Fig3_Missing_Data_Cr_Sens.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "import warnings as w\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment these two lines once on each computer. \n",
    "#!pip install Thermobar\n",
    "#!pip install \"https://github.com/PennyWieser/Thermobar_onnx/archive/refs/tags/0.02.zip\"\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-colorblind\")\n",
    "plt.rcParams[\"font.family\"] = 'arial'\n",
    "plt.rcParams[\"font.size\"] =12\n",
    "plt.rcParams[\"mathtext.default\"] = \"regular\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"dejavusans\"\n",
    "plt.rcParams['patch.linewidth'] = 1\n",
    "plt.rcParams['axes.linewidth'] = 1\n",
    "plt.rcParams[\"xtick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"xtick.major.size\"] = 6 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.major.size\"] = 4 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"axes.titlesize\"] = 14 # Overall title\n",
    "plt.rcParams[\"axes.labelsize\"] = 14 # Axes labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Loading Keith's calibration dataset and calculating missing data amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Keith=pd.read_excel('Supporting_Data_1.xlsx', sheet_name='Putirka_Cali')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  new_ix = Index(new_ix)\n"
     ]
    }
   ],
   "source": [
    "## Loading into thermobar format\n",
    "Keith_in=pt.import_excel('Supporting_Data_1.xlsx', sheet_name='Putirka_Cali_Thermobar') \n",
    "Keith_input=Keith_in['my_input']\n",
    "Keith_Cpx=Keith_in['Cpxs']\n",
    "cpx_comps=pt.calculate_clinopyroxene_components(cpx_comps=Keith_Cpx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading into thermobar format\n",
    "Petrelli=pd.read_excel('Supporting_Data_1.xlsx', sheet_name='Petrelli_Cali') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loading into thermobar format\n",
    "Petrelli_in=pt.import_excel('Supporting_Data_1.xlsx', sheet_name='Petrelli_Cali') \n",
    "Petrelli_input=Petrelli_in['my_input']\n",
    "Petrelli_Cpx=Petrelli_in['Cpxs']\n",
    "Petrelli_Liq=Petrelli_in['Liqs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text about Cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "698"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NoCr_Liq_Keith=Keith['Wt: Cr2O3_Liq']==0\n",
    "sum(NoCr_Liq_Keith)\n",
    "\n",
    "NoCr_Cpx_Keith=Keith['Wt: Cr2O3_Cpx']==0\n",
    "sum(NoCr_Cpx_Keith)\n",
    "\n",
    "Cr_Liq_Keith=Keith['Wt: Cr2O3_Liq']>0\n",
    "sum(Cr_Liq_Keith)\n",
    "\n",
    "Cr_Cpx_Keith=Keith['Wt: Cr2O3_Cpx']>0\n",
    "sum(Cr_Cpx_Keith)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prop Keith with no Liq Cr data\n",
      "58.21635012386457\n"
     ]
    }
   ],
   "source": [
    "print('Prop Keith with no Liq Cr data')\n",
    "print(100*sum(NoCr_Liq_Keith)/(sum(NoCr_Liq_Keith)+sum(Cr_Liq_Keith)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prop Keith with no Cpx Cr data\n",
      "42.36168455821635\n"
     ]
    }
   ],
   "source": [
    "print('Prop Keith with no Cpx Cr data')\n",
    "print(100*sum(NoCr_Cpx_Keith)/(sum(NoCr_Cpx_Keith)+sum(Cr_Cpx_Keith)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text missing text about water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keith Prop with water = 0\n",
      "73.50565428109854\n",
      "72.27958697378872\n"
     ]
    }
   ],
   "source": [
    "print('Keith Prop with water = 0')\n",
    "print(100*sum(Keith['Wt: H2O']==0)/(sum(Keith['Wt: H2O']>0)+sum(Keith['Wt: H2O']==0)))\n",
    "print(100*sum(Keith['Wt: H2O']==0)/(len(Keith)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrelli Prop with water = 0\n",
      "66.23529411764706\n",
      "66.23529411764706\n"
     ]
    }
   ],
   "source": [
    "Petrelli_nan=Petrelli.fillna(0)\n",
    "print('Petrelli Prop with water = 0')\n",
    "print(100*sum(Petrelli_nan['H2O_Liq']==0)/(sum(Petrelli_nan['H2O_Liq']>0)+sum(Petrelli_nan['H2O_Liq']==0)))\n",
    "print(100*sum(Petrelli_nan['H2O_Liq']==0)/(len(Petrelli_nan)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prop with P2O5 = 0\n",
      "56.06936416184971\n",
      "53.93169181890389\n"
     ]
    }
   ],
   "source": [
    "print('Prop with P2O5 = 0')\n",
    "print(100*sum(Keith['Wt: P2O5_Liq']==0)/(sum(Keith['Wt: P2O5_Liq']>0)+sum(Keith['Wt: P2O5_Liq']==0)))\n",
    "print(100*sum(Keith['Wt: P2O5_Liq']==0)/(len(Keith)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text about missing elements from Petrelli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrelli Prop with P2O5 = 0\n",
      "54.470588235294116\n",
      "54.470588235294116\n"
     ]
    }
   ],
   "source": [
    "Petrelli_nan=Petrelli.fillna(0)\n",
    "print('Petrelli Prop with P2O5 = 0')\n",
    "print(100*sum(Petrelli_nan['P2O5_Liq']==0)/(sum(Petrelli_nan['P2O5_Liq']>0)+sum(Petrelli_nan['P2O5_Liq']==0)))\n",
    "print(100*sum(Petrelli_nan['P2O5_Liq']==0)/(len(Petrelli_nan)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrelli Prop with Cr2O3 Glass = 0\n",
      "60.94117647058823\n",
      "60.94117647058823\n"
     ]
    }
   ],
   "source": [
    "Petrelli_nan=Petrelli.fillna(0)\n",
    "print('Petrelli Prop with Cr2O3 Glass = 0')\n",
    "print(100*sum(Petrelli_nan['Cr2O3_Liq']==0)/(sum(Petrelli_nan['Cr2O3_Liq']>0)+sum(Petrelli_nan['Cr2O3_Liq']==0)))\n",
    "print(100*sum(Petrelli_nan['Cr2O3_Liq']==0)/(len(Petrelli_nan)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrelli Prop with K2O Cpx = 0\n",
      "71.17647058823529\n",
      "71.17647058823529\n"
     ]
    }
   ],
   "source": [
    "Petrelli_nan=Petrelli.fillna(0)\n",
    "print('Petrelli Prop with K2O Cpx = 0')\n",
    "print(100*sum(Petrelli_nan['K2O_Cpx']==0)/(sum(Petrelli_nan['K2O_Cpx']>0)+sum(Petrelli_nan['K2O_Cpx']==0)))\n",
    "print(100*sum(Petrelli_nan['K2O_Cpx']==0)/(len(Petrelli_nan)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text about remaining elements after list wise deletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Petrelli_Cpx_na=Petrelli_Cpx.fillna(0)\n",
    "Petrelli_Liq_na=Petrelli_Liq.fillna(0)\n",
    "cpx_liq_combo=pd.concat([Petrelli_Cpx_na, Petrelli_Liq_na], axis=1)\n",
    "df=pd.DataFrame(data={\n",
    "                                'SiO2_Liq': cpx_liq_combo['SiO2_Liq'],\n",
    "                                'TiO2_Liq': cpx_liq_combo['TiO2_Liq'],\n",
    "                                'Al2O3_Liq': cpx_liq_combo['Al2O3_Liq'],\n",
    "                                'FeOt_Liq': cpx_liq_combo['FeOt_Liq'],\n",
    "                                'MnO_Liq': cpx_liq_combo['MnO_Liq'],\n",
    "                                'MgO_Liq': cpx_liq_combo['MgO_Liq'],\n",
    "                                'CaO_Liq': cpx_liq_combo['CaO_Liq'],\n",
    "                                'Na2O_Liq': cpx_liq_combo['Na2O_Liq'],\n",
    "                                'K2O_Liq': cpx_liq_combo['K2O_Liq'],\n",
    "                                'Cr2O3_Liq': cpx_liq_combo['Cr2O3_Liq'],\n",
    "                                'P2O5_Liq': cpx_liq_combo['P2O5_Liq'],\n",
    "                                'H2O_Liq': cpx_liq_combo['H2O_Liq'],\n",
    "                                'SiO2_Cpx': cpx_liq_combo['SiO2_Cpx'],\n",
    "                                'TiO2_Cpx': cpx_liq_combo['TiO2_Cpx'],\n",
    "                                'Al2O3_Cpx': cpx_liq_combo['Al2O3_Cpx'],\n",
    "                                'FeOt_Cpx': cpx_liq_combo['FeOt_Cpx'],\n",
    "                                'MnO_Cpx': cpx_liq_combo['MnO_Cpx'],\n",
    "                                'MgO_Cpx': cpx_liq_combo['MgO_Cpx'],\n",
    "                                'CaO_Cpx': cpx_liq_combo['CaO_Cpx'],\n",
    "                                'Na2O_Cpx': cpx_liq_combo['Na2O_Cpx'],\n",
    "                                'K2O_Cpx': cpx_liq_combo['K2O_Cpx'],\n",
    "                                'Cr2O3_Cpx': cpx_liq_combo['Cr2O3_Cpx'],\n",
    "    })\n",
    "\n",
    "    \n",
    "mask = (df != 0).all(axis=1)\n",
    "non_zero_rows = mask.sum()\n",
    "non_zero_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Petrelli_Cpx_na=Petrelli_Cpx.fillna(0)\n",
    "Petrelli_Liq_na=Petrelli_Liq.fillna(0)\n",
    "cpx_liq_combo=pd.concat([Petrelli_Cpx_na, Petrelli_Liq_na], axis=1)\n",
    "df=pd.DataFrame(data={\n",
    "                                'SiO2_Cpx': cpx_liq_combo['SiO2_Cpx'],\n",
    "                                'TiO2_Cpx': cpx_liq_combo['TiO2_Cpx'],\n",
    "                                'Al2O3_Cpx': cpx_liq_combo['Al2O3_Cpx'],\n",
    "                                'FeOt_Cpx': cpx_liq_combo['FeOt_Cpx'],\n",
    "                                'MnO_Cpx': cpx_liq_combo['MnO_Cpx'],\n",
    "                                'MgO_Cpx': cpx_liq_combo['MgO_Cpx'],\n",
    "                                'CaO_Cpx': cpx_liq_combo['CaO_Cpx'],\n",
    "                                'Na2O_Cpx': cpx_liq_combo['Na2O_Cpx'],\n",
    "                                'K2O_Cpx': cpx_liq_combo['K2O_Cpx'],\n",
    "                                'Cr2O3_Cpx': cpx_liq_combo['Cr2O3_Cpx'],\n",
    "    })\n",
    "\n",
    "    \n",
    "mask = (df != 0).all(axis=1)\n",
    "non_zero_rows = mask.sum()\n",
    "non_zero_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cpx_liq_combo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text about number of unique laboratories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique labs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of unique labs')\n",
    "len(Keith['Laboratory'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of analyses at MIT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.25258141382049243"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of analyses at MIT')\n",
    "sum(Keith['Laboratory']=='MIT')/len(Keith)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of analyses at Penn State\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.07148530579825259"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of analyses at Penn State')\n",
    "sum(Keith['Laboratory']=='Pennsylvania State University')/len(Keith)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of analyses at Lamont\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.06433677521842732"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of analyses at Lamont')\n",
    "sum(Keith['Laboratory']=='Lamont-Doherty Earth Observatory')/len(Keith)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of analyses at Hannover\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.04289118347895155"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of analyses at Hannover')\n",
    "sum(Keith['Laboratory']=='University of Hannover')/len(Keith)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of analyses at UO\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.04924543288324067"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of analyses at UO')\n",
    "sum(Keith['Laboratory']=='University of Oregon')/len(Keith)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading my newly compiled experimental dataset and finding missing data amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Fe3FeT from input file to calculate Kd Fe-Mg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_28972\\2907548969.py:7: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  notinKeith=(cpx_Glass['Citation_x'].str.contains('Mercer, C.N., and Johnston, A.D. (2008)'))|(cpx_Glass['Citation_x'].str.contains('Alonso-Perez, R., Müntener, O., and Ulmer, P. (2009)'))\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  new_ix = Index(new_ix)\n"
     ]
    }
   ],
   "source": [
    "out=pt.import_excel('Supporting_Data_1.xlsx', sheet_name='Cpx-Liq')\n",
    "myCpxs1=out['Cpxs']\n",
    "myLiquids1=out['Liqs']\n",
    "cpx_Glass=out['my_input']\n",
    "Cpx_Glass=cpx_Glass\n",
    "notinLEPR= cpx_Glass['Citation_x'].str.contains('notinLEPR')\n",
    "notinKeith=(cpx_Glass['Citation_x'].str.contains('Mercer, C.N., and Johnston, A.D. (2008)'))|(cpx_Glass['Citation_x'].str.contains('Alonso-Perez, R., Müntener, O., and Ulmer, P. (2009)'))\n",
    "newMask=notinLEPR|notinKeith\n",
    "OldMask=~newMask\n",
    "newMask=newMask.fillna(False)\n",
    "\n",
    "mask_1GPa=cpx_Glass['P_kbar_x']<13\n",
    "mask_1GPa=mask_1GPa.fillna(False)\n",
    "\n",
    "Press_new=mask_1GPa&newMask\n",
    "Press_new=Press_new.fillna(False)\n",
    "\n",
    "eq_tests=pt.calculate_cpx_liq_eq_tests(liq_comps=myLiquids1, cpx_comps=myCpxs1, P=cpx_Glass['P_kbar_x'], T=cpx_Glass['T_K_x'])\n",
    "\n",
    "sigma=1.5\n",
    "Eq_Mask=((eq_tests['Delta_EnFs_Mollo13']<0.05*sigma)\n",
    "        &(eq_tests['Delta_Kd_Put2008']<0.08))\n",
    "Cpx_Glass_Filt=Cpx_Glass.loc[Press_new]\n",
    "xT=cpx_Glass['T_K_x'].loc[Press_new&Eq_Mask]\n",
    "x=cpx_Glass['P_kbar_x'].loc[Press_new&Eq_Mask]\n",
    "x_All=cpx_Glass['P_kbar_x'].loc[Press_new]\n",
    "xT_All=cpx_Glass['T_K_x'].loc[Press_new]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text about ArcPL experiment prevalence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tokyo', 'MIT', 'Macquarie', 0, 'Hannover', 'Orleans', 'ETH',\n",
       "       'USGS', 'Bristol', 'Menlo Park', 'NHM',\n",
       "       'Geological Survey of Japan'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cpx_Glass_Filt['Laboratory_x'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "print(sum(Cpx_Glass_Filt['Laboratory_x']=='MIT')/len(Cpx_Glass_Filt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n"
     ]
    }
   ],
   "source": [
    "print(sum(Cpx_Glass_Filt['Laboratory_x']=='Hannover')/len(Cpx_Glass_Filt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.83673469387755"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NoCr_Cpx_New=Cpx_Glass_Filt['Cr2O3_Cpx']==0\n",
    "Cr_Cpx_New=Cpx_Glass_Filt['Cr2O3_Cpx']>0\n",
    "\n",
    "100*sum(NoCr_Cpx_New)/(sum(Cr_Cpx_New)+sum(NoCr_Cpx_New))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NoCr_Liq_New=Cpx_Glass_Filt['Cr2O3_Liq']==0\n",
    "Cr_Liq_New=Cpx_Glass_Filt['Cr2O3_Liq']>0\n",
    "\n",
    "100*sum(NoCr_Liq_New)/(sum(Cr_Liq_New)+sum(NoCr_Liq_New))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What ones don't report Cr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hamada2008_notinLEPR', 'Erdmann2016_notinLEPR',\n",
       "       'Berndt2005_notinLEPR', 'Costa2004_notinLEPR',\n",
       "       'Mandler2013_notinLEPR', 'Nandekar2014_notinLEPR',\n",
       "       'Blatter2013_notinLEPR', 'Melekhova2015_notinLEPR',\n",
       "       'Ulmer2018_notinLEPR', 'Almeev2013_notinLEPR',\n",
       "       'Andujar2015_notinLEPR', 'Cadoux2014_notinLEPR',\n",
       "       'Parat2014_notinLEPR', 'Sisson2005_notinLEPR',\n",
       "       'Koepke2018_notinLEPR', 'Husen2016_notinLEPR',\n",
       "       'Erdman_Koepke2016_notinLEPR', 'Marxer2022_notinLEPR',\n",
       "       'Nakatani2022_notinLEPR'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hamada, all zeros- Use synthetic starting mateiral, dont seem to add any Cr\n",
    "# Erdmann, al zeros - Natural starting material, but don't report Cr for it. \n",
    "# Berndt all zeros - Natural and synthetic starting materials - No mention of Cr. \n",
    "# Mandler all zeros - Starting material, mafic pumice, has <5 ppm Cr basd on whole-rock. but with Kd of 10-50, could\n",
    "# be 250 ppm of Cr = 0.025\n",
    "# Nandekar all zeros - synthetic apparent near-primary ol-tholeiite (10 wt% MgO), \n",
    "#but add no Cr even though it seems in nature there would be Cr\n",
    "# Blatter, does have most\n",
    "# Melekhova does have most\n",
    "# Ulmer, does have most\n",
    "# Almeev, has none - Synthetic starting material, with no Cr. \n",
    "# Andujar has none - Use natural Balos Basalt , can't find Cr data for it. \n",
    "# cadoux has none - Natural high Si samples. \n",
    "# Parat has none - mafic and crystal-poor Arenal-type basalt -187 ppm Cr. \n",
    "#Using a Kd of 30, means 0.5 wt% Cr expected\n",
    "\n",
    "# Sisson does have most\n",
    "# Koepke2018 has none - synthetic starting material, no Cr. \n",
    "# Husen has most\n",
    "# Erdman has none - basaltic andesite stage 4 dome clasts, no mention of Cr. \n",
    "Cpx_Glass['Citation_x'].loc[(Cpx_Glass['Cr2O3_Cpx']==0) & (Press_new)].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting values to loop over, this is using all of them, but you could do a subset for a larger dataset. \n",
    "index2=Cpx_Glass_Filt.index.values\n",
    "sam=np.ndarray.tolist(index2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Calculate P and T for measured Cr content for Cpx-Liq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Youve selected a P-independent function\n"
     ]
    }
   ],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "\n",
    "    # Cpx-Liq using measured Cr Contents.   \n",
    "\n",
    "    #Eq 31-33 \n",
    "    Eq3133M=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                     cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq31\", equationT=\"T_Put2008_eq33\",\n",
    "                                         )\n",
    "    Cr_31_T33_P_Meas=Eq3133M.P_kbar_calc\n",
    "    Cr_31_T33_T_Meas=Eq3133M.T_K_calc\n",
    "\n",
    "\n",
    "    # 32c 33\n",
    "\n",
    "\n",
    "    Eq32c33M=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                      cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq32c\", equationT=\"T_Put2008_eq33\")\n",
    "\n",
    "\n",
    "    Cr_32c_T33_P_Meas=Eq32c33M.P_kbar_calc\n",
    "    Cr_32c_T33_T_Meas=Eq32c33M.T_K_calc\n",
    "\n",
    "\n",
    "    # Neave and eq 33\n",
    "\n",
    "    NT33M=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Neave2017\", equationT=\"T_Put2008_eq33\")\n",
    "    Cr_Neave_T33_P_Meas=NT33M.P_kbar_calc\n",
    "    Cr_Neave_T33_T_Meas=NT33M.T_K_calc\n",
    "\n",
    "\n",
    "    # 32c 1996\n",
    "\n",
    "    Eq32c1996M=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                      cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq32c\", equationT=\"T_Put1996_eqT1\")\n",
    "\n",
    "\n",
    "    Cr_32c_1996_T_Meas=Eq32c1996M.T_K_calc\n",
    "    Cr_32c_1996_P_Meas=Eq32c1996M.P_kbar_calc\n",
    "\n",
    "\n",
    "\n",
    "    # Petrelli\n",
    "    Cr_Pet_T_Meas=pt.calculate_cpx_liq_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationT=\"T_Petrelli2020_Cpx_Liq\").T_K_calc\n",
    "\n",
    "    Cr_Pet_P_Meas=pt.calculate_cpx_liq_press(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs1.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Petrelli2020_Cpx_Liq\").P_kbar_calc \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same calculations with Cr=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Youve selected a P-independent function\n"
     ]
    }
   ],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    \n",
    "    myCpxs2=myCpxs1.copy()\n",
    "    myCpxs2['Cr2O3_Cpx']=0\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Cpx-Liq using Cr=0 \n",
    "\n",
    "    #Eq 31-33 \n",
    "    Eq3133Z=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                     cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq31\", equationT=\"T_Put2008_eq33\",\n",
    "                                         )\n",
    "    Cr_31_T33_P_NoCr=Eq3133Z.P_kbar_calc\n",
    "    Cr_31_T33_T_NoCr=Eq3133Z.T_K_calc\n",
    "\n",
    "\n",
    "    # 32c 33\n",
    "\n",
    "\n",
    "    Eq32c33Z=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                      cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq32c\", equationT=\"T_Put2008_eq33\")\n",
    "\n",
    "\n",
    "    Cr_32c_T33_P_NoCr=Eq32c33Z.P_kbar_calc\n",
    "    Cr_32c_T33_T_NoCr=Eq32c33Z.T_K_calc\n",
    "\n",
    "\n",
    "    # Neave and eq 33\n",
    "\n",
    "    NT33Z=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Neave2017\", equationT=\"T_Put2008_eq33\")\n",
    "    Cr_Neave_T33_P_NoCr=NT33Z.P_kbar_calc\n",
    "    Cr_Neave_T33_T_NoCr=NT33Z.T_K_calc\n",
    "\n",
    "\n",
    "    # 32c 1996\n",
    "\n",
    "    Eq32c1996Z=pt.calculate_cpx_liq_press_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                      cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Put2008_eq32c\", equationT=\"T_Put1996_eqT1\")\n",
    "\n",
    "\n",
    "    Cr_32c_1996_T_NoCr=Eq32c1996Z.T_K_calc\n",
    "    Cr_32c_1996_P_NoCr=Eq32c1996Z.P_kbar_calc\n",
    "\n",
    "\n",
    "\n",
    "    # Petrelli\n",
    "    Cr_Pet_T_NoCr=pt.calculate_cpx_liq_temp(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationT=\"T_Petrelli2020_Cpx_Liq\").T_K_calc\n",
    "\n",
    "    Cr_Pet_P_NoCr=pt.calculate_cpx_liq_press(liq_comps=myLiquids1.loc[sam].reset_index(drop=True), \n",
    "                                                        cpx_comps=myCpxs2.loc[sam].reset_index(drop=True), \n",
    "                                          equationP=\"P_Petrelli2020_Cpx_Liq\").P_kbar_calc \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cpx-only calculations no Cr. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weve reset the index of your Cpx compositions for Petrelli ML calculations, as non-consecative indexes or missing indices cause problems for iteration\n",
      "Weve reset the index of your Cpx compositions for Petrelli ML calculations, as non-consecative indexes or missing indices cause problems for iteration\n",
      "Weve reset the index of your Cpx compositions for Petrelli ML calculations, as non-consecative indexes or missing indices cause problems for iteration\n",
      "Weve reset the index of your Cpx compositions for Petrelli ML calculations, as non-consecative indexes or missing indices cause problems for iteration\n"
     ]
    }
   ],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    # Cpx-only pressure calculations with Cr=0 (Cpxs2)\n",
    "    Cr_cpx_comp_Wang21_NoCr=pt.calculate_cpx_only_press(cpx_comps=myCpxs2.loc[sam],\n",
    "                            equationP=\"P_Wang2021_eq1\")\n",
    "\n",
    "    Cr_cpx_comp_Pet_NoCr=pt.calculate_cpx_only_press(cpx_comps=myCpxs2.loc[sam], \n",
    "                                    equationP=\"P_Petrelli2020_Cpx_only\").P_kbar_calc\n",
    "\n",
    "    Cr_cpx_comp_Put2008_NoCr=pt.calculate_cpx_only_press_temp(cpx_comps=myCpxs2.loc[sam],\n",
    "                                        equationP=\"P_Put2008_eq32b\",\n",
    "                                        equationT=\"T_Put2008_eq32d\").P_kbar_calc\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Cpx-only temperature calculations with Cr=0 (Cpxs2)\n",
    "    Cr_cpx_comp_Wang21_T_NoCr=pt.calculate_cpx_only_temp(cpx_comps=myCpxs2.loc[sam], \n",
    "                                                          equationT=\"T_Wang2021_eq2\")\n",
    "\n",
    "    Cr_cpx_comp_Pet_T_NoCr=pt.calculate_cpx_only_temp(cpx_comps=myCpxs2.loc[sam], \n",
    "                                    equationT=\"T_Petrelli2020_Cpx_only\").T_K_calc\n",
    "\n",
    "    Cr_cpx_comp_Put2008_T_NoCr=pt.calculate_cpx_only_press_temp(cpx_comps=myCpxs2.loc[sam],\n",
    "                                            equationP=\"P_Put2008_eq32b\",\n",
    "                                            equationT=\"T_Put2008_eq32d\").T_K_calc\n",
    "\n",
    "    # Cpx-only pressure calculations with Cr=meas (Cpxs1)\n",
    "\n",
    "    Cr_cpx_comp_Wang21_Meas=pt.calculate_cpx_only_press(cpx_comps=myCpxs1.loc[sam], \n",
    "                                                        equationP=\"P_Wang2021_eq1\")\n",
    "\n",
    "    Cr_cpx_comp_Pet_Meas=pt.calculate_cpx_only_press(cpx_comps=myCpxs1.loc[sam], \n",
    "                                       equationP=\"P_Petrelli2020_Cpx_only\").P_kbar_calc\n",
    "\n",
    "    Cr_cpx_comp_Put2008_Meas=pt.calculate_cpx_only_press_temp(cpx_comps=myCpxs1.loc[sam],\n",
    "                                                          equationP=\"P_Put2008_eq32b\",\n",
    "                                 equationT=\"T_Put2008_eq32d\").P_kbar_calc\n",
    "\n",
    "\n",
    "    # Cpx-only temperature calculations with Cr=meas (Cpxs1)\n",
    "\n",
    "    Cr_cpx_comp_Wang21_T_Meas=pt.calculate_cpx_only_temp(cpx_comps=myCpxs1.loc[sam], \n",
    "                                                          equationT=\"T_Wang2021_eq2\")\n",
    "    Cr_cpx_comp_Pet_T_Meas=pt.calculate_cpx_only_temp(cpx_comps=myCpxs1.loc[sam], \n",
    "                                                         equationT=\"T_Petrelli2020_Cpx_only\").T_K_calc\n",
    "\n",
    "    Cr_cpx_comp_Put2008_T_Meas=pt.calculate_cpx_only_press_temp(cpx_comps=myCpxs1.loc[sam],\n",
    "                                                                equationP=\"P_Put2008_eq32b\",\n",
    "                                                                 equationT=\"T_Put2008_eq32d\").T_K_calc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What do cation sums do     myCpxs2=myCpxs1.copy()\n",
    "    myCpxs2['Cr2O3_Cpx']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  new_ix = Index(new_ix)\n",
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:2120: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  new_ix = Index(new_ix)\n"
     ]
    }
   ],
   "source": [
    "cat_sums_original=pt.calculate_clinopyroxene_components(cpx_comps=myCpxs1)\n",
    "myCpxs2=myCpxs1.copy()\n",
    "myCpxs2['Cr2O3_Cpx']=0\n",
    "cat_sums_noCr=pt.calculate_clinopyroxene_components(cpx_comps=myCpxs2)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cpx_with_Cr=cat_sums_original.loc[myCpxs1['Cr2O3_Cpx']>0]\n",
    "Cpx_with_noCr=cat_sums_original.loc[myCpxs1['Cr2O3_Cpx']==0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>SiO2_Cpx</th>\n",
       "      <th>TiO2_Cpx</th>\n",
       "      <th>Al2O3_Cpx</th>\n",
       "      <th>FeOt_Cpx</th>\n",
       "      <th>MnO_Cpx</th>\n",
       "      <th>MgO_Cpx</th>\n",
       "      <th>CaO_Cpx</th>\n",
       "      <th>Na2O_Cpx</th>\n",
       "      <th>K2O_Cpx</th>\n",
       "      <th>Cr2O3_Cpx</th>\n",
       "      <th>Sample_ID_Cpx</th>\n",
       "      <th>Si_Cpx_cat_6ox</th>\n",
       "      <th>Mg_Cpx_cat_6ox</th>\n",
       "      <th>Fet_Cpx_cat_6ox</th>\n",
       "      <th>Ca_Cpx_cat_6ox</th>\n",
       "      <th>Al_Cpx_cat_6ox</th>\n",
       "      <th>Na_Cpx_cat_6ox</th>\n",
       "      <th>K_Cpx_cat_6ox</th>\n",
       "      <th>Mn_Cpx_cat_6ox</th>\n",
       "      <th>Ti_Cpx_cat_6ox</th>\n",
       "      <th>Cr_Cpx_cat_6ox</th>\n",
       "      <th>oxy_renorm_factor</th>\n",
       "      <th>Al_IV_cat_6ox</th>\n",
       "      <th>Al_VI_cat_6ox</th>\n",
       "      <th>En_Simple_MgFeCa_Cpx</th>\n",
       "      <th>Fs_Simple_MgFeCa_Cpx</th>\n",
       "      <th>Wo_Simple_MgFeCa_Cpx</th>\n",
       "      <th>Cation_Sum_Cpx</th>\n",
       "      <th>Ca_CaMgFe</th>\n",
       "      <th>Lindley_Fe3_Cpx</th>\n",
       "      <th>Lindley_Fe2_Cpx</th>\n",
       "      <th>Lindley_Fe3_Cpx_prop</th>\n",
       "      <th>CrCaTs</th>\n",
       "      <th>a_cpx_En</th>\n",
       "      <th>Mgno_Cpx</th>\n",
       "      <th>Jd</th>\n",
       "      <th>Jd_from 0=Na, 1=Al</th>\n",
       "      <th>CaTs</th>\n",
       "      <th>CaTi</th>\n",
       "      <th>DiHd_1996</th>\n",
       "      <th>EnFs</th>\n",
       "      <th>DiHd_2003</th>\n",
       "      <th>Di_Cpx</th>\n",
       "      <th>FeIII_Wang21</th>\n",
       "      <th>FeII_Wang21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.900002</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>9.14000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>0.30000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.299999</td>\n",
       "      <td>0.59000</td>\n",
       "      <td>0.02000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.780864</td>\n",
       "      <td>0.566065</td>\n",
       "      <td>0.339780</td>\n",
       "      <td>0.825899</td>\n",
       "      <td>0.409036</td>\n",
       "      <td>0.043437</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.009649</td>\n",
       "      <td>0.030561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219136</td>\n",
       "      <td>0.189900</td>\n",
       "      <td>0.326876</td>\n",
       "      <td>0.196207</td>\n",
       "      <td>0.476918</td>\n",
       "      <td>4.006260</td>\n",
       "      <td>0.476918</td>\n",
       "      <td>0.011550</td>\n",
       "      <td>0.328230</td>\n",
       "      <td>0.033994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100291</td>\n",
       "      <td>0.624895</td>\n",
       "      <td>0.043437</td>\n",
       "      <td>0</td>\n",
       "      <td>0.146463</td>\n",
       "      <td>0.036336</td>\n",
       "      <td>0.643100</td>\n",
       "      <td>0.131373</td>\n",
       "      <td>0.643100</td>\n",
       "      <td>0.397640</td>\n",
       "      <td>0.011550</td>\n",
       "      <td>0.328230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.099998</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>8.32000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>0.29000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>0.71000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.816364</td>\n",
       "      <td>0.591093</td>\n",
       "      <td>0.388435</td>\n",
       "      <td>0.748519</td>\n",
       "      <td>0.370287</td>\n",
       "      <td>0.051983</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.009276</td>\n",
       "      <td>0.024144</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.183636</td>\n",
       "      <td>0.186650</td>\n",
       "      <td>0.342058</td>\n",
       "      <td>0.224783</td>\n",
       "      <td>0.433159</td>\n",
       "      <td>4.000582</td>\n",
       "      <td>0.433159</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.387754</td>\n",
       "      <td>0.001754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156949</td>\n",
       "      <td>0.603438</td>\n",
       "      <td>0.051983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.134667</td>\n",
       "      <td>0.024485</td>\n",
       "      <td>0.589367</td>\n",
       "      <td>0.195081</td>\n",
       "      <td>0.589367</td>\n",
       "      <td>0.352315</td>\n",
       "      <td>0.000681</td>\n",
       "      <td>0.387754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.299999</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>7.80000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.805281</td>\n",
       "      <td>0.679776</td>\n",
       "      <td>0.350084</td>\n",
       "      <td>0.768904</td>\n",
       "      <td>0.343597</td>\n",
       "      <td>0.043481</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>0.012663</td>\n",
       "      <td>0.019961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.194719</td>\n",
       "      <td>0.148878</td>\n",
       "      <td>0.377913</td>\n",
       "      <td>0.194625</td>\n",
       "      <td>0.427462</td>\n",
       "      <td>4.025654</td>\n",
       "      <td>0.427462</td>\n",
       "      <td>0.049400</td>\n",
       "      <td>0.300684</td>\n",
       "      <td>0.141109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149589</td>\n",
       "      <td>0.660059</td>\n",
       "      <td>0.043481</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105397</td>\n",
       "      <td>0.044661</td>\n",
       "      <td>0.618846</td>\n",
       "      <td>0.205507</td>\n",
       "      <td>0.618846</td>\n",
       "      <td>0.403518</td>\n",
       "      <td>0.049400</td>\n",
       "      <td>0.300684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.799999</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>6.53000</td>\n",
       "      <td>16.200001</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>0.64000</td>\n",
       "      <td>0.07000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.860560</td>\n",
       "      <td>0.574056</td>\n",
       "      <td>0.516529</td>\n",
       "      <td>0.669948</td>\n",
       "      <td>0.293423</td>\n",
       "      <td>0.047310</td>\n",
       "      <td>0.003405</td>\n",
       "      <td>0.016147</td>\n",
       "      <td>0.018354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.139440</td>\n",
       "      <td>0.153983</td>\n",
       "      <td>0.326069</td>\n",
       "      <td>0.293394</td>\n",
       "      <td>0.380537</td>\n",
       "      <td>3.999731</td>\n",
       "      <td>0.380537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.516529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.231272</td>\n",
       "      <td>0.526366</td>\n",
       "      <td>0.047310</td>\n",
       "      <td>0</td>\n",
       "      <td>0.106674</td>\n",
       "      <td>0.016383</td>\n",
       "      <td>0.546891</td>\n",
       "      <td>0.271847</td>\n",
       "      <td>0.546891</td>\n",
       "      <td>0.283669</td>\n",
       "      <td>-0.003942</td>\n",
       "      <td>0.520472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>51.750000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.43000</td>\n",
       "      <td>6.050000</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>16.510000</td>\n",
       "      <td>21.330000</td>\n",
       "      <td>0.33000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1.926453</td>\n",
       "      <td>0.916228</td>\n",
       "      <td>0.188347</td>\n",
       "      <td>0.850770</td>\n",
       "      <td>0.106613</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.073547</td>\n",
       "      <td>0.033066</td>\n",
       "      <td>0.468576</td>\n",
       "      <td>0.096324</td>\n",
       "      <td>0.435099</td>\n",
       "      <td>4.024870</td>\n",
       "      <td>0.435099</td>\n",
       "      <td>0.049739</td>\n",
       "      <td>0.138608</td>\n",
       "      <td>0.264082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117233</td>\n",
       "      <td>0.829480</td>\n",
       "      <td>0.023818</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009248</td>\n",
       "      <td>0.032150</td>\n",
       "      <td>0.809372</td>\n",
       "      <td>0.147602</td>\n",
       "      <td>0.809372</td>\n",
       "      <td>0.668119</td>\n",
       "      <td>0.049739</td>\n",
       "      <td>0.138608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>52.050896</td>\n",
       "      <td>0.412600</td>\n",
       "      <td>1.57460</td>\n",
       "      <td>11.582601</td>\n",
       "      <td>0.85630</td>\n",
       "      <td>14.671701</td>\n",
       "      <td>18.206402</td>\n",
       "      <td>0.20410</td>\n",
       "      <td>0.01440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>861</td>\n",
       "      <td>1.957975</td>\n",
       "      <td>0.822750</td>\n",
       "      <td>0.364369</td>\n",
       "      <td>0.733798</td>\n",
       "      <td>0.069808</td>\n",
       "      <td>0.014886</td>\n",
       "      <td>0.000691</td>\n",
       "      <td>0.027283</td>\n",
       "      <td>0.011674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042025</td>\n",
       "      <td>0.027784</td>\n",
       "      <td>0.428311</td>\n",
       "      <td>0.189685</td>\n",
       "      <td>0.382004</td>\n",
       "      <td>4.003234</td>\n",
       "      <td>0.382004</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>0.358591</td>\n",
       "      <td>0.015857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.239926</td>\n",
       "      <td>0.693058</td>\n",
       "      <td>0.014886</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012898</td>\n",
       "      <td>0.014563</td>\n",
       "      <td>0.706336</td>\n",
       "      <td>0.240391</td>\n",
       "      <td>0.706336</td>\n",
       "      <td>0.478539</td>\n",
       "      <td>0.005778</td>\n",
       "      <td>0.358591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>52.040802</td>\n",
       "      <td>0.540200</td>\n",
       "      <td>1.86220</td>\n",
       "      <td>10.797400</td>\n",
       "      <td>0.82140</td>\n",
       "      <td>14.709801</td>\n",
       "      <td>18.270199</td>\n",
       "      <td>0.22480</td>\n",
       "      <td>0.02500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>862</td>\n",
       "      <td>1.955806</td>\n",
       "      <td>0.824133</td>\n",
       "      <td>0.339357</td>\n",
       "      <td>0.735696</td>\n",
       "      <td>0.082483</td>\n",
       "      <td>0.016380</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.026147</td>\n",
       "      <td>0.015271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>0.038289</td>\n",
       "      <td>0.433940</td>\n",
       "      <td>0.178686</td>\n",
       "      <td>0.387374</td>\n",
       "      <td>3.996471</td>\n",
       "      <td>0.387374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.339357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.234381</td>\n",
       "      <td>0.708321</td>\n",
       "      <td>0.016380</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021909</td>\n",
       "      <td>0.011143</td>\n",
       "      <td>0.702644</td>\n",
       "      <td>0.230423</td>\n",
       "      <td>0.702644</td>\n",
       "      <td>0.486764</td>\n",
       "      <td>-0.008256</td>\n",
       "      <td>0.347613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>52.050838</td>\n",
       "      <td>0.570917</td>\n",
       "      <td>1.97225</td>\n",
       "      <td>10.900917</td>\n",
       "      <td>0.69875</td>\n",
       "      <td>14.550500</td>\n",
       "      <td>18.578165</td>\n",
       "      <td>0.23575</td>\n",
       "      <td>0.01800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>863</td>\n",
       "      <td>1.951999</td>\n",
       "      <td>0.813464</td>\n",
       "      <td>0.341878</td>\n",
       "      <td>0.746497</td>\n",
       "      <td>0.087171</td>\n",
       "      <td>0.017142</td>\n",
       "      <td>0.000861</td>\n",
       "      <td>0.022195</td>\n",
       "      <td>0.016105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048001</td>\n",
       "      <td>0.039170</td>\n",
       "      <td>0.427725</td>\n",
       "      <td>0.179762</td>\n",
       "      <td>0.392513</td>\n",
       "      <td>3.997312</td>\n",
       "      <td>0.392513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.341878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223116</td>\n",
       "      <td>0.704083</td>\n",
       "      <td>0.017142</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022029</td>\n",
       "      <td>0.012986</td>\n",
       "      <td>0.711482</td>\n",
       "      <td>0.221930</td>\n",
       "      <td>0.711482</td>\n",
       "      <td>0.491505</td>\n",
       "      <td>-0.006238</td>\n",
       "      <td>0.348116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>53.134750</td>\n",
       "      <td>0.358250</td>\n",
       "      <td>1.27350</td>\n",
       "      <td>9.903875</td>\n",
       "      <td>0.72850</td>\n",
       "      <td>15.471625</td>\n",
       "      <td>18.879126</td>\n",
       "      <td>0.20975</td>\n",
       "      <td>0.00625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>864</td>\n",
       "      <td>1.974544</td>\n",
       "      <td>0.857102</td>\n",
       "      <td>0.307786</td>\n",
       "      <td>0.751698</td>\n",
       "      <td>0.055776</td>\n",
       "      <td>0.015113</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.022930</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025456</td>\n",
       "      <td>0.030320</td>\n",
       "      <td>0.447203</td>\n",
       "      <td>0.160591</td>\n",
       "      <td>0.392207</td>\n",
       "      <td>3.995259</td>\n",
       "      <td>0.392207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.307786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.224604</td>\n",
       "      <td>0.735774</td>\n",
       "      <td>0.015113</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015207</td>\n",
       "      <td>0.005124</td>\n",
       "      <td>0.731366</td>\n",
       "      <td>0.216761</td>\n",
       "      <td>0.731366</td>\n",
       "      <td>0.527737</td>\n",
       "      <td>-0.009779</td>\n",
       "      <td>0.317565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>52.857998</td>\n",
       "      <td>0.559000</td>\n",
       "      <td>2.09300</td>\n",
       "      <td>11.825000</td>\n",
       "      <td>0.74800</td>\n",
       "      <td>15.159500</td>\n",
       "      <td>17.040001</td>\n",
       "      <td>0.18950</td>\n",
       "      <td>0.00600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>865</td>\n",
       "      <td>1.959832</td>\n",
       "      <td>0.837918</td>\n",
       "      <td>0.366661</td>\n",
       "      <td>0.676941</td>\n",
       "      <td>0.091461</td>\n",
       "      <td>0.013623</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.023491</td>\n",
       "      <td>0.015590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040168</td>\n",
       "      <td>0.051293</td>\n",
       "      <td>0.445341</td>\n",
       "      <td>0.194875</td>\n",
       "      <td>0.359784</td>\n",
       "      <td>3.985801</td>\n",
       "      <td>0.359784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.292865</td>\n",
       "      <td>0.695603</td>\n",
       "      <td>0.013623</td>\n",
       "      <td>0</td>\n",
       "      <td>0.037670</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.638022</td>\n",
       "      <td>0.283279</td>\n",
       "      <td>0.638022</td>\n",
       "      <td>0.435326</td>\n",
       "      <td>-0.028682</td>\n",
       "      <td>0.395344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>469 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SiO2_Cpx  TiO2_Cpx  Al2O3_Cpx   FeOt_Cpx  MnO_Cpx    MgO_Cpx    CaO_Cpx  \\\n",
       "0    46.900002  1.070000    9.14000  10.700000  0.30000  10.000000  20.299999   \n",
       "1    48.099998  0.850000    8.32000  12.300000  0.29000  10.500000  18.500000   \n",
       "2    48.299999  0.710000    7.80000  11.200000  0.40000  12.200000  19.200001   \n",
       "3    48.799999  0.640000    6.53000  16.200001  0.50000  10.100000  16.400000   \n",
       "37   51.750000  0.260000    2.43000   6.050000  0.17000  16.510000  21.330000   \n",
       "..         ...       ...        ...        ...      ...        ...        ...   \n",
       "861  52.050896  0.412600    1.57460  11.582601  0.85630  14.671701  18.206402   \n",
       "862  52.040802  0.540200    1.86220  10.797400  0.82140  14.709801  18.270199   \n",
       "863  52.050838  0.570917    1.97225  10.900917  0.69875  14.550500  18.578165   \n",
       "864  53.134750  0.358250    1.27350   9.903875  0.72850  15.471625  18.879126   \n",
       "865  52.857998  0.559000    2.09300  11.825000  0.74800  15.159500  17.040001   \n",
       "\n",
       "     Na2O_Cpx  K2O_Cpx  Cr2O3_Cpx  Sample_ID_Cpx  Si_Cpx_cat_6ox  \\\n",
       "0     0.59000  0.02000        0.0              0        1.780864   \n",
       "1     0.71000  0.01000        0.0              1        1.816364   \n",
       "2     0.60000  0.04000        0.0              2        1.805281   \n",
       "3     0.64000  0.07000        0.0              3        1.860560   \n",
       "37    0.33000  0.00000        0.0             37        1.926453   \n",
       "..        ...      ...        ...            ...             ...   \n",
       "861   0.20410  0.01440        0.0            861        1.957975   \n",
       "862   0.22480  0.02500        0.0            862        1.955806   \n",
       "863   0.23575  0.01800        0.0            863        1.951999   \n",
       "864   0.20975  0.00625        0.0            864        1.974544   \n",
       "865   0.18950  0.00600        0.0            865        1.959832   \n",
       "\n",
       "     Mg_Cpx_cat_6ox  Fet_Cpx_cat_6ox  Ca_Cpx_cat_6ox  Al_Cpx_cat_6ox  \\\n",
       "0          0.566065         0.339780        0.825899        0.409036   \n",
       "1          0.591093         0.388435        0.748519        0.370287   \n",
       "2          0.679776         0.350084        0.768904        0.343597   \n",
       "3          0.574056         0.516529        0.669948        0.293423   \n",
       "37         0.916228         0.188347        0.850770        0.106613   \n",
       "..              ...              ...             ...             ...   \n",
       "861        0.822750         0.364369        0.733798        0.069808   \n",
       "862        0.824133         0.339357        0.735696        0.082483   \n",
       "863        0.813464         0.341878        0.746497        0.087171   \n",
       "864        0.857102         0.307786        0.751698        0.055776   \n",
       "865        0.837918         0.366661        0.676941        0.091461   \n",
       "\n",
       "     Na_Cpx_cat_6ox  K_Cpx_cat_6ox  Mn_Cpx_cat_6ox  Ti_Cpx_cat_6ox  \\\n",
       "0          0.043437       0.000969        0.009649        0.030561   \n",
       "1          0.051983       0.000482        0.009276        0.024144   \n",
       "2          0.043481       0.001907        0.012663        0.019961   \n",
       "3          0.047310       0.003405        0.016147        0.018354   \n",
       "37         0.023818       0.000000        0.005360        0.007280   \n",
       "..              ...            ...             ...             ...   \n",
       "861        0.014886       0.000691        0.027283        0.011674   \n",
       "862        0.016380       0.001199        0.026147        0.015271   \n",
       "863        0.017142       0.000861        0.022195        0.016105   \n",
       "864        0.015113       0.000296        0.022930        0.010014   \n",
       "865        0.013623       0.000284        0.023491        0.015590   \n",
       "\n",
       "     Cr_Cpx_cat_6ox  oxy_renorm_factor  Al_IV_cat_6ox  Al_VI_cat_6ox  \\\n",
       "0               0.0                0.0       0.219136       0.189900   \n",
       "1               0.0                0.0       0.183636       0.186650   \n",
       "2               0.0                0.0       0.194719       0.148878   \n",
       "3               0.0                0.0       0.139440       0.153983   \n",
       "37              0.0                0.0       0.073547       0.033066   \n",
       "..              ...                ...            ...            ...   \n",
       "861             0.0                0.0       0.042025       0.027784   \n",
       "862             0.0                0.0       0.044194       0.038289   \n",
       "863             0.0                0.0       0.048001       0.039170   \n",
       "864             0.0                0.0       0.025456       0.030320   \n",
       "865             0.0                0.0       0.040168       0.051293   \n",
       "\n",
       "     En_Simple_MgFeCa_Cpx  Fs_Simple_MgFeCa_Cpx  Wo_Simple_MgFeCa_Cpx  \\\n",
       "0                0.326876              0.196207              0.476918   \n",
       "1                0.342058              0.224783              0.433159   \n",
       "2                0.377913              0.194625              0.427462   \n",
       "3                0.326069              0.293394              0.380537   \n",
       "37               0.468576              0.096324              0.435099   \n",
       "..                    ...                   ...                   ...   \n",
       "861              0.428311              0.189685              0.382004   \n",
       "862              0.433940              0.178686              0.387374   \n",
       "863              0.427725              0.179762              0.392513   \n",
       "864              0.447203              0.160591              0.392207   \n",
       "865              0.445341              0.194875              0.359784   \n",
       "\n",
       "     Cation_Sum_Cpx  Ca_CaMgFe  Lindley_Fe3_Cpx  Lindley_Fe2_Cpx  \\\n",
       "0          4.006260   0.476918         0.011550         0.328230   \n",
       "1          4.000582   0.433159         0.000681         0.387754   \n",
       "2          4.025654   0.427462         0.049400         0.300684   \n",
       "3          3.999731   0.380537         0.000000         0.516529   \n",
       "37         4.024870   0.435099         0.049739         0.138608   \n",
       "..              ...        ...              ...              ...   \n",
       "861        4.003234   0.382004         0.005778         0.358591   \n",
       "862        3.996471   0.387374         0.000000         0.339357   \n",
       "863        3.997312   0.392513         0.000000         0.341878   \n",
       "864        3.995259   0.392207         0.000000         0.307786   \n",
       "865        3.985801   0.359784         0.000000         0.366661   \n",
       "\n",
       "     Lindley_Fe3_Cpx_prop  CrCaTs  a_cpx_En  Mgno_Cpx        Jd  \\\n",
       "0                0.033994     0.0  0.100291  0.624895  0.043437   \n",
       "1                0.001754     0.0  0.156949  0.603438  0.051983   \n",
       "2                0.141109     0.0  0.149589  0.660059  0.043481   \n",
       "3                0.000000     0.0  0.231272  0.526366  0.047310   \n",
       "37               0.264082     0.0  0.117233  0.829480  0.023818   \n",
       "..                    ...     ...       ...       ...       ...   \n",
       "861              0.015857     0.0  0.239926  0.693058  0.014886   \n",
       "862              0.000000     0.0  0.234381  0.708321  0.016380   \n",
       "863              0.000000     0.0  0.223116  0.704083  0.017142   \n",
       "864              0.000000     0.0  0.224604  0.735774  0.015113   \n",
       "865              0.000000     0.0  0.292865  0.695603  0.013623   \n",
       "\n",
       "     Jd_from 0=Na, 1=Al      CaTs      CaTi  DiHd_1996      EnFs  DiHd_2003  \\\n",
       "0                     0  0.146463  0.036336   0.643100  0.131373   0.643100   \n",
       "1                     0  0.134667  0.024485   0.589367  0.195081   0.589367   \n",
       "2                     0  0.105397  0.044661   0.618846  0.205507   0.618846   \n",
       "3                     0  0.106674  0.016383   0.546891  0.271847   0.546891   \n",
       "37                    0  0.009248  0.032150   0.809372  0.147602   0.809372   \n",
       "..                  ...       ...       ...        ...       ...        ...   \n",
       "861                   0  0.012898  0.014563   0.706336  0.240391   0.706336   \n",
       "862                   0  0.021909  0.011143   0.702644  0.230423   0.702644   \n",
       "863                   0  0.022029  0.012986   0.711482  0.221930   0.711482   \n",
       "864                   0  0.015207  0.005124   0.731366  0.216761   0.731366   \n",
       "865                   0  0.037670  0.001249   0.638022  0.283279   0.638022   \n",
       "\n",
       "       Di_Cpx  FeIII_Wang21  FeII_Wang21  \n",
       "0    0.397640      0.011550     0.328230  \n",
       "1    0.352315      0.000681     0.387754  \n",
       "2    0.403518      0.049400     0.300684  \n",
       "3    0.283669     -0.003942     0.520472  \n",
       "37   0.668119      0.049739     0.138608  \n",
       "..        ...           ...          ...  \n",
       "861  0.478539      0.005778     0.358591  \n",
       "862  0.486764     -0.008256     0.347613  \n",
       "863  0.491505     -0.006238     0.348116  \n",
       "864  0.527737     -0.009779     0.317565  \n",
       "865  0.435326     -0.028682     0.395344  \n",
       "\n",
       "[469 rows x 45 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cpx_with_noCr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9XncsIyMD27ZtQ/PmzTW1/+677zBw4ECUlpY63ffTTz/hnnvuQbdu3ZCZmYnc3FzMnj0bf/75J9asWeN1H5WEh4cDV6+6nU1DUpLugT0RERERERH5l1cB9rvvvoukpCS3W9R8mV0/e/YsnnrqKU0JXEpKSvDPf/4TaWlpLutyL168GFFRUXj//fcRFhaGhIQEGI1GTJ48GampqZqDeI9wNo2IiIiIiKhGCfHmQYsXL0ZMTAyys7NRVFSEsrIypz9Kq9pajRs3Dn379kWfPn1U2+7cuRPz5s3D7NmzsWjRIsU2u3btwqBBgxAWFibdlpiYiLKyMuzatcvrfioxm826tiMiIgJ4BpuIiCgYeBVg//jjj5g2bRq6d+9uF7TqYe3atThw4ABeeeUVTe1vvfVWnDp1CrNnz0atWs4L8oWFhTh9+jTi4uLsbm/YsCHq1auHvLw8XfotslqturYjIiICgP/7v/+Tvt6xY4cfe0JERESueLVFPDo6ulLOEJ8+fRrJyclYv349TCaTpsc0bdrU7f1iIFuvXj2n+6KioqSSJ65cunTJqU14eDjPUBMRUZW64447pK+7d+/ux54QEQUQpao97m4nqmReBdiDBg1CZmYmJk+erFtHbDYbxo4di4SEBAwbNky35y0rKwOgfCbcZrMhJMT9In7Pnj2dbpszZw7mzp2rS/+IiIiIiMgzJpMJEUYjipKSXLaJMBo1L9oR6cWrAPvpp59GQkIC7r33Xjz44INo3LixYqB65513an7OV199FYcPH8aRI0ekTODiGbPS0lKEhISoBsNKoqOjAUBxpfrSpUuoX7++28d/9tln6Nixo91tmlavOZtGREQ6mj9/PpKTk1G3bl1/d4WIyO9iY2NxLDcXFovFZRuTycQa2FTlvAqw48vLT+Xn5+PDDz902c6TRGdZWVmwWCyIiYlxuq927dperxrXqVMHTZs2xYkTJ+xuP3fuHC5cuID27du7fXzdunUVt5e7Eh0dDYSEAG5m0xASIgX+REREWqxYsQKzZ8/2dzeIKAAUFBQwsIQQZNeE90nBxasA+7nnnvOpDJeS119/HRcvXrS7bd68eThw4AC2b9+OJk2aeP3cffv2xY4dO7Bs2TJp9TkrKwuhoaHo3bu3T/1WVFamWgebiIjIE0lJSZgxY4a0c0w+DvsyRhJRcCkoKEDb+HgUXb7ssk2E0YhjubkMPon8wKsAuzLOH7dt29bptgYNGiAsLAy33HILAGGb99GjR9G6dWs0bNhQ83PPmDED7777LgYMGIDk5GTk5eXh2WefxYQJE9CsWTPd3gPALOJERFQ5Vq1ahatXr2L58uVScG2z2WAwGHwqjUlEwcVisQjBtZvFnKKkJFgsFgbYRH7gVYAtKioqwueff45Tp05h4MCBqFOnDoqKitC4cWO9+mcnJycHvXr1wvr16zF69GjNj2vXrh0+/vhjpKSkIDExESaTCdOmTUN6errufeQWcSIiqgyOR52IqIaLjwc6d/Z3L4jIgdcB9tatWzFx4kTp/Mfu3btRUlKCwYMHY8GCBUhOTva5cxs2bLD7/q677pISnykZPXq0y8C7R48e+Oqrr3zuk5qYmBhNW8SVzpoTERG50qxZM7tkn7///juuu+46P/aIiIiIHHmelhvA/v37MXLkSDRo0ADPPvusdHtMTAxiY2ORkpKC999/X7dOBiVxVtHxj1LQTURE5MbMmTMxbtw46XuLxYJmzZrZjcFERETkf16tYD///PNo0aIFvv32W1y+fBkvvPACAODmm2/Gt99+i9tvvx3Lli3Dfffdp2tngwrLdBERkQ5ee+01vPPOO1i/fr10W4MGDbBt2zaMGzcOrVq1sgu+iYiIyH+8CrD/85//ICUlBUajEYWFhXb31atXD+PGjcPChQt16WCwMZvNms5gm83mqusUEREFrdWrV+Pf//43unXrJt1mMBiQkJCADRs2OK1uExERkf94FWAXFxfj2muvdXl/7dq1cdlN6YDqzGq1ajqDzSziRESkxenTp+2Ca7k+ffrg5MmTVdwjIgoI3C1JFJC8CrDbtm2Lffv24fHHH1e8f/v27bjhhht86liwkrKDq2R2ZBZxIiLSQpy0NhqNTveVlJSgVi2fCoIQUZAxmUyIMBpR5Ga3ZITRCJPJVIW9IiKRV6Py2LFjMXXqVNx2220YOnQoAGG72vnz55GamopPPvkEixcv1rWjwUJrdnBmESciIi26d++Ot99+G+PHj3e67+2338bf/vY3P/SKiPwlNjYWx3JzpUo+SkwmE2tgE/mJVwH25MmTsX//fjz99NNISUmBwWDAkCFDcPHiRdhsNvTr1w9Tp07VuatEREQ1z4wZM9C3b18UFRUhMTER1113HcxmMzIzMzFnzhxs2rTJ310koioWGxvLAJooQHkVYBsMBrz77rsYOnQo3n33XeTl5eHq1avo1q0bEhMTMXr0aLtanUREROSdrl27Ys2aNXjiiSfsJq8bNGiAVatWISEhwX+dIyIiIjs+HdwaPnw4hg8f7nT71atXceLECbRp08aXpyciIiIA999/P4YMGYLs7GxYLBY0adIEXbt2Re3atf3dNSIiIpLxapk5NDQU7777rsv733zzTXTp0sXrTgWzw4cPC1/k5gI5Oc5/yjM7Su2IiIg0CA8Px9133437778fd955Z5UE1x999BFuueUWGI1GNG/eHAsXLoTNZnP7mIyMDNx4442IjIxE27ZtsXbtWqc2X3/9NXr27Im6deuicePGePrpp1FcXGzXxmw244EHHoDJZEK9evWQmJiIM2fO2LUpLS3Fs88+i2bNmiEyMhJdu3bFl19+adfmxx9/hMFgcPrTrl07L38qRERErmlawT5z5gz27t0rfW+z2fD555/jypUrTm3Lysrw9ttvo6ysTL9eBpE///xTUx3sP//8s+o6RUREAe/ixYuYOXMmduzYgTNnzigGsgaDAaWlpVXSn/379+Pee+/FyJEj8fzzz+OLL77A7NmzUVZWhtmzZys+ZvPmzRg1ahSmTJmC/v3747333sP48eMRGRmJhx56CADw008/4Z577kG3bt2QmZmJ3NxczJ49G3/++SfWrFkDQAicBwwYgEuXLmHVqlW4cuUKZs6cib59++LQoUPS5MKUKVOwYcMGLFq0CM2bN8eyZcvQr18/5OTkIC4uDgBw6NAhAMCnn36KiIgIqa+RkZGV9aMjIqIaTFOAbTKZMHfuXJw+fRqAMMCvXr0aq1evdmorXhAkuQswqzGTyaSpDjZLJxARkVxKSgpWr14tbf/2d/mtefPmoWPHjnjrrbcAAP3798eVK1fw4osvIjk5WTFATU1NRWJiIpYvXw4A6NevH86fP485c+ZIAfbixYsRFRWF999/H2FhYUhISIDRaMTkyZORmpqK5s2bY/Pmzfjuu+/w/fff48YbbwQAdOzYETfddBM2bdqEpKQk/Pzzz1i9ejVWrlyJSZMmAQD69u2LuLg4LF68WFo5P3ToEFq0aIG77rqrsn9kQaWgoIBZqImIKoGm0Ts8PBxbt27Fd999B5vNhrFjx+Kxxx5D165dndqGhoaiUaNG6N27t+6dDQbSYKVSB9vdoEZERDXPBx98gMGDByMrK8vviUKLi4uxb98+zJs3z+72xMRELF68GNnZ2ejbt6/dfadOnUJeXp7iYzIzM5GXl4e4uDjs2rULgwYNQlhYmF2bSZMmYdeuXXjsscewa9cutG3bVgquAaB9+/aIj4/Hzp07kZSUhL1796K0tFQqFwoI1yuDBg3Ctm3bpNsOHTqEjh076vFjqTYKCgrQNj4eRZcvu2wTYTTiWG4ug2wiIg9pnh7v2LGjNEB99tlnGDNmDP7+979XVr+C1sWLF3VtR0RENcOFCxcwcOBAvwfXAHDy5EmUlJRI26xFYvLSvLw8pwA7tzzHiLvHNGvWDKdPn3Zq07BhQ9SrVw95eXnSczm2EZ9L3kY8w+3Yxmw249KlS6hbty4OHTqE9u3bo2vXrjh48CCio6MxevRopKen19gkcRaLRQiu3ey2K0pKgsViYYBNROQhr/afrV+/Xu9+EBER1WjdunXDgQMH8Oijj/q7K7BarQCAevXq2d0eFRUFQJgM8OYxrtqI7cTntVqtuOGGG1Tb1K9fX7GN+Hp//fUXfvvtN4SEhGDRokWIjY3F3r17sWjRIvz88894++23Fd8/AFy6dMnpfYaHhyM8PNzlY4KOym47IiLynNcHvH766Sds2LABv/76K65evep0v8FgwBtvvOFT54iIiGqKJUuWoFevXmjbti1GjhzptDJblcREpQaDQfF+pVV2V48Rc7OEhIS4fV6bzSY9b1lZmcs2oaGhqm3E16tXrx52796Ntm3bolmzZgCAnj17Ijw8HKmpqUhNTUW80gpueTtHc+bMwdy5cxXbExERAV4G2Lt378bAgQPdZjKt8QF2+VY5zbcTEVGN1rx5c9x8881ITk5GcnKyYpuqyiIeHR0NwHmlWjzepLRy7Ooxly5dkh7jqo3YTnze6Ohon9qIrxcZGYm7777bqc3AgQORmpqK7777zmWA/dlnnzmd3a5Wq9dERFQpvAqw586dizp16uCVV17BrbfeygFHpmnTpprKdDVt2rTqOkVERAFvypQp2LdvHxo2bIgbbrjBr+eDW7dujdDQUJw4ccLudvH79u3bOz2mbdu2UptOnTopPqZOnTpo2rSp0/OeO3cOFy5ckJ63bdu2OHjwoNNrnDhxArfddpvU5sKFCzh37hwaNmxo16ZFixaIjIzEsWPH8Omnn+LBBx+025ZeWFgIAG4retStW1dxKzuR3pjRnah68SrAPnjwIGbPni2V3KAKN998s6YyXTfffHPVd46IiALWBx98gPvuuw+bN2/2e4muiIgI3Hnnndi6dSuefvppaSt2VlYWoqOjpSBXrk2bNmjVqhWysrIwfPhw6fasrCzExcWhefPmAIRSWjt27MCyZcukCfqsrCyEhoZKFUj69u2Ld955B0ePHpWC7qNHjyI3NxepqakAgHvuuUd67MSJEwEI2c937NiB/v37AwDOnDmDiRMnIiwsDGPHjpX6tGnTJkRFRaFLly76/dCIvMCM7kTVj1cjuNFoRIMGDfTuCxERUY1VUlKCgQMH+j24FqWmpuLuu+/GiBEjMHbsWOzfvx9LlizBokWLEBkZiQsXLuDo0aNo3bq1tIKclpaGMWPGoEGDBrj33nuxfft2ZGZmYtOmTdLzzpgxA++++y4GDBiA5ORk5OXl4dlnn8WECROkc9IjR47EggULMGDAALz44osAgJkzZ6JDhw5S8N68eXM88sgjmDZtGgoLCxEXF4dly5bBarUiJSUFgHCO+q677kJycjL++usvtGvXDh9++CFefvllLFmyBNdcc01V/kgDD4+z+R0zuhNVP16N4n369MHOnTvx+OOP692foGcymRBhNKLIzRbxCKPR7bY0IiKqebp27YqcnBx/d0PSu3dvbNmyBXPmzMHgwYPRtGlTLFmyBNOnTwcA5OTkoFevXli/fj1Gjx4NABg9ejSKi4vx0ksvYd26dWjVqhU2btyIESNGSM/brl07fPzxx0hJSUFiYiJMJhOmTZuG9PR0qU14eDh2796NKVOm4LHHHkPt2rXRt29fLF++3G4C4vXXX8c111yDRYsW4dKlS+jSpQt2794tlQYLDQ3Fe++9h7lz52LZsmUwm81o3bo1Xn/9dYwfP74KfoqBidcqgaGgoEAqb6fIZFIOuokooBlsYrpND5w+fRp33HEHBg8ejIceegiNGzdWzCgazDNtOTk56NKlCw4cOIDOHpaw4FkaIqLqx5dxQevz9+nTB6mpqbj//vvRuHFjKWM2VZ3K/ncOFLxW8S8tW8NhNAKZmcCgQdX+95Eo0HkyNni1gn3TTTfhypUreO211/Daa68ptqmqTKeBKDY2loMSERF55JFHHkFoaChmzJiBGTNmKLapyWMr6YvXKv6lZWs4kpKA8trxRBQ8vAqwhw0b5rI2JhEREXmuQYMG3JJLVNPExwNcmSaqVrwKsDds2KBzN4iIiGq2ffv2+bsLRERE5COfUpXabDYcOHAA+fn5CA8PR2xsLDp27KhT14IXzzURERERkc/y8/3dAyLykNcB9v79+zFmzBicOHHC7vbWrVtj3bp1uOOOO3zuXDBiPUMiIvLG/PnzVdsYDAakpaVVQW+IKCCkpTGjO1GQ8SrA/vHHH9G3b1+UlpZi7NixuOmmm3D16lV8//33eOedd9C/f3/k5OQgLi5O7/4GPNYzJCIib8ydO9flfQaDATabjQE2UQ2TkZGBHj168JqRKIh4FWDPnz8foaGh+OabbxDvEESmpKSga9euWLBgQc0+q82kFURE5IFPP/3U6bbS0lL8+uuv2LhxI86cOYOdO3f6oWdEpDez2Sx84aoOdvnt0dHRDK6JgoxXAfbevXsxceJEp+AaAOLj4zFhwgS8/fbbPncuGEkfmDq1IyKimqFnz54u73vwwQfRs2dPvPTSS3j55ZersFdEVGlCQoRSXO7uJ6Kg49X/XKvVipYtW7q8v1WrVm6TfFVnVo31CrW2IyIiMhgMeOCBB5CVleXvrhCRDmJiYoCyMuFI4YEDzn8yMoCyMqEdEQUVr1awr7/+enz99deYMGGC4v1fffUVmjRp4lPHgpXWiYWaOgFBRETe+euvvzg5S1TdBMiRQlbAIdKPVwH24MGDsXLlSvTs2ROjRo2yu2/Dhg146623MHnyZF06GGwuXryoazsiIqrZiouL8e2332LFihVo3769v7tDRNUMK+AQ6curADstLQ3vv/8+xowZgxdeeAHt27eHwWDADz/8gBMnTiA2NrbGZjmVVhdUklZwFYKIiORCQkJgMBhc3m+z2bB06dIq7BER1QSsgEOkL68C7OjoaOzfvx+zZs3C1q1bcfz4cQBAvXr1MGrUKLz44oto0KCBrh0NKkxaQUREHrrzzjsVA+xatWqhSZMmGDt2rNtEaEREPgmQ7epEwc6rABsArrvuOrzxxhtYu3YtLBYLbDYbGjZs6Hb2vcYQk1a4mAV0G3wTEVGNtG/fPn93gYiqmsqORyIKPl4H2N999x0WLFiAf/7zn7juuusAANOnT0d+fj4WL16MNm3a6NbJYPLbb78JX6jMAkrtiIiIiKhGMZlMiDAaUeRm0SXCaITJZKrCXhGRHrwKsA8ePIg777wTRUVFmDVrlhRgh4eH46OPPsLnn3+Or776qsYG2URERFr9/PPP+PLLL3H//fdLt1mtVsyfPx+ffvopjEYjRowYgcmTJyM0NNSPPSUivcTGxuJYbi4zdxNVQ14F2HPnzkX9+vXxzTffoF27dtLtCxYswKOPPoqePXviueeewzvvvKNbR4NFVFSUru2IiKj6evnllzFjxgyUlpZi5MiRMBgMKCkpwV133YUjR44gNDQU9evXx7Rp07B7927s2LHD310mIp3ExsYygCaqhrwKsP/73/8iOTnZLrgWtW7dGpMmTcJrr73mc+eCkdFo1LUdERFVT//5z38wdepUxMXFYdKkSdLtK1aswOHDh9GyZUvs27cPzZo1w7///W889NBDWL9+PcaMGePHXhORN4KizjTPgxPpwqsA+9KlS4iIiHB5f/369XH+/HmvOxXMoqOjhS9UPqSkdkREVCP985//xPXXX48DBw6gTp060u0bN26EwWDAnDlz0KxZMwDA/fffj02bNuHNN99kgE0UZLTUmQ6LiMDWrCx06NChygNtngcn0pdXAXZ8fDy2bt2Kp556SvH+7du3Iy4uzqeOBavS0lJNZbpKS0urrlNERBRwvvjiC4wePdouuD579iyOHj2KWrVqYciQIXbte/fujXnz5lV1N4nIR1Kd6RUrgFmzgMJCpzYlRUUYNGgQIoxGHMvNrdIgm+fBifTlVYD96KOPYtKkSRg1ahSefvppxMfHw2Aw4NixY1ixYgX27NmDFStW6NzV4FCrVi2hTFd6OtCypXOD/HwgLU1oR0RENdbvv/+OFi1a2N2WnZ0NAOjcubNTro66devi0qVLVdU9ItKbySQE125KuRYlJcFisVR5MMvz4ET68SrKe/zxx7F//35kZGTg7bfftrvPZrPhgQcewJNPPqlLB4ONdEGUkKBcpisnB0hLY5IzIqIark6dOrhw4YLdbZ999hkMBgPuuusup/anT59GgwYNqqh3RFRpVEq5ElFw83oZdePGjRgzZgzee+895Ofn48qVK2jZsiUGDx6Mvn376tnHoFK/fn3hi4ULgYYNnRucO2ffjoiIaqQOHTrgs88+w9SpUwEAV69exfvvvw8A6N+/v11bm82GrKws/O1vf6vqbhL5XVAkCCMiKufTPuVevXqhV69eevWlWsjOzhbOYGdluW4UEoLs7GyMHj26yvpFRESBJSkpCY8//jgWL16MgQMHYtWqVTCbzbjhhhvQs2dPqZ3NZsMzzzyD3NxcTJ8+3Y89Jqp6WhKE+ePcMhGRKzwIrLPTp08LZ7DdnK9BUpLQjoiIaqyxY8figw8+wMyZMzFr1izYbDbUqVMHb775JgwGAwBgzZo1WLBgAQoKCtC9e3dOzFKNIyUI0+HcMlfCiagqMMDWWaGYGVLlfE2hQgZJIiKqOUJCQvDee+8hKysL2dnZqFevHsaMGYPWrVtLbU6fPo3ffvsNjz32GF566SUp8CaqcXw8txwQK+H5+ZXzvEQUUBhg6+yvv/7StZ2vOFtLRBS4DAYDhg8fjuHDhyve/+yzzyI9PZ2BNZGP9FwJ95RUZzotTXotRa5uJ6KgwgBbZyUlJbq280VAzNYSEZHXjEajv7tAVL34IYO3WGf6yJEjGDp8OEqSkly2jTAaYTKZqrB3RKQ3rwLsnJwcdOrUiTPqCrT+TKriZ+fP2VoiIiIKXNzhVrXEOtPHf/yRP3eias6rAHvgwIF4+OGHsXjxYr37Q5WB9RaJiIioHHe4+caXyQkx0Cai6surANtqtSIuLk7vvlQLtWqV/0hVztdI7Xzk7kM+l2d5iIiIgk5lry4H5Q63ADm3zMkJIlLjVZQ3ePBgrF+/HoMHD+Y5EQctW7bE4e+/B9ycr0FICFq2bOnza2n5kCciIqLgUaUBXBDscJMShAXIueWqmpzgFn6i4OVVgH3jjTfiww8/ROvWrfH3v/8djRs3dlqRNRgMeOONN3TpZDC55ZZb8P777wPp6YBSEJ2fD6Sl4ZZbbvH5tVQ/5HfuBMSMlUREROQXngRLQbm6XInEBGG6BZt6rYRX4uQEV8mJgptXAfZzzz0nfb1nzx7FNjU1wI6KihK+aNlSeWB0bKcH8UO+oABwMwBVBc64EhF57+LFi8jMzMSvv/6Kq1evOt1vMBiQxonToOJ1sBQEq8ve8vRaQencsvw5LBaL0/M5PkegrYS7w0kWouDmVYCdn5+vdz+qjTZt2gAhIapbxNu0aaPvCxcUCB/CSgN4FZ1b4owrEZH3vv32W9xzzz24cOECbDabYhsG2MGHwZI9X68VCgoKhHJXiYkoKSrS/By6r4RXhWo8yUJUnXkVYDdv3lzvflQb1157LVBW5r5RWZnQTk8WixBcywdwsxkYPtxtsB8WGQmz2YycnBzpNm8HGF5EEBF5b/bs2SgsLMT8+fNx6623Ijw83N9dIj0xWALg27WCptwzkZHAwoUomjrV6TmYwZuIqoJHAbbVasX69esxbdo0p/sWLVqE3377DSkpKYiJidGtg8Hmxx9/BAwGwMXqAwDAYMCPP/6Irl276t8BxwH8xx+F4Ds3F0hKwooVKzBj5kxp1reksBCDBg2yewqfV5l5EUFE5LH9+/cjOTkZs2fP9ndXqKbwZ2ZuL64VtATnSEoCAmCbd3XDI4BE2mkOsP/73/9iwIAB+PPPPzF06FCnVeytW7fim2++wcaNG7Flyxb07NlT984Ggx9++EFTgP3DDz9UTYdiY4U/5UwmkxBcc5WZiCighIaG6lJhgkhNMJ1HVhQIE/mOkxBmM2C1Csls4VwqNZgDUB4BJPKMpgD79OnT6Nu3L2w2G+bOnYsGDRo4tdm+fTvWr1+PefPmYfDgwfj+++/RtGlT3Tsc6C5fvixsEVeZXb2sZ2ktb2aaA2FwIiIiSbdu3fD5559j/Pjx/u4KBYJKXF3253lks9ksfOHqfYj3ByiXkxMhIXZHBJMc7g/mAJRHAIk8oynAXrRoEUpKSvDll1+is4ugrFGjRpg5cya6du2KPn36YMmSJVixYoWefQ0uKgHsuXPnUFBQ4NMHkZYZaEdSgjqlgS1QZ6qJiGqARYsW4c4778TSpUvx0EMPoXHjxv7uEvlBVa0u++M8ckFBAYYOHy584+r9BXjuAaXJidzcXCGg1jsA9ecWfiVcnCHSRFOA/fHHH+Phhx92GVzL9ezZE4mJidi5c2eNDLAvXryoqV1WVhZ27Nzp02ym/ENe+nB3xWwG5NlnldpGRABZWV71hYiIfPPggw+iVq1amDFjBmbMmKHYxmAwoLS0tIp7RrrQGCwFZbZrjSwWC0oKC9XPUBsMFSvdAcjl5IROAai/t/A7nrd23O5ORO5pCrB/+eUXdOnSRfOTduvWDe+//77XnQpmRW5KRthJT0dRWprP22mcPuRdfQh++qn62fCSEuDECa/7otoHfkATEbnUoEGDwD3zSl7zJljyZnU5qJJQqQWiNhusVqv3zx/k5WT9OcmiKVN7NRRU/38o4GkKsOvVq6c9cISQqKVOnTpedyqY/fXXX9oaRkXp+rqat4urzRofO1apfQjopClERH60b98+f3eBKklWZqbLgDE6OhodOnTw6OLdMRgwm80e14UOdNHR0a7vVJvIT0sL+usNf5UUUzxvLV4jVlNM4kZ60xRgt27dGt98843mJ/3Pf/6DZs2aed2pYKZ5IkLjVnI18kFWaQCPjo5GTEwMdu7cKWwPV5s1XrUKCAnxamtWdd7WRkRUlWw2GywWC8LDw1GvXj1/d4e8VFBQgLi2bVHs5tqgdlgYPtu3T/PYWFBQgLh27VBcWOi6kXjkSyybGoRJqJRKvmqZyA+LjMTWzZs9nrQgBzXovDWTuJHeNAXYw4cPx6xZszB79mzEK/3iyXz//ffIysrC008/7XPnhg4dipycHJw6dcptu4yMDCxcuBAnT55EbGwsUlJSMG7cOLs2jRs3xm+//eb0WLPZrGsimWuuuUb4ogq2SXsy46a59Et6OiCe0/aCv2ZciYiqg//973+YMWMGtmzZIuX0qFevHhITE7Fw4cKgXpGriY4cOYLi4mKnDNNyV0pK0LN3b5w4dkzT+HnkyBEhuFbbkQZUuwAp2CfyxbPMgdxHt6r7EcAaNKlAlUtTgP3oo49ixYoV6NOnD9atW4f+/fsrttu5cycef/xxGI1GTJw40aeOZWRkYNu2bU71th1t3rwZo0aNwpQpU9C/f3+89957GD9+PCIjI/HQQw8BAH777Tf89ttvWLZsGbp27Wr3eKWSY76Qnk9tK40OSWo8mXHTrDwQV5o59hbPtRARqbNarejWrRuOHz+ONm3aoFevXrh69Spyc3Pxxhtv4NNPP0VOTg5XtIOI1WoVcp/YbG7H6iserI5JO9XUggFfzjAHsICeyFcJQMVktFq2GwfUtZPJBBiNbq9tg31LPpGeNAXY9evXR2ZmJgYOHIiBAweiVatW6N69O5o0aYLS0lL8+uuvyM7ORkFBAerUqYMPPvjApxrYZ8+exVNPPYXrr79etW1qaioSExOxfPlyAEC/fv1w/vx5zJkzRwqwDx48CEBYEVcL2H1VUFDgdqYagHB/Xp5+LxrAM24810JEpM3zzz+PEydOYM2aNXj00Uft7lu3bh3Gjx+PhQsXYuHChX7qIfmkqsfq/HwgJ0f4ujzAy83NVQzMtAZzugV91WwlVFMenMhIYPNmwGpV3W4ccNdOsbHCv43FIu2QyMjIsNvVysUSogqaAmwAuP3223H48GGkpKTg/fffx8aNG+3ur1+/PkaPHo309HQ0adLEp06NGzcOffv2RUREhNukL6dOnUJeXh7mzZtnd3tiYiIyMzORl5eHuLg4HDp0CNHR0ZUeXAPClnOUlalv3frhh0rviz+Jg3Bubq4wQKSnS6vjAIDoaOFsGM+1EBEBALZt24aHH37YKbgGgLFjxyI7Oxtbt25lgF2DKAW04qKBy2BUzKGSluZ05CspKckpMNMazH2yZw963323T0GfyWRCWGQkStwFouHhQHGx6/sDkGLZVMfrQJNJCFTFSQ83AvJMcGys8KdcfHy8pvK95F5A7VQg3WgOsAGgadOmeOedd3Dp0iUcPnwYZ8+eRa1atdC0aVN06tQJtWp59HSK1q5diwMHDuCHH35QPcctnmWJi4uzu71NmzYAYBdgX3PNNRg6dCj27t2Lq1evYtCgQVi+fLmuW6EB4MqVK8IXajPVhw9X6XYaKRunxlljdzUP1f6zKw7Wjue6jUbhNVXO9BMR1RS//PKL0zEmudtvvx3//ve/q7BHVJXk4654baAY+IaECH+7ClIjI4W/09OBhATHF3EKzLQGcydPnvQ56IuNjcW+vXtxV+/erjOeFxcH5XZjp63reuxY8OcOxUreZcDAMgB3KpBuvIqI69ati27duundF5w+fRrJyclYv369pg9W8RyS43m0qPISWBcuXAAAHDp0CL/88gvGjx+PadOmITc3F8899xx69uyJgwcPui0pdunSJel5ROHh4QgPD1dsX+guq6dMTEwMvvrqqyr5D5Obm4vo6GjUjojAFXezxkajsLKMinNCStT+s2sZrJGUJGw1IiIiAEKSzNOnT7u8Pz8/n+evq6PyFWf5uBsWEYHFL74ojKUrVgA9egh3iOOnlgRnLVt6FpxpDeZ8DPq6du2K48eO1fjgKlBVRcnVgA0sq/joQkDuVCBd+L7krBObzYaxY8ciISEBw4YN0/SYsvJzzgaDwem5ACCkfJZ3/fr1iIiIQKdOnQAAPXr0wI033og77rgDGzdudJuQrWfPnk63zZkzB3PnzlVsf9nNh4Xc1atXfU5uoZVisOy4ZRsQgmsxKYrSzDfg2X/2AD4bTkQUaHr16oXXXnsNo0ePdtqZ9eOPP2LVqlUuk4xSkCooAIYPd7q5pKgIU6dOFb6ZMQNYvBgo350HIOjH16pOVMbVUu2qIlN7oAWWVTGp4FaQ/38mZwETYL/66qs4fPgwjhw5gtLyDNtioFxaWoqQkBApYBaJ254dV5gvXboEQDgXDkBxy1337t1Rv359fPfdd2779dlnn6Fjx452t7lavQYqgn417tppmdkTZ7cBqM+4yT/AzGZg2DD3pbhCQoBOndz+Z3eVKIWIiLwzZ84cbN++HR07dsTDDz+M9u3bw2Aw4Pvvv0dGRgYMBgPSfCij6I2PPvoIqampOHr0KBo2bIjHH38cM2fOdJrYltNSOvPrr79GSkoKDhw4gLp16yIpKQkvvPCC3fhqNpuRnJyM3bt3o6SkBH379sXKlSvtkqiWlpbiueeew1tvvQWLxYKOHTvipZdeQvfu3T3uU2WQjmcBwphsNttn987PB7SU3Jo6VT2BqpzBIO1Iq6nEoNpsNmPo8OEocbPDkNtw7VXZBEiABJbBXv6NAk/ABNhZWVmwWCyKZ6Jr166tuGrctm1bAMCJEyek1WnxewBo3749rFYrtm7dittvvx3t27eX2thsNpSUlKjORtWtW7fKt+TZzexFRwuz2w4DgzS7HRLiviSY0ShsLZN/KGzZAgwa5JQBEkBFcg6Vs+lKiVKoAmfLichT7dq1w0cffYSxY8dizZo1dve1atUKa9euxU033VRl/dm/fz/uvfdejBw5Es8//zy++OILzJ49G2VlZZg9e7biY7SUzvzpp59wzz33oFu3bsjMzERubi5mz56NP//8U3rfpaWlGDBgAC5duoRVq1bhypUrmDlzJvr27YtDhw6hdu3aAIApU6Zgw4YNWLRoEZo3b45ly5ahX79+yMnJkXYBaOmTXtxNPLgdq+PjhSRYrsaNiROBVasq/lYTGir8LSbUEhOflQf38vPe7nKuBCvFhYqqXi2tZpnSq7uALv9GQSdgAuzXX38dFy9etLtt3rx5OHDgALZv366YmbxNmzZo1aoVsrKyMFy2xSorKwtxcXFo3rw5Ll++jEmTJmHEiBF2mc/ff/99FBYW4q677tL1fUir7CofrI6r8YrEgcDd7HZ2NjB1KlasWIEZM2faJw3JyHAOrgEpePYpA2R6OorS0nwbkKrpIBOwZ4uIKOD16NEDeXl5yMnJwcmTJ2Gz2dC6dWt06tRJ27iho3nz5qFjx4546623AAD9+/fHlStX8OKLLyI5ORmRYjItGS2lMxcvXoyoqCi8//77CAsLQ0JCAoxGIyZPnozU1FQ0b94cmzdvxnfffYfvv/8eN954IwCgY8eOuOmmm7Bp0yYkJSXh559/xurVq7Fy5UpMmjQJANC3b1/ExcVh8eLFWLt2reY+6cFgMGgr01lWBhw4IHwvrlCbzcJ47WrcWLVKeKyW4BoASkuBQYOUXxsKR8dCQiqCcB/l5ubCbDZX1OuWiY6ORkxMTKVNMjtVMMnIEO5ISqqy1VJdtxszSCcKSgETYIur0XINGjRAWFgYbrnlFgDCVvCjR4+idevWaNiwIQAgLS0NY8aMQYMGDXDvvfdi+/btyMzMxKZNmwAARqMRM2bMQHp6Oho1aoT+/fvj8OHDmDt3LgYOHIi7775b1/cRFRWF3y0W9zPVISFSIjbNVAYGk8kkBNeOg0llBXCO57e9Uf4zqqxzLf5aRQ60s0VEFFwMBgO6dOmCLl26+K0PxcXF2Ldvn2IZzMWLFyM7Oxt9+/a1u09r6cxdu3Zh0KBBCAsLs2szadIk7Nq1C4899hh27dqFtm3bSsE1IOxKi4+Px86dO5GUlIS9e/eitLQUQ4cOldqEh4dj0KBB2LZtm0d90o2WMp3i1/It3FarEFyrPTYxEcjKUu+H4/O4S44m3nfwoPLutfJgThpPVYK+pKQk1YmGyphkVpzc9kOlEj22G/v9TDAR+SRgAmwtcnJy0KtXL6xfvx6jR48GAIwePRrFxcV46aWXsG7dOrRq1QobN27EiBEjpMfNnTsXjRo1wqpVq/DKK6+gQYMGmDBhgtOAq4fGjRvjp59+ct+orAyNGzfW/bUBeDSYyM9Ry2d9y++0byzWb/SEyiAsblGvjEA3IFaRA+RsEREFptDQULz11lt48MEHAQg7m9xuMYYQfIt5SirTyZMnUVJS4rYMpmOAraV0ZrNmzXD69GmnNg0bNkS9evWQl5cnPZdS4NumTRu7NnXr1nUaT9u0aQOz2YxLly5pLuepKy2f/a4CJ7XHli8uqK5sejP+KNTNFkUYjcJxObVjaSEhQP/+wM6dqpPM2dnZTsfUAOHsPQCXZVRdXTPYTW4D7vtZyXzdbhwsZ4J5HE4n3KlQ7WgKsFu1auXxExsMBvVAU8WGDRvsvr/rrrukxGdyEyZMwIQJE1w+T0hICJ544gk88cQTPvVHCyl52cSJgMK2dpw9C6xapTkZmsfk/xlVgmTxHPUne/ag99132wejjgNTZCSwebN9chYXtM689ujRo9I+eANmFbmgwPlMXfm/CxPFEdVsd955Jxo1amT3vVqAXVW0lsH09DGu2ojtxOe1Wq244YYbVNuIyUw9fT1370PkaZlOj4hbxHfudJ901JHRKIzHarlXHFc2zWb14Dg8XMjREhMjrWrLJ8ItFou2FfqdO4XvVYJ8l+VAvVj9LigoqFgg0GHVOhACx0A/E6zLQkYNDyy5U6H60hRgl5WVOQ36v/32G4qKinDNNdegXbt2KCsrw8mTJ3Hu3Dlce+21dgnFahKbzaZ+TiokRHGiwBcWi8V58FQLksvPUZ88eVJb3WrxPJesXraS2NhYfLJnD06ePOmyTatWrap/hkqVM3VMFEdUs3366ad23+/bt88/HVHgqgymSOk8uJbSme6e12azSc+rdN0htgktT+Dlro3a6zmW81TiaZlOj4jjkhhI5Odre1x0tDCODxoklNOMigIuXgS++07YOu4q94rVqi04PnhQvQ9q42p6urZJA6VyoBrqfDtOjmsJ9DwREDvggoAvCxkMLAXBslOBPKcpwD516pTd93v27MHAgQPx2muv4bHHHpMGKJvNhvXr12PSpEl4+umnde9sMAgLCxMGMaU604AwiKal2Z09c8mDGbyLFy9qGzzlQbIs8zoA9UFTfG5ZplOl7KNmsxlDExPtE645kA9Oes8U281k+5OGM3U8i01EWl29ehX5+fnS9ubK5KoMppiMVGnlWEvpTFdtxHbi80ZHR/vURu31HMt5KvG0TKdXoqOFyXFPVrHFrdNRUcDMmYB8rI2OFsZnxzFVDOCjo92P82lpisnQIoxGZGVm2rd13KHlOO6qJU1r2dJ95nTA5fE0cYwXV9aLLl9WD+w1rpYGzA64YOHFQgYDywqBvlOBvOPVGezZs2fj4YcfxuOPP253u8FgwNixY5GTk4O0tDTce++9unQymFy5ckX4QmWwlNopUJzZ07qNxosg2WMWi5C9PCTE9RYvwH67mUOfxcEJgK4zxXrPZOuCZ7GJSIPQ0FBkZGTggQceULz/zTffxLRp0/Dnn39Wel9at26N0NBQqeylSF4G05GW0pl16tRB06ZNnZ733LlzuHDhgvS8bdu2xUGF1dQTJ07gtttuk9pcuHAB586dkxKfim1atGiByMhITX1ypUrKdMbEaK9tDQA//yyMvwaDUBvbkUJZT6f7f/zRdU4VMUiVB5flY7ZdVvCCAuF+pbFWvP5Rey2LxfVziNcWRqNwnePwHIqBv9KihphMTm1bfWgoiouL1SfoTaYqS5wWCNvUKxMDS6rOvAqwjxw5gjFjxri8/8Ybb8S6deu87lQwKygo0FSmo6CgwOXd8pk9s9mMocOHo8TNwBAWGalYDkORPNjzNMBW6oPainlMjNvgUu+ZYs0z2VVB65Y/IqqRzpw5g71790rf22w2fP7554oTsGVlZXj77bcrL3+Hg4iICNx5553YunUrnn76aWmLdVZWFqKjo6UgV05L6UxAKKW1Y8cOLFu2TFoRzsrKQmhoKHr37i21eeedd3D06FEpCD569Chyc3ORmpoKALjnnnukx06cOBGAkP18x44d6N+/v0d90pU350qnTweWLlV/7OrVwt8hIcDy5cJ2cPH+pCT3ZT3FNhaL66DXTYWQfPmYZrFoy3ru7rUuXvT+OcoTmTkF/iKTSQjO1RYBFi0S+pGWhvPnz9vno1F6bGQksHBhefcqVtH9kag1LDISWzdvtksGp9SX6h6oEwUirwLs6667Dvv373dawRZ9/PHHuP76633qWLCSkoCobBG3WCyaPvQ6d+6M4z/+qNhO2opdWIilS5d63ed8jYGgmOwEEAaWJD3rSuq9yqtHGTEvSTsQ/B3gE1FAM5lMmDt3Lk6fPg1A2AW2evVqrBaDKBnxzLDbXUM6S01Nxd13340RI0Zg7Nix2L9/P5YsWYJFixYhMjLSq9KZADBjxgy8++67GDBgAJKTk5GXl4dnn30WEyZMQLNmzQAAI0eOxIIFCzBgwAC8+OKLAICZM2eiQ4cOUqDcvHlzPPLII5g2bRoKCwsRFxeHZcuWwWq1IiUlRXo9LX3SjZYs20qTJM2aaXtsaCiwZImwem0yVYybJhMQESFsF9djPHXsh8GANHFME5OYAeqvpTRh4OnOOyXuVpFzc4X7MzOFo1rl111Ogby4/TwnB0hLg9Vq1ZaPpnzngHwVXe/z2FoWH0qSkjDIoda5Y194npzIP7wKsEeOHImXXnoJLVq0wLRp03DNNdcAEBKfpaenY/v27Xj++ed17WiwKC4uVj9PFRKC4uJizR96rlitVufa115I0xgIxsfHo3OwbXX2Q4ZKcQdCdnZ2lV4ME1FwCQ8Px9atW/Hdd9/BZrNh7NixeOyxx9C1a1entqGhoWjUqJG0wlsVevfujS1btmDOnDkYPHgwmjZtiiVLlmD69OkAvC+d2a5dO3z88cdISUlBYmIiTCYTpk2bhvT0dKlNeHg4du/ejSlTpuCxxx5D7dq10bdvXyxfvhy1alVcurz++uu45pprsGjRIly6dAldunTB7t277c6pa+mTHmw2m3oW+LIyIYN4To7wvTgWmUwVgbfjBL3FIqyyWq3CKnf5OXjk5wvPIwaKWVkVeVZ8NX068MorQHGx+OYq7vNk8thdpvDyTO66iY52v2qtNZDXetQOcFlyTLdVYS/6It/xx/PkRP7hVYA9d+5cfPnll3j++efxwgsv4JprroHNZoPVaoXNZsOgQYPwzDPP6N3X4KFhBRuApg+9I0eOIHHECPdnij05D+SujFd1KpegNtCicjNUxsbGKtb3JCKS69ixo5RI67PPPsOYMWPw97//3b+dkhkyZAiGDBmieJ+3pTMBoEePHvjqq6/ctmnWrBm2bt3qtk14eDiWL1+O5cuXu22npU96sNlsyMnJQZcuXSpWqx1Xrbt0sX+QY2WOhISKoErprLMY4Ip1q8Vzyi7qRjvRsqrsalecWI3k4EFNgfbEiRPRvXt36fvo6GhYrVZh8lmcKFCzdq1Q9lTMmO6KWF7Mccefp6XQ1CgEvY6T6VW2KqzXpIEvguTakVvlqSp5FWBHRkbi888/x1tvvYWtW7dK5Zh69uyJkSNHYuTIkbp2MuhoWMGWBluVDz23W5bE7UqOtykRb3dsL/Z18WK/BaOVwtVAK6vtWZl1uJ1e05PbiahGWr9+vb+7QHqqVQsoKXHeEq60VdnVhb8nZ521cjXWywN9d9ccMTEV5T5VDBw4EAMHDrS7raCgwLNjVGLZU/HaKTRUWFl3zOgubg33B5WV5OoomEptcas8VTWvAmxAqB35yCOP4JFHHtGzP9WDxhVsXWlI6BEWGYnFCxdi6tSp9tvK770XGDu2coLR3FyhPueZMxW3nT0LANgpP8dVGWJjXSZXiY+Pr/QP0WAafIgoMPz888/YsmULLl26ZJfQrLS0FFarFR9//DF+/PFHP/aQ1Eif/a4u5pUm1tUCZL1XICdOFFaF5aKiADG7u06vF6Owqu7xMSr59YqYwFQWXEdHR6uOtZWuBlYLCaZSW9wqT1XN6wAbAL799lts3boVp0+fxqxZs1C3bl0cOHAAQ4cOVT+HVJ15soKtwmKxCOU43A0cZrPwwe64YisLkOPj46V6kQCcP2DkwahjbcvyfoiPtQsItayYu3i/dme/9V7lDYBVY38PPtwORRRc9u7di4SEBJSWlkrnecUt2OKY2sQxKKKA4/jZbzabYbVa8dprr2H//v3atmh7yt3xL6XbxVVhRyEhQvCqdeLXy7HW7hiV2nPIr1fEhQvZY2JiYqSft5SA1WFFGUlJAXFdUBXk2c0rW9CV2qqBEyHkH14H2MnJyVi5cqV0EfDoo4/ip59+wvDhw3HfffchMzMTtWvX1rOvwaOsTH07l0Y///yzEGArnHOTyGtNKnzQRUdHo3Pnzvb1HV0NKArnvZTOFn2yZ4/qjHFYZCSenDRJyHDu6ueRnQ0kJ6uuvJvNZuSISWFkHIPEQFs19tfgw+1QRMFn4cKFCAsLw4oVKwAATz75JN577z1cuHABL7/8Mn744Qd88cUX/u0kaaL02X/w4EHs/+orrxOSuiU+p5ZM5MuWVZT3khOvTxYtcl1aSxQdrfpa4lj7n//8RzpKKGexWFArLAyl7vprNCpvoZdl8BavA+x+3vJASsMOvwijEdHi9vhACsS96ItijXAiqlJeBdhvvvkmVqxYgYcffhj333+/dL7m73//O0aOHInMzEy88sormDZtmq6dDSo6zZJdvnxZW8Cena18XgrCOW6ngMvVQKPhvFdRUhLCw8M1rc5mZ2cL37j7eZSVIT09HQkJCXY3y8uQOZaiEDkGif5eNQ4U3A5FFHxycnIwfvx4TJw4ESUlJZgyZQpq166Nhx56CEOGDMHf/vY3zJ8/H2+88Ya/u0peuPHGG91PlouT6VpWo11R2h0nHlkTj6j16OH++kTLBHRMTMVrGQxYsXw5ejgE7SaTCWfOnEG3Hj2Aq1ddP1dICOakpWHevHmuS2k5jOnynXmqY1hsbMUOP4edffK+AlDfah4ZKewadMwCrzMtiwUu+6JUIzyQJg2IagCvAuxXXnkFd911F958803873//k25v3Lgx3n33XZw/fx4bNmyo2QG2ViofehfFjJlqAbubUhjR0dHKAZfStinH/oiDmwI9V2dbtmzpVAIsJyenogyZB0Fi0G1ZqkzcDkUUNC5duoSbbroJABAWFoZWrVrh8OHD6N+/P4xGI0aPHo2NGzf6uZfkrZtvvlkIoBMThZJaEycKd6xaBYSFCQnR1FagXVEaJ8Xs2S1b6pv8S36dUH4muk2bNoplPLOzs4XgWmWRQCq9pnHMUi0bqhI4unq84wS9NNFfVCTcUFjoXApNXGXXkdJiweHDhzHm0UcrJjZc9aVHD2lCQsv5dOaCIdKfVwH20aNHsXDhQpf3DxkyBCkpKV53qkaoVUv4kHQ3kBoMFRMY8lqZSsGjUuBcPvBZrdaKWUz54GU2ux7MxdvE8h/+DFhVBtxch4G0JqxQE1H1c+211+LChQvS9y1btsTRo0el75s0aYKz5UkiKYg1bCj8PW6c8PeqVUJwnZEhbL2WZ+gWV521nFN2HCfdjfHuiPW1Xb2W/PqgUycAysnM7GiomKIHX4+JKU3QHz92zC7Qlc55izsDoqOFgFZso9OqsOJigZhEFxB+L1RW/OXn013hNROR/rwKsGvVqoWrbrb7/PHHHwgNDfW6UzVCWJjzVmyzWThPXVgofG+z4f/+7/+Er9UCXqXBqzwZiNssnVq2n1ssHgfYYoKt/Pz8iueSc7My7im/1Z8kItLR7bffjoyMDDz++OOIiIhA27Zt8cEHH+Dq1asIDQ3FkSNHEBUV5e9ukpek4E+eYMxkAiIigKIi5XG8oABYsMB9kBwZqbyCKm7jHjAAaNHCdWIzR2J9bVevtXmz8Nzuyot56LJ4fE1tIkG8pnChMo6JOQa60r+jm2S2lboqLB6nS0vTtOLPXX0y3CpPVcSrAPuWW25BZmam4hbwoqIivPnmm+hUPqtJLojnsOQfjjk5QnCtd8ArzrI6DtDijLPO24gVE2w5vrY4UaAHL+tPMss2EQWSqVOn4u6770abNm1w+PBhPPTQQ3j55Zdxzz33oG3btli3bh3uu+8+f3eTvCQGfxs3bqyoohEbK2wXd5FnBLGxQkDr6n5AuN/dWCVO1KslTJVZsWIFZsycWbE1Whxn5fWnxTPNsM9c7c24aTQatSVoS0uTgld/jeHyIF7MEO9IPJontif/CrQEuFT9eRVgz5gxAwkJCRg8eDBGjBgBADh16hS2bt2KF154AcePH8eLL76oa0eDjtosWWGhMFiYzc5t9D43q/f5KxVaEmxJidlkHAdLp4znrla9vfh5Mct21eJkBpG6u+66C5s3b8bzzz+P6Oho3HrrrXjhhReQmpqKffv2oU2bNli0aJG/u0k+iI2NRUux1JRIbXu1/H75uCqesVZ7vPysN6BpFa9NmzY4fuxYRa3q+HhhDHaoMiKSZ672Zty0S+jquE0ekLbKZ2RkSMnU/DmGi8/ZvUcPXkcEASbAparmVYDdr18//POf/0RycjI++OADAMD48eMBCLU658+fj8GDB+vWyaCjZRbWYBASf3hz7kgpKZnSbSpbqXx6PS20JmYLDYXRaHQ9WFbCefAak2U7ALZDcTKDSLshQ4ZgyJAh0vezZs3Cww8/jP/973+48cYbK5JBUdCSykF5Qz6uOn6OFxTYb9kW75dvDQ8LU78+sdnw+++/29eqBjRXGcnOzpYel6/xOmSV2EdX1w05OUBaGuLj4xEbG4ucnBy/j+EBcR2hMsYzR00FbpWnquT1SD1p0iTcd9992Lp1K44dO4arV6+iZcuWGDp0KNq0aaNnH4OP2hYspfsLCpSDZZF824rj4KgU0JdvpUJIiN02Lq+CZYUzznpso0lPT0fLli3RqlUrhIeHa1/1lidz85UOuwUCcXU2kLZDBcRFCFGQ+u2339C4cWNcf/31/u4K6URKCOZuzPdEbq5zDhclISHAI48A992nnEht4kRhInvpUoSFhTm/hkhl3FTM+6I22StmVvdUIFTK8EcfNNT2RkgIc9QQ+YlXAfaoUaMwbNgw3HfffXjyySf17lPws9nUA0UxyM7PBz780H5gVPrANBqBzEzh6/Ls4EhPB+QJb2bMEDKRAhVlHMrKgKlTha9DQ70q/yEGwiJ3Z4sKCgqcZkxdSUhIkMpk5IgZSz0tR1YJ5TE8EairswG5HSoQLoSIAtC6devwz3/+EwcOHEBISIjdfU8//TR2796NefPmYcKECX7qIenJ5QSopzuOoqOdJ9jVrj1q1QIGDrS/r3x1WMpqvnSpel/dkfdh+3bhWsXd40NDgRtv9C7ArgLiJLrjeWunJK46Jm91KzdX+PlmZrqeKOneXfj9kB8f4GQ2UZXxKsDOysrCLbfcondfqhetwYQ8C6XawKi0nVwMLvPzheBaXjbC4YMVSUnK57dUBvU0F5kyHQNHLcGmr9LT04X+iO+jkgc0tcQtgbw6y+1QRIHvmWeewZIlS1CrVi0cP34cbdu2tbu/du3aOH/+PCZNmoSjR49i5cqVfuop6cVxAtRsNmPIsGG4opYpvLDQfry2Wism00Xyaw/HLeMAcO5cRQkuN+Nnfn4+CgoK7PoqladS43j9M28ekpKSkJGRIQR/TZrYt2/aFDhzRvhaXI1XOoMNYUz2ZeeVpzvO7K5rQkKcf95AlZU11TTZERIiTKA4TqIEuEDcCUjkC68C7Ouuu063moU1XXp5PcM0LeUWxNlSceu3UuAr3qblgz4qSv28eHg4sGWLcxIVhcBRCjbFFfZKIK2kuzrXpde583KaE7dwdZaIPLRjxw4sWbIEd911F9atW4cWLVo4tVm3bh3mzZuH4cOH45VXXsGgQYNwzz33VH1nSVeOE6DbtmzBoEGDXE/Wms1CJnGtK8kFBcoJybKyKlaKHat5yALbtLQ0zE9Px5LFi6WA1l0ApKZRo0bC9Ya7UmHi9YirQBbCmBweGYktmzd73Advdpw5XddUQllTrdR2pkkTIGpJ7wKMJ/8ugPvfQwbi1VewTcJ4FWCvWrUKDzzwAAoLC/GPf/wDjRs3Vky8EkhvNFAllNczdLVKbCctTQh4Fy0Stn1780HvODiLz3fxYsW285YtK7YabdmieSbULGZEF4NglZXxnTt3SivEWhOhXLlyxfXkgshgwOHDhwHo8B8uIwMAtK1CK60WyBKNBNp/fiLyr9deew0tWrTArl27ULt2bZftmjVrhl27diEuLg6vvPIKA+xqKEYtKBIXNdLThTrIjqu8771nv8VaQ0Iy6TpB5HCG+0pJCaaKR8wAYez1kl2WcDf9kVa63bQrTkrCiRMnPO6DTzvOxOsaP0+mV8edaVr/XY4cOYLEESMC7kgeVb5APY7pjlcB9ogRI1BUVITFixdj8eLFim0MBgNKS0t96hwpkK8me/NBXx4wIikJWLECmDWr4ow24By4ejATKu1qiI7WlHxD06SCg1OnTjk9j9NMt82GMWPGANDwH07tzJvW8mZmM9Cjh2L5EkCYdQ+0//xaBdusIVGw+OabbzB58mS3wbWofv36eOSRR/Dmm29WQc+oqmne/mu1Csk+5TlX3FG7TpCPgYWF6gG542NcPZfW/jhMTBeJdbdV+n3x4kXhi507hde1WISFAtHZs+Vdqjjm5bIPehL7o1RqjNxT+XexWq0BeySPKlcgH8d0xasAe9iwYTAYDHr3pXrRMACJWZw92nblydYftYzhFy+6HlDlg6mnYmIqBjyl55w4UVgVVzojrvJz+/333ytmwQHnc+UOjxH/wwH224rMZjPCIiNR4u49ignUtPz7WK2aypcE0n9+LXSdNWQ5ESI7Fy9e9Cg7eFxcHP78889K7BH5i9L239dffx2r16ypSIpaVmaXgAyAEHSvXatefssV8THh4UBxsXrwGRHh/nVcJB69KA9+RQrb2LM0JjorLS0VkqO5m6iXZdGOMBqRJSaKrUxif8on/8MiI2E2m5GTk8MxTS88kldzBdG/vVcB9oYNG3TuRjVjMLgfgAwGwGZDVmamlDhEV2az67PV4m3ybdaV8QsbG+v6HFL37s7bzk0mIYmLyuy90WgUvpYHsSr9z87OxoyZM1EizowrMRiA5cuFVWh5n2JjtQXYGvuiljQt0Ogxa6h1ZYblRKimiYmJqThao8G5c+dw3XXXVWKPyJ8ct/9evnxZW1WSsDDn8pWeXlcUF7u+T15G9MUXhbGx/BhZnz59sHfv3orkZVFRwgq7yN3xL6Vt7GLyVRW1atUCrl5V/9nIjnlVSe4g+eQ/gJLCQuFsPapwTPNlhwER6cLrOtjkRnkArXa/+KEr0etDUcwsqjLwSANjZXBzHhn5+cL98kEmNhbYvFlI4qLzirrdGTJACOQ3b65YQRef22TyfqJB4xlyMYgMi4zE1s2b7c7dBXTQ7cMkjNLKjJSMxU3W+2Bc8SfyRJcuXfD+++8jNTVVU/utW7ciLi6ukntFgaCgoKBi8kXL56+WushKHAJChY7YrzI7jKd79+7VlLwsMjLS9f3y9+fp9Y7az0brMS+9yF9Pfi1TBWOalslsceckj34RVS6vAuzevXurtjEYDJUXvAW6srKKwMGRmDzMaKyY5TWbheQibj4Ua4WFoVTLeSs5lYFH07+PN0F/drZwtluWLMVOWhqwcKFzlnMNZ8svi4O8J4OwwyAHMcum0mt4mKhMGtC0nicvv5gpSUpymmCpziu2LhOzJCQEzXYfIr2NHj0a9957L5YvX45p06a5bbt8+XLk5OTwDHYN4FXJy9jYiqNZvhzxcqSWLE1ccVaZ0FdKhOsRcWwuH4/Plp+xtjvz7HjsTE+ONa8dKd1exdtZ1bKMAxVn0YMtYRRRsPHqE2/fvn1u72/UqJF6RszqTC3LdUiIcN5J/sH7449CYKowKNYOC8OggQOxbds29XPVgOvVVIcByul55DUxNWzZdpwJtVgswuq8OMNdCeUsLl68qF5azJHWQe7gQWD8eJcTA0qJysQBLTs7W3t9UFEVz24TUWAZNGgQhg4diqeffhr//e9/8eSTT+Lvf/+7FIxcuXIF+/fvx6uvvootW7agZ8+euP/++/3ca6ps0tGcxET7zOBqxKNZ8mMHagGh4xlsV1yNo/KEoG7GWel4l5ZrGMf7xEUI2di8atUqbddaZrNimVHV1yxnN4mudu3hSd4WHXm6Gp2TkxOYCaO4tZ2qEa8C7DKF+oRXr17Fr7/+ig0bNmDlypX497//7XPngpaG7dlOYmMr2k+cKJxTBoCDB3Fl+XIhuJZ/uHsaZLqqiQlUPI/j1umFC4GpU7FixQr0kJ9NLqc6E1oJs7dFRUXOSc70IiaPURl0srOzER8fLw1asbGxiNe6DU0+UARRsgZvuRr4pbwDHpw/JaqONmzYgNDQUGRmZmLz5s2oVasWGjRogNLSUvzxxx8oKyuDzWbDkCFDsG7dOt9XAil4NGwo/K0WeOTnAzk5wtdmMzBsmPC12nWCvAqHGFy7moD3Ud26ddVzcQDCSrSrfivtRlO71rJapWua6OhozVuoRfJVYbPZDKvVivz8fKEKiuNre5O3xUc+JSINkGsQrVvbo6OjhW8YiNdcQfRvr9tIHRoaiqZNm2L27Nk4duwYnnnmGSEorKnUPrjclWVZtcr5PFNGhn3ZB7EkxdmzFW1r1QKmTBGCaMfHa62J6XguPCQEP//8s8uuOiXB+u47YMIE4MoV5V94eXZRVwO50uPKb/vrr7+E7+XvQaf/cOnp6cKgqfJvJ89K6jRoaV0tEGfWqzFN2xyHDxd2b3DVnmqounXrYtOmTXj00UexceNGfP311zh79ixq1aqFuLg49OzZEw888IDiJCdVc1p2bImruI4rufJrBosFeOYZ5xVq2WJJrfBw4RianpPWMrVq1XKdi0OuQwchaZtSUlKlsVntWku2oy8mJkbTFmrHQNTxiFNOTo6mawUleic6DcbyRY482dru6QQJVQ+e5BcIFJUyFd6zZ09Mnz69Mp66+ggJcX1ferpwNhWoON/k6oM8J6cimC4tdS7h4UhtQFCYIV66dCmWKjxveGQkFi1cWPG8JhPwxBNCcA0oD9RGIyCWylC6393FREgI/u///q/iey1JXVyUDFHSUunMvBJZVlJx0DKZTAiPjESxu76IOwSs1oqZ9WDhxSSGloHf26MCRNVN37590bdvX393gwKJfMeWu89Q+TWDeJt8rM/JEYJrN89TKo5djm10PM/tMheH4ziSlSWMj+U5a6TJb2/IymaZzWZ07txZv0DTi3HR7QS9LwJkNdpbLn83HHgzQULBT+skTCD921dKgH38+HHFbeQkU1jonEnbF0pbp7SedZKTf0iLK6zyLeuylfPiVavsM3RbLK7rasv7VR5YTpw4Ed3Ln1facpWWBtx7r+vHpqdXDJjypC5K7RIS7M+VOz6f2VwxiJf3QboPcP1YV9vBHTPHu9o+Jm7l84C/Mn7qMmuoNvC72bFARFRjXXut8LfaZ2inTtqCKy1BmLgN15HauKgiKirKaRwzm80Ii4xEiZvxJSwyElFRUR6/nqQSjpNpKj0ZGSlcY4jjvfjzy8gALBYUTZ0qHTdTen5X47njz1A6buXjv0+w0BqIU/UTbP/2XgXYn3/+ueLthYWF+Pbbb7Fy5UrcddddvvQr+KnNbP75JxAXByxaVLHCKgZ4jtu9tGwnlg+c3pbrkCsoELbvAspb1sV+iRMprraDK/1nKH+fTZo0cR5crrnGfb8cV5nd1dsGhODbYnEKppGU5FROTZohF39uRqNzpnOXL2NBcVGRMHiKmeJ1mlH26YyVj6pk1tDF72mgbfchIqpS7dtra6dnUtlhw5STnbnK1eJBdm3VI0Pya4pyJYWFzqU2PSG7xtCSfFfrZLbjuGg2mzE0MREl4tb2wkLnI3dGI9CqlZBMFXDeHl/O1Xju9lrAi+sWIqo8XgXYd911FwwGg+J9NpsN9erVw4IFC3zqWFAzGNwHt2JgV1zsVFMSISHA2rXAzTcL3zus+moiruyKWckTE4Xn82SLldbVaJHS144f9GazXdbPtLQ0521fyclOA6zEaHQ9u+5IaZLC8XnlK86uamN7un05Pl57Hy0W59ltKM9Q+/OMVWXPGmZkZHg8i09EVO1dvFj1rykPrl2Niwq5WlTPiQPuxzF35b70LDvmhqeT2Y7j0/Fjx2CxWCrOlivtYtNwbaWUTBWohGNXQZQwiijYeBVgP/fcc4oBdq1atdCkSRMkJib6tqUn2DmsjCreHxYG7N9vf7v44Xjzzb6vfMbGVgR6WVkVpT7kH5xathKprcLKz37JKX3QW62azpOJAZfTICUvf+HpwKDlHJtSbWxvttlrXU1wmFyJMBpRXFxcpVnZA0F8fDw6V8P3RUTkDbvSUFVJ6aiZ0rjouO1aw/gq7UbypdyX0nisY5DoTcIwtRVvp+sssa0vyVR9vBYIxoRRRMHGqwB77ty5OnejmtFapsuTD0h5GQ7H5xOJda4B+1IdcvIPVPnstLczli1bev5BrzI4REdH2wdcju1Vtr+HR0Ziy+bN0nYwKVD3ZlByfB0d61w6rtyaTCblAb6KZu+JiMj/xC3I2dnZLrcRq/I1B4u755Fl5rbLi1JZTCbhesXxZ+FhXeqdO3fCbDajQ4cOuuyOUl3xTkryfsu2QjJVzVT+7YMxYRRRsPEpydmVK1dQu7zclNVqxTvvvIPatWtjxIgRqF+/vi4dDFpqwZyLLfYuKZXhEEVGCtu62rUTth7JeVKWS56Uw49bhA4ePIiYmBjlWsnyxGYOK94iXQcGeYI3QNgVIL62GpWZdaWVW4vG2e2gwm1oREQeiY2NrRjX1D5Dd+6s+NpiEXbIKQWennwWi0e6lJ5HvBYJDQVuvFF10tuuhrE3YmOFxQDHrellZUKAL/ZJpS51WloaEBKC8LAw5B075nSdYHb3nkXl2ciBSq6U4SqZqhYO/VdajQ62hFFEwcarALuoqAhjxozBzz//jC+++AKFhYW47bbb8NNPP8Fms2HBggXYv3+/poQSpNHQocC2bcpbzxcuBL7+2v5cj1KpDgW1atdGqVhWSykph6/kA7d81tsNp7PZw4YBW7a43HpdqVuMXSV4g5stVBqSzNWE7VfchkbkG4vFgttuuw1vv/02unbt6u/uUBXTlK1altdEkXhkTetqr0jjkS40b+406Z2eno4E2dGx4uJinDx5suJxzm/Udb9E4vjveI0jvo7Sz0BMciq+Zvlqe3FRkeKqsFXje7Y65sTxpFJGFUwsV+qiAxFp4lWAvWDBAmzatAn3338/AOCdd97BiRMnMGnSJHTp0gVTp07F888/j1dffVXXzlYrnq5gb90q/C3/4DebhUzf8rO8Hq58vrRkCUwmU0WZrIkThYBSr21f3mxvU9oe7SLwr+wAzVUCLsDFoJWbK/Q9M9M+MV15ZnHx+SprwFM7D1ZcXIzw8HCX9yv1y9sSYdyGRuSbq1ev4tSpUyh03JlENYKrz1Dp2FOfPsDevepB8MSJQNu2wm3HjtmP8fIcJ67KUqpdU7ip5mE2mzEkMRFXxOzaStcEkZHCQoEWSv3RUjnFaBTKmXn7Gr6o4iNezGtC5H9eBdhZWVlITEzEO++8AwD44IMPUKdOHSxbtgxhYWE4duwY/v3vfzPAdicszPV9CrOdEydOxKpVq5w/+H/8UZihXbiwIpGZB5zKX3TvLgy+YjksX7f3enOWWGFwq5JM0wo/d60DldbV2h49enjfX5V/C7PZjO49ergvgxIaCly96raP8oQqvpYI4zY0UuOvGu9EwUDpM1Qab/buFW7QEhCKE9HHjtnf7mswqTBuKlYIUZsE+PZb5+dz9TqOt4uT2gcPus5ErlPuFG/Ir1+kyRFfrq147Ioo4HkVYOfn5yM5ORkAUFZWhs8++wx33HEHwsqDxri4OPz222/69bI6unrVOWmZ+OGocH7muuuus2/j6K+/vOuHOBCJJTJE0dHqM8IhIfalpuTcfdD7a3BQe10N55ZcqdTVWo1bzgGVMijihYybzO+OCVW8yapa1RigBS9/1ngnClbieLNx40bnQFaJ0lEn8XGuxkWNR7oUx6UBA4Bu3YCmTSvOg6sF8hkZwgSwu2uOyMiKiQJ346Kr13IofylXmTvh5BP1mrb9i/lwynfB5ebmwmQy8dgVURDxKsCuV68eisq3+/z3v//Fn3/+iT59+kj3m81mNGjQQJ8eBiu1YO7SJaBLF+f7Q0Iwfdo0NGvWDBfLa2BGRUXh22+/VT9D5Q1xIHLsr9XqvM0ZkLY6Y8AA4KOPnOt4yyn119NzYOVcZVLVcvGtaUALDwcWLRLqjpZv5fZ0tVlppUEe/FksFqdAUB78FRQUVAz68qQ1ACDWlZf1TykDOQD1C5lKyPzuLwzQglswTOAQBaLY2Fh00rrlefp04MEH7W/77jtg3LjK2b78f/8n/AkNBdas0fSQ9PR09OnTR/EIk7Tqu3lzxVZ0hYSn6enp2iYc4HxNERYZiVkzZmh6rC8J2+ST8dL7mj4deOWVihrkDvlwkpKSpHGMx66IgoNXAXaHDh3w7rvv4v7778eyZctgMBgwqPzD4JdffsHq1avRsWNHPfsZXDRkoURZGXDggP3t5YPE0qVLK9q4EhEBPPEEsHSpcHH63nvKW8Q9XS0uKVEuhyEXGQncdpswgLpTu7YQtE6dWnEBLc7KRkcLSUvElXO1c2BKF+AaL77lA5rZbMbQxESUiOfBRMXF0mSBt1u5HVdSzWYzhg4fjhI35yfFQROAfaCodJFQ/jvhqn9ua3FWUwzQqokAncDxt/r162P9+vW48cYb/d0VCkBOiWTlpTqBinFeDAgdx1cxC7d4JEzuyy9dJvi042ZHFJKSgDNn1J8DQMuWLdUT+Tm+X4ez3y2V3ocSx/EiNxclSUmYN2+epodbrVbk5ORUTIh7eJ0lTsZLk/9Llyr3S/Y84jjWuXPnKhnLuDOMyDdeBdizZs3CoEGD0KhRI9hsNgwaNAjt2rXDl19+Ka1ki+ezayR3gbH8fvGCUhwUxQ8zMXHJxIlAkyZAVJQwMIpBqThwKc2iyktbaQmUxbJc4nawJ55wLvUFVGQiBYT7HQeisDBgxAhhgBBLW0VHV6yAu7qAFvurdoGtcr+WwUDconX82DHdBw63K6kaBk1A2/Zub1bW/anKBmkGaFQNRURE4JFHHvF3NygYFBQIn4NKY5BY5lOpJnNCguvPzlWr1INHb3ZEeUtrIKvWztV4ISZ5VXm83eq3yoJKeGQkzGYzcpSO0gH4ZM8enDx5UnjOABnHuDOMyHdeBdh9+vTBp59+irfffhvNmjXDk08+CQC47rrr0LNnT6SmpqK7vHZwTaO2+iy/Xz4ohoQIt4mJSxxnj8VtzKJjx+w/3NVWzuVBMqBclkte6kskBvRqZ3szMir6Le97SIh9LWslPpzL1pLcSz4YVEbiLcWVVI2l0uyotI2Pjw+aAa2yB2m7LfVERDWV+DmoZ03m6Ghtu/F8qW8tozYRq3bMKzwiAiUlJagdEYErXhxDAyBkWld7z+Hh9mVDxV15DpVChLvMGJaYKO3wVBJhNCIrM9P16/kBd4YR+c6rABsAbr/9dtx+++12t91www3YtWuXz50KelpqR4osFmFQFEtmrFgBzJqlvIosbmMWS3yJAW16ekX5CYXSUHaSkoBp05yfOztbuN1mcx3kubpdDJ6VtpqJfXA8yy3SMoiHhwuvUVCgeGFw8ODBwBkMAmQGOhBU5iCtJXgnIqrOFANPrTWZ1SYnY2KEaxlxRxpQMZ5nZFR87bhtW+11Xdwu5pxxJO6CysrMdK4/jYrz0MOGD8fYsWNdv77BACxfDgwZ4nqCwWQCysoUq5ZI56W3bAEGDnR+bE4OkJZml9AsJycHxUVFws9LvqNPlJ+PorQ0HDx40HW//YnXM0Re8zrABoRzKJcuXUKZbLW2tLQUVqsVH330EZ599lmfOxi0tHww5eRUDDpRUcLfJpPzKrJY71oMuuWr0IDrrV/lH/h2brnFdb8cn1eLggKhb2I/lIjZxpWIg3hSErBpE3DlinOb4mJhpV3pPQIVSU1UfubiaqfZbHYaqKOjo6XzbJ5uW7ZbSVUoWaK6eu8lx+3XTgnSxCMFjv3Jz9ec+d0s9l1DiTCXKmGQloJ3veq1ExEFGcWEWWo8TWqmlIFcacLUFatV/bhaSAiixGsgGa27oLIyM1GstPtOJC5suMrx4sBteU6VCQX5rirpa4sFGD9eeeEE0JyYjYiCh1cB9rlz5/Dwww9j9+7dbtvV6ABbC3kW8Weesb9PHpTk5Chv3Rapbf1KTxf+TktzvTXKWxaLtr4dO+Y+qLvlFuE5vHmP8iDLMcmL7DXcXnzItu17sm25oKAAcW3bCrPUwos4Nxo2DMjLE75WSEDjzTZntxce4s9C6ahCaGjFeTwFiiU+tGwT9BetSW0osLGuK5FPNCe5FMdYx910LqSnpyOhPInZzp07hWAwN1d7GS8xgZcoLAx48kn728vK8PPPP+PDDz+0S9yWm5uraReUVS3XS1Uwm4GQEOXrDLHaiqv34VgmlYiCnlcB9uzZs/Hxxx/j1ltvRXR0NPbs2YOHHnoIZrMZn3/+OWrXro0tW7bo3dfqJTwc2L9f+FrjQOf14CEPuHQ6L+VErW9Ks+CiyEigTRttz6O0QizKzna9vV7+Wps3O6/sys6Qu9u27LhqnJ2djeKSEtevBwir8uvWAYsXK/ZN06qDAy3br8WkaPKtbsXFxYplUESOq/cx4g4DlddxymZb1RigBSXWdfXM/PnzkZycjLp16/q7KxQAvDoq4zjGqnx2tmzZUlrNLS4uRtrcufbXK95UKpFXSin/e+nSpUIFFUfidYsegbMHu7c8ZrW6HivV8rF4mY2ciAKXVwH2rl27MGDAAHz44Yf4/fff0bhxY0ybNg2dOnXCf/7zH/Tu3RtHjx5Fv3799O5v9VJFM63p6elo2bKlEMj5KRCaOHEiunfvLvTBcQAymVxvIXfkeCEursgC6rPE4iAXE+P6jLkb3mYKR1JSRdZ1tRlsTwdYDUnRXG5184QXkztut4170U5RdLRwdIABWlCSb3F1heVgKqxYsQKzZ8/2dzcoQNhNtAKebf82mYRynyqJUeU1n7t27Yr92dk4efIkPvvsM6x54w33jw8LEyaW5Z+/4lnkpKSKrN1q46erHC6ecrN7C5GR2q9D5MRdc65W9E0m9S31GnLRcBwjCi5eBdi//vorUlJSAAiZwxs3boxvvvkGnTp1QteuXTFq1Ci8/fbbmKaUTIsEaqW8dHT27FnPHuCq5IVSkKdxZtVoNDolDZEcOQKIST6ys53vlw8qSglX0tOFM+w//+y8HU3+HFrOjZUHe0rbthW3q2nNFC5eSLgSFVXtBlilhDRa2mkp7SURy9YpHQsIwrJmNVFlZPWvrpKSkjBjxgw8+OCDaNy4MQxiwksATZo08WPPyK8cq34oUbpd9vujSOH+rl27omvXroiPj8eaNWuc86fIq5WUlFRMfIsiIoAXX1R+PY1npL21YsUKzJg5EyXikS65wkJg6lT1cVb+c3TMjwM4j+FGI6CWJbx8p5hScjWRXyYauaJO5DWvAuzw8HBERERI37dq1QpHjx6Vvr/11luxdetW33sXzNQ+mFwF2FrPNWl5zfLvV8kDu+xs5b6JrxsWphzkqZ3DVfHbb78JK5VKzyM/K+w4GAPCAPXKK0I7pa3m4oy0eDGg1E8tg5zZDCQmlj+Fi/fq7XY1o1FbGbXp04GlSxUHWj0G2CqrSy3nwSDtSVIbSWysy4uyYCprRqRm1apVuHr1KpYvXy4F1zabDQaDAVevXvVz78ivTCbV3TyIjKyYsNaYP8XVRKl0vENcPZdnFld6TrNZyEdSVFQxzotjudhnF4lM9dKjRw8cP3bMqzHQ7XEWnVbgddtx5oZ4DaCU7BWoyMoeHhmJ4mo04U9U1bwKsNu1a4evvvoK48aNAyAE2IcOHZLu//PPP1FcXKxLB4OSlqRQShdDISEVwaK7s8auuAqMly0Tvp42DUhOdr96Xj7zPH36dPzz1VcrZnp1WHG3Kp1RclVj26G2JL75xvmxatnVAeGCYuFCYUBXG+ROnBAGf28GS7Ug8vJlbeXbKrHslJbgNSwyEls3b0aHDh18DkyjNdZRlW9B1FraS7ow4Aw71SAnTpzwdxcoUMXGOu/mEceVxEQgKwuYNEmYZJeXjFLZfZWfn48cpXPLAD7ZswcnT54UJqTln9dKz5mTI1QE0atOtxIN44G3O2aUjrNImdt1Sq4m7pyrrNVqTWf2yxc8wiMisGPHDpf5VXh0h8g9rwLsESNG4JlnnkFUVBQWLFiAu+++G2PHjsXatWvRoUMHvPbaa4iLi9O7r8GjrEy5JjSgXJta/jjA9SqylmBC/rria/XoIXxvswl/1AY4gwHdu3fHU089pTyYKAXIKn2LioqqCKSUBiP5bQUFwjZw+TYucabbm+zqWmdZxTqcngyWrlbl5UJC1J9bfJ7y96m0gu5JdnMlWoLXkqQkDBo0SHot+X2K3ASyPiVIU/k3iI6OZnIsqnGaNWuGEFnW/t9//x3XXXedH3tEAcXVbp6sLOFv8QiVUoUJF9LS0lyWkXLaTaSFN8GoSnKyqhoPfD7OojKOiuO+r2O9K1qToyI9HcVpaYiJian0FXWi6sqrAHvq1Kn48ssv8fLLL+P555/HAw88gH/+85+YMGGC1GbRokW6dTIoJSQoDyLy2tTigOH4obt1q30yMnGlVm3rV2Ghc/BuNDonEVMb4Gw2NG/e3G4wKSgoUH68lm1pISEwGo2u73d05IhwdksrTwdsV2fMPT2rDrjPHCo+d1KS/WSBUimxgwcrJmY6dXJORle+cuuU3dybFVy1n1d6OorS0pCdnY3o6GiERUSgxJcLl0oonRITE6M5OZZftsUT6WzmzJn4/fffsW7dOgDCxXKzZs0wffp0LFiwwM+9o4CmNCkuL2/pTnq6cD3jyHE3UWVSKS3ZoUMHvyRL1Jyg02IRKse4u04KDxcm2MPC3FYy0YXamMzyl0Q+8yrArlWrFrZt24bDhw+jTp06AIBPPvkEK1euxP/+9z/ce++96NWrl64drZbkdbAjI4XtW0uXKme5/vFHYWuXfGCUr1ZbLMI26MRE4OabgdJSoFYt4e+NG70LHsu53VYk35amtN27/LZmzZppH4jdbSX3hXjOXOl5DIaKVXJvslprDSLNZmFHgastWmlpqufQCgoKYDabERYZ6Vvg60p5wjV35cPEreQxMTF+C1C1rCZoPdNdGasFRHp57bXX8M4772D9+vXSbQ0aNMC2bdswbtw4tGrVSjqyRTWQ2kSr0vikNYhq2bJqa0sr7JzSmvyrqj/DNV/TiGfO5QngHBUXA5Mnq+eKIaKg4FWALfrb3/4mfR0VFYXU1FSfO1SjHDhQ8bXJVJFBW+v5UaUZ3aws4Y9DjUlviCt/UvZsVzPejtvSFAbzixcv4qK4TVrr+XK9Vz7T0oDatYXz744/E/mgN3y4MKHharDOzwc+/FCYCBCzn6sRX89qFYJrL8+hmc1mdO/Rw/056ogIbM3K8v4c9cWLqqvyJeVbuzVtH/PjOWmtZ7ordbWAyEerV6/Gv//9b3Tr1k26zWAwICEhARs2bMDMmTMrLcBetmwZXn31VZw5cwbt2rXDvHnzcN9997l9TGlpKZ577jm89dZbsFgs6NixI1566SV0FytAlMvIyMDChQtx8uRJxMbGIiUlxel9fP3110hJScGBAwdQt25dJCUl4YUXXkB4eLjUxmw2Izk5Gbt370ZJSQn69u2LlStXomnTplKbf/3rX5g4caJTXydMmIB//etf3vxo/K64uFgoVelu8lncxRYsHN5LhNEY/JUg0tOFv10lgAP0L0lGRH7lU4CdlZWFrVu34tSpU1i5ciXq1q2LnTt3YtKkSYiMjNSrj9WXY3CiMSkU0tIqkpeJ56vlxJrKYkAsfqB7sAr8zjvvYOU//4lSpa3anqzwms2AwWB/hsuxDwaDd6vG3hBLifiSaCUtzf1MtBLHrPpeTh5YrVZN56itVissFou0Zc6rlWwfJzjcZl0tV2XnpCthmzpRVTl9+rRdcC3Xp08fnDx5slJed8mSJZg1axbmzJmDW265BW+88QaGDRuGTz75BHfeeafLx02ZMgUbNmzAokWL0Lx5cyxbtgz9+vVDTk6OlJ9l8+bNGDVqFKZMmYL+/fvjvffew/jx4xEZGYmHHnoIAPDTTz/hnnvuQbdu3ZCZmYnc3FzMnj0bf/75p1AiCkIwP2DAAFy6dAmrVq3ClStXMHPmTPTt2xeHDh1C7dq1AQCHDh1C+/bt8cYbb9j1tVGjRpXxo6sS4eHhwoSxUu4VcYxQK32l1wRobm7FLjEfSno6rlYHxREeT36GHIuIagSvAuyysjKMGDEC27Ztk0qEXLx4EQUFBUhJScGWLVuwa9cuREVF6d3f6k1MCpWeLsxiLl1akf1TJK6ElpUJwbXSB7W4Ei5uCxcHPfFvtWDWYMBSpXrSYpA8bBiwZQvQoYPywC0fVD79VD0YNRiEduK5Y3elyrzJrl7+fOnp6WjZsqW2rJ9Kz52dXbEjwJPgGqj4d/SlDJucSv8dt3d7lIzGh+MEckpZVx25vHhidnAiSe3atXH58mXFXBYlJSWoVcunuXJFhYWFWLBgAZKTk6UJ0v79+6Nbt26YP38+9uzZo/i4n3/+GatXr8bKlSsxadIkAEDfvn0RFxeHxYsXY+3atQCA1NRUJCYmYvny5QCAfv364fz585gzZ44UYC9evBhRUVF4//33ERYWhoSEBBiNRkyePBmpqalo3rw5Nm/ejO+++w7ff/89brzxRgBAx44dcdNNN2HTpk3SZ+GhQ4dw22234fbbb9f9Z+V38pwvYp4XtTEuOlo4mqY2oS+r8qD8NA4JxtQWCVQ+26uiVJVetFbK0HTWnSoFc7CQv3g1Kq9YsQJbt25FWloahg8fLm0V79evH2bMmIHFixdj6dKlmDt3rp59DS6+BAhiQpGlS4HBg4GsLKSnp6Np06Y4c+YMtm/fjm+++Ub5sQUFwDPPCF+LZ4odP9zFAFkhkRYAbZnGBw0SBufNmyueR3y8N3Uily6tyHCqxF0yNbWfdfmKf6dOnVyWnHDibsD05mz4HXcIq9hVNdA69NGj0larVgkXBTrsKvA062pArXoTBYju3bvj7bffxvjx453ue/vtt+2Oa+nlv//9L6xWK4YOHSrdZjAYMHToUMyaNQuFhYWKO9X27t2L0tJSu8eFh4dj0KBB2LZtGwDg1KlTyMvLw7x58+wem5iYiMzMTOTl5SEuLg67du3CoEGDEBYWZtdm0qRJ2LVrFx577DHs2rULbdu2lYJrAGjfvj3i4+Oxc+dOJCUloaysDEeOHMEDDzyg288nIBUUaJ+EjIkRxu9Bg5zLX1qtwJdfCmOB1WqfwdthRdwx4eR3332HnTt3IisryzlPzDPPuB0vg+2zXWuljIzyWuHu8ppUKbVrAL0WAvyMOVjIn7wKsNevX4/77rsP8+bNw//+9z/p9rp16+LFF1/EqVOnkJmZWXMDbC0zmh6ei46KisLESZNQLM9GrcRi0VZrctAg5ceHhQkZvDVkmkZamtPzyJNfAcDOnTuF1Q+15xNXeNPThRVUcXJApJBMLSEhATs/+kj9Zz1tGrB0qfbgWkn5AAlXq99aJlTKyoCJE53fm9rj3D2/BywWC8IjI1Gslo1ea93wSuDTqjdRNTVjxgz07dsXRUVFSExMxHXXXQez2YzMzEzMmTMHmzZt0v01xZq8jiU327Rpg6tXr+Knn37CTTfdpPi4unXronHjxk6PM5vNuHTpktvnBoC8vDw0a9YMp0+fdmrTsGFD1KtXD3l5edLrKZUFbdOmjdQmLy8Ply9fxldffYVXX30V+fn5aNWqFWbPno1Ro0Zp/pkEtIICYWwSgwm1MUl+vzimFRQ4J+F0HC/EiXXZ+CCfSO3cuTPCwsKEANuxmsqQIXZjeHp6OhJkGcqD9rNd5frGVYI2RZUY3GqZwBZX3INtskMJc7CQP3kVYJ84cUIxWYioT58++OCDD7zuVNDTUgfbYHCu6+gmiPr2229RXJmlq0QlJdpWL8X3JkveMX36dPTq1UsKZE0mE1pqzVQaEeG8lUolqLz++uu1/axVtrgpEc+BSbW/XQ2QGsuUSbp31xZgu6iDLdUSV7t4Unj81KlTER4RgTlz5ggrR0qDjmNJtyqidRuX2M5V26C9QCNyo2vXrlizZg2eeOIJTBUzEgO49tprsWrVKrsgRYu//vpLWk1W0qhRI2nHS7169ezuE49+XbhwQfGxVqsV9evXd7pd/jgtz+2qjdhOfH2r1YobbrjBbZtDhw4BEM6yL1u2DLVr18bGjRvxyCOPoLi4WHFnAABcunTJ6X2Gh4fbJVgLGBaLEBivWAHMmqU+Jon3R0ZWJEETn0PjBL1SEFZQUIB8V0GiQ0LUli1bBs12cLf0PNJUicGtfALbbDYrZkGPjo72a3WQSsFz7+QHXgXYRqMRly5dcnn/2bNnERER4XWngp7amRtxBVtepkukVLcakLYY2a2k+sJNbUtNmSwtFqf3uXTpUruz2xFGI17UWp9V/v6io4UVbZULhJiYGG0/67Q0hEVGuhxQlGg+B6ZUpkyeXA4QbhMHS3dJYOS3yyYuxGDfZDLhyJEj6jskDAYgORno1cv+GEBuLoqTkiouRl0NOuLvXn6+8ySQu757Ses2rk/27EHvu+/2brsXz3RTkLv//vsxZMgQZGdnw2KxoEmTJujatauUxMsT586dw8MPP+zy/p49e+Kee+5RvM9Wnn8iRD5xKFNWVgaDweD2cWXlO7gc22lpI7YTX9/d64WGhgIAevXqhQ8//BC9evWStrX369cPv//+O5577jmMGzdO8Tl69uzpdNucOXMCe3dejx5CFQzxc1zc8l0+4Sw/bpaWliasRjt+XqoEJPIxSf5Zq+WzvDrx5EiTNCmsMhZlZGRUatZ0T49tEZF3vAqwu3Xrhg0bNiA5OdnpvvPnz2P16tXo2rWrz50LWhrO5Egc24nnmxxX6MQt1PK2PmTqdEnraq+GUk5FSUkVpblU9OnTB3v37hW+iYkR3qsYDEdHC1+LNbYBIClJSOwj9kFsY7EAM2YIK/GAtBW/pLAQg+Tb2VUGuVyHv7Fzp+vOO5YpE1fTlX4uYgZ4jUlRHEuUZGdnC+9JXKUoLHR+vM0mnGdftcptPW1VaWkuJy/0nGHXuo3r5MmTHm/34pnu6oPJaoTV07vvvlv6/vjx46hXr57HmbBbtGghBbOuvPrqqwCEEovXXHONdLs4ua60Sg0IK2BKq9vyx4k7cRzbaWkjthNf393riW0aNWqkuMo/cOBA7NmzB7/99pvTlnYA+Oyzz9CxY0e72wJy9dpxPHMckwAp+VlCQgI6d+6MnJwcIcD24uiUqwlo6bN8+nT3OVXKRXuxuyyQeHqkSctYFPQlyYgIgJcBdlpaGnr06IFu3bph2LBhMBgM+M9//oNDhw5h+fLl+P333zFjxgy9+xpcvN2SYrFUrIYCFSueDRtWtNGyLVmNq+DJ0/JqOm29kYJrpfdkNAJiBmxZYJUmBqvR0UJGc/HnVlLivG28pEQ4X37wILB8uWqA65SMxJPkZErb48pvy8jIkC4qYmJiFFfVxS1agFDnVL4lWhrIjx0Tguvp04FOneDwBBUTEmJ72e+Uy+17DhzLpchVSjCj9XfJg985rVvixJ8rL2wCE5PVCL755hukpKRg3759WL16NR5//HGEhYVh8+bN+Mc//qHra7Vt2xaAcCTs1ltvlW4/ceIEwsPD0apVK5ePu3DhAs6dO4eGsnHrxIkTaNGiBSIjI+2eu5Ps8+vEiRMAhCRlderUQdOmTaXbROfOncOFCxfQvn176fUOHjzo1I8TJ07gtttuAyAEyqdPn3Y6b11YWIjQ0FC7CQS5unXrKm5RDxSaztSq8aYyhxqNgbNPeVEChHxFWGkSUD5+f7Jnj9sJmpowSUhUU3gVYN92223YvHkzxo0bh1mzZgEAnnvuOdhsNkRFRWHdunVua2RqNXToUOTk5ODUqVNu22VkZGDhwoU4efIkYmNjkZKSgnHjxtm1+frrr5GSkoIDBw6gbt26SEpKwgsvvODf2WhPal6fPi3clp0tbAHbswc4ebJiVTc+XtgKdvCgtmBQaRVQvrqu0/bgn3/+2f3jxNunTwcefFD5fldb1sVZ8k8/BYYPt1/NdfwZqCSWC4uIwOIXXxTON6rVF9eStdxxq3/5uSpPZqfdBhXiOe7ly53fl3xCwmx2SlqTpnGyQMs2+WBYVRRfv3uPHjU+QAtWTFYjePrpp9GzZ0/YbDa88MIL2LBhAxo0aIBnnnlG9wC7W7duqFOnDrKysqQA22azYevWrejZs6fLsVPcWp6VlSXlaikuLsaOHTvQv39/AEICslatWiErKwvDhw+XHpuVlYW4uDg0b94cgFDea8eOHVi2bJn0ellZWQgNDUXv3r2lNu+88w6OHj0qBd1Hjx5Fbm4uUlNTAQgTuM8//zy6desmJVIrKytDVlYWbr/99sBcldZAPoEo5QvRSJfg3IHZbLY/tqXD0ZxgGGMATgISkT2vi2fee++9OH36NPbs2YNjx47h6tWraNmyJfr16+dy65gnMjIysG3bNmmgdWXz5s0YNWoUpkyZgv79++O9997D+PHjERkZKdXS/Omnn3DPPfegW7duyMzMRG5uLmbPno0///wTa9as8bmvXnPc3nz8OPDCC0BpacX94t/iFuXkZOF7eRAFCIGUPMhUG9jUVgHdbA9GZKTmDNOrV6/WNpHQq5fnK+HiLLm4Fc3dtmmlZGjR0cL2uNxclCQlVWwTdlVfXLx4UMvCvXlzRY3w8gsDb85VaQkq7CZY5LeJ/z5Wq3PSGrGNjxc/wXRBwQCtmqjhyWpyc3Px2Wef4YcffsDvv/+OkSNHIjw8vFLKTxmNRjz99NOYP38+wsLC0K1bN6xbtw4HDhzAp59+KrX75Zdf8Msvv6BTp04IDw9H8+bN8cgjj2DatGkoLCxEXFwcli1bBqvVipSUFOlxaWlpGDNmDBo0aIB7770X27dvR2Zmpl1G9BkzZuDdd9/FgAEDkJycjLy8PDz77LOYMGECmjVrBgAYOXIkFixYgAEDBuDFF18EAMycORMdOnSQgveJEyfi9ddfxz/+8Q/MmzcPRqMRr776Kr7//nu79xKMvD1Tq7S92dMg3ZHVarU/wuTj0RyOMe4Fy+SD3zEHC/mB1wE2AERGRuIf//iH7jPnZ8+exVNPPSVkiVaRmpqKxMRELF++HICQuOT8+fOYM2eOFGAvXrwYUVFReP/99xEWFoaEhAQYjUZMnjwZqampqkF8pZKXxxg/viK4VhIeLpTgEpNoAc5bxbVkEJVnDXXHYBDO8zoqLNR0vkqitHJsMAirryaT0Fcftoqlp6cLq7Imk9A3cdJCbVU7IkI4662VUs1LcVVbDN7FoN1hq398fLz3A51aUKEl6JC30XDEQMvFj68XFOLFQW5VDnIBGKDxIom0qlWrFqxWK3bu3ImuXbsiPDwcJ0+erLRtzHPmzEGtWrWwevVqvPTSS2jfvj22b9+O7t27S23Wrl2LefPmIT8/Hy1atAAAvP7667jmmmuwaNEiXLp0CV26dMHu3bul1WMAGD16NIqLi/HSSy9h3bp1aNWqFTZu3IgRI0ZIbdq1a4ePP/4YKSkpSExMhMlkwrRp05AuJoGEcCZ69+7dmDJlCh577DHUrl0bffv2xfLly4U8HRC2In/xxReYNWsWnnzySVy8eBG33nor9uzZg27dulXKz85vPAgmXAbnvgYkjonW5I8vL881atQo1c+1oJwYraIxJpgmH/yFOVjInzQF2J9//rlXT+7tNvFx48ahb9++iIiIwL59+1y2O3XqFPLy8oSSQzKJiYnIzMxEXl4e4uLisGvXLgwaNAhhYWF2bSZNmoRdu3bhscce86qfurJYKoJDteRo4iqs1SqsYlutwJdfCtuGVQY2AMpZQx3JV9bFcldiojVPMpm724puMrkuf+XITfZtp1Jg4nO6C7YBoKiooo53SIjbIMfp+cUB1HFLnAK/foArnbdWyHzueN7ao6DOiwuKQM026y7Yr4xAlxdJ5ImkpCTcdtttMJvNWLt2LQ4fPoz77rsPjzzySKW8nsFgQGpqqrTVWsncuXOdsmqHh4dj+fLl0sS3KxMmTMCECRPctunRowe++uort22aNWuGrVu3um1zww03CLWZqyktwURYRATMZjNyFI5/FRcX4/z58wiLiECJHgGJUqI1GaVyiy4/YwNwYtTfgnLyoYp5moSOSE+aAuy77rpLsYSFmqtXr3r8mLVr1+LAgQP44Ycf8PTTT7ttK14Mx8XF2d0uzpLn5eWhWbNmOH36tFObhg0bol69esjLy/O4j5p4OwPsyUDiahBUGdhUV4vDwytWYuVtxYsTtUzm8tv1Ghgds2+HhADlNVPdcgy23UxeaM14bkdpVVs2S9+yZUspiZb8Q17Lh7pZrRa5Fq4Cf4ffEcfz1gUFBYoXYSJfJwzsLg4Az7asV8Z2r/KJEnfbIysj0OVFEnli8eLF6NatGyIiItC/f3/k5+cjNTUVjz76qL+7Rn7mLpgwm80YkpiIkqIi+2oacmp5SiIjsXXzZnTo0MHnz6K0tDTFXCDhERHIO3aMn3We4OSDWyxLRv6ieYu4zWZDo0aNMHToUFx33XWV0pnTp08jOTkZ69ev13QBL2YEdtweF1UeeF24cMFlG7GdUnkPuUuXLjm1CQ8Pd58UxWBQr1Msbr0WgwJfgoOICOCJJzzbtu1OcXHFym658MhILFq4UEgCBmjLZK51K7oW6elCtuzyM9N2taW1qqqBqLxGuLtkYlqCNa01uxWVr1xL2+c9oGVVVUwKB6Did1csMeeJ+HjNW9ZbtWpVedu9xLOD/gp0eZFEGg0ePFj6umXLlgyuSeIqmPjwww9xpajI9eebeNTJzedfSVISYmJi3H7++Vp2q7ikBNu2bUOP8iSjuY7XR96MMUREfqApwF63bh02bdqEvXv3Ys2aNejVqxfuv/9+DB06VJeEZoAQwI8dOxYJCQkYNmyYpseUlc+2Oq6ui/U9Q0JCXLYR24WEhLh9jZ49ezrdNmfOHKctcXZcnV12vF8tYZarx4oz1OK531athIziWqmtAIqDrGz7cI8ePSpmxnNzhfvF7ekicSu5eEZcy1Z0rX1KSHAOQDSWm/KYWl+Uypoo/Tu6SapW6auS5VnLOzmW8NJAcVXVIYleSVFRxWSL+N6NRu/qbsu3rIsctq6LK/6Vvt2LgW5gq+HJas6fP4+XX34Zv/zyizS2lZSU4Pvvv8ehQ4f82zkKWNJkravPN512nGkuu+Xm6Jg0rsj5OsYQEVUxTQH26NGjMXr0aJw/fx6bN29GZmYmxo8fj0mTJqFfv3544IEH8I9//ANGo9Hrjrz66qs4fPgwjhw5gtLyRF9ioFxaWoqQkBCnYFicLXVcYb506RIAoH79+i7biO3UJgg+++wzdOzY0e421ZIeKith0mAhz2wtBqdqbDZA3MosT3S2YIH6Y4XOuw/qxe3hCsxasmiHhFQE3WqDbX6+8FqRkeor4WZzRdkwWTmsCKOxYtbcx4A7KipKvWyJwvsPi4zEv159FY8/8QRK3CVVEy8OtJ47F3kR8CtOinjy3ID9xVZOjrYcARaLdxc/Lo41OG5d93m7l6v3W1mTNaQLJqsRjB07Fj/99BMaNmwIq9WKli1b4sMPP8SkSZP83TWiCr4cHZs4EXAosyo91pcxpqo4vnezuSKXDZzzfPAMMFH15FEW8WuvvVZKSvLbb79JJTUefPBBGI1GDBo0CA888AAGDBiA2rVre9SRrKwsWCwWxRnQ2rVrK64at23bFgBw4sQJu5W6EydOAADat2+POnXqoGnTptJtonPnzuHChQtS3UxX6tat612GVq0zwR5u3wUAnD0r/C0/v3rsmPC12sA2ebKw1dqRGOArbA9PSkpChNGIFxcs0DZ5oHWrurv3Lj8PVljo1Cf5eTBAuLguUqu9qaJNmzbONUXT04Wz3uKkRlSUsE2t/OclD2RLtAagGpWUlGib0HC437Hetq6BidrvtdLqfgCojJqvVHWYrEbw6aefIi8vD7/88gvmz5+PLVu2YPPmzf4tN0lUTtPnrNrRsSZNKnWMqayqDS7fu8PZdsc8H0xiSVQ9eV2mq1GjRnjyySfx5JNP4ueff5aC7SFDhqB+/foYMmQI3njjDc3P9/rrrzslmZo3bx4OHDiA7du3o0mTJk6PadOmDVq1aoWsrCyp3iUgBOtxcXFS+a2+fftix44dWLZsmbT6nJWVhdDQUPTu3dubt69OLdCNiQF27LC/XWuip1WrhHPXYpZv8Ta1s98hIe6D3/BwYMsW55Xn8i3NEq2TByrvRUwCpsRoNLotn+Y4CB7LzcWRI0cwdPhw+wyoHgzA4vmy2NjYisHSzSRAWESElMAsV8vMvIfCwsKU63eLyoP89PR0JCQkSDfLfzZVHpgoXDwEwqqi2s/B1/qvVPmYrEbYPdWoUSPUrVsXR44cAQAMGzYMTzzxhJ97RuT+c3bnzp1CPhBPjo4p8XKMKSgoEK4RypO9ueJtwOu2rnhl5vaoZsdmWLaSqguf6mCLmjVrhunTp+O2227DSy+9hA8++AAbNmzwKMAWV6PlGjRogLCwMNxyyy0AhG3eR48eRevWrdGwYUMAQjbKMWPGoEGDBrj33nuxfft2KdgXzZgxA++++y4GDBiA5ORk5OXl4dlnn8WECRPQrFkzH9+9Ai2rjlevVgRiBQVCsKy2fTsyUljNFWtdO2YDlZ/7VsoIKvs+LCICW8uzgh88eFAY+LZsAQYOdPnyWgOlFStWYMasWW5LffiSBEz8ALZYLDCbzXbJwBYvXCh9nfLMM7iiJdh2UR/UXUbWocOHo6Sw0HVGVj0pnT8HhG3baWlo2bKl3TZqR1UZmHhc7qsKLw4COkCrZhdJVDlat26NTz/9FL169cKVK1fw888/IywsTNjtQqSgoKAA+VV4BMbV52yufHHBB96UlNRUFjIyEli4EEVTp3od8LocYzRMvHsaWAbDsRlP3xPLVlJ14nOA/cUXX2Dz5s3YsmULzGYzQkNDcc8992DkyJF69M9OTk4OevXqhfXr12P06NEAhPPhxcXFeOmll7Bu3Tq0atUKGzduxIgRI6THtWvXDh9//DFSUlKQmJgIk8mEadOmIT09Xfc+AtC2jVpMulZQILRz98EfEVFRImvQIGF7lZbtyOnpQnCmcL+YEbRz586IiYkRgl0fBz5Rjx49cPzHH12XCxk2DFeKi10/QXg4ii5fVhzkNA2UssmFsIgI/Ou114Tz0R4ORK4Gy5ycHOft4PKz9WrKy2+5qrkczDO0jmemXQnYi4MqDnQD9udAAemZZ57BwIEDcfToUYwbNw633347atWqVTUTfRR0nMZLtRwUOn7+OQZX+Y6v4Soj+NmzFflWFPogJr0Un1tLPW0t5RC9qkyiE7PZjO49engUWAb6sRlvgmWWraTqxKsA+4svvkBmZia2bNmCX3/9FSEhIbjzzjvx3HPPYdiwYWjQoIEunduwYYPd93fddZeU+ExOPBfuTo8ePfDVV1/p0i9N1GYsxTPqFosQXKt98CsFv2qv0bKl3zIiuwtOrxQXa0sCp0DzQFl+Pr0kKQk333yzy4Bf5NVA5M128Oxs4JlnADifxRKJg47Wkie+lkaRKygoqAj8d+6suBgSL470qM2NwLs48FegG2g/BwpsgwcPRl5eHho3bow5c+agffv2+OOPP/DII4/4u2sUgKTxUtz15m4SODRUtVSi1s8/t8GV+BqRkcJ2cfHaRhxrVq0S/igJCcF3332HSZMne7fKGaBVIqxWq1eBZSDvyvIpWA7QfyciT2gOsLOzs6WV6l9//RUGgwF33HEHZs+ejcTExEqrjV1tRUTYf+9J8qjqQMMHqNldMKf2eIcPdPlApLRtST4LXqkBjViCRMOgo7XkiebSKCqcLoqUtvAPG+bynL6nAuniwJ+BbiD9HCjw7dy5E6+88grOnj2LnJwcJCcn44EHHlCvbkE1V48ewI8/ChP68klo+RhUXCwcU3MokSjy5PNP80S4486LkBBg2TKhvy4ec+bMmeq7ylkdA8vq+J6INNAUYDdt2lQKqrt27YqZM2ciMTFRtwv7GqlOHc/ay2d9PchEXSmqaBut/Gy1Xir9jI/Wn00ADjpeXxSVC/atzAx0KdAtW7YMb7zxBmbMmIEpU6agfv36+N///ocpU6Zg3bp1/u4eBTIXpRAlDhM0riahPZpoVBnn5EF8dna2UAO7Rw+3j4mKitL03ERE/qQpwDabzTAYDGjVqhVq166Nbdu2Ydu2bW4fYzAYsHfvXl06WS1duSKcM/I0KC0srFgF1ZtKcBgdHR3050Ur7YyPySTUuVZLbjdtmvYyZrI+eXS7rzy4KJLjVmaiyrVq1Sp89NFHaN26NaZNm4b69esjMzMTN910k7+7RsFC41g11cV1hp5JprTm7JAL5OsLl5jEkqjG0bxF3Gaz4cSJE071pF0xiEm8SNnRo0CXLtrbyxOWZWdrC7I1rnRrPX/aoUOH6nNeVO/Z79hYYbCUbcGTB6JSuQ6lGuQKcsvPYIdHRqI4wCY0vLkoIiLf/fHHH2jRogUASPlIrrnmGly9etWPvaKgojBWSRPO4vdutmkXJSUhOztbSjYWFOO9Vl9+CaAiAamv70/rtZWeeVSIKDBoCrDLHMs9kTq9ZyzlCcvE51B7jWPH3GbjFHl6/tSfWSlz3b33QMk4Xc6XQFRMgBYeEYEdO3a4PI5R7S5wiMil2267Denp6Zg7d640if2vf/2LE17kGcft4o4TzirbtMXxKehKJqldM5UnV9Pr/Wm9tnJ3f43EFX+qBnSpg00OtNTBFkt5AUJbTz5QSkq0vYabbJyOK59Vfv5U7f0aDHazuk5np5Xee3kdS7fPr4MqyThdXkKuOC1NKqdGRDXbihUrcPfdd2PDhg24ePEibr75Zpw7dw4ff/yxv7tGgUzvgKX82iVYkolpGbOVsprr8f60XFtJAXZ1DCw9eE8sW0nViaYAOyUlBc8991xFcgkP/fHHH5g/fz6WL1/u1eODjpY62IBwv9azuxZLxWr0mTPCa6SnCyvbjvLzhQzQ8m3lDtuW/bXyaTKZEBYZ6bYmNcLDgeJiu1VbzQm4pk61m3wI2NJKaoOOmME7JATZ2dnanrOGUsoKL8efW81V3X434uLikJubiw8//BCnT59G06ZNMXDgQNSvX9/fXaMApCmwNBo93/0lG4NzFcYy6bbysUuhgevn1hqQqbRz7FdWZqaUODU6OhpWq1VYqRavKVzV5a4CgRxYevsZ6s17YtlKqk40Bdi5ublo3bo1ZsyYgVGjRmkuyXXmzBm88cYbWLlyJW6//XafOlptyc9DiWTBcHR0NIYlJqJY6cx1QoLyNq6cHCFAU6iD7e/zs7Gxsdi6eTMGDRrkeoLAYnF9xlzl7HR6ejo6deokBecBV1opOlp9QkWcSbdagaQkKdlMlW3HC6JZ9ErPCk9Bq7r+btSpUwcjRozwdzcoCCgFLFI+EF8DS7MZCAmRtlMrSk4WFgMUOAZXWgOyVq1aqU8ahIa67VeE0YiszEzhmwDIRh6ogaUvn6HevidW86DqQlOAvWPHDrz11ltITk7GrFmz0LNnTwwePBidO3fGDTfcgOjoaJSVlcFiseDUqVP48ssv8cknn2Dv3r2oX78+Fi5ciMcee6yy30vg0LpFXOSifIYYDOcdO2b3IbVz506kKdUoDhIdOnQQBkg378Hb2dqEhARtEwj+CiKtViAzU/hbTtx1kJEhnH+Lja3YsVA+EVHZ2/ECeRbdlUrLCk9Brzr9bnz++eeqbe68884q6AkFG9WAxWKpmOD3ZPyzWjXt1tNadcKTgMxdO6cJBIV+FSUlVUoZUF8EYmDp62doIL4noqqi+Qz2ww8/jCFDhuBf//oXVq5ciaeeesplpnCbzYbrr78e6enpmDx5MurWratbhwNdrVq1UFpaqr592wOOH1JKW7KCiT9na/0VRGreqicG13ItWyoPbjoL1Fl0TQJgFYICVDX43bjrrrsQGhoKg8EgZQ+XMxgMKCkp8UPPKNhoGos8mYBW+f8lHklzVVdb7JN4naNlfNHUrhr8vw8Y/FkSecyjJGd169bF008/jeTkZHz99df45JNPcPz4cZw7dw4hISGIiYlBixYt0K9fvxqblCkkJERYoXYXRDuuYNdA/prZ9FcQqfS60k4EL7fqVcb5Us44EwWeESNG4JNPPkFCQgLGjBmDnj17+rtLFKTcjYFmsxlDhw93nyPFwzPbZrMZ3e64A8WFhS7bhEdGIu/HHwFA05j2n//8BydPnlRsk5+fL3wRREediKj68SqLeEhICG6//Xaeq1ZgMBi0JSADvB4ANNdMlCdG46Ai8Wdwr7gTwYvZ4ep6vpSInP373//GhQsXsGnTJsyePRtmsxmjRo3CI488ItXFJtLK3Rh4/McfYbFYKrZaO17LREcL1xZiIKvixIkTQnDtZptxcVIS9u7di0mTJ6uOae9kZGDo8OGAu9rvGo7pVevM3UTkdyzTpbNatWqh+MoVbSvYXm5TjpGVkVAk3u6QKCzQzs96pRoNhponShTaVafzpUSkrl69ehg/fjzGjx+PEydOYOPGjejduzeaNWuGMWPGYPTo0f7uIlUDYvAtbSX3Md/LxYsXhS9UJpLPnDmjaUz74YcfhOBaraLIxInAuHGu7weCLucIEQUPBtg6i4yMxF9//aX64R8dHY29e/e6fB5323u1nKEKi4zE1s2b7UpdKT1nsJSxCcYEXGo0T5TI/g2d8GwUUY3Tpk0bzJ8/Hz169EBycjIeffRRBtikK83JxPSmdUwTj1W5uX5xd+zKZDIFb84RIgp4DLB1VlpaKnyhYZDw9py6XueIg2mbcVAn4HJBU7KZyEihHEqAZTwNONVoZwPprJr9bhw5cgQZGRl45513EB4ejoceegjvvfeev7tF1ZCm41T++v9lNgtJQV1dv6xaBbz5ptAPF++BOUc0qmafoURVgQG2zqQAW6d2rugxMATbNuPqNhjKJw3MZjOGJiaipKjIvlFhITBoEIDgW6GvCtVxZwPpozr9bpw9exbvvPMOMjIyUFBQgMTERLzzzjvo0aOHv7tGNZTW/19RUVGV0wGrVQiu1baKWyyKAbbmI1o1WHX6DCWqagywdeaqdJm37aoEtxn7jXzS4LhDvXNHwbZCXxWq484G0kd1+t1o3rw5rr32WowcORKDBw9GREQEAGD//v1Sm27duvmre1QDaf3/lZ2dXbkd8fL6xWq1IkdMAovg+SyoStXpM5SoqjHA1lmtWtp+pFrbUc1R3Vboqwp/buRKdfnduHr1Ks6dO4dXXnkFr7zyitP9BoMBV91lVSaqBB79/wqwbcaO58cdj8MFS36aylZdPkOJqprXUV5BQQH27t2LM2fOoEyhprPBYBBq/NYw4eHhurYjcivALlqISH9KYyxRMIiOjtZUNkvaSq51TNNYJszpceL38q3lDsfhgik/DREFJq8C7F27dmHo0KEoKiqCzWZTbFNTA+y6desKX6gMElI7Ii/wbBQREQW6Dh06IDwsDMWO+UVkwsPCcNttt2ka02688UYgNNR9KVQ5peczGoUEaS6C42DLTyPHlXeiwOBVgP3ss8/CaDRi2bJlaNOmDbc7y0RERGiarRXP0BF5g2ejiKqfixcvYubMmdixYwfOnDmjOIFtMBh8TpJJVNnkgd6WrCxYHSphREdHS6UqxbFK65i2Pzsbe/fu1bSIk5GRAaB8S7gYMLsp31VQUIBcdzvATCbloDsAcOWdKHB4FRkfO3YM8+bNw4QJE/TuT9CLiIgAXKzqS2y2wAqwuc04KPFsFFH1kpKSgtWrV6NJkybo2rUrJ68pKHkb6Gkd07p27Yrw8HAhwFa5fomXB8MqCdHMZjO69+hR0W9Xq9+Zmap99IdAW3nnajrVZF6N3g0bNuTA70JUVJQQYKuUjqi00hUe4DZjIqLA8cEHH2Dw4MHIyspCSEiIv7tD5JWqCPQ8uX5xF+TJWa1W1X4jKUkoERbIAqAyDFfTqabzKkoeN24c1q5di/Hjx8NoNOrdp6DWuHFj4QuVDzipnR9xmzERUeC4cOECBg4cyOCaqodKDPQ8uX7RGmBLAiBArUzutsDrdc0XaKvpRFXNqwB75syZ2L9/P9q2bYuBAwciJibGqa5zTU1yFh0drWu7ysZtxkREgaFbt244cOAAHn30UX93hSjgeXz9UsnH4YJlS7RjiTI53VeVq/lkBZErXgXY27dvx+7du1FaWorVq1crtqmpATYREZE3lixZgl69eqFt27YYOXJkQOx0IvInPYJWrdvJNS98KJQIC6ot0enpQEKC8+1cVSbSjVcB9pw5cxAVFYU5c+agffv2qF27tt79IiIiqlGaN2+Om2++GcnJyUhOTlZswyziVFPoFbRq3U6ueSt5WppTfpqg2hLdsiVXlYkqmVcB9vHjx5Geno6nnnpK7/5UH8zMTUREHpgyZQr27duHhg0b4oYbbuDkNdVoegatWraTaw2wMzIy0KNHD+Xn02FLtM+r9rz+JPI7rwLsxo0bIywsTO++VAvXXXcdYDC4r4NtMAjtiIiIyn3wwQe47777sHnzZlbqoOCnV6CnErQ6Ju3y+ayzhtJflbUK7cuqvZat8AgJAQIkB5AvguW8O9VcXo3go0ePxuuvv45HH30UderU0btPQa158+ZCgO2uFrbBILQjIiIqV1JSgoEDBzK4pqBW1SVAHZN2eXvWORBKl/qyaq+2FT43N1f4WcXEVEbXlVXCanpQnXenGsurUbxt27a4dOkS4uLikJCQgMaNGzttZaupSc4uXLgAlJWp1lG8cOFC1XeOiIgCVteuXZGTk+PvbhD5pMpLgMqvt3w46xxQpUu93GoeKJVhKnOyIqjOu1ON5VWA/eCDD0pfv/HGG4ptamqALWFpAiIi8sCLL76IPn36oHXr1rj//vvRuHFjhIaG+rtbRB6r0kBPx+utQAlQK1UVnNGukskKXmdTAPMqwP7000/17ke18ccff+jajoiIaoZHHnkEoaGhmDFjBmbMmKHYhlnEiQKYGKSazYDVWnF7eWkv8by4P84Ie7Oq7MtZ5xoxWUHkglcBds+ePfXuR7Xx+++/69qOiIhqhgYNGlTq+U6ioBQEWbGdgteQEOG4oAPxvLg/zgh7uqrMs85E3vMqwC4oKNDUjv/hiIiItNm3b5+/u0CkCz2yPGvKim00AgEwKSUPXqVkYgF4RtiTVWWedSbynlcBdosWLWAwGFTbXb161ZunJyIiIqIg5MnKJ+C+/vQne/YgPDxc+t4peDWZAB+DO71KPjkFr76cEQ6kVXuedSbymFcB9qhRo5wC7NLSUvz666/48ssv0apVK4wfP16XDgatQPpwJCKigDd//nzVNjU+gSgFPK0rn0eOHEHiiBG+bUG2WIQ/suf2RKBtgw6EUmFBg9fZFMC8CrA3bNjg8r4TJ06ge/fuaNCggbd9CmqlpaXC2Rt3W5pCQpikhoiI7MydO9flfQaDATabjQE2BQ+VlU+r1erxFmS9A9BA2wbt6py02WyGtTxpWnR0NCwWi10bfyRN8xdOQlAw8CrAdqdNmzaYOHEiXnrpJSmZQ41js/l2PxER1ThKFTrE3WEbN27EmTNnsHPnTj/0jKgSebAFudLKPwXQNmjHreYFBQXo3qNHwKyy+1tA1SsnckH3ABsArr/+ehw7dqwynjrgFRYWCgF0ejrQsqVzg/x8IC1NaEdERFTOXYWOBx98ED179sRLL72El19+uQp7RRRYalr5p0BbZQ8ENe13gIKP7gG2zWZDVlZWjd2aYTQahS8SEpRnQ3NygLS0inZEREQqDAYDHnjgAaSnpzPAJgoGep8R9tcqO886E3nMqwB77NixircXFhbi4MGDOH78OJ566imfOhasmjVrJnyh8oEktSMiItLgr7/+ks5hElFgqi5nhKvL+yDyB92TnIWFhWHUqFF44YUXvO1TULNarZqSnKldJOlVNoKIiIJbcXExvv32W6xYsQLt27ev0tdetmwZXn31VZw5cwbt2rXDvHnzcN9997l9TGlpKZ577jm89dZbsFgs6NixI1566SV0797drl1GRgYWLlyIkydPIjY2FikpKRg3bpxdm6+//hopKSk4cOAA6tati6SkJLzwwgt2pZvkhg4dinr16jldp2jtE+mohq58xsbG4pM9e3Dy5EmXbVq1ahXw13A860zkPa8C7Pz8fOUnq1ULJpPJ5cBXE1y+fBkoK3N7VgZJSUI7FwKtbAQREVW+kJAQpxKYcjabDUuXLq2y/ixZsgSzZs3CnDlzcMstt+CNN97AsGHD8Mknn+DOO+90+bgpU6Zgw4YNWLRoEZo3b45ly5ahX79+yMnJQVxcHABg8+bNGDVqFKZMmYL+/fvjvffew/jx4xEZGYmHHnoIAPDTTz/hnnvuQbdu3ZCZmYnc3FzMnj0bf/75J9asWWP3mlevXsXUqVOxbds2PPLII171ifShZeUzLDKyInDLzlZuVJWBuI6TAQUFBeh9993V4hqOZ52JvONVgN28eXO9+1FtXLx4UfhC5ayM1E4BE1oQEdU8d955p2KAXatWLTRp0gRjx451mwhNT4WFhViwYAGSk5OlsmD9+/dHt27dMH/+fOzZs0fxcT///DNWr16NlStXYtKkSQCAvn37Ii4uDosXL8batWsBAKmpqUhMTMTy5csBAP369cP58+cxZ84cKcBevHgxoqKi8P777yMsLAwJCQkwGo2YPHkyUlNTpWuRw4cPY/Lkyfj2228RGRnpdZ9IH/KVT7PZjKGJiSgpKrJrU1JYiKlTpwrfJCcLCxMKKnsLcmVsg+Y1HBF5HGCfPXsWTZo0sbtt+/bt+PTTT2E0GpGYmIhOnTrp1sEaLYDKRhDR/7d352FRVv3/wN8zIAMDAuog+iCoQBBo5ZLhkuJKaj4+P03NEhMtNdwtREPNNSsrNbfKXBJp0VCzTMP0ccutwqVFkjQVfTQFCxEFFObz+4OYL+PMwDAM28z7dV1cV5z7nPs+H6fhPp97OYeoYu3bt6+qu6Bz7NgxZGZmon///royhUKB/v3745VXXkFOTo7RZHbPnj3Iz8/Xa6dSqdCnTx9s3boVAHDhwgWkpqZizpw5em0HDBiATZs2ITU1FUFBQUhKSkKfPn3g5OSkV2fMmDFISkrCqFGjAADPPfcc3NzccPToUfTt29eiPpF1Fd35PH78eGFyXcpTfQkJCQgxsr2iH0Gu0MegSxnDpdx3d7zU49jpI/dENZHZCfbRo0cxYsQI/PHHH8gtdiUyKioKGzZsgPyztvMbb7yBefPmIS4uzvq9rQG8vb2tWo+IiKiyFQ3+7398OjAwEAUFBTh37hyaN29utJ2bmxsaNGhg0O7q1avIzs4ucd8AkJqaCl9fX1y8eNGgjpeXF9zd3ZGamqori4+Px8MPP1xiLKX1yc3NzWR7soJSks2QkBC0quQbClU9103kfXfNTT02zsnGiGoesxLstLQ09OjRAwUFBejduzfy8/Ph6OiIxMRExMfHw83NDatWrcJDDz2E5cuXY+bMmejQoUOlPcpWnZg7OzhnESciIqDwEeZDhw5h8ODBurLMzEzMnTtX93TYoEGDMG7cODg4OJT7eLdv3y7xzq23t7duIk53d3e9bbVr1wYAZGVlGW2bmZkJDw8Pg/Li7czZt6k6RfWKH7+k5NrcPhlLsLOzsw3iVKlUdj3PjK2oFnPdFL+rX8Jj45xsjKjmMSvBfueddyAiOHr0qN6JbNmyZVAoFJg2bZpuYPDee+8hOTkZy5Yts8sE29wriLzSSERES5cuRWxsLPLz8/H0009DoVDg7t276Ny5M37++Wc4ODjAw8MDkydPxrfffovt27eX+5jp6ekYOnSoye3h4eHo0aOH0W1FT6splUqj27VardH3yIu30/7zvu399cypU1TP1PEt7ZMxxsYws2bNwuzZs80+NlVP1eI96TK8BsjJxohqFrMS7F27dmH48OF6yfWtW7dw+PBhAIXvPxXXv39/LFu2zIrdrIH4rgwREZXgyJEjmDRpEoKCgnSTbwHAkiVL8NNPP6Fp06bYt28ffH198dlnn2HIkCFYt24dhg8fXq7jNmnSRJdcmrJixQoAhef6OnXq6Mqzs7MBwOgdYQDw9PQ0ene7eDtPT08AhnfBzalTVM/U8S3tkzH79+9HixYt9Mp499rGcK6bGq+qH/UnMsasBPvSpUsG71odOnQIBQUFCAoKQqNGjfS2NWjQADdu3LBeL2sQT09Ps9bBLho8lIhJOhGRzVq2bBkaNWqE5ORkuLq66srj4+OhUCgwa9Ys3etEgwcPxsaNG7F+/fpyJ9jmCA4OBgCcPXsWbdq00ZWfPXsWKpUK/v7+JttlZWUhPT0dXl5eeu2aNGkCFxcXvX0XnxT17NmzAIDQ0FC4urrCx8dHV1YkPT0dWVlZZVoP3Jw+GePm5mb0EXUiY4oSPd3kZTt26I/XPD2Bhg05hrOiavGoP5ERZiXYCoUCBQUFemUH/1m30NgjVBkZGbp3m+ySVgvMmwc0bWq47fx54J8lT0ypzAkteOWPiKhqfPfdd4iKitJLrq9cuYLTp0/D0dER/fr106vftWtXg5m3K0r79u3h6uqKxMREXYItItiyZQvCw8NN3skterQ8MTER0dHRAIC8vDxs374dPXv2BFA4uZi/vz8SExMxcOBAXdvExEQEBQXplt+KiIjA9u3bsWjRIt3xEhMT4eDggK5du5odizl9Iuu4f0xhkGwWJZn/V6FyO1hBjCZ694/1lMr/W45MrQb4qmC5VYtH/YmMMCvBDggIwM8//6xXtn37digUCkRERBjUT0pK0s0GapeUypKT6FLeHausCS145Y+IqOpcv34dTZo00SsrunjdqlUrgwvVbm5uuseaK5parUZMTAzmzp0LJycntG/fHmvXrkVycjL27t2rq3f58mVcvnwZLVu2hEqlQuPGjTFs2DBMnjwZOTk5CAoKwqJFi5CZmYkpU6bo2s2cORPDhw9HvXr10LdvX3z55ZfYtGkTNm7cqKsTGxuLTz/9FL169cJLL72E1NRUxMXFYfTo0WWaKNTcPlH5lDimKBoTFU8y/2ELM2Cbk+ghMrLw5kvv3oXJNcdV1sNH/amaMSvB7tu3LxYvXoyoqCiEhYXhs88+w88//wxPT0/07t1br+7WrVvx3//+t9Kuslc3DRs2LDx5lPJHtmHxK7hGVMaEFrzyR0RUdVxdXQ3eDd6/fz8UCgU6d+5sUP/ixYuoV69eJfWucEIvR0dHrFq1Cm+//TZCQ0Px5ZdfokOHDro6q1evxpw5c3D+/HndxYIPPvgAderUwZtvvons7Gy0bt0a3377rd6F96ioKOTl5eHtt9/G2rVr4e/vj/j4eAwaNEhX58EHH8SuXbswZcoUDBgwABqNBpMnT8a8efPKHIs5faLyMTfJvH/Na5t6Us7cRC8jo/CniI3cySeiQmYl2DExMfjkk0/Qvn171K1bF3/99RcA4K233tK9u7R//36sWbMGGzduRIMGDTBx4sSK6zVZF6/8ERFVuoceegj79+/HpEmTAAAFBQXYtm0bABg8uiwiSExMLHVJKmtSKBSYMWMGZsyYYbLO7NmzDWbVVqlUWLx4MRYvXlzi/kePHo3Ro0eXWKdjx444evSo2X2+cOGC0XJz+0RWUA3XvDapsue6mTnT5BOOtnAnn4gKmZVgu7u74/Dhw5gzZw4OHjyI4OBgjBs3Tm/Nzq+//hoJCQkIDQ1FYmKi3b6DffXqVbMmObt69WrldYqIiKqdyMhIvPjii1i4cCGefPJJvPfee7h69SoeeOABvflNRARTp05FSkoKXn755SrsMZFtqMy5boq7/+79/X2ymTv5RHbOrAQbAOrXr69btsOY4cOHo1+/fmjbtq3R9SbtRWZmplmPiGdmZlZ214iIqBoZMWIEvvrqK0ybNg2vvPIKRASurq5Yv3697jz64YcfYsGCBUhLS0OHDh0QFRVVtZ0msgHWmuvGYObwUlSru/dEVGHMTrBLY+qKnN3iY9dERFQCpVKJL774AomJiTh48CDc3d0xfPhwBAQE6OpcvHgR165dw6hRo/D222/b9QVsImsq71w35kwUS5WEy9pSNWO1BJvK5vz58zh+/Ljudz4aRERkfxQKBQYOHKi3XFVxcXFxmDdvHhNrompGb1I3oPDVQCZ6laqqHvUnKg0TbCvz9PQ0q97MmTMxs9hEF1W6HBZPCERE1ZJara7qLhCVjb2NKUJCCpfdUqtLnH+HiZ71VdaytkRlxQTbykpbfkun+DvaVbQcFq/8ERERkTXY9ZjCz6/wAsL9S28VW5aMiV7FqIxlbYnKigl2VakG72jzyh8RERFZg92PKfz8Cn/uw4nNiOwPE+yKUkMekeKVPyIiIrIGjimIiJhgW505j0hBrS58X4eIiIiIiIhshkUJtlarxbx587B69Wpcu3YNBQUFBnUUCgXy8/PL3cGaxtgjUikpKYiMjPy/9641GqOPERERERFRDVJDnlgkospjUYI9Z84czJs3Dx4eHnj00UehUqms3a8azeQjUtXgvWsiIqq+bt26hU2bNuHPP/80efG6+AoURFQ17HpSNyIqkUUJ9oYNGxAWFoY9e/ZYdQmRgoICvPXWW1i9ejX+97//ISgoCFOmTCm8+2tCXl4eZs+ejYSEBGRkZODBBx9ETEwMhgwZolevQYMGuHbtmkH7q1evokGDBlaLoUS8yklERCb8+OOP6NGjB7KysiAiRuswwSaqHux+UjciMsmiBPvKlSuYNm2a1dfnjIuLw+LFizFv3jw8+uij2LFjB4YOHQqlUolnn33WaJvBgwdj+/btiImJQbdu3XDixAmMHj0aGRkZmDhxIgDg2rVruHbtGhYtWoR27drpta9Xr55VYzCGVzmJiKg006dPR05ODubOnYs2bdrw6TAiC6SlpVVa0stJ3YjIGIsSbF9fX/z9999W7Uh2djaWLVuGyZMnY+rUqQCAbt26ITk5GcuWLTOaYJ84cQJffPEFXnvtNcTFxQEAunfvDldXV8TGxmLYsGHw9PTEiRMnAAD9+/dH48aNrdpvc/AqJxERlebw4cN46aWXMH369KruClGNlJaWhuCQEOTeuWOyjrNajTMpKRxzEVGFsSjBHj58OD744AOMGTMGtWvXtkpHnJ2dceTIEYPHtZ2cnJCVlWW0Tco/j1b/+9//1isPDw/H7du3sXfvXvTr1w8nT56Ep6dnlSTXRXiVk4iISuLg4ICmTZtWdTeIaqyMjIzC5LpoUtn7paQgNzISGRkZHJMRUYWxKMEOCAiAQqFASEgI+vTpgwYNGkCpVOrVKet7Yo6OjnjkkUcAACKCa9euYd26ddi9ezc+/PBDo228vLwAABcuXMBDDz2kKz937hwA4Pz58wCAkydPok6dOujfvz/27NmDgoIC9OnTB4sXL0bDhg3ND5yIiKiCtG/fHgcOHMDIkSOruitENRsnlSWiKmRRgv3MM8/o/nvVqlVG65RnIpZPPvlEN7FZ79698fTTTxutFx4eDn9/f0yYMAFqtRpt2rTBqVOnMHXqVCiVSty+fRtAYYJ9+fJljBw5EpMnT0ZKSgpeffVVhIeH48SJE3B1dTXZl+zsbIM76CqViu/GERGRVb355pvo1KkT3nnnHQwZMqTyJuAkIiIiq7Eowd67d6+1+6EnLCwM+/fvx5kzZ/Dqq6+iffv2+P777+Hs7KxXz8nJCUlJSRgxYgS6d+8OAGjYsCGWLl2Kp59+Wpc4r1u3Ds7OzmjZsiUAoGPHjmjWrBkef/xxxMfHIzo62mRfwsPDDcpmzZqF2bNnWylaIiIi4Nlnn4WjoyNiY2MRGxtrtI5CoUB+fn4l94yIiIjMZVGCbSzptKbAwEAEBgaiU6dOCAgIQLdu3bB582aDpbeK6h44cADXr1/HjRs38MADD+DSpUvQarWoW7cuABjMHA4AHTp0gIeHB06dOlViX/bv348WLVrolZX17nVlzmhJREQ1U7169biaBBERUQ1nUYJd5ODBg9i4cSPOnz8PlUoFPz8/DBw4EB06dCjzvq5fv46dO3eiV69eqF+/vq68TZs2AIBLly4ZtMnJycHmzZvRoUMHNG3aVNcuOTkZANCqVStkZmZiy5YtaNu2LUJDQ3VtRQR3794tdTDj5uYGd3f3MsdTxJwZLZ1cXLBvzx6jFwKIiMg+7Nu3r6q7QEREROVkcYI9efJkLF26FCKiV75s2TKMHTsWS5cuLdP+srOzERUVpbfkFgB88803AKCbAK04JycnjBs3DtHR0Xj99dcBAAUFBVi2bBkCAwPRvHlz5ObmYsyYMRg0aBDi4+N1bbdt24acnBx07ty5TP0sK3NmtLwbGYnOXbvi9zNneCebiIggIsjIyIBKpSrXRV4iu/TPKjNmlxMRWZFFCfbHH3+Md999F126dMHcuXPRvHlzFBQU4JdffsGcOXOwYsUKdOjQweTkZMb4+/vjueeew9y5c+Hg4IA2bdrgxx9/xPz58/HEE0+gZ8+eyMrKwunTpxEQEAAvLy84ODhgzJgxWLJkCXx8fBASEoLly5fj0KFD2LZtG5RKJdRqNWJjYzFv3jx4e3ujZ8+e+OmnnzB79mw8+eSTune3K1wpM1rezc3lshFERHbuxo0biI2NxebNm3Hr1i0AgLu7OwYMGIDXX3+dj5ATlUCj0cBZrUbuPxPlGuPk7Iy8vLxK7BUR2RuLEuwVK1agVatW2LVrFxwcHHTlnTp1wq5du9C2bVusXLmyTAk2UDgjeVBQENauXYtZs2ahYcOGmDhxImbMmAGFQoHjx4+jS5cuWLduHaKiogAAc+bMgVKpxMKFC/HXX3+hRYsW2LFjByIiInT7nT17Nry9vfHee+9h+fLlqFevHkaPHo05c+ZYEj4REZHVZWZmon379vj9998RGBiILl26oKCgACkpKVizZg327t2L48eP8442kQl+fn747+7d6NytG+7m5Bitczc3F127d8eZlBTe1CCiCmFRgv3TTz9hzpw5esl1EQcHBwwePBjz5s0r835VKhWmT5+O6dOnG93euXNng0fSa9Wqhfnz52P+/Pkm96tUKjF27FiMHTu2zH0iIiKqDPPnz8fZs2fx4Ycf4vnnn9fbtnbtWowcORKvv/667pUoIjKkUqkKk+sSXs3LjYzkU4NEVGHKNclZSbiMCBERkfm2bt2KoUOHGiTXADBixAgcPHgQW7ZsYYJNZI5SXs0jIqooSksaPfzww/jss89QUFBgsK2goACffvopmjdvXu7OERER2YvLly+XuJpE27ZtkZaWVok9IiIiorKy6A722LFjMXToUPTu3RuzZ8/WLX/166+/Yvbs2Thx4gTWrFlj1Y7WeJzRkoiISlCnTh1cvHjR5Pbz58/z/WsiIqJqzqIEe8iQITh69ChWrFiB3bt3620TEYwePVo3CZm902g0cHJxwd0SZrSESgVwRksiIrvWpUsXrFy5ElFRUQgKCtLb9ttvv+G9995Dz549q6h3REREZA6L38FetmwZBg0ahE2bNuGPP/6AiCAgIAADBgxAeHi4NftYo/n5+WHfnj3o3LUr7ubmGq+UlwdntZrLrxAR2bFZs2bhyy+/RIsWLTB06FCEhoZCoVDgl19+QUJCAhQKBWbOnFnV3SQiIqISlGuSs44dO6Jjx47W6ovNateuHX4/cwYZGRkm62g0Gs5mSURkxx588EF88803GDFiBD788EO9bf7+/li9ejXnNyEyF1/NI6IqYlaCfeDAAYSEhMDLy0v3uzk6depkec9sjJ+fHxNoIiIqUceOHZGamorjx4/rPR3WsmVLKJUWzUtKZFc0Gg2c1WrklvBqHp8aJKKKZFaC3blzZyQkJODZZ5/V/a5QKEptZ2yWcSIiIjJNoVCgdevWaN26dVV3hajG8fPzw5mUFD41SERVxqwEe9asWXj44Yd1v7/66qtmJdhERERknIODAzZs2KC7eK1UKks9tyoUCuTn51dG94hqLD41SERVyewEu7jZs2eX2iYnJ8eiDhEREdmDTp06wdvbW+93XrwmIiKq2Sya5Mzf3x9LlixB3759jW7/+OOPMWHCBNy4caNcnSMiIrJVe/fu1ft93759VdMRIiIrS0tL42P6ZLfMSrAzMjJw+vRp3e8XLlzADz/8AE9PT4O6Wq0W27ZtQ66pJamIiIiozAoKCnD+/HkEBgZWdVeIiExKS0tDcEgIcu/cMVnHWa3GmZQUJtlkk8xKsJ2cnDBw4EDdlSiFQoEFCxZgwYIFRuuLCHr16mW9XhIREdk4BwcHJCQk4JlnnjG6ff369Zg8eTJu3rxZyT0jIjJfRkZGYXKdkACEhBhWSElBbmQkMjIymGCTTTIrwXZ3d8eGDRtw5MgRiAjmzp2Lfv366U18VsTBwQHe3t4YPHiw1TtLRERkK/73v/9hz549ut9FBAcOHMC9e/cM6mq1Wnz88cfQarWV2UUiIsuFhACtWlV1L4gqndnvYEdERCAiIgIAsH//fowZMwbdunWrsI4RERHZMo1Gg9mzZ+PixYsACp8OW7VqFVatWmVQV0QAAJElrO1LREREVc+iSc7un5iFiIiIykalUmHLli04deoURAQjRozAqFGj0K5dO4O6RU+Hde3atQp6SkREROayKMEGgEuXLmHz5s3Izs7We2QtPz8fmZmZ2LVrF3777TerdJKIiMgWtWjRAi1atABQ+HTY8OHDERYWVrWdIiIiIotZlGDv2bMHvXv3Rn5+PkQECoVC9/ha0Rqe//rXv6zXSyIiIhu3bt06XLp0CUuWLOHFayIiohrKogT79ddfh5OTE5YsWQIAGD9+PL744gtkZWVh6dKl+PXXX/Hdd99Zs59EREQ2jRevicimpKSUrZzIRliUYB8/fhwjR45EdHQ07t69i4kTJ6JWrVoYMmSIbnbxuXPnYs2aNdbuLxERkU3ixWsisgUajQbOajVyS5iU0VmthkajqcReEVUeixLs7OxsNG/eHEDhGtn+/v746aef0LNnT6jVakRFRSE+Pt6qHSUiIrJlvHhNRLbAz88PZ1JSkJGRYbKORqPhGthksyxKsOvWrYusrCzd702bNsXp06d1v//rX//ClStXyt87IiIiO8GL10RUnaSlpVmcJPv5+TGBJrtlUYLdtm1bJCQk4MUXX4SzszOCg4Px1VdfoaCgAA4ODvj5559Ru3Zta/eViIjIZvHiNRFVF2lpaQgOCUHunTsm6zir1TiTksJEmug+SksaTZo0CSdPnkRgYCD++usvDBkyBOfPn0ePHj0QHR2NlStXomPHjtbuKxERkc0qunidm5sLAAgODsbBgwdRUFAAALx4TUSVJiMjozC5TkgAkpMNfxISkHvnTol3uInslUUJdufOnfH555/D29sbnp6eaNOmDV577TXs378fH3zwARo3bow333zT2n0lIiKyWbx4TUTVTkgI0KqV4U9ISFX3jKjasijBBoB+/fohOTkZSmXhLl555RVcvHgRJ06cwOnTp9G0aVOrdZKIiMjW8eI1ERFRzWdxgn2/a9euoUGDBnjkkUfg6GjRq91ERER2rbpdvF60aBECAgLg7OyMFi1aYNu2baW2yc/PR1xcHHx9feHi4oJ27drh0KFDBvUSEhLQrFkzuLi4IDg4GKtXrzao8/333yM8PBxubm5o0KABYmJikJeXZ/LY/fv3R1RUlEH5+++/D4VCYfDz4osvlhoPERFRWZQpwV67di1atmwJrVZrsC0mJgaNGjXCBx98YLXOERER2bOqvHj91ltvITY2FlFRUdi6dSsCAwPx1FNP4cCBAyW2mzhxIt59911MnToVmzZtgrOzM5544gmkpqbq6nz++ed47rnnEBERgS+++AJdu3bFyJEj8fHHH+vqnDt3Dj169IBarcamTZswZcoULF++HOPGjTM4ZkFBAcaPH4+tW7ca7dPJkycRGhqKI0eO6P1MnTrVwn8dIiIiE8RMsbGxolAopFatWvLbb78ZbB8+fLjUqlVLlEqlTJgwwdzdVlvJyckCQJKTk6u6K0REVA1U1HlhzZo10qJFCykoKDDYFhkZKd7e3vL+++9b9ZiluXPnjnh6esqUKVN0ZVqtVtq2bSvdunUz2S4tLU0cHR1lxYoVurLc3Fzx8/OT559/XlcWFBQkAwcO1Gs7aNAgCQgI0P0+atQo8fHxkby8PF3ZypUrRalUyoULF3Rlp06dko4dO4qLi4u4uLjIsGHDDPoVFhYmUVFRZsXO8z/R/30PkJwsEDH84feE7ExZzg1m3cHevn073nrrLXTu3BmpqakIDg42qLN27VqcO3cObdq0wfLly/Htt99a8TIAERGR7Zk6dSpeeOEF/Prrr/j9998NtteqVQt//fUXxowZg4kTJ1Zav44dO4bMzEz0799fV6ZQKNC/f3/s27cPOTk5Rtvt2bMH+fn5eu1UKhX69OmDHTt2AAAuXLiA1NRUvToAMGDAAJw7d053pzspKQl9+vSBk5OTXh2tVoukpCRd2XPPPQetVoujR4+ifv36Bn3SarX4+eef0aJFi7L/QxDZu5QU4Phxw5+UlKruGVG1ZVaCvXLlSjRp0gRJSUlo0qSJyXq+vr5ISkqCRqPB8uXLrdVHIiIim1OdL16n/DN4DgoK0isPDAxEQUEBzp07Z7Jd0fvS97e7evUqsrOzS9w3AKSmpiInJwcXL140qOPl5QV3d3e9x83j4+Px3Xff4eGHHzbap9TUVNy5cwdHjx5FUFAQatWqheDgYMTHx5f2z0BktzQaDZzVaiAyEmjd2vAnMhLOajU0Gk1Vd5Wo2jHrha4ffvgB48aNQ61atUqt6+HhgWHDhmH9+vXl7hwREZGtKn7xuqTza9HF66CgICxfvhw9evQo13Fv375t8l1lAPD29kZmZiYAwN3dXW9b0TrcWVlZRttmZmbCw8PDoLx4O3P2bapOUb3ixzeVWBc5efIkAODixYtYtGgRatWqhfj4eAwbNgx5eXkYOXKk0XbZ2dkGcapUKqhUqhKPR2QL/Pz8cCYlpcR1rjUaDfz8/CqxV0Q1g1kJ9q1bt9CoUSOzdxoUFISbN29a3CkiIiJbV1UXr9PT0zF06FCT28PDw00m8SICALpZzu+n1WqhUChKbFc0Uer99cypU1TP1PGN6dKlC77++mt06dIFLi4uAIAnnngC169fx6uvvooXXnjB6HHCw8MNymbNmoXZs2ebfWyimszPz48JNJEFzEqwGzZsiKtXr5q90/T0dKPvQREREVGhqrp43aRJE10ya8qKFSsAFPaxTp06uvLs7GwAMHqXGgA8PT2N3t0u3s7T0xOA4V1wc+oU1TN1fGO8vb3Ru3dvg/Inn3wSu3fv1s3Ufr/9+/cbvLfNu9dERFQasy4Bt27d2qy1L4ts2bLF4L0pIiIi+j/V+eJ10fvgZ8+e1Ss/e/YsVCoV/P39TbbLyspCenq6QbsmTZro1rw2tW8ACA0NhaurK3x8fAzqpKenIysrC6GhoWbHsn//fqPvW+fk5MDBwUHvAkJxbm5ucHd31/thgk1ERKUxK8GOiopCcnIyFi9eXGrdxYsX4/jx44iKiipv34iIiGxWdb543b59e7i6uiIxMVFXJiLYsmULwsPDTSaaRY+WF2+Xl5eH7du3IyIiAkDhZGb+/v56dYraBAUFoXHjxgCAiIgIbN++HXl5eXp1HBwc0LVrV7Nj2bNnD6KiovSSda1Wi8TERLRt25ZJMxERWZVZj4j36dMH/fv3R0xMDI4dO4bx48cjLCwMjo6Fze/du4fDhw9jxYoV2Lx5M8LDwzF48OAK7TgREVFNFhUVhb59+2Lx4sWYPHlyiXWLLl5X1gSiarUaMTExmDt3LpycnNC+fXusXbsWycnJ2Lt3r67e5cuXcfnyZbRs2RIqlQqNGzfGsGHDMHnyZOTk5CAoKAiLFi1CZmYmpkyZoms3c+ZMDB8+HPXq1UPfvn3x5ZdfYtOmTdi4caOuTmxsLD799FP06tULL730ElJTUxEXF4fRo0fD19fX7Fiio6PxwQcf4N///jfmzJkDtVqNFStW4JdfftGLhYiIyCrMXVz71q1bMmjQIFEoFKJUKsXJyUkaNmwoXl5e4ujoKEqlUhQKhfTv318yMzMtXMK7+ijLYuJERGT7KuK88NRTT4lSqZSnn35avvvuO7l3755u2927d2Xfvn0ycOBAUSqV0qVLF73tFU2r1cq8efPE19dXnJ2dpVWrVrJz5069OrNmzRIAcv78eV1Zbm6uTJo0SerXry9qtVo6duwox44dM9j/+++/L4GBgaJSqSQkJETi4+MN6hw4cEDCwsJEpVKJj4+PTJs2rcR/g8aNG8uwYcMMylNTU+Wpp56S+vXri4uLi3Tq1EkOHDhgdB88/xMR0f3Kcm5QiJQy08l9du3ahfj4eHz//fe4cuUKHB0d4ePjg/DwcDzzzDPo2LFjBVwGqHzHjx9H69atkZycjFatWlV1d4iIqIpVxHkhOzsbzz//PD7//HMoFAo4OjqiXr16yM/Px99//w2tVgsRQb9+/bB27doyTe5FluH5n4iI7leWc4NZj4gXFxERoXuPioiIiCzn5uaGjRs34vnnnze4eB0UFGRzF6+JiIhsXZkTbCIiIrIuXrwmInuSlpaGjIwMk9s1Gg3X4KYaiwk2ERERERFVirS0NASHhCD3zh2TdZzVapxJSWGSTTUSE2wiIiIiIqoUGRkZhcl1QgIQEmJYISUFuZGRyMjIYIJNNRITbCIiIiIiqlwhIQAnErRbtvyaABNsIiIiIiIiqhS2/poAE2wiIiIiIiKqFLb+mgATbCIiIiIiK7PlR2CJrMJGXxNggk1EREREZEW2/ggsEZnGBJuIiIiIyIps/RFYq0hJKVs5UQ3BBJuIiIiIqCLY6COw5aHRaOCsViM3MtJkHWe1GhqNphJ7RWQ9TLCJiIiIiKhS+Pn54UxKCt9PJ5vFBJuIiIiIiCqNn58fE2iy2dcEmGATERERERFRpbD11wSYYBMREREREVGlsPXXBJhgExERERFVBBt9BJaovGz5NQEm2EREREREVmTrj8ASkWlMsImIiIiIrMjWH4ElItOYYBMRERERWUlaWhoTayI7xgSbiIiIiMgK0tLSEBwSgtw7d0zWcVarcSYlpUKTbCb5RFWHCTYRERERkRVkZGQUJtcJCUBIiGGFlBTkRkYiIyOjwhLc6pLkE9krJthERERERNYUEgK0alUlh64OST6RPWOCTURERERka6owySeyZ8qq7gARERERERGRLWCCTURERERERGQFTLCJiIiIiIiIrIDvYBMRERERWVNKStnKichmVKs72AUFBXjjjTcQGBgIFxcXPPLII0hISCixTV5eHl555RX4+vrCxcUFLVu2xMcff2xQ7/vvv0d4eDjc3NzQoEEDxMTEIC8vr6JCISIiIiI7o9Fo4KxWA5GRQOvWhj+RkXBWq6HRaCq+MykpwPHjhj9M8okqVLW6gx0XF4fFixdj3rx5ePTRR7Fjxw4MHToUSqUSzz77rNE2gwcPxvbt2xETE4Nu3brhxIkTGD16NDIyMjBx4kQAwLlz59CjRw+0b98emzZtQkpKCqZPn46bN2/iww8/rMwQiYiIiMhG+fn54UxKCjIyMkzW0Wg0Fbo8VlGSnxsZabJOpSX5RHao2iTY2dnZWLZsGSZPnoypU6cCALp164bk5GQsW7bMaIJ94sQJfPHFF3jttdcQFxcHAOjevTtcXV0RGxuLYcOGwdPTEwsXLkTt2rWxbds2ODk5oXfv3lCr1Rg3bhxmzJiBxo0bV2qsRERERGSb/Pz8qnR96eqQ5BPZs2qTYDs7O+PIkSNo0KCBXrmTkxOysrKMtkn55xGXf//733rl4eHhuH37Nvbu3Yt+/fohKSkJffr0gZOTk67OgAEDMGbMGCQlJWHUqFFWjoaIiIiIqGpUdZJPZM+qTYLt6OiIRx55BAAgIrh27RrWrVuH3bt3m3yM28vLCwBw4cIFPPTQQ7ryc+fOAQDOnz+PnJwcXLx4EUFBQQZt3d3dkZqaWhHhEBERERERkZ2pNgl2cZ988gki/3lvpHfv3nj66aeN1gsPD4e/vz8mTJgAtVqNNm3a4NSpU5g6dSqUSiVu376NzMxMAIC7u7tB+9q1a5u8O14kOzvboI5KpYJKpbIgMiIiIiIiIrJV1TLBDgsLw/79+3HmzBm8+uqraN++Pb7//ns4Ozvr1XNyckJSUhJGjBiB7t27AwAaNmyIpUuX4umnn4arqyu0Wi0AQKFQGBxHRKBUljyRenh4uEHZrFmzMHv27DLFlJaWxndhiIiIiMiucAxM9qZaJtiBgYEIDAxEp06dEBAQgG7dumHz5s0YMmSI0boHDhzA9evXcePGDTzwwAO4dOkStFot6tatC09PTwAweqc6OzsbHh4eJfZl//79aNGihV5ZWe9ep6WlITgkBLl37pis46xW40xKCv/AEBEREZFN4BiY7FG1SbCvX7+OnTt3olevXqhfv76uvE2bNgCAS5cuGbTJycnB5s2b0aFDBzRt2lTXLjk5GQDQqlUruLq6wsfHB2fPntVrm56ejqysLISGhpbYLzc3N6OPl5dFRkZG4R+WhAQgJMSwQkoKciMjkZGRwT8uRERERGQTOAYme1Ty89GVKDs7G1FRUVi9erVe+TfffAMAugnQinNycsK4ceOwatUqXVlBQQGWLVuGwMBANG/eHAAQERGB7du3Iy8vT1cvMTERDg4O6Nq1a0WEY1xICNCqleGPsT84RERERES2gGNgsiPV5g62v78/nnvuOcydOxcODg5o06YNfvzxR8yfPx9PPPEEevbsiaysLJw+fRoBAQHw8vKCg4MDxowZgyVLlsDHxwchISFYvnw5Dh06hG3btuner46NjcWnn36KXr164aWXXkJqairi4uIwevRo+Pr6VnHkREREREREZAuqTYINAKtWrUJQUBDWrl2LWbNmoWHDhpg4cSJmzJgBhUKB48ePo0uXLli3bh2ioqIAAHPmzIFSqcTChQvx119/oUWLFtixYwciIiJ0+33wwQexa9cuTJkyBQMGDIBGo8HkyZMxb968KoqUiIiIiIiIbE21SrBVKhWmT5+O6dOnG93euXNniIheWa1atTB//nzMnz+/xH137NgRR48etVpfiYiIiIiIiIqrNu9gExEREREREdVk1eoOts1LSSlbORERERFRTccxMNkRJtiVQKPRwFmtRm5kpMk6zmo1NBpNJfaKiIiIiKjicAxM9ogJdiXw8/PDmZQUZGRkmKyj0Wi4/h8RERER2QyOgckeMcGuJH5+fvzjQURERER2hWNgsjec5IyIiIiIiIjICphgExEREREREVkBE2wiIiIiIiIiK2CCTURERERERGQFTLArWF5eHmbPno28vLyq7kqlY+z2GTtg3/Hbc+wA4ycqYu/fBXuOn7HbZ+yAfcdvz7HfTyEiUtWdqI6OHz+O1q1bIzk5Ga1atbJ4P1lZWfDw8MDNmzfh7u5uxR5Wf4zdPmMH7Dt+e44dsO34rXVeoOqN53/rsOf4Gbt9xg7Yd/y2HntZzg28g01ERERGLVq0CAEBAXB2dkaLFi2wbdu2Utvk5+cjLi4Ovr6+cHFxQbt27XDo0CGDegkJCWjWrBlcXFwQHByM1atXG9T5/vvvER4eDjc3NzRo0AAxMTEGd0dOnjyJ3r17w8vLC/Xq1UNERASOHz9uUZ+IiIjKiwk2ERERGXjrrbcQGxuLqKgobN26FYGBgXjqqadw4MCBEttNnDgR7777LqZOnYpNmzbB2dkZTzzxBFJTU3V1Pv/8czz33HOIiIjAF198ga5du2LkyJH4+OOPdXXOnTuHHj16QK1WY9OmTZgyZQqWL1+OcePG6dXp1KkT7ty5gzVr1mD9+vW4d+8eHn/8cZw5c6ZMfSIiIrIKIaOSk5MFgCQnJ5drPzdv3hQAcvPmTSv1rOZg7PYZu4h9x2/PsYvYdvzWOi/UBHfu3BFPT0+ZMmWKrkyr1Urbtm2lW7duJtulpaWJo6OjrFixQleWm5srfn5+8vzzz+vKgoKCZODAgXptBw0aJAEBAbrfR40aJT4+PpKXl6crW7lypSiVSrlw4YKIiIwfP17q168v2dnZujq3b98WjUYjY8eOLVOfivD8bx32HD9jt8/YRew7fluPvSznBt7BJiIiIj3Hjh1DZmYm+vfvrytTKBTo378/9u3bh5ycHKPt9uzZg/z8fL12KpUKffr0wY4dOwAAFy5cQGpqql4dABgwYADOnTunu6uclJSEPn36wMnJSa+OVqtFUlISACAkJAQxMTFwdXXV1VGr1WjUqBHOnTtndp+IiIisxbGqO0BERETVS0pKCgAgKChIrzwwMBAFBQU4d+4cmjdvbrRd0fvS97e7evUqsrOzS9w3AKSmpsLX1xcXL140qOPl5QV3d3ddEh4dHW3Qh9TUVPzyyy/o3r272X1yc3Mr+R+EiIjITEywTSi6Ol80ELBUdnY2gMJJWOztBM7Y7TN2wL7jt+fYAduOv+h8YOrubU1x+/ZtbN261eR2b29vZGZmAoDBTLC1a9cGUDhbrDGZmZnw8PAwKC/ezpx9m6pTVM/U8e/cuYNhw4bBxcUFEyZMMLtPxf9fLfp8jx8/rvv/uYiTk5PeHfWS2PJ3wRz2HD9jt8/YAfuO39ZjL8sYgAm2CRcuXAAAREZGWmV/4eHhVtlPTcTY7Zc9x2/PsQO2Hf+FCxfQoUOHqu6GxdLT0zF06FCT28PDw9GjRw+j2+SflT2VSuNvmGm1WigUihLbabVaADCoZ06donrGjp+VlYX//Oc/+PHHH7F161b4+vqa3afiis7/I0eONBpjWdnyd8Ec9hw/Y7df9hy/rcduzhiACbYJTzzxBBISEtCkSRO4uLhUdXeIiKiK5eTk4MKFC3jiiSequivl0qRJE11yacqKFSsAALdu3UKdOnV05UV3KIzdEQYAT09Po3eXi7fz9PQEYHgX3Jw6RfXuP/6lS5fw5JNPIjU1FZs2bUKfPn3K1KfinnjiCXz00Udo2LAhnJ2d9baV5Q42ERHZjrKMAZhgm6DRaDBkyJCq7gYREVUjNfnOdVkEBwcDAM6ePYs2bdroys+ePQuVSgV/f3+T7bKyspCeng4vLy+9dkUXrIvvu2XLlnp1ACA0NBSurq7w8fHRlRVJT09HVlYWQkNDdWU//fQTevbsiZycHHzzzTfo3LlzmftUnEajwbBhw0r9NyIiIvti7hiAs4gTERGRnvbt28PV1RWJiYm6MhHBli1bEB4eDpVKZbRd0aPlxdvl5eVh+/btiIiIAFA4uZi/v79enaI2QUFBaNy4MQAgIiIC27dvR15enl4dBwcHdO3aFUDhnesePXpAoVDg0KFDBsm1uX0iIiKyFt7BJiIiIj1qtRoxMTGYO3cunJyc0L59e6xduxbJycnYu3evrt7ly5dx+fJltGzZEiqVCo0bN8awYcMwefJk5OTkICgoCIsWLUJmZiamTJmiazdz5kwMHz4c9erVQ9++ffHll19i06ZN2Lhxo65ObGwsPv30U/Tq1QsvvfQSUlNTERcXh9GjR+ver54wYQKuX7+O999/H1lZWTh69Kiuvbu7O0JDQ83uExERkVVU5ILctmbnzp3SunVrcXFxET8/P1mwYIFotdoS22zYsEFCQ0PF2dlZgoKC5MMPPzSoc+zYMenUqZO4urqKt7e3vPzyy5Kbm1tRYVisrPHn5eXJggULJDg4WNRqtQQFBcmcOXMkLy9Pr563t7cAMPi5evVqRYdktrLGnpKSYjSm4OBgvXo14bMvS+zr1q0zGnfRz0cffaSrWxM+9+LS0tLEw8ND9u7dW2pdW/rei5gfuy1950lEq9XKvHnzxNfXV5ydnaVVq1ayc+dOvTqzZs0SAHL+/HldWW5urkyaNEnq168varVaOnbsKMeOHTPY//vvvy+BgYGiUqkkJCRE4uPjDeocOHBAwsLCRKVSiY+Pj0ybNk3u3bsnIoX/vzk6Opr8exMeHl7mPpXEnscA9nz+F+EYwN7HAPZ8/hfhGMASTLDNdOjQIalVq5ZERkbKzp07Zfr06aJQKGT+/Pkm22zatEkUCoVMmjRJvvnmG3nxxRcFgCQkJOjqnD17Vtzd3aVnz57y9ddfy9tvvy0qlUpeeOGFygjLbJbEHx0dLWq1Wl5//XXZvXu3LFy4UNRqtYwYMUJX588//xQAsmjRIjly5Ijez927dysjtFJZEvunn34qAGTv3r16MZ08eVJXpyZ89mWN/fr16waf4+HDh6VZs2bi6+sr169fF5Ga8bkXd+HCBQkODtZ9piWxpe+9SNlit5XvPNH97HkMYM/nfxGOAex9DGDP538RjgEsxQTbTBEREdKmTRu9stjYWHFzc5M7d+4YbRMUFCQDBw7UKxs0aJAEBATofh81apT4+PjoXd1ZuXKlKJVKuXDhghUjKJ+yxn/jxg1RKBSycOFCvfKFCxcKAN0f2Z07dwqAahXr/Sz57KdOnSpNmjQpcb814bO3JPb7LVmyRJRKpRw9elRXVhM+dxGRgoICWbt2rdStW1fq1q1r1gnGVr73ZY3dlr7zRPez5zGAPZ//RTgGsNcxgD2f/0U4BigvTnJmhry8POzbtw/9+/fXKx8wYACys7Nx8OBBgzYXLlxAamqq0Tbnzp1DamoqACApKQl9+vTRW/ZjwIAB0Gq1SEpKqoBoys6S+G/evIkXX3wRffv21SsPCgoCAPzxxx8AChej9/T01E1qU91YEjtQGFeLFi1K3Hd1/+wtjb24P//8EzNmzEB0dDTCwsJ05dX9cy/y008/ITo6GsOGDcOGDRtKrW9L3/uyxm4r33mi+9nzGMCez/8AxwD2PAaw5/M/wDFAeTHBNsMff/yBu3fv6v4nKRIYGAgAui9NcSkpKQBQYpucnBxcvHjRoI6Xlxfc3d2N7rcqWBJ/06ZNsXLlSt1yLEW2bNmCWrVq6fZ18uRJ1KlTB/3794eHhwfc3NwwePBgXL16tYKiKRtLYgcK47p58ybatWsHZ2dnNGjQANOmTcO9e/cAoEZ89pbGXtyrr74KBwcHzJ8/X6+8un/uRfz8/HD27FksWrQIarW61Pq29L0va+y28p0nup89jwHs+fwPcAxgz2MAez7/AxwDlBcTbDNkZmYCKJyRtLjatWsDALKysixqY6pOUT1j+60KlsRvzObNm7FhwwaMHTsWderUAVD4Rbt8+TLatGmD7du3Y9GiRdi3bx/Cw8Nx+/Zt6wVhIUtiv3btGq5du4bffvsNY8aMQVJSEkaNGoXFixcjKiqqxP0W7bs6fPbl/dyvX7+O+Ph4jBs3Dp6ennrbqvvnXqRu3bpo1KiR2fVt6Xtf1tiNqYnfeaL72fMYwJ7P/wDHAID9jgHs+fwPcAxQXlymywxarRYAoFAojG5XKg2vU5hqIyK6NiXtV0SM7rcqWBL//RITEzFkyBCEh4fjjTfe0JWvW7cOzs7OaNmyJQCgY8eOaNasGR5//HHEx8cjOjraChFYzpLY3d3d8e233yI4OFi3lEzRurEzZszAjBkzdH9cq/NnX97P/cMPP4RWq8XEiRMNtlX3z91StvS9L6+a+p0nup89jwHs+fwPcAwAcAxgLlv5zltLTf7eW4PtfJIVqOjK2/1Xlm7dugUA8PDwMLtNdna2ro2pOkX1jO23KlgSf3GLFi3C008/jccffxxfffUVVCqVblu7du10X7IiHTp0gIeHB06dOmWF3pePJbG7uLige/fuuhNrkSeffBIAcOrUqRrx2Zf3c09MTERERAS8vLwMtlX3z91StvS9L4+a/J0nup89jwHs+fwPcAwAcAxgLlv5zltDTf/eWwMTbDMEBATAwcEBZ8+e1Ssv+j00NNSgTdE7CCW1cXV1hY+Pj0Gd9PR0ZGVlGd1vVbAkfqDwatz48ePx8ssvY8CAAdixYwfc3Nx02zMzM7F27VqcPn3aoN3du3eh0WisHEnZWRL7mTNn8P777xv8Ac3JyQEAaDSaGvHZW/q5A8Dly5dx8uRJDBo0yGBbTfjcLWVL33tL2MJ3nuh+9jwGsOfzP8AxAMcA5rOV73x52Mr33ioqd9LymqtLly7Stm1b0Wq1urLY2Fjx9PQ0uVSBv7+/DBo0SK9s0KBBEhQUpPt9+PDh4ufnp7fA/MqVK8XBwUHS0tKsHIXlLIl/2rRpAkAmT56s167I7du3RaVSydChQ/XKt27dKgDk22+/tW4QFipr7Hv27BEAsmbNGr3yiRMnSu3ateWvv/4SkZrx2VvyuYuIbN68WQDI2bNnDbbVlM/9fnv37jVrmQ5b+t4XMTd2W/nOE93PnscA9nz+F+EYgGMA+z7/i3AMYAkm2Gbas2ePKBQKGTBggOzYsUNmzJiht97bzZs35ciRI7p13kRE1q1bJwAkOjpadu7cKdHR0QJANm7cqKuTkpIizs7O0qVLF/nqq6/knXfeEZVKJWPGjKn0GEtS1vhPnDghCoVCHn30UYOF5I8cOSI3b94UEZGZM2cKAImJiZHdu3fLokWLxN3dXZ588skqi/V+ZY09Pz9fOnfuLB4eHrJ06VLZtWuXTJw4URQKhbz99tu6/daEz96S/+9FRGbPni0qlcrkfmvC534/YycYW//eFzEndlv6zhPdz57HAPZ8/hfhGIBjAPs+/4twDGAJJthlsGXLFnnooYfEyclJmjZtqveHsuh/vnXr1um1ef/99yUwMFBUKpWEhIRIfHy8wX4PHDggYWFholKpxMfHR6ZNmyb37t2r6HDKrCzxF32BTP0UfUkLCgpk+fLl0qxZM3F2dhYfHx+ZMmVKiVdGq0JZP/vMzEyZNGmSNGnSRFQqlYSGhsqqVasM9lsTPntL/r+Pjo4Wb29vk/usKZ97ccZOMPbwvRcxL3Zb+84T3c+exwD2fP4X4RjA3scA9nz+F+EYwBIKkX+mtyMiIiIiIiIii3GSMyIiIiIiIiIrYIJNREREREREZAVMsImIiIiIiIisgAk2ERERERERkRUwwSYiIiIiIiKyAibYRERERERERFbABJuIiIiIiIjICphgExEREREREVkBE2wiIiIiIiIiK2CCTVTM9u3b8dRTT6FJkyZQqVTQaDTo1asXvvrqqwo75rfffouBAweiUaNGUKlUaNSoESIiIvDpp58iPz+/Qo5ZFXGaKz8/Hy1btsSyZcussr/c3FxcvnxZ97uIYPr06dBoNKhfvz6io6Nx+/Ztg3axsbEICQlBQUGBwbaXX34ZvXr1skr/iIioeuAYgGOAIhwDULkIEUlWVpY89dRTAkBatWolc+bMkVWrVsmrr74qjRs3FgASExNj1WPm5uZKZGSkAJAHHnhAZsyYIatXr5YFCxZIWFiYAJAOHTrIlStXrHbMqoizrObOnSvBwcFy7969cu/r+PHj0qRJE1m3bp2uLCEhQQDI+PHj5Y033hBXV1cZO3asXru0tDRxdnaWrVu3Gt1vZmamaDQavf0SEVHNxDEAxwDFcQxA5cUEm0hEBg0aJADkrbfeMtiWm5srnTt3FgCyevVqqx1zxIgRAkBefvllyc/PN9i+evVqcXR0lFatWsndu3etcsyqiLMs/vjjD3FycpLPPvvMKvtbt26dANA7Cfbo0UPatGmj+z0mJkZcXFxEq9XqyoYNGybt27cvcd8LFy6UOnXqSGZmplX6SkREVYNjAI4BOAYga2KCTXYvKSlJAMiAAQNM1vnjjz/EwcFBHnroIasc88iRIwJA+vTpU2K9qVOnCgB5++23y33MqoizrMaOHSuenp6Sl5dnlf0ZO7k++OCD8swzz+h+/+CDDwSAXLt2TUREfvrpJ1EqlXLw4MES9/3nn3+Kg4ODvPHGG1bpKxERVT6OAf4PxwAcA5B1MMEmuzdkyBABIPv37y+x3unTp/UeWQoPD5dmzZrJmjVrpH79+qJWq+WVV14x65hFV6737t1bYr0bN26Io6OjNGvWzKz9lqQ8cQYEBMiPP/4ojz/+uDg7O0vDhg1l9OjRkp6erqt38OBBcXBwkAceeEDu3LmjKz937pzUrl1bGjduLH///bfJ4964cUPUarVERUXplT/22GPi7u6u16e8vDxxdXUVR0dHycrK0pVrtVrx9vaW7t27y7BhwwSA3k9RPD179tS1ee2118TR0VG3/169eknfvn1L/Dcq0qVLF2nYsKHV7i4QEVHl4hhAH8cAHANQ+THBJrsXGBgojo6OZb5iGh4eLmq1Wjw9PeXNN9+UBQsWyIEDB8xq+8ADD4ijo6Pk5OSUWrfoXayiq6uWKk+cnp6e4uHhIX369JHly5fLCy+8IAqFQoKCguTWrVu6utOnT9c98iYicu/ePQkLCxNHR0c5cuRIicf55JNPBIBs27ZNr/y1114TAHr/tnv27NGdMLdv364rP3r0qACQFStWyOHDh2XUqFECQEaNGiUbNmwQEZH58+eLk5OTfP3113Lq1CkJDAzUnWz/+9//ioODg/z6669m/dssXbpUAMh3331nVn0iIqpeOAYoGccApnEMQKYwwSa7p1arxdvbu8ztwsPDdX/Iy8rV1VW8vLzMqls0Icnx48fLfJziyhvnyJEj9cqXLFkiAGT27Nm6snv37sljjz0mSqVSjh07pjvZLly4sNTjvPDCCwJALl26pFf+yy+/CACZPn26rmzq1Kni5eUlzs7OMnnyZF359OnTRaFQyOXLl0XE+ONh2dnZEhERoTs5h4aGyrlz50Sr1cqjjz4qzz//vNn/Nt99950AkHnz5pndhoiIqg+OAUrGMYBpHAOQKVymi+yeo6NjuZbC6Nu3b5nbiAhq1aplVl1HR0ddm/Iob5xz587V+33MmDHw8PDA5s2b9Y7x8ccfQ61WY/DgwXjjjTfQq1cvxMTElLr/33//Hc7OzmjUqJFeebNmzRAQEIBdu3bpynbt2oXu3bujTZs22LNnj678q6++wmOPPQYfHx+Tx3F1dcU333yDM2fO4NSpUzh16hT8/f2xceNG/Prrr5gzZw5EBG+++SYeeOAB+Pr6YsKECbhz547BvgIDAwEAZ8+eLTU+IiKqfjgGMA/HABwDkPmYYJPd8/Hxwd9//428vDyL2jdo0KDMbXx9fXHjxg3cu3ev1Lr/+9//AMDgpFNW5YlTo9EYxFmrVi34+/vj3LlzeuWBgYFYuHAhzp8/D5VKhfXr10OhUJR6jPT0dHh6ehrd1rdvXyQnJ+PGjRu4fv06Tp48ie7du6Nz5874+eefcf36daSlpeGnn35Cv379Sj2WQqFAUFAQHn74YTg6OuLevXuYPn06JkyYAB8fH8THx2P69OmYOXMmPvroI2zZsgWTJ0822E9Rf9PT00s9JhERVT8cA5SOYwCOAahsmGCT3QsPD4dWq8Xhw4dLrDdixAhERkbi6tWreuVFV5fLokuXLsjLyyv1mFlZWUhOTkZoaCjq169f5uMUV544VSqV0br5+flG4//vf/8LALhz5w62bdtmVv+USiW0Wq3Rbf/5z3+g1Wqxe/du7N69GyKC7t27o0ePHhAR7N27F9u3bwcA/L//9//MOl5x7733HjIzMzFt2jQAQHx8PMLDw/Hcc8+hW7duePHFF7F+/XqD/hUUFAAAHBwcynxMIiKqehwD6OMYgGMAKj8m2GT3nnnmGQDAihUrTNa5dOkSNmzYgF27dqFevXol7u/YsWPo2LEjPD098a9//Qvjx483eLTohRdegEKhwIIFC0p87GvJkiXIycnBiBEjdGU7duxAy5YtUbt2bfj5+WHWrFkmT0rWivPq1avIzs7Wq5uXl4fz588jKChIr3zdunVITEzEuHHj0Lx5c0yaNAm///57qf1r2LAhbty4YfTf4/HHH0e9evXwzTffYPfu3XjggQfg5+eHtm3bws3NDXv27MFXX32FkJAQBAcHl3qs4rKysjBv3jzExcXprkZfuXIF3t7eujpeXl7Iy8tDRkaGXtuiq9YNGzYs0zGJiKh64Big9Dg5BuAYgMqoKl78Jqpu+vXrJwDknXfeMdiWmZmpm8Wz+GQmRRN/FPf3339L3bp1ZfHixXLv3j25dOmStG7dWiZMmGCw38mTJwsAGT9+vN7yE0USEhLE0dFRWrdurVsC4vLly+Lk5CRbt24VEZHff/9dGjRoIOvXr6/wOOfMmaNX/4033jDY19mzZ8XNzU23TMcPP/wgjo6O8thjjxmNsbiRI0cKAElLSzO6/bnnnpMGDRpI06ZNJTo6Wlf+5JNPiq+vr6hUKomLi9NrEx8fLwBkzZo1Jo87ffp08fPzk9zcXF1Z9+7dJSwsTPf7lClTRKVSSUFBgV7b/fv3CwB5/fXXS4yNiIiqL44BOAbgGICsiQk2kRSeFItOIq1bt5bXXntNVq1aJVOmTBEvLy8BIGPHjtVrY+zkevLkSfnPf/6jV/buu+9Kq1atDI557949GTdunACQoKAgmTVrlqxZs0beeust3b47dOigmw2zyM2bN0VEJDc3Vw4dOiQajUZ27txZ4XG6uLjI0KFDZeXKlbr1JcPCwnRLfty7d0/atm0rSqVSb8mKuLg4ASAzZswosW+JiYkCQD799FOj2zdv3qyb9XPz5s268qKZTAHIDz/8oNcmKSlJAEiXLl1k9erVBmtVXrlyRdRqtXz00Ud65UUzj44fP17efPNNcXNzM5hBVeT/lg85efJkibEREVH1xTEAxwDFcQxA5cUEm+gfeXl5sn79eunSpYv4+PhIrVq1RKPRyJNPPmn05GXs5Ho/rVYrXbt2lVGjRpms891338mzzz4r/v7+olKppGHDhtKjRw/ZsGGDySu+BQUF4uLiIgBk4MCBpV4ZtkacP/74o7Rp00ZUKpU0adJE4uLi5M6dO7p6r776qgCQl156yeB4zZs3FwcHBzl48KDJft26dUtUKpXRk5hI4dIazs7OolQq5e+//9aV//rrrwJAGjVqZNDm3r17MmTIEFGr1eLm5iZnzpzR2z5y5Eh56KGHDK5Ka7VamT9/vjRq1Eg0Go2MGjVKb63PIt27d5fGjRubjImIiGoGjgE4BijCMQCVl0KknPP+E5FRBQUFGDduHHbu3ImjR49aNNOoKSKCu3fv4vLly+jfvz/atWuH999/32r7L65z587Yv39/uZcIMcfo0aOxceNGXLlyBWq1usKPVx5paWlo2rQp3njjDUyZMqWqu0NERNUIxwBlxzEA2QpOckZUATIyMtCzZ08cPnwYBw8etOqJFShcYkKlUiEgIAAzZszApk2brLr/qhIXF4c7d+7UiHhWr16NOnXqIDo6uqq7QkRE1QjHAJbhGIBsBRNsIis7ffo0WrdujTp16uDw4cPw9fW12r737NmDhx9+WO9Kcm5uLurWrWu1Y1Slxo0b4+WXX8b8+fNx9+7dqu6OSTdu3MDSpUvx2muvwc3Nraq7Q0RE1QTHAJbjGIBsBRNsIitKT09H9+7dMWDAAGzatAmurq5W3X/r1q1x/fp1zJ8/H/n5+UhJScG8efPw4osvWvU4VWnOnDlwdXUtcSmRqjZ37lyEhYVh9OjRVd0VIiKqJjgGKD+OAcgW8B1sIitasGABpk+fDrVaDYVCoSsPCAjAqVOnrHKMkydPYtKkSThx4gS8vLwwZswYTJo0CUolr5cRERFVFY4BiAhggk1ERERERERkFbzcRURERERERGQFTLCJiIiIiIiIrIAJNhEREREREZEVMMEmIiIiIiIisgIm2ERERERERERWwASbiIiIiIiIyAqYYBMRERERERFZARNsIiIiIiIiIitggk1ERERERERkBUywiYiIiIiIiKyACTYRERERERGRFTDBJiIiIiIiIrKC/w+25GB86Z28twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax2, ax1) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.plot(cat_sums_original['Cr2O3_Cpx'],\n",
    "         cat_sums_original['Cation_Sum_Cpx']-cat_sums_noCr['Cation_Sum_Cpx'], \n",
    "         'sk', mfc='cyan')\n",
    "#ax2.hist(Cpx_with_Cr['Cation_Sum_Cpx'], fc='cyan')\n",
    "#ax2.hist(Cpx_with_noCr['Cation_Sum_Cpx'], facecolor='None', ec='k')\n",
    "\n",
    "NoCr=np.sort(Cpx_with_noCr['Cation_Sum_Cpx'])\n",
    "HasCr=np.sort(Cpx_with_Cr['Cation_Sum_Cpx'])\n",
    "b=3\n",
    "# ax2.step(np.concatenate([NoCr, NoCr[[-1]]]),\n",
    "#          np.arange(NoCr.size+1)/NoCr.size, color='black', linewidth=b, label='NoCr')\n",
    "# ax2.step(np.concatenate([HasCr, HasCr[[-1]]]),\n",
    "#          np.arange(HasCr.size+1)/HasCr.size, color='orange', linewidth=b, label='HasCr')\n",
    "# ax2.legend()\n",
    "# ax2.plot([3.98, 3.98], [0, 1], '-r')\n",
    "# ax2.plot([4.02, 4.02], [0, 1], '-r')\n",
    "ax2.plot(cat_sums_original['Cr2O3_Cpx'],\n",
    "         cat_sums_original['Cation_Sum_Cpx'], \n",
    "         'sk', mfc='cyan')\n",
    "ax1.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax2.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax1.set_ylabel('Cation Sum$_{Meas Cr}$ - Cation Sum$_{Cr=0}$')\n",
    "ax2.set_ylabel('Cation Sum (Measured Cr content)')\n",
    "fig.tight_layout()\n",
    "fig.savefig('Cr_cation_sums.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets show this with a foreloop - obviously complex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "\n",
    "    linCr=np.linspace(0, 1, 10)\n",
    "    example_cpx1=myCpxs1.iloc[0:10]\n",
    "    example_cpx2=myCpxs1.iloc[0:10]\n",
    "    example_cpx1_cs=np.empty([10, len(linCr)], dtype=float)\n",
    "    example_cpx2_cs=np.empty([10, len(linCr)], dtype=float)\n",
    "    for i in range(0, len(linCr)):\n",
    "        example_cpx1['Cr2O3_Cpx']=linCr[i]\n",
    "        example_cpx2['Na2O_Cpx']=linCr[i]\n",
    "        example_cpx1_cs[:, i]=pt.calculate_clinopyroxene_components(cpx_comps=example_cpx1).Cation_Sum_Cpx\n",
    "        example_cpx2_cs[:, i]=pt.calculate_clinopyroxene_components(cpx_comps=example_cpx2).Cation_Sum_Cpx\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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efQUL2mdMN28CY8cC8+aZx5QxI3D3LgOwbNmAESOAjz8G3NzsM8aYFISJiIiIiMhzBQay1HzUcvMNG3JNVZky9hlTUBBLzU+dyv8DLLpx5w4DMHd3YMAATo10d7fPGJ9FQZiIiIiIiMQpJASYP5+ZpoAA9lWqBEyeDNSsaZ8xhYYC333HzZZv3mRfxozAvXsMwNzcODVyyBD7TY18EQVhIiIiIiISTVyl3T08WNCiaVP7FLQwDMDHh+vOzp5ln7s7s2D37gHOzkDHjoC3N5A/v+3HFx8KwkREREREBAADnS1bWPHQVNo9Vy6Wm+/UyT7l5gFg505WPPzrL7ZTp+am0KaKh02bchPm4sXtM774UhAmIiIiIiI4dIiBzo4dbKdPz3bv3vYrN3/sGAPCTZvYdnXlv48f89/atbkurVIl+4wvoRSEiYiIiIi8ws6c4bTD1avZdnUFevbktL8sWewzposXWdFw2TJm55ydOa7gYN7+xhvApElA3br2Gd/LUhAmIiIiIvIK+u8/FtyIWm7+ww9Zbr5AAfuMKSCA687mzOF0QwBIkwZ49IgBmIcHMH480KyZ/TZaTgwKwkREREREXiGBgcC0aSzvnlTKzT96BHz1FasuBgayzxR8PXoE5MnDdWkdO3IT5uTOAV6CiIiIiIi8SFzl5t98k4FPjRr2GVNYGLBoESsaXr/OvqjBV6ZMnBbZoweLcViTvz/g6wu0aWPd5wEUhImIiIiIOLRnlZufOBFo0sR+5ebXreNeXqdPsy91ahbcePSIgVjfvkD//twDzJquXeOeY99/z+qP1asz82ZNCsJERERERByQYQBbt7LCYcxy8507229a3x9/AAMHAn/+ybarK9d/PX7MMXXvzoAxZ07rjuPePWYBZ840V1ts2NC8Fs2aFISJiIiIiDiYQ4dY2n37drbTp2e7d29mmezBz49j+N//2E6ZEggPZ9Dj5AS0bcuiIIULW3ccjx8Ds2czE3j3LvuqVmW1xbfftu5zmygIExERERFxEGfPMou0ahXbSaHc/L//cs3X4sWcGunkxJLzYWG8vVEjVjy0dlGQsDCOYdQo4OpV9pUsyWCsUSPbTstUECYiIiIikszduMGCG/PnJ51y83fvmqf7mfb3cnEBQkOZAbNV9skwgJ9/BoYNA/75h3358/O9ad8eSJHCus8fFwVhIiIiIiLJ1IMHwPTpLDmfVMrNBwdzut+ECebpfqbgKzQUKFWK43v3Xetnn3bt4hTIAwfYzpKFwdinnwKpUln3uZ9HQZiIiIiISDLz5Ak3WR4zBrh1i32VKjHzVLOmfcYUHg4sXQqMGAFcucI+U9GN0FCgYEFm69q0sX726ehRVl7ctIntNGmAfv1YbTFDBus+tyUUhImIiIiIJBMREcDq1czmnD/PPg8PZp2aNrVfufnffmPG6cQJ9pkyX0+eANmzMzD7+GMGZdZ0/jyfa/lytm1ZbTE+FISJiIiIiCQD27ax3Pzhw2znzGkuN+/iYp8xHTzIMe3axXbKlFyTFhoKuLuzFH2fPkC6dNYdx40bwLhxXBMXGsq+1q2ZeSta1LrPnRAKwkREREREkrDDh5ll2rqVbXd3Bj59+gBp09pnTGfOsOKijw/bzs7M0oWFAW5uQI8enA6YNat1xxEYyPVwX35pXhNXvz7XnJUrZ93nfhkKwkREREREkqBz5ziNbuVKtl1cGNwMG2b94OZZ/vuP69C++85cYh5gAObsDHTsyHL0+fNbdxwhIcA337C0fUAA+ypW5Jq4WrWs+9yJQUGYiIiIiEgScvMmp9bNm8epdU5OQLt2DH4KFbLPmB48YMZp+nRzxsnJievBAK5HGzcOKF7cuuMIDweWLQNGjgQuXWKfvdfEJYSCMBERERGRJCAoiNPqpk7l/wHgnXc4ta5sWfuMKa4qjKaph4bBrNPEicCbb1p3HIYB/PILpzj+/Tf7cufmmrhOnbgWLTlJZsMVEREREXEsoaGc3jd6NLNgAFChAqfW1a5tnzHFVYUxRQpmoiIigPLlGXzVq2f97NO+fVwD98cfbGfMyDVyn3/O0vPJkYIwERERERE7iIhgYYthw4CzZ9lXtCin1jVvbr+pddu3M+jx9WXblPkKDweKFeO0w+bN2W9NJ0+y+MeGDWynSgX06sUALFMm6z63tSkIExERERGxsR07GOgcOsR2jhwsaNG1q/3KzR89ygBn82a2TcFXRASQK5d56p+1x3fpEt+LJUs4DdHZmWX4vb2BvHmt+9y2oiBMRERERMRGYgY66dJxL62+fa2/l9azXLzIKozLlrFtKrgREcGpf0OGAD17Wn/qX0AAs4Bz5nAtGsBiG+PHA6+9Zt3ntjUFYSIiIiIiVnbhAjBihDnQcXEBPv2UUxGzZ7fPmAICGODMnWsOegAGYKlTA717M0C09tS/hw+BGTNYkCQwkH01awKTJlm/4EdUhw9zCma3btZ/LgVhIiIiIiJWcuuWOdAJDWVf27bA2LFA4cL2GdPDh8DMmSz8YQp6TFKkYBAyYgSrD1qTqSDJmDHAjRvse/11Bl/169tuTdzZs8wErloFuLqy2EjBgtZ9TgVhIiIiIiKJzJTdmTKFe2wBgJcXA4xy5ewzprAwYNEirq26fj327a1bMyAqVsy64zBVXhw+nBtSAwxIx47lGKxd8MMk5sbTTk5Ay5a2WZOnIExEREREJJGEhgLff89y8//9x77y5Zl1qlvXPmMyDGD9eq7t+ucf9kXdaLl+fa7FKl/e+mPZupVr4g4fZjt7dmbdPv6YWShbuH+fUx9nzAAePWJfw4YsuV+mjG3GoCBMREREROQlGQawdi3XePn7s69wYQY3LVrYLrsT0969XNe1bx/bpuDLMLjeauJEbrhsbYcOMfjavp3tdOmAAQOAfv1sV5AkOJhFPyZMAO7cYV/lygyQq1e3zRhMFISJiIiIiLyE3bsZ6Bw8yHa2bMDIkbbN7sTk58fMl2mPrajBV/HiDETef9/66678/TntcM0atl1cgM8+Y7CaLZt1n9skPJzl7r29gStX2GfL9yAuCsJERERERBLg+HEGOr/+ynbatED//sAXXwDu7vYZ09WrDDYWLeLaKxPDAPLl4xqoDz9kAQ5run6dUzIXLGAQ5OQEtG/P57d20QsTw2AQOnQog1KA+4yNHg106ACkjBEJhYcDZ87Yphy+gjARERERkXi4fJmZLtNmwilTAt27c21Tjhz2GdO9eywC8tVXwOPH0W/LkoWZp08/BVKlsv043n2XWSdbrbcCgD17OP3RNA0zUyYGYz16sPx+VIbBoLVfP4753r3YxyQ2BWEiIiIiIha4fZtrqGbPBkJC2NeyJUvQFy1qnzGFhLD8/bhx5nVOJmnTMiv3xRdA+vTWHUdSWW914gSzk7/8wnbq1ECfPpwumjFj7OM3bwa6dGEG0WT5cvZZk4IwEREREZHnePQI+Pprlpe/f599tWoxwKhY0T5jiohgsDB8OHDpUvTbUqY0bwRt7cxcUllvdfEis5NLlzKzlSIF0LUr++La7+zYMe7XZpqmCHCa5OLFQI0a1h+vgjARERERkTiEhQE//MAAw5Qpef11Bl9eXvYp6GAYwJYtwKBBDCRi+vBDrnkqVMj644jPeitruXWLWcBvvjFvht2yJfcc8/CIffy//zL42rPH3Jc1KzBrFvcosxUFYSIiIiIiUZgCjCFDgFOn2FegAE/227a1X7l5X18GX6Yy71E1asRpkbZYd/XHHxyHJeutrOXBA+DLL4Fp04CgIPbVrcvpohUqxD4+MBDo1An4+Wfz/mhp07JQSN++tg+oFYSJiIiIiDwVc1+tLFk45e/TTwE3N/uM6fx5Ti1cuTL2bVWrcprk229bfxwnTjDY2riR7Rett7KGJ0+A+fOZ6bp1i31vvMH3IK7NsMPCgN69gW+/5f8Blsnv1Yv3sVXGLiYFYSIiIiLyyou5r1bq1KyWN2AAkCGDfcZ06xaDjXnzzFPtTEqVYtbn3Xetn8W5dIlrq3780bzeqksXTtOMa72VNUREACtWsALlhQvsK1qU2b/mzWNnJw2DQdbYseYqjc7OnHL43XdAmjS2GfezKAgTERERkVfWv/8Co0aZ99WyR4AR08OHnGo3dSqn3UVVsCCn0LVta/29vgICGOTMncsMFMCAZ9w4wNPTus9tYhjApk0MkE1r4HLm5PenSxdmtWL68Udmv+7eNffVrcuiHfbaQiAmBWEiIiIi8sq5d4+ZkpkzWV4dAJo2ZdBhi8164xIaCnz/PYtb/Pdf9NuyZWMW6OOPrT8t8uFDYMYM7vdlCgJr1eL7VamSdZ87qv37udfX7t1sp0/PtWi9e3M9V0zbt3Pdl6lKIwCUK8cqkvb6nj6LgjAREREReWWY9rMaP96cKalWjQFH5cr2GZNhAD/9xPVW/v7Rb0uXjlMi+/YF3N2tO47QUE7VGzMGuHGDfWXLMviyZTXIU6f4Xqxbx7abG9CzJ7NhWbLEPv7vv5kZPHHC3FewIEvnV6tmixHHn4IwEREREXF44eGcjjZihDlTUrIkAwxbrKt6lj17WNhi//7o/S4u5sAjWzbrjiEiAli9mgVIzp1jX+HCnHbYqpXtqkFeucKpoYsXc0zOzsBHH7Evf/7Yx1+7xuDLlCkDWG5+9myOOylTECYiIiIiDsswgF9/5bS2v/9mX968LNjw4YfWX1f1LCdPckymSoMmTk7mwKNAAeuPY+tWjuPwYbazZ2cRjm7dAFdX6z8/ANy5wyIjs2YBISHse/99ZitLlox9fFAQpx2uXWsuN58mDTN4X3xhmzG/LAVh9rRxI3/SnJwY6pv+jfp/S26zx/Gm/9vrspGIiIjICxw4wDVEpkyJPfaziunff1lUwpTtieqDD5h9iivwSGyHDjH4Mu055u5unvaYLp31nx8AHj3imrzJk4H799lXrRqzk2+9Ffv4sDBWrPzmm+jl5nv2ZBETewXUCZGkg7CmTZvi8OHDuHjx4nOPW7p0KSZOnIjz588jf/78GDBgALp27RrtmLVr12Ly5Mn4559/kCFDBtSpUweTJ09GjiglUq5fv45+/fph69atePLkCby8vDBz5kzkyZPHGi+PIX7Mn77kKK5gLTECvOTUl5RuS0hfcn4/dCFAkqDE/PsVVd++ffHVV1/BMF36FZE4nT7NfbXWrmXbzY3FHAYPZiBmD3EVAjGpXp23Vali/XGcOcP3Zs0atl1dgc8+Y3Bq7WmPJqGhwMKFLEBy/Tr7SpdmNqxhw9h/2g2Da/bGjGHgBvA0oGVLFjKxd7n5hEiyQdjSpUvx888/o8AL8rBr1qxBhw4d0Lt3b7zzzjtYt24dunXrhtSpU6Ndu3aRx7Rs2RLdu3fHuHHjcOPGDYwcORK1a9eGr68vUqVKhbCwMDRo0ABBQUH45ptvEBoaisGDB8PLywtHjx6FS1z1L19WpUqcoBwRwU9XRET0/79MX3zv8zIMg68jPDxx3heR+LJVVtgRbktqx1eowHlBDiQx/35F9fvvv+Prr7+21rBFHML16zxR/+47npY4OwMdO3JqX7589hlTXIVATMqWZeBRv771rylev86gZ8ECvjdOTkD79ny/Cha07nObGAYD42HDzAVIChTg1NBnldxftowbK9+5Y+6rVYsVD3PmtM24rcHJSIKX065du4ZSpUohbdq0SJEixXOvJHp6euL111/H6tWrI/tatWoFX19fnD17FgBQpkwZ5MuXD7/88kvkMX/99RcqVaqENWvWoHnz5lixYgXatm2Lv//+GyWf5oD9/PxQqlQpLFmyBO3bt4/13IcPH8Ybb7wBX19flC9fPpFevZ3EDM4sCeCsfZupndgBakL6ktJYXvY9TuzvV3h49PcmoY8pr6YVK7hzZjwl1d+/if33y+Thw4coU6YMnjx5gn///fe5mbCk+t6IWFNgIKejffmlOVPSqBEDnFKl7DOm8HAGECNGAJcvR7+tUCEGZbYoenH/PrNIX31lfm/efReYMAEoU8a6zx3Vjh3MRP71F9tZs7IQyCefxF1yf8cOrvuK+t69/jqDrxIlbDPm+IrP798kmQnr2rUrvLy8kCpVKuzateuZx128eBH+/v4YPXp0tP7mzZtj9erV8Pf3R9GiRVGvXj1Ur1492jEeHh4AgHNPS8Bs3rwZnp6ekQEYAJQoUQLFixfHr7/+GmcQ5lCcnJLXRFpxPKZAzhrBYlIP4O35mhPyfIl5TNas9v7kJarE/Ptl+jsFAP3790fOnDlRp04djB071lrDF0l2QkKA+fOZSQkIYF+VKlxjZK/S5IbBzYUHDwaOH49+W/bszMp16WL9ohfBwdxkefx4cxbJHu/NkSN8L7ZsYTttWhbP+OIL7vsV04kTQLt20cvNFyjAzbRr1bLNmG0hyQVhCxYsgK+vL06ePIn+/fs/99hTp04BQLQ/VABQtGhRAIj8IzZ9+vRY9/3pp58AAKWeXh45depUrMcxPZZ/zA0bRCTxRV3fpQsCkgxZ4+8XAGzduhVLlizBkSNHsHz5ciuMXCT5iYgAVq5kJuXCBfZ5ejLz9cEH9lsufOgQy83v3Bm9392dgcizNhlOTOHhwI8/sviHKYtUvDgzX++/b7v35tw5fn9WrmTbxQXo3p19UUoyRLp6lcFX1HLzmTOzYmLbtrYZsy0lqSDs0qVL6NevHxYtWoSsFlwdvXfvHgAgfYww2v3pTnaBgYFx3u/MmTMYMGAAypcvjwYNGkQ+VrFixWId6+7u/szHMQkKCop1jJubG9xetJ351Kn891nrJyz992Xum5DHsPexKsQgIkmMtf5+3b9/H126dMGYMWPivFD4PAn+2ySSxG3dyoqHR46wnSsXs0udOwMp7XRme+4c1zmtWhW939WV65kGD457k+HEZBgsvD1kCMvfA0CePFzz1aGD7d6b//5jZvLbb80VDNu2ZV/hwrGPDwxkZjBqufnUqRlEDhzouKd9SSYIMwwDnTt3RsOGDdGsWTOL7hPxdB2JU4zvjmmuvHMck2xPnTqFevXqwc3NDT4+PpHHRERExHoc02OleMFV+Ro1asTq8/b2xqhRo57/AgYP1lqYhLJHoJrUglFbvc6k8FzJ6YKDvHKs+ferT58+yJs3L/r27RvvcSX4b5NIEnX4ME+dtm5lO316BmO2yC49y82bDC7mzTMHHAD/HHTuzEDCFgVB9u7le7F3L9uZMjEY69nTdqX4AwOBadO4Lu/hQ/bVr8/sZLlysY9/8oQl8efONb93KVMCn37Kx7HVHmX2kmSCsDlz5uD48eM4ceIEwp5+J0x/jMLCwuDs7BwrqMqYMSOA2BmvoKAgAECGDBmi9e/cuRNNmzaFu7s7tm7dikKFCkV7rLgyXkFBQbEeJ6bdu3ejbNmy0fosutL40UeJs67Clus7EvPfl2EqCCGS1JiytfYONpPLv+3bx/3XORmx1t+vjRs3YuXKlTh06BAiIiIiv573uFEl+G+TSBJz/jynsK1YwbaLC/f5GjbMfstKHz5ksDF5sjngMGnalOuwXnvN+uM4eZKl5TdsYDtVKgalgwbZrhR/SAj37Ro/3rwur1IlltyPaw1XRIS53Pzjx+xzcgKaNWO5+bjWiTmiJBOE+fj4ICAgALly5Yp1m4uLS5xX7zw9PQEAZ8+eRbkof8RNVaVKRCmdsnz5cnTs2BEeHh7YtGkT8sYoiezp6Ykjprx2FGfPnkWlSpWeO/Z06dLFmlJikYUL438fR2JpUJgYQePLHvuisdg7oLXnc1rzvbHX63/Zz3ViPM6romLFZB+EWevvl7e3N4KDgyPXLsd83I8++giLFy9+5rgS/LdJJIm4dYsbF3/zDfeVArhmaOxYVhe0h9BQBgqjRgE3bkS/rVYtBh4vOG1MFFeuMMv2ww/8c+PszCl93t6cgmgLpuqPI0cCly6xz8ODa8+aNo09OcQwgKVLgT59opebr1aNFQ8dbLeSF0oyQdj8+fPx4MGDaH2jR4+Gr68vNmzYgNy5c8e6T9GiRVG4cGH4+PigRYsWkf0+Pj7w8PCI3KPl119/RYcOHfD2229j/fr1cWa2vLy8sHz5cvj5+UUGb35+fjh16hSGDx+emC9VTJycVIBBkqb4BJUvezHBlsF0fMdii3+LF7f3d/ulWevv16hRo9CzZ89o9/v222/x3Xff4a+//rJo7ZlIcmTKMk2dCph+tJ43rc0WDAP4+WdOhzxzJvptZcsyI1avnvVnpd++zfdh9mxmoADbZt4Avhe//srpjqYKhrlzMzDt1CnutWfbtjFIjFpuvmRJBmUxEvavjCQThJmuCkaVJUsWuLq6okKFCgA4bcPPzw9FihRBtqdbeo8YMQKdOnVClixZ0LhxY2zYsAGrV6/GqqcrI4ODg9G1a1e4u7tj2LBhkRWpTPLmzYu8efOiVatWmDBhAho0aIBJkyYBAAYPHozSpUtH+wMpIq+AqFMKRV7AWn+/ChYsiIIxdlDduHEjAEQ+rogjMWWZRo9mcQcAeOMNBjh16thvXHv2sEDE/v3R+4sUYfDTooX1/1w8egTMnMn34v599tWowfabb1r3uaP6809Oddyzh+2MGRmYfv45kCZN7OOPHWNRkKil+vPk4YbR77xjkyEnXUYS9tFHHxkFChSIbO/cudMAYCxatCjacfPmzTOKFi1quLm5GcWLFzeWLFkSedv27dsNAM/88vb2jjz28uXLRpMmTYx06dIZmTJlMlq1amVcu3btmePz9fU1ABi+vr6J9ZJFRMQCSf33b2L8/YqLt7e38aI/3Un9vRGJKSLCMHx8DMPDw5SqN4zChQ1j5UrDCA+337hOnjSM994zj8n0lT27YXzzjWE8eWL9MYSGGsb8+YaRK5f5+cuUMYxff+X7ZisnTxrG+++bx5AqlWEMGGAYt2/HffzFi4ZRs2b09y1DBsP47jvbjtvW4vP7N0kHYUmd/tCJiNiHfv8+m94bSU527zaMN980n6hny2YYs2YZRkiI/cZ05YphdO5sGM7O0YMId3fDmDDBMIKCrD+GiAjDWLMmemBasKBh/PijbQPTy5cNo1Mn83vh7GwYXbrwPYrL7duG0bKlYTg5mcft5mYY3t4MKB1dfH7/JpnpiCIiIiLyajhxgmuKfvmF7bRpgS++APr358bG9nDvHqf3zZhhXm8FsFR6796cdpc5s/XHsXMnp/z99RfbWbMCI0Zwo2NbFTi9c4drz2bNMr8XH3zAohtxLeV99Ijfz6jl5lOkYJn+GTPst4VAUqYgTERERERs4vJlc1U/w+CJ+scfs8Jezpz2GVNICDBnDqsuPt1HHQCXBpsqDtqict/Rowz0Nm9m2xSYfvGF7cq2x7X2rHp1Vn2sUiX28aGh3NNr7FhzuXkAaNSI675y5LDNuJMjBWEiIiIiYlVxZVZatGAJeg8P+4wpIoKl0YcOZcn3qGxZcfD8eWa6li9nO2VK4JNPuDearYKY0FDunDR6NHD9OvvKlOH3rEGDuMvN//AD0K8fcPeuub9SJWDJEiCOekUSg4IwEREREbGKx48ZeE2caM4y1ajBzXptsZ9WXAwD2LIFGDDAXGLdpGZNjq1iReuP4+ZNZpDmzzfvg9amDfuKFLH+8wN8L9au5cbX/v7sK1iQY2jbNu6qj7/9xqmRUQPXIkWARYu455dYRkGYiIiIiCSq8HBmREaOBP79l32lS3Oa2zvvWH8/rWfx9WXwtXNn9P6yZRl81a1r/bE9eABMn86voCD22WMftB07OP3R0rVnhw5xH7C//zb3Zc3KqZwtWtjve5pcKQgTERERkURhGMDGjSzScPIk+/LnZ2alXTuuAbOH8+eZ7Vm5Mnp/oUJc79S8ufX3+nryhFmvsWOBW7fYV6ECA9Pata373FEdOcLga8sWttOmZUGUL76IuyjK2bNAt27Arl3mvrRpgTFjgF694t6cWV5Mb5uIiIiIvLSYG/lmysTAp0cPIFUq+4zp1i0GPd98Y67aBwDZsrG/c2fAxcW6Y4iIAFasYJbpwgX2FSvGNWfNm9sug3TuHNeZmQJRFxdmvZ619uy//xhk+fgwuAYYcPXuzQAsrs2ZxXIKwkREREQkwU6fZubr55/ZTpXKXNI9Y0b7jOnhQ+Crr1hS/dEjc3+6dAwMe/WyfhBhGKx0OGQIKx8CQK5crLZoi+DP5MYN89ozUyDati37CheOffz9+wzMvvmG00oBBopt2rByYtasthm3o1MQJiIiIiLxdv06MGoU8P33PFl3duaaoVGjbFPSPS5hYazyN3y4ecofwICnb19m6myx19fBgwxCTWvP0qfnc/fubbs9swIDzWvPHj5k3zvvcO1Z2bKxjw8OZrn58eP5f5NatVhuPq6ATRJOQZiIiIiIWCwwkEUsZswwZ5kaN2bWqWRJ+4zJMID167m26dw5c79pr69Ro4A8eaw/Dn9/Ztp8fNh2dQV69mQZ/CxZrP/8ALcAmDeP5f8DAthXqRLXvtWqFfv48HAG0oMGRd8nrVQpBrS2qBT5KlIQJiIiIiIvFNfJfZUqLCxhz9Lke/cyy2Wq8mfSpAmzPrbYs+raNe6xZcoKOjkBHTqwr0AB6z8/YN73bMQI4OJF9nl4MDhu2jTuvb7WrWOQeO2auT93bk5dfPddVTy0JgVhIiIiIvJMEREs5jB8uLmwhKcnA5wPPrDfifo//7Dc/MaN0furV+e0OltkcO7fN2cFHz9mX6NGDHxKl7b+8wMMpn77jWvPjh9nX65czP517hx39cLffwc+/RTw8zP3ZcjA19Kli/2qWL5KFISJiIiISJy2beM0tcOH2c6Zk9mdZ53c28L168z2LFrEANGkVCngyy+BevWsP4bgYO6PNWECcOcO+956i1nBt9+2/vOb7N/PtWe7d7OdIQPbzyo8cuIE8NlnwB9/mPvc3Pg9HjwYSJ3aNuMWBWEiIiIiEsORIzwx37qVbXd3tvv0sV1hiZgCAxnkTJ/OqZEm+fMDU6faZq+v8HDgxx+5CfWVK+wrXpxZwcaNbZcV/OcfrjMzVaR0cwM+/5zZsLgKj1y8CPTrZz4e4HvVpQvHbqv1amKmIExEREREAHC64fDhXFsEsKrgZ5+x2ES2bPYZ05MnLJc+ciQDMZPMmVnJr0sX65d7Nwzgl1+YLTJtQp03L7OCHTrYLit49SqnGS5cyCygszPw0UccR758sY8PCOD3c8ECc7l5gFMmv/6am1WLfSgIExEREXnFBQSw4MbcuUBoKPuet5eULUREAKtXA198Eb1wRJo0DAptVe593z5mAU1T+DJlYsapZ0/bTd+7e5fVDb/+2lw+/v33GYTGVZEyKIjru6ZMiZ41rFiRxVXKl7fNuOXZFISJiIiIvKJMmxpPmWLOMtWty2l/9jxR37GD0+uiFo5ImZJrnYYNs81eX35+nPK3fj3bpk2oBw1iIGYLjx8Ds2ZxyqCpfPzbb/P789ZbsY9/8oSVDYcPj541LFSIAXb9+qp4mFQoCBMRERF5xYSFsbCFtzcLXQBAuXI8ubdFYYtnOX6cgZap0ATAoOHDD5n1scUm0Feu8H354QfzlL/Ondlnq02ow8KAxYs59fDqVfaVKsVgLK7S8RERwKpVzBqavp8A13pNm8b3TxUPkxYFYSIiIiKvCNOmxkOGsLgDABQsyACndWvrF7Z4lsuXWW5+9ero/Q0bshDHa69Zfwx37pin/Jmm8DVpwvemeHHrPz9g3rtr6FDz9yd/fmDMGKB9+9iBlGGweEqvXsDp0+b+VKm4hq5PH1U8TKoUhImIiIi8AvbuBQYO5BongFmSESOATz5hdT17uHOHRSXmzmX2x6RSJQZDb75p/TE8esQpf5Mmmaf8Va/OrGDlytZ/fpPdu1n4Y/9+trNk4dTLTz9lUBXTX39xeuSff5r7UqRgIZVRo2wzZVMSTkGYiIiIiAPz82Pma8MGtlOnZrnyAQO4r5Q9BAdzg+OxY82bHAOAhweDLy8v669dMk3JHDXKXPijdGkGYw0a2G7t1PHj/P78+ivbadIAffs++/vj78/bTN9Pk+bNWaq/YEGrD1kSgYIwEREREQd09SrXMZk2NU6RguXcvb2B3LntM6bwcGDJEgYRt2+b+3Pm5EbLrVpZf0qkYXC/rKFDzVP4ChRgQNi2re3WTl28yCmDS5dyTClTAt26MTuZK1fs469d422LF0ffpLpqVQauqniYvCgIExEREXEg9+6x2uFXX5mzTB98wKIOtlhbFRfDYKanZ08GHybp03PN1ccfA66u1h/H7t2sbnjgANtZsrCS4Kef2m5K5q1bfM3ffMNqhgDQsiW3CChWLPbx9+4xOzdjhvl4gFnDWbOYNZTkR0GYiIiIiAMICeHaqnHjuNYKYJZkypS4y5nbyl9/cd3Z4cPmPjc3rn8aMMA2e33FNeWvXz+gf3/bTckMCmIgNXUq8OAB++rWZXBcoULs44ODGWSNGcP7mmTPzoqH7drZr5CKvDwFYSIiIiLJWEQEsHw5MzqXLrGveHFmT957z377Qp07x8zXpk3mvhQpgO7dGVhkyWL9McR3yp81hIYC333H13zjBvvKl+f3J67tAExTNocMMR8PMFj19ub+aXEV6pDkRUGYiIiISDJkGMCWLZxed+wY+3Ln5sn+Rx8x4LCHmzc5ph9+4BhNWrRgBid/fuuPISCAU/7mzrVsyp81RESw5P7w4QxIAaBIEY6rRYvYWSzDYLGNAQOAM2fM/SlSsAqirTapFttQECYiIiKSzPj6MtDZvp3t9OmZOenVi1Pt7OHhQwY506cz+2NSqxYwezZQooRtxjBjBqdgmqb81a7NcvNxTfmzlq1bOd3SNAUzRw5m5Lp2jXvt2549nBp58GD0/tatOV1RFQ8dj4IwERERkWTi/HlmRFauZNvVFejRg322mN4Xl7AwZpyGDYu+dql0aWD+fKBKFeuPITQUWLCAe46ZpvCVK2ee8merKZm+vgy+tm1j292de7P16QOkSxf7+BMneHvUKZsAULMmq0WWK2ftEYu9KAgTERERSeJu3WIJ9XnzGHA4ObEww9ix9suSGAbg48M1SlHXLuXPz6CsYUPrBz+GAaxZwwDw7Fn2FS7MKX8tW9qucMXZs5x2uGoV2y4u3DR52DAgW7bYx1+8yHVpy5ZFn7JZogQzeap46PgUhImIiIgkUXFNr6tfnxmesmXtN649ezi1zt/f3Jc5M8favr1tgp8dOzgl89AhtrNl45Q/W5W7B4D//mMg/O23zAg6OfH1jxkTd3AcEMApm3Pm8HiTXLn4PW7bVhUPXxUKwkRERESSmLAw4PvvgVGjeKIPsKLelClAnTr2G9epUwy+9u0z96VJw3H27m2b4OfIEU7527KF7XTpuJ6qXz9O/7OFwEAWGZk+HXj0iH0NG3L9VpkysY83laefPJmBtYm7Oyse9uihioevGgVhIiIiIkmEYQDr1rHIxunT7CtUCJgwwbbT62K6fp3T69avN0+fc3FhIZBRo+Je75TYzp/nFL7ly83P/8knnAaYPbv1nx/gXmzz5jGbFRDAvkqVGFzVrBn7eFN5em9v8/EAx967NzB0KJApk02GLkmMgjARERGRJOCPP1ik4c8/2c6alUHHJ5/YbnpdTIGBzDp9+y33rwLMU+6mT497vVNiu3mTQY9pPRwAtGnDaYBFilj/+QHzXmwjRnA9FwB4ejI4btIk9to3U3n6IUPMx5u0b8/XU6CALUYuSZWCMBERERE78vPjyfqGDWynScOpdQMGsPS8PTx5wuzOhAlAcLC5v359BkO2KAby4AErBE6bZq666OXFKX/ly1v/+QFm/TZtYiB6/Dj7cudm9q9Tp7j3Ytu6lcH00aPR++vWBaZOte9aPkk6FISJiIiI2MHVq5ymtmgRMycpUgBduvAEP1cu+4zJMDieL74A7t0z91eowDVqca13SmxPnnAK35gxzIIBwBtvsBhJ3brWf36TAwdY+GP3brYzZGAw9qy92A4d4vE7dkTvL12aWcN69aw/Zkk+FISJiIiI2ND9+8wyffUV8Pgx+5o0YdbptdfsN65Nm4Bu3YB//zX3FS7M4Cuu9U6JzTSFb/hw4Nw59hUpwveleXPbrYc7fZprtX76iW03N5bhHzKEFSBjOnOGY169Onp/njwMHFXxUOKiIExERETEBkJCuH/WuHHAnTvsq1qVFQ/fest+4zpyBOjQAfj7b3NftmzA7NlAixa22eh42zZmkQ4fZjtHDpab79aNRSxs4epVbva8cCHXvzk7Ax99xL58+WIff/06s3XffWdeLweo4qFYRkGYiIiIiBVFRAArVjBbYirSULw4syTvvWebICculy8zyNi1y9yXLh03Ou7Rg9Mjrc3Xl1P8tm0zP//AgUDfvrapuAhw2uXkycDMmebMZOPGzMCVLBn7+Pv3ubbryy/NxwNcH9anz7MzZiJRJTgIu3z5MrZv346rV68iIiIi1u1OTk4YMWLESw1OREREJDnbsoUZHlORhty5mVnp2DHuog62cO8e8OmnwKpV5nLzrq5cBzZypG2yN+fOMShduZJtFxeWwB82zDYVFwEWHJk9m8HW3bvsq1qVAVnVqnEfP3cug1RTJtPkww9ZrVEVD8VSCfrx37x5M5o2bYrg4GAYpp/eGBSEiYiIyKvq8GEGX6YMT/r0zPj07h13UQdbCAnhWqdZs8yl3p2dGRDOmGGbSow3bjBYmT+fG1I7OXHN1Nix3A/NFsLDgSVLOGXwyhX2lSjBzGSjRrEzk+HhwNKlLE9vOt6kTh1mxcqVs83YxXEkKAgbOnQo0qRJgy+//BJFixZFSntdyhERERFJQi5cYIYn6obCPXoww5M1q33GFBHBIGvkSODRI3P/u+8CCxYAOXNafwwPHrDU/PTpwMOH7HvnHZabt1XJdsMA/vc/BqInT7IvXz6u6/rww9jTLw0D+OUXTi+Mul4OYJXIqVNZMl8kIRIUPZ0+fRqjR49G9+7dE3s8IiIiIslOQAALbsyda84ytWtn2wxPXFatAnr25PhMKlUCfvwR8PCw/vM/ecKs19ixwK1b7KtYkVP+atWy/vOb7N3LzOTevWxnysTA+FnFM/bt4/F//BG9P08eTl9s1842a+bEdkJCOD31+HHghx+s/3wJCsKyZcum7JeIiIi88h4+ZKn5KVOAwED2eXlxaps9p6j9/junGV64YO4rVownl1WqWP/5IyIYAA4fDpw/b37+CROAZs1sV4zk5ElmvkwbYadOzeIZAwcCGTPGPt7Pj8evXx+9P316vpbPP1fFQ0djGPysDhliLpzTvbv1K5YmaNeCrl27YsGCBXgUNactIiIi8ooIC2Np8mLFeHIeGMiga+tWYPNm+wVgp09zY+MaNcwBWI4cwLp1gL+/9QMww2AxkgoVuNbr/HlOd5w3jwFR8+a2CcCuXAE6d+a0wQ0bmLX6+GPg7FkGgjEDMNPxpUtHD8BcXFiw5MIFYMAABWCOZs8eoHJloE0bBmC5c3Oz8jfftP5zJyidNXjwYOzbtw+enp549913kStXLjjF+IlSYQ4RERFxNIbBk/QhQ4B//mFfwYKsmNe6tf025b15k+uatmwx97m7c9rfJ5/YJvA5dIjFR7ZvNz//oEHMPKVNa/3nB1i1cOJEFh8JCWFfs2b8/nh6Wna8Sbt2nGJasKDVhy025u/Pz+a6dWynS8d2v362K5yToCBsw4YN2Lp1K8LCwvDtt9/GeYyCMBEREXEk+/ZxGptpXVGWLKyY98kngJubfcb06BHLzS9dyimAAMcycCALcdhi9ciZM8wGrl7Ntqsr11oNHWq7YiSPHgFff81poPfvs69GDQahcWU14jrepHZtFt0oX9764xbbunWLhVjmzWM229mZGdJRo5gxtqUE/Wh6e3vD3d0d3t7eKFGiBFxstZW5iIiIiI398w8zX6ar5qlT84r5gAFAhgz2GVN4OAOfL79k8QuAU+46dmRwYYur+f/9xxPa774zl5v/8EP22Wq/rLAwTh8bNQq4do19ZcowuHrnndgZwLAwYOFC7tVmOt6kVCkGX/Xr228DbbGOx4+5GffEiea1m40aMUgvUcI+Y0pQEHbmzBmMHTsWvXr1SuzxiIiIiCQJ16/z5P777xn0ODsDXbqwL3du+4zJMFiBccgQln0HGDC8+y6LbmTObP0xBAaay82bygM0bMgT3DJlrP/8AN+Hdev4Ppw+zb4CBTh9sG3b2NNCDQP46Sdm5/z9o9+WJw+nK7Zvr4qHjiYigttFDB1q3uOtfHl+fm1ZnTMuCQrCcubMCVdX18Qei4iIiIjdBQay2uGMGeYg4/33GWQUL26/ca1fz6ptN26Y+ypXBpYtAwoXtv7zh4SYy82bSt5XqsRsQs2a1n9+k99/5/qd/fvZftG00J07uVbt4MHo/enT8+S8Vy9mN8Wx7NwJ9O/PjdMB7gk3YULcQbo9JGgIHTt2xPz58/HQtNueiIiISDL35Amn8hUpwszIo0esJrhnD7Mu9grADh7knl4ffGAOwDw92f/nn9YPwEzZhOLFgd69GYB5eABr1zIQslUAduIEp5DVqMHnTZOGwdf58xxXzADs6FFOSaxdO3oAljIl0Lcv7zdokAIwR3PqFPDee/y+Hz7MYHvSJGZM27dPGgEYkMBMmKenJ4KCguDh4YGGDRsiZ86csdaFqTCHiIiIJAcRESwqMWyYeU8rT09mvj74wH7rgy5cYOnsAwfMfTlzAt9+y5NMazOVmx88mAENAOTKxemYnTvbpugHAFy6xCIjP/7IMaVMCXTrxr6cOWMff/48g7Ply2Pf1qYNA2x7bqAt1nHjBuDtDSxYwOnDKVMyOzpyJJAtm71HF1uCfnzatm0b+f/vv/8+zmMUhImIiEhSt2MHKwn6+rKdMyeLNtgyyIjp7l0WuPj1VwYdAK/mT5nCSm62CAr/+ovB144d5ucfNIgZJ1uVmw8I4PSxOXPMxUdatuS6r2LFYh9/8yZvmzcPCA2NflutWnz/KlSw/rjFth49YoGayZOBoCD2NWnC7JeHh33H9jwJ+vWyc+fOxB6HiIiIiM0cP86gYtMmtt3dGYz17Wu7ICOmJ0+Azz4DFi/mlXyAU+wGDGBgaItpVHGVm+/Zk2unsmSx/vMDwMOHrGQ3ebK5kl3t2mzHFUQ9eMCT8GnTzCfhJqVKMfiKq1KiJG/h4cCSJfy8mipdVqzIgjHVqtl3bJZIUBBWo0aNxB6HiIiIiNXFNbXt0085fc1eU5YiIljWfdIk84bBKVIAHToA33xjmz3IkkK5+dBQlo8fNYrjAYCyZRl81asXO4h68oRTM8eM4f5PUeXJw/6PPlLFQ0e0dSuLbhw/znbBgpw+3LJl0lnz9SIJGubly5ct+npZTZs2RUELtilfunQpSpYsidSpU8PT0xMLFix45rGBgYEoWLAgFi9eHOu21q1bw8nJKdbXypUrX+JViIiIiL3ducOTNk9PXj03DKBVK+4B9vXX9gvAvv+eZeVHj2YA5uTEcu83bjAgsXYAFhjIoLRIEQZ8YWF8/qNHWfLeFgGYYbDIR6lSXMPz339cs7V8OaeJenlFD8AiIoAVK1go5PPPowdg7u48Gff355RSBWCO5cQJZjW9vBiAZczIDOg//wCtWyefAAxIYCasYMGCcLIgpxtuyqUnwNKlS/Hzzz+jwAt++tesWYMOHTqgd+/eeOedd7Bu3Tp069YNqVOnRrt27aIde+fOHTRu3BiXLl2K87GOHj2K9u3bo0ePHtH6i8U18VhERESSvMePgVmzeGJ+7x77atVidqViRfuN67ffgK5do28YXKkSg4tXqdx8zPLx2bIxK9m9O6dCRmUYzIAMHgwcORL9tpQpgR49ODUta1bbjF1s59o1XixYtIhBuIuL+fttq2myiS1BQViHDh1iBWFhYWH477//sHfvXhQuXBjdunVL8KCuXbuGXr16IW/evC88dvjw4WjevDlmzJgBAKhfvz7u3LkDb2/vaEHY+vXr0atXLwTFnCz81KNHj3DmzBkMGTIElStXTvDYRURExP7CwznlcORI8yatpUszyLDn+qCjR7lP0alT5r5ixYClSxkEWZspizRiBKsvAixeMHEiixnY6n05dozBlGlNXtq0zFR+8QWzWTHFLBQSVatWrHhYpIh1xyy2FxQETJ3KbJdpz74WLfh5Te7f7wQFYXFN5TM5e/YsqlatiiwvEZZ27doVXl5eSJUqFXbt2vXM4y5evAh/f3+MHj06Wn/z5s2xevVq+Pv7w8PDA/fu3UPTpk3Rvn17fP7556gYx6Wv48ePIyIiAmXLlk3wuEVERMS+DINZpsGDOXUJ4CatY8dyjyB7TU+7coXl0ffuNfflyMFs1PvvW//5k0q5+QsXzOXjTWvyundnX44csY8/c4ZbB6xZE/u26tV5gm6L4FVsKyyMWa+RI83rA6tUYTD21lv2HVtiSfSZk0WLFsWnn36KadOmJej+CxYsgK+vL2bPnv3CY089vYzkEaP+ZNGiRQEA/v7+AIA0adLAz88PP/zwA7I+I0d99OlvpHnz5iFnzpxwdXVFtWrVcCDq5hwiIiKSZP31F6vovfsuA7CMGVkZz9/ffgUaAgOZYSpQwByApUvHKZL//WebAOyvv4C6dZkBPHqU5ebHj2eA8/HHtgnAbt0C+vThmrxlyxiAtW7NjODs2bEDsOvXWTClRInYAVjx4sD//gfs2qUAzNEYBrdmeP11fjb/+48ZLx8f/vw4SgAGJDAT9iJ58+bF6dOn432/S5cuoV+/fli0aNEzg6Wo7j2d3J0+ffpo/e5P89iBT+uaurq6wtPT87mPZQrCHj9+jJUrV+L27duYNGkSatWqhf3796NMmTLPvG9QUFDkc5m4ubnBzRbljERERF5xZ88yW2Iqq+7mBvTqBQwZAmTKZJ8xhYZyDN99Zy437+rKEvgTJtiu3HzULJI9ys0HBQEzZjBj9eAB++rV43SyN96Iffz9+zx2xgzz9DOTXLlY8bBjR/vt4SbWc+QIt2PYvp3tzJmZCfv009jrAx1Bov8KMAwDPj4+FgVRMe/XuXNnNGzYEM2aNbPoPhEREQAQa32a8XRnQ+d4/Ibr27cvtm3bhsWLF6NmzZpo1qwZtm7dirRp02L8+PHPvW+NGjWQIUOGaF8TJ060+LlFRMSxJGZ137Vr16JSpUpInz498uXLh44dO+LGjRtWGHXyc/Mmq+MVL84AzMmJGS9/f2bA7BGAGQaDrPTpuWlweDgDrvbtWRhk0iTrB2D//cf9xkxZJCcnlrv39+ceSrYIwEJDgblzgaJFeSL94AFQvjynRG7ZEjsACwlh4FWkCLN0UQOwdOk4nfTMGRYzUQDmWK5c4c/tG28wAHN1ZTB27hw3B3fEAAxIYCasc+fOcfY/fvwYR44cwZkzZ9CrV694PeacOXNw/PhxnDhxAmFhYQDMwVRYWBicnZ1jBVUZM2YEgFhZKFPxjQwZMlj8/J6enrGyZRkzZkTVqlVx7Nix59539+7dsdaSKQsmIvJqSszqvmvWrEHLli3RvXt3jBs3Djdu3MDIkSNRu3Zt+Pr6IlWqVLZ4SUlOUBA355061bw5b4MGDHCeM3HF6hYvZqbLVIURYCntpUttUwI/MJBrZqZPNwcxDRsy62Sr9yUiglPHhg1jhhIwB1YtWsQOQMPDOT1x5Eju4RZVihQsWT9yJJA9u23GL7YTGMhCOV9+CQQHs69NG17EsOAaVvJnJICTk9Mzv9zc3IyOHTsaDx8+jNdj1qhRwwDwzC9vb+9Y9zlz5owBwFi9enW0/tWrVxsAjIsXL8a6z4ULFwwAxqJFi6L1r1ixwtiyZUus4728vIyqVavGOWZfX18DgOHr62v5CxURkZeWVH//Xr161ciUKZORN29eo0CBAs891sPDw2jRokW0vpYtWxpFihSJbJcuXdpo2LBhtGMOHjxoADDWrFkT5+Mm1fcmMTx5YhjffGMYOXIYBnNOhlGhgmHs2GHfcW3ZYhh585rHBBhG+fKGcfq0bZ4/ONgwZs40jKxZzc9fqZJh7Nxpm+c32baN3w/TGLJnN4zZsw0jJCT2sRERhrFxo2GULh39fTN9NW9uGP7+th2/2MaTJ4Yxd65hZMtm/n5Xr24YBw/ae2QvLz6/fxOUCbtgqmkaQ8qUKZE1a9YEZYHmz5+PB6bJwk+NHj0avr6+2LBhA3Lnzh3rPkWLFkXhwoXh4+ODFi1aRPb7+PjAw8PjhVcho5o7dy6uXbsGPz8/uD7Ne169ehV79+5Fnz594v16RETk1ZOY1X2LFi2KevXqoXr16tGOMRWjOnfuXKKPP6kyDODnn7nG62nNLRQuzCvmcWVXbOXYMaBdO+DkSXNfoULcDPrtt63//Eml3PyRI6y6uGUL2+nScTpZv378f0z79wODBgG//862kxO/xwBQtSoznFWq2GbsYjuGwYIqgwZxc2WAn9fJk1mgxl7bRthLgoKw+AQ3loqrcEaWLFng6uqKChUqAOC0Qz8/PxQpUgTZnub1R4wYgU6dOiFLlixo3LgxNmzYgNWrV2PVqlXxev6RI0eifv36aNq0KXr27Ik7d+5g1KhRyJQpE/r37//yL1BERByaqbrvyZMnX/h3w5Lqvh4eHpg+fXqs+/70008AgFKlSj33ORylaNQffwADBwJ//sl2tmycnvbxx/ZbK3LlCtd4mYIIgBsEz5kDtGxp/edPKuXmz583l5sHuIHup59yKmJc0wf/+YdFQX7+mW1T8GUYrJo4eTLQuPGrdzL+KvD15T5wpmtTWbPy8/rxx/zcvIrife3oWtSt3Z/asGED+vbti2HDhuFIzC3ME9Hhw4dRpUoV/PLLL5F9HTt2xLx587B161Z88MEH2LVrF5YsWYKW8fwtWLduXWzatAn3799Hq1at0KNHD5QvXx579+6NXHsmIiISF1N137lz5yZqdd+Yzpw5gwEDBqB8+fJo0KDBc58juReNOnWKV8erVWMAliYNT/jPnmWFP3sEYPfvA82bs9y8KQBLk4brsG7etE0AduiQ/cvN37zJyo+vvWYOwNq2ZZA1c2bsAOzqVaBbN6BkSXMABjD4ypGDBUz+/vvVzIY4usuXgQ8/BCpUYADm5saLB2fPAj16vLoBGADL14T9+eefRvHixQ03N7do/R999JHh7OwcuSbM2dnZGD9+fPwnUSZDjjzvXkQkKUtKv38jIiKM2rVrG61atYrs++ijj567Jmzp0qUGAOPs2bPR+v39/Q0AxooVK2Ldx8/Pz8iTJ4+RO3du4/z58898bNN7s3v3buP+/fvRvoKDg+P/Am3s6lXD6NrVMJydmSNJkcIwPv7YMK5ds9+YgoMNo2dPw0iZ0ryGxcXFMPr04foWWzhzxjBatjQ/v6urYfTtaxi3btnm+Q3DMB48MIzRow0jXTrzOOrXN4zDh+M+/s4dwxg0yDBSpYq95ittWsMYNYqPKY7n3j1+793czN/zDz80jEuX7D0y60r0NWGXL19GvXr1EB4ejoYNGyIsLAwpU6aEj48PlixZgnTp0uHbb79F6dKlMXv2bIwYMQJVq1ZFjRo1rBc9ioiIJAG2qO67c+dONG3aFO7u7ti6dSsKFSr0wnGlS5cuVqYtKbt/n2XlZ8wAHj9mX5MmXPf12mv2GVN4OMc0dqx5TE5OXIe2YAHwNHFpVTducG+sb78FwsL4/O3bs89WFeSePOF+Z2PGMAsGsJz45MlAnTqxj3/8mBswT5wI3L0b/bYUKZgV8/YGcua0/tjFtkJDgfnzgdGjgYAA9tWsyWxxXPvCvdIsiep69eplpE2b1jh27Fi0/urVq8eZ+apYsaLRrFkzy8PGZCopXYkVEXmVJKXfv9au7rts2TLDxcXFKFmypHHlypUXjicpvTeWCAkxjK++MowsWcxXzN96yzD++MN+Y4qIMIwffjCMTJmiZ29q1DCMf/+1zRgCAw1j5EhmjEzP36CBYRw9apvnNwzDCA83jJUrDaNIEfMYihQxjFWreFtMoaGG8f330StFOjmZ///BB4Zx6pTtxi+2ExFhGD//bBgeHubv92uvGcaGDbztVZHombAtW7agU6dOKBNlk4kHDx5g3759AIAOHTpEO75p06aYNWvWy0WHIiIiyYA1q/v++uuv6NChA95++22sX78+XvtfJnUREdxgeehQc2W/115j9sSea4O2bWNxiytXzH2lSnGvr9dft/7zP3nCrNeYMcCtW+yrWJFZp1q1rP/8Jtu3s4qdry/b2bMze9WtW+x1PIYBbNjA6pVPa87A2ZnfY8MAKldmxUNbVIwU2zt4kEU39uxhO1s2fn61sfbzWfTWXLlyJVYVpr179yI8PBweHh7ImzdvtNty5syJ27dvJ94oRUREkihrVfcNDg5G165d4e7ujmHDhkVWVDTJmzdvrL+/ycWOHax4aDrBz5mT05dsWdkvpmPHgA4dgOPHzX158gDffw/Ur2/95zcFpcOGseogABQrxumYzZol3XLzf/zBYO3pdfnI4CsiguOfOBFo2lQFNxzRxYu8iLJiBdupUgFffMGf7WQ0E9puLPpV5+TkhPDw8Gh9e56Gu3Gt+woICIis8CQiIvKqO3z4MGrVqoVFixahY8eOAFjdNyQkBNOmTcPChQtRuHDhaNV99+3bh+vXrwMAvLy8Yj2mt7c3Ro0aZauXkCiOH+cJ+6ZNbLu784Stb18gbVr7jOnKFeCjj4CdO819GTIA06czKLRF8LBtG9+Xw4fZzpGD5bu7dLFd9bjz54Hhw80n1C8qN//33zwB/9//2I4afGXLxvHHlTWT5O/ePV4cmDmTmVsnJ17AGDcOSKbXhezCoiCsSJEiOHHiRLS+jRs3wsnJKc4/DJs3b47c60RERORVs3jx4mjtmjVrRhbriKp79+7o3r17nI9Ru3btOO+THF2+zPLyP/7I6WkpU/IEf8QInrDbw507wGefMftkeptN5bOHD7dNRu7wYT7f1q1sm4LSPn3izjpZw82bPHmeN49FFQCWmx87lhtix3T5MqclLlnCgMu011dEBMv1f/EFp6YpE+J4njzh52T0aP78AEDt2iy6Ua6cfceWHFm0T1jjxo2xdOlSHDhwAACwcuVKnDhxAhkyZEDDhg2jHfvzzz9jx44dePfddxN/tCIiIpJs3L3LqWweHjxpNwygVSvuJ/X11/YJwB49YuYtRw5g1SqOyVSx7/ZtZnCsHYCdP89A5403GIC5uAC9ewPnzjEAtEUAFhTEdTtFigCzZjEAq1+fgeGyZbEDsDt3zN/LxYujB2DOznz/zpzhYyoAcyyGAfz0E/d5692bn4USJYBffmEW15ECsEOHuCbTFiz6NdO/f38sX74cb731FjJnzow7T8PfqVOnInXq1ACA3bt34/vvv8eqVauQM2dO9O7d23qjFhERkSQrOJgn9hMmcOoSwDLVU6awyIQ9hIUBX37JIMtUbh4AGjViufkcOaw/hriyTu3aMetkwa4DiSK+5eYfPWLAPGkStxEAGLSGh/Pk/L33eFuJErYZv9jWgQPMbu7dy3aOHPzs2HP9pjVcvsypt0uX8qJI3bpxZ4ITk0VvX/r06bFv3z6MHj0ae/bsgaenJ3r27InWrVtHHvPLL79g6dKlKFGiBHx8fLQmzBL16pkvJb3oy9k5cY+L77H2vF9iP0ZSH6c97psYr1lEXnnh4cyiDB9uri5YqhRP8Bs0sM+vCsPgNMi+fc1TqAAGg0uW2GYPsqAgBoBTp/L/ALNOEyfaLosQEQGsWcMTzXPn2FekCAPl5s35ZyCqsDBg0SIGrdeusS9lSvaHhwMVKvD11Kxpm/GLbZ0/zzV/T+sEIXVqZkL797fN/ni2EhjIiwgzZvDiEQC0bs0iI9ZmcQybPXt2zJkz55m3d+rUCU2aNEHlypXhpBMyy2zfbp6ILpLcJZeA01aPlxQCZGu+psR67Hr1WEJNkjXDADZvZnEJU3XBvHmZ4fnwQ2ZO7GHLFk6Tu3zZ3Fe4MIOL6tWt//yhocw6jR5tWdbJWuJbbn7dOpabP32afS4ufC1hYczYTZzIDatjBm6S/N29y2ytaYqqkxPQqROzX3ny2Ht0iScsjD+b3t7mrSBq1GBBHlttKp1oicTixYsn1kO9OpYvN2+ikZAv3df6901u433R41iTLZ5DHM+KFQrCkjlfXxaT2LGD7QwZeAX988959dweDh/mdKljx8x9WbPyxLJVK55YWpNhmLNOZ8+y73lZJ2uJb7n53bt5/P79bJsyX6GhQObMwMiRwCefsICJOJaQEGDuXF44uXuXffXqMdtpi/3xbMUwuJZtwACuTQUAT09OlX7vPev/bojKgWZzJkNRpnOK2ExiBHeJGTC+agFyYgXYtngOW7zWfPns/RMhCXT+PIOMlSvZdnVl4DV0KE/Y7TWmbt3MASHAin1jxrCggC3WsOzYwazToUNsZ8/O4KVbN75HthDfcvPHjzPz9euvbJvWfIWFMeDq04fBWcaMthm/2I5hAD4+/P6a9qcrVYoVD22xP54tHTnC9W2m7SiyZuV0248/ts9WCgrCRF41pmlgIiIJEBDA6Upz55qnK7VvzyvoBQrYZ0w3bzJQWLmSJ5UAA67PP+e4bLEH2dGjPJHdvJntdOm4fqZfP9utoYlvuflLlxggmrYOMGXowsP5fe3QgQFs/vy2Gb/Y1r59/Iz++SfbuXLxs9Kxo/2mEFvDlSu8KGH6nJsuLAwZwsy9vSgIExERkRd69Aj46iuuZwoMZJ+XF9tly9pnTA8eMIiYPZtZG4DBQ8uWnHpoixL4Fy/yBG/ZMrZTpuSUveHDbVNxEeD78OWXzF5YUvgjIIBTI+fMYbVEwLzuy3TfyZMdaxqamJ07xwsGPj5sp0nDKcVffGG7/els4cEDfo6nTzcX3WjbFhg/HihY0K5DA6AgTERERJ4jLIz7Qo0cCVy/zr5y5biGom5d+4wpJITVzMaMiV5uvkYNlpsvWtT6YwgI4Mnc3LnmQKZ1a2aiihSx/vMD8S83//AhA+kpU8yBtJsb38/QUAbTU6ZwLZA4njt3mOmaM4ffb2dnrp0cM4ZZMEcRFgZ8/z1/Z5l+LqpVYzBmry0y4qIgTERERGIxDOB//+MV81On2FewIAOP1q3tUxkvIoJl5fv1MxcPAIDixYGFC4HKla0/BlMgM3kyr7QDDHgmT7ZdVbX4lpsPDeX7M2oU8N9/7EuVitmBkBAuzRw/nnuWqeKh4wkJYeA1dqx537533mHAXbq0XYeWqAwD+O03Ft3w82NfsWJ8ne+/n/RWYigIExERkWj27+eJzB9/sJ0lC6fXffqpfSrjGQaLRnz2WfRy87lyMRNlixOsuAKZcuUYfNkycxTfcvNr17JYypkz7EudmtnD4GCuhxk2jGvnbLEvktiWYTBYHzwYuHCBfWXKcNqqo2U7jx3j+rZt29jOkoU/F927W14QxzCA9ev5WN7e1hurSYKud0RERGD06NHIly8fXF1dkSJFilhfKR1pG20REZFXwJkzQLNmQJUqDMBSpeLi9XPnuJDdHgHYgQOcJteokTkAc3fnlf3Ll4EPPrBuAGYKZEqV4lqv//7jXlnLl7MCoq1OZo8c4VqtunUZgKVLx/3Hzp1jcBozANu5k5nBFi34fTV97x4/5rF9+/K+AwYoAHNEe/cCb73FLRkuXABy5+ZFhMOHHSsAu3qVUyrLlWMA5urKYOzsWV5csDQAO3CA05mbNOHPlWm/Q2tKUKQ0evRojB07FhkyZECFChXgpg0jREREkr2jR4GffuKUtE6deDJirw1a//kH6NmTmR8TV1cGDUOHspiAtf3+OwsWHDjAdtaswIgRDMZsVW7+wgVmIZcvZ9vFxVz4I65y88eOMfOxaZP5+IgITkkDgDZtOPWwUCHbjF9s6+xZfv/XrmU7bVpmTvv1s02VUFsJCuIeZtOmsWgQwIBz4sT4fbbPn+fvk1Wr2E6Viu+VLQp3JCgI+/HHH/Hmm29i+/btSGOL34IiIiJidc2b8ypyx45AyZL2GcPVqwy0opabd3YGPvqIU/9sUfHwxAlmAH/5he00aVg5rn9/IH166z8/ANy6ZS78Yapa2KYN1/XEVfjjwgUGiMuXm8vNu7iYg6+aNXnSWqGCbcYvtnX7Nj8bps+LszPQtSsvpOTMae/RJZ7wcGDRIn7WTdOC33qLRTfisyb0zh0W0Zk927zVRocOfA9ttX1lgoKwa9euYfDgwQrAREREHIiTE0/U7eHuXZ4wzp7NEy2T+vU59dAWFQcvX2ZFtSVLGMikSMGNXEeOtN2J7MOHrPw4ZYq58IeXF6/wly8f+/i4grW0afk4ISEMpqdMARo0SHqFCeTlhYRwO4Zx44D799nXoAG/56VK2XdsiW3zZl4I+ftvtosU4YWZpk0t/2yHhPB3zLhx5iIldevy956tt9pIUBCWL18+3I1alkhEREQkAR4/ZrXBsWOjl5svWxaYPx+oVMn6Y7hzh0HOrFnmzFGLFjxR8/Cw/vMDDKAWLGAgeuMG+8qX50lmXFsBBAUxWJs61Rysubvz/w8fcg3QmDGOt/GukGEAq1dz6uHFi+x7/XV+HhxpzRfAzHT//sCWLWxnysQLI599Fr+iG6tWceqhqUhJqVJ8v+rXt88FigQV5ujUqRPmz5+PB6afehEREZF4CAtj0JE3L0+MTAFY/vwsjX/4sPUDsMePGeQULsy1JSEhXJx/4ABPcG0RgBkGN80tWZInlTducDwrVgB//RU7AAsNBb75hnuhjRzJoMs0RfLBAwZi48YB/v5Aly4KwBzRH3+weE7r1gzAcufmFD1fX8cKwK5f55TKsmUZgLm4cL3W2bMsFGRpAPb778Cbb3I6r6lIyfffcw3sO+/YL0OcoExYkSJF4OTkhOLFi6NRo0bImTMnnGNsLOHk5IQRI0YkyiBFRETEMRgGsGED0KtX9HLzGTPyqnSnTtYPHMLCOOVw5EiuQQO4X9LkybY9Kdu1i0UTDh5kO1s2junjj2OfYJrKjQ8bxpNQgAFXUBA3Xk6ZkgU7RoyIu2CHJH9nzjDz9dNPbKdNy3a/frYpVGMrDx/yosjUqfw/wPWqkybFb1ry6dP8+Vq/nu106VhoJ6kUKUlQENamTZvI/3/77bdxHqMgTERERKLas4dlo48dM/e5uTET1r+/9U8kTRtQDxli3sw1f35OhWzXznZZo2PHOIbffmM7bVq+/i++YGAV044dPJk8dMh8/JMn5mmIzZtzo+ZixWwzfrGtgABz0Y2wMBbd6NaNe9Y5WtGNH35g5c/r19lXuTKLbrz1luWPc/Mm35tvv+Vjpkhhfr9y5LDGyBMmQUHYzp07E3scIiIi4qBOnOD0oR07zH2mE8mxY21T8XDfPgYypg2oM2dmVumzz2y3T9bFi8x0LV3KgDBlSm4mO2JE3CeHR48y07F5M9tubjyhNGUHqlZltqBKFduMX2wrOJjrFMePNxfdaNiQRTfsVb3UWrZu5YUI0/5chQox89WiheWZ6UePuE5y0iRmiAHgvfeY4S5e3DrjfhkJCsJq1KiR2OMQERERB3PpEoOIlSuj97/3HvDll1zXZG2nTjHTtm4d26lSMSAcNIhTIG0hIICZqjlzmMECuKfRuHFxvwemcvPLlrGdMiWzX6YTcQ8Pnli+/74qHjoiUxGJIUOiF92YNi3uIi3J2cmT3JLClBXOmJGZsJ49Ld8cPjyc04tHjDBPL65Qge9XUg5ZEhSEmezZswerVq3ChQsX4Obmhvz586NFixaoWrVqYo1PREREkpmAAFbmmzs3ern5SpV4Zd8WFQ+vXuX0o4ULuVmxszPQuTP7bLUB9cOHwMyZDJgCA9lXuzbbce3XFVe5+SxZuAfU/ftc6zVqFIsVuLjY5jWIbe3Zw4yQaZ1gnjz8TLRv71hFVm7cYFZ4wQL+fKZMCfTowUAqSxbLH2fLFgZxpgxagQK84NG6NX/mk7IEB2F9+/bF119/DcO0k+JTs2bNQo8ePfD111+/9OBEREQk+Xj4kBmuiROjl5svVAj4+mvg3Xetn7m5d4/Ttb76yjyGDz7giZmtpiSFhTH4GzXKvLalbFkGX/XqxX4P4io3nz0717bcvm3eLHrAgLjXjEny5+/PrPHPP7OdLh3bffs6VtGNR4/4O2LyZPOUwSZN2I7Pmsbjx/nzYCpbnyGDOYNmq+nFLytBMeKyZcswc+ZM1KxZE3v27MHdu3cREBCAXbt2oWbNmpgzZw5WrVqV2GMVERGRJCg0FJg3D8iXj1e3TcFPlizAd9/xBLNRI+sGYCEhPLkrUsQcBFatCuzdyxNbWwRghsHKdaVKca3X9etAwYKcVujry02Xo74HcZWbz5aNV/Bv3jSvmztzhplFBWCOJyCAlUJLluTn1NmZVS7PnuWaRUcJwCIiOGXQ05PZrqAgoGJFlo//6SfLA7CrV5nRjlq2vk8f4Nw5ZhCTSwAGJDATNmfOHJQvXx5btmxBiii50erVq2PLli2oXLky5s6di1atWiXaQEVERCRpMZVNHzAgern51Kl5AmmLq/jh4cDy5Tyxu3SJfcWLc3H+e+/Zbs3U77+z/PWBA2xnzcoxde8ee21LXOXmM2dmJvHWLbYbNeJrcLQCDELBwcwOjx9vnqr67rvM4pYoYd+xJbadO5nJPXKE7QIFeKGkVSvLpww+eMBs2Zdfmi/ytGzJDHd8ytYnJQnKhB0/fhxt2rSJFoCZpEiRAq1bt8axqPVnRURExKFs386r0a1amQMwZ2dWG7x0yfpX8Q0D2LQJKF8e6NCBz5knDzdhPX4caNzYNgHY338z2DNt8pwmDYOvc+eY4YgZgO3YwTVxrVoxAHN352bLd+4wm1ehAk9a//c/BWCOKCKCG3G/9hqLwwQG8udo2zZg40bHCsD++Yc/h7VrMwBLn54XFv75hxsnWxKARc0Wjx9vznD/+SeLlyTXAAx4ycIczxMWFmathxYRERE7OXKEV7Vj7lbTtCmvVNui4uFff/EE1jSGDBlYSe7zz203fevyZU4hXLKEAWGKFNxkeeTIuPduilluPnVqrvsxZb4KFeJV/ZYtk35BAUmY33/nlLm//mI7Tx5+z9u3d6zv+a1bXA85f755n65PPgG8vS3fjsK0qfugQdx0GeCUxcmTucbTEaqCJuhbXqZMGaxcuRLhUUsePRUeHo4VK1agVKlSLz04ERERSRrOnWOAUL589ACsShVg/35g7VrrB2BnznAMlSpxDK6uDAjPn+fJmi0CsDt3OP3Sw4MbyxoG9zLy82NVw5gB2IULPMkuV44BWMqUPPl+/Jgnq5kycYrVqVPJo6KbxJ+/P4tP1KjBACxdOmZ1/P2ZxXWU73lwsPlCjKky6nvvMVs8e7blAdhffwE1azLYOn2aU3tnz2Y5+yZNHCMAAxIYhPXo0QO+vr5o2LAh/vzzT9y/fx/379/Hvn370KBBAxw5cgSffPJJYo9VREREbOzGDZaO9vDgOiaTYsU4ZW7vXuDNN20zhhIlOAYnJ+Cjj3gSO20a11NZ2+PHPMEsXJjPGRLCE8UDB4DVq/n+RHXrFgsGeHqa9/sqUICVE69e5TTFgQMZ3Pbta/meSJJ8BAQwO1uyJPepM2WEzp7l3nWOVHRj+XJ+1gcP5hTLcuU49XbDBk69tMSFC5ymWKkSs4apUjHDffYsf/4dbVuGBE1HbNeuHfbv3485c+Zg27Zt0W4zDAPdu3dHx44dE2N8IiIiYgeBgcD06SwUEBxs7s+alYvqO3ZkVseaHjxgwDN9OotWACxeMHEiULq0dZ/bJCyMGS9vb/NGsGXKMCCrX9+ycvMFCvC+ly7x+PbtgbFj2S+O51UqurFnD7PRLzPF8u5dvlezZnEzcycn4MMPuZl5vnzWG7u9JfjX56xZs9CyZUusXr0a58+fh2EYKFKkCJo3b44aSXl7ahEREXmmkBCu5fD25p5bJmnS8Op9nz5A2rTWHcOTJ8C337Isu2nN1JtvMvCx1SmGYTDTN2QIpxoCDJrGjgXatYt9ghkayo1nR49m5g7gCem9e+aqjXXr8kS8XDnbvAaxrYgIFosYMsT8PS9XjhcSate279gS25kznAL8MvuahYRw2uLYsQzEAKBOHV7AeBV+Rl7qGla1atVQrVq1xBqLiIiI2NG2bUCnTsC//5r7UqQAPv2UBScsXdORUBERnNo3bBjXeQGc9jhxIgt/2GotyN69PMHcu5ftzJm5Eexnn1lWbj57dq6HMWXOSpfmiWXMfcLEcSRGRig5uH2bQdOcOcwSOzsDXbvy4kNcBWniYvqZGTyYUxAB7q03ZQrwzjuvzs+IRUHY77//juLFiyPb09++v//+u0UPXr169YSPTERERGxq//7oAVjz5gyAbFHxcPt2Bj6+vmznyMEKa1262G4tiJ8fs33r17OdOjWv7A8cyAqMMe3YwTEfOsR2xozMCJjewzx5OKXqww8ZzIrj8fdnMPEyGaHkICSExTHGjTNnyBs04MWF+Gyl8McfrBBp2k8vVy4GdR07vno/IxYFYTVr1sTSpUvRtm3byLaTBWFqXNUTRUREJGlq04aBT5UqnEJl7YIbQOzS7e7uDHr69OEJrS1cvcrpl4sWMRvn7Mzgb9QoIHfuF485TRoed/YsT1DTp+eUtN69GciJ4wkI4HTZb74xZ4Q+/pifmRw57D26xGMYgI8PP++m7HSZMvz9UK+e5Y8TM1hNm5Y/5198Yf3pzUmVRUGYt7c3ypQpE9keOXKkRUGYiIiIJB9FigDHjrF4gLX/zF+8yCl+psqBLi6c9jh8uPWnPZrcu8d1Zl99ZS4+0qQJp5HFVdHtwgVuxBx1zEWLsrz82bMsVPLZZ7Z9DWJbwcEsIDF+PHD/PvsctejGn38ySPrzT7Zz5WIm7KOPLM9a3brFYHXevIRPX3RUFgdhUY0aNeqF93n8+HGCBiQiIiL2E5+pRQkREMAT2LlzWYADYAZu3DiWf7eF4GCuaRk/3lwQ4O23eSJdpUrs42/dMo85NJR9JUow8Dp1iu2WLXmMLaZuiu0ZBrByZfSiG2XLMiNUp45dh5bozp/n61y9mu00abg3Xv/+lmenHz8GZs7kdOaoFSInT7b+75jkIkFLBQsXLowNGzY88/Zly5Yhb968CR6UiIiIOJZHj5hhKlKEmacnT1gt0NeXewzZIgALDweWLOF+Rv37MwArUYJ7Gf3+e+wA7OFDBodFivCEMjSU982YkevHnjwBqlXjWrpVqxSAOao9e4DKlYG2bRmA5ckDLF7Mz64jBWB37/LnonhxBmBOTkDnzqyEOGqUZQFYRIT5Z2zIEPOeYdu3Axs3KgCLyqJMWEBAAPxM9VkBXLx4EX/99RcyZswY69iIiAisX78ewVE3FREREZFXUlgY11p5ewPXr7OvXDleEY/PmpKXYRjAb79xTcqJE+zLm5fTpDp0iD21KjQU+P57Tpn67z/2FSrE4gSnT7P92mt8De+99+pUc3vVJEYZ9uTgyRNOFxw9Grhzh3116zLL9/rrlj/O9u3MmB05wna+fLzw0ratY1WITCwWBWGurq5o0aIFAgICAABOTk6YMGECJkyYEOfxhmGgQYMGiTdKERERSVYMA1i3jlfDTYFLwYKcste6te1Oyg4e5In0rl1sZ8zICog9e8YummEYwNq1vP3MGfblzs0TblP5+Rw5eLLapYv1N6sW+wgIYMW+uXPN65i6dWM2yJHWMZl+RgcNMn/eS5Rg8BWfUvEnT7LIxq+/sp0+PX+GevVSYZrnsejXR/r06fHjjz/izz//hGEYGDNmDJo0aRKtWIdJihQpkCNHDrRu3TrRBysiIiJJ3x9/8KTMtKA/a1YWtOjePfY+W9bi78+9u3x82HZz40nh4MHc9yumXbt4MnrwINuZM/NK/rFjbCdkXYwkL8HB5jLspqIbDRtyraCjTaP76y8W3dizh+3s2ZkZjs/FhevXmeH+/ntOQ0yZ0rynYNas1hu7o7D4Go6Xlxe8vLwAALt378Znn32GOo40EVZEREReip8fM1+mZeNp0gD9+jF4SZ/eNmO4fp0nk999xzVgTk7cg2j0aAZVMR0/zsDst9/MYy5RAjh8mFOzTNXcRo1idThxPIbBNX1DhrBqJ8BpeNOmcVqeI7l0iVmq5cvZTpWKwdigQdwewhIPH/K9mTqV/we4mfqkSdxcXSyToET6zp07E3scIiIikkz9+y+viC9ezCviKVIwcPH2tl3gEhjIE8Pp01kEBAAaNWJ1tlKlYh9/8SKv2C9dypPwlCmB8uWBv/82b7783ns8sXS00uNi9scfDEJMGdDcuTll1tE22A4M5M/CjBlc2wjwNY4fH/fFibiEh3N958iR5vWdlSvz565qVeuM25EleDbzlStXsHbtWgQFBSEiIiKyPywsDPfu3cOWLVvwzz//JMogRUREJOm5d49BysyZ5n22mjblYnxPT9uMwVRUYOxYruUBeGI4eTJQvXrs4wMCOL45c8wl8itWZFBmOhGvWJFX+WvUsMlLEDs4c4YZ0J9+YjttWrb79XOsohthYcwKe3tzqwUAqFmTgdMbb1j2GIYBbNrEjPbJk+wrXJg/+82bqzBNQiUoCNu+fTsaNmyIsLAwGIYBJycnGIYBAJGbOOeOa4t5ERERSfbi2merWjWunalc2TZjiIjgFLJhw7iJMsDAb+JE4IMPYp8YPnrE0viTJ5v3LXr9deDBA66PAVgBceJEoEULVXNzVLdvM2CfM8exNw82DBbKGDDAvJedhwcvLsSnoufRo1wHuX0725kyMRP26ae2W9/pqBIUhE2cOBGurq746quvAACff/451q1bh8DAQHz99dc4efIk/vjjj8Qcp4iIiNhZeDiwbBmLbFy+zL6SJXlF/N13bXdFfOtWrmExlcLOlYtrtjp3jl1UIK4S+R4erNpmKrqROTNfk04sHVdIiLnoxr177GvQgEGJoxXdiBk4ZcnCn4/u3QEXF8se48oVYPhw4McfGdC5urKwzdChDMTk5SXoOs/hw4fRrVs3fPrpp+jSpQucnZ3h4uKCdu3aYefOnciZMyfGjBmT2GMVEREROzBdVS9XDvjoIwZgefMCCxcykGnUyDYB2OHDgJcXv44cYbGP8eM5tezjj6MHYIbB/Z1KleJt169zzG+9xcqJx44x4BowgOXn+/RRAOaITEU3ihdnYHLvHlCmDLBlCz/TjhSAXb0KdOrEtY3btzNwMn2+e/a0LAALDGSg5eHBTZcNA2jThttMTJ2qACwxJSgICwoKQqmnq1xdXV1RuHBhHD9+HACQJk0adOzYEXtMNS9FREQk2Tp4EKhdm5muEye4z9aUKQxkOnWyTfGC8+e54esbbzAL5uLCoOncOZ4wpk0b/fjff2ew1bQpTx6zZAFq1QJu3AD27eMxH37I26ZM0Ymlo9q7F6hShfvSXbjAohsLFzKYt9VG4bYQFMRMr4cHi+MYBtCqFfDPP/x8Z8z44scIDeUUzaJFOSU3OJhrKg8eZCXFggWt/CJeQQkKwjJnzoxA04RqAIUKFYKfn19kO3fu3Lh27dpLD65p06YoaMF3fenSpShZsiRSp04NT09PLFiw4JnHBgYGomDBgli8eHGs265fv442bdoga9asSJ8+PZo3b46rV6++xCsQERFJns6cAVq2BN58k3tombJG587xX1tswnrrFqdAvfYasGIFs23t2zN4mjEj9l5Ef//N9S41agD797PAQt26nEa5cydPNOvU4Un4kiVAgQLWfw1ie2fPsmDE228DBw4wSB8zxrYXDmwhPJx7dHl48PU9esSLD3/+CaxcyTWOL2LasLlUKWbLbt3i2sr16/lzX7GitV/FqytBQVjlypWxdOlSBD8theTp6Yk9e/YgPDwcAHDixAm4W7rZwDMsXboUP//88wuPW7NmDTp06AAvLy+sW7cOtWvXRrdu3bBs2bJYx965cwcNGzbEpUuXYt0WFhaGBg0a4K+//sI333yDefPm4eDBg/Dy8kJoaOhLvRYREZHk4sYN4LPPWJZ9zRrzPlv+/ryqHtdGx4ktKIjFEwoXBmbNYvBUvz6Dpx9/jH1yefkyT67LlAE2buRJdt26HOu2bZyCVqoU9wLbupXTKsXx3LkD9O3Lz+7atSy60a0bg7IRI2JnTJOzrVs57bBrV061LVyYP69//GF5cZyDB3nBokkT/nxnywbMncuMd+PGqnpodUYC7Ny500iRIoWRJ08e4/bt28bBgwcNJycno1atWsYnn3xiuLq6Gi1atEjIQxuGYRhXr141MmXKZOTNm9coUKDAc4/18PCI9VwtW7Y0ihQpEq1v3bp1Rv78+Y3MmTMbAIxFixZFu3358uUGAOPvv/+O7Dt58qTh5ORk/Pjjj3E+t6+vrwHA8PX1tfzFiYjIS9Pv32d7mfdm9WrDSJvWMHh93DDefdcwjh+3wiCf4ckTw/jmG8PIkcM8hjfeMIxt2+I+/vZtw+jf3zDc3MzH16hhGCVLmtu5cxvGwoWGERZmu9chthUcbBjTpxtGxozm7/s77xjGiRP2Hlni+/tvw2jQwPw6M2bkaw8Otvwxzp83jNatzY+ROrVhDBtmGPfvW2/cr4r4/P5NUCasZs2aWLNmDXLkyIGMGTOiYsWKGD9+PHbv3o358+ejQIECmDx5coIDw65du8LLywt16tR57nEXL16Ev78/mjZtGq2/efPmOHfuHPz9/QEA9+7dQ9OmTVGzZk1s3rw5zsfavHkzPD09UTLKCs0SJUqgePHi+PXXXxP8WkRERJKL11/nWhDTFMSNG4HSpa3/vIYB+PiwSMKnnzIbV6QIp1QdPMgphFE9fsxS84ULc7+jkBBOm6paFdi9m3sZububi3Y40hQ0MTMMZn9KlOCGy/fu8fO6eTOznnFt0p1c3bjB6oZlyvC1pUwJ9O7NLF+/fpYVlbl7l8VJXnuNP1tRs9zjxrHQjdhOgjdrbtKkCZo0aRLZHjJkCD788EPcvn0bJUuWRMqYNWIttGDBAvj6+uLkyZPo37//c4899XTjAw8Pj2j9RYsWBQD4+/vDw8MDadKkgZ+fHzw9PXHx4sVnPlbMxzE9limYExERcWQeHtwzq2xZ201F2r0bGDjQvFFytmwsMtCtG6u7RRUWBvzwA283LdkuXhzIn5/V7gyDJ6effMLpZ9mz2+Y1iO39+ScDrz//ZDtXLgYSH33kWAH348fAl19yG4igIPY1acKLEMWKWfYYISGcZjh2rHlfv3r1WO3w9detM255sQQHYTHduHEDOXPmRN68eRP8GJcuXUK/fv2waNEiZI252jYO955u9JA+RuhuWo9mKh7i6uoKT0/PFz5WsTg+ze7u7tGKkMQlKCgo1jFubm5wU61bERFJZmy1XurECWDIEOCXX9hOm5ZX6b/4glmsqAwD2LCBx5s2ns2Xj2titmwx9zVrBkyYwGBSHNP58/wcrF7Ndpo0DOK/+AJIl86+Y0tMERHck2/oUODff9lXoQIwfTqrFlrClCkcMoTvG8Ds4LRpXGMp9hWv6YgLFy5EuXLlEBEREeu2/v37I2/evJg/f36CBmIYBjp37oyGDRuiWbNmFt3HNA6nGJfrDMMAADjHY7v7iIiIWI9jeqwUL7ikUqNGDWTIkCHa18SJEy1+bhERkVeFqYjG668zAEuZkoVAzp7lhrIxA7C9e1nl7oMPGGxlzsxgKziYFdweP2YZ8r17OaVRAZhjMk2lK16cAZiTE9ClC6ebens7VgC2ezdQqRLQoQMDsHz5gKVLWenR0gBs715WSmzVigFYrlzAggXcyFkBWNJgcSZs0KBBmDp1KlKmTIkzZ87Eyiy5uLjgzp07+Oyzz+Dn54eZM2fGayBz5szB8ePHceLECYSFhQEwB1NhYWFwdnaOFVRlfLrxQcwsVNDTfG2GDBksfv6MGTPGmfEKCgp64ePs3r0bZcuWjdanLJiIiIjZnTvcf2jWLE6PAoAWLbhuK65pVX5+zAKsX8926tRAw4bcZHntWvYVK8ZpWk2aqJKbo3ryBPjmG5Zgv3OHffXqMZtTpox9x5bY/P2Z1TN95t3d+TPQu7flW0KcOQMMHgz89BPbadOaM4WOVB3SEViUKtq4cSOmTp2KmjVrwt/fP86pfQsXLsS5c+dQsWJFzJ49G1u3bo3XQHx8fBAQEIBcuXLBxcUFLi4uWLJkCS5dugQXFxeMGTMm1n1M4zh79my0flO7RIkSFj+/p6dnrMcxPVbx4sWfe9906dIhffr00b4UhImIiDBTNWUKC22YimjUrMmr+qtXxw7A/v2XZbdLl+bJaIoUwPvvcxrV2rXMmGXLBsyezQIcTZsqAHNEhsHvd4kS3Jj7zh0WbvntN05BdaQALCCA++GVLGn+zH/6KT/rgwdbFoCZHqNECQZgzs7Axx8zKBs5UgFYUmRRJmzu3LkoWLAgNm/eDBcXl2cely9fPmzevBkeHh6YPXs26sVjO/L58+fjwYMH0fpGjx4NX19fbNiwAblz5451n6JFi6Jw4cLw8fFBixYtIvt9fHzg4eGBAvHYhdHLywvLly+Hn59fZPDm5+eHU6dOYfjw4RY/joiIiHAj2SVLeAJoWtNSujQLCrzzTuzA6d49ZrVmzuRUQwDw8uK/UbNh/frxyr4quTmuAweYudm7l+0cOVhUolMnTl91FCEhzAyPGwfcv8++d99lwYwXXP+PFBwMfP01M8qmCV0NG/LCR5SC35IEWfRR/uuvv9CzZ8/nBmAmGTJkwEcffYQffvghXgOJK7uWJUsWuLq6okKFCgA47dDPzw9FihRBtmzZAAAjRoxAp06dkCVLFjRu3BgbNmzA6tWrsWrVqng9f6tWrTBhwgQ0aNAAkyZNAgAMHjwYpUuXjhbgiYiIyLMZBtd6DR7MTBXA6oVjxwLt2sWuXBccDMyZw5NIU+W2ypW5DmbdOm7U7OTEE/DRo4GXqP8lSdyFC5x+t3Il26lTcx3YgAGx1womZ4bBLPDgwYCpaPfrr7Poxgt2Z4oUEQGsWMH36/Jl9pUty2yzpY8h9mVREPbgwYN4VT308PDAfVNIn4gOHz6MWrVqYdGiRejYsSMAoGPHjggJCcG0adOwcOFCFC5cGEuWLEHLli3j9dhubm7YunUrevfujY8//hguLi7w8vLCjBkzElxuX0RE5FWyfz+zVHv2sJ0pEzBsGNCjB5AqVfRjw8NZbGDECODKFfaVKMEKcD//zMcCmDWbMsU2+5WJfdy7x6qWM2dyDZiTE0vNjxsH5Mlj79Elrj//ZDbX9PnOlYsXIDp0sLy0/q5dDE59fdnOm5eP0b49pyFKMmHJ7s8FCxY0xo4da/Fu0RMmTDDy5ctn8fHJVXx2xRYRkcST1H//NmnSxChQoMALj/vxxx+NEiVKGKlSpTI8PDyM7777LtYxBw4cMKpXr26kTZvWyJEjh/HFF18YwcHBz3xMe7w3//xjGE2bGgav8RtGqlSGMWiQYdy9G/vYiAjD2LjRMEqVMh+fN69hdOliGHnymPvKlTOMbdts9hLEDp48MYyvvzaMLFnM3/c6dQzjyBF7jyzxnTtnGC1amF9nmjSGMXq0YQQFWf4Yfn6G8d575sdwdzeMCRMM49Ej641b4ic+v38tipffeOMNrDdNyLbATz/9FOfGxyIiIo5u6dKl+Pnnn1943Jo1a9ChQwd4eXlh3bp1qF27Nrp164Zly5ZFHnPu3DnUq1cPadKkwerVqzFgwADMnj0bPXv2tOZLsNj169wYuWRJczEAU9nwSZOAp0WMIx04ANSqBTRqBPz9N2/v2pUZs++/5wbM+fMDP/4IHDqkaVWOyjA41bRkSRaTuH2ba6B++QXYupXT6hxF1NL6a9aYS+ufPWt5wYwbN1ioo3Rp4H//Y8asRw8+xpAhlldOlKTFonl2HTt2ROPGjTFjxgz07dv3ucfOmDEDhw8fjveasFfShQv818nJvEI5rv9b47aEPkbUtoiIRHPt2jX06tXLoin8w4cPR/PmzTFjxgwAQP369XHnzh14e3ujXbt2AIApU6bA3d0d69evh6urKxo2bIg0adKgZ8+eGD58eLwKUCWmwEAWD/jyS+DRI/Y1bswpZXEVAzh9mtMSTaXl3dyANm24lmXBAvZlyMD1Lb16xZ66KI7j0CEW3fj9d7azZWP5+a5dHavoRmgoS+uPHp3w0vqPHvFnbPJk4OnuS3j/fbbjKKUgyYxFH/dGjRqhadOm6N+/Pw4cOIDPP/8cb775ZuRaqdDQUOzbtw9z5szB2rVrUaNGDbRu3dqqA3cIRYrwclByltAALjGPTchx1n6MpPBeJMZrTIrPlZy/P8n9fbLG43t68izMQXTt2hVeXl5IlSoVdu3a9czjLl68CH9/f4wePTpaf/PmzbF69Wr4+/vDw8MDmzdvRqNGjeDq6hrtmM8++wybN2/Gxx9/bK2XEqeQEGDePK7VCQhgX5UqPCmsVi328dev8yR0wQKuAXN2Bpo355++H37gvy4uvKo/fDiQJYtNX47Y0KVLDMRNid5Uqbg2atAgx6p0aRis5jlwIDPCANc6TpsWd1XQuISHMxs8fDizwwBQsSIfw9LNmiXps/iaw+LFi5EiRQqsXr0aa9asQcqUKZElSxaEhYXh7t27iIiIgGEYaNKkCRYuXKhiFpZIm5blbUyBmHmar2XtpCDq2ERE4mvFCsBBLtotWLAAvr6+OHnyJPr37//cY0+dOgUAsabuFy1aFADg7++PfPny4dKlS7GOyZYtG9KnTw9/f/9EHP3zRUSwYt3w4eZJHJ6enHL4/vuxTyzjypS98w4LCCxdai5B37Ils2dFitjspYiN3b/Pz8mMGeZNuj/8kIUk8uWz79gSm68vs3y7d7OdPTuzfF26WJ7l27qV0xePH2e7QAFuct6qlYpuOBqLI6V06dJh1apV6NKlC5YsWYKDBw/i2rVrSJkyJTw8PFCjRg20adMG1eK6FCZxi7EvWoLEFaRF/X9825bellQeIzGPTaqvMakflxS/P8ntfUqKY7LV4zvIJfBLly6hX79+WLRoEbJmzfrC4+/duwcASB/j9bs/rcMdGBj4zGNMxwWaNgV6hqCgoFjHuLm5wc3N7YXji2rrVmYrjhxhO1cuZrfi2rMprkxZ5crAm28yA7JpE/vefptX9d98M15DkWQkNBT47jvA29v8WahZk2XYy5e369AS3ZUrzPL9+CPbqVIBffuyBL2lv+JOnGD2zPQzkiEDL3r07KnpuY4q3ukqLy8veJl2TxT7izrFR0REbM4wDHTu3BkNGzZEs2bNLLpPREQEAMApxu9v42lw6uzs/MxjTMc5v+CyeI0aNWL1eXt7Y9SoURaN8fBhnkRu3cp2+vQMxnr3jl1MwLRn0YgR0TNljRuz3PzMmea+yZPZrz9djskwWDxi4ECuBQT4fZ86lQVZHOn7/uABP8/Tp5uzu+3aMbubP79lj3HtGgt0LFrEnyNNz311aM6giIjIS5gzZw6OHz+OEydOICwsDIA5mAoLC4Ozs3OsgCnj07KBMTNVQU9X32fIkOGZx5iOy5Ahw3PHtXv3bpSNUWbOkizY+fM8AVyxgm3TSeGwYUDMJJ9hmDNlR4+yL1cu7nn0++888QY4LWvUKBZfcHF54RAkmfL15VQ603LIrFmZNe3WzbG+72FhwMKFDJ5u3GBftWoMxipWtOwxgoL48zFtmnnKbvPmnHr4dFayODgFYSIiIi/Bx8cHAQEByJUrV6zbXFxc4sw+eT4tbXb27FmUK1cusv/s2bMAgBIlSiBt2rTIkydPZJ/JrVu3EBgYiBIlSjx3XOnSpYtzKuPzLFvGaYahoWy3aweMHQsUKhT7WF9fBl/bt7OdPj3Xvpw7x+wAAKRJwzUyAwYAT2daigOKOR3Pzc08He8F1wqSnU2bGGiePMl20aLcTPyDDyzL8sUVwFWpwmDsrbesNmxJghSEiYiIvIT58+fjQYw1vqNHj4avry82bNiA3Llzx7pP0aJFUbhwYfj4+KBFixaR/T4+PvDw8IgsPe/l5YWNGzfiyy+/jMxi+fj4IEWKFKhdu3aiv5aqVXkiWb8+r8hHiQ8jnTvHE+5Vq9h2dWXgFhYGzJrFf52dgc6dmQWJ4+WLg3jwgEU3vvwy+nS88eNZUMKRnDjB4GvLFrYzZeJ6t08/5c/AixgG8NtvvCDh58e+IkX4/jVr5ljTNMUyCsJERERegmccG/ZkyZIFrq6uqFChAgBOKfTz80ORIkWQ7WlJ/hEjRqBTp07IkiULGjdujA0bNmD16tVYZYpuAAwcOBArVqxAgwYN0K9fP/j7+2Po0KHo3r078lmhtFzBgrzCH9d0qJs3mRWbN4+BlpMTK7blzQvMn2+uNdWwITNhpUol+vAkiQgL4+baI0fycwHEfzpecvHff1zruHChec3W559zym6mTJY9xpEjDOB27GA7c2a+d5YGcOKYFISJiIhY2eHDh1GrVi0sWrQIHTt2BAB07NgRISEhmDZtGhYuXIjChQtjyZIlaNmyZeT9XnvtNWzZsgUDBgxA8+bNkTVrVvTt2xdjx4612lhjBmAPHjDTMW2aecPY+vU5derbb837GJUvzzUuVkjQSRIRVzanWDFOx4trq4LkzLRR8qRJwMOH7GvWjBcYLN1S4coVBms//sj3ztWVm5EPHWp5ACeOS0GYiIhIIlu8eHG0ds2aNSOLdUTVvXt3dO/e/bmPVa1aNezfvz8xh2cRU4nx0aPN2Y4KFVg8YPlyTsUCWAVuwgSgTRvtY+TIjh1jNmfbNrYzZ+Zn4JNPHCubExHBveyGDjVfYKhUiVm+t9+27DECA817o5mmabZuzSm+BQtaZdiSDCkIExERkUiGAaxZw3VfppogRYoAH3/MSoiDB7MvY0Yeo32MHNu1a8zmLF7s+NmcXbtYSObwYbbju1Gy6cLFqFHArVvsq1aNWeRKlaw1akmuFISJiIgIAODAAQZVhw6xnT07T7j/+YfBl2FwTUzPngzAtI+R4woKYvAwdaq5hHqrVsx6Fi5s37ElttOnua/Zhg1sp0/PILN3b8suMMS1N5qHB6dpak88eRYFYSIiIgKAJ96HDgHp0jHQevIEGDcu+pSq8eMd7yRczMLDmfUaMQK4fp19VapwfVTlynYdWqILCOB0W1OxmRQpgO7dmcl6Wj/nhQ4d4jTN3bvZzpqV9//4Y8faG00Sn4IwERERAQDUqcN1LEFBwFdfAbdvs796dWZENKXKsW3dyoDi+HG2CxViIYrmzR0rmxMSAnz9NS8o3L/PvkaNmLkqXtyyx7h0idmy5cvZduS90cQ6FISJiIgIAJbQnj2be4EBwGuv8ST8vfcc6yRcojt5ksHXpk1sZ8zITFiPHgwuHIVpveOgQcDFi+x7/XUW3ahTx7LHuHeP68RmzmQwBwAffsiMcf781hi1OCoFYSIiIgKA61/OnQNy5OA0rS5dgJQ6U3BYN25wv6oFC1gVMGVKBl4jRjjeer8//2TRjT//ZDtXLmbCOnTgNMQXefKE++GNHm3OENesyXVzb7xhtWGLA9OvVhEREQHAvb9WrADefRdwd7f3aMRaTHtgTZ5s3vutaVOWVS9WzL5jS2wXLnCK4OrVbKdJwwIa/fsDadO++P6GAaxbx+zZmTPse+01Ts99911liCXhFISJiIhIpNat7T0CsZaICG4cPGyYeQ+sihU5Ha9aNfuOLbHdu8dM19dfM4vl5AR07Mhpg7lzW/YYBw4we7Z3L9vZszMT1rWrMsTy8vQREhEREXFwO3cyoDhyhO347oGVXISGstph1GmDdepw2mDZspY9xoULwJAhwKpVbKdOzfdu4EBliCXxKAgTERERcVD//MPg4X//Yzu+e2AlF6a9ugYMAPz92Ve8OKcNNmxo2bTBu3eZPZs1y5w9++gjYOxYIG9e645fXj0KwkREREQczK1b3K9q/nzu/ZUiBfDJJ4C3t+V7YCUXhw8zU7VrF9vZsjET1q2bZdMGQ0KAuXMZbN29y766dZk9e/11qw1bXnEKwkREREQcRHAwy6dPmAAEBrKvcWPugeXpad+xJbarV7m+bckSZsLiu1eXYQA+Pjz+/Hn2lSzJ4Kt+fRXdEOtSECYiIiKSzEVEACtXci3T5cvsK1+eAUWtWvYdW2ILCmJQOW0a8Pgx+9q0YeBZsKBlj7FvHyskmkrW58zJTFjHjiq6Ibahj5k9dejA35qmSy1R/42rLynd9jJ9iXm8JbdZ8/62fC8Terw1X7+1X7OtPj+v4v1FxGHs2cPpeH/9xXaePAxI2rd3rKIb4eHA4sXA8OHAf/+x7623WG7/zTcte4yzZ5n5WruW7TRpuI6sf38gXTqrDFskTgrC7GnZMgZhIiL2Yq2LH5be9u233KBIROLtzBnuX/Xzz2ynS8cAo29fBheOZNs2BprHj7NduDD3OWvWzLJrS7dvM9M1dy4rKDo7A507c+2YpSXrRRKTgjB7mj7dHIQZhvnfqP9Pire9TF9SftzEPN7e908q3w97vf6k8BzJRdSx28OTJ/Z7bpFkyhRQzJkDhIUxoOjWjYU4cua09+gSl58fM1W//sp2xozMhPXsyTVgLxIcDMyezf3B7t9n3zvvcDpj6dJWG7bICykIs6c+few9AhGxtqQQKCfm8Yl9W548EBHLhISYA4p799jXoAHLsJcsadehJbqbNxlUfvstpyGmTAl89hkwciSQJcuL7x8RwX2+hgwBLl1iX5kyfK+8vKw6dBGLKAgTEbGmqNPvREQSwDBiV/ErXZoTaurVs+/YEltwMPDVV1zT9uAB+95/n5krDw/LHmPPHq7xOniQ7dy5Gbh26MBS/SJJgYIwERERkSRq/36uhdq3j+2cORlQdOzoWAGFYZirO5oyV+XLM9CsWdOyx/D35xq5devYTpvWvEYubVprjFok4RSEiYiIiCQxFy4wIFm1im1HruK3dy/Qr585c5UnDzBxItCunWXVHQMCWGBj3jzHXyMnjkNBmIiIiEgSce8ep+LNnMm6NU5OzHqNHet4SyjPnWOmyseHbVPmql8/y6o7BgcDX38NjB9v3pj63XdZNdHR1siJ41EQJiIiImJnoaHM5IwezeqHAFCnDjckLlvWrkNLdHfvckrlrFnRy8WPGQPkyvXi+8e1MXXZsnyv6tSx6tBFEo2CMBERERE7MQxgwwZg4ECuaQKA4sVZxa9hQ8eq6xMaCnzzDQPNO3fYV68e131ZWi5+925OyTx0iG1H3ZhaHJ+CMBERERE78PVlQLFrF9vZsjFA6daNJdkdhSnQHDCAG0wDQIkSzFy9845lgebp0yy6sX492+nSMRPWp4/jbUwtrwYH+hEXERERSfquXAGGDQN+/JFtNzeugxo8GEif3r5jS2y+vqzuuHs329mzc9phly6WBZq3bpmLboSHsyKkqehGjhxWHbqIVSkIExEREbGBBw9YNGL6dBaVAFgBcMIEIH9++44tsf37LzB0aMIDzcePWZwk6n5hjRrx/StRwnrjFrEVBWEiIiIiVhQWBixcCIwcCdy4wb5q1RiMVaxo37EltqAgc6D5+DH74hNoRkQAy5czgLtyhX3lynHqYu3a1hu3iK0pCBMRERGxkk2buO7r5Em2ixYFpkwBPvjAsYpuhIcDixYBw4ebA8233wa+/NLyQHPXLk5dPHyY7bx5GbxZul+YSHKiIExEREQkkR0/zkIUW7awnTkzM2Gffgq4utp3bIltyxYGmidOsF2kCAPNJk0sCzRPnWLRjf/9j213d3PRjdSprTZsEbtSEGZPM2awZBBg/i2lf63zb1IYgyP+a8v32R6vUUQknq5fB0aMYFYoIgJwcQE+/5wZokyZ7D26xOXnx+Drt9/YzpiRgWaPHpYFmjdvssDGt9+ai2507w54e7OAh4gjUxBmT/378ze0iCRtSSHgjfqvI41h6lSgQQOIJHcPH3Id1JQp/D8ANG8OTJrEzJAjuXmTgdJ33zF4SpkS6NmTwWfmzC++/+PHvA49aZK56EbjxlxL9tpr1h27SFKhIMye2rVjJsyUDdO/1vvXWo+dXB7Tnu+pI3DE15RUBAbaewQiLyUiAliyhCXnr11jX+XKDMjeesu+Y0tswcHAV19Fr1jYpAmDp2LFXnz/iAhg6VK+V//+y7433mDRjZo1rTVqkaRJQZg9LVli7xGI2E5SCByj/psUH9ve74k93ruSJSGSXO3YwUISR4+yXbAgszstWzrWrGbDAFauZHn5y5fZ98YbDDRr1LDsMXbs4ASgI0fYzp+fwVybNiq6Ia8mBWEiYhtabyUiDuLUKWDgQGDjRrYzZGB25/PPgVSp7Du2xLZ3L/f3OniQ7fhWLPTz43v1yy9sp0/P8vO9eqnohrzaFISJiIiIWCCuQhKffsr1UVmz2nt0iev8eVYs9PFhO21aZsL69QPSpHnx/W/cMK8bi4gwv1cjRwLZsll37CLJgYIwERERkecIDgZmzmQGyLSMsXFjFuHw9LTv2BLbvXvAuHHArFnAkyfMdnXpAowZA+TM+eL7P3pkLroRFMS+Dz5g29HeK5GXoSBMREREJA6mtVBDhgCXLrGvXDluQOxohSRCQ4H585npu32bffXqcd1X6dIvvn94uLnoxtWr7KtQgfevXt1qwxZJthSEiYiIiMQQcy1UnjzMhLVv71iFJAyDmyQPHAicPs2+4sUZPL3zjmXLeLdvZ9ENU4GSAgWAiROBVq0c670SSUwKwkRERESeOneOa6HWrmU7vmuhkpMjR1jdcedOtrNl47TDrl2599eLnDzJ4O3XX9lOn56ZsF69HK9AiUhiUxAmIiIir7y7d4GxY4HZszk1L75roZKTq1eB4cOBH35gJszNDejbl9Mu06d/8f1v3GCBjQULWHQjZUpz0Q1HK1AiYi0KwkREROSV9eQJMHcug627d9lXvz4wdapla6GSk4cP+bqmTmUBDYD7dE2cyCmEL/LoEdfDTZ5sLrrRpAmLbnh4WG/cIo5IQZiIiIi8cgwDWLeO0+nOnmVfyZLAtGlcC+VIwsOBJUs4VfD6dfa99RYDqjfftOz+P/7I7Jmp6EbFilw3Vq2a9cYt4siS9HLJpk2bomDBgi88bunSpShZsiRSp04NT09PLFiwINYxBw8eRI0aNZAuXTrkzJkT/fv3R0hISLRjWrduDScnp1hfK1euTKyXJCIiInZ26BBQowbQtCkDsOzZWRnw6FHHC8C2bwfeeAPo3JkBWKFCwJo1wB9/WBaAbd/OKoedOjEAK1AAWL4c2L9fAZjIy0iymbClS5fi559/RoEX5MfXrFmDDh06oHfv3njnnXewbt06dOvWDalTp0a7du0AAOfOnUO9evXw1ltvYfXq1Th16hSGDRuG+/fv47vvvot8rKNHj6J9+/bo0aNHtOcoVqxY4r9AERERsanLl4GhQ4Fly9hOlYqFKQYNAtzd7Tu2xPbPP8CAAcDGjWxnyACMGAH07Mk1YC8Ss+hGhgzMpH3+uYpuiCSGJBmEXbt2Db169ULevHlfeOzw4cPRvHlzzJgxAwBQv3593LlzB97e3pFB2JQpU+Du7o7169fD1dUVDRs2RJo0adCzZ08MHz4cBQoUwKNHj3DmzBkMGTIElStXturrExEREdsJDOS6pRkzuPEyAHz4ITB+PJAvn33HltgCArjX17x5nEYY36IZKrohYhtJcjpi165d4eXlhTp16jz3uIsXL8Lf3x9NmzaN1t+8eXOcO3cO/v7+AIDNmzejUaNGcHV1jXZMREQENm/eDAA4fvw4IiIiULZs2cR9MSIiImIXYWEMRooVY/GJ4GBOQzx0iGukHCkACwlhwY2iRYE5cxiANW4M/P038PXXLw6gHj0Cxo3j/b/9lgFYkybMiFlyfxGJnyQXhC1YsAC+vr6YPXv2C489deoUAMAjRkmeokWLAgD8/f3x+PFjXLp0KdYx2bJlQ/r06SMDtaNPdxicN28ecubMCVdXV1SrVg0HDhx44TiCgoIQGBgY7SvmejMRERGxDcPgNLrXX2cW5+ZNBmLr1nFPrDfesPcIE49hAKtXc4PlgQOB+/eBsmWBHTuA9esBT8/n3z88HFi8mNUNR4xg1cOKFYHffwd++klVD0WsJUkFYZcuXUK/fv0wd+5cZLXgksu9e/cAAOljbGrh/nRid2Bg4DOPMR0XGBgIwByEPX78GCtXrsSKFSsQHByMWrVq4fjx488dR40aNZAhQ4ZoXxMnTnzh+EVERCRxHT8OeHkB774L+PkBmTMDM2cyI/T++4CTk71HmHj27weqVgVatQIuXABy5wYWLWKmr1atF99fRTdE7CfJrAkzDAOdO3dGw4YN0axZM4vuExERAQBwivEb1TAMAICzs/MzjzEd5+zMOLRv375o0aJFtCmQderUQbFixTB+/HisWrXqmePYvXt3rGmMbpasehUREZFEcf06MzkLFzI75OrKIhLDhgGZMtl7dInr4kVurGwq3pwmDYuLfPEFkDbti++vohsi9pdkgrA5c+bg+PHjOHHiBMLCwgCYg6mwsDA4OztHBkwmGTNmBIDIbJZJ0NMdBDNkyPDMY0zHZciQAQDg6ekJzxg5+4wZM6Jq1ao4duzYc8eeLl26ODNtIiIiYl0PH3K/qilT+H8AaNGChTgKF7bv2BLb/ftc2/bVV1wD5uQEdOzItVy5c7/4/iq6IZJ0JJkgzMfHBwEBAciVK1es21xcXODt7Y1Ro0ZF6zcFTWfPnkW5cuUi+88+3XWxRIkSSJs2LfLkyRPZZ3Lr1i0EBgaiRIkSAICVK1ciS5YsqFevXrTjHj9+bNHUSBEREbGdiAjzBsTXrrGvcmUGZG+9Zd+xJbawMAZOI0cCt26xr3ZtvlZL6ok9esSNmSdP5povgEU3Jk3Smi8Re0kyQdj8+fPx4MGDaH2jR4+Gr68vNmzYgNxxXOIpWrQoChcuDB8fH7Ro0SKy38fHBx4eHpF7jHl5eWHjxo348ssvI6cJ+vj4IEWKFKhduzYAYO7cubh27Rr8/PwiqyhevXoVe/fuRZ8+fazxkkVERCQBduzg1Luny7lRsCADipYtHWvNl2EAmzYB/ftzfRvAQhvTpnHN24tea3g48OOPwPDhXPMFsOjG9Ola8yVib0kmCIs5FRAAsmTJAldXV1SoUAEApxT6+fmhSJEiyJYtGwBgxIgR6NSpE7JkyYLGjRtjw4YNWL16dbQ1XAMHDsSKFSvQoEED9OvXD/7+/hg6dCi6d++OfE/r044cORL169dH06ZN0bNnT9y5cwejRo1CpkyZ0L9/fxu8AyIiIvI8cW1A7KhrmU6cYPC1ZQvbWbIAo0cDH38MuLi8+P7bt/P+pkC1QAFOZWzVCnBOUmXZRF5NyerH8PDhw6hSpQp++eWXyL6OHTti3rx52Lp1Kz744APs2rULS5YsQcuWLSOPee2117BlyxY8evQIzZs3x5dffom+ffti5syZkcfUrVsXmzZtwv3799GqVSv06NED5cuXx969eyPXlYmIiIjt3boF9OgBlCrFACxFCqBnT+DsWQZljhSA/fcfA62yZRmAuboymDp7lu/BiwKwkyeZJatblwFYhgxcL/fPP0CbNgrARJKKJJMJi8vixYujtWvWrBlZrCOq7t27o3v37s99rGrVqmH//v3PPaZevXqx1oSJiIiIfQQHc6Pg8eMBU32txo0ZVLxo/6vk5vFjYMYMZqtM67biU2BERTdEkpckHYSJiIjIq8cwgFWrgMGDgUuX2FeuHNcyWbL/VXISEQGsWMGS81eusK9SJRbSqFr1xfdX0Q2R5ElBmIiIiCQZ+/YB/foBBw6wnTs3s0Pt2zveVLo//uBr/esvtvPnZ/Bkybqt8HBg6VKuiVPRDZHkR0GYiIiI2N3588x8rVnDdtq05g2I06Sx79gS27lzfG1r17KdLh0wdCjQpw+QOvWL76+iGyLJn4IwERERsZt797jZ8KxZwJMnLLveuTMwdiwQx9ahydrdu+bXGhrKgKlbN1Y9zJHjxff38wMGDgRM9ckyZGDw1quXYxUnEXkVKAgTERERmwsNBebNYwBy+zb76tbldLoyZew7tsQWGgp88w1f65077Ktfn/t9lSr14vvfuAF4ewPffaeiGyKOQklrERGRlxQeHo5JkyahaNGiSJ06NV5//XUsXbr0ufcJCQnBkCFDkC9fPqROnRrlypXDsmXLYh23bt06vPHGG0iXLh2KFi2K0aNH48mTJ9Z6KVZnGMCGDQw+evViAFa8OLM7W7Y4VgAW9bX27s0ArGRJ4LffuAnziwKwR49YGbJoUWD+fAZgH3zAMvRff60ATCQ5UyZMRETkJQ0dOhQzZszA2LFjUaFCBfz666/48MMP4ezsjLZt28Z5n9atW2Pjxo3o378/6tSpgyNHjqB79+4ICAhA7969AQBbt25F06ZN0apVK0yaNAknTpzA0KFDcevWLcyePduWLzFRHD7MNV67drGdLRswZgzQtSuzO47kyBG+1p072c6WjVMsu3R58WuNiDAX3fj3X/ZVqMAsYfXq1h23iNiIIQnm6+trADB8fX3tPRQRkVdKUvr9++DBAyN16tTGwIEDo/XXqFHDqFy5cpz3OXz4sAHAGD9+fLT+OXPmGGnTpjXu3r1rGIZhtGnTxihQoIARFhYWecygQYMMV1dX48mTJ3E+dlJ6b0z+/dcwPvrIMJycDAMwDDc3wxg82DDu37f3yBLfv/8aRseO0V/rkCGWv9YdOwyjXDneFzCM/PkNY9kywwgPt+64ReTlxef3r4NddxIREbGtVKlS4c8//0TOnDmj9bu6uiLQtMNwDKdOnQIAvPfee9H6a9SogYcPH2Lnzp1o0qQJQkJCkDZtWqRIkSLymKxZs+LJkyd48OABMmfOnMivJnEFBXFj5WnTuBkxALRtC0yYwIp+juThQ2DqVH49esS+Nm1YtdCS13rqFItubNzIdvr0LLrRu7eKbog4Iq0JExEReQkpU6bE66+/jhw5csAwDPz333+YOHEitm3bhh49esR5n2zZsgEALl68GK3/3LlzAIALFy4AAHr27IkzZ85g6tSpuHfvHvbv34+vvvoKDRs2TNIBWHg48P33QLFinIL3+DE3Hj5wAFi2zLECsIgIYPFibow8ejQDsLfeAvbvB5Yvf/FrvXkT+OwzoHRpBmApUgA9egBnz7KMvQIwEcekTJiIiEgiWb58Odq3bw8AaNiwIVq1ahXncTVq1EDhwoXRq1cvpEmTBhUrVsSxY8cwaNAgODs74+HDhwCAmjVrYuDAgZFfAFCuXDksX778hWMJCgqKlYlzc3ODm5vby7zEF9q6lXtYHT/OduHCzIY1bcry845k506u+zpyhO1ChfhamzV78Wt9/Bj46itmyh48YF/jxry/p6dVhy0iSYAyYSIiIonkzTffxO7du/Htt9/i8OHDeOuttxAcHBzrOFdXV2zevBn58uVD3bp1kSFDBrRq1Qpjx44FAKRNmxYA8Mknn2DKlCkYPnw4du7ciYULFyIgIADvvPMOHpnmvD1DjRo1kCFDhmhfEydOTPwX/ZSfH/Duu4CXFwOwjBlZSMLPz7KgJDk5fRp4/32gdm0GYBkycBriqVNA8+bPf62mohuenpxu+OAB8MYbDOjWr1cAJvKqUCZMREQkkRQtWhRFixZF9erVUaRIEdSpUwdr165Fu3bt4jz2999/x82bN3H79m0UK1YMV65cQUREBDJnzoyrV6/iu+++w9ChQyODs5o1a6JixYooXbo0Fi5ciJ49ez5zLLt370bZsmWj9VkjC3bzpnkPq/BwVv777DPuYZUlS6I/nV3dvs1qjnPnAmFhnDr46ad8/ZaUi9+9m5kzX1+28+Xj+ri2bblxs4i8OhSEiYiIvISbN2/it99+Q4MGDZA9e/bI/ooVKwIArly5Eus+jx8/xtq1a1G1alUUKlQo8n6+T8/Oy5cvj8uXL8MwDFStWjXafUuVKoUsWbLg5MmTzx1XunTpkD59+pd6bc8THMzpdBMmmKfTffABMHky10c5kpAQYM4crm+7d499jRox+/Xaay++/+nTLLqxYQPb7u7AkCFAnz5A6tTWGrWIJGW67iIiIvISgoKC0LFjRyxYsCBa/6ZNmwAAr7/+eqz7uLq6omfPnvj2228j+8LDwzFr1iwULVoUpUqVQtGiRZEiRQrs2bMn2n1Pnz6N27dvo1ChQlZ4NS8WEcGCE56eDCQePADKl+feXz//7FgBmGEAP/3EDZa/+IIBWJkywLZtwP/+9+IA7NYtoGdP3n/DBmbOPvuMRTeGDFEAJvIqUyZMRETkJRQuXBgdOnTAmDFjkCJFClSsWBGHDh3CuHHjUL9+fbzzzjsIDAyEn58fihQpgmzZsiFFihT47LPP8NVXXyFPnjz4f3v3Hh/Tmf8B/DORi1xIQoI0cU/EtUsU9WtJtBHXUkS7XSqoa4mESKoSGyERQpWWZW0qrqs0tMW6a6xLtbI0WCwldVu2xCUR5DLJ8/vj6UyN3CaTOZNJfN6vl9drzzPnOef7ZHbPd79zznmeVq1aYdmyZTh27Bi+/fZbWFhYwNXVFaGhoVi4cCEAoGfPnrh27RpiYmLQqFEjjB071uRjPXYMmDYNOHFCbnt4yDthw4ZVv8fp/vUvOVZNDdygARAXBwQFyWKqNDk5wNKl8m+jmRvlrbfkXcJWrZSNm4iqBhZhREREFbRq1Sq0aNECq1evRnR0NNzc3BASEoKoqCioVCqcOnUKPXr0QFJSEkaOHAkAiImJgYWFBRISEnD//n20b98eu3btQkBAgPa4CxcuhIeHB1auXIlPPvkEbm5uCAgIQFxcHJydnU02vitX5HTpW7fKbXt7YMYMWaTY2ZksDJO4cQOIjATWr5fbtrZytseICMDBofS+hYXAl1/Ku1zXr8s2Hx+5TlqPHsrGTURVC4swIiKiCrKxsUFkZCQiIyOL/dzPzw9CCJ02KysrxMbGIjY2tsTjqlQqhIaGIjQ01Jjh6u3BAyA2Fvj8cyA/X97t+uADOTnFc2tTV3nFLSz9/vvy7lfDhmX3P3xYFmupqXK7Ot8lJKKKYxFGREREOvLygBUrZLF1/75sCwiQBUq7dpUbm7EVFABr18q7X//7n2zr3l1Or//KK2X3v3RJ3iX85hu57eDw+6Qb1e0uIREZD4swIiIiAiAnovj2W/no3c8/y7Y2bWTx1bt35camhIMH5SOVmoWlmzeXMx6+/XbZ65plZMgidcUKOV29hQUwbhwwezZQv77SkRNRVccijIiIiADI9a80S4/VqyeLjA8+kGt/VSf/+Q8QHg7s3Cm3nZzkumaTJgHW1qX3zcmRj2fGxQGZmbKtXz/5KGPr1oqGTUTVCJ9SJiIiIgDy/SUPD/k43c8/A+PHV68CLCMDCA4G2raVBZilJTBlipwyfurU0gswIeSkG61ayTuFmZlA+/ZyuvqdO1mAEVH5VKNLKxEREVWEk5MsSGxsKjsS48rNBZYtk4sta+5eDRgg7155e5fd/+hRuU6YZmp+d3d5J+z99znpBhEZhkUYERERaVWnAkyz2HJEBJCeLtvatwcWL9ZvyvjLl+WkG9u2ye3qPDU/EZkWizAiIiKqdlJTZbF09KjcdnOTU8a//37Ziy3fvy/vmi1f/vvU/GPGADEx1W9qfiKqHCzCiIiIqNq4fh2YORPYuFFu29rKO2HTp5e92HJuriy85s4FHj6UbX36yMcW27ZVNGwiesGwCCMiIqIq79EjYMECub5XTo5sCwqS7265u5feVwggOVk+aqh5bLFdO3msnj2VjZuIXkwswoiIiKjKKigAkpKAqCjg119lm6+vLKA6diy7//HjctKN48fltpsbEBsrC7iyHlskIjIUizAiIiKqkg4ckO99nT0rtz095cLSAwaUvdhyerqcin/LFrltZyfXDtPnsUUioopiEUZERERVyoULsmD6xz/ktrMzEB0NTJxY9mLLDx7IRxQ//xzIy5PF2qhR8j2wl15SPnYiIoBFGBEREVURd+/KGQpXrpSPIVpaApMnA7NmAXXqlN43Lw9YsQKYM0fOfgjI970WLQJefln52ImInsUijIiIiMxabq68cxUb+/tiy2+/LSfiaNGi9L5CAN98I2dIvHxZtrVuLYuv3r3LfmyRiEgJLMKIiIjILAkBbN0qC6hffpFtHTrIxZb9/Mrun5oqJ904ckRu16snHzscPVreRSMiqiy8BBEREZHZOXFCTrpx7Jjcfuml3xdbtrAove+1a3KtsL//XW7b2spiLCICqFVL2biJiPTBIoyIiIjMxvOLLdvZ/b7Ysr196X0zM4H4eGDJEvkIo0oli7a4OMDDQ/HQiYj0xiKMiIiIKt3ziy2rVMDIkfI9sLJmLczPB1atAmbPBjIyZFuPHvK9Lx8fpSMnIio/FmFERERUaYpbbNnPT7731aFD6X2FAHbskHfKLl6UbS1bAgsXAv36cdINIjJfLMKIiIioUjy/2LKXlyyg9Fls+eRJ+YjioUNy29VVTl8/ZgxgZaVo2EREFcYijIiIiEyqIost37gBREYC69fLbRsbYOpUYMYMwNFR2biJiIyFRRgRERGZREaGfG/LkMWWHz0C5s+Xjynm5Mi2YcPkpBuNGyseOhGRUbEIIyIiIkVVZLFltRpITJR3yu7ckW3duskJPDp1UjRsIiLFsAgjIiIiRVRksWUhgN275WOL58/LNi8vICEBGDiQk24QUdXGIoyIiIiMriKLLZ8+LRdXPnhQbtetK++ETZjASTeIqHpgEUZERERG8/xiy7a28k5YeHjZiy3fuiWnql+zRt4Js7YGpkyRE3E4OSkdORGR6bAIIyIiogp7frFlAAgKkhNnuLuX3vfxYzk1/cKFwJMnsu3dd4H4eKBpU2XjJiKqDCzCiIiIyGDFLbbs6yuLsY4dy+67dq3se/u2bOvaVb4z9uqrysZNRFSZWIQRERGRQQ4elO99nTkjtz095d0sfSbO2L9fLras6du0qbyTFhjISTeIqPor49VYIiIiIl3/+Q/w1luAv78sopycgE8/Bc6dk1PPl1ZEnTsH9O0LBAT83veTT+QCzkOHsgAjohcD74QRERGRXjIygJgYYMWK3xdbnjRJLrZct27pfX/9Ffjzn+WaX4WF5etLRFTdsAirTJoVK43BWD8dVtfjGIO5jcmcjmNO3xNgXn8bYx3HnGIxNnOMiXTk5gLLlgFz5/6eugYMkI8elrXY8pMn8i7Z/PlAdrZsGzxYbnt5KRs3EZG5YhFWmerUkT8HEhG9qDZtAv74x8qOgkogBLBtm5xiPj1dtrVvLx8ffOON0vsWFspp6mfOBG7elG2dOsm+3bopGjYRkdljEUZERERFpKbKSTeOHpXbbm5yuvkRI4AaNUrv+89/ysWWT56U240ayenm//jHshdqJiJ6EbAIq0yaxVDMhRA8jpLHMKbq+Lcxt+MYS3X8GxuTg0NlR0DPuXFD3r3asEFu29rKhZbDw8v+ui5elHfNtm+X27Vry2OFhAA1ayobNxFRVWLWRdjgwYNx6tQpXL16tdT9NmzYgPj4eKSnp6NRo0YIDw/HmDFjdPY5ceIEwsPDcfLkSTg4OGD48OGIi4uDjY2Ndp/bt29j2rRp2L9/P/Ly8hAQEIClS5fCvaxVJg31zLmJiIgq2+bNwKhRwNOncnvECHn3y8Oj9H6aCTtWrgTUanmnbPx4YPZswNVV8bCJiKocs30oYMOGDfj666/L3O+rr77CiBEjEBAQgG+++QZvvPEGxo4di40bN2r3uXLlCnr27Ak7Ozts2bIF4eHhWLZsGSZPnqzdR61Wo0+fPkhNTcWKFSuwcuVKnDhxAgEBAcjPz1dkjERERObEx0cWUd27y8cR164tvQDLyZGTc3h6yok71Go5df3Zs8Dy5SzAiIhKYpZ3wm7duoUpU6bAo6yf3gBERUUhMDAQn376KQCgV69euH//PqKjozFs2DAAQEJCAmrVqoVvv/0W1tbW6Nu3L+zs7DB58mRERUWhcePG+Oqrr3D69Gn8+9//Rps2bQAA7du3R9u2bbF582YMHz5cuQETERGZAS8v+R5X27alT1ophLxr9vHHgOZhlQ4dgEWLyp6wg4iIzPRO2JgxYxAQEIA333yz1P2uXr2KS5cuYfDgwTrtgYGBuHLlCi5dugQA2Lt3L/r37w9ra2udfQoLC7F3717tPt7e3toCDABat26NVq1aYdeuXcYaGhERkVlr1670AuzYMaBrV+C992QB5u4OrFkD/OtfLMCIiPRldkVYYmIiTp48iWXLlpW574ULFwAALZ5bpMTT0xMAcOnSJTx9+hTXrl0rso+rqytq166tLdQuXLhQZB/NsTT7EBERvaiuXAGGDgVefx348UfA3h6YMwe4dAkICuKsh0RE5WFWjyNeu3YN06ZNQ1JSElxcXMrc/+HDhwCA2rVr67TXqlULAJCVlVXiPpr9srKytMfyKmbVyGf3KUl2dnaRfWxsbHQm/SAiIqqKHjwAYmOBzz8H8vNlsfXBB3IiDje3yo6OiKhqMpvfrYQQGD16NPr27YshQ4bo1afwt4WOVc89NyF+m4bZwsKixH00+1n89tNdYWFhifvUKGNBFF9fXzg6Our8i4+P12sMRERE5igvD1iyBGjeHFi8WBZgvXoBaWnAqlUswIiIKsJs7oQtX74cZ86cwdmzZ6FWqwH8Xkyp1WpYWFhoCyYNJycnAChyFyo7OxsA4OjoWOI+mv0cHR21xyprn5L885//RPv27XXaeBeMiIiqIiGAr78GPvoIuHxZtrVtKyfd6NWrcmMjIqouzKYIS05ORkZGBtyK+WnNysoK0dHRmD17tk67t7c3AODy5cvo0KGDtv3yb1mjdevWsLe3h7u7u7ZN4+7du8jKykLr1q21x/rpp5+KnPvy5cvo3LlzqbE7ODgU+7hjmbp1A367U2cWzHEhV3Nhbn8bc4rHnGIxR+b09zGnWDTi4oCAgMqOgn7z00/AlCnA0aNyu0EDYO5cuXZYGQ+FEBFROZhNEfbXv/4Vjx490mmLiYnByZMnsX37drz00ktF+nh6eqJZs2ZITk7G0KFDte3Jyclo0aIFGjduDAAICAjAzp07sXjxYu0dquTkZNSoUQNv/DaVU0BAAP7+97/j/Pnz2sLs/PnzuHDhAqKiohQZM77/3ryKMCIiU7t/v7IjoGfcvi0LMFtbYPp0ICICcHCo7KiIiKofsynCNHe1nlW3bl1YW1vjlVdeASAfKTx//jyaN28O199WgJw1axZGjRqFunXrYsCAAdi+fTu2bNmCzZs3a48TERGBTZs2oU+fPpg2bRouXbqEmTNnYvz48WjYsCEA4N1338W8efPQp08fzJ8/HwAwY8YMtGvXTqfAM6qtW5U5bnVS2jzJpmZOsQDmF485Mbe/jTnFY06xAMBzj3JT5erTB0hIkNPP67FUJxERGchsijB9nDp1Cj169EBSUhJGjhwJABg5ciRyc3OxaNEirF69Gs2aNcO6devwzjvvaPu1bNkS+/btQ3h4OAIDA+Hi4oKpU6di7ty52n1sbGywf/9+hISEYNy4cbCyskJAQAA+/fRTWFoq9Gd6+21ljktERGQAlQoID6/sKIiIqj+zLsLWrFmjs+3n56edrONZ48ePx/jx40s9Vrdu3fDDDz+Uuk/Dhg2xbdu2csdJRERERESkL7OZop6IiIiIiOhFwCKMiIiIiIjIhFiEERERERERmRCLMCIiIiIiIhNiEUZERERERGRCLMKIiIiIiIhMiEVYJcrNzcXs2bORm5tb2aFUCo6f4+f4Of4Xdfzm7EX/bjh+jp/j5/hNMX6VKG7hLdLLqVOn0LFjR5w8eRI+Pj7l7p+VlQVHR0dkZmaidu3aCkRo3jh+jp/j5/gNHX9Fr7/VGXNTxXD8HD/Hz/GbIjfxThgREREREZEJsQgjIiIiIiIyIRZhREREREREJsQijIiIiIiIyIRYhBEREREREZmQZWUHUJU9ffoUAHDhwgWD+mdnZwMA0tLS4ODgYLS4qgqOn+MHOH6O37Dxa667musw/Y65qWI4fo4f4Pg5fuVzE6eor4CNGzdi+PDhlR0GEdELa8OGDRg2bFhlh2FWmJuIiCqXPrmJRVgFZGRkYO/evWjSpAlsbW0rOxwiohfG06dPcfXqVfTq1QsuLi6VHY5ZYW4iIqoc5clNLMKIiIiIiIhMiBNzEBERERERmRCLMCIiIiIiIhNiEaagPXv24JVXXoGdnR0aN26M+Ph4lPX054YNG9CmTRvY2trC29sbiYmJJorW+Mo7/ry8PMTHx6Nly5awt7eHt7c35syZg7y8PBNGbTyGfP8aarUanTp1gp+fn7JBKsiQ8f/jH/9A586dYWtrCw8PD4SEhODx48cmiti4yjt+tVqN+fPnw8vLC/b29mjfvj02b95swoiVc+PGDTg5OeHQoUNl7ludroHmirmJuYm5ibnpRc9NZpGXBCni2LFjwsrKSgwfPlzs3r1bREZGCpVKJWJjY0vss2XLFqFSqURoaKjYs2ePmDBhggAgNmzYYMLIjcOQ8U+cOFHY2dmJ+Ph4ceDAAZGQkCDs7OzE6NGjTRi5cRgy/mfNnTtXABC+vr7KBqoQQ8a/fft2YWFhIUaOHCkOHjwoPv/8c1GrVi3x3nvvmTBy4zBk/JGRkcLCwkLMnj1b7N+/X/u//6+++sqEkRvf1atXhbe3twAgUlJSSt23Ol0DzRVzE3MTcxNz04uem8wlL7EIU0hAQIDo1KmTTltERIRwcHAQT548KbZPixYtxNChQ3Xa3nnnHdG8eXPF4lRKecd/7949oVKpREJCgk57QkKCACDu3LmjaLzGZsj3r5GWliZsbW1FgwYNqmyiK+/4CwsLRbNmzYr893/JkiWiWbNm4vHjx4rGa2yGfP9ubm5i+PDhOm1dunQRfn5+isWppIKCArF69WpRp04dUadOHb2SXXW6Bpor5ibmJuYm5qZnvUi5ydzyEh9HVEBubi4OHTqEwYMH67QHBgYiOzsbR44cKdLn6tWruHTpUrF9rly5gkuXLikaszEZMv7MzExMmDABAwYM0Glv0aIFACA9PV25gI3MkPFr5OfnIygoCFOmTIG3t7fSoSrCkPGnpaUhPT0dwcHBOu0hISG4cuUK7OzsFI3ZmAz9/nNzc1G7dm2dNhcXF9y7d0+xWJV05swZTJw4EUFBQVi/fn2Z+1ena6C5Ym5ibmJuYm56kXOTueUlFmEKSE9PR15envYireHp6QkAxX5pmhW2y9PHXBky/qZNm+Ivf/lLkYv7tm3bYGVlVeRY5syQ8WvExMQgLy8PMTExisaoJEPGn5aWBgCwtbVF//79YWtrC2dnZwQHByMnJ0fxmI3J0O9/2rRpWLduHfbs2YOsrCxs3LgRe/bswfvvv694zEpo1KgRLl++jMWLF+v1f1Sq0zXQXDE3MTcxNzE3vci5ydzykmWFelOxHj58CABFfjmoVasWACArK8sofcyVscaydetWrF+/HiEhIXB2djZqjEoydPypqalYtGgRDh8+DBsbG0VjVJIh47979y4AYNCgQfjTn/6EsLAwpKamIjo6Gnfu3KlSLwEb+v0HBwfjyJEj6NOnj7Zt9OjRCA8PVyZQhdWpUwd16tTRe//qdA00V8xNDwEwNzE3MTc960XKTeaWl1iEKaCwsBAAoFKpiv3cwqLoDciS+ojfZqwpro+5MmT8z0tOTsawYcPg6+uL+fPnGzU+pRky/pycHAQFBSE0NBSdO3dWND6lGTJ+zSxjgwYNwoIFCwAAPXr0QGFhIT7++GPMmTOnyjwCY8j4c3Nz0a1bN/zvf//DypUr0bJlSxw9ehRxcXFwcHDA0qVLFY3ZHFSna6C5Ym5ibgKYm5ibmJv0pfT1r+pcPasQJycnAEUr5EePHgEAHB0d9e6TnZ1dYh9zZcj4n7V48WK8++67eP3117Fjx44q98ubIeOPiopCYWEhZs2aBbVaDbVaDSEnztH+56rCkPFrflXq37+/Tnvv3r0B/P5ISFVgyPi3bt2KM2fOYOPGjRg/fjx8fX0RGRmJhQsX4rPPPsPZs2cVj7uyVadroLlibnICwNzE3MTc9CzmppIpff1jEaaA5s2bo0aNGrh8+bJOu2a7devWRfpofkkpTx9zZcj4AfnLQnBwMMLCwhAYGIhdu3bBwcFB8XiNzZDxJycn4+LFi3BwcICVlRWsrKxw+PBhHD58GFZWVli7dq1JYjcGQ8bv5eUFQP7q9qz8/HwA8nn8qsKQ8V+7dg0A8Nprr+m0+/r6AgDOnz+vRKhmpTpdA80VcxNzE3MTcxNzk/6Uvv6xCFNAzZo10b17d2zbtk3nV6Lk5GQ4OTkVe0vf09MTzZo1Q3Jysk57cnIyWrRogcaNGyset7EYMn4AmDlzJpYtW4apU6fiyy+/rHK/MmoYMv4dO3YgNTVV55+Pjw98fHyQmpqKt956y5RDqBBDxt+9e3fY29tj06ZNOu3bt2+HpaUlunbtqnjcxmLI+Fu2bAkARWanOnbsGAA5OUB1V52ugeaKuYm5ibmJuYm5SX+KX/8qPMk9FevgwYNCpVKJwMBAsWvXLhEVFaWz1khmZqY4fvy4zhojSUlJAoCYOHGi2L17t5g4caIAIDZv3lxZwzBYecf/008/CZVKJV555RVx/PjxIv8yMzMrczjlZsj3/zxfX98quxaLIeP/5JNPBADx4YcfigMHDog5c+YIKysrERYWVlnDMFh5x69Wq0WXLl2Eq6ur+Mtf/iK+++47ER8fL+zt7cVbb71VmUMxipSUlCLrsVT3a6C5Ym5ibmJuYm5ibjKPvMQiTEHbtm0T7dq1E9bW1qJp06Zi0aJF2s80X35SUpJOn5UrVwpPT09hY2MjWrVqJdatW2fiqI2nPOOfNWuWAFDiv7IW0zNHhnz/z6rKiU4Iw8a/evVq0aZNG2FtbS2aNGki5s2bJwoKCkwcuXGUd/yZmZli8uTJws3NTfu///j4eJGbm1sJ0RtXccnuRbgGmivmJuYm5ibmphc9N5lDXlIJUYXeqiQiIiIiIqri+E4YERERERGRCbEIIyIiIiIiMiEWYURERERERCbEIoyIiIiIiMiEWIQRERERERGZEIswIiIiIiIiE2IRRkREREREZEIswoiIiIiIiEyIRRgREREREZEJsQgjMsDOnTsxZMgQNGnSBDY2NnBxcUGfPn2wY8cOxc65f/9+DB06FB4eHrCxsYGHhwcCAgKwadMmqNVqo59PrVajQ4cO+Pzzz41yvJycHNy8eVO7LYRAZGQkXFxcUK9ePUycOBGPHz8u0i8iIgKtWrVCQUFBsccNCwtDnz59jBIjEVFVNXv2bKhUKvTv37/EfQ4dOgSVSoXZs2crHo+xc5Yxc9Lz+QgwTk5iPqJyEUSkt6ysLDFkyBABQPj4+IiYmBixatUq8ec//1k0btxYABDTp0836jlzcnLE8OHDBQDh5eUloqKiRGJiopg3b57o0qWLACBee+01cevWLaOed86cOcLb21vk5+dX+FinTp0STZo0EUlJSdq2DRs2CAAiODhYzJ8/X9jb24tJkybp9Lt+/bqoWbOm+Prrr0s89sOHD4WLi4vOsYmIXjTR0dECgAAgVq9eXew+KSkpAoCIjo5WLA6lcpaxclJx+UgI4+Qk5iMqDxZhROXwzjvvCABi4cKFRT7LyckRfn5+AoBITEw02jlHjx4tAIiwsDChVquLfJ6YmCgsLS2Fj4+PyMvLM8o509PThbW1tfjyyy+NcrykpCQBQCcx9ezZU3Tq1Em7PX36dGFraysKCwu1bUFBQeL//u//yjx+QkKCcHZ2Fg8fPjRKvEREVc2zRZijo6O4ceNGkX1MUYQpkbOMmZOKy0dCGC8nMR+Rvvg4IpGe9u3bhy1btiAwMBDTp08v8rmNjQ1Wr16NGjVqYOnSpUY55w8//IDVq1ejf//+WLRoEWrUqFFknw8++ABhYWE4deoUPvvsM6Oc95NPPoGdnR0GDRpklOMV58aNG/D09NRue3l54enTp7h79y4A4OzZs1i/fj0WLFhQ5rFGjBiBrKwsrFy5UrF4iYiqgsDAQGRmZmLMmDEmP7dSOasq5STmI9IXizAiPa1btw4AEBwcXOI+TZs2xdmzZ3Hq1Cltm5+fH9q2bYvVq1ejfv36sLe3x8yZM/U659/+9jcA8jnz0kRERMDS0hJJSUl6Hbc09+/fR1JSEt5++21YW1tr27t06QJHR0edZ/nz8vLg4OAAKysrPHr0SNsuhECDBg3Qs2dPjBw5EqNGjQIAjBo1CiqVCgBQv359PHjwQNsnIyMDlpaWqFOnDgDgo48+Qv/+/fH666+XGXP9+vXRvXt3LF26FPn5+RX7AxARVWEffvgh3njjDezdu1ebQ8py6NAhDBw4EPXr14eVlRWcnZ3x5ptv4sCBA+U6txI5q7icZOx8BBgvJzEfkb5YhBHp6ccff4SlpSVeffXVUvdr1aoVLC0tddp++eUXhIWFISwsDFFRUXq/uHvkyBG9zlmnTh107NgR586dw507d/Q6dkn27t2LJ0+eFPnFceDAgcjKysLx48e1bUePHsXjx4+hVqtx+PBhbfuJEyfw66+/YtCgQRg/fjzGjRsHABg3bhzWr18PAOjZsye+++477Nq1C2fOnEFSUhL8/f1haWmJlJQU7Nu3D/Hx8XrHPWjQINy+fRsnTpyoyPCJiKo0lUqFL774Ag4ODggLC8P169dL3f/rr7/Gm2++iRs3buCjjz7CihUrEBQUhB9//BF9+vTBtWvX9D63EjmruJxk7HwEGDcnMR+RPliEEenp1q1bqFu3rs7dIX09efIEcXFxiIiIwMcff4xu3brpfU5nZ2fUrFmzzH09PDwAAP/973/LHd+zvvvuOwCAj4+PTvvAgQMByISosW/fPri6uqJmzZo4ePCgtn3Hjh1QqVQYOHAgunbtiq5duwIAunbtiuHDhwMAQkND4efnh379+uEPf/gDrK2tsXz5cgghEBERgZEjR6J169Z6x62JNyUlxYBRExFVH02aNMHChQvx6NEjfPDBBxBClLjv3LlzUa9ePRw5cgTTpk3DmDFjsGTJEiQkJECtVmPr1q16n1eJnFVcTjJ2PgKMm5OYj0gfLMKI9GRpaVmhqeAHDBhQ7j5CCFhZWem1r+buW2nJVh8///wzatasqU2QGm3atEHz5s2xb98+bdu+ffvg7++PTp06FUl6nTt3hru7e4nnsbe3x549e3Dx4kWcPn0ap0+fRrNmzbB582acO3cOMTExEEJgwYIF8PLyQsOGDTFlyhQ8efKk2ONpnuW/fPlyRYZPRFQtTJgwAf7+/jhw4ECp7yedOHECZ8+ehb29vbYtNzdX+6hedna23udUImcVl5OMnY8A4+Yk5iPSB4swIj25u7vjwYMHyM3NNah/gwYNyt2nYcOGuHfvnl7PlWt+TXy+eCqvu3fvwsnJqdjPBgwYgJMnT+LevXu4c+cO0tLS4O/vDz8/P5w9exZ37tzB9evXcebMGb1eoFapVGjRogVefvllWFpaIj8/H5GRkZgyZQrc3d2xbt06REZGYtasWVizZg22bduGqVOnFnssTcyal6iJiF50X3zxBWrXro2IiAhcvXq12H0sLS3xyy+/ICQkBL1794a3tzccHBwwadIkAEBhYaHe51MiZ5WUk4ydjwDj5STmI9IHizAiPfn6+qKwsBDff/99qfuNHj0aw4cPx+3bt3Xan39PTB89evRAbm5umefMysrCyZMn0bp1a9SrV6/c53mWhYVFiUl34MCBKCwsxIEDB3DgwAEIIeDv74+ePXtCCIGUlBTs3LkTAPD222+X+9wrVqzAw4cPMWPGDAByMhRfX1+MGDECb775JiZMmIC1a9cWG59m4cziZuMiInoRNWrUCIsWLUJ2djZGjx5d7F2n+Ph4dO7cGdu3b0e9evUwatQofPvtt/jmm2/KfT4lclZJOUnpfAQYnpOYj0gfLMKI9PTee+8BAJYvX17iPjdu3MD69euxb98+1K1bt9Tj/fjjj+jWrRucnJzw0ksvITg4uMhjDWPGjIFKpcK8efNKfWRjyZIlePr0KUaPHq1t27VrFzp06IBatWqhUaNGiI6O1usXTTc3N9y7d6/Y873++uuoW7cu9uzZgwMHDsDLywuNGjXCq6++CgcHBxw8eBA7duxAq1at4O3tXea5npWVlYW5c+di5syZ2l8Rb926hfr162v3cXV1RW5uLjIyMor01/zi6ObmVq7zEhFVZ2PHjkWvXr2QkpJSJH/duHEDkZGR6N69Oy5evIh169ZhxowZ6Nu3r84Mgxpl5a2K5KySlJSTlMxHQMVyEvMR6YNFGJGeunfvjkGDBmHr1q1YvHhxkc8zMzMxdOhQqNVqzJ49u9QJPB4+fIi+fftiyJAhyMjIwIkTJ3D8+HF8/PHHOvt17NgRoaGh2LdvH0JCQop9J23jxo2YO3cuOnbsiClTpgCQj3kMGjQI0dHRePToEb777jusWrUKGzZsKHOcTZo0QUFBAW7evFnksxo1aqBfv37Ys2cPDh06BH9/fwCAlZUVfH19sWfPHqSkpBR59EPza2BpRWBCQgLs7OwwefJkbZuHhwfS09O121euXIGNjQ1cXFyK9NfM4NW0adMyx0hE9CJJTEyEo6NjkUk2NMVNy5YtdXLWkydPtGt4afKOPnnL0JxVmpJykpL5CKhYTmI+Ir2YdGlooiruwYMHwtfXVwAQHTt2FHFxcWLVqlUiPDxcuLq6CgBi0qRJOn00+z8rLS1NDBw4UKdt6dKlwsfHp8g58/PzxeTJkwUA0aJFCxEdHS2++OILsXDhQu2xX3vtNXHz5k2dfpmZmUIIIXJycsSxY8eEi4uL2L17d5ljTE5OFgDEpk2biv1869atAoAAILZu3aptX7JkibY9NTVVp8/evXsFANGjRw+RmJgo8vLydD6/deuWsLOzE2vWrNFpT0pKEgBEcHCwWLBggXBwcBBjx44tNq64uDgBQKSlpZU5RiKi6iY6OloAECkpKcV+/sUXX2iv0dHR0UIIIfLy8oSnp6ewtLQUISEhIjExUcyZM0c0a9ZM2Nra6uQ0ffOWoTmrJKXlJCXykRAVz0nMR6QPFmFE5ZSbmyvWrl0revToIdzd3YWVlZVwcXER/fr1K7bIKa4Ie15hYaF44403xLhx40rc5+jRo+JPf/qTaNasmbCxsRFubm6iZ8+eYv369SI/P7/YPgUFBdpEOnTo0BL3e9ajR4+EjY1NicVOdna2qFmzprCwsBAPHjzQtp87d04AEB4eHkX65Ofni2HDhgk7Ozvh4OAgLl68qPP52LFjRbt27URBQYFOe2FhoYiNjRUeHh7CxcVFjBs3Tjx69KjYuPz9/UXjxo3LHB8RUXVUVhEmhBB9+/bVKcKEEOLy5ctiyJAhol69esLW1lY0b95cBAUFifT0dOHu7i48PT2LPVZZecuQnFWc0nKSEvlIiIrnJOYj0odKiArOZ01EFVJQUIDJkydj9+7d+OGHHwyaRbEkQgjk5eXh5s2bGDx4MLp27VrqVMUa48ePx+bNm3Hr1i3Y2dkZLR6lXL9+HU2bNsX8+fMRHh5e2eEQEVVrSuat4lSlnMR8RPriO2FElSgjIwO9e/fG999/jyNHjhg9kalUKtjY2KB58+aIiorCli1b9Oo3c+ZMPHnyRO/9K1tiYiKcnZ0xceLEyg6FiKhaUzpvFacq5STmI9IXizCiSnL+/Hl07NgRzs7O+P7779GwYUOjHfvgwYN4+eWXdWaTysnJQZ06dfTq37hxY4SFhSE2NhZ5eXlGi0sJ9+7dw2effYa4uDg4ODhUdjhERNWWknmrNFUlJzEfUXmwCCOqBHfv3oW/vz8CAwOxZcsW2NvbG/X4HTt2xJ07dxAbGwu1Wo0LFy5g7ty5mDBhgt7HiImJgb29falT8puDOXPmoEuXLhg/fnxlh0JEVG0pnbfKUhVyEvMRlQffCSOqBPPmzUNkZCTs7OygUqm07c2bN8fp06eNco60tDSEhobip59+gqurKz788EOEhobCwoK/vRARUfmYIm8RvUhYhBEREREREZkQfxInIiIiIiIyIRZhREREREREJsQijIiIiIiIyIRYhBEREREREZkQizAiIiIiIiITYhFGRERERERkQizCiIiIiIiITIhFGBERERERkQmxCCMiIiIiIjIhFmFEREREREQmxCKMiIiIiIjIhFiEERERERERmdD/A/CrnnOKawdMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "for i in range(0, 10):\n",
    "    ax1.plot(linCr, example_cpx1_cs[i, :], '-r')\n",
    "    ax2.plot(linCr, example_cpx2_cs[i, :], '-b')\n",
    "ax1.set_xlabel('Cr$_2$O$_3$ (wt%)')\n",
    "ax2.set_xlabel('Na$_2$O (wt%)')\n",
    "ax1.set_ylabel('Cation Sum')\n",
    "ax1.ticklabel_format(useOffset=False)\n",
    "fig.savefig('Cation_Sum_pertubation.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "\n",
    "    linCr=np.linspace(0, 10, 10)\n",
    "    example_cpx1=myCpxs1.iloc[0:10]\n",
    "    example_cpx1_cs=np.empty([10, len(linCr)], dtype=float)\n",
    "    for i in range(0, len(linCr)):\n",
    "        example_cpx1['Na2O_Cpx']=linCr[i]\n",
    "        example_cpx1_cs[:, i]=pt.calculate_clinopyroxene_components(cpx_comps=example_cpx1).Cation_Sum_Cpx\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "for i in range(0, 10):\n",
    "    ax1.plot(linCr, example_cpx1_cs[i, :], '-r')\n",
    "ax1.ticklabel_format(useOffset=False)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure for text - Cpx-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cpx_components=pt.calculate_clinopyroxene_components(cpx_comps=ExpY1_noise1)\n",
    "len(myCpxs1['Cr2O3_Cpx'].loc[sam])                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.8222194082283885"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Cr_cpx_comp_Wang21_Meas-Cr_cpx_comp_Wang21_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9891946881071725"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Cr_cpx_comp_Put2008_Meas-Cr_cpx_comp_Put2008_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-15.531381177808726"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Cr_cpx_comp_Put2008_T_Meas-Cr_cpx_comp_Put2008_T_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-34.986452899739334"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Cr_cpx_comp_Wang21_T_Meas-Cr_cpx_comp_Wang21_T_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.631028888888916"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(Cr_cpx_comp_Pet_Meas-Cr_cpx_comp_Pet_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-92.5533333333342"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(Cr_cpx_comp_Pet_T_Meas-Cr_cpx_comp_Pet_T_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Pet_Meas-Cr_cpx_comp_Pet_NoCr, '.c')\n",
    "\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Wang21_Meas-Cr_cpx_comp_Wang21_NoCr, 'ok', mec='w')\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Put2008_Meas-Cr_cpx_comp_Put2008_NoCr, '^k', mfc='r')\n",
    "\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Pet_T_Meas-Cr_cpx_comp_Pet_T_NoCr, '.c')\n",
    "\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Wang21_T_Meas-Cr_cpx_comp_Wang21_T_NoCr,  'ok', mec='w')\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_cpx_comp_Put2008_T_Meas-Cr_cpx_comp_Put2008_T_NoCr, '^k', mfc='r')\n",
    "\n",
    "## Adding error bars based on Ery's suggestion\n",
    "ax1.errorbar(1.5, 0, xerr=0, yerr=3.1, fmt='o', ecolor='cyan', \n",
    "             elinewidth=1, mfc='cyan',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Petrelli')\n",
    "ax1.errorbar(1.55, 0, xerr=0, yerr=1.66, fmt='o', ecolor='black', \n",
    "             elinewidth=1, mfc='black',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Wang')\n",
    "ax1.errorbar(1.6, 0, xerr=0, yerr=2.6, fmt='o', ecolor='red', \n",
    "             elinewidth=1, mfc='red',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008')\n",
    "\n",
    "\n",
    "ax2.errorbar(1.5, 0, xerr=0, yerr=53.8, fmt='o', ecolor='cyan', \n",
    "             elinewidth=1, mfc='cyan',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Petrelli')\n",
    "ax2.errorbar(1.55, 0, xerr=0, yerr=36.6, fmt='o', ecolor='black', \n",
    "             elinewidth=1, mfc='black',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Wang')\n",
    "ax2.errorbar(1.6, 0, xerr=0, yerr=51, fmt='o', ecolor='red', \n",
    "             elinewidth=1, mfc='red',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008')\n",
    "\n",
    "ax1.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax2.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax1.set_ylabel('Calc P$_{Cr=Meas}$ -Calc P$_{Cr=0}$ ')\n",
    "ax2.set_ylabel('Calc T$_{Cr=Meas}$ -Calc T$_{Cr=0}$ ')\n",
    "plt.tight_layout()\n",
    "fig.savefig('Cr_Cpx-only_points.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure for text - Cpx-Liq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure for Text - Cpx-Liq thermobarometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.950909090909363"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(Cr_Pet_T_Meas-Cr_Pet_T_NoCr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABmQi8riXX34ZDz30UGUXg6ha4F0oiMgb3E6Ut23bhsLCQotpN27cAABs2LABmZmZNuv8/e9/L2PxiIiIiIjsc3QXip6RkazIJqJycTtRXrx4MRYvXmx33pw5cyxeiwg0Gg0TZSIiIiLyOGd3oWCiTETl4VaiPGnSJG+VwyNEBG+++SYWLFiAY8eOISYmBr1798bUqVMRFhZW2cUjIiIiIg9S70JhnizzLhRE5Ak+lSjPmTMHzz33HJ5++ml069YNR44cwcSJE/HLL79g48aNvN0GERFVeZMmTUKrVq0quxh2uVIhffDgQTz55JP49ttv4efnh3vuuQcvvvgiIiIiKrfw5JPUu1AMP3gQReBdKIjIc3xmMC+j0YgZM2Zg+PDhmDFjBgCge/fuiIqKwv333489e/agQ4cOlVxKIiIi56pypXRpFdJZWVno1q0b6tWrh5UrV+LcuXMYO3YsTp06hQ0bNlR28clH8S4UROQNPpMoX7t2DQMGDEC/fv0spqekpAAAjh49ykSZiIiojFypkN64cSOuXLmCH374AbVr1wYAxMXFoVevXvj2229xyy23VOYhkA/jXSiIyNN8JlGOiIjA/PnzbaZ/9NFHAIAWLVpUdJGIiIh8hisV0uvXr0fnzp1NSTIA9OzZE6Ghofjyyy+ZKBMRUbXhM4myPdu3b8esWbNwzz33oHnz5naXuXHjBq5du2YxzWAwwGAwVEQRiYiIqgVXKqQPHDiABx54wGK+VqtFYmIiDh06VCHlJCIi8gRtZRfAW7Zt24ZevXohKSkJb731lsPlunTpgvDwcIuH2qWMiIiIHLOukL569ardu0yEhobaVEpbUyuuzR95eXneKjoREZFTHkmUDxw4gIKCAtPrb775Bvv27fPEpsvkv//9L+644w40bNgQX331FWrVquVw2a1btyIrK8vi8eyzz1ZgaYmIiEpX1WKtvQppEbF7hwkRgVbr/JSDFddERFSVlCtRLigowIABA9CiRQscPnzYNP2NN95Au3btMGLECIhIuQvpjjlz5uChhx5CRkYGvvnmG9StW9fp8iEhIQgLC7N4sNs1ERFVFVUx1jqqkA4PD7fbcnzjxg2Eh4c73SYrromIqCop1zXK8+bNw7vvvosHH3zQYuCOZ599FsHBwVi4cCFatmyJYcOGlbugrli0aBHGjh2L+++/HytXroS/v3+F7JeIiMhbqlqsnTNnDp555hnceuut+OSTTywS4NTUVBw5csRieaPRiN9//x333nuv0+2qFddERERVQbkS5bfffhsPPPAA3nnnHYvpLVu2xJtvvolr167h9ddfr5DgffbsWTzxxBNo2LAhRowYgb1791rMT0pKsjjBICIiqg6qUqwtrUK6R48emD17Ni5cuGCKuevXr8f169fRo0cPr5ePiIjIU8qVKB8/fhyjR492OL9bt2744osvyrMLl3355ZfIycnBiRMn0LlzZ5v5y5YtwyOPPFIhZSEiIvKUqhJrXamQfuyxxzB//nzccccdmDRpEi5duoSxY8firrvuQqdOnbxeRiIiIk8pV6IcERGBo0ePOpx/8uRJBAUFlWcXLhs0aBAGDRpUIfsi35WZm4vDOTloHBiIuICAyi4OEVGVibWuVkhv3rwZo0ePRv/+/REaGor77rsPc+fO9Xr5iIiIPKlcg3l1794dCxcuxP79+23mHT58GK+99hpuv/328uyCqMK8deYMGu7cidv37UPDnTvx1pkzlV0kIqIqE2sHDRoEEXH4UHtttWjRAps2bUJ2djbOnTuHRYsWITQ01OvlIyIi8qRytShPnDgRa9asQYcOHdCrVy80adIEGo0GBw8exNq1a6HT6TBlyhRPlZXIazJzczHs4EEYi18bAQw/eBA9IyPZskxElYqxloiIqOKVK1FOTk7Gtm3bMHLkSHz88ccWt6fIyMjAggULkJqaWu5CEnnb4ZwcU5KsKgJwJCeHiTIRVSrGWiIioopXrkQZAFq3bo2tW7fi0qVLOHHiBAoKCpCYmIiYmBhPlI+oQjQODIQWsEiWdQCSAwMrqURERCUYa4mIiCpWuRNlVVRUFKKiojy1OaIKFRcQgMWpqRh+8CCKoCTJi1JT2ZpMRFUKYy0REVHFcCtR1mq10Gg0bu1Ao9GgsLDQrXWIKsPg2Fj0jIzEkZwcJFfDUa85YjeRb2CsJSIiqnxuJcq33nqr28GbqDqJCwiolknmW2fOmAYj0wJYnJqKwbGxlV0sIioDxloiIqLK51aivGXLFi8Vg4jKiiN2U0XbtGkTxowZiblzX0X37t0ruzg+h7GWiIio8pXrPspEVPmcjdhN5GkigokTn8O+fQcwceJzFiMw27Np0ya0adMMmzZtqqASEhEREZVfuQfzOnXqFFavXo0bN27AaCw5XS8sLMTVq1exYcMG/Pbbb+XdDRE5wBG7qSJt2LABO3fuwhNPAPPm7cKGDRvQs2dPu8taJ9XdunWrml2KzwA4c8D99WJjlUcFYKwlIiKqWOVKlL/66iv06tULhYWFEBFoNBpT64J6MlSvXr3yl5KIHOKI3VRRRASTJ09ERoYOL75YhB07dJg8eSJ69OhhNwF2J6muVIsATBng/nqTJgGTJ3u6NDYYa4mIiCpeuRLlGTNmwN/fHy+//DIAYMSIEfj4449x7do1vPrqq9i/fz++/fZbT5STiJyo7iN2U/WgJr7r1gEaDTB5chHuvNN+AuxuUl2phgPovQpAU8vpBw4AAwYAq1YBTZvarldBrcmMtURERBWvXNco7927F0OHDsWjjz6KwYMHQ6vVQq/Xo3///ti8eTPq1q2LqVOneqqsROREXEAAbuMAXuQl5olvjx7KtB49gIwMJQG2vlZZTaonTy4yJdU7dypJdZUTC6BdU6BdO8uHmhw3tTOvXbsKS5QZa4mIiCpeuRLlGzduoEWLFgAAf39/NGrUCD/99BMAICgoCI888gi2bdtW/lISEVGlsk58AThMgN1Nqsk5xloiIqKKV65EuVatWrh27ZrpdWJiIn799VfT63r16uH06dPl2QUREVUyNfFNStIiOhrYu7fkER0NJCVpLRJgd5JqKh1jLRERUcUrV6KckZGBVatWITc3FwCQmpqKbdu2oaioCADw888/IzQ0tPylJCKiSpOfn4/MzJM4etSIDh2A9u1LHh06AEePGvHHH6eQn5/vdlJNpWOsJSIiqnjlGsxr9OjR6N69O5KTk/HTTz+hf//+ePXVV3HHHXcgNTUVS5cuxV//+ldPlZWIiCqBwWDA9u27cOHCBYfLxMTEwGAwIC8vD5mZJ5GZqSTVtozIz1eSaoPB4LUy+xLGWiIioopXrkT5tttuwwcffIDp06cjIiICaWlpeP755zFhwgRs2bIFycnJmDVrlqfKSkRUKTZt2oQxY0Zi7txX0b1798ouTqWIj49HfHx8qcu5k1STaxhriYjIHp6feFe5ul4DQJ8+fbBnzx5otcqmnn32WZw4cQI//PADfv31VyQmJpa7kERElUVEMHHic9i37wAmTnyOXYahBOY2rZph06ZNdufHx8ejXbt2aNeuHS5fvoxBjwzA5cuXTdPi4uIquMTVH2MtERGZ4/mJ95U7UbZ27tw51K1bF61bt4afX7karImIKp06MNUTT4ADUaE4ME94Dvt+PoCJE5wHZneWJfcw1hIR1Ww8P/G+MiXKS5cuRdu2bWE0Gm3mjRkzBnFxcVi0aFG5C0dEvmfTpk1o08Zxa6S313eH+W2OXnyRtzcCigPzd7vwxF3Azu+cB2Z3liVbjLVEnpGZm4vNV64gs3hAPKLqjucnFcPtRPmZZ57BkCFDsH//fhw+fNhmvl6vx+XLl/HYY49h1KhRHikkEfmG8nYTquhuRta3OarptzcSEUyeNBEZjXV4sT+Q0ViHyZPsB2Z3liVbjLXkLTUtaXzrzBk03LkTt+/bh4Y7d+LpI0dqzLGbq2mfu68ry/kJ/wfc51ai/Pnnn2POnDm47bbbcOjQIaSmptoss3TpUhw9ehRpaWlYsGABNm7c6LHCElHV404Lb3m7CVVkNyPz2toePZRpPXrU7FpbtYV48r3FgfneIoctxe4sS5YYa8lbrJPGt86cqewieVVmbi6GHTwItU+GEcDczEzE79yJp48erTEJQ0373H1dWc5P3jpzBg1eew0909uiwWuv8X/ARW4lygsXLkRCQgLWr1+PhIQEh8vFx8dj/fr1iI6OxoIFC8pbRiKqokpr4TVPotUf9vR0LV58EUhPd+9+uhXdzci6thZAjW5VNm8h7tFSmdajpf2WYneWJVuMteQN9pLG4QcP+nSyeDgnB7YXLijmnjpVI5LGmvi5+zp3z08yc3Mx9LffoF32JgoOn4B22ZsY9ttv/B9wgVuJ8q5duzBw4EDo9fpSlw0PD8fAgQOxc+fOMheOyNdV1W4waoI7e/Zsp63Fzlp4rZPo9evXY+fOXZgyxQiNBpgyxehWwlmR3aDVpDwpSYvoaGDv3pJHdDSQlOReku8LrFuIAThsKXZnWbLFWEveYC9pLAJwJCenMopTIRoHBjo90a0JSWNN/Nx9WVnOTw7n5EB27ULR/oN44gmgaP9BGHft4v+AC9xKlK9fv+7WbT1SUlKQlZXldqGIaoKK6ApVloGvRASjRj2OffsOYNq0fztsLS6thdc6iX7yyVFIT9dadBNytVW5ortB5+fnIzPzJI4eNaJDB6B9eyCjo/LcoQNw9KgRf/xxCvn5+R7db1WlthAn1dEiOhTY+3vJIzoUSKqjNbUUu7Ms2cdYS56WmZuLC/n5Nid9OgDJgYGVUaQKERcQgMWpqU5Pdn09abRXWeDrn7svs3d+oj4cnZ8kBwRAt2Ip0op79KWla6FbsRRJAQGVeCTVg1uJcmxsLM64cTJ/4cIFxMTEuF0oIl9X1q5Q7iS+ZR34av369fj110Po0we4cSPP4fXAM2fOdNjCa51EN2mixYEDh0ytyQDcalWu6G7QBoMB27fvwp49e7B79260atEMBUVAqxbNsHv3buzZswfbt++CwWDw6H6rqvz8fGSeOomj54zoMAFob/boMAE4es6IPzKVwOzOsmQfYy15klop+8CBAxCUnPjpACxKTUWcj58sD46Nxc62bR3O9/WkUa0s0BW/Nv/cPXEXiYruGVeRd76oiszPTxw9rM9Pfv3mGxTtP4hpxedg06YYUbT/IH795ptKPJLqwa2bL7Zv3x6ffPIJJkyY4NLyH330EVJSUspUMCJf5qwrlKOTFuvEt1u3btCoWaMd5i268+btQlJSQyxevBTdu3d3uI6I4MknR6FDB+DMGSAtDXjxRWDHDqXltkePHtBoNDAajZg1azrS0uCwhXfnzl1Yt06Zp9EYkZgIUzchldJNSGOxbXtlKulmZMTrrwOvzANGPQF07FjSzcjR+mUVHx9vugb0p19+xRN3AfPW/oqLFy+iZ8+eHttPdWAwGLB95y5cuHDB4TIxMTGmwOzOsmSLsZY8xbpSVgBoALzftCk6hYf7dJKcmZuLwzk5aBwYiLTwcCxJTcXwgwdRZLaMDsDMRo1wuLhF2Vffj8GxsegZGYkjOTlIDgxEXECA2+cU9rx15ozp/0sLYHFqKgbHxnrlGAD3z4N8lXp+4grLHnnKf7/5+Zqnz518jrjhs88+E41GIy+99FKpy7700kui1Wpl5cqV7uyiwuzZs0cAyJ49eyq7KFTFncrJka8vX5ZTOTke3aZ282aB2UO3ebPFPjZu3CitWzeVjRs3iojIunXrBIA88QQEgCQmxpvmWS9vNBolIyNNMjJ0YjRC0tIgOh0kPb2DGI1Gh+Vau3atAJDp05V9rFun9KZdt059vU5ERKZPn24xX32oyzVpkmLad24uJC5Ome7oER9fV3Jzc+2WKTc3V+Li6piW1Wktn0tbvzyMRqNkpKdJRmOdGFdBMhrrJCM9zel7SBVrjyj/fS7/kl/aI/IOlOfStrBnjwigPJe3nG7EHF+Kta5gPPaery9ftogz6mPz5cuVXTSXlSUGLzl92hRjtZs3y5LTp03b2nz5snx/9apsvnxZZp84YXe56sr6vMEZ63MKNb67ypXzGE8rb5lrIvU9c3S+5sn30BfjsVuJsojI3/72N9FqtfLAAw/It99+KwUFBaZ5+fn5smXLFrnvvvtEq9VK165dLeZXJQzMVY83EtLychRs3eHouJacPi3auXNF37ihaOfOtdi2mugCkIyMNCkqKnKa+Fovrya81omusx9Fo9EoTZumSIcOkIwM5WE0KusbjZCMDJ1kZKRJYWGhhIcHSWIiZPduyMKFkNTGyvPu3ZCkJI3odJC1ayEbN0Jat4S88w5k/nxl/717Q0ICISNHlkwr7ST/5MmTsmfPHpk/f74SJO9S1ps/f77s2bNHTp065fbn4gpTgHkGIu8ozwzOVYsvBmYR34m1rmA89p7KSGY8qSwx2NVjru7vjTXr8wBnFbrWlelqfHenEthUCVN8HoO5c71aCeOJMtc06nuWlKSV3bshe/aUPJTzNa1H30NfjMduJ8rXr1+X+++/XzQajWi1WvH395fY2FipXbu2+Pn5iVarFY1GI/fee69cvXq1TAdQERiYqxZPJKSe5k6r76xZsyye1dpc8+PSzJ0rDZs3lo0bN8rGjRulVasm0rhpYwEg7Tq2t/ihsq41tW7BtU58rZdv2rSkRbck0YWEhztuVXbUmmxd+zh58mTR60v2b6+FNyAA8r//QVq1UF63agHZtQuSmAjx91OmhQZDvv/e9R/qim7dtd6fvAO2KldBvhiYRXwn1rqC8di7lpw+LTqzGOZqslnZFddlTWRdbUUvb2u7O623ZVneej3r8wtr7rS2WrcylqV18VROjmi+/lp0zVOVc4DmqaL9+muv/c94osw1jXWPPHd79LnLF+Ox24myav369dK/f39p3LixBAcHS3h4uDRr1kweffRR+eabb8q62QrDwOwZngimVbVW1xREhw0T/2C9oFcv0TduKMMmTZLWrZvKhg0bTLW34eFBAkCCgg0CQBo3bSzvfP65+DduqKwX5Ce6urVNSXHbtHamHynroGZda5qerpXw8CCHiW/Hju0lI6ODaX6TJlqbRHfjRkhSouNWZbU1OSEB0rIlpFYtpdX3nnusW4uVsrRpo5Hdu0tahNUW3pdeekliYmpZ/Air85xNc+WHuqJbd633pz7Yqly1+GJgNlfdY60rGI+9T+1y7EpcdbXi2tvJdFkT2YpoUS6t9db6vXGntdfRftTzDHvru9Paqi6bbnVOkV6GFtrR775rcR4z+t13XV7XHUajUdp1bC9p6VqLMqela20aGsiS2iPP0cOTPfJ8MR6XOVGu7mp6YPZEgPNUK7A7wdDbgVmtub3rrrvEEABBr16iC9QrtaV6tbVUL3o9pHbtKIsA0amTZeIb1SiheD2NTVIMQOrVU7pQq12pMzKUll7rWtPSWnjVxwsvKN2cGzSATbfoxARlmaBASFiYbatyTk6OBAX5KeXV2bYSm7cW6/WQTz+FbNigtAqnJystrWmNIE2bpMiJEydk9+7d0qpFM0lvrBXjKkh6slaSkxIkIEAvaY1Klg8MMsiuXbtK/aGu6NZddX9JdbSyezpkj9lj93RIUh0tW5Vd5O3vrC8G5pqG703V4WryWBG9wMqTyLraiu7qcqWNGWJecWrvvSnrtbXqen362K9Yt17OWWuregwvvPCC03MKV8tWkV2hK/I6Wyo7X4zHHk2UL1y4IImJibJ9+3ZPbtYranJg9kRtsSdbgZ1ty7wMng7M1oFPbVXV6SB+xd2D1Wc1SI0erSSSakKpJrodOkCCgy0TX51OWR6AREdbzgsOtj9Y1tq1ay0Cj9pybJ34jhypPMfGQvz9Ie3bQzLSlW0EGux3ix59p/J3RIRtYMnNzZW6daMtEu8+HSxbf8PCgqV586bSsKFG6UbdEHZbeNeuXeuw9dfetKcnTy71s6ro1t3c3FyJq1dKd6X63hlAzJdUxMm0Lwbm0lSnWOuKmhyPvakslVSOKq7fP3vWYrue7gXmbCwPd7uNq+tp1MueSllPbW1/94sv7HZtLm3MEPME0d57o/36a2nXsb3bCaW6344dNZKRUXIOYb2+dcJa0uusZDnrlumkJI2Da1Y1Lie7FdUVuqKvs6Wy88V47NZ9lEtTVFSE48ePI8eHb9xeHZnf487V+/eq9128fd8+NNy5E29Z3dPT2e2N3OXoHn/rr1wxlaHBzp0YalXuoZMnIzTMgNmzZ5u2NXv2bIRZTbNHxPYew+vXr8ehQ4dQVAQUFgJPPKE816tXcquknj2BoiKgTx/ledo05X6+99wD/Plnyetp05T5PXsCKSnAxYvKOm1bK89//gmEBAIffACEhQA//qhs/4knRpruF/zVV0CbVsCRI8Dvvys3kh/xL+DgYeC1Bcrz+XOA0Qj83/8BO78DnrgLyMkDHnus+DMxKtOKjEDPVkB6MhBbB2jUSGO6jROg3P7n++/3YteuXQgNDkCHRODMVSCtEfBifyC9sRZJiY1w9eolnDghSEsDTp4COiYBPVoq++rRUll+0r8nYNK/JyCjsc40744WQFigMt96+dfnzoLRaP3fZPlZTZ40EUl1tIgOBfb+XvKIDgWS6mgxeVLJsXiCejskp/cp3Flz7qNcFmW9VziVjrG25nL1nrWlxXBHGgcGwt6JYb8DB0zb8GT8d1bWzNxcNAoIwMfNm+OlpCTsaNsWPSMjsfnKFezKyrJ5H9T76/7nyy8x7OBByJ490A97BLJnj9PfnriAAHSJiMCr0yZbnBOozG+1uHPnLsyYMcMUpzUaYPLkIuzcuQsbNmyw+94Yd+3C3u/32F3eGXW/vXsLdu4sOb+wXl9dTt0+YLucusyIEUB2djaOHhV06AC0b1/y6NABOHpU8Mcfpd/nXsT8lkPKNOtbRHpKfn4+MjNP4uhRo4MyG10qM1GZlDstN3P27FnRaDTy1VdfeXKzXlFTarCtW3TGHDlSajdnV2qLv7961Ss1yuo1VDZlUEdVHDZMeZ49W3QhyvXAgYF6CQ72kxkzZpiu4QkPD5KioiKHg2dYd4Nau3atNGmSUty12rL1NzBQXUZp3U1NhYSFQFJSIDNnKt2Pg4MhBj9lWoAeMngwpEkTZbnwcGVewwbKdjI6QkJDlL/Vga3CQyFffKG0QiclKS226kBYTVMhK1cq1w3DrIVXbfGtXRvSsgUkvbHSHTm9MaRFc0hgAEzdnNOTIBnJkLVjnV8XrA4aNv0++62/K1eulN27d0tiwwZOW3it55lafh0sP336dIf/F2zdrZ4q6rY0vliDXZrqFGtdUVPisavsta6eysmRMUeOmFpJ7fXQUNcrb3w2P2+waBk16+nl7R5ls0+cMB2rxajKVmOGaObOlTGHD8vJ7GxTi2l8SpLokuJFlxCv9Khqnir4+munvz2Ouka7PmaI0np7MjtbOR61zHPmiK55qs3yaWmQZs1SHLaCWrcmO7oLRVFRkSS1by2JjRy1EGslI6ODpKeXjGPStq1WWrZsKvWbJEuzFsp6zVpoJK5pY9m9e7dL16xWdFdo8+tsv9y+XRZt3ixfbt/uletsqex8MR77eS8Fp8pmr0XnpVOn7C4brC2pQ3ZUW/z5xYv4Z1ycxQ3mzQ2oUwdxAQFul3PTpk0YM2YkHnroEbz77nLUq5eArzevhfH2XsDtt0O/aD6KrmWj4NwF6E4tRUFuIXQzp6PoRh6eeAKYN68AADB58r+Rl1dQPC0bM2bMwOeff2JzY3oxqwl98cUi7NihwxNPjMThw4cRGgrk5QCnTwOB/kDLNkBODmDwA0aNAn4/CvgHAAV5wOVLwIznget/Ajqt0mp77AhQaARWvg00agRcu1Ey78RJpXV33lrluG9qDGw/XDLtk08AvV6p0U1LU5ZR5z38sLIdtYX324NKi2+HRGDvceDCBWDdM0ot8pR7gTtnKetPu694Wt+SaemNtcgxNMHnX66zaBE1Go2YM+sFdEgEPv8ByEi2bP3NaKzDawtexb8nTcHJUyeRFANTC68qOhRIjAHOXQWiQpR5IsDYd+F4+drAnFkv4Nlnn4VWa9uWobbuXrhwweH/UExMDFt3qxi1Zcr8d0IHIDkwsJJKRFT1mcdXLYDFqakAYBNz1R4aPSMjERcQYLGeBkoNojm1xddejM7MzcXhnBw0DgxEXEAABsfGIr+oCI8dOWKxnBHAjqws3FenDhanpmL4wYMoQkkvsLLEf0fnG2OPHVNeiEA7ZxZw+QK0c2YCN64h/88C6DauRUGBMu/VSeMw99Y7gOJW33nzjpq2pbw+CM1//oPHRz+GV16cj+7du5vOO+bOfRXdunXD4+OfQVq6Fi++aMS327V4fPwzONyjh6kldt06JZb+3/8Z8d132Zg8GTatt3feuQu/fvMNFrVsiX/+658oOHwC2gWvoOhEJiavs1x+2jTgzjsPYf369bjzzjtt3hd1v9OnAxMmwLR/6/29/fHHOHnqJArOKy3Etoy4ceMozp27YtrGjBlG3HnnAQDAW+uUltmX5gruvPMwLl68iPbt2zv9zNRzqKQkLaKjjdi7t2RedDSQlKTF5MkT0aNHD2jUQpdTfHw84uPjPbItIreUOy03U51quatzDbZ5rbGz648cteiUpUVZffT9+WeH81ypUZ41a5aEhvrLkCFDTCNHN2umtOQaDDqL64N1eoim+PZJOp0yAjPManxTUkpqZgMCIFqt9XXASotz3RjLGk61JlQd/OqFF0paJv2Krz/205a81sJymnrdrwaWLbzxVi2+5n9Hh8JiEKuwQEhYgOU0dbuRkSHSonnTkoGwGmttWnBNLbH3Ket1TNKYBrkqWqlsX922aeCrZMtWZesaX0etydatvykpyRLk77h1F4Dodc5fWz8C/LWSlZVVzm8GVTVlvb7QHb5Yg12a6hRrXVGd47EnOWpd1ZQSu53F7NLis6NxBN47e9ala5XV/auxfdasWab59qa5cszmLcmautEW44IADsYM0cM0InKHDlY9wzpqJCg00OYaY/X12ClT7F5ru+KTT5yOGWLdepuYCElq39p0q0W1fI6uB05MVG7l6GgE60aNlLtQJCXZ319Sklaap7UVjBsnAOShh4pj6siRsmjzZtmzZ4/s3r1b2rZtbbdFOzwcUlRk/5pmZyr6lkPk3MaNG6VpU/dvO+YNvhiPPdqiHB4ejmXLlqF58+ae3CyZsdeaq9Y6D46NtVjWUYuOEZa1zTooLcqbr1wx1SgvTk3F0IMHbWqlP7x40WHZigC8t3YtVk4Zj7lzX8XX332HV2dNxchn/o3b09MxZsxIHDlyDH/+mY8VK95Cfr5gxIjHcfDg4eJa3yLTtpTXAA4cNv29d69yve/Xm5Tn8+eBIAPQIBEwFirX6rZsCYSHAvc9AOzalYdAA3D2vDJt0qQJuOOOOzB58kSkp2vx6SdG7PsZCAwEQkOV/V6/btvSCwB9OgBrdpc8j+oJLNgItEtQWni3/Qb8eMKyxffgGWDuQ8AXPwCHzgKrHiuuSb7PtsVXnRYWBtSqVRu/7D9gaiGe3MeIv8w9ifRkLXq0VD7NHi2VFt93/qe0Vk/tK6ba5o2/ANdySrYNFG/nb8o+jpwrua5XrfFVW5MTooH3djpu/U2qo8HRI0ewbBjQPK5k3vbDwIi3gfnz56NRo0bQaDSoU6eOaf7PP/+MU8W9GcLDw1GrVi2L/52UlBSEhYU5/N+i6mlwbCx6RkbiSE4Okot/W6j8GGt9k6PWVUe0AM7n5+N8fr7Neup8Ixy3+DoaR6BnZCRuCg+3aZnWAOgUHm7TC2z27JfxwgtTcP16PqZN+zdWrVqKuXNfNU174YUpGDNmDE7n51u0XAMlY5QMe/dd6BbNR9HwERj6l79g0enT0C5dDJy/qIy1UXweEBSk9PhSxwx5+WVl3I81a4BpU4ymMUN27zYbM2Sq4M47c4qX24XAQD3y843F29yFH3/ah7Q0WFxrm5auxcSnn8SJQ0dNLbF5eUBmpvKw33oL6LOO4tmJzyEjQ4cZM4qweDFM1wPbExT0O/Ly8hBg9tmo1+RmZiq9zAoKHO3PiHp5p6H9ZDXap2uxapURh49qsfer9bhr5kzEBwZi/fr1+OGHfTYt0kqLNrBxo/I+mrdSb9iwAT179rRfYBT38trOXl5VgYjg8ccfx++HDuHxxx/Hb7/95rQVf/bs2Zg2cSImTpuGsWPHVmBJq7Fyp+XVVHWswXZWa+zoHoFjjhwxraO26Fi38gz89VebGuUlp0/br5m2vlY4NVX8DRB07CiaOXMkLCxAAEijdq1Mt1XSBvhJUrvWphphdSToTp2U1ykpSstuaKhyza6fn3JNb2io8rc6T69TRmzW6yBx9UtaYNWWXq0GEhZacs1vcrJtC6+p1XS67Tw/HaR9AiQ80LKl118H6dgI0iFRaZFNa2Tb6uromt61YyFN69m27qqtykUrLadpNZBAg5/FbZBMLcAOWnhjw2G6hdHuaZBW8ZCkGNi9rVFi7ZL3zfy63qysLDHota61/uoh2yfzlklUNfhiDXZNU53fG0/e/sxRi7KjVmLz65XtzV946pS8f/asw7LZbTWeO1cat0iRZ6dPl7pNk0pi/dy5suT0aTEajdK4uKeX2murQYP6Fr29AEit6FoW0+555hmHI+Cr98gFIO06tpeT2dmCWbNM5wzp6cX3+U0vaUFWxwzp2BEWI0Krrb7qa/MW1IwM5Q4R5nes6NhRIzqdsj3za23XrrXfGvzFF5BVqyBxcRpJbJoqmsQGFtf5aurXtWidXrlSKe/8+fNl/vz5xX8r25o/3/H1vCtXrhQAMnWqsj/1Mbb4fGDatGmyZ88e03L2Rp4ufbRoSKtWJa3VHEG6+lm7dq3oir9zOijj7ThSVFQkYUHKOD5hQco4Pp7mi/GYibKXA7Mng2hpXanVLlBfX74ss0+csBzE6/BhUxnMb5tgMUiGOk0dLCMuTkmC4+KU10OHiiYmWvz9IdBqRK8vCVpaHaRe40QBIEEBJcHS4Ffyt9oFOjgQEhUFMRTfl7hBvJKcaTVW3Z61Jd2brbs9q89qontTYzvdnaNLEtSMxkoSHRYWKI0aaSwGv0prBNEXl3NgZ/sJL1CSDH/5tGUyrXZrttfVuWk950muvQGvzKer23El8VUfpSW6detEyY4dO2wGv9i5c6esWrVKXnnlFZk2bZrNY8mSJRJTfPLj6MFBtaii+WJgrmmq63vjjdufWVdkjz961KbrtaaU7tjWA3BZl2vjxo3SsHlj0ahx33zgKXUArOJLoXT+OuV13dryxH/+I3UT6glge1/f0i6F0oUYBLNnm843zCv37Q2umdi2pen8wjoJDAqCfPmlWvltfxlHg0wNHGh/eTV5VR/btyvH4SzeRUSGWGxLTa7Ti7uAW3Zp7iBt09qZuoebEvh0rbTr2N4iMXX1dkjWt6my7kKdk5NTahdpvb783aZnzZolIf7Ou9h7UkXvr6oyGo1SJ1E5736i+LOrk5josJJDbShSl3U2iGpZ+WI81oiIoAbau3cv2rdvjz179qBdu3Ze2Ye9QTmsu0e7IzM3Fw137rTbxUoHYGajRnjm2DGH81dmZ2P6s0/h14xbgZUr4G/MRX6hDv66IuRrA+CvK0T+Q/+AduVyaAqUAbKKigCtFtBpgAKtFigwwt8PyC+E6dngB+QVKvsJMgDZeYBf8ZhMhUZl3aBg4PoNwE8HFBYBej+goBAIDADy85XuwzotkFIHOHDGstuz+ndYoNKlWH2dUhf4bS6Q/m+lm3NOHtAmAfhuqjJt73Hgs6eAu9oA639Suh3rdMoxAcrgVz1bAev2AX+ZC7RpoHRLTolVtqHRACLKtvadANo0VN6Lv7QFJnxQsr66bfW1Sp0eGwF8NkbptqYSAA+8CgQbgKXDlH0ZBegyDagTDnwwSlk+vxDoMw84m+X4/0KvAwqKgDq1o/Dxp5/j8uXLuHLlCgCgVq1aFl2gAaVLVFxcnL1NlerUqVOldrcq67aJymIvgPYA9gBw6Zf88l5gXXvgzj1ArXbOt7B3rzLSzZ49QDnjREXEnOqqqr431oNcmduVlYWMH36wubTpeEaGadldWVnYdu0aOoeFIdZgcLgte/s9kpODXdev45ljx2wugbKxZw/0i+ajoHFz+G/diPz+/wAefNA0vWj4CDyTm4sFs6Zi/PipeP+jD7D3+z3Q1a0N7bUrKAyJgJy/CE2D+pCTf5i6Oasx0PQ6vj7k1B8IDQWaN1di6XffAenpwI0bwP79wIYNSpdeQBmAqmdPYP16ZZomJhpy/iJ0zVNRNP91bG7TBl0iInDTTekA9mL79iLcdJMOWVlJOHDgEHQ6pcvxjh0lZenUSelWnZwMREQo0y9cAN57T9nnoEHK7Rffe6+kqzGgrPvAA8qtGhs3Br7/3izGpytf9SI7/dzr1InCxx9/Dn9/f4vpRqMRXW69Gc1b5OO775RtrVsH3HVXyXGr1OM3f0+s561bt87U3TkvLw/JyQ2RmXnO4UceH18Xr722GL1793a6zWbNmpli9nfffYd5s2dj4JAhSEhIgIhg2JAhyMnLQ3BAALZs2watVutWHDcajYgMDcW17GyEBQXhyvXrdgfl9JSK3l9VtvLTT/GPv/4VSQACdUBOEXAUwLJPPsHDvXtbLKu+b6nZ2fgOQDqAg154/3wyHpc7La+mvF2D7ez2CadycuS9s2flPSddohwZ+OuvtjXGc+dKXLNkpXa4Vy/R6iD+AZqSFuHUVNE3bigx9WNNtYdabUnLo3qLIn8/CLSW09QWYXVZ6xZdU0uvBhJUfBulepG2rbvmf6u3NeqUXNKKrM7T62y7PReugDSuo+wzPalkXtN6yt/2WmLNB7tSW2bTG0MCDJCQ4ADTQFnmXZutW5PttfSqrcmJtZUWXXe6Ott7uDvoVVStcFm+fLmsWrXK9Pjyyy95ewSqUFWpRt8Xa7BrmqrUw0tdZo5Vryy1VVa9pMlRC+5LJ0/KqZwcm1ht77ZO6r4WfPyxNG6RIv0eecQ0+NWpnBxlHbW1t1cvi9sildwqyU80EeHi5wfRqQNhGnSCWbPEP8RfiffNUiQwWPnbYNBbtN4CSivoyJGWXZI7dLB8nZZmeekUHLTiqsuGhVkOFKWuP2pU8TlI8TFa32ZI3ZZ1a7J1y7BWC/nkE0hcXOktpNbzP/3U/jbVVuXduyEtWmqlZctmcvLkSRGx/c1TW+fU8qldvh0NvJWYqLSE79rluIXYvCXw5MmT8sbmzYLXXxetTmnVVwcW1er1MvP77522OicmQjIyOpi2aTQapVmKMnBqsxRlADFPtDBWRCulvf3V1VXM/qoqo9EocUlJAkCaFp9jqs9xSUk2rcrq+/YCIK11yrM33j9fjMdMlL0UmB11kx5z+LBFlynN5s0y58QJl7Z5W48eSuJrMCjPer34+0MCQ5TRHNXRorUaq+QXtkmxOs068dVq7CTBDkZ5VrsV20uGg/yVZNZ6BOa0RkpX4g6JkEC9ZfKbGus42VVHlLaet+4Z59f8hptNc9a1OTEaEhJQkgDbS3j1OsjXz1l2J3cluTWfb/Ge3nWX4IEHBIMGKY8RI2TEjBmyYsUK070BrR9MhivHu198IY1TUuTdL76o7KJUOnevc9q4caO09uKInO+IEsjecXWFY+8ogfmYuoaTLbzzjhKY33F56w4xUXasIt4bV7pJW1yWZKe78xzze/u68pg7V3T1Y0QfEyG6iGDxC9ApCW/jhtJ17Fgl4R06VElsi5M/f3+ITqeV4BCry500MD3r9RDE1zONA6LTKeuaJ8C6yDCl4rpeSfxRR2FO66gxJa/q/L//3TIxddSl+YsvlNGSra8BNr/Xr73uz+q06dOV7sZJ7Vs77DrcqJFyPuNsdGm9HvK//ynlUa/bnT/f8nVsLKRu3ZLre5OTIQ0bOhpB2vJexebX+lr/5hUUFEhwsMGifDt2KPsqLUl3NM9ed+dTOTnKuQEgfdRzLHWdgQOlXv0Yp+ccoRHBpm2qI3Gr63/xxRcSGhQkaYAYAUkDJNTN61bV98V8G9669lXdX2hgoIQV/6+HaSGhgYFe219Vpl6bnGJ17p0C22uV1c+pAyAZxe9dhhbSwQufly/GY59LlNeuXSvt27eXwMBAadCggbzwwgt2++t7OzB/f/Wq08E3rINpXLNk2bhxo6SkpIheDwkM8pP6CfVFq9WKwQ+i8/OzSHStn0ODleRLA9vk196z2pJr/gUzv85X/Vud1ylZWc+6tbdDom0ynJ6kLLt2rO21uOrraX1LyqAmu44GvQryt0yorW93ZN6qbO+a38e6KwnvrmnKthLNWn93TIbUDXct4fV3Ml+rgTRp0kQaN24sTZs2laZNm0qrVq2kW7du0r9/f9N1vq+88oqsWrVKPvvmG4c9DpxxN+koa5IyZMgQMQBy1113lbq+s9ZFe/ufNWuWhJajNdLbiZe1N//4Q3TxxdftxcfLm3/8USH7rarcaUGw14rhadNECWTTXF3hp2nKb8RP6hpOtjBtmhKYp7m8dYdqYqJcleKx9e+tdvNm+f7qVVOr7mfnz7uW/KpjeKgDWYaFmcby0IcFiFYH0QfqxE8Pgd6v5HaHOuWhUSulA/RK4qSxTGLVZZVl/Gymqc8atTdWcWJsfk1wSorlvKAgZd7atfaT38aNlSTOlQGx4uNtk2DrVllHA0UlJipJdsn1xdNttqWWPzDQ9aTT36/kGml1G+npttcY+/s532ZkJCQlGbJwoWVL77Rp0wQoacUcMGCAaf/qeYP1+YNWC4kunvbKK69IoL+/1AckVQeJKT6f8NfrZdeuXQ4rwYuKisTg5yf1AQnwg0RBaQ1MAURnMMh/9u0zVaJ//u23ovHzk5Di/68QLUSj18uJP/8Uo9EoTVNSJMVs/dpRUQJABgES6qc8l/Z7bq2iWimt92d+burN/VVVRqNRUorjalMtJEMdi6dRSatyilm8NQ1ia/XeTffC++eL8dinEuX//e9/otfrZcCAAbJ27VoZP368aDQau/8EFd6i3LGjaXRohIWJn9aytddPCzEEKCNG+2ntd3VWW3RLS4TtJb/27vFbL9Iy8Q0PhIQY7I/8HOhvP+Gdfp/jZPjLpy2TWfMEN7WuZfJrL9F11ApsPW/+wOJRl2OUbtC7pznu+lxaMuyn04oWEF1xVyedTidardY0XwOIX3GlRb169eT//u//JECnkzr164sBkIhatUwJpppApqWliQGQtLQ0U0I5a9YsCdQXn2wBgl69bFo4rBNQ86SjYVyctGrSxCJZtE4gzZevGx0twXq9S8lpYWGh+Gs0yvtRfNyOkhxnrYtGo1HatS8eybS9MlhJUVGRctyABOr1btdk2tumN53KyTGNvqomhpriLoM1kbstCNatGM5G5CwrXwzMvqCqxGNnrcSmgaxiIpQE1x+iM2iVJLdXL0FcnPjplcRXVz9GMHSo6P2V1k61BVerVV7bS2itk1l1+YyMksuftFqlm7OaxKrL3nSTWcX1TZbz1DtGmCfG/n6Qnj2VSvOYGGXehg2QsBBlH//4h3LHgvh4ZXrrlpDBg5VpQUFKWV54ATJrljLgJqA8p6QovajuukuZ7++n3IUiJBByzz2QBnHKXSg6dSrpbaWe22i1tsmpQQ/5+GNlwKvw8CBT1+GFCyGpjSGPP64st3Kl0h08JFBJig1+SuLdqpXyd4MGkIbxyt0qAEidGMjMmcrxp6WVDBaq7l89D9DrlPOZ9u0hcfVKeuL5ae2fS8XH15GQgACLVkw/QFoC0tIsKVXXGz58uDSMj5dQs+Wja9UyJTUAJNhs+WlOflvUBD1QLbtVF9unJ082Lduha1e7SeSg8eNNv8PmXXS1gLQHJLx4WrhWee1qq3JFtlKq+1Nbk80Tw5rYqpyTkyMBfn5Sx+rcWD0nrgNIgJ+f5OTkmD6nhOL/13S1QauR8jrBw5+XL8bjCk2UDx06JGfNblbvaT169JC0tDSLaWPHjpWQkBDJzs62mO7JwGz+Y6zc3shPIqOiHLb+mndvtm7lNW8Rtr5+1zzxVa/ztb7u1/oa3/BAJfH10yqtv+r0YIP9LxjgeOTn+pG2rb3pSfaT4fQkxyM+rx1r25qckQxJqmPb7dnUCuykS3RpibC/Tkksd+/eLV988YV8+eWXsnv3bkls0EAASGKDBrJ7927ZtWuXBBQnwWqCqINZBYXVs7Y42bM3zw+QkMBAi22pz6FBQRJqNU+v0UhhYaHpf8peAmqddJgnsPZa7uwt78oP4sCBA5X/KTXownGS46x1cfS771rMG/3uu6bAr05zdpJgj71tetNXly6JLi7OIjHUxcXJ15cueXW/VZWpBQgQKX62/txVaiuG+XvX1MOtyocOHZLO48YJ+vWTzuPGyaFDh0pdftzgztKvE2Tc4M6yYcMGGTeus/TrBxk3rrPF+ocOHZJxnTtLP0DGde5c6rZLU5GJsrdjrSuqSjyGTqe0+jqIx2qM0GpKLkfyM4spasW1n1ZJfNW/AdvEMDhA2Y5BX7KMXqskq3q/kkTH30+JK+rdHvR+yrZ0OuXZ31951uuL7xDhV3LbRHUeoCTGAXql5dT6Uqrp05X9qucShuLK7gADJKOj/SSyeTNISHDJcVs86yDNmtqet5ifv5gvb927bdQoSOPk4oQqXbm+2LxV2HzZhATI998rSa/5Nu01GJifB6l33LAuu/VdMqz3Z29bAOSxxx6Tp59+2u4869Y59RzMr7iS3XyeFrZdZNXlA/397cZktVK5Huyf86WYxfOCggLRAXaTyJDAQGmSnGyzfwAy0Gqa+tqVFsaKbKU035+j89aa1Kqcm5srsVFRFr08zc/LdVpIvehoyc3NVW79adbYY++9M2i1kpWVVe5y+Wo89mqi/P3330uXLl1ERGTRokWi0WjEYDDIp59+6vF95ebmir+/v8yYMcOmDABk/fr1FtM9FZjtBQ1HibD6T2n9I6r+AJo/Rt9pv6tzqMHyfr7m1/3Wi7D/RVATX3VgK3V6k1jbL5hea9mN2nxesKHkel9XW3vN7/GrPv43SQnqavJr3v3Z3sOVa4ADzV63LH5u1qyZAJAHYZvo2WvpUpO4m9TPx2ybfayezeepf8c7mWdvm9bTBg4caCqfdQI6bdo0m6RDDaBr1661OZ4vv/zS4fLOAoramtwBkIzi9RwlOc5aF09mZ4suNdVinjYlRQL0eotp7rQq29umLjVVTlqddHvSyk8+Uf6/YZkYrvzkE6/ts6qy/rzFzuduTv2ftH7vPNWqvHTpUtHqdKKpVUvQtatoatUSrU4ny5Ytc7i8TqeVWiEa6dpM+T3TaCCRkRrp2hVSq5ZGdDqtLFu2TFlWq5VaGo10BaSWRiM6rdbhtl3hzUS5ImOtK6pTPHaU9NmruLaXGDrr4WWdLKrbtbc/wDbB02pKWkxtkj+NbaJrfl4RU1t5dnVwTXu3WLTXE826t5qzeebT7rrL8vX8+ZCGDTVSt25tu+UzxUk7+1HLbN4rrn6k87Kn1C05l6kVYrvN6FDLc6pgLSQ4IEBCrFox0xpBAjWWrXPmY69oNZBQs+XTGynTmlptQ13eUauyeWuy9TmfeRfb6dOnS/fu3W3OxczPzwDb/QdqlJZw82MO0UIaovRW5YpupVRbk4PM9mVqlGkECdLWvFZl0/2zHZx7r1y50rTsjh07pHFyoqQnay3fu2StNE5OlO+++67c5fHleOzVRPnWW2+ViRMnitFolAYNGsjbb78tn3/+uTRv3tzj+/r1118FgKxevdpi+uXLl4t/lOdbTPdkYHb0A28diOpHKj+YYYElLbDmP5ghBiUZzkguGYnZ2f18recZ9PYTXLVVWd2n9ajR7iS+6vW+ZWntNX+UlvwGaEtaW1sBEgLlpCMakDFQkr4AQHYB0hqQMED0sEzs/KB0JTIlio0amVpfrZPIJsnJEqjXK12RrLbjD0hHwCJ5LCyeri7XoXh/FkkjIEXF86y3GVo833ya2qpsLwENMhjsJh0pgDRp3FiaNm5ssXx8vXoOl3cWwNTWZLVW2FmS88gjj9hdZvr06TLrgw/szrM3zdVWZUfbnPXBB+X6Djti/X8iZu+vp1tGqwPr1mSx87mrvP3eHTp0SBkJduhQQXa2cnVjdrZgyBDR6nRy+PBhm+V1Oq0M7QrJXgY59KLy2zR0KCQ7W7muMTsbMmQIRKtVgvBQQLKLy54NyBBAdFqtzbZd5c1EuSJjrSuqQzxWEynzafbWc5QMAiW9phz18HJUHnsJqPU2nI0ZYi8RNa/gTqlrOc+VwTVDi88/1HkdEpWk3N5dKDok2m5HHUxT/Vtvta5Oq5yHmL8GlFZl88QyoxEkQFOyvPVlYP46SMdGJce47hllv8EG52VXz3fWjrVthAh2cGmZ+cPePHuXopVleetWZbU1OQKWy9nrYhsaFCRaKOdM1ud/ahLp70aZAIi/RuO0hbEiWynV/emLLwdzdG6qL6XMvsRoNEpGepok1dHaPfdOqqOVjPSSUdRNI8s7eO/WrVtXrvL4ejz2aqJcu3ZtERH55ZdfJCAgwDT6XmhoqMf3tX37dgFgM8BPQUGBAJDnn3/eYrr6Jm3dulWysrIsHq7eaB2w/cFVa3vtBSLzAbSc/cCuHausYz14ldriq3Z3Np/X0WwALXtfBEfdqecPLF/iW1rCG1w8vw0gN0NJXAElUVS7UA0AZBUgfy9+PR+QUzA7AUdJlyDrJGmd2d/25g+0er3yk08ctnTByX7UcqjzrBNJR4mlvWWdlnfgQJuEZC2ULt3WSUcGIE3tbMuV5e21Klu3JmfAcZJTWFgoeo3GYeti27ZtLeYVwbJiwHx5V1qV1WuT7a3vrWuVrf9P1IenW0arA7XyJhGQ3YDsMXvsBiQRlhUw3n7vxo0bJ7patQQ5OWbDCSnBWRcZKePGjbNZvlaoTnKWKb9/4/4PUisSkpNjsbZkZ0MCAjQSodFIjlXZswGJ1Olstu0qbybKFRlrXVEd4rGuOBmzaa1LVhK9YP+SimvrBFNdRq8tqYS23kf7BMvEMyNZSfpCA5R9m18KFRaoxHVXxwwJC4T4aWwT0bVjYTMwpiuDa9pL8BwlZ9Pvczxv3TPOk7CBnS1f60pJthydtwDKMarnQdblcVY+R40QaouzRRdWjdKia96Kaf2/YD24aGiA5bzSlrfXqnzlyhXRwfL/0/qcT/3M9Si5bZSjREirca1M6jmmo+7g5iqilVJlNBqlZfOm0ihGY/fctFGMRlq2aFpjKq9zc3Mlrl4dp+fe8fWVUdTdTarLwtfjsVcT5djYWLly5YrMnj1bunbtKiIiR48elfr163t8X99++60AkE2bNllMVwOzdRcw9U2y95g0aZJL+3T2A+9olGfz1mTra33VH1jrH3J3B7ayl+CGBCi3QFIHuypr4quO/KxeixsBpZUXUJKwvoD0L379GCBtAUm3Sm4yAEmFZTKnTrebeMF+kqUmc0VmyxVZrRtePE193aBRI4vWV/Mkzro12Xw7aqtyBpTWZPPlHJVdLV+6k+OyLq8fYHHLBjXxdZZ0BFltv7TlHbUqO2pNtpfkqMs6WsZ63rpStllaq7KpRtTB+uWtEbWmtog6SwxrUquydQuCvYfaglAR712/fv1E27WrZVAufmi7dpV+/frZLN+1udb0e9mvE6TrbfbWVgZC6mL1f6Y+umq1Ntt2lTcT5YqMta6o6vHYPDF0lFSZr+sowXSWzDlL8tT9utK652w71tOcJcPOBtfs2MgyqVen20ukwgIsk3rrxM+8VdnR7RrNuyQ7SkStKwgskjldyTEWrbRczlHZ1fKlJ9lvhHC1h527A4+Wtrz6/xdkMJhiclZWlsP/T+v1DXq9BGlLb+Awb0QprUw6oNTWWW+3UppzJzGsKU6ePOnwNqLmo6hXxHvn6/HYD140YMAAdOzYEWfOnMGSJUvw008/4a9//Sv+/ve/e3xfERERAIBr165ZTL9+/ToAIDw83O56W7duRZs2bSymGQyGUven0Wig0wLtEoAeLZVpPVoCaY2AH44D//sQ6NlKmZaRDLzzP8AowLUcYPLfAI1G3Q4w7T7gzlnK66l9gSkfAUl1gOhQYO/vJfuMCgGC/IE64bbzokOBxNrA6JVAkdG2vP464PeLgF4HdJhYMl2vA4qK//bTAgXFL/wAxAEIKp53HEA2gHFFwF8B1AZwn/peAEgGsL34bwFwCMBmAAcArCueri47GcCdxa+nFU9bD2CnnWWn2VnWejsbzZbbCKCn1bozAIxXXx87BljtZyOAawBaANhjZz/qdr4vXm8mgCwXym5+nI6Oy155r2dnm7YtAKYASAIQDWAvSkQXTz8OYKoby+sAXMvOxowZMzB+/HgAQFFREf6zYgUSALznZP1EAE+OHInDhw8j0cky5wBEFc8TAGNL2eYLU6fiueeeg1arhTURweQJE5zub/KECejRowc06hernPLy8nDi2DFkA+jgYJmg339HXl4eAgICPLLPqiwsLAxbt2/HkSNHsOXKFbx19iwEyv/c4Lp1cVtkJFJSUhAWFobc3Fyvv3cJCQnQbNgA5OQAgYElM3JyoPnxRyQMH26z/IYvNMjJBwL9gYRoYMO3dlfHtWsa7NMAOSIwm4UcAD9qNBiekFCmMntTRcZaV1T1ePzZXiA0AEiNBT7/QZlmvl5GshJfWzUA7mgB3DzF/jL7M4E1u+3P+3g30CHRdvqBP5Rzgc/2As/9taSch84o+zIv+4/Hle2nNbI9rn0ngO7NS6alJwGTVyvnFNblSU8CnnoHOHAaWPeM5fnH5L+VnH9Mu0+Ztv4nYOcR22XVc5XvjjrezsZfSpbb+IvyvpuvO+NTYPw9wJTi1zmi/K1ua+MvynlSi3hgz/GSMlmXoVawct4z4m0gK6f0spsfp6Pjem0jcFNjZbq98y0RYOy7QFKM43Owc1nKuq4snxQDvLdT+T3KvJyH69evIzw8HCEhIQgJDkB0YC7e2+l4/UYxGpy4WAANgN8vAB0m2P9+BOiB8CDXj+FCdgBCQkLsbwzFMXnSRCTV0SI61Gh7XHW0mDxposdissFgwPadu3DhwgWHy8TExLj0W+Er4uPjER8fX+pyFfHe+Xw8LlMq7oY1a9aYutkdO3ZMlixZ4pX95OTkiE6nk9mzZ1tMVwcP2bJli8X08tbuw07NoXWtojpNHeU5yF+5hZGjWr8gf2Wgq/IObAUoXYYAiKF4ekDxcyQgzYpbdQDl+t6/A/I0IFPMa9Gtam7Mu0FbT3O2fCxsW5W+h9JdSG1x2g3lOuQkO8vugtJi6qh1Kql43V1mf++GZetVaPF8e9tS950I5TpoZ61gekB2Fm/PlbKr6wUV79tR2dX17JVvByB1nXze6me73Y3l68J2sI3z58+Lvni+vrT96XSm68cd/h+W8tr64Q/IlStX7H7XcnNzJSYiwun6dSIjPVqbnJubK9HR0U73Wbt27RpVg23uVE6ObL582e5tsirivTNdEzVkiFvXRA25zfKaqMGDHV8TNQTV5xplkYqLta6oyvHY2Xgf9lr73G1BtF7f0Trm69nbh7vbd2V5e4Nrfj/VcnDN3dOUnmf2zlVKuzxLvUXjrmmOb9cYGgDZNdX+ttR9J0YX94IrpZUUUJ5dKbv5edauaWW/tMzVczB3l68bE2X6TczKyhKDXuvS+obi+WpvwvkDlde1oLRSx0TXcvsYAvydX1/MFl4y5+vxWCMiAi+5fPkyXn31VWRmZsJoVJo58/Pz8csvv+DHH3/0+P5uv/125OTkYPv27aZarGeeeQaLFy/G6dOnEWhWVbF37160b98ee/bsQbt27dzaj1p73SAK+GBUSUshoPxC3PcKcOoScKcGmDIFyCsEuk5XapHVFlt79DrH8/20QKFZS7EfACOUfWsBGKDUsOgB/AVAIID2AG6B0vJ7AUorcCiApcXbeABAcPFrTfH2ugCoA+AD2DkuABcBfF28T1eWP4mSFmuLYwVQ4OS1s2Wdzbe3rD+AfCfz1Wml7UfdjjtlL22+u9vyA1Do5HVpy5szaLU4f+UKwsLCICJo3Lgxjh496mRrxWXUatHUaDT93wiAvwP4LTISj0ydio2zZ+PUqVOlbsdaVlYWwsLCbKaLCNq0aYOff/4Z9n6uNBoNWrVqhR9++MFjLcoAcOrUqVJrYePi4jy2P19SEe/d8uXLMXjIECA8HMbWraH58Udorl3DW0uW4JFHHrG7/JAhgxEeCLRuYMR3R4GcfCAiQoM2bQQ//qjBtWsaLFnyFgBgyODBCAfQ2mjEjxoNrmk0WPLWW3a37YryxJzSVHSsdUVVjcdGAbpMA2LCgNBAIDsPeG+k/fVCApQX2fm2y6jbqRPueL8hAcCyYSWtlwLggVeBM1eVM77kOsCy4QA0yvRgA7C0eHlXtn/xOvD1c4BW69ryJy/Z73Fmfe7h7FzE2Tzr+faW9dcB+U7mq9NK24+6HXfKXtp8e/MiwkOwdt1GXBHBqbw8BFy9iqyrVzFyzx7lgyqOWRoAkyMjEaHXIyoqCrVq1UKdOnVw9uxZXLlyBZ///jv+e/68afkhxT1x1OWsfxO/++47HDlyBJcuXbLpmQEAtWvXxr/++U8YNUD7ROC7qUpxRID0fwN7jwM60eCHX35Bbm6uxbo///yzKUZLUBDyQkJQx98ftfR6AEBKSgrS0tIcv4lgfCRLvhyPvZoo//Wvf8WxY8dQu3ZtXL16FYmJifjiiy/w2GOP4aWXXvL4/r7++mt0794df/vb3zBo0CBs374dzz//PGbNmoWnn37aYtnyBmZXf8Qt1oMSsMqjrEmZq0meq8liWctTWmJHQGBgIPq98AKWXbyoTMjKwv/5+2PT668jJyfHrW0FBQVh7Nix0Ol0pmnh4eGoVauWRTDMy8tDw4YNce7cOYfbioqKwvjx4/Hkk09iHZQu46r1ULqNf/rppxg6dKjT7YSGheHRF15AXHCwS4HZlbLVrVsXx48fr1Fdrwg4cuQIBr31FrYdP47OCQlYOngwkpOTnS7/1sxBOP7rNiQ064zuD07Cpk1TcPz4NiQkdMbgwUtN6x85cgRvDRqE49u2IaFzZwxeutTptkvjzUS5omOtK6pyPHY1GdPrlMSj0E5yab4dZ+vbm69uF7DctrtJn6PjcrU8Wi3Qrl0H3LhxA4WFhTh+/CgKCx2fpfjpgLqxcYiMjERqair++OMPZGdn49yff+JsQQEQEADk5SE2MBBnjEYgPFx5BAQAtWvj6fh4tExKQvPmSp/xs2fPQqPRoE6dOqZ9qEnc7t27cfTsWfySnw9cvAhkZwNFRYBOhyi9HqEBAahduzauX7+OkJAQ5OXl4dKVK7h2/ToKi4pgSg3Vy3kMBjSrWxcN6tfHzTffjKysLPz555+4ceMGgoODUb9+fYSFhSEqKgoAHCawALD5yhXcvm+fzfuzuXVr3BYZaTM9MzcXDXfuhPm/kQ7A8YwMxJXj0p1XXnkFo0ePxrpnlC7uqvU/KV3JX3nlFYwcObLM2ydyh6/GY69eo7xlyxYcOnQImZmZmDp1KlavXo0PPvgAb775plf2d/vtt2P16tWYNGkS7rnnHtSvXx9z5szBU0895dH9iEipLVfWSTJQ/iQZcJ6UOpuf72C+09caDXRaLYqKSg7GPEmGVotCEWg1Guh0OhSYJWMIDgaMRgT7++NqQYES4IqKUKjXAzExQGEhYDRCe/IkjEVOIrtOBzRqpGyvQQPgyhUg36wUBgPg7w8EBaFTWBh26PXKshoNhqSkWNTYAs4D84EDB3D16lXsLyzEiQsXlMAMIDo8HEnh4QgMD8eWsDDg/PmSqttr13CTRgNNYSEuazQ48OefQEgIEBCA9JgYNE9IQKvERHRu29a0P3tlMFcUFoaMzEyLaV8C2P7Pf+LSxYu4++efS/6XLl+G5sYNvJKcjOQ6dWy2qQb5zNxcHM7JQePAQLuB2WAwYNcu59ex1K5dGz27d3d6rfAzY8bg+++/x0U1ybfy8YULmH7tGmbXrg0tgMWpqRgcG+twn66WraZdn0SK5ORk9JgxA9sA9IAyVkJpy88Y1QP4eRvQsgfQshu6ddsB2NlCcnIyZvToAWzbBvToAZQjKHtbRcdaV1TleKwmi86SSm/M12i10Go00Pr5oaioCDqdDgF6PfIKClBgNCrx16BX4otOVxKPiysUodcDauugTod8vR4hgYG4UVQEBAaioKAA0eHhuC0pCVFRUQgNDUVYWBhORkZiydmzKAgNhSYyEhMaNsQ9tWvbJIHmrYTn8vJwKi8P8QYD6hT/tjprJczMzcWRnBwkF8eYt86cwfCDB1EEJSlc5MJvPQCLk1ZHCeaPdhLMt86cwbCDB2GE0uNtYJ06WHXunNv7d0XjwEBTrzrzciWbX2Bp5nBODqzrWooAHMnJKXOiLCL473/ecXqd8H/eXYURI0Z4tKcVkSO+Go+9migbDAbUqVMHISEh+PnnnwEAf/vb3/D44497bZ99+vRBnz59vLZ9lSca4ndlZeF/167h5rAwpBUPbqIGm2CtFuk//FCSEJ0/D1y9ipmJiajn74+BBw9aJN7ayEic+L//Q1xAADJzc7H98GHkXb6MHBFToPv4wgU8f+KE8oMdGQnUrm1RHh2AGY0aYdyxY6ZgYx5cnAU+RzWsIxs2xHR1n3b8JyoKyQUl6fmk33/H55cumV53SUjAVn9/m/XGxMVhXmamqSxquc0tAzDJSY2tmjx2a9bMtMyurCx0/OEHi+UuAviybVukhYc7fA/UgG5uN4APy1BjvPnKFRitEuUiANnR0bgeFgbJy7OYJwBaOqjJBmxPIBwlp+aDQ9hLrF0dpCkmJgYNGjSwmZeZm4vnd+6EFP/fGQEMP3gQPSMjS32PXB24gqgmqoxY64qqEo+dtQCez8/HAwcO2MwbExeHJsHBFr/3RlhWeGsB7CyODQDw/rlzdrflqKVRZS8hNGedlAGW8Uc9l0gOCECwn5/F77YpNrVQRgoTAC8AGGYW91Tl+Z2NCwiw2N7g2Fj0jIy0SJ7Lss3Fqak2Mdd6W5m5uaYYByjv1apz57CjbVv8aTSWef/lLZfK3cTaFfn5+cg8dRKZ54wOBvEyIt/vFPLz81mJTFQOXk2Uk5KSsHnzZnTt2hUFBQU4deoU/P39kZ+fX/rKNUBaeLgpwKrMg82bqaklCU5MDBZ37mxKcPJTUhz+SMcFBOD+li1t9tcOwLDiRNzeyUERgGeOHTOdCFgnMs4Cn71AAABTT5yABva7nesA3NS4scV2PmvXzqICIdZgsFujPCouDqPi4kxlcbfG1lHyuM3OtUAA8L9r15AWHu7wPXBl/6W16qocBdXd16/jGavKAHWeo4Br7wSitOTU0Xuj0WgQFBGBbAetxQAQHBHhsPbaG7XqRI2snksV0sjy2dkWGjWyfK6iGGudc5aoJNuZp4USY+ICAix+79dfuWITd81j+E3h4WVKiKwTL3M6ADOLK4KLisv2ZHw8RtWvb/rdTAsPx0/Z2fjr/v02v9uV+btrnTyXhSsJt6Nj/NNodFpB4e1yqdxNrF3BkaCpKvLFeOzVRPmZZ57B3XffjV9//RVDhgxBRkYG/Pz88H//93/e3K3PcPZDXNbaWjVwZebm2j05KC2gWgc+8+TPUaCX4m0Pj43F4jNnYITzQGFdgeAswJiv7+oJirPksbOdwaQA4Gaz6faCf2k1xq626qrbtz7mGY0a4Zniln5zaqu/o8/f3ZOk0hLrvXv34sujR/HPQ4ds1l2UkoJeyckOA7M3atWJmlg9lyq8ieWzsy00aWL5XEUx1jpXWqJSWoxR/y4t7jrbT2kVpYNjY9EqKAhzMzPxwYULEFi2GveLiXG4X2e/277wu1tawl1Zx+hORYAnWtitsacVVTW+GI+9mijfc889OHToEOrUqYNJkyahadOmuHr1KgYOHOjN3foUZz/E5amttRfQZzZqhLFmLcqAkog5Cjb2kr/jGRn44MIFPGk1erIRwAMxMRjfsKFNoHDlBKK0AONOja2z5DE5MBA9IyOx/soV07yBderYtPy7s/+ytOpaH7O9MgPAf5s2xX0OrnUGPH8tVXx8PO4uvr7Yepu9Sulm7o1adSJirHWFpyqeS4u79rblSkWp9TJPFfeaclRJbc7Z7/ZtkZE+/7tbXWKLJ1rYiahieTVRBmAx8MP999/v7d2RG6wDOgCMtera6+jKL0fJ3/GMDNxXuzbGHD1qNzmzDhSutrS6EmBcPdlxlDzuun4d3fbtM912677oaIyJjy81SS5t/2Xt+mZ9zPbK3KkcCbw9riTW5Tkp8UatOhEx1rrCXhwxr6j1ZDddNXa6UlG6KysLQ83GHTECmJeZiVEu3l6ntN/tmvC7WxOOkYgqntbbO1i8eDFatWqF6OhonDx5En379sX169e9vVtyUVxAAG4rDtiHc3JsEmOBktBZKy35W5yaCnUMbOvW1c1XriAzN9fhCUSm1T3/yno8zpaxLt9Ms0HMAOW4V1+8iFg73YjNj8GV/asnMebc7Rbm7D0tzeDYWBzPyMDm1q1xPCPD6cifru7HnW3a20dpnxERuYex1n1vnTmDhjt34vZ9+9Bw5068deaMx7f5Smamw1ipLp9hPnCnnWVK48rvdk343a0Jx0hEFcurLcovvfQS3nrrLYwdOxajRo1CeHg4Ll68iFGjRmHp0qXe3DWVgTvddMtSg23devxkfHylDTLiStdme2WxPoaZjRqhQ2io0wG6PNUtrDw15u52+VLfiyIAv2VnIzM31+71eDwhIap8jLXuK8slMWXZ5rzMTJvBLNVYab087CzjKraoEhF5nldblF9//XV8+umnGDhwILRaLcLDw/HBBx/giy++8OZuqYzcabV0twbb3gnES6dOlbultTzMy+dKq6+9Yxh77JhLrRHlaYF1VGZvUI/R/KRu7qlTHmttISLPY6x1n7PKUU9v86n4eLux0tHYE6UN0ugIW1SJiDzLqy3KV65cQUJCAoCS+xxGRkaiqMh6XGSqKtyplXZnWXsnBEbY3g+5sgbgcKXV19FJDeBaa0R1aIF1dIyeaG0hIu9grHWfN0ZKdrTNUfXrY1T9+jax0t7y1vdmJiKiyuPVFuWOHTti2rRpAGC6t+obb7yBdu3aeXO3VE7u1Eq7uqyjFttRcXEeaWn1hNJafe0dg7nytkZUBc6O0ReOj8gXMda6rzzjPpRlm/Zipb3lF1vdm9nZmBhERORdXm1RnjdvHu644w4sX74c169fR+vWrXHhwgVs2LDBm7ulKqi0Ftuq0kpZ2u24HN0rGqh+96a0Rz1Ge9fN+cLxEfkixtqy8cZ1ve5u09nyrt4VgoiIvMOriXJqaioOHDiAL774AidOnED9+vVx9913I5xdimokXxhsxPwYdl+/jnHHjlV6t3FPU4/xlczMKtEtnoicY6wtO29cEuPuNh3dtsrTg40REZF7vJIof/PNNxav69ati7p16wIA9u3bBwC49dZbvbFrquKqw3W6pTHvRtcvJqZKJ/7m9wh198RtTnIyRsXFVenjI6rJGGt9l6t3YiAiIu/xSqJ82223QafTQaPRmAYWMafRaJCfn++NXRNVqKqc+Hui215VPj6imo6x1nd5Y7AxIiJyj1cG87r//vsRGRmJhx56CJs2bUJBQYHFg4GbyLscddvjgDBEvoOx1nd5Y7AxIiJyj1cS5f/+9784cuQIbr75ZowfPx5JSUmYMmUKjh8/7o3dEZEVb9wjlIiqFsZa31banRiIiMi7vHZ7qLCwMAwdOhTffvst1q9fj6KiItx+++3o0qULli9f7q3dEhEc346L3faIfAtjrW9z53aNRETkWV69j7IqOTkZU6dOxaJFi3D58mUMHjy4InZLVGOx2x5RzcNYS0RE5DlevT0UAPz8889YtWoV3n33XRgMBvTv3x8ff/yxt3dLVOP5wu24iMg1jLVERESe5ZVE+fTp03j33XexatUqnDx5En379sW7776Lzp07e2N3ROQAR60m8l2MtURERN7jlUS5YcOGqFWrFh544AHcc889CCg+Ud++fbtpmZtuuskbuyYiIqoRGGuJiIi8xyuJclFRES5cuIAFCxZgwYIFNvM1Gg2Kioq8sWsiIqIagbGWiIjIe7ySKBuN1jemISIiIk9irCUiIvKeChn1moiIiIiIiKi6YKJMREREREREZMariXJmZqbdv4mIiMgzGGuJiIg8z6uJcrNmzUx/t2rVypu7IiIiqpEYa4mIiDzPq4myiNj9m4iIiDyDsZaIiMjzvJooazQau38TERGRZzDWEhEReR4H8yIiIiIiIiIyw0SZiIiIiIiIyAyvUSYiIqrGGGuJiIg8z6uJ8tq1a01/f/75597cFRERUY3EWEtEROR5ft7c+C233GL6++abb/bmroiIiMrmDIAzB2ynHzhg+WwtNlZ5VDLGWiIiIs/zaqJMRERU5S0CMGWA4/kDHMybNAmYPNkbJSIiIqJK5tVEeerUqXjyyScREhLizd0QERGV3XAAvVcBaOreelWgNRlgrCUiIvIGrybKL7/8MsaPH+/NXRAREZVPLIDYpgDaVXZJyoSxloiIyPO8migPGDAAY8eOxUMPPYS6detCo9GY5tWrV8+buyYiIqoRGGuJiIg8z6uJ8uuvv46ioiLMmzfPFLhFBBqNBkVFRd7cNRERUY3AWEtEROR5Xk2Ujxw54s3NExER1XiMtURERJ7n1UQ5Pj4eWm3JrZrPnz+PmJgYb+6SiIioRmGsJSIi8jxt6YuUzbhx4zBkyBDT64sXLyI+Ph7PPfect3ZJRERUozDWEhEReYdXEuWFCxfi3XffRf/+/U3ToqKisGbNGixfvhxLlizxxm6JiIhqDMZaIiIi7/FKorx48WL897//Rbdu3UzTNBoNevXqheXLl2PhwoXe2C0REVGNwVhLRETkPV5JlE+cOIGbbrrJ7rxu3brh2LFj3tgtERFRjVEZsfb69esYO3YskpOTERwcjBYtWuC1116D0Wi0WG7dunXo0KEDgoKC0LBhQ8yYMQMi4vHyEBEReYtXEmW9Xo/s7Gy78/Lz8+Hn59UxxIiIiHxeZcTaBx98EEuXLsUTTzyBTz/9FPfccw9GjRqF559/3rTM9u3b0bt3bzRt2hQfffQRHn74YYwfPx4vvPCCx8tDRETkLV5JlG+++Wa88847due98847aNWqlcf36WotNxERkS+o6Fi7d+9efPHFF3j99dfx+OOPo1u3bpg+fTqGDx+O2bNnm1qMp0yZgjZt2mDlypW48847MX36dDz99NOYOXMmcnJyPFomIiIib/FK0+7YsWPRo0cP5Obmom/fvoiJicGZM2fw/vvvY9KkSXjvvfc8vs8HH3wQO3fuxJQpU9CkSRNs3rwZo0aNwuXLlzFx4kSP74+IiKgyVUasHTZsmMU10QCQkpKCGzdu4Pz584iIiMCWLVswZcoUi2X69u2L2bNnY9u2bejRo4fHy0VERORpXkmUO3XqhDfffBOPP/44Ro8ebZoeFRWF119/Hb169fLo/tRa7vfffx/33XcfAOX6rCtXrmD27NmYMGECNBqNR/dJRERUmSo61rZr1w6LFi2ymf7RRx8hJiYGtWvXxsGDB5Gfn4+UlBSLZZKTkwEAhw4dYqJMRETVgtcuFu7Xrx/69OmDb7/9FhcuXEC9evXQqVMn6PV6r+yvtFruOnXqeGW/RERElcVTsfbPP//EmjVrHM6vU6cO7rjjDpvpL730Er755hu8/PLL0Gq1uHr1KgAgLCzMYrnQ0FAAwLVr1xzu48aNGzbzDQYDDAaDq4dBRETkMWVOlLOyslBYWIioqCiHyxgMBpvk1RtcqeUmIiKqDqZOnYp7770XLVq0cGl5T8TaCxcu4OGHH3Y4v0uXLjaJ8iuvvIIxY8bgwQcfxMiRIwHANC6Io15cWq3joVG6dOliM23SpEmYPHlyacUnIiLyOLcT5Y0bN2LMmDH45ZdfAACJiYn497//jb///e8eLxzguVpuR1iDTUREVcnkyZORnJzscqLsCQkJCS7fvsloNOLpp5/GSy+9hP79+2P58uWmxDgiIgKAbcvx9evXAQDh4eEOt7t161a0adPGYhpjMRERVRa3EuUdO3bg7rvvRlFREZo3bw6dTofffvsN//jHP5CTk4Phw4d7vICequV2tr411mATERHZys/PR79+/bBmzRqMHj0aL730kkXrcVJSEnQ6HY4cOWKxnvq6WbNmDrcdEhJi02WbiIiosrh1e6g5c+YgMjISu3fvxk8//YQffvgBhw4dQps2bTBt2jSvFFCt5Xb02LJli2lZo9GIp556CqNHj8ZDDz2EFStWlDqI19atW5GVlWXxePbZZ71yLERERNXZwIED8fHHH2PevHmYN2+eTYwNCAjArbfeio8++siihfrDDz9EREQEOnbsWNFFJiIiKhO3WpS/++47PP7442jbtq1pWnx8PKZPn46//OUvOHbsGBo1auTxQrqitFpuR1iDTUREVU1VvFPDJ598gv/+97/o3bs3MjIysHPnTov5bdu2hcFgwIQJE9C9e3fcf//9GDRoELZv3445c+Zg1qxZCAwMrKTSExERucetRPnixYto2LChzfTWrVtDRHDmzJlKS5TNa7nNb5NBRERU3QwYMAADBgxweXmNRoPCwkIvlghYvXo1AODTTz/Fp59+ajP/999/R0JCAm6//XasXr0akyZNwj333IP69etjzpw5eOqpp7xaPiIiIk9yK1EuKCiwe8sJdbCNvLw8z5TKTa7WchMREVUHUVFRCA4OruxiWFixYgVWrFjh0rJ9+vRBnz59vFwiIiIi7/HafZQrkqu13ERERNXByy+/jIceeqiyi0FERFRjuTWYV1W1YsUKpwN+MUkmIiIiIiIiV7ndorxt2zab66Bu3LgBANiwYQMyMzNt1vHWPZaJiIiIiIiIPM3tRHnx4sVYvHix3Xlz5syxeC0i0Gg0TJSJiIiIiIio2nArUZ40aZK3ykFERERQYm2rVq0quxhEREQ1GhNlIiKiKoSxloiIqPL5xGBeRERERERERJ7CRJmIiIiIiIjIDBNlIiIiIiIiIjNMlImIiIiIiIjMMFEmIiKqBg4cOICCggLT62+++Qb79u2rxBIRERH5Lo8kygzeRERE3lFQUIABAwagRYsWOHz4sGn6G2+8gXbt2mHEiBEQkUosIRERke8pV6LM4E1ERORd8+bNw7vvvot+/fqhdu3apunPPvssBg0ahIULF+LNN9+sxBISERG56gyAvYDuABAJ5Rl7ARwonq++Nn8csLchr3PrPsrW1OD94IMP2gTv4OBgLFy4EC1btsSwYcPKXVAiIqKa6O2338YDDzyAd955x2J6y5Yt8eabb+LatWt4/fXXGWuJiKgaWARgChAO4C4AGGA13/p15SlXi7J58DZPlNXg3bdvX7z++uvlLiQREVFNdfz4cdx+++0O53fr1s2iVxcREVHVNRzAHiBrFbAWyjP2lPJYVSklLVeL8vHjxzF69GiH87t164YvvviiPLsgIiKq0SIiInD06FGH80+ePImgoKAKLBEREVFZxSqPIgBXABQ1BdCucovkQLlalBm8iYiIvKt79+5YuHAh9u/fbzPv8OHDeO2115y2OBMREZH7ytWirAbvhx9+GM2bN7eYpwbvnj17lquARERENdnEiROxZs0adOjQAb169UKTJk2g0Whw8OBBrF27FjqdDlOmTKnsYhIREfmUciXKDN5ERETelZycjG3btmHkyJH4+OOPLe4mkZGRgQULFiA1NbUSS0hEROR7ypUoM3gTEVFFOFP8sHbA6tla8ZVQ1V7r1q2xdetWXLp0CSdOnEBBQQESExMRExNT2UUjIiLySeVKlAEGbyIi8r7im0k45OhmEpMATPZ4aSpPVFQUoqKiKrsYREREPq/cibKKwZuIiLxlOIDeZVjPF1qTiYiIqOJ5LFEmIiLyFl/pQu0KrVYLjUbj1joajQaFhYVeKhEREVHN41aizOBNRETkXbfeeqvbsZaIiIg8y61EmcGbiIjIu7Zs2VLZRSAiIqrx3EqUGbyJiIgq3qlTp7B69WrcuHEDRqPRNL2wsBBXr17Fhg0b8Ntvv1ViCYmIiHxLua9RZvAmIiLynq+++gq9evVCYWEhRAQajcZ0O0a1l1e9evUqs4hEREQ+p1yJMoM3ERGRd82YMQP+/v54+eWXAQAjRozAxx9/jGvXruHVV1/F/v378e2331ZuIYmIiHyMtjwrq8F7wYIFeO2116DRaPDJJ59g5cqV6NChAwIDAxm8iYiIymHv3r0YOnQoHn30UQwePBharRZ6vR79+/fH5s2bUbduXUydOrWyi0lERORTypUoM3gTERF5140bN9CiRQsAgL+/Pxo1aoSffvoJABAUFIRHHnkE27Ztq8wiEhER+ZxyJcoM3kRERN5Vq1YtXLt2zfQ6MTERv/76q+l1vXr1cPr06cooGhERkc8qV6LM4E1ERORdGRkZWLVqFXJzcwEAqamp2LZtG4qKigAAP//8M0JDQyuziERERD6nXIkygzcREZF3jR49Gj/++COSk5Nx+fJl9O/fH7///jvuuOMOPProo1i4cCE6d+5c2cUkIiLyKeVKlBm8iYiIvOu2227DBx98gDp16iAiIgJpaWl4/vnnsXXrVixatAgNGzbErFmzKruYREREPqVciTKDNxERkff16dMHe/bsgVarhO1nn30WJ06cwA8//IBff/0ViYmJlVxCIiIi31KuRBlg8CYiIqpo586dQ926ddG6dWv4+flVdnGIiIh8TrkTZWsM3kREROW3dOlStG3bFkaj0WbemDFjEBcXh0WLFlVCyYiIiHxfmRJlBm8iIiLveeaZZzBkyBDs378fhw8ftpmv1+tx+fJlPPbYYxg1alQllJCIiMi3uZ0oM3gTERF5z+eff445c+bgtttuw6FDh5CammqzzNKlS3H06FGkpaVhwYIF2LhxYyWUlIiIyHe5lSgzeBMREXnXwoULkZCQgPXr1yMhIcHhcvHx8Vi/fj2io6OxYMGCiisgERFRDeBWoszgTURE5F27du3CwIEDodfrS102PDwcAwcOxM6dOyugZERERDWHW4kygzcREZF3Xb9+HXFxcS4vn5KSgqysLC+WiIiIqOZxK1Fm8CYiIvKu2NhYnDlzxuXlL1y4gJiYGC+WiIiIqOZxK1Fm8CYiIvKu9u3b45NPPnF5+Y8++ggpKSleLBEREVHN41aizOBNRETkXY888gj27NmDefPmlbrsvHnzsHfvXjzyyCPeL5iV69evo2HDhnb3vW7dOnTo0AFBQUFo2LAhZsyYARGp8DISERGVlVuJcnUJ3kRERNXVX/7yF9x7770YM2YM+vXrh//9738oLCw0zS8oKMDWrVtx//33Y8yYMejSpQv69etX4eV84okncPLkSZvp27dvR+/evdG0aVN89NFHePjhhzF+/Hi88MILFV5GIiKisvJzZ2Hz4P3dd99hxIgRSE9Ph5+fspmCggJs374dr732GlavXl1pwfv69eto0aIFunbtiuXLl1f4/omIiMpj+fLl0Ol0eP/99/HBBx/Az88PUVFRKCwsxJUrV2A0GiEi6NOnD5YuXWqKwxXlyy+/xPvvv4/w8HCbeVOmTEGbNm2wcuVKAMCdd96JgoICzJw5E08++SQCAwMrtKxERERl4VaLMqAE7759++L999/HrbfeiuDgYNSrVw8xMTEICgrC7bffjg8//BD33HMP1qxZU+HBG3Bcy01ERFQdhISE4L333sO6devw4IMPomHDhrh27Rry8/ORkpKC4cOHY+vWrVi9erXdZNWbrly5gqFDh2L27NmIiIiwmJeXl4ctW7bg3nvvtZjet29f3LhxA9u2bavAkhIREZWd21msGrwHDx6MFStW4Pvvv8fp06fh5+eHlJQUdOnSBQ8++CA6d+7sjfKWylktNxERUXXSo0cP9OjRo7KLYWHEiBFo2rQphg8fjpkzZ1rMO3bsmCmZN5ecnAwAOHToUJU7HiIiInvK3NxbFYO3eS23dfAmIiIi+/7880+sWbPG4fw6dergjjvuwJo1a/DJJ5/g559/hkajsVnu6tWrAICwsDCL6aGhoQCAa9euOdzHjRs3bOYbDAYYDAZXD4OIiMhjKr5ftBc5q+UmIiIi+y5cuICHH37Y4fwuXbqgTZs2+Oc//4k5c+YgISHB7nJGoxEA7CbRAKDVOr7iq0uXLjbTJk2ahMmTJzsuOBERkZdU+UTZU7XcjrAGm4iIarqEhIRSb9/Ut29fNGvWDIMHD7YYhVtEUFhYCJ1OZ7pm2TquXr9+HQCcXha1detWtGnTxmIaYzEREVWWKp8oe6qW29n61liDTUREZGn16tUAAH9/f4vpK1aswIoVK7B582ZkZGRAp9PhyJEjFsuor5s1a+Zw+yEhITZdtomIiCpLlU+UPVXL7aiVmTXYREREpdu1a5fNtN69e6N9+/aYNGkSUlNTERAQgFtvvRUfffQRxowZY4q9H374ISIiItCxY8eKLjYREVGZVPlE2RWu1HLfdtttdtdlDTYREVHpOnToYDPN398fUVFRFvMmTJiA7t274/7778egQYOwfft2zJkzB7NmzeI9lImIqNrwiUTZlVpuIiIi8r7bb78dq1evxqRJk3DPPfegfv36mDNnDp566qnKLhoREZHLfCJRdrWWm4iIiDzn+PHjdqf36dMHffr0qdjCEBEReZDj+zQQERERERER1UA+0aJsj6NabiIiIiIiIiJn2KJMREREREREZIaJMhEREREREZEZJspEREREREREZpgoExEREREREZnx2cG8iIiIiIiIqBLlnFEe1rIOWD4DwNkLwLmLyt+GaCCgtvL3gQOWz9ZiY5WHhzFRJiIiIiIiIs87vAj4ZYrj+TsGlPy9GsBHTrY1YID96ZMmAZMnl6FwzjFRJiIiIiIiIs9rPByI6+3asm0uAP+y06JcGi+0JgNMlImIiIiIiMgbAmOVhytqAWjm1dK4hYN5EREREREREZlhokxERERERERkhokyERERERERkRkmykRERERERERmmCgTERERERERmWGiTERERERERGSGiTIRERERERGRGSbKRERERERERGaYKBMRERERERGZYaJMREREREREZIaJMhEREREREZEZJspEREREREREZvwquwBEREQel3NGeag0FwDtRSDndyASQM6XQNYBQPs7EArg+peA8QBgiAYCapttKLb4QURERGeKH9YOWD1bq47RlIkyERH5nsOLgF+mlLxuWfwIB1AfACZaLh9q9dpkEoDJni8fERFRNbQIwBQn8wc4mF4doykTZSIi8j2NhwNxvUteay4AWRdLX89uizIREREBwHAAvUtdylZ1jKZMlImIyPcExioPIiIi8pjq2IW6rDiYFxEREREREZEZJspEREREREREZpgoExEREREREZlhokxERERERERkhomyB+Tl5WHy5MnIy8ur7KJ4XU051ppynACP1RfVlOMEataxUulq0v9DTTnWmnKcAI/VF9WU4wR881g1IiKVXYjKsHfvXrRv3x579uxBu3btyrWta9euITw8HFlZWQgLC/NQCaummnKsNeU4AR6rL6opxwlUn2P1ZMzxNYzHZVNTjrWmHCfAY/VFNeU4gepzrO7EHLYoExEREREREZlhokxERERERERkhokyERERERERkRkmykRERERERERmmCgTERERERERmfGr7AJUlpycHADAgQMHyr2tGzduAAB+/PFHhISElHt7VVlNOdaacpwAj9UX1ZTjBKrPsaqxRo09VILxuGxqyrHWlOMEeKy+qKYcJ1B9jtWdeFxjbw/1zjvvYMCAAZVdDCIiqkFWrVqF/v37V3YxqhTGYyIiqmiuxOMamyhfvHgR69evR0JCAgIDAyu7OERE5MNycnJw/Phx9OzZE9HR0ZVdnCqF8ZiIiCqKO/G4xibKRERERERERPZwMC8iIiIiIiIiM0yUiYiIiIiIiMwwUbZj3bp16NChA4KCgtCwYUPMmDEDpfVQX7VqFZo3b47AwECkpqZiyZIlNst8//336NKlC0JCQlC3bl2MGTMGeXl53jqMUrl7nPn5+ZgxYwaaNGmC4OBgpKamYurUqcjPz7dYrm7dutBoNDaPs2fPevuQHHL3WH/77Te7x9CkSROL5arzZ7p8+XK7x6g+3n77bdOyVfEzVZ06dQoRERHYsmVLqctWx++pOVePtTp/VwHXj7O6fk/JdYzH9lXn73hNicdAzYvJjMe2qvN3VVWjY7KQhf/973+i1+tlwIABsnbtWhk/frxoNBqZPn26w3Xef/990Wg0Mnr0aFm3bp3885//FACyatUq0zJHjhyRsLAwufPOO+WLL76QuXPnisFgkCFDhlTEYdkoy3E++uijEhQUJDNmzJBNmzbJ7NmzJSgoSAYNGmRa5uzZswJAXnrpJdmxY4fFIz8/vyIOzUZZjvU///mPAJDNmzdbHMOPP/5oWqa6f6bnz5+3+Yy2b98uzZs3l/j4eDl//ryIVM3PVHX8+HFJTU01fVbOVMfvqTl3jrW6fldF3DvO6vg9JdcxHjMei1Tf73lNi8mMx/ZV1++qqqbHZCbKVnr06CFpaWkW08aOHSshISGSnZ1td52UlBS57777LKbdf//9kpSUZHo9bNgwqV+/vuTl5ZmmLVy4ULRarRw/ftyDR+Aad4/z0qVLotFoZPbs2RbTZ8+eLQBMP+Br164VAJVyTI6U5TN95plnJCEhwel2q/tnas/LL78sWq1Wdu7caZpWFT/ToqIiWbp0qdSqVUtq1arl0g94dfyeirh/rNX1u1qWz7Q6fk/JdYzHjMci1fd7XlNiMuPxZofLV+fvKmOygl2vzeTl5WHLli249957Lab37dsXN27cwLZt22zWOX78OA4dOmR3naNHj+LQoUMAgPXr1+Mvf/kL/P39LZYxGo1Yv369F47GsbIcZ1ZWFv75z3+id+/eFtNTUlIAAMeOHQOg3GQ8IiICDRs29FLp3VOWYwWU42jTpo3TbVf3z9Ta2bNnMWHCBDz66KNIT083Ta9qnykA/PTTT3j00UcxcOBArFy5stTlq+P3VOXusVbX76q7xwlUv+8puY7xmPFYVR2/5zUpJjMeO1Zdv6sAY7KKibKZY8eOIT8/3/QPrEpOTgYA05fX3IEDBwDA6To5OTk4ceKEzTK1a9dGWFiY3e16U1mOMzExEQsXLkRqaqrF9I8++gh6vd60rR9//BGRkZG49957ER4ejpCQEPTr1w9nzpzx0tE4V5ZjBZTjyMrKQqdOnRAQEIC6deti3LhxKCgoAACf+Eyt/fvf/4ZOp8P06dMtple1zxQAGjRogCNHjuCll15CUFBQqctXx++pyt1jra7fVXePE6h+31NyHeMx47GqOn7Pa1JMZjx2rLp+VwHGZBUTZTNXr14FAISFhVlMDw0NBQBcu3atTOs4WkZdzt52vaksx2nP6tWrsXLlSjz++OOIjIwEoHxJMjMzkZaWhs8//xwvvfQStmzZgi5duuDPP//03EG4qCzHeu7cOZw7dw6//fYbHnvsMaxfvx7Dhg3DvHnz8Mgjjzjdrrrt6vaZnj9/HitWrMC//vUvREREWMyrap8pANSqVQtxcXEuL18dv6cqd4/VnurwXXX3OKvj95Rcx3jMeAxU3+95TYrJjMfuqQ7fVYAxWeVX2QWoSoxGIwBAo9HYna/V2tYrOFpHikc11Gq1TrcrIna3601lOU5rH374Ifr3748uXbpg5syZpunLli1DQEAA2rZtCwDo3LkzmjdvjltuuQUrVqzAo48+6oEjcF1ZjjUsLAwbN25Eamoq4uPjAQBdunSBwWDAhAkTMGHCBNOX3Fc+0zfffBNGoxGjRo2ymVfVPtOyqI7fU0+pLt9Vd1XH7ym5jvFYwXhcPb/njMmOVcfvqadUl+9qWVTX72ppql6JKpFaa2ddo3H9+nUAQHh4uMvr3Lhxw7SOo2XU5ext15vKcpzmXnrpJTzwwAO45ZZb8Nlnn8FgMJjmderUyfRFV918880IDw/Hvn37PFB695TlWAMDA9G9e3fTF1119913AwD27dvnc5/phx9+iB49eqB27do286raZ1oW1fF76gnV6bvqrur4PSXXMR4zHgPV93vOmOxYdfyeekJ1+q6WRXX9rpaGibKZpKQk6HQ6HDlyxGK6+rpZs2Y266jXHThbJzg4GPXr17dZ5sKFC7h27Zrd7XpTWY4TUGp7RowYgaeeegp9+/bFl19+iZCQENP8q1evYunSpfj1119t1svPz0d0dLSHj6R0ZTnWgwcP4o033rD5Iufk5AAAoqOjfeYzBYDMzEz8+OOPuP/++23mVcXPtCyq4/e0PKrjd9Vd1fF7Sq5jPGY8Bqrv95wx2bHq+D0tj+r4XS2L6vpdLVVFDrFdHXTt2lUyMjLEaDSapo0dO1YiIiIcDuffqFEjuf/++y2m3X///ZKSkmJ6/Y9//EMaNGggubm5pmkLFy4UnU4nJ0+e9PBRlK4sxzlu3DgBIE888YTFeqo///xTDAaDPPzwwxbT16xZIwBk48aNnj0IF7l7rF999ZUAkLfeesti+qhRoyQ0NFQuX74sIr7xmYqIrF69WgDIkSNHbOZV1c/U3ObNm126bUF1/J5ac/VYq+t3VeXKcVbX7ym5jvGY8bg6f89rYkxmPLZVXb+r5mpyTGaibOWrr74SjUYjffv2lS+//FImTJhgcQ+0rKws2bFjh+neZyIiy5YtEwDy6KOPytq1a+XRRx8VAPLee++Zljlw4IAEBARI165d5bPPPpMXX3xRDAaDPPbYYxV+jCLuH+cPP/wgGo1GOnToYHMz9B07dkhWVpaIiEycOFEAyJgxY2TTpk3y0ksvSVhYmNx9992VcpxlOdbCwkK57bbbJDw8XF599VXZsGGDjBo1SjQajcydO9e03er+maomT54sBoPB4Xar4mdqzt4PuK98T625cqzV+buqcuU4q+v3lFzHeMx4XJ2/5zUxJjMe+8531VxNjslMlO346KOPpGXLluLv7y+JiYkWH7D6z7Js2TKLdd544w1JTk4Wg8EgTZs2lRUrVths95tvvpH09HQxGAxSv359GTdunBQUFHj7cBxy5zjVL7Gjh/rlKSoqkgULFkjz5s0lICBA6tevL08//bTT2tOK4O5nevXqVRk9erQkJCSIwWCQZs2ayeLFi222W50/U9Wjjz4qderUcbjNqvqZquz9gPvS99ScK8da3b+rIq5/ptX1e0quYzz2ve94TYnHIjUvJjMe+9Z3VVWTY7JGpHiYOSIiIiIiIiLiYF5ERERERERE5pgoExEREREREZlhokxERERERERkhokyERERERERkRkmykRERERERERmmCgTERERERERmWGiTERERERERGSGiTIRERERERGRGSbKRERERERERGaYKBM58fnnn+Nvf/sbEhISYDAYEB0djbvuugufffaZ1/a5ceNG3HfffYiLi4PBYEBcXBx69OiB//znPygsLPTKPivjOF1VWFiItm3bYv78+R7ZXm5uLjIzM02vRQTjx49HdHQ0YmJi8Oijj+LPP/+0WW/s2LFo2rQpioqKbOY99dRTuOuuuzxSPiIissV4zHisYjymCiNEZOPatWvyt7/9TQBIu3btZMqUKbJ48WL597//LQ0bNhQAMmbMGI/uMzc3VwYMGCAApHHjxjJhwgRZsmSJvPDCC5Keni4A5Oabb5bTp097bJ+VcZzumjp1qqSmpkpBQUG5t7V3715JSEiQZcuWmaatWrVKAMiIESNk5syZEhwcLI8//rjFeidPnpSAgABZs2aN3e1evXpVoqOjLbZLRETlx3jMeGyO8ZgqEhNlIjvuv/9+ASBz5syxmZebmyu33XabAJAlS5Z4bJ+DBg0SAPLUU09JYWGhzfwlS5aIn5+ftGvXTvLz8z2yz8o4TnccO3ZM/P395b///a9Htrds2TIBYBFA77jjDklLSzO9HjNmjAQGBorRaDRNGzhwoNx0001Otz179myJjIyUq1eveqSsRETEeCzCeMx4TJWFiTKRlfXr1wsA6du3r8Nljh07JjqdTlq2bOmRfe7YsUMAyF/+8henyz3zzDMCQObOnVvufVbGcbrr8ccfl4iICMnLy/PI9uwF5iZNmsiDDz5oer1o0SIBIOfOnRMRkZ9++km0Wq1s27bN6bbPnj0rOp1OZs6c6ZGyEhHVdIzHJRiPGY+p4jFRJrLSv39/ASBbt251utyvv/5q0f2oS5cu0rx5c3nrrbckJiZGgoKC5Nlnn3Vpn2rt9ebNm50ud+nSJfHz85PmzZu7tF1nynOcSUlJsnv3brnlllskICBAYmNjZfjw4XLhwgXTctu2bROdTieNGzeW7Oxs0/SjR49KaGioNGzYUK5cueJwv5cuXZKgoCB55JFHLKZ37NhRwsLCLMqUl5cnwcHB4ufnJ9euXTNNNxqNUqdOHenevbsMHDhQAFg81OO58847Tes8//zz4ufnZ9r+XXfdJb1793b6Hqm6du0qsbGxHmthICKqyRiPLTEeMx5TxWKiTGQlOTlZ/Pz83K417dKliwQFBUlERITMmjVLXnjhBfnmm29cWrdx48bi5+cnOTk5pS6rXh+l1rCWVXmOMyIiQsLDw+Uvf/mLLFiwQIYMGSIajUZSUlLk+vXrpmXHjx9v6r4mIlJQUCDp6eni5+cnO3bscLqfd999VwDIJ598YjH9+eefFwAW7+1XX31lCraff/65afrOnTsFgLz22muyfft2GTZsmACQYcOGycqVK0VEZPr06eLv7y9ffPGF7Nu3T5KTk02B+uuvvxadTif79+936b159dVXBYB8++23Li1PRESOMR47x3jsGOMxeQITZSIrQUFBUqdOHbfX69KliykIuCs4OFhq167t0rLqYB979+51ez/mynucQ4cOtZj+8ssvCwCZPHmyaVpBQYF07NhRtFqtfPfdd6ZAPXv27FL3M2TIEAEgp06dspj+yy+/CAAZP368adozzzwjtWvXloCAAHniiSdM08ePHy8ajUYyMzNFxH5Xrxs3bkiPHj1Mgb1Zs2Zy9OhRMRqN0qFDBxk8eLDL7823334rAGTatGkur0NERPYxHjvHeOwY4zF5Am8PRWTFz8+vXLd96N27t9vriAj0er1Ly/r5+ZnWKY/yHufUqVMtXj/22GMIDw/H6tWrLfbxzjvvICgoCP369cPMmTNx1113YcyYMaVu//DhwwgICEBcXJzF9ObNmyMpKQkbNmwwTduwYQO6d++OtLQ0fPXVV6bpn332GTp27Ij69es73E9wcDDWrVuHgwcPYt++fdi3bx8aNWqE9957D/v378eUKVMgIpg1axYaN26M+Ph4jBw5EtnZ2TbbSk5OBgAcOXKk1OMjIiLnGI9dw3jMeEzewUSZyEr9+vVx5coV5OXllWn9unXrur1OfHw8Ll26hIKCglKX/eOPPwDAJmC5qzzHGR0dbXOcer0ejRo1wtGjRy2mJycnY/bs2fj9999hMBjw9ttvQ6PRlLqPCxcuICIiwu683r17Y8+ePbh06RLOnz+PH3/8Ed27d8dtt92Gn3/+GefPn8fJkyfx008/oU+fPqXuS6PRICUlBa1atYKfnx8KCgowfvx4jBw5EvXr18eKFSswfvx4TJw4EcuXL8dHH32EJ554wmY7ankvXLhQ6j6JiMg5xuPSMR4zHpP3MFEmstKlSxcYjUZs377d6XKDBg3CgAEDcObMGYvpag2zO7p27Yq8vLxS93nt2jXs2bMHzZo1Q0xMjNv7MVee4zQYDHaXLSwstHv8X3/9NQAgOzsbn3zyiUvl02q1MBqNduf99a9/hdFoxKZNm7Bp0yaICLp374477rgDIoLNmzfj888/BwDcc889Lu3P3Ouvv46rV69i3LhxAIAVK1agS5cu+Pvf/45u3brhn//8J95++22b8hUVFQEAdDqd2/skIiJLjMeWGI8Zj6liMVEmsvLggw8CAF577TWHy5w6dQorV67Ehg0bEBUV5XR73333HTp37oyIiAjUq1cPI0aMsOkmNGTIEGg0GrzwwgtOu3C9/PLLyMnJwaBBg0zTvvzyS7Rt2xahoaFo0KABJk2a5DCgeeo4z5w5gxs3blgsm5eXh99//x0pKSkW05ctW4YPP/wQ//rXv9CiRQuMHj0ahw8fLrV8sbGxuHTpkt3345ZbbkFUVBTWrVuHTZs2oXHjxmjQoAEyMjIQEhKCr776Cp999hmaNm2K1NTUUvdl7tq1a5g2bRqee+45U4306dOnUadOHdMytWvXRl5eHi5evGixrlpzHRsb69Y+iYjIFuNx6cfJeMx4TF5UGRdGE1V1ffr0EQDy4osv2sy7evWqaaRL84FC1EE1zF25ckVq1aol8+bNk4KCAjl16pS0b99eRo4cabPdJ554QgDIiBEjLG61oFq1apX4+flJ+/btTbc7yMzMFH9/f1mzZo2IiBw+fFjq1q0rb7/9ttePc8qUKRbLz5w502ZbR44ckZCQENMtKXbt2iV+fn7SsWNHu8dobujQoQJATp48aXf+3//+d6lbt64kJibKo48+app+9913S3x8vBgMBnnuuecs1lmxYoUAkLfeesvhfsePHy8NGjSQ3Nxc07Tu3btLenq66fXTTz8tBoNBioqKLNbdunWrAJAZM2Y4PTYiInIN4zHjMeMxVRYmykR2XLlyxRSA2rdvL88//7wsXrxYnn76aaldu7YAkMcff9xiHXuB+ccff5S//vWvFtNeeeUVadeunc0+CwoK5F//+pcAkJSUFJk0aZK89dZbMmfOHNO2b775ZtOIkaqsrCwREcnNzZX//e9/Eh0dLWvXrvX6cQYGBsrDDz8sCxcuNN0TMT093XR7i4KCAsnIyBCtVmtxe4bnnntOAMiECROclu3DDz8UAPKf//zH7vzVq1ebRsZcvXq1abo62icA2bVrl8U669evFwDStWtXWbJkic39FU+fPi1BQUGyfPlyi+nq6JwjRoyQWbNmSUhIiM0ooyIlt8r48ccfnR4bERG5hvGY8dgc4zFVJCbKRA7k5eXJ22+/LV27dpX69euLXq+X6Ohoufvuu+0GPnuB2ZrRaJTbb79dhg0b5nCZb7/9Vh566CFp1KiRGAwGiY2NlTvuuENWrlzpsNa3qKhIAgMDBYDcd999pdYOe+I4d+/eLWlpaWIwGCQhIUGee+45yc7ONi3373//WwDIk08+abO/Fi1aiE6nk23btjks1/Xr18VgMNgNgCLKbSQCAgJEq9XKlStXTNP3798vACQuLs5mnYKCAunfv78EBQVJSEiIHDx40GL+0KFDpWXLljY100ajUaZPny5xcXESHR0tw4YNs7g/pap79+7SsGFDh8dERETuYzxmPFYxHlNF0oiUc0x7InJJUVER/vWvf2Ht2rXYuXNnmUbjdEREkJ+fj8zMTNx7773o1KkT3njjDY9t39xtt92GrVu3lvt2GK4YPnw43nvvPZw+fRpBQUFe3195nDx5EomJiZg5cyaefvrpyi4OERE5wHjsPsZjqok4mBdRBbh48SLuvPNObN++Hdu2bfNoUAaU2ykYDAYkJSVhwoQJeP/99z26/cry/+3dPW7iUBQG0EvKmMZIKREFfRoXbIAlsIf8VJbSQQWYLICaFWQPaWlJGthDIlqEIop0ozdSMtJkQNaIc0oX715Xn74nWR4Oh7Hb7f6L91ksFpHnedzd3dW9CgDfkMc/I485R4oynNh6vY6iKCLP81gul9Fut4929vPzc1xfX/92m7zf76PVah1tRp06nU48PDxEVVXx8fFR9zrf2m63MZ/PYzabRbPZrHsdAL4gj39OHnOOFGU4off39+j3+zEYDOLp6SmyLDvq+UVRxNvbW1RVFYfDITabTUyn07i9vT3qnDqNx+PIsuyPv82o22QyiV6vFzc3N3WvAsAX5PG/k8ecG98owwk9Pj7GaDSKy8vLaDQav553u914fX09yoyXl5coyzJWq1VcXV3F/f19lGUZFxfuwQAgQh4Df09RBgAAgIQrLgAAAEgoygAAAJBQlAEAACChKAMAAEBCUQYAAICEogwAAAAJRRkAAAASijIAAAAkFGUAAABIKMoAAACQUJQBAAAgoSgDAABA4hNtCsqGo94PmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_Pet_P_Meas-Cr_Pet_P_NoCr, '.c')\n",
    "\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_31_T33_P_Meas-Cr_31_T33_P_NoCr, '^k')\n",
    "\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_Neave_T33_P_Meas-Cr_Neave_T33_P_NoCr, '^k', mfc='r')\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_32c_T33_P_Meas-Cr_32c_T33_P_NoCr, '^k', mfc='yellow')\n",
    "ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_32c_1996_P_Meas-Cr_32c_1996_P_NoCr, '^k', mfc='orange')\n",
    "\n",
    "#Temp\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_Pet_T_Meas-Cr_Pet_T_NoCr, '.c')\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_31_T33_T_Meas-Cr_31_T33_T_NoCr, '^k')\n",
    "#ax1.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_30_T33_P_Meas-Cr_30_T33_P_NoCr, '^c')\n",
    "\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_Neave_T33_T_Meas-Cr_Neave_T33_T_NoCr, '^k', mfc='r')\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_32c_T33_T_Meas-Cr_32c_T33_T_NoCr, '^k', mfc='yellow')\n",
    "ax2.plot(myCpxs1['Cr2O3_Cpx'].loc[sam], Cr_32c_1996_T_Meas-Cr_32c_1996_T_NoCr, '^k', mfc='orange')\n",
    "ax1.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax2.set_xlabel('Cr$_2$O$_3$ Cpx (wt%)')\n",
    "ax1.set_ylabel('Calc P$_{Cr=Meas}$ -Calc P$_{Cr=0}$ ')\n",
    "ax2.set_ylabel('Calc T$_{Cr=Meas}$ -Calc T$_{Cr=0}$ ')\n",
    "fig.tight_layout()\n",
    "\n",
    "## Adding error bars based on Ery's suggestion\n",
    "ax1.errorbar(1.5, 0, xerr=0, yerr=2.9, fmt='o', ecolor='cyan', \n",
    "             elinewidth=1, mfc='cyan',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Petrelli')\n",
    "ax1.errorbar(1.55, 0, xerr=0, yerr=5, fmt='o', ecolor='orange', \n",
    "             elinewidth=1, mfc='orange',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008 1997 T1-eq32c')\n",
    "ax1.errorbar(1.57, 0, xerr=0, yerr=5, fmt='o', ecolor='yellow', \n",
    "             elinewidth=1, mfc='yellow',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008 1997 T1-eq32c')\n",
    "ax1.errorbar(1.6, 0, xerr=0, yerr=1.4, fmt='o', ecolor='red', \n",
    "             elinewidth=1, mfc='red',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008 eq33-NP17')\n",
    "\n",
    "\n",
    "ax2.errorbar(1.5, 0, xerr=0, yerr=51, fmt='o', ecolor='cyan', \n",
    "             elinewidth=1, mfc='cyan',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='Petrelli')\n",
    "ax2.errorbar(1.55, 0, xerr=0, yerr=45, fmt='o', ecolor='orange', \n",
    "             elinewidth=1, mfc='orange',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='eq33-eq32c')\n",
    "ax2.errorbar(1.57, 0, xerr=0, yerr=27, fmt='o', ecolor='yellow', \n",
    "             elinewidth=1, mfc='yellow',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008 1996 T1-eq32c')\n",
    "ax2.errorbar(1.6, 0, xerr=0, yerr=45, fmt='o', ecolor='red', \n",
    "             elinewidth=1, mfc='red',  ms=5,\n",
    "             mec='k', capsize=5, \n",
    "             label='P2008 eq33-NP')\n",
    "\n",
    "fig.savefig('Cpx-Liq-Cr-variable-points.png', dpi=200)"
   ]
  },
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   "cell_type": "code",
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   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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