trial faulted strata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eff272ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gempy as gp\n",
    "from gempy.assets import topology as tp\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7b2f217e",
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_model = gp.create_model('Tutorial_ch1_1_Basics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "76056179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active grids: ['regular']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Tutorial_ch1_1_Basics  2022-08-31 14:11"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = 'https://raw.githubusercontent.com/cgre-aachen/gempy_data/master/'\n",
    "# Importing the data from CSV-files and setting extent and resolution\n",
    "gp.init_data(geo_model, [0, 2000., 0, 2000., 0, 750.], [50, 50, 50],\n",
    "             path_o=data_path + \"/data/input_data/getting_started/\"\n",
    "                                \"simple_fault_model_orientations.csv\",\n",
    "             path_i=data_path + \"/data/input_data/getting_started/\"\n",
    "                                \"simple_fault_model_points.csv\",\n",
    "             default_values=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f540b825",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>X</th>\n",
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       "      <th>surface</th>\n",
       "      <th>G_x</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.000002</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
       "      <td>1000</td>\n",
       "      <td>1950</td>\n",
       "      <td>416.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>366.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>1000</td>\n",
       "      <td>333.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000002</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>11</th>\n",
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       "      <td>1000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000002</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>1000</td>\n",
       "      <td>483.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Shale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>14</th>\n",
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       "      <td>466.666667</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1000</td>\n",
       "      <td>300</td>\n",
       "      <td>333.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>19</th>\n",
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       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>21</th>\n",
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       "      <td>333.333333</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1000</td>\n",
       "      <td>1700</td>\n",
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       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
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       "      <td>316.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>266.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1300</td>\n",
       "      <td>1000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1600</td>\n",
       "      <td>1000</td>\n",
       "      <td>383.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1900</td>\n",
       "      <td>1000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1700</td>\n",
       "      <td>500</td>\n",
       "      <td>366.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1700</td>\n",
       "      <td>1500</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1100</td>\n",
       "      <td>150</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1100</td>\n",
       "      <td>300</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1100</td>\n",
       "      <td>1500</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1100</td>\n",
       "      <td>1700</td>\n",
       "      <td>473.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1100</td>\n",
       "      <td>1950</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>433.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>600</td>\n",
       "      <td>1000</td>\n",
       "      <td>366.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1300</td>\n",
       "      <td>1000</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1600</td>\n",
       "      <td>1000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1900</td>\n",
       "      <td>1000</td>\n",
       "      <td>566.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1700</td>\n",
       "      <td>500</td>\n",
       "      <td>533.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1700</td>\n",
       "      <td>1500</td>\n",
       "      <td>516.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Siltstone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>300</td>\n",
       "      <td>1000</td>\n",
       "      <td>466.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>600</td>\n",
       "      <td>1000</td>\n",
       "      <td>433.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1300</td>\n",
       "      <td>1000</td>\n",
       "      <td>633.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1600</td>\n",
       "      <td>1000</td>\n",
       "      <td>616.666667</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1900</td>\n",
       "      <td>1000</td>\n",
       "      <td>633.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1700</td>\n",
       "      <td>500</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1700</td>\n",
       "      <td>1500</td>\n",
       "      <td>583.333333</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>700</td>\n",
       "      <td>1000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>600</td>\n",
       "      <td>1000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1100</td>\n",
       "      <td>1000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">orientations</th>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>Shale</td>\n",
       "      <td>0.316229</td>\n",
       "      <td>1.000019e-12</td>\n",
       "      <td>0.948683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>400</td>\n",
       "      <td>1000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>Sandstone_2</td>\n",
       "      <td>0.316229</td>\n",
       "      <td>1.000019e-12</td>\n",
       "      <td>0.948683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>Main_Fault</td>\n",
       "      <td>-0.948683</td>\n",
       "      <td>9.998257e-13</td>\n",
       "      <td>0.316229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      X     Y           Z    smooth      surface       G_x  \\\n",
       "surface_points 0   1000    50  450.000000  0.000002        Shale       NaN   \n",
       "               1   1000   150  433.333333  0.000002        Shale       NaN   \n",
       "               2   1000   300  433.333333  0.000002        Shale       NaN   \n",
       "               3   1000   500  466.666667  0.000002        Shale       NaN   \n",
       "               4   1000  1000  533.333333  0.000002        Shale       NaN   \n",
       "               5   1000  1500  433.333333  0.000002        Shale       NaN   \n",
       "               6   1000  1700  400.000000  0.000002        Shale       NaN   \n",
       "               7   1000  1950  416.666667  0.000002        Shale       NaN   \n",
       "               8      0  1000  366.666667  0.000002        Shale       NaN   \n",
       "               9    300  1000  333.333333  0.000002        Shale       NaN   \n",
       "               10   450  1000  316.666667  0.000002        Shale       NaN   \n",
       "               11  1300  1000  500.000000  0.000002        Shale       NaN   \n",
       "               12  1600  1000  483.333333  0.000002        Shale       NaN   \n",
       "               13  1900  1000  500.000000  0.000002        Shale       NaN   \n",
       "               14  1700   500  466.666667  0.000002        Shale       NaN   \n",
       "               15  1700  1500  450.000000  0.000002        Shale       NaN   \n",
       "               16  1000    50  350.000000  0.000002  Sandstone_1       NaN   \n",
       "               17  1000   150  333.333333  0.000002  Sandstone_1       NaN   \n",
       "               18  1000   300  333.333333  0.000002  Sandstone_1       NaN   \n",
       "               19  1000   500  366.666667  0.000002  Sandstone_1       NaN   \n",
       "               20  1000  1000  433.333333  0.000002  Sandstone_1       NaN   \n",
       "               21  1000  1500  333.333333  0.000002  Sandstone_1       NaN   \n",
       "               22  1000  1700  300.000000  0.000002  Sandstone_1       NaN   \n",
       "               23  1000  1950  316.666667  0.000002  Sandstone_1       NaN   \n",
       "               24     0  1000  266.666667  0.000002  Sandstone_1       NaN   \n",
       "               25   300  1000  233.333333  0.000002  Sandstone_1       NaN   \n",
       "               26  1300  1000  400.000000  0.000002  Sandstone_1       NaN   \n",
       "               27  1600  1000  383.333333  0.000002  Sandstone_1       NaN   \n",
       "               28  1900  1000  400.000000  0.000002  Sandstone_1       NaN   \n",
       "               29  1700   500  366.666667  0.000002  Sandstone_1       NaN   \n",
       "               30  1700  1500  350.000000  0.000002  Sandstone_1       NaN   \n",
       "               31  1100   150  506.666667  0.000002    Siltstone       NaN   \n",
       "               32  1100   300  506.666667  0.000002    Siltstone       NaN   \n",
       "               33  1100  1500  506.666667  0.000002    Siltstone       NaN   \n",
       "               34  1100  1700  473.333333  0.000002    Siltstone       NaN   \n",
       "               35  1100  1950  490.000000  0.000002    Siltstone       NaN   \n",
       "               36     0  1000  433.333333  0.000002    Siltstone       NaN   \n",
       "               37   300  1000  400.000000  0.000002    Siltstone       NaN   \n",
       "               38   600  1000  366.666667  0.000002    Siltstone       NaN   \n",
       "               39  1300  1000  566.666667  0.000002    Siltstone       NaN   \n",
       "               40  1600  1000  550.000000  0.000002    Siltstone       NaN   \n",
       "               41  1900  1000  566.666667  0.000002    Siltstone       NaN   \n",
       "               42  1700   500  533.333333  0.000002    Siltstone       NaN   \n",
       "               43  1700  1500  516.666667  0.000002    Siltstone       NaN   \n",
       "               44     0  1000  500.000000  0.000002  Sandstone_2       NaN   \n",
       "               45   300  1000  466.666667  0.000002  Sandstone_2       NaN   \n",
       "               46   600  1000  433.333333  0.000002  Sandstone_2       NaN   \n",
       "               47  1300  1000  633.333333  0.000002  Sandstone_2       NaN   \n",
       "               48  1600  1000  616.666667  0.000002  Sandstone_2       NaN   \n",
       "               49  1900  1000  633.333333  0.000002  Sandstone_2       NaN   \n",
       "               50  1700   500  600.000000  0.000002  Sandstone_2       NaN   \n",
       "               51  1700  1500  583.333333  0.000002  Sandstone_2       NaN   \n",
       "               52   700  1000  300.000000  0.000002   Main_Fault       NaN   \n",
       "               53   600  1000  200.000000  0.000002   Main_Fault       NaN   \n",
       "               54   500  1000  100.000000  0.000002   Main_Fault       NaN   \n",
       "               55  1000  1000  600.000000  0.000002   Main_Fault       NaN   \n",
       "               56  1100  1000  700.000000  0.000002   Main_Fault       NaN   \n",
       "orientations   0   1000  1000  300.000000  0.010000        Shale  0.316229   \n",
       "               1    400  1000  420.000000  0.010000  Sandstone_2  0.316229   \n",
       "               2    500  1000  300.000000  0.010000   Main_Fault -0.948683   \n",
       "\n",
       "                            G_y       G_z  \n",
       "surface_points 0            NaN       NaN  \n",
       "               1            NaN       NaN  \n",
       "               2            NaN       NaN  \n",
       "               3            NaN       NaN  \n",
       "               4            NaN       NaN  \n",
       "               5            NaN       NaN  \n",
       "               6            NaN       NaN  \n",
       "               7            NaN       NaN  \n",
       "               8            NaN       NaN  \n",
       "               9            NaN       NaN  \n",
       "               10           NaN       NaN  \n",
       "               11           NaN       NaN  \n",
       "               12           NaN       NaN  \n",
       "               13           NaN       NaN  \n",
       "               14           NaN       NaN  \n",
       "               15           NaN       NaN  \n",
       "               16           NaN       NaN  \n",
       "               17           NaN       NaN  \n",
       "               18           NaN       NaN  \n",
       "               19           NaN       NaN  \n",
       "               20           NaN       NaN  \n",
       "               21           NaN       NaN  \n",
       "               22           NaN       NaN  \n",
       "               23           NaN       NaN  \n",
       "               24           NaN       NaN  \n",
       "               25           NaN       NaN  \n",
       "               26           NaN       NaN  \n",
       "               27           NaN       NaN  \n",
       "               28           NaN       NaN  \n",
       "               29           NaN       NaN  \n",
       "               30           NaN       NaN  \n",
       "               31           NaN       NaN  \n",
       "               32           NaN       NaN  \n",
       "               33           NaN       NaN  \n",
       "               34           NaN       NaN  \n",
       "               35           NaN       NaN  \n",
       "               36           NaN       NaN  \n",
       "               37           NaN       NaN  \n",
       "               38           NaN       NaN  \n",
       "               39           NaN       NaN  \n",
       "               40           NaN       NaN  \n",
       "               41           NaN       NaN  \n",
       "               42           NaN       NaN  \n",
       "               43           NaN       NaN  \n",
       "               44           NaN       NaN  \n",
       "               45           NaN       NaN  \n",
       "               46           NaN       NaN  \n",
       "               47           NaN       NaN  \n",
       "               48           NaN       NaN  \n",
       "               49           NaN       NaN  \n",
       "               50           NaN       NaN  \n",
       "               51           NaN       NaN  \n",
       "               52           NaN       NaN  \n",
       "               53           NaN       NaN  \n",
       "               54           NaN       NaN  \n",
       "               55           NaN       NaN  \n",
       "               56           NaN       NaN  \n",
       "orientations   0   1.000019e-12  0.948683  \n",
       "               1   1.000019e-12  0.948683  \n",
       "               2   9.998257e-13  0.316229  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "24c5accc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_446ea_row0_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "#T_446ea_row1_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_446ea_row2_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_446ea_row3_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_446ea_row4_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "#T_446ea_row5_col3 {\n",
       "  background-color: #ff3f20;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_446ea_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
       "      <td id=\"T_446ea_row0_col0\" class=\"data row0 col0\" >Main_Fault</td>\n",
       "      <td id=\"T_446ea_row0_col1\" class=\"data row0 col1\" >Fault_Series</td>\n",
       "      <td id=\"T_446ea_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_446ea_row0_col3\" class=\"data row0 col3\" >#443988</td>\n",
       "      <td id=\"T_446ea_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
       "      <td id=\"T_446ea_row1_col0\" class=\"data row1 col0\" >Shale</td>\n",
       "      <td id=\"T_446ea_row1_col1\" class=\"data row1 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_446ea_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_446ea_row1_col3\" class=\"data row1 col3\" >#015482</td>\n",
       "      <td id=\"T_446ea_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
       "      <td id=\"T_446ea_row2_col0\" class=\"data row2 col0\" >Sandstone_1</td>\n",
       "      <td id=\"T_446ea_row2_col1\" class=\"data row2 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_446ea_row2_col2\" class=\"data row2 col2\" >2</td>\n",
       "      <td id=\"T_446ea_row2_col3\" class=\"data row2 col3\" >#9f0052</td>\n",
       "      <td id=\"T_446ea_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_446ea_row3_col0\" class=\"data row3 col0\" >Siltstone</td>\n",
       "      <td id=\"T_446ea_row3_col1\" class=\"data row3 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_446ea_row3_col2\" class=\"data row3 col2\" >3</td>\n",
       "      <td id=\"T_446ea_row3_col3\" class=\"data row3 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_446ea_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
       "      <td id=\"T_446ea_row4_col0\" class=\"data row4 col0\" >Sandstone_2</td>\n",
       "      <td id=\"T_446ea_row4_col1\" class=\"data row4 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_446ea_row4_col2\" class=\"data row4 col2\" >4</td>\n",
       "      <td id=\"T_446ea_row4_col3\" class=\"data row4 col3\" >#728f02</td>\n",
       "      <td id=\"T_446ea_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_446ea_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_446ea_row5_col0\" class=\"data row5 col0\" >basement</td>\n",
       "      <td id=\"T_446ea_row5_col1\" class=\"data row5 col1\" >Strat_Series</td>\n",
       "      <td id=\"T_446ea_row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "      <td id=\"T_446ea_row5_col3\" class=\"data row5 col3\" >#ff3f20</td>\n",
       "      <td id=\"T_446ea_row5_col4\" class=\"data row5 col4\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "       surface        series  order_surfaces    color  id\n",
       "4   Main_Fault  Fault_Series               1  #443988   1\n",
       "0        Shale  Strat_Series               1  #015482   2\n",
       "1  Sandstone_1  Strat_Series               2  #9f0052   3\n",
       "2    Siltstone  Strat_Series               3  #ffbe00   4\n",
       "3  Sandstone_2  Strat_Series               4  #728f02   5\n",
       "5     basement  Strat_Series               5  #ff3f20   6"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(geo_model,\n",
    "                         {\"Fault_Series\": 'Main_Fault',\n",
    "                          \"Strat_Series\": ('Sandstone_2', 'Siltstone',\n",
    "                                           'Shale', 'Sandstone_1', 'basement')},\n",
    "                         remove_unused_series=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "28df62af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_series</th>\n",
       "      <th>BottomRelation</th>\n",
       "      <th>isActive</th>\n",
       "      <th>isFault</th>\n",
       "      <th>isFinite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault_Series</th>\n",
       "      <td>1</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strat_Series</th>\n",
       "      <td>2</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "              order_series BottomRelation  isActive  isFault  isFinite\n",
       "Fault_Series             1        Erosion      True    False     False\n",
       "Strat_Series             2        Erosion      True    False     False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9a56653e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fault colors changed. If you do not like this behavior, set change_color to False.\n"
     ]
    },
    {
     "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>Fault_Series</th>\n",
       "      <th>Strat_Series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault_Series</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strat_Series</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fault_Series  Strat_Series\n",
       "Fault_Series         False          True\n",
       "Strat_Series         False         False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.set_is_fault(['Fault_Series'])\n",
    "geo_model.faults.faults_relations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "20501ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_series</th>\n",
       "      <th>BottomRelation</th>\n",
       "      <th>isActive</th>\n",
       "      <th>isFault</th>\n",
       "      <th>isFinite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault_Series</th>\n",
       "      <td>1</td>\n",
       "      <td>Fault</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strat_Series</th>\n",
       "      <td>2</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "              order_series BottomRelation  isActive  isFault  isFinite\n",
       "Fault_Series             1          Fault      True     True     False\n",
       "Strat_Series             2        Erosion      True    False     False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.faults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "77d2bd12",
   "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>Fault_Series</th>\n",
       "      <th>Strat_Series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault_Series</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strat_Series</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fault_Series  Strat_Series\n",
       "Fault_Series         False          True\n",
       "Strat_Series         False         False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.faults.faults_relations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "88c06f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grid Object. Values: \n",
       "array([[  20. ,   20. ,    7.5],\n",
       "       [  20. ,   20. ,   22.5],\n",
       "       [  20. ,   20. ,   37.5],\n",
       "       ...,\n",
       "       [1980. , 1980. ,  712.5],\n",
       "       [1980. , 1980. ,  727.5],\n",
       "       [1980. , 1980. ,  742.5]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f256ef49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting kriging parameters to their default values.\n",
      "Compiling theano function...\n",
      "Level of Optimization:  fast_compile\n",
      "Device:  cpu\n",
      "Precision:  float64\n",
      "Number of faults:  1\n",
      "Compilation Done!\n",
      "Kriging values: \n",
      "                         values\n",
      "range              2926.174978\n",
      "$C_o$            203869.047619\n",
      "drift equations         [3, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.core.interpolator.InterpolatorModel at 0x22b8ed06e20>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.set_interpolator(geo_model,\n",
    "                    compile_theano=True,\n",
    "                    theano_optimizer='fast_compile',\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c9e57abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>2926.174978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$C_o$</th>\n",
       "      <td>203869.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drift equations</th>\n",
       "      <td>[3, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                        values\n",
       "range              2926.174978\n",
       "$C_o$            203869.047619\n",
       "drift equations         [3, 3]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.get_data(geo_model, 'kriging')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dd32c97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sol = gp.compute_model(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "947d8557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, show_data=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d7c795d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "edges, centroids = tp.compute_topology(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "60fa39de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 5), (3, 4), (3, 5), (4, 5)}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cd737635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([19.59982026, 25.0286342 , 40.0263125 ]),\n",
       " 2: array([25.17945109, 24.59032874, 31.92319292]),\n",
       " 3: array([25.41815961, 24.53094463, 28.00142508]),\n",
       " 4: array([25.69517906, 24.55702479, 23.10089532]),\n",
       " 5: array([27.72107715, 24.04185422, 10.48556819])}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "702edbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p0qW/e5xSqWTHjh34+fmhUCgYP348rq6uKBSKJM3z+++/s2rVKkJCQtDQ0KBbt2507tyZokWLquyzxMfHc/r06YT76S5cuIC9vX3C/XS2trZoamqqbD4hhBAiK5KVuEzCzMyMo0eP0qhRI+zt7Vm0aNF3V+UUCgVubm5ERUUxZswYRo0ahaOjI/v27UvSylrx4sUZOXIk165dIygoiBs3bmBubo6bmxu//fbbd5+ITS4tLS2qVq2Kn58fR44c4eHDhwwZMoTHjx/TrVs3ChUqRKtWrViyZAk3b95M9XxCCCFEdiQrcRnQ1atXcXd3J1euXAQHB1OqVKkfHvvlyxc2bNjAuHHjKFSoEOPHj6d27drJmu/t27ds2rSJkJAQrly5ktB7zsrKKnUf5AcePnxIREQE4eHh7N+/Hz09PZydnXF2dsbJyYkCBQqkybxCCCFEZiIrcZlQ+fLlOXbsGPXr18fOzo4lS5b8cJVNQ0ODdu3acenSJTw9PenevTtOTk4cP348yfPlzJmTrl27cujQIU6cOEGuXLlwdXXFzs6OBQsW8OLFC1V9NACKFStG586dWblyJb///jvbt2+nQoUKrFy5EmNjYypVqsTIkSPZv38/Hz58UOncQgghRFYhK3EZ3OXLl3F3dydfvnwEBQVRsmTJnx7/6dMnVqxYQUBAAObm5owfPx57e/tkz/v582ciIiJYvnw5e/bsoWHDhnh4eODk5JSm97P9/fffnD59OmGVLiYmBgcHh4T76WxsbFLc+04IIYTITGQlLpOrUKECJ06coE6dOtjZ2bFs2bKf3vumra2Np6cnsbGxNGnShObNm9OkSRMuXLiQrHk1NTWpX78+v/76K7dv36ZmzZqMHj2aUqVKMWbMGOLi4lL5yb5PR0eH6tWr4+/vz7Fjx3jw4AH9+vXj/v37dOzYkcKFC9O2bVuWLVvGnTt30iSDEEIIkRlIEZcJaGlpMXr0aA4ePMiSJUtwcXHh/v37Pz1HR0eH3r17ExcXh5OTEy4uLrRq1YrLly8ne/78+fPTp08fzp49y86dO3n79i1Vq1alVq1arFixIk235MqbNy9NmjRh3rx5XL16lXPnzuHi4sKhQ4eoUqUKJiYm9O7dm82bN6v8sq8QQgiRkUkRl4lYWFhw4sQJqlevjq2tLcHBwYk+kaqnp8eAAQOIi4ujSpUq1K1blw4dOnD9+vUUZbCysmLWrFk8ePCAAQMGsGnTJgwNDfH09OTYsWNpvnODkZER7u7urFmzhkePHrFlyxbKli1LcHAwJUuWpHLlyvj4+HDw4EE+fvyYplmEEEIIdZJ74jKpmJgYunbtSpEiRVi2bFmSv6e//vqLuXPnMnv2bFxdXfHz88PY2DhVWf7444+E3nNfvnyhW7dudOnShWLFiqVq3OT6+PEjJ0+eTLif7sqVK1StWjXhfjpLS0u5n04IIUSmIffEZVFWVlacPn0aR0dHKlasSGhoaJJWwXLnzo2Pjw+xsbGULFkSe3t7evbsyb1791KcpWjRogwfPpwrV66wYsUKbt26hYWFBa6urmzatCndVsR0dXWpVasWEyZM4OTJk9y9excvLy9u3rxJ69atKVq0KB06dGD58uWJXo4WQgghMjop4jIxbW1t/Pz8CA8PZ/bs2bi5ufHw4cMknZsvXz78/f2JjY2lYMGCVKxYEW9vb37//fcU51EoFDg4OLB06VIePHhAu3btWLhwIYaGhgwYMCDZD1ekVv78+WnevDkLFy7kxo0bnD59GicnJ8LDw7G1tcXMzIy+ffuydetWXr16la7ZhBBCiNSSIi4LsLGx4fTp09jZ2WFjY8PKlSuTfG/aL7/8wqRJk7h69Sr6+vpYWloyaNAgHj9+nKpMOXLkoHPnzhw4cIBTp06RL18+mjZtiq2tLfPmzeP58+epGj8lSpYsSffu3Vm3bh2PHz9m/fr1lCxZkkWLFmFoaIijoyN+fn4cPnxYJTtXCCGEEGlJirgsQkdHh3HjxrFnzx5mzJhB06ZN+eOPP5J8fqFChZgxYwaXL1/m8+fPlC9fnhEjRvDs2bNUZzM2Nsbf35/bt28zbdo0Tp48SZkyZWjTpg27d+/m8+fPqZ4juTQ0NLCxsWHYsGHs3buXp0+fMnHiRD59+sSQIUMoWLAgrq6uzJo1i0uXLqX5AxtCCCFEckkRl8XY2tpy9uxZrK2tsba2Zs2aNckqQIoWLcrcuXOJjo7m1atXmJmZ4evrq5L2HRoaGjg7O7NmzRru3LlD3bp1GTt2LCVLlmT06NHExsameo6U0tPTo27dukyePJkzZ85w+/ZtunXrxvXr12natGnCLhMrVqxI1SVnIYQQQlWkiMuCdHR0CAgIYNeuXUyePJnmzZvz6NGjZI1hZGTE4sWLiYqK4uHDh5QtW5bx48fz+vVrlWTMly8fvXr14vTp0+zZs4ePHz9SvXp1atSoQUhICG/evFHJPClVoEABWrVqxeLFi7l58ybHjh2jRo0a7Ny5E2tra8zNzenfvz/bt29X2XcihBBCJIcUcVmYnZ0dUVFRmJubY21tzbp165J9WbBUqVIEBwdz4sQJYmNjMTExYcqUKSpt8GthYUFgYCAPHjxg6NChbN26FSMjI7p168aRI0cyxKVMY2NjevbsyYYNG3j8+DGrVq2iWLFizJkzh+LFi1O9enXGjRvHsWPH+PTpk7rjCiGEyAakT1w2cebMGdzd3TEzM2PRokUULlw4ReNcvXqVcePGERkZyfDhw+nduzf6+voqTguPHz9m9erVLF++nL///ht3d3e6du2aIf89vH//nqNHj7J//37Cw8O5desWNWvWxNnZmXr16lGuXDkUCoW6YwohhMhkpE+cAMDe3p6oqChMTU2xtrZmw4YNKRqnfPnyrF+/nn379nHkyBFMTEyYP3++ynvBFS5cmCFDhnDp0iXWrFnD/fv3sbKyomHDhmzYsCFD7cagr69PvXr1mDp1KufOnSM2NpaOHTty8eJFXFxcMDQ0pGvXrqxevTpZD5sIIYQQPyMrcdnQqVOncHd3x8LCgoULF2JgYJDisaKiovDz8+PixYuMGTOGbt26oa2trcK0/9+7d+/YsmULISEhXLhwgfbt29OtWzcqVqyYYVe6lEolN2/eTNhF4uDBgxQvXjxhla5mzZrkypVL3TGFEEJkQLISJ75RpUoVzp07R+nSpbGysmLTpk0pHqtSpUrs3LmT9evXs2nTJszMzAgNDSU+Pl6Fif+RI0cOOnbsyP79+zl79iwFCxakZcuW2NjYMGfOHJW0Q1E1hUKBiYkJvXv3ZvPmzTx9+pTg4GAMDAyYPn06RYsWpVatWgQEBHDy5Mk0+d6EEEJkTbISl82dOHECd3d3bGxsWLBgAQULFkzVeIcPH8bPz4+HDx8yduxY2rVrh6amporSfuvLly8cOnSIkJAQtm/fjrOzM926daNBgwZoaWml2byq8vbtW44cOZJwP929e/eoXbs2zs7OODs7Y2pqmmFXGYUQQqQtWYkTP+Xo6MiFCxcwMjLC0tKS3377LVXj1axZk4MHD7Jo0SIWLFiAlZUVGzdu5MuXLypK/DUNDQ3q1q3LqlWruHv3LvXr12fChAmUKFGCkSNHcv369TSZV1Vy5sxJw4YNmTFjBtHR0Vy7do3WrVsTFRWFs7MzJUuWxMPDg3Xr1vHkyRN1xxVCCJGByEqcSHDs2DHc3d2xt7dn3rx5FChQIFXjKZVK9uzZg5+fH58+fcLf358mTZqky8rSlStXCA0NZdWqVRgbG9OtWzfatGlDnjx50nxuVVEqldy4cSNhle7QoUOUKlUq4X66GjVqkCNHDnXHFEIIkUZkJU4kWbVq1YiOjqZw4cJYWloSFhaWqvEUCgUuLi6cPn0af39//Pz8qFy5Mrt3707z3m/m5uZMmzaNe/fuMXLkSHbt2kWJEiXo2rUrhw4dSrOVQVVSKBSYmZnh7e3N1q1befbsGYsWLSJv3rxMnDiRwoULU7duXSZNmsTp06fVsn2ZEEII9ZGVOPFdR44coVu3bjg4ODB37lx++eWXVI/55csXfvvtN8aOHUvevHkZP348Tk5O6XbP15MnT1izZg3Lly/n3bt3Cb3nSpQokS7zq9qbN2+IjIxMWKl7+PAhderUoV69ejg7O1OmTBm5n04IITIxWYkTKVKjRg2io6MpUKAAlpaWbNu2LdVjamho0KpVK2JiYujbty99+vShdu3aHD58WAWJE1eoUCEGDRpETEwM69ev548//qBixYrUr1+fX3/9lQ8fPqRLDlXJlSsXrq6uzJo1i0uXLnH58mWaN2/OiRMnqFWrFsbGxvTo0YMNGzZkyCd3hRBCpI6sxIlERUZG4uHhQbVq1ZgzZw758+dXybjx8fGsXr2a8ePHU6ZMGQICAnBwcFDJ2En14cMHtm7dyvLly4mKiqJt27Z4eHhQqVKlTL2KpVQquXr1asIq3eHDhzExMUl46rV69eppstOGEEII1ZGVOJFqtWrVIjo6mjx58mBpacnOnTtVMq6Wlhbu7u5cv36d1q1b06ZNG1xdXYmKilLJ+Emhp6dHu3bt2LdvH+fPn6do0aK0bdsWKysrZs2axdOnT9MtiyopFArMzc3p378/27dv59mzZ8yZMwd9fX38/f0pVKgQzs7OTJ06laioqExxj6AQQoivyUqcSJaDBw/i4eFB7dq1mTVrFvny5VPZ2B8/fiQoKIhJkyZhb2/P+PHjsbKyUtn4SfXlyxeOHDnC8uXLCQsLo27dunTr1g0XF5dM0XsuKV6/fk1kZGTCThJPnjyhbt26CffTlS5dWt0RhRAi25OVOKFSderU4eLFi+jr62Npacnu3btVNrauri7e3t7ExcVRq1Yt6tevT5s2bbhy5YrK5kgKDQ0NatWqxYoVK7h37x6NGjViypQpGBkZMXz4cK5evZquedJCnjx5cHNzY+7cuVy5coULFy7g6upKZGQkjo6OlClThl69erFp0yb+/PNPdccVQgjxHVLEiWTLlSsXCxcuJDQ0lN69e9O9e3devXqlsvH19fUZNGgQN2/epFKlStSuXZtOnToRGxursjmSKk+ePHh6enLs2DEOHjyIhoYGTk5OODo6snTpUpV+bnUyNDSka9eurF69mj/++IOtW7diZmZGSEgIpUqVwt7entGjR3PgwAE+fvyo7rhCCCGQIk6kgpOTEzExMWhpaWFpacm+fftUOn7OnDkZMWIEcXFxmJmZ4ejoiIeHB7dv31bpPElVrlw5pkyZwr179xgzZgz79u2jZMmSdO7cmQMHDmSZ+8oUCgWWlpYMGjSInTt38uzZMwIDA9HU1MTHx4eCBQvSoEEDZsyYwYULF7LM5xZCiMxGijiRKnny5GHJkiUEBwfTo0cPevbsyevXr1U+h6+vL7GxsRQvXhw7Ozt69erF/fv3VTpPUmlpaeHq6sqmTZuIi4vDzs6OQYMGUaZMGfz9/bl7965acqUVHR0datasSUBAACdOnOD+/fv07t2b27dv07ZtW4oUKUL79u0JDg7m3r176o4rhBDZhhRxQiXq1avHxYsXUSqVWFpaEh4ervI58ufPT0BAANevXydfvnxYW1vTr18//vjjD5XPlVQFCxZkwIABXLhwgc2bN/Ps2TMqVaqEs7Mza9as4f3792rLllby5ctHs2bNWLBgAdevX+fs2bPUq1ePiIgI7OzsMDU1xdvbmy1btvDy5Ut1xxVCiCxLijihMnny5GHZsmUsXbqU7t2706tXL/766y+Vz1OwYEGmTJnC1atX0dbWpkKFCgwZMkStG8QrFApsbW2ZN28eDx48oGfPnqxatQpDQ0N69erF6dOn03yrMXUpUaIEHh4erF27lkePHrFx40ZKly7NkiVLMDIywsHBAV9fXyIjI/n777/VHVcIIbIMKeKEyjVo0ICLFy/y6dMnLC0tiYiISJN5ChcuzMyZM7l06RIfP36kfPnyjBo1iufPn6fJfEmlp6dHmzZt2LNnD9HR0ZQoUYIOHTpgYWFBYGAgjx8/Vmu+tKShoYG1tTVDhw5lz549PH36lMmTJ/P582eGDRtGwYIFadSoETNnzkxYuRVCCJEyUsSJNJE3b16Cg4NZuHAh7u7ueHt78+bNmzSZq1ixYsyfP5/z58/z/PlzTE1NGTt2bIa4lGdoaMjo0aOJjY1l8eLFXLp0iXLlytG0aVPCwsL49OmTuiOmKT09PerUqcOkSZM4ffo0d+7coXv37sTGxtK8eXOKFi1Kp06dCA0N5cGDB+qOK4QQmYoUcSJNNWrUiJiYGN6+fYuVlRWHDh1Ks7lKlCjB0qVLOXPmDPfu3aNs2bJMmDAhTS7pJpdCoaBGjRqEhIRw7949mjZtyowZMzAyMmLo0KFcvnxZ3RHTxS+//ELLli1ZtGgRcXFxCfu87t69GxsbG8qXL0///v3Ztm2byh+QEUKIrEaKOJHm8ufPT2hoKPPmzaNTp07069ePt2/fptl8xsbGhISEcOzYMa5evYqJiQnTpk1L0zmTI3fu3Hh4eHDkyBEOHz6Mjo4ODRo0oHLlyixevDhDrCCml9KlS9OjRw/Wr1/PkydPWLNmDcWLF2fevHkUL16catWqMXbsWI4ePZrlVy2FECK5pIgT6cbV1ZWLFy/y6tUrrKysOHz4cJrOZ2pqypo1azhw4ABnzpzBxMSE2bNn8+HDhzSdNzlMTU2ZNGkSd+/exd/fnwMHDlCqVCk6duzI/v37s1UPNg0NDWxtbRkxYgTh4eE8efIEf39/Pnz4wIABAyhYsCBubm7MmTOHK1euyP10QohsT/ZOFWqxbds2evfuTatWrZg0aRI5c+ZM8zkvXLjA2LFjiYqKYvTo0XTv3h1dXd00nze5nj9/ztq1awkJCeHPP/+ka9euuLu7Z/v9TJ89e8aBAwfYv38/4eHhfPz4EWdnZ+rVq4eTkxPFihVTd0QhhFAp2TtVZEhNmjTh4sWLPH/+HBsbG44ePZrmc9rY2BAWFsaWLVvYvn07pqamBAUFZbjLdAUKFKBfv36cO3eOrVu38vLlSypXrkzdunVZtWoV7969U3dEtShYsCBt2rRh6dKl3Lp1iyNHjlC1alXCwsKwsLDAwsKCgQMHsnPnzgxxH6QQQqQ1WYkTard161b69OlD27ZtmThxIjly5EiXeY8fP46fnx937tzBz8+PDh06oKWllS5zJ9fHjx/Zvn07ISEhnDhxglatWuHh4UGVKlVQKBTqjqd2nz9/5ty5cwmrdGfOnKFixYoJK3X29vYZ9r+tEEL8iKzEiQyvWbNmxMTE8OjRI2xsbDh+/Hi6zFu1alX2799PcHAwQUFBWFhYsG7dugx5H5quri6tWrVi586dXLx4EWNjY7p06YK5uTnTpk1T664VGYGmpib29vaMGjWKAwcO8PjxY3x8fPjrr7/o3bs3BQsWpFmzZsyfP59r167J/XRCiCxBVuJEhrJ582b69u1Lx44dCQgIQF9fP13mVSqV7N+/H19fX968eYO/vz/NmzdHQyPj/p2jVCo5fvw4y5cv57fffqN69ep4eHjg6uqKjo6OuuNlKE+ePCEiIiJhpU6pVH51P13hwoXVHVEIIb4hK3EiU2nZsiUxMTHcv3+fihUrcvLkyXSZV6FQUK9ePU6cOMHUqVOZNGkSlSpVYvv27Rl21UahUFCtWjWCg4O5f/8+rVq1Yvbs2RgaGjJo0CAuXryo7ogZRqFChWjfvj3BwcHcvXuXiIgI7O3t2bhxI+XKlcPa2pohQ4awZ8+eDNOKRgghEiMrcSLD2rhxI/369aNLly6MHz8ePT29dJtbqVSydetWxo4di76+PuPHj6d+/fqZ4v6zuLg4QkNDWbFiBYULF8bDw4P27duTP39+dUfLkOLj4zl79mzCKt25c+ews7PD2dkZZ2dn7Ozs0NTUVHdMIUQ2JCtxItNq3bo1MTEx3Lp1i4oVK3Lq1Kl0m1uhUNC8eXMuXLjAkCFDGDhwIDVq1ODAgQPpliGlTExMmDBhAnfu3GHixIkcPnyY0qVL0759e/bt28fnz5/VHTFD0dLSwsHBgTFjxhAZGckff/zBsGHDeP78OZ6enhgYGNCiRQsWLVpEbGxshl2ZFUJkP7ISJzI8pVLJhg0b6N+/Px4eHowbNy7d+7t9/vyZdevW4e/vj6GhIQEBAVSvXj1dM6TGn3/+ybp16wgJCeHJkycJvefKlCmj7mgZ3qNHj4iIiCA8PJzw8HC0tbUT7qerW7cuBgYG6o4ohMiiZCVOZHoKhYK2bdsSExPD9evXsbW15cyZM+maQVNTk06dOnH16lU6d+5M586dadCgAadPn07XHCn1yy+/4O3tzdmzZ9mxYwdv3rzB0dGRWrVqsWLFCrkP7CeKFClCx44dCQ0N5cGDB+zevRtra2vWrFmDiYkJtra2DB8+nPDwcN6/f6/uuEKIbESKOJFpFC5cmM2bN+Pj40Pjxo3x8fHh48eP6ZpBS0sLDw8Prl+/TvPmzWnZsiVubm6cP38+XXOkhpWVFbNmzeLBgwcMGDCATZs2YWhoiKenJ8eOHZPLhT+hUCgoX748/fr1Y9u2bTx79ox58+aRM2dO/P39KVSoEE5OTkyZMoWzZ8/KpWshRJqSIk5kKgqFgg4dOhAdHc3ly5exs7MjKioq3XPo6OjQq1cvYmNjqVevHq6urrRo0SJTPRGqo6NDixYt2L59O1euXMHU1BRPT0/KlSvHlClTePjwobojZnja2tpUq1aNsWPHcvToUX7//XcGDhzIH3/8QdeuXSlUqBCtW7dO2GVCCCFUKUlF3L+NUCtWrJjwc/bsWV69eoW3tzeVKlWidu3abNy4MeEcpVJJYGAgDg4O2NvbM2HCBPmrVKhMkSJF2LJlCyNGjMDFxQVfX1/+/vvvdM+hp6dH//79iYuLo1q1atSrV4/27dtz7dq1dM+SGkWLFmX48OFcuXKF0NBQbt26hYWFBa6urmzatCndVzwzqzx58uDm5sacOXO4fPkyMTExuLm5ceTIEapVq0aZMmXw8vJi06ZNPH/+XN1xhRCZXJKKuKtXrzJo0CDOnz+f8GNnZ4evry85cuTg+PHjzJ07lxkzZiT88lqzZg2HDh1i27Zt7Nq1i3PnzrF27do0/TAie1EoFHTq1IkLFy5w4cIF7O3t1XZZM0eOHAwZMoS4uDisrKyoUaMGXbp0IS4uTi15UkqhUODo6MjSpUu5f/8+7dq1Y8GCBRgaGjJgwACio6PVHTFTKV68OF26dGHVqlU8fPiQsLAwypcvT0hICKVLl8bOzo5Ro0YRERHBhw8f1B1XCJHJJLmIK1++/FevvX37lv3799O/f390dXWxsrKicePGCatxYWFhCZcTDAwM8PLyYsOGDar/BCLbK1asGNu2bWPIkCE0aNCAcePGqWVVDiBXrlyMGjWKuLg4ypQpg4ODA56enty9e1cteVIjZ86cdO7cmYMHD3Lq1Cny5s1LkyZNsLW1Zf78+fz555/qjpipKBQKLCwsGDhwIDt37uTZs2fMnDkTbW1tfH19MTAwoH79+kyfPp3z589nyO3fhBAZS6JF3Pv377lz5w4rV66kWrVquLi4sGnTJu7evYuWlhZGRkYJx5YuXZrY2FgAbt26hYmJyVfvxcXFyU3TIk0oFAq6dOnC+fPnOXPmDJUrV1brqlHevHkZO3YsN27coHDhwtja2tKnTx9+//13tWVKDWNjY8aPH8/t27eZNm0ax48fx9jYmDZt2rBnzx65VSIFdHR0qFmzJuPHj+f48eM8ePAAb29v7t69S/v27SlcuDDt2rVL2GVCCCH+V6JF3LNnz7C1taV9+/YcPHiQgIAApkyZwsGDB7/poK+np5dwSeD9+/dfva+vr8+XL1/UtkIisofixYuzY8cOBg4ciLOzM+PHj+fTp09qy/PLL78wceJErl27Rq5cubCysmLgwIE8evRIbZlSQ0NDA2dnZ9auXcudO3eoW7cufn5+lCxZktGjRyf8ESeSL2/evDRt2pT58+dz7do1oqKiaNCgQcIWYaampvTp04fffvuNFy9eqDuuECIDSLSIMzIyYvXq1dSqVQsdHR3s7Oxo2rQpZ8+e/eYejg8fPpAjRw7gn4LuvzdDv3//Hi0trXRv0iqyH4VCgbu7O+fPn+fEiRNUqVJF7U+NGhgYMG3aNC5fvgyAubk5w4YN4+nTp2rNlRr58uWjV69enD59mj179vDx40eqV69OjRo1CAkJ4c2bN+qOmKmVKFGCbt26sXbtWh49esTGjRspU6YMS5cupUSJElSpUoUxY8Zw6NAhefBEiGwq0SLu8uXLLF269KvXPn78SNGiRYmPj/+qDcHt27cTLqGWKVOG27dvf/WesbGxqnILkShDQ0N27dqFt7c3devWZeLEicTHx6s1U5EiRZg9ezYxMTG8ffuWcuXK4ePjk+nvL7OwsCAwMJAHDx4wZMgQtm7dipGRER4eHhw5ckRuo0glDQ0NrK2tGTJkCHv27OHZs2dMnToVpVLJiBEjMDAwwMXFhZkzZxITEyPftxDZRKJFXI4cOZg/fz579uzhy5cvnDhxgp07d9KxY0ecnJwIDAzk/fv3xMTEsGPHDtzc3ABo0qQJwcHBPHr0iGfPnrFkyRKaNm2a5h9IiP9SKBR0796dc+fOcfjwYRwcHLh06ZK6Y2FoaMjChQuJiori8ePHmJqa4u/vz6tXr9QdLVW0tbVp1qwZYWFhXL16FXNzc3r16oWpqSmTJk3iwYMH6o6YJejq6lK7dm0mTpzIqVOnuHv3Lj169CA2NpaWLVsm7DIREhIi37kQWZkyCSIiIpSNGzdWWltbK+vXr6/cvXu3UqlUKl+8eKHs37+/0t7eXlmrVi3lxo0bE86Jj49Xzpw5U1mtWjVl5cqVlQEBAcr4+PikTKe8f/++0tTUVHn//v0kHS9EUnz58kW5dOlSZcGCBZWTJk1Sfvr0Sd2REsTGxiq7dOmiNDAwUE6aNEn5119/qTuSynz58kV58uRJpZeXlzJ//vzKhg0bKtevX6/88OGDuqNlWbdv31YuW7ZM2aZNG2WBAgWUZmZmyr59+yq3bt2qfPnypbrjCSGSKLF6SKFUZrx198Q2fBUiNe7evUv37t15/fo1oaGhmJubqztSgmvXruHv78+BAwcYNmwYffr0SbjPNCt49+4dW7ZsISQkhOjoaNq1a4eHhwcVK1ZUd7Qs68uXL0RHRxMeHs7+/fs5ceIElpaW1KtXD2dnZxwcHNDW1lZ3TCHEdyRWD8m2WyLbKVmyJOHh4Xh4eFCzZk2mTp2q9nvl/lWuXDnWrVuX8MvWxMSEuXPnZplGsDly5KBjx47s37+fM2fOULBgQZo3b46NjQ1z5szh2bNn6o6Y5WhoaFCxYkWGDx/Ovn37ePr0KQEBAXz8+JFBgwZRsGBBGjduzOzZs7l8+bLcTydEJiIrcSJbu3PnDt27d+ft27eEhoZSrlw5dUf6yvnz5/Hz8+PChQv4+Pjg4eGBjo6OumOp1JcvXzh06BAhISFs374dZ2dnunXrRoMGDdDS0lJ3vCzv2bNnHDx4kPDwcMLDw/n48SPOzs4JP8WKFUu3LAsWLGDLli3cu3fvp62BFAoFenp6lC1blj59+tCgQYN0yyhEekq0Hkq/K7tJJ/fEifT0+fNn5YIFC5QFChRQTps2Lcn3bqankydPKhs0aKAsVaqUMjg4OEPdz6dKL1++VC5ZskRZpUoVZbFixZQjRoxQXrt2Td2xspWbN28qFy9erGzVqpXyl19+UZqbmysHDBig3L59u/L169dpNu+UKVOUOXPmVALJ+ilevLhy//79aZZLCHVKrB6Sy6ki29PQ0KBPnz6cPn2anTt3UqNGDa5fv67uWF+pUqUKe/bsYdWqVaxevZry5cuzevXqLLdTQt68eenZsycnT54kPDycz58/U6tWLapVq0ZwcDB//fWXuiNmecbGxnh5ebFx40aePHnCihUrKFy4MLNmzaJYsWLUqFEDf39/jh8/rrJG2kqlko0bN/L27dtkn/v777+zZMkSleQQIrORIk6I/2NsbMyBAwfo0KED1apVY+bMmRmuSKpevToHDhxg6dKlLF68GAsLC9avX58l99k0Nzdn+vTp3L9/n5EjR7Jjxw6MjIzo2rUrkZGRcu9WOtDU1MTOzo5Ro0YRERHB48eP8fX15c2bN3h7e2NgYEDTpk2ZN28e165dS/F/k4cPH3Lz5s0U54yLi0vxuUJkZlLECfEfGhoa9O3bl1OnTrF161Zq1aqVIbeSqlOnDkeOHGH27NkEBgZiY2PDli1bsmRho62tjZubG1u2bOHGjRtYW1vj7e2NiYkJEyZM4P79++qOmG3kyJGD+vXrM336dM6fP09sbCzt27cnOjqaBg0aJOwysWbNmmRtLff27dtU7TohO1aI7EqKOCG+o0yZMhw6dIg2bdrg6OjI7NmzM9xql0KhoEGDBpw6dYqJEycyfvx47Ozs2LlzZ5Ys5gAKFSrE4MGDuXjxIr/++isPHz7ExsaGBg0a8Ouvv2aZp3gzCwMDA9q1a0dQUBB37tzhwIED2Nvbs3nzZsqXL4+VlRWDBw9m9+7diV4q/d6/2WnTphEXF8dff/3FjRs38Pb2TquPIkSmJEWcED+goaFB//79OXHiBJs2baJ27doZ8rKNQqHAzc2NqKgoRo8ezYgRI3B0dCQ8PDzLFnMKhQJ7e3sWLlzIgwcPcHd3Z/ny5RQvXhxvb2/Onj2bZT97RqVQKBKeFv3tt994+vQpy5Yto0CBAkydOpUiRYpQu3ZtJkyYwKlTp5LU1ufdu3c0adKEPHny0Lx5c8aOHUuLFi3S4dMIkTlIESdEIsqWLUtkZCTNmzfHwcGBefPmZbhVOfin6GzZsiUxMTEMHDiQfv36UatWLQ4dOqTuaGlKX1+f9u3bs2/fPs6fP0+RIkVo27Yt1tbWzJo1i6dPn6o7YrakpaVFlSpV8PHx4dChQzx69IgRI0bw4sULevTogYGBAS1atGDhwoVf7bP9X+PGjePKlSsolUouX77M4cOHqVGjRjp/EiEyLinihEgCTU1NBg0axLFjx1i3bh1169bl1q1b6o71XRoaGrRr145Lly7h6elJ9+7dcXJy4vjx4+qOluZKlCiBr68vsbGxzJ07lwsXLlC2bFlatGjB9u3bM0xT5+woZ86cuLi4EBgYSExMDFevXqVly5acOXOGLl26JHopXEtLi0qVKnHhwoX0CSxEJiBFnBDJYGZmxpEjR3Bzc6Ny5cosWLAgQ67KwT+/9Lp06cK1a9do37497du3x8XFhTNnzqg7WprT0NCgdu3arFixgnv37tGoUSMmT56MkZERw4cP5+rVq+qOmO0VKVKEjh07EhISwuHDh9HV1f3p8bNmzeLevXusWrUqnRIKkfFJESdEMmlqajJkyBCOHj3KqlWrcHJy+uHloIxAW1sbT09Pbty4gZubG82bN6dp06bZZkUjT548eHp6cvz4cQ4ePIiGhgZOTk44OjqydOlSXr16pe6I2Z5CoUChUPzwfR8fH2rUqEGzZs0y7B9NQqiDFHFCpFC5cuU4duwYLi4u2Nvbs2jRogz9C0ZXV5c+ffoQGxtLnTp1cHFxoXXr1ly+fFnd0dJNuXLlmDJlCvfu3WPMmDHs3buXkiVL0rlzZw4ePJih//tlV8OGDaNDhw44Ozvz4sULdccRIkORIk6IVNDU1GT48OEcPnyYkJAQ6tevz927d9Ud66f09fUZOHAgcXFxVK5cmbp169KxY0du3Lih7mjpRktLC1dXVzZv3kxcXBx2dnYMHDiQMmXKMH78+Az/3zC7GDJkCB4eHtStW5dnz56pO44QGY4UcUKogLm5OcePH8fZ2Rk7OzuWLl2a4Vtc5MyZk2HDhhEXF4e5uTnVqlXD3d09wz6wkVYKFizIgAEDuHDhAps3b+bJkydUqlQJZ2dn1q5dy/v379UdMduaMWMGpUuXTugV99dff7Fr1y51xxIiw5AiTggV0dLSYuTIkRw6dIilS5fSoEED7t27p+5YicqdOzc+Pj7ExsZSsmRJ7O3t6dmzZ6bIrkoKhQJbW1vmz5/PgwcP6NGjBytXrsTQ0JDevXtz+vTpDF+YZzUKhQI9PT1y586d8NOoUSN1xxIiw5AiTggVq1ChAidPnqR27dpUqlSJoKCgTPHLP1++fPj7+3Pjxg0KFCiAjY0Nffv25eHDh+qOlu709PRo27Yte/bs4cKFCxgaGtKhQwcsLCwIDAzk8ePH6o4ohBBSxAmRFrS0tBg9ejQHDhxg0aJFuLi48ODBA3XHSpICBQowefJkrl27hp6eHhYWFgwePDjbFi5GRkYJK5WLFi3i0qVLmJmZ0axZM8LCwvj06ZO6Iwohsikp4oRIQ5aWlpw8eZJq1apRsWJFQkJCMsWqHPyzT+mMGTO4fPky8fHxlC9fnhEjRmTbG8wVCgU1a9YkJCSE+/fv06RJE2bMmIGRkRFDhw7NVk/5qpqOjg7a2topPl9LS0uFaYTIPKSIEyKNaWtr4+vrS0REBHPnzqVx48b8/vvv6o6VZEWLFmXu3LlER0fz6tUrzMzM8PX15eXLl+qOpja5c+fGw8ODI0eOcPjwYXR0dKhfvz5VqlRh8eLF2fq7SYnixYtTvHjxVJ0vRHYkRZwQ6cTKyorTp09TuXJlbGxsWLFiRaZZlYN/LisuXryYs2fP8vvvv2NiYkJAQACvX79WdzS1MjU1ZdKkSdy7d49x48Zx4MABSpUqRceOHYmIiJDec0mgra1NzZo1U3Sunp4eTZs2VXEiITIHKeKESEfa2tqMHTuW8PBwZs6ciZubW6Z7cKB06dIsX76cEydOcOPGDUxMTJg6dSpv375VdzS10tTUxMXFhQ0bNnDz5k0cHBwYOnQoxsbGjB07NkPv6pERLFiwgG7dulGmTBk0NTUTPV5XVxcjIyPy5MlDt27d0iGhEBmPFHFCqIGNjQ1nzpzB1tYWGxsbVq1alalW5QDKli3LqlWriIyM5Ny5c5QpU4aZM2dKXzX+eTikX79+nD9/nq1bt/Ly5cuExsqrV6/m3bt36o6Y4WhpabF8+XIuXrxIVFQUx44d++7P7t27yZEjB0ePHuX27dvY29szdepUdccXQi0Uygz4m+PBgwc4OTkRERGBoaGhuuMIkabOnTuHu7s7pUuXZsmSJRQpUkTdkVIkJiaGsWPHcvr0aUaNGkWPHj0S3dQ8O/n48SPbt29n+fLlnDx5klatWuHh4UGVKlV+um+o+JaLiwvu7u60bduWe/fuYWtry5EjRyhfvry6owmhUonVQ7ISJ4Sa2dracubMGSwtLbG2tmbNmjWZblUO/rnnb8uWLWzbto3du3dTtmxZli5dKi04/o+uri6tWrVi165dXLx4kdKlS9OlSxfMzc2ZPn06jx49UnfETKNVq1Zs3LgRgBIlSuDv74+np6fcfyiyHSnihMgAdHV1mTBhAjt37mTSpEm0aNEi0/Zlq1SpEjt37mT9+vVs2rQJMzMzQkNDiY+PV3e0DKN48eKMGjWK69evs2zZMq5du0b58uVp0qQJW7Zs4e+//1Z3xAytWbNmhIeHJ9yH2bt3bxQKBYsWLVJzMiHSlxRxQmQgdnZ2nDt3jvLly2NlZcWvv/6aKVflABwdHdm3bx+hoaGEhIRgbm7OmjVr+Pz5s7qjZRgKhYLq1asTHBzM/fv3adGiBbNnz8bIyIjBgwdz8eJFdUfMkAoUKICDg0PCPqoaGhoEBQUxbty4bLddnMjepIgTIoPR1dVl0qRJbN++nfHjx9O6dWuePHmi7lgpVrNmTQ4dOsSiRYtYsGABVlZWbNy4US59/Y9cuXLh7u5OZGQkx44dI0eOHDRq1Ah7e3sWLlzIixcv1B0xQ/nvJVWAcuXKMWDAAHr37p1p//ARIrmkiBMig6pcuTLnzp3DxMQEKysrNmzYoO5IKaZQKHBycuLYsWPMmDGDadOmYWtrS1hYmPzC/Q4TExMmTJjAnTt3mDBhAocPH6Z06dK0b9+effv2yWom0Lx5c/bu3fvVk77Dhw/n/v37rFu3To3JhEg/UsQJkYHp6ekxZcoUtm7dip+fH23atOHp06fqjpViCoUCFxcXTp8+jb+/P35+flSuXJndu3dLMfcdmpqaNGjQgF9//ZVbt25RvXp1Ro8eTenSpfH19eXmzZvqjqg2BQsWTPi38y8dHR2CgoIYPHhwtt0eTmQvUsQJkQk4ODhw/vx5SpYsiZWVFZs3b1Z3pFRRKBQ0bdqU8+fPM3z4cIYMGUK1atWIiIiQYu4HfvnlF7y9vTl79iw7duzgzZs3ODo6Urt2bVasWJEtmy23atWKTZs2ffVa5cqV6dChAwMHDlRPKCHSkRRxQmQS+vr6TJ8+nc2bNzN69GjatWuX6VcbNDQ0aN26NRcvXqRv37707t2b2rVrc/jwYXVHy9CsrKyYNWsWDx48oH///mzcuBFDQ0M8PT05fvx4timEmzdvzu7du79pMB0QEMDx48e/WqUTIiuSIk6ITKZq1apcuHCB4sWLJ/Rmy+w0NTXp0KEDV65coVu3bri7u1OvXj1Onjyp7mgZmo6ODi1atGDHjh1cuXIFU1NTPDw8KF++PFOnTs10W7olV6FChbC1tWXPnj1fvZ4zZ06WLl1K7969+euvv9SUToi0J0WcEJmQvr4+gYGBbNiwgeHDh9OxY0eeP3+u7lippqWlhbu7O9euXaN169a0adMGV1dXoqKi1B0twytatCjDhw/n6tWrhISEEBcXh4WFBa6urmzevDnL9p5r3br1N5dUAZydnalTpw4+Pj5qSCVE+pAiTohMrHr16kRHR2NgYIClpSVhYWHqjqQSOjo69OzZk9jYWFxcXGjSpAnNmzcnJiZG3dEyPIVCgaOjI8uWLeP+/fu0a9eO+fPnU7x4cQYOHEh0dLS6I6pUixYt2LlzJx8+fPjmvcDAQDZt2sSJEyfUkEyItCdFnBCZXI4cOZg9eza//vorgwcPpnPnzvz555/qjqUSurq69O3bl7i4OGrWrEn9+vVp27YtV69eVXe0TCFnzpx07tyZgwcPcurUKfLkyYObmxuVKlVi/vz5WeLfSeHChbGxsWHv3r3fvPfLL78we/ZsunfvzsePH9WQToi0JUWcEFlEzZo1iYmJIX/+/FhZWbFjxw51R1IZfX19Bg0axM2bN7G1taVWrVp07tyZ2NhYdUfLNIyNjRk/fjy3b99mypQpHD9+HGNjY9q2bcuePXsyde+5H11S/fe9smXLMmnSpHROJUTakyJOiCwkZ86czJ07l9WrV9O/f3+6du2apTr958yZkxEjRhAXF4epqSmOjo54eHhw+/ZtdUfLNDQ1NalXrx5r167l9u3b1K5dGz8/P0qWLImPj0+mLIz/fbjje6ttCoWChQsXsnDhQi5duqSGdEKkHSnihMiCateuTUxMDLlz58bS0jJhj8msIk+ePPj6+hIbG0vx4sWxs7OjV69e3L9/X93RMpX8+fPTu3dvTp8+zZ49e/jw4QPVq1enZs2ahISE8ObNG3VHTJKiRYtiaWlJeHj4d98vXrw4EyZMwNPTM1OvOArxv6SIEyKLypUrF/Pnz2flypV4e3vj4eHBy5cv1R1LpfLnz09AQADXr18nb968WFtb079/f/744w91R8t0LCwsCAwM5P79+wwePJitW7diZGSEh4cHR44cyfC951q3bv3VXqr/q0ePHujq6jJ//vx0TCVE2pIiTogsrm7dusTExKCrq4ulpeU3PbWygoIFCzJ16lSuXr2KlpYWFSpUYOjQoTx58kTd0TIdHR0dmjVrRlhYGFevXsXc3BwvLy/MzMyYNGkSDx48UHfE72rZsiXbt2//4QMMGhoaLFu2jICAAO7cuZO+4YRII1LECZEN5M6dm0WLFhESEkKvXr3w9PTk1atX6o6lcoULF2bmzJlcunSJDx8+UL58eUaNGpUleuipQ5EiRRg6dCiXL19m1apV3L17FysrK1xcXNiwYUOGeuKzWLFimJubExER8cNjTE1NGTJkCF5eXhl+ZVGIpJAiTohsxNnZmZiYGDQ1NbG0tGTfvn3qjpQmihUrxvz58zl//jzPnz/H1NSUsWPHZrnLyelFoVBQpUoVlixZwoMHD+jUqRNLliyhePHi9OvXj/Pnz6s7IpD4JVUgYYV21apV6ZRKiLQjRZwQ2UyePHlYsmQJQUFBeHp60rNnT16/fq3uWGmiRIkSLF26lDNnznD37l3Kli3LxIkTZSumVMiRIwcdO3YkIiKCM2fOUKBAAZo3b46NjQ1z585V636+LVu2ZNu2bT/dnUJbW5ugoCCGDRsml9tFpidFnBDZVP369bl48SJfvnzBysqK/fv3qztSmjE2NiY0NJSjR49y5coVTExMmD59Om/fvlV3tEytdOnSjBs3jlu3bjFz5kxOnz6NiYkJrVq1YteuXcTHx6drHkNDQ8zMzDhw4MBPj6tUqRJdu3ZlwIAB6ZRMiLQhRZwQ2VjevHkJCgpi8eLFeHh4ZPkNw83MzFizZg0HDhxIKDhmz5793S2bRNJpaGhQt25dVq9ezd27d6lXrx7jx4+nZMmSjBo1ihs3bqRblqRcUgUYN24cZ86cyVJNsUX2I0WcEIKGDRsSExPDx48fsbKySnQlI7OrUKECGzduZPfu3Rw8eBATExMWLlyYoW7Uz6zy5s2Ll5cXJ0+eJDw8nPj4eGrWrEn16tUJDg5O8z8SWrZsSVhYGJ8+ffrpcTly5GDZsmX06dMny95OILI+KeKEEADky5eP5cuXs2DBArp06YK3t3emafaaUjY2NoSFhbFlyxa2b9+OmZkZQUFBiRYAImnMzc2ZPn069+/fZ/jw4ezYsQMjIyPc3d2JjIxMkydEixUrRqlSpQgLC+PPP//87s+rV6/48uULderUoX79+owcOVLlOYRID1LECSG+0qhRIy5evMibN2+wsrIiMjJS3ZHSnL29Pbt372bt2rX8+uuvlC9fnpUrV6b7PV1Zlba2Nk2aNGHLli3cuHEDKysrvL29KVu2LBMmTFDJThuXL1+madOmmJmZcf36dTp37ky5cuW++2NmZoaFhQWdO3fGz8+PsLAwjhw5ooJPKkQ6U2ZA9+/fV5qamirv37+v7ihCZGvbtm1TFitWTNmvXz/lmzdv1B0n3Rw8eFBZvXp1pZmZmXLt2rXKz58/qztSlvPlyxfl6dOnlb1791b+8ssvyvr16yvXrVunfP/+fbLHev78ubJ8+fJKINk/Tk5Oyk2bNilNTU1TNLcQaSmxekhW4oQQP+Tm5sbFixd58eIF1tbWHD58WN2R0kXt2rU5fPgw8+bNY86cOVhZWbF582a+fPmi7mhZhkKhwN7enoULF/LgwQPc3d0JDg7G0NAQb29voqKikny5dfHixVy9ejVFOU6ePImxsTEWFhZMmDAhRWMIoS5SxAkhfuqXX35h1apVBAYG0q5dOwYOHMi7d+/UHSvNKRQK6tWrx4kTJ5g6dSqTJk2iUqVKbN++Xbr9q5i+vj7t27cnPDycc+fOUaRIEVq3bo21tTWzZs3i6dOnPz3/ypUrKZ777du3bN26lfnz57N06VJiYmJSPJYQ6U2KOCFEkjRt2pSLFy/y9OlTrK2tOXr0qLojpQuFQoGrqytnz57Fz8+P0aNH4+DgwN69e6WYSwMlSpTA19eXuLg45s6dy/nz5ylbtiwtWrRgx44d371P8WfNfZPi48ePFC1alMmTJ9O9e3e5F1JkGlLECSGSrECBAqxZs4Zp06bRpk0bhgwZwvv379UdK10oFAqaN29OdHQ0gwcPZuDAgdSoUYODBw+qO1qWpKGhQe3atVm5ciX37t3DxcWFSZMmYWRkxIgRI7h27VqiYyxbtox79+7x6tUrbt26xejRo396vIeHB7lz52bOnDmq+hhCpCkp4oQQyda8eXNiYmJ4+PAhNjY2nDhxQt2R0o2GhgZt27bl0qVL9OrVi549e1K3bt1sszKpDnny5KFHjx4cP348oWiuW7cujo6OLFu27IctYebPn4+5uTl58+alatWqdOjQAXd39x/Oo1AoWLp0KZMnT+bmzZtp8VGEUCkp4oQQKVKwYEHWrVvHpEmTaNGiBcOGDcs2q3IAmpqadOrUiatXr9KpUyc6d+5MgwYNOH36tLqjZWnlypVj6tSp3Lt3Dx8fH/bs2fPDXReio6MTeh3q6OgAYGVl9dPxTUxMGDFiBD179pTL5SLDkyJOCJEqLVu2JCYmhrt371KxYkVOnjyp7kjpSktLCw8PD65fv07z5s1p2bIlbm5unD9/Xt3RsjQtLS0aN27M5s2badSo0Q+PGzFiBG/evOHu3bsUKlSIFStWJDr2oEGDePXqFaGhoSpMLITqSREnhEg1AwMDNmzYwPjx42nWrBkjRozIdvuR6ujo0KtXL2JjY6lXrx6urq60bNmSS5cuqTtalqerq/vD96ZOnUquXLmoXLky8+fPJy4uLtHxtLS0CAoKYsSIETx69EiVUYVQKSnihBAq06ZNG2JiYrh58ya2tracOXNG3ZHSnZ6eHv379ycuLo6qVavi7OxM+/btk3Qjvkg7Z86c4f3790l+aMHGxgZPT0/69euXxsmESDkp4oQQKlWoUCE2btyIn58fjRs3ZtSoUdlyY/kcOXIwZMgQ4uLisLKyokaNGnTp0kVumFcjhUKBra1tko/39fUlOjqarVu3pl0oIVJBijghhMopFAratWtHdHQ0165do1KlSpw9e1bdsdQiV65cjBo1iri4OMqUKUOVKlXw9PTk7t276o6WpRUuXJhevXpRoEAB4J+Vtb59+yZrL2B9fX2CgoLo27cvL1++TKOkQqScFHFCiDRTpEgRfvvtN0aPHk2jRo0YM2ZMtlyVA8ibNy9jx47lxo0bFC5cGFtbW/r06cPvv/+u7mhZ0ufPn2nevDmXL1/mzZs3hIWFsWHDBkaOHJmscWrWrEnjxo0ZPnx4GiUVIuWkiBNCpCmFQkGHDh2Ijo7m4sWL2NnZce7cOXXHUptffvmFiRMncu3aNXLmzImlpSUDBw6UG+hV7NmzZzRo0IAiRYqQK1cuSpYsydChQ1P0R8TUqVPZvXs3hw4dUn1QIVJBijghRLooWrQoW7duZcSIETRs2BA/P79Ub5eUmRkYGDB9+nSuXLmCUqnE3Nyc4cOHJ7pPqEh/efPmZcGCBfTo0SNb9UIUGZ8UcUKIdKNQKOjUqRMXLlzg/Pnz2Nvbc+HCBXXHUqsiRYowZ84cYmJiePPmDeXKlcPHx4c///xT3dHEfzRp0gRbW1v8/f3VHUWIBFLECSHSXbFixdi2bRuDBw+mfv36+Pv7/3DrpOzC0NCQhQsXEhUVxePHjzE1NcXf359Xr16pO1qGp62tnarz/93NITFz585l+fLl2fp2AJGxSBEnhFALhUJB165dOX/+PKdOnaJy5cpER0erO5balSpViqCgIE6ePMnNmzcpW7YskydPTtg+SnzL1NQ0xefmyJEDV1fXJB1buHBhpk2bhqenJ/Hx8SmeUwhVkSJOCKFWxYsXZ+fOnfTv3x9nZ2cCAgKy/aoc/LOH58qVKzl8+DDR0dGYmJgQGBjIu3fv1B0tw+nTpw9ly5ZN0bnFihWjcuXKST6+a9euFCxYkMDAwBTNJ4QqSREnhFA7hUJBt27dOHfuHMeOHcPBwYGLFy+qO1aGUK5cOX799VfCw8M5fvw4JiYmzJ07N9tta/YzBgYGrF27lgYNGmBoaEiePHnInTv3Nz+5cuUCIF++fJiYmNCgQQOeP3/OlStXkjyXQqFgyZIlTJ8+ndjY2LT6SEIkiRRxQogMw8jIiN27d9O7d2/q1q3LxIkT5bLV/7G0tGTz5s3s3LmT8PBwypYty+LFi7P1E77/ZWdnx549e7h27RqXLl367s/ly5exs7Nj7ty5XL58mT179jBt2jQ6dOiQrKK4dOnS+Pj40KNHD758+ZKGn0qIn0tyEffs2TMcHR05ePAgAK9evcLb25tKlSpRu3ZtNm7cmHCsUqkkMDAQBwcH7O3tmTBhAp8/f1Z9eiFElqNQKPD09CQqKorIyEgcHR25fPmyumNlGBUrVmT79u1s2rSJLVu2YGZmxvLly6XY/T85c+bEyMiIEiVKfPenTZs2HDt2LOFhhu7du2NiYsKoUaOSNU///v15//49wcHBafExhEiSJBdxPj4+X2074uvrS44cOTh+/Dhz585lxowZCRs8r1mzhkOHDrFt2zZ27drFuXPnWLt2rcrDCyGyrhIlSrB371569OhB7dq1mTJlihQq/1GlShX27t3LqlWrWLVqFeXLl2f16tXyB3MimjRpwvbt2xNW0BQKBcuWLWPTpk3s2bMnyeNoamoSFBTE6NGjefjwYVrFFeKnklTErVu3Dn19fYoWLQrA27dv2b9/P/3790dXVxcrKysaN26csBoXFhZG165dKVSoEAYGBnh5ebFhw4a0+xRCiCxJoVDQs2dPzp49y/79+6lWrVqy7l/KDqpXr87BgwdZsmQJixYtwsLCgg0bNshlvh8wMzMjd+7cREVFJbz2yy+/sGLFCjw8PHjy5EmSx7K0tKRXr154e3ujVCrTIq4QP5VoEXfnzh1CQkIYN25cwmt3795FS0sLIyOjhNdKly6dcJPnrVu3MDEx+eq9uLg4+UcuhEiRkiVLEh4eTrdu3ahZsybTpk2TFaf/UbduXY4ePcrs2bOZMWMGNjY2bNmyRf5/9zuaNm3Ktm3bvnqtbt26dO7cme7duyfrOxszZgzXrl1j8+bNqo4pRKJ+WsTFx8czbNgwfHx8yJcvX8Lr7969Q09P76tj9fT0Em4Mff/+/Vfv6+vr8+XLF7kBVwiRYgqFgl69enHmzBn27NlD9erVE27hEP9QKBQ0aNCAU6dOMXHiRMaPH4+dnR07d+6UYu4/mjRpQlhY2DevBwQE8Mcff7Bo0aIkj6Wrq0tQUBD9+/eXXTZEuvtpEbdw4ULKly9PrVq1vnpdX1//myd5Pnz4QI4cOYB/Crr/bjL8/v17tLS00NXVVVVuIUQ2Vbp0afbv30+nTp2oXr06gYGBsir3PxQKBW5ubkRFRTF69GhGjBiBo6Mj4eHhUswBDg4OPHr0iNu3b3/1uo6ODmvWrGHs2LHJepimWrVqtGjRgmHDhqk6qhA/9dMibteuXezcuRM7Ozvs7Ox4+PAhgwcP5tChQ8THx391M+ft27cTLqGWKVPmq/9x3L59G2Nj4zT6CEKI7EZDQwNvb29Onz7N9u3bqVmzJjdu3FB3rAxHQ0ODli1bEh0dzYABA+jbty+1atUiMjJS3dHUSlNTk8aNG39zSRX+uWdu8uTJyW47MnnyZPbv309ERIQqowrxUz8t4vbs2UNUVBRnz57l7NmzFCtWjJkzZ+Lt7Y2TkxOBgYG8f/+emJgYduzYgZubG/DPUnVwcDCPHj3i2bNnLFmyhKZNm6bLBxJCZB/GxsYcOHCAdu3aUbVqVWbNmiWrct+hqalJ+/btuXz5Mp6ennh4eODs7Mzx48fVHU1tmjZt+t1LqvBP25EyZcokq+1I7ty5WbhwIT179pRdNUS6SXGz34CAAOLj46lVqxb9+/dn2LBhWFtbA9ChQwfq1q1Lq1atcHV1xdbWlm7duqkstBBC/EtDQ4N+/fpx8uRJfvvtN2rVqiWd9H9AS0uLLl26cO3aNdq1a0f79u1xcXHhzJkz6o6W7urVq8fZs2d58eLFN+/9t+3I3r17kzymq6srVapUwc/PT5VRhfgxZQZ0//59pampqfL+/fvqjiKEyEQ+f/6snDVrlrJAgQLK2bNnKz9//qzuSBnahw8flAsWLFAWL15c2aRJE+WFCxfUHSldubm5KVevXv3D9yMiIpTFihVTPnnyJMljPnnyRFmoUCHl6dOnVRFRZHOJ1UOy7ZYQIsvQ0NBg4MCBnDhxgo0bN1KnTh1u3ryp7lgZlq6uLn369CE2NpY6derQsGFDWrdunW12yPjZJVX4p+1Ip06d8PDwSPIDIQYGBgQGBuLp6cmnT59UFVWI75IiTgiR5ZQtW5bIyEiaNm1KlSpVmDdvnjS//Ql9fX0GDhxIXFwclStXpm7dunTs2DHLPyzSuHFj9u3b91U3hf8VEBDAw4cPk9V2pGPHjhQvXpzp06erIqYQPyRFnBAiS9LU1GTw4MEcO3aMtWvXUrduXW7duqXuWBlazpw5GTZsGHFxcZibm1OtWjXc3d2z7PdWuHBhzM3NOXTo0A+P0dHRYe3atfj5+SV5hVKhULBo0SJmzpzJ9evXVZRWiG9JESeEyNLMzMw4evQojRs3pkqVKixcuFBW5RKRO3dufHx8iI2NpWTJktjb29OzZ0/u3bun7mgq973dG/7Xf9uO/GzV7r9KlizJ2LFj8fT0lH9vIs1IESeEyPI0NTUZOnQoR44cYeXKldSrV487d+6oO1aGly9fPvz9/blx4wYFChSgYsWK9O3bN0tt+N6kSRO2bduW6D1vnp6eyW470qdPH+Lj41myZElqYwrxXVLECSGyjXLlynH06FEaNGiAvb09ixcvlh0MkqBAgQJMnjyZq1evoquri4WFBYMHD07WZvEZVbly5dDX1+fcuXM/Pe7ftiMbN25k3759SRpbU1OToKAg/Pz8ePDggSriCvEVKeKEENmKlpYWw4cPJzIykuXLl1O/fn3u3r2r7liZQqFChQgMDOTSpUt8+vSJcuXKMXLkSJ4/f67uaCmmUCgSVuMSU6BAAVasWEG3bt14+vRpksavUKECffv2pXfv3vIHg1A5KeKEENmSubk5x48fp27dutjZ2bFs2TL5JZtExYoVY968eURHR/Py5UtMTU3x9fXl5cuX6o6WIom1Gvmvf5/cTU7bkVGjRnH79m3Wr1+fmphCfEOKOCFEtqWlpcWoUaM4ePAgS5YsoWHDhty/f1/dsTINIyMjFi9ezNmzZ/n9998xMTEhICCA169fqztaslStWpXff/89ySuyEyZM4OHDhyxevDhJx+vo6BAUFMSgQYMy9aqlyHikiBNCZHsWFhacOHGCmjVrYmtrS3BwsKzKJUPp0qVZvnw5J06c4Pr165iYmDB16lTevn2r7mhJoqmpiaura5IuqcL/bzvi6+vLlStXknSOg4MDbdu2ZfDgwamJKsRXpIgTQghAW1sbHx8fIiIiWLBgAY0aNZKb0ZOpbNmyrF69mkOHDhEVFUWZMmWYNWsW79+/V3e0RCXnkiqkrO3IhAkTiIyMTNZ+rEL8jBRxQgjxH1ZWVpw6dQpHR0cqVqxISEiIrMolk7m5ORs2bGDfvn0cPnwYExMT5s+fn+RiRx3q1avH6dOnk3Vfn6enJ8bGxkluO5IrVy6WLFlCr169ePPmTQqTCvH/SREnhBD/Q1tbGz8/P/bv38/cuXNp3Lgxv//+u7pjZTpWVlZs2bKFbdu2sXv3bsqWLcvSpUsz5J6iuXLlombNmuzevTvJ5/zbduTfgjUpGjRoQI0aNRgzZkxKowqRQIo4IYT4AWtra06dOoW9vT0VK1Zk5cqVsiqXApUqVWLnzp2sX7+ejRs3YmZmRmhoKPHx8eqO9pWk7N7wv1LSdmTWrFmsX7+ekydPpiSmEAmkiBNCiJ/Q0dFh3Lhx7N27lxkzZtCkSZMstWNBenJ0dCQ8PJzQ0FBCQkKoUKECa9eu5fPnz+qOBkDjxo3Zs2cPf//9d7LOc3JyomPHjnTv3j1JRX6BAgWYNWsWnp6eyZ5LiP+SIk4IIZKgYsWKnD17looVK2JjY8Pq1atlVS6FatasyaFDh1iwYAHz5s3DysqKTZs2qX2P0aJFi2JmZkZkZGSyz50wYQK///57ktuOtG3bltKlSzNlypRkzyXEv6SIE0KIJNLR0WH8+PHs3r2bqVOn0rx5cx49eqTuWJmSQqHA2dmZ48ePM2PGDKZMmYKtrS1hYWFqLY6TunvD/0pu2xGFQsHChQuZN29ektuUCPG/pIgTQohkqlSpEmfPnsXCwgJra2vWrVsnq3IppFAocHFx4cyZM/j7++Pn50flypXZvXu3Wr7Tf1uNpGRuMzMzJk2alOS2I0ZGRvj7++Pp6ZlhLimLzEWKOCGESAFdXV0mTJjAjh07mDBhAi1btuTx48fqjpVpKRQKmjZtyvnz5xk+fDhDhgyhWrVqREREpGsxZ25ujo6ODtHR0Sk6v0ePHhgbGzN69OgkHd+rVy80NTVZuHBhiuYT2ZsUcUIIkQr29vZERUVhZmaGtbU169evl1W5VNDQ0KB169ZcvHgRb29vevfuTZ06dThy5Ei6zK9QKGjSpEmyGv/+7/nLli1j/fr1SWo7oqGhwbJly/D390/ytl9C/EuKOCGESCU9PT0mT55MWFgY48aNo3Xr1jx58kTdsTI1TU1NOnbsyJUrV3B3d6dr167Ur18/XdpyJHf3hv+V3LYj5cqVY9CgQfTu3Vv+ABDJIkWcEEKoSJUqVTh//jzGxsZYWVmxceNGdUfK9LS0tHB3d+fatWu0atWKNm3a4OrqSlRUVJrNWa1aNe7du8f9+/dTPEZy244MHz6cBw8esHbt2hTPKbIfKeKEEEKF9PT0mDZtGlu3bsXX15e2bdvy7NkzdcfK9HR0dOjZsyexsbG4uLjQpEkTmjdvTkxMjMrn0tLSolGjRil6SvW/JkyYwIMHD1iyZEmix2praxMcHMyQIUOS3DRYCCnihBAiDTg4OHD+/HmMjIywtLRk8+bN6o6UJejq6tK3b1/i4uKoUaMG9evXp23btly9elWl86Rk94b/9d+2I0nJZ29vT8eOHRk4cGCq5hXZhxRxQgiRRvT19ZkxYwabNm1i1KhRtG/fnufPn6s7Vpagr6/P4MGDiYuLw9bWllq1atG5c2diY2NVMn79+vU5ceIEr169StU45cqVY+LEibRv3z5JbUfGjx/PyZMn2blzZ6rmFdmDFHFCCJHGqlWrxoULFyhatCiWlpZs3bpV3ZGyjFy5cjFixAji4uIwNTXF0dERDw8P7ty5k6pxc+fOTfXq1dmzZ0+qMyan7UjOnDlZsmQJffr04a+//kr13CJrUygz4KMwDx48wMnJiYiICAwNDdUdRwghVObo0aN069aNKlWqMHfuXH755Rd1R8pSXrx4wcyZM1m4cCGtW7fGx8cHIyOjZI3x7NkzQkJC2LdvHw8ePKBRo0bfPU5DQ4Py5cvTvn179PX1fzrm8+fPsba2Zvny5dSvXz/RDB4eHuTMmZN58+YlK7vIWhKth5QZ0P3795WmpqbK+/fvqzuKEEKo3Nu3b5UDBgxQFitWTBkWFqbuOFnS06dPlcOHD1fmz59f2a9fP+XDhw+TdF5AQICySJEiSiDJP2XLllVu37490bHDw8OVxYoVUz59+jTRY58/f64sWrSo8tixY0nKLbKmxOohuZwqhBDpLEeOHMyePZu1a9cyaNAgunTpwosXL9QdK0spWLAgU6dO5erVq2hpaVGhQgWGDh360yc/T58+zbRp05K9H25sbCxDhw5N9J43Z2dnOnTokKS2I7/88gtz5szB09MzSffSiexJijghhFCTWrVqERMTQ968ebG0tGTHjh3qjpTlFC5cmJkzZ3Lp0iU+fPhAuXLlGD16NH/++ec3x4aGhqb4PrQbN26wZcuWRI+bOHEi9+/fT1LbkVatWmFqasrEiRNTlElkfVLECSGEGv1739OqVavo378/7u7uvHz5Ut2xspxixYoxf/58zp8/z7NnzzA1NWXs2LFffdffK+ySSqlUcvHixUSPS07bEYVCwYIFC1i0aFGSxhbZjxRxQgiRAdSpU4eYmBhy5syJpaUlu3fvVnekLKlEiRIsXbqU06dPc/fuXcqWLcvEiRP566+/+PLlS6rG/vz5c5KOS07bkeLFizNx4kQ8PT2TPL7IPqSIE0KIDCJXrlwsWLCAFStW0KdPH7p3757qPmXi+4yNjQkNDeXo0aNcvnwZExMTrl279tNz9PX1iYuLU0kvuh49elCqVCl8fHwSPdbT0xN9fX3mzp2b6nlF1iJFnBBCZDB169YlJiYGbW1tLC0t2bt3r7ojZVlmZmasXbuWiIiIRC+nTpgwIdkPPfyIQqEgKCiIX3/9lfDw8J8eq6GhwbJly5g4cSK3b99Wyfwia5AiTgghMqDcuXOzePFigoOD6dmzJz169OD169fqjpVlWVhYULVq1R++X7lyZRwcHAgODlbZnAULFiQ0NJRu3bolur9u2bJlGTp0KF5eXok+2SqyDynihBAiA6tXrx4XL15EoVBgaWmZ6KqNUD1tbW2WLVtG3759U33f3P9ydnamffv2SWo7MmTIEJ4+fcrKlStVmkFkXlLECSFEBpcnTx6WLl3K0qVL6d69O15eXrIlUzry9fXl0KFDnD9/Pk3GnzBhQpLajmhraxMcHMzw4cN5/PhxmmQRmYsUcUIIkUk0aNCAixcvEh8fj6WlJREREeqOlOVVqFCBLl26MGbMmDSbQ1dXl7Vr1zJmzJhE247Y2tri7u5O//790yyPyDykiBNCiEwkb968BAcHs2jRItzd3enTpw9v3rxRd6wsy87OjpIlS/L69WuUSiWhoaGYmJigVCopWbKkyub5t+1Ihw4dEm07Mm7cOM6dO8e2bdtUNr/InKSIE0KITMjFxYWLFy/y/v17rKysOHjwoLojZUkrVqxAoVAk/Li7uxMXF4dCoeDu3bsqnatnz56ULFky0bYj+vr6LF26FG9vb2lBk81JESeEEJlUvnz5CAkJYd68eXTu3Jl+/frx9u1bdccSKZSctiN16tShYcOGjBw5Mp3SiYxIijghhMjkXF1duXjxIq9fv8bKyorIyEh1R8qyVqxYQdmyZdNs/H/bjri7uyfadmT69Ols27aNw4cPp1kekbFJESeEEFlA/vz5WbFiBbNnz6ZDhw4MGDBAVuUyqX/bjnh6ev607Ui+fPmYP38+PXr04MOHD+mYUGQUUsQJIUQW4ubmxsWLF3n+/DnW1tYcOXJE3ZGyDQ0N1f1KnThxInfv3mXp0qU/Pa558+ZYWloyfvx4lc0tMg8p4oQQIov55ZdfWL16NYGBgbRt25ZBgwbx7t07dcfK8HLmzJmq84sUKaKiJP+0HVm3bh0+Pj6Jth2ZP38+QUFBXLhwQWXzi8xBijghhMiimjZtysWLF3n8+DE2NjYcO3ZM3ZEytAYNGqR4NU1DQwNdXV2V5klq25EiRYowZcoUPD09iY+PV2kGkbFJESeEEFlYgQIFWLt2LVOmTKFVq1YMGTKE9+/fqztWhtSqVSsaN26c7PP09fVxd3fH19dX5Q8ZJLXtSLdu3cibNy+zZ89W6fwiY9NSdwAhhBBpr0WLFtSsWZO+fftSsWJFQkJCcHR0VHesDEVLS4tNmzaxdOlSIiMjef369U+P19DQoGDBgrRo0YJmzZoRERFB69atiYyMpFy5cirJ9G/bERsbGxo2bIizs/MPj1u6dClVqlShefPmlClTRiXzi4xNoUxsx101ePDgAU5OTkRERGBoaKjuOEIIkaVs2rSJvn370qVLF8aPH4+enp66I2UZK1aswN/fnxMnTlC4cGGVjRseHk63bt24cOECBQsW/OFxM2bMYPfu3ezfvx+FQqGy+YV6JFYPyeVUIYTIZlq1akVMTAy3b9+mYsWKnDp1St2RsoyuXbvSpUsXGjdurNIWL/Xq1aNdu3aJth0ZOHAgr169Yvny5SqbW2RcUsQJIUQ2VKhQITZu3Ii/vz9NmjRh5MiR0mtMRcaOHUuFChVo3749nz9/Vtm4SWk7oqWlRXBwMKNGjeKPP/5Q2dwiY5IiTgghsrE2bdoQExNDbGwslSpV4syZM+qOlOn9e3/au3fvGDhw4E9XzpJDV1eXtWvX4uPjw7Vr1354nLW1NT169KBv374qmVdkXFLECSFENle4cGE2bdqEr68vjRs3xsfH56ctLUTidHR02Lx5M4cOHWLWrFkqG7d8+fJMmDAh0bYjvr6+XLp0id9++01lc4uMR4o4IYQQKBQK2rVrR3R0NJcvX8bOzo6oqCh1x8rU8ubNy65du5g5cyabNm1S2bheXl6UKFGCMWPG/PAYPT09li1bRr9+/Xj58qXK5hYZixRxQgghEhQpUoQtW7YwcuRIGjVqhK+vL3///be6Y2VaRkZGbN++nT59+nD8+HGVjPlv25F169axf//+Hx5Xs2ZNmjRpwrBhw1Qyr8h4pIgTQgjxFYVCQceOHblw4QLR0dHY2dlx7tw5dcfKtCpWrMjKlStp0aIFsbGxKhmzYMGChISE4O7uzrNnz3543NSpU9mzZw8HDx5UybwiY5EiTgghxHcVLVqUsLAwhg4dSsOGDRk7dqysyqVQw4YNCQgIwMXFhadPn6pkzKS0HcmTJw8LFy6kR48esn9uFiRFnBBCiB9SKBR06dKFCxcuEBUVReXKlYmOjlZ3rEypR48etGnThiZNmqhs67N/244sW7bsh8e4ublhZ2fHuHHjVDKnyDikiBNCCJGoYsWKsX37dgYOHEi9evXw9/fn06dP6o6V6UyYMAFjY2M6d+7Mly9fUj1eUtuOzJ07lxUrVshl8SxGijghhBBJolAocHd359y5c5w8eZIqVaoQExOj7liZioaGBsuXL+fZs2cqe+CgfPnyBAQE/LTtSKFChZg2bRrdu3eX4jsLkSJOCCFEshgaGrJr1y769u2Lk5MTEyZMkMIgGXR1ddmyZQu7du1i3rx5KhkzKW1HunTpQqFChQgMDFTJnEL9pIgTQgiRbAqFAg8PD86dO8eRI0dwdHTk0qVL6o6VaeTPn59du3YxZcoUwsLCUj3ev21H1q5d+8O2IwqFgiVLljBjxgxu3LiR6jmF+kkRJ4QQIsWMjIzYs2cPvXr1ok6dOkyePJn4+Hh1x8oUSpcuTVhYGD169OD06dOpHq9gwYKEhobi7u7O8+fPv3tMqVKlGDNmDD169FDJPXlCvaSIE0IIkSoKhQJPT0+ioqI4cOAAVatW5cqVK+qOlSnY2dkRHBxMs2bNuHXrVqrHq1evHm3btv1p25F+/frx8ePHnz7RKjIHKeKEEEKoRIkSJdi3bx/du3enVq1aTJ06VVblksDNzQ0fHx8aNWrEn3/+merxJk2axJ07d35YpGlqahIUFMSYMWP4/fffUz2fUB8p4oQQQqiMQqHAy8uLM2fOsG/fPqpVq8bVq1fVHSvD8/b2xs3NjWbNmvHhw4dUjZWUtiMWFhb07t0bb2/vH67YiYxPijghhBAqV6pUKcLDw3F3d6dmzZpMnz6dz58/qztWhjZ16lSKFClCt27dUn2/2n/bjvxolw0fHx9u3LjBpk2bUjWXUB8p4oQQQqQJDQ0NevfuzenTp9m1axc1atTg+vXr6o6VYWloaLBy5Uru3bvH6NGjUz2el5cXRkZGP2w7oqurS1BQEAMGDFDJZVyR/qSIE0IIkaZKly5NREQEHTp0oFq1agQGBsqq3A/o6ekRFhbGb7/9xpIlS1I11r9tR9asWUNERMR3j6latSotW7ZkyJAhqZpLqIcUcUIIIdKchoYGffv25dSpU2zbto2aNWtKr7IfKFiwILt27WLcuHHs2rUrVWMZGBgQGhpK165df9h2ZNKkSRw4cIDw8PBUzSXSX5KKuF27duHi4kLFihVxdXVNaCT46tUrvL29qVSpErVr12bjxo0J5yiVSgIDA3FwcMDe3p4JEybIX15CCJHNlSlThoMHD9K2bVuqVq3K7NmzpV/Zd5iYmLBlyxa6du2a6v1OE2s7kjt3bhYvXoyXlxdv375N1VwifSVaxN2+fZvRo0czceJEzp8/j4+PDwMHDuTPP//E19eXHDlycPz4cebOncuMGTMSnoRZs2YNhw4dYtu2bezatYtz586xdu3aNP9AQgghMjYNDQ369+/PyZMn2bx5M7Vr1yYuLk7dsTIcBwcHlixZgpubG3fv3k3VWJMmTeL27dsEBQV9930XFxccHR3x8/NL1TwifSVaxJUuXZpjx45ha2vL27dvefLkCTlz5kRHR4f9+/fTv39/dHV1sbKyonHjxgmrcWFhYXTt2pVChQphYGCAl5cXGzZsSPMPJIQQInMwMTHh0KFDtGjRAgcHB+bOnSurcv+jRYsWDBs2jEaNGvHy5csUj/Nv25FRo0b98OGS2bNns2bNGs6cOZPieUT6StLl1Jw5c3L//n3s7OwYOXIkgwYN4t69e2hpaWFkZJRwXOnSpYmNjQXg1q1bmJiYfPVeXFyc9KMRQgiRQFNTk4EDB3L8+HHWr19PnTp1VLJzQVYycOBAnJ2dadGixQ/bhSSFubn5T9uOGBgYEBgYSPfu3VM1j0g/SX6woWjRosTExBASEsLUqVM5cOAAenp6Xx2jp6eX0KTw/fv3X72vr6/Ply9f5B+GEEKIb5iamnL48GGaNm1K5cqVWbBggazK/cfMmTPJmzfvT7fTSopevXphaGj4w7YjHTp0wNDQkGnTpqV4DpF+klzEaWlpoa2tjaOjI/Xr1+fSpUvfdJX+8OEDOXLkAP4p6D5+/Jjw3vv379HS0kJXV1dF0YUQQmQlmpqaDB48mGPHjrF69WqcnJy4ffu2umNlCJqamqxZs4br168zduzYFI+TWNsRhULB4sWLmT17tuy0kQkkWsRFRkbi7u7+1WufPn2iRIkSxMfH8/Dhw4TXb9++nXAJtUyZMl/9j+/27dsYGxurKLYQQoisyszMjKNHj9KoUSPs7e1ZtGiRrMoBOXLkYPv27axZs4bly5eneBwDAwNCQkJ+2HakRIkSjBs3jh49esj3nsElWsSZm5tz6dIltm7dypcvX4iMjCQyMpK2bdvi5OREYGAg79+/JyYmhh07duDm5gZAkyZNCA4O5tGjRzx79owlS5bQtGnTNP9AQgghMj9NTU2GDRvGkSNHCA0NpV69ety5c0fdsdSuUKFC7Nq1i9GjR7Nv374Uj1O/fn3atGlDjx49vnt5tnfv3nz58oXFixenJq5IY4kWcQYGBixevJiVK1diZ2fHnDlzWLBgAWXKlCEgIID4+Hhq1apF//79GTZsGNbW1sA/19Xr1q1Lq1atcHV1xdbWlm7duqX5BxJCCJF1lC9fnmPHjlG/fn3s7e1ZsmRJtn9AzszMjI0bN9KpUyeio6NTPM7kyZO5devWd9uOaGpqEhQUhJ+fH/fv309NXJGGFMoM+L+GBw8e4OTkREREBIaGhuqOI4QQIgO4fPky7u7u5M+fn6CgIEqUKKHuSGq1fv16hg4dyokTJ1L8u/LKlSvUrFmTY8eOYWZm9s3748eP5/Tp02zfvh2FQpHayCKZEquHZNstIYQQmUKFChU4ceIEtWvXplKlSixbtixbr8q1bduWfv364erqyuvXr1M0RmJtR0aOHMndu3f59ddfUxtXpAEp4oQQQmQaWlpajB49mgMHDrB48WJcXFyy9eW+YcOGUbVqVVq3bs2nT59SNEavXr0oXrw4vr6+37yno6NDUFAQgwYN4tmzZ6mNK1RMijghhBCZjqWlJSdPnqR69erY2tqyfPnybLkqp1AomDdvHtra2vTq1StF34FCoSA4OJjVq1dz4MCBb96vUqUK7du3Z9CgQaqILFRIijghhBCZkra2NmPGjCEiIoJ58+bh6urK77//ru5Y6U5LS4tff/2VCxcuMHHixBSNkVjbkYCAAI4ePcqePXtSG1eokBRxQgghMjUrKytOnz6Ng4MDFStWZMWKFdluVS5Xrlzs2LGDoKAgVq9enaIx6tevT+vWrb/bdiRXrlwsWbKEXr168ebNG1VEFiogRZwQQohMT1tbGz8/P/bt28fMmTNxc3P7qhl9dlC0aFF27tzJ4MGDv3tZNCl+1nakfv361KpVCx8fn9RGFSoiRZwQQogsw8bGhjNnzlCpUiVsbGxYtWpVtlqVq1ChAuvXr6ddu3Zcvnw52efr6uqydu1aRo0axfXr1795f+bMmWzYsIETJ06oIq5IJSnihBBCZCk6Ojr4+/uzZ88epk+fTtOmTfnjjz/UHSvd1KlTh5kzZ+Lq6pqiz21ubs748ePp2LHjN21HChQowOzZs/H09Pxqf3ShHlLECSGEyJJsbW05e/Ys1tbW2NjYsGbNmmyzKtepUyc8PT1p3Lhxiu5h6927N8WKFftu25E2bdpQpkwZJk+erIqoIhWkiBNCCJFl6ejoEBAQwM6dO5k8eTItWrTg0aNH6o6VLnx8fKhYsSJt27YlPj4+Wef+rO2IQqFg4cKFzJ8/P0WXbIXqSBEnhBAiy7OzsyMqKory5ctjbW3NunXrsvyqnEKhYNGiRcTHx9OvX79kf96ftR0xNDQkICAAT09PPn/+rMrYIhmkiBNCCJEt6OrqMmnSJHbs2MGECRNo1aoVT548UXesNKWtrc3GjRs5fvw406dPT/b5P2s74uXlhba2NgsWLFBVXJFMUsQJIYTIVuzt7YmKiqJs2bJYWVmxYcMGdUdKU3ny5GHnzp3Mnz+f9evXJ/v8SZMmcfPmTYKDg796XUNDg2XLljF+/Hju3r2rqrgiGaSIE0IIke3o6ekxZcoUwsLCGDt2LG3atOHp06fqjpVmDA0N2bFjB/369ePIkSPJOldPT49169YxcuTIb9qOmJmZMXjwYLy8vLL85emMSIo4IYQQ2VaVKlU4d+4cpUqVwsrKik2bNqk7UpqxsrJi9erVtGrV6rs94H7mZ21Hhg0bxh9//JHinSJEykkRJ4QQIlvT19dn2rRp/Pbbb/j4+NCuXTuePXum7lhpon79+kyePJlGjRrx+PHjZJ37b9sRPz+/r17X1tYmODiYoUOHZvl7DDMaKeKEEEIIwNHRkQsXLmBoaIilpSVbtmxRd6Q04eHhQceOHWnSpAnv3r1L8nn/th1ZtWoVBw8e/Oo9Ozs7OnfuzMCBA1WcVvyMFHFCCCHE/9HX12fGjBls2rSJ4cOH06FDh2/aa2QF/v7+mJmZ0aFDh2S1CDEwMGD58uV06dLlm+9l/PjxnDp1ip07d6o6rvgBKeKEEEKI/1GtWjWio6MpXLgwlpaWhIWFqTuSSikUCoKCgnj9+jWDBw9O1rkNGjSgVatW9OzZ86uHGXLkyMHSpUvp3bs3r1+/VnVk8R1SxAkhhBDfkSNHDmbNmsX69esZMmQInTp14s8//1R3LJXR0dHht99+Y//+/cyePTtZ506ePJm4uLhv2o44OTlRr149Ro0apcKk4kekiBNCCCF+okaNGkRHR1OgQAEsLS3Zvn27uiOpTL58+di1axfTp09n8+bNST7v37Yjo0aN4saNG1+9N2PGDLZu3crRo0dVHVf8DynihBBCiETkzJmTOXPmsHbtWgYOHEjXrl158eKFumOpRMmSJdm+fTu9evXixIkTST7P3Nwcf39/OnTo8FXbkfz58zN37lw8PT358OFDWkQW/0eKOCGEECKJatWqRXR0NLlz58bS0jLL3MRva2vLihUraNGiBXFxcUk+r3fv3hQtWvSbtiMtWrSgfPnyTJw4UdVRxX9IESeEEEIkQ65cuZg/fz6rVq2ib9++dOvWjZcvX6o7Vqo1atSIcePG0ahRoyT3yfu37cjKlSu/ajuiUChYsGABixcvJiYmJq0iZ3tSxAkhhBApUKdOHWJiYtDX18fS0pI9e/aoO1KqeXl50aJFC5o2bcr79++TdE6hQoUICQn5pu1IsWLFmDRpEp6enslqYyKSToo4IYQQIoVy587NwoULCQ0NpVevXnh6evLq1St1x0qVSZMmUaJECbp27cqXL1+SdM6/bUf+dw9VT0/PhPsJhepJESeEEEKkkpOTEzExMWhqamJpacm+ffvUHSnFNDQ0CAkJ4dGjR4wYMSLJ502ePJnY2FiWL1+e8JpCoWDZsmVMmjSJW7dupUXcbE2KOCGEEEIF8uTJw5IlSwgKCqJHjx707Nkz0za91dPTY+vWrWzfvp0FCxYk+Zy1a9cycuTIr9qOmJiYMHz48G9W6UTqSREnhBBCqFD9+vWJiYlBqVRiaWnJ/v371R0pRX755Rd27drFxIkTk9wbr0KFCowbN+6btiODBw/m+fPnrFixIq3iZktSxAkhhBAqljdvXpYtW8bSpUvx8PCgV69e/PXXX+qOlWzGxsZs3boVDw8Pzp49m6Rz+vTp803bES0tLYKDgxk+fDiPHz9Oq7jZjhRxQgghRBpp0KABFy9e5NOnT1hZWXHgwAF1R0q2ypUrExQURJMmTbh9+3aix/+o7UjFihXx8PCgX79+aRk3W5EiTgghhEhDefPmJTg4mAULFtC1a1e8vb158+aNumMlS9OmTRk1ahSNGjVK0k4VhQoVYvny5XTt2vWr/WbHjh3LhQsXCAsLS8u42YYUcUIIIUQ6aNSoETExMbx9+xYrKysOHTqk7kjJ0q9fP1xcXGjevDkfP35M9PiGDRvSokULevbsmfBAg76+PsuWLcPb2zvTt2LJCKSIE0IIIdJJ/vz5CQ0NZd68eXTq1Il+/frx9u1bdcdKshkzZlCwYEE8PDyS1ENuypQp37QdqVWrFq6urgwfPjwto2YLUsQJIYQQ6czV1ZWLFy/y6tUrrK2tOXz4sLojJYmGhgarVq3i9u3b+Pr6Jnr8j9qOTJ06lZ07dxIZGZmWcbM8KeKEEEIINcifPz8rV65k5syZtG/fnoEDB/Lu3Tt1x0qUvr4+YWFhbNiwgaVLlyZ6/PfajuTLl4/58+fTo0ePJG/vJb4lRZwQQgihRk2aNCEmJoanT59ibW3N0aNH1R0pUQYGBuzatQs/Pz92796d6PF9+vShSJEijB07NuG1Zs2aYW1tzfjx49MyapYmRZwQQgihZgUKFGDNmjVMnz6dNm3aMHjw4Ay/Kle2bFl+++03unbtyvnz5396rEKhYPny5axYseKrtiPz5s0jODg40fPF90kRJ4QQQmQQzZo1IyYmhj/++AMbGxuOHz+u7kg/VbVqVRYuXIibmxv37t376bHfaztSpEgRpk6diqenJ/Hx8ekROUuRIk4IIYTIQAoWLMi6deuYPHkyLVu2ZNiwYRn6vrFWrVoxaNAgXF1dE20b8r22I+7u7uTPn5+ZM2emR9wsRYo4IYQQIgNq2bIlMTEx3Lt3j4oVK3Ly5El1R/qhwYMHU6tWLVq2bPnVnqnf82/bkZCQEOCfS61Lly5l2rRpxMXFpUfcLEOKOCGEECKDMjAwYP369QQEBNCsWTNGjBjBhw8f1B3rGwqFgjlz5pAjR46vVtm+59+2IyNGjCA2Nhb4Z4/WUaNGJXqu+JoUcUIIIUQG17p1a2JiYrh58ya2tracPn1a3ZG+oampybp167h8+TL+/v4/PbZChQqMHTv2q7YjAwYM4K+//iI4ODg94mYJUsQJIYQQmUChQoXYuHEjY8eOpUmTJowaNSpJ21+lp5w5c7Jjxw5WrlxJaGjoT4/19vamcOHCCW1HtLS0CA4OZtSoUTx8+DAd0mZ+UsQJIYQQmYRCoaBt27ZER0dz/fp1bG1tOXPmjLpjfaVw4cLs3LmTESNGsH///h8e9722I1ZWVnh5edG3b9/0ipupSREnhBBCZDKFCxdm8+bN+Pj40LhxY3x8fDLUqlz58uXZuHEjHTp04OLFiz887nttR8aMGcOVK1fYvHlzesXNtKSIE0IIITIhhUJBhw4diI6O5vLly9jZ2XHu3Dl1x0pQs2ZN5syZg6urK7///vsPj/u37YiXlxdKpRI9PT2WLVtGv379ePHiRTomznykiBNCCCEysSJFirBlyxZGjBhBw4YN8fPzS7TNR3pp3749vXv3xtXVlb/++uuHx02ZMoXr168ntB2pUaMGzZo1Y9iwYekVNVOSIk4IIYTI5BQKBZ06deLChQucP38ee3t7Lly4oO5YAIwcOZIqVarQunVrPn369N1jvtd2ZMqUKezbt48DBw6kZ9xMRYo4IYQQIosoVqwY27ZtY8iQIdSvX59x48apfVVOoVCwYMECNDQ06NOnzw/7wFlYWHzVdiRPnjwsXLiQnj17Zvh9ZNVFijghhBAiC1EoFHTp0oXz589z5swZqlSpQnR0tFozaWlpsX79eqKiopg8efIPj/u37ci4ceMAaNy4Mfb29gltSMTXpIgTQgghsqDixYuzY8cOBgwYgLOzMwEBAT+8nJkecufOzY4dO1iyZAlr16797jH/th0JDQ3l0KFDAMyZM4eVK1dy9uzZdEybOUgRJ4QQQmRRCoUCd3d3zp8/z/Hjx3FwcPhpy4+0VqxYMXbu3MnAgQOJjIz87jGFChUiODiYLl268Oeff1KoUCGmT59O9+7d1VqEZkRSxAkhhBBZnKGhIbt27aJPnz7UrVuXiRMnEh8fr5YsFhYWrFu3jjZt2nD16tXvHuPi4kLz5s0T2o507tyZIkWKMGPGjHROm7FJESeEEEJkAwqFgu7duxMVFUVkZCQODg5cunRJLVmcnJyYNm0ajRo14tGjR989ZurUqVy/fp3Q0FAUCgVLliwhMDCQ69evp3PajEuKOCGEECIbKVGiBHv37sXLy4s6deowefJktazKde3aFXd3dxo3bszbt2+/ef/ftiPDhw8nNjaWUqVK4evrS48ePfjy5Uu6582IpIgTQgghshmFQkGPHj04e/YsERERVK1alStXrqR7Dj8/PywtLWnXrt13C8l/24507NiRT58+0bdvX/7++2+WLl2a7lkzIinihBBCiGyqZMmShIeH4+HhQc2aNZk2bRqfP39Ot/kVCgVLly7lw4cPDBgw4Ls95Ly9vTEwMGDs2LFoamoSHByMr68vDx48SLecGZUUcUIIIUQ2plAo6NWrF2fPnmXv3r1Uq1aNa9eupdv82trabNq0iSNHjhAYGPjdfCEhIQltRypUqECfPn1+2jg4u5AiTgghhBCUKlWK8PBwunTpQvXq1ZkxY0a6rcrlzZuXnTt3MmfOHDZu3PjN+/9tO/LixQtGjx5NXFzcd4/NTqSIE0IIIQRAwtZYp0+fZseOHdSoUYMbN26ky9xGRkZs374db29vjh079s37/207oqOjQ3BwMAMGDOD58+fpki8jkiJOCCGEEF8xNjbmwIEDdOjQgapVqzJr1qx0WZWzsbFh1apVtGzZ8rvF49SpU7l69SqhoaE4OjrSunVrhgwZkua5Miop4oQQQgjxDQ0NDfr27cupU6fYsmULtWrVIjY2Ns3nbdCgARMmTKBRo0Y8ffr0q/f09PRYt25dQtuRSZMmcejQIfbt25fmuTIiKeKEEEII8UNlypTh0KFDtG7dGkdHR+bMmZPmfdo8PT1p164dbm5uvHv37qv3LCws8PPzo2PHjujq6rJo0SK8vLx48+ZNmmbKiKSIE0IIIcRPaWhoMGDAAE6cOMHGjRupXbs2cXFxaTpnQEAAJiYmdOrU6ZtLuX379sXAwIBx48bh4uJCtWrV8PX1TdM8GZEUcUIIIYRIkrJlyxIZGUnz5s1xcHBg3rx5abYqp1AoCA4O5sWLFwwdOvSb90JCQggJCSEyMpLZs2ezbt06Tp06lSZZMiop4oQQQgiRZJqamgwaNIhjx46xbt066taty61bt9JkLl1dXX777Tf27t3LnDlzvnrv37YjnTt3RlNTk5kzZ+Lp6cnff/+dJlkyIinihBBCCJFsZmZmHDlyhMaNG1O5cmUWLFiQJqty+fPnZ9euXUybNo0tW7Z89Z6LiwvNmjXDy8uLdu3aUaJECaZOnaryDBmVFHFCCCGESBFNTU2GDh3K0aNHWbVqFc7Ozty5c0fl85QqVYqwsDB69uz5zSXTadOmcfXqVVasWMGiRYuYM2cOV69eVXmGjEiKOCGEEEKkSrly5Th27BgNGzbE3t6exYsXq3xLLDs7O0JCQmjWrBk3b95MeP2/bUf+/vtv/P398fT0TPMnaDOCJBVxZ8+epXXr1lSqVAlnZ2d+/fVXAF69eoW3tzeVKlWidu3aX21/oVQqCQwMxMHBAXt7eyZMmJCum+oKIYQQIv1oamoyfPhwIiMjWb58OfXr1+fu3bsqnaNx48b4+vrSqFGjr3Zq+LftSIcOHfD09ARg4cKFKp07I0q0iHv16hV9+vShc+fOnDlzhjlz5jBz5kyOHz+Or68vOXLk4Pjx48ydO5cZM2YkbJq7Zs0aDh36f+3de1BU9fsH8PfCwi4biGRaVH6tMCfFXVxRvMQYsI6WIpipmTQihmIZeIlMnegiykANlkQGijpmUYIXFAREycbGRCV0d1JKGxlTEQFFhGKRy+f3hz83FRGWyy4r79cMf3A+55x9Pg8P7MM5e875GXv27EFmZiYKCgqQnJzc6RMiIiIi8xk0aBB+/fVXaDQaDBs2DOvXr+/Qo3LvvPMO/P394e/vD71eb1h++7YjK1euRFJSEj755BP8/fffHfa6XVGLTVxxcTFeeukl+Pn5wcrKCq6urhgxYgQKCgpw4MABhIWFQSaTQaVSwdfX13A0bvfu3QgMDESfPn3Qu3dvhISEICUlpdMnREREROYllUqxbNky/Pzzz1i/fj3Gjx/foQ1VdHQ0nnrqKQQGBhpOm0okEmzatAmbNm1CaWkpFi5ciLfffrvDT+t2JS02cQMHDsTnn39u+L6yshL5+fkAbv2Q+vbtaxh79tlnDY/kOHfuHPr373/X2F9//fVQJ5OIiIj+4+rqiry8PHh5ecHd3R0bN27skD7AysoKW7ZswaVLl7B8+XLD8scff9xw25F58+bh77//xg8//NDu1+uqjLqwoaqqCvPnzzccjZPL5XeNy+Vyw6HNmpqau8bt7OzQ2NjYre7fQkRE1N1JpVKsWLECP/30E9atW4cJEybg4sWL7d6vXC7H7t27kZaWhm+++cawfMKECZg8eTJCQ0ORlJSEJUuWoLy8vN2v1xW1uom7cOECZsyYAUdHR8THx0OhUNx1LhoA9Ho9FAoFgFvJra2tNYzV1NRAKpVCJpN1UOhERERkKZRKJfLy8jB69Gio1Wps3ry53UflevXqhczMTKxcuRIZGRmG5bdvO1JYWIiZM2di0aJF7Yy+a2pVE3fq1ClMnz4dnp6eWLduHeRyOfr164f6+noUFxcb1isqKjKcQnVxcUFRUdFdY88991wHh09ERESWwsbGBhEREThw4ADi4uLg6+uLS5cutWufLi4u2LVrF4KCgvDbb78BuHUgKTk5Ge+//z5mz56Nw4cPIysrqyOm0KW02MSVl5cjODgYQUFBWL58Oaysbm1ib28PjUaD2NhY1NTUQKfTISMjA5MmTQIA+Pn5YePGjSgpKUF5eTkSExPh7+/fubMhIiKiLs/NzQ3Hjh2Dh4cH1Go1tmzZ0q6jciNHjsT69evh5+dnuNmwUqlEREQEgoOD8fXXX2P+/PmoqqrqoBl0DRLRQtYSEhLwxRdfGE6T3jZr1iwEBQXh448/xpEjR6BQKPDuu+9i6tSpAICGhgbExcVhx44dqKurw6RJk7B8+XJYW1u3GNTFixeh0WiQm5uLp59+uh3TIyIioq7s5MmTCAwMxP/+9z8kJibiySefbPO+1q5di8TERBw+fBhOTk4QQmDixIlQq9W4dOkSevTogbi4uA6MvnO11A+12MSZA5s4IiKi7uPmzZtYtWoVEhMTERsbi4CAAEgkkjbta9GiRdBqtcjOzoZMJsOVK1cwZMgQbNiwAXPnzsXOnTsxatSoDp5B52ipH+Jjt4iIiMisbG1tsXLlSmRmZiImJgaTJ09GSUlJm/YVGxsLJycnBAcHQwhhuO3IggULsHr1arz11lt3XXhpydjEERERUZfg7u6O/Px8KJVKuLm5ITk52ejPyllbW+O7777D2bNn8dFHHwG4ddsRf39/7Nu3D/3790dUVFRnhG9ybOKIiIioy5DJZFi1ahX27t2L1atX47XXXsOVK1eM2odCocCePXuQnJyMpKQkAEBMTAxOnz4Nb29vrFu3Dr///ntnhG9SbOKIiIioyxk2bBgKCgrwwgsvQKVSYdu2bUYdlevTpw+ysrLw4YcfYt++fbCzs0NycjKioqIQGhqK4OBgNDQ0dOIMOh+bOCIiIuqSZDIZoqKikJ6ejk8//RTTpk1DaWlpq7cfMGAAtm/fjjfffBNardZw25G9e/fCxsYG8fHxnRh952MTR0RERF2ah4cHCgoK0L9/f6hUKqSmprZ6W09PT8THx8PX1xcXL15EaGgoevXqhcGDByMyMtJwXzlLxCaOiIiIujy5XI7o6GikpaUhIiIC06dPR1lZWau2ff311xEWFoYJEybgxo0b2Lx5M9LS0jBlyhSEhIS0+/Ff5sImjoiIiCzGyJEjceLECfTr1w8qlQo7duxo1Xbh4eHw9PTEtGnT8OijjyIpKQn79u1DcXExtm7d2slRdw42cURERGRR7Ozs8Pnnn2PHjh1YsWIF3njjDZSXlz9wG4lEgri4ONja2iIkJAQTJkyAn58fnJ2dER4ebtRn7boKNnFERERkkUaPHo0TJ07A2dkZKpUKu3bteuD6UqkUP/74I3Q6HSIjI/HZZ5/h8uXLcHd3R1hYmImi7jhs4oiIiMhiKRQKrFmzBikpKVi6dCkCAgJw9erVZte3t7dHRkYGNm/ejNTUVCQnJyM/Px9HjhxBenq6CSNvPzZxREREZPE8PT2h1WrRu3dvqFQq7Nmzp9l1n3jiCezduxfh4eEoKytDREQEFAoF3nnnHdy4ccOEUbcPmzgiIiJ6KCgUCnz55Zf44YcfsHjxYsyaNQsVFRX3XXfQoEHYtm0bZsyYAW9vbzzzzDPo1asXli1bZuKo245NHBERET1UxowZA51Oh549e0KpVCIjI+O+63l7e+OLL76Ar68voqOjUVJSgpSUFPzyyy8mjrht2MQRERHRQ+eRRx5BXFwcvvvuO4SFhWH27Nm4fv16k/UCAgIwb948BAUFIT4+HlZWVggKCoJerzd90EZiE0dEREQPLS8vL+h0Otjb20OpVCIzM7PJOitWrIC7uzs2b96MadOmQa/XY+XKlWaI1jhs4oiIiOihZm9vj/j4eGzZsgULFizAnDlzUFlZaRiXSCRYt24dGhoaoNfrYW9vj6+++gpardaMUbdMau4AiIiIiEzBx8cHOp0OS5cuhVKpxIYNGzB+/HgAgI2NDVJSUjBmzBiMHz8emzZtwsyZMxEeHo68vDzU1dU9cN+2traGJ0LIZDJTTAcS0QUfGHbx4kVoNBrk5ubi6aefNnc4RERE9JA5cOAAgoODMXbsWMTGxsLR0REAcOnSJYwaNQoeHh7YtWsXGhsbW71PiUSCYcOGIT09HY8//ni7Y2ypH+LpVCIiIup2xo4dC51OB2tra6hUKuzfvx8A8NRTTyEjIwPp6elGNXAAIITA8ePHsWTJks4IuQk2cURERNQt9ejRA4mJidiwYQOCg4MREhKCqqoqKJXKdh1JM9Vn6djEERERUbc2btw46HQ6NDQ0QKlUIjs7Gw0NDW3eX2VlJerr6zswwvvjhQ1ERETU7Tk6OiIpKQlZWVkIDg6+7z3lWksIYfSp2LbgkTgiIiKi//fKK6/g+PHjaO66z4MHD0Kv16Oqqsrw5ezsbOIob2ETR0RERHSHnj17omfPns2OR0ZGwsHBwfB1+fJl0wV3BzZxRERERBaITRwRERGRERYtWoSrV69Cq9Vizpw5ZouDFzYQERERtdIHH3yA06dPQ6/XY+zYsUhJScH169exc+dOk8fCI3FERERErXTs2DFUV1ejvr4e2dnZ2LJlC6ZNm2aWWNjEEREREbVRY2MjJBKJWV6bTRwRERFRKzg6OmLSpElQKBSQSCTw8vJCYGAgtm/fbpZ4+Jk4IiIiolawsbHBxx9/jO+//x4AcP78eSxevJhNHBEREVFXVl5ejmHDhpk7DAOeTiUiIiKyQGziiIiIiO4gkUjafbGCKS52YBNHREREdAe5XA4HB4c2b9+jRw/Y2Nh0YET3xyaOiIiI6A4SiQSDBw9u8/ZKpbIDo2kemzgiIiKie8TExEClUhm9nZubG2JiYjohoqZ4dSoRERHRPVxcXHDo0CEkJSVBq9Xi5s2bD1xfJpNBrVZjzpw56NGjh0liZBNHREREdB+Ojo547733zB1Gs3g6lYiIiMgCsYkjIiIiskBs4oiIiIgsEJs4IiIiIgvEJo6IiIjIAnXJq1MbGhoAACUlJWaOhIiIiMg8bvdBt/uie3XJJq6srAwAEBAQYOZIiIiIiMyrrKwM/fr1a7JcIoQQZojngfR6PX7//Xf07t0b1tbW5g6HiIiIyOQaGhpQVlaGwYMHQy6XNxnvkk0cERERET0YL2wgIiIiskBs4oiIiIgsEJs4IiIiIgvEJo6IiIjIArGJIyIiIrJAbOKIiIiILBCbuA5QUFCAKVOmYOjQoRg/fjzS09MBAJWVlViwYAHc3d3h5eWF1NRUwzZCCMTGxmLkyJEYPnw4Vq1a1ewdmbuD5nKo0+kwcOBAqNVqw1dCQgIA5vBeR44cweTJk6FWq/H6669Dq9UCYB0aq7k8shZbptPp4Onpafi+PbWXkZEBjUYDtVqNkJAQlJeXm3Qu5mJsDu+sR7VajeDgYMN4d80h0DSPAFBRUQGNRoMzZ84Ylll8HQpql/r6ejFy5EiRlZUlhBDi+PHjYtCgQeLChQsiNDRUhIeHC71eL7RarfDw8BCFhYVCCCG2bt0qfH19xZUrV0Rpaal49dVXxbfffmvOqZjNg3K4bds2MW/evPtuxxz+58KFC8LNzU1s27ZN1NXViYMHDwoPDw9RWlrKOjTCg/LIWmxeY2OjSE1NFe7u7sLDw8OwvK21V1hYKIYOHSpOnjwpampqxIoVK8S7775rlrmZSltyWFRUJIYMGSIaGxub7K875lCI5vN4/Phx8fLLL4sBAwaIP//807Dc0uuQR+La6caNG7h27RoaGhoghIBEIoGNjQ2sra1x4MABhIWFQSaTQaVSwdfX1/Bf1O7duxEYGIg+ffqgd+/eCAkJQUpKiplnYx4PyuHp06fxwgsv3Hc75vA/hw4dwoABAzB9+nRIpVJ4eXlBpVIhOzubdWiEB+WRtdi8hIQEfPvtt5g/f75h2T///NPm2ktPT4dGo4GbmxvkcjnCw8ORm5uLq1evmmV+ptCWHN6uSYlE0mR/3TGHwP3zmJ+fj4ULFyIkJKTJ+pZeh2zi2snJyQkzZ87EkiVL4OrqioCAAERERKCiogJSqRR9+/Y1rPvss8/i7NmzAIBz586hf//+d4399ddfEN3wARrN5dDZ2RmFhYUoKCiAj48PvLy8EBMTg5s3bwJgDu/U2NjY5JEsVlZWOHz4MOvQCM3l8fz586zFB3jttdewe/duKJVKw7Lz58+3ufbuHXNycoKDgwPOnTtngtmYR1tyWFhYiOrqavj7+2PUqFEICwvDlStXADTNb3fIIXD/PD7//PPIzc3F5MmTm6xv6XXIJq6dbv/RX7t2LU6ePImEhARERUWhurq6yZuBXC6HXq8HANTU1Nw1bmdnh8bGRsObQnfSXA7/+OMPODk5wcfHBxkZGdi6dSuOHj2KuLg4AMzhnTw9PaHT6ZCVlYW6ujocOnQIeXl5+Pfff1mHRmguj7W1tazFB+jTp0+To0Htqb17x26P19TUdNIMzK8tObS1tcWQIUOwceNG5OTkQKFQIDQ0FEDT/AIPfw6B++fR0dHxvs8dBSy/DtnEtVNOTg50Oh1efvll2NrawsvLC15eXvjqq68Mv2i36fV6KBQKALd+EWtraw1jNTU1kEqlkMlkJo2/K2guh2lpaUhISEBQUBAUCgX69u2LkJAQ7N+/HwBzeKdnnnkGX375Jb755ht4enoiIyMDkyZNglQqZR0aobk8Ojg4sBaNZGdn1+bau7NRuXP89rbdRUs5DA0NRWRkJB577DE4ODjggw8+gFarRWlpKXPYSpZeh2zi2uny5ctN/tuWSqVwdXVFfX09iouLDcuLiooMh2ZdXFxQVFR019hzzz1nmqC7mOZy+O+//yImJgbV1dWG5bW1tYY3RubwP9XV1XB2dsaePXtw9OhRfPbZZzhz5gz8/PxYh0ZoLo+DBg1iLRqpX79+ba69e8euXbuGyspKuLi4mCj6rqGlHK5fvx6nTp0yjN3+OyqTyZjDVrL0OmQT106jR49GYWEhduzYASEEjh07hv3792PixInQaDSIjY1FTU0NdDqd4b96APDz88PGjRtRUlKC8vJyJCYmwt/f38yzMY/mcjh16lTs378f8fHxqKurw/nz55GQkIApU6YAYA7vdP36dcyYMQOnTp3CzZs38f3336O4uBjjxo1jHRqhuTz6+PiwFo1kb2/f5trz9fVFTk4O8vPzUVtbizVr1mDMmDFwcnIy55RMrqUcnjt3DtHR0aioqEBVVRVWr14NjUYDR0dH5rCVLL4OzXVZ7MMkNzdX+Pn5CbVaLSZOnChycnKEEEJUVFSIsLAwMXz4cPHSSy+J1NRUwzb19fVizZo14sUXXxQeHh4iMjJS1NfXm2sKZtdcDs+ePSsCAwPF0KFDxejRo8XatWsNl9Mzh3dLS0sT3t7eQq1WizfffFOcOXNGCME6NFZzeWQttiwvL++u2zq0p/b27t0rxo0bJ9RqtZg7d64oLy836VzMxZgcVlVViWXLlokRI0aIoUOHiiVLlojr168bxrtrDoVomsfb7r3FiKXXoUSIbnL5FBEREdFDhKdTiYiIiCwQmzgiIiIiC8QmjoiIiMgCsYkjIiIiskBs4oiIiIgsEJs4IiIiIgvEJo6IiIjIArGJIyIiIrJAbOKIiIiILND/AQRecDCAy4G3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot.plot_topology(geo_model, edges, centroids)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2dbe5477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, cell_number=[5], show=False)\n",
    "gp.plot.plot_topology(geo_model, edges, centroids, scale=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "488a99b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False False False False False False False False]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False  True  True False False  True]\n",
      " [False False False False False  True  True False False  True]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False False False False False False]\n",
      " [False False False False False  True  True False False False]]\n"
     ]
    }
   ],
   "source": [
    "M = tp.get_adjacency_matrix(geo_model, edges, centroids)\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8e15c828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp.plot_adjacency_matrix(geo_model, M)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1da958c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surface\n",
      "Main_Fault     #527682\n",
      "Sandstone_2    #728f02\n",
      "Siltstone      #ffbe00\n",
      "Shale          #015482\n",
      "Sandstone_1    #9f0052\n",
      "basement       #ff3f20\n",
      "Name: color, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768 at 0x22B95035A90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gempy.plot._vista import Vista\n",
    "gpv = Vista(geo_model)\n",
    "gpv.plot_topology(edges, centroids)\n",
    "gpv.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0053c306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 2, 2: 5, 3: 5, 4: 6, 5: 6}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lith_lot = tp.get_lot_node_to_lith_id(geo_model, centroids)\n",
    "lith_lot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f12e48c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0, 2: 0, 3: 0, 4: 0, 5: 0}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fault_lot = tp.get_lot_node_to_fault_block(geo_model, centroids)\n",
    "fault_lot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "569585c5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mtp\u001b[49m\u001b[38;5;241m.\u001b[39mget_lot_lith_to_node_id(lith_lot)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'tp' is not defined"
     ]
    }
   ],
   "source": [
    "tp.get_lot_lith_to_node_id(lith_lot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "6a940d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing GemPy\n",
    "import gempy as gp\n",
    "\n",
    "# Importing aux libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "a7c76acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_model = gp.create_model('Model1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "0edacde0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active grids: ['regular']\n"
     ]
    }
   ],
   "source": [
    "geo_model = gp.init_data(geo_model, extent=[0, 1000, 0, 200, -800, 0], resolution=[100, 10, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "1d4a6fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting kriging parameters to their default values.\n",
      "Compiling theano function...\n",
      "Level of Optimization:  fast_compile\n",
      "Device:  cpu\n",
      "Precision:  float64\n",
      "Number of faults:  0\n",
      "Compilation Done!\n",
      "Kriging values: \n",
      "                      values\n",
      "range            1296.14814\n",
      "$C_o$               40000.0\n",
      "drift equations         [3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.core.interpolator.InterpolatorModel at 0x20777316820>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.set_interpolator(geo_model, theano_optimizer='fast_compile', verbose=[]) ## to help in computing and gathering all data ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "7dad1906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7abb1_row0_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_7abb1_row1_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7abb1_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7abb1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7abb1_row0_col0\" class=\"data row0 col0\" >surface1</td>\n",
       "      <td id=\"T_7abb1_row0_col1\" class=\"data row0 col1\" >Default series</td>\n",
       "      <td id=\"T_7abb1_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_7abb1_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "      <td id=\"T_7abb1_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7abb1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7abb1_row1_col0\" class=\"data row1 col0\" >surface2</td>\n",
       "      <td id=\"T_7abb1_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "      <td id=\"T_7abb1_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_7abb1_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "      <td id=\"T_7abb1_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface          series  order_surfaces    color  id\n",
       "0  surface1  Default series               1  #015482   1\n",
       "1  surface2  Default series               2  #9f0052   2"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.set_default_surfaces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "5eb68b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>G_x</th>\n",
       "      <th>G_y</th>\n",
       "      <th>G_z</th>\n",
       "      <th>smooth</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "       X    Y      Z  G_x  G_y  G_z  smooth   surface\n",
       "0  350.0  0.0 -300.0  0.0  0.0  1.0    0.01  surface1"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surface_points(X=500, Y=0.01, Z=-100, surface='surface1') \n",
    "geo_model.add_surface_points(X=700, Y=0.01, Z=-100, surface='surface1') \n",
    "geo_model.add_surface_points(X=200, Y=0.01, Z=-300, surface='surface1') \n",
    "geo_model.add_surface_points(X=0, Y=0.01, Z=-500, surface='surface1')\n",
    "geo_model.add_surface_points(X=800, Y=0.01, Z=-300, surface='surface1') \n",
    "geo_model.add_surface_points(X=1000, Y=0.01, Z=-500, surface='surface1') \n",
    "geo_model.add_orientations(X=350, Y=0, Z=-300, surface='surface1', pole_vector=(0, 0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "bf5a2b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.visualization_2d.Plot2D at 0x2069142bd00>"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, cell_number=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "93803b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Lithology ids \n",
       "  [2. 2. 2. ... 1. 1. 1.] "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.compute_model(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "9304b4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>1296.14814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$C_o$</th>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drift equations</th>\n",
       "      <td>[3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                     values\n",
       "range            1296.14814\n",
       "$C_o$               40000.0\n",
       "drift equations         [3]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.additional_data.kriging_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "d04d1b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rescaling factor</th>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centers</th>\n",
       "      <td>[500.0, 0.005, -300.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                                  values\n",
       "rescaling factor                  2000.0\n",
       "centers           [500.0, 0.005, -300.0]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.additional_data.rescaling_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "332553b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.visualization_2d.Plot2D at 0x2068353b6d0>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/PJL/PLLL/D398e3336LsLCwuxraIyIiIrofjX5oDwC6du2KHTt2iF0GERERJABa21rDxqpJfMQ2CuVaHfLLKiHGwpi8ykRERHWkpdwCKycMhk+HtrCRW4tdTrNRrqnE5dRsvP3jcZRodA16bAYpIiKiOiABsHLCYPT18QQkErHLaVZsbCzQ18cTKycAM7f+3qA9U41+jhQREZE5aG1rDZ8ObRmixCKRwKdDW7S2bdieQAYpIiKiOmBjZcHhPJHZyK0afG4agxQRERE1EQ3fG8ggRUREZGY0VQ07YZpMx8nmREREZqCkohLfHD2HvacSUViugYONHGP6dsKkwAfQUmmeQ4Z/HjmIrV+EwmAwYMWGzXB0blNvx/r1p52IvxSHOYuW1tsxTMEeKSIiIpGVVFRi6hd7se3oBRSWVz8/trBcg21HL2DqF3tRUlEpcoW1O3ZgP4JHj8OXP/5cbyFKo1Hju68+x/bNYfXy/veLPVJEREQi++boOaRcL6q1LeV6EbYePY9ZD/er02N+v+ULHD8UCYPBgI7enTD51TnY/f1WtGhph2dfngEAOBN9At+ErcO6r3fg6IH9OBq5D7qqKlzPzkL7jl64HHcOV+LO42pyEuYuWYZd336N6OO/ozA/D0obWzw28TkEPfIoAODyhVh8t/lzZKanwtG5DZ6bOhO9eveDQa9HxA/f4mjkr9BWVsLPfwCemzYLSqUNAODTD5ZALpdj+MNjUVJc+/dITOyRIiIiEtnPpxLv0J5Qp8eLiz2Nk8eOYGXYFmz4NhytWjth1/av77hfwqU4THjxZXz61XYsWrkGXbv3xLOvzMD895bjzyMHEfPnMfxn5VpsDv8FEye/gm2bPoNGrUZxUSFWvbcII0aPw+adezFx8lSsXbYE5WVl2PfTTvwV9QeWfLIea77ajsrKSmwNW2885vS5b+GNxR/Azt6hTr8HdYVBioiISESaKh2K/h7Ou5XCcg0q63ACulJpi5LiIvz+2z5cv5aJl16fhxnzF95xP/tWrdHDtw+UNrY3tfUZOAiLVqyBnYMDCvLzYGlphSqtFmWlJYiNOYk2Lu0Q+NDDkMpk6N0/AItWrIGlpSV+j9yHJ0ImobWTMxRKJSZOnorjRw5Cq9UCABxaO9bZedcHDu0RERGJSG5pAXsb+W3DlIONHNaWdfeR7dm5C6a98RYO7I3Azm1b4OTcFs9NffWO+9k53LpXSK/TYevn6xEXewatnZ3RwdMbACAIAoqLCtHK0anG6726+AAA8nNz8PnqFdj06UfGNguZDPk51+Hi5m7K6TUoBikiIiKRje3bCduOXrhNe+c6PV5+bg5c3Nyx+ON10KjVOLA3AqEr3kf/B4dCp6syvq6stLjGfpLbrNP0w9dfwiAI2PBtOKysrJCXcx1/HPwNQHWvUmF+Xo3XR+z4Fv0HB8LeoTVefn0euvv2BgDodDrkZGehjUu7ujrdesWhPSIiIpFNCnwAnm3sa23zbGOPFwJ71enxkuIvY9V7C5FzLQtyhQJKGxsobW3Rzr09zp2KQXFRIYqLCnFo3893/Z7qigpYWVlBJpOhtKQY3/19l51ep4Nvv/7IvZ6N44cPwKDX40x0FPbt/hG2LVviwREPYfd336CwIB86nQ47t36Fjxe/1aDPy7sf7JEiIiISWUulNTZNG4OtR8/j51MJxnWkxvbtjBcCe9X5OlL9BwciXZWMpQteh7qiHO3c2mP2oqVo5+qOhMtxmP/K87Czb4Wg0Y9if0T4Xb3n+OcmI2z1CkydMBZKG1sMHh6MNi7tkJmeij4DBmHB0hXYtukzfL1xHZzatsUbiz9Ai5Z2eHTCs9BV6fDuGzNRUV6Gjt6dMH/pCshksjo95/oiEQShsYS+OpeRkYGgoCCouj4GndXNE+eIiIjuVodWNtg5bzwgu/8+isoqXZ3OiWo29Do8tTocqQXlNTZbaMvgcSUChw4dgpubW50ekkN7REREZoYhqvFgkCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiIT8bYAIiIztCU8FnlKK5xxtcOFNi2Q4GSLKlntv/vaaHVwLNPC0mCAVACkggCpABgkQIHSCvlKK+ilta9I7VakRs/sEvRPL0T74upHlEwZ71tfp0XU5DBIERGZobWDPHChbUsIkpoBqH1hBbrllMG1RI02pZVoW1YJW63+tu+llwCFCivk2lghq6UcF9u0wCVnW2gtZMiwVyDDXoFfu7ZB15xSjErIqc/TImpyGKSIiMzQeRc7AIBNpQ49r5egR3Ypul8vhV2l7p7fSyYAjhVaOFZo4ZNbhqDkPFRJJUhwtEFc25Y4084OubbWuOLcAlecW9T1qZAJDBotpHIrscugu8AgRURkhjrnlmFoSh76ZBbD0lD3D6CwNAjonlOG7jlleOp8Fs65tMRvnZ2R4MSnPIhFX1yB3C8OoDD8JPQFZZC1soXD+AFwmhYMmZ1S7PJq9eeRg9j6RSgMBgNWbNgMR+c2dX6Mw/v3Ym/4DhQXFqKdmzuefeVVdO1Rt88evB8MUkREZujto0kNdiwpAL9rJfC7VoLkVkp8OLxzgx2bqumLK5AycS0qE6/9b1tBGfI2HUTpkYvw3DHHLMPUsQP7ETx6HMY/P6Ve3v/iubP48evNePvDVWjv4YnjhyOxeulCrPlqO1q0tKuXY94rBikiIpFZ6g0ITszFw/E5sKm6/Xyn+uZVUIEt4bFIs1Pgmz5uULWyEbWe5iL3iwM1QtQ/VSZeQ+4XB9D2zXF1eszvt3yB44ciYTAY0NG7Eya/Oge7v9+KFi3t8OzLMwAAZ6JP4JuwdVj39Q4cPbAfRyP3QVdVhevZWWjf0QuX487hStx5XE1Owtwly7Dr268Rffx3FObnQWlji8cmPoegRx4FAFy+EIvvNn+OzPRUODq3wXNTZ6JX734w6PWI+OFbHI38FdrKSvj5D8Bz02ZBqbRBQV4uRj/5NDp6eQMAhowYhe1fhiEj9Sp8ej5Qp98PUzFIERGJbOWvl+GgqRK7jBraF6ux6HAijng5YncPF6gtZWKX1KQV7jx5+/bwk3UapOJiT+PksSNYGbYFShtbbAldg13bv4ZEevtVkRIuxeHtDz+BZ6cuUNrYYtlbc+A/OBAPjX0cfxyKRMyfx/CflWth5+CAqN8P4st1qzBoWDAqKzVY9d4ivDBtFh4Megixp6KxdtkShG7diSP79+KvqD+w5JP1UNrYYvP6Vdgath7T572DB4MeqnH8+IsXoFZXwK19xzr7XtwvriNFRCQycwtRN0gBBCXnYdlvl8UupUkzaLTQF5bd9jX6gjIYKuvu50SptEVJcRF+/20frl/LxEuvz8OM+QvvuJ99q9bo4dsHSpub59L1GTgIi1asgZ2DAwry82BpaYUqrRZlpSWIjTmJNi7tEPjQw5DKZOjdPwCLVqyBpaUlfo/chydCJqG1kzMUSiUmTp6K40cOQqvV1nj/jLSrWLf8XYx/bjJa2JnHsB7AHikiIroDB8293ylId08qt4LMwfa2YUrWyhZSa8s6O6Zn5y6Y9sZbOLA3Aju3bYGTc1s8N/XVO+5n5+Bwyza9Toetn69HXOwZtHZ2RgfP6uE4QRBQXFSIVo5ONV7v1cUHAJCfm4PPV6/Apk8/MrZZyGTIz7kOFzd3AMD5M38hdOX7eOTxCXh0Qsg9n299YpAiIqonr55Q4at+7VFpUT0s1i+9EM+ezUDLO6z7ZI62hMcCABJb2+CzgR1RIq+7D3UCHJ4agLxNB2/dPn5AnR4vPzcHLm7uWPzxOmjUahzYG4HQFe+j/4NDodP9r+errLS4xn4S1L6wKwD88PWXMAgCNnwbDisrK+TlXMcfB3+rrr+1Iwrz82q8PmLHt+g/OBD2Dq3x8uvz0N23NwBAp9MhJzsLbVzaAQCORv6KrV9swEuvzUXA0KA6Of+6xKE9IqJ6snGgByotZGipqcLMKBVmRKc2yhD1T53yy7H4UALaF1aIXUqT4jQtGNadXGpts+7kAqdpwXV6vKT4y1j13kLkXMuCXKGA0sYGSltbtHNvj3OnYlBcVIjiokIc2vfzXb+nuqICVlZWkMlkKC0pxnebwwBU91T59uuP3OvZOH74AAx6Pc5ER2Hf7h9h27IlHhzxEHZ/9w0KC/Kh0+mwc+tX+HjxWxBQPZfrvxvXYsHSFWYZogD2SBER1SvvvDLMOHm1SQ2PtVZX4Z3fEzHjcfO4a6opkNkp4bljToOtI9V/cCDSVclYuuB1qCvK0c6tPWYvWop2ru5IuByH+a88Dzv7Vgga/Sj2R4Tf1XuOf24ywlavwNQJY6G0scXg4cFo49IOmemp6DNgEBYsXYFtmz7D1xvXwaltW7yx+AO0aGmHRyc8C12VDu++MRMV5WXo6N0J85eugEwmw887d0Cn0+HjJW/VONbshUvxQF//Ov2emEoiCELdr/TWSGRkZCAoKAiqro9BZ8VF6Iiobo1IzMWE85mwaKL/y/KZfDV1aGWDnfPGA7L776MwVFbV6ZyoZkOvw1Orw5FaUF5js4W2DB5XInDo0CG4ubnV6SE5tEdEVAemRl+F7O8VyK10ekw7eRUh55puiAKq501tDo/F0OS8O7+Y7glDVOPBoT0iojqwqX9HAECbUg1mnbgK1xKNuAU1ECmA585mwCCR4Jhna7HLIWpwDFJERHXEM78cs/9MQYtGPqH8XkkBvHAmHTqpBFEdW4ldDlGD4tAeEVEdeCCrGAuOJTe7EHWDFMDk02lil2EGmvBYbqPQ8N9/Bikionv0WcR5+OSUGr9+UJWPWSdUsNYbRKxKfDIB+GL3OXT9+3tjoTdg4ZEEkatqOEUVWhSWclkIMRWWVqCoQnvnF9YhDu0RUbPQUmmNx/p1gfXfz4zT6vT4KSYeJRWV9/xenw72RJJj9Z2+j17KxrhL2bdZprB5sTQIePXkVSwb3hk5ttb4bIDHbV/f0dkeo3y9oNMbIJNKkFVYhn1nEqE3NL6enWJNFT7eE4XJQ3uivXNryK0sAP5kNAABGq0OaTn5+O/vF1DcwI9cMrsgtWzZMlhaWuKtt/63ZkRUVBSWL1+OjIwMdOvWDR9++CE8PKr/cWZkZGDRokU4f/48nJ2d8fbbb2PYsGFilU9EZsirrQPG9O6Erw7HokxT/duqrdwKLw33xS9nkpCUXXBP73cjRD17NgNBvGPtJrZaPV77MwXLhndGseLWd5+N8vVCC4U1Nh08A8Pfwam9ox3eHBeANXtPorKq8Q2THrqchWMJ2WhtYwVrCz7ouaFU6vTIL9eiSoReYbMJUoWFhfjoo4/w008/YcqUKcbteXl5mDVrFlatWoXBgwdj06ZNmDdvHnbv3g0AmD17NgICArB582ZERUXhjTfewMGDB9GqFSc8ElG1cX27YM3ekzW2lWm0WLcvBnPHDLip7W48dyYdw1Py66rEJse1tBIv/5WGzwJq75FyaqmEU0sbbDt2vsb2tLxirP81Bi8OfQBfHDjTEKXWuSq9AdnN5K5NMqM5UiEhIZDJZBg5cmSN7ZGRkfDx8cHw4cNhZWWFGTNmID09HXFxcUhOTkZCQgJmzpwJS0tLBAYGwt/fHxEREeKcBBGZnd6ebRGVkH7L9j+upKGvV+2P5riVZ89mMETdhT5ZxRh9ObvWtrF9O+PHExdrbSvXVMEgCLCQmc1HFNEtNdhPqU6nQ0lJyU1/ysqqn3b99ddf48MPP4RSWXMZ/JSUFHh5eRm/lslkcHd3R1JSElJSUuDq6gq5XG5s9/DwQGJiYsOcFBGZPR9XJ8RevX7L9nNXr8PH1emW7bXhcN7de/xi7UFKKpHcduguq6AMji3q9rEoRPWhwYb2YmJiMHny5Ju2u7q64vDhw2jTpk2t+6nVatja1nx8i0KhgFqthkQigUKhqNEml8uh0bBLlYiq5ZaUo51DC6RcL6y13cXBFrkl5bW20f271W/rMuntJ2G3bqFAifrebwQgamgNFqQCAgIQHx9/z/spFIqbgpFarYZSqay1TaPR3NSrRUTN1+G4q3glyA9hkadrbX/YzxubD51t4Kroz/gMDO3eAb9fTK21vaXCGhWVDXv3FZEpzGay+a14enpi//79xq/1ej3S0tLg7e0NuVyOzMxMaLVaWFlZAQBUKhX69+8vVrlEZGZ0egNScoowopcHDp5X1WgL6umB1Nxi6O5wp8+jl7Lx2KXah6jozraExwIAfurWFj93awsAiEvLwaxR/XA1txhXc4pqvH7mqL7Ye4ZTNKhxMPsgFRwcjFWrViEyMhJDhw7Fpk2b0LZtW3Tr1g0SiQTe3t5Yu3Yt5syZgxMnTiA6Ohrvvvuu2GUTkRn5LTYZAzq74tWRfVGl10MQAGtLGc6kZOPQBdVt9x2anIdxDFF1YuzlbMS1bQFVKxsAwIb9f+Gpgd0wurc3NFodLGRSWMik+L9TCUjPKxG5WqK7Y/ZBysnJCRs3bsTy5cvx1ltvwcfHB6GhoZBIqsfXQ0NDsWTJEgwcOBCOjo5Ys2YNXFzu7Q4cImr6TiZk4mRC5j3v99zZDC6pWEcsBOCVmDS8N6IztH+vsbTzxCUAgEQCCI1vDU4iSASh+f7oZmRkICgoCKquj0FnZXvnHYio2bkxLEV155CXI7b7uYldBjUjFtoyeFyJwKFDh+DmVrc/e2bfI0VE1FBkBgF6qQRdcsvwxh/JsGqEjylpDIYl5+FsOztcatMCNlodPoi8AnuNDlPG+4pdGtE942pnRER/00slcCnR4LUoFUNUPZICmHIqDQqtHuVWFvjOl71T1HgxSBER/a2Fpgqz/0yBshE+462xaaWuwlMXsgAAp9zsEevSUuSKiEzDIEVE9Lc5f6bAuVwrdhnNxhBVPrzzqp9usZ29UtRIMUgRUbOzJTwWn+8+Z/wQlxkEzP0jGR6FapEra16kACadTofMYEC+jZXY5RCZhEGKiJodA4Av/TsgybH6bt3Jp9LQ43qpuEU1U66llXg4PkfsMohMxiBFRM3Ozl7tcNrNHgAw/kIWAtJqfw4fNYyxl6/DuZTP1aPGiUGKiJqd3zo7AwACU/LYG2IGLA0Cnj+bLnYZRCZhkCKiZql7dgme5arlZqN7ThkGpBUAABRVeqzZG8fFUKlRYJAiombHrUiNV09ehQWXijIrT5/LgkKrh9pShvAe7cQuh+iuMEgRUbMz588UKHQGscugf7Gr1OHxi9cAAFEdWyG5lVLkiojujEGKiJqdVuoqsUugWxiWkgfX4uplKL7zdRW5GqI7Y5AioibNM78cAGCpN2DhkQTOuzFzMgF45lwmAEDVykbkaojujEGKiJq0lNbVH8Yvx6TCO79C5GrobnTLKUOfjCKxyyC6KwxSRNTkPXkhC/0yi8Uug+7B0+ezYKHnPDYyfwxSRNSkjUjMxSNcK6rRcazQYlQCrxuZPwYpImoytoTH4qnzWcavB6YWYOK5TK4V1UiNvsIgReaPQYqImoyo9g7Y2at6/SHfrGJMPpXG/+QaMWsO7VEjwP9jiKjJ+KpfewCAV345pkVzwU0iqn8MUkTUZAgSCdyK1Jh9PAXWeqYoIqp/FmIXQERUV9qUajDvj2TYVunFLoXqyI11v77s1x4nOrSCbaUOZdb86CLzwR4pImoy5h9Lhl2lTuwyqB48GXcNlnoDQxSZHQYpImoyWvPRL01WK3UVRnI5BDJDDFJERNQoPHIlB3YMy2Rm2EdKRI1WS00V3jyahHallWKXQg1Arjfg8YvX8HXf9mKXQmTEHikiapRaaKqw4FgyQ1QzM/hqgdglENXAIEVEjdKCY8lwLdGIXQY1MH5okbnhzyQRNUpuDFFEZAY4R4qIzJ7UIMAglcC5tBLz/0iGY4VW7JJIRFvCY5Fta43FD3WFXirBkxeyMDo+B1PG+4pdGjVD7JEiIrNnkErgWqzG20cTGaIIANC2rBJDU/IAAL90bYMSK5nIFVFzxSBFRGavY0EF3jqaBHsNF9uk/xl7KRuKKj00ljLs6dZW7HKomWKQIiKzt+BYEmy1fOwL1dRSq8foK9cBAEc9HUWuhporBikiMnsKnUHsEshMBSfmonW5FgapROxSqJniZHMiMktPnc/EqIRc8OORbsfSIODJuCxs6t9R7FKomWKPFBGZFQu9AdNPXsXDDFF0l/qnF8GjoFzsMqiZYpAiIrPy5rEk+GcUiV0GNSISAM/EZopdBjVTDFJEZFa88yvELoEaIe8C/tyQOBikiEh0QUm52LTrHLaEx4pdCjViH++7CAt99Y0JYy9l8+eJGgSDFBGJxlJvwEsxqXg2NhMWgiB2OdTIOVZUYURS9SKdv3V2RoHCUuSKqDlgkCIi0Sw8kohBaYVil0FNyJgr12FbqYPWQord3V3ELoeaAbMJUhs3bsTQoUPRt29fPP/880hISDC2RUVFYcyYMfD19UVISAhUKpWxLSMjA5MmTYKfnx9GjhyJI0eOiFE+EZmgQ5Fa7BKoiVFW6THuUjYAIKpjK5GroebALILU7t27sWfPHmzbtg0nT57EwIEDMW3aNBgMBuTl5WHWrFmYO3cuYmJiEBAQgHnz5hn3nT17Nnr16oWYmBgsXLgQ8+bNQ0FBgYhnQ0S3YqPV4dUTKmwJj+X8Fao3gSl5cCnRiF0GNRNmEaQKCwsxffp0uLu7w8LCAi+88AKysrKQnZ2NyMhI+Pj4YPjw4bCyssKMGTOQnp6OuLg4JCcnIyEhATNnzoSlpSUCAwPh7++PiIgIsU+JiP7FJ6cUSw/Eo29msdilUBNnIQBPn+dyCNQwGmxlc51Oh4qKm29PlUqleOmll2psO3z4MOzt7dG2bVukpKTAy8vL2CaTyeDu7o6kpCTY2NjA1dUVcrnc2O7h4YHExMT6OxEiMsm8Y8nm8ZsbNQs9s0vRPbsEF9u2FLsUauIaLEjFxMRg8uTJN213dXXF4cOHjV//9ddfePfdd/H+++9DKpVCrVbD1ta2xj4KhQJqtRoSiQQKhaJGm1wuh0bDLl0ic8MQRQ1JAmDiuUwsZpCietZgQSogIADx8fG3fU1ERASWLl2KxYsXY+zYsQCqQ9O/g5FarYZSqay1TaPRQKlU1m3xRHRPbCt1ePp8JgJSC/mYFxKNa2klghJzcaiTE6x1eqzYfxn2Gh2mjPcVuzRqQszml8TPPvsMK1aswMaNG/HEE08Yt3t6eta4S0+v1yMtLQ3e3t7w8vJCZmYmtFqtsV2lUsHb27tBayeimpb9dhmDGKLIDIy7lA2lVodKCxl+4nIIVA/MIkjt2rUL33zzDb777jsMHDiwRltwcDDi4uIQGRkJrVaLsLAwtG3bFt26dYOXlxe8vb2xdu1aaLVaHD16FNHR0Rg1apRIZ0JEANBSqxe7BCIAgG2VHo9evg4AON6xFVLtFXfYg+jemEWQ2rRpE8rLyzF+/Hj4+fkZ/yQnJ8PJyQkbN27Ehg0b0L9/f0RFRSE0NBQSSfXvuqGhoYiPj8fAgQOxfPlyrFmzBi4u/K2DiIiqDU/KQ5tSDQSJBN8/4Cp2OdTESASh+T6XISMjA0FBQVB1fQw6K9s770DUzLkXqZH+92/0MoMBQUl5GHP5Omyr2ANF5u1c25ZYN9hT7DJIJBbaMnhcicChQ4fg5uZWt+9dp+9GRE3ajRDll1mECRey0KZMe4c9iMxDr+wSLodA9YJBiojumndeGcZfuIbO+eVil0J0T24sh/CucwsYpLwNguqOWcyRIqLG4Z3fkxiiqNFyLa1EUHKu2GVQE8MeKSK6pS65ZRh7ORs+OWVcyoCahHGXruOkuwNK5Zbom16IV6NTAYBrS5HJ2CNFRLV650gi3jqahG4MUdSEKKv0eDLuGgDglLsD4h1tRK6IGjsGKSKqVScO4VETNfhqAToUVj/7dbuvG/T8TYHuA4MUERE1K1IAIbGZAIAMewV+93QUtyBq1DhHiqiZci1WIygpDwPTCmGtN4hdDlGD6pRfjoGpBTjRoRUiurcVuxxqxBikiJoRqUGAX1YxhifnoWsu5z5R8zb+QhbOtLNDuRU/Csl0/OkhakY+2XcJDpoqscsgMgsOGh0evZyNnb342BgyHedIETUjDFFENQUn5sGlRCN2GdSIsUeKqIlxL1Jj8NV8DEgrRAstn4FHdDsWgoBnYzOwaog3AODFU2kYcrUAANeWorvDIEXUBNhW6jAgrRCDUgvQoUgtdjlEjUq3nDL0TS/EKXcHhPdsh95ZxbDlLyF0lzi0R9QErNl7ESHnMhmiiEw08XwWrHV6lFlbYFcPF7HLoUaEQYqoCbAQBLFLIGrUWqmr8Oil6wCAYx6tkeKgFLkiaiw4tEdkJtbsjcPJ9g440b4V0u0VNdrcitQYkF6I/mmFaK3mhHGi+hCcmIvjHVvhWks5tvV2E7scaiQYpIjMxLzR3SFI/reyk0OFFgPSCzEgrRDuxbyriKi+WQgCnj+bgY8DvZHKHim6SwxSRGZCkEigqNKjb0YRBqYVonNuGcfeiRpY19wyDEgtwMkOrcQuhRoJBikiM/HqCRV6XSuBlYHznYjE9PT5LJxzsYPaSiZ2KdQIMEgR1bMJ5zPxR8fWuNZSXmN7l9wyDEwtQN/MYiireKs1kbmwq9RhfFwWtvV2F7sUagQYpIjq2Y//ePxEm1INAlILMTCtEI4VWhGrIqLbCUzJZ5Ciu8IgRVTPrKv06J9eiMFXC+BVUMEHBRM1ApyfSHeLQYqonq3dexHWeoPYZRARUT1gkCIy0cNXruMPj9Yos/7fPyN7tRYPqgrw4NUCDt0RNXJbwmOhkUmx+KGuyLexQufcMiQ42YpdFpkZBikiE/3atQ0AQCII6JldgsCUfPTKLoGMN90RNRlyvQHPxmZg/SBPhiiqFYMUkYlaaKoQqMpHYEo+VxsnasJ8r5WgT0YRTrvZi10KmSEGKSITrdp3CZZc84moWQiJzcAl5xZcW4puwiBF9A9bwmORbifHr52d8Ze7A/TS6nvsZAYDBqQVYkRSHjoUqUWukogamoNGh6cuZGFrHy6JQDUxSBH9w9pBHjjvYmf82qZSh2EpeRienAd7jU7Eym4tEyVYL/0Lsw390A4txS6HqMkaospnkKKbMEgR/cONEOVcVomRCTkISC2Atd58h+8qoMUqWQyKXdpj1bUYLNMHQglLscsiapK4thTVhkGK6B/ci9R45Mp19M0sMvu77wwQECo7g7L2XYA+Y1F66mdsSD+N+fr+kHLZTyKiBsEgRQSgc24ZRl+5jh7XSxtNBNkluYKUFjLo/B6BBICu9yNILtqCXcVX8JTgI3Z5RE3SlvBY6CQSvB/UGRn2CrQp1eB6C/mdd6Qmiz2V1Kx1zSnFW78n4u2jSejZiELUKWTioGUGtIOfgURafReRRCqDdvAzOGiZjtPIErlCoqbLQhAw+XQaJILAEEUMUtR8vfV7It48lowueeVil3JPMlGCL2XnoB0cAom85gKBErkttINDsEkWiyyUiFQhUdPnUajGyIQcscsgM8AgRc1WYwtQwP8ml1f5joKklWutr5G0ckOV7yisksWgAlwolKi+PHYxG21KNWKXQSLjHClqclbvjcNP3V3wZ8dWECTVg3VuRWo8GXcNvbJLGs3w3b/VmFzu0fv2L/bojdL8TE4+J6pHVgYBk0+l46Oh3hAkEjx6KRuPXcoGAEwZ7ytucdRg2CNFTc47o3xw3KM1BIkEjuWVeCUmFe8djMcDjThEATUnl98NXe9HkNJCht2SK/VcGVHz1Tm/HEFJeQCAX7q2QZod50w1NwxS1ORoLWSw0eowMTYTy/dfwcC0wkb/g17b5PI7kUhlqBz8DA5w8jlRvXoy7hocyyqhl0qwpW976CSN+Vc2uleN/fOF6CbBiTlY8etlPJSUCwvBzBeDugu3m1x+J5x8TlT/rPUGTDmdDgBIc1Bir08bkSuihsQgRU3OM+eyYFulF7uMOlGBKqy+w+TyO+Hkc6L61zW3DEGJuQCqh/io+eBkc2r0XEo0eDY2A91yysQupU4ZIGCD7DRK72Zy+Z149EYZJ58T1avxcddwoW1L5LSwFrsUakBmEaS0Wi2WL1+O/fv3o6qqCv7+/njvvffQpk11qo+KisLy5cuRkZGBbt264cMPP4SHhwcAICMjA4sWLcL58+fh7OyMt99+G8OGDRPzdMgEdkprPDO4BwCgSqeHtaUFKiqr8O0fF6DTG26534TzmRiRmNckhvD+bZfkCpL/sXL5/arq/QhSirZgd/EVjOfK50R1SiKTwml6MF7zdEKq0gIWMhkkAL7/8yKKyrlEQlNmFkN7n332GZKTk7F//36cOHEC9vb2+OCDDwAAeXl5mDVrFubOnYuYmBgEBARg3rx5xn1nz56NXr16ISYmBgsXLsS8efNQUFAg1qmQCeyU1pj+UB98dfgsPo88ja8Ox2Ljb6fwf6cSMG/sAEilt44RoxKaxjyofzNlcvmdcPI5Uf2QSKVQLH8G08uLsfhEHDYfisXnkaex+fBZTAvuDXsb3snXlJlFkHr99dfx5Zdfwt7eHvn5+SgvL4eDgwMAIDIyEj4+Phg+fDisrKwwY8YMpKenIy4uDsnJyUhISMDMmTNhaWmJwMBA+Pv7IyIiQtwTonsycVB3fLo3GlW6mj1PBWVq/BB1CaN7dxKpMnFkoQSba5lcLggCBE0ZhLxUCFfPAhcOwDLqBwil1bdeC6V5sIz6AbhwAMLVs9Wv05RB+EfQvDH5/EtOPieqM3aP98OiqxnIK6vZ81SlM2DN3pN4OqCbSJVRQ2iwoT2dToeKioqbtkulUtja2kImkyE0NBSfffYZnJ2dsX37dgBASkoKvLy8jK+XyWRwd3dHUlISbGxs4OrqCrn8f2nfw8MDiYmJ9X9CVGckEgm0utonh1/NKcIo3+rrP+ZyNsZevg5LQ9PrgbqhAlVYJYtBZfvuQEkOkHkZ1sW5kJTmQ6suhAxStJa1QFuDDdz1SpyUXkNOxzyghSNQmgf77EwMyDIgXZaGbGk58vWl0MMAK4UDhBatUWnnBLRojUr37liVHoNl+kAoYSn2aRM1ark93JF8ManWtiqdAS20emwJj+UinU1UgwWpmJgYTJ48+abtrq6uOHz4MABg6tSpeOWVV7Bq1Sq89NJL+OWXX6BWq2FrW/OWb4VCAbVaDYlEAoVCUaNNLpdDo+F4dGNyuzlQ/2x/4mJ2Q5Qjqmiko0KohEtGKtqm5cFdr0Rb2KAN2sAZtrCFFfCPb1epxICcqO9wI1p2kHbE4/AB9Kj+A6AMWuSUl+F6eRmys3ORLktFtrQcBUIlopGBYfBo6NMkalJK7zCJschCirg2LRqmGGpwDRakAgICEB8ff9vXWFtX3+nw5ptvYseOHUhISIBCobgpGKnVaiiVylrbNBoNlEpl3RZP9crK4vZzgO7U3pQMgyeGGTxrhKXbeUHfHS+g+/821LKfLaxgi1bwRKvqDf8IWUR0/5wMt/8Ha2Uhw1d92zdQNdTQzGKO1DvvvIPvvvvO+LVer4fBYEDLli3h6ekJlUpVoy0tLQ3e3t7w8vJCZmYmtFqtsV2lUsHb27tB66f7U1CmRrtWtf+2FtitA6ITMxu4IiKiu6fcdw6PdHGvtc3VzgYlJRUoVnAIvakyiyDVq1cvbNmyBRkZGVCr1fjwww/Rp08fuLu7Izg4GHFxcYiMjIRWq0VYWBjatm2Lbt26wcvLC97e3li7di20Wi2OHj2K6OhojBo1SuxTonuQ/GMUJgZ0R3d3pxrbx3Rtj1evl2Pmx79iS3isOMUREd1B6alkTMpV49GuNXuderVrjTUebpBujBSpMmoIEkEQ/95xQRDw2Wef4YcffkBVVRUGDRqERYsWoVWr6qGIkydPYvny5UhPT4ePj0+NdaQyMzOxZMkSxMbGwtHR8Z7WkcrIyEBQUBBUXR+DzureHr1Bde9F1zbwd2gBLQS01gmw3ncWZWdUd96RiMgM2Pp2hGZ0bxRYSmBrkMAhJgnF+2MhAFg3yAPnXezELrHZstCWweNKBA4dOgQ3N7c6fW+zCFJiYZAyD1Y6Ayacz8SwlHyut01ETVKxtQXeGNtD7DKarfoMUmaxsjk1b+8djEfbskqxyyAiqjd2lTqxS6B6YhZzpKj5kP19d4vUIODRS9nYtCuWIYqImoUt4bEYlly9gK5EELDwSILIFVFdYI8UNSi9VArHskpMjUmFd8HNC7QSETVlE85n4oqTLa61lGOTfwexy6E6wB4palD90wrx3sF4higiapas9QKmRafCQm9Ano212OVQHWCQogY1NSYVSt1drjZJRNQEtS9W46kLfHB4U8GhPap37kVqTI++CpdSzoUiIgKAEUl5uNimBZdEaALYI0X1amhyHv5zOIEhiojoHyQAppxKF7sMqgPskaJ680p0KgamF4pdBhGRWWrJJRGaBPZIUb1hiCIioqaOPVJ031xKNLjWUg6g+q68SafTIddzQjkR0Z1sCY+FXgJ8MsQbCU62UGj1UFvJxC6L7gF7pOi+XWsph9Qg4JnYDEyNSWWIIiK6BzKh+o5m20odQ1QjxCBF962lpgoLjiUhOCmPz8ojIjJBK3UVXv4rVewyyAQMUnTflhxKQJe8crHLICJq1Hpll+LhK9fFLoPuEedI0T1xK1Ijw14BABh0tQAvnEmHpUEQuSoioqbhiYvXkORog0RHWyiq9Hj3YDycy7UAgCnjfcUtjmrFHim6Jxn2CkgEARNjMzHlVBpDFBFRHZIJwPSTf8+XspRh44CO0Eo5acKcMUjRPVFo9XjjeAoeSsrlfCgionrgoKnC1JhUSAQBaQ5KfOvnBv7Kar4YpOie/OdIAnpcLxW7DCKiJq3H9VKMu5QNADju0RrHPFqJXBHdCoMU3VKfjCLj37vmlGL9ngt81AsRUQMZc/k6el0rBgBs93UTuRq6FQYpuqXTbvYAgAdV+Zj7RzJsq/TiFkRE1IxIAbwckwbH8kroZPy4Nle8MnRbT53Pwoun02HBAXoiogZnW6XHzBNXYcmFjs0WgxTd0owTKjyckMNJ5UREIupQpMbzZzLELoNugUGKAFSHJou/f+OxrdThnSOJ6JdZLHJVREQEAINTCzAsOc/49asnVNgSHiteQWTEIEUAgM8HdIROJoVzWSUWHUlAp3yuVE5EZE6eic2Ed14ZAOCrfu2R8ffD4klcDFIEABAkEngUlGPhkUS0KdOKXQ4REf2LhSDg1ZNXYaeuQqWFDKEBHmKXRGCQor/1yC7BgqPJaFmpE7sUIiK6BXuNDrP+noqRa2stdjkEBqlmKeync8a1SQAg4GoBXv8zBXLeFUJEZPa8Cio4+dyMMEg1Q6sf9MJ5FzsAwKj463jpVBqXNyAiakQeTC1AUGKu2GUQGKSapSRHWwDAhHOZmHDhGpc3ICJqhJ4+nyl2CQTAQuwCqOFJBAEvnkrHg6kFYpdCREQm4kiCeWCPVBO3JTwWH/52GQ4V1XfiWegNePXEVYYoIqImYEt4LD747TIUfz/Cy61ILXJFzQ+DVBOXZifHykBvFCqtYKXTY/afKeiTxYU2iYiaCtfSSsw4eRUSQUCGvULscpodBqkm7pNAb5TKLaHQ6jH/WDK655SJXRIREdWxHtdL8Uws50yJgUGqiSu3soBtpQ5vHkuCd0GF2OUQEVE9CUrOw7Ak3snX0BikmriWmiq8dTQJHThuTkTUpEkAhJzLRI/sEgCA1CBgzvFkPpOvnjFINXFv/54E1xKN2GUQEVEDkAnA9JNX4VqshkEqQVj/jkiz47yp+sQg1cS1LasUuwQiImpASp0Bc46nVD+Tz1KGTwd7il1Sk8YgRURE1MS0Vldhzp8psNbpUaywFLucJo0LcjYxzqWVePNYElqpq8QuhYiIRNShSI0ZJ69ifYAnDFI+w6K+sEeqCWlTqsFbRxmiiIioWq/sUjx3lg84rk8MUk3Im0eT4KBhiCIiov8ZqsoXu4QmjUGqCXHQ6MQugYiIqFkxuyAVHh6O/v3719gWFRWFMWPGwNfXFyEhIVCpVMa2jIwMTJo0CX5+fhg5ciSOHDnS0CWLwlJvAAA4l1Vi9d6LXCeEiIhuaUt4LL7cFQtfPiKszt0ySP31118oLS297c6VlZWIiIios2LS09OxcuXKGtvy8vIwa9YszJ07FzExMQgICMC8efOM7bNnz0avXr0QExODhQsXYt68eSgoaPoP5K2SSeFYVsnhPCIiuisyAZgWfRWd8viosLp0yyD1/PPPY9y4cTh9+vQtdy4tLcU777xTJ4Xo9Xq8+eabmDBhQo3tkZGR8PHxwfDhw2FlZYUZM2YgPT0dcXFxSE5ORkJCAmbOnAlLS0sEBgbC39+/TsOduWpdrsWbx5I5sZyIiO6atV7A63+q4ManXdSZ2w7tubq64oUXXkBoaCgMBsN9HUin06GkpOSmP2Vl1cl406ZN6NSpEwIDA2vsl5KSAi8vL+PXMpkM7u7uSEpKQkpKClxdXSGXy43tHh4eSExMvK9aG4MFx5LgWKEVuwwiImpkbKr0eON4sthlNBm3XEdKIpHg008/xc6dO/HZZ5/hxIkTWLVqFdq1a2fSgWJiYjB58uSbtru6umL9+vXYs2cPdu3ahbi4uBrtarUatra2NbYpFAqo1WpIJBIoFDWXvpfL5dBomv4jUZzLGaKIiMg0vDmp7twySAmCAIlEghkzZmDgwIGYP38+xo0bh6VLl+KRRx655wMFBAQgPj7+pu0ajQbjx4/HsmXLYGNjc1O7QqG4KRip1Woolcpa2zQaDZRK5T3XZ87s1FUoVljCRqvDgqNJaF/c9IMiERHVry3hschoKcdHgd4ot7ZAS00VSuRcBf1e3dVde76+vtizZw+GDh2KuXPnYuHChVCr62Z8NS4uDunp6Zg+fTr69u2L6dOno7i4GH379kVWVhY8PT1r3KWn1+uRlpYGb29veHl5ITMzE1rt/3pnVCoVvL2966Q2c1GssIS8So83/khhiCIiojrjVqLB3OPJkFfpGaJMdMsgJZHUXE7exsYGn3zyCT755BNERkbi8ccfx6VLl+67gL59++LcuXM4deoUTp06hc8//xx2dnY4deoU2rVrh+DgYMTFxSEyMhJarRZhYWFo27YtunXrBi8vL3h7e2Pt2rXQarU4evQooqOjMWrUqPuuy5xY6g2Y/WcKPAsrxC6FiIiaGI9CNeYcT4GVTi92KY3SLYOUIAi1bh87diwiIiJgb2+PGTNm1FthNzg5OWHjxo3YsGED+vfvj6ioKISGhhqDXmhoKOLj4zFw4EAsX74ca9asgYuLS73X1ZBmnlChS1652GUQEVET1Tm/HK9Hqe78QrqJRLhFYsrMzES7du1u6pm6wWAwYOPGjYiOjsa2bdvqtcj6kpGRgaCgIKi6Pgadle2ddxAJF9skIqKGcL5tC4QGeEAvlcKlRIMFx5Iwd0wPscu6bxbaMnhcicChQ4fg5uZWp+99yx4pV1fXW4YoAJBKpZg1a1ajDVFERERUU6/sUrx64ipkBgOutZTj4yFNa85xfTC7R8QQERGRePyulRjDVHZL+Z13aOYYpIiIiKgGv2slmPl3mKLbu+U6UiSOQVcLMOVUGm49qEpERFT/fP8OUxsHdIROJoVzaSUWHEtCa3UVpoz3Fbs8s8EeKTPz4mmGKCIiMg++10ow64QKFnoDclpYY8WwTsi2tRa7LLPCIGVmZLWvOkFERCSKXtmlmP1n9TpTBUorrBzKCej/xCBFREREt9U9pwzz/kiBgiug34RzpETmUKHFoiOJaKWuErsUIiKiW+qUX443jyZh9YNeKLNmfLiBPVIistHqMPePZIYoIiJqFDoUqfHO74lil2FWGKRE9NqfKriWVopdBhER0V1z4edWDQxSIuqcz+fnERERNWYc5GwAX4XH4pvebjjm6QgAeCY2A8FJeSJXRUREZJobz4CtlEkRNqADzrvYiVuQiNgj1QB+6epsDFEPJeQwRBERUZNgrTfgtSgVhqTki12KaBikGsDuHu0AAH3TCzHhfJbI1RAREdUdmQBMOpMudhmiYZBqIN55ZXjlrzR+w4mIqMlpzk/k4BypBtCmVIPXolSwNHDZciIiappuzJtKaG2DDQEezWatKXaQNIA5x1PQQqsXuwwiIqJ61zm/HIsPJcC1WC12KQ2CQaoBtCnXil0CERFRg3Gq0GLhkeaxcCeDFBEREdU5hc4gdgkNonkMYIrgibgsjLmSI3YZREREorkxb+qUqx2+6tselZYycQuqB+yRqgeDVfkYzRBFREQEAOibWYzFhxPgUqIRu5Q6xyBVx7rmlOKFMxnN+lZQIiKif2tXWon/HE4Qu4w6xyBVx2aeuAoLgcscEBER/VtTnDfFIHUf1u+5gDal1d2UNlodVuy/BJsqLnNARER0K1vCY7ElPBZLDsYbP0MbMwap+xA2sCOut5BDZhAw88RVtCnjMgdERER3o2ORGu8eTMBgVeN+Th+D1H247NwCAPDC6XR0zS0TuRoiIqLGRa43YMrpdEyNvip2KSZjkLpPo+Kv48HUArHLICIiarQGpBeJXYLJuI7UffDNKsb4C9fELoOIiKjR2xIeCwOAo56t8WOvdqi0qF5zqm96IZ6LzUTLSh0AYMp4X/GKrAV7pO7DKzGp/AYSERHVESmAYSn5eD8yHl1zSgEAp9wdsHBkVxzxbA1zvOePPVL3oSnexklERCQ2pwot5h9LxlHP1tjZsx0qrCywrbc7/ujYWuzSbsIOFSIiIjI7N3qnPvztMvqnFQIArrZSiltULdgjdY+m/JWGwZxcTkRE1CAcNDpMi0nFEFU+vvVzw7WWcgDV6zeOvXwdw5LzMO2JB0Srj0HqHoyKz2GIIiIiEoFPbhmWHohHZGcn/OzTBuVWFtjxgCsOejmKWheH9u5Sr2vFGH8hS+wyiIiImi0LQcAj8TlYsf8yhqTkQyIIyLO1FrUmBqm7NC2ad+gRERGZA3uNDi+eScfSA/Hoda1Y1FqYDWrxWcR5uBarAfz9DL1fL/EOPSIiIjPjVqLBnD9VePNoEjzzy0WpgUGqFl/6d0CmnQJSg4DpJ6+iTTmfoUdERGSuuuaWYdGRRMz9IxleDRyoONm8FrHt7AAAT5/PRPccPkOPiIjI3EkA9Lheiu7XS3HJ2Rarh3g3yHHZI3ULD6ryMSIpT+wyiIiI6B5IgAbtBGGPVC2888rw3NkMSMQuhIiIiEyyJTzW+PciCy3CPOvnOM2+R6prVx8I/9o288RVWBr+vZWIiIgao1a+9ZSiYEZBavTo0XjggQfg5+cHPz8/jB492tgWFRWFMWPGwNfXFyEhIVCpVMa2jIwMTJo0CX5+fhg5ciSOHDlyT8fN6zYaM8YG4kGf9sZtdn8/YZqIiIgaL5vhPVD8cQi2BPWqt2OYRZDSaDRQqVQ4cuQIzp49i7Nnz+KXX34BAOTl5WHWrFmYO3cuYmJiEBAQgHnz5hn3nT17Nnr16oWYmBgsXLgQ8+bNQ0HB3a8+fiIpGxt+Ow17Gzn6ernU+bkRERFRw1P6e+OXBzvhjZhLOJZcfwtqm0WQSkhIgKOjI1q1anVTW2RkJHx8fDB8+HBYWVlhxowZSE9PR1xcHJKTk5GQkICZM2fC0tISgYGB8Pf3R0RExD3X8POpBEy1aVFjTJWIiIgap9Ixfth6UXXnF96nBptsrtPpUFFRcdN2qVSKS5cuwcLCAk8//TRSU1PRrVs3LFq0CF5eXkhJSYGXl5fx9TKZDO7u7khKSoKNjQ1cXV0hl8uN7R4eHkhMTDSpxgSFBbpbyCDo9CbtT0REROKTSKVIVFo2yLEaLEjFxMRg8uTJN213dXXF1KlT0bNnTyxYsACOjo7YuHEjXnnlFezbtw9qtRq2trY19lEoFFCr1ZBIJFAoFDXa5HI5NBqNSTVqDQZIpJKbJp8TERFRIyKVQNtAN401WJAKCAhAfHz8LdsnTpxo/Psbb7yB7du34/Lly1AoFDcFI7VaDaVSWWubRqOBUqk0qcZOBgkMWk40JyIiaswEnR5euoYJUmYxR+qHH35AVFSU8Wu9Xg+dTgdra2t4enrWuEtPr9cjLS0N3t7e8PLyQmZmJrTa/z3CRaVSwdv73lcz7e/mhDZ/pdzfiRAREZFZaBOTjIFuzvV+HLMIUjk5Ofjwww9x7do1aDQarFy5Ep6enujatSuCg4MRFxeHyMhIaLVahIWFoW3btujWrRu8vLzg7e2NtWvXQqvV4ujRo4iOjsaoUaPu+thKa0vM6tsJ88oFlO2OrsezJCIiooZSFvEX3ijT4fW+XWAjr7/5Umaxsvn06dNRVlaGp556CuXl5ejXrx8+++wzSKVSODk5YePGjVi+fDneeust+Pj4IDQ0FBJJ9brjoaGhWLJkCQYOHAhHR0esWbMGLi53v4zB1A4adFoVgbK8qvo6PSIiIhJB2ebD8Le3Qf+J/fBePR1DIghCs51bnZGRgaCgIMxI6Qx7nZXY5RAREVE9qH5ETAIOHToENze3On1vsxjaIyIiImqMGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiITMUgRERERmYhBioiIiMhEDFJEREREJmKQIiIiIjIRgxQRERGRiRikiIiIiEzEIEVERERkIgYpIiIiIhMxSBERERGZiEGKiIiIyEQMUkREREQmYpAiIiIiMhGDFBEREZGJGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiITMUgRERERmYhBioiIiMhEDFJEREREJmKQIiIiIjIRgxQRERGRicwmSB04cACjRo2Cn58fJkyYgCtXrhjboqKiMGbMGPj6+iIkJAQqlcrYlpGRgUmTJsHPzw8jR47EkSNHxCifiIiImiGzCFKXLl3CwoULsWzZMpw+fRojRozA7NmzAQB5eXmYNWsW5s6di5iYGAQEBGDevHnGfWfPno1evXohJiYGCxcuxLx581BQUCDWqRAREVEzYhZBaseOHXjqqafQt29fSKVSTJ48GatXr4bBYEBkZCR8fHwwfPhwWFlZYcaMGUhPT0dcXBySk5ORkJCAmTNnwtLSEoGBgfD390dERITYp0RERETNQIMFKZ1Oh5KSkpv+lJWV4dKlS1AqlXjhhRfQv39/TJ06FTY2NpBKpUhJSYGXl5fxfWQyGdzd3ZGUlISUlBS4urpCLpcb2z08PJCYmNhQp0VERETNmEVDHSgmJgaTJ0++aburqytkMhl27NiBsLAwdOnSBevXr8eMGTOwd+9eqNVq2Nra1thHoVBArVZDIpFAoVDUaJPL5dBoNPV6LkRERERAAwapgIAAxMfH19o2evRoBAcHo2fPngCq5z19/fXXSElJgUKhuCkYqdVqKJXKWts0Gg2USmX9nAQRERHRP5jFHCkPDw+UlpYavxYEwfjH09Ozxl16er0eaWlp8Pb2hpeXFzIzM6HVao3tKpUK3t7eDVo/ERERNU9mEaQef/xx7N27F6dOnUJVVRXWrl2LDh06oHPnzggODkZcXBwiIyOh1WoRFhaGtm3bolu3bvDy8oK3tzfWrl0LrVaLo0ePIjo6GqNGjRL7lIiIiKgZMIsgFRQUhPfeew+LFy+Gv78/zp8/j40bN0IikcDJyQkbN27Ehg0b0L9/f0RFRSE0NBQSiQQAEBoaivj4eAwcOBDLly/HmjVr4OLiIvIZERERUXMgEQRBELsIsWRkZCAoKAgzUjrDXmcldjlERERUD4ostAjzTMChQ4fg5uZWp+9tFj1SRERERI0RgxQRERGRiRikiIiIiEzEIEVERERkIgYpIiIiIhMxSBERERGZiEGKiIiIyEQMUkREREQmYpAiIiIiMhGDFBEREZGJGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiITMUgRERERmYhBioiIiMhEDFJEREREJmKQIiIiIjIRgxQRERGRiRikiIiIiEzEIEVERERkIgYpIiIiIhMxSBERERGZiEGKiIiIyEQMUkREREQmYpAiIiIiMhGDFBEREZGJGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIykVkEqZdffhl+fn7GPw888AC6dOmCM2fOAACioqIwZswY+Pr6IiQkBCqVyrhvRkYGJk2aBD8/P4wcORJHjhwR6zSIiIiomTGLILV582acPXvW+GfUqFEYM2YMevfujby8PMyaNQtz585FTEwMAgICMG/ePOO+s2fPRq9evRATE4OFCxdi3rx5KCgoEPFsiIiIqLkwiyD1TwcPHsTJkyexdOlSAEBkZCR8fHwwfPhwWFlZYcaMGUhPT0dcXBySk5ORkJCAmTNnwtLSEoGBgfD390dERIS4J0FERETNgkVDHUin06GiouKm7VKpFLa2tsbXrFixAm+99ZZxW0pKCry8vIyvl8lkcHd3R1JSEmxsbODq6gq5XG5s9/DwQGJiYj2fDREREVEDBqmYmBhMnjz5pu2urq44fPgwAGDfvn2wtrbGqFGjjO1qtdoYqm5QKBRQq9WQSCRQKBQ12uRyOTQaTT2cAREREVFNDRakAgICEB8ff9vX7N69GxMmTIBU+r8RR4VCcVMwUqvVUCqVtbZpNBoolcq6K5yIiIjoFsxmjlRZWRn++usvPPzwwzW2e3p61rhLT6/XIy0tDd7e3vDy8kJmZia0Wq2xXaVSwdvbu8HqJiIioubLbIJUXFwcnJ2d0aZNmxrbg4ODERcXh8jISGi1WoSFhaFt27bo1q0bvLy84O3tjbVr10Kr1eLo0aOIjo6uMTRIREREVF/MJkhlZmbCycnppu1OTk7YuHEjNmzYgP79+yMqKgqhoaGQSCQAgNDQUMTHx2PgwIFYvnw51qxZAxcXl4Yun4iIiJohiSAIgthFiCUjIwNBQUGYkdIZ9jorscshIiKielBkoUWYZwIOHToENze3On1vs+mRIiIiImpsGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiITMUgRERERmYhBioiIiMhEDFJEREREJmKQIiIiIjIRgxQRERGRiRikiIiIiEzEIEVERERkIgYpIiIiIhMxSBERERGZiEGKiIiIyEQMUkREREQmYpAiIiIiMhGDFBEREZGJGKSIiIiITMQgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCIiIiIyEYMUERERkYkYpIiIiIhMxCBFREREZCIGKSIiIiITMUgRERERmYhBioiIiMhEDFJEREREJmKQIiIiIjIRgxQRERGRicwmSG3cuBEPPvgg+vXrh5deegnp6enGtqioKIwZMwa+vr4ICQmBSqUytmVkZGDSpEnw8/PDyJEjceTIETHKJyIiombILILU4cOHERERgV27diEqKgrt27fHokWLAAB5eXmYNWsW5s6di5iYGAQEBGDevHnGfWfPno1evXohJiYGCxcuxLx581BQUCDWqRAREVEzYhZB6urVqzAYDDAYDBAEATKZDHK5HAAQGRkJHx8fDB8+HFZWVpgxYwbS09MRFxeH5ORkJCQkYObMmbC0tERgYCD8/f0REREh7gkRERFRs2DRUAfS6XSoqKi4abtUKsXo0aPxww8/IDAwEDKZDM7Ozvj+++8BACkpKfDy8jK+XiaTwd3dHUlJSbCxsYGrq6sxdAGAh4cHEhMT76omvV4PACixqLqfUyMiIiIzduNz/sbnfl1qsCAVExODyZMn37Td1dUV33zzDXr37o0vvvgCTk5OWLFiBd544w18//33UKvVsLW1rbGPQqGAWq2GRCKBQqGo0SaXy6HRaO6qptzcXADA9vaqO7ySiIiIGrvc3Fx06NChTt+zwYJUQEAA4uPja22bNm0agoOD0bFjRwDAf/7zH/Tu3RsJCQlQKBQ3BSO1Wg2lUllrm0ajgVKpvKuaevToge3bt8PJyQkymezeT4qIiIjMnl6vR25uLnr06FHn791gQep2srKyoNVqjV9LpVJIpVJYWFjA09MT+/fvN7bp9XqkpaXB29sbcrkcmZmZ0Gq1sLKyAgCoVCr079//ro4rl8vRt2/fuj0ZIiIiMjt13RN1g1lMNh86dCi++uorpKenQ6vVYvXq1ejUqRM8PDwQHByMuLg4REZGQqvVIiwsDG3btkW3bt3g5eUFb29vrF27FlqtFkePHkV0dDRGjRol9ikRERFRMyARBEEQuwitVotPP/0Ue/fuhVarRe/evbF48WK0a9cOAHDy5EksX74c6enp8PHxwYcffggPDw8AQGZmJpYsWYLY2Fg4Ojri7bffxrBhw8Q8HSIiImomzCJIERERETVGZjG0R0RERNQYMUgRERERmYhBioiIiMhEzTZIXbp0CePHj4evry/GjRuH2NhYsUtqFk6dOoWnnnoKffr0wYgRI7Bjxw4AQHFxMWbOnIk+ffpg6NCh2Llzp3EfQRCwevVqDBgwAP369cOyZcvqZXXa5i4vLw8DBw40Pvib10Rc2dnZmDZtGnr37o0hQ4Zg69atAHhdxHbmzBk88cQT6N27N0aOHImff/4ZAK+LWM6fP4/Bgwcbv76f67B3714EBQXBz88P06ZNQ15e3t0VITRDGo1GePDBB4Xt27cLWq1W2LlzpzBo0CChsrJS7NKatKKiIqFfv37Cnj17BL1eL8TFxQn9+vUT/vzzT+G1114T5s+fL2g0GuHcuXOCv7+/cPnyZUEQBGHbtm3CmDFjhOvXrws5OTnC448/LmzdulXks2l6pk6dKnTt2lU4fPiwIAgCr4mIDAaD8PjjjwsrV64UtFqtkJCQIPTr1084ffo0r4uIdDqdMGDAAOHXX38VBEEQ/vrrL6Fbt25Ceno6r0sDMxgMws6dO4U+ffoI/v7+xu2mXofLly8LvXv3FmJjYwW1Wi0sXLhQmDVr1l3V0ix7pE6ePAmpVIqQkBBYWlpi/PjxcHBwMP4mTvUjKysLgYGBePTRRyGVStG9e3f0798fZ86cwcGDB/H666/D2toavXr1wpgxY4y/SezZsweTJk2Cs7MznJycMG3aNPz4448in03T8v3330OhUMDFxQUAUF5ezmsionPnziEnJwfz58+HpaUlOnXqhB07dqBNmza8LiIqKSlBQUEB9Ho9BEGARCKBpaUlZDIZr0sD+/zzz7F161ZMnz7duO1+/t/6+eefERQUhAceeAByuRzz58/HoUOHkJ+ff8dammWQUqlUNR6EDNzbw47JND4+Pvjkk0+MXxcXF+PUqVMAAAsLC7i7uxvb/nk9UlJS4O3tXaMtKSkJAlfuqBNXr17Ff//7X7z33nvGbampqbwmIrp48SI6deqETz75BIMGDcLIkSNx7tw5FBcX87qIyMHBASEhIZg7dy66d++OZ599FosXL0ZhYSGvSwN78sknsWfPHvTs2dO47X7+3/p3m4ODA1q0aIGUlJQ71tIsg1RFRcV9PeyY7l9paSmmT59u7JWSy+U12v95PdRqdY12hUIBg8FQ47FCZBqdTocFCxZg0aJFsLe3N26vqKjgNRFRcXExoqOjjT3lK1aswAcffMDrIjKDwQC5XI5169YhNjYWn3/+OZYvX46ysjJelwbm7OwMiURSY9v9/Pv4d9uNdrVafcdammWQut+HHdP9SU9Px8SJE2FnZ4cNGzZAqVTe9nrI5XJUVlYa29RqNSwsLGBtbd2gdTdFGzduhI+PDwIDA2tsv9O/EV6T+mVlZQU7OztMmzYNVlZWxonN69ev53URUWRkJM6fP49Ro0bBysoKQ4cOxdChQxEaGsrrYgbu5/+t2jpT1Gr1XeWCZhmkPD09oVKpamxTqVQ1uvWofly8eBETJkzA4MGDsXHjRsjlcnTo0AE6nQ5ZWVnG1/3zenh5edW4XiqVCp6eng1ee1O0b98+/PLLL+jbty/69u2LrKwszJ07F7///juviYg8PDygVquh0+mM2/R6Pbp168brIqJr167d1ItkYWGB7t2787qYgfv5LPl3W0FBAYqLi2+aBlSbZhmkBg4cCK1Wi23btqGqqgrh4eHIy8urcQsl1b28vDy8/PLLmDx5Mt555x1IpdU/fra2tggKCsLq1auhVqtx/vx57N27F2PHjgUAPProo/jqq6+QnZ2NvLw8fPHFFxg3bpyYp9Jk7N+/H6dPn8apU6dw6tQptGvXDmvWrMHMmTN5TUQ0aNAgtGzZEqtXr4ZOp8OZM2dw4MABjBo1itdFRAEBAbh8+TJ27doFQRAQExODAwcOYPTo0bwuZuB+PkvGjBmDyMhInDp1CpWVlVizZg2GDBkCBweHOx+4Du9GbFQuX74sPP3004Kvr68wbtw44ezZs2KX1OSFhYUJnTt3Fnx9fWv8WbNmjVBYWCi8/vrrQr9+/YTAwEBh586dxv10Op2wZs0aYdCgQYK/v7/wwQcfCDqdTsQzabqGDRtmXP6A10RcV69eFaZMmSL069dPGDZsmBAeHi4IAq+L2A4dOiQ8+uijgp+fnzB69GghMjJSEAReF7GcPHmyxvIH93MdfvnlF+Ghhx4S/Pz8hFdeeUXIy8u7qxr40GIiIiIiEzXLoT0iIiKiusAgRURERGQiBikiIiIiEzFIEREREZmIQYqIiIjIRAxSRERERCZikCKiJi0xMRE9e/bEpk2bbmqbPXs2nnjiCVRVVYlQGRE1BQxSRNSkderUCTNnzkRoaCiSk5ON23/66SccOXIEH3/8MSwtLUWskIgaMy7ISURNnk6nw4QJE2BpaYnvv/8eWVlZGDduHF577TW8+OKLYpdHRI0YgxQRNQvx8fF48sknsWDBAhw5cgSCIODrr7+GRCIRuzQiasQYpIio2diwYQPCwsKgUCjw888/w8XFReySiKiR4xwpImo2hg8fDp1Oh/bt26NNmzZil0NETQCDFBE1C1qtFgsXLkSfPn0QHx+Pr7/+WuySiKgJYJAiomYhNDQUmZmZ+PTTT/Hiiy9i7dq1UKlUYpdFRI0c50gRUZN35swZPPfcc1i+fDkee+wxVFZWYty4cbC3t8d3330HqZS/UxKRafi/BxE1aRUVFXjrrbcwZMgQPPbYYwAAa2trLF++HOfOneMQHxHdFwYpImrSVq5ciZKSEnzwwQc1tvfu3RvPPfcc1q5di5SUFJGqI6LGjkN7RERERCZijxQRERGRiRikiIiIiEzEIEVERERkIgYpIiIiIhMxSBERERGZiEGKiIiIyEQMUkREREQmYpAiIiIiMhGDFBEREZGJ/h/wiX/8VHJgYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, cell_number=[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "59d79c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7c1d9_row0_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_7c1d9_row1_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_7c1d9_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_7c1d9_row3_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7c1d9_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7c1d9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7c1d9_row0_col0\" class=\"data row0 col0\" >surface1</td>\n",
       "      <td id=\"T_7c1d9_row0_col1\" class=\"data row0 col1\" >Default series</td>\n",
       "      <td id=\"T_7c1d9_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_7c1d9_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "      <td id=\"T_7c1d9_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c1d9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7c1d9_row1_col0\" class=\"data row1 col0\" >surface2</td>\n",
       "      <td id=\"T_7c1d9_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "      <td id=\"T_7c1d9_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_7c1d9_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "      <td id=\"T_7c1d9_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c1d9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7c1d9_row2_col0\" class=\"data row2 col0\" >surface3</td>\n",
       "      <td id=\"T_7c1d9_row2_col1\" class=\"data row2 col1\" >Default series</td>\n",
       "      <td id=\"T_7c1d9_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "      <td id=\"T_7c1d9_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_7c1d9_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c1d9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7c1d9_row3_col0\" class=\"data row3 col0\" >basement</td>\n",
       "      <td id=\"T_7c1d9_row3_col1\" class=\"data row3 col1\" >Default series</td>\n",
       "      <td id=\"T_7c1d9_row3_col2\" class=\"data row3 col2\" >4</td>\n",
       "      <td id=\"T_7c1d9_row3_col3\" class=\"data row3 col3\" >#728f02</td>\n",
       "      <td id=\"T_7c1d9_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface          series  order_surfaces    color  id\n",
       "0  surface1  Default series               1  #015482   1\n",
       "1  surface2  Default series               2  #9f0052   2\n",
       "2  surface3  Default series               3  #ffbe00   3\n",
       "3  basement  Default series               4  #728f02   4"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surfaces(['surface3', 'basement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "4d506ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>smooth</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>700.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>800.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-400.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>800.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "         X     Y      Z    smooth   surface\n",
       "0    500.0  0.01 -100.0  0.000001  surface1\n",
       "1    700.0  0.01 -100.0  0.000001  surface1\n",
       "2    200.0  0.01 -300.0  0.000001  surface1\n",
       "3      0.0  0.01 -500.0  0.000001  surface1\n",
       "4    800.0  0.01 -300.0  0.000001  surface1\n",
       "5   1000.0  0.01 -500.0  0.000001  surface1\n",
       "6    500.0  0.01 -400.0  0.000001  surface2\n",
       "7    200.0  0.01  500.0  0.000001  surface2\n",
       "8      0.0  0.01 -700.0  0.000001  surface2\n",
       "9    800.0  0.01 -500.0  0.000001  surface2\n",
       "10  1000.0  0.01 -700.0  0.000001  surface2"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surface_points(X=500, Y=0.01, Z=-400, surface='surface2') \n",
    "geo_model.add_surface_points(X=200, Y=0.01, Z=500, surface='surface2') \n",
    "geo_model.add_surface_points(X=0, Y=0.01, Z=-700, surface='surface2')\n",
    "geo_model.add_surface_points(X=800, Y=0.01, Z=-500, surface='surface2') \n",
    "geo_model.add_surface_points(X=1000, Y=0.01, Z=-700, surface='surface2') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "18add1ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Lithology ids \n",
       "  [4. 4. 4. ... 1. 1. 1.] "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.compute_model(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "58b0750d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n",
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\pyvista\\plotting\\tools.py:571: PyvistaDeprecationWarning: The usage of `parse_color` is deprecated in favor of the new `Color` class.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x2068dd92d60>"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, cell_number=5, legend='force')\n",
    "gp.plot_3d(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "aea22800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>smooth</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>700.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>800.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-400.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>800.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-500.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>500.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-600.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>800.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>surface3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "         X     Y       Z    smooth   surface\n",
       "0    500.0  0.01  -100.0  0.000001  surface1\n",
       "1    700.0  0.01  -100.0  0.000001  surface1\n",
       "2    200.0  0.01  -300.0  0.000001  surface1\n",
       "3      0.0  0.01  -500.0  0.000001  surface1\n",
       "4    800.0  0.01  -300.0  0.000001  surface1\n",
       "5   1000.0  0.01  -500.0  0.000001  surface1\n",
       "6    500.0  0.01  -400.0  0.000001  surface2\n",
       "7    200.0  0.01   500.0  0.000001  surface2\n",
       "8      0.0  0.01  -700.0  0.000001  surface2\n",
       "9    800.0  0.01  -500.0  0.000001  surface2\n",
       "10  1000.0  0.01  -700.0  0.000001  surface2\n",
       "11   500.0  0.01  -600.0  0.000001  surface3\n",
       "12   200.0  0.01  -700.0  0.000001  surface3\n",
       "13     0.0  0.01 -1000.0  0.000001  surface3\n",
       "14   800.0  0.01  -700.0  0.000001  surface3\n",
       "15  1000.0  0.01 -1000.0  0.000001  surface3"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surface_points(X=500, Y=0.01, Z=-600, surface='surface3') \n",
    "geo_model.add_surface_points(X=200, Y=0.01, Z=-700, surface='surface3') \n",
    "geo_model.add_surface_points(X=0, Y=0.01, Z=-1000, surface='surface3')\n",
    "geo_model.add_surface_points(X=800, Y=0.01, Z=-700, surface='surface3') \n",
    "geo_model.add_surface_points(X=1000, Y=0.01, Z=-1000, surface='surface3') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "63f3a69b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Lithology ids \n",
       "  [3. 3. 3. ... 1. 1. 1.] "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.compute_model(geo_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "7ee6292f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\gempy\\plot\\plot_api.py:261: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  p.fig.show()\n",
      "C:\\Users\\AHMED\\anaconda3\\lib\\site-packages\\pyvista\\plotting\\tools.py:571: PyvistaDeprecationWarning: The usage of `parse_color` is deprecated in favor of the new `Color` class.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x20684233b20>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.plot_2d(geo_model, cell_number=5, legend='force')\n",
    "gp.plot_3d(geo_model, kwargs_plot_structured_grid={'opacity': .2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "fc5ebf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_series</th>\n",
       "      <th>BottomRelation</th>\n",
       "      <th>isActive</th>\n",
       "      <th>isFault</th>\n",
       "      <th>isFinite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Default series</th>\n",
       "      <td>1</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fault1</th>\n",
       "      <td>2</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                order_series BottomRelation  isActive  isFault  isFinite\n",
       "Default series             1        Erosion      True    False     False\n",
       "Fault1                     2        Erosion     False    False     False"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_features('Fault1')   ### add fault ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "d843afd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_series</th>\n",
       "      <th>BottomRelation</th>\n",
       "      <th>isActive</th>\n",
       "      <th>isFault</th>\n",
       "      <th>isFinite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault1</th>\n",
       "      <td>1</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Default series</th>\n",
       "      <td>2</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                order_series BottomRelation  isActive  isFault  isFinite\n",
       "Fault1                     1        Erosion     False    False     False\n",
       "Default series             2        Erosion      True    False     False"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.reorder_features(['Fault1', 'Default series'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "9269f330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fault colors changed. If you do not like this behavior, set change_color to False.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_series</th>\n",
       "      <th>BottomRelation</th>\n",
       "      <th>isActive</th>\n",
       "      <th>isFault</th>\n",
       "      <th>isFinite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fault1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fault</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Default series</th>\n",
       "      <td>2</td>\n",
       "      <td>Erosion</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "                order_series BottomRelation  isActive  isFault  isFinite\n",
       "Fault1                     1          Fault     False     True     False\n",
       "Default series             2        Erosion      True    False     False"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.set_is_fault('Fault1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "e1f64125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b910b_row0_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_b910b_row1_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_b910b_row2_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_b910b_row3_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "#T_b910b_row4_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b910b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b910b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_b910b_row0_col0\" class=\"data row0 col0\" >surface1</td>\n",
       "      <td id=\"T_b910b_row0_col1\" class=\"data row0 col1\" >Default series</td>\n",
       "      <td id=\"T_b910b_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_b910b_row0_col3\" class=\"data row0 col3\" >#015482</td>\n",
       "      <td id=\"T_b910b_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b910b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_b910b_row1_col0\" class=\"data row1 col0\" >surface2</td>\n",
       "      <td id=\"T_b910b_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "      <td id=\"T_b910b_row1_col2\" class=\"data row1 col2\" >2</td>\n",
       "      <td id=\"T_b910b_row1_col3\" class=\"data row1 col3\" >#9f0052</td>\n",
       "      <td id=\"T_b910b_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b910b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_b910b_row2_col0\" class=\"data row2 col0\" >surface3</td>\n",
       "      <td id=\"T_b910b_row2_col1\" class=\"data row2 col1\" >Default series</td>\n",
       "      <td id=\"T_b910b_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "      <td id=\"T_b910b_row2_col3\" class=\"data row2 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_b910b_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b910b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_b910b_row3_col0\" class=\"data row3 col0\" >basement</td>\n",
       "      <td id=\"T_b910b_row3_col1\" class=\"data row3 col1\" >Default series</td>\n",
       "      <td id=\"T_b910b_row3_col2\" class=\"data row3 col2\" >4</td>\n",
       "      <td id=\"T_b910b_row3_col3\" class=\"data row3 col3\" >#728f02</td>\n",
       "      <td id=\"T_b910b_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b910b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_b910b_row4_col0\" class=\"data row4 col0\" >fault1</td>\n",
       "      <td id=\"T_b910b_row4_col1\" class=\"data row4 col1\" >Default series</td>\n",
       "      <td id=\"T_b910b_row4_col2\" class=\"data row4 col2\" >5</td>\n",
       "      <td id=\"T_b910b_row4_col3\" class=\"data row4 col3\" >#443988</td>\n",
       "      <td id=\"T_b910b_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface          series  order_surfaces    color  id\n",
       "0  surface1  Default series               1  #015482   1\n",
       "1  surface2  Default series               2  #9f0052   2\n",
       "2  surface3  Default series               3  #ffbe00   3\n",
       "3  basement  Default series               4  #728f02   4\n",
       "4    fault1  Default series               5  #443988   5"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surfaces('fault1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "a87bf212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8b04a_row0_col3 {\n",
       "  background-color: #443988;\n",
       "}\n",
       "#T_8b04a_row1_col3 {\n",
       "  background-color: #015482;\n",
       "}\n",
       "#T_8b04a_row2_col3 {\n",
       "  background-color: #9f0052;\n",
       "}\n",
       "#T_8b04a_row3_col3 {\n",
       "  background-color: #ffbe00;\n",
       "}\n",
       "#T_8b04a_row4_col3 {\n",
       "  background-color: #728f02;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8b04a_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >surface</th>\n",
       "      <th class=\"col_heading level0 col1\" >series</th>\n",
       "      <th class=\"col_heading level0 col2\" >order_surfaces</th>\n",
       "      <th class=\"col_heading level0 col3\" >color</th>\n",
       "      <th class=\"col_heading level0 col4\" >id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8b04a_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
       "      <td id=\"T_8b04a_row0_col0\" class=\"data row0 col0\" >fault1</td>\n",
       "      <td id=\"T_8b04a_row0_col1\" class=\"data row0 col1\" >Fault1</td>\n",
       "      <td id=\"T_8b04a_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_8b04a_row0_col3\" class=\"data row0 col3\" >#443988</td>\n",
       "      <td id=\"T_8b04a_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b04a_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
       "      <td id=\"T_8b04a_row1_col0\" class=\"data row1 col0\" >surface1</td>\n",
       "      <td id=\"T_8b04a_row1_col1\" class=\"data row1 col1\" >Default series</td>\n",
       "      <td id=\"T_8b04a_row1_col2\" class=\"data row1 col2\" >1</td>\n",
       "      <td id=\"T_8b04a_row1_col3\" class=\"data row1 col3\" >#015482</td>\n",
       "      <td id=\"T_8b04a_row1_col4\" class=\"data row1 col4\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b04a_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
       "      <td id=\"T_8b04a_row2_col0\" class=\"data row2 col0\" >surface2</td>\n",
       "      <td id=\"T_8b04a_row2_col1\" class=\"data row2 col1\" >Default series</td>\n",
       "      <td id=\"T_8b04a_row2_col2\" class=\"data row2 col2\" >2</td>\n",
       "      <td id=\"T_8b04a_row2_col3\" class=\"data row2 col3\" >#9f0052</td>\n",
       "      <td id=\"T_8b04a_row2_col4\" class=\"data row2 col4\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b04a_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_8b04a_row3_col0\" class=\"data row3 col0\" >surface3</td>\n",
       "      <td id=\"T_8b04a_row3_col1\" class=\"data row3 col1\" >Default series</td>\n",
       "      <td id=\"T_8b04a_row3_col2\" class=\"data row3 col2\" >3</td>\n",
       "      <td id=\"T_8b04a_row3_col3\" class=\"data row3 col3\" >#ffbe00</td>\n",
       "      <td id=\"T_8b04a_row3_col4\" class=\"data row3 col4\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b04a_level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
       "      <td id=\"T_8b04a_row4_col0\" class=\"data row4 col0\" >basement</td>\n",
       "      <td id=\"T_8b04a_row4_col1\" class=\"data row4 col1\" >Default series</td>\n",
       "      <td id=\"T_8b04a_row4_col2\" class=\"data row4 col2\" >4</td>\n",
       "      <td id=\"T_8b04a_row4_col3\" class=\"data row4 col3\" >#728f02</td>\n",
       "      <td id=\"T_8b04a_row4_col4\" class=\"data row4 col4\" >5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "    surface          series  order_surfaces    color  id\n",
       "4    fault1          Fault1               1  #443988   1\n",
       "0  surface1  Default series               1  #015482   2\n",
       "1  surface2  Default series               2  #9f0052   3\n",
       "2  surface3  Default series               3  #ffbe00   4\n",
       "3  basement  Default series               4  #728f02   5"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.map_stack_to_surfaces(geo_model, {'Fault1': 'fault1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "d0f78b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>G_x</th>\n",
       "      <th>G_y</th>\n",
       "      <th>G_z</th>\n",
       "      <th>smooth</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>fault1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>surface1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "       X    Y      Z  G_x  G_y  G_z  smooth   surface\n",
       "1    0.0  0.0    0.0  0.3  0.0  0.3    0.01    fault1\n",
       "0  350.0  0.0 -300.0  0.0  0.0  1.0    0.01  surface1"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_model.add_surface_points(X=0, Y=0, Z=-0, surface='fault1')\n",
    "geo_model.add_surface_points(X=200, Y=0, Z=-100, surface='fault1')\n",
    "geo_model.add_surface_points(X=400, Y=0, Z=-300, surface='fault1')\n",
    "geo_model.add_surface_points(X=600, Y=0, Z=-500, surface='fault1')\n",
    "geo_model.add_surface_points(X=800, Y=0, Z=-700, surface='fault1')\n",
    "geo_model.add_surface_points(X=1000, Y=0, Z=-800, surface='fault1')\n",
    "geo_model.add_orientations(X=0, Y=0, Z=-0, surface='fault1', pole_vector=(.3, 0, .3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "c030d41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gempy.plot.vista.GemPyToVista at 0x20682fb32e0>"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.compute_model(geo_model)\n",
    "gp.plot_2d(geo_model,cell_number=5, legend='force')\n",
    "gp.plot_3d(geo_model, kwargs_plot_structured_grid={'opacity': .2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bf54649",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "246a838e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}