nuclear_inventory_starting_kit.ipynb

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    "# __Prediction of the isotopic inventory in a nuclear reactor core__\n",
    "\n",
    "*Benjamin Dechenaux, Jean-Baptiste Clavel, Cécilia Damon (IRSN), François Caud, Alexandre Gramfort (DATAIA, Univ. Paris-Saclay)*\n",
    "\n",
    "This challenge was done with the support of [DATAIA](https://dataia.eu) in collaboration with [IRSN](https://www.irsn.fr/FR/Pages/Home.aspx):\n",
    "\n",
    "<div>\n",
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "  <tr style=\"background-color:transparent;\">\n",
    "    <td align=\"left\"; style=\"background-color:transparent; width: 45%;\">\n",
    "        <a href=\"https://dataia.eu\">\n",
    "            <img src=\"https://github.com/ramp-kits/nuclear_inventory/raw/main/DATAIA-h.png\" width=\"500px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "    <td align=\"right\"; style=\"background-color:transparent; width: 45%;\">\n",
    "        <a href=\"https://www.irsn.fr/FR/Pages/Home.aspx\">\n",
    "            <img src=\"https://github.com/ramp-kits/nuclear_inventory/raw/main/IRSN(logo).svg.png\" width=\"300px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "  </tr>\n",
    " </table>\n",
    " </div>\n",
    "\n",
    "## Introduction \n",
    "\n",
    "Matter contained inside a nuclear reactor undergoes irradiation that causes successive cascades of nuclear reactions, modifying its atomic composition. Knowledge of this time evolving composition is an important parameter used to model the behavior of a nuclear reactor. But it is also a crucial input for __safety__ studies associated with its operation and is a key input __for the mitigation of a severe accident__. Knowing at a given instant the composition of a reactor allows to quickly assess what radioactive isotopes may be released in the environment. \n",
    "\n",
    "The modelling of the change in the atomic composition of irradiated materials over time is usually achieved using time expensive Monte Carlo simulations of the system being studied. Although precise, this calculation scheme has been found inadapted in crisis (i.e. accidental) situations, where faster calculation schemes have to be developped. \n",
    "\n",
    "This project aims at constructing a machine learning surrogate model capable at predicting the time changing nuclear inventory of a typical reactor of the French fleet. "
   ]
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    "## Description of the data \n",
    "\n",
    "The atomic composition of the material subjected to irradiation usually involves thousands of isotopes. However, most of them have short lifetimes (characteristic decay time) and are not of critical interest for the safety of the facility or the mitigation of a reactor accident. \n",
    "\n",
    "In this project, we propose to track the temporal evolution of __26 isotopes__ that have a significant impact on the reactor behaviour or are isotopes whose compositions are a key input for the study of the mitigation of an accident (they might be isotopes that are usually found to be released in the environment in accidental scenarios, or are important for safety reasons in general). \n",
    "\n",
    "For non-proliferation reasons, the isotopes were renamed and each of them are identified by a unique letter of the alphabet (e.g. the first isotope considered is named \"A\").   \n",
    "\n",
    "So we'll be interested in the evolution of the content in isotopes \"A\", \"B\", ..., \"Z\" inside a reactor with time. The isotope content at each time is expressed in some kind of density unity ( ~ number of atoms per cubic cm).  \n",
    "\n",
    "### Modelling the irradiation in a nuclear reactor\n",
    "\n",
    "To model the irradiation conditions of the nuclear materials, we suppose a simplified scheme for the temporal evolution.\n",
    "\n",
    "Matter is put inside the reactor for a grand total of __1825 days__ (i.e. 5 years). \n",
    "\n",
    "This total period is subdivided into 5 different irradiation __cycles__ where each cycle corresponds to a 300 days period with a certain configuration of the reactor (its operating power). Each cycle is charaterized by a single parameter *pi* (with i running from 1 to 5).  \n",
    "\n",
    "Between 2 cycles, a period of 65 days was added and correspond to a maintenance period for the reactor, where the fuel is not irradiated. Note that isotopes do evolve during this period, yet during these intercycle periods, we don't track the evolution of the isotopes content.\n",
    "\n",
    "To summarize, the nuclear fuel is put inside of a reactor for a total of 1825 days with the following history :\n",
    "\n",
    "* The fuel is irradiated for 300 days (from T = 0 to T = 300) with parameter *p1*\n",
    "* The fuel is put to rest for 65 days\n",
    "* The fuel is irradiated for 300 days (from T = 365 to T = 665) with parameter *p2*\n",
    "* The fuel is put to rest for 65 days\n",
    "* The fuel is irradiated for 300 days (from T = 730 to T = 1030) with parameter *p3*\n",
    "* The fuel is put to rest for 65 days\n",
    "* The fuel is irradiated for 300 days (from T = 1095 to T = 1395) with parameter *p4*\n",
    "* The fuel is put to rest for 65 days\n",
    "* The fuel is irradiated for 300 days (from T = 1460 to T = 1760) with parameter *p5*\n",
    "* The fuel is put to rest for 65 days\n",
    "\n",
    "\n",
    "For the sake of the exercise, the composition of each isotope will be tracked on a __20 days__ time interval basis, except for the periods where the fuel is put to rest, where the interval is 65 days (again, the evolution of fuel composition is not being tracked, but this doesn't mean it doesn't evolve...) . \n",
    "\n",
    "\n",
    "So in the end, for a given set of input data (which consists of the inital composition of isotopes A -> H and the 5 parameters p1,...,p5), the result is a time series of length 81 (initial composition + 80 timesteps).\n",
    "\n",
    "See the 'One sample' section below to see plots of those time series."
   ]
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    "### Description of the available data\n",
    "\n",
    "The database we built is composed of a total of __920__ different reactor histories (i.e. time series), that were generated varying the initial conditions (i.e. the input parameters, see below) of the system and performing a detailled simulation of the reactor evolution in each case.  \n",
    "\n",
    "For each of the 920 simulations, the composition of the 26 isotopes were tracked. These are the output data of the database (see below). \n",
    "\n",
    "\n",
    "#### Input data \n",
    "\n",
    "The input data is composed of the initial composition of the matter before irradiation and the 5 parameters p1, .. p5 that specify the global operating conditions of the reactor for each irradiation cycle.\n",
    "\n",
    "**This initial composition of the matter is only composed of isotopes __A to H__ ** (all the other are always equal to zero at the initial time T=0).\n",
    "\n",
    "For each of the generated data point, the initial conditions (which is the input for any surrogate model) is composed of those 13 paramaters :\n",
    "\n",
    "* Initial composition in terms of isotopes A --> H (8 parameters)\n",
    "* Operating conditions of the reactor p1, ... , p5 (5 parameters)\n",
    "\n",
    "For each of the 920 differents simulations that were done, a unique set of values was picked in this initial input space using the latin hypercube method.\n",
    "\n",
    "#### Output data \n",
    "\n",
    "For each point in the input space, a simulation of the time evolving composition of matter was performed. The result of a simulation is a time series of length 81 (initial composition + 80 timesteps) for each isotope (form A to Z. Note that isotopes I -> Z always have zero composition at T=0). \n",
    "\n",
    "Those times series are stored in CSV output files that can be found under the _data/train_ and _data/test_ folders.\n",
    "\n",
    "__Remark__\n",
    "\n",
    "To ease the manipulation of the time series, the additional p1, ..., p5 input parameters were added to each of their corresponding timesteps (e.g. a time series named p1 which have the same value for each entry of the time series)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements for running the notebook \n",
    "\n",
    "To properly run the notebook, the following Python modules must be imported :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Required for running the notebook \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import shuffle\n",
    "import seaborn as sns\n",
    "import string\n",
    "import os\n",
    "\n",
    "\n",
    "# sklearn dependences are used to build a baseline model \n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download the data executing this python script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!python download_data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data \n",
    "\n",
    "The 920 simulations have been split into two *training* and *testing* datasets.\n",
    "\n",
    "* The *training* dataset is composed of 690 simulations and is accessible in CSV format under the __train__ folder\n",
    "* The *testing* dataset is composed of 230 simulations and is accessible in CSV format under the __test__ folder\n",
    "\n",
    "Training and testing sets can be loaded as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "\n",
    "def get_file_list_from_dir(*, path, datadir):\n",
    "    data_files = sorted(glob(os.path.join(path, \"data\", datadir, \"*.csv.gz\")))\n",
    "    return data_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "690"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_files = get_file_list_from_dir(path=\".\", datadir=\"train\")\n",
    "len(train_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>0.001753</td>\n",
       "      <td>0.005820</td>\n",
       "      <td>0.012624</td>\n",
       "      <td>0.003452</td>\n",
       "      <td>3.845987e-07</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>0.003044</td>\n",
       "      <td>0.016795</td>\n",
       "      <td>0.001108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55890 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     times         A         B         C         D         E         F  \\\n",
       "0      0.0  0.317729  3.265168  0.205470  1.316402  0.848286  0.364322   \n",
       "1     20.0  0.316280  3.263388  0.204874  1.310603  0.847751  0.363267   \n",
       "2     40.0  0.314831  3.261604  0.204346  1.305081  0.847190  0.362233   \n",
       "3     60.0  0.313387  3.259812  0.203877  1.299572  0.846631  0.361196   \n",
       "4     80.0  0.311945  3.258014  0.203466  1.294071  0.846071  0.360163   \n",
       "..     ...       ...       ...       ...       ...       ...       ...   \n",
       "76  1700.0  0.169739  3.433316  0.123249  0.965266  0.508759  0.633691   \n",
       "77  1720.0  0.169704  3.433221  0.123440  0.965082  0.508757  0.631893   \n",
       "78  1740.0  0.169668  3.433125  0.123616  0.964899  0.508754  0.630100   \n",
       "79  1760.0  0.169632  3.433030  0.123777  0.964714  0.508753  0.628313   \n",
       "80  1825.0  0.169636  3.433026  0.124242  0.964725  0.508783  0.622953   \n",
       "\n",
       "           G         H         I  ...         V         W         X         Y  \\\n",
       "0   0.635073  0.173054  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "1   0.634779  0.172712  0.000002  ...  0.000018  0.000004  0.000079  0.000080   \n",
       "2   0.634489  0.172364  0.000015  ...  0.000040  0.000016  0.000267  0.000159   \n",
       "3   0.634213  0.172015  0.000044  ...  0.000062  0.000035  0.000494  0.000239   \n",
       "4   0.633918  0.171660  0.000090  ...  0.000085  0.000062  0.000736  0.000318   \n",
       "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
       "76  0.613137  0.324423  0.008339  ...  0.001746  0.005862  0.012580  0.003470   \n",
       "77  0.613151  0.325956  0.008353  ...  0.001748  0.005861  0.012591  0.003469   \n",
       "78  0.613166  0.327481  0.008366  ...  0.001750  0.005860  0.012601  0.003468   \n",
       "79  0.613180  0.329002  0.008378  ...  0.001752  0.005859  0.012612  0.003467   \n",
       "80  0.613182  0.334266  0.008411  ...  0.001753  0.005820  0.012624  0.003452   \n",
       "\n",
       "               Z        p1        p2        p3        p4        p5  \n",
       "0   0.000000e+00  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "1   1.808307e-04  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "2   2.418617e-04  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "3   2.624485e-04  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "4   2.693822e-04  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "..           ...       ...       ...       ...       ...       ...  \n",
       "76  1.302972e-05  0.021140  0.040212  0.003044  0.016795  0.001108  \n",
       "77  1.302924e-05  0.021140  0.040212  0.003044  0.016795  0.001108  \n",
       "78  1.303008e-05  0.021140  0.040212  0.003044  0.016795  0.001108  \n",
       "79  1.302903e-05  0.021140  0.040212  0.003044  0.016795  0.001108  \n",
       "80  3.845987e-07  0.021140  0.040212  0.003044  0.016795  0.001108  \n",
       "\n",
       "[55890 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain = pd.concat((pd.read_csv(f) for f in train_files))\n",
    "dtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these dataframes, data have been concatenated one on top of the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 690 samples * 81 (length of each time series) = 55890 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_files = get_file_list_from_dir(path=\".\", datadir=\"test\")\n",
    "len(test_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.040281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.132701</td>\n",
       "      <td>4.772421</td>\n",
       "      <td>0.399662</td>\n",
       "      <td>2.116149</td>\n",
       "      <td>0.784623</td>\n",
       "      <td>0.116937</td>\n",
       "      <td>0.761039</td>\n",
       "      <td>0.138697</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.040281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.131602</td>\n",
       "      <td>4.768180</td>\n",
       "      <td>0.397866</td>\n",
       "      <td>2.101764</td>\n",
       "      <td>0.785669</td>\n",
       "      <td>0.119713</td>\n",
       "      <td>0.759411</td>\n",
       "      <td>0.137147</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.040281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>0.130507</td>\n",
       "      <td>4.763926</td>\n",
       "      <td>0.396161</td>\n",
       "      <td>2.087453</td>\n",
       "      <td>0.786703</td>\n",
       "      <td>0.122439</td>\n",
       "      <td>0.757784</td>\n",
       "      <td>0.135616</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.040281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.129422</td>\n",
       "      <td>4.759661</td>\n",
       "      <td>0.394536</td>\n",
       "      <td>2.073188</td>\n",
       "      <td>0.787729</td>\n",
       "      <td>0.125117</td>\n",
       "      <td>0.756160</td>\n",
       "      <td>0.134112</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>0.007466</td>\n",
       "      <td>0.002743</td>\n",
       "      <td>0.040281</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.349419</td>\n",
       "      <td>5.362785</td>\n",
       "      <td>0.334786</td>\n",
       "      <td>2.214174</td>\n",
       "      <td>1.027648</td>\n",
       "      <td>0.302531</td>\n",
       "      <td>0.156442</td>\n",
       "      <td>0.200398</td>\n",
       "      <td>0.013778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.004512</td>\n",
       "      <td>0.020048</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.048688</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1720.0</td>\n",
       "      <td>0.349006</td>\n",
       "      <td>5.362041</td>\n",
       "      <td>0.334873</td>\n",
       "      <td>2.212055</td>\n",
       "      <td>1.027713</td>\n",
       "      <td>0.301910</td>\n",
       "      <td>0.156495</td>\n",
       "      <td>0.200820</td>\n",
       "      <td>0.013851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002462</td>\n",
       "      <td>0.004540</td>\n",
       "      <td>0.020128</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.048688</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.348592</td>\n",
       "      <td>5.361306</td>\n",
       "      <td>0.334943</td>\n",
       "      <td>2.209939</td>\n",
       "      <td>1.027774</td>\n",
       "      <td>0.301292</td>\n",
       "      <td>0.156549</td>\n",
       "      <td>0.201237</td>\n",
       "      <td>0.013922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.020208</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.048688</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1760.0</td>\n",
       "      <td>0.348177</td>\n",
       "      <td>5.360567</td>\n",
       "      <td>0.334995</td>\n",
       "      <td>2.207827</td>\n",
       "      <td>1.027837</td>\n",
       "      <td>0.300674</td>\n",
       "      <td>0.156603</td>\n",
       "      <td>0.201652</td>\n",
       "      <td>0.013989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.048688</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1825.0</td>\n",
       "      <td>0.348189</td>\n",
       "      <td>5.360571</td>\n",
       "      <td>0.335734</td>\n",
       "      <td>2.207923</td>\n",
       "      <td>1.027850</td>\n",
       "      <td>0.298108</td>\n",
       "      <td>0.156606</td>\n",
       "      <td>0.204159</td>\n",
       "      <td>0.014191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.004566</td>\n",
       "      <td>0.020375</td>\n",
       "      <td>0.006569</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>0.035835</td>\n",
       "      <td>0.048688</td>\n",
       "      <td>0.008009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18630 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     times         A         B         C         D         E         F  \\\n",
       "0      0.0  0.133810  4.776655  0.401556  2.131205  0.783545  0.114118   \n",
       "1     20.0  0.132701  4.772421  0.399662  2.116149  0.784623  0.116937   \n",
       "2     40.0  0.131602  4.768180  0.397866  2.101764  0.785669  0.119713   \n",
       "3     60.0  0.130507  4.763926  0.396161  2.087453  0.786703  0.122439   \n",
       "4     80.0  0.129422  4.759661  0.394536  2.073188  0.787729  0.125117   \n",
       "..     ...       ...       ...       ...       ...       ...       ...   \n",
       "76  1700.0  0.349419  5.362785  0.334786  2.214174  1.027648  0.302531   \n",
       "77  1720.0  0.349006  5.362041  0.334873  2.212055  1.027713  0.301910   \n",
       "78  1740.0  0.348592  5.361306  0.334943  2.209939  1.027774  0.301292   \n",
       "79  1760.0  0.348177  5.360567  0.334995  2.207827  1.027837  0.300674   \n",
       "80  1825.0  0.348189  5.360571  0.335734  2.207923  1.027850  0.298108   \n",
       "\n",
       "           G         H         I  ...         V         W         X         Y  \\\n",
       "0   0.762691  0.140276  0.000000  ...  0.000000  0.000000  0.000000  0.000000   \n",
       "1   0.761039  0.138697  0.000004  ...  0.000036  0.000015  0.000145  0.000141   \n",
       "2   0.759411  0.137147  0.000028  ...  0.000078  0.000059  0.000491  0.000282   \n",
       "3   0.757784  0.135616  0.000083  ...  0.000122  0.000133  0.000910  0.000422   \n",
       "4   0.756160  0.134112  0.000173  ...  0.000167  0.000233  0.001355  0.000562   \n",
       "..       ...       ...       ...  ...       ...       ...       ...       ...   \n",
       "76  0.156442  0.200398  0.013778  ...  0.002450  0.004512  0.020048  0.006538   \n",
       "77  0.156495  0.200820  0.013851  ...  0.002462  0.004540  0.020128  0.006558   \n",
       "78  0.156549  0.201237  0.013922  ...  0.002474  0.004568  0.020208  0.006578   \n",
       "79  0.156603  0.201652  0.013989  ...  0.002485  0.004597  0.020288  0.006597   \n",
       "80  0.156606  0.204159  0.014191  ...  0.002488  0.004566  0.020375  0.006569   \n",
       "\n",
       "           Z        p1        p2        p3        p4        p5  \n",
       "0   0.000000  0.044514  0.011609  0.007466  0.002743  0.040281  \n",
       "1   0.000331  0.044514  0.011609  0.007466  0.002743  0.040281  \n",
       "2   0.000443  0.044514  0.011609  0.007466  0.002743  0.040281  \n",
       "3   0.000481  0.044514  0.011609  0.007466  0.002743  0.040281  \n",
       "4   0.000494  0.044514  0.011609  0.007466  0.002743  0.040281  \n",
       "..       ...       ...       ...       ...       ...       ...  \n",
       "76  0.000094  0.014510  0.024052  0.035835  0.048688  0.008009  \n",
       "77  0.000094  0.014510  0.024052  0.035835  0.048688  0.008009  \n",
       "78  0.000094  0.014510  0.024052  0.035835  0.048688  0.008009  \n",
       "79  0.000094  0.014510  0.024052  0.035835  0.048688  0.008009  \n",
       "80  0.000003  0.014510  0.024052  0.035835  0.048688  0.008009  \n",
       "\n",
       "[18630 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtest = pd.concat((pd.read_csv(f) for f in test_files))\n",
    "dtest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One sample\n",
    "Let us first take a look at one of the 690 train samples (or simulations). We will put times as the index to ease plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>J</th>\n",
       "      <th>...</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.316280</td>\n",
       "      <td>3.263388</td>\n",
       "      <td>0.204874</td>\n",
       "      <td>1.310603</td>\n",
       "      <td>0.847751</td>\n",
       "      <td>0.363267</td>\n",
       "      <td>0.634779</td>\n",
       "      <td>0.172712</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.314831</td>\n",
       "      <td>3.261604</td>\n",
       "      <td>0.204346</td>\n",
       "      <td>1.305081</td>\n",
       "      <td>0.847190</td>\n",
       "      <td>0.362233</td>\n",
       "      <td>0.634489</td>\n",
       "      <td>0.172364</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.313387</td>\n",
       "      <td>3.259812</td>\n",
       "      <td>0.203877</td>\n",
       "      <td>1.299572</td>\n",
       "      <td>0.846631</td>\n",
       "      <td>0.361196</td>\n",
       "      <td>0.634213</td>\n",
       "      <td>0.172015</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.0</th>\n",
       "      <td>0.311945</td>\n",
       "      <td>3.258014</td>\n",
       "      <td>0.203466</td>\n",
       "      <td>1.294071</td>\n",
       "      <td>0.846071</td>\n",
       "      <td>0.360163</td>\n",
       "      <td>0.633918</td>\n",
       "      <td>0.171660</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700.0</th>\n",
       "      <td>0.240961</td>\n",
       "      <td>3.157960</td>\n",
       "      <td>0.208606</td>\n",
       "      <td>1.019774</td>\n",
       "      <td>0.808000</td>\n",
       "      <td>0.297971</td>\n",
       "      <td>0.615205</td>\n",
       "      <td>0.167178</td>\n",
       "      <td>0.008587</td>\n",
       "      <td>0.011986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001778</td>\n",
       "      <td>0.009769</td>\n",
       "      <td>0.013276</td>\n",
       "      <td>0.004203</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1720.0</th>\n",
       "      <td>0.240071</td>\n",
       "      <td>3.156546</td>\n",
       "      <td>0.208798</td>\n",
       "      <td>1.016284</td>\n",
       "      <td>0.807374</td>\n",
       "      <td>0.297375</td>\n",
       "      <td>0.614916</td>\n",
       "      <td>0.166946</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.012124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001806</td>\n",
       "      <td>0.009983</td>\n",
       "      <td>0.013436</td>\n",
       "      <td>0.004251</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740.0</th>\n",
       "      <td>0.239182</td>\n",
       "      <td>3.155125</td>\n",
       "      <td>0.208985</td>\n",
       "      <td>1.012798</td>\n",
       "      <td>0.806741</td>\n",
       "      <td>0.296792</td>\n",
       "      <td>0.614635</td>\n",
       "      <td>0.166708</td>\n",
       "      <td>0.008814</td>\n",
       "      <td>0.012261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.010199</td>\n",
       "      <td>0.013596</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760.0</th>\n",
       "      <td>0.238295</td>\n",
       "      <td>3.153707</td>\n",
       "      <td>0.209168</td>\n",
       "      <td>1.009316</td>\n",
       "      <td>0.806106</td>\n",
       "      <td>0.296210</td>\n",
       "      <td>0.614338</td>\n",
       "      <td>0.166470</td>\n",
       "      <td>0.008927</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.010418</td>\n",
       "      <td>0.013755</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825.0</th>\n",
       "      <td>0.238300</td>\n",
       "      <td>3.153707</td>\n",
       "      <td>0.210896</td>\n",
       "      <td>1.009524</td>\n",
       "      <td>0.806160</td>\n",
       "      <td>0.293671</td>\n",
       "      <td>0.614346</td>\n",
       "      <td>0.168961</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>0.012432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001866</td>\n",
       "      <td>0.010348</td>\n",
       "      <td>0.013931</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               A         B         C         D         E         F         G  \\\n",
       "times                                                                          \n",
       "0.0     0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "20.0    0.316280  3.263388  0.204874  1.310603  0.847751  0.363267  0.634779   \n",
       "40.0    0.314831  3.261604  0.204346  1.305081  0.847190  0.362233  0.634489   \n",
       "60.0    0.313387  3.259812  0.203877  1.299572  0.846631  0.361196  0.634213   \n",
       "80.0    0.311945  3.258014  0.203466  1.294071  0.846071  0.360163  0.633918   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "1700.0  0.240961  3.157960  0.208606  1.019774  0.808000  0.297971  0.615205   \n",
       "1720.0  0.240071  3.156546  0.208798  1.016284  0.807374  0.297375  0.614916   \n",
       "1740.0  0.239182  3.155125  0.208985  1.012798  0.806741  0.296792  0.614635   \n",
       "1760.0  0.238295  3.153707  0.209168  1.009316  0.806106  0.296210  0.614338   \n",
       "1825.0  0.238300  3.153707  0.210896  1.009524  0.806160  0.293671  0.614346   \n",
       "\n",
       "               H         I         J  ...         V         W         X  \\\n",
       "times                                 ...                                 \n",
       "0.0     0.173054  0.000000  0.000000  ...  0.000000  0.000000  0.000000   \n",
       "20.0    0.172712  0.000002  0.000183  ...  0.000018  0.000004  0.000079   \n",
       "40.0    0.172364  0.000015  0.000413  ...  0.000040  0.000016  0.000267   \n",
       "60.0    0.172015  0.000044  0.000643  ...  0.000062  0.000035  0.000494   \n",
       "80.0    0.171660  0.000090  0.000873  ...  0.000085  0.000062  0.000736   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "1700.0  0.167178  0.008587  0.011986  ...  0.001778  0.009769  0.013276   \n",
       "1720.0  0.166946  0.008701  0.012124  ...  0.001806  0.009983  0.013436   \n",
       "1740.0  0.166708  0.008814  0.012261  ...  0.001833  0.010199  0.013596   \n",
       "1760.0  0.166470  0.008927  0.012398  ...  0.001860  0.010418  0.013755   \n",
       "1825.0  0.168961  0.009283  0.012432  ...  0.001866  0.010348  0.013931   \n",
       "\n",
       "               Y         Z        p1        p2        p3        p4        p5  \n",
       "times                                                                         \n",
       "0.0     0.000000  0.000000  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "20.0    0.000080  0.000181  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "40.0    0.000159  0.000242  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "60.0    0.000239  0.000262  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "80.0    0.000318  0.000269  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "...          ...       ...       ...       ...       ...       ...       ...  \n",
       "1700.0  0.004203  0.000189  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "1720.0  0.004251  0.000189  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "1740.0  0.004300  0.000189  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "1760.0  0.004349  0.000189  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "1825.0  0.004330  0.000006  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "\n",
       "[81 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smpl1 = dtrain.reset_index(drop=True).iloc[:81]\n",
    "smpl1 = smpl1.set_index('times')\n",
    "smpl1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot each individual isotope content vs times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Isotope A vs time')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize': (10, 6)})\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=1.3, rc={\"lines.linewidth\": 2.5})\n",
    "\n",
    "sns.lineplot(data=smpl1['A']).set(title=\"Isotope A vs time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Isotope J vs time')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=smpl1['J']).set(title=\"Isotope J vs time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots reveal irradiation and maintenance cycles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot all input composition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Input compos vs time')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphabet = list(string.ascii_uppercase)  # to ease the manipulation of the data\n",
    "\n",
    "# The input compositions are isotopes A -> H  \n",
    "input_compos = alphabet[:8]\n",
    "\n",
    "sns.lineplot(data=smpl1[input_compos]).set(title=\"Input compos vs time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need a data scaling to better see all the curves. Isotope B is for example much higher than the other isotopes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Input composition (normalized) vs time')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smpl1_max = smpl1.max()\n",
    "smpl1_norm = smpl1 / smpl1_max\n",
    "\n",
    "sns.lineplot(data=smpl1_norm[input_compos]).set(\n",
    "    title=\"Input composition (normalized) vs time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the rest of the isotopes (except Z):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f459a3d4520>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lineplot(data=smpl1[alphabet[8: -1]])\n",
    "g.set(title=\"Isotopes I to Y vs time\")\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.2, 0.5), ncol=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f459a354e80>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lineplot(data=smpl1_norm[alphabet[8: -1]])\n",
    "g.set(title=\"Isotopes I to Y (normalized) vs time\")\n",
    "g.legend(loc='center right', bbox_to_anchor=(1.2, 0.5), ncol=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z alone:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Isotope Z vs time')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=smpl1['Z']).set(title=\"Isotope Z vs time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to see if there is a pairwise correlation between the time variation of isotopes. \n",
    "Let's compute the first discrete difference of element and plot the heatmap of the correlation between first isotopes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smpl1_diff = smpl1.diff()\n",
    "sns.heatmap(smpl1_diff[input_compos].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that for example A and D are positively correlated in their temporal variation (coefficient 1.00) and F and H are negatively correlated (coeff < -0.75).\n",
    "\n",
    "And between all isotopes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(smpl1_diff[alphabet].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It shows that the \"input\" isotopes that mainly decline have a strongly negative correlation in their time variation with almost all the other isotopes which are created in the five years time. Except for example for C and F which behave differently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input and output data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To separate input data from output data, use the index of the dataframes or the \"times\" column.\n",
    "\n",
    "The input data for instance is composed of the value of each T=0 entry in the datasets. To obtain those values, simply select every entry having T=0 in the train or test datasets : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(690, 32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain.loc[0].shape  # equivalent to dtrain.loc[dtrain[\"times\"] == 0.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As said before, the train dataset regroup a total of 690 simulations that were performed varying the 13 input parameters listed above. Here the dataset is found to have 32 parameters at T=0, but only 13 are non zero, as can be seen using :  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times    0.000000\n",
       "A        0.317729\n",
       "B        3.265168\n",
       "C        0.205470\n",
       "D        1.316402\n",
       "E        0.848286\n",
       "F        0.364322\n",
       "G        0.635073\n",
       "H        0.173054\n",
       "I        0.000000\n",
       "J        0.000000\n",
       "K        0.000000\n",
       "L        0.000000\n",
       "M        0.000000\n",
       "N        0.000000\n",
       "O        0.000000\n",
       "P        0.000000\n",
       "Q        0.000000\n",
       "R        0.000000\n",
       "S        0.000000\n",
       "T        0.000000\n",
       "U        0.000000\n",
       "V        0.000000\n",
       "W        0.000000\n",
       "X        0.000000\n",
       "Y        0.000000\n",
       "Z        0.000000\n",
       "p1       0.023597\n",
       "p2       0.006906\n",
       "p3       0.008332\n",
       "p4       0.035934\n",
       "p5       0.016353\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As adverstised, the initial compositions for isotopes \"I\" -> \"Z\" are zero, leaving only 13 input parameters. \n",
    "At T= 1825 days the (81th timestep), the composition has evolved :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1825.0</td>\n",
       "      <td>0.238300</td>\n",
       "      <td>3.153707</td>\n",
       "      <td>0.210896</td>\n",
       "      <td>1.009524</td>\n",
       "      <td>0.806160</td>\n",
       "      <td>0.293671</td>\n",
       "      <td>0.614346</td>\n",
       "      <td>0.168961</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001866</td>\n",
       "      <td>0.010348</td>\n",
       "      <td>0.013931</td>\n",
       "      <td>0.00433</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>0.008332</td>\n",
       "      <td>0.035934</td>\n",
       "      <td>0.016353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     times         A         B         C         D         E         F  \\\n",
       "0      0.0  0.317729  3.265168  0.205470  1.316402  0.848286  0.364322   \n",
       "80  1825.0  0.238300  3.153707  0.210896  1.009524  0.806160  0.293671   \n",
       "\n",
       "           G         H         I  ...         V         W         X        Y  \\\n",
       "0   0.635073  0.173054  0.000000  ...  0.000000  0.000000  0.000000  0.00000   \n",
       "80  0.614346  0.168961  0.009283  ...  0.001866  0.010348  0.013931  0.00433   \n",
       "\n",
       "           Z        p1        p2        p3        p4        p5  \n",
       "0   0.000000  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "80  0.000006  0.023597  0.006906  0.008332  0.035934  0.016353  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain.iloc[[0, 80]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get all of the unique timesteps, one can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0.   20.   40.   60.   80.  100.  120.  140.  160.  180.  200.  220.\n",
      "  240.  260.  280.  300.  365.  385.  405.  425.  445.  465.  485.  505.\n",
      "  525.  545.  565.  585.  605.  625.  645.  665.  730.  750.  770.  790.\n",
      "  810.  830.  850.  870.  890.  910.  930.  950.  970.  990. 1010. 1030.\n",
      " 1095. 1115. 1135. 1155. 1175. 1195. 1215. 1235. 1255. 1275. 1295. 1315.\n",
      " 1335. 1355. 1375. 1395. 1460. 1480. 1500. 1520. 1540. 1560. 1580. 1600.\n",
      " 1620. 1640. 1660. 1680. 1700. 1720. 1740. 1760. 1825.]\n"
     ]
    }
   ],
   "source": [
    "timesteps = dtrain[\"times\"].unique()\n",
    "print(timesteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'p1', 'p2', 'p3', 'p4', 'p5']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The input parameters are composed of the input_compos + parameters p1 to p5 \n",
    "input_params = input_compos + [\"p1\", \"p2\", \"p3\", \"p4\", \"p5\"]\n",
    "input_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization of the data\n",
    "\n",
    "It is important to note that the composition data that make up the database are very heterogeneous. Indeed, the isotopes present in this database __have typical compositions that can present orders of magnitude of differences__. This can pose serious problems of normalization to succeed in learning the data at best. \n",
    "\n",
    "Let us for instance plot the distributions of the isotopes that makes up the input parameter composition (i.e. isotopes A to H) at the initial T=0 and final T=1825 times : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='isotope', ylabel='composition (a.u.)'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_initial = pd.DataFrame(\n",
    "    dtrain.loc[0, ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']].melt(\n",
    "    value_name=\"composition (a.u.)\", var_name=\"isotope\")\n",
    ")\n",
    "temp_final = pd.DataFrame(\n",
    "    dtrain.loc[80, ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']].melt(\n",
    "    value_name=\"composition (a.u.)\", var_name=\"isotope\")\n",
    ")\n",
    "temp_initial['time'] = 'initial'\n",
    "temp_final['time'] = 'final'\n",
    "\n",
    "temp = pd.concat([temp_initial, temp_final], axis=0)\n",
    "\n",
    "# plot a violin plot for both the initial and final compositions of the input_compos  \n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"notebook\", font_scale=1.3, rc={\"lines.linewidth\": 2.5, 'figure.figsize':(14, 8)})\n",
    "sns.violinplot(\n",
    "    data=temp,\n",
    "    x=\"isotope\",\n",
    "    y=\"composition (a.u.)\",\n",
    "    hue=\"time\",\n",
    "    split=True,\n",
    "    inner=\"quartile\",\n",
    "    linewidth=1,\n",
    "    palette={\"initial\": \"cornflowerblue\", \"final\": \".85\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the figure, most matter inside the reactor is made up of isotope B. \n",
    "\n",
    "The difficulty lies in the fact that we want, in the end, to have a precise __relative__ error on each individual isotope because it can happen that an isotope which is present in small quantities still has a non-negligible impact on the safety of the reactor and on the mitigation of an accident.\n",
    "\n",
    "__So we can't just be good in the absolute overall composition of the matter: we need to be sufficiently precise for each individual isotope !__\n",
    "\n",
    "\n",
    "The differences between the isotopes for the final compositions, adding all of the 26 isotopes, is even more visible:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='isotope', ylabel='composition (a.u.)'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.DataFrame(\n",
    "    dtrain.loc[80, alphabet].melt(\n",
    "    value_name=\"composition (a.u.)\", var_name=\"isotope\")\n",
    ")\n",
    "sns.violinplot(data=temp, x=\"isotope\", y=\"composition (a.u.)\", inner=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There, we want to be sufficiently precise on each individual isotopes, even if there exist ~ up to 5 orders of magnitudes between some isotopes ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.71932, 0.0006211064)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtrain.B.max(), dtrain.Z.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__To achieve good overall performances for this challenge, it is believed that the first key is to find a clever way to  normalize the input and output data.__\n",
    "\n",
    "In this notebook, we propose to normalize data just before feeding it into the models, i.e. normalize X and Y matrices. But just to see the effect of normalization, let's visualize isotope composition at a given time step after scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change timestep in loc[ ] to see compositions at different times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='isotope', ylabel='composition (a.u.)'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V9Gv9oiMRqq6rCsUtDkeia5QnUL2eoMH3NS2k4SY7Em3LbNyrXg+7SCOhqF9c135PDHCYx+zNzMxg06ZN2LRpE1qtFvbt24dNmzZh+/btAIC1a9fi0ksvxZ49ewBYT9l/9md/hs9//vP43//9Xzz55JP4u7/7OyxYsACvetWrDuVPIaOfKvLzyBI00oGq6zob40rVd/TTQtdP13U2+job904Vejk+up/uVT+tWb18z3kEkb2gY1T0vBv3iSeewFVXXeW8/u53v4vvfve7OP/883H77bdjamoKW7du9agWH/zgB5HP5/GBD3wA1WoV55xzDm6++WYlcU+zAX7iZEVa00G/PN2pRLPZTP07VC0e/aRA9RPZm40xkMb1SMsOdjodj1uzl+CfA4ZhKO9rP2Wkq1qz+uVh5Igjey984Qvx+9//PvT9K664AldccYXnWLFYxIc+9CF86EMfSrt7qWC23EK5XE5pm/1EIPvl6Q7oLyPXT0rBkahC9tMY4JGWbWm32ygUCqm0nRT+69hut1EsFpV+R6+7cXn0kxtXxcNJELGbmpqSbq83H2mOcGTKXjpIo+zGbEDVGJgNVUdVXOBsxBemoUKmqUCpwGxc1zS+I61Qhl5W+Gejr71O9tLY2q2X7zmPIy5m70hEvxY+7fWYvSN99wA/2UvjGvSTGzeNOLi0xlU/uXF72U3mv4693NcgZU8F+DHa62QvDczGA48KZMreEQB+MKZFoI7EjDnesPWTcUorGzeNhf9IJHv9pEL2k7rLI61Qhozs9XbiTy8/kKYNRux0jgM0Gg3p+ZWRvR5EP2U28egnZa+fyJ6q6+rfMYHf+UIVVJGJfiJQsxF2caSTPVXXtZ/JXhrXtZ8ezvopdlkFmMs2p+mBx0WRkb0eRD9NnLTbVIl+jdlTdV39C30aC7+qNvvJ3cgvwmnNAVULPU/wVc0B/2+ejXsnC/917OWF3983VSJAGg+9aQkUaTw89fL45MHcuH6yJ5uRm5G9HkQ/FWntJ/Qr2et1ZY8fo6ra7CcFajYSNNIge/30EJFWcH4vx2/5+6aqr/3kxk0DvXzPeTAFT8/I3uGLLBs3HfQr2VMFP9nzv5YFP5bSICVpIQ0XVq8re/w9VzUH/PcqjXunyg76r2Mvk5TZUCH7qaRPP+0kpAKOsqdrgcdFkZG9HsRsLB5HItnr15g9VfcqLbLHX0tVbfoJQxqLcj+ViVGllqXhxp0NspfWQt/LySSzoez10zZ8qgi/f3z26lrISJ1f2cti9g4jzAbZ6+Un2rTAG7ZeneBBSMvI1Wo1Je3OBtlT1S4PVQSKn69plbJQRaD4e24YhpJ54L83qsZVGuinpId+cuOmRfbSsNP+udSrYQdZgsYRAH7i9HIAcb/hSK+zV61WPa9VLMqdTsdzLVWRsrRUSB5pxOylocAFvZaF/56raNffZhr3ShXSco3ypCmtkj5pENNM2etNJbrdbjv90n2/OyN7PYA05Pu0JlGvZ2GlgX6N2VM1rvyLsgqyl5aqkwbZ819HVUY+jaLKaSU9+O+P/wFABrNBzFUhLQLFt5NWeICqMZCGG3c2Crarwmx4DZKCn5eZG7cHkQbZSysrMa2yCxnUQ9UTrX9hV7HQp6EWBrWjwiD7H5zScOOqQlokOo37lcZDRFpIi+zxhF/VeEhLgUrDjTsbKmRaVQnSIHtJ10We0OXs363B+l92fmVkTyHSeKpPK/hbVV/7SSHrV2VPFfwLfZJ9FhnSUIrSajetxTONGNu0XKNpjIG0CD+PtLJx01Dh0iqAncaDfz+RPVWYjbCDpHaA7yNT9hjpk7WFGdlTCFUEYjaUvX7aE1HVAppGzF4/kUb/wq6CQPnbVEEegHRUyNnIRg56LYM01DLTNGeF8KsaAzzS2j0hjbhNVW36bX8ayl5ablxV61YapcfSekDloZbsafb/etd7IsjInkKkMXHSqjeWBtnp9ZqAaZC9fnJj+xdh2XpNUW2qWujTIKZpkT3/g5MKZScNtazZbHYRkV5W9maj3I4qYjY2Nub8rUrZ84/PNB78e53s8UgrnCUNJToNZY+Rvozs9QDSmDjNZjMVQtFPMXu9TPb6SdmcDWWvWq0qWaTTUKDSioPz/940lL007lVa7aoi/GnYFj8JUUX2tm/f7vydVsxeGkXQVRHqfiZ7vahEByt7GdnrGaRVpDSNAPA0snF7vQB0Gu6L2SB7KoycaZpdSl4ayh6ghkSlQUzTeqJPw42bxu8Put+9rO6mMbfSUvbSKL8zG4kE/VRYu5cT1fxI040rOw4ysqcQqp6S+mmS95MbM42iyv0Ss1ev17vGp4qFvp8IRFpZo2koe2n8/qA2evVeAf1F9tJQy9Iar2n0Na19t9NAWuOVR9L1hb9+jOSy/zOy1wNQZZz6iez1kxszDfSLG3tqaop0TBRpkD3DMFKJL0zrid5/f3qV7AXd7zQIv4o2gf6K2ePvuao2+4nspbU7TxpIQzX3QyXZ87txZYl0RvYUIq2npDQmzpFI9tJw484G2VPhvkhLgUuj3Wq12nVde5nszYYbVwXZ6ycVFugvsseP114nezzSEih6VdnrdDpd817Vw4n/e5KAv56svp6OTNnrGagyTmnEFqWhPqhsZzaQRl/75feHLfRJ+5+GWhT0eRUq5Gy5G9Mie0kfLGbrutbrdSVxxvx1nJiYSNwekF6CBn9vVMVYz4YS3U+hRyqQViiDH0nnKhunOU3vcuPKxoRmZE8hVEycoFpYKia5fwFSNclnY7u0LBs5+XdMTk52HTMMI/GDRBoEIi13o9/Qt1otJYu9fyypuF/+39tqtRKrJbPlxgXUEGnetmzdujVxe8DsbEGmikDORpZzWsper7pxg8ZqkG1MiqTXlc31gp5zjvFkT8bGZGRPIVQQn0aj0dWOikk+G8per2fjptHX2SC7Kty4YQQsqaFLIxYwLQUq6KEpjbmlYkykQaD6KW4T8M5XVWrZbLhxVfV1NhIJetkbBahfU9J64PEj6frKxmUhl3eOMTeuaZpSYywjewqhYuIETWjVT8lAOmQvLZfmkZ6goQJhBq0XiVlapGS25lavJtQ4n9dds5+U7Hc6ndRcY2k8SM2GG1eVWpxGbFlaHp5+2R85LdviR9Lr6pA9j7LX/b4IMrKnECqe6NJakNKIKwLS2QDcD1WEil/YVBm5fnbjAoqJmW1Nki5KYU/fScdsP82tNNSyILI3PT2daHyFhZikQUxUIC03rups3KAYTRVj1W+j01L20lAhVWC2yF7SscvGZSHX7cbl3xdBRvYUIi1lLw3DmQbZSysOThWh2rlzp5J2eKR1XXkodeP6mkqi7BiG4R2bmu+7JBH0eRXxhf00t1JVTDmy1263E13XsOunWtlTZQP6JRs36Po1Go3E5DSNrf2A/tiCDAi2dzMzM8pVZFVu3Dyv7HHGO1P2DjF6WdnzD2ZVgzsNN24aAe9AfxFT1WALveab8UkW5S5VyG47qWsw7PNJ252NRIKg1zLtBSlmScmec/107yBI0m5a4QFAOmQvrW29VJO9sOuX9Lr616i0yF4vFioG0rMtfiS1Aey+hJG9LGbvEEPFxElrQZoNZW98fFxJm2klk0R9hyzSIqaq22TGzE/2khi5roVHDzku267ulSGTGuR+eZAKu37K3Lha+mQvDZulAmm4cU3T9MxRFW3OFtlTVUEirULFvFdDpR3UfdRHNdlLOnYdssfNVd7Bk5G9Q4xeTtBIKzCXH3Q7duxQ0mZaxJQ3FmkUagbS6atKN65Kstf1WdXKnmKyl1bWqGrXWBqZ06ZpBrpxk7abFjEF+kfZC3INJ+1vPyl7aVWQ8EOFbWX1GnO+OaCijqPK+oVsTPH9zJS9HkI/uXHTcLmmVbuvl+vszYaypwJ+ZU9ToMLNNtlL0teg7EYgnaB35WTPJvtJfn+1WnXnlUKy10Xq7Lb7xY2rwuUaRBiTksiwe5KUlPjHpqpkEj9UlTNR/YDukD0tF3hcFZLaADb2+X5myl4PQQXZC1qQVAS7zoYbt9fVsjTanI0EDRXwK3tpkr1Go5GoAHAayl6tVvOOK7vpNMhe0gW0+7omJ1CexSxNsmcPrH4pvaJiW6+gNpK2m1b2vH9s9nLokR8qyR4fC8cfV4Wk64BD9jxzNVP2egYqJg4jdvwyl8YOGqpICW880oqD6+UyKWkRU5WxKvV63Vl8/GQvyULfZSD1iPcE4JK9kOMS6Fp87OurgpSozvDsJntWX5VcU6CL7CVptztuU52yl0YR9DTcuGmSPU1TR8yBYLKXxjZ8vbgFGeDGlfuVPRXx5rzNVpGkBQA5rk0t4H0RZGRPIVSQPbbtjM7dZBXKXlrZuGkoe2m5nNNosx8SNPgFgg0rtoYkWei7PsvZT9lFyTCM7gQNBSVdusie3bYKBcK/sCt34SkgUJ4xwMiePRiUKHtsQOnqlD3envayGzeI2CXdG9ZJJPApUEnJXtAalfQaBM0hFdv7+ZF0DBiG4ZA6puyxXSlUJRcyqMrGzQVslwbIrV8Z2UuANLZJCiJ7KibNbCRo9LqyF/UdsuiHzGHvQu/9X4myx6wIZ01kjefMzIx7DRnZ05Mb5G5lz+qsCtVctVoURvaUEXPdS8yUkD3dO7B6NWbPb0tV2Nagh3HVZI/9n5TsBY3NpGQvzSSdpASHB1+YnSl7Od3ajuzgwYOJ2vZDnbIXTNEyZW+WkUawa1pkLy21bDbIXi9n485GzF7SNqPI3vT0tLRhcsiXQrIX6G7UkytQs6nsJZ2vLolmZDc5KYsie2pc7uqVvdmI2ZPdVJ5HENlL6o1x3biM7OU9x2URtEaloewB6bhyk4AndEzZY/+rJnuqEjR0jxv3MI/Zu/vuu3HZZZdh1apVuOSSS/CDH/wg9jPPPfccrrvuOlxwwQVYvXo1Lr/8cvznf/6n8r6lUbOIGSM+86bZbCZe8NNSy9KI2Uur8CePI4ns8Yu5n+wB8guI0y7zNCgge15S4lX2kpCSsJi93iZ7XgLVaDSkSYRzjzVNKYnsUvbs/1XsSjAbyh6QXIlNk+z5lT3VCRpA8t+fprLH3/ekdvDAgQPO30zRy9tkWrUbN+ma5SqQwXX2Djs37qOPPorrrrsOF198MX784x/jyiuvxIc//GH88pe/jPzcO9/5Thw4cABf/epX8ZOf/AR//Md/jA984AN46KGHlPYvDVLikD3fvlaqnhTCXssiDbKXVl9VGo6wdvrNjQvIkyjnaZhX9nTfe4KIIntJDHKXu1aRstdut7vmZtKFPozsed6TbbNchhMEabeb5Lr6Y/ZYQoFpmold5KrJXrvdDpyfScl50O9M+tvDyF7SrNE03LhsDPjXLNXKXtIx4FH2NK+yxxNBFUhD2eNx2JG9W265BatXr8a73/1urFy5EldddRUuvfRSfOMb3wj9zOTkJJ5++mm85S1vwQte8AIcffTReMc73oF58+bh8ccfV9q/NMgem3i+qhOJDVI/uXHT2qybR6bsue/LKDumaXaTPQAYsP6TJXse4sEmQc5148retzBlr1qtJhoLQbFZSeO10iB77Lpq5Yp7kHPjyl6DrkLNXF+TqlCqyV6YDU1D2UtC9lqtlvP52YjZS/r7GanTfRmuKuI2VYIndHlb2WMKX7VaTTxveahS9rQQN67MmpinnDQ2Nob7778fjzzyCPbt24dGo4G5c+dixYoVOO+883DWWWcJfzEFGzZswOte9zrPsQsvvBAf//jH0el0kMvluj4zMjKCk046CT/+8Y9x7rnnYmhoCP/1X/+F6elpXHDBBUr7lybZy5Q9F2m4cftJ2Ut6r5xCoqXubFz+fRFUq1V38eSn4QCAaUVkL+dV9trtNqanpzE8PCzVXw+4NpvNJkqlkkx3Axf1pIuGGwvZTaASu8crZYBNU7vdVquFWq2GgYEBoTYNw3BJtC++Ekiu7Kh+yAsjNUkfpIPU4SRkjydJLFaPd+Oapim9q07QNUj6+1l/dV0Hf8t6LUGDkb2Cnnfco3mOoB44cABLly6Vbp/vn7qiysF6nMy1iCR7Dz74IL75zW/i17/+NdrtNpYuXYo5c+agVCrhueeew1133YXPf/7zWLp0KV73utfhjW98I4aGhoQ7EYb9+/djwYIFnmMLFy5Es9nExMQE5s2bF/i5m2++GX/7t3+L8847D/l8HqVSCV/4whdwyimnKOsbkDLZ04KPyyItAsX3Kw21MOi1LI5UNy5b6Atl91hSsudxe3iUPQ2AmZzs5XW3sJRP2ZIhe86CrGmAaXomWLValSZ7qlWdZrPZFQen6brDz5K6x7VyBSbrM1fWYWJiQpjszczMOGNT0zSrjwqVPdVegzASngbZSxIewKt3un09GdljBFt2nU1T2ctpObQCjqtC0jEwNjYGAJhTGnGOMYWPvZ+E7PFIul6zdcSToJGwjl8o2Xvb296G9evX46KLLsKaNWtw9tlnY2RkxHOOaZp45pln8Mtf/hI//elPceutt+LTn/40XvrSlwp3JAz+J5i4G26aJj71qU8hl8vh9ttvx/DwMO655x68733vwy233IIzzzyT/N1PPPFE5Pv79u3zvH7++eexfv16cvtBcOIffL97w4YNXcRXBE8//bTn9XPPPZe4r0D3hH7ooYccAyWLZ5991vN606ZNSggfT8RmZmaU/P4tW7Z4Xj/22GPYtWtX4nZ5BWfbtm2J+vr8888DAPIhZO93v/sdlixZItSm53f7lT0Ae/bskeqz0+5AwT3IqUUPPPAAVqxYIdyuM6Z0DeiYnjZ/85vfYOHChcJtetrlsGPHDun75VU27Qubc2/Wxo0bpfrKFjpUKoBD9tx2H3jgARxzzDFCbXoJf3d8wGOPPSbcTx78Q4hhGInn6+7duwOPP/roo4myMbdv3+6+0HTANLB161bp/m7dutX5myl7GqdA3X///aFCRxz89gqwxlQScrJ3714ALiHVoMGEic2bNye+Z/zD1NNPP51on3B2n+aURp1j/E4aDz/8sJIi2+y7kvx21g89RNnbvHlzFx+LQyjZO/300/HZz34Wc+fODf2wpmk48cQTceKJJ+Kv/uqvcO+99yopAMywYMGCLkI1NjaGQqGA0dHRwM888MADuPPOO3Hvvfc6RvHUU0/Fhg0b8M1vfhNf+MIXyN+/atWqyCd+flIClup4zjnnkNsPAiNK/iF98skn47jjjpNu1/8ksHjx4sR9BbpJ6RlnnCGtkjAUi0XP6+OPP155X8vlspI2/cT2tNNOE140g/DTn/7U+Xv58uWJ+nrTTTcBAPIVoGOLGJoG5IpApwkMDg4Kt++JHeLJ3qD138zMDM4880zk86RIEQff/e53rT8q3Ody7n1btGiR1LX48Y9/bP3h+LHdNlesWIGTTz5ZuE2rme7Fp1QqSd+vp556yn3BFz8u5IFWG5VKRbjtZrPpqFpaueKohLxiKGMPAvvKzYek9rBcLnteJ52vTz75ZODx4447LlEo0s9//nPnb13PwegYGBgYkO4vr0D6Y/YA4JhjjpEer5s2beo6dswxxyS6tm4ygdvXjtFOdA0Y+DGwcuXKRO19+ctfBmApe1NNS6QocMreyMiIkjUBkLdTDMyueEuvuAi6Z41GI1KgCpVg3vOe90QSvSC8+MUvxsUXXyz0mSicddZZWLdunefYfffdh9WrVwfG6wHuRPG/H3Z+EvjVJhVPBW7pFS3wuCzScuOmUZE+jevqR69nDvNI2lemFhW8a6ej9MnEgTlKEeCxItqgO25lMtyYwqKFKHtJijV72uLaTOJyU52J6b2u3IW1XawyCpQnQafCJWjk9MBzqPBcN811OQe+L4HZcuMmjbHkvRuagr2BvTF73WQvSZJGGqVnXDeubv+vpkyMSpimif379wMA5pZdRSyn5Zy4Pc/ck2ifRxpuXJ7uHXbZuFdffTU2bNiANWvWYMuWLbjjjjtw11134a1vfatzztq1a3HppZdiz549ACyCOG/ePHzwgx/EE088gWeffRY333wz/vd//xeXXHKJ0v6lkTXKJqPqbFz/4FNFStLYazKtvvLop5i9JG12Oh1ncchXvO8V7NcyBMohcjl4rcig+6eM8XTITAjZky2R4InZ4/9HsoVZNdnjf5/GkTGtYjHzRNcUcEgjAE/MngyJ9Fw3XoVUVFhZdTburJA9BfXwvDF73WQvSduq9/Ftt9uON8/f116K2eOzbeeWvV7BOfbrJGRP9f7YLtnroTp7P/rRj/CjH/0oaTOBOPPMM3HDDTfg5z//OV7zmtfg1ltvxfXXX4+Xv/zlzjlTU1PYunWrc3HnzJmDm2++GYAVd3j55ZfjRz/6ET75yU/iVa96ldL+qb7BpmmG1tlLapDSUPZM0+z6zWmQPRVqIZBOgsZsKHtJ+sqXKymUvWMqibLHnpJ5cme9dr9D1Hi2Wi135wCe7Glw3LqysVVpKXuqg/MD4+AAaDZJkyG7/P31KHuaBhSs66yM7EEDitbAOiKVPQVEx81uzTlKYa8qezzxdLYgU7hlHo8kY4C3RXyCBgDMtV+rJHtJ18FgZa/7fRGIBdQE4IMf/CA0TcNrX/vapE0F4qKLLsJFF10U+v4VV1yBK664wnPs1FNPxb/+67+m0h8eqrdLq9frzoD23+SkBinNzGEevUz2eBwpZI9f6FUqe45h7CJ7AecQ4VWgCt43BwpArS2t7DkLj9ZN9pIszKrLbjjXrFzyPsoPWDfrwIEDwqU3+OuqVbwZt1plAGZrQmoM8L9d09z4P61YhFmvJlZ2/DYqSckRILzYddI4c57UMDd2EqLDPlsquRm3qpS9IPucZG0JdDmn5MZVRfbmlrzK3lwFyp6fRKsKu9JCYvYOCdm77bbbkjbRt1DtxuWNjp/sJTVIae72wSONmL0jvfSKKrLXFbNnk73JycnQupVhcMmeBlQ5I1yBZZVMTv0jwkPkfGRPGyjAHKtJkb1Op+MSMJ1z49r9VE32WEkSGWLCrqvmK4OiDVqv6/U6qtUqBgf9LDscHhJd8TH+SgWYnJBS9rwxe9yiVCrDRHI3nt+WtNttFAqFkLPjEWZDk5BzwzA8v5MRnenpaekxEET2AA3FYgXNZk25GzcJMQn67b3oxvWQPb8b11b29u/fL33PVJO9IDcuEhZVTuzGPf/883H++ecnbaYvoVqB4o1OzjfgkrpEggxnUqRRoBPoL2Wv14mph+z5lT3brWuappBryDTNUGVP0zWn/Irok7InXs2v7A0Wus6hwjN3bLKnAUDJajPJ4tlFImHdL9l54F5XX807jvxJK6b5PLSCn0TLJ34417VQ9KqQBSubPumWYao9J9390UKO08HXGgRcN26r1ZJWzNhcLJW8k4uRvyRuXNU22+PG1b0JGs1mU8l6wJDEDka7cS3y12g0pMeC/7om+d3879ShTtnr6QSNXodqUuJ5SvJlaKiuRt/Lyl4/x+z1srKX9/EHnvyJuPFmZmYcY6YNBTwF24JEErLHyJ2DATe2TPSp1kPm+AxXm+wpUfZ89bBkH87ilD3+HCqce+tz4VrHKt5zBMAWRq3oLbXEXquM2Qt6LQp3IffuDZzEa+J/UNAUuFvZ58plb+FkRv5UKXssHlAV2fMre/73DyXYnCnnSqjkvS6OOVx2rqwrV6Wyx9v7HG+vEu4mQnLjvuUtb4l8X9O0yP1qD1eoNkbeYFe7zg4AA+m4RJIijWrsQW2oKr3Ckz1VsXVpKXuqtglyFnENyPvKH/IxfAcPHiQXK/a4Z4O8iYOWfzQR2at4TZM2UIAJ61pMTk4KlYXyKCE672602kyilHgSPzre4/Pnzxdqq9VqufdrsAJw/dIG3Jvlrz0aB3Zd+Tacdm0CODU1hWaz2VXjMgrOb/eRPRTVKHupeU50HTA6NkHvJOqnf+z4yd6iRYuE2+TduM2mS0TL5eHA7xSBl+xpME2Vyh5L0PBegySbAfBIYgfZHBj1qXqAV+k7cOCAVJ3UtJS94MIrKSp7rVar69++ffvw4IMP4plnnknFzdYPUG2M+EmcsxclxuyTboCdRu26oN+bhhs3jTp7qsheWvUL+cmsguzly921G3llT6TOmofEDQYoe4MB54m0W8l7yo5YbbokRNSV61EX+GtgK3tJ5pZDFHzXVoZAsOQLAND8MXkKlD1/cgbgVRBF1T1G9jQfQVSh7AVl+ie1r10qrP1/kn76lStdwXZxbDymoezxtlSzl39V2biusqduyzzeZiWJ2WM2Y06pe6tFnuzJZvv7XfZJkl48btzZ2huX4fbbbw88/txzz+Fv/uZvcM011wh/8eEA1WoZv+C6BSo1tCBX9JTHbCl7/eLGTUvZU9UuP5mTtOkUVO4WdTzHRIycl+x1v68NWtslzczMoFaroeJPCgiBQ+L88XrwxvCNjY1h5cqV5P56lT3OeFaK3e8Loquki/+4AHjFVPPF7GmFgqWYNZvSyl5Xcobv2MGDB4WUKIfQFksAPw9ssler1WAYhtT2iUH2SRnZ03VLhdWTk70oZU9mXPFxbt4EDaBkExV1blyt65goWF8qhQFHeeKVvaQiBW+zVZC9IGVvtDjcdZ4o/NdQWcyeJxv3ECZoHH300firv/orfOYzn0nSTN9CtbuRETpd07qUPdmdAxjSIFCz5cbtJ7KnStnj+6dC2Qsie7kSHN+AvLIXcAJ3TMR4OvFq/ng9wBPDJ6pseX4bt/WaVk6u7HUVa/YfF4Dndw0EqHA2ART5/e1221mQ/XGA/mOiqoYTs1fwKXv2a9M0pRWOoDmvzI1rkxHVu10AyWP2+M/4EzTKZev19PS0tE0IitlLYrPZ3Bkoqi8T40cSO8jGdpCyN1gYcHbR6D1lj7Mrh7qo8sjIiLPR+pEG1dlibKANc26RvE36jiSyl4Yb1/9U2OtuXNVkzxeTDMB6si9IFFZ2CFwR0ArhblzPuSLtBih7/DHRp2+HzGmal5SVrXlWr9eln8Q9cWBBxwXAK3baUEAyhU32REraeAsqB5A9n7InAuc3+skeF8Mnq5qlUcPTKdjt7ONrLfAqlb2kxY/5z7AYPQam7JmmKU1QPW5chcreQNGd9P6YPVWQtYP1et1JwhkpdpM9TdMwYl/b3iN7wRRNRuWUJnuGYeCZZ57BjTfeSA7sPtygWoFiA2207BpPXtlLImPPFtlLo10VbaZFytJS9vgJn6TNKGUPcJM0RMheaEFlhgHxXTQ6nY7bhyA3bl4HSnJ7WDrtlgvegGdunsmESXQ6HTeTU6Wyl9OBUqnrfRbHJ+LGjayxB3gydOVj9nx95cifbKZrGrbFIUiMkCnY2swt1u3GAer5onS7PNnrVvZc9Uy2z0FuXBUxe7yyp2m6sv1xVSSq8eN6pDgUeA473gtkj1+rtk/sw44p6+F2slF17FdqMXunn356V3C3YRgwTRODg4O46aabhL/4cIBqBYoNypFSEW37ZjJlj7ljRka6Yw4oSIPspbWDRhrZuEHV+EULCVPa7SVlr9FoOIttvhygwMEqtFyDGNlxFbiQ4qMSyt7ExITzO7XBkIzQgQLQ6Agre85vK/varXjJnmjmpEe9UxCz55C4wcHgwq62sjc+Po5Wq0UqMBy1ewYAaPm8tWVaqyW00HU6Hbf8TkjpFUBeNVPtNTBNs4uYabq160ej0RDORGZwQm/0HIyONX6L5SHUpw9IPUDw5Khb2XOJyuTkJJYtWybUtj/pRUfy0iuuG3cYkzU76xvAQGkIU/WJxDF7PFSQvdEANy7gkj3Z2PggsidboJkXdf73ud85f++cPpg+2XvHO97R1elisYilS5fiD//wD6UJSL9DNYFylL1SCWP2Ip3jZNzx8XHpa52GWjZbyp6KDN8gt20aZE9VfCHfX1mXM2+4UlH2AjyNAIAynJpBVGIWtXsGgzZYgHmwLu3G1SpFgFfHy+73yBj5wB0k7F05kih7/uQM5yt8GblHHXVUbJuh++LyqFSEyZ7n9/lJEve6V8herVZzt6DSc9bWbr7YMtFSOQDvGs4BHWvuF2yyl9SN60/QSKrsdTodL0mwx2wSm8W7cRnZs16rIXsqEjT4uT1cDHZHDCsme4C1bpXLAfEzMYgicibhnDCQyN673vUu4YaPBKgkUKZpOoZ5tFx0yF6eUwzGx8elagAF9S0tZS8NspeGsseOyTzNR7WbRiygrFroKagcYnPygjF7pmm6pCDEjatpGswBANNyZE8bCDFLXGFlEbhu3CJQ49xYlWRuXO/OHFw5D9NIpOwFJVIAycleoBsX9v64k5PChbWdz0coe7I17FTbFm9Gtk3yfFmjMmSPV/bYjC3aipzMmPLG7IWTPRkS5X9o1hMmaHQ6Hcc1Puhzj7LXKmP2VDz0Doe4cRkJlI2NDyJ7tVpNOdkTOcePbAeNBFBJoKrVqjPpRkp8goZ7i2TjCYL6lhaB6tWYvbT6mpayx7erwsiFkT22ZRo//qLAn6eFuXEBR/WjjlmvshfhxoW3Hh0F7Drw5A6Ax62rTNmzH85EyZ5pmu41CFH2+OPUJA1n8crlLHdtAJjiJ3INPCSu4Ff2krtxVe/hGkT2NM62yipQztjiVMJiguLHjBzpeh55XxV0XumTIVFd1y9hggbfhwEfiaoUk2/t5oesG5cf12Exe4wENptNqXi7oM/Ixu31LNlbs2YNvvzlLydtpi8RREqUBJFywdk82UuSkZuGazSI2KiIWVNZs4ghrb6mVROQX0xl+0khe/xximH2jMEwNy4AVKyFhEr2POeFKHus1l6r1SIvdnzpEd5tC8AqqqzJZ7t7MiKZAm+3J5otOTk56YydoJ0uAK+yJ0z2ypXw+CFbfZBW9krewZVWzJ6K2DKAI2YJM2dN0+TInjtmCxWL7MmMKb6gcnfo1CBY/Q2Z/vptE6vbJmuzvGTPK/Oz1yqVPVk3LrtWOU1HOded+AQAwwW3/zIPfkcM2VuzZk3SZvoSKgmEN4iUz8Z1SykmUfZmSy1Lo1izCrKXVl/TcuPy40G2n/yCUEiD7EXVSh4IOD8CbjmXHLRCSBwlF8tHbdfzm3zKnqZpDgGUMfAeQsdia3W52m3e2oUh/vFSyWmf6h534xXD3UlaueKcS11Eoty4vNLXKzF7gW5cjuzJ3P9arebYUb7kSqlixVVT1fKgfvrj9azv0J0MXdmCzTySZuN6yZ438SENN66skMKu1VAhJPEJwFBxoOt8ERwRZO+ee+7B3XffnbSZvoTKuBKeyPFuXAAYtl8nUfaCFKgkpVyA9FyjKjeVZkgrvjAtZU9FgoZbXw7IhXhGRcmeV4GLcONyiR8Uw+S0GxavB0jV2uMXcc2fjQs4BFBmbnkWMt3rxk1C9rTBEGVP0wBb9RP+/eUIZm4re4ZBjzX0/D5/zJ6mOcdU1IOLOkZF0C4qSXe74MeMnnPHbbEyEngOBWxM+eP1GJJsmdZN9qzrIOuRilb2krlFg5DUDvKEzo8hTtlTRfZkRYqeJXvLli0TTgE/XKDS1RDmxrVeJyd7Qf1KSkzSco3Olsu5l8keT8RlrykzxrkioOnBxIwPC1Kp7LF4PiqBcLf0Ci8nwm+ZRlW5Pb8pgOwxAiizeEa5cUXb85RICXHj8u8JK3sBdfucNrkgcupCx/92vxuXPyZL9tKL2dNckqfpzgSQWeA994yP2VNA9kohJULKCeIBw5Q9QM7G8GN80KdE8jF8SXYo4ZHUjcsTOj/SIHuHnbJ3JEMlgfC4cX2Lkgqyl0ZNvLByJknhN/S9nDmclhtXJdnLlwDTMDG22UTNXp9aNesYT/YoRpk3hGbJhPm0AbA1z24TgFV+xQbFRcbGthZSdgUAUHHVE+pc8Ch7/gQNIJEb11nsSgV330pbNapWq0JjwUPeovYSFiR7brxiRFZgQrLnV/b4Y72i7DmxdaUB8PtO6WX5RAKe7Ol6sLInGnrDx+wFoZygv2Exe0HvURCVoMG/VuXKlXXjsu8fKkQoe5zqJ9PfoIcTWbJHIbUyxJdUegUAfv3rX+M73/kOtm7dGvjD7rnnHuEv73eoJFDMKAwWCp6kDMCqu8efI4MwFXJoKNioUODUrYJb/6dXlb20agKmlY2rYh9fXtl75h4T49vc9+rj1rEVL3WPURYQhxTlAaw1YW7hjM5BwLzLAP5Y96h+ExMTWL58eWS7DnmrRJikct4ZbFLKXilgZ45yEab/PCKcz/CqmS/Dc+7cuaS2nN9TyEOLKJasVSowQSN7fGkMrVSCaRgwtm6BaX+XWavBNAyP6kdd6BwSVyh6slqdfpbKMKFW2VORoKGVveqOVh4EpseSK3tcvc7SwKjzt0hNSL7wc1DMHn9cjRtX87w3GBYrGgJPAWgfkaoUkpGnICSN2RuMInsF9TF7fenG/dWvfoW3v/3tMAwDW7ZswSmnnIIVK1Zg3759yOVyeMlLXiL8xYcDVBKIoK3SGNgx0ZITPFS7RQCO7Gndx5LA39dOp5OYRKa120cvu3HZQmt04CF6DOPbgINb3dwCirvVMdx5AFsCxuIWE3jSBLgdO+KMfbvddklJpQDTMGE8fQA4aBvQWgumYVejL1tkkKrERSVoAHCUvampKWED6hIIrl3OXS6yaLhkNyrrxX1/amoqdq7x99MsFtG++xdo37MWqNtbmI0fRPvuX8Dkak1SyRm7p0EuXMBN2lCp7Kkge7pPMWPkT0bZdcme5lH2ShWX7Il4Y2q1mjPXw2P25Mlel7LHFexPouyVCwNde7jyMXyqyJ7M2scT6MHCAAzTwAO7NmDH9B4AwGRzGoZpIK/nnUxdmf6qdOOmpeyRyN6Xv/xlvOlNb3Kybv/6r/8aX//61/GLX/wCuq5j9erVwl98OECla5A9AY4GxNbMKVvHGo2GdJHSNJStNNy4hmEE9iupupeWsjcbZE+2TYfsRXx8aqebvEFZmB1DGGFrzOdNjxs3jvTwC61ZzsH4r80w1m4BajbJPdiA8V+brWtix/QJZ+Pmc9ByAebODpEQSU7oarscrOyJEAjHjR1ThJXPqo1r33M/xw/C2La16xxj21aYzz8f/BlK22GxgGWXlMpA9cOp48b1KXtJ3LjMZhfLQ54n3lyhhHxRzN3u70OcG7darQrbhS5lj3PjythXZ/eMgFi4SgpkT0bNajQaznUazFfwpUduxY2P3o6pljV+d87swZceuRWGaWCgID9mVSrRlN+ZGtnbsmULXvKSl0DXdWia5ly8o446Ctdeey2++tWvCn/x4QCVZI9l482tBJG9ctd5IjBNU/mTMuB14/qPySLMoCdV4fpJ2fNPZNlrysiLGfHx5ox6socpExCIBfQstAfqMLeOd51jbh2H+fsxaLabl7o483F1QdC42nuiRt6tYZec7LlZs5a7tbN5K8yDVvtmrQ6T7RssQPY8yt5E+PUy9+5x/hZX9kIyh4uuSiKzMLlzMxkhYXD761P2EmS3MmWvyLltGYq2uidL9uLcuIB4n6PcuDJ2i42vckCWK08AVSVoJM0Y3l3dh/V7Hu86Z/2ex3HfzvWOK1f0upqmOevKnsyaQCJ7+XwemqZB0zTMnz8fO3fudN4bGRnxvD6SoEotMgzDIXHzAuphzeMIILWYqr9PQQMoKdkJmnyycRUMYX1KSkxnK0FDRcyifyLL9tNRgWNmOSN7NXuLvig4hjvOcnCezThjzxtXcyLcQJrPTwIlSbLnL6jMUJIje56CuiFuXCmyVyqhffev0Lnnf4G6PebHJ9C++1fWHOaUtLhr4PECRMwfs1p1SCplDADgYgFD3Li2stdqtaQWPWe+a4rJXtlLTJjSNz09LbyAMiJXGpjT9R6L2xOJs/Yqe9HZuP7zKeh24yYje2wMBCl7pULFUQ5lay0CyffG5W3P3lo48d449hQGbWVPlJw2m01P39hVTTMbVwYksnfiiSdi+/btAICzzz4bN910Ex577DFs2rQJN954I1asWJFK53odqurMHTx40GkrmOy5x9j+mSJIi0A5xlFhzF5Yn5ISU9XZfQyzseewzDXtdDrutmYR5fAAIGfzHUqIAJXsabrmEL44EuVZtBrhv9WcbgLlXPdnIuAqOmrJnseNNsDNWYkdb0zTdH9PrQ5z27Pd52x7FsbTmz0qYhyZ9NzPkNI7DuxCyNSF2VVMQ8ged1xG2XHmpi+JQAY84dRLPjduySV/oqTEJXvdyl5pcI7nHAr48VcsDuCZZ+7HwYM7AAC1mlXwOsn+uFHZuDLXlt3XcqFb3dU1HSVJ8sSDJ1FJlb1aK/xB5kBtHAO2sifaXz+pY/GLquoLBiG1BI3Xve51juF697vfjYmJCfz5n/85rrjiCjz99NP4+7//e+EvPhygikDs3bvX+Xt+QIA2T/b27NnT9X4cwghUUrLHBhy/jCQt1Dybyl4a9ft6JcOXV2jiyJ5u8x2KcXIIREybAByyF6cWBdarC4Ot7FWrVdJYc2PLQty4kmTPk4nJKe+apjnfRVV16vW6e48jrpX5/C6hzFkP2YsbBMWC/fXxyh4f9I5iCe3NT8I4aHkmzFrVcjlzZC9RDJQCZc9bE9Cn7JXkYstM03TubyDZqyRT9n7zm+/gnnu+hHrdOjY+vgNr137RKarsP5+C7r1xkyVoMHJcCclyrdhkL4myx5MambWF/25+l5MgDOblYvb8c0a3xyxVJfcjrZg9UumV17zmNc7fK1euxJ133olHH30UtVoNZ511FubNmyf8xYcDVLkGd+/e7fy9MKB6fjGXw5xyCeP1Rk+SvbhjIggje0lJVBpFpYPa6BXXsIe4xSl7+YDPBKDT6bjnUB4TbbIXpxh6tx2LblIr5mDCus7NZhOliGLBQHzMHiOPXf2IgWcR96vxlTLQaJEXev57zYjxY87MOAkl/s8FwTsGom+Yli/A9H8mol02zo3ntqDz2ENuH8fH0Lz7P5E7bbVzTBXZU7Gtl+ZT9njyJ3L/p6amnHkZ5cat1Wqo1WqoxGVZw0vennvuka73t217CEuWnBx4PgWq3bhsXvvLrjBYx8d6xo2bi5kDA5LktJvs6QA60mTvkNfZ4zE4OIgLLrhA5qOHDTqdTiCxkVmYebK3KGSrpEWDFYzXG9i1a5dw+2m5RoOUvaRu3DCj0y9u3DRi9pKSvRgbB1Y1Is44iRBIAADRPSzkbiy5T+czMzOxZM+NLQsxdRwJlCV72oCX7GkDZZjjU1JkDwE16zzI561zDCO2v555H6fsFazrQyF7nhjLg90JY51tT0OfvyjwfCpcN657PWQfTr37+HrtqyzZ4+8tX0TZOcYRwIMHDwqRPV3PwTCC7ejevZudv0XLxUSRPdFraxgGR/aCf1vZVspkK0iw7wn6mwohsmfvHdloNNBut5HP0+iR//cxZU/2d8966ZX169cLNzY9PY2nnnpK+HP9iLAFWOYJiSW4jJSKKIcMsEWDllFSSfaSxhQExeylpez1C9nrFQIpYrwZ2Yvru4cMksiedVLcOHOMYp4gFxZcshdHTpvNpvObzEIexjO7YB60jL9Za1q1+/I5wC7JIrLYe3e8CFD2/OdEQITsWvvOWgQ1ToEQInu2vEsZNxTy1hlzQ1MOtRs3kuxxr0UUHZ7sBSp7ErtoOAk/EaSkWh1HPi+3xZ8/wznJDhr83AtX9pKTvaTKHv/d/lqAflTy7lgQ6XOwsifvxp11svfOd74Tb3zjG/Gzn/0sttO///3v8bnPfQ6vfOUr8Zvf/Ea4E/2IsMkhQ/Z27LCCcBcPhlf4Zu+NjY0JD6LDwY2blESprsjP4O9Xr7hx+X7FxuwRyZ6HtFHcuMRYQGc8Fyhkzz0nbh54FKite9C5+1Ggbt+f8Rl01j5iZ7haHZVSdjR4S6/AVfqoRdA9JCNOhgWAIi2Zwkv2YtrMWySaMs9I7sO6u1j2FNkrqSF7fPJNsLInXliZkb242DKWkSu6faabsGWTPW6sidpXngyV88HKXkkB2ePXExmvEfvugp73kNsgVPLuQ5tIn/3jhpE9Wff1rLtx7777btx00034h3/4B3zoQx/CKaecgpNOOgnz5s1DoVDA1NQUnn/+eWzcuBF79uzBmWeeic9//vO48MILhTvRj1BJ9piyt3gonOwdNeTGmuzcuRMrV64ktz+bCRq9GrPnJz+mqWYHEf/vTYvsmabpcbvEwfPb4uLgcu5nor5HiDwAjnWJG2fO+xSyl6e79TzGdqybcJhb98B8aqcVt1dtyCl7lbKVecxjwHUHVavV2G2o+IVF07XIEoYAoBWs+Lq4BckZA7k84m6YlsvDBG1OkK6Tpjnu5kNN9jzXtxBO9kQW+Dg3bhJlj9+NIwjl8jCmp8eE3bgO2YM1xvzbpYmAf9DK5wrY8NwD2DNpiRbTjUkYpuGQQFmFC0ieoMG+u5wPzhrnUeE2ChcZC/75wNzFssWkZz1BY2RkBB/4wAdw3XXX4a677sL999+Phx9+GPv27UOj0cDo6CiOP/54/Mmf/An++I//GCeddJLwl/czVJG9arXqLBxLhgZhmCYe3LEbOyatATTZaMIwTRzFEcEdO3YoIXtJ3bj9quwxspdGhq8KN66f7JmmCcMwkMtFP/GH9i1O2dOtDWdN00Sn0wmNVREhkAAc6xJ3TVxSEk/2NAGyRyElxvNj0IoF4X1cnQV8oHsR4Qsfj4+Px5I9r3uccGGJ8XXOGCBcV9hqEsV+0RYxzcrIrVWldqcIS9AwDAN6XFyjD143ri++suAu8CKkxCVamrWDhg/FynDAubQ2qcqebDaupmmAqc6Nu27z3dh+4Bnn9Z6pnbjt/hswZ2B+17miSOrGZd9dyUXH9gJAiTtHZG302w1e2RN9SKcilQSNcrmM1772tXjta18r06fDFqrIHnPhApar9obfPIKHd7oZtzunZnDDbx7BNWevCvwMBd11gABDAdlxlD2FdfZUKqY8PMpewLGkbUYdE0VY/UZZshdnajSu2VarRSN7FHCKYRSc9ykxezm1ZA/TNSk3LntA8ydnAPAQwAMHDmDZsmWRbYmTPau/ceqDM44o48a+rurInuUyNWtVqTpr7N5qmu5ROpvNJsoxW8r54SlDVPC53PUckC8A7ZaQmsOIVqE8CC2AfOq5AnKFMjqtOons8bUW45U9SzWUd+Pqnv8B8bWAv6Y80WN4fOfDOH3J2c65MiQdSO7GZf0s5YoxZwKlvHuOzFiwHpmBnP072+02arUaBgbCPXZBOKR742bohip34/PcvpS7pmc8RI/h4Z17sH7XXozaZRf4z1Dgn8jspitL0OCQVNlLi+z5lT3/MRmE7aCSxjUQNXQiyh4fJhZ1nUXaBOCQvbh75ypQhEa5c+KuCTlmppgXOx+cshdQBF3zkb04CCVSAFZGrv9zAXDIHmWRtQkhJT6UTvbkN5YPUvY8xwXgEJNcwSJ3PmiFsvc8AtgCXwzZ6cJ6j77vLl+ke1aUPViXlv2dxI0bhvGam6mtYp9Y2b1xAaCYCym9xKGku2RPpL8OSbcNaY57epZRtdNCRvYkEVaNXHTS8FvN7ZgKfwLeuHcMi+24PdHt6fykjtlPVW5claVXVGY58whKWEgj6UNFuyp2ZhG5DzzZi/pc2mQvSCHpgk53PVEVJVZYmUpKDMNwyJ4WQPZ4AkiJ13LmobX6xp6v5WluXCGyZ59DGTfOdYprtyRXpNY0TaVkj10nRur8YGqfDNkrBLhwGdh7lN/Pq39xZK9ixwPWajUhW8OrpRY05G3FS9RmUdaORss9R3at4cdj2mSvwJ0jcj3Yvcvb9y3PGVTRuEogU/Z6Dv6Fhq1BoosyK6UyWipivB5uyMZqNSy2a/AlJXu65NOcH2lslxZ2/ZLWr/PUnVNEdtOKL1RN9uL4A0/2oq6z8D3Qu/sSBOd9ijUSUPbI7sOSq+xRjOjU1JS78AS5ccslZ05QyJ5DYIh1vVjmbBzxYdeHQqI1zvUUB4e8xBVqtpU9UTeuRx33DV6ZOctInFYIduXJKHvsGhR8RZp5FEp0ZU+E7PH744q4cvkEDQZG9kRJNOU+8G7iQ0X2mN0s6PFkr8idI3I92D1gil6Ou38Z2RPA3XffjcsuuwyrVq3CJZdcgh/84Aekz91///14wxvegNWrV+Occ87BX/7lXyaq5O2HfzDIyuGM7C2KKLvCwM45ePCg0OTp7qv1vyo3Lm+Ok5Ky2VT2krpx01L2gn6r6HWVVfaiDKqnDwRlj2WpGoYR2a7zXlxBZcCz+Csje3bdOsMwaO4pboHlt0pzjumaU46Fshi7CSrEmMwcrUyKS6IJZt4eBJQF1d0fOY7sWSRKVNmL2vkjGdkLDtLX7FgtmaD8aLJn2WvKuuMle9Gkv8Jl+oq4Cf1uXAAopKjs6QrIXlI3rkv24h+k+HNE7C2b43l7PuS4eSGyXV7a6Gmy9+ijj+K6667DxRdfjB//+Me48sor8eEPfxi//OUvIz/33//933jnO9+Jl73sZfj+97+Pf//3f8cb3/hGoQD3OHTHwWmBx+PA9sUN2ibND/4cfj/dODibgNuv2f+qslFnw42blEQGxeylkaDh/y4ZqNiGT4SYUZU94XtLdA+7CjGB7OkSZC+uULHglmmep/VySJaffZzyZC+SjQzAUQDJZE/gulLmGZXswc58rVarQmPHS/bUuXGRD7lX9nEZspcrVrDrmQcwfdBKmmvWJq29geESQcqY4klb3DrFK3siylG3GxdOgeZ0lD333qlQ9mTWFmY3cwSyxytyIvaWEbqc48bNdb2nGrO2Xdps4ZZbbsHq1avx7ne/G4C1L++GDRvwjW98Ay9/+csDP9PpdPDJT34SV199Nf7qr/7KOb5ixQqlffMbWhllr9PpYN++fQCA+ZUyDtSiJ8SCAS/ZO+aYY0jf48Ss2OlCuiJlz1kYOHucVIHrjoW0MpyUunEDjiVtk0cvkL00YvY8T9aUmD3iziqO4aKQEu6UOIPnKCps4Ieh6LpvKCpMnLLHjpsHaYuxm6BCexjVcm58XVSWo0v2KMqeq8LGge7GdV3c09PTGB0djTjbhTdhRQ9/jwiX7BXQeOYhdA5a3hSjNgXTMBz3LtUe8FuFHXj+CTz3xN3OezMHd+KRX3wJq//oXchLK3vpkj1ecZNV9ij3QU+41R0rOcUgswawORC3VRrgxtyJfFetVnN+GyN5uqajlCug0aHvj81DhshRQCZ7pmniiSeewK5duwJv3Ktf/WqlHQOADRs24HWve53n2IUXXoiPf/zj6HQ6gU9AGzduxM6dO7Fw4UK84Q1vwLZt27BixQpcd911eOELX6isb13KnkbbGorH+Pi4M5jnVSoAxiPPn8cFfu/fv5/8Pe4kBzomI6amspg1LeCYLLommb1OJyWRacTspVW/UEX9PqGYPWLMpbAbRZjsUdp0T4ozih5lL4r7Fg+tsuco5FRlz7coFYvBsWiue5yeoCFE9mLduO61ESF7UcqezNxi89Q4uBszd3/dOW6M78L02q8CgnFrtVrNGXsz47u73t+zdT12/v4+5O0Czq1WC81mM/Q+Ad59cTWNTvZE3LiussfF7Em4sAF/gebgeZikaDPQbYtklD2X7MU/SPGEkGrrxsbcjGOeLM4pDWNP9QB5y8TZAInsPfPMM/ibv/kbPPvss4EGVtO0VMje/v37sWDBAs+xhQsXotlsYmJiAvPmzev6zHPPPQcAuOGGG/CBD3wAp556Kn70ox/hLW95C374wx8qK/7cneEq7sblB8rcEIWAxxxuYREhe44b1yZ5qpQ9NwbEPWYYRigRp4CRPabFqFD2TNNMhez1srInQsz89y8Mwk+cxHZlS9XEfc6r7EWAI3sUFcazwJZDgv7LRZigxau5WbPUmD13UWq1WqEkQsiNa5/D1JQotdCpQRabjes+nIrE7XnnqndHEZk5yz5j1rqJUWvrI8gvOVGobcoYGduxEaOLT/B8hkL2yhGlXBhKXJygHNlTp+xpmhZqF5ImaPhtfhKypxPmgK6JK3s8mfOQvfIQ9lQPeNb4Qw0S2fvYxz4GwzDwxS9+ESeeeGLkoFUNf/XpuAWHLQB//ud/jv/7f/8vAOC0007D/fffj+9973v4yEc+Qv7uJ554IvS9Z57xFpJkg+nAgQNYv349qf1NmzY5f88JUwg4FHM5DBTyqLbaeOqpp8jfw1zF7FKy/6vVKrmNIDiBz7559Jvf/AalUvzvCYJTMNpmecwDt2fPHum+ttttDzHgEzQefvhh6QrnTz/9dODxJ554IlGtvS1btnQd27hxoxDhe/bZZ+lfyK3ZGzduDE0q2LZtG71NwEP2NmzYgEolOC7VSYoQvA3PPvts5JhwDHFczB5H9jZu3IhCITpzz7nv+Ryg6TCeeQ7mQWvRNWt1mIbpKHuTk5N46KGHIgvKOgsCJUEF8JCs9evXh+7Q4aiKJLLntvnwww+HPqx5dqPQ9cit3Xg37oYNG8gJcr///e8D+8Xemzt3LqkdhjiiadSs92dmZkg2Zs+e7lqoftSmD2D+cne8P/jgg13CBQ82X0ul8FIuDLqeQ7E4gGazis2bN5P6zD/wehI07HhFkXULcCtCaJoOmMG2Tucm9FNPPRW7k4wffoIo2kcg2HUdBp4Q7tixg/RdjzzyiPN3nosLnGuT9p07dwr3OWxd4TExMSHcLonsbdy4EZ/97Gdx0UUXCTWeFAsWLHCICsPY2BgKhUKoS2DRokUA0LWd2MqVK4V3nli1alUoafETQTZQNE3DOeecQ2qf/20jJRqBHikVUW21USgUyN9z22232X309rXVapHbCAJ7+vFPo9NOOw1z5syRavPee+8F4Cp6DCMjI9J99Rt7fu1btWqVcEV+hq1btwYeX758eaLryj8EMBx77LFCbXoMhoAb96STTsIpp5wSeB7LHCeDa/eMM87AyEj3HqIAuOsvFrN39NFHR16TsPIdXeDI3qJFi2Kv8z333GP9USqis/Z+mNs4uzI+hc7a+4GjrK2iTNPEKaecguHhcMXme9/7nvUHdYcBjhSuWrUq0MMBAN/5zne6zg8Fd41Wr14d+kDvKfsUF7PHbU22ZMkS8vj17F7gu3eU++NH3IOX2bZUrVarhbPPPjv24e93v/sd6Xvz3O8//vjjceKJJ4ae++1vfxsAjewBQLk8hGazinK5TLoebN9rIDhBQ9d1oev6X//1X1ZbEXOW/56lS5cK3zd/CMTAwIBwG+yhhUL2+N9CHa+bN292/i5wyh4je1NTU6QxxYMiFIyOjnb1r9FoRApUJOuycOHCVPZ3i8NZZ52FdevWeY7dd999WL16deiT5+mnn45SqdS1EG/bti122yIRMIPEBggjUDLbrABiZM//2TiEZeN2Oh3pWDi+8Kl/aCRxjzpuXJ8KmSTLN6z0TNB7Igj7nWnE7In2UyhBQ6ZOYhrmQHGbrruRTvYopVfcBAXNS/RsmNt2AONT3eeHQKhECuAhWVELg4wbN65NT0xj3HX1xexR4bkHvmuSxI0bBra2GYZBct9RbTyL2aN8hl2fUkQpFx7FonUe1T3urUbAu3FLXe9TwNy+USSK5wwydtZ/L2RCeURCT/j+Uj/HVPlSruC5FnPtLe1arZbUDjJpgGRd3v72t+Pmm29OvIiJ4uqrr8aGDRuwZs0abNmyBXfccQfuuusuvPWtb3XOWbt2LS699FJHWh8aGsJf/uVf4vbbb8ddd92F7du34wtf+AKeeeYZvP71r1fWNzZ5c1pyspfXNZSIMW5DRXGy5w/MVUF2+M/5bX6ScRKUjRt0XARhO4gEvZekXRVtAsH3RPT3C8XXcVYgrUyw2YanZl4c2cm7c4/ianSMdxQxnnDJTawbkapAMhBrDbrt0rNxPZ8LgIe0CWTjysbsuf2y/hcpfAxY1yeWJAjGlpHJHqfs0ckebR9VRgqpfeF/F+9eLUiWXgmq2ecHr5TJ2G8VZM/pi6BYRbWDzDvHyB3DPO613zt5qEBy427YsAG7du3CK1/5SpxzzjldLlRN0/CJT3xCeefOPPNM3HDDDfiXf/kX3HTTTViyZAmuv/56T9mVqakpbN261TOY3ve+96FQKOD666/HzMwMTj75ZNx888044YQTgr5GCmyS6ZoGmC7po2Re8X0HLAJHHYxDRbGtnQA+QQOe/9l7Q0M01wEPz+biWvh7onAyE33Knkqy5//9suBjFnnbkOT3A8GGV7SfQgkakp871IgyyFEZnX5ommape8026d45hCfi+00u4F2I7FEWGZ1GzIRIpJSyF0Mic3nrHK5UCQWBRZXtayM6D0jn+xSoKJc7QJ/fOW57trjPsIeMYpFG9th5iZU9iRqDAF9jlebGPdRkjwqWXSxK9uZXRtDh5s28ijuG9u7d2xVWdihAInvMlVoqlQJ9wmm6eC+66KLIWMErrrgCV1xxhedYPp/He9/7Xrz3ve9NrV9scuq6BhheOTsu84qBTdTBmIBwHknInuaQvWTyOuB9ovQre0rInv26H5Q9/xqdhrInep/chZ6w1hN30Og1RNkdryuQYJ/yOaDZJt07h/BEKVscEYpzYYqrqTR3kxujpUUmUtgnOX9GjQGP8hlXekXTrMLK9aqkG5cbvLoOdIyUyF5ayh7NjWuapvN+sRicxOQHI3tUW+vPcGZgyl6r1RKqouA+lEcpey55kim94rf5SctvUcDKyFA5DSN788oj2Fcdd47PL492nXOoQSJ7//3f/512P/oOzOix2jw5bnBUq1VSxhhzxQ4V6WSPEcNGo0FWEN2MpG43riwx4Y2Mfy0VeYr3w6/ssXWtF8mePxuZkb6kyl5Qn0TJnrPQE87lzyETD8pp3DmzHfMrouwBAAq57s+FgLQzB/edZLIncY2i7peIskeNV/Jk42rR2bgAoBWLMOtVoa0qnflTKLhjUyJMBiDOG8GHX3rMHs2NW6/XnWteKNDIXqHg7k5CQbiy5/axXq+TM2bdElnRYyun59E2Wol2v/B/pwxEH6go9qrZbDoZ/wsqox6yN6c8hJymo2MaQrtdpYme3i6tl8EMOCNQvLJHVd0Y2RsUIHs8MaTE7bXbbbfWkH2Mv+myZIc33v4aRkn2IA5z4yYp1hxF9lQkaPj72mvKXiy4c6JUnaDyIaVSCcuWLcPrXvc6LFu2zMpeD7CrUaVHZBFlkL27MBAas+P24q5zs9l0x2IU2dO9Sn9aiLoGQguclLJHIJHFUvfnYuA8ROW5B1nbvoo+SNHInthOD3Q3bgls8EWRMv69AkcQo1C04wH5As9R8D6cd9fZA8RsrLt7UvQYYOVIZB7W/Z+RWQPY/Agr/MxDlBDyit2CgTme93RNxzx7D2NKqR4eFKIp8/BM3kFjbGwMt912Gx566CFMTExgdHQU5513Hq666irMnz9f+Iv7Hc7eiM7mx/QneQZG1oYF6hYOc1m7k5OTkbWbgOA9YZNWNgf8ZC/8PVE4yST261TcuBHviSBI2Uvapv/zTC2c7eSoIAQZmAULFuBrX/sa8vk83vzmN+Oaa67BjpqdoSqq7HHnl0olLFiwABdccAHWrVuH/fv3o2GaNEURfrJHMJ55S6WKW+zJCQoarIlhmLH2IC3VUyxmz/0zatFziEmhCBKLliB7zlgvcGWv7N8gOg8o5E10D1enEoOWg2mGK1aapiNfLKPdrEUSRL6PBf43RyBvK3KmaUYW1mbw2hSO7HHfJ3Jtqcoe2/pNhbInswY4D5kEIscTQsrDKU/i5le6S8EtqMzBvup4fyl727dvx+WXX45vfOMbyOVyOPnkk5HP5/GNb3wDl19+ObZv3552P3sKpukacOa+zQkqe6ZpumSvJKfsUbZi8mRhhSRoyIBfwPzzQiQ+x48wZS+JAhdVeiUNspfUjev5vCICCQSrcH4bGLXQBxnACy64APm89cyYz+dxwQUXAMwmE8me8x733YxEXnPNNfja175mPdQ06AuGKNmjKnvU0iMaNMB+MCOTPe73B98r9r7Z/VkiwtuluXEdokN8QGX7zorMCWdeFbjvsMdeOm5cMWXPuQYEQsDKr0T1m//OfJ5K9txrQ7EN3od+PmYvIdmLGX9smzIZF6wKsueU1UH83DJMsXnF1x1dNNAdtrXQVvuE65MSIPOASCJ7n/nMZzA0NIRf/OIXuP322/H5z38et912G9auXYuRkRF89rOfFf7ifkatVuvaYJl3ZVLIztTUlDMBRgV2m+DPpWyyHBSYq4LseMgeIzsB74nCX7svbTeuqgQN/v8kMYv+PqkikEAwger41rYoIxIUvL1u3TpnHLfbbSuZiz2PmNGfZaCTSMNDiKKevj0LA8Uu2v2LW1CE6szZD2Zx8yFIfQgmu82u86KuQVAsYHC7DXK8oKvsEW2WTQpF5kQg2ZNU9kh2g/vtFELhKnvxy2fOdrdGKZs8EcvlaA/+/HmU3xiq7OXkyB5VqWPjU4WyJ7MGMLtjcLt8BM2BmVbVcw4lUWX3bmtf5Jyme0qtMDACOD4+LmS/03Ljksjeb37zG1x33XVYunSp5/iSJUtw7bXX4oEHHhD+4n6GV9Viblx69h0Az5ZUoyH7awaBL75M2WSZnyBBMXuyJCqI7LFLkAbZS0vZ68WYPRX7+LoEwj0WRKA6Le85UeTB8579mf379ztG85prrrH2bGbrBxf6FWU8g5StYBKpk9VC77gmGEZ7v1llblwAWgJlL5jstrrOEzX6ke0S4BIxGilhhE1k/DrEsOgSEUZQeilBQ0TZi1rseVIjQ/YoBNVbF7W79Aogdo9YiECsG9f+Lpksf//a5N/2kgJmdzockQuaA/VOU5jssd1k5ldGPes/A6/2iah7acQ3A0Sy1263Q7eUKpVKiXY36Efwblq+qDLbLoXixt2/f7/zN2VfXIahYgEFezBQyF5czJ4s2WELWDnvtpuz/09SMdwfs8cGqMrix2m7cZOSvaAahrLKHu+QCyJQuQI5DM4xkDwajQZ27NiB73//+9ixY4d1/xh3MWgKlEt23GPBJDKXorJnnRTnbhJKULCVvbh4Nee6GnFk117guQUv6J5EIbJdG6T6hQXiA2oSN66CBA2a608shllE2WPlV6JIKj/mdJ12P3M59zxRshdUegWQI3tx0BSSPUDcHewoi4bLUYLmQDlX9BBCCuFiBG7xYPB2hYsHXbLn2WZQAWQIIWlkveAFL8A3v/lNvPjFL/YEgjabTdxyyy1YtWqV8Bf3M/gndT5Wb7BYxHi9RgpG5snevAp9b1ZN0zC3UsLemRqpfo9H2QuI2ZNV9hihq/BhNXa7SZS9MLWMbe0Wt0l9EHi10DQt056zynZJk13DMFJR9kzTVEL2nCdT0+VHjEDxSQ+FuSArcB5iQWGIhvs5knuYIxk8iXSgFciuYc+iQFG/bOMpRPbi2iW6cZ3fYRiA/cAYdK8w1w4C50hhFNkLuubB7c4jq4UuESvAbMXbDi1vXYNms0mu4+bGBZZg1rxlbtrtNrnkFEAke4LFfx3iRiF7hBIpvFiic/urBiUpBe1vSxFbXFulhcbsidhCarkglcoeYPWReu8Bd37wql3QHDiusgwdLtkmbp0xTdMhcEHxegCweMAlgSJkL62ELRLZe8973oOrr74ar3jFK/CKV7wCCxYswP79+/HLX/4Sk5OTuOWWW1LpXK/Co+xxzGmgYJE9irLFZ+iIkD0AmF+pkMme54nOfoLVQt4XAVvAKgW3NfawIavstVotx9jqPgIFWAYrEdmDyxXyeaDTTLZdnGt4vX1NWlSaN4yybcapcAxFDeDsYCR5ECZ7tu2MW+BdskNoVEbZo8B244rF7MUs9sLKngHATRTx3ytnETBo7qag88PbpZE9Z77kCwCB7IEbM41GAwMD8TtEuHX2+Gxc3fM+dcGnPMyKVidw3bjxxJWi7Hnnu/s7gzLdq1W75Bf33RQi5eyipOexf9wiHjP1SY87WMQWum7cOFhnyGzDGHQvROc1m1ttTtkLnAMDGtqG+6AXZ7PGx8ede3pUiLI3WKxgqFDBdKuGHTu699AOA0W1Sy1m7+yzz8Z3vvMdnHXWWbj77rvx1a9+FXfffTfOOuss5/iRBG/ZEU7ZK9BidACX7I2UiigSq5YzzB8oe9qIAj9heFLCyFRSZW+As7nMjSur7AXFqvEqpCyJ8scBAu52qLIqXFBfVSh7/t/Ihpe0sgfEEjOTqOwJE23bvsYtzOw7TQrZ67jnqFT2NFFlT9diVzqNSPbYdTWp4TAdmgIhtCAQXe4u2SO6j/NiZILf09iboOH2SSRuj+T24y4TRSVzwzfoCRpR89cMUVWDYstarUbXeSJkr9NpodqwqkCMTezA2gdvdc5JEr8cBtZLVWRPtI/s+rWM+HHAu3rjbB1P3sLIHuC6eFUre6m5cQHgtNNOw5e+9CXhLzgc4XXjepU9gFZTitXoWTBAq5jOg31mbGws1rUZ5MYFgIKuodExpevXOW7cgoaZpjWRkyp7QbtyaCmTPVmyG0X2kric/QuZrLIn8t38WhH1OQ9pE1D24vriKnuIf/wkujCFSz1wbsIouMkDBXsrqAiUXBdm1HhwjhPJntlxb1jUtXVItBlPBvjFOIpEs/mi5fPObw9yNzaZ6u1T9uLgmVdFzuOhe5U9KmjjwDUMced3Oh3XnpASNFw3rmmaQgR83bp1ePOb34x8Pu/ElgXV4aO0GVYebfOOR52/0yB71CzvIATZZlF7zWxWm0D2mtw5cTaLJ29hMXuARQQ3j+/oCWVPLLo3A4Dw3SMGCrQneYAne2IuXABYaJM90zSxb9++rixpHh5ljzteyFlkL7EbtwjM+DYUaLVaaDQa1m4KAuCJDnto5se9bEmTILLH1jPZ3x9F9tj7MmSvSxXk1EKRxYInQnEP1Xxd2CgCJUz2bNsZltzV9Z2GEe8aJZI9j0JDuWT24I1TdtzSI4R7y51Tq9VCx4MzT6jKnk1IisUiLRaS5B6nBac79oRLEAhyN+6s1rvOE4qHAzxuXH5/XxE7QEse1JyA3jiyF1bCJAw5m+wZhkEqfsxPrKDYsrlzV1hnJdjtIQypkL0EUEH22JxrGS0U9Og52xYge4y8adCwMCRmDwAW2URw//79qNfrsbYQSK/0Sqi1/OhHP4q3v/3tWL58OT760Y/GfvEnPvEJ4S/vVzBjU8p5A88reZqyZxiGM/mClL2owFwAmM99Zs+ePXSyxwfm2oubjLLFF5UeKLjxPjlu/E1PTycie0HKnkqyl58Fsjc8PCzcbpiyZ5om6vU6KhWaEuy59jHrAue9iLxnIm0CcMhe3ALnKltm/OMnUdXyurYoCRpawOe64Sp78c/JWsE9p1qtYmSkuxYXwF2ftpgbN25+eWMB49qkXVeHsHHqX5C78d9//l9d54nEwwHwFm6WdOOSEwM0HTA7sed7k6coyp57j6rVauBc4JVUgxRf6VVrKUkvooWXlUHCfcsQ1B/R9YrNkZbRjiV7zY77MBJHypiyt2BgFPmI2E0+SWPXrl1YsWJFbJ9n3Y1733334S//8i+dv6Mw25ucH2qwCV/xGcWKbfDi3AwHDx50jGYQ2Qt6Up6ZGnff59TAuLg9/mmav00Fvft9KphbCgDK3CXgx9/09LTwNnpBZI93Pctuw+Yv5wK4IUdJEjQYgsiebLtdMXs+NzaV7PGLSpy95T0cUcTMYwAFyF4cKXG+s+MmKISBj+uLIiVByl7oFmyAQyaoblyeyIWi6FX2wuBcV6rrmVP2ouCSvY6T5RsKjvGHkQeTV764yR7kbnRKs3DnUVQ2D9krBLtxUyN76MQqZkFF6qOQI2S78teb4nIHAINwv7znh7eraTpM00iF7LGrKcMRVMTssTnS6LQwkI+2nU3D/b64ucVn4hqmgQd3bcKOaUvAmWzMwDAN6JruKb9CJXuU+6mU7P33f/934N8ZXGNTznkvX7lAKzPAl12ZH5CJG/Sk/Muf/cR5n8/ejZPno9y4/vep4GMWPdm4XFafTJIGT+aCEjRkyZ4TZzRLMXv+92XbBbzVHURilUSIGU/2ooiZMNlrmd2fC4BL9giNtt1FK8ogBxGLwH18q+PWm5yCGuUud+5PPu9RwwKRd+d/1HhwrrlhEK+rdcPiiL9DhtsdoBizgLAdgXK5ULvFX1M+EzW4nMvCrvMo8XMeIlfks3Hd+yEyD8juTidxOfqeeh/yKG5cMbLX6dAIvyGQOWqdH0X2NJimGJESJW+Hiuwx29PsxNt5EWWP1dhbVJmLL63/dzy8+0nnvZ0z+/Gl9f+Od53zOk9ZFmqSBn+tAh9QGw0pskf6xEMPPRS60FarVTz00EPCX9zPYIa7nPcqC+Uc7Ul+bGzM+XtuANkLLPrILRzFXM7ZI5dvKwhB2bgAkLdZlIyyx48Fj7KXkJh5YyHVtAlw10BhzF6csidL9uKUPSp4YxWv7Hn3jaS0CYoAYQ+tOFLiujEJjXJ9jSJ7QYtb8O4RNoHhBlrUwugWFSYoe1xMIUnZA2gbttu2IW5BcmMBCQSiHe8a9lwXX2F2f2FtLWBSiCt7waVXZO1ANGhkxDOvBZW9MJvAj+NOh2aP2233PEq4TBTp1WwaIBXSo+CMMKhw47I50iCQPf6cqLk1PT3tiBkz7bqH6DE8vPtJ3Pf8YxguDqCcs+4vi9OPA0/kArc3hJyyR/rEVVddhc2bNwe+t2XLFlx11VXCX9zPYJO25AsQL3KELIpEeLZKK3UvWEG7Bwz6XFZsj9yJiYnIvjIyl/NVisgniNmbFbJnj0wVMXvByp482QX6i+zF2Vu2vpRKpdjSK3yx5likQfY6NGXPU7vM/j949wj79xBJiVt6JF5N4V29UfbAc30obrwWjeyx62MSYgFNmxCS4yCpSg13HkVl88bscSRGMhtXNbwPz/HLp07Y1ownax0CKfGfR6k5GKfsAekqezJQqew1/JuAB4A/J2pu8aRtohHuwdq4fws0TcOCgTldn4sCT+QCH1CRItmL2z6HIiMfTmADzl8fr5SjGXeeoA0FTNTIJ2Ubw3ZZByrZy/u2diooUva8btzgc0Tb1TSeQGlOLLxs/T6H7HHH7Bq6St24/BekFbNHBU8gYpU9ewjEkQdN09yiuBSyZ1/auEK6bswahei450T117FZ3KAM3oLNXoyJi5ebjUqwecSHP8/1obgd7Tkbd10dEtEmzHGCWuhZByTIHiV+LjQbF3Bq9sk+9EWDpkB5t+EjBNITihbz17zVoj0ksnp7/s+HIeras1qxsrYwLahQ9pgd7JgGzJh7XGvTrikfJ19vh/dnrGbVM1xQGe36XBR0Xzxs1wMqFMfsPfXUU/j973/vvL733nu7avU0Gg387Gc/w/Lly4W/uJ/BBlwxl0ebD+okkj22aOc0DSWCQhAEajIIM06FnNcw5RMkaPDGtsSNoKRlUhjZK+Vh7/Zhx3wVgWZbbcxeLmHMXq8reyJkj3n5KLsbDAwMWHUUKW7cZndfguCQEhPxay5HCMkuRxvB2Y3dvzlK2XO2ncrn4ukBMRPVc68IZVJMe87GXVfnfUriB0u4IhAHWVAWKE+2q2//Xa1YgtluCdkWsgLFng1i+hhW3SAMOrcmhMUs8vOOTvas65TP50nKXqQbV0LZmw0EzRlRu8rPESNGNa9zZC/KFvJx8gXCXsbzbbJHKX8DeMdgYDwsFJO9X/ziF1izZg0AazDccMMNgeeNjo7i+uuvF/7ifoZL9nKoclyJV/qiSBSbVIWc+A1jKNjfFTf4XWXP/3l5ZY83tmVe2QMciibjamHKXdlnu5j40ksJGh7DqHn+S9Suc93sCylLIL2uwehzDaK7FQAGBwdJbZod0ymq7HwmBN6+xjTccokYKTNZQoGK/PqA0iOh4OZ31HgQVvbstuiKaRtxN4wSB+ghN9TEB8HSG45t0XPOvroOCiUA0ymRPbb1YfT53gcBQj00QoIKfx+bTZrdbDYtW0DNzqe4cZMoe3HlwmQQZO9E+8jbnliyZ7txC4VCZA1PRtpymo48gXTNK1sluKampkgVFXhPaeAe4aAl5fgR+ove9KY34U/+5E9gmiYuuugirFmzBqeeeqrnnGKxiAULFhxxpVfYgPPX1ynoNLcNm3g5QsxHGJh3Km5CuX31K3vyE5wncp4kP81S5eptOWWPkb2Sz8aX7Vp+MmSv0+kEKjV6Qjcuu7+67pp8ldm4do1Xz3oiQqD5BSQuDIyF/1CVPQDxyh53WePInkh8oWm7cXO5nFTR6iQwTVOM7OVpD3+e60OJ2Wu2uj8XAM+iEqcYtuIJpEdNkCB7FDXCLVrdrVZphZLwgyR9bbKue1wfRcmeTiB7hUIBpVIJjUYDjQbNxjWb1nmUWp4swzwMMgka/vYC9/GdSpZIoyJmj58DnZi5VWtbtjduXrGkyDmlIVDGwLyKW19zbGws1hNKmSdKlb3h4WFnIN1zzz1YuHAhefPpwx1sEBa6yJ57A6KMu1MYM0Gmkr+tMDhu3BCyl9iN61tvizbZk1H2HDduwdvXJMpeWJ1Btg4bhhFZJicMjJTx1XdU1tnTNIv3yLpx8/k8CoUCWq1WvBtXRtmL4yTczxcie3GkxHbjUhWNJEVd/fDMFYoqT7QHnusT8/vNTscpkxJHzoUU02a8a9ijdhjEAtDceVFqCYO7L27AtmB2wobIgyR5Xhs0Zc+T+BNRv9F0lEJ3DEQRrsHBQTQaDYfExYGRwri5BcSXvJFx4zq/z34dlEhwz89+iZzt5pRR+Txx0dBgQnzHJ36OxCl7jOzFzSuH7JVpRfNHS0PO3wcOHIgle5QxK6PskejhsmXLMqLHwY2D814+XumLMu7OFi5xdbqi+mB/Nk7dCCN7Sdy4Tsyh3q0YlvKa5xwRODuT+H4Sey2ToBFG9vi5IltYGvAmZfLtJ038UBEHSE2mYG5cirI3NDREapMne85nQiDkxmxa5IFM9qgI2YyehzDZI8bseZU9GikD4q+riGJoEpU9piiYRNtlcuSIQvacotXFgHhMe4ci9cqeGywap5gE7cwSVB6jxRQ6YoIKE1bqdSrZm/Z8LgpxtkiTSNDwk7egRIJivuz8fhmyxxM72SSSNMjegQMHADBlLx5zSu49iiuVBqRH9kJn36pVq/Ctb30LZ5xxBk4//fTYSfPEE08If3m/gg3qvM8Nm8/RnuTZQtUyDLQNg+T396PWphVWdWL2fAkaSXbQYMY2qE4ry5xNpuwBDa5bpQRu3LCi0vxa3Ww2hQPTHWXPdw1yOUt4SZqg4VEhC1ZCpQzZm5iYiHfjypC9WDeua9zFYvZi2m3RVC1n0ZYQ9lSRPU3XHH981Dzz/Ja4jFVuPMddVxHFkLUb12axWLTGIbH4L58cQhEMHLsR5Ma1CaCIbRFdFGXcY0Gq1k/u+qX9Lq30zOioFcRfr0+RCGq9PuX5XBTi1LAkyh5DUCLBsqHjMNO0+kneyYSDJwnOvo6iNpAfz6rcuA7ZKw9jhpBQM6fsksKDBw/Gnj/rbtx3vOMdWLx4sfP3kRaXFwWXQHkNCU/+oow7/zQ23WxhTllsD1n2OX9bQXBj9rzHk+yN69YZ7H6PHZMhO+ypvsjttwu4yl6tVovc3SAI3lIJ7p/8XJEhvMwQ+deSfN4ie4lLuvAqZF6O7DESlYobV0DZixujXrITR0qM7s8EwCV7xGAJ7nvDCIJHJdbdbNzIbdhyOtDuRI6xXC6HgYEBa/zHLIomtwCqU/ZMMtkrlUrWOKSUcwE851EeqBwily+i9czjMA5awfBGbQa50fnecwggkT0z/t5HIWi7uJyz1Vu8YgzA2Te5Xp9EpRJP4Or1ac/nohCv7Il7eVzy5hLFrkz3Yc1RDUWVvU6n4+m3rumAmYzsxSp7rXiy12q1rGoEAEZLg9gxFZ9hO1ioIKfp6JgGiezNurJ37bXXOn+/613vEm74cEWn426W7VfkCsRs3Llz5zp/T9QbUmRvot7oaisIbMLUmgbGG1a/JxsdR5iwYrrECJSj7OW7P1PMWURNNEHDNN3P+EkkUwvb7TZarZZQSAEfr+JR9nLB51DByJ7fM5V0Zw6/Gxdw4wLTcOOapqQbNw4CMXsiblyTqOwJG0OOZIV91jNOOGUveBs2a0Gwnio6sWNseHjYJnsxi2LDXQDjFnpvkk5EuwZdhS2Xy5ZaTCR7Jve7KTs9MNtiHtyL+j3fddsZ34eOHf8nQvZEFZC4ceO1k9Z1C1K1KvOO85wThzlz5gCgkb1Op4Vm07KVFGUvluzZ0VwiNoutgXGrBiN7lN1TePj7zNy4onZVhOxVCcoeX9d2pBQfLwlY24iOlAZxsD51SMmedDro9PQ0nnrqKamFsp/Bk7idkxPYMTkOAJhs1J0B6T/Pj/nz5zt/H6iJK2Btw8CkbfT5toLAJs1zU21M2arIrukOfrvL+l7DMITvISMdxZyJR57vYM+kvR9uw0QhZ3rOoYLtJwy45I6B209emESGxuypUvZ8fc0nUDb5z6kge46yFyPq+M+PggzZE4rZi038kCB7lPXWcGO2RN24wduwtT3nxS24bixknLLnthN3XT3vR7XLvUe+Vy2ict10BwJlfLExbjKyzMGcPOCcQ3UL0hZFesZw0PtRRfBNgmIM8G7cGcQNWObCBVySGAWqGzesckEQnOsfIxJokjF7XXuES7px+V2B4t241kNElG3x7H5VJNpCACNFixjGbYIA0MZsajto3HTTTfiXf/kX5/XDDz+Ml73sZbj88stxySWXdBVbPpzBG/z7d2zDpG3MdkxN4JuPPBB4nh/MPQ4A+6risW1j1bpjDvi2gsAPTh67pl2CJ+pyZBPuYBX41kNtzNgf3zsFPHdQjuzx5xe7Mny1wPMoCHPj5rjEEhmyx/rhr4mtas9dlcpelJ01UyJ7ZsMlT3HtCiUo2MpenALlTVwiLDRGfMKTZ55wBjl4G7a857y4Mea4umPd2O64inOPe+5VFDkSUPacBwiizWCJH4VCQSxmLwbU+UVZFPkhF7fQ8u9TCIxpuHY2qm2XtJkwYjKda/bODN7PhYOaoEE5l4FKtpkAIhqz57d1rB1RG6hpmjOmo5Q9E6azg0bUHODX05FSvCfEPddqU5WyR0l28oNE9n70ox950oU/85nP4IQTTsCXv/xlzJs3D1/4wheEv7hfETUZHtmzg3TevHnznEVl74x4PTr+M0cddVTkuRQlTJbszQR8bNy21aIJGvwk3j9hYr+tFs7UXbVQpt0wN66ekhuXvVZJ9mTbdEgWkexR3LiUUg8AHGVvaGgoNkTAQwRi3Zg0Zc9DLCiigp1dGkVIvMqeO4CCt2Gz2yEqey7Zi1kU63Rlr1wuu2Qn6rpyi2Dc/WX9NBu0RZedR31IoM5v6nmqlT3vIhs/sHjiRiN7QCcm+aVWc9WhuDAegB6zB9BtjOvGjZ7bOuTInv/+MrInk/hHInum6ZRCo5K94aIA2bPPVaXsKY3Z47Fnzx4cc8wxACzD9vjjj+O2227Deeedh06ng49//OPCX9yvoBKjqPN0XcfSpUuxfft27JkWJ3u7p92s1GXLlkWeSyEyomSPMuHq9bpQLCBP9jZscY3o2BTw0NNG4HkU8As0u9S1OqBrZuA5VIQlaLDXMm5c0+TqSKl040atSZz9oyh7lFIPADxkj4LBwUFrHEbKkHDq7FGyRt3PERSYdjzZ8yp7LikI3obNjqezBwSZ7MW6ca0LWy6XY5UypmpMTU1FtmsKKHtOPxt1IKg8ih822aOMm04nOpGFB3XR95O3oGSalkCCBk/2KMqewRG3KNWYJ3siyp7KbFxAvbKnSSp7YWRPxq6yMR3lxu0QH3gmJ91rP1wkPvgCGLLJHv/5MJC24UvLjZvP550B89BDD6FcLuPss88GYA02lp1yJEAF2QNckrZrWrycyC6btZRKpdiYPcokE1WMKIZWxHDH9WEHV5pIlJjy7bISZQengN/+zjXUShM0Eih7rVbLfWIOIHtpK3tC2bhxsG+TCNkDEB2zZ9JJiScZgBIv1O50f84Hz/Un7mmt5RW7ce0+UEm3e11pyl5cu66yRyNbZr1GahcQW8ip5/oXxaCaeGhUQ8/3w0PYKGTPcO87nezFKXsuYVCi7MH74EJBkJ0KbNs+odeVPUOQ7GkABpyM63gMFS3bWqvVSGtYHOFLzY176qmn4t/+7d/w1FNP4dZbb8WLXvQi5wlox44dsYTjcELsUxKXih6Fo48+GgCwd6aGtuBE2DVlpd0vX7481jhRnj5FlS3qhBMx3lQSJ0p41q9fH3h89373b6UJGgli9jwV4/kyMUnJXhRSS9CId4nwcEu6RGWNBpwfApEt2AA4ZC+qPIinZiPVjZKjZRFSyRtL0BAme1GKIWd/4tzjDilptUhbuzGyR4ktS4Ps+ZW6oGQas80V742xp7yaKqrsRSmxIspevW4Rjnw+Twq9UK3seX93jBtXUYIGcwez8lsiYNcoinBSlT1W2H+gUHZ+GwWDBde2yu7xziO1bNz3vOc9+O1vf4vLL78cmzdv9pRiWbt2Lc4880zhL+5XxE0cNgDijBEje4ZpOq7c+RGLLf/eTnvPwbhtV6iKlYiRNQyDTPZEMmepREZU2XvmmWdizxFV9nh3a5cbN0E2rqeIaMBuH7Ju3CiIKnuUxQWA48alkj13z12aDKmynAsAoNnu/pz/FH7sEZU9YTduHNJQ9gTcuLzb0CQ8pJo1eokQkfFNtUF+hSQomUbLu2puHNnzKsbxv99ou/c9SjUeHh52vpuq7I2OjpJcfvFkT0zZEyNbcmTPv3bwiR6y5Vei3LiG6RLsqDnAvJg8eSP1gVMBKZ7QuPuaWszeWWedhf/5n//B1q1bceyxx3oMzZ/92Z/h2GOPFf7ifkWckdE1DR1C8UdG9gCLvC0bGcLpi+bjvud2Bp5/+iJLPa232xizy7WwOMowpEGg0jDIAF1dE1XhKAGxomSv2Ww6xqsrQSPnniMKz/1S4MalFLHlbTDlfE8B4CgIkj13Zw41pMRDXONco4BTKiWK8IbF7EVC1I0bB1myRyi9QsmY9bgNY8p0mIYB1K1xQlH2ROwF1Q75F82gmngYOSr0fD/4OUIhMJ2WO2ejyJ6u6xgeHsbExARB2aPvngGkk6BBBWtZFdkDrHEisuORSjcu61clT4hX5TDAnU9R9tIge+Qov8HBQaxatarLyLzsZS/DihUrhL+4XxFnZJiyF2e4eLK3y5aGX3zMUpy7tLuUyrlLF+PFxywFAOzmEjrilD3qxBWZ4CIGWUTZS4vsUYyMKNnjr5df4ElSZy9O2eNj+igguXEFEzQAgro3rAH2bRKO2Yu6XQLKnlA5FwCmqLJHduNa5ylz49bF3LgiiilFtfWQvdjMYXf+z5s3L7bt2YjZC6yJxz1ZiZG9+LnY4VzEcfOLFcmmkj3K7hmAmLKnIp6MBxt1ogkFUWRP1A3quHEjAoJ51S9qHrD1r5ynF/a3znfJHmXsHlKy98wzz+B973sfXvrSl2L16tV46Utfir/7u7/D5s2bhb+0nxE30Khkb2hoyHna3TVVdT573QtX49rzz8SwXWxu6fAgrnvhaqddPhOXJ4xBSIPsiUw0EbJHJVxpkD3RNvnJ6o/ZY69brVaiqvGe3T647xC5V2m4cQGCWrcMDtmjun1JpERA2fPWmCOoCg1r/EWRKNG9cfnzlCh7g4NOnT0qiXZ3UYmvs0e5Vx7SFkNKzKprKw4V2RPd5jOOlIiSvTan7MWpUXSyZ4kDVGVPJGaPYl+EyB7hGgWBrTOMiPNkT7SwPsW7UM65ZCzqfLaul3JiZK+Uc5NzZJJM/EgtG/fxxx/Hn/7pn+Lee+/FBRdcgDe+8Y244IILcO+99+JP//RP8cQTTwh/MRV33303LrvsMqxatQqXXHIJfvCDH5A/axgGrr76apx88sn48Y9/rKQ/VLJHIUVMmeMJnK5peNHyJVg2YhnzkVLREwi6a4pediUNsicy0USIIZXsiRKotJW98YMA8xTX614OIFu/EIB3H1/uIU6kTRGyVygUyBlejjEMWruO14AVbuepZI/k7i24F0JI2Yvbb9Y0nVTtuH0xHVCfrHUa2aOQN33JYqceoDjZi8/GpYwXITduddr5m0L2RBZB2Zi9pOd7rhFF2WvVnHbjtoujkr1Gw1L2qOpunM3QJYoqU3fGYLXrRMkJWztY33Jarus9KkiKddkizrquR5JyNo+L/qf8GBS48yniQhrZuKRP/PM//zNOOeUU3HzzzZ4LV61W8Za3vAX//M//jG9+85vCXx6HRx99FNdddx3e/va347LLLsN9992HD3/4w5g7dy5e/vKXx37+q1/9Kmk/RhHEkZ2cPTgpA3LZsmV44oknsEegsDI7d968ebHGOVP20id7Tz3lHp+cBLZtc1/X63WyWgb4lL2AmD3/d8eB8t0s01ckBsaxAcOA9lId5v8aQA3AXEC7VAemXfeNcDZuBLShIsx9VW8fQsAyFavVKm1XDlvdilpAPeNEp5EILZezygMS9sZ1MHcOcHDc285xx0A7elnw+REgjT97IaacWygUMDo6asXCRizg2uCwR9mjVGw4dMqeOz4oBcALhYK1pzghpKLdtPpZqVRiCQ8je1E2a3BwHnbvfhIAfQzEXSvejU29rvl8nmSLWdiJqNuRJ3sds+MhpCwjlgretpw+/yRsHHvK8/45i1+AuUXr2lcqlcgxwOZxThf7PXmOrKoge6m5cR999FG87W1v6zKwAwMDeNvb3oZHHnlE+IspuOWWW7B69Wq8+93vxsqVK3HVVVfh0ksvxTe+8Y3Yzz788MP4zne+g3/8x39U2qe4TBrdXgQoA3LpUisOb7LRRJVIYljmbpyqBxx6sicyKftV2fOD351ONG4vLmYv7rv9oCzeeZvjkbNswRnPFqCdqANM7KkAmq45NfY851LbBICju2ORtBVzYI5YrhPq1lvk2nXcrhRRC6gnXpKqGHH7jkahXC47Blw7Zhlyr3wpULYfVOeMIn/RSwHuYYCq7JHInq0SUB9MHOKWD68bl1t+rEfZo9SD6wU3LuV8dxtCihs3fr9VBjYHoto96qiTHJtGHQMi26WJbkNnxtQ16pg2OZIke0zRU6XsXXHCH+FvzrwKwwXr2i0dXIx3rX4T6h3rGsXZKzaPc5qYUsmTQ9E1LAipuXF1XQ9dEFutltQXU7BhwwZceOGFnmMXXnghHn300cgLNj4+jve///341Kc+RXIfiCBuoPHKXlwwPSN7gFVvjwKm7PGfDcOhJntpuHFlyqSobjOtMjFh2bg6p+zJunErAdNgznEAqwggokA6xjOsK9xxmThA/YXLoF9yPFCxf/jcEvQ/WgmtRY8tAwS2IGu4D1pRZM+xOZpGJxE6jexpmuYs3lqzhdzK46DNnWO9rpSh6bpbFRz0hZ5XbLXl3TG++nErhJQ9gHPJahpyx53Y9X7uuBORO/F0mDMW2RsZGVG3L26C4roUUO6rQ8pi3K0A0G7QyR67p4bRxrHHntP1/nHHnYejjz6j6/w4pLGDBisQHWdfO/Y1iiooHQQmFDBFL4myx1/7RqeJFy5ZjWVDViLkSHEIuqaj3nEVWAr8o6RUKmHZsmV43eteh2XLlqFUKnmujdgjxyF045577rlYs2YNzj33XKviuI2xsTHceOONOO+884S/mIL9+/d7vg8AFi5ciGaziYmJiVAi9//9f/8fLr300i6iKIqgWMTnn38+8jM5rmL4unXrIgcPXxZk70wVx82Jzq6qt9uYtIuqmqYZWjCYYdOmTZHvM2zfvj22LYYnn3wy9hxds8SUrVu3ktvdvn076bznnnuO3CZAUwC2bdsm1Obvfvc70nmPPPKIVdqBiKefftr5O0zZe/TRRz37M0aBdxfMOx4or9awfZ2Jdh0ozwFOeKWGp+6yDFKn0yFfA0fdDhOjm66Re+6550gL6I4d7r7SWtuAdsI8GE/sBWrTQKVgKYYta+HI5/OkvjrfG6PsmZyyt2fPntC2d+3aZTcc+9VdMAwjts+OAQ9bcLlF+/nnnydd1927dzt/5170IuDgKWjfdy9QrwFz5iJ/0SVofvvfAFgPZ5Tr6ixitRkU/+SN6Gx9Cs377gHqVWhz5qN40WugaZrjxq1UKqR2t27dGnsONA0wrfFCaZNSZ5PHli1bYtUdZ1cIgrLXaladz8T19+DBg87fL3nJW3HiiRfi3ntvQb0+iTlzluHii9+NAweedc7Zu3cv6Rrs3bs38n3r92gATLItdGL2YpS9tr2DyNTUlJCNZXaTKXq6pkODBhMmnn76aaG2nHkLoNYOJr7seNw8DSPObGeWfD6PN7/5zbjmmmswM+E+kPBXiXKN40j0M888I7wrCYnsfeADH8Ab3vAGvPKVr3QI3/79+7F+/XqUy2V84QtfEPpSEfgNWtxFuOOOO7Bnzx588YtfTPzdq1at6or5u/322yM/k+NUzpUrV2LJkiWh55500klYs2YNAJqyt48755xzzsE553Q//fGIIwXW9LZcLHFtMTz22GOx5zCyV6lUyO3yRCcKixYtIrcJ0J4oRdukbg94/PHH4wUveAG5XZ7whsXsrVixAmeddRapPdM0oes6DMOA0QbmrdSwd5OJqV1AwXa5dmy1bMGCBeRrsHHjRtx7771Ay7uvqgOOq5x99tmkOpz8Ams2O8F8qmmRPep4/cUvfmGNqyiTMVQBai5rPe+880LrVzpjX6xkGADLjRXX5/nz52NsbMzZJcMPkyOB55xzTmw2PuAl/FrbgL7yBHR+txHmrhq0SsVSDNvWOcuXLydd10cffRTr1693CibnV56C9u8egbGrCq0yYLUJN0Fj2bJlpHbdBZBZpgDoOmB0MDg4SGpTtL7b8ccfH9vuokWLsGPHDqKyZ12jxYsXx7Z78OBB/OQnP7E+165j5coXYePGtdi1axKVygh0XUez6a4BZ5xxBskWfO9734s5Q0MhX0Sr3cC8efNI13VgYABTU1OxruxWxxpbRx11lJCNZeOWkT0NGgYKA5hpzWB4eFioLZ7s1dvBD/81+3jcWsDEG8M3roJ2ZvnlT3/hvM8/GKxcuTK2/3He0lNPPRWrVq3yHGs0GpHJsiT/68qVK/Gf//mf+Iu/+AtMTk5iw4YNmJycxF/8xV/gxz/+MVauXElpRhgLFizAvn37PMfGxsacIOEgrFu3Dps2bcLq1atx2mmn4bTTTgMAfPCDH8Rll12WuE9xC32OW6Xjzh0eHnYWuX2EJI293DlHHXVUxJkW4rOwaOfxYL8pKj6dvZdGzF4vJGiktbVbXFFl0TY1TXOMUyfksrFi/VJuXCBY3eOOCZdeARwFzw/TJnvUOECnkG/EgqQvn+9R9qKK/zoPniIEwqRnIzq/qxlysxK6cRmp64I9/qlJOo5HxTQthTAETNmjhtI4KnyUYkncoShNsGtPSdBoNaxrQBmz/DnNZvB60Gy6oTHUeUCxV3m7lAjVvjARJM6+tuxYOJFESdM0uXXGnTeDeev3Uh+2GXjbUoshe3F2kBG6to/oB+3MUubKs/DnU1ywh2wHDcB6MvngBz8o/AVJcNZZZ2HdunX4m7/5G+fYfffdh9WrV4f+2I985CP427/9W8+xV7/61XjPe96DSy65JHGfYskeZ9TZpslROOqoo7B582bsqxKUPe4cCtmLj9UAYIoRCDeWItw7ltMBdNSSPU2z1pY0YvZECWRaMXvuYuersyeZoAFYxmtmZgZGyE+0bbFQgkYs2eN+tlTplWbIImqTPWqbTtHZjgntuEUwt3ndWdqKxdBOWgrzIUtV1nU9kkTxRpqy0FvfbXR9NgwOiQh147rHqQu9py5cwNwxDcMpoSJM9gCYtRloA919MU0TZk2S7OkaECaa2Yt/WmSP4hpncZ2mGa/sMbJHKYDM39NGI4zsucepY4BiMwq5Imqg2yyX7MUpe+Jkr1qtulm8XGLGYGEQqImTPZ7A1TvB14IpfnHXlHmKWr4t7YJ2ZjluwN0ggSd7FG9T3DhMLUGDYXp6GuvXr8fPf/5zrF+/Xviii+Lqq6/Ghg0bsGbNGmzZsgV33HEH7rrrLrz1rW91zlm7di0uvfRS7NmzB4CVuHDSSSd5/gEWWT3uuOMS9Yd/4ghDTjCQlJG2fQJu3FKpRNp+KDYLy/5fhOzwZC8M7D2R8RHXB/Z1Kskem0+9kqDBFjA/L+CVPdFFji3gocpey3seBZ6n34CfaHIxe9R2vW2GLKK24kdVIXn1X7/gVOQuOhMo24Z2ziByF6+2jGrN3ZUiyoh6kgw6NLLHSCHFwLvKXrQbl5qNDPgW2aA5xo196oLsIXvVkCSsRt1JjBFX9iKWJUFlj5RIwwfSi5C9uKLShoG2Tc4oZVL4h5hWK3g94N241IcekrKXl1P2jIiYBhNyZI8XSfgs1iE7g5YiovAoFovOw1ZczF6cbWH2rOEzqIE7s/DFqjvuPVBRDk6G7JGUPdM0ccMNN+DWW2/11E4bGBjAVVddheuuuy6VjNwzzzwTN9xwA/7lX/4FN910E5YsWYLrr7/eU2NvamoKW7duFVZnZFCr1WKz6nSd7sYFXLK3v1qDYZqeAsp+MGXvqKOOIhmleDeuJe2JqEXsNzH1Lrhd77kUxJI9W4UUvc9RQawsMkiUlFH7IEv2/PU6+QoXomTP2SooTNmTcON6VbiAEzgCSXU3lMtlK6jfNGGGuHGZ4ifsxoWV4aqdsATGxmdh7joIrVK0kj4AmPVW1/lB8BCsNrF8gn0ehZxR3bjU3w/43bgBDzUSZI8vo8LUOz/445SyKwCXYRvpxhVT9kRLr1BA3pO27j7sUz5Dc+O6ZE+lspe3d3ig2iw2rqKUPf49kYdJft3glT1ZsgdY9m1qaipQ2TNN0yFjdLInWGmBI4cUW5uGskcie5/73Odwyy234Oqrr8Yf/dEfOQkad911F772ta+h1Wrh/e9/v/CXU3DRRRfhoosuCn3/iiuuwBVXXBHZxu9//3slfaGQF17ZowxKlsDRMgyM1xuYVwmfFCyuj+LCBSj1laz/ZchelLLHdpGYnp6GaZokg5sWgYpV9iQIJLUPqsge/1p0qyAnZi9gnTcME8wTJR2z1wxP0BBxDeu6jkqlYv2+RhjZE3Pj8gusWWuGJ9Hayl7cguy5Ri2iGmyPLcr1dRbvdjvYTdyk1QLjEU/2WsHnRoAnxSxJww/+OMULAdCUPU3TYOLQxuxR96Rt1d31QtSNG072rOMi6i7FvjGyR10L3ESFcLLHJzGI2BePsqeI7LGEknqAstfoNJ2s4ri55ZC9sPjXENTb7logQnzDkBrZ+8EPfoB3vvOduPbaa51jS5cuxRlnnIGBgQHccccdqZG9XgLFLatrGgp6Di2jQ6ozxxO3vTPVULJnmqaTsRuV4csjTTdu1NagjAh2Oh3yLhKUZJIOFCdo2P0UbZN6vmi7jMj5a9XqOTdmUdaNG6Ts8cfkyV7ACRJkj51frVYDEzTMjuEEicq4cRmhCwJL0BAje8R7a5NCkaK6AAJducyNS03OAHwxewF95uP4qIvQwMCAu4uEQrLnEI0eT9CgK3su2RNV9sJi9hoCCR9OPwQSNKg2y7ErKSh7fEky3o07XLBc4dVqFa1WS6h2H5u7Qdm4vNoXZ1vYdQ9zB4eBP59iCw7ZDhqNRgNnn3124HtnnXXWrLhQewHUIsGD9hMX5XyeuLHdMYIw0WiiYbuQVZE9XZDsmKYpFLMH0F25sQZJQoUEYpQ96nf7MNtkT9M0sH20pZW9GLIntV0aEEn2RBYkz/lBMXuc2ifjxuUzbrtAVPb4uCuzQZwzDTpB85K9gPbtYyIk2uOaDVIjuDFKLiirac61NevxZI9Kjmgxe+m6cSnnU93SzZqrQFE+k8vlnHsQp+yJEH4a2RNz47J+RrlxeSKoRNkrDgWeQwGbM7UANy6v9sX1k7VTDcnqDUOtJUb24pBagsbLXvYy/PKXvwx873/+53/w4he/WPiL+xFUsjdgB7tSEzTYjdsbUX5l97T73ZTdMwB60gOVQNVqNScGLpLscaOKmpEb1weZMjFATMxeysqeaF8ZkWu3gOlx61ijbtWyYw+xolsFOe6WAA8e79qVzsYNJHtiO10wRJI9Tu2jtjs0NOQaxRBlzzQMZweNOAXKE2RfJz501N3kjzjwC3hQRq4p4cbVdd0lfEFu3Ja4sge4BM4M2cmCJ4HiZC9e2Wu328KJVapAViqrrkJF/Qy7t0zB80OU7Jmm6bFXYTs95OxtelQqe4akssfqw2rQPGRvpDDSdQ4VrrIXTfbibAu7P/V2k1RUm2GGI4eUexf30CETi0py4/6f//N/8MlPfhJjY2O46KKLMG/ePBw4cABr167Fhg0b8P/+3//Db3/7W+f8MBWw30FdaCv2ykzZ0qdQKGDRokXYvXs3dkcoe7zqRyV78TF7VtAadYLzxC3qwSLHguFAJ3vxm3XTzvODko2bFtkTXYzYtZo84B6bmQDW/w8cZU+U7EVl48oqe7EJGpLKnrPnqCJlT9d1jIyMWAtDmLLHKXRxpMQTdxWlFNowTdMhhaIxWwgqrCzhxgWsha7RaMAMUAvNtriyB3DXKkTZY/X3hoeHyS4n9sDH4vICwWc4NhpS20ZFgbKIUolbs2aRPTYOKRgeHsb+/ftDyR47Th0DnU7HYwODdnqoz0xjsGLdT1GyF4Wkbtzh4jA0LtJ2uDjcdQ4VkWSvU+86Lwz8da+26F6masut4yfjgvUjtZg9VufuzjvvxJ133ulkzfnfZ8H41G26+g1UF1rZNkDU85cvX47du3dj13T4Qs7ey+Vy6mL2BNUyD9kjunGVkT2Ndp4fcdm4QHpuXFGyF2bA9jwLDNh2TpbsGe1u4ssreyLGOJfLoVQqWQSioSZBA4hW9ngCKEIiR0dHMT4+7mTcdoHbPSNuQfYmJhDGYavtlGihuPE8C3gQ2WuIK3uAdR/Gx8eD3bjc2Bche+xamSFFldlxqqoHUGP23EWuXq/HXgvRRZFC9orFIoaHh2NDVBoz1nyeM2cOeYFnCnA42Zv2nBcHv60K2unhF3f+D/J2FhjVZlHshYFkyh6L0WMYKcore44iF+DGFYmn4+foTEh5nCDMtGh1/BgOWTbubbfdJtzw4Qgq0SjZE4d6/vLly/Hwww9j9/RMaPmVXVPW5F+yZAn5aZbqxqWSHZ5o6Jx65wc/DqmENy2yR0nQEI0DTGO3D8MwIn8bq/gjugm4Y2RNwF8DVlbZAyyj12g0gpU9+3LKKnuB2biSZM8hcCFKHB/LF0dMisUiBgcHrXlQq1tbrYVhqGKdY4OiBnm3jPP21+x0nEEgo+wBCK7fJxGzB3Bu3BiyR1W0DMNw7RChzh5As1tpxOwBFnmPInuVoXlo1MYB0JVAgCd7wW3X68nI3rp16/DmN78Z+Xze2emhWChJu3GjIKvsMSI3Uhzx2G8VbtygHTT4Y2LKHn0tYudS71scUiN7559/vnDDhyPIBSdtMkY9n+3F2ewY2F+tYdFg99PFjqlpz7kUqE7Q8JK9+Hb9n4lCnMubtUlxjfOgxOyJEkjqbh9xNRl5xJFiZjdFC5nzRtYft2dIKnuAZfTGxsa6yJ5pmE6dPVGy5xjSGDeuCNlxSUnIXBAge4BVIHhmZgZmtQ792KXoPP1s4Hn6ssUwZ+qez8XBsxD45y6n9IkuGE4wfVA2bsttV0SJdUhcq2kRUX+7DTGy57FVhJi9rs+Enk4hb2JFlQFrH+Nnnw2+9wAwf9np2P74LwBYrlMq2PWq17sf6gyj48TsUa+r3wYF7fSwaHQFdDvrlfogS6nJyBM1kULCBw8eBGCRvYmG6+0YKAwgp+XQMTvOOVSwsV1vN7oEAN61G2ezvMoefd1gKiDVdqURsydMD8fHx7Fnz56uf0cCqApY0Z44VLLHbxT//GT3JG92Ok7MHmVTeYZ4ZU8jncfAExIq2aMqe3EkTusjsscgouzFkTj2IJeE7Plr7SUhe45R9A9xboqIKlDO+R0TZtt335puZ0XduADCEzQ4skdZQJ2Fe6YG7aRjoR23rOsc7bhl0E46FuC2N5w/f35s25VKxXliN/22g7M9otc1cmcO+xhzzVPhuVaNgDkpqOxRyZ7mi9mLg6gCQj2f3U89113+Y/GKc7D05AtRnznoOZcCNl7r9amuTFeeAFLVQr+tCtvpQdcsgYL6gEoZK3wCA3VsmaaJAwesoOXRovfhS9d0x5UrS/ZMmF2uXF7Zi3vg4R+0RMhe3yh7k5OT+Md//EfcddddoQvj4Rqnx4Mt3uEOTAs5wackL9mbwtlLFnne3zk143yfCNmjxuy1Wi0YhhE7gHiiFUX2NLh751LJGVXZa7VaaDab5IKiUWRPT8mNK6PsxZUSYJ6ter0u9Ps9yp6Pe3YSuHEdo9UwAX694y6lNNkDgIb3Gpt161pqmibnxm20gl36XMweRdljZM+cqVklcS7+A5hbn0fn3g1WMsacYeQu/gNomgZjutb1uSiwvXknJye7YvZMLvtXdMFwE1/CM3wHBgaE1AKexJkBawI7lqayR3mYom2XJng+XAJnGB2cedFf43f33Y5WbQqDc5di9SXvgmkYaNamPOdS4MRCmkZXrb16fbLrvDhQbZDomkWxP6W86xKl2qtarebY4zmlOXh2yquejhZHcbBx0CGEVPA2w+/KZa91XY+1g16yRxcepltuwpIKpJaN++EPfxjr1q3D61//eqxYsUKomOHhBDYR/AkqfuRt0kSdOCMjI5g/fz7Gxsbw7ES3cvMcd2zFihXk/lLJHjs3bqBTyR40oJS31lEK2Ws2m7GGm+ehtVqNZDz8mWhd3eTUQupOH6xdlecB8dllfNempqbICwgfgxLlxhWJ1wI441mHl+xxdlTUsHnO98ft2eTPU06FAIfAmfBk3jqou2SHMqYc0latwTQMaLoObeXRMDZuhrlrH7RK2dmGDXYppUqlQia+w8PDFtnzl3bhHkikSXQz4KHGble0TQ/Z8yl7ZrvlyMjk3Sb4+X8I3biiyh5MA/OWnYKhuctwsPYkipURaLqO+tQBp10Rsscn8tRqXptQE6zbB9BtkGY/TUY9HPOgKHVzB+Zj58R2AHSyx5M4v7IHAKOlUWAKicieP9au2q4558SNl6QJGqLzTCVIZG/dunX46Ec/ite+9rUpd6e3wSZOnPnICZI9ADj++OMxNjaG5ya63biMAObzeSxfvpzcZmzMnu/cOLLHq7pxNrRokz2Ki5QS18d/38zMDEmFoSZ9GIaBVqtFNkhxBpS1K3L/45Q9fg0aHx8nLyAeN66P6xgtazHSNE14c25X2QPAczouOzcR2av7rp2t7Im2GbeLhmkfo5KSRYts1d0EMFMHhsPdPqYderFo0SLygwT7fX43Lv+a2lcGZ4FpdMcryZZz8Sp7PrLHvZZS9qIsrKCyJ1rmgkr2eKW2Pt1NPJgL139uHHj3LE/urNfidfuo5E0XJHu8rTxu/onYNva05/0XLD0X84cWA7us8mLU68qTuDmlOV3vzynO6TqPAn5819re8coKJFM8BqVSydk9hurGbXXaaNrGl2q/DlnM3ty5c8lPEocz3IkQcyPs/yMzQX1YuXIlAGDn1DQavk3Wt09Yk/64444TUlXj3JP8eKG4Mtk5OS2e7BVyGrldSlyfTBxg3HmaRJtAOspeXHYZn6AoUmPKo+yFuHFLpZKw8XAWcT9/qgecQ4Rnh4oustfuOocCz/64QUkaxK3SGByyB8CMKJVkvW+NqYULF5LaBngS7Zs3dXmyF5X4YTbkFAdvzUGfwiFB9jwPRpFjUUzZI5EMU1zZ85C9me74MZ4AipC9KGWvWh0PPC8KVPKm2b+barP4deilJ70Kb3zhtRiy6+AtHl6Kq/7gOnRs14HImjU2Nub8HUj27GPj4+NCD9MeZc9H9phCRyF7mqY5c2WmSVP2eFKoStlLjey97W1vwze/+U3hemRHKsRvg0v2TADPTXpdudvGJz3nUBG/K4WY4WTtFQgPy8UcrQ8ATdmTyfCNI3AyBBIgKHZ2uyrduLoCsudX9thrme17PASCX08SuHE9hMtH9syGmFuQwaOARCh7VKVk8eLF7oupmDFjv+/5TAzc7GGfsme/1nVdOMvZm0wR7B4Wva5UZY+8jyxnfyIXspSVPer5PIFrBJC9hqSyx2dt8+TOem3N+3w+T55bIoKDCHgCZ5omVh/9IiwesZKVhkoj0DU9FbI3t+SSXBF1L8r9Wm1Z85R6Tdl5VGWP/z5VMXsyILlxX//612PHjh14xStegbPPPrtrAmuahk984hOpdLAvYRskkYl2wgknOH8zcgcArY6Bqr2lEX8OBbHKHvc3xd3KjGueYA/zuvczUZBx41IQd55MmwDBjWv/L/LkGafs8WRPJBONErMnGq8H+MiBIrLnVYt8na1Zr0WK9AKE/XHrYmSPquyZrbaTZMF/Jg5uXcAGMMwnrLikTDQTz6uYeuc5I5Gi96pcLruFtX27aMi4celzxZ20lM9QyJvJxexRyd7cuXOh67pVHzNI2bOPFQoFISI9MjKCXC6HTqcTQPYOOt9NVXaoaxCrzEA9nydwbb/LwDkuTvb2799vfUYvYDDf/VDDE8CxsTHy3OLJ3nTLO15Fy6I4yh4xZo8/71AqeySy98Mf/hBf//rXoes6NmzY0HXzZL64HyE6wUSM8pIlSzAwMIBqtYrtHNmrcwZNhOy1Wq1YY8grWxQFjhG3HOFn5RSTPf5SUolZXAFimT18AbpiJ0L24gicprtZviIFRXnVruPjOuy1SrJn1t19cUW3siqXyygWi2g2m6FuXFGy5+mnT9kzTdM5Rm23XC5jzpw51q4cUcrelDtGZZQ9tNse9yLbsUNUgev6TIMje6bpvBa9ruwze/fuDVD2xPfFJbtxubdUkT0+G5dqs3O5HObOnYuxsTE0fKQMgHNs/vz5QuujruuYO3cu9u/fH0D23DaVQ1Cg8JA9f00ndtwmgTLK3pziHDy450HsmNkBAJhsTsIwDY+yx4ghBd4sWj/Zk1f2Kvn4WOc03LgyIFnjG264AZdccgmuv/76QypDHmq4hsCaEKVSCQsWLPAUqDRN9zlRhOzpuo4TTjgBjz32GLaOT6Jsy2f1tlty4vjjjye3R1HqeCNEyZplxpUne8HXoIG8Tq/hJxqzp4zsSWzrBtBKrwD0eBmAptYVy1ZJM1llr9PybZcmuYctEK/syZASTdMwMjJiGfEad41NAHbdPdF2PbteVH1st9m26gNBbJeDxYsXW4R7KkLZm3TfO+qoo8hte68rd2HthzFZUub0i7cLXB00mfsVSvZsW6JpGnm98M4pawIF2ZYmN4QpD100G+w2KvKAwiooNGbGu95jx2SI2fz5822y553n7DWlQLcwTDdZiwKewHX8LgPfcRllr9ap4cYnbnSO76zuxJrH1uCNJ7+x61wK8vm8I6ZMN73zdrplvabOAVfZEyd7h5I/kdjI+Pg4Xv/61x/RRA9wDQczDWxT6WuuuQZf+9rXsGDBAky3ms7TkajiyZS75yennId6puwtX75cSIGhkDee7FDOZ8Y1x/2uoGtQbYkRnrRi9uIInJaysidSVJlC4Er27ReJVdF13VH3upS9BDF7cWRPhpQAHOniyZ5hdr8vABbMbvpj9mqumi3SLiNvZhTZm5Ijex5ixv1u1OUVuND4OiM52QMA1HwPa3V3lweqW9Qzp+y5HmRbwG0+T1H2SOTNlCd7AFAPIHvMjStDzNhnZnzu4RmJNtPaLo7kxu3Ik73pVrc9Xr9vPR4/8DhyWs5zLhVsjE+13LlpmAY69kMPld8wslclunGrKSh7MrGYJLJ3/vnn48knnxRu/HCD33AFbSrdaLecwSPqxmJkr22YaNrGjyl7ovF6aWS4MoPMfy7oGjTbbvIHhRhRiJamaU6sIJXsxZYz4X5H3Lk8qMoelRQahkFyzTKyJ1o9nil3YW5cGQMUSvZqlhGSfTB0ExS4BaRjdr0vApdA+pIeOKVPhuxhuuolZDxsslcqlYQqGYQpe2ZNLpEC8NUm5Pbr5dtPcl27lb2qcJveuWJNoCDbAm57N8qDpKgNFknoYL+/6cuatY5Z9kSG7DESybtxTdNwtkoTSfgghx5FbhPQDb70Stuf+WWjZZNAkQL4fIJGEDYd2OS4ckXJHhuP0013rWtzG4ZTbQCzbdV2A9HbK1hII2ZPBiSy9853vhPf+973cOutt+Lpp58+YrdLY4aAsep169Y5Cz/bVLqUL6BjGyHRQGo+25Ypeh37u0QzcSnkjVfoKOcz48rbj6BrUMy7RIpCeBh5izNLpYL3/DjE7kqhuXF7Ksme6HnT09Okc2XJHjMwbV9YJts1SMYAVSoV94ldobLnqkXByl4iUtKl7LmvRQiZE4NnmE7hZD+Ysrd48WIhdcWz4Chy4+q67u7MwBEzk2tfRjF1r+sM+EWPxeyJtBlE3IJsCwoucaDYFlFlT0SFYkSuWZvytNFpN9G2CYVMyTLWbqMx7WyZZnCu0jSUPfY9VLLLX6dWiLLX6YjF7E1MTMTawQP1Aw7Z27dvH6ldBjYHpjllr2O4Y4j6gMqHvYyUwm3n/Ir1fcyNWy6XhR8+wiASIsRAzsYFgH/8x38MHTxHwnZpDtmzXwdtKr1iaNQhe6Jp/0cffbRTsLHuq7UnSvZEXaMUdc1xT3PHgq7B0qH4OnxBfdV1oBMxhksFq44tlezF7koBoFICZmrpKHtUshf3NMvAyN74+DhpezsGR9lrwLl5pum6cWVi9lh83djYmLKYPcBH9obthT0hKXEW3FoTqLhkweSUPpFFmXfLmpMz0Ia7rx+L2RNx4QIhyp5hOGQiCYkeHx933MEAPJMtkWLa6XgIuYyy51m87AkUZFswx012obiyKIurVnLDY0QWY3csmjA50tCsTwWcQwcf52fY7XY6cmSPugaJhh7puo58Po92ux2u7HXElD2qUjevPA+YoNtNBjYep5ozKFesWLtOAmUPAFaMLsGDu34XeN7pC6wY+6rE7hlplMwhjexPfepTR0zGbRT8E4ffVJpBG9bQtm+UKIvP5/M49thj8cwzz6Dhe2oVSc4AqK5RoJjT0OyYQqVHeLYXdg3YKSIxe3FDrFTQAND7SqlHx8ieSIYrNRaPeh5VqWNrUqfTweTkpLBxajcBFk/M25IkLtexsTGAH6r2T5Yle85vahvuU1VCZc8hco2W1+liK3uapgm1u2TJEvdFQNyeaZrOcVGyNzw87G7HyJM9G0mu6/bt263kCfaQoMiNC8BDdhjZEyHQQXYiyLbomubcQ4qyR1GVcqOL0Nm7DYCYzeavmcGTvdpU4DlU8GSOqWO8sieS9EF9IGTKnog3qlgsot1uo+WPD7HRMprOeRRQyR7vxhXZ5pKN16nmNBZWrGvMK3vUe8U/HJ+2YAXOPeoUPLzbG+Z27lGn4MLlZwCQI3txSE3Zu+KKK4QbPhxBNQRM2ZORbB2y1/YOQlF3wNRU9x67QRgs6Gh2OuTzAbGi0ZSJ6Ch7MaeWbbtNTaagELhyiX4uQxyJYz+ZSvaoCRecAIEDBw6QyR4jCO26S/Z4NU6W7DmfY21xa29iNy7gkjz7/1wuJ6VCeq4TLx1X3Tg4ERWebX9mmmZwkka9Cdi1MUXJXi6Xw9DQkDUfbTtiJiS7ns/Va8CAfQ3t9iuVCnlB5hHocjZNZw9eEVWLrmS4RoKy4PH3VZ+zGMa4N+SosGI19HnL3NcCblxvMo07+FucsidDznmyF6TsiVxXcoKM/T0ia1a5XEa1Wg0ne/Zx6laMomSv1WphcnKSPCfYvWgaLRg2ueVj9qjt8Pay1q7jXef8KR7atQm3PvFzTDWrWDq4AO8650+duHWZfXHj5kNqCRoM09PTuP/++/Hzn/8c999/v1AG4+EA8sQRjH/gccwxx9htuDfz2GOPFW7H75YslUpYtmwZXve612HZsmUolUowTRODRWsIiJA91WDxgnFkj8XsUXe7oBC4gbL1pVR1rdPpxC8yKZG9Mpc0K5KR6yh7DVfR46puSKtFzudYWwra9BjcjpfsjY6OSnkYvKSEdzeKFVRmKBQKTpB8ENnjiy2Lkj2Av67qlD03Zq87QSNx5jQA2GSBJz2pkD3B269pmkPgCseeicGLroFWtuaDPmcJhi5+OzTu+oqQHX7x9pC9hnv/ZR6k+Ad7pugx0qdpWipkz7BJj4iyx/bdbviDgW002nXPeXEgk72yXK09fpwz9y2fTEmtSsA/cM4069A1HS9cejqWDVnbIo6UBp29hgF3792+UPYA4Etf+hK+8Y1vWBXT7YlZLpfxtre9Dddee63wF/cjZkPZO/roo7uOLV++XLgdRvZ0WGswK2OQz+fx5je/Gddccw1mJvdgpGQZA5EtuChmWeS5g4/ZA8Jr91luXFrMXqfTIZG9Cpf0QHEJULaVYy1QtxcUdeOKfAbgFvoOnBuTNtlLmo1rtecle7JtelRx3khKkj3AInH79u0LrrUnWWOPYWRkBDt27OB+vxo3LgAr0cP0titL9jzXtdNNTEW8ETJKhUgB4FarBRgdlE44F42Nv0J71xT0yjA0XYfJuUhlyZ5h8mTPfRiVGbP8eGSKHvtfVIUmkz1JZQ8Amu3gmq5NmwRSS4axGLyclvPE0vkxr+Qqn/v37yfHs/PXtc0UU/t/kQdJ/p5WQ347DxllLw4iW3EykGj8Lbfcgi9/+ct4zWteg1tvvRV33nknbr/9dlx++eW48cYbcdtttwl/cT+COhEMiR00GJYtW0Y6FgdG3lgB5OAyMSaGbGVPJEFBhMlRJhBT6tipQfW16k0Ikb3JyUnS08+A/dDZarVI7VJ2GmG/g0r2mEoXN1z8blwqPHXWDO///vdF4HzOia1T2Cbgkp2OeOFjHnHKnkzGJMvI5YsnO+1yO2v0mrJndZC120nUppeYd7z/Q/5+ye0uHg7Hbofs9AAua1REOfYqe+49anPKnkzYAb/3LSNhTOETvabkNcv+HhEiyUhcI4DwGKYhTPb4rdKiwG+ZJmIHg5Q95sYVeeDx7rNLIXtWBrwI8Y97kJFR9khs5Nvf/jbe/OY34xOf+ATOP/98HH/88TjvvPPw8Y9/HG9605vwb//2b8Jf3I+gkrckbtygxcETEE6EQ/Zs4xVcJkZzyB5F2fMXlY4CG6syMXthtfuKtg1oNBqxTzZUIzDAeRgonyEpe1w2LuUJjCmQWsxwyRc05GzbLaPsAV2iDgB5ZafLeClI+ggkewmVvdCYvZp4bBmDM0+rdZj+e2yrfYODg1JP824sJCNl7sOjTAFswE/MvCRS9v4Xi0Xn9zlkh7u+8mRPbSYic+OaITs9sPsnEq8HeIuRm9zTU9smALquk+PV/GD3xO/GFb2macbsMSJbb3WH1TTadad2H5XwMmUvr0f3gSd78m5c637xyh4VlUrFua5x++O2jQ7qbWvNUEn2UlP2du7ciZe85CWB773kJS/Bzp07hb+4H0Ele0zZkyF7lUqla0LLqAOMQDBlj5UxYIrZ/v37MVjQHDfu1NQUIfGA7Z8Y//3slDiy1+l0HLWMkb2w2n0lzgbE7fhBTcsfrLj9o3yGtg2d+zdFCWTEjTK8imXvZyjwZE3aNoKtTSKxKn50GS+VSR8AF2Bo/S+rQA0MDHA1AQ23yZa4oWfw7Hc77SsqPD3TfY4AnGvgk2FZpq4MQku6+N8ThDO2Eip7aVZ7cJJPQkqEQLAeHEMul3Pa9ih7NtmrVCqJ75er7MmpsNTfxLY2kyF7tQCyxxNAYbKnRfehoBcwXBj2fIaCQDeuKU6i+W0AZ5rR60E1pa3SUlP25s+fj40bNwa+t3HjxnQ2Zu5BiJI9GTcu0F0hXaRiOgMje2yPWr6MwY4dO9BoNKxaaSW96zNhEDFcVGWPT7ZgpwYR03LRdeMC8a5cqhEY4DwMlKdEETcuQCOHImSPr7VHRZCyx3jEyMiI9IIURvby+Tw5KNsPT8ato+yFfB8RntIqAXFwMgrUokWLnL/9SRrMjcufIwKX7NkHDMN7PEmb8BITQBXZY5nD1v+FQkHoIYI8BrkHTepnHEIW4sZlx2Uykp1xzil7HXtLN9k5AKgje7lcjnSdDIl9bJmqG0T2ak0xstdoNJyEzzhlDwBGS9Z8FnHjDgwMOAKMk6AhoewB7nyaakYnC/Lv94Wyd9lll+FLX/oSbrnlFmvja9PEvn37cNttt2HNmjV49atfLfzF/Qh6zSKx8/3gyV0+nxceiHxyQi4mxXW05KqPcQTCceMKKHtx14Ane6yrYcS0WAj+XBD8xC04GxkY5MgehSBS9hCGANljNfMA7169YWDKnkhCTZSyJ+9qCwg45khZEqXGade3FVkSstNN9pKVM/EQuRnfmJhJRva6r6sZfFwAge7xoPcEEabszZkzR2gM0M91+061rw6BaatV9gCX0PGLs2G77ZKQPX/MnmlPXNE5oGkaSa1jhZFFlD3Wl1pzxillwlBtupU6KOOLJ20UssfKr4goe/xDHyN5BuS8BqydyWa06MCTPRE7kwbZI93Z6667Ds899xz+6Z/+CZ/+9Kc9HfrjP/5jXHfddcJffDhDdJ9BP/iAcVGjCXiTE/Ix9nCUU/biXIOsH2HbgfKgZsp5yF5cgoIE2dM0i5wGZSPXpnagWNBQyFtl0VS5cXl+HUcOp6amnGtFUvbs9UNE2WN7oxqG0ZWgkWShDyN7MkHpPIaHh60tGH0DTQnZUZT0sHDhQudvc9odi6ZhAE1LJVFG9uybleT3e0iCT9lL0q6zgPmycUUJtMzDAZXsucpeMNkzbRIoE1/HPsPH7HXaYvXlgsDul+nLSpW5V042cgQ6Esqe00eYXXF7PNmjEFQP2Ytx4wLAaFFc2QOsuX7gwIGubF9RG+AWaKYreypLEVF3Z+JBInvFYhFf/OIX8dRTT+Hhhx/GxMQERkdHce655+Kkk04S/tJ+hahBklU3eEMpozrwpC0f04fRsqvsxZEdJ2aP0Ac2VuPiFoPcuGEocqM1juw5afw60O4EJ33c9TOrMv/QAHBwkrbXIkXZ0wTIHp8FTVL27PVjYmKCXD1e13UMDw9bn/GRPdngfCCIlIQcl203FbLXrezJkL1isYjR0VHrmnJkjydSMuEXQLiyl4REVyoVh/DDVEf23Ixsb4avqGIs4wURdePGxezJuHGdz/DKXoL2GNi99i/6Muo2hcCJ7mMLeG3HTMNbp3W6IbaLCL/2UJQ9lqRx8OBBdDodcnw8u34d3xwQva5sfE80omsNj3Pv9wXZYzjppJOOKHLnh2gtKNn97XjjK1Orj3/aiXPjjnBu3Dhlz83GNRFXHiGJGzcMfMxeHIliyl4uZ5G9devW4c1vfrOzn+O6detQsC/tYMUiexRlj1LQmV+D4s7nyR5lvSvYyl6n00G1WiUTAIeY+GL2kpC9ru+220xK9px2ffMnCdlRTfYAK5Z5YmICqAbvNytL9rp+p32zkvx+XdcxODjo2ZmDIYl7vGsBk0z68NiJKLvJvUcliI761g7OpGfKngw5czJ9uewkGeLkR9gckhkDJLJniGck87Zj2kf2ZhquXaOMBY9AQSF7xTkArESFyclJcvkk1hd+mzRAfA6wXU5mWnU0O20Uc8F9Hrd3U9F1/ZCTPdJsufPOO/G1r30t8L2vf/3r+PnPfy78xRnCkTRrxxv/EH1uMaehkqftIiGSjcvWUpkEjTDwyl5cgoZD9iKykUu2bR8c0DyfiYL/e4NiAaPO98Oj7BGEiiLXvEhtRE9hZcBh40ncuEwtcqDIjesmaIQcl4CrQHVXlZadb84uGnzMHkf2ZBPXukl0cmUP4Eu6qFNMu5N05Ny4MsqeONkLc+PKx9g55ChA2UtC9sKSW2Qy5yn9aEv0mScv0w1vDPF00yI5AwMDJBLN1iwNGnJxNaggX2uPjXV/jKGoHeTn9ngjfPcppuzNmTNHqDrHISN7X/va10IHQalUCiWChxuoSp0GRorklD2+CKWMK9j7lBT/eebKjVO22GClxexZ/6tU9orE0ivNZtMhQozsBSd9WO8N2Zf7wIEDsYGvfqUuqAA0t61xLNnjtxykuHELnN2kFIFmcMieGXxcBl113+y2ZUu5MKSh7IW5RsvlsvSi7KgJte4tyADvHqciCPudykh0Gm5cBlOuJqJ3IaTZTVGyhxBlDwli7BxljxursnX7eMw22ZOJ2ePVtKm698Fzqj4OgD4HGGEbLY4662cUWDYuIFaGynXjJouF5GN2D9TCH7rHahNd51MQN7apW3F62qSctG3bNpx88smB75144onYtm2b8Bf3I6i1bdgGyDK1cADvE6YMYWSDv5jTYgkUAKf8CjUbl0L2DCLZ40lbrLJHTNDgFTrKw9SgbT9N04x9SmTkjPU1KBaQr/AQ58blCRvlXuU5sieyNzVTWnzrfCKyB/gIiGplzww5LoFuBSp5hquzkNUablKWPefL5TJ55wA/Uid7PrukRDElHg+Dh+xF2hf3TWqIi6vsBZdNYseTkD2PsidRs86PMFInM6bSitnjlb0pn7LHyB/VdcnWrJEibdywBA1ATtnzDzFRO8AnXzFCF4QxmwiKJmvFqYCplV7J5/OhRODAgQOpFsS8++67cdlll2HVqlW45JJL8IMf/CDy/J07d+KjH/0oLr74Ypxxxhl4+ctfjuuvv15sO7AQzBbZ441OEmWPr6EXhZGiNbCUll4hunF5shdHdnK65ih1UcoeT/YoD/98YeU4V66f7AUVgC4U3PfjCNlsKXtO5pxishe0KCVV9lSqGgxhyp6SeDXT5LZ2M7zvSSBsQZcljwxBimmpVEpESsKuX2rKHvcWVdlj181shcTs2cdlri/rN99jo5Oc7IX1JW03rkjcIr+t23TdR/Zs8keNpWOEjXfPRoEneyLKnqpYSF6p21cdDzzHME2M1az3RMneIVP2Vq9ejVtvvbWLTbbbbdx2221YvXq18BdT8Oijj+K6667DxRdfjB//+Me48sor8eEPfxi//OUvQz+zdetW1Ot1fOQjH8FPf/pTXH/99fjVr36F97///Yn7Q2XTus1aZNg34J1wMsoeI23DRSLZIyp7Im5cdk6cwWOlTHI6jdgydS+qBArvjiYpewKFlf1kLygWsFgE2C2cmgqP5wBc0qpz/QyrCQgA+UL3ZylQtSj7EbT4KFOgOORyuUSlLLozh5Mre2lsQQZYvzUofiwNZS8pMQ8bP6LXNU1lz7mWnVZXQWkAMO29XWVi9pw+8G5cia3H/FBJ+CkErmPIxRkydXvSdtsyTNkkh+rGZYSNJ3FRKOfLKOes+yWi7AXNIb7YMhWVSsUhsvvs3+rHeH0KLXssiG55yrjDmYuOxXDRuueLB9xrI0P2SKPxXe96F6688kq86lWvwuWXX45FixZhz549+M///E/s2rULd9xxh/AXU3DLLbdg9erVePe73w0AWLlyJTZs2IBvfOMbePnLXx74mQsvvBAXXnih8/qYY47Be97zHrzvfe9DtVpNZNyo5C1vSzSyZI9n9TLKHiMYw6UcGu14dZHtjzs9PR2Zxp6mG7dItIvFvLWlaRTR8ZA9krLn/h1nONi11XWg0/HGAjJomkX2Go14ZY+RVp7EBdUEnJjZYf0egS3jeIQtvkkzZ9Mge0EL2sDAgJpCzQwK3LhpkT3A+r3+BxpliilHTJLeq7A+JSJ7kcqe+x51gebHk9nyunJNw3CKLcuQPbcPasleWF8SJZFEoN0Rj9kDLOVu+/btmKyPo6BbnzVgot62bBNF4TYMwxEaRkoj2FeLL4EFWMSwXqsrIXsyWLJkCQ4ePIg9M8Hfv6/qKo6iW54yMnf0yAI0Om08ObYDw6UK9lQtxTS1BI0zzjgDt956K+bNm4c1a9bgIx/5CL785S9jwYIFuPXWW3HGGWcIfzEFGzZs8BA3wCJzjz76qBCRmpmZQaVSSVTRHKBfYN0mezLs2w8ZZY+5rIcKNGVvqOgazSglytlqhuCdZufEGWRGWAoCZI//XBCcGns5WoZrueSeF5ekwpO9yH7aIlRc+AD7HTyJC4wDtIdSPyh7Sd2NabTZXc4keYart1Cxt6RLTyumCslePp8PVFxF26Ure97vpsBzLVteAm1yr5OUNTEVx+wFrVW6rkslfcQre6bjxhVVz5m6Nc0laLBkD4Cm7E1OTjrrOSupQgFL0hBx4wbdY9k5sHTpUgDA3png79/NkcBly5aR2zVN0+Eaec7lwyeupFpn76yzzsK3v/1t1Ot1p6hyUvIUh/3793fVqlq4cCGazSYmJiZIA2lsbAxr1qzBG97wBuH0/ieeeMLz+tlnnyV9Lm9/z8zMDNavXy/0nf7vqVarwm2wp6TBoo4xAh8Y4ty9DzzwQGjmEHNxUkIRO7btm5ycjOz/rl27ANCVvUJOA2Bi7969oe0+88wzAICBclw1QAu6rmGgbO169fTTT0f21ykREFcT0Lave/bsiWxvz549ALxkL6gmYM628fyauG3bNvLYYNfZjyeffFLYhcEjKG7w+eeflxr3DM8991zXMU3TErXZRYxtUiY7RwGfCszInh0UWavVEvU36CFvy5YtsWEBUQhSQNrtdqJ+AhaZ8O8ZvXnzZlKRcoadO3c6f0fuQMRdl+3bt5P6zuYYAJhN7zgwuY3s9+3bJ3wtXKLRrewdOHBA+to2m93xhYVCAb/97W+F24rzLliX1Oq/6DVggsZkfRxzBqxyJDzZo1wD3jbxWbZxYC7fXbt2JbKDpmlK3SfmaTjYmEItIPln14wlHOi6jh07dmD37t2kdjudjjP/c1qwl2/r1q3CfRZ+9CiXy6mTPB5+142I0jUxMYFrrrkGJ5xwgtSWbqtWrfI86Tz22GOkzxVsspfL5XDOOecIfy9PcM8991yhNjqdjmMoWP28OFS4YsUrVqwIzbxm5LdjIvbp27AXv4ULF0b2/z/+4z8AAPkcra9MASwWi6Htfu973wNgkT0qKjbZ03U9tF3TNN0Yu7it3Vi1h3Y78vf/8Ic/BOAlcSwO8IILLsC6deuwf/9+sHANTdeg6SZMw6r1RB0bQUrTwMAAzj//fNLnw/Dggw/i4Ycf9hw788wzcdppp0m3GeT6EfmtQejyBNh25LjjjpNu10O8nK1JrP+OP/74RP1dsGABduzY4Tl23nnnSdfuA4Ddu3d31UQ96qijEvUTsFzWfhL6ohe9SEiN9fyuSBvvvnfyySeT+s7fe6OL7LmvTzvtNOFrsW7dOrshXtlz47Rkr21Qct/Q0JBUe/fccw8ef/zx0PeHBuY4fx977LFC37Flyxbce++9aBstp3ZdmyN75513XuxGDLz9oMbs8efOzMyQ+xxE9hYtWiR1Xaenp7F27VoAwO7pbo8QO7Z06VIhO8uHb3iVPRdB87bRaHQJVDzEK1nOIhYsWND1dDg2NoZCoRAbE3PgwAG86U1vwpw5c3DjjTcm2rqGgUmncbQkpydz4x577LG47LLLcOaZZ+L1r3+90Gf5gVIhunErXOXlqFIhvFsijnIzN26c24EpAlQ3bsEe+1EJGuxpuyJI9vjPBqFarToLB5XsTUyEp+UD7hM8n6ARVRMQcIlh0NN/GFTGqsS1kdTlGvT5pG3mcrnANpK4MT2/3RfImkYsXBru8TTGgKZpwoKA1+VJy8alKtJ8/GC3sue+lrlnrN9mgLKXpM6erutdLlXZBKW4fixf5JIxWTcu4Cp6/O4UFO8bb3Nl3LjVarVLWQ6Dyjlw9NFHO38zFY8HO8afRwH/cJIPid9PrfTKocJZZ53lPjnZuO+++7B69erIib5v3z688Y1vxMKFC/GVr3wlURYfD0be4gLFC3bfksTsXXfddfjsZz9LTl1n4MlaiaiWlTgFMIrs8UYjjuy1Rcke0ZOYz3k/FwQWJ1cp0QP62blRMXZ8tnKsG9ceco1GI5KYsgcIkQgDpuyLxG2kEQMGhCdTqG4zKdEB1JMdD4FUWAAaCP69ST0qaZE9f18rlYpwMo2H7BGzcalkykP2Gl77lpTsuXX23GOs9EqS8Aig+37L3n8mdOgBO1OcdMy5OGn52c5rmQQNBpfsuXaJkqDBhxeIuHF5YkhN0lBpW5YtW+aM813T3ioOHcNwEjdEyR7PG3K6956xsm6plV45VLj66quxYcMGrFmzBlu2bMEdd9yBu+66C29961udc9auXYtLL73UicvYs2cPrrzySoyMjOBjH/sYJicnsW/fPuzbt09ICQkC+3ycGSvYNyjp98mA/84ikewVuQJ3UX32kL0YtidK9vKCZC+sn6ZpOoStLMDxy4RSKSL72JY4uxxFINmk1QXWBXauCNlTGZjMo58UqDTUzbBCxaprDXZtTaegzbBjovDf70QlTABQs3GpCRCRZK/hxpzKJNW4fWAxm26MS5IEDUA92dM0Da/9w3dhoGTV1pw/ugxX/OG7PUpcMrLXsf+37NLw8DDpGjCiVsqVUMm7Yym4BJV7/3liSCV7xWKxq0+yc6BUKmHx4sUAgF0+N+7e6kF0bLf28uXLhdrlVbucr/hqLkGlj54me2eeeSZuuOEG/PznP8drXvMa3Hrrrbj++us9ZVempqawdetWZ9G87777sG3bNvz2t7/FK17xCrz4xS92/m3YsCFRf8jKnp5c2ZMFT4IK1Dg47ryoPnvr/0W3ybYMi3Ofs++jkj3W1zCyV6/XHRJUEvDcs3NrtVroNeCVPaob1/85P9ikFRFCGDcXIXuFQqFLaVChlqWhwhWLxS5ik1ZflamQvgmhWt1MS9lU0a4KUuJV9tSWXvGSPW9CkVlXRfacFp2/krhxAfVkr2N0cOqxL8SCOVZm6GB5BLquO5m4/LlU8Mqd340rXFDZ58IN2opypu3eL/58kYxcFQ8nDEy12+lT9vjXxxxzjFCbvF3P68FkL9Vs3EOFiy66CBdddFHo+1dccQWuuOKK0Ncq4cRXxWh7zI1rGEZk3bo04H0qoDEIfueKqEHEu8MjQ6hNNxs3zoXOrmme+NjBLmUYIeOzQ4sCtpa3cdVqNTAmlI+/iyN7vKoYFbfnkD0JN67IDi2apmFgYMCjXKZBoPL5fOJFTtM0VCoVz73sRTeu5/NG75O9NMguoIaUUMcMf5WpnykWiyiVSmg0GjB8ZI9/nciNy/onQUbDoIrsuX00PS5Whk4Csjc6OgpN02CaJjqm141LrTXp7Ivrc+EGlaD65U/cDRVklD3Amgeq7ODRRx+Nhx56CLtnxrByjltehXfrJlH2dN/CoOs60DkMlb1eg+PGjVX23Ms6265c3thQxSL+90RlO1OVPf4tqrKXo2wMC7dIchjZ42MOqUkfgLf0S1jcopCyx9nlKGWPETYRZU+TUPaAbqOWxkKvgpQEtdOrKmRQoWLPcUmkcV3TioVUkUggo+yJPFSwbQF5JY9/PTw8LEXOupQ9rn9JkwL9Y0A29pzvB0/sGHhlT/RBLZfLOcqp48Y17Zp5xC0DHbLny8QN2oqS7ZoBWPvostpzcfVReaicW4zItYyOs1sG4CZnjI6OCm9JGSXYJHHj9ryy10twyV70eQXOaDQaDWULoDCIBII/LYrs8ZNkMMKODZesWnj+zwSBmt3KkItJTuCTIQoFtx9x4M8NK1bMSBulWDP/s0nKngTZEy24PRsESlVZptlyY2Zu3D5z40JOORsdHcW+ffu6yV7DJXsy6FL2etiNC3iJnXvMFSRkCCUrveOP2RNV9vz74gaVoDqufJzzvq7pGCmOYKI5Iazs8VDhxgWAJndtmbInmpwBeD02XcqebfxFvDrOZ4U/cQSD7Mblou1nO26Pj3WibGtmnUczoLwhWDIaPnSOmeten7iJxAYtlewxomMYRiDZ4ZVUahwg4N1WLeyeMdJGsQ2FgvubosiejLIH7hqIQNXikXabQDcxUdHubLpxVZefUfH7g2IhVai7KpS9XC7n9E0rhn9eK7rXQUbZM+reBCyjNu15XxRdfZBIIAmD/zqqIXvdnqYkyh7gkjqm6LF6exSyV6vVnIdrv7IXXILKaygZQRQheyptFu+i5cke2z1DZOcMBt6u+39vRvZmCaKlV4DZd+Pyk7VNZHstThGOmuz8pDh2HrBqSffwWbVEx0mL3esTZ/gdZY9Idvjzgga8N22d1ibgLWocphoy0kZZyzTNPS/KjesQVgllT1TK7yey528njazRsGMi6Cc3LouFjPoeGfjdlbLuRvagqQ2GEy991C04L0KmGPEw697dJEyb/MnuZRwVs6fajauE7AXE7CVJ0AA4Im10Ao9HwVNjz6fsUcAIokiChkqbNW/ePOfzTbvkjmEamGpaoUAyZC86FEvrOoeKjOwJQLT0CjD7yp6H7HVMzKuEG0T2Hk8Ko8gev1C0DA1XvjCPvzwv77h0Fw0DV74wj1ZHC/zMbIAnaiKVKngSGZeNWyLaBkph5SQxe6KYDbKnqqZlGn31j8WgwrWiCCJ7pVKpZ4Lz/UjDPaxqDDDbo40uQP647h1Y8sedBn3BEve1BNljSh4De02NL/OjixxJxhQGIRVlrx2k7CVz4zJS15Ege7wiJ0P2DrWyp2mas0du07DWDV7hY++JwEv2vMZe0vQDyMieEJwdNGJWWz5dWiZFOgn4gVvvmDhtQfjkZe/VO65CFmX8+bYbbUtSPnN5DotHrOsxVNKgaxoa7eDPzAZEi7mKtCFav4+Rwqg6e/3uxlW1IPkxG2SvXC4nHi+9HAdHaVdFX/2ER1bRYuRNMw2UL349yhf9ObSylSGrzVmI8sWvhyZJphzlrlUHwOaNmYKy587JnlT2UnDjOmTP9JI9ShwkT9JGiuKudKbsjY+Pk+2h6rnFCF3LVvaanHoqo+x54DNPsvHaQEb2hEB14+YPobLHlw+otw38wdEDOPuo7sF89lFl/MHR1kJVb7kDJ8r95FH22uGDrcm9p1rZ47816D54M4vVtQuIuXH585S7cf2fJSINFa6fyV5aSQ8qilX3k2KqalsvR6nrtKHpOgorXwB97kIAgF4ZhKbrMDvuQiqinvLKnWmHP5imARhimaN+dBM6dW5c/3WUbS8uQaOVUNlz6xh67RGF7PG2UWRfXAZGEA3DiHyo5qHqujIcddRRAFyS1+LGKHtPGj4T7ywXEg+pGdkTAFPpyrko1+gg8pxPcLaVvWKx6DydVVsGdE3DX583H+84Zx6Gi9btXjKUw1+fN98J9qy15ZS9MDS4B7y4BTXn1CQEhiPCnNh77OFN1/XAHQX4Y0Jkjzs3aCI1Gg1ntw9RshdlhBhhExL2JJ/u0iBmuVzOc73SIiUq2k0jXi0tZU8VgfJjNsheUmXPjFJoOFehjBvX0wYX8yqr7Pmvn+lz5yeBqnvF9yNI2UtSZw8IJ3WiZG+4IJ4RzRNEatye/zcmnQN+QtfidhCRefDzCBbEahIUZGRPACwgft5A+A1ctXAJcofQjQu4k2yqaWe6ahrOXzaApcOWcRwp5RyiBwBTDde4Rj3hFgoFh0hGkj3uvbiFj7XXMYDjFocPR/YeWwfCnuq9hg0YGginUfx7bY6gBhlpvghnRLKgty+2TZmZmQlNpnDInoQ3MSnZU0UgeOOU9CmZ4UhX9maL7KloV1WChkPejIjEIyd7P/hhLwz8bg4ma99ITvb8v5V34yYds2nU2Wu1A5Q9Lo7vUJG9ocJQ1z6wpO8uut9xqJS9RYsWeV4zsuc/ToW3oobXxjPylyl7KYMt2MuGRnDuku4tUM5dcgxefMzxnv3sZIofJgUjbNMNWgwDI4VAfFAtWyAbEW7cRovuxmUGvmOYeMGxGk5a1j2IT1qm4QXHavZ53s/54Sd7yyJUdP69Tsftc5CR5skedRs2RgpN0/TsBsFDJoWeyYCiYystAsEbJ1VtptHXNAhkWnsOq16QGPjfrGmakhhX9W7c8HHNiJpoWRPPQ6y9GJtcbBV1ay8/usmeOmVPlRIfp+yxY4VCQWr/ZX47Oh6UecAImoyqB1gkkSEqEY6HqocTBrY/LkPLHr9pkL2OvV7IJIBlZE8ABvdU+e4X/iHedd4fYsRe0ZcNj+LdL/xD6JrmyaCRCaRMCvaUOtGgkQF23tDQUGyArkv2ws8RSdBgE63VsRafP/mDHF77BzkM2PNv/jDwJ3/gugpZmZiwdvnjzZaJk4/TsCJgt5oVy4GTj9O4c6P7PD3tZvEVqGSPO4//PA9H2RPZLk1yfVYVSO8HTxiSZiAyqDbIQH+TPVWxkHy7Mgt7XJtBr6kgKXsdObLnUe463cqebMxe12/llL2kY1bVHPAqe42u95myJ9t+ENmrVCqke8QI2mBBbs7wyh6V7PW6sscTOT/ZY68zspcy3PgqDbqm40XLj8OykTkAgJFS2al2zS/Gh4LsLVhg1aIar9NUo/G6ZfTmz58fey5zW1HIXrlcjl1Q2CLWsj+j6xpOPVrHAjvDd7CsQediIFliSNjix7sOGk2LiFxygY6LL9BQsef43GHgkgt0D0lpNIPbYPBsw0bkM/x5YbtySCl7kp/tZ2VPBTFNIxM1LbLnJ81pkOi0yJ7svWK/MTpmz/CcS8Xg4CAXE6jOjesfQ3zfk44vVfOVH/etCGVP9r4Fkb0wtc8P5vGQJXsDeTeMIsx74of/dyadWwMDA557zVytCxculGqPJ3L+DOeOmSl7PYVDrewxsjfR6KBDKKx80CZ77HNRILlx7fcohWVZe01iaCM7L8yQDg4OOiSOEThd13DCMTrm2h7qShkeAgkAdfuBt1AoBBLJpGSPaogokE3Q8Bs1Vcpe1HfIwt+3pHXrgHTi1dIie373qqp7xf9mFS5cII2YvQhjYL8nOh40TXPVO5vksazcgYEB6evbnaDBSilpypU92T563LgBdfaY2ierHqsgezxpE0FRLyKn5TxtxX7Gdx2TzgNN0wKFEop4EgTehnZ8Dz5te+zK2NmM7AmAPQnHZchQtx9LC+yJwjBprtwDVTrZE1H2KGSPGYV6k0Zc6k3v5/zI5XKOMlerB54SiJpN9kZGRgInP6/MUT1I/Hlxyt6hSNDoJ7KnAv3kxvVD1XVNw42rus4eIpQ9M8Fi55K9tud/WRcu6wffF0b2VNRwVKXsFYtFLgwmXNmTnQ9J5gB7iC7n5L5b0zRU8hVPW3FQNZd4BBG7efPmSbXl2RjB9JM967XMVnwZ2RMAM45xapnB3SBVBlUEfMDovmo02Wu0DUzaCRqUmkCOshdRPlBE2WPErN5tgwLBagJGPTmyiTdTo5Ohqn1u2NMYK7sCeLdWiwJ/nspt82SVPdXuC8p3HOp2eKRB9nK5XNci3Mtkj28nLWUvsRs3KvGoI0/2nC3TmLKXsMYeg8eFZy/Gaew5LHtdeZWx1e5+Am4mVPby+bz0HGB2sZiTn+9FvehpKw5p2L0gYqdC2eNr9pmm6QhNmbKXMpzMUTM6VqpziJU9nrTtn4n2j+7nyCCF7LnKXlQ2rvfcKDCyV+2OGw5ElVPgwuCSPVqbADBd837WD744NvWhir/1PFk8VOgnN24aBtnfpqr4Qv/ClgbZ62USrT5BI8Jm2WRPxq46cXm+mD3ZeD0G3s4xZU9FPKjK+erGRgclaLD6ofLzQXYOMLvICJsMCrp1nQ4l2QvK5lZRqLvJkz3OoyhzrzKyJwB2E9pR2WIAmtyTaRrGNQ6LFy92ntr3VqPJHv/+kiVLIs60wAxbPaLZekxcHQ/2RNTqWNmzUTAM0yF7UaUSmBt7mhgmZ5qmc25YUC1fL1HTgCgey97jRd0kiRiqkFbQPw8Z90IQ0uibX8lKi+ypUHX86CcSLfsdzuciS69Y81DGrjqkzpeNS9nDNQpesme1qYLwq1TindjoVjfZa7bqnnNkIEv2nAoXIuUIfGD1+ag1bVXZKB7+BwY+nEgUuVzO6SO/9RrvyZEZ/xnZEwCbbM2Y+ma89HooyF6xWHTSvvfEKHt7psX28WOTuNHuTgtnYKofZcLzpG06Jsau1nR3uoiKh2Cktd6MJ5DsPJYNHEZ4/S7ToyJ4cdB7KhN1ZFvyLxZpGL1eJnt+qCJ7fnKXhrLXy9dVVTKJ68aNiBGxF/QkblxnBily4/IhJcyNS01QiILKhzNG5ILduNaxJGENs6Fuh4HZVmrIVBpzwD+GRkdHE4VJMNvU4Agsv97K3KuM7AmAXeBGzBNEnSN7qupjiYJtzsyTuSAwMjg0NER6wuUXtrC4vbp9nDLhebfpVEyM3SQXfxsVD8ETtsng8nbedrlzqHsZrlgBLD+6+/jyo633AO8WaGFkz0n6MYFKxOXyvGc3JerK8hs5VTFbadTZmw2yp+pBzE/20lD2epns+ZE4azTCvrK9cRMpe8TjVPB2jpG9NMrvJAkJcmOtA8hey4phSTJuZecANekxCqKfTeMh1792JlWLHa7BPfhkZG8WwSZwtRUdG1Dj4rvSMPwUHH20xUR2T7cjVSVGBpctW0Za/HkjFuTKNU3TIXuU384XnpyMSaaarLq/I6pgJa9Qjk/FG4LxSfec5csDKjDDayBM03LlvvjFwIUvdvfAHRmxjrHL2DGCP8+DJ3sLlob3kX+P3x9YBGkt9PxiqSpGNQ2D7Ec/uXFVXY/ZuK6y44wRODNgSy8H9uIns9iFkbqkC7NH2TM7XcdkoXK+um7c7kBm5sZNQlBlyZ7rLXPX1HnlcK9N0HtNwTqB/UD22P2qc6VyeLIn43LPyJ4AHLIXZYwAVLkbNJtyNg9G9hodM7K48q5p67ccc0z39m9B4I1YLcBF2mi7bkaKweNj5CZmoonZhATZO0jYLpGdo+t6qBuXNyTMi6/rwLHHAmwNKZd9cXqd4M/zYOTINIBlK4HFAbdh8THWewymImVPFXjS1OukJI19fP2Gt1/IXlqVAmTHmUPgouyr/Z4MUQ9bgJMqe143rjXpZeO1eKicA074jY/stTstdOy4sCQxe7Jkj/Wr1nH7dfq800PPD3qv3qkLfWcatsV/v5OSfZfs8cpesoLdGdkTALuh083ozMrphpvddChi9gCX7AHAzulg41ltGZiw988NU7T88JC9AIGz2gw+NwzlctmJvxuPIXvjtru1UqlEGuhKpeKQwYOEHXSYsrdkyZLQ++UtTBrfJmDtzRv0eR7s+zr2dnHnvAw466XuvrqDo8A5L/OSFEYiRcfWbKg6/UT2VJFfv+FNI3QjjesqWxpC5DtE4MwRoxNafsVsydeECyNgSVWYoHZVKHsq54BTRaHpdZ/UuddJ+uwnWlQywj5Xbbn9uGDJBThn4Tld556z8BxcsOQCzzHDNFBri7mh07At/muXlOyz31ILUfZkRKSM7AmABWFONRoelu3HZLPuOf9QgFfqdk4Fkz3+OFXZ4wdxkLLHH6MOeBZfeDAmvu7gtOmcH+dyPu644wAABybi3bhjE97PBIGfzNSSeU0C8WULHCNwmq5h6QoNQ3Ps98vWMR7sXFF1YzbIniq1qJfdjX7wC5umaamUW0rDPZ6W10H2unoW64CsUQAw7eMy6mkYqUuD7KlQ9lSOI7d4vZfsNTiSlUSRllW3WYLeRNN9Ktc1HdeecS3++gV/jeGCdR2XDizFtWdc25W1O9mcdGL2qEWM05ifqkM5HLLHzQO+5JtM+xnZE4BTlBMmpiLUvYnGoSd78+fPdwbErqlgKWondzyK6PDgDeNMjLJHNaLM7XogJr6Ovc/IYRRW2FkS41NAuxPebqtlOgka7DNB4I13jLDr4P9v77zjoji3Pv7bXXoTkI6KYgEVEJSIimKJvUSj1xLbNWpiSTS+iSEao9HEN5bEEqNXTbuaZixXTYw3eT/qzdVEYyFBMbErdqlLW5Bd2N33j90ZZgu4OzMLA5zv58OHZWb27LPDzPP85pzznIdbWq+6zp8RbDVNQDSH8exLUeyJ9Rm1UZ9SykuQmSPW+eDacdRSjnyvAZMSJlbEnl6vZ5+g+ISxqrsHhQozIcuF1YSY1xLTHq2ugq0FCADl6lKLY/hg/v+w9f/DrNqkLFeabJfL5EgKTkK4p2Fs8HHxsVqehfs+W9eidUTfYu5pFvogVZUyRp69OoEb9ih4XP1sAqVxn6PCJLYgk8kQEREBAHjwBM+es7OzzbNQuQKuTG05WJRylj2zVewxIecytXWbAFBRqUdhqenxNcEIN72+5lBuPmdfTYKXGzYut3EZNq7Yqy7szNy0FTzEnr2dc33y7NWG2BPLs1cbM+7rk9jj21aT3De1lZtMowaTEcxHmLi4uFj8r2QymeCB2VGePTHhtkfHSSYu11SFU4R4OPmKPSbdRqlWorKmYtrVkPs4l31tq9hzRD9oLsyFFtVm/l+lVjx7MpmMPHuOhnsxKW0Qe7ZefI6CCc0+LKmw2rEzIrB58+Y2d9Bubm6sJ0NlxbNXyhE49oo9AMgvtj4AKTkhXkbE1kSbNm3Y17kF1Q9qeZx9bdu2rfY47rrBNi7BCGZdbldX12rPBTNoVRO1skCn04Op7GPvIFUbAkqsz6iNZQbFamttiD1HiGhHeSH52uUKEr3a8ibjbuMrTMxFmKenp+DrwJqwExoaFhtue7T6KrH3WCTPHt+lCJm+XKfXIassy+7PfVD6AIDhmrPFCQDUTt8iVOyxYfdKTVVZSH1VWR8+34HEnh1wZ4DmPba+PEOpRsPOoKlrscfcSKUVehSrLXMMmTCuLeKJCxOeVlnxwjHb5HK5zbPcuJ+fV43Yy+Pk3tmSXxgeHs6K0ryC6o9j9nl6etbo3fT29mZv4FIbxV4ZZ1WO6gZAphO2NTSs4Tg87B1QasOz5wgPlKMQy7NXG5Ow6tN55YuJ2Cu3LBGiV1dt4ytMxJ41ac2mWHbFpDrPXpm6hH0tZFYyX7HHjabcU92z+3OZ94SGhtr8mbVxDwh9AOReP4xHTyuwhiOJPTvw8/NjB4gcVYnVY3LKqrbbGhp1FNwbyTyUW1ahQ0G54aa3V+wxSbUl5ZbCjNnm4+Nj800VEhLCCrOcakKuuUVVItKWJziFQoHWrQ31Smry7DH7WrduXaNHQiaTITg4GABQzb/eghKjN5J5nzXY5eLUgK6G3EIGrsPD1oRkBgrjOuYzakPsiXVeuXYcFcblCzfHWf/YcraWvqxqW03LJdaEuQgTQ5Q5KmdPTLhCzkTslRvqTslksjrx7LVo0YLtl24V3bLrM/V6PW4VG94TGRlp8/tqw7MntIYnV5yzYs/4m6/XmMSeHXDrsGWXViP2ONttmUjgSLgizlzscWfi8hd7lvuKjdvsESJyuZwVprmF1gegHKPYa968uc2DKxOWzS8EtDpLu5VaPZSFhtft2rV7oj1GZBbZULtPpwNKjMfV5InkDlpWnBkWcI+xV+zVhoASKzRYn8K43OvRUaFRsc6Ho9onBtwlpqyKPc42vpPfxC6RYc2GXC6vs2L61cEVe1pObtxjo2fPnodza5j3ybb20S4uLmzfe63wml2fmfs4F4XqQgBATEyMze+rD2LPJOyuI7FXJzACLqsasZfFcftUV6C3tggICGA7nYdmM3K54s/WmbgMTHi6yIpnr9i4jZvjZgvMk1lOkd6qxyHHKALtaSvTiWi11idp5BcCjAasKV+PgRFtpaon19orK60qvlyTJ5Lr9bMyvlna5RxTU2Fpa9RGJycWjmor99pyxBJkjipc3RjEnkKhqKp4UGbZv+o42/h69hyxhqtCoTDJ0fLy8pLceTYVe1WevdLHhidSoZUjrE18sRVGqN0pucPWzLOFq4VXLWzYgqP6FjELtlv17BlFH98HlPrT+0sEZuDOVpWgUmctD86gKvz9/ets9QwG7oxc81p7jPizZyYuAzPLuLwCUFeaCrMi4/q29s5EZsSeugIoMsuJK1PrUWLsA5jQrC1wvXW5SksByd1mi2eP6wEtLKz52ALO/poEKtf7a2V8s6DM6C10dXW127NXGwn5YoUGa6NDFuszuALPUd5TR/zvpCZIgKqHRJ3K0n2uLzVs8/b25p0TZe7ZE6uP5tqp637fGk5OTqxHSMfx7JWWG8YrvuKZQYi46dSpEwBDSbO/lH/Z/L6L+RcBGM63PeNCfcgHrsmzxze3ksSenTBiT6vXmYRsGRixZ2uRYkdTNSPX1BX1iMdMXAbuxJNCzhJmen1V7T17J6dwb9Ycs1Au9297cjOaNWvGDgo5ViZp5BpLNHl6etoUcud6/wqUNRwIQJlv+C2TyWrsiIKCgljRUWrDah/MJRcaGiqZwdoRuV/1KYzL7dillgdX32D6Db2Vm0GnKjI5hg/m4dXGIvaAKkHHDeOWPhZH7AkRN3FxcWzY80LeBZveo9VpWbGXmJho171cG/2mULFn4ok1ijymzh559moJroh7UGLaIen0enabVMRe1YxcHSo5jsiHKsMNz6ed3NBjIcfrXqG1fowtcAsaZ9cg9ux9gmNKsFjz7OUYt7Vr186mDiA0NJQdLJRPEnvG/c2aNatxGr6zszNbVLqk8IlNQIlRtNqbZ1nfqI0O2RGrfdSnULkUhSmTmqBTFVq0T68qBCBM7DkijGtuR6pij4kEaJnaTdBD9bgQQN169lxdXREfHw/AIPZqWp2K4XrRdZRVGkJATz31lF2fVxv3qFCxxy2vwi2VA1DOXq3BHWTvFZu6i/LKVFAbbyR78+AcBVfMaYyzPXV6QPlYa7HfVrhCTsnx7HHFpL1ij+tdyzabPZtlFHu+vr52hy65kzS4Visr9SgwRopsCeEChk4iKioKAJCXV/1xej2Qb/TsMcfXBOOtLLbifeRSodaj3FjOpabVPmqb+hpirE9LuzkCqXiGubD9RoUG+vKqfA49AJ3xSUdIlQO+a7jaY1csm2LDij2jZ0+n06FSqzHZxxeh4qZ79+4ADMumXS+8/sTjz2WfA2C4h5OSkuz6rNpIERF6PmQyGevd05qli1EYt5bw9PRknz7vFRWa7LtXXPW3VMQed3KARqsz/ravZp05TZs2ZQe4glKO2OPY5dMhM1676sK4bdq0sXuAYsSeTgdUcCLZeYUGUcY9xhY6dOgAACgqqn6N3OLiqn3M8TXBCLfyUkBjZdILQxHHmyglscf1wIglIGpDiNSnVSkcgRTbyu039CWcpx+dDqjQWBxjL+ZCTCwvXH0Ue9xwbl2LvR49erAi7FzOuRqP1el1SMtJA2DI97NX/NSXvoXx4JnPDWiwYu/o0aMYPnw4YmJiMHDgQOzfv/+J79FoNHjvvffQvXt3dOrUCdOnT0dmZqZobWIG2vsccQeYevqkIvaCgoLYG5EReVyx16xZM7ttKhQKVvDmc8QeE8Z1dnbmtVQc4+EqKjOsFAEYBqT8YtP99sD12pmIPY73kLvaxpPgire8XOvHcLfbIva4bSyswWNYxNlni8eQqBmxOn2uZ0+K3rLqkGJbubmzuuL8qh2VVRPMhFQ5cJRnjzthRKpiz7xP5oo9e6snmCPUu+3r64u4uDgAwNnsszWGcq8VXkOhphAA0KtXL0Gf6yjE8Paznj2zc9Egw7gXLlzA/PnzMWDAAHz33XeYPHkylixZgp9//rnG961ZswaHDh3C6tWrsXv3bri4uGD69Ol4/Nj2ad01wYi9R6pik8WJ7xYZxF5QUJBkimoqFAo2J8ya2GP22QvT4eaX6uHqZBg0mDImQUFBvJ5suGJOYxSOFdoqu3zEHnclDVOxZ/jt4eFh1+DRvn17dpDMqUbsMds9PDxsyq3jCreaxF6B0W5QUJDgJ3FHIUUBUR1itVXKq1LUN7j3oq6oSuzptVVij2+fBfBfw/VJcMVebSyfxwcLsaetrHafvYghbvr06QPAEMq9WnC12uPOZJ8BYAjHSlXsienZ05l54BukZ2/Hjh2Ij4/HK6+8gtatW2Pq1KkYPHgwPvvss2rfo1KpsHv3brz66qvo3bs3oqOj8f777yMvLw///ve/RWkXIzr00EPNKbjGhHWlFGIDUK3YCwwM5F38kemUlaV6PB0lR3wzOVyM9zvfYtJcMcd4rrm1kPmcV4VCwdpl+jZnZyC/0LaVM8zx8vJi7eXmWD+G2R4TE2PTTe/t7c16WAuqsanX61Fo3BcdHW1ze4nqaQyrUtSEFNvq7u7OCg9dUT7gbOyfjB2BTCYT5Nnju9LDk+D2o0IL6joKS89eRbX77EUMcdOzZ09WNP6W/ZvVYyp1lWy+XufOnQUt8SY23PtJjPPBfDeFTI4WPlWe1wbp2UtPT0dycrLJtuTkZFy4cAFardbqey5evIiKigr07NmT3ebt7Y24uDj88ccforSLKzoY1e2iUOCRsTaUVMVehVFAVRo7TiFPyIygK68EmnrK8VyiE7uiBl+xFxQUxIZZ/L1l6NBChkAfgxBzcnLiFXIGqkRkpQ5o0wLo3EGG/CLTffbAFPDMzzcUTnYypqs4OQNlZYBKZXqcPTYLcqpC2FzKSgC18fx27NjR7jbXFlIUENXhiNU+GqNnU2yY+1xXlA/XLn3h1CYOci/DwBcUFCRo5qejxB73GqiN5fP4YC7omFvVw8ND8IofYjw4+fj4oEuXLgCAtJw0VOoqLY65XHAZJRWG+lN9+/YV/Jliwr2fxLi3GFH3uFKD9gGGe8LV1ZX3w4Skp5Hl5eVZ5BIEBgZCo9GgqKjIaigrzzhN0vzCDggIQG5uNXG3avjzzz+tbtdqtVAoFNBqtQj3boJmPr6ICw7DheyH7DG///67XZ/lSCoqqp7g4kPccDVPDcCQW8e3ndyQeF6pHv56GRt61Wq1vO0GBATg7t27KFMDU/o5YfcJww3ftGlTZGRk8LLJPGVptUC3TnJotYDW6N1UKBR2t5VJ6tbpgPw8ICbG4C2MjgZyOJ45FxcXm20zYX9tJVCiNBWQAKDMrjrWyclJlOtLrGu0vLxq3bxLly4hPz+/hqP5IVZbuWI0PT1dlPBTcXFVAeDKykqH3Pti2bx79y77WqVSiWZ32LBhOHz4MDp16iTYJjOY6QrzIA8Mh/vT41C6bwsAwwAoxH6hWTX0mzdvQqWyYemaJ5CVlcW+fvTokSSvgUpOFCrQrzncXbxQVJoLT09P0dvL115ERATOnDmD0opS/KX8C25OBjHO/D6TZQjhKhQKeHh4SKof5PYtFy9eFPwgwfQreuiRY6w76e7uzru9khZ7gKVC5us50Ov1dqvtmJiYalV0REQEbt26hRKNGot7DsCJOzfZff369ZPMBA3AcA4PHDgAAOjZ3BPnswyDc2xsLPskZS/+/v748ssvARjy9rj/lq5du/K227FjR9y9exd5xYZl0/KKDYajo6N523RycsL3338PAFAWAVpOvmtKSopNkyi4REZG4ptvvgFgEHcxsQDzTHLLeBm4uLhgxIgRNs9SCwsLw759+wAA+dlA206AkwvQytg0pXEs8fDwwNChQ0UJE/A9n9bs/PjjjwAMoRUhsyWtIZPJRGurn58flMYiiJ07dxZlebOCgqqJWU5OTqK1ddy4cdizZw8CAgJEs8n14Hh5eYlmt3Pnzhg9ejTCwsIEX5uZmZk4e/YsoCmH/nEp4O7J5u916NBBUJuLikxroyYkJIiyrOX58+fZ1+Hh4aKdVy5i2PTx8UFxcTGaBbZFTsE9AIa+R+z28rUXHR2NAwcOoKKiAmezz+KZVs/A3ckdA5sPRKWuEr/nGoRO165dLaJ+td1Wc2QyGatPEhISBHtLlUolDh8+DAB4pDL0MTX1BWq1uloHFSDxMK41b1x+fj6cnZ2rjdUznkBz74JSqRQ844gLI+buGyvhPjD+VigUvMONjoLbmV3Oq/LCCBmUuTbzVXqTWbl8w7hAVamYcg1QXGb44W7nA3eSREGxHgVFeqv7bMXPz4/9H5tP0mA8e+3bt7dLSISEhLDFYvOzAN9AGRJSZPANkLHbAINAl9qEgClTpqBt27YYOnSoqEKPKYmzYMEC0WzOnTsXgOF6Eqs+nqPCoRMmTMCLL76Id955xyH2xUQmk/Fajcca3HtdV5RnWCe30lB2RWjfav7wLtX8OkfBRLxUZYVQlRWYbJMCnp6eSExMBACczzuPlt4tMTtmNiKbROJqwVW2kHJKSkpdNvOJiBnGBYCs0kKLbfYiabGXkJCAU6dOmWw7efIk4uPjq+1UYmNj4ezsjJMnT7LbVCoVLly4gM6dO4vWNkYkFJY/RqlGzZZhadasmeSKrAYEBLAX3/X8quJw9hY+5uLm5sZ2EvmlVWJPJpMJGvC5nfmNR1UuOCFiz9fXl11iprAYKDQuOSZk/WImby4/r2oyiUZjqL8H2FZyhYtMJmNLDyizAT0nb69MpcdjYzHl2NhYXu11JAEBAdiyZYuoogwA1q5diw8//BCDBw8WzWavXr3wj3/8A5s2bXJIzp6YOYteXl7429/+ZldpoCfBXfDe3mK0tQW3D9AV5UFXWPVEJSTPGLCsB+cIsSfmNdCjRw8AwIgRI0Sxx6Q+lTwuYFfPkNrMfsZjp6pQ4XpRVYFlxqunUCgEX7vM2Ddz5kxBdqpDrBxGW7bZiqTF3rRp05Ceno7Nmzfj1q1b+Oqrr/DTTz9hxowZ7DFHjhzB4MGDkZ1tSGry8vLCuHHjsH79epw4cQJXrlzBwoULERAQgKFDh4rWNq5H6EFJER6qpLVMGhdnZ2f2hr5bXJW/x9TK4wsj6pRleiiNYq9p06aCEpS5Hf3NR8JLxJjbLSzRo7BEb/FZ9sKIuYoKoNgo8LiravCZRMGIvUqN6dJp3Hw95hi+MN7DAQMGCLJTG3h6epqUuhEDmUyGNm3aiLqklVQnOlgjNDQUM2fOxMCBA/Hss8/WdXOsEhwczD7M6wvzTUqwCPXsmTsJxAjjmyPm9bBw4UIsW7ZMNFHCjAP5RQ/ZOntSE3vdunVjxVJ6bjoAg4A+n3cegKEPFFrabPXq1UhNTcWoUaME2akOMa4Ba2vg8l0XF5B4zl6nTp2wadMmbNiwAdu2bUNoaChWrlxpMgunpKQEmZmZJpMQFi1aBCcnJ6SmpqKsrAxdunTB559/LmqxS26nc7+4ELmlpRbbpURgYKBJaFuhUAheDzEkJAR//fWXySoaQsN4QUFBbO7DnZwqu0LzasLDw3H58mUUq6py9oSEm02KK+cDvn4GLx9D+/bt7bbJ9drlZwM+xj6Yyddzc3MT7OVZtmwZTp06hWeeeUaQHaKK+iT2AEMuoJRxcnJCcHAwHj58CF2xEjKjp8zZ2VnQurjWcITYExMvLy+TyhJCYYRdRaWa3SalMC5g8F5FR0fj0qVLuKS8BADILstGfrlh/OratavgzwgPDxfsQHA0jUrsAUD//v3Rv3//avePHj0ao0ePNtnm4uKCN998E2+++abD2hUaGgq5XA6dTocL2Q+hh3BvkSMxv6H9/f0Fu5oZYVdUDmiNHbKQ0DBg+N81bdoUeXl5qDTO7vX09BR0kXPbWsqpqy1EQDZv3hzu7u54/PgxlPkA2gD5xuXMwsPDebU3PDwcfn5+KCgoQEE20MqoFxnPXvv27QWnCERFRdHqGw6kPpWekTJhYWFGsZcPuXEFAa7HTyzqm1AXirUHfKEP/Y4gISEBly5dwl3VXRSpi/CnsmrigZjpWGISGBjIRhjFuK6seS+FeDQlHcaVMs7Ozqywych5wG4X4i1yJOauejFc99wwsEptuY0v5t5BoQLSmk2hduVyOTuBgHGYKo2/ucuf2YNMJmM9hsxqGRq1HqXGyh5Srq/XmOEKPEctst7YYB7EdMUF0BmXoZRq31qfsCbsuHmcUiEhIYF9fa3wGq4WGlbU8PPzk1SlCy4vvfQSAMPym2Lk7TPlZbgIcXpQzyQARkBoOAWexZjG7wgcIfashVTEEHvWaisKxRFtZUKqRUWGYspMuTkhoVYm/FteCjwu1YOTm273pA+i9iHPnjiwD2cVauiMeQxiPPQ5ivriIbRWxUJKq1AwtGvXjn1wull8EzeLDDWtxM7hFZNu3bphx44d2LBhg2htNM8tFpJrTGJPAOadj4uLiyRd4oDlDS3G05w1ASWGMDMXe2KUzLGWlyI0V4VZKUWnA+7fs9zOB26uX1Ge6Vq5FH6VPlIdiOob1oSdlMVefRH55uOATCYTNMPTUbi5ubGrG6XnprP5elJfKjIsLEzUGd4k9iSCuWeIW+JEapiLOzFucGtiSQyPobkNMQS0I8QeN5xw54717fbCXau3SAkUG0PDoaGhgvMWCccj1fu/vmHN6y5G1KCxYy72vLy8JFe3k4FJk8kqq1qdhG+KTH2FxJ5EsLYkm1QxF3diuO49PT0tyqyIIfbMhakYXkgPDw+TJy5rbbcXbu0/pva3h4eHIBHp4eHB5iYV5wPFxkkfrVu35m2TcCzc62r8+PF12JKGg7W+VMr961NPPcW+jo+Pr7uGPAHzB0ZHPECKlVtpreA9nyL49RkxxZ7kZ+NKGfNBXWpT2LmYz+IRWqcIMHgx/Pz8TGYgiSEiHRFyZsIVzIosYrTTw8ODnT3LEBYWJti7ExkZiQcPHqAwD9CUV20jpImLiwvee+89XLt2TbTit40dPz8/ttoBg5T719jYWCxcuBByuZxX2aXawtXVFa6urlCrDTPqxBR7s2fPxg8//CBacXXzmrUeHh6SqwnoaMzLxQlZgo3EngDMw4tSzdcDHCP2AIPHkFvQWoyQgKOePps0acKKPbHyVEJDQ03EnhgTdBiPoaZqZTtJFusmqkhMTGSXeSKEo1Ao0KRJE5N7S8oDvUwmw8CBA+u6GTbh6enJij2xxgHAehk0IZh7CMV4kK5vmIs7IbWCKYwrAHPvkBRnNTGI6Q7mwv3OYn1/8w5IrLZyRaNYAtI8aVyMJHJrS8NJtX4jQTgKbn/i5ubW6NaxdRRcASHmSjJi44iqDPUNNzc39rVMJhN0D5DYE0Bt5D+IBfeiAYQ9IXDhCjOxnhIdJUy5dsSy6YhQvrWcF6mW9CEIR8FN35Dyg3R9wxH9oCNwcXExuQYao9jjjtNubm6CPJsk9gRgLqDEdImLjXl4VSyx54iOQ8w8BS6OaKsjysSYzzr08fERdak/gqgPcB+epVgepL7C7Uuk3q9wx9TGKPjNPXtCILEnAPOTL+WnJHPMhSpfHCGgzF3VYnVI3O8slk3zySNi5G36+fmZVGBvjE+0BMHtT6T8IF3fcEQ/6Ci4Y2xjFPxipi6Q2BMRsTxQtYHQsiMM3I5DLJvmIlqsC55rRyyxa94BidEhyeVyExEp5cR0gnAUXIFXnx6kpQ43yiP1PEjuWCDlNClHwf3/kGdPQoglIGoDsW7y2vjOYs3A4toRY+1CwFLcidUhccWeFNeuJAhHw/U6SV2U1Ffq03mVuhfSEXAdKEJXaSGxJyL1Sew5OzuLYqc+fWcuUly7kIsjZjkTRH2CO7g3tpIbjoQrGqQu9rhip76ONULgfn/y7EkIqd84XMTybIklGusr5qF7sa4B7k1OISyiMdIYB/fagCsaxEq9cRRMkXJnZ2dBy1DWV7jjq1CxR0WVRaQ+CR9HhEalusaiIzEPLTjivNanXFCCEAtH9ad9+/bFzz//jKSkJIfYr09IfcwaPHgwWrZsiaZNm0p60QJHwf3/CA3jktgTEanfOI4gOjqafd27d+86bEnd4Kgn49jYWJw6dQoAGuUTLUE4KnQ7d+5cxMfHN1qxxxUNcrm0g3symUzSy885GjHHFxJ7Annuueewa9cuBAcHN8qwQ4sWLfDpp59CrVajbdu2otkdNmwYDh8+jL59+4pm0xE4qrN85plnEBAQAA8PD0kvrE4QjiI8PJx93alTJ9HsNmnSBEOGDBHNHkE4Cm66FYVx65gpU6YgNjYWrVu3lvxTUlJSEs6cOSO6O9wR67bOmzcPw4cPF9Wr1b17d+zevRsA0KVLF9HstmvXDteuXUO3bt1Es+ns7NwoPaUEwdChQwe88cYbePz4Mfr161fXzWmQ0MQXacMVexTGrWOcnJzqzQLoc+fORUREBJKTk+u6KU9ELpejdevWotps3749Vq9eDblcjnbt2olm93//93+RkZGBzp07i2aTIBo7MpkMTz/9dF03o8FBAq/+wM2DJ7FH2ExoaChmzpxZ182oM2QymUMEWZMmTdCrVy/R7RIEQYjNwIEDcfLkSQAQ/YGaEBeu2KMwLkEQBEEQNpGUlIR33nkH3t7eCA0NrevmEDVAYVyCIAiCIOxGLpeLml9MOA7uPAChcwKkPaOAIAiCIAiiESLmpE8SewRBEARBEBKDK/aErpFOYo8gCIIgCEJiNG3aFGFhYQCAGTNmCLJFOXsEQRAEQRASQ6FQYPv27SgoKEBISIggW+TZIwiCIAiCkCCurq6ChR5AYo8gCIIgCKJBQ2KPIAiCIAiiAUNijyAIgiAIogFDYo8gCIIgCKIBQ2KPIAiCIAiiAUNijyAIgiAIogFDYo8gCIIgCKIBI3mxd/ToUQwfPhwxMTEYOHAg9u/fX+PxDx8+xNKlSzFgwADExcWhb9++WLlyJYqLi2upxQRBEARBENJB0itoXLhwAfPnz8esWbMwfPhwnDx5EkuWLIGfnx/69u1r9T2ZmZkoLy/HW2+9hVatWuHevXtYvnw57t27h+3bt9fyNyAIgiAIgqhbJC32duzYgfj4eLzyyisAgNatWyM9PR2fffZZtWIvOTkZycnJ7N8tWrTA//zP/+C1115DWVkZPDw8aqXtBEEQBEEQUkDSYi89PR1jx4412ZacnIwVK1ZAq9VCoVDYZKe0tBTu7u5wc3Oz6Xi9Xg8A0Gg09jWYIAiCIAiilmH0CqNfzKl1sadWq6FWq2s8xs3NDS4uLsjLy0NAQIDJvsDAQGg0GhQVFcHf3/+Jn5efn4/Nmzdj4sSJkMttS1GsqKgAAFy7ds2m4wmCIAiCIOqaiooKq46tWhd7H3/8MTZv3lzjMampqZgxYwYAQCaTmeyrTrVao6ioCC+88ALatGmD+fPn2/w+T09PtGvXDs7OzhafTxAEQRAEISX0ej0qKirg6elpdX+ti73p06djwoQJNR7j5eUFAAgICEBubq7Jvvz8fDg7O6NJkyY12lAqlZg+fTr8/f3xj3/8Ay4uLja3US6Xw9vb2+bjCYIgCIIg6pKaUtVqXex5enpWqzzNSUhIwKlTp/DSSy+x206ePIn4+Pga8/Vyc3Mxbdo0hIWFYfPmzXB1dRXcboIgCIIgiPqIpOvsTZs2Denp6di8eTNu3bqFr776Cj/99BMb4gWAI0eOYPDgwcjOzgYAZGdnY/LkyfDx8cHy5ctRXFyM3Nxc5Obm0oQLgiAIgiAaHZKejdupUyds2rQJGzZswLZt2xAaGoqVK1ealF0pKSlBZmYmO6ni5MmTuH37Nm7fvo1+/fqZ2Pviiy+QlJRUq9+BIAiCIAiiLpHp7ZnxQBAEQRAEQdQrJB3GJQiCIAiCIIRBYo8gCIIgCKIBQ2KPIAiCIAiiAUNijyAIgiAIogFDYk8ADx8+RExMDPr16wedTifY3qJFixAVFcX+dOnSBePHj8fx48cF21YqlVizZg0GDRqE2NhYJCcnY+rUqTh8+LDdbee2s2PHjkhKSsJzzz2Hjz/+GKWlpYLaaX4OmJ/k5GSH2D169Chvm4WFhfjggw8wdOhQdOrUCV27dsWoUaOwceNGthSQvW2cNm2axfb79+8jKioKaWlpvNv6pM+Qkl1rts6fP4+kpCQsXLiQnXkvhl2+MNfTggULLPZ9++23iIqKsqgGYI/dtWvXmmzPyspCVFQUzpw5w7fJyMnJwdtvv40+ffogJiYGycnJWLhwIW7fvs3bJve+6tChA/r27Ytly5ahoKCAt01HtJXbzvbt2yMlJQWpqam87lMu5eXl2LhxIwYOHIi4uDgkJSVhzJgx+OKLL3jbtNZPcX/4XFdTpkzBkiVLLLYL6Vtmz55d7QIJZWVl6Ny5Mz777DOb7X377bfo2LEjVCqVyfZBgwYhPj7eonRar1698P7779tkW6/X4+9//zsmT55sMt7p9XpMmzYNEydO5D2Gv/jiixg3bhwqKytNtmdkZKBjx444cuSIXfbOnDnzxGuAD5IuvSJ19u7di8GDByMjIwO//vorUlJSBNtMTEzExo0bAQDFxcXYtWsXXnrpJfzwww9o2bIlL5tZWVl47rnn4O3tjVdffRUdO3ZERUUF0tLS8NFHH6FTp05o1qwZr3bqdDoUFhYiLS0Nn3zyCfbt24evv/4agYGBvNrKtc2lpiLaQuw+aSWW6nj06BEmTpwIJycnvPTSS4iOjoaLiwvu3buHY8eO4fPPP8fixYsFt5kA/vvf/2LBggV47rnnkJqaKpklDMPCwnDs2DEolUqTdbr37t2LsLAw3nZdXV3x5ZdfYtKkSQgPDxejqXjw4AEmTJiAoKAgrFy5EpGRkXj48CG2bt2KMWPGYOfOnYiJieFlm7mvtFot/vzzTyxduhRZWVn4+OOPJdVWbp91584dvPvuu3jllVfw7bff8monAKxYsQJnzpzBkiVLEBUVBZVKhUuXLuHhw4e8bf7666/s6/T0dMybNw8HDhxg+1Qx+kIxGD9+PGbPno3r16+jbdu2JvsOHz4MjUaDZ5991mZ7PXr0QGVlJdLS0tCnTx8Ahrq59+/fh5+fH86fP4+uXbsCAG7evImcnBx0797dJtsymQyrV6/GiBEj8Pnnn2PmzJkAgB07diAjIwPfffcd5HJ+vq/33nsPI0aMwLZt2/Dyyy8DMDwEpKamYuTIkRgwYIBd9hISEkyuAYabN2+ywpIPJPZ4otVq8a9//QurVq1Cy5YtsXfvXlHEnrOzM3tTBwYGYuHChfjyyy9x+fJl3mJv+fLl0Gg0+Oabb9il6ACgVatWeOaZZwS3Mzg4GFFRUejfvz9GjBiBdevWYfXq1bzsmtsWEzHtrlixAhUVFTh06JDJOY2MjETv3r3tWsOZqJ79+/dj2bJleO211/D888/XdXNMiIiIQEhICL7//nvWY3jlyhXcvHkTEydOxE8//cTLbkJCAsrKyrB+/XqsW7dOlLa+88470Ol02LlzJ3u9hoWFYfv27Xj22WexePFifP/997yENPe+CgkJwfXr17Fp0yaUl5fXuHxTbbfVvM8aN24cVq5cCZVKZXIP28PRo0exYMECPP300+y26OhoXrYYuH0U8zDq7+/vkD5RCL1790ZoaCj27t2LN99802Tf3r170b9/f5OHoCfRokULhIeH4/Tp06zY++233xAbG4tWrVrh9OnTrNg7ffo0nJ2d0aVLF5vth4aGYunSpViyZAl69uwJuVyO9evXY/ny5WjevLnNdswJCAjAypUrMX/+fKSkpCAuLg7vv/8+tFqtVW/qk3BxcbH4XxcUFGDp0qXo2rUrbycChXF58t///hcA0L17d4waNQo///yzxTq+QqmoqMCePXvg7OzM+6m7sLAQx48fx+TJk612aK6urqItJxccHIwRI0bgyJEjooS1pcqTzikAyXif6jMff/wxli1bhlWrVklO6DGMHTsW+/btY//es2cPhgwZwls8AIZr54033sDhw4dx8eJFwW0sKirCiRMnMGnSJIt2OTk5YcaMGbh27RquXr0q+LMAw/qcOp3OIqwlpbZmZ2fj//7v/6BQKHh7dADDQH/ixAnBYev6iFwux5gxY/Ddd9+ZhFivXbuGCxcu8PJAdevWDadPn2b/Pn36NJKSkpCUlGSy/cyZM0hISIC7u7td9keOHIn+/fsjNTUVCxcuREpKCsaMGWN3O815+umnMWbMGKSmpuLo0aPYvXs33n//fZuXhq2JiooKzJ8/Hy4uLti4cSNvzy6JPZ7s2bMHI0eOhFwuR7NmzZCQkIADBw4Itnv27FkkJCQgISEBsbGxWL9+PT744APeTx53796FTqdDmzZtBLfNFlq3bg2VSiWo8+OeA+bn1VdfFdw2c7t8vZrMOW3durXJ9gkTJgi2be27Dx8+nJet+kxaWhrWrVuHNWvWYMSIEXXdnGoZMmQIsrKykJ6eDrVajUOHDmHs2LGC7SYmJuLpp5+2yN3jw507d6DT6SxCbQzM9lu3bgn+rBs3buDrr79GXFwcL8HryLYy91anTp2QkpKCc+fOYfLkyfDw8LDbFsO7776L69evo0ePHhgxYgSWLFmCY8eO8bZX3xg7dixKSkpM8tL27NmDFi1a2Bxi5dK9e3dcuXKFHT/OnDmDbt26ISkpCRkZGSgrK4Ner2e382HZsmXIzMzEo0eP8O677/KyYY3FixdDr9dj3rx5mDVrFuLj40Wxu2LFCly/fh3btm0T9BBJYVweZGVl4ZdffkFqaiq7bdSoUdi2bRteeOEFQV6duLg4rFmzBoBhKbjDhw9j8eLFCAsLQ1xcnN326iqcKNY5YBDSIVdn19nZmZed6s7phg0boNFo8OWXX/KeVGPtu2dnZ2PKlCm87NVXIiMjUVlZiW3btiEpKQkBAQF13SSruLu7Y9iwYdi3bx+eeuopBAYGonPnziZeCL4sXLgQw4cPx7Fjx9CxY0fedmztA/jes4yI0mq10Gg0SEpKwjvvvMPLlq1tdXFxsds2c2+p1Wr8+9//xqlTpwQ/RCYmJuLIkSPIyMhAeno6zp49i3nz5qFPnz7YsmVLg/fwh4SEoFevXtizZw+GDRvGPvDMmDGD13fv3r07K+aio6ORl5eHhIQEuLm5ITQ0FGlpaQgICEBhYSF69OjBq80HDx6EXC6HSqXC1atXeYlSa7i7u2PGjBlYvnw55syZI4rNHTt24ODBg9ixY4egUDNAYo8XkwWegQAADHhJREFUe/fuhVartfA4aLVa/Pbbb7wvQsAQAomIiGD/jomJwc8//4ydO3fyyt+JiIiAXC7HjRs37E4U5cPNmzfh5eUFX19f3jbMz4FYiGW3unMaGhoKgP+kj+raKJWE7NrE398f69evx7Rp0zBp0iTs3LkTISEhdd0sq4wdOxZTpkzB1atXRfHqMbRq1Qrjx4/HBx98gE8++YS3nZYtW0Iul+P69etW+4Dr16+zn8cHRkQpFAoEBQXxEmL2tpVP/jL33mrXrh3u3LmDFStWYNWqVbzbCxjuT8YLP336dHz//fd4/fXXce7cOTbHrK7x8vJCSUmJxfbi4mIAEJTKM27cOLz00ku4e/cuzp8/D5VKZdfEDC4BAQFo164dTp8+jcLCQsTHx7N5n0woNyAgAJ6enoiNjbXb/rVr17Bhwwa8/fbb+Ouvv7B48WIcOnQI3t7evNprjpOTk8lvIRw/fhxr167FypUrkZiYKNgehXHtRKfT4V//+hfmzp2LgwcPmvwMHDgQe/bsEf0zFQqFxbRzW/H19UVKSgq+/vpriyntAKBWq6FWq4U2EYDBA3Xo0CEMGDBAUB6M1GHO6VdffWW1AyXEwd/fH1988QW8vLwwadIk3L9/v66bZJWYmBi0bNkSV65cwciRI0W1/fLLLyMnJ0dQv9KkSROkpKTgm2++sSiNpNVq8c9//hPt2rXjXdKBEVHNmjUTJPRsbWtMTIwoaSkvv/wyDh48KEpeJJfIyEgAQH5+vqh2hRAZGYlLly5Bq9WabM/IyIBCoRD0ENynTx8EBQVh37592Lt3L/r16ydoMgmTt8fk6zEwYo+ZqGGvoNJoNHj99dfRq1cv/O1vf0NqaipcXV2xcuVK3m11FDdu3MCrr76K559/HqNHjxbFZsMdkR3E8ePHkZWVhQkTJqBdu3YmP6NGjcLRo0ehVCp526+oqEBubi5yc3ORmZmJLVu24MaNGyazvezl7bffhkKhwJgxY/DDDz/g5s2buH37Nvbv349Ro0bxmljCtDM7OxtXr17FN998g/Hjx8PX1xevvfYa77ZybZv/SIm3334bTk5OGDVqFA4ePIgrV67g3r17OH78OI4fP96gxW5t4uvri507dyIoKAiTJk0SVBMOMNT/unz5ssmPGLlqX3/9NU6dOmXX7ENb8Pf3x4svvoidO3cKsrNs2TIAwLRp03Dq1Ck8evQIv//+O2bNmoX79+9j1apVkgk51tTWR48eiZLHCBjyi/v06WNRjskeJk+ejF27duHixYt48OABfvvtN6xYsQI+Pj4mQqWumThxIvLy8vDWW2/hzz//xN27d/HDDz9g06ZNGD16NHx8fHjbZsaWXbt24dy5c4K92927d0dmZiZOnDhhkpeXlJSEy5cv49y5c7zy9T788EPk5uay4s7d3R1r1qzBoUOHBNVbFRulUolZs2ahffv2mDp1qtWx0Fy02wKFce1kz5496Ny5M4KDgy329erVC25ubti/fz9bx8de0tLS0LNnTwCGPLUWLVpg5cqVGDVqFO82h4WF4cCBA9i+fTs2bdqEhw8fwsfHB23atMG8efN41QRj2qlQKODt7Y1WrVph4sSJmDRpkuAZSNxzwCUjI0O0mcNCYc7pp59+iu3bt+PBgwcAgObNm6NXr16NLsdOp9M5LNzs5eWFzz77DHPmzMHkyZPxz3/+s9oE/idx4cIFi3spOjoa3333naA2ipFTWh3Tpk3Drl278OjRI942wsPDsX//fmzZsgWLFy9GXl4emjRpgh49emDfvn28Q7iOwLytzODWtm1bHDp0yGrfy5eZM2di4sSJOHPmDC9xlpKSgkOHDmHTpk1QqVRo2rQpEhMTsWrVKtGFvxDCw8Px7bffYuPGjZg9ezZUKhWaN2+OGTNmYOrUqYLtjx07Ftu2bUNYWJjVvtseGK+dVqs1yVMPCgpCREQEMjMz7c6zS0tLw+eff44tW7aY/F/i4+PxwgsvYOnSpUhISEDTpk0FtV0Mjh8/jvv37+P+/fvVlnM7duyY3bVxZXoqCEYQhECmT5+O4OBgwflPBGGNH374AYsWLcLSpUsxfvz4um4OQdQ7yLNHEARvlEol/vjjD5w9e5aEHuEwhg8fDh8fH2RkZKCgoAB+fn513SSCqFeQZ48gCN5MmTIFd+7cwciRI7FgwYJGOXOYIAhC6pDYIwiCIAiCaMDQlEGCIAiCIIgGDIk9giAIgiCIBgyJPYIgCIIgiAYMiT2CIBo9H330ETp06CCqzcuXL+Ojjz6yunINQRBEbUJijyCIRs/YsWOxa9cuUW1evnwZmzdvJrFHEESdQ3X2CIJo9ISEhCAkJKSum0EQBOEQyLNHEESjxzyM++mnn2LQoEGIjY1FUlISJkyYgD/++IPdr9Fo8MEHH6B3796IiYnBoEGDsGPHDnb//v37sXjxYgBA7969ERUVhaioKHZ/VlYWXn31VXTt2hVxcXEYO3YsfvnlF5M2LVq0CAMGDMBvv/2GkSNHIjY2FsOHD8eJEycs2n/q1ClMmjQJ8fHx6NKlC+bPny9oeTWCIBoW5NkjCILgcPDgQaxfvx7z5s1D586dUVpaij///BPFxcXsMa+//jqOHTuGefPmoWPHjvj111+xatUqFBUV4ZVXXkGfPn0wZ84cbN26FVu3bjVZj7O0tBSTJ0+GWq3GokWL4O/vj127dmHWrFn45JNPkJyczB5bWFiIJUuWYM6cOQgICMCXX36JuXPn4uDBg2jTpg0AwzqZL7/8MoYOHYoXXngBZWVl2Lx5M6ZOnYrvvvvOoev2EgRRPyCxRxAEwSEjIwNRUVGYM2cOu61fv37s66tXr+Knn37CG2+8genTpwMAevbsibKyMnz22Wd4/vnn4e/vjxYtWgAAOnToYBIi3r9/P+7du4d9+/YhNjYWAJCSkoLhw4dj06ZNJmKvuLiY9SACQHJyMvr374+tW7di3bp1AIBVq1ahb9++7N8AEBcXh8GDB+PAgQOYNGmS2KeIIIh6BoVxCYIgOMTExODy5ctYuXIl0tLSoFarTfanpaUBAIYNG2ayfdiwYVCr1bh48WKN9tPS0hAREcEKPQCQy+UYMmQIMjIyTD7P1dUVKSkp7N8uLi7o168fLly4AAC4ffs27t27h6FDh6KyspL9CQkJQatWrUxCzwRBNF7Is0cQBMHh2WefxePHj7F792589dVXcHd3x+DBg/HGG2/A19cXRUVFAICAgACT9zF/M/uro6ioyOK9zPt1Oh1KSkrg6uoKAPDz84NMJrM4LicnBwCQn58PAHjttdfw2muvWdgMCgqy5SsTBNHAIbFHEATBQSaTYdKkSZg0aRKUSiWOHj2K1atXQ6vVYu3atWjSpAkAIC8vD8HBwez7GOHF7K+OJk2a4MqVKxbb8/PzIZfL4e3tzW4rKCiAXq83EXx5eXkIDAwEAPj6+gIwTOZITEy0sOnl5WXjtyYIoiFDYVyCIIhq8Pf3x7hx49CjRw9cv34dAFhR9eOPP5oce/jwYbi6urLhWRcXFwCwCAMnJibi9u3buHTpErtNp9Phxx9/RFxcHOvVY97LnX2r0Wjwn//8B/Hx8QCAyMhIhIeHIzMzE7GxsRY/rVq1EulMEARRnyHPHkEQBIelS5fCy8sLCQkJ8PX1xdWrV/Hrr79i8uTJAICoqCgMGTIE69atg0ajYWfj7t69G3PmzIGPjw8AoHXr1gCAr7/+GkOHDoVCoUBsbCxGjx6NnTt3Ys6cOViwYAH8/Pzw7bff4tatW/j0009N2uLj44MVK1Zg7ty57Gzc/Px8zJ49G4DBC/nWW29h3rx5KC8vx8CBA+Ht7Y2cnBycOXMGvXr1wqBBg2rx7BEEIUVker1eX9eNIAiCqEs++ugjbN26FZcuXcKBAwewb98+3LhxA2VlZQgLC8OIESMwe/ZsODkZno81Gg0+/PBDHDp0CEqlEmFhYZg4cSKmTZtmYnfjxo3Yu3cv8vPzodfrcfXqVQCGOntr167FL7/8gvLyckRHR2PevHkmkzEWLVqE33//HStWrMCqVauQmZmJiIgIpKamsrNzGdLS0rB161acP38eFRUVCA4OxlNPPYWZM2ciMjLSsSePIAjJQ2KPIAhCgjBi78iRI3XdFIIg6jmUs0cQBEEQBNGAIbFHEARBEATRgKEwLkEQBEEQRAOGPHsEQRAEQRANGBJ7BEEQBEEQDRgSewRBEARBEA0YEnsEQRAEQRANGBJ7BEEQBEEQDRgSewRBEARBEA2Y/wf639yl4emTowAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dtrain at a certain timestep\n",
    "temp = dtrain.loc[50, alphabet]\n",
    "# scaling\n",
    "temp = temp / temp.max()\n",
    "\n",
    "temp = pd.DataFrame(\n",
    "    temp.melt(\n",
    "    value_name=\"composition (a.u.)\", var_name=\"isotope\")\n",
    ")\n",
    "\n",
    "sns.violinplot(data=temp,x=\"isotope\",y=\"composition (a.u.)\", inner=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple baseline algorithm  \n",
    "\n",
    "To benchmark the performances of a machine learning algorithm, we first try to build a simple baseline method.\n",
    "\n",
    "Our goal is to predict the composition of matter inside the reactor **at any given time** by just using its initial composition (isotopes A --> H) and the parameters p1, ..., p5.\n",
    "\n",
    "The baseline algorithm presented here simply performs a multioutput linear regression for each isotope. Here we have  80 timesteps as output in the predictive model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first reshape the data into a form that will be more useful for the rest of this notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>...</th>\n",
       "      <th>Y_R</th>\n",
       "      <th>Y_S</th>\n",
       "      <th>Y_T</th>\n",
       "      <th>Y_U</th>\n",
       "      <th>Y_V</th>\n",
       "      <th>Y_W</th>\n",
       "      <th>Y_X</th>\n",
       "      <th>Y_Y</th>\n",
       "      <th>Y_Z</th>\n",
       "      <th>times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>3.737475e-08</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>1.808307e-04</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>1.455940e-07</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>2.418617e-04</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>3.190400e-07</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>2.624485e-04</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>5.524009e-07</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000736</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>2.693822e-04</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.317729</td>\n",
       "      <td>3.265168</td>\n",
       "      <td>0.205470</td>\n",
       "      <td>1.316402</td>\n",
       "      <td>0.848286</td>\n",
       "      <td>0.364322</td>\n",
       "      <td>0.635073</td>\n",
       "      <td>0.173054</td>\n",
       "      <td>0.023597</td>\n",
       "      <td>0.006906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>8.406218e-07</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000983</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>2.717239e-04</td>\n",
       "      <td>100.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55195</th>\n",
       "      <td>0.210802</td>\n",
       "      <td>3.523721</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>1.164630</td>\n",
       "      <td>0.514956</td>\n",
       "      <td>0.986465</td>\n",
       "      <td>0.590926</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003093</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.002046</td>\n",
       "      <td>9.069067e-05</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.012580</td>\n",
       "      <td>0.003470</td>\n",
       "      <td>1.302972e-05</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55196</th>\n",
       "      <td>0.210802</td>\n",
       "      <td>3.523721</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>1.164630</td>\n",
       "      <td>0.514956</td>\n",
       "      <td>0.986465</td>\n",
       "      <td>0.590926</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>9.225339e-05</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>0.012591</td>\n",
       "      <td>0.003469</td>\n",
       "      <td>1.302924e-05</td>\n",
       "      <td>1720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55197</th>\n",
       "      <td>0.210802</td>\n",
       "      <td>3.523721</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>1.164630</td>\n",
       "      <td>0.514956</td>\n",
       "      <td>0.986465</td>\n",
       "      <td>0.590926</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003101</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.002049</td>\n",
       "      <td>9.380330e-05</td>\n",
       "      <td>0.001750</td>\n",
       "      <td>0.005860</td>\n",
       "      <td>0.012601</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>1.303008e-05</td>\n",
       "      <td>1740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55198</th>\n",
       "      <td>0.210802</td>\n",
       "      <td>3.523721</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>1.164630</td>\n",
       "      <td>0.514956</td>\n",
       "      <td>0.986465</td>\n",
       "      <td>0.590926</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>9.534032e-05</td>\n",
       "      <td>0.001752</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.012612</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>1.302903e-05</td>\n",
       "      <td>1760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55199</th>\n",
       "      <td>0.210802</td>\n",
       "      <td>3.523721</td>\n",
       "      <td>0.082074</td>\n",
       "      <td>1.164630</td>\n",
       "      <td>0.514956</td>\n",
       "      <td>0.986465</td>\n",
       "      <td>0.590926</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.021140</td>\n",
       "      <td>0.040212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>1.006643e-04</td>\n",
       "      <td>0.001753</td>\n",
       "      <td>0.005820</td>\n",
       "      <td>0.012624</td>\n",
       "      <td>0.003452</td>\n",
       "      <td>3.845987e-07</td>\n",
       "      <td>1825.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55200 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D         E         F         G  \\\n",
       "0      0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "1      0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "2      0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "3      0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "4      0.317729  3.265168  0.205470  1.316402  0.848286  0.364322  0.635073   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "55195  0.210802  3.523721  0.082074  1.164630  0.514956  0.986465  0.590926   \n",
       "55196  0.210802  3.523721  0.082074  1.164630  0.514956  0.986465  0.590926   \n",
       "55197  0.210802  3.523721  0.082074  1.164630  0.514956  0.986465  0.590926   \n",
       "55198  0.210802  3.523721  0.082074  1.164630  0.514956  0.986465  0.590926   \n",
       "55199  0.210802  3.523721  0.082074  1.164630  0.514956  0.986465  0.590926   \n",
       "\n",
       "              H        p1        p2  ...       Y_R       Y_S       Y_T  \\\n",
       "0      0.173054  0.023597  0.006906  ...  0.000003  0.000039  0.000034   \n",
       "1      0.173054  0.023597  0.006906  ...  0.000012  0.000081  0.000069   \n",
       "2      0.173054  0.023597  0.006906  ...  0.000027  0.000122  0.000104   \n",
       "3      0.173054  0.023597  0.006906  ...  0.000047  0.000163  0.000139   \n",
       "4      0.173054  0.023597  0.006906  ...  0.000073  0.000202  0.000175   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "55195  0.242705  0.021140  0.040212  ...  0.003093  0.001547  0.002046   \n",
       "55196  0.242705  0.021140  0.040212  ...  0.003097  0.001547  0.002047   \n",
       "55197  0.242705  0.021140  0.040212  ...  0.003101  0.001547  0.002049   \n",
       "55198  0.242705  0.021140  0.040212  ...  0.003105  0.001547  0.002051   \n",
       "55199  0.242705  0.021140  0.040212  ...  0.003105  0.001545  0.002051   \n",
       "\n",
       "                Y_U       Y_V       Y_W       Y_X       Y_Y           Y_Z  \\\n",
       "0      3.737475e-08  0.000018  0.000004  0.000079  0.000080  1.808307e-04   \n",
       "1      1.455940e-07  0.000040  0.000016  0.000267  0.000159  2.418617e-04   \n",
       "2      3.190400e-07  0.000062  0.000035  0.000494  0.000239  2.624485e-04   \n",
       "3      5.524009e-07  0.000085  0.000062  0.000736  0.000318  2.693822e-04   \n",
       "4      8.406218e-07  0.000108  0.000097  0.000983  0.000398  2.717239e-04   \n",
       "...             ...       ...       ...       ...       ...           ...   \n",
       "55195  9.069067e-05  0.001746  0.005862  0.012580  0.003470  1.302972e-05   \n",
       "55196  9.225339e-05  0.001748  0.005861  0.012591  0.003469  1.302924e-05   \n",
       "55197  9.380330e-05  0.001750  0.005860  0.012601  0.003468  1.303008e-05   \n",
       "55198  9.534032e-05  0.001752  0.005859  0.012612  0.003467  1.302903e-05   \n",
       "55199  1.006643e-04  0.001753  0.005820  0.012624  0.003452  3.845987e-07   \n",
       "\n",
       "        times  \n",
       "0        20.0  \n",
       "1        40.0  \n",
       "2        60.0  \n",
       "3        80.0  \n",
       "4       100.0  \n",
       "...       ...  \n",
       "55195  1700.0  \n",
       "55196  1720.0  \n",
       "55197  1740.0  \n",
       "55198  1760.0  \n",
       "55199  1825.0  \n",
       "\n",
       "[55200 rows x 40 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = dtrain[alphabet].add_prefix('Y_')\n",
    "train_data[\"times\"] = dtrain[\"times\"]\n",
    "train_data = train_data[ train_data[\"times\"] > 0.]\n",
    "temp = dtrain.loc[0][input_params].reset_index(drop=True)\n",
    "temp = temp.loc[temp.index.repeat(80)].reset_index(drop=True)\n",
    "train_data = pd.concat([temp, train_data.reset_index(drop=True)], axis=1)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>...</th>\n",
       "      <th>Y_R</th>\n",
       "      <th>Y_S</th>\n",
       "      <th>Y_T</th>\n",
       "      <th>Y_U</th>\n",
       "      <th>Y_V</th>\n",
       "      <th>Y_W</th>\n",
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       "      <th>Y_Y</th>\n",
       "      <th>Y_Z</th>\n",
       "      <th>times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>7.151626e-08</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>2.741408e-07</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>5.910356e-07</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>1.007280e-06</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>4.776655</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>2.131205</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.114118</td>\n",
       "      <td>0.762691</td>\n",
       "      <td>0.140276</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.011609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>1.509132e-06</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>100.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18395</th>\n",
       "      <td>0.457563</td>\n",
       "      <td>5.532067</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>2.744948</td>\n",
       "      <td>0.997465</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>0.141960</td>\n",
       "      <td>0.215506</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004523</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>9.559488e-05</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.004512</td>\n",
       "      <td>0.020048</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18396</th>\n",
       "      <td>0.457563</td>\n",
       "      <td>5.532067</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>2.744948</td>\n",
       "      <td>0.997465</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>0.141960</td>\n",
       "      <td>0.215506</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.002970</td>\n",
       "      <td>9.788469e-05</td>\n",
       "      <td>0.002462</td>\n",
       "      <td>0.004540</td>\n",
       "      <td>0.020128</td>\n",
       "      <td>0.006558</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18397</th>\n",
       "      <td>0.457563</td>\n",
       "      <td>5.532067</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>2.744948</td>\n",
       "      <td>0.997465</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>0.141960</td>\n",
       "      <td>0.215506</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>1.001580e-04</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.020208</td>\n",
       "      <td>0.006578</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18398</th>\n",
       "      <td>0.457563</td>\n",
       "      <td>5.532067</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>2.744948</td>\n",
       "      <td>0.997465</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>0.141960</td>\n",
       "      <td>0.215506</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004602</td>\n",
       "      <td>0.002215</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>1.024140e-04</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18399</th>\n",
       "      <td>0.457563</td>\n",
       "      <td>5.532067</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>2.744948</td>\n",
       "      <td>0.997465</td>\n",
       "      <td>0.341679</td>\n",
       "      <td>0.141960</td>\n",
       "      <td>0.215506</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.024052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004602</td>\n",
       "      <td>0.002213</td>\n",
       "      <td>0.002994</td>\n",
       "      <td>1.115262e-04</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.004566</td>\n",
       "      <td>0.020375</td>\n",
       "      <td>0.006569</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1825.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18400 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D         E         F         G  \\\n",
       "0      0.133810  4.776655  0.401556  2.131205  0.783545  0.114118  0.762691   \n",
       "1      0.133810  4.776655  0.401556  2.131205  0.783545  0.114118  0.762691   \n",
       "2      0.133810  4.776655  0.401556  2.131205  0.783545  0.114118  0.762691   \n",
       "3      0.133810  4.776655  0.401556  2.131205  0.783545  0.114118  0.762691   \n",
       "4      0.133810  4.776655  0.401556  2.131205  0.783545  0.114118  0.762691   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "18395  0.457563  5.532067  0.350256  2.744948  0.997465  0.341679  0.141960   \n",
       "18396  0.457563  5.532067  0.350256  2.744948  0.997465  0.341679  0.141960   \n",
       "18397  0.457563  5.532067  0.350256  2.744948  0.997465  0.341679  0.141960   \n",
       "18398  0.457563  5.532067  0.350256  2.744948  0.997465  0.341679  0.141960   \n",
       "18399  0.457563  5.532067  0.350256  2.744948  0.997465  0.341679  0.141960   \n",
       "\n",
       "              H        p1        p2  ...       Y_R       Y_S       Y_T  \\\n",
       "0      0.140276  0.044514  0.011609  ...  0.000008  0.000074  0.000065   \n",
       "1      0.140276  0.044514  0.011609  ...  0.000033  0.000152  0.000131   \n",
       "2      0.140276  0.044514  0.011609  ...  0.000074  0.000226  0.000197   \n",
       "3      0.140276  0.044514  0.011609  ...  0.000128  0.000298  0.000265   \n",
       "4      0.140276  0.044514  0.011609  ...  0.000194  0.000368  0.000332   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "18395  0.215506  0.014510  0.024052  ...  0.004523  0.002202  0.002959   \n",
       "18396  0.215506  0.014510  0.024052  ...  0.004550  0.002206  0.002970   \n",
       "18397  0.215506  0.014510  0.024052  ...  0.004576  0.002211  0.002982   \n",
       "18398  0.215506  0.014510  0.024052  ...  0.004602  0.002215  0.002994   \n",
       "18399  0.215506  0.014510  0.024052  ...  0.004602  0.002213  0.002994   \n",
       "\n",
       "                Y_U       Y_V       Y_W       Y_X       Y_Y       Y_Z   times  \n",
       "0      7.151626e-08  0.000036  0.000015  0.000145  0.000141  0.000331    20.0  \n",
       "1      2.741408e-07  0.000078  0.000059  0.000491  0.000282  0.000443    40.0  \n",
       "2      5.910356e-07  0.000122  0.000133  0.000910  0.000422  0.000481    60.0  \n",
       "3      1.007280e-06  0.000167  0.000233  0.001355  0.000562  0.000494    80.0  \n",
       "4      1.509132e-06  0.000214  0.000360  0.001807  0.000702  0.000498   100.0  \n",
       "...             ...       ...       ...       ...       ...       ...     ...  \n",
       "18395  9.559488e-05  0.002450  0.004512  0.020048  0.006538  0.000094  1700.0  \n",
       "18396  9.788469e-05  0.002462  0.004540  0.020128  0.006558  0.000094  1720.0  \n",
       "18397  1.001580e-04  0.002474  0.004568  0.020208  0.006578  0.000094  1740.0  \n",
       "18398  1.024140e-04  0.002485  0.004597  0.020288  0.006597  0.000094  1760.0  \n",
       "18399  1.115262e-04  0.002488  0.004566  0.020375  0.006569  0.000003  1825.0  \n",
       "\n",
       "[18400 rows x 40 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = dtest[alphabet].add_prefix('Y_')\n",
    "test_data[\"times\"] = dtest[\"times\"]\n",
    "test_data = test_data[test_data[\"times\"] > 0.]\n",
    "temp = dtest.loc[0][input_params].reset_index(drop=True)\n",
    "temp = temp.loc[temp.index.repeat(80)].reset_index(drop=True)\n",
    "test_data = pd.concat([temp, test_data.reset_index(drop=True)], axis=1)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and test data:\n",
    "From these dataframe, let us extract X_train, y_train, X_test and y_test for each isotope:\n",
    "\n",
    "For isotope A:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>...</th>\n",
       "      <th>A71</th>\n",
       "      <th>A72</th>\n",
       "      <th>A73</th>\n",
       "      <th>A74</th>\n",
       "      <th>A75</th>\n",
       "      <th>A76</th>\n",
       "      <th>A77</th>\n",
       "      <th>A78</th>\n",
       "      <th>A79</th>\n",
       "      <th>A80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005192</td>\n",
       "      <td>4.990765</td>\n",
       "      <td>0.126878</td>\n",
       "      <td>2.347393</td>\n",
       "      <td>0.315230</td>\n",
       "      <td>0.374373</td>\n",
       "      <td>0.078563</td>\n",
       "      <td>0.129959</td>\n",
       "      <td>0.027372</td>\n",
       "      <td>0.022840</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>0.004728</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.004716</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.004705</td>\n",
       "      <td>0.004700</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.004703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006727</td>\n",
       "      <td>4.876231</td>\n",
       "      <td>0.174605</td>\n",
       "      <td>3.000470</td>\n",
       "      <td>0.931660</td>\n",
       "      <td>0.317459</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>0.110730</td>\n",
       "      <td>0.020134</td>\n",
       "      <td>0.012753</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006498</td>\n",
       "      <td>0.006491</td>\n",
       "      <td>0.006485</td>\n",
       "      <td>0.006479</td>\n",
       "      <td>0.006473</td>\n",
       "      <td>0.006467</td>\n",
       "      <td>0.006461</td>\n",
       "      <td>0.006456</td>\n",
       "      <td>0.006449</td>\n",
       "      <td>0.006461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007188</td>\n",
       "      <td>6.176172</td>\n",
       "      <td>0.109065</td>\n",
       "      <td>1.556984</td>\n",
       "      <td>0.296715</td>\n",
       "      <td>0.204958</td>\n",
       "      <td>0.154517</td>\n",
       "      <td>0.026233</td>\n",
       "      <td>0.027025</td>\n",
       "      <td>0.020994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005332</td>\n",
       "      <td>0.005297</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>0.005228</td>\n",
       "      <td>0.005193</td>\n",
       "      <td>0.005158</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>0.005089</td>\n",
       "      <td>0.005055</td>\n",
       "      <td>0.005060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008880</td>\n",
       "      <td>4.154490</td>\n",
       "      <td>0.077639</td>\n",
       "      <td>3.082386</td>\n",
       "      <td>0.109124</td>\n",
       "      <td>0.401981</td>\n",
       "      <td>0.252849</td>\n",
       "      <td>0.127539</td>\n",
       "      <td>0.016481</td>\n",
       "      <td>0.008402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007459</td>\n",
       "      <td>0.007448</td>\n",
       "      <td>0.007438</td>\n",
       "      <td>0.007427</td>\n",
       "      <td>0.007416</td>\n",
       "      <td>0.007406</td>\n",
       "      <td>0.007395</td>\n",
       "      <td>0.007385</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>0.007387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011813</td>\n",
       "      <td>5.558284</td>\n",
       "      <td>0.438273</td>\n",
       "      <td>2.416261</td>\n",
       "      <td>0.662015</td>\n",
       "      <td>0.691550</td>\n",
       "      <td>0.417485</td>\n",
       "      <td>0.031835</td>\n",
       "      <td>0.017720</td>\n",
       "      <td>0.038619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010632</td>\n",
       "      <td>0.010628</td>\n",
       "      <td>0.010623</td>\n",
       "      <td>0.010618</td>\n",
       "      <td>0.010616</td>\n",
       "      <td>0.010612</td>\n",
       "      <td>0.010609</td>\n",
       "      <td>0.010605</td>\n",
       "      <td>0.010602</td>\n",
       "      <td>0.010611</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0.904926</td>\n",
       "      <td>9.835680</td>\n",
       "      <td>0.103366</td>\n",
       "      <td>0.609510</td>\n",
       "      <td>0.320723</td>\n",
       "      <td>0.024184</td>\n",
       "      <td>0.164953</td>\n",
       "      <td>0.086149</td>\n",
       "      <td>0.025203</td>\n",
       "      <td>0.043971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.543602</td>\n",
       "      <td>0.539840</td>\n",
       "      <td>0.536089</td>\n",
       "      <td>0.532356</td>\n",
       "      <td>0.528637</td>\n",
       "      <td>0.524942</td>\n",
       "      <td>0.521263</td>\n",
       "      <td>0.517594</td>\n",
       "      <td>0.513942</td>\n",
       "      <td>0.513945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.934583</td>\n",
       "      <td>9.466261</td>\n",
       "      <td>0.027023</td>\n",
       "      <td>0.384777</td>\n",
       "      <td>0.138869</td>\n",
       "      <td>0.226331</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.075807</td>\n",
       "      <td>0.042623</td>\n",
       "      <td>0.043409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.527536</td>\n",
       "      <td>0.523719</td>\n",
       "      <td>0.519918</td>\n",
       "      <td>0.516134</td>\n",
       "      <td>0.512357</td>\n",
       "      <td>0.508596</td>\n",
       "      <td>0.504846</td>\n",
       "      <td>0.501101</td>\n",
       "      <td>0.497381</td>\n",
       "      <td>0.497383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0.957282</td>\n",
       "      <td>10.214110</td>\n",
       "      <td>0.008380</td>\n",
       "      <td>0.111240</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>0.003537</td>\n",
       "      <td>0.015444</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.013751</td>\n",
       "      <td>0.037612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.490326</td>\n",
       "      <td>0.482109</td>\n",
       "      <td>0.473979</td>\n",
       "      <td>0.465930</td>\n",
       "      <td>0.457960</td>\n",
       "      <td>0.450073</td>\n",
       "      <td>0.442256</td>\n",
       "      <td>0.434517</td>\n",
       "      <td>0.426852</td>\n",
       "      <td>0.426853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>0.959747</td>\n",
       "      <td>10.057000</td>\n",
       "      <td>0.111717</td>\n",
       "      <td>0.656046</td>\n",
       "      <td>0.067865</td>\n",
       "      <td>0.107541</td>\n",
       "      <td>0.193227</td>\n",
       "      <td>0.013821</td>\n",
       "      <td>0.029813</td>\n",
       "      <td>0.033931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.516493</td>\n",
       "      <td>0.512444</td>\n",
       "      <td>0.508408</td>\n",
       "      <td>0.504393</td>\n",
       "      <td>0.500389</td>\n",
       "      <td>0.496402</td>\n",
       "      <td>0.492426</td>\n",
       "      <td>0.488471</td>\n",
       "      <td>0.484538</td>\n",
       "      <td>0.484540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>0.968041</td>\n",
       "      <td>9.749631</td>\n",
       "      <td>0.002714</td>\n",
       "      <td>0.039926</td>\n",
       "      <td>0.018918</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.032481</td>\n",
       "      <td>0.025001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365408</td>\n",
       "      <td>0.362404</td>\n",
       "      <td>0.359414</td>\n",
       "      <td>0.356436</td>\n",
       "      <td>0.353474</td>\n",
       "      <td>0.350524</td>\n",
       "      <td>0.347589</td>\n",
       "      <td>0.344674</td>\n",
       "      <td>0.341766</td>\n",
       "      <td>0.341767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A          B         C         D         E         F         G  \\\n",
       "0    0.005192   4.990765  0.126878  2.347393  0.315230  0.374373  0.078563   \n",
       "1    0.006727   4.876231  0.174605  3.000470  0.931660  0.317459  0.026470   \n",
       "2    0.007188   6.176172  0.109065  1.556984  0.296715  0.204958  0.154517   \n",
       "3    0.008880   4.154490  0.077639  3.082386  0.109124  0.401981  0.252849   \n",
       "4    0.011813   5.558284  0.438273  2.416261  0.662015  0.691550  0.417485   \n",
       "..        ...        ...       ...       ...       ...       ...       ...   \n",
       "685  0.904926   9.835680  0.103366  0.609510  0.320723  0.024184  0.164953   \n",
       "686  0.934583   9.466261  0.027023  0.384777  0.138869  0.226331  0.002109   \n",
       "687  0.957282  10.214110  0.008380  0.111240  0.001545  0.003537  0.015444   \n",
       "688  0.959747  10.057000  0.111717  0.656046  0.067865  0.107541  0.193227   \n",
       "689  0.968041   9.749631  0.002714  0.039926  0.018918  0.019527  0.001690   \n",
       "\n",
       "            H        p1        p2  ...       A71       A72       A73  \\\n",
       "0    0.129959  0.027372  0.022840  ...  0.004739  0.004734  0.004728   \n",
       "1    0.110730  0.020134  0.012753  ...  0.006498  0.006491  0.006485   \n",
       "2    0.026233  0.027025  0.020994  ...  0.005332  0.005297  0.005262   \n",
       "3    0.127539  0.016481  0.008402  ...  0.007459  0.007448  0.007438   \n",
       "4    0.031835  0.017720  0.038619  ...  0.010632  0.010628  0.010623   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "685  0.086149  0.025203  0.043971  ...  0.543602  0.539840  0.536089   \n",
       "686  0.075807  0.042623  0.043409  ...  0.527536  0.523719  0.519918   \n",
       "687  0.001633  0.013751  0.037612  ...  0.490326  0.482109  0.473979   \n",
       "688  0.013821  0.029813  0.033931  ...  0.516493  0.512444  0.508408   \n",
       "689  0.000877  0.032481  0.025001  ...  0.365408  0.362404  0.359414   \n",
       "\n",
       "          A74       A75       A76       A77       A78       A79       A80  \n",
       "0    0.004722  0.004716  0.004711  0.004705  0.004700  0.004694  0.004703  \n",
       "1    0.006479  0.006473  0.006467  0.006461  0.006456  0.006449  0.006461  \n",
       "2    0.005228  0.005193  0.005158  0.005124  0.005089  0.005055  0.005060  \n",
       "3    0.007427  0.007416  0.007406  0.007395  0.007385  0.007375  0.007387  \n",
       "4    0.010618  0.010616  0.010612  0.010609  0.010605  0.010602  0.010611  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "685  0.532356  0.528637  0.524942  0.521263  0.517594  0.513942  0.513945  \n",
       "686  0.516134  0.512357  0.508596  0.504846  0.501101  0.497381  0.497383  \n",
       "687  0.465930  0.457960  0.450073  0.442256  0.434517  0.426852  0.426853  \n",
       "688  0.504393  0.500389  0.496402  0.492426  0.488471  0.484538  0.484540  \n",
       "689  0.356436  0.353474  0.350524  0.347589  0.344674  0.341766  0.341767  \n",
       "\n",
       "[690 rows x 93 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_target_A = train_data.groupby(input_params)['Y_A'].apply(list).apply(pd.Series).rename(\n",
    "    columns=lambda x: 'A' + str(x + 1)).reset_index()\n",
    "train_target_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>p1</th>\n",
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       "      <td>0.006922</td>\n",
       "      <td>0.006935</td>\n",
       "      <td>0.006947</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.018082</td>\n",
       "      <td>4.220983</td>\n",
       "      <td>0.253170</td>\n",
       "      <td>1.590942</td>\n",
       "      <td>0.582395</td>\n",
       "      <td>1.075491</td>\n",
       "      <td>0.570636</td>\n",
       "      <td>0.130346</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>0.001281</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014952</td>\n",
       "      <td>0.014871</td>\n",
       "      <td>0.014790</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.014631</td>\n",
       "      <td>0.014551</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>0.014316</td>\n",
       "      <td>0.014322</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.018187</td>\n",
       "      <td>8.435444</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.118998</td>\n",
       "      <td>0.031198</td>\n",
       "      <td>0.009453</td>\n",
       "      <td>0.007955</td>\n",
       "      <td>0.009778</td>\n",
       "      <td>0.046906</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001510</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.001101</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.001053</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024323</td>\n",
       "      <td>7.804812</td>\n",
       "      <td>0.109733</td>\n",
       "      <td>0.485782</td>\n",
       "      <td>0.370916</td>\n",
       "      <td>0.187885</td>\n",
       "      <td>0.122907</td>\n",
       "      <td>0.108180</td>\n",
       "      <td>0.045575</td>\n",
       "      <td>0.039759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007914</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.007865</td>\n",
       "      <td>0.007841</td>\n",
       "      <td>0.007817</td>\n",
       "      <td>0.007793</td>\n",
       "      <td>0.007769</td>\n",
       "      <td>0.007746</td>\n",
       "      <td>0.007722</td>\n",
       "      <td>0.007723</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027303</td>\n",
       "      <td>3.948096</td>\n",
       "      <td>0.222217</td>\n",
       "      <td>1.507915</td>\n",
       "      <td>0.348863</td>\n",
       "      <td>1.006527</td>\n",
       "      <td>0.345910</td>\n",
       "      <td>0.118197</td>\n",
       "      <td>0.003194</td>\n",
       "      <td>0.031200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021365</td>\n",
       "      <td>0.021278</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>0.021105</td>\n",
       "      <td>0.021018</td>\n",
       "      <td>0.020933</td>\n",
       "      <td>0.020847</td>\n",
       "      <td>0.020762</td>\n",
       "      <td>0.020677</td>\n",
       "      <td>0.020683</td>\n",
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       "      <td>0.027587</td>\n",
       "      <td>0.195462</td>\n",
       "      <td>0.026856</td>\n",
       "      <td>0.021091</td>\n",
       "      <td>0.022773</td>\n",
       "      <td>0.005447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600267</td>\n",
       "      <td>0.592330</td>\n",
       "      <td>0.584438</td>\n",
       "      <td>0.576583</td>\n",
       "      <td>0.568773</td>\n",
       "      <td>0.561010</td>\n",
       "      <td>0.553282</td>\n",
       "      <td>0.545612</td>\n",
       "      <td>0.537980</td>\n",
       "      <td>0.537983</td>\n",
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       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.897279</td>\n",
       "      <td>10.004340</td>\n",
       "      <td>0.027873</td>\n",
       "      <td>0.186502</td>\n",
       "      <td>0.048653</td>\n",
       "      <td>0.083877</td>\n",
       "      <td>0.038836</td>\n",
       "      <td>0.031195</td>\n",
       "      <td>0.040411</td>\n",
       "      <td>0.041649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376968</td>\n",
       "      <td>0.369650</td>\n",
       "      <td>0.362413</td>\n",
       "      <td>0.355268</td>\n",
       "      <td>0.348203</td>\n",
       "      <td>0.341228</td>\n",
       "      <td>0.334327</td>\n",
       "      <td>0.327512</td>\n",
       "      <td>0.320783</td>\n",
       "      <td>0.320784</td>\n",
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       "      <th>227</th>\n",
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       "      <td>0.007526</td>\n",
       "      <td>0.037894</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>0.015970</td>\n",
       "      <td>0.008717</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>0.024353</td>\n",
       "      <td>0.043407</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283028</td>\n",
       "      <td>0.278262</td>\n",
       "      <td>0.273537</td>\n",
       "      <td>0.268883</td>\n",
       "      <td>0.264269</td>\n",
       "      <td>0.259712</td>\n",
       "      <td>0.255200</td>\n",
       "      <td>0.250743</td>\n",
       "      <td>0.246337</td>\n",
       "      <td>0.246338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.972700</td>\n",
       "      <td>9.890109</td>\n",
       "      <td>0.029839</td>\n",
       "      <td>0.235201</td>\n",
       "      <td>0.051008</td>\n",
       "      <td>0.056772</td>\n",
       "      <td>0.037784</td>\n",
       "      <td>0.017580</td>\n",
       "      <td>0.009303</td>\n",
       "      <td>0.018152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639333</td>\n",
       "      <td>0.630878</td>\n",
       "      <td>0.622467</td>\n",
       "      <td>0.614114</td>\n",
       "      <td>0.605806</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>0.589378</td>\n",
       "      <td>0.581238</td>\n",
       "      <td>0.573178</td>\n",
       "      <td>0.573179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.999898</td>\n",
       "      <td>10.007760</td>\n",
       "      <td>0.038949</td>\n",
       "      <td>0.502331</td>\n",
       "      <td>0.081502</td>\n",
       "      <td>0.219961</td>\n",
       "      <td>0.130346</td>\n",
       "      <td>0.091305</td>\n",
       "      <td>0.030037</td>\n",
       "      <td>0.019715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.685723</td>\n",
       "      <td>0.677157</td>\n",
       "      <td>0.668648</td>\n",
       "      <td>0.660182</td>\n",
       "      <td>0.651756</td>\n",
       "      <td>0.643388</td>\n",
       "      <td>0.635066</td>\n",
       "      <td>0.626794</td>\n",
       "      <td>0.618565</td>\n",
       "      <td>0.618567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A          B         C         D         E         F         G  \\\n",
       "0    0.007282   5.636715  0.412482  2.093307  1.554593  1.225205  0.175682   \n",
       "1    0.018082   4.220983  0.253170  1.590942  0.582395  1.075491  0.570636   \n",
       "2    0.018187   8.435444  0.017857  0.118998  0.031198  0.009453  0.007955   \n",
       "3    0.024323   7.804812  0.109733  0.485782  0.370916  0.187885  0.122907   \n",
       "4    0.027303   3.948096  0.222217  1.507915  0.348863  1.006527  0.345910   \n",
       "..        ...        ...       ...       ...       ...       ...       ...   \n",
       "225  0.877807  10.121250  0.056728  0.613247  0.027587  0.195462  0.026856   \n",
       "226  0.897279  10.004340  0.027873  0.186502  0.048653  0.083877  0.038836   \n",
       "227  0.916215  10.383010  0.007526  0.037894  0.022747  0.015970  0.008717   \n",
       "228  0.972700   9.890109  0.029839  0.235201  0.051008  0.056772  0.037784   \n",
       "229  0.999898  10.007760  0.038949  0.502331  0.081502  0.219961  0.130346   \n",
       "\n",
       "            H        p1        p2  ...       A71       A72       A73  \\\n",
       "0    0.049742  0.045963  0.026232  ...  0.006851  0.006862  0.006874   \n",
       "1    0.130346  0.009426  0.001281  ...  0.014952  0.014871  0.014790   \n",
       "2    0.009778  0.046906  0.001916  ...  0.001510  0.001443  0.001380   \n",
       "3    0.108180  0.045575  0.039759  ...  0.007914  0.007889  0.007865   \n",
       "4    0.118197  0.003194  0.031200  ...  0.021365  0.021278  0.021191   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.021091  0.022773  0.005447  ...  0.600267  0.592330  0.584438   \n",
       "226  0.031195  0.040411  0.041649  ...  0.376968  0.369650  0.362413   \n",
       "227  0.000682  0.024353  0.043407  ...  0.283028  0.278262  0.273537   \n",
       "228  0.017580  0.009303  0.018152  ...  0.639333  0.630878  0.622467   \n",
       "229  0.091305  0.030037  0.019715  ...  0.685723  0.677157  0.668648   \n",
       "\n",
       "          A74       A75       A76       A77       A78       A79       A80  \n",
       "0    0.006886  0.006898  0.006909  0.006922  0.006935  0.006947  0.006956  \n",
       "1    0.014710  0.014631  0.014551  0.014472  0.014393  0.014316  0.014322  \n",
       "2    0.001319  0.001260  0.001205  0.001152  0.001101  0.001053  0.001053  \n",
       "3    0.007841  0.007817  0.007793  0.007769  0.007746  0.007722  0.007723  \n",
       "4    0.021105  0.021018  0.020933  0.020847  0.020762  0.020677  0.020683  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.576583  0.568773  0.561010  0.553282  0.545612  0.537980  0.537983  \n",
       "226  0.355268  0.348203  0.341228  0.334327  0.327512  0.320783  0.320784  \n",
       "227  0.268883  0.264269  0.259712  0.255200  0.250743  0.246337  0.246338  \n",
       "228  0.614114  0.605806  0.597561  0.589378  0.581238  0.573178  0.573179  \n",
       "229  0.660182  0.651756  0.643388  0.635066  0.626794  0.618565  0.618567  \n",
       "\n",
       "[230 rows x 93 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_target_A = test_data.groupby(input_params)['Y_A'].apply(list).apply(pd.Series).rename(\n",
    "    columns=lambda x: 'A' + str(x + 1)).reset_index()\n",
    "test_target_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.005363</td>\n",
       "      <td>0.425858</td>\n",
       "      <td>0.233685</td>\n",
       "      <td>0.519969</td>\n",
       "      <td>0.206268</td>\n",
       "      <td>0.241581</td>\n",
       "      <td>0.067691</td>\n",
       "      <td>0.240884</td>\n",
       "      <td>0.547926</td>\n",
       "      <td>0.458792</td>\n",
       "      <td>0.047443</td>\n",
       "      <td>0.385955</td>\n",
       "      <td>0.832317</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006949</td>\n",
       "      <td>0.416085</td>\n",
       "      <td>0.321590</td>\n",
       "      <td>0.664632</td>\n",
       "      <td>0.609623</td>\n",
       "      <td>0.204855</td>\n",
       "      <td>0.022807</td>\n",
       "      <td>0.205243</td>\n",
       "      <td>0.403038</td>\n",
       "      <td>0.256179</td>\n",
       "      <td>0.091628</td>\n",
       "      <td>0.285955</td>\n",
       "      <td>0.939493</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.527008</td>\n",
       "      <td>0.200877</td>\n",
       "      <td>0.344886</td>\n",
       "      <td>0.194153</td>\n",
       "      <td>0.132258</td>\n",
       "      <td>0.133134</td>\n",
       "      <td>0.048624</td>\n",
       "      <td>0.540978</td>\n",
       "      <td>0.421714</td>\n",
       "      <td>0.377074</td>\n",
       "      <td>0.563385</td>\n",
       "      <td>0.890780</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009173</td>\n",
       "      <td>0.354499</td>\n",
       "      <td>0.142997</td>\n",
       "      <td>0.682777</td>\n",
       "      <td>0.071404</td>\n",
       "      <td>0.259396</td>\n",
       "      <td>0.217858</td>\n",
       "      <td>0.236398</td>\n",
       "      <td>0.329918</td>\n",
       "      <td>0.168780</td>\n",
       "      <td>0.949347</td>\n",
       "      <td>0.572730</td>\n",
       "      <td>0.324807</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012203</td>\n",
       "      <td>0.474284</td>\n",
       "      <td>0.807217</td>\n",
       "      <td>0.535224</td>\n",
       "      <td>0.433183</td>\n",
       "      <td>0.446254</td>\n",
       "      <td>0.359711</td>\n",
       "      <td>0.059008</td>\n",
       "      <td>0.354717</td>\n",
       "      <td>0.775757</td>\n",
       "      <td>0.477787</td>\n",
       "      <td>0.711153</td>\n",
       "      <td>0.880216</td>\n",
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       "      <td>...</td>\n",
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       "      <th>685</th>\n",
       "      <td>0.934802</td>\n",
       "      <td>0.839271</td>\n",
       "      <td>0.190380</td>\n",
       "      <td>0.135012</td>\n",
       "      <td>0.209862</td>\n",
       "      <td>0.015606</td>\n",
       "      <td>0.142126</td>\n",
       "      <td>0.159681</td>\n",
       "      <td>0.504507</td>\n",
       "      <td>0.883250</td>\n",
       "      <td>0.142299</td>\n",
       "      <td>0.711716</td>\n",
       "      <td>0.454782</td>\n",
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       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.965438</td>\n",
       "      <td>0.807748</td>\n",
       "      <td>0.049772</td>\n",
       "      <td>0.085232</td>\n",
       "      <td>0.090867</td>\n",
       "      <td>0.146050</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.140511</td>\n",
       "      <td>0.853211</td>\n",
       "      <td>0.871958</td>\n",
       "      <td>0.175744</td>\n",
       "      <td>0.545873</td>\n",
       "      <td>0.418075</td>\n",
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       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0.988886</td>\n",
       "      <td>0.871562</td>\n",
       "      <td>0.015433</td>\n",
       "      <td>0.024641</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.013307</td>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.275266</td>\n",
       "      <td>0.755523</td>\n",
       "      <td>0.489252</td>\n",
       "      <td>0.128342</td>\n",
       "      <td>0.622672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>0.991432</td>\n",
       "      <td>0.858156</td>\n",
       "      <td>0.205763</td>\n",
       "      <td>0.145320</td>\n",
       "      <td>0.044407</td>\n",
       "      <td>0.069396</td>\n",
       "      <td>0.166487</td>\n",
       "      <td>0.025618</td>\n",
       "      <td>0.596790</td>\n",
       "      <td>0.681584</td>\n",
       "      <td>0.732292</td>\n",
       "      <td>0.825660</td>\n",
       "      <td>0.488552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.831928</td>\n",
       "      <td>0.004998</td>\n",
       "      <td>0.008844</td>\n",
       "      <td>0.012379</td>\n",
       "      <td>0.012600</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.001626</td>\n",
       "      <td>0.650187</td>\n",
       "      <td>0.502205</td>\n",
       "      <td>0.292049</td>\n",
       "      <td>0.930102</td>\n",
       "      <td>0.253247</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D         E         F         G  \\\n",
       "0    0.005363  0.425858  0.233685  0.519969  0.206268  0.241581  0.067691   \n",
       "1    0.006949  0.416085  0.321590  0.664632  0.609623  0.204855  0.022807   \n",
       "2    0.007425  0.527008  0.200877  0.344886  0.194153  0.132258  0.133134   \n",
       "3    0.009173  0.354499  0.142997  0.682777  0.071404  0.259396  0.217858   \n",
       "4    0.012203  0.474284  0.807217  0.535224  0.433183  0.446254  0.359711   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "685  0.934802  0.839271  0.190380  0.135012  0.209862  0.015606  0.142126   \n",
       "686  0.965438  0.807748  0.049772  0.085232  0.090867  0.146050  0.001817   \n",
       "687  0.988886  0.871562  0.015433  0.024641  0.001011  0.002283  0.013307   \n",
       "688  0.991432  0.858156  0.205763  0.145320  0.044407  0.069396  0.166487   \n",
       "689  1.000000  0.831928  0.004998  0.008844  0.012379  0.012600  0.001456   \n",
       "\n",
       "            H        p1        p2        p3        p4        p5  \n",
       "0    0.240884  0.547926  0.458792  0.047443  0.385955  0.832317  \n",
       "1    0.205243  0.403038  0.256179  0.091628  0.285955  0.939493  \n",
       "2    0.048624  0.540978  0.421714  0.377074  0.563385  0.890780  \n",
       "3    0.236398  0.329918  0.168780  0.949347  0.572730  0.324807  \n",
       "4    0.059008  0.354717  0.775757  0.477787  0.711153  0.880216  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "685  0.159681  0.504507  0.883250  0.142299  0.711716  0.454782  \n",
       "686  0.140511  0.853211  0.871958  0.175744  0.545873  0.418075  \n",
       "687  0.003028  0.275266  0.755523  0.489252  0.128342  0.622672  \n",
       "688  0.025618  0.596790  0.681584  0.732292  0.825660  0.488552  \n",
       "689  0.001626  0.650187  0.502205  0.292049  0.930102  0.253247  \n",
       "\n",
       "[690 rows x 13 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_target_A[input_params]\n",
    "# scale X_train\n",
    "max_X_train = X_train.max()\n",
    "X_train = X_train / max_X_train\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.007212</td>\n",
       "      <td>0.007237</td>\n",
       "      <td>0.007263</td>\n",
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       "      <td>0.009734</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.007495</td>\n",
       "      <td>0.007492</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.007488</td>\n",
       "      <td>0.007486</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.007484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.007814</td>\n",
       "      <td>0.007768</td>\n",
       "      <td>0.007722</td>\n",
       "      <td>0.007676</td>\n",
       "      <td>0.007630</td>\n",
       "      <td>0.007637</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009284</td>\n",
       "      <td>0.009342</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.009409</td>\n",
       "      <td>0.009442</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>0.009511</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>0.009582</td>\n",
       "      <td>0.009617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>0.011183</td>\n",
       "      <td>0.011175</td>\n",
       "      <td>0.011167</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.011152</td>\n",
       "      <td>0.011145</td>\n",
       "      <td>0.011138</td>\n",
       "      <td>0.011131</td>\n",
       "      <td>0.011149</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012348</td>\n",
       "      <td>0.012421</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>0.012506</td>\n",
       "      <td>0.012549</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>0.012639</td>\n",
       "      <td>0.012685</td>\n",
       "      <td>0.012731</td>\n",
       "      <td>0.012779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015950</td>\n",
       "      <td>0.015956</td>\n",
       "      <td>0.015961</td>\n",
       "      <td>0.015966</td>\n",
       "      <td>0.015974</td>\n",
       "      <td>0.015981</td>\n",
       "      <td>0.015988</td>\n",
       "      <td>0.015995</td>\n",
       "      <td>0.016003</td>\n",
       "      <td>0.016016</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0.941855</td>\n",
       "      <td>0.943366</td>\n",
       "      <td>0.942399</td>\n",
       "      <td>0.941445</td>\n",
       "      <td>0.940489</td>\n",
       "      <td>0.939533</td>\n",
       "      <td>0.938591</td>\n",
       "      <td>0.937660</td>\n",
       "      <td>0.936715</td>\n",
       "      <td>0.935777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.815546</td>\n",
       "      <td>0.810511</td>\n",
       "      <td>0.805485</td>\n",
       "      <td>0.800478</td>\n",
       "      <td>0.795484</td>\n",
       "      <td>0.790519</td>\n",
       "      <td>0.785571</td>\n",
       "      <td>0.780632</td>\n",
       "      <td>0.775709</td>\n",
       "      <td>0.775711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.968933</td>\n",
       "      <td>0.966641</td>\n",
       "      <td>0.961778</td>\n",
       "      <td>0.956888</td>\n",
       "      <td>0.951965</td>\n",
       "      <td>0.947015</td>\n",
       "      <td>0.942026</td>\n",
       "      <td>0.936994</td>\n",
       "      <td>0.931933</td>\n",
       "      <td>0.926850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791442</td>\n",
       "      <td>0.786307</td>\n",
       "      <td>0.781188</td>\n",
       "      <td>0.776086</td>\n",
       "      <td>0.770986</td>\n",
       "      <td>0.765903</td>\n",
       "      <td>0.760830</td>\n",
       "      <td>0.755757</td>\n",
       "      <td>0.750712</td>\n",
       "      <td>0.750714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0.997376</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735618</td>\n",
       "      <td>0.723834</td>\n",
       "      <td>0.712163</td>\n",
       "      <td>0.700597</td>\n",
       "      <td>0.689130</td>\n",
       "      <td>0.677773</td>\n",
       "      <td>0.666503</td>\n",
       "      <td>0.655336</td>\n",
       "      <td>0.644262</td>\n",
       "      <td>0.644261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>0.998681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998694</td>\n",
       "      <td>0.997368</td>\n",
       "      <td>0.996024</td>\n",
       "      <td>0.994670</td>\n",
       "      <td>0.993306</td>\n",
       "      <td>0.991914</td>\n",
       "      <td>0.990504</td>\n",
       "      <td>0.989092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774875</td>\n",
       "      <td>0.769379</td>\n",
       "      <td>0.763893</td>\n",
       "      <td>0.758431</td>\n",
       "      <td>0.752977</td>\n",
       "      <td>0.747541</td>\n",
       "      <td>0.742112</td>\n",
       "      <td>0.736709</td>\n",
       "      <td>0.731328</td>\n",
       "      <td>0.731329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994033</td>\n",
       "      <td>0.985478</td>\n",
       "      <td>0.976948</td>\n",
       "      <td>0.968438</td>\n",
       "      <td>0.959950</td>\n",
       "      <td>0.951474</td>\n",
       "      <td>0.943019</td>\n",
       "      <td>0.934582</td>\n",
       "      <td>0.926152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.548208</td>\n",
       "      <td>0.544110</td>\n",
       "      <td>0.540027</td>\n",
       "      <td>0.535955</td>\n",
       "      <td>0.531902</td>\n",
       "      <td>0.527860</td>\n",
       "      <td>0.523835</td>\n",
       "      <td>0.519834</td>\n",
       "      <td>0.515839</td>\n",
       "      <td>0.515838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.005417  0.005439  0.005448  0.005457  0.005466  0.005475  0.005485   \n",
       "1    0.007030  0.007071  0.007093  0.007116  0.007139  0.007163  0.007187   \n",
       "2    0.007485  0.007501  0.007498  0.007495  0.007492  0.007490  0.007488   \n",
       "3    0.009284  0.009342  0.009375  0.009409  0.009442  0.009477  0.009511   \n",
       "4    0.012348  0.012421  0.012463  0.012506  0.012549  0.012593  0.012639   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "685  0.941855  0.943366  0.942399  0.941445  0.940489  0.939533  0.938591   \n",
       "686  0.968933  0.966641  0.961778  0.956888  0.951965  0.947015  0.942026   \n",
       "687  0.997376  0.999992  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "688  0.998681  1.000000  0.998694  0.997368  0.996024  0.994670  0.993306   \n",
       "689  1.000000  0.994033  0.985478  0.976948  0.968438  0.959950  0.951474   \n",
       "\n",
       "           A8        A9       A10  ...       A71       A72       A73  \\\n",
       "0    0.005495  0.005505  0.005516  ...  0.007110  0.007107  0.007104   \n",
       "1    0.007212  0.007237  0.007263  ...  0.009748  0.009746  0.009744   \n",
       "2    0.007486  0.007485  0.007484  ...  0.008000  0.007953  0.007906   \n",
       "3    0.009546  0.009582  0.009617  ...  0.011191  0.011183  0.011175   \n",
       "4    0.012685  0.012731  0.012779  ...  0.015950  0.015956  0.015961   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "685  0.937660  0.936715  0.935777  ...  0.815546  0.810511  0.805485   \n",
       "686  0.936994  0.931933  0.926850  ...  0.791442  0.786307  0.781188   \n",
       "687  1.000000  1.000000  1.000000  ...  0.735618  0.723834  0.712163   \n",
       "688  0.991914  0.990504  0.989092  ...  0.774875  0.769379  0.763893   \n",
       "689  0.943019  0.934582  0.926152  ...  0.548208  0.544110  0.540027   \n",
       "\n",
       "          A74       A75       A76       A77       A78       A79       A80  \n",
       "0    0.007101  0.007097  0.007094  0.007091  0.007089  0.007085  0.007099  \n",
       "1    0.009742  0.009740  0.009739  0.009737  0.009736  0.009734  0.009752  \n",
       "2    0.007860  0.007814  0.007768  0.007722  0.007676  0.007630  0.007637  \n",
       "3    0.011167  0.011160  0.011152  0.011145  0.011138  0.011131  0.011149  \n",
       "4    0.015966  0.015974  0.015981  0.015988  0.015995  0.016003  0.016016  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "685  0.800478  0.795484  0.790519  0.785571  0.780632  0.775709  0.775711  \n",
       "686  0.776086  0.770986  0.765903  0.760830  0.755757  0.750712  0.750714  \n",
       "687  0.700597  0.689130  0.677773  0.666503  0.655336  0.644262  0.644261  \n",
       "688  0.758431  0.752977  0.747541  0.742112  0.736709  0.731328  0.731329  \n",
       "689  0.535955  0.531902  0.527860  0.523835  0.519834  0.515839  0.515838  \n",
       "\n",
       "[690 rows x 80 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_A = train_target_A.iloc[:, len(input_params):]\n",
    "# scale y_train_A\n",
    "max_y_train_A = y_train_A.max()\n",
    "y_train_A = y_train_A / max_y_train_A\n",
    "y_train_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <th>E</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.018679</td>\n",
       "      <td>0.360173</td>\n",
       "      <td>0.466292</td>\n",
       "      <td>0.352408</td>\n",
       "      <td>0.381084</td>\n",
       "      <td>0.694009</td>\n",
       "      <td>0.491669</td>\n",
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       "      <td>0.025735</td>\n",
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       "      <td>0.880378</td>\n",
       "      <td>0.965078</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.018788</td>\n",
       "      <td>0.719790</td>\n",
       "      <td>0.032890</td>\n",
       "      <td>0.026359</td>\n",
       "      <td>0.020414</td>\n",
       "      <td>0.006100</td>\n",
       "      <td>0.006854</td>\n",
       "      <td>0.018124</td>\n",
       "      <td>0.938942</td>\n",
       "      <td>0.038497</td>\n",
       "      <td>0.155534</td>\n",
       "      <td>0.389847</td>\n",
       "      <td>0.417847</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.025126</td>\n",
       "      <td>0.665978</td>\n",
       "      <td>0.202108</td>\n",
       "      <td>0.107605</td>\n",
       "      <td>0.242705</td>\n",
       "      <td>0.121241</td>\n",
       "      <td>0.105899</td>\n",
       "      <td>0.200516</td>\n",
       "      <td>0.912311</td>\n",
       "      <td>0.798647</td>\n",
       "      <td>0.968925</td>\n",
       "      <td>0.715934</td>\n",
       "      <td>0.141899</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.028204</td>\n",
       "      <td>0.336888</td>\n",
       "      <td>0.409283</td>\n",
       "      <td>0.334017</td>\n",
       "      <td>0.228275</td>\n",
       "      <td>0.649507</td>\n",
       "      <td>0.298041</td>\n",
       "      <td>0.219083</td>\n",
       "      <td>0.063933</td>\n",
       "      <td>0.626713</td>\n",
       "      <td>0.926961</td>\n",
       "      <td>0.330878</td>\n",
       "      <td>0.550075</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>225</th>\n",
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       "      <td>0.863638</td>\n",
       "      <td>0.104482</td>\n",
       "      <td>0.135840</td>\n",
       "      <td>0.018051</td>\n",
       "      <td>0.126131</td>\n",
       "      <td>0.023140</td>\n",
       "      <td>0.039094</td>\n",
       "      <td>0.455852</td>\n",
       "      <td>0.109425</td>\n",
       "      <td>0.350386</td>\n",
       "      <td>0.631457</td>\n",
       "      <td>0.897085</td>\n",
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       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.926902</td>\n",
       "      <td>0.853662</td>\n",
       "      <td>0.051338</td>\n",
       "      <td>0.041312</td>\n",
       "      <td>0.031836</td>\n",
       "      <td>0.054126</td>\n",
       "      <td>0.033461</td>\n",
       "      <td>0.057822</td>\n",
       "      <td>0.808922</td>\n",
       "      <td>0.836620</td>\n",
       "      <td>0.093961</td>\n",
       "      <td>0.718253</td>\n",
       "      <td>0.763929</td>\n",
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       "      <td>0.008394</td>\n",
       "      <td>0.014885</td>\n",
       "      <td>0.010305</td>\n",
       "      <td>0.007510</td>\n",
       "      <td>0.001265</td>\n",
       "      <td>0.487485</td>\n",
       "      <td>0.871916</td>\n",
       "      <td>0.895170</td>\n",
       "      <td>0.351277</td>\n",
       "      <td>0.484065</td>\n",
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       "      <th>228</th>\n",
       "      <td>1.004813</td>\n",
       "      <td>0.843915</td>\n",
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       "      <td>0.033377</td>\n",
       "      <td>0.036635</td>\n",
       "      <td>0.032555</td>\n",
       "      <td>0.032586</td>\n",
       "      <td>0.186215</td>\n",
       "      <td>0.364629</td>\n",
       "      <td>0.702929</td>\n",
       "      <td>0.035355</td>\n",
       "      <td>0.676715</td>\n",
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       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1.032909</td>\n",
       "      <td>0.853954</td>\n",
       "      <td>0.071737</td>\n",
       "      <td>0.111271</td>\n",
       "      <td>0.053330</td>\n",
       "      <td>0.141940</td>\n",
       "      <td>0.112308</td>\n",
       "      <td>0.169237</td>\n",
       "      <td>0.601263</td>\n",
       "      <td>0.396029</td>\n",
       "      <td>0.139562</td>\n",
       "      <td>0.454501</td>\n",
       "      <td>0.876515</td>\n",
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       "</table>\n",
       "<p>230 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D         E         F         G  \\\n",
       "0    0.007523  0.480976  0.759716  0.463687  1.017233  0.790619  0.151370   \n",
       "1    0.018679  0.360173  0.466292  0.352408  0.381084  0.694009  0.491669   \n",
       "2    0.018788  0.719790  0.032890  0.026359  0.020414  0.006100  0.006854   \n",
       "3    0.025126  0.665978  0.202108  0.107605  0.242705  0.121241  0.105899   \n",
       "4    0.028204  0.336888  0.409283  0.334017  0.228275  0.649507  0.298041   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.906787  0.863638  0.104482  0.135840  0.018051  0.126131  0.023140   \n",
       "226  0.926902  0.853662  0.051338  0.041312  0.031836  0.054126  0.033461   \n",
       "227  0.946463  0.885974  0.013861  0.008394  0.014885  0.010305  0.007510   \n",
       "228  1.004813  0.843915  0.054957  0.052099  0.033377  0.036635  0.032555   \n",
       "229  1.032909  0.853954  0.071737  0.111271  0.053330  0.141940  0.112308   \n",
       "\n",
       "            H        p1        p2        p3        p4        p5  \n",
       "0    0.092199  0.920067  0.526934  0.865575  0.576617  0.589161  \n",
       "1    0.241601  0.188677  0.025735  0.694389  0.880378  0.965078  \n",
       "2    0.018124  0.938942  0.038497  0.155534  0.389847  0.417847  \n",
       "3    0.200516  0.912311  0.798647  0.968925  0.715934  0.141899  \n",
       "4    0.219083  0.063933  0.626713  0.926961  0.330878  0.550075  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "225  0.039094  0.455852  0.109425  0.350386  0.631457  0.897085  \n",
       "226  0.057822  0.808922  0.836620  0.093961  0.718253  0.763929  \n",
       "227  0.001265  0.487485  0.871916  0.895170  0.351277  0.484065  \n",
       "228  0.032586  0.186215  0.364629  0.702929  0.035355  0.676715  \n",
       "229  0.169237  0.601263  0.396029  0.139562  0.454501  0.876515  \n",
       "\n",
       "[230 rows x 13 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = test_target_A[input_params]\n",
    "# scale X_test the same way X_train was scaled\n",
    "X_test = X_test / max_X_train\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>...</th>\n",
       "      <th>A71</th>\n",
       "      <th>A72</th>\n",
       "      <th>A73</th>\n",
       "      <th>A74</th>\n",
       "      <th>A75</th>\n",
       "      <th>A76</th>\n",
       "      <th>A77</th>\n",
       "      <th>A78</th>\n",
       "      <th>A79</th>\n",
       "      <th>A80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>0.007175</td>\n",
       "      <td>0.007142</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.006958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006851</td>\n",
       "      <td>0.006862</td>\n",
       "      <td>0.006874</td>\n",
       "      <td>0.006886</td>\n",
       "      <td>0.006898</td>\n",
       "      <td>0.006909</td>\n",
       "      <td>0.006922</td>\n",
       "      <td>0.006935</td>\n",
       "      <td>0.006947</td>\n",
       "      <td>0.006956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.018060</td>\n",
       "      <td>0.018038</td>\n",
       "      <td>0.018016</td>\n",
       "      <td>0.017994</td>\n",
       "      <td>0.017972</td>\n",
       "      <td>0.017951</td>\n",
       "      <td>0.017929</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.017886</td>\n",
       "      <td>0.017865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014952</td>\n",
       "      <td>0.014871</td>\n",
       "      <td>0.014790</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.014631</td>\n",
       "      <td>0.014551</td>\n",
       "      <td>0.014472</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>0.014316</td>\n",
       "      <td>0.014322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.014747</td>\n",
       "      <td>0.013688</td>\n",
       "      <td>0.012674</td>\n",
       "      <td>0.011708</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.009925</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.008346</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001510</td>\n",
       "      <td>0.001443</td>\n",
       "      <td>0.001380</td>\n",
       "      <td>0.001319</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.001101</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.001053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023882</td>\n",
       "      <td>0.023448</td>\n",
       "      <td>0.023021</td>\n",
       "      <td>0.022600</td>\n",
       "      <td>0.022186</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>0.021378</td>\n",
       "      <td>0.020984</td>\n",
       "      <td>0.020595</td>\n",
       "      <td>0.020213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007914</td>\n",
       "      <td>0.007889</td>\n",
       "      <td>0.007865</td>\n",
       "      <td>0.007841</td>\n",
       "      <td>0.007817</td>\n",
       "      <td>0.007793</td>\n",
       "      <td>0.007769</td>\n",
       "      <td>0.007746</td>\n",
       "      <td>0.007722</td>\n",
       "      <td>0.007723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027292</td>\n",
       "      <td>0.027281</td>\n",
       "      <td>0.027270</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>0.027248</td>\n",
       "      <td>0.027237</td>\n",
       "      <td>0.027226</td>\n",
       "      <td>0.027215</td>\n",
       "      <td>0.027204</td>\n",
       "      <td>0.027194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021365</td>\n",
       "      <td>0.021278</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>0.021105</td>\n",
       "      <td>0.021018</td>\n",
       "      <td>0.020933</td>\n",
       "      <td>0.020847</td>\n",
       "      <td>0.020762</td>\n",
       "      <td>0.020677</td>\n",
       "      <td>0.020683</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.873388</td>\n",
       "      <td>0.868957</td>\n",
       "      <td>0.864537</td>\n",
       "      <td>0.860111</td>\n",
       "      <td>0.855689</td>\n",
       "      <td>0.851269</td>\n",
       "      <td>0.846855</td>\n",
       "      <td>0.842436</td>\n",
       "      <td>0.838020</td>\n",
       "      <td>0.833612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600267</td>\n",
       "      <td>0.592330</td>\n",
       "      <td>0.584438</td>\n",
       "      <td>0.576583</td>\n",
       "      <td>0.568773</td>\n",
       "      <td>0.561010</td>\n",
       "      <td>0.553282</td>\n",
       "      <td>0.545612</td>\n",
       "      <td>0.537980</td>\n",
       "      <td>0.537983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.885411</td>\n",
       "      <td>0.873605</td>\n",
       "      <td>0.861860</td>\n",
       "      <td>0.850191</td>\n",
       "      <td>0.838585</td>\n",
       "      <td>0.827061</td>\n",
       "      <td>0.815588</td>\n",
       "      <td>0.804189</td>\n",
       "      <td>0.792859</td>\n",
       "      <td>0.781609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376968</td>\n",
       "      <td>0.369650</td>\n",
       "      <td>0.362413</td>\n",
       "      <td>0.355268</td>\n",
       "      <td>0.348203</td>\n",
       "      <td>0.341228</td>\n",
       "      <td>0.334327</td>\n",
       "      <td>0.327512</td>\n",
       "      <td>0.320783</td>\n",
       "      <td>0.320784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.906209</td>\n",
       "      <td>0.896269</td>\n",
       "      <td>0.886418</td>\n",
       "      <td>0.876653</td>\n",
       "      <td>0.866961</td>\n",
       "      <td>0.857349</td>\n",
       "      <td>0.847811</td>\n",
       "      <td>0.838355</td>\n",
       "      <td>0.828977</td>\n",
       "      <td>0.819667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283028</td>\n",
       "      <td>0.278262</td>\n",
       "      <td>0.273537</td>\n",
       "      <td>0.268883</td>\n",
       "      <td>0.264269</td>\n",
       "      <td>0.259712</td>\n",
       "      <td>0.255200</td>\n",
       "      <td>0.250743</td>\n",
       "      <td>0.246337</td>\n",
       "      <td>0.246338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.969906</td>\n",
       "      <td>0.967113</td>\n",
       "      <td>0.964323</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.958754</td>\n",
       "      <td>0.955970</td>\n",
       "      <td>0.953193</td>\n",
       "      <td>0.950415</td>\n",
       "      <td>0.947641</td>\n",
       "      <td>0.944873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639333</td>\n",
       "      <td>0.630878</td>\n",
       "      <td>0.622467</td>\n",
       "      <td>0.614114</td>\n",
       "      <td>0.605806</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>0.589378</td>\n",
       "      <td>0.581238</td>\n",
       "      <td>0.573178</td>\n",
       "      <td>0.573179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.993294</td>\n",
       "      <td>0.986688</td>\n",
       "      <td>0.980099</td>\n",
       "      <td>0.973519</td>\n",
       "      <td>0.966947</td>\n",
       "      <td>0.960396</td>\n",
       "      <td>0.953863</td>\n",
       "      <td>0.947327</td>\n",
       "      <td>0.940804</td>\n",
       "      <td>0.934300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.685723</td>\n",
       "      <td>0.677157</td>\n",
       "      <td>0.668648</td>\n",
       "      <td>0.660182</td>\n",
       "      <td>0.651756</td>\n",
       "      <td>0.643388</td>\n",
       "      <td>0.635066</td>\n",
       "      <td>0.626794</td>\n",
       "      <td>0.618565</td>\n",
       "      <td>0.618567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.007246  0.007210  0.007175  0.007142  0.007109  0.007077  0.007046   \n",
       "1    0.018060  0.018038  0.018016  0.017994  0.017972  0.017951  0.017929   \n",
       "2    0.017000  0.015852  0.014747  0.013688  0.012674  0.011708  0.010791   \n",
       "3    0.023882  0.023448  0.023021  0.022600  0.022186  0.021779  0.021378   \n",
       "4    0.027292  0.027281  0.027270  0.027259  0.027248  0.027237  0.027226   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.873388  0.868957  0.864537  0.860111  0.855689  0.851269  0.846855   \n",
       "226  0.885411  0.873605  0.861860  0.850191  0.838585  0.827061  0.815588   \n",
       "227  0.906209  0.896269  0.886418  0.876653  0.866961  0.857349  0.847811   \n",
       "228  0.969906  0.967113  0.964323  0.961538  0.958754  0.955970  0.953193   \n",
       "229  0.993294  0.986688  0.980099  0.973519  0.966947  0.960396  0.953863   \n",
       "\n",
       "           A8        A9       A10  ...       A71       A72       A73  \\\n",
       "0    0.007016  0.006987  0.006958  ...  0.006851  0.006862  0.006874   \n",
       "1    0.017908  0.017886  0.017865  ...  0.014952  0.014871  0.014790   \n",
       "2    0.009925  0.009110  0.008346  ...  0.001510  0.001443  0.001380   \n",
       "3    0.020984  0.020595  0.020213  ...  0.007914  0.007889  0.007865   \n",
       "4    0.027215  0.027204  0.027194  ...  0.021365  0.021278  0.021191   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.842436  0.838020  0.833612  ...  0.600267  0.592330  0.584438   \n",
       "226  0.804189  0.792859  0.781609  ...  0.376968  0.369650  0.362413   \n",
       "227  0.838355  0.828977  0.819667  ...  0.283028  0.278262  0.273537   \n",
       "228  0.950415  0.947641  0.944873  ...  0.639333  0.630878  0.622467   \n",
       "229  0.947327  0.940804  0.934300  ...  0.685723  0.677157  0.668648   \n",
       "\n",
       "          A74       A75       A76       A77       A78       A79       A80  \n",
       "0    0.006886  0.006898  0.006909  0.006922  0.006935  0.006947  0.006956  \n",
       "1    0.014710  0.014631  0.014551  0.014472  0.014393  0.014316  0.014322  \n",
       "2    0.001319  0.001260  0.001205  0.001152  0.001101  0.001053  0.001053  \n",
       "3    0.007841  0.007817  0.007793  0.007769  0.007746  0.007722  0.007723  \n",
       "4    0.021105  0.021018  0.020933  0.020847  0.020762  0.020677  0.020683  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.576583  0.568773  0.561010  0.553282  0.545612  0.537980  0.537983  \n",
       "226  0.355268  0.348203  0.341228  0.334327  0.327512  0.320783  0.320784  \n",
       "227  0.268883  0.264269  0.259712  0.255200  0.250743  0.246337  0.246338  \n",
       "228  0.614114  0.605806  0.597561  0.589378  0.581238  0.573178  0.573179  \n",
       "229  0.660182  0.651756  0.643388  0.635066  0.626794  0.618565  0.618567  \n",
       "\n",
       "[230 rows x 80 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_A = test_target_A.iloc[:, len(input_params):]\n",
    "y_test_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now apply a multioutput algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.25346852 0.20156435 0.21988226 0.17279623 0.14846828 0.23597558\n",
      " 0.16959568 0.22835256 0.20292559 0.26815275]\n",
      "0.2101181794822215\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "cv = ShuffleSplit(n_splits=10, test_size=0.25, random_state=57)\n",
    "scores = cross_val_score(model, X_train, y_train_A, cv=cv, scoring='neg_mean_absolute_percentage_error', n_jobs=-1)\n",
    "errors = -scores\n",
    "print(errors)\n",
    "print(errors.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
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       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>...</th>\n",
       "      <th>A71</th>\n",
       "      <th>A72</th>\n",
       "      <th>A73</th>\n",
       "      <th>A74</th>\n",
       "      <th>A75</th>\n",
       "      <th>A76</th>\n",
       "      <th>A77</th>\n",
       "      <th>A78</th>\n",
       "      <th>A79</th>\n",
       "      <th>A80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006891</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>0.006115</td>\n",
       "      <td>0.005730</td>\n",
       "      <td>0.005347</td>\n",
       "      <td>0.004967</td>\n",
       "      <td>0.004588</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.003837</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030477</td>\n",
       "      <td>0.031052</td>\n",
       "      <td>0.031618</td>\n",
       "      <td>0.032173</td>\n",
       "      <td>0.032719</td>\n",
       "      <td>0.033255</td>\n",
       "      <td>0.033780</td>\n",
       "      <td>0.034296</td>\n",
       "      <td>0.034801</td>\n",
       "      <td>0.034810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019652</td>\n",
       "      <td>0.021211</td>\n",
       "      <td>0.022758</td>\n",
       "      <td>0.024294</td>\n",
       "      <td>0.025817</td>\n",
       "      <td>0.027329</td>\n",
       "      <td>0.028829</td>\n",
       "      <td>0.030318</td>\n",
       "      <td>0.031795</td>\n",
       "      <td>0.033260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042582</td>\n",
       "      <td>0.041583</td>\n",
       "      <td>0.040592</td>\n",
       "      <td>0.039609</td>\n",
       "      <td>0.038634</td>\n",
       "      <td>0.037667</td>\n",
       "      <td>0.036709</td>\n",
       "      <td>0.035758</td>\n",
       "      <td>0.034816</td>\n",
       "      <td>0.034822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.014498</td>\n",
       "      <td>0.010851</td>\n",
       "      <td>0.007249</td>\n",
       "      <td>0.003691</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.003295</td>\n",
       "      <td>-0.006725</td>\n",
       "      <td>-0.010113</td>\n",
       "      <td>-0.013459</td>\n",
       "      <td>-0.016765</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052174</td>\n",
       "      <td>-0.052683</td>\n",
       "      <td>-0.053174</td>\n",
       "      <td>-0.053648</td>\n",
       "      <td>-0.054105</td>\n",
       "      <td>-0.054544</td>\n",
       "      <td>-0.054968</td>\n",
       "      <td>-0.055374</td>\n",
       "      <td>-0.055763</td>\n",
       "      <td>-0.055763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021342</td>\n",
       "      <td>0.018394</td>\n",
       "      <td>0.015486</td>\n",
       "      <td>0.012614</td>\n",
       "      <td>0.009777</td>\n",
       "      <td>0.006976</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>-0.003883</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085446</td>\n",
       "      <td>-0.084755</td>\n",
       "      <td>-0.084059</td>\n",
       "      <td>-0.083360</td>\n",
       "      <td>-0.082655</td>\n",
       "      <td>-0.081947</td>\n",
       "      <td>-0.081235</td>\n",
       "      <td>-0.080519</td>\n",
       "      <td>-0.079800</td>\n",
       "      <td>-0.079800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029087</td>\n",
       "      <td>0.030858</td>\n",
       "      <td>0.032614</td>\n",
       "      <td>0.034356</td>\n",
       "      <td>0.036084</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>0.039498</td>\n",
       "      <td>0.041184</td>\n",
       "      <td>0.042856</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043116</td>\n",
       "      <td>0.042981</td>\n",
       "      <td>0.042847</td>\n",
       "      <td>0.042714</td>\n",
       "      <td>0.042582</td>\n",
       "      <td>0.042451</td>\n",
       "      <td>0.042321</td>\n",
       "      <td>0.042192</td>\n",
       "      <td>0.042062</td>\n",
       "      <td>0.042068</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.872249</td>\n",
       "      <td>0.866714</td>\n",
       "      <td>0.861206</td>\n",
       "      <td>0.855725</td>\n",
       "      <td>0.850270</td>\n",
       "      <td>0.844842</td>\n",
       "      <td>0.839442</td>\n",
       "      <td>0.834067</td>\n",
       "      <td>0.828719</td>\n",
       "      <td>0.823398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.554449</td>\n",
       "      <td>0.549374</td>\n",
       "      <td>0.544335</td>\n",
       "      <td>0.539334</td>\n",
       "      <td>0.534370</td>\n",
       "      <td>0.529443</td>\n",
       "      <td>0.524552</td>\n",
       "      <td>0.519698</td>\n",
       "      <td>0.514883</td>\n",
       "      <td>0.514886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.889980</td>\n",
       "      <td>0.882714</td>\n",
       "      <td>0.875487</td>\n",
       "      <td>0.868297</td>\n",
       "      <td>0.861146</td>\n",
       "      <td>0.854033</td>\n",
       "      <td>0.846960</td>\n",
       "      <td>0.839924</td>\n",
       "      <td>0.832927</td>\n",
       "      <td>0.825968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.514757</td>\n",
       "      <td>0.509884</td>\n",
       "      <td>0.505048</td>\n",
       "      <td>0.500249</td>\n",
       "      <td>0.495488</td>\n",
       "      <td>0.490764</td>\n",
       "      <td>0.486078</td>\n",
       "      <td>0.481429</td>\n",
       "      <td>0.476818</td>\n",
       "      <td>0.476818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.909943</td>\n",
       "      <td>0.903698</td>\n",
       "      <td>0.897486</td>\n",
       "      <td>0.891307</td>\n",
       "      <td>0.885159</td>\n",
       "      <td>0.879044</td>\n",
       "      <td>0.872962</td>\n",
       "      <td>0.866910</td>\n",
       "      <td>0.860891</td>\n",
       "      <td>0.854904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.526946</td>\n",
       "      <td>0.522686</td>\n",
       "      <td>0.518457</td>\n",
       "      <td>0.514259</td>\n",
       "      <td>0.510094</td>\n",
       "      <td>0.505960</td>\n",
       "      <td>0.501858</td>\n",
       "      <td>0.497787</td>\n",
       "      <td>0.493749</td>\n",
       "      <td>0.493748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.967548</td>\n",
       "      <td>0.962412</td>\n",
       "      <td>0.957295</td>\n",
       "      <td>0.952198</td>\n",
       "      <td>0.947121</td>\n",
       "      <td>0.942063</td>\n",
       "      <td>0.937027</td>\n",
       "      <td>0.932009</td>\n",
       "      <td>0.927011</td>\n",
       "      <td>0.922034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.625969</td>\n",
       "      <td>0.620853</td>\n",
       "      <td>0.615772</td>\n",
       "      <td>0.610726</td>\n",
       "      <td>0.605716</td>\n",
       "      <td>0.600742</td>\n",
       "      <td>0.595802</td>\n",
       "      <td>0.590898</td>\n",
       "      <td>0.586030</td>\n",
       "      <td>0.586031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.993329</td>\n",
       "      <td>0.986782</td>\n",
       "      <td>0.980262</td>\n",
       "      <td>0.973771</td>\n",
       "      <td>0.967306</td>\n",
       "      <td>0.960869</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>0.948080</td>\n",
       "      <td>0.941727</td>\n",
       "      <td>0.935403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637890</td>\n",
       "      <td>0.632332</td>\n",
       "      <td>0.626811</td>\n",
       "      <td>0.621327</td>\n",
       "      <td>0.615882</td>\n",
       "      <td>0.610475</td>\n",
       "      <td>0.605105</td>\n",
       "      <td>0.599773</td>\n",
       "      <td>0.594481</td>\n",
       "      <td>0.594483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.006891  0.006502  0.006115  0.005730  0.005347  0.004967  0.004588   \n",
       "1    0.019652  0.021211  0.022758  0.024294  0.025817  0.027329  0.028829   \n",
       "2    0.014498  0.010851  0.007249  0.003691  0.000176 -0.003295 -0.006725   \n",
       "3    0.021342  0.018394  0.015486  0.012614  0.009777  0.006976  0.004210   \n",
       "4    0.029087  0.030858  0.032614  0.034356  0.036084  0.037798  0.039498   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.872249  0.866714  0.861206  0.855725  0.850270  0.844842  0.839442   \n",
       "226  0.889980  0.882714  0.875487  0.868297  0.861146  0.854033  0.846960   \n",
       "227  0.909943  0.903698  0.897486  0.891307  0.885159  0.879044  0.872962   \n",
       "228  0.967548  0.962412  0.957295  0.952198  0.947121  0.942063  0.937027   \n",
       "229  0.993329  0.986782  0.980262  0.973771  0.967306  0.960869  0.954461   \n",
       "\n",
       "           A8        A9       A10  ...       A71       A72       A73  \\\n",
       "0    0.004212  0.003837  0.003464  ...  0.030477  0.031052  0.031618   \n",
       "1    0.030318  0.031795  0.033260  ...  0.042582  0.041583  0.040592   \n",
       "2   -0.010113 -0.013459 -0.016765  ... -0.052174 -0.052683 -0.053174   \n",
       "3    0.001478 -0.001219 -0.003883  ... -0.085446 -0.084755 -0.084059   \n",
       "4    0.041184  0.042856  0.044514  ...  0.043116  0.042981  0.042847   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.834067  0.828719  0.823398  ...  0.554449  0.549374  0.544335   \n",
       "226  0.839924  0.832927  0.825968  ...  0.514757  0.509884  0.505048   \n",
       "227  0.866910  0.860891  0.854904  ...  0.526946  0.522686  0.518457   \n",
       "228  0.932009  0.927011  0.922034  ...  0.625969  0.620853  0.615772   \n",
       "229  0.948080  0.941727  0.935403  ...  0.637890  0.632332  0.626811   \n",
       "\n",
       "          A74       A75       A76       A77       A78       A79       A80  \n",
       "0    0.032173  0.032719  0.033255  0.033780  0.034296  0.034801  0.034810  \n",
       "1    0.039609  0.038634  0.037667  0.036709  0.035758  0.034816  0.034822  \n",
       "2   -0.053648 -0.054105 -0.054544 -0.054968 -0.055374 -0.055763 -0.055763  \n",
       "3   -0.083360 -0.082655 -0.081947 -0.081235 -0.080519 -0.079800 -0.079800  \n",
       "4    0.042714  0.042582  0.042451  0.042321  0.042192  0.042062  0.042068  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.539334  0.534370  0.529443  0.524552  0.519698  0.514883  0.514886  \n",
       "226  0.500249  0.495488  0.490764  0.486078  0.481429  0.476818  0.476818  \n",
       "227  0.514259  0.510094  0.505960  0.501858  0.497787  0.493749  0.493748  \n",
       "228  0.610726  0.605716  0.600742  0.595802  0.590898  0.586030  0.586031  \n",
       "229  0.621327  0.615882  0.610475  0.605105  0.599773  0.594481  0.594483  \n",
       "\n",
       "[230 rows x 80 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train_A)\n",
    "y_pred_A = model.predict(X_test)\n",
    "y_pred_A = pd.DataFrame(y_pred_A, columns=y_train_A.columns) * max_y_train_A\n",
    "y_pred_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE=0.3392\n"
     ]
    }
   ],
   "source": [
    "MAPE = mean_absolute_percentage_error(y_test_A, y_pred_A)\n",
    "print(f\"MAPE={MAPE:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treating all isotopes at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005417</td>\n",
       "      <td>0.005439</td>\n",
       "      <td>0.005448</td>\n",
       "      <td>0.005457</td>\n",
       "      <td>0.005466</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.005505</td>\n",
       "      <td>0.005516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791647</td>\n",
       "      <td>0.792379</td>\n",
       "      <td>0.793139</td>\n",
       "      <td>0.793866</td>\n",
       "      <td>0.794622</td>\n",
       "      <td>0.795352</td>\n",
       "      <td>0.796098</td>\n",
       "      <td>0.796821</td>\n",
       "      <td>0.797535</td>\n",
       "      <td>0.797532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007071</td>\n",
       "      <td>0.007093</td>\n",
       "      <td>0.007116</td>\n",
       "      <td>0.007139</td>\n",
       "      <td>0.007163</td>\n",
       "      <td>0.007187</td>\n",
       "      <td>0.007212</td>\n",
       "      <td>0.007237</td>\n",
       "      <td>0.007263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891521</td>\n",
       "      <td>0.892401</td>\n",
       "      <td>0.893314</td>\n",
       "      <td>0.894185</td>\n",
       "      <td>0.895065</td>\n",
       "      <td>0.895927</td>\n",
       "      <td>0.896823</td>\n",
       "      <td>0.897639</td>\n",
       "      <td>0.898526</td>\n",
       "      <td>0.898522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.007501</td>\n",
       "      <td>0.007498</td>\n",
       "      <td>0.007495</td>\n",
       "      <td>0.007492</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.007488</td>\n",
       "      <td>0.007486</td>\n",
       "      <td>0.007485</td>\n",
       "      <td>0.007484</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866115</td>\n",
       "      <td>0.867118</td>\n",
       "      <td>0.867951</td>\n",
       "      <td>0.868897</td>\n",
       "      <td>0.869789</td>\n",
       "      <td>0.870677</td>\n",
       "      <td>0.871363</td>\n",
       "      <td>0.872320</td>\n",
       "      <td>0.873218</td>\n",
       "      <td>0.879094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.009284</td>\n",
       "      <td>0.009342</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.009409</td>\n",
       "      <td>0.009442</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>0.009511</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>0.009582</td>\n",
       "      <td>0.009617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308313</td>\n",
       "      <td>0.308591</td>\n",
       "      <td>0.308891</td>\n",
       "      <td>0.309178</td>\n",
       "      <td>0.309460</td>\n",
       "      <td>0.309742</td>\n",
       "      <td>0.310033</td>\n",
       "      <td>0.310309</td>\n",
       "      <td>0.310587</td>\n",
       "      <td>0.310586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.012348</td>\n",
       "      <td>0.012421</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>0.012506</td>\n",
       "      <td>0.012549</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>0.012639</td>\n",
       "      <td>0.012685</td>\n",
       "      <td>0.012731</td>\n",
       "      <td>0.012779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.840706</td>\n",
       "      <td>0.841477</td>\n",
       "      <td>0.842276</td>\n",
       "      <td>0.843048</td>\n",
       "      <td>0.843833</td>\n",
       "      <td>0.844618</td>\n",
       "      <td>0.845397</td>\n",
       "      <td>0.846158</td>\n",
       "      <td>0.846919</td>\n",
       "      <td>0.846917</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0.941855</td>\n",
       "      <td>0.943366</td>\n",
       "      <td>0.942399</td>\n",
       "      <td>0.941445</td>\n",
       "      <td>0.940489</td>\n",
       "      <td>0.939533</td>\n",
       "      <td>0.938591</td>\n",
       "      <td>0.937660</td>\n",
       "      <td>0.936715</td>\n",
       "      <td>0.935777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.464552</td>\n",
       "      <td>0.464854</td>\n",
       "      <td>0.465288</td>\n",
       "      <td>0.465636</td>\n",
       "      <td>0.465994</td>\n",
       "      <td>0.466309</td>\n",
       "      <td>0.466683</td>\n",
       "      <td>0.467043</td>\n",
       "      <td>0.467328</td>\n",
       "      <td>0.470475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.968933</td>\n",
       "      <td>0.966641</td>\n",
       "      <td>0.961778</td>\n",
       "      <td>0.956888</td>\n",
       "      <td>0.951965</td>\n",
       "      <td>0.947015</td>\n",
       "      <td>0.942026</td>\n",
       "      <td>0.936994</td>\n",
       "      <td>0.931933</td>\n",
       "      <td>0.926850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430151</td>\n",
       "      <td>0.430507</td>\n",
       "      <td>0.430812</td>\n",
       "      <td>0.431161</td>\n",
       "      <td>0.431496</td>\n",
       "      <td>0.431834</td>\n",
       "      <td>0.432148</td>\n",
       "      <td>0.432508</td>\n",
       "      <td>0.432802</td>\n",
       "      <td>0.435715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0.997376</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.662031</td>\n",
       "      <td>0.662120</td>\n",
       "      <td>0.662163</td>\n",
       "      <td>0.662208</td>\n",
       "      <td>0.662279</td>\n",
       "      <td>0.662278</td>\n",
       "      <td>0.662345</td>\n",
       "      <td>0.662416</td>\n",
       "      <td>0.662534</td>\n",
       "      <td>0.662535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>0.998681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998694</td>\n",
       "      <td>0.997368</td>\n",
       "      <td>0.996024</td>\n",
       "      <td>0.994670</td>\n",
       "      <td>0.993306</td>\n",
       "      <td>0.991914</td>\n",
       "      <td>0.990504</td>\n",
       "      <td>0.989092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499401</td>\n",
       "      <td>0.499734</td>\n",
       "      <td>0.500127</td>\n",
       "      <td>0.500505</td>\n",
       "      <td>0.500890</td>\n",
       "      <td>0.501301</td>\n",
       "      <td>0.501699</td>\n",
       "      <td>0.502053</td>\n",
       "      <td>0.502394</td>\n",
       "      <td>0.502394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994033</td>\n",
       "      <td>0.985478</td>\n",
       "      <td>0.976948</td>\n",
       "      <td>0.968438</td>\n",
       "      <td>0.959950</td>\n",
       "      <td>0.951474</td>\n",
       "      <td>0.943019</td>\n",
       "      <td>0.934582</td>\n",
       "      <td>0.926152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267516</td>\n",
       "      <td>0.267660</td>\n",
       "      <td>0.267818</td>\n",
       "      <td>0.267979</td>\n",
       "      <td>0.268112</td>\n",
       "      <td>0.268263</td>\n",
       "      <td>0.268408</td>\n",
       "      <td>0.268515</td>\n",
       "      <td>0.268661</td>\n",
       "      <td>0.268661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 2080 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.005417  0.005439  0.005448  0.005457  0.005466  0.005475  0.005485   \n",
       "1    0.007030  0.007071  0.007093  0.007116  0.007139  0.007163  0.007187   \n",
       "2    0.007485  0.007501  0.007498  0.007495  0.007492  0.007490  0.007488   \n",
       "3    0.009284  0.009342  0.009375  0.009409  0.009442  0.009477  0.009511   \n",
       "4    0.012348  0.012421  0.012463  0.012506  0.012549  0.012593  0.012639   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "685  0.941855  0.943366  0.942399  0.941445  0.940489  0.939533  0.938591   \n",
       "686  0.968933  0.966641  0.961778  0.956888  0.951965  0.947015  0.942026   \n",
       "687  0.997376  0.999992  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "688  0.998681  1.000000  0.998694  0.997368  0.996024  0.994670  0.993306   \n",
       "689  1.000000  0.994033  0.985478  0.976948  0.968438  0.959950  0.951474   \n",
       "\n",
       "           A8        A9       A10  ...       Z71       Z72       Z73  \\\n",
       "0    0.005495  0.005505  0.005516  ...  0.791647  0.792379  0.793139   \n",
       "1    0.007212  0.007237  0.007263  ...  0.891521  0.892401  0.893314   \n",
       "2    0.007486  0.007485  0.007484  ...  0.866115  0.867118  0.867951   \n",
       "3    0.009546  0.009582  0.009617  ...  0.308313  0.308591  0.308891   \n",
       "4    0.012685  0.012731  0.012779  ...  0.840706  0.841477  0.842276   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "685  0.937660  0.936715  0.935777  ...  0.464552  0.464854  0.465288   \n",
       "686  0.936994  0.931933  0.926850  ...  0.430151  0.430507  0.430812   \n",
       "687  1.000000  1.000000  1.000000  ...  0.662031  0.662120  0.662163   \n",
       "688  0.991914  0.990504  0.989092  ...  0.499401  0.499734  0.500127   \n",
       "689  0.943019  0.934582  0.926152  ...  0.267516  0.267660  0.267818   \n",
       "\n",
       "          Z74       Z75       Z76       Z77       Z78       Z79       Z80  \n",
       "0    0.793866  0.794622  0.795352  0.796098  0.796821  0.797535  0.797532  \n",
       "1    0.894185  0.895065  0.895927  0.896823  0.897639  0.898526  0.898522  \n",
       "2    0.868897  0.869789  0.870677  0.871363  0.872320  0.873218  0.879094  \n",
       "3    0.309178  0.309460  0.309742  0.310033  0.310309  0.310587  0.310586  \n",
       "4    0.843048  0.843833  0.844618  0.845397  0.846158  0.846919  0.846917  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "685  0.465636  0.465994  0.466309  0.466683  0.467043  0.467328  0.470475  \n",
       "686  0.431161  0.431496  0.431834  0.432148  0.432508  0.432802  0.435715  \n",
       "687  0.662208  0.662279  0.662278  0.662345  0.662416  0.662534  0.662535  \n",
       "688  0.500505  0.500890  0.501301  0.501699  0.502053  0.502394  0.502394  \n",
       "689  0.267979  0.268112  0.268263  0.268408  0.268515  0.268661  0.268661  \n",
       "\n",
       "[690 rows x 2080 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_all = []\n",
    "for i in alphabet:\n",
    "    y_train_all.append(\n",
    "        train_data.groupby(input_params)['Y_'+i].apply(list).apply(pd.Series).rename(\n",
    "        columns=lambda x: i + str(x + 1)).reset_index().iloc[:, len(input_params):]\n",
    "    )\n",
    "    \n",
    "y_train_all = pd.concat(y_train_all, axis=1)\n",
    "# scale y_train_all\n",
    "max_y_train_all = y_train_all.max()\n",
    "y_train_all = y_train_all / max_y_train_all\n",
    "y_train_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>...</th>\n",
       "      <th>Z71</th>\n",
       "      <th>Z72</th>\n",
       "      <th>Z73</th>\n",
       "      <th>Z74</th>\n",
       "      <th>Z75</th>\n",
       "      <th>Z76</th>\n",
       "      <th>Z77</th>\n",
       "      <th>Z78</th>\n",
       "      <th>Z79</th>\n",
       "      <th>Z80</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007210</td>\n",
       "      <td>0.007175</td>\n",
       "      <td>0.007142</td>\n",
       "      <td>0.007109</td>\n",
       "      <td>0.007077</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.006958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.018060</td>\n",
       "      <td>0.018038</td>\n",
       "      <td>0.018016</td>\n",
       "      <td>0.017994</td>\n",
       "      <td>0.017972</td>\n",
       "      <td>0.017951</td>\n",
       "      <td>0.017929</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.017886</td>\n",
       "      <td>0.017865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.015852</td>\n",
       "      <td>0.014747</td>\n",
       "      <td>0.013688</td>\n",
       "      <td>0.012674</td>\n",
       "      <td>0.011708</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.009925</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.008346</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.023882</td>\n",
       "      <td>0.023448</td>\n",
       "      <td>0.023021</td>\n",
       "      <td>0.022600</td>\n",
       "      <td>0.022186</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>0.021378</td>\n",
       "      <td>0.020984</td>\n",
       "      <td>0.020595</td>\n",
       "      <td>0.020213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.027292</td>\n",
       "      <td>0.027281</td>\n",
       "      <td>0.027270</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>0.027248</td>\n",
       "      <td>0.027237</td>\n",
       "      <td>0.027226</td>\n",
       "      <td>0.027215</td>\n",
       "      <td>0.027204</td>\n",
       "      <td>0.027194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000009</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.873388</td>\n",
       "      <td>0.868957</td>\n",
       "      <td>0.864537</td>\n",
       "      <td>0.860111</td>\n",
       "      <td>0.855689</td>\n",
       "      <td>0.851269</td>\n",
       "      <td>0.846855</td>\n",
       "      <td>0.842436</td>\n",
       "      <td>0.838020</td>\n",
       "      <td>0.833612</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000541</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.885411</td>\n",
       "      <td>0.873605</td>\n",
       "      <td>0.861860</td>\n",
       "      <td>0.850191</td>\n",
       "      <td>0.838585</td>\n",
       "      <td>0.827061</td>\n",
       "      <td>0.815588</td>\n",
       "      <td>0.804189</td>\n",
       "      <td>0.792859</td>\n",
       "      <td>0.781609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.906209</td>\n",
       "      <td>0.896269</td>\n",
       "      <td>0.886418</td>\n",
       "      <td>0.876653</td>\n",
       "      <td>0.866961</td>\n",
       "      <td>0.857349</td>\n",
       "      <td>0.847811</td>\n",
       "      <td>0.838355</td>\n",
       "      <td>0.828977</td>\n",
       "      <td>0.819667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.969906</td>\n",
       "      <td>0.967113</td>\n",
       "      <td>0.964323</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.958754</td>\n",
       "      <td>0.955970</td>\n",
       "      <td>0.953193</td>\n",
       "      <td>0.950415</td>\n",
       "      <td>0.947641</td>\n",
       "      <td>0.944873</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.993294</td>\n",
       "      <td>0.986688</td>\n",
       "      <td>0.980099</td>\n",
       "      <td>0.973519</td>\n",
       "      <td>0.966947</td>\n",
       "      <td>0.960396</td>\n",
       "      <td>0.953863</td>\n",
       "      <td>0.947327</td>\n",
       "      <td>0.940804</td>\n",
       "      <td>0.934300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 2080 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.007246  0.007210  0.007175  0.007142  0.007109  0.007077  0.007046   \n",
       "1    0.018060  0.018038  0.018016  0.017994  0.017972  0.017951  0.017929   \n",
       "2    0.017000  0.015852  0.014747  0.013688  0.012674  0.011708  0.010791   \n",
       "3    0.023882  0.023448  0.023021  0.022600  0.022186  0.021779  0.021378   \n",
       "4    0.027292  0.027281  0.027270  0.027259  0.027248  0.027237  0.027226   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.873388  0.868957  0.864537  0.860111  0.855689  0.851269  0.846855   \n",
       "226  0.885411  0.873605  0.861860  0.850191  0.838585  0.827061  0.815588   \n",
       "227  0.906209  0.896269  0.886418  0.876653  0.866961  0.857349  0.847811   \n",
       "228  0.969906  0.967113  0.964323  0.961538  0.958754  0.955970  0.953193   \n",
       "229  0.993294  0.986688  0.980099  0.973519  0.966947  0.960396  0.953863   \n",
       "\n",
       "           A8        A9       A10  ...       Z71       Z72       Z73  \\\n",
       "0    0.007016  0.006987  0.006958  ...  0.000334  0.000334  0.000334   \n",
       "1    0.017908  0.017886  0.017865  ...  0.000548  0.000548  0.000548   \n",
       "2    0.009925  0.009110  0.008346  ...  0.000239  0.000239  0.000239   \n",
       "3    0.020984  0.020595  0.020213  ...  0.000081  0.000081  0.000081   \n",
       "4    0.027215  0.027204  0.027194  ...  0.000319  0.000319  0.000319   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.842436  0.838020  0.833612  ...  0.000542  0.000542  0.000542   \n",
       "226  0.804189  0.792859  0.781609  ...  0.000467  0.000467  0.000466   \n",
       "227  0.838355  0.828977  0.819667  ...  0.000295  0.000295  0.000295   \n",
       "228  0.950415  0.947641  0.944873  ...  0.000420  0.000420  0.000420   \n",
       "229  0.947327  0.940804  0.934300  ...  0.000534  0.000534  0.000534   \n",
       "\n",
       "          Z74       Z75       Z76       Z77       Z78       Z79       Z80  \n",
       "0    0.000334  0.000334  0.000334  0.000334  0.000334  0.000334  0.000010  \n",
       "1    0.000548  0.000548  0.000548  0.000548  0.000548  0.000548  0.000016  \n",
       "2    0.000239  0.000239  0.000239  0.000239  0.000239  0.000239  0.000007  \n",
       "3    0.000081  0.000081  0.000081  0.000081  0.000081  0.000081  0.000002  \n",
       "4    0.000319  0.000319  0.000319  0.000319  0.000319  0.000319  0.000009  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.000541  0.000541  0.000541  0.000541  0.000541  0.000541  0.000016  \n",
       "226  0.000466  0.000466  0.000465  0.000465  0.000465  0.000464  0.000014  \n",
       "227  0.000294  0.000294  0.000294  0.000294  0.000294  0.000293  0.000009  \n",
       "228  0.000420  0.000420  0.000419  0.000419  0.000419  0.000419  0.000012  \n",
       "229  0.000533  0.000533  0.000533  0.000533  0.000533  0.000532  0.000016  \n",
       "\n",
       "[230 rows x 2080 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_all = []\n",
    "for i in alphabet:\n",
    "    y_test_all.append(\n",
    "        test_data.groupby(input_params)['Y_'+i].apply(list).apply(pd.Series).rename(\n",
    "        columns=lambda x: i + str(x + 1)).reset_index().iloc[:, len(input_params):]\n",
    "    )\n",
    "    \n",
    "y_test_all = pd.concat(y_test_all, axis=1)\n",
    "y_test_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5278524309265932"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "cv = ShuffleSplit(n_splits=10, test_size=0.25, random_state=57)\n",
    "scores = cross_val_score(model, X_train, y_train_all, cv=cv, scoring='neg_mean_absolute_percentage_error', n_jobs=-1)\n",
    "errors = -scores\n",
    "errors.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
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       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>...</th>\n",
       "      <th>Z71</th>\n",
       "      <th>Z72</th>\n",
       "      <th>Z73</th>\n",
       "      <th>Z74</th>\n",
       "      <th>Z75</th>\n",
       "      <th>Z76</th>\n",
       "      <th>Z77</th>\n",
       "      <th>Z78</th>\n",
       "      <th>Z79</th>\n",
       "      <th>Z80</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006891</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>0.006115</td>\n",
       "      <td>0.005730</td>\n",
       "      <td>0.005347</td>\n",
       "      <td>0.004967</td>\n",
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       "      <td>0.004212</td>\n",
       "      <td>0.003837</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000010</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019652</td>\n",
       "      <td>0.021211</td>\n",
       "      <td>0.022758</td>\n",
       "      <td>0.024294</td>\n",
       "      <td>0.025817</td>\n",
       "      <td>0.027329</td>\n",
       "      <td>0.028829</td>\n",
       "      <td>0.030318</td>\n",
       "      <td>0.031795</td>\n",
       "      <td>0.033260</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>0.000016</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.014498</td>\n",
       "      <td>0.010851</td>\n",
       "      <td>0.007249</td>\n",
       "      <td>0.003691</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.003295</td>\n",
       "      <td>-0.006725</td>\n",
       "      <td>-0.010113</td>\n",
       "      <td>-0.013459</td>\n",
       "      <td>-0.016765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021342</td>\n",
       "      <td>0.018394</td>\n",
       "      <td>0.015486</td>\n",
       "      <td>0.012614</td>\n",
       "      <td>0.009777</td>\n",
       "      <td>0.006976</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>-0.003883</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029087</td>\n",
       "      <td>0.030858</td>\n",
       "      <td>0.032614</td>\n",
       "      <td>0.034356</td>\n",
       "      <td>0.036084</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>0.039498</td>\n",
       "      <td>0.041184</td>\n",
       "      <td>0.042856</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000313</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
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       "      <td>0.861206</td>\n",
       "      <td>0.855725</td>\n",
       "      <td>0.850270</td>\n",
       "      <td>0.844842</td>\n",
       "      <td>0.839442</td>\n",
       "      <td>0.834067</td>\n",
       "      <td>0.828719</td>\n",
       "      <td>0.823398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000530</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.889980</td>\n",
       "      <td>0.882714</td>\n",
       "      <td>0.875487</td>\n",
       "      <td>0.868297</td>\n",
       "      <td>0.861146</td>\n",
       "      <td>0.854033</td>\n",
       "      <td>0.846960</td>\n",
       "      <td>0.839924</td>\n",
       "      <td>0.832927</td>\n",
       "      <td>0.825968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.909943</td>\n",
       "      <td>0.903698</td>\n",
       "      <td>0.897486</td>\n",
       "      <td>0.891307</td>\n",
       "      <td>0.885159</td>\n",
       "      <td>0.879044</td>\n",
       "      <td>0.872962</td>\n",
       "      <td>0.866910</td>\n",
       "      <td>0.860891</td>\n",
       "      <td>0.854904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.967548</td>\n",
       "      <td>0.962412</td>\n",
       "      <td>0.957295</td>\n",
       "      <td>0.952198</td>\n",
       "      <td>0.947121</td>\n",
       "      <td>0.942063</td>\n",
       "      <td>0.937027</td>\n",
       "      <td>0.932009</td>\n",
       "      <td>0.927011</td>\n",
       "      <td>0.922034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.993329</td>\n",
       "      <td>0.986782</td>\n",
       "      <td>0.980262</td>\n",
       "      <td>0.973771</td>\n",
       "      <td>0.967306</td>\n",
       "      <td>0.960869</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>0.948080</td>\n",
       "      <td>0.941727</td>\n",
       "      <td>0.935403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 2080 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.006891  0.006502  0.006115  0.005730  0.005347  0.004967  0.004588   \n",
       "1    0.019652  0.021211  0.022758  0.024294  0.025817  0.027329  0.028829   \n",
       "2    0.014498  0.010851  0.007249  0.003691  0.000176 -0.003295 -0.006725   \n",
       "3    0.021342  0.018394  0.015486  0.012614  0.009777  0.006976  0.004210   \n",
       "4    0.029087  0.030858  0.032614  0.034356  0.036084  0.037798  0.039498   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.872249  0.866714  0.861206  0.855725  0.850270  0.844842  0.839442   \n",
       "226  0.889980  0.882714  0.875487  0.868297  0.861146  0.854033  0.846960   \n",
       "227  0.909943  0.903698  0.897486  0.891307  0.885159  0.879044  0.872962   \n",
       "228  0.967548  0.962412  0.957295  0.952198  0.947121  0.942063  0.937027   \n",
       "229  0.993329  0.986782  0.980262  0.973771  0.967306  0.960869  0.954461   \n",
       "\n",
       "           A8        A9       A10  ...       Z71       Z72       Z73  \\\n",
       "0    0.004212  0.003837  0.003464  ...  0.000333  0.000333  0.000333   \n",
       "1    0.030318  0.031795  0.033260  ...  0.000552  0.000552  0.000552   \n",
       "2   -0.010113 -0.013459 -0.016765  ...  0.000241  0.000241  0.000241   \n",
       "3    0.001478 -0.001219 -0.003883  ...  0.000079  0.000079  0.000079   \n",
       "4    0.041184  0.042856  0.044514  ...  0.000313  0.000314  0.000314   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.834067  0.828719  0.823398  ...  0.000530  0.000530  0.000530   \n",
       "226  0.839924  0.832927  0.825968  ...  0.000455  0.000455  0.000455   \n",
       "227  0.866910  0.860891  0.854904  ...  0.000294  0.000294  0.000293   \n",
       "228  0.932009  0.927011  0.922034  ...  0.000406  0.000406  0.000405   \n",
       "229  0.948080  0.941727  0.935403  ...  0.000521  0.000521  0.000521   \n",
       "\n",
       "          Z74       Z75       Z76       Z77       Z78       Z79       Z80  \n",
       "0    0.000333  0.000333  0.000333  0.000333  0.000333  0.000333  0.000010  \n",
       "1    0.000552  0.000552  0.000552  0.000552  0.000552  0.000552  0.000016  \n",
       "2    0.000241  0.000240  0.000240  0.000240  0.000240  0.000240  0.000007  \n",
       "3    0.000079  0.000079  0.000079  0.000079  0.000079  0.000079  0.000002  \n",
       "4    0.000314  0.000314  0.000314  0.000314  0.000313  0.000313  0.000009  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.000530  0.000530  0.000530  0.000529  0.000529  0.000529  0.000016  \n",
       "226  0.000454  0.000454  0.000454  0.000454  0.000454  0.000454  0.000013  \n",
       "227  0.000293  0.000293  0.000293  0.000293  0.000293  0.000293  0.000009  \n",
       "228  0.000405  0.000405  0.000405  0.000405  0.000405  0.000405  0.000012  \n",
       "229  0.000521  0.000520  0.000520  0.000520  0.000520  0.000520  0.000015  \n",
       "\n",
       "[230 rows x 2080 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train_all)\n",
    "y_pred_all = model.predict(X_test)\n",
    "y_pred_all = pd.DataFrame(y_pred_all, columns=y_train_all.columns) * max_y_train_all\n",
    "y_pred_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE=1.3653\n"
     ]
    }
   ],
   "source": [
    "MAPE = mean_absolute_percentage_error(y_test_all, y_pred_all)\n",
    "print(f\"MAPE={MAPE:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A proposed machine learning algorithm should at least beat this number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A naive MLP attempt\n",
    "\n",
    "Let's see now if one can do better with a multilayer perceptron (MLP)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "model = MLPRegressor(\n",
    "    solver=\"adam\", hidden_layer_sizes=(100,100,100), max_iter=300,\n",
    "    batch_size=100, random_state=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(batch_size=100, hidden_layer_sizes=(100, 100, 100), max_iter=300,\n",
       "             random_state=57)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>...</th>\n",
       "      <th>Z71</th>\n",
       "      <th>Z72</th>\n",
       "      <th>Z73</th>\n",
       "      <th>Z74</th>\n",
       "      <th>Z75</th>\n",
       "      <th>Z76</th>\n",
       "      <th>Z77</th>\n",
       "      <th>Z78</th>\n",
       "      <th>Z79</th>\n",
       "      <th>Z80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.025002</td>\n",
       "      <td>0.047413</td>\n",
       "      <td>0.047657</td>\n",
       "      <td>0.031935</td>\n",
       "      <td>0.033642</td>\n",
       "      <td>0.056091</td>\n",
       "      <td>0.020563</td>\n",
       "      <td>0.011744</td>\n",
       "      <td>0.034383</td>\n",
       "      <td>0.037488</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.064956</td>\n",
       "      <td>0.049174</td>\n",
       "      <td>0.059578</td>\n",
       "      <td>0.048637</td>\n",
       "      <td>0.052147</td>\n",
       "      <td>0.059152</td>\n",
       "      <td>0.078862</td>\n",
       "      <td>0.060323</td>\n",
       "      <td>0.062352</td>\n",
       "      <td>0.063733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.080815</td>\n",
       "      <td>0.058416</td>\n",
       "      <td>0.070292</td>\n",
       "      <td>0.039943</td>\n",
       "      <td>0.060017</td>\n",
       "      <td>0.050410</td>\n",
       "      <td>0.044948</td>\n",
       "      <td>0.029302</td>\n",
       "      <td>0.047457</td>\n",
       "      <td>0.049800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.042448</td>\n",
       "      <td>0.044963</td>\n",
       "      <td>0.028303</td>\n",
       "      <td>0.015428</td>\n",
       "      <td>0.024725</td>\n",
       "      <td>0.014375</td>\n",
       "      <td>0.022633</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.025022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.057138</td>\n",
       "      <td>0.046485</td>\n",
       "      <td>0.061710</td>\n",
       "      <td>0.055913</td>\n",
       "      <td>0.059679</td>\n",
       "      <td>0.066038</td>\n",
       "      <td>0.072629</td>\n",
       "      <td>0.065307</td>\n",
       "      <td>0.070598</td>\n",
       "      <td>0.072602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000009</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.873506</td>\n",
       "      <td>0.879221</td>\n",
       "      <td>0.870521</td>\n",
       "      <td>0.870107</td>\n",
       "      <td>0.850982</td>\n",
       "      <td>0.860596</td>\n",
       "      <td>0.843687</td>\n",
       "      <td>0.844033</td>\n",
       "      <td>0.835398</td>\n",
       "      <td>0.828234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.887949</td>\n",
       "      <td>0.885970</td>\n",
       "      <td>0.869607</td>\n",
       "      <td>0.857264</td>\n",
       "      <td>0.843754</td>\n",
       "      <td>0.843269</td>\n",
       "      <td>0.850758</td>\n",
       "      <td>0.834175</td>\n",
       "      <td>0.810197</td>\n",
       "      <td>0.836554</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.889007</td>\n",
       "      <td>0.885526</td>\n",
       "      <td>0.872733</td>\n",
       "      <td>0.873183</td>\n",
       "      <td>0.850967</td>\n",
       "      <td>0.844750</td>\n",
       "      <td>0.866006</td>\n",
       "      <td>0.852681</td>\n",
       "      <td>0.831363</td>\n",
       "      <td>0.855886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.919666</td>\n",
       "      <td>0.906649</td>\n",
       "      <td>0.908895</td>\n",
       "      <td>0.910081</td>\n",
       "      <td>0.888506</td>\n",
       "      <td>0.890943</td>\n",
       "      <td>0.898566</td>\n",
       "      <td>0.894921</td>\n",
       "      <td>0.886417</td>\n",
       "      <td>0.874892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.995991</td>\n",
       "      <td>0.995543</td>\n",
       "      <td>0.982222</td>\n",
       "      <td>0.990868</td>\n",
       "      <td>0.968226</td>\n",
       "      <td>0.971487</td>\n",
       "      <td>0.960916</td>\n",
       "      <td>0.963961</td>\n",
       "      <td>0.935899</td>\n",
       "      <td>0.939749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000442</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 2080 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1        A2        A3        A4        A5        A6        A7  \\\n",
       "0    0.025002  0.047413  0.047657  0.031935  0.033642  0.056091  0.020563   \n",
       "1    0.064956  0.049174  0.059578  0.048637  0.052147  0.059152  0.078862   \n",
       "2    0.080815  0.058416  0.070292  0.039943  0.060017  0.050410  0.044948   \n",
       "3    0.042448  0.044963  0.028303  0.015428  0.024725  0.014375  0.022633   \n",
       "4    0.057138  0.046485  0.061710  0.055913  0.059679  0.066038  0.072629   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "225  0.873506  0.879221  0.870521  0.870107  0.850982  0.860596  0.843687   \n",
       "226  0.887949  0.885970  0.869607  0.857264  0.843754  0.843269  0.850758   \n",
       "227  0.889007  0.885526  0.872733  0.873183  0.850967  0.844750  0.866006   \n",
       "228  0.919666  0.906649  0.908895  0.910081  0.888506  0.890943  0.898566   \n",
       "229  0.995991  0.995543  0.982222  0.990868  0.968226  0.971487  0.960916   \n",
       "\n",
       "           A8        A9       A10  ...       Z71       Z72       Z73  \\\n",
       "0    0.011744  0.034383  0.037488  ...  0.000336  0.000356  0.000351   \n",
       "1    0.060323  0.062352  0.063733  ...  0.000528  0.000517  0.000520   \n",
       "2    0.029302  0.047457  0.049800  ...  0.000245  0.000237  0.000249   \n",
       "3    0.002131 -0.000340  0.025022  ...  0.000082  0.000076  0.000089   \n",
       "4    0.065307  0.070598  0.072602  ...  0.000320  0.000325  0.000314   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "225  0.844033  0.835398  0.828234  ...  0.000486  0.000486  0.000484   \n",
       "226  0.834175  0.810197  0.836554  ...  0.000421  0.000405  0.000416   \n",
       "227  0.852681  0.831363  0.855886  ...  0.000304  0.000287  0.000283   \n",
       "228  0.894921  0.886417  0.874892  ...  0.000394  0.000389  0.000379   \n",
       "229  0.963961  0.935899  0.939749  ...  0.000457  0.000452  0.000450   \n",
       "\n",
       "          Z74       Z75       Z76       Z77       Z78       Z79       Z80  \n",
       "0    0.000339  0.000344  0.000337  0.000343  0.000348  0.000347  0.000010  \n",
       "1    0.000516  0.000528  0.000518  0.000520  0.000517  0.000523  0.000015  \n",
       "2    0.000264  0.000253  0.000241  0.000256  0.000249  0.000238  0.000007  \n",
       "3    0.000102  0.000080  0.000082  0.000101  0.000092  0.000087  0.000003  \n",
       "4    0.000325  0.000322  0.000319  0.000313  0.000321  0.000317  0.000009  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "225  0.000476  0.000488  0.000479  0.000487  0.000487  0.000489  0.000014  \n",
       "226  0.000417  0.000417  0.000414  0.000428  0.000415  0.000420  0.000012  \n",
       "227  0.000289  0.000286  0.000281  0.000300  0.000289  0.000290  0.000009  \n",
       "228  0.000372  0.000386  0.000379  0.000382  0.000385  0.000380  0.000011  \n",
       "229  0.000442  0.000459  0.000450  0.000454  0.000452  0.000455  0.000013  \n",
       "\n",
       "[230 rows x 2080 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_all = model.predict(X_test)\n",
    "y_pred_all = pd.DataFrame(y_pred_all, columns=y_train_all.columns) * max_y_train_all\n",
    "y_pred_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE=0.9728\n"
     ]
    }
   ],
   "source": [
    "MAPE = mean_absolute_percentage_error(y_test_all, y_pred_all)\n",
    "print(f\"MAPE={MAPE:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick submission test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, make sure the local processing goes through by running the\n",
    "```\n",
    "ramp-test --submission <submission folder>\n",
    "```\n",
    "If you want to quickly test the that there are no obvious code errors, use the `--quick-test` flag to only use a small subset of the data (training and testing sets with 5 elements each).\n",
    "\n",
    "```\n",
    "ramp-test --submission <submission folder> --quick-test\n",
    "```\n",
    "\n",
    "See the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html) for more details."
   ]
  }
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