LSTM Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a33361b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Attempting to make predictions on the same mechanistic model but now using an LSTM and then hybridizing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72de0d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torchvision\n",
    "import torchvision.transforms as tr\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "from scipy.integrate import odeint\n",
    "from scipy.integrate import solve_ivp\n",
    "import random\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.utils.data import Dataset\n",
    "import torch.optim as optim\n",
    "import warnings\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "writer = SummaryWriter()\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5aa41160",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Mechanistic Model\n",
    "def odes(t, m):\n",
    "    # X is biomass concentration\n",
    "    # S is substarte concentration\n",
    "    # V is volume \n",
    "    X, S, V = m\n",
    "    # Declaring the constants\n",
    "    k = 1\n",
    "    \n",
    "    # If time is lesser than 15\n",
    "    if t < 15:\n",
    "        # Flow Rate\n",
    "        F = 1\n",
    "        # Substrate Concentration coming in\n",
    "        s0 = 60\n",
    "      \n",
    "    # If time is greater than 15\n",
    "    else:\n",
    "        F = 0\n",
    "        s0 = 0\n",
    "        \n",
    "\n",
    "    #Declaring mu or growth rate equation\n",
    "    mu = (5*S)/(10+S+(S**2)/0.1)\n",
    "    # Declaring the differential equations\n",
    "    \n",
    "    # Change in biomass concentration with time\n",
    "    dxdt = (mu*X) - X*(F/V)\n",
    "    # Change in substarte concentration with time\n",
    "    dsdt = -k*mu*X + (F/V)*(s0 - S)\n",
    "    # Change in volume with time\n",
    "    dvdt = F\n",
    "    \n",
    "    # Returning the above values to calculate the change at the next iteration or time point\n",
    "    return [dxdt, dsdt, dvdt]\n",
    "\n",
    "# Declaring the initial conditions of the model\n",
    "# (Biomass Conc, Substrate Conc, Volume)\n",
    "m0 = (5, 5, 10)\n",
    "\n",
    "# Declaring a time vector\n",
    "t = np.arange(0.0, 20.0, 0.2)\n",
    "\n",
    "# Solving the differential equation using the odeint method\n",
    "sol = odeint(odes, m0, t, tfirst = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d032d889",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Biomass Concentration</th>\n",
       "      <th>Substrate Concentration</th>\n",
       "      <th>Volume</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>4.987693</td>\n",
       "      <td>5.992699</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.139673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.965100</td>\n",
       "      <td>6.957977</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.123431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>4.935479</td>\n",
       "      <td>7.894710</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>0.110848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>4.900981</td>\n",
       "      <td>8.802722</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.100844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>3.010314</td>\n",
       "      <td>36.989689</td>\n",
       "      <td>25.000002</td>\n",
       "      <td>0.026304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>19.200001</td>\n",
       "      <td>3.018436</td>\n",
       "      <td>36.981564</td>\n",
       "      <td>25.000002</td>\n",
       "      <td>0.026310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>19.400000</td>\n",
       "      <td>3.026582</td>\n",
       "      <td>36.973419</td>\n",
       "      <td>25.000002</td>\n",
       "      <td>0.026315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>19.600000</td>\n",
       "      <td>3.034751</td>\n",
       "      <td>36.965248</td>\n",
       "      <td>25.000002</td>\n",
       "      <td>0.026321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19.799999</td>\n",
       "      <td>3.042945</td>\n",
       "      <td>36.957058</td>\n",
       "      <td>25.000002</td>\n",
       "      <td>0.026327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time  Biomass Concentration  Substrate Concentration     Volume  \\\n",
       "0    0.000000               5.000000                 5.000000  10.000000   \n",
       "1    0.200000               4.987693                 5.992699  10.200000   \n",
       "2    0.400000               4.965100                 6.957977  10.400000   \n",
       "3    0.600000               4.935479                 7.894710  10.600000   \n",
       "4    0.800000               4.900981                 8.802722  10.800000   \n",
       "..        ...                    ...                      ...        ...   \n",
       "95  19.000000               3.010314                36.989689  25.000002   \n",
       "96  19.200001               3.018436                36.981564  25.000002   \n",
       "97  19.400000               3.026582                36.973419  25.000002   \n",
       "98  19.600000               3.034751                36.965248  25.000002   \n",
       "99  19.799999               3.042945                36.957058  25.000002   \n",
       "\n",
       "          mu  \n",
       "0   0.161290  \n",
       "1   0.139673  \n",
       "2   0.123431  \n",
       "3   0.110848  \n",
       "4   0.100844  \n",
       "..       ...  \n",
       "95  0.026304  \n",
       "96  0.026310  \n",
       "97  0.026315  \n",
       "98  0.026321  \n",
       "99  0.026327  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting biomass conc, substrate conc and volume from the generated solution by\n",
    "# transposing the matrix\n",
    "X = sol.T[0]\n",
    "S = sol.T[1]\n",
    "V = sol.T[2]\n",
    "\n",
    "# Computing the growth rate values\n",
    "mu_list = []\n",
    "for s in S:\n",
    "    mu = 0\n",
    "    mu = (1*s)/(1+s+(s**2)/1)\n",
    "    mu_list.append(mu)\n",
    "\n",
    "# Generating the dataset\n",
    "mdf = pd.DataFrame()\n",
    "mdf[\"Time\"] = t\n",
    "mdf[\"Biomass Concentration\"] = X\n",
    "mdf[\"Substrate Concentration\"] = S\n",
    "mdf[\"Volume\"] = V\n",
    "mdf[\"mu\"] = mu_list\n",
    "relu = nn.ReLU()\n",
    "mdf_tensor = relu(torch.tensor(mdf.to_numpy(), dtype = torch.float32))\n",
    "mdf = pd.DataFrame(mdf_tensor.numpy(), columns = [\"Time\", \"Biomass Concentration\", \"Substrate Concentration\",\"Volume\", \"mu\"])\n",
    "mu_list = mdf[\"mu\"]\n",
    "mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0e0980d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df[\"Time\"] = t\n",
    "list_b = []\n",
    "list_s = []\n",
    "for index in range(len(mdf)):\n",
    "    if mdf.iloc[index].at[\"Biomass Concentration\"] > 0:\n",
    "        list_b.append(np.log(mdf.iloc[index].at[\"Biomass Concentration\"]))\n",
    "    else:\n",
    "        list_b.append(0)\n",
    "    \n",
    "    if mdf.iloc[index].at[\"Substrate Concentration\"] > 0:\n",
    "        list_s.append(np.log(mdf.iloc[index].at[\"Substrate Concentration\"]))\n",
    "    else:\n",
    "        list_s.append(0)\n",
    "df[\"Biomass Concentration\"] = list_b\n",
    "df[\"Substrate Concentration\"] = list_s\n",
    "df[\"Volume\"] = V\n",
    "df[\"mu\"] = mu_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40df9afe",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Biomass Concentration</th>\n",
       "      <th>Substrate Concentration</th>\n",
       "      <th>Volume</th>\n",
       "      <th>mu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>1.606973</td>\n",
       "      <td>1.790542</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.139673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>1.602433</td>\n",
       "      <td>1.939889</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.123431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.596450</td>\n",
       "      <td>2.066193</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>0.110848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.589435</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.100844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.102044</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>19.2</td>\n",
       "      <td>1.104739</td>\n",
       "      <td>3.610420</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>19.4</td>\n",
       "      <td>1.107434</td>\n",
       "      <td>3.610199</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>19.6</td>\n",
       "      <td>1.110130</td>\n",
       "      <td>3.609978</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>19.8</td>\n",
       "      <td>1.112826</td>\n",
       "      <td>3.609757</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Time  Biomass Concentration  Substrate Concentration     Volume        mu\n",
       "0    0.0               1.609438                 1.609438  10.000000  0.161290\n",
       "1    0.2               1.606973                 1.790542  10.200000  0.139673\n",
       "2    0.4               1.602433                 1.939889  10.400000  0.123431\n",
       "3    0.6               1.596450                 2.066193  10.600000  0.110848\n",
       "4    0.8               1.589435                 2.175061  10.800000  0.100844\n",
       "..   ...                    ...                      ...        ...       ...\n",
       "95  19.0               1.102044                 3.610639  25.000001  0.026304\n",
       "96  19.2               1.104739                 3.610420  25.000001  0.026310\n",
       "97  19.4               1.107434                 3.610199  25.000001  0.026315\n",
       "98  19.6               1.110130                 3.609978  25.000001  0.026321\n",
       "99  19.8               1.112826                 3.609757  25.000001  0.026327\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c142118",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Biomass Concentration</th>\n",
       "      <th>Substrate Concentration</th>\n",
       "      <th>Volume</th>\n",
       "      <th>mu</th>\n",
       "      <th>S(t-1)</th>\n",
       "      <th>S(t-2)</th>\n",
       "      <th>S(t-3)</th>\n",
       "      <th>S(t-4)</th>\n",
       "      <th>S(t-5)</th>\n",
       "      <th>S(t-6)</th>\n",
       "      <th>X(t-1)</th>\n",
       "      <th>X(t-2)</th>\n",
       "      <th>X(t-3)</th>\n",
       "      <th>X(t-4)</th>\n",
       "      <th>X(t-5)</th>\n",
       "      <th>X(t-6)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>1.573359</td>\n",
       "      <td>2.354639</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>0.085990</td>\n",
       "      <td>2.270307</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>2.066193</td>\n",
       "      <td>1.939889</td>\n",
       "      <td>1.790542</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.581672</td>\n",
       "      <td>1.589435</td>\n",
       "      <td>1.596450</td>\n",
       "      <td>1.602433</td>\n",
       "      <td>1.606973</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.4</th>\n",
       "      <td>1.564640</td>\n",
       "      <td>2.430046</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>0.080338</td>\n",
       "      <td>2.354639</td>\n",
       "      <td>2.270307</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>2.066193</td>\n",
       "      <td>1.939889</td>\n",
       "      <td>1.790542</td>\n",
       "      <td>1.573359</td>\n",
       "      <td>1.581672</td>\n",
       "      <td>1.589435</td>\n",
       "      <td>1.596450</td>\n",
       "      <td>1.602433</td>\n",
       "      <td>1.606973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6</th>\n",
       "      <td>1.555622</td>\n",
       "      <td>2.498030</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>0.075524</td>\n",
       "      <td>2.430046</td>\n",
       "      <td>2.354639</td>\n",
       "      <td>2.270307</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>2.066193</td>\n",
       "      <td>1.939889</td>\n",
       "      <td>1.564640</td>\n",
       "      <td>1.573359</td>\n",
       "      <td>1.581672</td>\n",
       "      <td>1.589435</td>\n",
       "      <td>1.596450</td>\n",
       "      <td>1.602433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.8</th>\n",
       "      <td>1.546387</td>\n",
       "      <td>2.559754</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>0.071378</td>\n",
       "      <td>2.498030</td>\n",
       "      <td>2.430046</td>\n",
       "      <td>2.354639</td>\n",
       "      <td>2.270307</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>2.066193</td>\n",
       "      <td>1.555622</td>\n",
       "      <td>1.564640</td>\n",
       "      <td>1.573359</td>\n",
       "      <td>1.581672</td>\n",
       "      <td>1.589435</td>\n",
       "      <td>1.596450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1.536998</td>\n",
       "      <td>2.616134</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.067770</td>\n",
       "      <td>2.559754</td>\n",
       "      <td>2.498030</td>\n",
       "      <td>2.430046</td>\n",
       "      <td>2.354639</td>\n",
       "      <td>2.270307</td>\n",
       "      <td>2.175061</td>\n",
       "      <td>1.546387</td>\n",
       "      <td>1.555622</td>\n",
       "      <td>1.564640</td>\n",
       "      <td>1.573359</td>\n",
       "      <td>1.581672</td>\n",
       "      <td>1.589435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>1.102044</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026304</td>\n",
       "      <td>3.610858</td>\n",
       "      <td>3.611076</td>\n",
       "      <td>3.611294</td>\n",
       "      <td>3.611511</td>\n",
       "      <td>3.611727</td>\n",
       "      <td>3.611942</td>\n",
       "      <td>1.099350</td>\n",
       "      <td>1.096657</td>\n",
       "      <td>1.093964</td>\n",
       "      <td>1.091272</td>\n",
       "      <td>1.088581</td>\n",
       "      <td>1.085890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.2</th>\n",
       "      <td>1.104739</td>\n",
       "      <td>3.610420</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>3.610858</td>\n",
       "      <td>3.611076</td>\n",
       "      <td>3.611294</td>\n",
       "      <td>3.611511</td>\n",
       "      <td>3.611727</td>\n",
       "      <td>1.102044</td>\n",
       "      <td>1.099350</td>\n",
       "      <td>1.096657</td>\n",
       "      <td>1.093964</td>\n",
       "      <td>1.091272</td>\n",
       "      <td>1.088581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.4</th>\n",
       "      <td>1.107434</td>\n",
       "      <td>3.610199</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026315</td>\n",
       "      <td>3.610420</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>3.610858</td>\n",
       "      <td>3.611076</td>\n",
       "      <td>3.611294</td>\n",
       "      <td>3.611511</td>\n",
       "      <td>1.104739</td>\n",
       "      <td>1.102044</td>\n",
       "      <td>1.099350</td>\n",
       "      <td>1.096657</td>\n",
       "      <td>1.093964</td>\n",
       "      <td>1.091272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.6</th>\n",
       "      <td>1.110130</td>\n",
       "      <td>3.609978</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>3.610199</td>\n",
       "      <td>3.610420</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>3.610858</td>\n",
       "      <td>3.611076</td>\n",
       "      <td>3.611294</td>\n",
       "      <td>1.107434</td>\n",
       "      <td>1.104739</td>\n",
       "      <td>1.102044</td>\n",
       "      <td>1.099350</td>\n",
       "      <td>1.096657</td>\n",
       "      <td>1.093964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.8</th>\n",
       "      <td>1.112826</td>\n",
       "      <td>3.609757</td>\n",
       "      <td>25.000001</td>\n",
       "      <td>0.026327</td>\n",
       "      <td>3.609978</td>\n",
       "      <td>3.610199</td>\n",
       "      <td>3.610420</td>\n",
       "      <td>3.610639</td>\n",
       "      <td>3.610858</td>\n",
       "      <td>3.611076</td>\n",
       "      <td>1.110130</td>\n",
       "      <td>1.107434</td>\n",
       "      <td>1.104739</td>\n",
       "      <td>1.102044</td>\n",
       "      <td>1.099350</td>\n",
       "      <td>1.096657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Biomass Concentration  Substrate Concentration     Volume        mu  \\\n",
       "Time                                                                        \n",
       "1.2                1.573359                 2.354639  11.200000  0.085990   \n",
       "1.4                1.564640                 2.430046  11.400000  0.080338   \n",
       "1.6                1.555622                 2.498030  11.600000  0.075524   \n",
       "1.8                1.546387                 2.559754  11.800000  0.071378   \n",
       "2.0                1.536998                 2.616134  12.000000  0.067770   \n",
       "...                     ...                      ...        ...       ...   \n",
       "19.0               1.102044                 3.610639  25.000001  0.026304   \n",
       "19.2               1.104739                 3.610420  25.000001  0.026310   \n",
       "19.4               1.107434                 3.610199  25.000001  0.026315   \n",
       "19.6               1.110130                 3.609978  25.000001  0.026321   \n",
       "19.8               1.112826                 3.609757  25.000001  0.026327   \n",
       "\n",
       "        S(t-1)    S(t-2)    S(t-3)    S(t-4)    S(t-5)    S(t-6)    X(t-1)  \\\n",
       "Time                                                                         \n",
       "1.2   2.270307  2.175061  2.066193  1.939889  1.790542  1.609438  1.581672   \n",
       "1.4   2.354639  2.270307  2.175061  2.066193  1.939889  1.790542  1.573359   \n",
       "1.6   2.430046  2.354639  2.270307  2.175061  2.066193  1.939889  1.564640   \n",
       "1.8   2.498030  2.430046  2.354639  2.270307  2.175061  2.066193  1.555622   \n",
       "2.0   2.559754  2.498030  2.430046  2.354639  2.270307  2.175061  1.546387   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "19.0  3.610858  3.611076  3.611294  3.611511  3.611727  3.611942  1.099350   \n",
       "19.2  3.610639  3.610858  3.611076  3.611294  3.611511  3.611727  1.102044   \n",
       "19.4  3.610420  3.610639  3.610858  3.611076  3.611294  3.611511  1.104739   \n",
       "19.6  3.610199  3.610420  3.610639  3.610858  3.611076  3.611294  1.107434   \n",
       "19.8  3.609978  3.610199  3.610420  3.610639  3.610858  3.611076  1.110130   \n",
       "\n",
       "        X(t-2)    X(t-3)    X(t-4)    X(t-5)    X(t-6)  \n",
       "Time                                                    \n",
       "1.2   1.589435  1.596450  1.602433  1.606973  1.609438  \n",
       "1.4   1.581672  1.589435  1.596450  1.602433  1.606973  \n",
       "1.6   1.573359  1.581672  1.589435  1.596450  1.602433  \n",
       "1.8   1.564640  1.573359  1.581672  1.589435  1.596450  \n",
       "2.0   1.555622  1.564640  1.573359  1.581672  1.589435  \n",
       "...        ...       ...       ...       ...       ...  \n",
       "19.0  1.096657  1.093964  1.091272  1.088581  1.085890  \n",
       "19.2  1.099350  1.096657  1.093964  1.091272  1.088581  \n",
       "19.4  1.102044  1.099350  1.096657  1.093964  1.091272  \n",
       "19.6  1.104739  1.102044  1.099350  1.096657  1.093964  \n",
       "19.8  1.107434  1.104739  1.102044  1.099350  1.096657  \n",
       "\n",
       "[94 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from copy import deepcopy as dc\n",
    "def prepare_dataframe_for_lstm(df, n_steps):\n",
    "    df = dc(df)\n",
    "    df.set_index('Time', inplace=True)\n",
    "    for i in range(1, n_steps+1):\n",
    "        df[f'S(t-{i})'] = df['Substrate Concentration'].shift(i)\n",
    "    for i in range(1, n_steps+1):\n",
    "        df[f'X(t-{i})'] = df['Biomass Concentration'].shift(i)\n",
    "    df.dropna(inplace=True)\n",
    "    return df\n",
    "\n",
    "lookback = 6\n",
    "df = prepare_dataframe_for_lstm(df, lookback)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbb38d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets start implementing the neural network model using pytorch\n",
    "\n",
    "# Defining the hyperparameters\n",
    "input_size = 2\n",
    "hidden_layer_size = 50\n",
    "output_size = 1\n",
    "learning_rate = 000.1\n",
    "batch_size = 8\n",
    "num_epochs = 300\n",
    "\n",
    "# Define early stopping criteria\n",
    "patience = 20  # Number of epochs without improvement before stopping\n",
    "min_delta = 0.000000000025  # Minimum improvement in the validation loss\n",
    "\n",
    "# Initialize variables for early stopping\n",
    "best_val_loss = float('inf')\n",
    "epochs_without_improvement = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11ac6f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df)\n",
    "new_train, validation_set = train_test_split(train, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d7e55bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PandasDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        # This converts the dataframe passed into a numpy array and then into a pytorch tensor\n",
    "        self.dataframe = torch.tensor(dataframe.values)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataframe)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.dataframe[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25536554",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_wrapped = PandasDataset(new_train)\n",
    "validation_wrapped = PandasDataset(validation_set)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_wrapped.dataframe, batch_size=batch_size, shuffle=True)\n",
    "validation_loader = torch.utils.data.DataLoader(dataset=validation_wrapped.dataframe, batch_size=7, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c5d6bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS NEEDS TO BE CONVERTED TO AN LSTM\n",
    "\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size, num_layers):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first = True)\n",
    "        self.output_layer = nn.Linear(hidden_size, output_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        hidden_states = torch.zeros(self.num_layers, x.shape[0], self.hidden_size)\n",
    "        cell_states = torch.zeros(self.num_layers, x.shape[0], self.hidden_size)\n",
    "        out, _ = self.lstm(x, (hidden_states, cell_states))\n",
    "        out = self.output_layer(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd889388",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNet(2, 30, 1, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98659b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)\n",
    "#scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience = 20, factor = 0.65, eps = 1e-8, verbose = True,\n",
    "                                                #threshold = 1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0e213975",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Validation Loss: 0.00073569\n",
      "epoch 0 = loss: 1.8703439235687256\n",
      "Epoch: 1, Validation Loss: 0.00005392\n",
      "Epoch: 2, Validation Loss: 0.00027966\n",
      "Epoch: 3, Validation Loss: 0.00015243\n",
      "Epoch: 4, Validation Loss: 0.00005446\n",
      "Epoch: 5, Validation Loss: 0.00006568\n",
      "Epoch: 6, Validation Loss: 0.00003061\n",
      "Epoch: 7, Validation Loss: 0.00000526\n",
      "Epoch: 8, Validation Loss: 0.00000470\n",
      "Epoch: 9, Validation Loss: 0.00001011\n",
      "Epoch: 10, Validation Loss: 0.00000766\n",
      "epoch 10 = loss: 2.800462425511796e-05\n",
      "Epoch: 11, Validation Loss: 0.00000529\n",
      "Epoch: 12, Validation Loss: 0.00000118\n",
      "Epoch: 13, Validation Loss: 0.00000819\n",
      "Epoch: 14, Validation Loss: 0.00001436\n",
      "Epoch: 15, Validation Loss: 0.00000792\n",
      "Epoch: 16, Validation Loss: 0.00000492\n",
      "Epoch: 17, Validation Loss: 0.00000211\n",
      "Epoch: 18, Validation Loss: 0.00000803\n",
      "Epoch: 19, Validation Loss: 0.00000208\n",
      "Epoch: 20, Validation Loss: 0.00000531\n",
      "epoch 20 = loss: 2.9107990485499613e-05\n",
      "Epoch: 21, Validation Loss: 0.00003019\n",
      "Epoch: 22, Validation Loss: 0.00001828\n",
      "Epoch: 23, Validation Loss: 0.00003418\n",
      "Epoch: 24, Validation Loss: 0.00001603\n",
      "Epoch: 25, Validation Loss: 0.00000312\n",
      "Epoch: 26, Validation Loss: 0.00000322\n",
      "Epoch: 27, Validation Loss: 0.00000289\n",
      "Epoch: 28, Validation Loss: 0.00000538\n",
      "Epoch: 29, Validation Loss: 0.00000617\n",
      "Epoch: 30, Validation Loss: 0.00001589\n",
      "epoch 30 = loss: 7.404036296065897e-05\n",
      "Epoch: 31, Validation Loss: 0.00002218\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the training loop\n",
    "loss_curve = []\n",
    "val_curve = []\n",
    "best_loss = 1000000.0\n",
    "best_weights = None\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    losses = []\n",
    "    for i, (data) in enumerate(train_loader):\n",
    "        \n",
    "        # Prepocessing the data\n",
    "        input_data = data[:, 4:].to(torch.float32)\n",
    "        input_data = input_data.reshape(8, 6, 2)\n",
    "        target_output = (data[:, 3].to(torch.float32)).view(8, 1)\n",
    "        \n",
    "        # Forward pass\n",
    "        predictions = model(input_data)\n",
    "        l = loss(predictions, target_output)\n",
    "        losses.append(l)\n",
    "        \n",
    "        # Bacward Pass\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    model.eval()\n",
    "    # Calculate and log the training loss\n",
    "    mean_loss = sum(losses)/len(losses)\n",
    "    loss_curve.append(mean_loss)\n",
    "    writer.add_scalar('Loss/Train', mean_loss, epoch)\n",
    "    #scheduler.step(mean_loss)\n",
    "    \n",
    "    if mean_loss.item() < best_loss:\n",
    "        best_loss = mean_loss.item()\n",
    "        best_weights = model.state_dict()\n",
    "\n",
    "    val_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for i, (data) in enumerate(validation_loader):\n",
    "            inputs = data[:, 4:].to(torch.float32)\n",
    "            inputs = inputs.reshape(7, 6, 2)\n",
    "            target = (data[:, 3].to(torch.float32)).view(7, 1)\n",
    "            outputs = model(inputs)\n",
    "            lo = loss(outputs, target)\n",
    "            val_loss += lo.item()\n",
    "    val_loss /= len(validation_set)\n",
    "    \n",
    "    val_curve.append(val_loss)\n",
    "    writer.add_scalar('Loss/Validation', val_loss, epoch)\n",
    "    # Check for improvement\n",
    "    if val_loss < best_val_loss - min_delta:\n",
    "        best_val_loss = val_loss\n",
    "        epochs_without_improvement = 0\n",
    "    else:\n",
    "        epochs_without_improvement += 1\n",
    "    \n",
    "    # Early stopping condition\n",
    "    if epochs_without_improvement >= patience:\n",
    "        break\n",
    "\n",
    "    print(\"Epoch: {}, Validation Loss: {:.8f}\".format(epoch, val_loss))\n",
    " \n",
    "    with torch.no_grad():\n",
    "        if epoch % 10 == 0:\n",
    "            print(f'epoch {epoch} = loss: {mean_loss}')\n",
    "\n",
    "# Restore the model to the best weights\n",
    "model.load_state_dict(best_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87e57c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10))\n",
    "    ax1.plot(loss_curve)\n",
    "    ax1.set_title(\"Training Loss Curve of Model\")\n",
    "    ax1.set_xlabel(\"Epoch Number\")\n",
    "    ax1.set_ylabel(\"Training Loss\")\n",
    "    ax2.plot(val_curve)\n",
    "    ax2.set_title(\"Validation Loss Curve of Model \")\n",
    "    ax2.set_xlabel(\"Epoch Number\")\n",
    "    ax2.set_ylabel(\"Validation Loss\")\n",
    "    ax1.set_xscale('log')\n",
    "    ax1.set_yscale('log')\n",
    "    ax2.set_xscale('log')\n",
    "    ax2.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87e2d5ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_input_features = df[['S(t-1)', 'S(t-2)', 'S(t-3)', 'S(t-4)', 'S(t-5)','S(t-6)', \n",
    "                        'X(t-1)', 'X(t-2)', 'X(t-3)', 'X(t-4)', 'X(t-5)','X(t-6)']].values\n",
    "df_input_tensor = torch.tensor(df_input_features, dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    df_predictions_list = []\n",
    "    for row in df_input_tensor:\n",
    "        row = row.reshape(1, 6, 2)\n",
    "        df_predictions = model(row)\n",
    "        df_predictions_list.append(df_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8cb1c7cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([[0.0871]]),\n",
       " tensor([[0.0835]]),\n",
       " tensor([[0.0805]]),\n",
       " tensor([[0.0778]]),\n",
       " tensor([[0.0755]]),\n",
       " tensor([[0.0734]]),\n",
       " tensor([[0.0714]]),\n",
       " tensor([[0.0697]]),\n",
       " tensor([[0.0681]]),\n",
       " tensor([[0.0666]]),\n",
       " tensor([[0.0653]]),\n",
       " tensor([[0.0640]]),\n",
       " tensor([[0.0628]]),\n",
       " tensor([[0.0617]]),\n",
       " tensor([[0.0607]]),\n",
       " tensor([[0.0597]]),\n",
       " tensor([[0.0588]]),\n",
       " tensor([[0.0579]]),\n",
       " tensor([[0.0571]]),\n",
       " tensor([[0.0563]]),\n",
       " tensor([[0.0555]]),\n",
       " tensor([[0.0548]]),\n",
       " tensor([[0.0541]]),\n",
       " tensor([[0.0534]]),\n",
       " tensor([[0.0528]]),\n",
       " tensor([[0.0522]]),\n",
       " tensor([[0.0516]]),\n",
       " tensor([[0.0510]]),\n",
       " tensor([[0.0505]]),\n",
       " tensor([[0.0500]]),\n",
       " tensor([[0.0495]]),\n",
       " tensor([[0.0490]]),\n",
       " tensor([[0.0485]]),\n",
       " tensor([[0.0480]]),\n",
       " tensor([[0.0476]]),\n",
       " tensor([[0.0471]]),\n",
       " tensor([[0.0467]]),\n",
       " tensor([[0.0463]]),\n",
       " tensor([[0.0459]]),\n",
       " tensor([[0.0455]]),\n",
       " tensor([[0.0452]]),\n",
       " tensor([[0.0448]]),\n",
       " tensor([[0.0444]]),\n",
       " tensor([[0.0441]]),\n",
       " tensor([[0.0438]]),\n",
       " tensor([[0.0434]]),\n",
       " tensor([[0.0431]]),\n",
       " tensor([[0.0428]]),\n",
       " tensor([[0.0425]]),\n",
       " tensor([[0.0422]]),\n",
       " tensor([[0.0419]]),\n",
       " tensor([[0.0416]]),\n",
       " tensor([[0.0413]]),\n",
       " tensor([[0.0410]]),\n",
       " tensor([[0.0408]]),\n",
       " tensor([[0.0405]]),\n",
       " tensor([[0.0403]]),\n",
       " tensor([[0.0400]]),\n",
       " tensor([[0.0398]]),\n",
       " tensor([[0.0395]]),\n",
       " tensor([[0.0393]]),\n",
       " tensor([[0.0390]]),\n",
       " tensor([[0.0388]]),\n",
       " tensor([[0.0386]]),\n",
       " tensor([[0.0384]]),\n",
       " tensor([[0.0381]]),\n",
       " tensor([[0.0379]]),\n",
       " tensor([[0.0377]]),\n",
       " tensor([[0.0375]]),\n",
       " tensor([[0.0373]]),\n",
       " tensor([[0.0371]]),\n",
       " tensor([[0.0369]]),\n",
       " tensor([[0.0367]]),\n",
       " tensor([[0.0367]]),\n",
       " tensor([[0.0369]]),\n",
       " tensor([[0.0371]]),\n",
       " tensor([[0.0371]]),\n",
       " tensor([[0.0372]]),\n",
       " tensor([[0.0372]]),\n",
       " tensor([[0.0372]]),\n",
       " tensor([[0.0373]]),\n",
       " tensor([[0.0373]]),\n",
       " tensor([[0.0374]]),\n",
       " tensor([[0.0374]]),\n",
       " tensor([[0.0375]]),\n",
       " tensor([[0.0375]]),\n",
       " tensor([[0.0375]]),\n",
       " tensor([[0.0376]]),\n",
       " tensor([[0.0376]]),\n",
       " tensor([[0.0377]]),\n",
       " tensor([[0.0377]]),\n",
       " tensor([[0.0378]]),\n",
       " tensor([[0.0378]]),\n",
       " tensor([[0.0378]])]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e9bf9d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Growth Rate (mu)')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Br5+FbBK4dhB0/38Gsctk/YiIbISjg8ioW92J9MUcIFMIHQU8tQXwrDP16xkoaw8dZZ5xERER2SnOAJlK6Cigy+Oy3V63/wLc/WTLXpz5ISIiMjkGQKbk4MhEZyIiIgvAJTAiIiKyO5wBMrMaqcCdEkRERCbGAMiMWCuDiIjIPLgEZia7cyWYmXJc5eTkotJyzEw5jt25knpeSURERPpiAGQGNVIBi3flqS2LKG9bvCsPNVIW6SYisgUikQg7d+409zAMIiMjAyKRCH///be5h6IXBkBmcKTghsrMT20CAElpOY4U3DDdoIiIbEBWVhYcHR0RGxur9WvbtWuHlStXGn5QZJEYAJnBtVv1Bz+69CMisjjSGtlZiKe+lf1XWmOSb7thwwbMmjULhw4dQmFhoUm+J1knBkBm0NJDswMGNe1HRGRR8lKBlWGyg6C3T5f9d2WYrN2I7ty5g6+//hozZ87EiBEjsGnTJpU+qampiIyMhFgsho+PD8aOHQsAGDx4MC5duoT4+HiIRCKIRLLduIsWLUKPHj2UrrFy5Uq0a9dO8fi3337D0KFD4ePjAy8vLwwaNAjHjx/XauyDBw/GrFmzMGfOHDRv3hx+fn747LPPcOfOHUybNg0eHh7o0KEDfvzxR8VrNm3ahGbNmildZ+fOnYqxqxMVFYV58+YptV2/fh3Ozs7Yv38/ACAlJQWRkZHw8PCAv78/JkyYgGvXrtV7TU3+jgBg48aNCAkJgVgsRpcuXZCUlKR4rrKyEi+//DICAgIgFovRrl07JCYm1vs9DYEBkBn0Cm6BAC8x6vsRFUG2G6xXcAtTDouISH95qbKzD8uuKreXSWTtRgyCtm3bhs6dO6Nz586YNGkSNm7cCEF4kEv5ww8/YOzYsXj88ceRk5ODvXv3IjIyEgCwY8cOtG7dGkuWLIFEIoFEovlGlFu3bmHKlCk4ePAgfvnlF3Tq1AnDhw/HrVu3tBr/5s2b4ePjgyNHjmDWrFmYOXMmnnzySfTt2xfHjx9HTEwMJk+ejLt372p13domTpyIrVu3Kv29bNu2DX5+fhg0aBAAWTCydOlSnDx5Ejt37kRBQQGmTp2q8/cEgPXr1+PNN9/Eu+++i/z8fLz33nt4++23sXnzZgDAqlWrkJqaiq+//hpnzpxBSkqKSgBlaNwGbwaODiIsHBmKmSnH6zseFQtHhrIeEBFZF2kNsHsuUO8WDxGwe57sWCAjHAOUnJyMSZMmAQBiY2Nx+/Zt7N27F4899hgA4N1338XTTz+NxYsXK17TvXt3AECLFi3g6OiomPXQxpAhQ5Qer1u3Ds2bN0dmZiZGjBih8XW6d++Ot956CwAwf/58LFu2DD4+PpgxYwYA4J133sGaNWvw+++/o0+fPlqNUW78+PGIj4/HoUOHMGCA7GSC//znP5gwYQIcHGRzIs8995yif/v27bFq1Sr06tULt2/fhru7u07fd+nSpfjwww8VM27BwcHIy8vDunXrMGXKFBQWFqJTp07o378/RCIRgoKCdPo+2uAMkJnEhgVgzaRw+HspL3P5e4mxZlI46wARkfW5lKU686NEAMquyPoZ2JkzZ3DkyBE8/fTTAAAnJyeMHz8eGzZsUPQ5ceIE/vGPfxj8e1+7dg1xcXF46KGH4OXlBS8vL9y+fVvrHKRu3bop/uzo6Ahvb2907dpV0ebn56f4frry9fXF0KFD8eWXXwIACgoKkJ2djYkTJyr65OTkYPTo0QgKCoKHhwcGDx4MADrnVF2/fh2XL1/G9OnT4e7urvj697//jfPnzwMApk6dihMnTqBz586YPXs29uzZo/M9aoozQGYUGxaAoaH+rARNRLbh9l+G7aeF5ORkVFdXo1WrVoo2QRDg7OyMmzdvonnz5mjSpInW13VwcFBaLgKAqqoqpcdTp07F9evXsXLlSgQFBcHV1RVRUVGorKzU6ns5OzsrPRaJREpt8tweqVSq8djUmThxIl555RV88skn+M9//oOHH35YMRN2584dREdHIzo6GikpKfD19UVhYSFiYmLqvZ/GxiEf7/r169G7d2+lfo6OspnA8PBwFBQU4Mcff8TPP/+Mp556Co899hi+/fbbRu9HVwyAzMzRQYSoDt7mHgYRkf7c/QzbT0PV1dXYsmULPvzwQ0RHRys9N27cOHz55Zd4+eWX0a1bN+zduxfTpk1Tex0XFxfU1CjvVvP19UVRUREEQVAEICdOnFDqc/DgQSQlJWH48OEAgMuXL6O4uNhAd1c/X19f3Lp1C3fu3IGbm5vasakzZswYvPDCC9i9ezf+85//YPLkyYrn/vjjDxQXF2PZsmVo06YNAODo0aONjqOhvyM/Pz+0atUKFy5cUJppqsvT0xPjx4/H+PHj8c9//hOxsbG4ceMGWrQwTj4sAyAiIjKMoL6AZ6As4VltHpBI9nxQX4N+2++//x43b97E9OnT4eXlpfTcP//5TyQnJ+Pll1/GwoUL8Y9//AMdOnTA008/jerqavz444944403AMjqAB04cABPP/00XF1d4ePjg8GDB+P69ev4v//7P/zzn//E7t278eOPP8LT01PxPTp27IgvvvgCkZGRKCsrw+uvv67TbJO2evfujaZNm2LBggWYNWsWjhw5onbnW11ubm4YPXo03n77beTn52PChAmK59q2bQsXFxd88skniIuLQ25uLpYuXdrg9TT5O1q0aBFmz54NT09PDBs2DBUVFTh69Chu3ryJhIQEfPTRRwgICECPHj3g4OCAb775Bv7+/iq73AyJOUBERGQYDo5A7PL7D+ou5d9/HLvM4AnQycnJeOyxx1SCH0A2A3TixAkcP34cgwcPxjfffIPU1FT06NEDQ4YMwa+//qrou2TJEly8eBEdOnSAr68vACAkJARJSUn49NNP0b17dxw5cgSvvfaa0vfYsGEDbt68iZ49e2Ly5MmYPXs2WrZsadB7VKdFixZISUlBWloaunbtiq1bt2LRokUavXbixIk4efIkBgwYgLZt2yrafX19sWnTJnzzzTcIDQ3FsmXL8MEHHzR4LU3+jp5//nl8/vnn2LRpE7p27YpBgwZh06ZNCA4OBgC4u7tj+fLliIyMxCOPPIKLFy8iLS1NkZhtDCKh7sIdoaysDF5eXigtLVWKYImIbF15eTkKCgoQHBwMsVjHWmR5qbLdYLUToj1byYKf0FGGGSjZrYZ+RrX5/OYSGBERGVboKNlW90tZsoRndz/ZspcRtr4T6YoBEBERGZ6DIxA8wNyjIKoXAyALVCMVuDWeiIjIiBgAmZu0RmmaePftYCz+/ozSafEBXmIsHBnK4ohEREQGwgDInNQkCnYTWqBb1bOQoJeirai0HDNTjrNCNBGZDPfHkKUy1M8mt8GbSz0HBvrjBtY4r0SMwxFFm/ytXrwrDzVS/k+JiIxHXplX2yrGRKYi/9mU/6zqyuwzQElJSXj//fchkUjw8MMPY+XKlYoD2tTJzMxEQkICTp8+jcDAQLzxxhuIi4tT6rNy5UqsWbMGhYWF8PHxwT//+U8kJibqvqXT0Bo4MNBBBEgFYKHzF0iviIT0fowqAJCUluNIwQ1WjiYio3FyckLTpk1x/fp1ODs7G7UOC5G2pFIprl+/jqZNm8LJSb8QxqwB0LZt2zBnzhwkJSWhX79+WLduHYYNG4a8vDylwkxyBQUFGD58OGbMmIGUlBQcPnwYL774Inx9fTFu3DgAwJdffol58+Zhw4YN6Nu3L/78809MnToVAPDRRx+Z8vbq18iBgQ4iIBAl6OXwB36Rhio9d+1WeT2vIiLSn0gkQkBAAAoKCnDp0iVzD4dIhYODA9q2bas4dkNXZg2AVqxYgenTp+P5558HIJu5+emnn7BmzRokJiaq9F+7di3atm2LlStXApBVnzx69Cg++OADRQCUnZ2Nfv36KUp7t2vXDs888wyOHDmicj2z0fAgwJb4W7XNw0JmsYjIZrm4uKBTp05cBiOL5OLiYpCZSbMFQJWVlTh27BjmzZun1B4dHY2srCy1r8nOzlY56C4mJgbJycmoqqqCs7Mz+vfvj5SUFBw5cgS9evXChQsXkJaWhilTptQ7loqKClRUVCgel5WV6XFnGtDwIMBraKb4swiAv5dsSzwRkbE5ODhYTtoAkRGYLQAqLi5GTU0N/PyUgwE/Pz8UFRWpfU1RUZHa/tXV1SguLkZAQACefvppXL9+Hf3794cgCKiursbMmTNVAq3aEhMTsXjxYv1vSlONHBgoFYAieOOItAuAByfqLBwZynpAREREBmD27La6a3iCIDS4rqeuf+32jIwMvPvuu0hKSsLx48exY8cOfP/99w2eZjt//nyUlpYqvi5fvqzr7WimgQMDBYggEgGLqyYrEqD9vcTcAk9ERGRAZpsB8vHxgaOjo8psz7Vr11RmeeT8/f3V9ndycoK3t2xn1Ntvv43Jkycr8oq6du2KO3fu4F//+hfefPNNteuGrq6ucHV1NcRtaS50FPDUFpU6QCLPQNTEJGKquD+GsxI0ERGRUZgtAHJxcUFERATS09PxxBNPKNrT09MxevRota+JiorCrl27lNr27NmDyMhIODs7AwDu3r2rEuQ4OjpCEATLK+xVz4GBjg6OiDL32IiIiGyYWXeBJSQkYPLkyYiMjERUVBQ+++wzFBYWKur6zJ8/H1euXMGWLVsAAHFxcVi9ejUSEhIwY8YMZGdnIzk5GVu3blVcc+TIkVixYgV69uyJ3r1749y5c3j77bcxatQovYsmGQUPDCQiIjI5swZA48ePR0lJCZYsWQKJRIKwsDCkpaUhKCgIACCRSFBYWKjoHxwcjLS0NMTHx+PTTz9FYGAgVq1apdgCDwBvvfUWRCIR3nrrLVy5cgW+vr4YOXIk3n33XZPfHxEREVkmkWBx60LmV1ZWBi8vL5SWlsLT09PcwyEiIiINaPP5bfajMKhxNVIBRwpu4BqToomIiAyCAZCF250rweJdeZCUPjgCI8BLjIUjQ7ktnoiISEdmrwNE9dudK8HMlONKwQ8AFJWWY2bKcezOlZhpZERERNaNAZCFqpEKWLwrT02d6Ae1oxfvykONlClcRERE2mIAZKGOFNxQmfmpTQAgKS3HkYIbphsUERGRjWAAZKGu3ao/+NGlHxERET3AAMhCtfTQ7BRmTfsRERHRAwyALFSv4BYI8BKjvs3uIsh2g/UKbmHKYREREdkEBkAWytFBhIUjQwHUPS/+weOFI0NZD4iIiEgHDIAsWGxYANZMCoe/l/Iyl7+XGGsmhbMOEBERkY5YCNHCxYYFYGioPytBExERGRADICvg6CBCVAdvcw+DiIjIZnAJjIiIiOwOZ4AskbQGuJQF3P4LcPcDgvoCDo5KXXhAKhERke4YAFmavFRg91yg7OqDNs9AIHY5EDoKAA9IJSIi0heXwCxJXirw9bPKwQ8AlElk7XmpPCCViIjIABgAWQppjWzmp4HjT4Xd87A09RQPSCUiItITAyBLcSlLdeZHiQBR2RW0uX2ygR48IJWIiEgTDIAsxe2/NOrWEn832ocHpBIRETWMAZClcPfTqNs1NGu0Dw9IJSIiahgDIEsR1Fe226uB408Fz1a47N6dB6QSERHpiQGQpXBwlG11B1Df8aei2GV4e1TXBnrwgFQiIiJNMACyJKGjgKe2AJ51avl4BsraQ0fxgFQiIiIDEAmCwD3TdZSVlcHLywulpaXw9PQ0/QBYCZqIiEhr2nx+sxK0JXJwBIIHNNiFB6QSERHpjgGQDeGsEBERkWYYANkIng9GRESkOSZB2wCeD0ZERKQdBkBWrkYqYPGuPJ4PRkREpAUGQFbuSMENlZmf2ng+GBERkSoGQFZO03O/eD4YERHRAwyArJym537xfDAiIqIHGABZuV7BLRDgJeb5YERERFpgAGTlHB1EWDgyFADPByMiItIUAyAbwPPBiIiItMNCiNZAg7PBYsMCMDTUX6USNABkny9hdWgiIqJaGABZurxUYPdcoOzqgzbPQCB2uez0+Frqng/G6tBERETqcQnMkuWlAl8/qxz8AECZRNael1rvS1kdmoiIqH4MgCyVtEY289NQjefd82T96mB1aCIiooYxALJUl7JUZ36UCEDZFVm/OlgdmoiIqGEMgCzV7b907sfq0ERERA1jAGSp3P107sfq0ERERA1jAGSpgvrKdns1VOPZs5WsXx2sDk1ERNQwBkCWysFRttUdQL01nmOXqdQDAlgdmoiIqDFmD4CSkpIQHBwMsViMiIgIHDx4sMH+mZmZiIiIgFgsRvv27bF27Vql5wcPHgyRSKTy9fjjjxvzNowjdBTw1BbAs07NHs9AWXudOkC1sTo0ERFR/cxaCHHbtm2YM2cOkpKS0K9fP6xbtw7Dhg1DXl4e2rZtq9K/oKAAw4cPx4wZM5CSkoLDhw/jxRdfhK+vL8aNGwcA2LFjByorKxWvKSkpQffu3fHkk0+a7L4MKnQU0OXxRitBq8Pq0EREROqJBEEwWzGY3r17Izw8HGvWrFG0hYSEYMyYMUhMTFTpP3fuXKSmpiI/P1/RFhcXh5MnTyI7O1vt91i5ciXeeecdSCQSuLm5aTSusrIyeHl5obS0FJ6enlrelWVjdWgiIrJV2nx+m20JrLKyEseOHUN0dLRSe3R0NLKyVGvbAEB2drZK/5iYGBw9ehRVVVVqX5OcnIynn366weCnoqICZWVlSl+2iNWhiYiIZMwWABUXF6OmpgZ+fsrbuP38/FBUVKT2NUVFRWr7V1dXo7i4WKX/kSNHkJubi+eff77BsSQmJsLLy0vx1aZNGy3vxvKxOjQREdEDZk+CFomUc08EQVBpa6y/unZANvsTFhaGXr16NTiG+fPno7S0VPF1+fJlTYdvNVgdmoiI6AGzJUH7+PjA0dFRZbbn2rVrKrM8cv7+/mr7Ozk5wdvbW6n97t27+Oqrr7BkyZJGx+Lq6gpXV1ct78C6sDo0ERHRA2abAXJxcUFERATS09OV2tPT09G3r2pxPwCIiopS6b9nzx5ERkbC2dlZqf3rr79GRUUFJk2aZNiBWylWhyYiInrArEtgCQkJ+Pzzz7Fhwwbk5+cjPj4ehYWFiIuLAyBbmnr22WcV/ePi4nDp0iUkJCQgPz8fGzZsQHJyMl577TWVaycnJ2PMmDEqM0P2itWhiYiIHjBrHaDx48ejpKQES5YsgUQiQVhYGNLS0hAUFAQAkEgkKCwsVPQPDg5GWloa4uPj8emnnyIwMBCrVq1S1ACS+/PPP3Ho0CHs2bPHpPdjyeTVoWemHIcIUEqGZnVoIiKyN2atA2Sp7LEO0NuPh6C5myuLIxIRkdXS5vPbrDNApAdpjcGqQ9+8U4mlP7A4IhER2Q/OAKlh8TNAeanA7rlA2dUHbZ6BssNTGzgfTB15ccS6PwTyuR+eG0ZERNbCKipBk47yUoGvn1UOfgCgTCJrz0vV+FIsjkhERPaKAZA1kdbIZn4aCll2z5P10wCLIxIRkb1iAGRNLmWpzvwoEYCyK7J+GmBxRCIislcMgKzJ7b8M2o/FEYmIyF4xALIm7uqPCNG1H4sjEhGRvdI5AKqqqsLly5dx5swZ3LjBHBGTCOor2+3VUMji2UrWTwPy4oio54oCgOFhsi3zTIQmIiJbolUAdPv2baxbtw6DBw+Gl5cX2rVrh9DQUPj6+iIoKAgzZszAb7/9ZqyxkoOjbKs7ANWQ5f7j2GUa1QOSiw0LwJpJ4fD3Ul7mktdATD58Ec+s/wX9l+/D7lyJbuMmIiKyMBrXAfroo4/w7rvvol27dhg1ahR69eqFVq1aoUmTJrhx4wZyc3Nx8OBBfPfdd+jTpw8++eQTdOrUydjjNwrrrAPUShb8aFkHSK5GKuBIwQ2k5xVhw+GLKs+zLhAREVk6bT6/NQ6AnnzySbzzzjvo2rVrg/0qKiqQnJwMFxcXPP/885qP2oJYfAAE6FwJuiE1UgH9l++rd2u8CIC/lxiH5g7hMRlERGRxjBIA2ROrCICMIPt8CZ5Z/0uj/bbO6IOoDt4mGBEREZHmWAmadMK6QEREZC90Ogz10UcfhUhU/xLIvn37dB4QmQ/rAhERkb3QKQDq0aOH0uOqqiqcOHECubm5mDJliiHGRWYgrwtUVFqu9rANAGjh5oyisnJkny9Br+AWzAUiIiKrZNAcoEWLFuH27dv44IMPDHVJs7DXHCDgwenwgPoTx2oL8BJj4chQ7gojIiKLYLYcoEmTJmHDhg2GvCSZWH11gdQpKi3HzJTjrA9ERERWR6clsPpkZ2dDLGZ+iLWLDQvA0FBZBeii0ntY+kM+btypVOknQLY1fvGuPAwN9edyGBERWQ2dAqCxY8cqPRYEARKJBEePHsXbb79tkIGRDgxYG8jRQYSoDt7IPl+iNviREwBISstxpOAGt8YTEZHV0CkA8vLyUnrs4OCAzp07Y8mSJYiOjjbIwEhLaqtDB8qOztCxOjTArfFERGSbdAqANm7caOhxkD7yUoGvn4VK2nKZRNb+1BadgyBujSciIlukdxL07du3UVZWpvRFJiStkc38qN2zdb9t9zxZPx3It8Y3lN1Te2s8T40nIiJroFMAVFBQgMcffxxubm7w8vJC8+bN0bx5czRr1gzNmzc39BipIZeylJe9VAhA2RVZPx04OoiwcGQoANXz5+Vu3KlC/LYTPDWeiIishk5LYBMnTgQAbNiwAX5+fg1WhSYju/2XYfupId8av3hXXr0HpcrJt8bz1HgiIrJkOgVAv//+O44dO4bOnTsbejykLXc/w/arB7fGExGRLdFpCeyRRx7B5cuXDT0W0kVQX9lur3oXqESAZytZPz3Jt8b7ezXReGs8ERGRJdJpBujzzz9HXFwcrly5grCwMDg7Oys9361bN4MMjjTg4Cjb6v71s5AFQbWTkO8HRbHLdK4HpA63xhMRkbXTKQC6fv06zp8/j2nTpinaRCIRBEGASCRCTY1uO45IR6GjZFvd1dYBWqZXHSB1uDWeiIisnU4B0HPPPYeePXti69atTIK2FKGjgC6PG6wSdEN4ajwREVk7nU6Dd3Nzw8mTJ9GxY0djjMns7Pk0eE3x1HgiIrI0Rj8NfsiQITh58qROgyPbwFPjiYjImum0BDZy5EjEx8fj1KlT6Nq1q0oS9KhRhs05IcvErfFERGStdFoCc3Cof+LIFpKguQSmvezzJXhm/S+N9ts6ow9PjSciIqPQ5vNbpxkgqVSq08DIDKQ1JkmM5tZ4IiKyJjoFQGQl8lLr2Rq/3Gxb48/+dZs7w4iIyOw0ToL+6quvNL7o5cuXcfjwYZ0GRAaSlyorjlj3oNQyiaw9L9Wg306TU+MBYPX+czw0lYiIzE7jAGjNmjXo0qULli9fjvz8fJXnS0tLkZaWhgkTJiAiIgI3bvAYBLOR1shmftRuUL/ftnuerJ+BaHJqfG3cGUZEROakcQCUmZmJDz74APv27UNYWBg8PT3RqVMndO3aFa1bt4a3tzemT5+Odu3aITc3FyNHjjTmuKkhl7JUZ36UCEDZFVk/A9Jma7w8NFu8Kw81Uq3z8ImIiPSiVQ7QiBEjMGLECJSUlODQoUO4ePEi7t27Bx8fH/Ts2RM9e/ZscIcYmcjtvwzbTwu1t8YfPncdq/efr7dv7UNTuTOMiIhMSackaG9vb4wePdrQYyFDcfczbD8tyU+N584wIiKyVJyusUVBfWW7verNxhEBnq1k/YxI251hXAojIiJTYQBkixwcZVvdAagGQfcfxy4zSj2g2rgzjIiILBUDIFsVOgp4agvgWecAUs9AWbuB6wCpw51hRERkqXQ6CsPW2dRRGCaqBN2Q3bkSLN6VB0lp47k+IgD+XmIcmjuEhRKJiEgrRj8KQ66yshIFBQXo0KEDnJxYVNoiOTgCwQPMOgTuDCMiIkuj0xLY3bt3MX36dDRt2hQPP/wwCgsLAQCzZ8/GsmXLtLpWUlISgoODIRaLERERgYMHDzbYPzMzExERERCLxWjfvj3Wrl2r0ufvv//GSy+9hICAAIjFYoSEhCAtLU2rcZFhyXeGdfLz0Kj/j7kSJkYTEZHR6BQAzZ8/HydPnkRGRgbE4gc7fR577DFs27ZN4+ts27YNc+bMwZtvvomcnBwMGDAAw4YNUwRUdRUUFGD48OEYMGAAcnJysGDBAsyePRvbt29X9KmsrMTQoUNx8eJFfPvttzhz5gzWr1+PVq1a6XKrZGCa7gzbkn2JidFERGQ0OuUABQUFYdu2bejTpw88PDxw8uRJtG/fHufOnUN4eDjKyso0uk7v3r0RHh6ONWvWKNpCQkIwZswYJCYmqvSfO3cuUlNTlY7iiIuLw8mTJ5GdnQ0AWLt2Ld5//3388ccfcHZ21mgcFRUVqKioUDwuKytDmzZtbCMHSB0z5gXVSAX0X74PRaXlag/qqEueBbRmUjhiwwIa7EtERPZNmxwgnWaArl+/jpYtW6q037lzByKRZomrlZWVOHbsGKKjo5Xao6OjkZWl/oiG7Oxslf4xMTE4evQoqqqqAACpqamIiorCSy+9BD8/P4SFheG9995DTU39514lJibCy8tL8dWmTRuN7sEq5aUCK8OAzSOA7dNl/10ZZvDDUeuj7c4wHplBRETGoFMA9Mgjj+CHH35QPJYHPevXr0dUVJRG1yguLkZNTQ38/JSrEfv5+aGoqEjta4qKitT2r66uRnFxMQDgwoUL+Pbbb1FTU4O0tDS89dZb+PDDD/Huu+/WO5b58+ejtLRU8XX58mWN7sHqmPiE+Ppoc2YYoJwYTUREZAg6bd1KTExEbGws8vLyUF1djY8//hinT59GdnY2MjMztbpW3RkjQRAanEVS1792u1QqRcuWLfHZZ5/B0dERERERuHr1Kt5//3288847aq/p6uoKV1dXrcZtdRo9IV4kOyG+y+MmWQ6rvTPsx1wJtmRfavQ1h89dR6/gFtweT0REetNpBqhv3744fPgw7t69iw4dOmDPnj3w8/NDdnY2IiIiNLqGj48PHB0dVWZ7rl27pjLLI+fv76+2v5OTE7y9ZVumAwIC8NBDD8HR8cGHeEhICIqKilBZWanNbdoWM50Q3xD5zrBhGub2rN5/nknRRERkEDpXgu7atSs2b96M3Nxc5OXlISUlBV27dtX49S4uLoiIiEB6erpSe3p6Ovr2VX9GVVRUlEr/PXv2IDIyUpHw3K9fP5w7dw5SqVTR588//0RAQABcXFw0Hp/NMeMJ8Y3R9MgMgNWiiYjIMHQKgBwdHXHt2jWV9pKSEqWZl8YkJCTg888/x4YNG5Cfn4/4+HgUFhYiLi4OgCw359lnn1X0j4uLw6VLl5CQkID8/Hxs2LABycnJeO211xR9Zs6ciZKSErzyyiv4888/8cMPP+C9997DSy+9pMut2g4znxDfEG0So5kUTUREhqBTDlB9O+crKiq0mmUZP348SkpKsGTJEkgkEoSFhSEtLQ1BQUEAAIlEolQTKDg4GGlpaYiPj8enn36KwMBArFq1CuPGjVP0adOmDfbs2YP4+Hh069YNrVq1wiuvvIK5c+fqcqu2Q35CfJkE6vOARLLnjXxCfH3kidGaHJnBatFERKQvreoArVq1CgAQHx+PpUuXwt3dXfFcTU0NDhw4gIsXLyInJ8fwIzUhmzoLrDb5LjAAykHQ/XkXEx2S2pAaqYCP0v/E6v3nGu37bFQQhoUFMDGaiIgAaPf5rVUAFBwcDAC4dOkSWrdurbTc5eLignbt2mHJkiXo3bu3jkO3DDYbAAGyIGj3XOWEaM9WQOwyswc/ctnnS/DM+l807h/gJcbCkaEslEhEZOeMFgDJPfroo9ixYweaN2+u8yAtmU0HQIBFnBDfEFaLJiIiXRg9ALJ1Nh8AWYHduRLMTDkOQH3GUl0iAP5eYhyaO4TLYUREdkqbz2+dkqAB4H//+x9SU1NRWFioUl9nxYoVul6WzMXCZoW0SYoGmBhNRETa0SkA2rt3L0aNGoXg4GCcOXMGYWFhuHjxIgRBQHh4uKHHSMamNi8oEIhdbta8IF2qRf94vz4QE6OJiKghOtUBmj9/Pl599VXk5uZCLBZj+/btuHz5MgYNGoQnn3zS0GMkY7KQ88Hqo2216C3Zl/DM+l9YMZqIiBqkUwCUn5+PKVOmAACcnJxw7949uLu7Y8mSJVi+fLlBB0hG1Oj5YJCdDyatMeWo1NKmWjTAitFERNQwnQIgNzc3VFRUAAACAwNx/vx5xXPyU9nJCljg+WD10aZaNMCK0URE1DCdAqA+ffrg8OHDAIDHH38cr776Kt59910899xz6NOnj0EHSEZkweeDqSNPjPb3EmvUX54Y/VH6n8g+X8JAiIiIFHTaBn/hwgXcvn0b3bp1w927d/Haa6/h0KFD6NixIz766CPFURbWym62wRccBDaPaLzflO+B4AHGH4+GaqSCVonRciyYSERk21gHSE92EwBJa4CVYY2fDzbnlEUVSpTTtmI0CyYSEdk2bT6/dVoCq8+OHTvQrVs3Q16SjMnBUbbVHYBqZs39x7HLLDL4AbRPjGZeEBERyWkdAK1fvx5PPvkkJkyYgF9//RUAsG/fPvTs2ROTJk1CVFSUwQdJRhQ6SnYIqmedGRHPQIs4HLUh2iZGA8wLIiIiGa2WwD744AMsWLAA3bp1Q35+PgDgzTffxIoVKzBr1iy89NJL8PHxMdpgTcVulsBqs7BK0NrYnSvRuGJ0XcwLIiKyHUbLAQoJCcHrr7+O5557DhkZGRgyZAiGDBmCb7/9Fs2aNdN33BbDLgMgKydPjD587jpW7z/f+AvuY14QEZHtMFoA1LRpU/zxxx9o27YtAMDV1RUHDhxA79699RuxhWEABKudEdL2JHmAB6kSEdkKox2GWl5eDrH4QQ0WFxcX+Pr66jZKslwWejaYJuR5QTNTjkMEzU6Sr50X1K+jD88RIyKyA1rNADk4OODf//433N3dAQBz587F66+/rpL3M3v2bMOO0sTsegZIfjaYSuhwPyCw8MRoOeYFERHZH6MtgbVr1w4iUcO/GYtEIly4cEHTS1okuw2AFHWB6jsew7LrAtXFvCAiIvtitCWwixcv6jMusnTanA1mQZWh6yM/Sb5XcAtsP35F47wgAbIgaPGuPAwN9edyGBGRDTJoIUSyclZ2Npim9KkXtOlwAWsFERHZIAZA9IC7n2H7WRBtD1KVW/pDPvov34fduRIjjYyIiMyBARA9ENRXluNT7zyJCPBsJetnhWLDAnBo7hBsndEHLz/aQePXFZWWY2bKcQZBREQ2hAEQPWDlZ4NpQp4XFD+0s8bniPEMMSIi28MAiJRZ8dlg2tA2L4hniBER2RattsHXJpVKce7cOVy7dg1SqVTpuYEDBxpkcOZit9vga1NXCRqwyurQDdG1XhBrBRERWR6j1QGS++WXXzBhwgRcunQJdV8uEolQU1Oj7SUtCgMgNay4OnRjaqQCNh0uwNIf8jV+DWsFERFZHm0+v3VaAouLi0NkZCRyc3Nx48YN3Lx5U/F148YNnQZNFkxeHbpujaAyiaw9L9U84zIQRwcRpvYL1jgnCJAtiQkAFnx3Ct/lXOGyGBGRldFpBsjNzQ0nT55Ex44djTEms+MMUC02Vh26IbtzJZiZchyAZmeI1cVlMSIi8zL6DFDv3r1x7tw5nQZHVkab6tBWTtdaQXLcLk9EZD00Pgrj999/V/x51qxZePXVV1FUVISuXbvC2dlZqW+3bt0MN0IyLxutDl2f2LAADA311+kMMfms0YLvTuFelRT+nmKeLE9EZKE0XgJzcHCASCRSSXpWXOj+c0yCtjEFB4HNIxrvN+V7qzgfTBs1UgH9l+/T+AwxdbgsRkRkOkY5DLWgoEDvgZEVkleHLpNAfWbM/RwgK60O3RB5raCZKcchgm55QfJlMe4WIyKyLBrnAAUFBSm+Ll26hFatWim1BQUFoVWrVrh06ZIxx0umZgfVoRuib14Qq0gTEVkmnXaBOTo6QiKRoGXLlkrtJSUlaNmyJZfAbJHaOkCtgOj3ADdvmyqOqE6NVMCRghsoKr2HpT/k4+adSq1nhF5+tCP6dfRhXhARkZEYZQmsNnmuT10lJSVwc3PT5ZJk6UJHAV0eV64EfbcE+Gm+TRZHrEt+hhgANHFx1GlZbPX+c1i9/xzzgoiILIBWM0Bjx44FAPz3v/9FbGwsXF1dFc/V1NTg999/R+fOnbF7927Dj9SEOAOkAXlxRJUQ4H5gbEPnhqmj6xEaAKtIExEZi9FmgLy8vADIZoA8PDzQpEkTxXMuLi7o06cPZsyYocOQyapIa2TLYWrnPwQAImD3PNmMkQ0uhwHK2+W1XRbjdnkiIvPTKgDauHEjAKBdu3Z47bXXuNxlr7QpjmhjW+Nr03dZ7MadKsRvOwGA2+WJiExNp0rQgYGBuHq1oQ9Asml2VhxRE6wiTURkXXRKgv7www8RFxcHPz8/DBo0CIMHD8agQYPQpUsXQ4+PLJG7n2H72QhWkSYish46bYMHgKKiIuzfvx+ZmZnIyMjA2bNn4evri8GDB+Orr74y9DhNiknQjVAckNpIcUQbOCBVV6wiTURketp8fuscAMnduXMHhw4dwldffYWUlBQIgoDq6mp9Lml2DIA0oNgFBigHQfezYAYvALw72HRtoMboe7o8d4sREWnH6AHQjz/+qJj5OXnyJB5++GEMHDgQgwcPxoABA9C8eXOdB28JGABpSF1xxCYtZP+9d+NBm43WBtKEPtvl5Zo1ccanE8PRp703l8SIiBpg9ADIwcEBvr6+ePXVV/HCCy8otsfrIikpCe+//z4kEgkefvhhrFy5EgMG1L9zKDMzEwkJCTh9+jQCAwPxxhtvIC4uTvH8pk2bMG3aNJXX3bt3D2KxZgmqDIC0IK15UByx5DyQkQh7rQ1UH0NUkQa4JEZE1BhtPr912gW2YsUK9OvXD++//z46d+6M8ePHY82aNcjPz9fqOtu2bcOcOXPw5ptvIicnBwMGDMCwYcNQWFiotn9BQQGGDx+OAQMGICcnBwsWLMDs2bOxfft2pX6enp6QSCRKX5oGP6QlB0fZVveHnwCOb0L9tYEgqw0kte5jUnQh3y7/RHhrvPdEGADVU9U0wZ1iRESGo3cO0KlTp5CZmYn9+/dj165d8Pb2hkSi2f+ge/fujfDwcKxZs0bRFhISgjFjxiAxMVGl/9y5c5GamqoUaMXFxeHkyZPIzs4GIJsBmjNnDv7++2+N76GiogIVFRWKx2VlZWjTpg1ngLRRcBDYPKLxflO+t+naQJrQd1mshZsz3h7xMHeKERHVYfQZILmcnBz8/PPP2LNnD/bt2wepVIrWrVtr9NrKykocO3YM0dHRSu3R0dHIyspS+5rs7GyV/jExMTh69CiqqqoUbbdv30ZQUBBat26NESNGICcnp8GxJCYmwsvLS/HVpk0bje6BamFtII3FhgXg0Nwh2DqjDz56qjtauLloNSMkL6D4zPpf0H/5Ps4IERHpQKcAaNSoUWjRogUeeeQRfPnll3jooYfwxRdf4MaNG/jtt980ukZxcTFqamrg56dcK8bPzw9FRUVqX1NUVKS2f3V1NYqLiwEAXbp0waZNm5CamoqtW7dCLBajX79+OHv2bL1jmT9/PkpLSxVfly9f1ugeqBbWBtKKIZfF4lKO4+Of/8R/T1xB9vkS1Ej1mtQlIrILOhVCfOihh/Cvf/0LAwcO1HuJqO6p8vWdNN9Q/9rtffr0QZ8+fRTP9+vXD+Hh4fjkk0+watUqtdd0dXVVOtiVdBDUV7bbq7HaQEF9TT0yiyevIq3Lspj8b/qjnx8E+EyWJiJqnE4zQB988AFGjBihV/Dj4+MDR0dHldmea9euqczyyPn7+6vt7+TkBG9vb7WvcXBwwCOPPNLgDBAZgIOjbKs7ANW5jPu1gcKnAKe/k+UL2WEydEPky2JfTu+NZk2c9boWk6WJiBqncw5QZmYmRo4ciY4dO6JTp04YNWoUDh48qPHrXVxcEBERgfT0dKX29PR09O2rfpYgKipKpf+ePXsQGRkJZ2f1HxqCIODEiRMICOBvw0YXOkq21d2zzt91k+ay+kAZ7wHbp8uSpVeGyeoIkYKjgwj9Ovlg2biuEEG3JTFANiskQHasxnc5XBYjIlJHp11gKSkpmDZtGsaOHYt+/fpBEARkZWXhu+++w6ZNmzBhwgSNrrNt2zZMnjwZa9euRVRUFD777DOsX78ep0+fRlBQEObPn48rV65gy5YtAGTb4MPCwvDCCy9gxowZyM7ORlxcHLZu3Ypx48YBABYvXow+ffqgU6dOKCsrw6pVq/DFF1/g8OHD6NWrl0bjYh0gPbE2kN4MUUCxNi6LEZE9MHohxJCQEPzrX/9CfHy8UvuKFSuwfv16reoBJSUl4f/+7/8gkUgQFhaGjz76CAMHDgQATJ06FRcvXkRGRoaif2ZmJuLj4xWFEOfOnatUCDE+Ph47duxAUVERvLy80LNnTyxatAhRUVEaj4kBkIEozgy7Wk8HnhnWEEMVUAQUi5CIf6wT2vm4oaUHt9ATke0xegDk6uqK06dPo2PHjkrt586dQ1hYGMrLDfNbq7kwADIQ1gYyGH3PFVOHs0JEZGuMXgeoTZs22Lt3r0r73r17WUOHHmBtIIOR7xTz9zJcRXMmSxORPdNpG/yrr76K2bNn48SJE+jbty9EIhEOHTqETZs24eOPPzb0GMlasTaQQcWGBWBoqD+OFNzAtVvluFh8Fyt//hOAbrNC8tcs+O4U7lVJWVmaiOyKzkdhfPfdd/jwww8V+T4hISF4/fXXMXr0aIMO0By4BGYgihyg+moDAWjqA8QmAh4BshpBzAXSCpOliYgeMGoOUHV1Nd59910899xzNrvcxQDIgPJSga+fvf+gkR81z0BZLSHuCtMKk6WJiGSMngTt7u6O3NxctGvXTtcxWjQGQAaWlwrsntvAbjA5bo3XF5OlicieGT0J+rHHHlPamk7UoNBRwJxc2W6vsetly15q3f/I3j2PlaJ1xGRpIiLN6JQEPWzYMMyfPx+5ubmIiIiAm5ub0vOjRvG3d6rDwVG21b3gIHC3uIGOAlB2RVZIkVvjdWKsZOl520/BQ+yMPu29uSRGRFZPpyUwB4f6J45EIhFqaqz7t3cugRnRqW9lx2E0Zlwy0PWfxh+PnTBksjSXxIjIUmnz+a3TDJBUKtVpYETcGm8etWeF9E2WLiotR1zKcSZKE5FV0ykAItJZUF/Zbq/GtsbfksiWy7g13mAcHUSI6uANAGji4oiZKccVu760Ie//0c9nFW2cFSIia6PVEti9e/ewd+9ejBghO95g/vz5qKioUDzv6OiIpUuXQiw2XAKmOXAJzMi4Nd4iGHJZjNvnicgSGG0b/Lp16/D9999j165dAAAPDw88/PDDaNKkCQDgjz/+wBtvvKFySKq1YQBkAtwabxHkNYQMkSxdF2eFiMjUjBYADRw4EPHx8XjiiScAyAKgkydPon379gCAlJQUfPrpp8jOztZj+ObHAMhEpDWy3V63JMDu+Q3sDuOp8abCWSEismZGS4L+888/8dBDDykei8VipR1hvXr1wksvvaTlcMlucWu8xZEnS/9yvgQv/ec4/r5XpfO1mCtERJZMq0KIpaWlcHJ6EDNdv35dqRq0VCpVygki0ghPjbcojg4i9Ovkg2XjukIExSKkQch3kH3885/474kryD5fghqpoWpWExFpTqsZoNatWyM3NxedO3dW+/zvv/+O1q1bG2RgZEc03fJ++y/ZshmXwUxCXlXakIetclaIiCyFVjlAr7zyCn7++WccO3ZMZafXvXv3EBkZicceewwff/yxwQdqSswBMjFNTo2X464wkzNmojTAXCEiMhyjJUH/9ddf6NGjB1xcXPDyyy/joYcegkgkwh9//IHVq1ejuroaOTk58POz7iJ2DIDMQOOt8dwVZm6GTJSuD2eFiEgXRj0NvqCgADNnzkR6ejrkLxWJRBg6dCiSkpIUO8KsGQMgM9Fmazx3hZmVsWeF5Kb3a4fHQv05I0REGjFqACR348YNnDt3DgDQsWNHtGjRQpfLWCQGQGYkrQF+XQv8tKDxvlO+564wC2HsWSHOCBGRJox+FhgAtGjRAr169dL15UTqOThqnhSdnyr7L4/LMDtDn0BfF88fIyJD03kGyJZxBsjMCg4Cm0do3p+J0RaJs0JEZGomWQKzZQyAzEybXWEAmBhtuYyZK8TdY0RUFwMgPTEAsgDaHJgKgInR1oGzQkRkTAyA9MQAyEJovCusFiZGWzzOChGRsZgkCZrI6EJHAV0el50Blp8KHPms8dcwMdriOTqIENXBW/G4s7+7wWaFWGmaiDTFGSA1OANkgZgYbdPks0LpeUXYcPiiYibHEDgrRGQ/uASmJwZAFoiJ0XbDVJWm3348BM3dXHHtVjmDIiIbwQBITwyALBQTo+2GqSpN18alMiLrxwBITwyALBgTo+2SKWaFuFRGZP0YAOmJAZCFk9Zolxjd619AyCgmRls5zgoRUWMYAOmJAZCVYGK0XTPFrJAcD2Ulsg4MgPTEAMhKMDHa7pl6VojJ00SWjQGQnhgAWREmRlMtppwVkuMyGZHlYACkJwZAVoaJ0VSLqWeFmDxNZDlYCZrsCytGUy3GrDStTn3Vp7lURmTZOAOkBmeArBgTo0mN2rNCLT3EuHmnEkt/4FIZka3hEpieGABZMSZGk4a4VEZkexgA6YkBkJVjYjTpwFwJ1FwqIzIcBkB6YgBkA3RJjB7wOtB+EPOC7JgxD2XVFJfKiHTHAEhPDIBshLYVo+WYF0Qwz4xQbSy+SKQ9BkB6YgBkY7RNjGZeEN1nKcnTXCYj0gwDID0xALIxWidGA8wLovqY40yyurhMRqQeAyA9MQCyQVonRt8X8x7QO45BENXLHEtl3FFGpJ42n98OJhpTvZKSkhAcHAyxWIyIiAgcPHiwwf6ZmZmIiIiAWCxG+/btsXbt2nr7fvXVVxCJRBgzZoyBR01WJ3SUbEnLU8vfmH9aIJs9yks1zrjI6sWGBeDQ3CHYOqMPPn66B7bO6IOkCeEI8BIb7XvWLr74ylcn8Mz6X9B/+T6k/X4V2edL8N8TV5B9vgQ1Uv5+S1Qfs84Abdu2DZMnT0ZSUhL69euHdevW4fPPP0deXh7atm2r0r+goABhYWGYMWMGXnjhBRw+fBgvvvgitm7dinHjxin1vXTpEvr164f27dujRYsW2Llzp8bj4gyQDZMnRhdkAgfe1/BFzAki7VnKUhnzh8ieWM0SWO/evREeHo41a9Yo2kJCQjBmzBgkJiaq9J87dy5SU1ORn5+vaIuLi8PJkyeRnZ2taKupqcGgQYMwbdo0HDx4EH///TcDIFKmS8FE5gSRHsy9q0yO+UNky6ziLLDKykocO3YM8+bNU2qPjo5GVlaW2tdkZ2cjOjpaqS0mJgbJycmoqqqCs7MzAGDJkiXw9fXF9OnTG11SA4CKigpUVFQoHpeVlWl7O2RtHBxlW92/fhbQqNqLAJRdAfYnslYQ6SQ2LABDQ/3NvqusqLQccSnHmT9Eds9sAVBxcTFqamrg5+en1O7n54eioiK1rykqKlLbv7q6GsXFxQgICMDhw4eRnJyMEydOaDyWxMRELF68WOt7ICsnzwvSpmDiwfdlX6wVRDqoe1ArAMSE+Zu0+CIPbyWSMftp8CKR8j8wQRBU2hrrL2+/desWJk2ahPXr18PHx0fjMcyfPx8JCQmKx2VlZWjTpo3GrycrJj9J/te1soRnTZVJZLNHzAsiPcmDoqgO3ugV3MIsy2SS0nK8+J8cpTZ/T1c806stZ4nIZpktAPLx8YGjo6PKbM+1a9dUZnnk/P391fZ3cnKCt7c3Tp8+jYsXL2LkyJGK56VSKQDAyckJZ86cQYcOHVSu6+rqCldXV31viayVg6Nsq3v2ai1yggQAImD3PFkAxeUwMgBLWSYDgKKyCpVZIuYOkS0xWwDk4uKCiIgIpKen44knnlC0p6enY/To0WpfExUVhV27dim17dmzB5GRkXB2dkaXLl1w6tQppeffeust3Lp1Cx9//DFndah+WucEAcwLImNoaJnMnDvKmDtEtsYitsGvXbsWUVFR+Oyzz7B+/XqcPn0aQUFBmD9/Pq5cuYItW7YAeLAN/oUXXsCMGTOQnZ2NuLg4tdvg5aZOncpdYKQ5XQ5RlWNeEJmIJe0oY+4QWRKr2AUGAOPHj0dJSQmWLFkCiUSCsLAwpKWlISgoCAAgkUhQWFio6B8cHIy0tDTEx8fj008/RWBgIFatWlVv8EOkNXlOkNa1gsC8IDIZS1kqU5c7xKCIrAWPwlCDM0AEQMczxAA09QFiEwGPAC6LkUlZwuGt6jAoIlOxmkKIlooBECnoeoaYHJfFyMwsoSK1OgyKrFvdYLtXcAsA0LnNUO87AyA9MQAiJfrkBfEYDbIwlpI/pA6DIuPSJGiJCGqOY5duNthH3cxis6ayQsR/363Sus2QOwwZAOmJARCp0OkMsVq4LEYWxFKXytSxp+33dd8XTYIRTds0DVocREDtM3TV9TE0eXi7ZlK43u8zAyA9MQCieumaF1Qbl8XIAtVdKtt6pBBFZZYTEE3v1w6Phfpb1IyQIZeB1AUomgQjmrZZOhEAfy8xDs0dotf7ywBITwyAqEH65gVxWYysgD3nDmkS2Bh6GYhkts7oo1IHSxtWsw2eyCrpcoaYkvsfId/HA9XlXBYji1S3IGNnf3eLyB0yxtb7xma/NA1Y1D2vaRvJXLtlup8vzgCpwRkg0og8L+iWBNg9H7hbAi6LkS2zttwhdUER0PhMDpmPKWeAGACpwQCItMZlMbJT1hQUcenJcpkjB4hLYESGwGUxslONnV1mSUERAx/LJA93Fo4MNWmCO2eA1OAMEOmMy2JEalnTTBFpj3WAbAQDIDIILosRNYhBkXnpWgdI0/wqVoK2QgyAyGD0qiJ9X5PmwJObgXb9uSRGNk8eFKXnFWHD4YsQwfxb783FkHWANA1aNC2+aCm1mOpiAKQnBkBkUIZaFuOSGNkZSz62Qx1DByiGrARtyUGLITEA0hMDIDIavZbF7v8uPHgB4N0BcPdjojTZPEtYJrO2ZSB7xgBITwyAyKgMsSwmx1khskPGDor8PV3xTK+2aOfjxoDFyjAA0hMDIDI6g+0WY6I0EaBZUKTNTA6DG+vEOkBEls7BEQgeIPuzk/j+spgu6Z6sH0QEaFaPiDM5VBtngNTgDBCZHJfFiIj0xiUwPTEAIrOQ1gAFB4FvpwL3bupxISZLE5F9YgCkJwZAZFZ6F1BUg7NCRGQHtPn8djDRmIhIU/JzxTz1LwuvUCYBvp4MZCwHTn0rm2mS1hju+kREVoYzQGpwBogsgnyn2O2/gJLzQEbi/Sc4K0REpA53gRHZgto7xQCgZYjhEqWB+7NCz3ILPRHZJQZARNYidBTQ5XHDnTbPLfREZMcYABFZE4PVD6rlbjGwY4bsz1wWIyI7wSRoImvFZGkiIp0xCVoNJkGTVWGyNBERACZBE9kXoydLX5XNCvV5Eeg8nHlCRGQTOAOkBmeAyOoZc1aIM0JEZKE4A0Rk74w5KyTPE+JRG0RkxTgDpAZngMgmyWeFDLKFvg7PQCA6EXDzls06MSgiIjPgDBARqTLGFnq5sqvAt1OU27hURkQWjNvgieyRMbbQ18Ut9URkwbgEpgaXwMhuGHsLfV2cFSIiI+ISGBFpxthb6OtiAjURWQjOAKnBGSCya/JZoTNpwC9JMFieUH2YQE1EBqLN5zcDIDUYABHdl5dq3Bmh+nCpjIh0wABITwyAiGoxdZ5Qbaw+TURaYACkJwZARA0wx6wQl8mISAMMgPTEAIioEbVnhdz9ZEUVf5pv+qCIy2REVAsDID0xACLSgcmXyu4nZ3NHGRHdx23wRGR6pt5SLw+sMt570MSlMiLSEGeA1OAMEJGBmDOBWo5BEZHd4AwQEVkGk88KqcFzyohIDbOfBZaUlITg4GCIxWJERETg4MGDDfbPzMxEREQExGIx2rdvj7Vr1yo9v2PHDkRGRqJZs2Zwc3NDjx498MUXXxjzFohIU6GjgDm5wJTvgXHJsv8+uVkWkJgSzykjsntmnQHatm0b5syZg6SkJPTr1w/r1q3DsGHDkJeXh7Zt26r0LygowPDhwzFjxgykpKTg8OHDePHFF+Hr64tx48YBAFq0aIE333wTXbp0gYuLC77//ntMmzYNLVu2RExMjKlvkYjqqjsrBAAhI01bfZr5Q0R2z6w5QL1790Z4eDjWrFmjaAsJCcGYMWOQmJio0n/u3LlITU1Ffn6+oi0uLg4nT55EdnZ2vd8nPDwcjz/+OJYuXarRuJgDRGRG5qo+rY5HABAxjbvMiKyEVeQAVVZW4tixY5g3b55Se3R0NLKystS+Jjs7G9HR0UptMTExSE5ORlVVFZydnZWeEwQB+/btw5kzZ7B8+fJ6x1JRUYGKigrF47KyMm1vh4gMJXQU0OVx89cZAoBbEs4SEdkoswVAxcXFqKmpgZ+fn1K7n58fioqK1L6mqKhIbf/q6moUFxcjICAAAFBaWopWrVqhoqICjo6OSEpKwtChQ+sdS2JiIhYvXqznHRGRwTS0TGbOHWVMqCayGWbfBSYSiZQeC4Kg0tZY/7rtHh4eOHHiBG7fvo29e/ciISEB7du3x+DBg9Vec/78+UhISFA8LisrQ5s2bbS9FSIyJkvYUaaOPKGaBRmJrIrZAiAfHx84OjqqzPZcu3ZNZZZHzt/fX21/JycneHt7K9ocHBzQsWNHAECPHj2Qn5+PxMTEegMgV1dXuLq66nE3RGRyFrNUxoRqImtktgDIxcUFERERSE9PxxNPPKFoT09Px+jRo9W+JioqCrt27VJq27NnDyIjI1Xyf2oTBEEpx4eIbERjS2XmCorqWypjUERkMcy6BJaQkIDJkycjMjISUVFR+Oyzz1BYWIi4uDgAsqWpK1euYMuWLQBkO75Wr16NhIQEzJgxA9nZ2UhOTsbWrVsV10xMTERkZCQ6dOiAyspKpKWlYcuWLUo7zYjIhllb/hCDIiKzMGsANH78eJSUlGDJkiWQSCQICwtDWloagoKCAAASiQSFhYWK/sHBwUhLS0N8fDw+/fRTBAYGYtWqVYoaQABw584dvPjii/jf//6HJk2aoEuXLkhJScH48eNNfn9EZCEsNn9ITVDErfdEJsGzwNRgHSAiO1D7nDJzbrVvDGeJiDSmzec3AyA1GAAR2am6h7ce32R5ARFQf1AEKAd1DJTIzjAA0hMDICICYD2zRADQpDkAEXDvxoM2zh6RnWEApCcGQERUr7qzROZIqNYHgyKyYVZxFAYRkVWy1IRqTXE3GhEAzgCpxRkgItKKNS2VaYq70cgKcQlMTwyAiEhvthYUMfGarAADID0xACIio7C1oIiJ12RhGADpiQEQEZmMtWy91weDIjIRBkB6YgBERGZja7NE9eGSmuHU/Zmp7+9Rk7Y2vYHLvxrmWtq0Geg9ZgCkJwZARGRRNAmKmrSQ/bf2cpS1Ubekpi4ZGzDuB7K5ggBdxqH2Z0HN36OmbSIHQJAa5lqatnkGArHLgdBR0BcDID0xACIii6fJb/22OHtkig9kcwQBuo7DJohk/3lqi95BEAMgPTEAIiKbYS9LamTlRLKZoDmn9FoOYyFEIiKSqVu4EQBCRjIoIgsjAGVXZD+XdX9ejYQBEBGRvWksKLLV3Whk+W7/ZbJvxQCIiIhUg6KBr9lH4jVZFnc/k30rBkBERKRKk6Uze0m8JhO4nwMk/5kyAQZARESkGXVBEcAcI9LT/V1gsctMWvOJARARERmWronXXFIzDHV/j5q2qWy91+NamrZ5BsqCHwPUAdIGAyAiIjI+XZbU1CVjm+ID2RxBgK7j8GwFRL+nWUVtTdqsvBK0NlgHSA3WASIishCGPObB0oMAXcfBo0IUWAhRTwyAiIiIrI82n98OJhoTERERkcVgAERERER2hwEQERER2R0GQERERGR3GAARERGR3WEARERERHaHARARERHZHQZAREREZHcYABEREZHd4VlgasiLY5eVlZl5JERERKQp+ee2JodcMABS49atWwCANm3amHkkREREpK1bt27By8urwT48C0wNqVSKq1evwsPDAyKRyNzDMZiysjK0adMGly9ftpszznjPtn/P9na/gP3ds73dL8B71vWeBUHArVu3EBgYCAeHhrN8OAOkhoODA1q3bm3uYRiNp6en3fyDkuM92z57u1/A/u7Z3u4X4D3rorGZHzkmQRMREZHdYQBEREREdocBkB1xdXXFwoUL4erqau6hmAzv2fbZ2/0C9nfP9na/AO/ZFJgETURERHaHM0BERERkdxgAERERkd1hAERERER2hwEQERER2R0GQDYiMTERjzzyCDw8PNCyZUuMGTMGZ86cafA1GRkZEIlEKl9//PGHiUatn0WLFqmM3d/fv8HXZGZmIiIiAmKxGO3bt8fatWtNNFrDaNeundr37KWXXlLb39re4wMHDmDkyJEIDAyESCTCzp07lZ4XBAGLFi1CYGAgmjRpgsGDB+P06dONXnf79u0IDQ2Fq6srQkND8d133xnpDrTX0D1XVVVh7ty56Nq1K9zc3BAYGIhnn30WV69ebfCamzZtUvu+l5eXG/luGtfYezx16lSVcffp06fR61rrewxA7XslEonw/vvv13tNS36PNfk8soR/ywyAbERmZiZeeukl/PLLL0hPT0d1dTWio6Nx586dRl975swZSCQSxVenTp1MMGLDePjhh5XGfurUqXr7FhQUYPjw4RgwYABycnKwYMECzJ49G9u3bzfhiPXz22+/Kd1veno6AODJJ59s8HXW8h7fuXMH3bt3x+rVq9U+/3//939YsWIFVq9ejd9++w3+/v4YOnSo4vw+dbKzszF+/HhMnjwZJ0+exOTJk/HUU0/h119/NdZtaKWhe7579y6OHz+Ot99+G8ePH8eOHTvw559/YtSoUY1e19PTU+k9l0gkEIvFxrgFrTT2HgNAbGys0rjT0tIavKY1v8cAVN6nDRs2QCQSYdy4cQ1e11LfY00+jyzi37JANunatWsCACEzM7PePvv37xcACDdv3jTdwAxo4cKFQvfu3TXu/8YbbwhdunRRanvhhReEPn36GHhkpvPKK68IHTp0EKRSqdrnrfk9BiB89913isdSqVTw9/cXli1bpmgrLy8XvLy8hLVr19Z7naeeekqIjY1VaouJiRGefvppg49ZX3XvWZ0jR44IAIRLly7V22fjxo2Cl5eXYQdnBOrud8qUKcLo0aO1uo6tvcejR48WhgwZ0mAfa3mPBUH188hS/i1zBshGlZaWAgBatGjRaN+ePXsiICAA//jHP7B//35jD82gzp49i8DAQAQHB+Ppp5/GhQsX6u2bnZ2N6OhopbaYmBgcPXoUVVVVxh6qwVVWViIlJQXPPfdco4f2WvN7LFdQUICioiKl99DV1RWDBg1CVlZWva+r731v6DWWrLS0FCKRCM2aNWuw3+3btxEUFITWrVtjxIgRyMnJMc0ADSAjIwMtW7bEQw89hBkzZuDatWsN9rel9/ivv/7CDz/8gOnTpzfa11re47qfR5byb5kBkA0SBAEJCQno378/wsLC6u0XEBCAzz77DNu3b8eOHTvQuXNn/OMf/8CBAwdMOFrd9e7dG1u2bMFPP/2E9evXo6ioCH379kVJSYna/kVFRfDz81Nq8/PzQ3V1NYqLi00xZIPauXMn/v77b0ydOrXePtb+HtdWVFQEAGrfQ/lz9b1O29dYqvLycsybNw8TJkxo8LDILl26YNOmTUhNTcXWrVshFovRr18/nD171oSj1c2wYcPw5ZdfYt++ffjwww/x22+/YciQIaioqKj3Nbb0Hm/evBkeHh4YO3Zsg/2s5T1W93lkKf+WeRq8DXr55Zfx+++/49ChQw3269y5Mzp37qx4HBUVhcuXL+ODDz7AwIEDjT1MvQ0bNkzx565duyIqKgodOnTA5s2bkZCQoPY1dWdKhPuF0BubQbFEycnJGDZsGAIDA+vtY+3vsTrq3sPG3j9dXmNpqqqq8PTTT0MqlSIpKanBvn369FFKHO7Xrx/Cw8PxySefYNWqVcYeql7Gjx+v+HNYWBgiIyMRFBSEH374ocGgwBbeYwDYsGEDJk6c2Gguj7W8xw19Hpn73zJngGzMrFmzkJqaiv3796N169Zav75Pnz4W9xuEptzc3NC1a9d6x+/v76/ym8K1a9fg5OQEb29vUwzRYC5duoSff/4Zzz//vNavtdb3WL7DT917WPe3wrqv0/Y1lqaqqgpPPfUUCgoKkJ6e3uDsjzoODg545JFHrPJ9DwgIQFBQUINjt4X3GAAOHjyIM2fO6PTv2hLf4/o+jyzl3zIDIBshCAJefvll7NixA/v27UNwcLBO18nJyUFAQICBR2caFRUVyM/Pr3f8UVFRil1Tcnv27EFkZCScnZ1NMUSD2bhxI1q2bInHH39c69da63scHBwMf39/pfewsrISmZmZ6Nu3b72vq+99b+g1lkQe/Jw9exY///yzTsG6IAg4ceKEVb7vJSUluHz5coNjt/b3WC45ORkRERHo3r271q+1pPe4sc8ji/m3rFPqNFmcmTNnCl5eXkJGRoYgkUgUX3fv3lX0mTdvnjB58mTF448++kj47rvvhD///FPIzc0V5s2bJwAQtm/fbo5b0Nqrr74qZGRkCBcuXBB++eUXYcSIEYKHh4dw8eJFQRBU7/fChQtC06ZNhfj4eCEvL09ITk4WnJ2dhW+//dZct6CTmpoaoW3btsLcuXNVnrP29/jWrVtCTk6OkJOTIwAQVqxYIeTk5Ch2PC1btkzw8vISduzYIZw6dUp45plnhICAAKGsrExxjcmTJwvz5s1TPD58+LDg6OgoLFu2TMjPzxeWLVsmODk5Cb/88ovJ70+dhu65qqpKGDVqlNC6dWvhxIkTSv+2KyoqFNeoe8+LFi0Sdu/eLZw/f17IyckRpk2bJjg5OQm//vqrOW5RSUP3e+vWLeHVV18VsrKyhIKCAmH//v1CVFSU0KpVK5t9j+VKS0uFpk2bCmvWrFF7DWt6jzX5PLKEf8sMgGwEALVfGzduVPSZMmWKMGjQIMXj5cuXCx06dBDEYrHQvHlzoX///sIPP/xg+sHraPz48UJAQIDg7OwsBAYGCmPHjhVOnz6teL7u/QqCIGRkZAg9e/YUXFxchHbt2tX7PxtL9tNPPwkAhDNnzqg8Z+3vsXzbft2vKVOmCIIg2z67cOFCwd/fX3B1dRUGDhwonDp1SukagwYNUvSX++abb4TOnTsLzs7OQpcuXSwqAGzongsKCur9t71//37FNere85w5c4S2bdsKLi4ugq+vrxAdHS1kZWWZ/ubUaOh+7969K0RHRwu+vr6Cs7Oz0LZtW2HKlClCYWGh0jVs6T2WW7dundCkSRPh77//VnsNa3qPNfk8soR/y6L7gyUiIiKyG8wBIiIiIrvDAIiIiIjsDgMgIiIisjsMgIiIiMjuMAAiIiIiu8MAiIiIiOwOAyAiIiKyOwyAiIiIyO4wACIim7No0SL06NHD3MMgIgvGStBEZFVEIlGDz0+ZMgWrV69GRUWFTgeHEpF9YABERFalqKhI8edt27bhnXfewZkzZxRtTZo0gZeXlzmGRkRWhEtgRGRV/P39FV9eXl4QiUQqbXWXwKZOnYoxY8bgvffeg5+fH5o1a4bFixejuroar7/+Olq0aIHWrVtjw4YNSt/rypUrGD9+PJo3bw5vb2+MHj0aFy9eNO0NE5FRMAAiIruwb98+XL16FQcOHMCKFSuwaNEijBgxAs2bN8evv/6KuLg4xMXF4fLlywCAu3fv4tFHH4W7uzsOHDiAQ4cOwd3dHbGxsaisrDTz3RCRvhgAEZFdaNGiBVatWoXOnTvjueeeQ+fOnXH37l0sWLAAnTp1wvz58+Hi4oLDhw8DAL766is4ODjg888/R9euXRESEoKNGzeisLAQGRkZ5r0ZItKbk7kHQERkCg8//DAcHB78zufn54ewsDDFY0dHR3h7e+PatWsAgGPHjuHcuXPw8PBQuk55eTnOnz9vmkETkdEwACIiu+Ds7Kz0WCQSqW2TSqUAAKlUioiICHz55Zcq1/L19TXeQInIJBgAERGpER4ejm3btqFly5bw9PQ093CIyMCYA0REpMbEiRPh4+OD0aNH4+DBgygoKEBmZiZeeeUV/O9//zP38IhITwyAiIjUaNq0KQ4cOIC2bdti7NixCAkJwXPPPYd79+5xRojIBrAQIhEREdkdzgARERGR3WEARERERHaHARARERHZHQZAREREZHcYABEREZHdYQBEREREdocBEBEREdkdBkBERERkdxgAERERkd1hAERERER2hwEQERER2Z3/B9bkHvmizcLsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.index, df_predictions_list, label = \"Neural Network\")\n",
    "plt.scatter(df.index, df[\"mu\"], label = \"Actual mu values\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Growth Rate (mu)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44f0b895",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_input = test[['S(t-1)', 'S(t-2)', 'S(t-3)', 'S(t-4)', 'S(t-5)','S(t-6)',\n",
    "                     'X(t-1)', 'X(t-2)', 'X(t-3)', 'X(t-4)', 'X(t-5)','X(t-6)']].values\n",
    "df_input_tensor = torch.tensor(df_test_input, dtype=torch.float32)\n",
    "with torch.no_grad():\n",
    "    df_predictions_list = []\n",
    "    for row in df_input_tensor:\n",
    "        row = row.reshape(1, 6, 2)\n",
    "        df_predictions = model(row)\n",
    "        df_predictions_list.append(df_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b6ce20a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Growth Rate (mu)')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bxmSIiIiIbBqTISIionomEomwb98+c4dhFCkpKRCJRLh37565QzEaJkNERNRgpKWlwd7eHtHR0Xof27ZtW6xevdr4QZHFYzJERETGJ68EpMeAC98o/iuvNMnXbt68Ga+++iqOHz+OnJwck3wnWT8mQ0REZFxZicDqIOCLEcCeGYr/rg5SlNejBw8eYPfu3Zg1axZGjBiBrVu3atRJTExEaGgoxGIxPDw8MHbsWABAREQEbty4gblz50IkEkEkUuyjtmTJEvTo0UPtHKtXr0bbtm1V70+ePIkhQ4bAw8MDEokEAwcOxJkzZ/SKPSIiAq+++irmzJmDZs2awcvLC59//jkePHiAF154Aa6urmjfvj1++OEH1TFbt25F06ZN1c6zb98+VezahIWF4c0331Qru337NhwdHXHkyBEAwPbt2xEaGgpXV1d4e3tj4sSJyMvLq/acuvyMAGDLli0ICAiAWCxG586dsW7dOtVnZWVleOWVV+Dj4wOxWIy2bdsiPj6+2u80NiZDRERkPFmJwO7ngaI/1MuLZIryekyIdu3ahU6dOqFTp06YPHkytmzZAkEQVJ9///33GDt2LIYPH46MjAwcOnQIoaGhAIC9e/eiVatWWLZsGWQyGWQymc7fW1xcjKlTp+LYsWP45Zdf0LFjRwwbNgzFxcV6xf/FF1/Aw8MDJ06cwKuvvopZs2bh2WefRd++fXHmzBlERUVhypQpePjwoV7nfdykSZOwY8cOtZ/Lrl274OXlhYEDBwJQJCbvvvsuzp07h3379kEqlWLatGkGfycAbNy4EQsXLsTy5cuRnZ2N9957D4sWLcIXX3wBAFizZg0SExOxe/duXLp0Cdu3b9dIpuoTN2olIiLjkFcCB+YDELR8KAAQAQfeBDoPB+zsjf71CQkJmDx5MgAgOjoa9+/fx6FDhzB48GAAwPLlyzFhwgQsXbpUdUz37t0BAM2bN4e9vb2qN0QfgwYNUnv/2WefoVmzZkhNTcWIESN0Pk/37t3x9ttvAwAWLFiAFStWwMPDAzNnzgQAvPPOO1i/fj3Onz+PPn366BWj0vjx4zF37lwcP34c4eHhAICvvvoKEydOhJ2don9k+vTpqvrt2rXDmjVr0KtXL9y/fx8uLi4Gfe+7776LDz/8UNUT5+/vj6ysLHz22WeYOnUqcnJy0LFjR/Tv3x8ikQh+fn4GfY+h2DNERETGcSNNs0dIjQAU3VLUM7JLly7hxIkTmDBhAgDAwcEB48ePx+bNm1V1zp49i6efftro352Xl4eYmBg88cQTkEgkkEgkuH//vt5jlrp166b6f3t7e7i7u6Nr166qMi8vL9X3GcrT0xNDhgzBl19+CQCQSqVIT0/HpEmTVHUyMjIwevRo+Pn5wdXVFREREQBg8Bis27dv4+bNm5gxYwZcXFxUr//3//4frl69CgCYNm0azp49i06dOiE2NhYHDx40uI2GYM8QEREZx/0/jVtPDwkJCaioqEDLli1VZYIgwNHREXfv3kWzZs3QqFEjvc9rZ2en9kgJAMrLy9XeT5s2Dbdv38bq1avh5+cHZ2dnhIWFoaysTK/vcnR0VHsvEonUypRjgeRyuc6xaTNp0iTMnj0bn3zyCb766it06dJF1UP24MEDREZGIjIyEtu3b4enpydycnIQFRVVbXtqi0MZ78aNG9G7d2+1evb2ih7C4OBgSKVS/PDDD/jpp5/w3HPPYfDgwfjmm29qbY8xsGeIiIiMw8XLuPV0VFFRgW3btuHDDz/E2bNnVa9z587Bz89P1QvSrVs3HDp0qNrzODk5obJSfdabp6cncnNz1W72Z8+eVatz7NgxxMbGYtiwYejSpQucnZ2Rn59vvAZWw9PTE8XFxXjw4EG1sWkzZswYlJSU4MCBA/jqq69UjxYB4LfffkN+fj5WrFiB8PBwdO7cudaeqNp+Rl5eXmjZsiWuXbuGDh06qL38/f1V9dzc3DB+/Hhs3LgRu3btwp49e3Dnzh0dfhJ1x54hIiIyDr++gJuvYrC01nFDIsXnfn2N+rXfffcd7t69ixkzZkAikah99o9//AMJCQl45ZVXsHjxYjz99NNo3749JkyYgIqKCvzwww944403ACjWGTp69CgmTJgAZ2dneHh4ICIiArdv38b//d//4R//+AcOHDiAH374AW5ubqrv6NChA/7zn/8gNDQURUVFeP311w3qhdJX79690bhxY7z11lt49dVXceLECa0z6Kpq0qQJRo8ejUWLFiE7OxsTJ05UfdamTRs4OTnhk08+QUxMDDIzM/Huu+/WeD5dfkZLlixBbGws3NzcMHToUJSWluLUqVO4e/cu4uLi8NFHH8HHxwc9evSAnZ0dvv76a3h7e2vMlqsv7BkiIiLjsLMHolf+9abq9O6/3kevMPrg6YSEBAwePFgjEQKAcePG4ezZszhz5gwiIiLw9ddfIzExET169MCgQYPw66+/quouW7YM169fR/v27eHp6QkACAgIwLp16/Dpp5+ie/fuOHHiBObNm6f2HZs3b8bdu3fRs2dPTJkyBbGxsWjRooVR26hN8+bNsX37diQlJaFr167YsWMHlixZotOxkyZNwrlz5xAeHo42bdqoyj09PbF161Z8/fXXCAwMxIoVK/DBBx/UeC5dfkYvvvgiNm3ahK1bt6Jr164YOHAgtm7dquoZcnFxwcqVKxEaGoonn3wS169fR1JSkmpQd30TCVUf9BGKioogkUhQWFioltkSETV0JSUlkEql8Pf3h1gsNuwkWYmKWWWPD6Z2a6lIhAJHGSdQslk1/Y4aev/mYzIiIjKuwFGK6fM30hSDpV28FI/G6mE6PZExMBkiIiLjs7MH/MPNHQWRTjhmiIiIiGwakyEiIiKyaUyGiIhIA+fWkKWqj99NJkNERKSiXBFY39WTiUxF+bup/F01Bg6gJiIiFQcHBzRu3Bi3b9+Go6OjydZ5IdKFXC7H7du30bhxYzg4GC+FYTJEREQqIpEIPj4+kEqluHHjhrnDIdJgZ2eHNm3aqPZqMwYmQ6Ykr+S6G0Rk8ZycnNCxY0c+KiOL5OTkZPQeSyZDpqJ1RVZfxdL1XJGViCyMnZ2d4StQE1kZPgw2haxEYPfz6okQoNjMcPfzis+JiIjILJgM1Td5paJHSOsOzn+VHXhTUY+IiIhMjslQfbuRptkjpEYAim4p6hEREZHJMRmqb/f/NG49IiIiMiomQ/XNxcu49YiIiMiomAzVN7++illjqG49BBHg1lJRj4iIiEyOyVB9s7NXTJ8HoJkQ/fU+egXXGyIiIjITJkOmEDgKeG4b4OajXu7mqyjnOkNERERmY/ZkaN26dfD394dYLEZISAiOHTtWY/3U1FSEhIRALBajXbt22LBhg9rnEREREIlEGq/hw4fXZzNqFzgKmJMJTP0OGJeg+O+cC0yEiIiIzMysK1Dv2rULc+bMwbp169CvXz989tlnGDp0KLKystCmTRuN+lKpFMOGDcPMmTOxfft2/Pzzz/j3v/8NT09PjBs3DgCwd+9etSXkCwoK0L17dzz77LMma1e17OwB/3BzR0FERESPEQmCoG01QJPo3bs3goODsX79elVZQEAAxowZg/j4eI368+fPR2JiIrKzs1VlMTExOHfuHNLT07V+x+rVq/HOO+9AJpOhSZMmOsVVVFQEiUSCwsJCuLm56dkqIiIiMgdD799me0xWVlaG06dPIzIyUq08MjISaWnaFyBMT0/XqB8VFYVTp06hvLxc6zEJCQmYMGFCjYlQaWkpioqK1F5ERERkG8yWDOXn56OyshJeXurr63h5eSE3N1frMbm5uVrrV1RUID8/X6P+iRMnkJmZiRdffLHGWOLj4yGRSFSv1q1b69kaIiIislZmH0AtEqlPNxcEQaOstvraygFFr1BQUBB69epVYwwLFixAYWGh6nXz5k1dwyciIiIrZ7YB1B4eHrC3t9foBcrLy9Po/VHy9vbWWt/BwQHu7u5q5Q8fPsTOnTuxbNmyWmNxdnaGs7Ozni0gIiKihsBsPUNOTk4ICQlBcnKyWnlycjL69tW+GnNYWJhG/YMHDyI0NBSOjo5q5bt370ZpaSkmT55s3MCJiIioQTHrY7K4uDhs2rQJmzdvRnZ2NubOnYucnBzExMQAUDy+ev7551X1Y2JicOPGDcTFxSE7OxubN29GQkIC5s2bp3HuhIQEjBkzRqPHiIiIiOhxZl1naPz48SgoKMCyZcsgk8kQFBSEpKQk+Pn5AQBkMhlycnJU9f39/ZGUlIS5c+fi008/ha+vL9asWaNaY0jp999/x/Hjx3Hw4EGTtoeIiIisj1nXGbJUXGeIiIjI+ljdOkNEREREloDJEBEREdk0JkNERERk05gMERERkU1jMkREREQ2jckQERER2TQmQ0RERGTTmAwRERGRTWMyRERERDaNyRARERHZNCZDREREZNOYDBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMERERkU1jMkREREQ2jckQERER2TQmQ0RERGTTmAwRERGRTWMyRERERDaNyRARERHZNCZDREREZNOYDBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMERERkU1jMkREREQ2jckQERER2TQmQ0RERGTTmAwRERGRTWMyRERERDaNyRARERHZNCZDREREZNOYDBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMERERkU1jMkREREQ2jckQERER2TQHcwdA1qdSLuCE9A7yikvQwlWMXv7NYW8nMndYREREBmEyRHo5kCnD0v1ZkBWWqMp8JGIsHhmI6CAfM0ZGRERkGD4mI50dyJRh1vYzaokQAOQWlmDW9jM4kCkzU2RERESGYzJEOqmUC1i6PwuCls+UZUv3Z6FSrq0GERGR5WIyRDo5Ib2j0SP0OAGArLAEJ6R3TBcUERGRETAZIp3kFVefCBlSj4iIyFIwGSKdtHAVG7UeERGRpWAyRDrp5d8cPhIxqptAL4JiVlkv/+amDIuIiKjODE6GysvLcfPmTVy6dAl37nCcSENnbyfC4pGBAKCRECnfLx4ZyPWGiIjI6uiVDN2/fx+fffYZIiIiIJFI0LZtWwQGBsLT0xN+fn6YOXMmTp48WV+xkplFB/lg/eRgeEvUH4V5S8RYPzmY6wwREZFVEgmCoNNc6I8++gjLly9H27ZtMWrUKPTq1QstW7ZEo0aNcOfOHWRmZuLYsWP49ttv0adPH3zyySfo2LFjfcdfL4qKiiCRSFBYWAg3Nzdzh2NxuAI1ERFZIkPv3zonQ88++yzeeecddO3atcZ6paWlSEhIgJOTE1588UWdA7EkTIaIiIisj6H3b50fk3399de1JkIA4OzsjH//+986J0Lr1q2Dv78/xGIxQkJCcOzYsRrrp6amIiQkBGKxGO3atcOGDRs06ty7dw8vv/wyfHx8IBaLERAQgKSkJJ3iISIiItti1tlku3btwpw5c7Bw4UJkZGQgPDwcQ4cORU5Ojtb6UqkUw4YNQ3h4ODIyMvDWW28hNjYWe/bsUdUpKyvDkCFDcP36dXzzzTe4dOkSNm7ciJYtW5qqWURERGRFdH5M9rinnnoKIlH1Y0QOHz6s03l69+6N4OBgrF+/XlUWEBCAMWPGID4+XqP+/PnzkZiYiOzsbFVZTEwMzp07h/T0dADAhg0b8P777+O3336Do6Ojrk1Sw8dkRERE1qfeH5M9rkePHujevbvqFRgYiLKyMpw5c0anR2mAogfn9OnTiIyMVCuPjIxEWlqa1mPS09M16kdFReHUqVMoLy8HACQmJiIsLAwvv/wyvLy8EBQUhPfeew+VlZXVxlJaWoqioiK1FxEREdkGB0MO+uijj7SWL1myBPfv39fpHPn5+aisrISXl5dauZeXF3Jzc7Uek5ubq7V+RUUF8vPz4ePjg2vXruHw4cOYNGkSkpKScPnyZbz88suoqKjAO++8o/W88fHxWLp0qU5xExERUcNi1DFDkydPxubNm/U6purjNkEQanwEp63+4+VyuRwtWrTA559/jpCQEEyYMAELFy5UexRX1YIFC1BYWKh63bx5U682EBERkfUyqGeoOunp6RCLddubysPDA/b29hq9QHl5eRq9P0re3t5a6zs4OMDd3R0A4OPjA0dHR9jb26vqBAQEIDc3F2VlZXByctI4r7OzM5ydnXWKm4iIiBoWg5KhsWPHqr0XBAEymQynTp3CokWLdDqHk5MTQkJCkJycjGeeeUZVnpycjNGjR2s9JiwsDPv371crO3jwIEJDQ1WDpfv164evvvoKcrkcdnaKjq/ff/8dPj4+WhMhIiIism0GPSaTSCRqr+bNmyMiIgJJSUlYvHixzueJi4vDpk2bsHnzZmRnZ2Pu3LnIyclBTEwMAMXjq+eff15VPyYmBjdu3EBcXByys7OxefNmJCQkYN68eao6s2bNQkFBAWbPno3ff/8d33//Pd577z28/PLLhjSViIiIGjiDeoa2bNlilC8fP348CgoKsGzZMshkMgQFBSEpKQl+fn4AAJlMprbmkL+/P5KSkjB37lx8+umn8PX1xZo1azBu3DhVndatW+PgwYOYO3cuunXrhpYtW2L27NmYP3++UWImIiKihsWgdYYed//+fcjlcrUya1+bh+sMERERWR+TrjMklUoxfPhwNGnSBBKJBM2aNUOzZs3QtGlTNGvWzJBTEhEREZmFQY/JJk2aBADYvHkzvLy8apwKT0RERGTJDEqGzp8/j9OnT6NTp07GjoeIiIjIpAx6TPbkk09yYUIiIiJqEAzqGdq0aRNiYmJw69YtBAUFaWyI2q1bN6MER0RERFTfDEqGbt++jatXr+KFF15QlYlEItVWGjVtikpERERkSQxKhqZPn46ePXtix44dHEBNREREVs2gZOjGjRtITExEhw4djB0PkUqlXMAJ6R3kFZeghasYvfybw96OiTcRERmXQcnQoEGDcO7cOSZDVG8OZMqwdH8WZIUlqjIfiRiLRwYiOsjHjJEREVFDY1AyNHLkSMydOxcXLlxA165dNQZQjxo1yijBkW06kCnDrO1nUHVp9NzCEszafgbrJwczISIiIqMxaDsO5W7wWk/YAAZQczsO86mUC+i/8rBaj9DjRAC8JWIcnz+Ij8yIiEiNSbfjkMvl1b6sPREi8zohvVNtIgQAAgBZYQlOSO+YLigiImrQDEqGiOpLXnH1iZAh9YiIiGqjczK0c+dOnU968+ZN/PzzzwYFRLathavYqPWIiIhqo3MytH79enTu3BkrV65Edna2xueFhYVISkrCxIkTERISgjt3+BiD9NfLvzl8JGJUNxpIBMWssl7+zU0ZFhERNWA6J0Opqan44IMPcPjwYQQFBcHNzQ0dO3ZE165d0apVK7i7u2PGjBlo27YtMjMzMXLkyPqMmxooezsRFo8MBACNhEj5fvHIQA6eJiIiozFoNllBQQGOHz+O69ev49GjR/Dw8EDPnj3Rs2fPGmeaWQvOJjM/rjNERET6MvT+bVAy1NAxGbIMuq5AzZWqiYgIMPz+bdCii0SmYG8nQlh79xrrsAeJiIjqyvqfaZHNUq5UXXVdIuVK1QcyZWaKjIiIrAmTIbJKlXIBS/dnaWzZAUBVtnR/FirlfApMREQ1YzJEVokrVRMRkbHUKRkqKyvDpUuXUFFRYax4iHSi6wrUP2TKkH61gD1ERERULYOSoYcPH2LGjBlo3LgxunTpgpycHABAbGwsVqxYYdQAibTRdQXqbek38M+Nv6D/ysMcQ0RERFoZlAwtWLAA586dQ0pKCsTiv29KgwcPxq5du4wWHFF1alupuioOqiYiouoYlAzt27cPa9euRf/+/SES/X07CgwMxNWrV40WHFF1alqpWhsOqiYiouoYlAzdvn0bLVq00Ch/8OCBWnJEVJ+ig3ywfnIwvCW6PTLjoGoiItLGoGToySefxPfff696r0yANm7ciLCwMONERqSD6CAfHJ8/CDtm9sHzYX46HaPr4GsiIrINBq1AHR8fj+joaGRlZaGiogIff/wxLl68iPT0dKSmpho7RqIaPb5S9bb0G7XW13XwNRER2QaDeob69u2Ln3/+GQ8fPkT79u1x8OBBeHl5IT09HSEhIcaOkUgntQ2qFkGxVUcv/+amDIuIiCwcN2rVghu1Wi/lFh0A1FanViZI6ycHc88yIqIGytD7t0E9Q/b29sjLy9MoLygogL29vSGnJDKK6gZVe0vETISIiEgrg8YMVdeZVFpaCicnpzoFRFRX0UE+GBLojRPSO8grLkELV8WjMXs7znQkIiJNeiVDa9asAaCYPbZp0ya4uLioPqusrMTRo0fRuXNn40ZIZIDHB1UTERHVRK9k6KOPPgKg6BnasGGD2iMxJycntG3bFhs2bDBuhERERET1SK9kSCqVAgCeeuop7N27F82aNauXoIiIiIhMxaAxQ0eOHDF2HERERERmYVAyBAD/+9//kJiYiJycHJSVlal9tmrVqjoHRkRERGQKBiVDhw4dwqhRo+Dv749Lly4hKCgI169fhyAICA4ONnaMRERERPXGoHWGFixYgNdeew2ZmZkQi8XYs2cPbt68iYEDB+LZZ581doxERERE9cagZCg7OxtTp04FADg4OODRo0dwcXHBsmXLsHLlSqMGSERERFSfDEqGmjRpgtLSUgCAr68vrl69qvosPz/fOJERERERmYBBY4b69OmDn3/+GYGBgRg+fDhee+01XLhwAXv37kWfPn2MHSMRERFRvTEoGVq1ahXu378PAFiyZAnu37+PXbt2oUOHDqqFGYmIiIisAXet14K71hMREVkfk+5aX529e/eiW7duxjwlERERUb3SOxnauHEjnn32WUycOBG//vorAODw4cPo2bMnJk+ejLCwMKMHSURERFRf9EqGPvjgA7z88suQSqX473//i0GDBuG9997Dc889hzFjxiAnJwefffZZfcVKREREZHR6DaBOSEjAhg0bMH36dKSkpGDQoEE4fPgwrly5gqZNm9ZTiERERET1R6+eoRs3bmDw4MEAgIiICDg6OmL58uVMhIiIiMhq6ZUMlZSUQCwWq947OTnB09PT6EERERERmYre6wxt2rQJLi4uAICKigps3boVHh4eanViY2ONEx0RERFRPdNrnaG2bdtCJBLVfEKRCNeuXatzYObEdYaIiIisj6H3b716hq5fv65vXEREREQWzaiLLhpi3bp18Pf3h1gsRkhICI4dO1Zj/dTUVISEhEAsFqNdu3bYsGGD2udbt26FSCTSeJWUlNRnM4iIiMhKmTUZ2rVrF+bMmYOFCxciIyMD4eHhGDp0KHJycrTWl0qlGDZsGMLDw5GRkYG33noLsbGx2LNnj1o9Nzc3yGQytdfjA7+JiIiIlMy6N1nv3r0RHByM9evXq8oCAgIwZswYxMfHa9SfP38+EhMTkZ2drSqLiYnBuXPnkJ6eDkDRMzRnzhzcu3fP4Lg4ZoiIiMj6WMTeZPooKyvD6dOnERkZqVYeGRmJtLQ0rcekp6dr1I+KisKpU6dQXl6uKrt//z78/PzQqlUrjBgxAhkZGTXGUlpaiqKiIrUXERER2QazJUP5+fmorKyEl5eXWrmXlxdyc3O1HpObm6u1fkVFBfLz8wEAnTt3xtatW5GYmIgdO3ZALBajX79+uHz5crWxxMfHQyKRqF6tW7euY+uIiIjIWui9zpCSXC7HlStXkJeXB7lcrvbZgAEDdD5P1an6giDUOH1fW/3Hy/v06YM+ffqoPu/Xrx+Cg4PxySefYM2aNVrPuWDBAsTFxaneFxUVMSEiIiKyEQYlQ7/88gsmTpyIGzduoOqQI5FIhMrKylrP4eHhAXt7e41eoLy8PI3eHyVvb2+t9R0cHODu7q71GDs7Ozz55JM19gw5OzvD2dm51piJiIio4THoMVlMTAxCQ0ORmZmJO3fu4O7du6rXnTt3dDqHk5MTQkJCkJycrFaenJyMvn37aj0mLCxMo/7BgwcRGhoKR0dHrccIgoCzZ8/Cx8dHp7iIiIjIthjUM3T58mV888036NChQ52+PC4uDlOmTEFoaCjCwsLw+eefIycnBzExMQAUj69u3bqFbdu2AVAkYWvXrkVcXBxmzpyJ9PR0JCQkYMeOHapzLl26FH369EHHjh1RVFSENWvW4OzZs/j000/rFCs9Rl4J3EgD7v8JuHgBfn0BO3tzR0VERGQQg5Kh3r1748qVK3VOhsaPH4+CggIsW7YMMpkMQUFBSEpKgp+fHwBAJpOprTnk7++PpKQkzJ07F59++il8fX2xZs0ajBs3TlXn3r17eOmll5CbmwuJRIKePXvi6NGj6NWrV51ipb9kJQIH5gNFf/xd5uYLRK8EAkeZLy4iIiID6bzO0Pnz51X/f/XqVbz99tt4/fXX0bVrV41HVN26dTNulCbGdYaqkZUI7H4eQNVfmb8GtT+3jQkRERGZjaH3b52TITs7O4hEIo0B06oT/fWZrgOoLRmTIS3klcDqIPUeITUiRQ/RnAt8ZEZERGZR7xu1SqVSgwKjBuJGWg2JEAAIQNEtRT3/cJOFRX+rlAs4Ib2DvOIStHAVo5d/c9jbVb9MBRERKeicDCnH8QDA0aNH0bdvXzg4qB9eUVGBtLQ0tbrUQNz/07j1yKgOZMqwdH8WZIV/b0jsIxFj8chARAdxJiURUU0Mmlr/1FNPaZ1CX1hYiKeeeqrOQZEFctG+9pPB9choDmTKMGv7GbVECAByC0swa/sZHMiUmSkyIiLrYFAyVN0q0QUFBWjSpEmdgyIL5NdXMSYI1T12EQFuLRX1yGQq5QKW7s/SGNIO/D3Mfen+LFTKzbYfMxGRxdNrav3YsWMBKAZLT5s2TW3V5srKSpw/f77aBRPJytnZK6bP734eioTo8ZvrXwlS9AoOnjaxE9I7Gj1CjxMAyApLcEJ6B2Htta/STkRk6/RKhiQSCQBFz5CrqysaNWqk+szJyQl9+vTBzJkzjRshWY7AUYrp81rXGVrBafVmkFdcfSJkSD0iIlukVzK0ZcsWAEDbtm0xb948PhKzRYGjgM7DuQK1hWjhKta7HmedERGpM2gFal9fX/zxxx/o2LGjseMha2Bnz+nzFqKXf3P4SMTILSzROm5IBMBbokh4AM46IyLSRudFFx/XuXNnXL58GV5eXhg4cCAiIiIwcOBAdO7cuT5iNDkuukjWRDmbDNA6kgvrJwcjOshHVa+a9cMxZ/ATaOvRmL1FRGS16n0F6qpyc3Nx5MgRpKamIiUlBZcvX4anpyciIiKwc+dOQ05pMZgMkbWprcenUi6g/8rDNQ62fhx7i4jIGpk8GVJ68OABjh8/jp07d2L79u0QBAEVFRV1OaXZMRkia1TTWKD0qwX458ZfdD5X1V4lIiJrUO/bcTzuhx9+UPUInTt3Dl26dMGAAQOwZ88ehIdzLAmROdjbiaqdPq/vbDIBioRo4beZeFRWCW9JIz46I6IGy6BkaPjw4fD09MRrr72GH3/8UTXlnogsk66zzh4nACh4UIa5u88B4KMzImq4DFqBetWqVejXrx/ef/99dOrUCePHj8f69euRnZ1t7PiIyAiUs87q0q/D7T2IqKGq85ihCxcuIDU1FUeOHMH+/fvh7u4Omcy6/7LkmCFqiKqbdaav5k0csWhEF3i7cdYZEVkWQ+/fBvUMKWVkZOCnn37CwYMHcfjwYcjlcrRq1aoupySiehId5IP1k4PhLdH/kdnj7jwox9xdZ/HPjb+g/8rD7CkiIqtnUM/QqFGjcPz4cRQVFaFHjx6IiIhAREQEBgwY0CB6UtgzRA3Z47POruc/wEc/XdbYbU5XXKOIiCyJSWeTPfHEE3jppZcaTPJDZEuqzjrr5O2qsUaRrpQJ1Ec//a4q40BrIrI2dR4z1BCxZ4hsjbK3KLeoBO9+dxF3HpQbfC72FhGRuZi0ZwgAUlNT8cEHHyA7OxsikQgBAQF4/fXXuc4QkRV6vLeokaNdnQZas7eIiKyNQQOot2/fjsGDB6Nx48aIjY3FK6+8gkaNGuHpp5/GV199ZewYiciEjDXQ+nGclk9Elsygx2QBAQF46aWXMHfuXLXyVatWYePGjVa/3hAfkxE99uis8BHe/T4bdx+U1WlKvgiAt0SM4/MH8ZEZEdULk06tv3btGkaOHKlRPmrUKEilUkNOSUQWRvno7JngVnjvmSAAqNOijQIAWWEJTkjvGCU+IiJjMSgZat26NQ4dOqRRfujQIbRu3brOQRGRZTHmozN990kjIqpvBg2gfu211xAbG4uzZ8+ib9++EIlEOH78OLZu3YqPP/7Y2DESkQWIDvLBkEDvOq9RZMg+aURE9cmgZGjWrFnw9vbGhx9+iN27dwNQjCPatWsXRo8ebdQAichy1GWNIuWYoV7+zesxQiIi/emdDFVUVGD58uWYPn06jh8/Xh8xEZGV0LW3SDnWaPHIQA6eJiKLY9BsMhcXF2RmZqJt27b1EJL5cTYZkeEOZMo0eou4zhARmYJJF10cPHgwUlJSMG3aNEMOJ6IGrGpvEVegJiJLZ1AyNHToUCxYsACZmZkICQlBkyZN1D4fNWqUUYIjIutUdWwREZElM+gxmZ1d9TPyRSIRKisr6xSUufExGRERkfUx6WMyuVxuyGFEREREFsfgjVqJ6p28EriRBtz/E3DxAvz6Anb25o6KiIgaGL2SoUePHuHQoUMYMWIEAGDBggUoLS1VfW5vb493330XYjEXVaM6ykoEDswHiv74u8zNF4heCQRyTBoRERmPXsnQtm3b8N1336mSobVr16JLly5o1KgRAOC3336Dr6+vxgauRHrJSgR2Pw+NdY2LZIry57YxISIiIqPRa2+yL7/8EtOnT1cr++qrr3DkyBEcOXIE77//vmpFaiKDyCsVPUJaN3j4q+zAm4p6RERERqBXMvT777/jiSeeUL0Xi8VqM8t69eqFrKws40VHtudGmvqjMQ0CUHRLUY+IiMgI9HpMVlhYCAeHvw+5ffu22udyuVxtDBGR3u7/adx6REREtdCrZ6hVq1bIzMys9vPz58+jVatWdQ6KbJiLl3HrERER1UKvZGjYsGF45513UFKiuUP1o0ePsHTpUgwfPtxowZEN8uurmDWG6rZuEAFuLRX1iIiIjECvFaj//PNP9OjRA05OTnjllVfwxBNPQCQS4bfffsPatWtRUVGBjIwMeHlZ97/auQK1malmkwFa9z7nbDIiItLC0Pu33ttxSKVSzJo1C8nJyVAeKhKJMGTIEKxbtw7t2rXTL3ILxGTIAmhdZ6glEL2CiRAREWllsmRI6c6dO7hy5QoAoEOHDmjevLkhp7FITIYsBFegJgtVKRdwQnoHecUlaOEqRi//5rC3q+7RLhGZikn3JgOA5s2bo1evXoYeTlQ7O3vAP7z2ekyayIQOZMqwdH8WZIV/j530kYixeGQgooN8zBgZERmKe5ORdeO2HWRCBzJlmLX9jMaSoLmFJZi1/QzWTw5mQkRkhfSaTUZkUZQDrasu0qjctiMr0TxxUYNUKRewdH9WTWujY+n+LFTKDRp5QERmxGSIrBO37SATOyG9o/ZorCoBgKywBCekd0wXFBEZBZMhsk7ctoNMLK+4+kTIkHpEZDk4Zoisk67bcWT/9aiMg6qpjlq4io1aj4gsB3uGyDrpuh3Hic+BL0YAq4M4hojqpJd/c/hIxDWtjQ4fiWKavb4q5QLSrxbgv2dvIf1qAccdEZkYe4bIOim37SiSQfu4oSqUg6q5ejUZyN5OhMUjAzFr+xmIoHVtdCweGaj3ekOcqk9kfuwZIutkZ6+YPg+g+n3MHsdB1VR30UE+WD85GN4S9Udh3hKxQdPqlVP1qw7MVk7VP5Apq3PMRFQ7g1egbsi4ArUV0bbOUG2mfqfbYo5E1TDGCtSVcgH9Vx6udoaaCIok6/j8QVzdmkhHht6/zd4ztG7dOvj7+0MsFiMkJATHjh2rsX5qaipCQkIgFovRrl07bNiwodq6O3fuhEgkwpgxY4wcNVmMwFHAnExFgtPrJd2O0XXwNVE17O1ECGvvjtE9WiKsvbtByQqn6hNZDrMmQ7t27cKcOXOwcOFCZGRkIDw8HEOHDkVOTo7W+lKpFMOGDUN4eDgyMjLw1ltvITY2Fnv27NGoe+PGDcybNw/h4ewBaPCU23YE6DgWSNfB10T1iFP1iSyHWZOhVatWYcaMGXjxxRcREBCA1atXo3Xr1li/fr3W+hs2bECbNm2wevVqBAQE4MUXX8T06dPxwQcfqNWrrKzEpEmTsHTpUrRr184UTSFLoBxUXdN8H7eWinpEZsap+kSWw2zJUFlZGU6fPo3IyEi18sjISKSlaV8oLz09XaN+VFQUTp06hfLyclXZsmXL4OnpiRkzZugUS2lpKYqKitReZIVqHFT91/voFVxviCxCfU7VJyL9mC0Zys/PR2VlJby81B9ZeHl5ITc3V+sxubm5WutXVFQgPz8fAPDzzz8jISEBGzdu1DmW+Ph4SCQS1at169Z6toYsRuAoxfR5tyqzetx8Oa2eLIpyqj5Qbepu0FR9ItKf2dcZEonU/6ALgqBRVlt9ZXlxcTEmT56MjRs3wsPDQ+cYFixYgLi4ONX7oqIiJkTWLHAU0Hm4YiuO+38qxghxBWqyQMqp+lXXGfI24TpDxpgZR2TtzJYMeXh4wN7eXqMXKC8vT6P3R8nb21trfQcHB7i7u+PixYu4fv06Ro4cqfpcLpcDABwcHHDp0iW0b99e47zOzs5wdnaua5PIkigHVRNZuOggHwwJ9DZLQsIFH4kUzJYMOTk5ISQkBMnJyXjmmWdU5cnJyRg9erTWY8LCwrB//361soMHDyI0NBSOjo7o3LkzLly4oPb522+/jeLiYnz88cfs7SEii6Scqm9KygUfqy40p1zw0ZBFJImslVkfk8XFxWHKlCkIDQ1FWFgYPv/8c+Tk5CAmJgaA4vHVrVu3sG3bNgBATEwM1q5di7i4OMycORPp6elISEjAjh07AABisRhBQUFq39G0aVMA0CgnIrJVlXIBS/dnad3IRoBizNLS/VkYEujNR2ZkE8yaDI0fPx4FBQVYtmwZZDIZgoKCkJSUBD8/PwCATCZTW3PI398fSUlJmDt3Lj799FP4+vpizZo1GDdunLmaQERkdfRZ8NHUPVZE5sDtOLTgdhxE1JD99+wtzN55ttZ6H0/ogdE9WtZ/QERGYrXbcRARkWlxwUcidUyGiIhsDBd8JFLHZIiIyMZwwUcyp0q5gPSrBfjv2VtIv1qASrn5R+uYfdFFIiIyPUtY8JFsj6WubcUB1FpwADUR2QquQE1K9f27UN3aVspvMMbaVobev9kzRERkw8yx4CPVTNekxJjJS3332Fj62lZMhoiIyGJZa8+VtrgB1Fp290Ep3v0+u9akxJjJiylWI7f0ta2YDBERkUWy1PEltdEWd9PGjgCAew/LayzTpmpSYszkxVQ9NnnF1SdChtQzNs4mIyIii6O84VftTVDe8A9kytTKLWWGUnVx33tYrpH0aCvTRtmSpfuzUFYhrzF5UdbTtf369NjUhaWvbcWeISIisij69lZYSg9STXHXlTIp+U/6daM+bjJVj41ybavcwhKtPx8RFDMZzbW2FXuGiIjIoujTW6FvD1J9qi1uY7hx56FO9XRNXkzVY2Ppa1sxGSIiIoui6408t/CRUR8Z1ZUpxrv4NW+sUz1dkxdTrkauXNvKW6Iem7dEbJRB2nXBx2RERGRRdL2R33lQZlEzlOpzvIvyMdKUsLbYdFxqtMdNyh6bWdvPQASonbM+emyig3wwJNDb4mYIsmeIiIgsiq69Fc1dnHU6n6lmKNUWt6EeT0qcHOyM/rjJ1D02yrWtRvdoibD27mZPhAD2DBERkYXRtbdC0shJp/OZaoZSTXHXRdUtUupjKxVL7bExFW7HoQW34yAiMr/aZolVygX0X3m41kdGx+cPMulNXdd1hpo1doRQpcxHIsai4YFo1sTJpCtQNxSG3r+ZDGnBZIiIyDLUdsNXziYDtPcgmWtgrqErUDOhqRsmQ0bEZIiIyHpYyjpDZH7cqJWIiGySrY93obpjMkRERFZPOUOJyBCcWk9EREQ2jckQERER2TQmQ0RERGTTmAwRERGRTWMyRERERDaNyRARERHZNCZDREREZNO4zhBRQyGvBG6kAff/BFy8AL++gJ29uaMiIrJ4TIaIGoKsRODAfKDoj7/L3HyB6JVA4CjzxUVEZAX4mIzI2mUlArufV0+EAKBIpijPSjRPXEREVoLJEJE1k1cqeoSgbb/lv8oOvKmoR0REWjEZIrJmN9I0e4TUCEDRLUU9IiLSimOGiKzZ/T/1r8eB1kREapgMEVkzFy/96nGgNRGRBiZDRNbMr68imSmSQfu4IZHic7++fw+0rlpPOdA6YgHg3p69RURkc5gMEVkzO3tFr87u5wGIoJ7oiBT/iV6h+G9tA61T3vu7iL1FRGRDOICayNoFjgKe2wa4+aiXu/kqygNH6TDQugplb1HKSuDCN4D0GGekEVGDxZ4hooYgcBTQeXj1A6N1HWitwt4iIrIdTIaIGgo7e8A/XPtnug60rkmRDNg9BYh4i2OLiKhBYTJEZAtqHWitC/YWEVHDxDFDRLZAOdAagGpgtTFwbBERNQAiQRAM/Wdig1VUVASJRILCwkK4ubmZOxwi49G2zpCxsbeIiMzE0Ps3kyEtmAxRg/b4CtQFV/967FV1Wn5d/NXzxHWLiMjEDL1/c8wQka2pOtC6RYCRe4s4toiIrAt7hrRgzxDZHFP1FinXPSIiqgfsGSIiw5mkt0gEHHhTsR4SH5kRkQVhMkREmqou4lhwFUiJ/+vDOkzNL7qlOGd16yEREZkBkyEi0q6+eov0Xg2biKh+MRkiIt1o7S0yYGyRMVbDJiIyIiZDRKS7OvUWiRSzyvz61lt4RESGYDJERIbTubfor9lk0Ss4eJqILA6TISKqG116i9x8FYkQp9UTkQViMkRExlW1t4grUBORhWMyRETGV7W3iIjIgpl91/p169bB398fYrEYISEhOHbsWI31U1NTERISArFYjHbt2mHDhg1qn+/duxehoaFo2rQpmjRpgh49euA///lPfTaBiIiIrJhZk6Fdu3Zhzpw5WLhwITIyMhAeHo6hQ4ciJydHa32pVIphw4YhPDwcGRkZeOuttxAbG4s9e/ao6jRv3hwLFy5Eeno6zp8/jxdeeAEvvPACfvzxR1M1i4iIiKyIWfcm6927N4KDg7F+/XpVWUBAAMaMGYP4+HiN+vPnz0diYiKys7NVZTExMTh37hzS09Or/Z7g4GAMHz4c7777rk5xcW8yIiIi62Po/dtsPUNlZWU4ffo0IiMj1cojIyORlpam9Zj09HSN+lFRUTh16hTKy8s16guCgEOHDuHSpUsYMGBAtbGUlpaiqKhI7UVERES2wWzJUH5+PiorK+Hlpb4arZeXF3Jzc7Uek5ubq7V+RUUF8vPzVWWFhYVwcXGBk5MThg8fjk8++QRDhgypNpb4+HhIJBLVq3Xr1nVoGREREVkTsw+gFolEau8FQdAoq61+1XJXV1ecPXsWJ0+exPLlyxEXF4eUlJRqz7lgwQIUFhaqXjdv3jSgJURERGSNzDa13sPDA/b29hq9QHl5eRq9P0re3t5a6zs4OMDd3V1VZmdnhw4dOgAAevTogezsbMTHxyMiIkLreZ2dneHs7FyH1hCRTZFXch0logbEbMmQk5MTQkJCkJycjGeeeUZVnpycjNGjR2s9JiwsDPv371crO3jwIEJDQ+Ho6FjtdwmCgNLSUuMETkS2LSuxmhW2V3KFbSIrZdbHZHFxcdi0aRM2b96M7OxszJ07Fzk5OYiJiQGgeHz1/PPPq+rHxMTgxo0biIuLQ3Z2NjZv3oyEhATMmzdPVSc+Ph7Jycm4du0afvvtN6xatQrbtm3D5MmTTd4+ImpgshKB3c9rbkxbJFOUZyWaJy4iqhOzrkA9fvx4FBQUYNmyZZDJZAgKCkJSUhL8/PwAADKZTG3NIX9/fyQlJWHu3Ln49NNP4evrizVr1mDcuHGqOg8ePMC///1v/O9//0OjRo3QuXNnbN++HePHjzd5+4ioAZFXKnqEoG01EgGACDjwpmIrEj4yI7IqZl1nyFJxnSEi0iA9BnwxovZ6U7/jViREZmLo/Zt7kxER6eL+n8at9zgOyCYyKyZDRES6cNE+y9XgekockE1kdmZfZ4iIyCr49VUkKahuHTQR4NZSUU9XHJBNZBGYDBER6cLOXtFbA0AzIfrrffQK3R9v1TogG4oB2fJK/WMlIr0wGSIi0lXgKOC5bYCbj3q5m6+iXJ/HWjfSNHuE1AhA0S1FPSKqVxwzRESkj8BRiunzdR3wXJ8DsolIL0yGiIj0ZWdf9+nz9TUgW1+cyUbEZIiIyCyUA7KLZNA+bkik+FyfAdn64kw2IgAcM0REZB7GHpCtL85kI1JhMkREZC7GHJCtD85kI1LDx2REROZkrAHZ+tBnJhu3FiEbwGSIiMjcjDEgWx+cyUakhskQEZGtsZSZbFQ3xp4JaKqZhRY4g5HJEBGRrbGEmWy60vXGaWk3WG3xAIaXVW2LsWcCmmpmoYXOYBQJgqDtT4JNKyoqgkQiQWFhIdzc3MwdDhGR8SlnkwFQT4j+mslWnwO4daXrjdPSbrDa4mnUDIAIeHTnsbLmAATg0d2ay6q2RXXtqt6+Dbx2xj6fGb/H0Ps3Z5MREdkic81k05WuU/8tbYmA6uJ5dFc9EQIU7x9Peqore7wtxp4JaKqZhRY+g5GPyYiIbJU5ZrLpotYbp0hx43wiWrd6nYebpk01xl0Xj7VFLDHuTEBTzSy08BmMTIaIiGyZqWey6ULXG+fJjZZ1g6017rr4qy3SY7pVN/aMwbrOLLTwGYx8TEZERJZF1xvi3evGPV9dmeJ7qi5WXh1jzxis68xCC5/ByGSIiIgsi643xGZtjXu+ujLF9/j1V4zrqjYrEgFuLXWfCaicWWis85n7ewzEZIiIiCyLrjfOJ2da1g221rjr4q+2+Icbd087U+2RZ+69+GrBZIiIiCyLrjdOByfLusHWGHddVGmLsWcCmmpmoQXPYOQ6Q1pwnSEiIgugdf2gloqkoNZ1hrTUMxWt6ww1V/zXoHWGqmkLV6DWYOj9m8mQFkyGiIgsBFegNn9brAiTISNiMkRERGR9uAI1ERERkQGYDBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMERERkU1jMkREREQ2jckQERER2TQmQ0RERGTTHMwdgCVSLspdVFRk5kiIiIhIV8r7tr6bazAZ0qK4uBgA0Lp1azNHQkRERPoqLi6GRCLRuT73JtNCLpfjjz/+gKurK0QikbnD0aqoqAitW7fGzZs3bW7/NLbdNtsO2Hb72XbbbDtg2+3Xt+2CIKC4uBi+vr6ws9N9JBB7hrSws7NDq1atzB2GTtzc3GzuD4cS226bbQdsu/1su222HbDt9uvTdn16hJQ4gJqIiIhsGpMhIiIismlMhqyUs7MzFi9eDGdnZ3OHYnJsu222HbDt9rPtttl2wLbbb6q2cwA1ERER2TT2DBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMWZj4+Hg8+eSTcHV1RYsWLTBmzBhcunSpxmNSUlIgEok0Xr/99puJojaeJUuWaLTD29u7xmNSU1MREhICsViMdu3aYcOGDSaK1rjatm2r9Tq+/PLLWutb+3U/evQoRo4cCV9fX4hEIuzbt0/tc0EQsGTJEvj6+qJRo0aIiIjAxYsXaz3vnj17EBgYCGdnZwQGBuLbb7+tpxYYrqa2l5eXY/78+ejatSuaNGkCX19fPP/88/jjjz9qPOfWrVu1/j6UlJTUc2v0U9t1nzZtmkYb+vTpU+t5reG6A7W3X9s1FIlEeP/996s9pzVce13ubeb8M89kyMKkpqbi5Zdfxi+//ILk5GRUVFQgMjISDx48qPXYS5cuQSaTqV4dO3Y0QcTG16VLF7V2XLhwodq6UqkUw4YNQ3h4ODIyMvDWW28hNjYWe/bsMWHExnHy5Em1dicnJwMAnn322RqPs9br/uDBA3Tv3h1r167V+vn//d//YdWqVVi7di1OnjwJb29vDBkyRLV3oDbp6ekYP348pkyZgnPnzmHKlCl47rnn8Ouvv9ZXMwxSU9sfPnyIM2fOYNGiRThz5gz27t2L33//HaNGjar1vG5ubmq/CzKZDGKxuD6aYLDarjsAREdHq7UhKSmpxnNay3UHam9/1eu3efNmiEQijBs3rsbzWvq11+XeZtY/8wJZtLy8PAGAkJqaWm2dI0eOCACEu3fvmi6werJ48WKhe/fuOtd/4403hM6dO6uV/etf/xL69Olj5MhMb/bs2UL79u0FuVyu9fOGdN0BCN9++63qvVwuF7y9vYUVK1aoykpKSgSJRCJs2LCh2vM899xzQnR0tFpZVFSUMGHCBKPHbCxV267NiRMnBADCjRs3qq2zZcsWQSKRGDe4eqat7VOnThVGjx6t13ms8boLgm7XfvTo0cKgQYNqrGON177qvc3cf+bZM2ThCgsLAQDNmzevtW7Pnj3h4+ODp59+GkeOHKnv0OrN5cuX4evrC39/f0yYMAHXrl2rtm56ejoiIyPVyqKionDq1CmUl5fXd6j1pqysDNu3b8f06dNr3Sy4oVz3x0mlUuTm5qpdW2dnZwwcOBBpaWnVHlfd70NNx1iDwsJCiEQiNG3atMZ69+/fh5+fH1q1aoURI0YgIyPDNAEaWUpKClq0aIEnnngCM2fORF5eXo31G+p1//PPP/H9999jxowZtda1tmtf9d5m7j/zTIYsmCAIiIuLQ//+/REUFFRtPR8fH3z++efYs2cP9u7di06dOuHpp5/G0aNHTRitcfTu3Rvbtm3Djz/+iI0bNyI3Nxd9+/ZFQUGB1vq5ubnw8vJSK/Py8kJFRQXy8/NNEXK92LdvH+7du4dp06ZVW6chXfeqcnNzAUDrtVV+Vt1x+h5j6UpKSvDmm29i4sSJNW5U2blzZ2zduhWJiYnYsWMHxGIx+vXrh8uXL5sw2robOnQovvzySxw+fBgffvghTp48iUGDBqG0tLTaYxridQeAL774Aq6urhg7dmyN9azt2mu7t5n7zzx3rbdgr7zyCs6fP4/jx4/XWK9Tp07o1KmT6n1YWBhu3ryJDz74AAMGDKjvMI1q6NChqv/v2rUrwsLC0L59e3zxxReIi4vTekzVnhPhr0XVa+tRsWQJCQkYOnQofH19q63TkK57dbRd29quqyHHWKry8nJMmDABcrkc69atq7Funz591AYa9+vXD8HBwfjkk0+wZs2a+g7VaMaPH6/6/6CgIISGhsLPzw/ff/99jUlBQ7ruSps3b8akSZNqHftjbde+pnubuf7Ms2fIQr366qtITEzEkSNH0KpVK72P79Onj8X+q0AfTZo0QdeuXatti7e3t8a/APLy8uDg4AB3d3dThGh0N27cwE8//YQXX3xR72MbynVXziDUdm2r/iuw6nH6HmOpysvL8dxzz0EqlSI5ObnGXiFt7Ozs8OSTT1r974OPjw/8/PxqbEdDuu5Kx44dw6VLlwz6e8CSr3119zZz/5lnMmRhBEHAK6+8gr179+Lw4cPw9/c36DwZGRnw8fExcnSmV1paiuzs7GrbEhYWppp1pXTw4EGEhobC0dHRFCEa3ZYtW9CiRQsMHz5c72MbynX39/eHt7e32rUtKytDamoq+vbtW+1x1f0+1HSMJVImQpcvX8ZPP/1kUGIvCALOnj1r9b8PBQUFuHnzZo3taCjX/XEJCQkICQlB9+7d9T7WEq99bfc2s/+Z12u4NdW7WbNmCRKJREhJSRFkMpnq9fDhQ1WdN998U5gyZYrq/UcffSR8++23wu+//y5kZmYKb775pgBA2LNnjzmaUCevvfaakJKSIly7dk345ZdfhBEjRgiurq7C9evXBUHQbPu1a9eExo0bC3PnzhWysrKEhIQEwdHRUfjmm2/M1YQ6qaysFNq0aSPMnz9f47OGdt2Li4uFjIwMISMjQwAgrFq1SsjIyFDNmFqxYoUgkUiEvXv3ChcuXBD++c9/Cj4+PkJRUZHqHFOmTBHefPNN1fuff/5ZsLe3F1asWCFkZ2cLK1asEBwcHIRffvnF5O2rSU1tLy8vF0aNGiW0atVKOHv2rNrfA6WlpapzVG37kiVLhAMHDghXr14VMjIyhBdeeEFwcHAQfv31V3M0sVo1tb24uFh47bXXhLS0NEEqlQpHjhwRwsLChJYtWzaI6y4Itf/eC4IgFBYWCo0bNxbWr1+v9RzWeO11ubeZ8888kyELA0Dra8uWLao6U6dOFQYOHKh6v3LlSqF9+/aCWCwWmjVrJvTv31/4/vvvTR+8EYwfP17w8fERHB0dBV9fX2Hs2LHCxYsXVZ9XbbsgCEJKSorQs2dPwcnJSWjbtm21f4FYgx9//FEAIFy6dEnjs4Z23ZVLA1R9TZ06VRAExVTbxYsXC97e3oKzs7MwYMAA4cKFC2rnGDhwoKq+0tdffy106tRJcHR0FDp37myRyWFNbZdKpdX+PXDkyBHVOaq2fc6cOUKbNm0EJycnwdPTU4iMjBTS0tJM37ha1NT2hw8fCpGRkYKnp6fg6OgotGnTRpg6daqQk5Ojdg5rve6CUPvvvSAIwmeffSY0atRIuHfvntZzWOO11+XeZs4/86K/giQiIiKySRwzRERERDaNyRARERHZNCZDREREZNOYDBEREZFNYzJERERENo3JEBEREdk0JkNERERk05gMERERkU1jMkREDc6SJUvQo0cPc4dBRFaCK1ATkVURiUQ1fj516lSsXbsWpaWlBm1wSkS2h8kQEVmV3Nxc1f/v2rUL77zzDi5duqQqa9SoESQSiTlCIyIrxcdkRGRVvL29VS+JRAKRSKRRVvUx2bRp0zBmzBi899578PLyQtOmTbF06VJUVFTg9ddfR/PmzdGqVSts3rxZ7btu3bqF8ePHo1mzZnB3d8fo0aNx/fp10zaYiOodkyEisgmHDx/GH3/8gaNHj2LVqlVYsmQJRowYgWbNmuHXX39FTEwMYmJicPPmTQDAw4cP8dRTT8HFxQVHjx7F8ePH4eLigujoaJSVlZm5NURkTEyGiMgmNG/eHGvWrEGnTp0wffp0dOrUCQ8fPsRbb72Fjh07YsGCBXBycsLPP/8MANi5cyfs7OywadMmdO3aFQEBAdiyZQtycnKQkpJi3sYQkVE5mDsAIiJT6NKlC+zs/v73n5eXF4KCglTv7e3t4e7ujry8PADA6dOnceXKFbi6uqqdp6SkBFevXjVN0ERkEkyGiMgmODo6qr0XiURay+RyOQBALpcjJCQEX375pca5PD096y9QIjI5JkNERFoEBwdj165daNGiBdzc3MwdDhHVI44ZIiLSYtKkSfDw8MDo0aNx7NgxSKVSpKamYvbs2fjf//5n7vCIyIiYDBERadG4cWMcPXoUbdq0wdixYxEQEIDp06fj0aNH7CkiamC46CIRERHZNPYMERERkU1jMkREREQ2jckQERER2TQmQ0RERGTTmAwRERGRTWMyRERERDaNyRARERHZNCZDREREZNOYDBEREZFNYzJERERENo3JEBEREdm0/w9sEobCj4kHiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(test.index, df_predictions_list, label = \"Neural Network\")\n",
    "plt.scatter(test.index, test[\"mu\"], label = \"Actual mu values\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Growth Rate (mu)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eacb76f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.nn.modules.loss.MSELoss"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d259450",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}