{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "# Example 2: Implementing GeMpy into PyMC3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing and data\n",
    "import theano.tensor as T\n",
    "import theano\n",
    "import sys, os\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "# Importing GeMpy modules\n",
    "import gempy as GeMpy\n",
    "\n",
    "# Reloading (only for development purposes)\n",
    "import importlib\n",
    "importlib.reload(GeMpy)\n",
    "\n",
    "# Usuful packages\n",
    "import numpy as np\n",
    "import pandas as pn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This was to choose the gpu\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
    "\n",
    "# Default options of printin\n",
    "np.set_printoptions(precision = 6, linewidth= 130, suppress =  True)\n",
    "\n",
    "#%matplotlib inline\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "# Setting the extent\n",
    "geo_data = GeMpy.create_data([0,10,0,10,0,10], [50,50,50])\n",
    "\n",
    "\n",
    "# =========================\n",
    "# DATA GENERATION IN PYTHON\n",
    "# =========================\n",
    "# Layers coordinates\n",
    "layer_1 = np.array([[0.5,4,7], [2,4,6.5], [4,4,7], [5,4,6]])#-np.array([5,5,4]))/8+0.5\n",
    "layer_2 = np.array([[3,4,5], [6,4,4],[8,4,4], [7,4,3], [1,4,6]])\n",
    "layers = np.asarray([layer_1,layer_2])\n",
    "\n",
    "# Foliations coordinates\n",
    "dip_pos_1 = np.array([7,4,7])#- np.array([5,5,4]))/8+0.5\n",
    "dip_pos_2 = np.array([2.,4,4])\n",
    "\n",
    "# Dips\n",
    "dip_angle_1 = float(15)\n",
    "dip_angle_2 = float(340)\n",
    "dips_angles = np.asarray([dip_angle_1, dip_angle_2], dtype=\"float64\")\n",
    "\n",
    "# Azimuths\n",
    "azimuths = np.asarray([90,90], dtype=\"float64\")\n",
    "\n",
    "# Polarity\n",
    "polarity = np.asarray([1,1], dtype=\"float64\")\n",
    "\n",
    "# Setting foliations and interfaces values\n",
    "GeMpy.set_interfaces(geo_data, pn.DataFrame(\n",
    "    data = {\"X\" :np.append(layer_1[:, 0],layer_2[:,0]),\n",
    "            \"Y\" :np.append(layer_1[:, 1],layer_2[:,1]),\n",
    "            \"Z\" :np.append(layer_1[:, 2],layer_2[:,2]),\n",
    "            \"formation\" : np.append(\n",
    "               np.tile(\"Layer 1\", len(layer_1)), \n",
    "               np.tile(\"Layer 2\", len(layer_2))),\n",
    "            \"labels\" : [r'${\\bf{x}}_{\\alpha \\, 0}^1$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 1}^1$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 2}^1$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 3}^1$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 0}^2$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 1}^2$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 2}^2$',\n",
    "               r'${\\bf{x}}_{\\alpha \\, 3}^2$',\n",
    "        \n",
    "                        r'${\\bf{x}}_{\\alpha \\, 4}^2$'] }))\n",
    "\n",
    "GeMpy.set_foliations(geo_data,  pn.DataFrame(\n",
    "    data = {\"X\" :np.append(dip_pos_1[0],dip_pos_2[0]),\n",
    "            \"Y\" :np.append(dip_pos_1[ 1],dip_pos_2[1]),\n",
    "            \"Z\" :np.append(dip_pos_1[ 2],dip_pos_2[2]),\n",
    "            \"azimuth\" : azimuths,\n",
    "            \"dip\" : dips_angles,\n",
    "            \"polarity\" : polarity,\n",
    "            \"formation\" : [\"Layer 1\", \"Layer 2\"],\n",
    "            \"labels\" : [r'${\\bf{x}}_{\\beta \\,{0}}$',\n",
    "              r'${\\bf{x}}_{\\beta \\,{1}}$'] })) \n",
    "\n",
    "\n",
    "\n",
    "layer_3 = np.array([[2,4,3], [8,4,2], [9,4,3]])\n",
    "dip_pos_3 = np.array([1,4,1])\n",
    "dip_angle_3 = float(80)\n",
    "azimuth_3 = 90\n",
    "polarity_3 = 1\n",
    "\n",
    "\n",
    "\n",
    "GeMpy.set_interfaces(geo_data, pn.DataFrame(\n",
    "    data = {\"X\" :layer_3[:, 0],\n",
    "            \"Y\" :layer_3[:, 1],\n",
    "            \"Z\" :layer_3[:, 2],\n",
    "            \"formation\" : np.tile(\"Layer 3\", len(layer_3)), \n",
    "            \"labels\" : [  r'${\\bf{x}}_{\\alpha \\, 0}^3$',\n",
    "                           r'${\\bf{x}}_{\\alpha \\, 1}^3$',\n",
    "                           r'${\\bf{x}}_{\\alpha \\, 2}^3$'] }), append = True)\n",
    "GeMpy.get_raw_data(geo_data,\"interfaces\")\n",
    "\n",
    "\n",
    "GeMpy.set_foliations(geo_data, pn.DataFrame(data = {\n",
    "                     \"X\" : dip_pos_3[0],\n",
    "                     \"Y\" : dip_pos_3[1],\n",
    "                     \"Z\" : dip_pos_3[2],\n",
    "            \n",
    "                     \"azimuth\" : azimuth_3,\n",
    "                     \"dip\" : dip_angle_3,\n",
    "                     \"polarity\" : polarity_3,\n",
    "                     \"formation\" : [ 'Layer 3'],\n",
    "                     \"labels\" : r'${\\bf{x}}_{\\beta \\,{2}}$'}), append = True)\n",
    "\n",
    "\n",
    "GeMpy.set_data_series(geo_data, {'younger': ('Layer 1', 'Layer 2'),\n",
    "                      'older': 'Layer 3'}, order_series = ['younger', 'older'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gempy.Visualization.PlotData at 0x7f12289b0550>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddFH/qwTiWF7WBL1bu6HXy0VOm0O52RMjWFVk8Rp0t6F6vyq2Ncjn/+9yU+s2faXpU3I0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\nMf8PnJkqv+00KGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12289b0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GeMpy.plot_data(geo_data, direction='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n",
      "<class 'gempy.visualization_vtk.InterfaceSphere'>\n"
     ]
    }
   ],
   "source": [
    "GeMpy.visualize(geo_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am in the setting\n",
      "I am here\n",
      "[2, 2]\n"
     ]
    }
   ],
   "source": [
    "# Select series to interpolate data:image/png;base64,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(if you do not want to interpolate all)\n",
    "new_series = GeMpy.select_series(geo_data, ['younger'])\n",
    "data_interp = GeMpy.set_interpolator(geo_data,\n",
    "                                    #verbose = 'potential_field_at_all'\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>formation</th>\n",
       "      <th>labels</th>\n",
       "      <th>order_series</th>\n",
       "      <th>series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Layer 1</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 0}^1$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>Layer 1</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 1}^1$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Layer 1</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 2}^1$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Layer 1</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 3}^1$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 0}^2$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 1}^2$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.009619</td>\n",
       "      <td>2.302988</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 2}^2$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 3}^2$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 4}^2$</td>\n",
       "      <td>1</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Layer 3</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 0}^3$</td>\n",
       "      <td>2</td>\n",
       "      <td>older</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Layer 3</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 1}^3$</td>\n",
       "      <td>2</td>\n",
       "      <td>older</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Layer 3</td>\n",
       "      <td>${\\bf{x}}_{\\alpha \\, 2}^3$</td>\n",
       "      <td>2</td>\n",
       "      <td>older</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X         Y         Z formation                      labels  \\\n",
       "0  0.5  4.000000  7.000000   Layer 1  ${\\bf{x}}_{\\alpha \\, 0}^1$   \n",
       "1  2.0  4.000000  6.500000   Layer 1  ${\\bf{x}}_{\\alpha \\, 1}^1$   \n",
       "2  4.0  4.000000  7.000000   Layer 1  ${\\bf{x}}_{\\alpha \\, 2}^1$   \n",
       "3  5.0  4.000000  6.000000   Layer 1  ${\\bf{x}}_{\\alpha \\, 3}^1$   \n",
       "4  3.0  4.000000  5.000000   Layer 2  ${\\bf{x}}_{\\alpha \\, 0}^2$   \n",
       "5  6.0  4.000000  4.000000   Layer 2  ${\\bf{x}}_{\\alpha \\, 1}^2$   \n",
       "6  8.0  6.009619  2.302988   Layer 2  ${\\bf{x}}_{\\alpha \\, 2}^2$   \n",
       "7  7.0  4.000000  3.000000   Layer 2  ${\\bf{x}}_{\\alpha \\, 3}^2$   \n",
       "8  1.0  4.000000  6.000000   Layer 2  ${\\bf{x}}_{\\alpha \\, 4}^2$   \n",
       "0  2.0  4.000000  3.000000   Layer 3  ${\\bf{x}}_{\\alpha \\, 0}^3$   \n",
       "1  8.0  4.000000  2.000000   Layer 3  ${\\bf{x}}_{\\alpha \\, 1}^3$   \n",
       "2  9.0  4.000000  3.000000   Layer 3  ${\\bf{x}}_{\\alpha \\, 2}^3$   \n",
       "\n",
       "   order_series   series  \n",
       "0             1  younger  \n",
       "1             1  younger  \n",
       "2             1  younger  \n",
       "3             1  younger  \n",
       "4             1  younger  \n",
       "5             1  younger  \n",
       "6             1  younger  \n",
       "7             1  younger  \n",
       "8             1  younger  \n",
       "0             2    older  \n",
       "1             2    older  \n",
       "2             2    older  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_data.interfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0., ...,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_interp.interpolator.tg.final_block[0].eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((1,3))[-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# This are the shared parameters and the compilation of the function. This will be hidden as well at some point\n",
    "input_data_T = data_interp.interpolator.tg.input_parameters_list()\n",
    "debugging = theano.function(input_data_T, data_interp.interpolator.tg.whole_block_model(),\n",
    "                            on_unused_input='ignore',\n",
    "                            allow_input_downcast=True, profile=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 0]\n"
     ]
    }
   ],
   "source": [
    "# This prepares the user data to the theano function\n",
    "input_data_P = data_interp.interpolator.data_prep() \n",
    "\n",
    "# Solution of theano\n",
    "sol = debugging(input_data_P[0], input_data_P[1], input_data_P[2], input_data_P[3],input_data_P[4], input_data_P[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.      ,  0.      ,  0.      , ...,  1.      ,  1.      ,  1.      ],\n",
       "        [ 0.368047,  0.382449,  0.396848, ...,  1.180642,  1.19662 ,  1.212601]],\n",
       "\n",
       "       [[ 0.      ,  0.      ,  0.      , ...,  1.      ,  1.      ,  1.      ],\n",
       "        [-0.043672, -0.041708, -0.039591, ...,  1.180642,  1.19662 ,  1.212601]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Visualization.PlotData at 0x7fa9d6fb0630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQJdeeumg12xpadGzzz6rvXv3auXKlbrqqquG3eSTrW1S1KlubYORYfscnIqT2dqmqyes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\nmabQihDT45kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9d80b1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GeMpy.plot_section(geo_data, 32,  block = sol[1,0,:], direction='y', plot_data = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.contour?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GdhxQKpWcOHGCb7/9ljt37jB8+HDefvtttS24fdYUCgWJiYmcPXuWM2fOcPny\nZQBsbGzw9/dn4MCBuLq6atWkl6ZACh6JxgkhiI2NZe/eveTl5TFkyBDGjx+v8S1UTp06xbp16zA3\nN2fx4sVq2WhUqVTy448/smnTJhwcHPj444/VMt70e3K5nP/85z+sX7+e4uJiXnrpJebMmaO2zVYb\nSkFBAefOnePs2bOcP3+eoqIiWrRogYeHB35+fgwcOBArKyutXADdmEjBI9EaCoWCsLAwDhw4gIGB\nARMmTMDf31+j4z8ZGRmsWLECmUzGwoULsbe3V0u78fHxfPrpp+jp6fHJJ5/g5OSklnZ/r7q6mh9/\n/JHNmzcjl8sZP34806ZNaxS7SyuVSq5cuaLaxDM2Npba2lo6deqEt7c3Pj4+OncsiK6QgkeidYqL\ni9mzZw8XL16kV69evPbaa2o91O33SkpK+OKLL0hNTSUkJITnn39eLa+ICwoK+Pjjj0lJSeH111/n\nlVde0VgIl5aWsn37dnbt2kV1dTWjR49m5syZ9OrVSyP1NITq6mqio6MJDw/n4sWLpKamAjxwLIi3\ntzft27fXcKW6TwoeidZKT0/nhx9+4M6dO/j4+PDKK69gYmKikVoUCgVbt27l8OHDah33USgUbNiw\ngV27duHt7c3ixYtp165dg7f7RyoqKtizZw8//PADRUVFDBs2jBkzZmjFxJBnraioiIiICC5evMiF\nCxe4ffs2ANbW1nh7e+Pl5YWHh4dGHw9dJQWPRKsplUrOnDnDvn370NPT49VXX8Xb21tjffD14z69\ne/dm8eLFaht/CQ8PZ+XKlRgaGrJ06VJcXV3V0u4fqamp4cCBA2zdupV79+7h7OzMxIkTtWZtVkPI\nysoiIiJCFUb3799HT08POzs7PD098fb2xt3dXVrE+jdIwSPRCfVdPbGxsTg6OjJjxgyNdXmkp6fz\n+eefI4Rg8eLFWFlZqaXdvLw8li9fTkJCApMmTWLWrFkaf5JXKBScOHGC3bt3Ex8fT/v27XnxxReZ\nMGFCo/47FkJw+/ZtIiMjiYiIIDIykry8PPT19bGzs8PLywsvLy/c3NykIHoEKXgkOiU2Npbt27cj\nk8mYNGkSgwcP1sjVT1FREStXruT69evMmTOHYcOGqaXduro6du/ezcaNG7GwsODjjz/W+PTzehkZ\nGezdu5dDhw5RVVWFr68vEydOxNfXt9Ev3hRCcOvWLVUIRUVFSUH0J6TgkeiciooKfvzxRy5cuIC1\ntTUzZ84lvGJyAAAgAElEQVTUyIJAuVzO999/z/Hjxxk5ciQhISFqe4JNS0vj008/JT8/n3nz5jFm\nzBitmQJcVVXFkSNH2LNnD2lpabRv357nn3+e0aNHY21trTV1NiQhBDdv3iQiIoKoqKgHgsjW1hYv\nLy9GjhyJi4uLpkvVCCl4JDorOTmZrVu3UlFRwZQpU/D391f7k5oQgtDQUDZu3IiVlRULFy5U22Bz\nVVUV3377LUeOHMHT01PrNvoUQpCamsrhw4c5evQoxcXF9OnThzFjxvD88883qUPe6oMoKiqKyMhI\n1SJh6ehrDZOCR/Ikqqur2bFjB+Hh4Tg6OhISEqKRmW8pKSmsWrUKQ0NDPvzwQ7UeMx0eHs7q1aup\nra1l3rx5jBw5UuuuKuRyORcvXuTw4cOcOXMGuVyOq6srQUFBBAQE0LFjR02XqFZCCGQyWZPdMUEK\nHkmjEBcXx5YtWwAICQnB2dlZ7TXk5+ezfPlysrKyeOutt/D391db22VlZXzzzTccP34cV1dX3n//\nfbUeMvc4ysrKOH78OMeOHSM2NhalUomTkxMBAQEMHTqUbt26abpESQOTgkfSaJSUlLBx40YuXbrE\nkCFDeOWVV9R+7k9tbS1r167l7NmzjB07lunTp9OsWTO1tR8REcGXX35JWVkZM2fOZMKECWpt/3EV\nFxdz+vRpTp48SWRkJAqFAhsbGwYNGsSgQYOwsbHRip3LJc+WFDySRkUIwcmTJ9m1axcdO3bkzTff\nVPusLyEEhw4dYuvWrdja2vL++++rdZFhZWUlGzZs4JdffqFfv37Mnz9fbVv9PI3y8nLOnz/P6dOn\nuXDhApWVlTz33HMMHDiQQYMG4enpSatWrTRdpuQZkIJH0ijdu3ePtWvXcv/+fV555RUCAgLUPu6R\nnJzMF198gZGREYsWLVL7tj+XLl1izZo1ZGRkEBgYyJw5c7Rq8sGfkcvlxMfHqzb0zMzMxMjICFdX\nV3x9ffHx8cHCwkLrxrIkf48UPJJGSyaT8dNPP3Hq1ClcXV0JCQnB2NhYrTXk5+ezatUqbt68yeuv\nv05QUJBanyzr6uo4cuQIGzZsoLa2ltdee42JEyfq1KB2/RqZs2fPcvHiReLj41EoFHTt2lUVQp6e\nnhrfzVzy90nBI2n0YmJi2LRpE8bGxrz55ptYWlqqtX25XM6GDRsICwsjICCAOXPmqH3HgfLycrZu\n3covv/xCp06dePPNNxk4cKBOXjFUVVURExPDxYsXuXjxInfv3qVZs2bY29urFmra29tjaGio6VIl\nf0AKHkmTkJeXx9q1a7lz5w4vv/yy2k4W/a3jx4/z/fffY25uzsKFCzWy6PX27dt8++23xMbGYmdn\nx4wZM3Bzc9PJAKp39+5d1R5qMTExVFRUYGxsjJubG56ennh4eGBpaanT97GxkYJHDYQQKJVKFAoF\nCoWCuro6hBCqj9ffhBCqPw49Pb0Hbs2aNcPAwABDQ0MMDAykmT5PQKFQsGfPHo4dO4azszOzZ89W\n+2B1RkYGq1atoqqqirfffhtPT0+1tg+//j5GR0ezefNm0tLSGk0Awa+PcUpKCpGRkURHR5OUlIRC\noaB9+/Z4eHjg4eGBu7s7PXr00Pn7qsuaVPDUP/nL5XLVTSaT/eG/ZTLZQ/9+1Of+/nvWf6w+ZBQK\nxTO/L78NolatWtGyZcuH3rZp0wYTExPatWuHiYkJJiYmap9erI3i4uLYsGEDbdu25Z133qFHjx5q\nbb9+zU1sbCyjR49m+vTpGukWEkIQGRnJ1q1bSUtLw9bWlhkzZuDu7t5onpSrqqpITEwkJiaGqKgo\nrl69ilKppGvXrnh4eODm5oa7u3ujONhOl2hd8LzxxhsPTT39o6sDpVKpemKvq6t74P36J/7fBkNd\nXd1j1aSvr4+RkRGGhoYYGho+8P6j/l0fBPXvGxgYqAKi/v36m76+Pvr6+ujp6T3w/m/v729v9ffp\nt/dHoVAgk8morq6murqaqqoq1duqqirKy8uRyWQP3KcWLVrQrl07OnXqpLp17tyZTp06YWJi0mie\ncP7K/fv3+eabb8jLyyM4OBgfHx+1tl8/5Xr79u306tWL999/X2NPfkIIoqKi2Lp1K1evXsXKyorx\n48czZMiQRjdOUlpaSnx8PNHR0cTExJCRkQFAz549cXd3V92a2k4K6qaW4MnIyGDnzp2YmpqiVCp5\n9913H/qc+uB5+eWXadOmzYNF/v/upkc9Wdc/kf/2Sb7+Y78PhPoA+DthYmRkpNUL7/4OIQQ1NTWU\nlpZSUlJCaWkppaWlFBUVkZeXR15eHkVFRdT/CjRv3hwzMzN69uypunXt2lXnfw5/pKamhq1btxIR\nEUFgYCCTJ09W+y7K6enpfPHFF5SVlTFv3jx8fX3V2v5v1QfQ7t27SUhIoH379rzwwguMHTu20Z66\nWVhYSGxsLDExMcTGxnLr1i0ALCwsVCHk6uraaO+/pqgleLZt24aVlRWenp5MnTqVH3744aHPacxj\nPNpMLpdTUFBAbm4uubm5ZGVlcffuXfLy8gAwNDSkW7du9OrVi379+tGvXz+1T0luSPULTn/88Ud6\n9+7NvHnz1P4kU1FRwXfffUdERARBQUGEhIRofLrzjRs32L9/P8ePH0cIwdChQxk/fjz9+/fXaF0N\nLS8vj5iYGNUtKysLgL59+6q65dzc3DR2Em5joZbgyc7OZtGiRXTr1o2OHTvy3nvvPfQ5UvBol+rq\najIzM7l79y6ZmZncuHGDwsJC9PT06N69O/3798fa2hpLS0uNH0b2LGRkZPCvf/2Luro63nrrLayt\nrdXafv0u15s3b6ZLly68++67at1o9I+UlpZy+PBh/vOf/5CXl0f//v0ZOXIkQ4YMaRJHPufk5BAT\nE6Pqmqs/ddTKykp1ReTi4iKtIXpMagmeFStWMHnyZMzNzXnrrbdYvnz5Q68YpODRfgUFBVy7dk11\nKysrw8DAAFtbW9zc3LC3t9fpECotLeW7774jPT2dyZMnM2zYMLWPed2+fZsvv/ySrKwsJk6cyPjx\n47XiEDWFQsHFixc5cuQI0dHR6Onp4eXlRVBQEN7e3jr9uP9dQggyMzNV3XIxMTHk5+fTrFkzbGxs\ncHd3x8PDAycnJ1q2bKnpcrWaWoInOjqaY8eOYWpqSmFhIZ988slDf9BS8OgWIQTZ2dmkpqYSFxdH\nZmYmzZs3x8HBATc3N6ytrXVybEihULB7927CwsLw8fEhODhY7U+qcrmcXbt28csvv2BhYcH8+fPp\n2bOnWmv4M4WFhZw8eZKwsDAyMjJo3bo1gwYNwt/fHxcXl0Y3IeGP1B9//duuueLiYgwNDXFwcMDd\n3R1PT09pMesjaN2sNil4dNP9+/eJi4sjNjaWvLw8jI2NcXV1xc/PTye3ub9w4QJbtmzBzMyM+fPn\na2SW07Vr11izZg25ublMmTKFF154QevC/NatW4SFhXH69GlycnJo3bo1Pj4+DBo0CHd3d42PVamT\nUqkkIyND1TUXFxdHRUUFLVu2xNnZGQ8PDzw9PbGysmry6/Ck4JE8U/XdEbGxsURHR1NeXo6lpSUD\nBw7E0dFRp1753b59mzVr1lBbW8tbb72Fra2t2muora1l586dHDp0iH79+vHmm2/Sq1cvtdfxV4QQ\nZGRkqDb1vHv3Li1btsTLywtvb288PT2b3IC8QqHgypUrREdHEx0dTWJiIrW1tZiYmODu7s7YsWPV\nemaTNpGCR9JgFAoFiYmJnD9/nuvXr6teDfv5+dGhQwdNl/e3lJWVsXbtWtLS0pg0aRLDhw/XyFqn\nlJQU1q5dS05ODmPHjtXIWUN/V30X1NmzZwkPDycjIwN9fX3s7Ozw9vbG29ubXr16NZk1Y/Vqa2tJ\nTk4mKiqK6OhoAgMDmT59uqbL0ggpeCRqce/ePcLDw4mOjqa2thYXFxdGjhypEyvG6+rq2LNnD6Gh\noXh4eBASEqKRJ325XM7PP//Mvn37MDU1Zfbs2bi5uam9jseVl5dHZGQkERERxMXFIZPJ6Nq1K56e\nnri7u+Ps7Cyds9PESMEjUauamhoiIyM5ceIEJSUluLm5MWrUKJ577jlNl/aXoqKi2LRpEx07duSd\nd97RWGhmZ2ezfv16kpOTcXd3JyQkhC5dumiklsdVU1NDQkICERERREdHc//+fQwMDLCzs8PNzQ0P\nDw/69u3b5MdAGjspeCQaIZfLuXjxIqGhoVRUVODj48PIkSO1fm1IVlYW33zzDSUlJcyePRtXV1eN\n1CGEIDw8nC1btlBeXs6oUaMYP378Q7t+aLP68cDo6GhiY2NJTEykpqYGExMTXFxccHFxwc3NTSeu\niiWPRwoeiUbV1tZy9uxZjh8/jlwuZ/DgwYwYMUJrxy/g140nN2zYQHx8vOoJX1Ozzaqrq/n55585\nePAgBgYGjBs3jtGjR2v1z++PyGQyUlNTiYmJIT4+nrS0NIQQdOvWDVdXV1UYtW3bVtOlSp6SFDwS\nrVBVVcWJEyc4deoUrVq1Yty4cVq9Tb8QgiNHjrB3716sra154403NDprq7i4mD179hAWFkbbtm2Z\nOHEiw4YN04rFp0+qrKyMhIQE4uLiiI+PJysrCz09Pfr27asKIQcHB50M2aZOCh6JVikoKGD//v0k\nJydjaWnJxIkTtfr3ISUlhXXr1qGvr8/cuXM1MuX6t3Jycvjpp584d+4cXbp0YfLkyfj5+Wnd+p8n\nkZOTQ3x8vOpWXFysGh+qDyJra2udDtumQgoeiVa6cuUKe/bsIT8/Hz8/P8aMGaO1m5OWlJSwbt06\n0tLSGDt2LC+++KLGB8dv3rzJjh07iI+Pp1OnTrz44osEBAQ0mgWdQghu3rypCqHExESqq6tp2bIl\nDg4OuLi44OrqioWFhcYfC8nDpOCRaC2FQsHp06c5evQoBgYGvPLKK7i4uGi6rEdSKpUcOHCAAwcO\n0L9/f+bOnasVW+lfv36dn3/+mcjISFq3bs3zzz/PiBEjtH4Sx+NSKBSkpaWpgiglJQW5XP7ARAVX\nV1fMzMw0XaoEKXgkOqCkpIQ9e/aQlJSEl5cXL7/8stb261+5coX169ejUCh4/fXXcXJy0nRJwK9T\nsA8cOMDp06cRQjBo0CCGDx9O3759HxhHu379Ol26dNH53ZZrampISUlRjQ9du3YNIQRmZmaqEHJ2\ndm50AawrpOCR6AQhBBEREezdu5d27doRHByMubm5pst6pLKyMjZs2EBycjJDhw5l0qRJWhOUZWVl\nHDt2jKNHj1JUVIS5uTkBAQH4+/vTtm1bhg8fzg8//EDnzp01XeozVVZWRmJiIvHx8apNbesnKri6\nuqp2V28sXZHaTgoeiU65f/8+W7Zs4d69e4wZM4bAwECt7MNXKpWcOHGCPXv2YGpqyqxZs7CystJ0\nWSp1dXUkJiZy4sQJYmJiADAzM2P//v1ERkZiYWGh4QobVm5uLnFxcapbSUkJRkZGDBgwQBVElpaW\nWvm71RhIwSPROXK5nEOHDnHy5En69+/P9OnTtbbLJCcnhw0bNnDjxg2GDRvGhAkTtO5VdWlpKaGh\noWzatIlWrVrRunVrfH198fHxwc7OrlHMiPszSqWSmzdvEhsbS2xsLMnJychkMtq1a6cKITc3N53Y\nXUNXSMEj0VlXr15l+/bt1NXVMWPGDGxsbDRd0iMplUpCQ0P5+eef6dChA7NmzaJfv36aLusB8+bN\n47vvvqNdu3YsXLiQ9PR0CgoKMDExwcvLq8mEEPy6qPny5cuqIMrIyACgV69eqm19pPVDT0cKHolO\nKy8vZ+vWraSlpTFixAief/55re0euXfvHhs2bODWrVsMHz6ccePGacWTV1xcHO7u7tQ/FURHR+Pq\n6kp6ejoXL17k4sWLqhBydXVVPfE2lVM2i4uLiYuLU508WlhYiJGRkeqwNw8Pjya52/bTkIJHovOU\nSiXHjh3jv//9LzY2NgQHB2vtbsd1dXUcOXKEAwcO0KZNGyZPnoy7u7vGnrQUCgUeHh4kJCSoPnb+\n/Hn8/PxU/1YqlaSnpxMREUFMTAzZ2dkYGhpib2+Pm5sbrq6ujW4ywh+pXz9UH0L13XKdOnXC09MT\nDw8PXF1dtfb3T1tIwSNpNFJSUtiyZQtt2rRh7ty5Wr1jc25uLjt27CA5ORlbW1umTp2qkTUm3377\nLe+8884DHzt+/DiBgYF/+DVZWVmq02avXLlCXV0dPXr0UK2XsbGx0akD/55GTU0NiYmJREVFERUV\nRXZ2NgYGBjg4OODp6YmXlxc9e/aUroZ+RwoeSaOSm5vL+vXrKS0tJTg4GHt7e02X9IeEECQmJrJj\nxw6Ki4sZMWIEY8eOVVv3271797C2tqa8vPyBjx8+fJhRo0b9re9RUVGhmqackJBASUkJLVq0YMCA\nATg7O+Ps7KzVLwCepfrdtutDKCkpCblcTteuXVUnsTo6Omrd5BJNkIJH0uhUV1ezZcsWUlNTGTt2\nLMOGDdPqV5wymYzDhw9z5MgR2rRpw/jx4/Hx8WnwsaqXX36Zffv2PfTxffv2MX78+Mf+fkqlklu3\nbqlCKC0tDaVSSbdu3XBycsLJyQl7e3utGNdSh+rqahISEoiMjCQyMpK8vDxatGiBq6srXl5eeHl5\nNdmZclLwSBolpVLJoUOHCAsLw9XVlddeew0jIyNNl/WncnNz+emnn0hISMDMzEy1Q3dDBNC1a9f4\n+uuvcXZ25u2336a2thY9PT2EEOzcuZMpU6Y8dRsVFRUkJSWRmJhIYmIiBQUFGBgYYGtrqwqipjIo\nL4Tgxo0bREREEBkZyZUrV5gwYQJvvfWWpkvTCCl4JI1abGwsO3bsoEuXLsyZM0cr9k/7Kzdu3ODn\nn3/m8uXL9OjRg/Hjx+Pk5NQgT9AJCQmq/e927drFmjVrmDVrFjNnznym7dR3QyUmJpKQkEBqaioy\nmYz27dvj6OioCqKmctZOSUkJCoWCjh07aroUjWgUwVNdqyA5I5+yShltjY1w6PscLZtLW6NrO3U9\nbnfv3uXf//43CoWCN954g169ej3zNhpCWloa+/fv59q1a1hYWDB27FgcHR2f6RXQP//5TxYuXEiL\nFi0oLi5GqVSSkZGBg4PDM2vjUWpra7ly5QoJCQkkJiZy9+5d9PT06NOnj2qSQt++fZvEuqGmSOeD\n50T0HU7G3kUmr1N9zMiwGQFuPQn00M69vCTqf9zKyspYv3499+7dIzg4GEdHx2feRkMQQpCSksIv\nv/zC9evX6dq1K0FBQfj6+j6TQerAwEBOnjxJQEAAJ06ceAYVP5mCggLVJIXk5GQqKytp06YNjo6O\nuLi44OTkhKmpqcbqkzxbOh08J6LvcDTi1h/+/0jv3lL4aCFNPW4ymYxt27aRlJSkOp9GV8YXhBCk\np6cTGhpKQkICxsbGDB48mMDAwCd+Qq6ursbU1JTa2lpWrVrFBx988IyrfjJ1dXUPHHFw69avvyuW\nlpY4Ozvj6uoqXQ3pOJ0NnupaBZ9sjHzgFfPvGRk249NZXrSQut20hqYfN6VSycGDBzl+/DgeHh5M\nmTJF59ac5ObmEhYWxvnz51EoFHh6ejJ48GD69ev3WEF68uRJ1Xqd+Ph4nJ2dG6rkp1JUVERCQgLx\n8fEkJSWproacnZ1xcXHB2dm5yYwNNRY6+4ycnJH/p09eADJ5HckZ+XjYdVVTVZK/ounHTV9fnxdf\nfBEzMzN27txJbm4us2fP1tpNRh+lc+fOTJ06lZdeeomzZ89y4sQJLl68SKdOnfD19cXX1/dvTdM9\ndeoUAB06dNDqrsf27dsTEBBAQEDAQ1dD586de+h4g969e2vttkmSX+nsFc/x6DuE/kl3TT2pu027\naNPjdvv2bf79738DMHv2bHr37t2g7TUUpVLJlStXuHDhArGxschkMqytrfH19cXd3f0P183IZDKi\no6PJzs5m4sSJaq762SgsLFSdsZOcnEx1dTXt27dXdck5OjpK29doIZ0NnqiUHPacuPaXnzcpsL90\nxaNFtO1xKykp4fvvvycrK4tXX30VDw+PBm+zIVVXVxMbG0t4eDhpaWmqzSybwo7KcrmcK1euqM7Y\nuXfvnmrdUP3mpk1lFwVtp7PBo+mxAsmT0cbHTS6X8+OPPxIdHU1QUBBjxoxpFF01eXl5REdHEx0d\nzZ07dzAyMsLJyUkVQtq+oPZpZWdnq0IoJSUFhUKBubk5Hh4eeHh4YGlpqTOTSxobnQ0ekGa16Spt\nfNyEEISFhXHo0CGsrKyYMWMGbdq0UWsNDSknJ4fo6GhiYmLIzMykRYsWODg44OLigoODQ6Pvjqqs\nrCQhIYHo6Gji4+OprKykQ4cOqhCys7PTuUkmukyngwekdTy6Slsft9TUVLZu3YqhoSEzZ87E0tJS\nY7U0lHv37hETE0N8fDx37tyhWbNm2NraqmaI6dJEiyehUChISUlRXQ0WFBRgbGys6o5zdnZu9EGs\naTofPAA1j1gBL3WvaT9tfdyKiorYvHkzt2/f5oUXXtCp9T6PKy8vj4SEBOLi4khPTwegT58+qp2l\nzczMGu19h//bQy0qKkrVJWlgYICjo6PqfB0TExNNl9noNIrgkUietbq6Og4cOMDJkycZMGAAU6dO\nxdjYWNNlNaiysjLVXmopKSmqA87qQ6hfv36NftFmdnY20dHRREVFkZaWhp6eHjY2NqqzdZrqbtLP\nmhQ8EsmfSEpK4ocffqBVq1bMnDlTZ6dcPy6ZTEZqaqpqL7XS0lKMjY1V5+w0haOvi4uLiY6OJjIy\nkkuXLlFXV4elpSWenp74+PjQrVs3TZeos6TgkUj+Qn5+Pps2bSIrK4ugoCBGjhyJgYHmuwTVpf6c\nnfoQyszMxMDAADs7Ozw8PHBycmr0V4MVFRXExsYSFRVFfHw8MpmM3r174+Pjg6+vr0ZOj9VlUvBI\nJH9DXV0doaGhhIaGYmZmxvTp05vsK968vDzV0dfXr19XTU5wd3fH2dm5Uc0GfJSamhri4+MfWLDb\np08f/Pz88PX1pVOnTpouUetJwSORPIY7d+6wfft28vLyGD16NIGBgY1izc+TKiwsJDY2ltjYWDIy\nMtDT08Pa2ho3NzdcXV0b/cB8dXU1cXFxhIeHEx8fj1wux8rKioEDB+Lj4yPtqP0HpODRMOksId0j\nl8s5dOgQp06donfv3kybNk16lcuvu0DExcURExNDWloaAP369cPNzQ03NzedOITvaVRWVhIdHU14\neDhJSUkolUrs7Ozw8/PD29tb2sj0N6Tg0SBtXcsi+XsyMjLYvn07ZWVlDBkyhKCgoEY/4P53lZaW\nkpCQQGxsLFeuXFENzNdv5NnYg7qsrIyIiAjCw8NJSUlBX18fZ2dnBg4ciLu7e5P/PZGCR0O0cfW+\n5PHV1NRw7NgxTp8+TfPmzRk1ahS+vr6Nftrx46ioqCAxMZHY2FhSUlKQy+WYm5urroQa+8B8YWEh\nFy5cIDw8nPT0dJo3b467uztBQUEMGDBA0+VphNYFz4IFCx7oF9XT06NDhw74+/s3mj9mbdyvTPJ0\nioqKOHToENHR0XTu3JmXXnoJOzu7Rr348klUV1eTlJREbGwsycnJyGQyunXrplon07lzZ02X2KCy\ns7MJDw/n/Pnz+Pn5MWnSJE2XpBFaFzzDhg17aGqmUqmkf//+TJ8+vVGEj7bt0Cx5du7evcvPP/9M\neno6VlZWvPDCC5ibS1euj1JbW8ulS5eIiYkhISEBmUyGhYUF3t7eeHh4NOqte4QQ1NXVNalp+b+l\ndcEzadKkB6ZjCiGorKykqqqKvn37EhwcrPPho01n0kiePSEEycnJ/PLLL+Tn52NlZUVQUBD9+/eX\nroD+QE1NDYmJiURERHD58mWUSiXW1tZ4eXnh5ubW6NcJNTVaFzzvvPMOpqam/LaskpIS8vLyqKqq\nok+fPsycOVOnXylIVzxNQ11dHQkJCYSFhXHv3j169eql6tdvylOw/0p5eblqsWZaWhr6+vo4ODjg\n6emJs7MzzZs313SJkqekdcHz2Wef0bFjxwf+r6ysjOzsbHJzc6mqqqJ3797MmjVLZ8NHGuNpWoQQ\npKSkEBYWxo0bN+jSpQsBAQG4uLg06oPZnoWioiLVtjW3bt2iefPmODs74+Xlhb29vc4+BzR1Whc8\n69atU021rC+toqKC/Px8cnJyuH//PlVVVZibm/P666/r7Bka0qy2pikjI4OwsDBSU1Np3rw5Tk5O\neHp60rdvX+kq6C/cv3+fqKgoIiMjyc7OpnXr1ri6uuLl5YWVlZX089MhWhc8O3fufOh42qqqKgoL\nC1Xhk5ubS2VlJd26deP5559HX1//ob5zU1NTrR+clNbxNF35+fmqXZALCwtp3749np6eeHt706FD\nB02Xp9WEENy9e5fIyEjVz8/U1BQPDw98fHwwNzeXxtK0nNYFz6FDhzAzM3tgjKempoaSkhIKCgoe\nCp8/oq+vT3BwMP369VNH+U9MW8+kkaiHUqnk+vXrREVFqWZ2WVpa4ubmhpOTE61bt9Z0iVqt/ucX\nERFBTEwM5eXl9OjRQ7VbQGPfskdXaV3w/H4BqRCCqqoqKisrHwif+/fvU1hYSF3do8dJqqqqUCqV\nzJgxAysrK3XdDYnkidVvPhkbG0t6errqLBhXV1ccHByk8aC/oFAouHTpEuHh4SQmJgLg4OCAn58f\njo6O0niQFtH64IFf98aqra2lqqrqgfD5ffD89q6UlpZSVFSEUqlk+vTpWFtbq+2+SCRPq7S0VBVC\nt2/fxtDQEFtbWwYMGICtra2079dfKCsrIzIykvPnz3P37l3atGmDt7c3fn5+0roqLaATwSOEoLq6\nGj09PcrLyykuLqawsJCysrJHXvEIISgpKSEnJ0cVPtOmTcPGxkZdd0cieWby8/OJi4sjOTmZO3fu\noKenh7m5Ofb29tjZ2dGjRw9pTONP3Llzh/DwcCIiIigvL8fc3FzVFdfYj3DQVjoRPPDrVY9MJsPI\nyLBEes8AACAASURBVIji4mKKi4spKyvj9+XX/7u8vJzc3FxycnIoLCxEqVTy2muvYWdnp5b7I5E0\nhNLSUlJTU7l8+TJXr16ltrYWExMTbGxssLKywsrKSroa+gMKhYKkpCTCw8NJTk4GwMnJCT8/Pxwc\nHHR+Ybou0Zngqb/q0dfXx8jIiIKCAioqKlAqlQ+FD/w6BbuwsFAVPkVFRdTV1TFlypQmuzGfpHGR\ny+Vcv36dlJQUrl69Sk5ODgDdu3fHysoKa2trLC0tMTIy0nCl2qe0tFS1e3RmZiYmJib4+PgwcODA\nJnvAnzrpTPDA/1311G8pXlpaSl1d3QPBU/9+dXU1RUVFFBUVcf/+fVX4KBQKpkyZgoODAwA18hou\n512jvLaCNs1bY9+pPy0MpUHcpqCxnYVUUlJCWloaV69eJS0tjbKyMgwNDbGxscHBwQF7e3tpltzv\nCCG4ffs258+fJzIyksrKSiwtLRk6dCju7u5SaDcQnQoepVJJdXU1zZs3/9MZKkIIKioqVMFTHz71\nM+EUCgWvvPIKxSbVnL0VgaxOrvpao2aG+Pf2ZoiF9zO/jxLt0djXUAkhuHfvHqmpqSQlJXH79m30\n9fWxtLTE0dERBweHRn8w2+OSy+UkJCRw6tQprl69SuvWrfHz82PIkCEPrS2UPB2dCp76qdVGRkZ/\nuWPB3wkfPdtW6HV99L5PwywHSeHTSDXFXSNKSkpITk4mKSmJ9PR0lEol5ubmODk54ejo2OiPI3hc\n2dnZnD59mvDwcKqqqrC1tWXo0KE4OTlJ07KfAbUET2lpKf/6178wMjKic+fOTJs27aHP+bsHwVVV\nVWFgYPC3LoGFEKpZcH8YPjat0DN7OHyMmhmyZNA8WhhIGxI2JtI+eb8e0ZySkkJSUhKpqanI5XK6\ndu2Ko6MjTk5OdO/eXZol9//V1tYSHR3NqVOnuHnzJu3atcPf35/BgwdLV4xPQS1/Wfv27cPExASF\nQvHUp4vq6emhVCr/9uf+2XTJwsJCFFeqQIBetwcDRlYnJyU3DdduDk9Vr0S7JGfk/2noAMjkdSRn\n5DfancGNjY3x8PDAw8OD2tra/9femYdJUd/5/11dXX1O33OAyAAqAjqcEVhQQAERIRqiokaTsIgo\noj7ZxOzjEbM/k11l9UnybBYhIipRNIlRjqBBQIZLuQTDGWQURJBjZpi+766uqt8fs1X0zPTM9MxU\nVR/1fSXzyPRUV327u7re9blx7NgxHDx4EFu3bsVHH30Ej8eDkSNHYuTIkejfv7+me6AZjUZMnDgR\nEydOxDfffIPa2lqsX78e69atw8iRIzFlyhRce+21mn6PuoMqwnPmzBlMnToVEydOxMKFCzF58uRu\n31FRFJU1i62j7bOJj3h8r9eL9BcxQBBAXd4yqSCUbL8lD6E4CUVTsm5X7IiNSkeOHIl0Oo26ujoc\nPHgQe/bswebNm+F0OiVL6KqrrtL0BbZ///6YN28efvCDH2Dnzp2ora3FSy+9hKqqKtx0002YOHEi\nqQvKEVWEJ3PMgdFo7NHkPZ1Oh3Q63aXndGb5+Hw+sMfjzdtmiI/dSIZPlRp2a25ZSrluV0ro9Xpc\ne+21uPbaa3HvvffixIkTOHDgAA4cOIBt27ahrKwMo0aNwvjx41FdXa1Zd5zFYsHNN9+MqVOnoq6u\nDrW1tXjvvfewatUqjB07Frfccgv69++f72UWNKoIzz333INFixZh586dGDZsWI+Cc6LFIwhCl078\nnMVHAKi+JhhoBjVVpMdbqTF8YAXWbDvRaYxn+MAKFVdVeNA0jUGDBmHQoEG4++67cerUKRw4cAD7\n9u3Djh070KdPH4wbNw5jxozR7F0+RVFS0W4wGMT27dtRW1uLTz/9FEOGDMH06dMxYsQITVuJ7VFU\nWW1Ac/VxMpmE2Wzu1gcqJhz4fD4p6SCzyJRlWVCDzLjlpltIVluJosWsNrngOA7Hjh3Drl27cPjw\nYVAUhWHDhmHcuHG45pprNF/9n06nsW/fPmzYsAFff/01qqqqcMstt2DChAmkyWsGRZe2I4pNd/Wy\nI8uHoih4vV6wdXEYBvHAFd1eJqGAEUWllOt4lIKmaQwdOhRDhw5FKBTCZ599hl27dmHp0qVwOBwY\nO3Ysxo0bp9m6F71ej3HjxuFf/uVf8NVXX2HDhg1YuXIl3n//fUyePBk333wzyYZDEVo8Yi1PZ0Wk\nnZEt1bqhoUFKtWZZFt/97ncxceLEbh+DUNiQWUjyIAgCTp8+jd27d2Pfvn2Ix+MYMGAAxo0bh+uu\nu07qNKJVGhsbsWnTJmzfvh0sy2LMmDGYPn06rrhCu3e2RSc8QHMdAsMwPW5nIQgCQqEQAoFAu+Iz\nc+ZMTJo0qUfHIRC0AsuyOHjwIHbv3o3jx49Dr9dj5MiRGDduHK6++mpNxztisRi2b9+OTZs2oamp\nCdOnT8f999+f72XlhaK8vdPpdN12tWVCUVTWTr6ZqdZ///vfIQgCbrzxxh4fj0AodRiGwejRozF6\n9Gj4fD7s3bsXu3fvxmeffSaN9x43blyLTFetYLFYcOutt2LatGn4/PPPNT0dtSiFpytFpLnsSxSf\nbGLm9Xqxfv16jBkzBhaLRZZjEghawO1249Zbb8X06dNx4sQJ7N69G7W1tfjoo49QU1ODSZMmYciQ\nIZqzgmiaxpgxY/K9jLyieeER92cymcAwDIxGIwwGA4xGI8xmMxiGAcuyYFm28x0RCIQ2UBSFgQMH\nYuDAgbj77ruxb98+bN++HS+//DIqKysxadIkjBs3TvOxIC1RlMIjFpF2tZaHQCDkF5PJhAkTJuCG\nG27AiRMnsG3bNqxatQrr1q3DmDFjMGnSJDIPRwMUpfCIYkOEh0AoTjKtoEAggE8++UT6ufrqqzFx\n4kSMGDFC83VBpUpRCk9Pa3m6Q4Ek/xEIJYfT6cRtt92G6dOnS+15XnvtNWkq6A033ACXy5XvZRJk\npCiFR7RyeJ4nd0QEQonAMAzGjBmDMWPG4Ntvv8WOHTuwefNmbNiwAcOGDcOkSZMwaNAg4uUoAYpW\neLrapbq7iMfw+/1wOp2KH49AIAB9+/bF/fffj+9///vYu3cvduzYgd///vfo1asXpk6dijFjxnQ6\nDJJQuBRlASkAxONxKRtNDnieR1NTE4LBIPx+P5qamqRi0mAwCL1ej8cff5xMaiQQ8oAgCKirq8OW\nLVtw5MgROBwOTJ48GRMmTCDZcEVI0SbQy1VEmrk/j8cDh8MBp9MJj8eDqqoqVFVVSUPsFi9ejPr6\netmOSSAQckPsBL1w4UL88pe/xODBg/G3v/0Nv/jFL7B27VoEg8F8L1F1PvnkE/zsZz9DLBbr8b4W\nLVoEAHjrrbd6vK9coJ977rnnVDlSJ4RCIbz11luYM2dO1m4CreF5HhzHgWEY2Xy+Op0OJpMJPM9L\n7jwxhpROpxGPx7F//34MGTJEs63gCYR8Y7PZMGLECIwbNw4sy+KTTz5BbW0tfD4fKisrUVZWlu8l\nqsLLL78Mv9+Pzz//HF988QX279+Pyy67DI8++ihYlsXbb7+NhoYGrFy5Uuql9+WXX6KiogIvvfQS\nDh06hK+//hoAsGLFCowYMQJr1qzBlClT8Nxzz+GLL77Axx9/jIkTJ+L73/8+AODVV1/FjTfe2GM3\nZ1FbPEpA0zTKy8vhcDjgcrng8XjQq1evFpbPyy+/jPPnzytyfAKBkBtutxuzZ8/G888/jxkzZuDw\n4cP41a9+hVdeeQUnT57M9/IUZ9y4cRg1ahRmzZqFxx9/HHV1deA4DtXV1bjnnnsAALfccgtuvvlm\n2Gw2LFy4EHv37oXJZILD4YDFYsGnn36KYcOGoV+/frj22msBAJ9++ilGjRqFRx99FABw7tw5uN1u\n3Hvvvbjyyitx5syZHq+9KJMLAGUz20TxAVqmUYtWUCAQwJIlS7Bw4UJS7EYg5Bmr1Ypbb70VU6dO\nlUZ2/+Y3v8EVV1yBm2++GcOGDSvptjytr1GZrb0MBgN0Op30X47jsHbtWkycOBHDhg3D/v37c9qn\n0WgE0HzDL0fXmKIVHvFEUiqlOlN8spEpPrkkQxQz6XQaZ8+elSa/Am3rmrLF25TKW2ntWu3duzes\nVjKmXOswDIMJEybg+uuvx+HDh/Hxxx9j2bJlqKqqwrRp0zB27NiSK7+44oor8OGHH2Lfvn05C+w1\n11yDd999FwcPHgRFUfj666+RSqWwb98+AMCECRPw/PPPo6GhASaTCZdddpns6y7arDZAvvEIHcFx\nXItsN6/Xi4aGBjQ0NCAQCECn0+GRRx5B3759FVtDPkkkEli2bBnOnTuX76W0i9VqxSOPPILKysp8\nL4VQYJw4cQKbNm3CkSNHUFFRgRkzZmDMmDElbQEVA0UtPHKnVLdHKpWC3+/vUHwWLFiA6upqRdeh\nNvF4HH/4wx/Q0NAAs9nc4m5R7SI+sT1SNktLHAy4cOFCku5OyMrp06fx97//HUeOHEFVVRVmzpyJ\n73znO0SA8kRRC08ymQTP86rk8UciEUSj0XbFh6IoPPzww+jfv7/ia1GDWCyGpUuX4uLFi7BYLHA6\nnT2a+KoUPM8jFAohHA7DYDBg4cKFmh27TOicU6dO4cMPP8SxY8fQu3dvzJw5EyNHjiQCpDJFLTyp\nVAosy8JisSh+By5e4FiWhc/nQyAQyCo+Dz30EAYMGKDoWpQmGo1i6dKlaGpqgtVqhdvthtvtVvTL\n2d3TUBAEBINB+Hw+hMNhMAyDRx55RBG/NKF0OHnyJD744APU1dWhT58+mDlzJoYPH04ESCWKWnjS\n6TSSySTMZrMqJwzLsgiHwxAEAU1NTW3ERyximz9/ftHOU49EIliyZAl8Pp8kOuXl5XC73TkHZlu7\nxbp6inVle/GGwOv1SuKj1+vxyCOPkIxDQqd89dVX+PDDD/Hll1/i8ssvlwSoUPvBxZNpHPrqIkLR\nFOxWA4YPrIDZWHieiM4oauHheR7xeBwmk0m1bJVoNIpkMgmKotDY2NhCfMTfBUHA/PnzceWVV6qy\npp6QYBM40liHcDICJk3j01VbEQgEUFZWBpfLhfLycng8Htjt9jbvcU+sFLkQBAGRSAQ+nw9NTU2S\n+NA0jQULFpRs0gdBXurq6vDhhx/ixIkT6Nu3L2677TbU1NQUlAB9vPc0Nu87gxTLSY8ZGBpTR1fj\n5rH98riyrlN8UplBPrpUWywWsCwLvV7fIpYg1vhQFAW/349ly5a1iD2Ja219Inf2e2ePt3cR7+ji\nLlokqXQKKT4NiNuyzY+XlZXB4/FIPw6HA3a7vU2boq4ISE/EprPXIlq74vsPAOFwGIsXL27RYSLz\nMxg+fDhmzJhBXCsEAMCgQYNw9dVXo66uDh988AGWLl2Kq666CnfeeWdBxG0/3nsa63edavN4iuWk\nx4tJfIpeeOQeg53LMfV6PXieh8PhkB7L/DvQfOHjOC7rPgoJA/QAJf0Co9HYRnTcbjf69evXIrmg\nPQHqSJhy/VtXtuE4DhcuXGjzOEVRiMViSCaTWfe7Y8cOxGIx3HXXXUR8CAAu9YMbNGgQ/vnPf2L1\n6tV48cUXcd1112HWrFnweDx5WVc8mcbmfR13C9i87wwmjOgDUydut9WrV4OmaXzve9+Tc4kAmtvu\nrFy5Elu2bOl02y4LTzQaLahiPbmbhXYVUXxaX1TFnm/dQSnroNPtKMBsMkvuNVF0qqur22S0ZRPb\nfNGvX/Y7vfbquwRBQDgcxv79+8FxHO655x4iPgQJiqJQU1ODIUOGYNeuXfjggw/w3HPPYfLkyZg+\nfbrq3bAPfXWxhXstGymWw6GvLmJsTe8u7VsQBPzqV7+Cx+PBuXPn8Itf/AKPPfYY3nzzTfh8Pjz/\n/PP44Q9/iNraWpjNZuj1esycORM///nPceONN2LBggXSvqZOnYrt27fndNwOhWfWrFn4r//6L9TU\n1EiPPfHEE3jllVe69OKUhKKovFgWmRduh8ORVXg6E4FcRELJwHw2jEYj3G43HA4HrI6yrKJTaBgM\nhqziYzabs4oix3GgaRqBQAAHDhwAz/P4wQ9+QMSH0AKapjFhwgSMHj0aGzduRG1tLXbu3IkZM2Zg\n4sSJqn0vQtGUrNu1prq6GjzPw+/3o66uDsOGDcPevXtRV1eH22+/HStWrMCVV14Jnudx7NgxzJw5\nE1VVVS1EB0CX4qkdvnM2mw1vvPEGxo0bh9mzZ3frRSmNTqdDOp2W4hZqkO04rYfEmc3mFlldXW0p\nk4ubqfXjcsRcjEYjHA4HeDOFBp0fQwpcdEQMBgP69++Pb775RnqsPfHneR46nU6Kxx06dAg8z+O+\n++4ruZYqhJ5jMpnwve99DxMmTMCHH36I999/H9u2bcP3vvc9jBo1SvHrjt2aW2eWXLfL5Pjx46iv\nr8czzzyD+vp6cByHO++8E4sXL0Y4HMb999+PVatW4f7770d5eTnq6+uRTqd73AG8w6uK1WrF7373\nO/zhD3/Ac889h1/84hc9OpgSiHepagpPe2SKj8FgyFkUuvu39v7e+rit3xdBEMALAgDh//4vbk/B\naDCAMwF/v7AVtw+5ucNjFxoMw6B///44ffo0gLafQSY0TUvvSyAQwJEjR/D222/jhz/8IREfQlbc\nbjd+/OMfY/LkyVizZg1ee+019O/fH3fccQcGDhyo2HGHD6zAmm0nOnS3GRgawwdW5LS/devW4ejR\nowCABQsW4OTJk1i6dCmMRiM2bdqEX/7yl/B6vaipqQFN03jggQfw3//93/B4PHC73Zg5c2abffp8\nPixfvhynT5/Giy++iFmzZmHQoEHtrqHDdOoFCxZIbrUdO3bgrbfeQjAYxHvvvZfTC+wK3UmnBi6l\nVBuNRtVM31gshlQqlXUUtljQGAgEWqwxG121hNrbvrPn5fp3AAhyYfy9fgcoCnhm0uMw6Y2dPqfQ\nYFkWp0+fRjQabfM3sb4oHA4jEAjA5/PB6/UiEAggHo+jX79+uOqqq9pkIep0OtTU1JCWPASJL774\nAqtXr8bZs2cxfPhw3HXXXR02Fu4J7WW1icwYP0DWrLaXXnoJs2fPVqwYvsMr9d133y39e+LEiejf\nvz9eeOEFRRbSXTJTqgsBiqKa4yNWK3ieb1NEKdd/geYGngzDoLy8vF23XnvP33f2ED47exA6igKF\nS2nIDQkvOIHDtKsmFaXoAM2WT79+/eDz+cBxHOLxOABAr9fjsssuA03TOHHiRIvniK//7NmzWRui\n8jyPrVu34uGHHya1QQQAwJAhQ/D0009j3759WLNmDX79619j2rRpmDZtmuyNi0VRUaOO59VXX4XJ\nZFK0A0tRF5CKqNUsNPN4iUQCLpdLleO1R0NDA4xGY1bLKxe2fL0L207tQopjpccMNIMbB4zH5CvG\ny7XMvCAWlnq9XvA8D6fTCafT2WKcxsmTJ6WOE2Lng0QikXV/6XQakUgENE3j4YcfLrmGsISekUgk\nsH79etTW1sLlcuGee+7B0KFD5T9Ols4FnaVQFyIlITxqNgsFmoUnHo/D7XarcrxsCIKACxcuwGaz\n9WgMdyKdxNGG4wglo7AbraipGly0lo6IIAjwer0IBoOwWCwoLy/POqpXFJ/MDhTtza/nOA7BYBCh\nUAg6nQ4PP/xwu2ncBO1y4cIFvPvuu6irq8PQoUMxe/ZsVFTkFnvREiUhPCzLIpVKqdIsFGi+u4nF\nYnC5XHlLaOB5HhcuXIDL5WoxcVDrCIKAixcvIhwOSyPMO6K1+MRisXbjbn6/XxqPodPpMH/+/KJv\nCEuQH0EQ8Pnnn+P9999HNBrFtGnTcMstt8jifstscWUzlmFo5SCYGHU8PXJSfDZaFsSLv1qZbfnO\nngMg1S6RDKxLCIKAhoYGRKNRVFRUwG63d/ocnU6HK6+8UvpM27t5SafTLd7rUCiEV199tagbwhKU\ngaIoXHfddaipqcH69euxceNG7NmzB3feeSdGjhzZ7etHNtf4B8c/LkrXeEkIT6bfXs0iwHymcKfT\naQBEeER4nkdDQwNisRiqqqq6VGeg0+kk8RCbvGbbf+tMt1AohOXLl2PevHm46qqrZHgVhFLCZDLh\njjvuwPjx4/Hee+9h+fLlGDRoEO6+++4uj+3Y8vUubDrRtitAimOlx4tJfEpCeNTObCMWT2EhWjrx\neBy9e/fulutRFJ9IJJJVeFiWbVGcKhIKhfD666/jgQceULSWg1C89OrVC4899hiOHj2K9957Dy+8\n8AKmTZuGW2+9NWvssTUJNoFtp3Z1uM22U7swvvo7ncZnlerVlkgk8B//8R9wu92or6/HokWLOoy5\nl4zwqNmzLdO1ly8yq++1jCAIaGxsRCwWQ69evXoU79LpdB2654xGI+rq6to8nik+V199dbePTyhd\nKIrC0KFDMXjwYHz00UfYuHEjGhsb8eCDD3b63CONdS3ca9lIcSyONhzHdX2Gd2ldcvVqi8fjeOCB\nBzB48GC89NJLOH36NAYPHtzucUtCeIDmi4baFk8+hUfsN6Z1vF4vIpEIqqqqFG9ea7PZMGjQoDbi\nQ1EUgsEg3njjDcydO7fDim2CtmEYBrfffju+853v5HzTGE5GctoulGxbMJ0LcvRqc7lccLlc2Llz\nJwRB6FB0gBISHnE8ghpxl0IQHrXjWYVIPB5HMBiE2+3uce+oXLHZbBg8eDCOHz8OoKXbNRgMYsWK\nFZgzZw6GDBmiynoIxUlXpuPajLmd23Zj12+85OzVtmLFCphMJjz55JOdHrdkrlyZPdvUglg8+YPn\neTQ2NvaogLa7lJWVYfDgwXA4HNKUVpfLBYfDAYqi8Mc//hHHjh1TdU2E0mVo5SAY6I5jQQaaQU1V\nx1aGyLp16/D888/j+eefR2VlZZtebf3794fX68XVV1/dolfbokWL8Le//S3rPg8fPozVq1fjzJkz\nePHFF/HFF190uIaSqOMB1O3ZxvM8AoEArFYrjMauFVt6vV5ZBkrV19fDbDZ3WqdSqjQ1NSEUCuHy\nyy+XvT1JrkQiEdTV1Um9+bxeL7xeL0KhEDiOw49+9KMWI0UIhO7SXlabyLSrJsma1aZ0r7aSsXjU\nzGzriavtZz/7WbttWXJFEARNWzyJRALBYBAulytvogO0tHycTqc0tdVut4OmaaxcuRKHDx/O2/oI\npcPkK8Zj2lWT2lg+BpqRXXTU6NVWUjEetTLbeiI8J0+exBNPPIElS5Z0+/jicbUY4xEEAU1NTTAY\nDKq72LJhtVqlmE/r2GIoFMI777wDQRAwfHjXso0IhNZMvmI8xld/R/EWVw899JCs+8tGSV251M5s\n647wOBwOLF26FO+//363jy2+Ri0KTyQSQTKZhMfjKZhUcqvViiFDhkgxH9HycTgcoGkaf/rTn3Dg\nwIF8L5NQAjAsjwFnk7j2ZAwDzibBsIXRlb+rlIzFA6g7jbQnwgMA8+bNw6hRo7rVbkWrwsPzPHw+\nHywWS8H1p7NYLC2y3TIJBoP485//DEEQMGrUqDysjlAKNGyuRWPtVnDJSyOuz69dh8opN6Fq6pQ8\nrqzrlNSVS8005+4Kj1igGAqFcM899yCZTHZ5H1oVnkAggHQ6LUtyhhKI4uN0OttYPnq9Hn/5y1/w\n+eef53uZhCKkYXMtLqzf2EJ0AIBLpnBh/UY0bK7N08q6R0lduTJ7tilNTy0eANi/f39OOe+t0aLw\npNNpBAIBOByOvCYUdEam+Ljdbrjdbng8HjidTuj1erz77rvYt29fvpdJKCK4eByNtVs73Kaxdiu4\nHJKWVq9e3W5KdE+IRCJ44okn8MILL+CJJ57oNIGqpK5cama2dTeRoXX68+9//3usXbu2S/sQX1+h\nxDjUwOfzgaKovA/fywWz2Yzq6mrY7XZJeETxYRgG7733Hvbu3ZvvZRKKhMDhI20sndZwyRQCh450\ned+CIOC5557D4sWL8dRTTyEcDmPOnDkAmr9zTzzxBA4cOIDf/OY3WLJkCZYtW4azZ8/i3nvvxSuv\nvCLtJ5VKYeHChXjmmWdgs9lw/vz5Do9bcsKjVoKBHBaPyNy5c7M2oGwPMYalFeFJp9MIh8NwuVxF\nk0IeDodhs9lgt9vbuN0YhsGqVauwZ8+efC+TUASkQ6Hctgvntl1rqqurYTab27TM+fDDD6WWOQzD\nSC1zALRpmeN2u1FVVYWnn34aPp8P/fv37/CYJSU8gHqZbT2N8WQSCARw7733gmU7bgQoorV2OZFI\nc6+qnkxaVROe58FxHCoqKiSxyazzEcVn9erV2LWr467DBII+h7lSAKC35bZdJmLLnAcffBB9+/aV\nWub89a9/xaeffoobbrgBAHD//ffj8ccfx9NPPw0AbVrmnDt3Dn6/H4sWLUJNTQ127NjR8Vq7vNIC\nR63MNrE3XFdpbfHodDqsWbMGgUAAJ0+e7LS5HpDfOUD5IBKJwGKxFI21I85KMhgM6Nu3b7ufVTAY\nxNq1ayEIAq6//no1l0goIpzDhuL82nUduttoowHO4UNz2t+6detw9OhRAMCCBQvatMz55S9/Ca/X\ni5qamhYtczweD9xuN2bOnNlmnwaDAYsWLUJ5eTkuXLiAu+66q8M1lKTwAM13nUpeqLo79dThcMBi\nseB///d/8eCDD4LneXi9XsydOzfnfWhJeFiWRTKZRGVlZb6XkjOi8Oj1euh0ug5bQAWDQfztb38D\nz/OYMGGCWkskFBG02YzKKTfhwvqN7W5TOeUm0KbOR2DfcccduOOOO1o89vrrr7fZ7pprrsGdd94J\nABgxYgRGjBjR4u/PP/98i98rKirwu9/9rtPji5Scv0atzLbuNiWtrKzE2rVrMW/ePFx33XUAgJUr\nV3ZpH1pytUUiEVAUpfjIAznJFB4AkviIbrbMmI+YcPDBBx+gdmtxpcT2hASbwL5zh7Dl653Yd+4Q\nEmzP2kiVOlVTp6D3jFtAG1tmdNJGA3rPuEXWOh7SMqcbiEF3pYWnuzVDQ4YMkVrm//jHP8b+Hkpt\nMQAAIABJREFU/fuxbds2fPvtt+jbt29O+9CSxRONRmE2m4tKaNPpNGiabrFmUXwyP7fMfwcCAWz8\naCNO+b7Fg3f+q5rLVZ0tX+/CtlO7Wgw3++D4x7hxwPiiGt+sNlVTp6D8husROHQE6XAIepsdzuFD\nc7J0ugJpmdNN1EgwkCN1+95774Ver4cgCHjnnXdyfp5WhCedTiOZTKo2a0cuWJbN2iFdp9OhT58+\n0JcZYP+/hIPy8nLJ8jEYDPhy7zEsf39FHlatDmKX5dYTNVMci00ntmPL1yTZoiNokwmesaNRNXUK\nPGNHyy46aqGa8AiCgMceewxLly5V/Fii8CjZwUCO+T8VFRWYPn06gGZ3W6770orwxONxAOhwdnuh\nIQgC4vE4TO1cEFJcCqu+3YiQLtIy263cIxXHfvXZF9iwaYPKK1eeBJvAtlMdC8u2U7uQSHe9mweh\nuFBNeFasWIFhw4apciw1hsLJ1Z7nRz/6EYDm+TqdFV2JaEV4EokEGIZRfL6SnLAsC47j2u0ld6Sx\nDol0Ehvrd8KvC18SH7cH5eXlkvhs2bwFmzZtUnn1ynKksa6NpdOaFMfiaEPbfneE0kIV4dmzZw9M\nJpNqreHVSDCQK5Z0++23Y82aNbhw4ULO43C1IjwdWQ6FSiwWA4B21x1ONtck8eCxqX4nvFRQEh+3\n243y8nLJ7bZ582Zs2LAhr5Nu5UR87Z0RSkYVXgkh36hyK7l582Y4HA4cPnwY58+fx2233ZZzIL07\nqNU6p7tFpJmYTCbMmjWrS8/RgvCk02mwLFsULXIyEcWyvWQIm/FSvEqAgM0NuzG58l9Q4XA238xk\nPC8QCGDLli0QBAHTp08v+s8887V3hN1YPBmMhO6hivA8++yzAIC9e/fi888/V1R0APVa56iRPdfR\nsUsZsclgMVk8YnynowF1QysH4YPjH0suJwECtjTuwY2VY1BldwMAxE+Woij4/X5s3boVPM9jxowZ\nRf25t37t2TDQDGqqOi+iJhQ3qjrPx44di7Fjx6pyLJ1OB47jFD9GPtwgpeJ66YhEIgG9Xg+GYTrf\nuEBIJpMQBKHDWUEmxoQbB4zHphPbpccECNjW+BkmVozGZY7yFttTFAWfz4ft27eD53l897vfLVrx\nEV/7xq+2AedSEELpNtuU23vjgzXr2t1He6/d7XZj0qRJRdPdQusUT9S2i6jROoeiKMXFLRtacLWl\nUikYjfKO9FWaSCQCmqY7XbdYq5JZyyJAwG7fAdzRbxoccEifr/hfv9+PTz75BDzP4/bbby/az/+m\nAePwxa4jOH38JHR0S3ckBQr1jedQj3Nd3m86ncaZM2fwox/9iIhPEVDSwgMo2zpHFDeC/LAsW1T1\nO4IgSB2pcxGFyVeMx/jq7+Bow3GEklHYjVbUVA2GkTagsbEx6z78fj927twJnucxa9asohMfQRCw\nbt06nD58EkajUdbPNxaL4dixY3jzzTcxZ84cIj4FDhGeHh5DrBcqtotAISMIAtLpdFG52WKxGHie\n71IHbZPeiOv6tM30rKysbGHxZI7ACAQC2L17NwRBwPe///2iOe8EQcDatWuxe/duGI1GuFyuNg1z\ne/JawuEwfD4fjh8/jhUrVuBf//VfiyoNX2uU7CejRuuczHqhYrkAFAPieIhiunCEw2EYDAZZpqNS\nFIWKiop2z6lAIIA9e/aA53nccccdBd9OSBAErF69Gnv37oXRaJTSxh0OR87fm862M5vNoCgKXq8X\nX375Jd544w088MADRXUOaYmS/lSUzmzLTNtW68svJhaUstCJwlMsFg/HcYhGo/B4PLJ9LhRFoby8\nvM3Av0zL57PPPgPP87jrrrsKVnwEQcCqVavw2WefwWQyweVyoby8HOXl5Tm72tp7TzMfN5lM0u9e\nrxcnTpzA66+/jgceeKBoziMtUfLCw7KsYhaJGh0StEixWTzioDq5Y1KZ4iP+nkkgEMD+/fvB8zzu\nvvvughMfnufx/vvvY//+/W1Ex+VytXm/emL9ZCZ0UBSFpqYmnDx5EsuXL8f06dOh0+naPM/pdGad\nCExQnuL4ZncTpV1hao1g0Bocx0Gv1xeFVScIAkKhEMxms2JC2dqSyhQiv9+Pf/zjH+B5Hvfee2/B\niA/P8/jrX/+Kf/zjHzCZTJJ7zePxwOVywel0olevXm0+4678HolEkEo1D0fLZtV4vV588803eOWV\nV7KukaZpzJs3D1dddVW3XiOh+5S08IhJBUq5wtTqkKA1iilmFovFkEql0Lt3b0WP43a7QVEUzp49\n2+Zvfr8fBw8eBM/zuO+++/IuPjzP491338WBAwfaFZ0BAwb0KF0+FotJ48XNZjNCoVDWc0bs/t4a\nsdj3tddew7x58zBw4MBur4XQdUpaeNRKMFBTeORqTlrIFMtrEwQBPp8PJpNJlQ7aYvugs2fPtrGA\n/H4/Dh8+DEEQcN999+UtnZjnefz5z3/GoUOHWoiO2IPO5XKhf//+PRIdjuMQCARgNpul991ut0t/\nz8wEZBim3fMpFAohEAjg9ddfx9y5czFo0KBur4nQNUpaeADlOxjkq3tBqVMMFo/o6rnssstUW6/L\n5Wph+bROPjhy5Ajefvtt/PCHP1RdfHiex5/+9CccPnxYEp2Kioo2lk5PMv8EQYDf7wdFUW3iM3a7\nvcX7QVEUDAZDu99/UfwCgQDeeOMNzJ07F4MHk3Y9aqAJ4VE6wYC42uSlGFxtPM/D5/PBYrGoPi/I\n6XS263YDgH/+859YuXKlqlX8HMfhnXfewdGjR2E2m+FyuVBRUSFZOnKIDtDsYksmk3C73VlfW+sC\nXr1e3+L7mS0TTrQYV6xYgTlz5uCaa67p0RoJnaMJ4QGUTTBQu3uBHF2xCT0jFAohnU4rHttpj85q\nYNSs4uc4DitXrsSxY8dgNpvbda/1VHTS6TSCwWALF1s2MrPlGIZp98awdUKC3+/Hm2++iR//+Me4\n9tpre7RWQsdoRniUSjAg3Qvkp9DfS47j4Pf7YbPZZCkY7S52ux3V1dU4c+YMgLZut+PHj+OPf/wj\n5syZo2hq+qefftpCdET3mpyik+li66j7t0hZWZmUVt3eTZq4psz3LBAI4M0335SsytaxtL59+2L2\n7NmkNqiHlLzwKJ15lo/uBaVu8dA0LY1FKER8Ph8EQYDb7c73UmCz2STxyVbvU1dXhxUrVmDu3LmK\niU8wGIRer5cskbKyMunfbrdbFnEOhUJIpVIoLy/P+QbSarVKMZ72et+1jgmJx4pGsw+jO3jwIPx+\nPx566CEiPj2g4IRH7guq0rN5lLaoslHqwsMwjOKdxbtLIpFAKBSCx+MpmAJXm82Gfv36SZaPiDhS\n4auvvirqKv54PI5IJAK73d7lbDiGYdp9zZk98YBLwmMwGNpNwQ6Hwzh9+jSWLVuGhx56KK8WbzFT\nGN8chaFpWrE4DCkilR/xgl5ojUIFQUBTUxMMBkPBVbyXlZVJ4tPaReTz+XDy5Em8/vrrmDdvnqLv\nqdw3Cul0Gn6/HyaTSfbOEDqdDpWVlW0eNxqNWRMSeJ6XhObMmTNYtmwZHn74YSI+3aDghEeJC7iY\n8qzEHXQ+iki1YPEAhSc84XAYyWRS1fTprmC1WtGvXz+cPn0aQEsR8Pv9+Prrr7F8+XLMnz9f1vdV\nqXOR53l4vV7odDopjVxuKIpq0w3cYDC0+302GAzStt9++y3+8Ic/YMGCBUU3OyrfFJzwKHESi1aJ\n2IpFTpR25bV3zFIWHvEzYllW9VTl9uA4Dl6vV4pfFCoWiwX9+/fHN998Iz2WaQF98803ePXVVzF/\n/nxV7tS7KxaCICAYDCKdTqOiokJRN7bYDVz8d0dFpwzDtHhN586dw9KlS/HII48U1Zj2fFOQwiO3\nZaJGggERHvnQ6XSgaVpqFloIeL1eAM190wods9ncRnxEKIrC6dOnsWTJEowaNapL37PW55z4e11d\nXY/Wm41YLIZYLAan06maQIqjKDJTsFu/P6KlmCmEFy5ckMSnkG9KComCEx5A/uFtaiQYEOGRF4PB\nUDCZbcFgEOFwGOXl5QWTUNAZZrMZAwYMaCM+4oW0vr4eH330EYDsXoauCpJer4der4fBYABN09JP\nd6yAZDKJQCAAi8UCq9Xa5ed3F7EbuMVikbodZL4PYoukzMdFa7K+vh5Lly7FwoULifjkQMF9i8Te\nanIXvSnZOkftIlKl2wAVAlarFU1NTeA4Lq9jjGOxGJqammC321v0AysGTCYTBgwYgFOnTrXoXwY0\nB9DlvHlhGAZutxsul0t6ry6//PIuCwfLsvB6vTAajTnV68gNRVEdrlkcOCdum/nfhoYGvPzyy3j0\n0UdhsViUX2wRU5DCo4S7TRQHJRIM1C4i1YLFIwpPLBbr0jhpOUmlUmhoaIDZbG4xF6eYMBqNWS0f\nud1XBoOhRXuc6urqLmf+iXE0mqalbtyFBsMwuOyyywC0tIbEfzc1NUnio6a1VmwUnPAoVZCZOSJB\nCWuqu/vm4nEEDh9BOhSC3m6Hc9hQ0J2Y6loQHtFtE41G8yI8HMehvr4eNE2jqqqqIC+CuZIpPqLV\nYzKZwAs8IACgAArZh82JZJ5z4r8zO6UzDAOHwwGXy4Xq6uouW4eiG4vneVRWVipeE9ed750IwzDo\n06dPG3cbADQ2NsLr9eLll1/GY489RsSnHQpOeETkLsjMTDAoFOFp2FyLxtqt4JIp6bHza9ehcspN\nqJo6pcPjaaFuyGq1IhAIqF5IKggCGhoawHEc+vTpk1dXn1wYDIYWCQftFUkC3ctE0+v1cDgc3Rad\nQCAgdSZQOo7W3e9dJnq9Xkqrb+1yu3jxInw+HxYvXozHHntM9vqjUqDghEdMBFCqgwHHcbLXhmRa\nU7nSsLkWF9ZvbPM4l0xJj7f3JdCCxQM0C4/f70c8HlfNZy4WicbjcfTu3bukigMNBgPqaT9sVhP0\nDAO0PocyLB/poRxFiGEY9O3bt1txsEgkImWwKV0P05PvXWv0en2HTWL9fj8WL16MBx54ABaLpY1A\nMQxTUudXVyg44QEuJRgoEedRIigv3vXkKjxcPI7G2q0dbtNYuxXlN1wPOktWkJIFsYWEwWCAXq9H\nOBxWTXgCgQBCoZCU3VRKJNgENn/zCQwUg3GeEWAoBoL0PwBoPp8GegaA1l2y8rLFMlr/3t0MtHg8\njlAohLKyMsXdUj393mUjm+WTSSAQwG9/+9uszx09ejRmz56d03FKjYIUHlEg5HaLqZFgkAuBw0da\nmPnZ4JIpBA4dgWfs6KzHApRxGxYSYifipqYmuFwuRe8ORXePz+eD0+ksuJY4cnCksQ4pjkUKLD5u\n2NXudmVuG67rNVzx9SSTSfh8PpjNZlUyBnv6vWsPmqZbWD6ZGYQ6nQ6pVPZjhkKhnI9RahSk8Igf\nmtzuJCUv2F2xptI5nnDpcPbttCI8QHMDTL/fD7/fj6qqKkWOwfM8Ll68iEgkAqfTWRBdp5UgnIzk\ntF0omb0zs5yIadMGg0Gxdjit6en3riNE8Wn9Omiabld4xG4JWqQghQdQxt2mtPDkWmmvz/HuTm/L\nvp2WGpOKfbqamppgs9lkd3+l02lcuHABLMuisrIyb6nbamAz5hbkthsVdnllpE17PB7V3MU9/d51\nRmvLR3wsmUxm3b4UrepcUaePfzfITKuWi8wEA7mhaVoSys5wDhsK2tix24g2GuAcPjTr37QkPEDz\nwDOz2Yz6+vp256R0h3g8jrNnz0rZa6UsOgAwtHIQDHTHiTUGmkFN1WDF1iA2/hQEoUuzdeSgp9+7\nXNDpdOjduzdcLhfcbjc8Hg8qKiqy/rhcrm4fp9gpWOHJrBGQE6VSkbsiBrTZjMopN3W4TeWUm9oN\ncGpNeCiKQq9evWQVn1AohPPnz4NhGFx++eWa6C5sYky4ccD4Dre5ccB4mPTKvBdirU46nYbH41Hd\nTdzT712uZIpPez9ut7vkb3Q6oqBdbUq0z1EqwaCrbjwxZbN1PQFtNHRaT9DVLLpSQKfToVevXmho\naEB9fT08Hg8cDkeXe4olk0kEg0FEIhHYbDapMaRWmHxFs/BsO7ULKe6Sa9hAM7hxwHjp73IjdptO\nJpPweDx5SyPuyfeuK4jna+Y03dbnmZbreyihQApCzp49iylTpqC2thaXX345gGZfsFh3I9fFgeM4\nJBIJmEwmWQVNnAlvtVq7dPfMJRIIHDqCdDgEvc0O5/ChOd1x1dfXw2w2a85PLNbZhEIhmEwmVFRU\ndHoRS6fTCIfDCIfDYFkWNE1LPcW0JDqZJNJJHG04jlAyCrvRipqqwYpZOkDzLKNQKASn01kQ1fzd\n/d51h8xLrFbPt9YUrMUDXMoUK4YEA9EK6Wr8iDaZupS6KT2Ppku+UWg2xPb1ZWVlaGxsxNmzZ1FW\nViaNUtDpdNKPIAiIRCKSa85qtaK8vLxFo0etYtIbcV0f5VOmASAajSIUCsFmsxWE6ADd/951B62f\na9koaOERkbN9jtIdDNRyf2mlbU57mM1m9O3bFz6fD4lEQqr7av2eGAwGeDwe2Gy2kk89L0QSiYQ0\n4kDLMQ1CSwpaeDLn6Mht9ShhLag5rqCjNE2toNPpUF5e3uIxsaODeM7o9Xpyx5knUqkUfD4fTCYT\nnE4n+RwIEgWb1SaiRLplZssZufdLLJ78It6s6PV6WWODhK6RTqfh9Xqh1+tVKxAlFA8FLzxKjK3u\nTlPPXPerhKC1d6xc64YIBDURa3UoioLH41G1VodQHBT8GaFE6rASYgZcss7UcLeJ4qnFBANC4SLW\n6nAcl5daHUJxUPDCA8jvGlOqg4FSllRHxyLCQygUMmt13G637Mk7hNKhKIRHiS4GSsRIxHUSi4eg\nRaLRKKLRKBwOB0wK1cQQSgNNC4/c8RiKolRLqe5u3RCBoATxeBzBYBBWq1XTFfm5wrJsu12rtUDR\nCI/ccR6l3GJqpVSLIkeEh5BvUqkU/H4/TCaT5jppdId0Oo1IJKLpcoiiEB5AmTgPIL+rSs0iUiI8\nhHwjjjggadO5wfM8IpEIdDpdwXRxyAdFJTyAfO62zOJUOckseFUamqaRTqcVPw6BkA0xgw0ASZvO\nAUEQEA6HIQiC1OZJqxTdK5dTKJQQHjWD/nq9XuplRyCoTSAQQCqVgtvtJmnTnSCKDsdxpH0Tikh4\nlLBQlEgwUDOlWq/XS+1hCAQ1iUajiMVicDqdmphl1BMEQUA0GkU6nYbNZoNeX9CdylShaIQHkD+7\nTQmRUDOlWjyBibuNoCbJZFJq/KnlOEUuCIKAWCyGVCoFq9VKapv+j6ISHrknbyohEmqmVBPhIagN\nx3Hw+XwwGAxwOp35Xk7Bk0gkkEwmYbFYiGWYQVEJj5hWXQwJBmqmVLMs2/nGBEIPEQQBXq8XAOB2\nu0kGWyckEgnE43GYTCZSUNuKohIeAFI9j1zio1SCgVppzgzDEIuHoDiCICAQCIBlWdKDLQeSySRi\nsRiMRiPMZnO+l1NwFJ3wyJ1WrVSCgVpdqvV6PbF4CIoTi8WkZILORo1rnVQqhWg0CoPBAIvFQizD\nLBSd8BRDgoGaKdUMw4DneVJISlAMlmVJMkGOpFIpRCIRGAwGWK1WIjrtUJTCI2f7HCUSDNQcjyBm\nyRCrh6AEPM/D5/NBr9eTdjidIIoOwzBEdDqhKBPKxeC9HOOwlaoPUquBp5jZxrIsCWASZEWM63Ac\nh4qKClkr7bl4HIHDR5AOhaC32+EcNhR0EcdCWJaVRKesrIyITicUpfBkutvk+ICVms2jVmYbwzDE\n4iHITjQaRTweh8vlkrX+pGFzLRprt4JLXurOfH7tOlROuQlVU6fIdhy1YFkW4XAYer2eiE6OFJ2r\nDZA/zqNUgoGamW1EeAhykkqlEAwGYbFYYLFYZNtvw+ZaXFi/sYXoAACXTOHC+o1o2Fwr27HUIFN0\nbDYbEZ0cUcXiOXnyJJYsWSJNJXzyySd7tL/MOI8caZ2ZCQZypYnSNI1kMimbVdYRDMMgFoupcixC\n6SPGdRiGkbVIlIvH0Vi7tcNtGmu3ovyG60EXgduYiE73Uc3ieeaZZ/Dss8/iyy+/lGV/clop4glT\nrJltYnqrlgdLEeRBjOvwPC/7mIPA4SNtLJ3WcMkUAoeOyHZMpSCi0zNUEZ4rr7wSHo8Hb7zxBm67\n7TZZ9imnu01MMJBTJNROqQZIZhuh58RiMcTjcTidTtn7iqVDody2C+e2Xb4gotNzVHG1pVIpvPDC\nC/jud7+L6667TpZ9ZlopcmTbyC08ama2iQkGxOIh9IR0Oo1gMAiz2SxrXEdEb7fntp0tt+3ygZgy\nTUSnZ6hi8bz55ps4e/Ysamtr8eKLLyISifR4n3L3bSv2BAODwUCEh9BtxKFuOp1OseafzmFDQRs7\n7npAGw1wDh+qyPF7SmadDhGdnqGKxTN//nzMnz9f9v3KWc+T2flazgQDtdxfBoMB0WgUHMeRPlqE\nLhMKhcCyrOz1OpnQZjMqp9yEC+s3trtN5ZSbCjKxIFN0SMp0zynKdGoROeM8co9cACCNR1CjZxtJ\nMCB0l2QyiUgkArvdrngftqqpU9B7xi1tLB/aaEDvGbcUZB0PER35KcoCUhE54zxKdDDITDBQeuog\nTdNSCjfphkvIFZ7n4ff7YTAYUFZWpsoxq6ZOQfkN1yNw6AjS4RD0Njucw4cWpKWTTCalhp+kDY58\nFL3wyB3nUSqzTWnhoSiKxHkIXUZMnS4vL1f1okqbTPCMHa3a8boDER3lKGpXGyBvUoDcCQY6nU61\noXAAYDQawbKsKtNPCcVPZuq00jdGxUYikSCioyBFLzzFEOdRa1CbOFo3mUyqcjxC8ZJOpxEIBGA2\nm4lrthWJREIa4kZERxmI8GSglPCoZfHo9XopzkMgtIcgCPD7/aAoCk6nk1xYM8gUHTLETTlKQnjk\nms8j96wf4NI0UrXcX0ajkQgPoUMikQhSqRRcLpdiqdPFSDweRywWg8lkIqKjMCVx1skZmxFToOVC\n9J2rGedJp9NkIikhKyzLIhQKwWq1kvlNGcTjccTjcZhMJpjNZiI6ClMSwiO3u03O2hvxjlLtOE8i\nkVDleITiQXSxkWmiLSGioz5EeFohd5yHoijo9XrVLBCapsEwDBEeQhvE7gRyd50uZojo5IeSER65\nYjPFntkGAGazWZoFRCAAl7oT2Gw2xbsTFAtEdPJHSQgPAKmQtKcXWyUSDPR6vWqtc4Bmd5sgCCTJ\ngADgUncCsbklodkVTUQnf5SM8MiZnaNU6xy1rB6GYUDTNHG3EQAAwWAQPM/D7XaTCywupUwT0ckf\nJSM8ck4RlTvBQG3hoSgKJpMJ8XicuNs0jpgibLfbSXcCNLscxTodIjr5o+SER84EA7ku2hRFqVpI\nCgAWiwU8zxOrR8NwHIdAICBV4GudVCqFaDRKikMLgJIRHuBSPU9PES0UueM8aiYYGAwGMAyDWCym\n2jEJhYMgCAgEAhAEgWSx4dJoA4PBQESnACgp4SmGBAM1G3haLBYkEglSTKpBYrEYEokEnE6n5gcD\nsiwrzdMhvdcKg5ITHkA+d5ucF2zRv66m1WOxWACAWD0aI51OIxgMwmw2S+eAVkmn0wiHw9Dr9WSI\nWwFBhKcdlOhgQFGUqsKj0+lgNpsRjUZJkoFGELsT6HQ6OJ3OfC8nr3Ach3A4DJqmYbPZiOgUECUn\nPHIXksqZYKB2nAdotno4jiMD4jRCOBwmDUDRHJ8Nh8OgKIqITgFScmemXHEeJToYiMKjpvVhNBpB\n0zQikYhqxyTkh1QqhXA4jLKyMqlnnxYRBAHhcBiCIMBms2lagAuVkvtE5DrJ5KwLElG7UzXQ/DrK\nysqQSCRUt7YI6sHzPHw+HxiGgd1uz/dy8oYgCIhEIuA4DjabTfOJFYVKyVWUZcZ5emJeUxSFeDyO\nb775BocPH0Z1dTWqq6vRu3fvbhfiic9jWVbVYj6LxYJQKIRoNEq6EpcoYncCj8ejWbeSIAiIxWJg\nWRZlZWWkYLaAKblPJtNS6an1Y7PZkEwmcd9990mP0TSNPn36SEKU+TNkyBBcccUVHa4tH3EenU4H\nq9WKaDRKXA8liNidwOl0gmGYfC8nbyQSCSSTSVgsFtIItcApOeEBLsV5ekIqlcK5c+dQVVWF3r17\n48KFCwCa3WRnzpzBmTNnpG3Lysrwk5/8BOPGjet0vwzDSK1s1LwztVqtiEQiiMViKCsrU+24BGUR\nuxOIUzO1SiqVkpp+kgF3hU9JCo9Yg9OVi/vOnTuxY8cOHDlyBEeOHMHx48dx2WWX4dixYxgxYoQk\nPJmUlZXh8ccfxxNPPAGPx5PTcUThSafTqt6d6vV6mM1mRCIRUkRXIgiCAJ/PBwBwOp2a/UzFAlGD\nwQCz2Zzv5RByoCSFpztfwD/+8Y947bXXWjx25swZeL1ejB49Gh999JH0uNVqxWOPPYaf//znKC8v\n79JxaJoGRVFgWVZ1t4jVakU8HkcikSBf0BJATJ0uLy/XbBCd53lEIhHo9XpyQ1VElLTwdMXiGTp0\nKOx2O2pqajBs2DAMHToUQ4cOhcvlws0334xf//rXsFgskuBUVFR0e20Mw4Bl2W49vycYjUYYDAaE\nQiGYTCbyJS1iEokEwuEwbDabZlOnxbRpAKQrQZFRksIj0pUEgwULFuDxxx9vc/JGo1FUV1fj5z//\nOf793/8dlZWVPV4XwzBIpVLgOE71O1W73Y6mpibE43FNxwSKGY7j4Pf7YTQaNTvYTRAERKNRcBwH\nu91OEmaKjJIUHrGDQVcSDNrLgkmlUvB4PHjxxRdlO7lFFxvLsqoLj9FohMlkQjAYhMlkIl/YIiMz\nrqPlrtPJZBKpVApWq5WkTRchJXvVkSOzDYBU9yJnIalOp4Ner8+Luw1ofk2ib5xQXIhxHbfbrdm4\nDsuy0jA3rboZi52SFh6g573WxOaecncbEOM8+WjeKXbqFSu8CcUBieu0TCYgruLihQhOBWsXAAAJ\nq0lEQVRPDoidquVEdO3ly+oRGycGg8G8HJ/QNUhchyQTlBJEeHKApmlZRyQAzWKm0+ny1jVap9PB\nbrcjHo8jmUzmZQ2E3BAEAV6vF4C24zqxWEzqwUZik8VNyX56cls8gLxxHoqiYDAY8uZuAyC1FhFH\nJBMKD3GENcuy8Hg8mo3rJJNJqR0OSSYofkpWeIBmwZBTeOSOhxgMBgiCkDd3G0VRcDqd0pRGQuER\njUalPmxa7T/GcRyi0SgYhtFsbKvUKGnhkWs2D0VRisR5aJrOq7sNaE5yKCsrQzgcJmMTCoxEIoFg\nMIiysjJYrdZ8LycviGMOxEa3WnUzlholLzxyQdO01P9NLgrB3QZAmltCXG6FA8uy8Pl8MBqNmp6v\nI8Z1ysrKSFynhCjpT1KJOI/cF+Z8u9uA5tfmcDiQTCYRj8fztg5CM+JQN51OB7fbrdm7/FQqReI6\nJUpJC4+IXJltgPxxnkJwtwGAyWSC2WyWAtmE/CB2JuA4Dh6PR7N3+SSuU9qU9FndndY5He1LHLcg\nJ6K7LZVK5dXNJSYa0DQNn88nezyL0DliBlsymYTb7dbsUDexDxsAEtcpUUpaeAD5WucAlwpJ5RYI\n8Y4u35aG6NoRh4uReI+6hMNhKYNNy8PMEokE0uk0ieuUMCX/qcqV2QY0u8Xk2lfr/dI0XRCFnAzD\nwOl0Ih6PS3edBOWJRCJSOxytZrABzTdf4iRRrVp8WkATwiMXSsV5AEjZbYXg4rJYLLBarQgGgwUh\nhqVOLBZDMBiE1WrVbDsc4JKLjaZpMqiwxNGM8MgV56EoShFxEN1t+U4yEHE4HDAYDFKgm6AMiUQC\nfr8fFosFDodD0/GMaDQKnudJHzYNUPLCIyKXe0ys55EbnU4HhmGQTCYLIrZCURTcbjcAwOfzFcSa\nSo1EIgGv1wuTyQSn06npi20qlZLm62i1LZCWKHnhkTOzDbgU51HK6uE4rmA6CNA0DbfbjVQqRZIN\nZCaZTEoFolqu1QGa65bE1GmttgXSGiUvPIC8mW1KxnkYhoFOpyuouIrRaITT6ZTiEER8ek4ymYTX\n6wXDMJoXHZI6rU00ITwAZMtGU6qeR9y30WhEKpUqiCQDEavVCofDgWg0ilAoRMSnB4juNYZhNF0g\nKpJMJsGyLKxWq+bfCy2hiU9a7rsopep5gEtJBoVk9QDNg7ccDgcikQgRn24Si8Xg9XphMBiI6KDZ\nayCOsCYuNm2hiQZImZltcogQTdNIp9Oy7S8TnU4Hg8GAZDIJk8lUUK6HsrIyCIKAUCgEALDb7QW1\nvkImEokgGAzCbDZrepibSGbXaTLCWnto4pZLzpRqQNk4D9DcN43n+bx3MsiGzWaD3W6XCh6J5dMx\nolCLdTpEdJpJJBLgOI7EdTSK5iweufYnxnmUqK7W6/WgaRqJRKIgXRBikaPociOWT3bE3muxWAw2\nmw02m428TwDS6TTpTqBxNCE8gLyZbUCz1SPO0VHiYmIymRCNRpFOpwuyJXym+PA8r/k6lNZwHAe/\n349kMgmXy0XcSf8H6U5AADTiagOUER4AimWfGQwGUBRVcEkGmdhsNinV+uLFiwVTf5RvkskkGhsb\nwbIsPB4PEZ0M4vE4cbERtCU8gHzuNjEjSak4D0VRMJlMSCaTBZVa3Rqr1YqKigpwHIeLFy8WtFAq\njSAICIfDaGpqgl6vR2Vlpaa7TLcmnU4jkUjAbDYXpBVPUA9VPv0vv/wSr732Gux2OwYMGID7779f\njcO2QO7MNoqiFGufI2I0GhGPx5FMJgvaLWEwGFBZWQmfz4empiY4HA7N3dFmutbKyspI3KsVYhYb\nTdNEjAnqCM9rr72Gn/70p+jduzcefPBBzJ49u03QXLyA19fXK7IGQRCQTqeliZ9ykE6nwbKsomnP\nsVgMLMsWxYVMvOOvr6+HxWLRTKdlnufh9XqlRAvxfSBcIpFISKJM3puW9OrVS3MWoCqv1uv1olev\nXgAgFSGKDShFLl68CAB5sYYIBAIhX9TW1uLyyy/P9zJURRXh6dWrF+rr69G7d28EAgG4XK4229TU\n1OCdd95BRUUF6U5LIBA0g3hTriUoQYUKwJMnT2LZsmWw2+0YOHAg7rnnHqUPSSAQCIQCRRXhIRAI\nBAJBRDPp1AQCgUAoDIjwEAgEAkFViPAQCAQCQVUKInm8EApM88XJkyexZMkSuN1uMAyDJ598Mt9L\nUhVBEPD444/jmmuuwcKFC/O9HFUJBoNYvHgxDAYDqqqqMGfOnHwvSTW++uorvP3223C5XOB5Hj/7\n2c/yvSRVCIfDePXVV3H06FGsWLECv/3tb5FOp+H1evHUU0+1KTMpVQrC4hELTJ999lls3boVqVQq\n30tSlWeeeQbPPvssvvzyy3wvRXVWrFiBYcOG5XsZeeG9996Dw+EAwzCaq+PYuXMnbr31Vvzbv/0b\nDh48mO/lqAbLsnj44YchCALOnDkDn8+HJ598EnfccQf+8pe/5Ht5qlEQwpOtwFQrXHnllfB4PHjj\njTdw22235Xs5qrJnzx6YTCYMHz4830vJC2fOnMHw4cPx05/+FKtWrdLUbKNp06Zh6dKlePrppzX1\n+bvdbpSVlQEAmpqaUFVVBQCoqqqSiui1QEEIj1hgCqDdAtNSJZVK4Ve/+hWGDRuGWbNm5Xs5qrJ5\n82Z4vV6sWbMGe/bswbfffpvvJalKeXm59G+j0aho379C480338R//ud/YtGiRTh16hSCwWC+l6Q6\nvXv3RkNDAwDg/Pnz6NOnT55XpB4FUcej5QLT5cuXY+/evRg4cCAA4NFHH5XuiLTC3r178fnnn2su\nxtPQ0IBFixahqqoKvXr1wty5c/O9JNXYu3cvNmzYAJfLBa/Xi+eee67gexHKwcGDB7Fx40Zs2LAB\n06dPlx73+Xx46qmnNHPTXRDCQyAQCATtUBCuNgKBQCBoByI8BAKBQFAVIjwEAoFAUBUiPAQCgUBQ\nFSI8BAKBQFAVIjwEAoFAUBUiPARNsnPnTjz11FMAAJ7n8eCDD+LkyZN5XhWBoA1IHQ9Bs/zmN7/B\nNddcg/Pnz8Nms2mqcJlAyCdEeAiaJZ1O46GHHoLdbsf//M//5Hs5BIJmIK42gmYJh8PQ6XTw+/1I\nJBL5Xg6BoBmIxUPQLD/5yU/w6KOP4sKFC9i2bRv+3//7f/leEoGgCYjFQ9Akb731FkaOHImrr74a\nkyZNAs/z2LJlS76XRSBoAmLxEAgEAkFViMVDIBAIBFUhwkMgEAgEVSHCQyAQCARVIcJDIBAIBFUh\nwkMgEAgEVSHCQyAQCARVIcJDIBAIBFX5/97GKwhcy0VCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9d800c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GeMpy.plot_potential_field(geo_data, sol[1,1,:].reshape(50, 50, 50), 22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G_x</th>\n",
       "      <th>G_y</th>\n",
       "      <th>G_z</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>dip</th>\n",
       "      <th>formation</th>\n",
       "      <th>labels</th>\n",
       "      <th>order_series</th>\n",
       "      <th>polarity</th>\n",
       "      <th>series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.258819</td>\n",
       "      <td>1.584810e-17</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Layer 1</td>\n",
       "      <td>${\\bf{x}}_{\\beta \\,{0}}$</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.342020</td>\n",
       "      <td>-2.094269e-17</td>\n",
       "      <td>0.939693</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>Layer 2</td>\n",
       "      <td>${\\bf{x}}_{\\beta \\,{1}}$</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>younger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.984808</td>\n",
       "      <td>6.030208e-17</td>\n",
       "      <td>0.173648</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Layer 3</td>\n",
       "      <td>${\\bf{x}}_{\\beta \\,{2}}$</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>older</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        G_x           G_y       G_z    X    Y    Z  azimuth    dip formation  \\\n",
       "0  0.258819  1.584810e-17  0.965926  7.0  4.0  7.0     90.0   40.0   Layer 1   \n",
       "1 -0.342020 -2.094269e-17  0.939693  2.0  4.0  4.0     90.0  340.0   Layer 2   \n",
       "0  0.984808  6.030208e-17  0.173648  1.0  4.0  1.0     90.0   40.0   Layer 3   \n",
       "\n",
       "                     labels  order_series  polarity   series  \n",
       "0  ${\\bf{x}}_{\\beta \\,{0}}$             1       1.0  younger  \n",
       "1  ${\\bf{x}}_{\\beta \\,{1}}$             1       1.0  younger  \n",
       "0  ${\\bf{x}}_{\\beta \\,{2}}$             2       1.0    older  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you change the values here. Here changes the plot as well\n",
    "geo_data.foliations.set_value(0, 'dip', 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You need to set the interpolator again\n",
    "new_series = GeMpy.select_series(geo_data, ['younger'])\n",
    "data_interp = GeMpy.set_interpolator(new_series, verbose= ['cov_function'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you change it here is not necesary. Maybe some function in GeMpy with an attribute to choose would be good\n",
    "data_interp.interpolator._data_scaled.foliations.set_value(0, 'dip', 40)\n",
    "# In any case, data prep has to be called to convert the data to pure arrays. This function should be hidden I guess\n",
    "input_data_P = data_interp.interpolator.data_prep()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sol = debugging(input_data_P[0], input_data_P[1], input_data_P[2], input_data_P[3],input_data_P[4], input_data_P[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Visualization.PlotData at 0x7fd1ef06eba8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1ef072a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GeMpy.plot_section(new_series, 13,block= sol, plot_data = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyMC3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_interp = GeMpy.set_interpolator(geo_data, u_grade = 0)\n",
    "\n",
    "# This are the shared parameters and the compilation of the function. This will be hidden as well at some point\n",
    "input_data_T = data_interp.interpolator.tg.input_parameters_list()\n",
    "# This prepares the user data to the theano function\n",
    "input_data_P = data_interp.interpolator.data_prep() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create the op. Because is an op we cannot call it with python variables anymore. Thats why we have to make them shared\n",
    "# Before\n",
    "op2 = theano.OpFromGraph(input_data_T, [data_interp.interpolator.tg.whole_block_model()], on_unused_input='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi...\n",
      "Average ELBO = -0.012037: 100%|██████████| 200000/200000 [00:07<00:00, 25793.75it/s] \n",
      "Finished [100%]: Average ELBO = -0.0012071\n",
      "100%|██████████| 6/6 [00:00<00:00, 18.53it/s]\n"
     ]
    }
   ],
   "source": [
    "import pymc3 as pm\n",
    "theano.config.compute_test_value = 'ignore'\n",
    "model = pm.Model()\n",
    "with model:\n",
    "    # Stochastic value\n",
    "    foliation = pm.Normal('foliation', 40, sd=10)\n",
    "    \n",
    "    # We convert a python variable to theano.shared\n",
    "    dips = theano.shared(input_data_P[1])\n",
    "    \n",
    "    # We add the stochastic value to the correspondant array\n",
    "    dips = T.set_subtensor(dips[0], foliation)\n",
    "\n",
    "    geo_model = pm.Deterministic('GeMpy', op2(theano.shared(input_data_P[0]), dips, \n",
    "                                     theano.shared(input_data_P[2]), theano.shared(input_data_P[3]),\n",
    "                                     theano.shared(input_data_P[4]), theano.shared(input_data_P[5])))\n",
    "\n",
    "    trace = pm.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['foliation', 'GeMpy'], array([[0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1]]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace.varnames, trace.get_values(\"GeMpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEZCAYAAACq+/FtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ed5f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEZCAYAAACq+/FtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ed85908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ebcfcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ec4ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEZCAYAAACq+/FtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ebcfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEZCAYAAACq+/FtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff23ea83748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in trace.get_values('GeMpy'):\n",
    "    GeMpy.plot_section(new_series, 13, block = i, plot_data = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipyvolume.pylab as p3\n",
    "import ipyvolume.serialize\n",
    "ipyvolume.serialize.performance = 1 # 1 for binary, 0 for JSON\n",
    "#p3 = ipyvolume.pylab.figure(width=200,height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0431015df72c484cbf1fbbf91c4c57a8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lith0 = trace['GeMpy'][0] == 0\n",
    "lith1 = trace['GeMpy'][0] == 1\n",
    "lith2 = trace['GeMpy'][0] == 2\n",
    "lith3 = trace['GeMpy'][0] == 3\n",
    "p3.figure(width=800)\n",
    "\n",
    "p3.scatter(geo_data.grid.grid[:,0][lith0],\n",
    "           geo_data.grid.grid[:,1][lith0],\n",
    "           geo_data.grid.grid[:,2][lith0], marker='box', color = 'blue' )\n",
    "\n",
    "p3.scatter(geo_data.grid.grid[:,0][lith1],\n",
    "           geo_data.grid.grid[:,1][lith1],\n",
    "           geo_data.grid.grid[:,2][lith1], marker='box', color = 'yellow', size = 1 )\n",
    "\n",
    "p3.scatter(geo_data.grid.grid[:,0][lith2],\n",
    "           geo_data.grid.grid[:,1][lith2],\n",
    "           geo_data.grid.grid[:,2][lith2], marker='box', color = 'green' )\n",
    "\n",
    "p3.scatter(geo_data.grid.grid[:,0][lith3],\n",
    "           geo_data.grid.grid[:,1][lith3],\n",
    "           geo_data.grid.grid[:,2][lith3], marker='box', color = 'red' )\n",
    "\n",
    "p3.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cholesky  (Under development)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'C' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-c22109665cca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Cholesky solution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcholesky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mU\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcholesky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve_triangular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve_triangular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'C' is not defined"
     ]
    }
   ],
   "source": [
    "# Cholesky solution\n",
    "L = np.linalg.cholesky(C)\n",
    "U = sc.linalg.cholesky(C)\n",
    "Y = sc.linalg.solve_triangular(L,b, lower=True)\n",
    "x = sc.linalg.solve_triangular(L.conj().T, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy as sc\n",
    "Y = sc.linalg.solve_triangular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "debugging.profile.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_interp.interpolator.tg.dips_position_all.set_value(input_data_P[0])\n",
    "data_interp.interpolator.tg.dip_angles_all.set_value(input_data_P[1])\n",
    "data_interp.interpolator.tg.azimuth_all.set_value(input_data_P[2])\n",
    "data_interp.interpolator.tg.polarity_all.set_value(input_data_P[3])\n",
    "data_interp.interpolator.tg.ref_layer_points_all.set_value(input_data_P[4])\n",
    "data_interp.interpolator.tg.rest_layer_points_all.set_value(input_data_P[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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