33Xabpk2bJp/Pp5kzZ+qBBx7QibjvDTUlAACMjiEvJXnkkUe0atUqG0zOP/98/eQnP9GKFSu0fft2TZ06tc/HfOYzn9Hhw4d1//33a9asWaqsrFQoFNKJoD0SUU0wYK+7A15azAMAkCyhZO3atbrhhht044032tt33nmnnn76aa1bt05r1qw55vOfeuopvfDCC9qzZ4/y8vLsfdOnT9eJwgQS22A+KjmDbkZKAABIhumbYDCoLVu2aPny5T3uN7c3bdrU52P++Mc/avHixfrud7+ryZMna86cOfrqV7+q1tbWAad7GhoaehzJMHXjcjg0LoslwQAAJHykpKqqSuFwWIWFhT3uN7crKir6fIwZIXnppZds/cnjjz9uv8bNN9+smpqafutKzIjL7bffrqRaeRP0aFy2Sy6XI9FPCQCAk9KwCl0djp5vzNFo9Jj7OkUiEfuxhx9+WOecc44uu+wyOwX04IMP9jtasnr1atXX13cdZWVlSorGaTmMkgAAkBQjJQUFBXK5XMeMipjC1d6jJ52KiorstE1OTk7XffPmzbNB5sCBA5o9e/YxjzErdMyRDOjmCgBAEo6UeL1euwR4w4YNPe43t88777w+H2NW6Bw6dEhNTU1d9+3cuVNOp1NTpkxRsqObKwAASTp9c+utt+q+++6z9SDvvvuuvvzlL6u0tFQrV67smnq55ppruj7/c5/7nPLz83X99dfbZcMbN27U1772NX3hC19QWlqaTpxQ4mXlDQAAybQk+Morr1R1dbXuuOMOlZeXa8GCBVq/fr1tjGaY+0xI6ZSZmWlHUm655Ra7CscEFNO35Nvf/rZOtBbz+WzGBwDAqHFETXFHkjNLgk1Niil6zc7OHtPvffGzT2pvc5Mm/nWefvi5mTr71OQf3QEA4ER8/2bvm+OgxTwAAGODUDKA1lBIjR3t8N1235shz3YBAIBBIpQMoKpjzxtH2CGfw63MNBqnAQAwWgglgypy9Wp8rrvfBnEAAGDkCCUDoEcJAABjh1AymFASNPUktJgHAGA0EUoGwL43AACMHULJAFgODADA2CGUDLabKzsEAwAwqgglA2CHYAAAxg6hZJCFroyUAAAwuggl/TBbAvXYIZjpGwAARhWhpB+NoXYFIxF7Pdfllc/LqQIAYDTxTnucIldnu0sTsr2j+ksAAACEkn7RzRUAgLHFSEk/WHkDAMDYIpT0g5ESAADGFqHkuMuBvex7AwDAGCCUDKKbK8uBAQAYfYSSflBTAgDA2CKU9IOaEgAAxhahpB+VrbHpG0+7R+OyXWP5OwEAICURSvoQjkZV2x6w1/O9frmcjrH+vQAAkHIIJX2oCQZkG8xHpcJ0/5j/UgAASEWEkoFazAfdmpDrHuvfCQAAKYlQMkCRq5vdgQEAGDOEkj5UBbr1KMmlyBUAgLFAKOkDy4EBABh7hJKBurkGGSkBAGCsEEr6wEgJAABjj1DSh8OtR2tKxrP6BgCAMUEo6cPhtlZ76Q97lZFG4zQAAMYCoaQPVYFYN9fxPr8cDkIJAABjgVDSSyAcVnOk3V4vTKObKwAAY4VQ0k+RqyIOFWX7xuwXAQBAqiOUDLDypiCHFvMAAIwVQkm/LebpUQIAwFgilPRT5BobKaHFPAAAY4VQ0gv73gAAkBiEkl4qO3qUuIJe5dM4DQCAMUMo6aWipXuhK9M3AACMFUJJL+UtsZGSTHnl9dA4DQCAsUIo6afQtcBL4zQAAMYSoaSbaDSq2lBs+qYwnVACAMBYIpR00xQKqV0Re31yRtqY/iIAAEh1hJI+Gqc5Qk5NzPUm6ncCAEBKIpT02WLey8obAABOhFByzz33qKSkRH6/X4sWLdKLL77Y7+c+//zzcjgcxxzvvfeeknnfm/xclgMDAJDUoeSRRx7RqlWrdNttt2nr1q1atmyZVqxYodLS0gEft2PHDpWXl3cds2fPVrI50nZ035vxNE4DACC5Q8natWt1ww036MYbb9S8efN05513qri4WOvWrRvwcRMmTNDEiRO7DpfLlbzdXM1ICY3TAABI3lASDAa1ZcsWLV++vMf95vamTZsGfOyZZ56poqIiXXLJJXruuecG/NxAIKCGhoYex1g42BQLJe6gR7lZlNsAADCWhvTOW1VVpXA4rMLCwh73m9sVFRV9PsYEkXvvvVePPvqoHnvsMc2dO9cGk40bN/b7fdasWaOcnJyuw4zEjIXy5tj0TbbTJ5eTbq4AAIwl93AeZApVezcd631fJxNCzNFp6dKlKisr0/e+9z1dcMEFfT5m9erVuvXWW7tum5GSsQgmnTsE59PNFQCA5B4pKSgosLUgvUdFKisrjxk9GciSJUu0a9eufj/u8/mUnZ3d4xgLtaFYi/kJaXRzBQAgqUOJ1+u1S4A3bNjQ435z+7zzzhv01zGrdsy0TjKJRKNqiATt9cmZhBIAAJJ++sZMq1x99dVavHixnYox9SJmOfDKlSu7pl4OHjyohx56yN42q3OmT5+u+fPn20LZX/7yl7a+xBzJpDYYVNQRtdeLs2gxDwBA0oeSK6+8UtXV1brjjjtsv5EFCxZo/fr1mjZtmv24ua97zxITRL761a/aoJKWlmbDyRNPPKHLLrtMydg4zRl0q3C8J9FPBwCAlOOImirVJGcKXc0qnPr6+lGrL3nxyGFd8+pGeRrT9OuzPqRFpzCFAwDAWL5/04yjVzdXGqcBAJAYhJIOFS0tXaGkgH1vAAAYc4SSDqWNsW6uvpBXGX4apwEAMNYIJb26ueY4ff02ggMAAKOHUNKhsqOmJM/jG8XTDQAA+kMo6VDTHgsldHMFACAxCCUdOru5FqXTOA0AgEQglEgKhMNqc7TbEzI1h1ACAEAiEEokVQdjG/Ep4lBxDk3TAABIBEKJpKqOFvOmR8mEcbSYBwAgEQglduVNrEcJ3VwBAEgcQomkAx2N01xBj/JzXAn8dQAAkLoIJaaba0MslKSFvfJ6aJwGAEAiEEokHWqKhZJsJ43TAABIFEIJ3VwBAEgKhJJuS4LH+1gODABAohBKJNVHYqGkKINQAgBAohBKJDUrFkqKs+jmCgBAoqR8KGkOhRRyRuzJmJ6bnrBfBAAAqS7lQ8mRjm6ujpBTU/KYvgEAIFFSPpQcbjnazbWAxmkAACRMyoeSvXUt9kS4gx7lZqX86QAAIGFS/l24tL6jm2vEK6eTbq4AACRKyoeSg80d3VwddHMFACCRUj6UVLbGCl1z3YQSAAASKeVDSXUwFkro5goAQGKlfCipC3d0c02ncRoAAImU8qGkSUF7IqbQzRUAgIRK6VASiUbV5oyFkml0cwUAIKFSOpTUtwcVdUbt9Rl5TN8AAJBIKR1KDjTFlgM7g24V5XkT/XQAAEhpKR1K9lQf7eaa4adxGgAAiZTSoWR/fUtXN1eHg1ACAEAipXQo6Zy+yXIwdQMAQKKldCg53NLZzdWf6KcCAEDKS+lQUtXZzdVLi3kAABItpUOJt9UvX22mpqVnJvqpAACQ8typfAZWzZ2vHfsDWjY7PdFPBQCAlJfSoeSDZ6XbAwAAJF5KT98AAIDkQSgBAABJgVACAACSAqEEAAAkBUIJAABICoQSAACQFAglAAAgKRBKAABAUiCUAACAEzeU3HPPPSopKZHf79eiRYv04osvDupxL7/8stxut84444zhfFsAAHASG3IoeeSRR7Rq1Srddttt2rp1q5YtW6YVK1aotLR0wMfV19frmmuu0SWXXDKS5wsAAE5Sjmg0Gh3KA84991ydddZZWrduXdd98+bN08c//nGtWbOm38ddddVVmj17tlwul/7whz/ojTfeGPT3bGhoUE5Ojg022dnZQ3m6AAAgQYb6/j2kkZJgMKgtW7Zo+fLlPe43tzdt2tTv4372s5/p/fff17e+9a1BfZ9AIGB/kO4HAAA4uQ1pl+CqqiqFw2EVFhb2uN/crqio6PMxu3bt0te//nVbd2LqSQbDjLjcfvvtx9xPOAEA4MTR+b492EmZIYWSTg6Ho8dt881632eYAPO5z33OBow5c+YM+uuvXr1at956a9ftgwcP6tRTT1VxcfFwni4AAEigxsZGO40T11BSUFBga0J6j4pUVlYeM3rS+SQ2b95sC2K/+MUv2vsikYgNMWbU5JlnntHFF198zON8Pp89OmVmZqqsrExZWVl9hp+RJDgTdMzXplaF8zbaeL1x3sYKrzXOW7K83sz7vckCkyZNGtTXGlIo8Xq9dgnwhg0b9IlPfKLrfnP7Yx/72DGfb57c22+/fcxy4meffVa///3v7bLiwXA6nZoyZYpGi3mehBLO21jh9cZ547WW3Pgbje95G8wIybCnb8y0ytVXX63Fixdr6dKluvfee+1y4JUrV3ZNvZjploceesiGiQULFvR4/IQJE2x/k973AwCA1DbkUHLllVequrpad9xxh8rLy224WL9+vaZNm2Y/bu47Xs8SAACAuBS63nzzzfboy4MPPjjgY//t3/7NHsnA1K2YZcrd61fAeeP1llz4O+Wc8VpLnb/RITdPAwAAGA1syAcAAJICoQQAACQFQgkAAEgKhBIAAJAUUjqUmEZupoGb6ZtimsKZ/XnQP7NqynTU7X5MnDiRU9bLxo0bdcUVV9gOhuYcmV2xuzO15eZcmo+npaXpwgsv1LZt21L6PB7vnF133XXHvPaWLFmiVGb2CDv77LNtp2vT/8ns1L5jx44en8NrbXjnjdfbsdatW6fTTz+9q0Ga6VP25JNPxv21lrKh5JFHHtGqVat022232Tb4y5Yt04oVK+ixchzz58+3vWg6j94deyE1Nzdr4cKFuuuuu/o8Hd/97ne1du1a+/HXX3/dBrsPf/jDthVzqjreOTM+8pGP9Hjtmf5IqeyFF17Qv/zLv+jVV1+1XbVDoZDdsd2cy0681oZ33gxebz2Zrurf+c537NYx5jBbxJhO7p3BI26vtWiKOuecc6IrV67scd8pp5wS/frXv56w55TsvvWtb0UXLlyY6KdxQjF/Yo8//njX7UgkEp04cWL0O9/5Ttd9bW1t0ZycnOiPf/zjBD3L5D5nxrXXXhv92Mc+lrDndCKorKy05+6FF16wt3mtDe+8GbzeBmfcuHHR++67L66vtZQcKQkGg9qyZYtNx92Z25s2bUrY8zoR7Nq1yw7PmWmvq666Snv27En0Uzqh7N27125o2f21ZxoOffCDH+S1dxzPP/+8HW43O47fdNNNdiNQHFVfX28v8/LyeK2N4Lzxeju+cDis3/zmN3Z0yUzjxPP/aykZSqqqquxJ7b2zsbndewdkHHXuuefaPY2efvpp/fSnP7Xn6rzzzrPbDmBwOl9fvPaGxkytPvzww3Yzz+9///t2eNgMHwcCAV56HfP5Zl+yD3zgA137ivFaG9554/XWPzNdn5mZaQOH2e/u8ccf16mnnhrX19qw2syfLEyxXO8XaO/70PONodNpp51mE/LMmTP185//3P5hg9feaDF7bnUybx5mQ1Cz39YTTzyhT37ykyn/0vviF7+ot956Sy+99NIx54L/zw39vPF669vcuXP1xhtvqK6uTo8++qiuvfZaW6MTz9daSo6UFBQUyOVyHZPgzHBw76SH/mVkZNhwYqZ0MDidq5V47Y1MUVGRDSW89qRbbrlFf/zjH/Xcc8/ZYkReayM7b7ze+uf1ejVr1iz7jwKziskUp//gBz+I6//XnKl6Ys0SYFN53Z25baYjMDhm6Pzdd9+1bxAYHFOLY/6Au7/2TI2T+dcGr73BM1OGZWVlKf3aM/8KNf/Sf+yxx+y0lnltdcdrbXjnrS+83vo/l+Z9IK6vtWiK+s1vfhP1eDzR+++/P7p9+/boqlWrohkZGdF9+/Yl+qklra985SvR559/Prpnz57oq6++Gr388sujWVlZnLNeGhsbo1u3brWH+RNbu3atvb5//377cVOhbqrSH3vssejbb78d/exnPxstKiqKNjQ0RFPVQOfMfMy89jZt2hTdu3dv9LnnnosuXbo0Onny5JQ+Z//8z/9sX0fmb7K8vLzraGlp6focXmtDP2+83vq2evXq6MaNG+3f4FtvvRX9xje+EXU6ndFnnnkmrq+1lA0lxt133x2dNm1a1Ov1Rs8666weS8JwrCuvvNK+yEyYmzRpUvSTn/xkdNu2bZyqXsybpnlj7X2YZYaGWT5nllebJXQ+ny96wQUX2D/iVDbQOTNvFsuXL4+OHz/evvamTp1q7y8tLY2msr7Olzl+9rOfdX0Or7Whnzdeb337whe+0PV+af4WL7nkkq5AEs/XmsP8Z2hjKwAAAPGXkjUlAAAg+RBKAABAUiCUAACApEAoAQAASYFQAgAAkgKhBAAAJAVCCQAASAqEEgAAkBQIJQAAICkQSgAAQFIglAAAgKRAKAEAAEoG/w+wvTcHNpBWrgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_progress_matrix[0:epochs:5,0], color='royalblue')\n",
    "plt.plot(train_progress_matrix[0:epochs:5,1], color='lightseagreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9829213e-7b84-4d35-ba15-f1489ed98de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig01.NN_prediction.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(9,5))\n",
    "[axes1, axes2] = PlotFuncsXC.crt_subplot2grid_matrix([10,2,10], [1,0,1], [10,1,10,1,10,1,10], [1,0,1,0,1,0,1])\n",
    "axes_full = axes1 + axes2\n",
    "\n",
    "xdata = np.linspace(-1, 1, 201)\n",
    "\n",
    "for i in np.arange(8):\n",
    "    if i==2:\n",
    "        plotdata = X_val[y_val==i].numpy().T/2\n",
    "    else:\n",
    "        plotdata = X_val[y_val==i].numpy().T\n",
    "\n",
    "    axis = axes_full[i]\n",
    "    axis.plot(xdata, plotdata[:,5], color='black', linewidth=1, zorder=2)\n",
    "    axis.plot(xdata, plotdata[:,1::], color='lightgrey', linewidth=0.5, zorder=0)\n",
    "    axis.set_title('Type %d' % (i+1), loc='left', fontsize=10)\n",
    "    axis.set_title('n=%02d' % (plotdata.shape[1]), loc='right', fontsize=10)\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1,1])\n",
    "\n",
    "\n",
    "for axis in axes_full[0:4]:\n",
    "    axis.set_xticklabels([])\n",
    "for i in [1,2,5,6]:\n",
    "    axes_full[i].set_yticklabels([])\n",
    "for i in [3,7]:\n",
    "    axes_full[i].yaxis.tick_right()\n",
    "    axes_full[i].yaxis.set_label_position(\"right\")\n",
    "\n",
    "figname = plotdir + 'fig01.NN_prediction.png'\n",
    "print(figname)\n",
    "#fig1.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa4b923e-7d2b-4281-961d-9290376b8d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "numstrs = ['0', 'I', 'VII', 'IV', 'IX', 'V', 'VI', 'VII', 'VIII']\n",
    "typelist = ['0', '0', '0', '0', '0', 'A', 'B', 'C', 'D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "462ca941-1d26-49ca-8354-eb55bf0e555a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig01.NN_prediction.group1.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(9,5))\n",
    "[axes1] = PlotFuncsXC.crt_subplot2grid_matrix([10,2,10], [1,0,0], [10,1,10,1,10,1,10], [1,0,1,0,1,0,1])\n",
    "axes_full = axes1\n",
    "\n",
    "xdata = np.linspace(-1, 1, 201)\n",
    "\n",
    "for i,axis in zip([0,2,1,3], axes_full):\n",
    "    if i==2:\n",
    "        plotdata = X_val[y_val==i].numpy().T/2\n",
    "    else:\n",
    "        plotdata = X_val[y_val==i].numpy().T\n",
    "\n",
    "    axis.plot(xdata, plotdata[:,5], color='black', linewidth=2, zorder=2)\n",
    "    axis.plot(xdata, plotdata[:,1::], color='lightgrey', linewidth=0.5, zorder=0)\n",
    "    axis.text(-0.95, 0.95, 'Type %s' % numstrs[(i+1)], ha='left', va='top', fontsize=10)\n",
    "    axis.text(0.95, -0.95, r'$N_{testing}$=%02d' % (plotdata.shape[1]), ha='right', va='bottom', fontsize=10)\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1.02,1.02])\n",
    "\n",
    "\n",
    "for axis in axes_full[0:4]:\n",
    "    axis.set_xticks([-1, 0, 1])\n",
    "    axis.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
    "    axis.set_yticklabels([-1, -0.5, 0, 0.5, 1], fontsize=10)\n",
    "for i in [1,2]:\n",
    "    axes_full[i].set_yticklabels([])\n",
    "for i in [3]:\n",
    "    axes_full[i].yaxis.tick_right()\n",
    "    axes_full[i].yaxis.set_label_position(\"right\")\n",
    "\n",
    "figname = plotdir + 'fig01.NN_prediction.group1.png'\n",
    "print(figname)\n",
    "#fig1.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5945a945-cf9a-4615-b284-1ca396103b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig02.NN_prediction.group2.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(9,5))\n",
    "[axes1] = PlotFuncsXC.crt_subplot2grid_matrix([10,2,10], [1,0,0], [10,1,10,1,10,1,10], [1,0,1,0,1,0,1])\n",
    "axes_full = axes1\n",
    "\n",
    "xdata = np.linspace(-1, 1, 201)\n",
    "\n",
    "for i in np.arange(4,8):\n",
    "    if i==2:\n",
    "        plotdata = X_val[y_val==i].numpy().T/2\n",
    "    else:\n",
    "        plotdata = X_val[y_val==i].numpy().T\n",
    "\n",
    "    axis = axes_full[i-4]\n",
    "    axis.plot(xdata, plotdata[:,5], color='black', linewidth=2, zorder=2)\n",
    "    axis.plot(xdata, plotdata[:,1::], color='lightgrey', linewidth=0.5, zorder=0)\n",
    "    axis.text(-0.95, 0.95, 'New type %s' % typelist[(i+1)], ha='left', va='top', fontsize=10)\n",
    "    axis.text(0.95, -0.95, r'$N_{testing}$=%02d' % (plotdata.shape[1]), ha='right', va='bottom', fontsize=10)\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1,1])\n",
    "\n",
    "\n",
    "for axis in axes_full[0:4]:\n",
    "    axis.set_xticks([-1, 0, 1])\n",
    "    axis.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
    "    axis.set_yticklabels([-1, -0.5, 0, 0.5, 1], fontsize=10)\n",
    "for i in [1,2]:\n",
    "    axes_full[i].set_yticklabels([])\n",
    "for i in [3]:\n",
    "    axes_full[i].yaxis.tick_right()\n",
    "    axes_full[i].yaxis.set_label_position(\"right\")\n",
    "\n",
    "figname = plotdir + 'fig02.NN_prediction.group2.png'\n",
    "print(figname)\n",
    "#fig2.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c309e65-56e9-4673-abc0-03c6e58e7fa3",
   "metadata": {},
   "source": [
    "## 3. real world sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "80150f33-0c46-48c1-940e-aaad0ee4345a",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = '/raid1/xdchen/collaborations/Pei/NN_type/data/data_blue.mat'\n",
    "data_blue_full = sio.loadmat(infile)['data_blue'][:,0]\n",
    "data_blue = data_blue_full[111:187]\n",
    "\n",
    "infile = '/raid1/xdchen/collaborations/Pei/NN_type/data/data_red.mat'\n",
    "data_red_full = sio.loadmat(infile)['data_red'][:,0]\n",
    "data_red = data_red_full[115:184]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2b07ef1c-06f7-47d2-8263-6483502593ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_blue_rescale = np.zeros((1,201))\n",
    "data_blue_rescale[0] = np.interp(np.linspace(-1, 1, 201), np.linspace(-1, 1, 76), data_blue)\n",
    "data_blue_rescale[0] = data_blue_rescale/np.maximum(data_blue.max(), data_blue.min()*-1)\n",
    "#data_blue_rescale[0] = (data_blue_rescale-data_blue.min())/(data_blue.max()-data_blue.min())*2-1\n",
    "\n",
    "data_red_rescale = np.zeros((1,201))\n",
    "data_red_rescale[0] = np.interp(np.linspace(-1, 1, 201), np.linspace(-1, 1, 69), data_red)\n",
    "data_red_rescale[0] = data_red_rescale/np.maximum(data_red.max(), data_red.min()*-1)\n",
    "#data_red_rescale[0] = (data_red_rescale-data_red.min())/(data_red.max()-data_red.min())*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "85686e6a-de56-4cd8-be41-29e287cff0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    probs = torch.softmax(model(torch.tensor(data_blue_rescale, dtype=torch.float32).to(device)), dim=1)\n",
    "    preds = torch.argmax(probs, dim=1).cpu().numpy()\n",
    "    pred_blue = preds[0]+1\n",
    "    print(pred_blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "773ce7c3-f76a-4728-9f59-164862d7d96f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    probs = torch.softmax(model(torch.tensor(data_red_rescale, dtype=torch.float32).to(device)), dim=1)\n",
    "    preds = torch.argmax(probs, dim=1).cpu().numpy()\n",
    "    pred_red = preds[0]+1\n",
    "    print(pred_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6e62399d-bec4-442f-9c8b-64a401105d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig03.NN_prediction.RealWorldSample.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(7,3.5))\n",
    "[[ax1,ax2]] = PlotFuncsXC.crt_subplot2grid_matrix([10,1], [1,0], [10,1,10], [1,0,1])\n",
    "\n",
    "ax1.scatter(np.linspace(-1, 1, 201), data_red_rescale[0], s=0.5, color='red')\n",
    "ax2.scatter(np.linspace(-1, 1, 201), data_blue_rescale[0], s=0.5, color='blue')\n",
    "\n",
    "# some guess\n",
    "ax1.plot(np.linspace(-1, 1, 201), (Xdata_full[ydata_full==(pred_red-1)].T)[:,50], linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "ax2.plot(np.linspace(-1, 1, 201), (Xdata_full[ydata_full==(pred_blue-1)].T/2)[:,25], linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "for axis in [ax1,ax2]:\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1,1])\n",
    "    axis.set_xticks([-1, -0.5, 0, 0.5, 1])\n",
    "    axis.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
    "    \n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position(\"right\")\n",
    "\n",
    "for axis in [ax1, ax2]:\n",
    "    axis.set_title('Step 2', loc='left', fontsize=12)\n",
    "#ax1.text(-0.95, 0.95, 'Sample #1', ha='left', va='top', fontsize=12)\n",
    "#ax1.text(0.95, -0.95, 'Identified type: %s' % numstrs[pred_red], ha='right', va='bottom', fontsize=12)\n",
    "#ax2.text(-0.95, 0.95, 'Sample #2', ha='left', va='top', fontsize=12)\n",
    "#ax2.text(0.95, -0.95, 'Identified type: %s' % numstrs[pred_blue], ha='right', va='bottom', fontsize=12)\n",
    "\n",
    "figname = plotdir + 'fig03.NN_prediction.RealWorldSample.png'\n",
    "print(figname)\n",
    "#fig3.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde49824-6983-4677-b4aa-21a0c30cd675",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py312-E2S",
   "language": "python",
   "name": "py312-e2s"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
