Fig2_Laboratory_Diferences_Using_1038.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b2a19ad-95f3-45c1-aa88-ecd4b785e285",
   "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": "markdown",
   "id": "b24cd763-0c8f-4cf9-a306-35ec7275c924",
   "metadata": {},
   "source": [
    "# If you haven't already, install Thermobar and Thermobar_onnx\n",
    "- You only need to do this once per PC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adcaf4f3-b7db-4122-b021-1291b718584d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!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,
   "id": "5bbb9916-6b5d-467c-b583-e4062c1b7dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.style.use(\"seaborn-colorblind\")\n",
    "plt.rcParams[\"font.family\"] = 'arial'\n",
    "plt.rcParams[\"font.size\"] =10\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\"] = 10 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"xtick.labelsize\"] = 10 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"axes.titlesize\"] = 12 # Overall t_MITle\n",
    "plt.rcParams[\"axes.labelsize\"] = 12 # Axes labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af144583-ebc0-43df-8101-10055a7fe5f6",
   "metadata": {},
   "source": [
    "## Reading in Gales Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "736f0d2b-0f44-4892-af06-7423e57acd65",
   "metadata": {},
   "outputs": [],
   "source": [
    "gale=pd.read_excel('Supporting_Data_1.xlsx', sheet_name=\"Gale_Differences\")\n",
    "gale_df=pd.DataFrame(data={'Sample_ID': gale['short'],\n",
    "                     'SiO2': gale['SiO2'],\n",
    "                        'Al2O3': gale['Al2O3'],\n",
    "                      'MgO': gale['MgO'],\n",
    "                     'CaO': gale['CaO'] ,\n",
    "                     'Na2O': gale['Na2O'],\n",
    "                     'TiO2': gale['TiO2'],\n",
    "                     'FeOt': gale['FeOt']}\n",
    "                        \n",
    "                        )\n",
    "gale_df_cpx_head=gale_df.add_suffix('_Cpx')\n",
    "\n",
    "df_MIT=gale.loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"]\n",
    "df_TAS=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"]\n",
    "df_TUL=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"]\n",
    "df_HI=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"]\n",
    "df_RI=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"]\n",
    "df_LaMere=gale.loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"]\n",
    "df_Cape=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"]\n",
    "df_SUNY=gale.loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"]\n",
    "df_RNAS=gale.loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"]\n",
    "df_UBC=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"]\n",
    "df_ANU=gale.loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"]\n",
    "df_Houston=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f84fbf-469c-450b-a728-57dcbd6e7d53",
   "metadata": {},
   "source": [
    "## Loading in Cpx B1038 from Kraw2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35b41342-7db7-4541-8172-499804f018fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_9536\\3439944529.py:10: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  Cpx_B1038_Mean=Cpx_B1038.mean(axis=0).to_frame().T\n"
     ]
    }
   ],
   "source": [
    "out_Cpx_B1038=pt.import_excel('Supporting_Data_2.xlsx',\n",
    "                                 sheet_name='B1038', suffix=\"_Cpx\")\n",
    "Cpx_B1038_all=out_Cpx_B1038['Cpxs']\n",
    "Cpx_B1038_input_all=out_Cpx_B1038['my_input']\n",
    "\n",
    "# As has all minerals, need to filter out things that arent Cpx\n",
    "IsCpx_B1038=Cpx_B1038_input_all['phase']==\"cpx\"\n",
    "Cpx_B1038=Cpx_B1038_all.loc[IsCpx_B1038].reset_index(drop=True)\n",
    "\n",
    "Cpx_B1038_Mean=Cpx_B1038.mean(axis=0).to_frame().T\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161f8d40-dd0c-47f8-a31e-cd67b65bfd93",
   "metadata": {},
   "source": [
    "## Correcting Cpx data for various labs\n",
    "- MIT corrected to Lamont by multiplying, as Gale supplement says \"Interlaboratory bias factors may be multiplied by raw oxide data produced from a given method/laboratory to correct major elements to a common basis (for this study, consistent with EMP - Lamont-Doherty Earth Observatory of Columbia University). \"\n",
    "- Then take values corrected to Lamont, and divide by the other offsets in Gale's table, as that would be like if it was measured in another lab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "793c66d3-7c8e-4b8b-bcad-b14b471fe7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cpx_B1038_MIT=Cpx_B1038.copy()\n",
    "Cpx_B1038_MIT['SiO2_Cpx']=Cpx_B1038['SiO2_Cpx']*df_MIT['SiO2'].values\n",
    "Cpx_B1038_MIT['Al2O3_Cpx']=Cpx_B1038['Al2O3_Cpx']*df_MIT['Al2O3'].values\n",
    "Cpx_B1038_MIT['MgO_Cpx']=Cpx_B1038['MgO_Cpx']*df_MIT['MgO'].values\n",
    "Cpx_B1038_MIT['Na2O_Cpx']=Cpx_B1038['Na2O_Cpx']*df_MIT['Na2O'].values\n",
    "Cpx_B1038_MIT['TiO2_Cpx']=Cpx_B1038['TiO2_Cpx']*df_MIT['TiO2'].values\n",
    "Cpx_B1038_MIT['FeOt_Cpx']=Cpx_B1038['FeOt_Cpx']*df_MIT['FeOt'].values\n",
    "Cpx_B1038_MIT['CaO_Cpx']=Cpx_B1038['CaO_Cpx']*df_MIT['CaO'].values\n",
    "\n",
    "\n",
    "Cpx_B1038_MIT_TAS=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_TAS['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_TAS['SiO2'].values\n",
    "Cpx_B1038_MIT_TAS['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_TAS['Al2O3'].values\n",
    "Cpx_B1038_MIT_TAS['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_TAS['MgO'].values\n",
    "Cpx_B1038_MIT_TAS['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_TAS['Na2O'].values\n",
    "Cpx_B1038_MIT_TAS['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_TAS['TiO2'].values\n",
    "Cpx_B1038_MIT_TAS['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_TAS['FeOt'].values\n",
    "Cpx_B1038_MIT_TAS['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_TAS['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_Houston=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_Houston['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_Houston['SiO2'].values\n",
    "Cpx_B1038_MIT_Houston['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_Houston['Al2O3'].values\n",
    "Cpx_B1038_MIT_Houston['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_Houston['MgO'].values\n",
    "Cpx_B1038_MIT_Houston['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_Houston['Na2O'].values\n",
    "Cpx_B1038_MIT_Houston['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_Houston['TiO2'].values\n",
    "Cpx_B1038_MIT_Houston['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_Houston['FeOt'].values\n",
    "Cpx_B1038_MIT_Houston['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_Houston['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_Cape=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_Cape['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_Cape['SiO2'].values\n",
    "Cpx_B1038_MIT_Cape['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_Cape['Al2O3'].values\n",
    "Cpx_B1038_MIT_Cape['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_Cape['MgO'].values\n",
    "Cpx_B1038_MIT_Cape['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_Cape['Na2O'].values\n",
    "Cpx_B1038_MIT_Cape['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_Cape['TiO2'].values\n",
    "Cpx_B1038_MIT_Cape['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_Cape['FeOt'].values\n",
    "Cpx_B1038_MIT_Cape['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_Cape['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_SUNY=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_SUNY['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_SUNY['SiO2'].values\n",
    "Cpx_B1038_MIT_SUNY['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_SUNY['Al2O3'].values\n",
    "Cpx_B1038_MIT_SUNY['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_SUNY['MgO'].values\n",
    "Cpx_B1038_MIT_SUNY['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_SUNY['Na2O'].values\n",
    "Cpx_B1038_MIT_SUNY['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_SUNY['TiO2'].values\n",
    "Cpx_B1038_MIT_SUNY['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_SUNY['FeOt'].values\n",
    "Cpx_B1038_MIT_SUNY['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_SUNY['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_RNAS=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_RNAS['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_RNAS['SiO2'].values\n",
    "Cpx_B1038_MIT_RNAS['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_RNAS['Al2O3'].values\n",
    "Cpx_B1038_MIT_RNAS['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_RNAS['MgO'].values\n",
    "Cpx_B1038_MIT_RNAS['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_RNAS['Na2O'].values\n",
    "Cpx_B1038_MIT_RNAS['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_RNAS['TiO2'].values\n",
    "Cpx_B1038_MIT_RNAS['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_RNAS['FeOt'].values\n",
    "Cpx_B1038_MIT_RNAS['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_RNAS['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_UBC=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_UBC['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_UBC['SiO2'].values\n",
    "Cpx_B1038_MIT_UBC['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_UBC['Al2O3'].values\n",
    "Cpx_B1038_MIT_UBC['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_UBC['MgO'].values\n",
    "Cpx_B1038_MIT_UBC['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_UBC['Na2O'].values\n",
    "Cpx_B1038_MIT_UBC['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_UBC['TiO2'].values\n",
    "Cpx_B1038_MIT_UBC['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_UBC['FeOt'].values\n",
    "Cpx_B1038_MIT_UBC['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_UBC['CaO'].values\n",
    "\n",
    "Cpx_B1038_MIT_ANU=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_ANU['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_ANU['SiO2'].values\n",
    "Cpx_B1038_MIT_ANU['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_ANU['Al2O3'].values\n",
    "Cpx_B1038_MIT_ANU['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_ANU['MgO'].values\n",
    "Cpx_B1038_MIT_ANU['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_ANU['Na2O'].values\n",
    "Cpx_B1038_MIT_ANU['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_ANU['TiO2'].values\n",
    "Cpx_B1038_MIT_ANU['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_ANU['FeOt'].values\n",
    "Cpx_B1038_MIT_ANU['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_ANU['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1038_MIT_TUL=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_TUL['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_TUL['SiO2'].values\n",
    "Cpx_B1038_MIT_TUL['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_TUL['Al2O3'].values\n",
    "Cpx_B1038_MIT_TUL['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_TUL['MgO'].values\n",
    "Cpx_B1038_MIT_TUL['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_TUL['Na2O'].values\n",
    "Cpx_B1038_MIT_TUL['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_TUL['TiO2'].values\n",
    "Cpx_B1038_MIT_TUL['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_TUL['FeOt'].values\n",
    "Cpx_B1038_MIT_TUL['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_TUL['CaO'].values\n",
    "\n",
    "\n",
    "Cpx_B1038_MIT_HI=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_HI['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_HI['SiO2'].values\n",
    "Cpx_B1038_MIT_HI['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_HI['Al2O3'].values\n",
    "Cpx_B1038_MIT_HI['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_HI['MgO'].values\n",
    "Cpx_B1038_MIT_HI['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_HI['Na2O'].values\n",
    "Cpx_B1038_MIT_HI['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_HI['TiO2'].values\n",
    "Cpx_B1038_MIT_HI['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_HI['FeOt'].values\n",
    "Cpx_B1038_MIT_HI['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_HI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1038_MIT_RI=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_RI['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_RI['SiO2'].values\n",
    "Cpx_B1038_MIT_RI['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_RI['Al2O3'].values\n",
    "Cpx_B1038_MIT_RI['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_RI['MgO'].values\n",
    "Cpx_B1038_MIT_RI['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_RI['Na2O'].values\n",
    "Cpx_B1038_MIT_RI['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_RI['TiO2'].values\n",
    "Cpx_B1038_MIT_RI['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_RI['FeOt'].values\n",
    "Cpx_B1038_MIT_RI['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_RI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1038_MIT_LaMere=Cpx_B1038_MIT.copy()\n",
    "Cpx_B1038_MIT_LaMere['SiO2_Cpx']=Cpx_B1038_MIT['SiO2_Cpx']/df_LaMere['SiO2'].values\n",
    "Cpx_B1038_MIT_LaMere['Al2O3_Cpx']=Cpx_B1038_MIT['Al2O3_Cpx']/df_LaMere['Al2O3'].values\n",
    "Cpx_B1038_MIT_LaMere['MgO_Cpx']=Cpx_B1038_MIT['MgO_Cpx']/df_LaMere['MgO'].values\n",
    "Cpx_B1038_MIT_LaMere['Na2O_Cpx']=Cpx_B1038_MIT['Na2O_Cpx']/df_LaMere['Na2O'].values\n",
    "Cpx_B1038_MIT_LaMere['TiO2_Cpx']=Cpx_B1038_MIT['TiO2_Cpx']/df_LaMere['TiO2'].values\n",
    "Cpx_B1038_MIT_LaMere['FeOt_Cpx']=Cpx_B1038_MIT['FeOt_Cpx']/df_LaMere['FeOt'].values\n",
    "Cpx_B1038_MIT_LaMere['CaO_Cpx']=Cpx_B1038_MIT['CaO_Cpx']/df_LaMere['CaO'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72264c56-9337-4c2d-929d-7d446bf0aadc",
   "metadata": {},
   "source": [
    "## Loading in B1038 liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb36eebe-01d7-4523-b218-d9f7837cd148",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_Liq_B1038=pt.import_excel('Supporting_Data_2.xlsx', \n",
    "                sheet_name='B1038_Glass', suffix=\"_Liq\")\n",
    "Liq_B1038=out_Liq_B1038['Liqs']\n",
    "Liq_B1038_input_all=out_Liq_B1038['my_input']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2959f89d-3068-4ad1-909f-be419069659c",
   "metadata": {},
   "source": [
    "### Corecting liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d0c34d5-c934-4410-a6c6-f70ad376f605",
   "metadata": {},
   "outputs": [],
   "source": [
    "Liq_B1038_MIT=Liq_B1038.copy()\n",
    "Liq_B1038_MIT['SiO2_Liq']=Liq_B1038['SiO2_Liq']*df_MIT['SiO2'].values\n",
    "Liq_B1038_MIT['Al2O3_Liq']=Liq_B1038['Al2O3_Liq']*df_MIT['Al2O3'].values\n",
    "Liq_B1038_MIT['MgO_Liq']=Liq_B1038['MgO_Liq']*df_MIT['MgO'].values\n",
    "Liq_B1038_MIT['Na2O_Liq']=Liq_B1038['Na2O_Liq']*df_MIT['Na2O'].values\n",
    "Liq_B1038_MIT['TiO2_Liq']=Liq_B1038['TiO2_Liq']*df_MIT['TiO2'].values\n",
    "Liq_B1038_MIT['FeOt_Liq']=Liq_B1038['FeOt_Liq']*df_MIT['FeOt'].values\n",
    "Liq_B1038_MIT['CaO_Liq']=Liq_B1038['CaO_Liq']*df_MIT['CaO'].values\n",
    "\n",
    "\n",
    "Liq_B1038_MIT_TAS=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_TAS['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_TAS['SiO2'].values\n",
    "Liq_B1038_MIT_TAS['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_TAS['Al2O3'].values\n",
    "Liq_B1038_MIT_TAS['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_TAS['MgO'].values\n",
    "Liq_B1038_MIT_TAS['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_TAS['Na2O'].values\n",
    "Liq_B1038_MIT_TAS['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_TAS['TiO2'].values\n",
    "Liq_B1038_MIT_TAS['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_TAS['FeOt'].values\n",
    "Liq_B1038_MIT_TAS['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_TAS['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_Houston=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_Houston['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_Houston['SiO2'].values\n",
    "Liq_B1038_MIT_Houston['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_Houston['Al2O3'].values\n",
    "Liq_B1038_MIT_Houston['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_Houston['MgO'].values\n",
    "Liq_B1038_MIT_Houston['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_Houston['Na2O'].values\n",
    "Liq_B1038_MIT_Houston['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_Houston['TiO2'].values\n",
    "Liq_B1038_MIT_Houston['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_Houston['FeOt'].values\n",
    "Liq_B1038_MIT_Houston['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_Houston['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_Cape=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_Cape['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_Cape['SiO2'].values\n",
    "Liq_B1038_MIT_Cape['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_Cape['Al2O3'].values\n",
    "Liq_B1038_MIT_Cape['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_Cape['MgO'].values\n",
    "Liq_B1038_MIT_Cape['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_Cape['Na2O'].values\n",
    "Liq_B1038_MIT_Cape['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_Cape['TiO2'].values\n",
    "Liq_B1038_MIT_Cape['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_Cape['FeOt'].values\n",
    "Liq_B1038_MIT_Cape['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_Cape['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_SUNY=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_SUNY['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_SUNY['SiO2'].values\n",
    "Liq_B1038_MIT_SUNY['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_SUNY['Al2O3'].values\n",
    "Liq_B1038_MIT_SUNY['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_SUNY['MgO'].values\n",
    "Liq_B1038_MIT_SUNY['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_SUNY['Na2O'].values\n",
    "Liq_B1038_MIT_SUNY['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_SUNY['TiO2'].values\n",
    "Liq_B1038_MIT_SUNY['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_SUNY['FeOt'].values\n",
    "Liq_B1038_MIT_SUNY['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_SUNY['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_RNAS=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_RNAS['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_RNAS['SiO2'].values\n",
    "Liq_B1038_MIT_RNAS['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_RNAS['Al2O3'].values\n",
    "Liq_B1038_MIT_RNAS['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_RNAS['MgO'].values\n",
    "Liq_B1038_MIT_RNAS['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_RNAS['Na2O'].values\n",
    "Liq_B1038_MIT_RNAS['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_RNAS['TiO2'].values\n",
    "Liq_B1038_MIT_RNAS['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_RNAS['FeOt'].values\n",
    "Liq_B1038_MIT_RNAS['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_RNAS['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_UBC=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_UBC['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_UBC['SiO2'].values\n",
    "Liq_B1038_MIT_UBC['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_UBC['Al2O3'].values\n",
    "Liq_B1038_MIT_UBC['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_UBC['MgO'].values\n",
    "Liq_B1038_MIT_UBC['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_UBC['Na2O'].values\n",
    "Liq_B1038_MIT_UBC['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_UBC['TiO2'].values\n",
    "Liq_B1038_MIT_UBC['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_UBC['FeOt'].values\n",
    "Liq_B1038_MIT_UBC['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_UBC['CaO'].values\n",
    "\n",
    "Liq_B1038_MIT_ANU=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_ANU['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_ANU['SiO2'].values\n",
    "Liq_B1038_MIT_ANU['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_ANU['Al2O3'].values\n",
    "Liq_B1038_MIT_ANU['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_ANU['MgO'].values\n",
    "Liq_B1038_MIT_ANU['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_ANU['Na2O'].values\n",
    "Liq_B1038_MIT_ANU['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_ANU['TiO2'].values\n",
    "Liq_B1038_MIT_ANU['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_ANU['FeOt'].values\n",
    "Liq_B1038_MIT_ANU['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_ANU['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1038_MIT_TUL=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_TUL['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_TUL['SiO2'].values\n",
    "Liq_B1038_MIT_TUL['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_TUL['Al2O3'].values\n",
    "Liq_B1038_MIT_TUL['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_TUL['MgO'].values\n",
    "Liq_B1038_MIT_TUL['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_TUL['Na2O'].values\n",
    "Liq_B1038_MIT_TUL['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_TUL['TiO2'].values\n",
    "Liq_B1038_MIT_TUL['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_TUL['FeOt'].values\n",
    "Liq_B1038_MIT_TUL['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_TUL['CaO'].values\n",
    "\n",
    "\n",
    "Liq_B1038_MIT_HI=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_HI['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_HI['SiO2'].values\n",
    "Liq_B1038_MIT_HI['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_HI['Al2O3'].values\n",
    "Liq_B1038_MIT_HI['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_HI['MgO'].values\n",
    "Liq_B1038_MIT_HI['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_HI['Na2O'].values\n",
    "Liq_B1038_MIT_HI['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_HI['TiO2'].values\n",
    "Liq_B1038_MIT_HI['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_HI['FeOt'].values\n",
    "Liq_B1038_MIT_HI['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_HI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1038_MIT_RI=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_RI['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_RI['SiO2'].values\n",
    "Liq_B1038_MIT_RI['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_RI['Al2O3'].values\n",
    "Liq_B1038_MIT_RI['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_RI['MgO'].values\n",
    "Liq_B1038_MIT_RI['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_RI['Na2O'].values\n",
    "Liq_B1038_MIT_RI['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_RI['TiO2'].values\n",
    "Liq_B1038_MIT_RI['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_RI['FeOt'].values\n",
    "Liq_B1038_MIT_RI['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_RI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1038_MIT_LaMere=Liq_B1038_MIT.copy()\n",
    "Liq_B1038_MIT_LaMere['SiO2_Liq']=Liq_B1038_MIT['SiO2_Liq']/df_LaMere['SiO2'].values\n",
    "Liq_B1038_MIT_LaMere['Al2O3_Liq']=Liq_B1038_MIT['Al2O3_Liq']/df_LaMere['Al2O3'].values\n",
    "Liq_B1038_MIT_LaMere['MgO_Liq']=Liq_B1038_MIT['MgO_Liq']/df_LaMere['MgO'].values\n",
    "Liq_B1038_MIT_LaMere['Na2O_Liq']=Liq_B1038_MIT['Na2O_Liq']/df_LaMere['Na2O'].values\n",
    "Liq_B1038_MIT_LaMere['TiO2_Liq']=Liq_B1038_MIT['TiO2_Liq']/df_LaMere['TiO2'].values\n",
    "Liq_B1038_MIT_LaMere['FeOt_Liq']=Liq_B1038_MIT['FeOt_Liq']/df_LaMere['FeOt'].values\n",
    "Liq_B1038_MIT_LaMere['CaO_Liq']=Liq_B1038_MIT['CaO_Liq']/df_LaMere['CaO'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1253be0-62ad-4b38-bc44-545c0c45438d",
   "metadata": {},
   "source": [
    "## Calculating Cpx-Liq P and T For B1038\n",
    "- Here we calculate a mean composition of Cpx and Liq for different labs, where we have corrected both the liquid and the Cpx compositions for EPMA lab offsets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a532eef6-b34a-422f-bfbc-9f2bd8a6b614",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    ## Correcting liquid and cpx\n",
    "    Cpx_Liq_B1038_Calc_Meas=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1038_Calc_Corr_MIT=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_TAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_TUL=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_HI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_RI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1038_Calc_Corr_LaMere=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1038_Calc_Corr_Cape=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1038_Calc_Corr_SUNY=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_RNAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_UBC=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1038_Calc_Corr_ANU=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    \n",
    "    Cpx_Liq_B1038_Calc_Corr_Houston=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31d8016-2e55-4c18-bcaa-9f4f7aaca82c",
   "metadata": {},
   "source": [
    "## Cpx only pressure and temperatures for B1038"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2cd5d0d-a78d-42de-b3cb-055a1fdce8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    Cpx_B1038_Calc_Meas=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038, H2O_Liq=13)\n",
    "    \n",
    "    Cpx_B1038_Calc_Corr_MIT=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT, H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_TAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_TAS, H2O_Liq=13)\n",
    "\n",
    "    Cpx_B1038_Calc_Meas=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T, H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_MIT=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT.mean(axis=0).to_frame().T, H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_TAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_TUL=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_HI=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_RI=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_LaMere=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_Cape=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_SUNY=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_RNAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_UBC=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_ANU=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1038_Calc_Corr_Houston=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7214e99-f379-48b0-ac41-b8beee8242a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Figure showing correction factor and Cpx only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9265137-1d27-4dcd-8ac0-d86c334495cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_9536\\1838567419.py:37: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax1.plot([1, 1.12], [1, 1], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_9536\\1838567419.py:38: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax1.plot([1, 1], [0.93, 1.09], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_9536\\1838567419.py:76: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax2.plot([0.95, 1.07], [1, 1], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_9536\\1838567419.py:77: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax2.plot([1, 1], [0.93, 1.01], '-k', lw=0.3, color='grey')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1020x880 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12*0.85,10*0.88))\n",
    "\n",
    "\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], 'ok', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], 'ok', mfc='cyan', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], 'ok', mfc='yellow', ms=13)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], 'pk', mfc='white', ms=10)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], 'ok', mfc='grey', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], 'ok', mfc='blue', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], 'ok', mfc='magenta', ms=7)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], 'sk', mfc='red', ms=8)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], '*k', mfc='k', ms=10)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], 'pk', mfc='orange', ms=10)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], 'dk', mfc='white', ms=7)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], '+k', ms=15, lw=2)\n",
    "\n",
    "ax1.set_xlabel('MgO Correction Factor')\n",
    "ax1.set_ylabel('FeO$_t$ Correction Factor')\n",
    "ax1.plot([1, 1.12], [1, 1], '-k', lw=0.3, color='grey')\n",
    "ax1.plot([1, 1], [0.93, 1.09], '-k', lw=0.3, color='grey')\n",
    "\n",
    "### \n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], 'ok', ms=7, label='HI')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], 'ok', mfc='cyan', ms=7, label='RI')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], 'ok', mfc='yellow', ms=13, label='M_MIT')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], 'pk', mfc='white', ms=10, label='SUNY')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], 'ok', mfc='grey', ms=7, label='Houston'\n",
    "        )\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], 'ok', mfc='blue', ms=7, label='Tulsa')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], 'ok', mfc='magenta', ms=7, label='Cape Town')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], 'sk', mfc='red', ms=8, label='La Mere')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], '*k', mfc='k', ms=10, label='Tasmania'\n",
    "        )\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], 'pk', mfc='orange', ms=10, label='Russian NAS')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], 'dk', mfc='white', ms=7, label='UBC')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], '+k', ms=15, lw=2, label='ANU')\n",
    "\n",
    "ax2.set_xlabel('Na$_2$O Correction Factor')\n",
    "ax2.set_ylabel('Al$_2$O$_3$ Correction Factor')\n",
    "\n",
    "ax2.plot([0.95, 1.07], [1, 1], '-k', lw=0.3, color='grey')\n",
    "ax2.plot([1, 1], [0.93, 1.01], '-k', lw=0.3, color='grey')\n",
    "ax1.plot(1, 1, '*k', ms=20, mfc='grey')#, label='Lamont')\n",
    "ax2.plot(1, 1, '*k', ms=20, mfc='grey')#,  label='Lamont')\n",
    "\n",
    "# handles, labels = ax2.get_legend_handles_labels()\n",
    "# fig.legend(handles, labels, loc='upper center', ncol=7)\n",
    "\n",
    "#### Thermobarometry - Cpx only\n",
    "y='T_Put_Teq32d_Peq32b'\n",
    "x='P_Put_Teq32d_Peq32b'\n",
    "ax3.errorbar(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], \n",
    "       xerr=2.6, yerr=51, fmt='None', ecolor='grey', elinewidth=0.8, mfc='None', ms=0,\n",
    "             mec='k', capsize=5, label='1 sigma thermobarometry')\n",
    "ax3.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=13)\n",
    "# plt.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], '.k', mfc='yellow')\n",
    "# plt.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '.r')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_TAS[x], Cpx_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_TUL[x], Cpx_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_HI[x], Cpx_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_RI[x], Cpx_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_Cape[x], Cpx_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_SUNY[x], Cpx_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_RNAS[x], Cpx_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_UBC[x], Cpx_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_ANU[x], Cpx_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_Houston[x], Cpx_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "\n",
    "ax3.set_xlabel('Cpx-only P (kbar, Putirka 2008 eq 32b)')\n",
    "ax3.set_ylabel('Cpx-only T (K, Putirka 2008 eq 32d)')\n",
    "ax3.plot()\n",
    "\n",
    "## Cpx-Liq, Liq and Cpx correctd\n",
    "y='T_K_calc'\n",
    "x='P_kbar_calc'\n",
    "ax4.errorbar(Cpx_Liq_B1038_Calc_Meas[x], Cpx_Liq_B1038_Calc_Meas[y], \n",
    "       xerr=1.4, yerr=45, fmt='None', ecolor='grey', elinewidth=0.8, mfc='None', ms=0,\n",
    "             mec='k', capsize=5, label='1 sigma thermobarometry')\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Meas[x], Cpx_Liq_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=13)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_MIT[x], Cpx_Liq_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_TAS[x], Cpx_Liq_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_TUL[x], Cpx_Liq_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_HI[x], Cpx_Liq_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_RI[x], Cpx_Liq_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_LaMere[x], Cpx_Liq_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_Cape[x], Cpx_Liq_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_SUNY[x], Cpx_Liq_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_RNAS[x], Cpx_Liq_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_UBC[x], Cpx_Liq_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_ANU[x], Cpx_Liq_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax4.plot(Cpx_Liq_B1038_Calc_Corr_Houston[x], Cpx_Liq_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax4.set_xlabel('Cpx-Liq P (kbar, Neave & Putirka, 2017)')\n",
    "ax4.set_ylabel('Cpx-Liq T (K, Putirka 2008 eq 33)')\n",
    "ax4.plot()\n",
    "\n",
    "ax1.set_title('Glass Correction Factors (Gale et al. 2013)')\n",
    "ax2.set_title('Glass Correction Factors (Gale et al. 2013)')\n",
    "ax3.set_title('Average Cpx-Only PT \\n (Krawczynski et al., 2012, Exp B1038)')\n",
    "ax4.set_title('Average Cpx-Liq PT \\n (Krawczynski et al., 2012, Exp B1038)')\n",
    "\n",
    "ax1.annotate(\"a)\", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=14)\n",
    "ax2.annotate(\"b) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=14)\n",
    "ax3.annotate(\"e) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=14)\n",
    "ax4.annotate(\"f) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=14)\n",
    "\n",
    "ax3.set_xlim([5, 12.5])\n",
    "ax3.set_ylim([1490, 1560])\n",
    "ax4.set_xlim([-4.5, 3])\n",
    "ax4.set_ylim([1340, 1410])\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "fig.savefig('Lab_Corrections_B1038_NP2017.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e11c4ae-b321-47a4-b4ad-3f33b05c1269",
   "metadata": {},
   "source": [
    "## Supplementary Figure - Other equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd46f1b7-2e79-44a3-be85-57dee158f2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    ## Correcting liquid and cpx\n",
    "    Cpx_Liq_B1038_31_Calc_Meas=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_MIT=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_TAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_TUL=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_HI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_RI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_LaMere=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_Cape=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_SUNY=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_RNAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_UBC=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    Cpx_Liq_B1038_31_Calc_Corr_ANU=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")\n",
    "    \n",
    "    Cpx_Liq_B1038_31_Calc_Corr_Houston=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq31\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7dcda9f5-9ae3-468f-a305-57378d76792b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    ## Correcting liquid and cpx\n",
    "    Cpx_Liq_B1038_32c_Calc_Meas=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_MIT=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_TAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_TUL=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_HI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_HI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_RI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_LaMere=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_Cape=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_SUNY=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_RNAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_UBC=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_ANU=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")\n",
    "    \n",
    "    Cpx_Liq_B1038_32c_Calc_Corr_Houston=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1038_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Put2008_eq32c\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "699a9287-c08e-4ab8-91c8-134d311f4c6b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F2xwCTCry2jNpPDkNIJ3xeoQ0n0mnFsTS8J58nn3/8stNue1WJ3VZGJl9xisVvH6P3LYHZ5/DAvWTvudzc3V8kb3+dmDRRmIsTE4ter9If/R8jdRffyqwarZ+S+Bf2ee9CXAf8ASwSO61mwNz8uu8eFlYF5yzwDnLOcs5q0Ms7vLXsTSMxvJBwfrFSP+RtiVdzr22kcvQkxoeRMSdQDelEXjuyZV1johppMvKWwMDSYnvVlL/aIBvA2OApYFlmP8S8sSIODUi5ubqvYaUxE7NrbsR2C3rorA96Uf34Vz5xNzjT4BOkjqTfhDGAuMlTSCdbXwti7nBFUB3YHKR96CY/qSzhVOz7hafAfeSkmaf5l7c1HvZwvqbpOS3pMv5+0XEC1nRDL7sStFgUdJ72WIR8UJEPEY6i7s+8M0SXv4d0h8x+eXE3L5fI92QPBQ4LSL+U/D6R3OPxwFLAcs1Ute7uTrWB3YkJbk7Wxhri96viJgcEU+T3u//AN+XJOCPwG8j4s6IeALYh/Td2TX38g9JfzAt3cKYzDoy5yznLOcs56wOwQ2qjiWyfws/13ci4pQsIRxOusnxtCKvn9HwQNK5pAQxm/SfbtOCbe8jJaetSGeBHgEGSVqX9APyT9KZxSZJ+hHpLMl3I2J2ruh+YBYp0e0N3FKQ0IrtW1kS2o70A3ovsBcpUa2X2+4M0og5v9eXo900pQvwKvP/wK5PGpXnzeZe3IL3smxZ/FeTLvPvGxGjc8X/IXVNyVueNLJQc/vtrjSs7mIN6yLibeBjSvthnRIRrxUs7xRssz7pDNg2RV6f/040JPO5RbaDdMa3oY6XIuI+UgLZUdKARl6T1+T7Jenb+fsVIp2+e4kv/wjrQ+pW01A+FXiN1GWm8BjmtCAes47OOcs5yznLOatDcIOqY2n4T1/44zHvP0JEPA/8EjhV0gZN7OtI4PiIODkibiadMYTUJQJSctoqWx7Jztp8RDpj97eImJn953yf1Ec8vVhaQ9K7kpaStDVwLrB/4VmeLFHdSupXviMpmTRL0mbATyPikYg4mXS25V0gPzfDbUBD2eEt2O3LwCrAhw0/fqQfrV+Q/g9FUy+m+feyNS4knSkbEhG3F5Q9DmyQTzCkM7KPt3DfNwI7NDyRtBrwFeDF8sOdn6TvALsAOwO7StqtYJMNco8HkT7L90qpIvu3Swu2be79OoM0alTasdSFlFhfJJ3F+4J0f0RDeXfS2cb8memvkpJr4Rl5s4WRc5ZzVp5zlnNW/WrrPoZeqreQ/iO+C+yaW3cQC974uCjwb9IwnF35sj96l9w2L5P6CK9GulHyqWyb/E23b5P+Q66VPb+F9B/vyNw2p5HOiH2blAzuJ3WtWJGUTH9BOlMy72bf3Gu3JPVVfq0g/knM38d9XvykH7NZpFFp+pL6Bk8nnQHsS67fOHAK6Udl6cKygvo6k4ZovZf0Y7QZ6SzPLVn5V7PX7kluONiWvpf54yF168i/B4sDyzTyeW+a7ecU5r9Zevlc3C+Q5hsZQErInwN9i+zrTAr6owOXkX5YtyAlhieAO3LlSwG9Gomt4f3ctDC2bFkUWAKYApyRveZc0k2xPXOvfy6re1vS2bifNVLfQaTvY76OjUndL16keB/2wv7oTb5fpO4jM4Dvkm70vor03V48K7+E1CXnW6Tv+k3AK+T6wwPHAuNq/VvhxUt7WHDOcs5yznLO6iBLzQPwUuEPNI16dH7u+UEUn+tgC1IiOZviyWmL7IdhevYDdQrwWP7HAbgWeDf3/JhsP31z67oA55PO+k0F/kTqN39Qtu0CS+61yv7zn1MQ+yQaSU7Z8/2zH6QZwOvA0dn6hh+8huS0KKlbxNWFZUXer1VJExJ+TjpTcwXzz/1wDSlR/6CR97rR95L5k9NBBe/BmRS5ITcru6Cx9zD3XqwO/CN7L14Atm9kX2eyYHLqDvyW9EfE1OwYe+bKHwKubWR/fZuILUhn+C7O3o9uufreIN070fD6U7P63yfdW1D05uIi36eGM2ojyW7ALfKa+ZJTS94v0h89r2Wf5d+A/rmybqQz6ZNJZ77vYsERk24Gzqr174QXL+1lwTkLnLOcs5yz6n5R9oZZB6E0w/ZIUoKo6w9XUg/S2cuvR0TFLtnXG0n3R8T2tY6jkNJs7CdHxJFV2HdfUqJaI1J3lbonaXHS2cz1I6KlN5ebdWjOWR2Pc5Zz1sLI91B1MBExlnT2aKcah9IqkvYC/gA8u5Anpv1JN0u3Rz8i3TNgLfM90nwrTkxmGeesjsU5q0NxziqBr1B1QNnoMFdGRLGZteuCpFdIXS92i4gJtY6nViR1jYhZtY6jmGrG1tHO9klalDSE7rdjwRGjzBZqzlkdh3OWc9bCyg0qMzMzMzOzMrnLn5mZmZmZWZncoDIzMzMzMytTSyYOa3ckuZ+imVkHEBGVmCy0XXPOMjPrGBrLWXXZoALwvV9mZvVN6vBtqXmcs8zM6ltTOWuh7PIXEWyyySY89NBDtQ7FzMwMSVxwwQWNlk+fPp0111yTl19+uQ2jMjOzllgoG1SS+NWvfsXhhx/O9OnTax2OmZlVkaR1JR0i6RRJP5J0oKR1ah1XKbp3786pp57KYYcd5qtdZmbtzELZoALYaqutWGqppfjDH/5Q61DMzKzCJH1F0k8lvQk8DZwM7ArsBZwOPCfpDUk/kbRkDUNtsQMOOIDXX3+dO++8s9ahmJlZTodtUH3yySf84Ac/oE+fPiyyyCIss8wyfP/73+fjjz+et82+++7Lb3/7W2bPnl27QM3MrKIkHUaalHIF4HBg8YhYKyI2j4hNImJ1YEngKGAtUuNqWM0CznzwwQfsvvvudO/enVVXXZWLLrpovvIuXbqw5557Ntk10MzM2l6HbVANHTqU0aNH88tf/pL777+fH/7wh9x4442cc84587bZc889mTx5Mo899lgNIzUzswrrB3wtIo6JiL9ExBeFG0TEZ1nZQcDXgNXbOshCF1xwAT179uTOO+9kzz335MQTT+Sqq66ab5s999yTRx99lDfffLNGUZqZWSGV0xdbUk9gK2BjYFlgDvAu6Yzg3yNiRiWDLFJ/NBX3jBkz2HXXXfm///s/dtxxx3nrd9ttN/7zn/8wbty4eeuWXnppjj/+eE4//fRqhmxmZgUkLTTDpjeXayWx5ZZb8vDDD89bt++++zJu3DgmTpw4b92nn35Kz549ue666zjwwAOrFrOZmc2vqZxV0rDpklYHTgH2A/4H/Bv4EOhM6jZxDLC4pBuACyLi1Wb2J+BaYEJEXCCpO3Ap8HVAwBPAMRFR0sgR3bp14/777wdg0qRJvPLKKzz//PP8+9//plu3bvNt27t3byZNmlTK7s3MrB2T9ADw64i4t9axlGLIkCHzPf/Od77Dn/70Jz788EO++tWvArDEEkvwla98xXnLzKwdaXGXP0nnAncBrwAbRcQqEbFDRAyNiH0jYruIWA7YCPgvMEbSz5vYX3/gb6QbhBucRmrkrZct3YGflHpQAHfddRf9+vVj1VVXZf/99+evf/0rPXr0WGB0pB49ejB16tRyqjAzs/bpW8Adki6XtEytg2mp5ZZbbr7nyyyTQn/77bfnW++8ZWbWvpRyD9WbwLoR8auIeKmxjSLipYg4E1gHeKuJ/R0DXAmMyq17GDg3IuZGxBxgPNCnhBgBePXVV9l7773ZdtttefPNN3n//fcZM2YMa6211gLbfvTRR/PO/JmZLYxGjhxJ37596dSpE3379mXkyJG1DqkSBgMbAq9K+rmkFWsdUHM++uij+Z6/++67AAvkKOctM1tY9V1+eSQVXfouv3zN4mpxgyoiLs8aOS3dfmZE/L6J8mMj4saCdfdHxCsAkvoAJzB/g2ueQYMGzVtGjBgxX9nTTz/NzJkzOeWUU1h55ZUB+Pzzzxk7dux8V6gigrfffpvevXu39LDMzDqUkSNHMmzYMCZPnkxEMHnyZIYNG9YRGlXPR8TXSSP57QK8IWmMpMMlrVnj2Ir6y1/+Mt/z2267jTXWWIMVV/yyLfjxxx8zbdo05y0zWyhNfvddAoouk7OTULVQ0j1UeZK+BRwJ9AdmAi8CF0bE+NYGJWkj4A7gkoj4c7Ft8gNLFNpwww3p3LkzJ598MkcddRQffPABF1xwAe+88w6LLrrovO1eeuklPv74Y7797W+3NmQzs7p02mmnMW3atPnWTZs2jdNOO43999+/RlFVTkTcBNwk6RvAPsCPgcslzQSmZl3V24UHHniAH/7whwwePJg77riDu+66i5tvvnm+bR577DEkse2229YoSjMzK1TWsOmSDgHuBT4DLgeuy4oek/Td1gSUvf6vwCkR0eg9WE1Zc801uf7663nuuefYaaed+PGPf8ygQYP4/e9/z5QpU+b1R7///vtZeeWV+frXv96akM3M6taUKVNKWl+vImJsRBwfEWsAywA7AMfVOKz5DB8+nAkTJrDzzjtz3333MXLkSPbdd9/5trn//vvZbLPNWGGFFWoUpZmZFSp32PS3gFMj4vqC9UeQGkKrlrCva0ldMy6Q9B3gKmCniGj0ElRLhqBtifXXX5+DDz6YE044odX7MjOrR3379mXy5MkLrO/Tp0/VR5Kr1rDpkh4E9oiIjyu973JUKmfNnDmTlVZaiSuuuILdd9+99YGZmdUZSTT2aypYYPC5itfdSM4qd2LfJYAni6wfCyxd5j4BLiC9H1dKeiZbLm3F/hr1wAMPMHXqVI444ohq7N7MrC4MHz6cHj16zLeuR48eDB8+vEYRtV5EbNNeGlOVdP3117Paaqux22671ToUMzPLKfcK1TnA+sD3I+KjbF0PUte/yRHxw4pGuWD9rTrbFxFsvPHGnHfeee6HbmYLvZEjR3LaaacxZcoUevfuzfDhw9vk/ilP7Nty06dPZ+DAgfz5z3+mf//+FYrMzKy+SGIaaaCFiUA/YAjQjdpeoWpxg0rSmzDvKpuAlYAvgDeAucCqwKLA+IjYuLVBNxNLRbpPmJlZ7VSxy99qLd02Il6vdP2FnLPMzCpjha9+lY//N42v8TW2Yiv+wT94mqeZwQz6LLcck955p2p1N5WzShnl76cVisfMzKyaHiUNPAHZScsi2zSs79xWQZmZWfmmT5+O5i7KzVzNbnzZ9Xk0ozlqyaN4aVLVz481qqwuf03uUOoUEXMrutMF6/DZPjNbqE2fPp077riDiRMn0q9fP4YMGUK3bt1qHVZJqniFaingbqAHsBcwu7FtI2LBETkqH49zlplZK914441cf8T1/OWzvyxQtuPiO3Lg5QcydOjQqtVfkUEpJC0i6XxJb0v6UNIdktYu2GY5YFYr4zUzsyY8+eST9OnTh+HDh/PAAw8wfPhwevfuzZNPFhsraOETEf8jTebbExgSEZMbW2ocqpmZtdDEiRMZ9PmgomUbfb4Rr7/e+BWqM888s0pRJaWM8vdzYA/gJOBYYFlgnKRdCrbr8DcYm5m1xowZMzjnrDNYcbmvcO7ZZzJjxowWv3b69OnsvPPObLvttuyzzz5861vfYp999mHbbbdl5513LmlfHVk2YNKBpKtUZmZW5/r168e4xYrPqvTUYk+x2mqN3z571llnVSssoLQG1T7AwRFxUzbz/DeAK4HbJOXHcHW/BjOzRtx7770MXHtVxv/lAkYd+TFPjzmfgWuvypgxY1r0+jvuuINlllmGtdeer4MAa6+9Nsssswy33357NcKuSxHxz4iobhY1M7M2MWTIEJ7r8hyjGT3f+tGM5rkuzzFkyJAaRVZag2px4N2GJ5GcAPwBuEXS4ArHZmbWYUyaNIndd/k2Pxi2Nxfv8w63HzeNLdaC24+bxsX7vMPxh+/F7rt8u9nJdCdOnMjSSxef7m/ppZfm9ddfZ+rUqeyxxx5MnTq1CkcCI2+8kb79+9Opc2f69u/PyBtvrEo9lSBpJUmbSPqmpEGSVqp1TGZm1rSJEydy4olHs9xyPencuRPLLdeTn/zk/7j06ks5asmj2HHxHTlNp7Hj4jty1JJHMfr+0TW9j7iUBtVY4BxJ3fMrI+IHwE3A7cD3KxibmVmHsc03N2VA1weZcO40Bm8wf9ngDWDCudNYp8uDbPPNTZvcT79+/fjggw+Kln3wwQesttpq3HXXXdx5553cfffdlQk+Z+SNNzLsxz9m8rBhxH33MXnYMIb9+MftqlGl5MeSJgNTgMeAh4B/AVMkvSnppFrGaGZWqtZ0F68nY8aMYdNN16N79yt59NFP+eKL4NFHP6V79ysZNuwAfn/N7znw8gPpfnZ3Drz8QF7/7+tsvHFVZ2xqVinzUK0G/BlYDdgpIv6eKxPwa+AHpItXVR2G1iMmmVm92XXwNuyz6kMc8I3Gt/njI3Dr5G0Yfe/fG91mxowZ9O7dm2233Xa+bn8vvfQSf/vb35gyZQqDBw/moYceYptttuHvf298X+Xo278/k4cNgw03/HLl+PH0GTGCSS++WNK+qjjK3/nAfsDJpJOB75LmTVwUWB7YknRf8MiIOKXS9ReJxznLzFrl3nvv5fijD2W9FT7hpO2nceH9PXjuvz254KLLmDZtWl2P+Jo3ceJENt10Pe66axqbbbZg+WOPwa679uDxx5+jX79+je5n5I03cto55zDllVfoveaaTH7ppVZP+luRiX2zHXUFvg68HBELnCKVtCmwZ0T8qNxgWxiHk5OZ1ZUbbriBUZccxejjP2t0m11/uzj7HHcZBxxwQJP7evLJJ9l5551ZZpllWHrppZkyZQpvvfUWs2en0cEXWWQRZs6cOe/fBttuuy0PPPBAq46jU+fOxH33QZfcNIazZ6MddmDunDkl7auKDaoPgT0i4uEmttkKGBURy1a6/iJ1OWeZWVkmTZrECccezgvPPMrvhs7fw+G3Y+DHf+rKSiutxCqr9OaDDz7g/fff55577qn5FZtynXji0XTvfiU//3njg4b/5Cdd+eKLYfz615cULW/oSTHtpJNg3XVhwgT4v//jhpEj2b8Vw6pXrEHVyM6/CSwN/DsiXmrVzlpep5OTmdWVqVOn0nvl5Zjymy/oVWTcuanToPeJi/Lmf96jZ8+eze6vYR6q119/nRkzZvDrX/+a6dOnN7p9jx49uOeee9h6661bcRR1c4XqXdLJvbFNbLMNcGtEfLXS9RepyznLzMqyau/lGbrRB/xstzl0W+TL9dNnQp8TurDt4L2K9lZ45JFHOOKII7j22mvp27dv2wdepuWW68mjj35KExefmDgRttiiJ++8M/99wmeeeWbZo/mdccYZzQ6tXpEGlaTFSN36tgIeAM4G/gKsD3wMLAncARwUEY2fgq0AJyczq0dNdftrSXe/pjz44IPssssuTJs2bYGySjWmoPiZvx4XXsiIX/2q5DN/VWxQDScNmX468AjwNvN3+fsG8Avgxtb2qJB0APAj0gi304DjI2JcwTbOWWZWlsbyxo3/hOF/XYV99j90gdf86U9/onv37kybNo211lqLO+64o42ibb3OnTvxxRcxXyeIQrNmQffunZg9u3iviKI9KbbZBnXqVHJPiryKTOxLakxtAlwKbES6yfczYOXsDN8awMrARWVHambWge2z/6GMenrxomWjnl6cvYceUva+t9lmG2655ZYF+s5369aNW265pSKNKYD9hw5lxK9+RZ8RI9AOO9BnxIiyGlPVFBGnAb8BzgReAT4FZmb/vko6Ifg70j1WZZO0FnA+sGNEbACcSxqgycysIhrLGxPfg6WX71P0NV/96ld54403ePzxx5kwYQK33nprVUd+raSll16cyc1MuT5lStquMb3XXDN18yu2vkpKaVDtCRwSERcD3wVWBX4UEf8FiIjXSRP+7lnxKM3MOoDvfOc7PD6xC9qfBZYnXu/Cd77znVbt/+OPP6ZLly506tSJ7t2706lTJ7p06cLHH39cmQPI7D90KJNefJG5c+Yw6cUX21VjqkFE/Doi+gArARuTeldsQjoJ2DcizouIua2s5gvgsIY8CIwDlpe0SBOvMTNrse985zs89MIsphZ0Pui3LHzwTvGWx+TJkznssMPo2bMnF198MSeccELVRn6ttKFDD+Cqq7o2uc2VV3Zl6NDvNVo+/Gc/o8eFF8L48TB7dvo3W18tpTSoAGYDRMRk4Ebgk4LyRYCOOYajmVkr9erVi3ff/4iIWGB59/2P6NWrV6v2f9VVVzFt2jTWX399Ro8ezfrrr8+0adO4+uqrK3QE9UNSd0nbkxpTb0XE2IgY19D4kdRNUvmXBIGImBQR92T7axjt9q6ImFm47aBBg+YtI0aMaE21ZrYQ6dWrF1t9YzPufnr+9UM2hvff+y8vvTT/8AUvvfQS77///rx7iQYPHszAgQPp3LlzXeSCY489iSuu6MpjjxUvf+yx1KA65pgTG91HsZ4UDeurpZR7qK4B+gPHRMRTBWXdgR1IXR/+HBGNH2UFuD+6mdWTSZMmcdBBB1X95uDdd9+db37zm5xwwgl06tSJOXPmcNFFF/HII49w5513Vq3eclXxHqq1Sff4fhUQ0AX4eUScndtmOeDtSkzzkd1jfC2wCqn738cF5c5ZZla2xkaJfXIifHN4F5ZZdgV69+7NlClTeOedd/j973/PYYcdNm+7sWPHsv322zNnzpyKj/xaDWPGjOHAA/fisMNmcdhhs+jdO3Xzu/LKrlx5ZVeuv/5WBg8eXNI+s3zTqrgqNSjFEsBlwLSIGFZQtifpitWVwP9FxBetirj5WJyczKxu7L777rzyyit1d3NwtVWxQfVX4C1gGDAXOBy4ABgNfC8i5laqQSWpN3A38CJwcEQsMNSic5aZtcbUqVNZc/W+vPfBxwuUde0Ms3LjLHTq1IntttuO++67b9667bffnr///e/MyQ3IUMnBiqph4sSJXHrpb7jxxj/ywQefsfTSizN06Pc45pgTm5x/qjHtpkHVTAXdACKiTbr7OTmZWb249957+cEPfsBTTz3F1772NS6++OKSz6x1VFVsUE0FBkXEq7l1WwD3kiaoPwBYllY2qLITjc8C10VEo2P1OmeZWbX07duXyQWjOPTo0YNbb01Xce6991723nvv+UaAbe+NqWqodoOq1HuoioqIGW3VmDIzqxczZszg+OOP5+KLL553c/Dxxx/PjBn+uayy/wG98ysi4p/AbsAewNVUJv8dC/QB9pD0TG6p+txWZmYAw4cPp0eP+Sc3nDNnDkcffTRTp07lsMMOm68xVemRX+vFGWecUdX9V6RBZWZmCzr//PNZb7312HHHHYEvbw6+4IILahxZh/dH4BpJh0lapmFlRDwE7AXsS+r+1yoR8YuI6BwRGxQsH7Z232ZmLbH//vszYsQI+vTpgyT69OnDVVddxfrrr8+mm27KZ599VvWRX+tBc5P2tlZFuvy1NXefMLN6sOKKKzJq1Ci22GKLeevGjh3Lvvvuy3/+858aRtY+VLHLX2fgJ8D3gcOzhlS+fBPSVaq1KzEoRQvicc4yszY1adIkNtlkE95//3022GADzjvvPE4++WSeffZZttpqK/7+9/ImkV+YVf0eqrbm5GRm9eCcc85h/Pjx3H77l3O97rHHHmy00Ub89Kc/rWFk7UO1GlQl1D8gIl5og3qcs8yszdXbyK/tXcUbVKXM3RERFR/03snJzOrBjBkzGDhw4LyBKBoGqJgwYQLdunWrdXg1V+sGVVtxzjIzq3/VaFA9CGwKBPAqabb4NYElgCm5TSMiViu5gubrd3Iys7owZswYjj/+eMaNG8dGG23kUf5y3KAyM7N6UY0G1XBgfeD7DTffZhMbXg5MiYhTWxFvS+p3cjKzuuF5qIpzg8rMzOpFNRpUHwGbR8SLBevXAp6MiJ5lRdry+p2czKxuTJo0iYMOOohrr72Wvn371jqcdsMNKjMzqxdN5awuZe7zE2AQaWb4vK2B98vcp5lZh9S3b18eeuihWodhOZK6ApsBM4DxETGrxiGZmVmdKrdB9XPgCknfAp4BBGwM7Emagd7MzKw9Wwp4CDgbuEDSNhExp7YhmZlZPSp72HRJOwGHAmsD04B/A7+OiGdL3I+Aa4EJEXFBtm5J4GHgkIgYV+Q17j5hZlbnatnlT1InYJWImCxpiYj4tIp1OWeZmdW5anT5IyLuBe6V1AWYU062kNQfuBTYBJiQrdsJ+A3Qt9zYzMzMmhIRc4HJ2eOqNabMzKzjK7tBJekY4ESgN7C2pFOAD4CfZomqJY4BrmT+odaPJ3UbHFVubGZmZgCSFgeOBAYAnRtWA4sCG0TEmrWKzczMOoZO5bxI0v8BPyb1PW+4kfd+4BDg3JbuJyKOjYgbC9btGBFPlhOXmZlZgSuBH5EaUPuR8t6awF7ADTWMy8zMOoiyGlSks33DIuJ6YC5ARNwKfA84sEKxNWnQoEHzlhEjRrRFlWZmVn92BPaPiKGke31/ExGbkLqWr1vTyMzMrEMot8vfysCrRda/CXyl/HBabty4BcaqMDMzK7Qo8Er2+AXSiLRPAX8AxtYqKDMz6zjKvUL1GHBw7nlkIyadDDzR6qjMzMwq49/At7PHzwNbZo+/QmpsmZmZtUq5Darjge9LGg90Ay4HXid1rTixQrGZmdWliRMncuKJR7Pccj3p3LkTyy3XkxNPPJqJEyfWOrSF0RnApZKGAX8EdpU0BrgNuK+mkZmZWYfQmnmougFDgf6kroMvAzdExGeVC6/Ruj2nh5m1S2PGjOHAA/fi8MNnceihs+jTByZPhquu6soVV3Tl+utvZfDgwbUOs11oq3moJPUFukTEa5LWJ40k+yHw24iY3gb1O2eZmdW5pnJW2Q2qWnJyMrP2aOLEiWy66Xrcddc0NttswfLHHoNdd+3B448/R79+/do+wHamLRpUklaLiNeLrF8E+HFEtHhk2lbE4JxlZlbnmspZ5Xb5MzOzApdcciGHHz6raGMKYLPN4LDDZnHppb9p28AWbmMlDcivkPRt0v1UJ9QkIjMz61B8hcrMrEKWW64njz76KU1dfJo4EbbYoifvvDO17QJrp9roCtUvgGHATsBbwEXAHsAVpInoP6xm/VkMzllmZnXOXf7MzNpA586d+OKLoEsTE1LMmgXdu3di9uw5bRdYO9WG91AdA/wcEPA08IOIeLba9ebqd84yM6tzVevyJ6mLpCGSfihpD0ldW7M/M7N6tvTSizN5ctPbTJmStrPqkdQpvwCXAYeRBlC6FJiQKzMzM2uVFicTSctKulvS55L+IWll0qSI1wD7AVeSktTKVYrVzKxdGzr0AK66qunzSlde2ZWhQ7/XRhEttGYDswqWm0nTfNxcsN7MzKxVWtzlT9KfgOWBX5GGS/8Gadb5fSPiE0ndgauB7hGxe3XCnReLu0+YWU2ceeaZnHnmmUXLPMpfaarV5U/SVrmnXUgNrKIi4h+Vrr9IPM5ZZmZ1riL3UEn6H/CNiPh31nj6BNg8Ip7MbdMfGBsRX61A3E3F4uRkZjWR/aA2Wt4wD9Vhh83isMNm0bt36uZ35ZVdufJKz0OV10aDUjwPDI2I56pZTzMxOGeZmdW5St1D9TnQ0FBaCuhMumKVtzypoWVmtlAaPHgwjz/+HF98MYwttuhJ9+6d2GKLnnzxxTAef/w5N6ba3lcAjwBiZmZVU8oVqjNIN/WOAgYDM4HppCFoXwDWA84FboqIU6sRbC4Wn+0zs5po7gqVtVwbXaE6DzgAGAlMAmbkyyPi6mrWn8XgnGVmVucqNmy6pCNIjakPgbOB3sD1QB/gC9LAFCdFxMzWBt1MHE5OZlYTblBVThs1qN5oojgiYrVq1p/F4JxlZlbnqj4PlaRlgA8jYm6rd9ay+pyczKwm3KCqnLaah6rWnLPMzOpfUzmrieknG93Zt4DNgJWBRYFpwH+Bx4C/tyJOMzOzVsvy1MMRMTt73JiIiAcrUJ+Aa4EJEXFBa/dnZmb1pZR5qPployXdAWxHutG3M7A08G3gDknPSVq1KpGamVXBYp07I6nFC9BkeWNDqlubeoA0eFLD46aWVslGt/0bsFdr92VmZvWplCtUVwATgI0jYnphoaQewFXACFIDy8ys3Zs2dy6NdcYSLNC9z13+2r+I6FTscZUcQ7p/eEqV6zEzs3aqlESzCXBWscYUQERMIw1UUWQ6SzMzs7Yn6XVJSxVZv6Kk91q7/4g4NiJubO1+zMysfpVyheo1YGfgpSa22R14szUBmZmZtYakfYBdsqd9gT9ImlGwWR9gVlvFNGjQoHmPhw0bxrBhw9qqajMzq7JSGlQnAKMl7QY8DLxNGip9UdKEvt8gXcUaUuEYzczMSvEgsCOp1ybAXOaf3DeAZ4GT2iqgcePGtVVVZmbWxlrcoIqIByWtAwwjNZxWAHqQJkn8D/AIcHBETK5GoGZmZi0REe8DhwBImgScn3VLNzMzq7iShk2PiLeA06sUi5mZWaU9AmzaMEJjoYjwdB9mZtYqJU3sK6kPcCjF56F6HLgiIqo+0pEnSTSzSlmsc2emzS0+J3mPTp34fM6c+dZ5lL/KaYuJfSU1NuH8DOCtiFizmvVnMThnmZnVuYpM7CtpR+A24FFgLPAuC95DdYKk3SPib62O2sysDRQ2mJpzxhlnVCkSq4bCYdMldQb6Ab8D/lSToMzMrENp8RUqSS8A10fEeU1sczJwQESsW6H4GqvHZ/vMzOpcW1yhaqLuAcBfImKVNqjLOcvMrM41lbNKmYeqDzC6mW3uJp35MzMza89WBHrVOggzM6t/pQxK8SjwU0nDio2WJKk7cCbwRIViMzNbwPTp07njjjuYOHEi/fr1Y8iQIXTr1q3WYVk7Jen6Iqt7AjsAnpDXzMxarZQuf72Bu4A1gPEsOA/VBsBkYLeIeL0aweZicfcJs4XQk08+yW7b78Z6s9dj0OeDGLfYOJ7r8hyj7x/NxhtvXOvwrERtNCjFNQWrApgJPAn8MSJmVrP+LAbnLDOzOtdUzipplL9sZ9+i+DxUjwP/iIjGRlSqGCcns4XP9OnT6bdiPy77+DJ2Y7d560czmqOWPIrX//u6r1TVmVreQ9WWnLPMzOpfpe6hAtKcHRHxi4g4PiIOi4hjs+cPRsRcSSXv08ysOXfccQfrzV5vvsYUwG6kK1a33357jSKz9kZSZ0mnSXpa0mOSTpHUtdZxmVl9mDFjBueccw4rrrgi5557LjNmzKh1SNbOtbjxI2lRSedLelvSh5LukLR2wTbLAbNK2KckXSfph9nzzpIukvSSpNckHdniIzGzDm3ixIkM+nxQ0bKNPt+I11+vak9jqy+/BE4m3dM7Dvgx8PuaRmRmdeHee+9l4MCBjB8/nlGjRvH0008zcOBAxowZU+vQrB0rZVCK4cDuwEnZ82OBcZK+GxF/zm3Xou4bkvoDl5K6D07IVh8BrAkMBJYAHpP0dET8q4Q4zawD6tevH9cvdj18tmDZU4s9xYGrHdj2QVl79V1gv4i4B0DSrcAYSUdFxOzahmZm7dGkSZM44YQTeOGFF7j44osZPHgwAFtssQVjxozh+OOPZ8CAAVx00UX07du3tsFau1NK97x9gIMj4qaIuIk0ke+VwG2S8n1wWtpR/Jjs9aNy6/YAromI2RHxEXAzcEAJMZpZBzVkyJA0AEXB7A2jGc1zXZ5jyJAhNYrM2qHlgadzzx8BumbrzcwWsM022zBgwAAmTJgwrzHVYPDgwUyYMIF11lmHbbbZpkYRWntWyhWqxYF3G55kd9ieIEnALZL2YP4E1qSIOBZA0va51asAb+aevwWsV0KMZtZBdevWjdH3j2a37XfjstmXsdHnG/HUYk/NG+XPA1JYTmdgTsOT7P7eGcAitQvJzNqzddddl/79+zeaS7p160b//v1Zbz3/WWoLKuUK1VjgnGy+qXki4gfATcDtwPcrEE/+CpfIJcW8QYMGzVtGjBjRymrNrB5svPHGTHx7IgdefiDdz+7OgZcfyOv/fd1DppuZWavss88+jBo1qsltRo0axd57791GEVk9KWUeqtWAPwOrATtFxN9zZQJ+DfyAdPGqc4sDkK4Fno+ICyQ9AFwaEXdkZWcAS0bEiQWv8RC0ZmZ1rlrDpkuaC/wG+Dy3+mRgBPBRftuIOL3S9ReJxznLrJ2bOnUqvXv3ZsqUKfTq1avR8jfffJOePXvWIEKrtaZyVou7/EXE65LWB74OvFxQFsCJkm4B9mxFrKOBQyTdTepi+F3AI/2ZmVkpHga+VrDuUdKAR3lu5ZgZAL169WKrrbbi7rvv5oADFrx9/6677mLrrbd2Y8qKKuUeKiJiFvDPJsofJ03wW67LgH7As6S+7pdHxD9asT8zM1vIRMTWtY7BzOpPQ7e/Yg2qUaNGsc8++9QgKqsHLe7y1564+4SZWf2rVpe/9sY5y6w+TJ06lTXXXJP33ntvgbJll12WV155pWh3QFs4NJWz3KAyM7OacIPKzMzqRVM5q5RR/szMzMzMzCynpHuo8iR1BVYkzfcBaYjzRYENI2JkBWIzMzMzMzNr18rq8idpCGn42a8UKf5PRPRubWDN1O/uE2Zmda6tu/xJWjUi3sge7wN8KyKqPpKsc5aZWf2rRpe/nwO3AeuQ5vTYHPgOMAk4o8x9mpmZVYWkHsArkiZI+jVwA/B2jcMyM7MOoNwrVF8A60TEREn3Ab+PiNGStgd+HRGFc31UlM/2mZnVvypO7NsZOApYHRgVEf/M1m9Jmu9wSeBfEbFppetuJB7nLDOzOleNK1QfAz2yxy8BG2aPXwZWLXOfZmZmlTACOBlYAxgjaS9J6wFXAHOAE4A1JJ1UuxDNzKyjKPcK1ZXAWsCRwJrABcB+wJ7AHhGxZiWDLFK/z/aZmdW5Kl6h+hjYMiImSNoKuJM0aNJ4YN+IeEvSHqQeFVU/CeicZWZW/5rKWeWO8ncC8BtgI+CPpIbUY8DnwP5l7tPMzKwS3gY2k/Rf0sm+XsALwDcjYk62zfPA8jWKz8zMOpCKTewraQlgOrB4RHxckZ02XpfP9pmZ1bkqXqH6NnArsDjwHnAqqSfFTyPisuweq8uAARGxRaXrLxKPc5aZWZ1rKmeV2+XvVxHx4yLrDwLOi4jlSt5pafU7OZmZ1blqDpsuqRfpnt5/R8RMSTuRRqd9gzQoRVdgh4h4uhr1F8TinGVmVueq0eVvf0lLAkdERGQ3+15GGpzigjL3aWZmVhERMRV4Jvf8XklrA7sB04DbIuKjGoVnZmYdSLlXqPoAY0h90P8DHAvcBZwUEZMqGWAj9ftsn5lZnWvriX1rxTnLzKz+VbzLX7bTpYC7gU2AIRFxV/khlly3k5OZWZ1zg8rMzOpFRRpUks4usroHcAzwHHBfw8qIOL2MOFvMycnMrP51lAaVpJ2BX5CGZn8OODQiPsmVO2eZmdW5SjWoHmxhfRER32ppcOVwcjIzq38doUElaRnSkOxbRMSrks4DloiIo3PbOGeZmdW5aozydwjw54h4r7XBlcPJycys/rVlg0rSFqSJ6G8F+gCvRsQXFdjv/sDQiNg5e94XeBZYsiFROWeZmdW/pnJWpzL3eSHQs/yQzMzMqk/SspKeAO4HrgCWAYYDL0pavQJVrAK8mXv+Fik/LpHfaNCgQfOWESNGVKBaMzNrL8q9QjUKeBn4RUR8XvGomq/fZ/vMzOpcW1yhknQLIOD7pEl+1wc+AK4HFomInVq5/1OBVSLiqOx5F2AWaZL7z7N1zllmZnWuGvNQrQzsCfxE0ofAjHxhRPQuc79mZmaVtC3wzYiYLqU8GBGfSDoFeKIC+59CGu22wUrAR7U42WhmZrVRboPqD9liZmbWns0ljUhbaAUKTgaW6X7gQklrRMSrwJHA6Ars18zM6kRZDaqIuK7SgZiZmVXBjcDFko4EAugp6dvApcAtrd15RLwn6WDgVkmLABOBA1u7XzMzqx+lDJv+MLBrRHws6RFSYioqIr5Zofgai8X90c3M6lwb3UO1CGmOqGOARbLVs4Ergf+LiEpcpWouBucsM7M6V6l7qP4GzMweP9DqqMzMzKpvVkScJOmnwGqkvDcxIj6rcVxmZtZBtLhBFRFn5Z6+AdxSOIeHpMWAQyoUm5mZWWu9nY30d3NEPF7rYMzMrOMppcvfssDi2dNXgU2BDws2Wx+4MSK6VyzC4rG4+4SZWZ1roy5/+wJ7AYNJw6U3NK7GV7Peghics8zM6lxTOauUBtWewCi+vHcqv8PIPb82Iqp6lcrJycys/rVFgypXVw9gF1LjagfgHeCmiDizDep2zjIzq3MVaVBlO+oNdAJeB74OvJ8rDuCziPhfK2JtaRxOTmZmda4tG1S5OtcF9gZOACIierVBnc5ZZmZ1rmINqvbCycnMrP61VYNK0sakyeiHkCbeHQPcDPzZo/yZmVlLVLxBlQ2h3qjWDpsu6TjgWGA68CJwTP7Kl5OTmVn9a6N7qKYAy5FGqr0ZuCMiPq1mnUVicM4yM6tzlRo2Pe9vRfazGrAzcHaZ+wRA0jbAycCmEfGWpO8BI0j93s3MzEoxHLg1IgoHUTIzM6uIinb5yxo/342InVuxjx8CAyPioOz5YsD/gCUiYma2zmf7zMzqXBt2+VseWAvo3LAKWBTYMCKGt0H9zllmZnWuGleoGjMWuLyV+3gCOF5Sn4iYDBxMmt3+q8B/GzYaNGjQvBcMGzaMYcOGtbJaMzPraCQdCVxMakzlR6QN4HHSFSwzM7OyldWgkrRakdU9gVOBSa0JKCIekXQWcIekucDVpCtUM/PbjRs3rjXVmJnZwuEU4Fzgl6RJ6TcBlgCuB0bXMC4zM+sgyh2UYi5fzkc1bzXwJnBIRBTeY1XKvpcAlouI17LnKwHPAUs39Jlw9wkzs/rXRoNSzATWjIhJkv4MXB8Rf5K0BWnexDWqWX8Wg3OWmVmdq0aXv1ULngfpCtK7FcgaKwJ/k7RORHwCnEaafNHZyMzMSvUusAyp98RLwIbAn4C3SfnGzMysVUpqUGVXj7YBZgCPZw2eioqIlyX9EnhCUifSfVnHVroeMzNbKNwMXC/pMOAvwEhJzwC7AK/WMjAzM+sYWtzlT9KmwJ+BpbJV7wP7RsRD1QmtyVh8wcrMrM61UZe/LqSpOCZExF2SzgWOAD4EDo6Ix6pZfxaDc5aZWZ2ryMS+kh4B/gP8AJgDnA9sHhFrVSrQlnJyMjOrf201bHqtOWeZmdW/SjWoPiXN2dEwWMRSwAfAVyPio0oF28JYnJzMzOpcG85DtTNwIrA6sBVwGPBmRIyodt1Z/c5ZZmZ1rqmc1amE/SwGzLtnKiL+B0wHerUuPDMzs+qQdADwR+BhYFnSfFTvAL+WdGItYzMzs46hlAZVMXMrsA8zM7NqORk4IiLOJnVXJyIuBQ4Cjq9hXGZm1kGUMspfAJ2ykfcgzTtVuC5tGDG3QvGZmZm1Rj+g2EzwzwDLt20oZmbWEZVydUmkQSlmZctMYHHg5dy6hsXMzKw9mADsnHvecDPTIaRJ483MzFqllCtU21QtCjMzs+o4CbhH0rbAIsDpktYENiDNRWVmZtYqLR7lrz3xiElmZvWvDUf5Wx44GuhPOpH4MvD7iJhS7bqz+p2zzMzqXEWGTW9PnJzMzOqf56EyM7N60VTOKqXLn5mZWbsn6foWbhoR8f2qBmNmZh2eG1RmZtbRzGmmfCugL9Cmk9KbmVnH5AaVmZl1KBFxcLH1klYGfktqTN1AGrDCzMysVSraoJK0GClBzQDujogXK7l/MzOzUknqDJwInA5MBraOiIdrG5WZmXUUpcxD1RLdgYOBfwHDsyRmZmZWE5K+AYwHzgDOBTZ0Y8rMzCqprAaVpCslrV64PiI+iIhVI+KhiBgSEc31Yzfr0GbMmME555zDiiuuyLnnnsuMGTNqHZLZQkHS0pKuAx4GXgP6R8SvImJ2FeqSpOsk/bDS+zYzs/av3CtUQ2j+pl+zhdq9997LwIEDGT9+PKNGjeLpp59m4MCBjBkzptahmXVoko4gzTW1JbArsBfwtqROhUsF6uoP/C2rw8zMFkJlzUMl6aekRHURqT/6fKfdI+L1SgTXRP2e08ParUmTJnHCCSfwwgsv8Lvf/Y7BgwfPKxszZgzHH388AwYM4KKLLqJv3761C9Ssxqo1D5WkubmnTSaLiGhV13RJlwCPAtsDz0fEBUW2cc4yM6tzFZ/YtyBZwZcJS6R5Pap675STk7Vnq666KkOHDuVnP/sZ3bp1W6B8xowZnH322dx000288cYbNYjQrH2oYoNqq5ZuGxH/aMH+dgLuKlJ0SERcn21zLW5QmZl1WNWY2HfVVsRj1qGtu+669O/fv2hjCqBbt27079+f9dZbr40jM1s4tKSRVOL+7qWVo+IOGjRo3uNhw4YxbNiw1oZlZmbtRFkJIiImNzzO9UEXsCiwIakboNlCaZ999mHUqFEccMABjW4zatQo9tlnnzaMysxqady4cbUOwczMqqTcLn9bApcB/YsUz4qI4qfmK8TdJ6w9mzp1Kr1792bKlCn06tWr0fI333yTnj171iBCs/ahWl3+asFd/szMOramcla5Ixz9ljQM7U7A58CewHHAh8D3y9ynWYfQq1cvttpqK+6+++6i5XfddRdbb721G1NmZmZmHUC5Dap1gFMi4j7gKeCLiPg9cDTwo0oFZ1avGrr9FTNq1Cj23nvvNo7IzKopIg4qdnXKrN6NHHkTffsOpFOnzvTtO5CRI2+qdUhm7U65Xf7+B2waEa9IugyYEhG/kNQbeCEilqh0oAX1u/uEtWtTp05lzTXX5L333lugbNlll+WVV14p2h3QbGFSyy5/khYDTiJN+3F3RLxYxbqcs6wujRx5E8OGnca0aVcB3wDG0qPHoYwYMZz999+v1uGZtalqDJs+CuhM6ub3LeCHwLeB/YD/i4g+5YfbovqdnMzM6lyNG1RLA08CBwPHA3tHRFUmrHfOsnrVt+9AJk++GNgmt/ZB+vQ5jkmTnq9VWGY1UY0G1fLAH0nzcvwBeIA00e8c4KiIuLL8cFtUv5OTmVmda4sGlaQrgV9GxGvVrKeZGJyzrC516tSZiBlA19zaWUjdmDu3KucfzNqtig9KERHvRMS3I+LiiJgFbA2sC/StdmPKzMysBENIJ/vMrES9e/cHxhasHZutN7MG5Q5KgaReko6WdDFwAbAp8L+KRWZmZtZ6vwb+IGmwpHUkrZZfah2cWXs2fPhp9OhxKPAgMAt4kB49DmX48NNqHJlZ+1Jul7+NSd383geeITXMvka6JrxTRDzbqqCkPYCzgLmkRtrhETExV+7uE2Zmda6NuvzNLVjVkDwERER0rmb9WQzOWVa3Ro68idNOG86UKS/Su3d/hg8/zQNS2EKpGvdQPQfcD/yoIUtI6gxcDAyKiK+3ItjuwAfA+hHxmqQTge0iYufcNk5OZmZ1ro0aVE0OkhQRk6tZfxaDc5aZWZ1rKmd1KXOfawB75TNERMyR9FtgfJn7bNCZdOawYUzpxUnD2pqZmZUk32CS1NDNXcCiwIZA1RtUZmbWsZXboLoXGAqcWbB+L1JH27JFxGeSjgQelfQhqYG1RWv2aWZmCydJWwKXAcXuop8FdGvbiMzMrKNpcYNK0vW5p52B0yXtTJrHYy5plL/NgWtbE5CkdYHTgXUiYqKk44HbJG2QvyI2aNCgea8ZNmwYw4YNa021ZmbWMf0WeI00ie8o4EBgRdIJweNqF5aZmXUULb6HStI1LdxnRMQhZQck/RBYNyK+nz3vDMwElouID7J17o9uZlbn2ugeqhnABhHxkqQHgV9FxBhJewGnRMSgZnZRiRics8zM6lxF7qGKiINzO2w0O0jqV3qI83kaOFbSchHxLrA78EZDY8rMzKwE00i9KABeAjYAxgD/AtaqUUxmZtaBlDsP1Y3ZlaN5JC0q6UxgQmsCioi/A+cDD0l6FjgW2K01+zQzs4XW34BfSloJeBT4rqRlgT3w3IlmZlYB5Q6b/gIwCdgzImZI2ok0ZPpiwKkRcXVFo1ywfnefMDOrc23U5W954I/AXcAfSHMobgnMAY6KiCurWX8Wg3OWmVmdq8Y8VF8hJSdIc0btBFwCnBURn5QbaAn1OzmZmdW5tmhQFalTwDrAxxHxnzaq0znLzKzOVbxBle10UWAk6R6nbSPiH2VHWHrdTk5mZnWurRpUknoB+5OGTp8J/Bu4MSKmV7vurH7nLDOzOleRBlXBsOkNOgNDgP+Q+qYDEBEHlhFnizk5mZnVvzbq8rcxqZvf+8AzpHuHvwZ0BXaKiGerWX8Wg3OWmVmdq8gof6T+5sXW3VxWVGZmZtV3FXAF8KOGVk02qNLF2fqv1zA2MzPrAMru8ldLPttnZlb/2ugK1XRg/Yh4pWD9WsD4iOhRzfqzupyzzMzqXFM5q8XDpku6RdKaJWy/jqRRLd3ezMysCu4FhhZZvxfwYBvHYmZmHVApXf4uB26X9DZwJ/BX4LVcFwoBA0jD0e4HLAccU9FozczMmlFwz29n4HRJOwNPkib5XRfYHLi27aMzM7OOpqQuf5K6APsAR5KS0WzgY9KVrq+QEtVjpH7pN0dEsfuuWs3dJ8zM6l+1uvxJuqaFm0ZEHFLp+gs5Z5mZ1b9qDZveE9gQWBYI4B3gOc9DZWZmLdFG91A1mjAk9YuIidWsP6vHOcvMrM5VpUFVS05OZmb1r40aVDcBB+R7TGTzKP4E+LEHpTAzs5aoyKAUZmZmdWg94C5J3QAk7USa2PdI4NhaBmZmZh2Dr1CZmVlNtNEVqq8Ad2VPPwB2Ai4BzmqLLupZDM5ZZmZ1zl3+zMys3WmLBlVWz6LASGB3YNuI+Ee16yyo3znLzKzONZWzShk2vdiOO0XEXEnLk4ZLfyYiXm3NPs3MzFqjYNj0Bl8As4CrJD3asDIiDmyzwMzMrEMqq0ElaVNgFHCgpH8D44CewKKSvhsRd1QwRjMzs1IUm7JjDnBzWwdiZmYdX1ld/rKze08DpwDHA8OAtYH9gRMjYmAlgyxSv7tP2HxGjryJ004bzpQpL9K7d3+GDz+N/fffr9ZhmVkT2qrLXzVJOgD4EWn6kGnA8RExrmAb5ywzszpXjVH+NgTOj4jPgF2BOyNiBvAA0K/MfZqVZeTImxg27DQmT76YiBlMnnwxw4adxsiRN9U6NDOrAUm3SFqzhO3XkTSqjHrWAs4HdoyIDYBzgdtL3Y+ZmdW3cq9QvQXsB7wJTCQlk79K2pPU0FqtsmEuUL/P9tk8ffsOZPLki4FtcmsfpE+f45g06flahWVmzajWFSpJ3wJ+B7wN3An8FXitIXFIEjCAdO/vfsBywDER8UCJ9fQFBkTEPdnzZYG3gMUjYmZuO+csM7M6V/FR/iSdDRwDzATeATYizelxPnBaRFxUdrQtq9/Jyebp1Kkz6QJp19zaWUjdmDu32K0UZtYeVLPLn6QuwD6k3LQ5MBv4mNQz4yvAXOAx4Arg5vzEv0X2tRNfDr2ed0hEXJ9tI+CPQLeI2Kvg9c5ZZmZ1rirDpkvaA+gDjIyI97OE0yki/lx+qC2u28nJ5vEVKrP61IbDpvckdVVflnSv0zvAc5Wah0rSYsC1wCqkHhsfF5THRhttNO/5sGHDGDZsWCWqNjOzNlK1eahqNWy6G1SW13AP1bRpVwHfAMbSo8ehjBgx3ANTmLVjHWRQit7A3cCLwMERMb3INs5ZZmZ1ruKDUkjaVNKbwFaSliMNm34V8Hx25cqszey//36MGDGcPn2OQ+pGnz7HuTFlZlUnaQngIeD2iPhuscaUmZl1fB423czMaqLer1BJ+glpZL8JBUXbRsSHue2cs8zM6lw1BqWYDqwdEZMlPQ48HhEnSOoDvBQR3VsXcrP1OzmZmdW5em9QtZRzlplZ/avGPFQfAr2zIWM3Bu7J1g8C/lvmPs3MzKpCUqfs3+Ul7S1pjVrHZGZmHUO5DaqrSXN7PAY8B/xN0tHA9aS5P8zMzGrO9/yamVm1edh0MzOribbo8lfre36zGJyzzMzqXDWHTV8JWBN4HOgZEe+WvbPS6nVyMjOrc23UoKrpPb9ZDM5ZZmZ1rhrDpi8uaRTwJvBXYAVghKRHJS1bfqhmZmYV5Xt+zcysqsq9h+pCYGlgVaBh3o2TSDPQt+oeKkkHSnomt7whaVbW993MzKwUvufXzMyqqtxh0/8L7BQR4yV9CqwfEa9LWh94MCKWqkhwUlfgYeDaiLg8t97dJ8zM6lxbDZtey3t+s/qds8zM6lxTOatLmfvsDswssn5RoJLJ8WTgvXxjyszMrBQRcYekThExV9LywGLAMzUOy8zMOohyu/yNBn4hacnseWRzelwMVOSMn6SlSd0ITyxWPmjQoHnLiBEjKlGlmZl1MB423czMqq3cLn89gWuA3UlXpD4jnfG7DzggIv7X6sCkU4E1I+KgImXuPmFmVuc8bLqZmdWLig+bLmkA8G/SoBT9SV0HX46Il1oTaEEdzwLHR8Q/ipQ5OZmZ1TkPm25mZvWiGvdQPQgMjoingNfLjqwRkr4CrA48Wul9m5nZQqVh2HSRhk3/Wbbew6abmVlFlHsP1VvASpUMpMDqwH8jYlYV6zAzs47Pw6abmVlVldvl71pS//PxwCRgRr48Ig6sQGxN1e/uE2Zmdc7DppuZWb2oRpe/AG4oPyQzM7O2kQ2bvhIwMLuP6qmIeLfWcZmZWcdQ1hWqWvPZPjOz+tdGg1IsThqVdk9gLrAm8BtgGWD3iHivmvVnMThnmZnVuaZyVovvoZLUWdJpkp6W9JikUyR1rVyYZmZmFXchsDRpVNrp2bqTSD0tfA+VmZm1Wild/n4JHAGMBGYDPwb6AYdXIS4zM7NK2BXYKRs2HYCIeC0bmOLBmkZmZmYdQikNqu8C+0XEPQCSbgXGSDoqImZXJTozM7PW6Q7MLLJ+UdLE9GZmZq1SyrDpy5Nmm2/wCNA1W29mZtYejQZ+IWnJ7HlIWgO4GGiTUf7MzKxjK6VB1RmY0/AkIuaShktfpNJBmZmZVchxwCzSBL+LAc8CL2XPf1DDuMzMrIMod9h0MzOzerAKsBdpUIr+pLz3ckS8VNOozMyswyi1QXWypM9zzxcBTpT0UX6jiDi91ZGZmZm13oPA4Ih4Cni91sGYmVnHU0qD6mHgawXrHgUGFqzzZBtmZtZevAWsBDxV60DMzKxjanGDKiK2rmIcZmZm1fAccJuk8cAk0r2/80TEgbUIyszMOg7fQ2VmZh1ZADfUOggzM+u4FFF/PfQkRT3GbWZmX5JERHT4uaCcs8zM6l9TOauUYdPNzMzaPUmdJZ0m6WlJj0k6RVLXWsdlZmYdU1kNKkmdKx2ImZlZhfwSOBl4AhgH/Bj4fU0jMjOzDqusLn+SPgBGATdGxCMVj6r5+t19wsyszlWry5+kN4EjI+Ke7PlWwBigZ0TMrnR9LYjHOcvMrM5Vo8vfAaQ5qEZLelPSBZI2KjtCMzOzylkeeDr3/BGga7a+oiQdK+kFSc9LGi1p2UrXYWZm7VtZDaqI+EtEHAosBxwFLA38WdIrks6UtFYlgzQzMytBZ2BOw5OImEsaLn2RSlaSnUj8IbB5RAwEXgXOqWQdZmbW/rVqUIqImAX8BfgTcBewMnAI8KSkhyUNaH2IZmZm7U9EPAWsERFTJXUjTSD8YY3DMjOzNlbWPFSSugDbA3sDuwGzgFuB7SNirKQewB+A0cDqFYrVzMyspU6W9Hnu+SLAiZI+ym8UEac3tyNJO5FOGhY6JCKul7Q7cCXwBVB0f4MGDZr3eNiwYQwbNqzZAzAzs/pQ7qAU/yN1qbgLuBG4PyLmFGyzFzA8Iire/c83+JqZ1b8qDkrxEGlC3+ZERHyrgvUeDvwEWD3rZtiw3jnLzKzONZWzym1Q7QvcFRHTWxtcOZyczMzqX71P7CtpdWD5iBibPe8MzASWjYgPc9s5Z5mZ1bmmclaLu/xJyp/Fex/YTCqeByPi7yVFaGZmVn9WAG6StEFEfADsDzyfb0yZmVnH1+IrVJLmNr8VkLpQVHXiX5/tMzOrf/V+hQpA0lHAMcBs4G3gmIh4o2Ab5ywzszpX8S5/tebkZGZW/zpCg6olnLPMzOpfRbr8FdmpSEPEdgXyO4/Cs3NmZmZmZmYdUbnDpg8GriD1H29oTEX2OEgjAJqZmdVUwf2/TfL9v2ZmVo5yR/l7DniJNCP8J4XlETG59aE1Wb+7T5iZ1bm26PIn6Q3Syb9FgE9Jc0UtzZfDqs87KVit+3+ds8zM6l9TOatTmfvsB/wsIiZExOTCpfxQE0nrSnpI0nhJ4yRt1Np9mpnZQulC4Blg3YjoFRHLAn2AvwEnRESnbHHPCjMzK0u5V6jGADdGxB8rHpDUA5gIHBoR90raDTgvItbObeOzfWZmda6NrlC9C2wfEc8WrB8APBIRS1Wz/qwu5ywzszpXjUEp/glcJmlXUuNnZr4wIk4vc78A2wMTI+Le7PldgAe5MDOzcgSwMvBswfp1gBltH46ZmXU05TaotgWeJPVDX7qgrLWn4dYE3pF0FbA+8DHw41bu08zMFk6XAtdL+i2pUSVgY+BY4NRaBmZmZh1DWQ2qiNim0oHkdAV2AraJiCeyLn/3SuoTEV80bDRo0KB5Lxg2bBjDhg2rYkhmZlaPIuIcSR8ChwInA9OAfwP7R8SfaxqcmZl1CGVP7CtpEPAjoD9pmPSXgUtaO+yspIOBYyNio9y694FvRsSL2XP3Rzczq3Oe2NfMzOpFUzmr3EEp9gJuBG4DHiU1qDYHdgf2jojRrQh2edLZw29HxFOSvgncCvSOiBnZNk5OZmZ1rloNKklnA7+MiGnZ40a18p7flsbjnGVmVueq0aB6AbgiIi4qWH8CcFBEbFB6mPPt55vA+cBipDlDfhARY3PlTk5mZnWuig2qB4E9IuLj7HGjqtyFvSEe5ywzszpXjQbVNGC9iHitYP3qwISI6F5WpC2v38nJzKzOucufmZnVi2pM7PsiMLjI+p3xEOdmZtbOSdpc0nO1jsPMzOpfucOmnwHcJmlz4Ils3SbAEGBoJQIzMzOroiWAAbUOwszM6l9ZV6iyoWYHA4sAw4ADgbnA5hFxW+XCMzMzMzMza7/KvUJFNjz63yWtArwdEXMqF5aZmZmZmVn7V+49VHkvAH0qsB8zMzMzM7O6UvYVqpwOP0KTmZnVD0lvAs0Nq9etLWIxM7OOrxINKjMzs/bkp7UOwMzMFh6VaFDdAHxSgf2YmZm1WkRcV2y9pJVJ9/zObeOQzMysAyvrHipJ32x4HBFHRcQH2fpOkn5YqeDMzMwq6N9A31oHYWZmHUu5g1L8TdLvJPVoWCFpA+BJ4LRKBGZmZlZhvufXzMwqrtwG1TeBbwETJO0o6TzgX6Szf2tXKjgzMzMzM7P2rKx7qCLiMUkbAtcD9wBzgD0j4u5KBmdmZlZBvufXzMwqrtx7qJYCLgb2Aq4GngJGSDqggrGZmZm1iu/5NTOzaiu3y9+rpG5/20TE4cDmwC+ASyX9o1LBmZmZtZLv+TUzs6oqt0F1KbBBRIwFiOR3wEDg4wrFZmZm1lq+59fMzKpKEc1NJt/Ei6UlgbWAucBLEfFpheJqrt5oTdxmZlZ7koiIqo+8J6kr6Z7ffajBPb/OWWZm9a+pnFXuPVSLS7oJeB94DHgCeF/SCEmLlB+qmZlZ5fieXzMzq7Zyu/yNAAYA3wZ6Al8BdiHdS/XryoRWPR9++CG77LILH374Ya1DMTOz6mqTe34l7S6pKr00nLPMzNq3chtUuwAHRcRDEfFZREyNiAeAQ4H9KxdedVx33XXce++9XH/99bUOxczMqqvq9/xKWgO4gCpNHOycZWbWvpV1D5WkV4HjI2JMwfqtgGsiYrUKxddY/WX3R48I+vXrx6qrrsqkSZN47bXXkKrehd/MzAq01T1UWV1LUoV7frPRAx8EhgM3RsTiRbZxzjIzq3MVv4cKOA+4UtLxkjaWtKGkg0k3/f5R0rcalnKDrpZHHnmEmTNn8o1vfIMZM2YwduzYWodkZmZVUol7fiXtJGl2keVA4PJsea4a8TtnmZm1f+VeoZrbwk0jIjqXXEHz9Zd9tm/fffflk08+YdNNN+Wxxx6jV69e3HLLLRWO0MzMmtMWV6gk3Ujq3nc8MA7oDGwMXAQ8FBHHtmLfRwMbR8TBkvoCzzd2hWqjjTaa93zYsGEMGzasRXU4Z5mZtQ9N5axWDZteKy1tUO25557cfvvt861bYoklOOqoo+jRowfTpk3jsssu49NP5+/5MWTIEG677baKxmxmZvNrowbVJ8DWEfF0wfpNgL9ExFdase9/AT2A2cAipC6FE4CdIuLt3HbOWWZmda6iDSpJGwMTImJGbt2uwHsR8XirIm15DC1KTi+88AK77747vXr1YocddmCRRRrv3TFz5kzuu+8+PvnkE+644w4GDBhQyZDNzKxAGzWo2uSe3+auUDlnmZnVt4rcQyWpi6Q/Ao8DmxQUDwX+KekKSRXv4leuAQMG8Mwzz7DWWmtx7bXX8t577xXd7t133+Xaa69l7bXXZvz48U5MZmYdR93c8+ucZWZWn1p8hUrSycBxwNCIeLhI+TbALcDPI+KiSgZZpK6S76G6/PLLOeWUUzjhhBMWKPvNb37DeeedxxFHHFGhCM3MrDltdIWqpvf8ZjE4Z5mZ1blKjfJ3EHBcscYUQEQ8CPwIOKzkCNvAhhtuSK9evYqWLbnkknzta19r44jMzKzaIqJTC5d207sCnLPMzOpJKQ2q3sDTzWzzCLBq+eFUz7hx41huueUAeOaZZ7j00kt55plnAFhuueUYN25cDaMzM7NKy7r4dStYt6ukTWsVU0s5Z5mZ1Y9SGlTv0HxjqTfwQfnhVM/jjz9Or169+POf/8wLL7zAiBEjeP7557n77rvp1asXjz/eJuNpmJlZldXjPb+FnLPMzOpHKQ2q24GzJHUtVpitPxO4t7VBSbpQ0hRJz2RLqyfdePLJJ3nkkUdYa621GD9+PLvvvvu8m38feeQRnnzyydZWYWZm7cNJwDbANhHxj3xBRHwX2A7YjXRfcLvknGVmVj9KGZSiF/Av4AvgYtIEiVOBrwBfB44FugFbRMQ7rQpKegw4KSIebaS85Bt8d999d3bbbTcOPvjgBcquueYaRo8ezZ133llOuGZmVoZqDUoh6UXg1Ii4o4ltvg/8KCIGVrr+InU5Z5mZ1bmKDEoREVOBTYEngAtJDapXgSeBc4C/A5tUoDG1KLAh8GNJEyTdJql3a/YJcOeddxZNTAAHH3xwixLTiBEjWhtGXfBxdjwLy7H6OC1T1/f8Qvk5a2H8bixsx7ywHS8sfMe8sB0v1P8xl9Llj4j4KCIOB5YGBgLfIM0Mv2xEHBcRlbh/akVS4+ynwHqkPvCjJc3XIhw0aNC8pa0+hHr/sFvKx9nxLCzH6uO0TF3f89saC+N3Y2E75oXteGHhO+aF7Xih/o+5SzkvioiZwIsVjqVh328AOzU8l3QB8DOgL/BGw3qPcGRmZo1ouOd3u4iYVVhYyXt+zczMympQVZOk9YD1I+KP+dXAAknRzMysiHNJ9/w+Jampe36/W7MIzcysw2h3DSpgLvA7SWOzq1VHAc9FxFs1jsvMzOpAREzN5pr6Feme38WyIgH/A24CzqpQN3UzM1vItbsGVUQ8L+k44O5sjpC3gP1qHJaZmdWRiPgIOFzSMUA/YEnSPVMTI2JuLWMzM7OOpcXDprcnkuovaDMzW0A1hk1vb5yzzMw6hsZyVl02qMzMzMzMzNqDkoZNNzMzMzMzsy+5QWVmZmZmZlamhb5BpeQ6ST/MrftA0jO5Zf9s/RqSHpb0b0n/krR27jWHZOtflXRZNs9Ju1HKcebKD5F0d5F1HeI4JW0s6Z/ZugmSDsi9pl0fJ5R8rNtIGifpWUmPS/p67jXt+ljL/O6uKul/kgbl1nWY45T0nez48mVLZGXt+jitdIXfDUm9JN0q6fnssz45t22H+G6UeMxLSRopabyklyR9L1dWF8fc0uOVtE7BZztBUkgakpXXxfFCyZ/xOpLGZsc8XtIOubK6OOYSj3cbSU9Lek7Sg5LWz5XVxfFC8byWK7td0iW553X7NzYAEbHQLkB/4O/A58APs3VrAa80sv2/gKHZ48HA86RheAcCbwLLkBqpNwE/rvXxteI4lwL+AHwK/Dm3vsMcZ/a5TQG2y56vDLwHrNHej7OMY10kO7YNs+e7AC93tM8095puwD+Bz4BBHfE4gV8ApxZZ366P00vFvhu/Ay7KHi8GTAI26yjfjTKO+S7gvOzxysBH2b91ccylHm/Bay8EblwIPuOHgEOyxxuS5pXrUi/HXMrxAr2y7/C2WdnawMvAovVyvI0dc67sx8D7wCW5dXX5N3bDsrBfoToGuBIYlVu3OTBH0iPZmYHTJXWWtBLpS30zQESMARYn/cfeDbgrIt6PNBzv5cABtB8tPs6sbB/gbaDwjEJHOs5FSfPQPAAQaZ6z90lJuL0fJ5RwrBExE1gpIsZLErAa8GH2mvZ+rKV+dwEuBa4lDZHdoKMd5+bAt5SuOD4i6ZvZ+vZ+nFa6Yt+NH/Dl7/MKpN+zqdnzjvDdaPExS1oK+DZwFsz7Ld+ENN9YvRxzqZ8xAJK2BPYCjsxW1cvxQunH3Jk0MTfAEsCM7HG9HHMpx7sGMDUi/gYQES8Bn5AaW/VyvFD8mJG0NbAj6cR9w7p6/hsbWMi7/EXEsRFxY8HqLsADpA/7m8AOwHHAKsDbMf/8JW+R/gBfhdR6LlzfLpR4nETEHyLibOCLgtd0mOOMiBkRcVXDRpKGkX6kH6edHyeU9ZnOkrQc6VjOJ014Cu38WEs9TkmHAV0j4oqC13So4yQ1iP8AbAD8BLhDUrv/LbLSFftuRDJb0g2ks7gPkc5gQwf4bpR4zKsD/wX+T6kL9zjgaxExjTo55jI+4wbnA6dFxCfZ87o4XijrmI8BfiLpLdLv4lERMZs6OeYSj/cVYDFJ20O6PQEYQGp01cXxQvFjlrQi8Ftgf2BOrqhu/8ZusFA3qIqJiCsi4riI+DwiPgZ+DexBeq8Kx5gX6QtRWNawvt1q4jib0iGPU9IppLOb34mI6dThcULzxxoR70bESqSzXNdIWpM6PNbGjlPS10hnao8s8rIOc5xZ2ZCIuDVLyGOBR0ln6evuOK18EXEAsDSpm/bp2boO/d0ocsxdgVWBTyJiC+C7wG8kbUQHOOZinzGApM1JXaDyf7DW/fHCgscsqRtwC3BQRKxMOsF0uaRV6ADHXHi8WQN5d+BUSc8CB5K6zs2kjo83u+/pJuDEiPhvQXHd/43tBlUBSd+TtF5+FTCLdL/NClmXqQYrklrKU7LHhevbrSaOsykd6jglLSrpJmA/Uj/tZ7Nt6u44ofFjzW58zTesngaeBdalDo+1ic/0QKAn8KikZ0jHMlLSrnSg45S0pKRTC36L8r9TdXWcVjpJO2RneomIz0h/pHytI383GjtmUvd0gGuysteAscDXqeNjbuJ4G+wLXF9wRr9ujxeaPOaBQI+I+HNW9jjwAqlrZ90ecxP/jzsBn0XE1hGxfkQcB6wJvEYdHy8wiHTLwa+zHH0ksK+kK+kAf2O7QbWggcDZ2X1T3YFjgVsi9ct+jfQjhtIIM3OBCaQbYneVtGz2ZRgG3FmL4EtQ9DibeU1HO84bSH+Abx4Rk3KvqcfjhMaPdQ5wtaQtACQNIPVVfoL6PNbG/o+eEBFrRsQGEbEB6Q+t/SPiLjrQcZIGizkGaBjVa0PSH49/oT6P00q3D3CGkkWz53+nY383ih5zRLwBPA18HyDr2rw5MI76PubGPuMGWwF/K3hNPR8vNH7MrwG9sqtySOoHrAOMp76PubHjDeBeZaPUStqXdM/Yc9Tx8UbEYxGxSi5H/4GUuw/rCH9ju0G1oLNIN7NOIH15HyXdVAfpSsaRkp4HhgN7R8TciHgOOJv0H+El0h+w57V14CVq6jiL6kjHKWkz0s28qwMNQ6c/I2mHOj1OaORYszNfuwMXZWeFriaNpPNWnR7rQv3djYg5pJt0f5j9Fl0D7BsRH9TpcVrpTiKNBDYBeCpbftvBvxtFjzkr2wPYQdILpPtQzo6IJ+v8mJs6XkgDF0zKv6DOjxca/15/TPqMfytpAnArMCwiJtb5MTd2vAEMBa7IvtOHA7tn3Xjr+XibU9d/Yyt9bmZmZmZmZlYqX6EyMzMzMzMrkxtUZmZmZmZmZXKDyszMzMzMrExuUJmZmZmZmZXJDSozMzMzM7MyuUFVh7LJG8+X9LqkaZJelnSK0izU7Y6kkLRdia95KHtdY8vW1Ym2xfGtLemf2eODJC0wyZykS7LPZ8sW7G/rIsf4maSx2RDvLY1rL0nLZ4/PlDS2iW0fknRuS/fdTL39JN0t6SNJb0m6UGl2+4byPpLul/S5pBclDS54/TaSnsver4ckrZ4rk6Qzsv1+JGlUNtdM1UnqKekaSR9K+p+kEZIWb+lxF9nfWElnZo+PlDS8DQ7DrN1q63zW2O91rrys30XnLOesXFl7zllrS3og+6wmS/pRrmyApH9I802uay3kBlWdkbQUaULWTUlzEwwATgWOI5spvoMYAqyQLSeRZsVeIbc8WrvQALiENC9CUZLOIH0+e0TEIyXsd2W+PMavAf8B7pHUq7kXSuoDjAIafjwvAHYtoe6ySFoEuBv4gjSh5v6kea+GZ+UCRgMfAhsD1wG3SVo1K1+FNHHfDaSZ1N8BRivNFg9pEr9hwAHAlsDypLm02sLvSf/HtgO2BzYBfpPF3eRxt8CVwBBJa1Y2ZLP60E7z2RDgl2W+zjnLOQvab87qCowBpgAbkCYB/5mk/QEi4gVgMtkk2VaiiPBSRwtwBfA80K1g/Vak2bU3qXWMRWIOYLtWvP4wYFKtjyMXz+bAa7nnBwFv5Z4fCcwG9ixhn1tn71OXgvXLZ+t3b8E++mbbrt7COh8Czq3A+/ENYCaweG7dUOCd7PG3gOnAErnyBxrqJiX5sbmyHsAnDd8ZUmK7KFf+HWB6G3zOIjV6Ns6tOx54pSXH3cg+xwJn5p6fDFxVq++yFy+1XGqRzwp/r6t0XM5ZzlkX5crbS87qC9wCdM+V3w6MyD3fBHiVbJ5aLy1ffIWqjkhaFPgucElEzMiXRcQ/SD8CE7JtQ9Lhkl6T9KmkmyT1zMrOyS4FL50930zS7Ka6JEjaRdLTkqZnl7/3zpU9JOlnkv6SXf6eIGmnIvv4bnb5e5Hcuh2zS9Mlde+QtLKkO7PL8VMk/bJhv1l3hrGSTs3qe0fSUEn7ZNt+JOnnuX1NknSipGey/Y2RtGIT1R8F3NlIXHuTzgQeGhG3lXJMjZid/fuFpL7Z55rvWpDvIvFG9u+r2Xswryx7/rikWyVNlXRYQdx9JL0t6YLs+RKSrpT0nqSZSt1w9mwkxpeBnSLis9y6ABbNHm8KjI+IT3PlY4HNcuUPz3thxDTg6Vz5h8Dg7DPvTppN/anG3rCs28Lfs+/qa5JOyndhkHRE9j2YKunn2ff3sML9RHJYRDzZ8B6Rku7fWnjcSNpD0ivZ9+q3LNgr4C5gP0lLNnY8Zh1RLfNZM3HN61am5GeS3pX0vqTjsnxRUhf2bF/OWThnFdNWOSsiJkXEvhExPftubwF8ky9zGsC/SFcMv91YvFacG1T1pR/pi/5kscKIeDD7j93gbOAEYBtgIOlsIMC5wHvAz7Mf9CuByyPioWL7lfQt0lmM64H1gRHAjZK+ntvsJ8DNwEbAS8CVkjoX7OouoAvpMnSDfYBbI2JWo0e9YDwC7gA+yurbH9gF+EVus42BNbN//5TFfCywcxbrTyStm9v+TOBC0tmZbtnxNlb3jsBfi5RtS+oCcHtEXNfS42mMUneY84EPgMda8JKGz2Mz0lmoQg1nnr5O6u7QUM9Xgb8AYyLih9nq3wD9SZ/VAFLyuCL7I2g+EfF+RDyQ218n0nvd0G1kBeDtgpe9S+oq0pLys0ldM94EPiUlgKFFjo8sef0FeBxYj9R16IQsHpQa+r8DTiedtV2fL5NgoySNBCYBywFnteS4Ja1D+u5dRvqediusKyJeBP5HOiNvtjCpST4r0VHA/5G6wm1P6sLVVMOlKOesRjlntWHOKvAWqZH4GHBrw8pIl6n+Bgwu8hprSq0vkXlp+UL6z9Siy+PZdifmnm8DzAGWyp5vSTqTdDPpLNHiTezrduCWgnU3A6Oyxw8Bt+XK1svq752LpeFS+A3AddnjRUgJZutmjmW+7hPAtqQzQJ1z67Yi/YB1IXVnmE12uR5YN4thm9z2HwN7Z48nAb/Nla2abb9BkVj65o8tW3cQ8Bnpkv+DpK4EC7y2mWPcOtvvZ9kyLXv+KrBtQd2r5153JlnXg8LygrKDsrJ8F4aHSAn5UdIPaueCY1o393yt7PWrtuBYLsriH5A9vwoYWbDNIQ2fKTAROLyg/Hrg2tx39V+kPu6bAPeR7rtYpEjdhwLPFqz7PjAx912+Lle2NKlrx2HNHNMAUhJ7BBgPdGrBcZ8PPJQr7wr8l1yXv2z9/VSgG4sXL/W0ULt8dhBNdPkj162MdNXhrFzZQFrQhR3nLOesdp6zSPd+7Uq65+13BWWnkuvS6KVli69Q1ZcPsn+/0sLt8zfBjiNdkVwLINJNp9cC+wLHRHbpW9IflEZ/aVh6k876PFFk3/1zzyfmHn+S/VusG9+NwG7ZmcTtST9iDxfZrin9gSWBqQ1xAveSGmh9sm0+iC8v10/P/p2c28cMcl2zyL1XEfEG6apB/vgaLNuw/4L1i5FuBt2WdGn/WpU3StVGpJtFBwJLR8QaEfG3pl/SYh/G/F0YIN2Uuinwn4iYk1t/PbCGpN9Jup8v35/Cq47zZF0Ifpvtc79IN7jCgu812fNpzZVnZ1f/SPrj4c6IeIJ0VbM/xW9e7g8MyH+HSVeI+mbfubWBZxo2jogPSH8ANCkiXoiIx0j/X9YnnXFs7rjXAZ7N7WNW/nnOh3z5vTJbWNQqn5Wi8PfiedIVh1I5Z5XHOasKOSsrHxcRd5EGUDlCuVsxcE4qixtU9WUi6Ufz68UKJd0iaffcqtm5xw0/KnOzbTuRfgDnkM72NTid9OPYsLxN+vEo1Jn5f6hmFgupyLr7gVmk/rl7k658zS2yXVO6kH5Q8nGuD6xBusQO8x97g6bqKdy+cyPbR/Zv4f+ddyLilOxYDif9MX1aE/U1ZmJEvBYRr0fEh43UndelhH0X+xwnkEanOkbSBrn115POBH5M+nHfuakdZ9+nq0ldZPaNiNG54v+QblTOW550taa58mVIf3A831AQEVOB14DVioTShXQWc4Pcsh7pD6/ZpIRY+L38opFj6q40pO9iubrfJr0nDfdrNHXcFKmrWNfWzqT/h2YLk1rls1K0+PeiGc5ZX3LOml+b5SxJK0kqbNT9m9Sw75lb55xUBjeo6kh2NuYm4NjCfsGStiGdBXk/t3qD3ONBpD/mXsqeH0vqJrAncIKkr2V1vJf9ODYss4EXSZet8zYj3dhZ6jHMJl2q343Ur/umUveR1bsK6ezVaxHxGunH7BeU/53eoOGB0g20vYDnimz3Tvbv0gXr5/34ZGcxfwmcWvCD31oNjdb8D1/+B7pY8mrOfRFxJ+n+tt9nZ+x6kvp7D42I0yPiDmCpbPvG5qe4MHvNkIgo7Mv/OLBB/keeNMrS47nybzQUSOoBbJit/5CUPNbLlXcndRXJXxVt8DLpPoRJue/GBsDJ2R8O/ybdo9Cwr8Wy7RtzI7BDbvvVSGfUX2zBcT9fUFfn/HHkfJXU/95soVHDfFaKwt+LVVjwt78lnLO+5Jw1v7bMWf2B2yXlrz5tBLyfXflq4JxUjlr3OfRS2kI6+/EGqZvcNqQbe79PSjxX57YL0pmvrUmNoRfIhmcm/bB/Anw/e34Dqa94l0bqbEheJ5DOqJ1AOnPy7az8IXL3gLBgv+j5+pyT+hdPJzeMazPHXNgfvTPpLNW9fHmD5ktk93mx4JCwq2cx9M2tewc4IHs8iXQGZzfSj+DDwN8aiUWkH5pdc+vmqy9btyjph/BZoGvusyvat59GhqAt2KYTaf6IW0lJ6XvAVL7sc/7VbB97km72PpP5+6MXxjjvcyP9MTKd1E+8K6lby/nZZ7k98Hq274FF4to0KzuF9EfCvCX3eb1Amm9kAGmo8M8bPo+sjumks6PrkP7Iep6szzdpBKrJpFG/+mflrwCLFolliezzuTbb9tvZ8/Oy8g1ISf5IUleKG7LYi/ZHJ53pnAhsQfp/8ARwRwuPew3SGdbTSWcbf0v6I+bMgjreA3ap9W+LFy9tvVCbfHZQtv8dC5YtsvL87+J3st/CfbLfrr9S3j1UzlnOWe0hZ3UlNbrvzeraJavrBwX7uBW4oNa/D/W21DwAL2V8aGmUoctJCWZ69iN4UsOPYLZNAOeQktXU7D9Zt6zsLtLoLsqeL59t8+Mm6tybdIbji+w/5JBc2bwfuex5X5puUCmL/ZwWHu8Cc3qQfkzvzn7kPiCN+NRwQ+9BlJ6cLiIlvM9Iow0t1UQ81wPn557PV19u/RakLhhn5+o5s5F9bk0zySnb7tukRDyDNEHfD5l/Poxrss/oB5SQnLLn55D+0FiKlKhfy32/DiWNCvS9IjFdkMVebOmS+wz+kcX9ArB9wT4GZ8c1Dfg70C9X1o109nQyaRCTu8jdYF0kng2zY5tO6uJzXv59Jf1x9Hr2WTfst7Hk1J3UEHqH9H/kGqBnCcf9bVKinQ6MBO5h/nmo1s7KetT6d8WLl1ostHE+48vBDgqX57Pywt/F40hduT4m/a6W3KDK1jlnOWc19h61Sc7Kyntn8XxC6rr4E3JzTpH+Pnub3IAoXlq2NPwAWQcjKUhXkB5oduM2ll0efxf4eqRho2sdzyTSj/SVLdz+G6Q/jvtGCf+Bsm4sm0TEL8sK1Kqi1M+/wnWfBawYEYe3dd1m9aI95bP2EItz1sKtmjkr+8z/APSP0u9vX6j5HiprU5L2Iv1nfbY9NKbKERFjSWfuFpi8uBnHk5tLwxZu2ahKB5DOlpqZVYVzlpXgSFJ3QzemSlTKaCtmlfBz0vdut1oH0kpHkyaQvKeE1+wTJUxgbB3e4aS53Eoe3MXMrETOWdYkSQOBlUjdBK1E7vJnZmZmZmZWJnf5MzMzMzMzK5MbVGZmZmZmZmVyg8rMzMzMzKxMblCZmZmZmZmVyQ0qMzMzMzOzMrlBZWZmZmZmViY3qMzMzMzMzMrkBpWZmZmZmVmZ3KAyMzMzMzMrkxtUZmZmZmZmZXKDyszMzMzMrExuUJmZmZmZmZXJDSprdyTtJykknVTrWCpJUn9JN0p6R9Knkp6QtGeV65SkIySNlzQ9q/sWSQNL2Eff7PNYvYz6J2WvbVhmSXpd0ilZ+dYF5YXLQ6XWaWZWa85jFavvWkk3NFEekrYrc9+F+eYLSS9KOiQrP6iZ/HRtmYdlHZAbVNYe7Qe8Bny/1oFUiqTNgCeAacDOwIbAzcDNkg6vYtUjgHOB3wPrZHV/Bjwuaesq1pt3ErBCtqwGnAWcK+l7wKO5shWA/wIn5J4PaaMYzcwqyXmsbawAPNyK1+/Dl/lmTeAa4CpJ3wRuyZWtnG2/Z27dD1pRr3UwXWodgFmepK8AOwCHADdI2jAixtc4rFaRJOBaYFREHJYr+o2knsAvJY2MiGkVrnc34EBg44h4Llv9BnCopC+AayWtFRFfVLLeIj6JiHdyz6+TtB+wZ0T8EZhXJmkuMLVgezOzuuE81nYqkCs+KtjHr7IrVEMi4mFgOoCkhr+X/+f8ZMX4CpW1N3sBX5DODL0CHAQg6ZeSxuY3lHSypCezx70kXSdpatYVYYSkJbKyrSW9JemSrPxMSV0lnZ+tnyVpsqSjcvvuLunKbPv/SDpU0mxJfZurr4jNSWe+zi9S9jtgJ2BGFuc7ko6R9IGk9ySdntUnSf+QNO9MXHb870laupF6DwfuyjWm8s4CegPbZ/uaJOlYSY9KmibpX5I2LnyRpFMk/btg3ZGSitXRlNmkz9nMrKNxHqtcHmuScl3+JPVU6o74qaRXJB2YlZd68cD5yUrmBpW1N0OBMRExGxgNDJXUldStYHNJK+S23TtbD3A1sDSwJakrwlqks2kNVgJ6Al/L1p8M7EpKfA3bXixpxWz73wHfAHYE9gV+DHTO7a+5+vLWBz6NiJcKCyLio4h4IiLmZqu+ChxMaugMA06SdFRERPb865KGKt3PdAZwTER80Ei9GwP/KlYQEe+SEv2mudVnAL/K1s0ALiny0puAtTX/PVj7ZOublf0BMCQ7vrta8hozszrjPFa5PFaKK4EBwNak7ng/L+XFkrpJOprUPf7uCsRjC5OI8OKlXSzAisAcYL/s+aZAALtlz/8NHJ09XjXbdhWgHzAXWCq3rz7Za1ch/bgGMDBXvjuwZe75otk22wCLk85ObZ8r3yEr79tcfUWO61TgrRYcf0OcG+bWnQU8k3v+U+BNUp/xUc3sbxZwWBPljwGXZ48nARfmynYF5mSP+2ZxrZ49HwuclT1ejnQ2b9VG6phEapx9li1zgE+B8xrZ/i3goFp/F7148eKlnMV5rOJ57FrghibKA9gO+EqWi7bJlR2blXdp4rXTc/lpLvAhcGKRbbtk229d6++Yl/a5+AqVtSffJSWXe7PnTwBv8+VNvbfw5SAFewOPRsSbQH9AwBRJn0n6DHgh227N3P4nNTyIiDuBbpIulHRPrqwzsDawCPBk7rWP5R63tL4GHwBLNnHceTNi/r7247L6GpxHapB8DTi6YaWkFxpikdQQy/+A5Zuoa0VS8mgwMff4E6CTpM4s6EbSVSlIn8OTEfFGE/WcBWyQLX2AJSPi5Ca2NzOrV85jlc1jLbUG6bifya17tAWvO4KUm9YnDTyxdET8psS6zTwohbUr+wFdgQ8lNazrBOyS9a++CfippK+SEtG12TZdgM9JP4qF/kvq+gbpSgkAks4l/ZBeDfyR9KM+KSue3bBZbj/5x83VV+hJYDFJAyJiviQhaVlgJOlMGqREnNeZdNaswTKkhlA34OvAPdn6nUjvHaQrUwCPA4OKxIOk5UndR57IrZ5ZbNMi6/4E/FbSANLn0Fx3v/cj4rVmtjEz6wicxyqbx1qqYTCM/DG25D6ot52frBJ8hcraBUlrkP74P5Evr2ZsAOxC+oHdLyJeASYAh2Vlt2YvfxlYDOgcEa/lfhx/TepvXsyRwPERcXJE3Jy9HtKP8WukxsVGue3zj0uqLztT9zxp+PBCx5ASypvZ88Uk9cuVDwLyAz5cQrov6mzgMkmLZ3VMboglIiZn2/4e2LXY4BLAmaSzpn8pUtakSH3dHwC+R+rO8qdS92Fm1tE4j1Ulj7XU66QGVD7fbVjiPszK5itU1l7sB3wM/CEiZuTWPy/pUVJ3iYtJZ/dOBx6KNLACEfGipL8Af5R0HOkM3mWkRPFfSWsVqe9D0hnDJ0jzSfwuW79oRHwm6RrScLANw8M2lEdz9TVyfEcD90uaTWrofEG6Sfg04NCImJY7m3mFpONJNwgfT0pWSNqddOPwusAU0pDoP8+2WUBE3CfpUuBepYl0/05KlEcABwC7RPlDpt9ImuPqkfAQsmZm4DxW8TyWWUHSjgXrJkfEiw1PsrovJfWeOJjUgD23iX2aVVatb+Ly4iUiAF4ELm2k7ACym3FJN+fOJf1457dZmtTlYCopoY0Cls/KtqbgxlRgC9IZs+mke4dOIfUv/1lWvjhwA+lG1f8AP8n2sUJz9TVxjF8njRz0Hqn/+GPArrnyhjhPAj4iDdBwXFbWM3s+PLf9YFLXik2bqff7pLOBnwHvkkaUGlCwzSRyA1jk3zMKBqXIvT8zCj+HInXPt98WfA88KIUXL17qcnEeq3weI3WJjCLLJVl5ANtljxcF/pDFNYk0gmBzg1Js18LP1oNSeGlyUURgZvPLzqI9EBGfZc83Bv4JLBYRpfbtbmmdWwMPAl0jDbfbbknqA7xESswf1zgcMzMrsLDnsfYUi3V8vofKrLgzSF0HVpe0IWkyw9HVSkL1QtJikvYidQ25zY0pM7N2y3nMrI24QWVW3P6krm7jSQMwvE66iXhhF8AVpBGaPPS5mVn75Txm1kbc5c/MzMzMzKxMvkJlZmZmZmZWJjeoOiBJa0v6Z/b4IElvFdnmEknTJG3Z9hHWhqS+kkLS6qWUtXDfe2WT5Zbz2km5YW1LfW0/SXdL+kjSW5IulNQtV95H0v2SPpf0oqTBjeznAEljC9b1lHSNpA8l/U/SiIb5QloQV8P72diyZDnH20R9BxXsf5akl7Phc4ttv6ik5yVtV7C+yfdL0imS3sz+7/wlm3emoUySzsg+h48kjZK0XK6+8UoTYJpZjnNWcc5Zzlm57Z2z2jk3qDqmS0gT5hUl6QzgcGCPiHikzaJq394kzePxRqkvVBrxbhRpiNo2I2kR0vC1XwCbk/rL7w4Mz8oFjCbNVbIxcB1wm6RVC/azDXB5kSp+DwwAtgO2BzYBflNimJuR3tfCZWqJ+2mJ/+b2vwbwC+BySd/Ib5Ql75tIx5Zf3+T7lf0B8UPS/531SUMC/1lSw+/osGw5ANgSWB64GiDSfF8XA7+q6BGbdQzOWaVzzlqQc5ZzVu3Uetx2L5VdSD9Sr+WeHwS8lXt+JDAb2LPWsdbgvelLwXxK7WG/lDhXU+513wBmAovn1g0F3skef4s0P8kSufIHgHNzz88gzSc1ARibW///7N15mBTV1cfx72GRARSiIuIGAyiKIKBAxB3cIi6ACy5IEtzGnWg0xoiJuCUxGlcMEdeggL6oiAtEExV3VBRkcYsgYFxQiOLCjuf949ZA0fTM9PTUTHfP/D7PUw/ddaurzu1u+sytunWvAXcBPWPrhgIf5vK9Lud4G3zPY+tfBG6NPd8VmAG8Q8ocJBW9X8CFwCmxsi7RPraNnk8Ebo6VHwUsjz3fBPgf0KYm3hMtWgphUc4q971RzlLOUs4qkEVXqGqfs4HH0hWY2UDCmcDT3P2R2Pre0SXfEWa21MyGm1lDM7s+Wr/azBaY2dnR9reY2YTY6y8wsx/NbIvoeQszW2tm25lZfTO70sw+NbNvzWyimW1TzuX1583sxOjy8yaxYxwWXcZvaKG7wXlm9mp0GfsNC/NrlG57jpnNM7MVZjbTzI4s4/0oMbPvzayXVdB9wsy2N7PHosvqC83sz7H4Ss8Q/sfMhqR5bZnvZRV9ABzu0RwjESdMbgjQC5ju7t/Fyl8mnIErdQDhTN4jsXV4cLq7vxnVoQ0h8T2bQNxE+zzNzFaZ2S7R87bR+/uL2OdxcvS+fRN9PxtW8jBfpTzfD3iGDd+DUuW+X+5+k7vfG8XaHDgXmAN8EW27BOgbfVcaAycBb5XuyN1XAf8CzqxkHURqM+Us5SzlrPWUswpVrlt0WpJbCGdovgJ+Fls3hDAz+UGEy+z/l+Z1vQk/aqOB9oQzNZcTfvx6Ae2AKwlnCbcFDiectagXvX4CYdb3I6PnJwAzo8dXA59Fr9mF8MPwL6A+4fJy6XJwFN9RQBPCTOdHxmK8B7gjejw/qucAwhmXF4HXo7LdCWfA+gNtgGHAMuAnxM5AAf2AH1g/w/q6sjLe1zeBe6M67AfMBv4alfeMXtsLaJzm9WW+l7H6VPpsX5rj1CNM2vh49Pw24OGUbc4GPkjz2uHEzvallI2J6vcx0CrDWMp8P1O2ew54Jnr8L+CplNd/EL3fvQnf4+vK2M8QUs72AfsQumn0KuM1qWf7Mnq/CF0knHCW9ICUOs+MytZE8bZOee3phASY898LLVpyvaCcBcpZylnr1ylnFfCS8wC0JPhhrv8P3Tq2bgjwPfAtYcbwVUC3lNf1jl7XObZuALBf7HmjaJs+hOSxAugW/XAvBp4C/hRtexeh360BX8Z/eAnJ749EiS1a1yz6EfpLbN0DwD+ix5sQ+v72jp7PJ0oM0fN+wNro8dGEJNctel6PcDarSez9+Tkh+R2b5r1Ll5wOIpzJqR9bd0B0nAblvbai9zJWnySS082ERNwpen43MCZlm1OB+WleO5yyk1MnwhmvlwjzmdTLIJbS9+SH6PsXX8bFttuR0GVhTPQZb5fy+qNj254SfQ4bHZ/wPf8xdoyV0esfBRqVEWNqcsro/SL80bMHob/+UqBttH4/4I3o894TeBp4Hdgk9tq9gbXxdVq01NUF5SxQzlLOUs6qFYu6/NUupaOxLE5Z35TwH+kgwuXc+8q4DD2/9IG7PwYUWRiB56lYWX13X0a4rNwb6ExIfA8T+kcDHAJMBloAW7HhJeS57n6Zu/8YO+69hCR2WWzdWKB/1EXhUMKP7oux8rmxx98C9cysPuEH4WVgupnNIpxt/CiKudSdQGNgQZr3IJ2OhLOFS6PuFt8DkwhJs01FLy7vvczw+OWy4BbC5fyT3H1OVLSC9V0pSjUivJcZc/c57v4a4SxuV2D/Srz8KMIfMfHlwti+PyLckDwIGObun6a8/tXY42nAFsDWZRxrUewYXYHDCEnusQxjzej9cvcF7v424f3+FPilmRlwP3CLuz/m7q8DxxO+O/1iL19C+IOpRYYxidRmylnKWcpZylm1ghpUtYtH/6Z+rl+4+6VRQjiDcJPjsDSvX1H6wMyuISSINYT/dL1Stn2akJwOIJwFegnoYWa7EX5AXiGcWSyXmf2GcJbkRHdfEyt6BlhNSHQDgYdSElq6fVuUhA4m/IBOAo4jJKouse2uIIyY8zdbP9pNeRoA/2HDH9iuhFF5PqnoxRm8l1mL4r+HcJn/BHefGCv+lNA1Ja4VYWShivbb2MKwuk1L17n7Z8A3VO6HdaG7f5SyfJGyTVfCGbA+aV4f/06UJvMf02wH4Yxv6THed/enCQnkMDPrVMZr4sp9v8zskPj9Ch5O373P+j/C2hC61ZSWLwU+InSZSa3D2gziEantlLOUs5SzlLNqBTWoapfS//SpPx7r/iO4+2zgz8BlZtatnH2dBQx199+6+4OEM4YQukRASE4HRMtL0Vmbrwln7J5191XRf86vCH3Ew4vNdjKzRWa2hZn1Bq4BTk49yxMlqocJ/coPIySTCpnZXsDl7v6Su/+WcLZlERCfm+ERoLTsjAx2+wGwA7Ck9MeP8KP1J8L/IS/vxVT8XlbFXwlnyo5x90dTyqYC3eIJhnBGdmqG+x4L/Kz0iZm1AzYH3ss+3A2Z2VHAkcARQD8z65+ySbfY4x6Ez/LLyhwi+rdBBttW9H5dQRg1KuzYrAEhsb5HOIu3knB/RGl5Y8LZxviZ6S0JyTX1jLxIXaScpZwVp5ylnFW4arqPoZbqWwj/ERcB/WLrhrDxjY+NgHcJw3A2ZH1/9AaxbT4g9BFuR7hR8q1om/hNt58R/kPuHD1/iPAf76zYNsMIZ8QOISSDZwhdK7YlJNM/Ec6UrLvZN/ba/Qh9lT9KiX8+G/ZxXxc/4cdsNWFUmmJC3+DlhDOAxcT6jQOXEn5UWqSWpRyvPmGI1kmEH6O9CGd5HorKt4xeeyyx4WAzfS/j9SF064i/B5sCW5XxefeK9nMpG94s3SoW9xzCfCOdCAn5B6A4zb6Gk9IfHRhJ+GHdh5AYXgcmxMq3AJqXEVvp+9krNbZoaQRsBiwErohecw3hpthmsdfPjI59EOFs3O/LON4QwvcxfoyehO4X75G+D3tqf/Ry3y9C95EVwImEG73vJny3N43KRxC65BxI+K6PAz4k1h8eOA+YluvfCi1a8mFBOUs5SzlLOauWLDkPQEvCH2gY9ej62PMhpJ/rYB9CIrmK9Mlpn+iHYXn0A3Up8Fr8xwG4D1gUe35utJ/i2LoGwPWEs35Lgf8j9JsfEm270RJ7rUX/+a9OiX0+ZSSn6PnJ0Q/SCmAecE60vvQHrzQ5NSJ0i7gntSzN+9WWMCHhD4QzNXey4dwP9xIS9a/KeK/LfC/ZMDkNSXkPhpPmhtyo7Iay3sPYe7Ej8EL0XswBDi1jX8PZODk1Bm4h/BGxNKpjs1j5FOC+MvZXXE5sTjjDd1v0fhTFjvcx4d6J0tdfFh3/K8K9BWlvLk7zfSo9ozaG6AbcNK/ZIDll8n4R/uj5KPosnwU6xsqKCGfSFxDOfD/OxiMmPQhcmevfCS1a8mVBOQuUs5SzlLMKfrHoDZNawsIM22MICaKgP1wza0I4e/lTd0/skn2hMbNn3P3QXMeRysJs7L9197OqYd/FhES1k4fuKgXPzDYlnM3s6u6Z3lwuUqspZ9U+ylnKWXWR7qGqZdz9ZcLZo8NzHEqVmNlxwN+Bd+p4YjqZcLN0PvoN4Z4ByczPCfOtKDGJRJSzahflrFpFOasSdIWqFopGh7nL3dPNrF0QzOxDQteL/u4+K9fx5IqZNXT31bmOI53qjK22ne0zs0aEIXQP8Y1HjBKp05Szag/lLOWsukoNKhERERERkSypy5+IiIiIiEiW1KASERERERHJUiYTh+UdM1M/RRGRWsDdk5gsNK8pZ4mI1A5l5ay8bVCZ2QDgfnffLF257v0SESlsZrW+LbWOcpaISGErL2flZZc/M9uJMAFctWRbd2fPPfdkypQp1bF7ERGRKuvduzdHHnkkAMuXL6dDhw588MEHOY5KRERS5V2DKpoY7wHg19V4DP7yl79wxhlnsHz58uo6jIiI1AFmdrSZzTSzGWb2nJm1T/oYjRs35rLLLuP000/X1S4RkTyTdw0q4I5omVmdBznggAPYYost+Pvf/16dhxERkVrMzBoTTgIe4+7dgCeAW6vjWIMHD2bevHk89thj1bF7ERHJUl41qMzsHGCNu99T0bY9evRYt4waNWqj8rVr1/LHP/6R9u3b06RJE7p167ZREjrhhBO45ZZbWLNmTVJVEBGRuqU+oXt68+j5psCKyu7k+++/54wzzmCLLbagRYsWXHfddRtt06BBA4499lhuuOGGKgUsIiLJyquJfc3sDaAJsAbYBNgZmAUc7u6fxbbziuIeOnQod9xxB7///e/Za6+9+L//+z/uvvtupkyZwr777gvAggULKC4u5sUXX2S//farrmqJiEgaZlYrRvkzs18AdwJLCA2sfdz9o1h5hTnryCOPZOrUqfzlL39hyy235IorruDdd9/l0EMP5cknn1y33QsvvEDv3r1ZuHAhO+ywQ/VUSERENlJezsqrK1Tu/lN37xx1mzgcWO7u3eKNqUz873//429/+xuXX345l19+OQcddBB33HEH++67Ly+++OK67dq0acOWW27J888/n2xFRERqgaVLl3L00UezdOnSXIeSt8xsN+APwK7uvi1wLfCIpQwHVV6vipkzZ/LUU09x++23c+qpp9K/f38mT55MvXobp+g99tgDQHlLRCSP5O2w6VXx+uuvs3btWo466qgN1qcb1a9169bMnz+/ZgITESkgjz/+OI899hhPPPEEgwcPznU4+epnwCvuPjd6fjtwE7AlsLh0o2nTppW5g1deeQWAvn37rlu3zTbbsNdee2207Wabbcbmm2+uvCUikkfy6gpVnLvPd/dNs3nt//73PwBatmxZ4bZNmjTR2VcRqfXGjBlDcXEx9erVo7i4mDFjxlT4mnvuuWeDfyWtt4EDzGzr6PkA4GN3X1z2Szb09ddf07BhQ5o1a7bB+latWqXdXnlLRCQzY8aOpbhjR+rVr09xx46MGTu2Wo6Ttw2qqmjePNwb/NVXX22wfsaMGUyfPn2DdV9//TVbbrlljcUmIlLTxowZQ0lJCQsWLMDdWbBgASUlJRs1qg4++GDMbN3y6quvAuEKSnz9wQcfnItq5CV3fw64HphiZu8A5wH9K7OPLbfcktWrV/PNN99ssH7JkiVpt1feEhGp2JixYym55BIWlJTgTz/NgpISSi65pFoaVbWyQfXTn/6UBg0abHAjL8CZZ57J9ddfv+65u/PZZ5/RunXrmg5RRKTGDBs2jGXLlm2wbtmyZQwbNmyj7Zo0abLu+apVqzb4F8LVkcsvv7waoy087n67u3d0967u3sfd51Tm9b179wbg0UcfXbfu66+/ZurUqRtt+80337Bs2TLlLRGRCgy7+mqWXXQR7L47NGgAu+/OsosuYtjVVyd+rFp5D1XLli0566yzuOaaa2jYsCHdu3dn/PjxzJgxg9tvv33ddu+//z7ffPMNhxxySA6jFRGpXgsXLsxofZ8+fXjyySc58sgjN2qAQWhMPfXUU3z62WcUd+zIwg8/pHWHDlz7+99z8qBB1RJ7XbDzzjszePBgLrjgAlasWEHr1q3505/+xOrVqzfa9rXXXsPMOOigg3IQqYhI4Vj44Yew224brtxtt7A+YbWyQQVw880306JFC0aMGMHixYvp3LkzkyZNokePHuu2eeaZZ9h+++356U9/msNIRUSqV+vWrVmwYEHa9an69OnDQw89xMCBA1mxYv10SkVFRTz00EN8+tlnlFxySTjrt9tuLJg1i5JLLgFQo6oK7r77brbaaiuuuOIKVq1axWmnncZ22223UcP2mWeeYa+99mKbbbbJUaQiIvlt+PDhDB8+nNYdOrBg1qxwharUrFm07tAh8WPm1TxUmcpkTo9MdO3alVNOOYULLrig6kGJiOSp0nuo4n+cN2nShFGjRnHyySdvtP0DDzzA2WefzbJly2jUqBErV66kSZMmjBw5ksuvvZYFJSUbJqjp02kzahTz33uvUnHleh4qMzsY6Av0BFoCa4FFwDTgKXd/IaHjJJKzVq1axXbbbcedd97JgAEDqh6YiEgtFOWWdfdQlZ4AZNYsmvz1r4z6y1+yOgFYMPNQ1aR///vfLF26lDPPPDPXoYiIVKuTTz6ZUaNG0aZNG8yMNm3alNmYgnC1ZNmyZXTt2pWJEyfStWtXli1bxj333FOjXSiqi5n90sw+BEYTGlKTgRuBEcBzQFtgrJm9Z2a/zF2kGxo9ejTt2rWjf/9KjXkhIlInnTxoEKP+8hfajBqF/exntBk1KuvGVEXq5BUqd6dnz55cd9116ocuIpJiwIAB7L///lxwwQXUq1ePtWvXcvPNN/PSSy8x44MPCvoKlZlNAeYDf3P3NyrYtg9wJrCtu+9fhWNW+QrV8uXL6dy5M08++SQdO3as0r5ERGqz0itU1bTftDmr0g2qmuoiUUEMiXSfEBEpFMuXL2fChAnMnTuX9u3bc8wxx1BUVFTjcSTZhSJHDapd3f3dSr6mU2VH7kt5vXKWiEgNyesGVdTtYRiwKfAs8C6wBKhPmBG+K7A38C3wZ3f/R9VDLzMWJScRqTPefPNNjjjiCLbaaitatGjB4sWL+eqrr3jqqafo2bNnjcczZuxYhl19dZVH+cv1PVQ1RTlLRKTm5G2DKhddJCo4hpKTiNQJy5cvp02bNhx00EHssssu69a///77PPvssyxcuDAnV6qSoAaViIgkLRcNqkwHpTjH3YdU1JgCcPfn3f1E4OzKBCkiIhubMGECW2211QaNKYBddtmFrbbaaoPJYKViZnZgpkuuYxURkfSGDx+OmaVdgDLLzIzhw4cnHk9G81BVtr959Jqs+5uLiEgwd+5cWrRokbasRYsWzJs3r4YjKni3ALtGj8u7OuaELu0iIpJnSueaSqe6rlCVp84Omy4iUgjat2/P4sWL05YtXryYdu3a1XBEBa8HMBGYCTRx93plLGpMiYhkqbhVqzKvEBW3apXr8BKX6T1UGXd9cPfnqhRRBtQfXUTqihUrVtC6dWvdQ5XscRsBU4F/uvvvauB4ylkiUqeYGel+9ZYDTYCmTTdh2bJVtGixKSef/HPOO+8i2rdvn9yx83RQillk2EWiJs7qKTmJSF2Sb6P8JSWXg1KYWUdgP3cfVQPHUs4SkTolXYPqTaA/RbRlD3rbAbzV9AVm1Hubw45ezVNPNWL06Ifp27dvMsfO0wZVI2AcYfb4vdx9RbIhVo6Sk4jUNaXzUM2bN4927drlbB6qJGmUPxGRZCxdupQhQ4Zw33330bx581yHs1GDajnQniJG8iD96b9u/UQmcvZPTmTshBUMHNiEqVNnVvlKVd42qKKd1GgXiQpiUXISESlw+dSgMrP9gRbAe+7+XsL7Vs4SkWp1//3384tf/IL777+fwYMH5zqcjRpUY4HR7M0/eWWjbQ/bdB9+ccerzJrVkJUrS7jxxhFVP3aeDpuOu68EBgEfJxWYiIhITTKzpmZ2h5m9b2YjzKylmb0NPA/cCcw2s4fNbNMchyoisoH58+fTu3dv5s+fv1HZPffcs8G/+WYu0IMD0pZ1/2E/5s2D009fzdix99dsYAmp1Ch/7v5eTfQ3FxERqSY3AnsCtwPdgdeA74Ht3X1LYCdge+DmXAUoIjJmzBiKi4upV68excXFjBkzhgsuuIAvv/ySCy+8kIMPPniDkfNeffVVAF555ZUN1h988ME5rknQHpjGC2nL3mr6Eu3aQevWsHjx91U+1hVXXFHlfVRWxl3+AMysA1AEzHH3tSllDQk3+GqUPxERqVAuuvyZ2WLgUHd/28zaEHpd7OXur8e26QH8y903T+iYylkiwooVK7j+uj8x8m+3cs65v+LiSy6lqKho3T2yc+fOpX379qxcuZLzzjuPZcuWrXtto0aN2GabbXjnnXfYY489OP3007n66qs32CZVkyZNeOqpp+jdu3cN1G5Dxa1asWDRog3WFVHEg2XcQzXv8xV8+inss08zvvhiaU2Hm5HyclZGE/ua2bbAY4T5OwAWm9nF7j46ttkWwL/QRIgiIpLf1gC4+wIzGwt8m1K+CZDTwZdEpHaZNGkSQ885jS7bfMv4s5bx18nX0/meOzj/wku59tpr143i+uCDD/LRRx+xatWqDV5fv359Ro4cSbNmzbjtttsYOnQojz76KMccc0zaRlUuG1MA87/4YoPnF154Dl9/PYqzJ57IyDV70P2H/Xir6UvMbPA2E59ZQVER3HVXQwYN+nlO4q2qTEf5ewRoDJwWrfoVcDFwfekAFWa2NfC5u1f7ZME62yciUvhydIXqXqAjcK67v5VS1hj4GXA98KS7X5jQMZWzROqo+fPnc8F5ZzBnxqvcOmgZfbutL3vsTTjhbw055phjN5pn8OGHH2bNmjVAaEwddNBBPP300+u2Ofroo+nevTvdunVj4MCBrFix/hxQUVER48eP58gjj6z2+mVq7ty59OrVhfHjl/HZZzBvHrRrB8ccA0VF8Npr0K9fMqP8VZckBqXoDVzs7p9Hy6XACcCvzey62HbKGCJSZ5V3w7DkjaHAR8CZacoOBx4CngEurcmgRKR26rN/Lzo1fJ5Z12zYmAJYtgrat2m1QWMKYJdddmGbbbZZ93yTTTbhD3/4wwbbXHTRRYwcOZJvvvmGBg0aUK9ePRo3bky9evVo0KAB33zzTTXVKDvt27dn9OiHGTiwCbNmNeSkk2DgQPj0U/jd7xrSr18TRo9+OG8bUxXJtEG1HNgsvsLdHwF+CVxkZlclHZiISC6sWLGCq6+8gm233pxrrhq+wVm/isRvGJb85O7fuftgdy9JU/wU0Nzdz41GthURqZLdOnWk4zZrKdpk47K5X8JWrdqkfV3r1q3XPV61ahVXXbXhn9p//etfOfvss7n77rtZtmwZXbt2ZeLEiXTt2pVly5bl5Wh/ffv2ZerUmaxcWcI++zSjceN67LNPM1auLGHq1JmJTOqbK5k2qB4D7jSzg82sSelKd38QOAsYBtyRfHgiIjVn0qRJdN6lLdP/eQPjz/qGtydfT+dd2jJ58uSMXjtnzhymTp3KrFmzMnqN5Bd3X5HrietFpHY5/uTTGP92+lkY2reExV8sSFv25Zdf0qJFC8yM7bbbjtmzZ6/LK5MmTWL27NlcfPHFNG/enOuvv55p06ZxyCGH8Oabb/KXv/yFZs2aVVudqqJ9+/bceOMIvvhiKWvWrOWLL5Zy440jCvbKVKlM76FqCtwCnAgc7e7/Sik/GhgFbOHu1T4ohfqji0iSyuvjPnkGDB3bhE7d9ubmEXdSXFy80etXrFhB586dGTFiBIcddhiTJ09m6NChzJo1i6KiopqqRsHJp4l9q5NylkjdtXTpUlpvvzULb1pJ8yYblq1YBTv8qgEHH37cRvdQPfvssyxcuHBdDinNK9OmTaN79+7cdtttBX1FpxCVl7MqO2x6AwB3X5OmrAnQx92fyjbQSsSh5CQiiWnbuhWDui/m9/3Td8tYsQqueqw+495uwccLv9io/Oqrr2b69Ok8+uij69aV3jB8+eWXV2foBU0NKhGpC/r17cPxbacweN+Ny658BK6bXETbtu1o0aIFixcv5quvvuKpp56iZ8+eG2w7YMAAPvzwQ3beeWcmTJhQQ9FLqSoPmx6zP7CXmW0PNAKWAZ8Dr0XzT1V7Y0pEJGmhj/uUtI0pgKJNoOM2a+nSede05SNHjmT8+PEbrLvooos44YQT1KASEanjjj/5NMaPmMbgfTeetPat/27KiBG3UFRUxLx582jXrh3HHHNM2t4NN998M0OGDOGmm26qibClEjLt8tcemAjsALwNLAJWEhpVrYDdgQVAf3f/uNqiXR+PzvaJSGIeeOABxo84m4lDy56hvd8tm3L8+SMZPHjwRmW6QpWdXF+hMrNTM93W3bO+w1s5S6RuW7p0KR12LObLxd9sVNayxU/48KP5NG/evOYDk0qpcpc/M3uO0Ig61d2XpylvAtwNtHD3Q6oYbybxKDmJSGLK6+MOsHQZtL6wEZ98+mXaG31L76Eq7dM+adIkfvWrX+keqgrkQYPqeaAXYcqP/xBOFHYgjGq7MLapu3u7KhxHOUtEpMAlMQ/VnsCV6RpTAO6+DLgK2Cu7EEVEcqd58+YcsO9ePPF2+vLH34Le++1d5qhJRUVF62auX7p0KUOHDuXWW29VYyr/vQo8C+zg7l3d/afAdsA4YJy7t42WrBtTIiJS+2XaoPoIOKKCbQYAn1QpGhGRHClvaNvxb2/KwEHl9w7r27cvnTp1Yq+99mK33XbT6EuF4RzgN+6+pHSFu/8AXA2cl7OoRESkoGTa5a8P4R6qGcCLwGdseA/VvoSrWMe4+9PVFWwsHnWfEJFEJdHHff78+QwZMoT77rsv7fDqsqE86PK3ALjc3e9PWX8mcIm7JzIxinKWiEjhS2TYdDPbATiD0HDaBmgCrAA+BaYC97h7+tnJEqbkJCJS+PKgQXUmYY7FcYQThgb0BI4FBrv7wwkdRzlLRKTAJTYPVb5QchIRKXy5blBFMRwOnAbsQpgK5F3gRnd/J8FjKGeJiBS4pK5QHQGcDBQBLwB3uPuKWPnmwER337/qIVcYi5KTiEiBy4cGValo4vq11ZFclLNERApflUf5i+bqeBj4DvgSuBKYZmbFsc02AfapWqgiIiI1x8zONbOPCFen2prZKDP7o5llmh9/YWYzYsvHZrbazLau3shFRCRfZDrK32+A09z9THc/C9gZWAq8YmY7Jh2UmZ1nZnPMbLaZTTSzlkkfQ0RE6jYz+zVwCWHaj9XR6meAU4FrMtmHu492927u3o1w/9UXwHnuvij5iEVEJB9lOsrfd0BXd58XW9cEeBooBvYDlgOfuXv9KgVk1h14JDreUjO7AdjM3c+MbaPuEyIiBS7XXf7M7EPgfHd/Op7nzOwQ4F53376S+7sc6Onu/VPWK2eJiBS4JCb2fQ8YGF8RTeZ7BPAV8Dywa1WCjO33LWCnqDFVRJhkcUkFLxMREams7YH/pFn/CbB5ZXZkZi2Ai4AL05X36NFj3TJq1KhKByoiIvkr0ytUBwFPANMJXRmmx8q2BJ4C9gDqV/UKVWy/A4C7CPNd9Xb3/8TKdLZPRKTA5cEVqmeBV93999EVqi7AAuBuoI27H1iJfV0GdHD3IWnKlLNERApcUqP87QQcDzzk7h+llDUEfg0c5+49qxhv6nHPAH4H7OjuP0brvHv37uu2KSkpoaSkJMnDiohINcuDBlUnYDKhF0RnQm+LDoRJ6w+rzNDpZvYOMNTdX0hTpgaViEiBK6h5qKJBLlq5+8vR8/rAKqCluy+J1ik5iYgUuFw3qKIYioBBQEegAfAB8IC7f1+JfWwO/Bf4ibuvTlOunCUiUuDKy1kNajqYDGwDjDOzbu6+mDD31ezSxpSIiEhSovkU76nibnYEPk/XmBIRkdov765QAZjZ2cC5wBrgM+Bcd/84Vq6zfSIiBS4frlDVBOUsEZHCV1Bd/jKh5CQiUvjUoBIRkUKRxLDpIiIiIiIikiKxBpWZNTOzqvZDFxERqVFm1sDMjjGzi83s6GjkWhERkYwkeYWqPtA2wf2JiIgkysxamtkTZvaDmb1gZtsDLwP3AicR5j+cFa0XERGpUGINKnf/2t37JLU/ERGRajACaA6cAHwKvAp8Dezg7t2B7QmT2I/IWYQiIlJQMhqUwswybniVTr5bnXSDr4hI4cvFoBRm9j9gX3d/18waA98Ce7v7m7FtOgIvu/uWCR1TOUtEpMAlMQ/VGiDTbFA/w+1ERERq2g9AaUNpC0LOapWyTStCQ0tERKRCmTao1JVPRERqg7uAsWY2HugLzAKGmVlTYA7QBbgGGJe7EEVEpJAkOg+VmW3v7v9NbIdlH0fdJ0REClyu5qEyszMJjaklwFVAa2A00AZYSWh0XeTuqxI6nnKWiEiBS3Ri36hv+fVAJ9Z37zOgEbClu1d7lz8lJxGRwpdvE/ua2VbAkqTvBVbOEhEpfEncQxU3ijA64J+Am4HfEM7qnQuckmWMIiIiNcbMDgT2Iozq1whYBnwOvAY8l8PQRESkwGRzhWo5sJe7zzCzl4E/uPtzZnY68Et33686Ak2JQWf7REQKXI5G+WsPTAR2AN4GFhG6+TUiDEaxO7AA6O/uHyd0TOUsEZECl/QVqtXAN9Hj9wnJ5zngX8AN2QQoIiJSQ+4kDETR092XpxaaWRPgbkJvjENqODYRESlA2Uzs+zLwmyjpTAMGRPNU7QmsSDI4ERGRhO0JXJmuMQXg7ssIA1XsVaNRiYhIwcqmQfVr4GDgHOB+oAXhitVY4PbEIhMREUneR8ARFWwzAPik+kMREZHaIOth082sibsvi+bu6E0YGWlqksGVc2z1RxcRKXA5uoeqD+EeqhnAi8BnbHgP1b6Eq1jHuPvTCR1TOUtEpMAlOmx6PlByEhEpfDmch2p7oITQcNoGaELosv4pMBW4x90XJHg85SwRkQKnBpWIiOSdfJuHqrooZ4mIFL6kR/kTEREpWGbWBjiN9PNQTQXudPeFuYtQREQKSaJXqMyshbsvTmyHZR9HZ/tERApcju6hOgx4BHiVMGpt6jxUpfdQDXD3ZxM6pnKWiEiBS7TLn5mtBVq5+1cp64uB2e6+abaBViIGJScRkQKXowbVHGC0u19Xzja/BQa7+24JHVM5S0SkwFW5QWVmvyR0j4Bw9u51wgS/cdsA7u4dqhBrRpScREQKX44aVN8DPdz9/XK22RWY5u5NEjqmcpaISIErL2dlOg/Vw8C/gdLuDy9Hj0uXfwMj0azyIlJJw4cPz3UIUre8ClweTU6/ETNrDAwnnDgUERGpUDZd/n4JPOjuK6snpIxi0Nk+kVoiOuOT6zAkB3J0hao18DiwEzCdjeeh6gYsAPq7+7yEjqmcJSJS4BIfNt3MugDnExLSycDRwEfu/s+qBFqJ4ys5idQSalDVXbkcNt3MDqTseahecPcfEzyWcpaISIFLdNh0M/sZ8CjwECEZNQK2Am40syHuPrYqwYqIiFQ3d38OeK6scjOrl2SjSkREaq9M76GKuxa40N1PBdYAuPsVwFDg9wnGJiIFaO7cuVx44TlsvXUz6tevx9ZbN+PCC89h7ty5uQ5NBDNrZGbXm9lnZrbEzCaY2S4p22zNxgMviYiIpJVNg6oj8K806/8FFFcpGhEpaJMnT6ZXry40bnwXr776HStXOq+++h2NG99Fr15dmDx5cq5DFLmW0E39IuA8oCUwzcyOTNkuJ10RRUSk8GQzKMVs4I/uPtbMvgO6uvs8M7sAOMPdO1VDnKkxqD+6SJ6ZO3cuvXp14fHHl7HXXhuXv/Ya9OvXhKlTZ9K+fft163UPVd2Vo0EpFgInu/tL0XMDbgLOBo5394nRFarP3L1+QsdUzhIRKXBJDJsedznwdzO7iXAP1ilmNgb4C2GoWRGpg0aM+CtnnLE6bWNq+HDYe29YvHgZO+64I2a2bgE2eJ66aFh1SdimwKLSJx5cAPwdeMjM+uYqMBERKUzZjvLXldBdoiOhUfUBcJO718i8HTrbJ5J/tt66Ga+++h2xi08bmTsX9tmnGV98sXTdOl2hqrtydIXqcWA5MMTdl6eU3QucCFwB/ElXqEREpFTiw6bnmpKTSP6pX78eK1c6DcoZO3T1amjcuB5r1qxdt04NqrorRw2qdsCTQDvg8Gi0v9IyA24EfkW4eKUGlYiIAMl3+RMR2UiLFpuyYEH52yxcGLYTyZVost6uwEHAzJQyd/cLgb0JDSsREZEKqUElIokYNGgwd9/dsNxt7rqrIYMG/byGIhJJz91Xu/sr7r64jPKp7v6bTPdnZruZ2RQzm25m08yse3LRiohIvlOXPxFJhEb5k8rKRZe/pJlZE2AucJq7TzKz/sB17r5LbBvlLBGRAqcufyJS7dq3b8/o0Q/Tr18Tfve7hsydG+6ZmjsXfve7hvTr14TRox/eoDElUgscCsx190nR88eB43MYj4iI1LBybh9Pz8yeB9KdanNgFfA58H/u/s8qxiYiBaZv375MnTqT22+/iX32uZ/Fi7+nRYtNGTTo50ydeqEaU1IbdQC+MLO7CfdmfQNcktOIRESkRmUzse+VwO+Ah4HXCLPJdycMNXs/sAIYDFzo7vcmGu36GNR9QqSGNK1fn2U//pi2rEm9evywdm3askwNHz5cc03VUfnQ5c/MGgLbAqUj+hnQCNjd3cdk8PphwDCgj7u/HnX5uwNo4+4ro228e/f1t1WVlJRQUlKSbEVERKRaJTpsupm9DIxz99tT1p8O/MLd9zezY4A/xvuQV/IYg4HfEK56LQOGuvu0WLkaVCI1xMzSXpKG8Jen/i9KtnLdoIpy1Shg8zTFn7p76wz2cQpwnrt3j637Ctjf3d+LnitniYgUuKTvodoD+Fea9S8CPaPH04AKE1E6ZrYzcD1wmLt3A64BHs1mXyIiIuX4I/AIsCvwNWG49KOA+YTJfTMxGWhbOrKfme1POBn4cdLBiohIfqr0PVTAO8BQMxvq7j/CuskQzwPejbbpCfw3y5hWAqe7++fR82lAKzPbxN1XZblPERGRVG2BI9x9rpm9BbRy94lmtoYwD1WF3dbd/QszGwD8zcyaEnLYMe6+ojoDFxGR/JFNl7/uhDNyy4DphF4/XYGmhDN7DYBngXPc/Z4qBRcaavcDRe5+XGy9uk+I1BB1+ZPqkgdd/hYBB7v7LDO7Bfja3YebWRvgXXdvmtBxlLNERApcovdQRTtsRhiEYjdgDTCHcF/VD2ZWDDR393eyDxmiM333ATsQuv99EyvTDb4iNUQNKqkuedCgugvYGTiLMFrfDcBJwLHA0e7eIaHjqEElIlLgEm9QVTczaw08AbwHnOLuy1PKlZxEaogaVFJd8qBBtSlwE/ASoTfE/YQG1Q/Aye7+RELHUc4SESlwSY/y15ZwI29PoCHhb6p1MhkVqYL9b0a4T+sf7n5lGdsoOYnUkOoeNl3qrlw3qNKJctByYNN4z4gq7lM5S0SkwJWXs7IZlOJuoBVwC/BtVQIrw3lAG+BoMzs6tv4gd19SDccTkXKowSS1lZn9xd03mITX3b8zsyHAdcDWOQlMREQKSjZXqL4DDnD3t6snpIxi0Nk+EZECl+srVGb2KfAUcKa7u5l1AUYCuwM3uPsfEjqOcpZIglasWMH111/PyJEjOeecc7j44ospKirKdVhSyyU9D9U8oFnVQhIREcm5vYF9gYfM7CbgLeALYNekGlMikqxJkybRuXNnpk+fzvjx43n77bfp3LkzkydPznVoUodlc4Xq58AfCDfyzgM2mBvK3Z9LLLqyY9DZPhGRApfrK1RRDFsQBkHakzB/1OPVcAzlLJEqmj9/PhdccAFz5szh1ltvpW/fvuvKJk+ezNChQ+nUqRM333wzxcXFuQtUaq2kB6VIf3d64O5ev1I7zIKSk4hI4ctFg8rMrkqzuglwLjATeLp0pbr8ieSPtm3bMmjQIH7/+9+n7d63YsUKrrrqKsaNG8fHH3+cgwiltkt0UAp3z6aboIiISD7Yr4z1U1PK1QISySO77bYbHTt2LPNeqaKiIjp27EiXLl1qODKR7Eb5w8waAtsCpVejDGgE7O7uYxKKTUREJFHu3qf0sZmdCjzp7l/mMCQRycDxxx/P+PHjGTx4cJnbjB8/nuOPP74GoxIJsunydwwwCtg8TfGnVZ2HKsMY1H1CRKTA5foeKjP7Gujp7h9V83GUs0SqaOnSpbRu3ZqFCxfSvHnzMss/+eQTmjXT2GmSvKRH+fsj8AiwK/A1YZSko4D5wBVZxigiIlLT/g0MMbOmuQ5ERMrXvHlzDjjgAJ544om05Y8//ji9e/dWY0pyIpsGVVvgL+7+AWGI2VbuPgk4G7goyeBERESq0fbAZcC3ZvalmS2ML7kOTkQ2VNrtL53x48czcODAGo5IJMimy98i4GB3n2VmtwBfu/twM2sDvOvu1X6mT90nREQKXx50+ftleeXu/o+EjqOcJZKApUuX0qFDB778cuPbHlu2bMmHH36YtjugSBKSHjb9LmBn4CygA3ADcBJwLHC0u3eoWrgZxaDkJHXS8uXLmTBhAnPnzqV9+/Ycc8wxmh1eClauG1Q1RTlLRKTwJd2g2pQwqe9LwP3RchLwPTDY3dN3bk2QkpPURW+++Sb9D+1PlzVd6PFDD6Y1ncbMBjOZ+MxEevbsmevwRCotR/NQvQj0c/dvzOwlyhke3d33T+iYylkiIgWuyg0qM/sr8DzwkrsvTVO+GbDc3ddUNdhMKDlJXbN8+XLab9uekd+MpD/9162fyETO/snZzPt8nq5UScHJUYPqCuB6d18WPS6Tu1+Z0DGVs0REClwSE/v2Igw60dDMZgJTouVFd1/q7t8lEaiIpDdhwgS6rOmyQWMKoD/9GblmJI8++iiDBg3KUXQihSOlkfQx8JC7r4xvE436d2qNBiYiIgUrowaVu+9jZg2APYA9gb2AW4DWZvYOKQ2s6glVpO6aO3cuPX7okbas+w/dmTdvXg1HJFKYzKwlsGn09F7gPTNbkrJZV+AvwG01GZuIiBSmTK9QEXXneyNaboN1iWkvYCAwDiiqzD5FJDPt27dndNPR4U7FFG81fYtftPtFzQclUpj2A8az/t6p12NlDpR257ivBmMSEZECls2gFPWAHsCBwL6EiX0bAq8Az7v7dUkHmSYG9UeXOmXFihW026ad7qGSWiVXo/yZWWvCPIzzgJ8CX8WKHfje3f+X4PGUs0REClwSg1J0ITSgDgL2B+oTGlBTouXNmhqQIopHyUnqnPgof91/6M5bTd/SKH9S0DRsuoiIFIokGlQ/Ap8CdwDPAW/UZAMqTTxKTlInlc5DNW/ePNq1a6d5qKSg5bpBFQ2hXiYNmy4iIqWSaFA9BBxAuEfqJcIQ6s8DM3KRJZScREQKXx40qFKHTW8AtAOOAK5y9xsTOo5ylohIgUtsYl8z60To+ncgoeufAS8SNbDcfWbVw80oDiUnEZECl+sGVVnM7OfAie5+REL7U84SESlwiTWoUnZqwO5Ab8LAFIcCK919qyzjrMyxlZxERApcHjeo2gJz3L1JQvtTzhIRKXBJTOybusNtCSMjlS5dgR8I3QFFRETynpm1S7O6GXAZML9moxERkUKV6T1UvQkT+pY2oLYFFhIaUC8SJvT9sNqi3Dgene0TESlwub5CFQ24lJpMDPgEONXdn03oOMpZIiIFLqlR/j4gajwRGlCfJBplJSg5iYgUvjxoULVJWeXAKmBRkklGOUtEpPAl0eVva3f/quLNRERE8puZbQb0AVYAU9392xyHJCIiBSyjBpUaUyIiUhuYWS/gSWCLaNVXZnaCu0/JXVQiIlLI6uU6ABERkRp0PfBvYBugJTCJMGm9iIhIVrIeNj2X1B9dRKTw5eIeKjP7Dtjd3T+Knm8BLAa2dPevq+mYylkiIgWuvJylK1QiIlKXNAXW3TPl7v8DlgPNs92hmf3VzBaa2YxoeSiBOEVEpEBkNQ9VOmbWFLiIcJPvE+7+XlL7FhERqUY/UrUTjHsDJ7r7qwnFIyIiBSTJK1SNgVOAN4Brzax+gvsWERFJggP1zKx0qZ9mXT0zyyg/mlkjYHfgEjObZWaPmFnraoxfRETyTKXvoTKzu4A/l/Y/zwX1RxcRKXw5uoeqrMl8N0oq7l7hiUEzawvcDlwCzAEuBgYBe5QmKuUsEZHCV+WJfVN29j+gu7t/nERw2VByEhEpfDlqUB2Q6bbu/kIW+zdgKdC1NE+amXfv3n3dNiUlJZSUlFR21yIikkNJN6guB/YDbgYWEO6ZWsfd52UXZqViUINKRKTA5aJBlTQz60JoPN0fPTfCoBcd3f2/0TrlLBGRApd0g+rHlFWlOzDAM+kiUVVKTiIiha+WNKg6Ay8Ruvh9bGbnACe7+z6xbZSzREQKXHk5K5tR/tpWMR4REZFawd1nm9n5wBPRABf/BU7KcVgiIlKDqjSxb2wUJAMaESZLfCWJwCo4rs72iYgUuNpwhSoTylkiIoUv0Yl9zWw/M5ttZmuB1dGyCvgOeLZKka4/hpnZP8zs4iT2JyIiIiIiUh2ymYfqFuAj4HDgB+BY4HxgCfDLqgZkZh0JDbPjqrovERGRyjKzpmb2BzO7JMpJIiIiZcqmQbUrcKm7Pw28Bax0978B5wC/SSCmc4G7gPEJ7EtERKSyNFG9iIhkLJsG1TKgdKS/94Fu0eM3gJ2rGpC7n+fuY6u6HxERkfKY2V1mtmPqendf7O5t3X2Kux/j7mtzEZ+IiBSGbBpUzwJ/NrPtgFeBE82sJXA08L8kgytPjx491i2jRo2qqcOKiEjtcQygxpKIiFRJNsOmnw/cT0hEfwdOB74gJKWzkwutfNOmTaupQ4mISO10I/B3M7uZHE1ULyIiha9Kw6bDulnhdwW+cfdPE4kq7Pc+YLa735CmTEPQiogUuFwPm15TE9UrZ4mIFL6kJ/bFzJoDJwMdCUOmvwvovicRESkkmqheRESqrNJXqMysJ/Bv4CtgBuE+rD2AhsDh7v5OwjGmi0Fn+0REClyur1DFVedE9cpZIiKFr7yclU2DaibwDPCb0gwRDSl7G9DD3X9axXgziUHJSUSkwOW6QWVm+wEjCb0tUq1296KEjqOcJSJS4MrLWdmM8rcTMCqeHaIhZW8BOmcXooiISI2r1onqRUSkbsimQTUJGJRm/XHA81ULR0REpMZU90T1IgVnzJhxFBd3pl69+hQXd2bMmHG5Dkkk72U0KIWZjY49rQ/8wcyOAN4kTPK7G7A3cF/SAYqIiFSTdBPVTyahiepFCs2YMeMoKRnGsmV3A/uyYMHLlJScBsDJJ5+U2+BE8lhG91CZ2b0Z7s/d/dSqhVQx9UcXESl8eXAP1XjCScLzgQOBi4FDgJOAX7t7m4SOo5wlBaG4uDMLFtwG9ImtfZ42bc5n/vzZuQpLJC8kPShFmZnBzNq7+9wsYqxsDEpOIiIFLg8aVK0IE9U/Tpio/t/AfkQT1bv7XQkdRzlLCkK9evVxX0EYuLnUasyK+PHHtbkKSyQvJD0oxdhoVL/4ARqZ2XBgVhb7ExERqXHu/oW7H+Lut7n7aqA3oQt7cVKNKZFC0rp1R+DllLUvR+tFpCzZNKi6AI+bWRGAmR1OmNj3LOC8BGMTERGpVmbW3MzOMbPbgBuAXsD/chyWSE5ce+0wmjQ5jTDG2GrgeZo0OY1rrx2W48hE8ls2Xf42J3SPAFhMGG52BHClu3+bbHhlxqDuEyIiBS4PuvzVyET1yllSSMaMGcewYdeycOF7tG7dkWuvHaYBKURI+B6qaIeNgDHAAOAgd3+hShFW/vhKTiIiBS4PGlQ1MlG9cpaISOGrcoMqZdj0UvWBY4BPgVdLV7r7L7KMM2NKTiIihS8PGlTLga7u/mHK+p2B6e7eJKHjKGeJiBS4JAalWJtmWQU8CLyUsl5ERKQQaKJ6ERGpsqy6/OWazvaJiBS+XFyhSulx0QzoB7xFmonq3f2MhI6pnCUiUuCqfIXKzB4ysw6VOOCu0YSJIiIi+STeo+Jr4B/AbKAx0BSYBzxA6NYuIiJSoQYZbncH8KiZfQY8BvwL+Ch2E68BnQgTIp4EbA2cm3i0IiIiVeDup5Q+rmii+pqLSkREClnGXf7MrAFwPGG+qb2BNcA3hKtcmxO6SrwG3Ak86O7Vdj+Vuk+IiBS+PBiUYhwwOJ6volFsfwdcokEpRESkVHUMm94M2B1oCTjwBTBT81CJiEim8qBBNQeYDxzr7iuiiepvI3T9u8zd70noOMpZIiIFLvEGVa4pOYmIFL48aFDVyET1ylkiIoVPDSoREck7uW5QRTFU+0T1ylkiIoWvvJyV6aAUIiIiBa+MiepXAquBu82sRieqFxGRwqcGlYiI1CXpBkxaS5ioXkREpNKy7vJnZvXc/Ucza0UYLn2Gu/8n0ejKPra6T4iIFLh86PJXE5SzREQKX5Un9k3ZWS8z+wQ4wMy2BqYBdwOzzezoqoUqIiJSfTRRvYiIJC2bLn83AhOBN4GhhPmoWgInA1cDExKLTkREJFmaqF5ERBJV6S5/ZrYc2MXdF5jZVGCqu19gZm2A9929cXUEmhKDuk+IiBS4XHX5q+mJ6pWzREQKX9Kj/C0BWkdn8XoCv4/W9wA+zy5EERGRmuHua4CxwNikJqo3swHA/e6+WcLhiohInsumQXUPoZvEKmAm8KyZnQNcDwxLLjQREZHqFTWcqjT3lJntBNwA1PoBNkREZGNZjfIXDT7RBhjj7l+Z2eFAPXd/MukAyzi+uk+IiBS42jDKn5k1AZ4HrgXGuvumabZRzhIRKXCJT+zr7hNShk1vCsyoQowiIiKF6I5omVneRj169Fj3uKSkhJKSkmoOS0REako2g1L0AsYDvwDeBd4CmgGNgBPdvdpH+dPZvvy1YsUKrr/+ekaOHMk555zDxRdfTFFRUa7DEpE8VOhXqKLu7j3d/RQzKwZm6wqViEjtlOg8VGw4bPpprB82/RzCsOlSR02aNInOnTszffp0xo8fz9tvv03nzp2ZPHlyrkMTESmTmdWL/m1lZgOje6IyMQToaWYzgElAYzObYWbbVk+kIiKSjzRsulTZ/PnzueCCC5gzZw633norffv2XVc2efJkhg4dSqdOnbj55pspLi7OXaAikldyfYUqyR4XukIlIlK7JX2FqnTY9GLCsOlPRes1bHod1adPHzp16sSsWbM2aEwB9O3bl1mzZrHrrrvSp0+fHEUoIpKWelyIiEiVZdOgKh02/TU2HDZ9NHBrcqFJodhtt93o2LFjmfdKFRUV0bFjR7p06VLDkYmIlGt34Hp3/x7oBzzm7iuAfwPtK7Mjd5+f7uqUiIjUfpVuULn7H4DTgeuAQ939R2A+cIK735xodFIQjj/+eMaPH1/uNuPHj2fgwIE1FJGISEbU40JERKosq3moAMxsO6ADMBVo5u6LkgysgmOrP3oeWbp0Ka1bt2bhwoU0b968zPJPPvmEZs2a5SBCEclHeXAP1VXAuYSJ6r8AugNnEU1Un9RJQuUsEZHCl+g9VGa2qZmNBz4B/gVsA4wys1fNrGXVQpVC1Lx5cw444ACeeOKJtOWPP/44vXv3VmNKRPKKelyIiEgSsrmH6q9AC6AtsDxadxHgJHQPlZkdYWYzzewDMxtvZvpLPM+V1+1P3f1EJF9FI/nd6u5fxSaq/yDHYYmISAHJZtj0z4HD3X26mX0HdHX3eWbWFXje3beoUkBmWwFzgH3c/T9mdh2wmbufE9tG3SfyzNKlS+nQoQNffvnlRmUtW7bkww8/TNsdUETqrjzo8lcjE9UrZ4mIFL6kh01vTOhvnqoRkERiPBR4093/Ez0fCZxsZjlLulKx5s2bs2jRItx9o2XRokVqTIlIPtKw6SIiUmXZNKgmAn8ys59Ezz2aVf424MkEYtqBcH9Wqf8SzhhulsC+RURESiU2bLqIiNRd2TSozgdWE4abbQq8A7wfPf9VQjGl6xuxNv6kR48e65ZRo0YlcFgREaljNGy6iIhUWYMsXrMDcBxhUIqO0T4+cPf3E4ppIbBn7Pl2wNfu/kN8o2nTpiV0OBERqaNKJ6pfxYYT1V8PDMthXCIiUkCyGZTiS6Cvu79VLQGFoddnAftGg1L8CWjl7qfEttENviIiBS7Xg1JEMRwNtAHGRCP9HQ7Uc/ckurCXHkM5S0SkwJWXs7K5QvVfwlWjamlQufuXZnYK8LCZbQLMJYzAJCIikih3nxBNVN/ZzKYCb9XkRPUiIlL4srlCdR9wMjCdMAHiini5u1d740dn+0RECl+ur1CZ2abAvcCxwI9AB+AmYCtggLtvPA9EdsdRzhIRKXBJD5vuwAOEuaJ+IAwWEV9EREQKQbVPVC8iIrVfpa9Q5QOd7RMRKXx5cIWqWieqjx1HOUtEpMBV+QqVmdU3s2Fm9raZvWZml5pZw2TDFBERqVHVPVG9iIjUAZl2+fsz8FvgdWAacAnwt+oKSkREpAZU90T1IiJSB2TU5c/MPgHOcvenoucHAJOBZu6+pnpDTBuPuk+IiBS4POjy14wwKMUAwhWp7wkT1j8NDHb3/yV0HOUsEZECV17OyrRBtRpo7e6fR8/rASuBtu7+3ySDzYSSk4hI4cuDBlUn4F2qb6L60uMoZ4mIFLgk5qGqT2wEP3f/0cxWAJskEJ+IiEguPM/6iern5ToYEREpTNkMmy4iIlIblE5ULyIikrVMr1AB/NbMfog93wS40My+jm/k7n9IJDIREZHqNRN4xMxyNlG9iIgUvkwbVC8Ce6SsexXonLJOncRFRKRQlE5ULyIikjVN7CsiIjmR60EpaopylohI4avyxL4iIiK1gSaqFxGRpKlBJSIidYkmqhcRkUSpy5+IiORELrr85WKieuUsEZHCl2iXPzOrX/WQREREcqIV8Hbs+UtAw2i9iIhIpWXT5W+RmY00s/0Sj0ZERKR6bTRRPWG4dE1ULyIiWcmmQTWYkHgmmtknZnaDmXVPOC4REREREZG8l/U9VNGoSD8Djov+/Q4YC4xz9w8SizD9sdUfvRLGjBnHsGHXsnDhe7Ru3ZFrrx3GySeflOuwRKSOy9E9VD8CNwHxiep/C4wCqmWieuUsEZHCV17OqtKgFGbWADgU6A/8HFgM/ASYAZzt7nOy3nn5x1VyytCYMeMoKRnGsmV3A/sCL9OkyWmMGnWtGlUiklM5alBNIbNJ6N3dD0zomMpZIiIFLtEGVawRNZDQkFoNPEy4MvWymTUB/g7s7e47VinysmNQcspQcXFnFiy4DegTW/s8bdqcz/z5s3MVloiIJvYVEZGCkXSD6n+Em3ofJ3Txe8bd16ZscxxwrbvvnF3IFcag5JShevXq476CMIhVqdWYFfHjj2vLepmISLVTg0pERApFosOmA2cDrdz95+4+ObUxBeDuD1dXY0oqp3XrjsDLKWtfjtaLiEhVmdl5ZjbHzGab2UQza5nrmEREpOZkdIXKzDLuR+7uz1UpogzobF/mdA+ViOSr2nCFKhrl9hGgq7svNbMbgM3c/czYNspZIiIFrryc1SDDffw7w+2c0B1Q8kRpo2nYsPNjo/ypMSUikgR3f8vMdnL31WZWBGwHfJzruEREpOZUaZS/XNHZPhGRwpfrK1RJ9r4wswHAXcBKoLe7/ydWppwlIlLgEh823cyMcBauIRDfsbt7tZ+ZU3ISESl8edCg+hjYhjBZ/XeExlAL1g+rXhqbu3tGvS/M7Azgd8CO7v5jtM67d+++bpuSkhJKSkoSqYOIiNSMpEf56wvcSUhC65JN9DjjpFMValCJiBS+PGhQnQcMBk4rnTfRzHYA7gaecPfbMtjHjoSBml6OntcHVgEt3X1JtE45S0SkwCXdoJoJvA9cDXybWu7uC7IJspIxKDmJiBS4PGhQLQIOdfd3UtZ3Al5y9y0y2Md+wDigm7svNrNfABe5e9fYNspZIiIFLolBKeLaAwPd/YOqhSUiIpJTDmwPvJOyfldgRUY7cH/JzK4FppjZGuAzYECSQYqISH7LpkH1IvBTQA0qEREpZLcDo83sFkKjyoCewHnAZZnuxN1HAiOrJUIREcl72TSoXgFGmlk/YC6hr/g67v6HJAITERGpTu5+tZktAU4DfgssA94FTnb3J3ManIiIFIxs7qF6vpxid/eMh6HNlvqji4gUvlzfQ1VTlLNERApf4sOm55qSk4hI4ctFg8rMrgL+7O7LosdlSqrHhXKWiEjhS3pQCsysB/AboCNQn3A/1YiKJj4UERHJsf2AGwnd+/bLcSwiIlILZNPl7zhgLPAI8CqhQbU3YVSjge4+MeEY08Wgs30iIgVOXf5ERKRQJD0P1RzgTne/OWX9BcAQd++WXZiVikHJSUSkwOVrg8rM9gb+7u5dEtqfcpaISIErL2fVy2J/bYF0ox89Ceycxf5ERETyyWZAp1wHISIihSGbBtV7QN80648APq5aOIEF/zCzi5PYn4iIiIiISHXIZlCKK4BHoi4Rr0fr9gSOAQZVNSAz60iYbHFPYFZV9yciIiIiIlJdKt2gcvcnzawvcC5QAqwgXLXa293fSiCmc4G7gIUJ7EtERERERKTaZDVsejQ8+nNmtgPwmbuvrczrzexw4PE0Rae6+3nRNodmE5uIiEhZzOwToKIRIopqIhYREakdsmpQxcwBugHzKvMid59U1WP36NFj3eOSkhJKSkqqsjsREakbLs91ACIiUrtUtUGVs+Fup02blqtDi4hIgXL3f6Rbb2bbE3pc/FjDIYmISIHLZpQ/ERGR2uZdoDjXQYiISOGpaoPqAeDbJAIRERHJobybYFhERAqDVXb2djPb391fTLO+HvBrd78hqeDKiUGzzouIFLjyZp2vaWb2HdDV3St1T3CG+1bOEhEpcOXlrGyuUD1rZreaWZPYAboBbwLDsgtRREQkp9TjQkREspJNg2p/4EBglpkdZmbXAW8Q+p/vkmRwIiIi1cXM9i997O5nu/viaH09M7s4d5GJiEghqXSXPwAzawiMBo4H1gLHuvsTCcdW3vHVfUJEpMDlusufma0GRgKXuvuyaF034G6gnbtvntBxlLNERApcol3+zGwL4DbgOOAe4C1glJkNrlKUIiIiNUs9LkREpMqyGZRiCbAIKHH3l83MgPOBq4EZ7n5A8mFuFIPO9omIFLhcX6GKYqj2HhfKWSIihS/pQSluB7q5+8sAHtwKdAa+yTpKERGRGqQeFyIikoSs7qECMLOfADsDPwLvu/t3CcZV0bF1tk9EpMDl+gpVTfW4UM4SESl8Sd9DtamZjQO+Al4DXge+MrNRZrZJ1UIVERGpMepxISIiVZbNPVRjCclmKDANqA/0BG4Gprj7eQnHmC4Gne0TESlwub5CFYvjJ1RjjwvlLBGRwldezsqmQfUt0Nvd305Zvyfwz6SGma0gBiUnEZECl+sGlZltCtxJuIeqfrR6FWGQivPcfVVCx1HOEhEpcEkPSrEI2DrN+iLg6yz2JyIikgujgE7AIUAzYHPgSGBv4MYcxiUiIgUkmytUpwNXAtcR7qFaA3QDhgP3AS+UbuvuzyUT5kYx6GyfiEiBy4MrVDXS40I5S0Sk8CXd5e/HDDd1d69f8WaVp+QkIlL48qBB9R9gqLtPTll/AHCvu7dL6DjKWSIiBa68nNWgsjtz92y6CYqIiOSb64C7zCxtjwszO7B0w+rqcSEiIoWvUleozKwnMMvdV8TW9QO+dPep1RBfWXHobJ+ISIHLgytUNdLjQjlLRKTwVXlQCjNrYGb3A1OBPVOKBwGvmNmdZlYtXfxERESS5u71MlyU20REpEyZdt+7COgD9HH3F+IF7n4icDDQnzDDvIiISF4zs55mVpSyrp+Z9cpVTCIiUpgybVANAc539xfTFbr788BvgNMTiktERCRx6nEhIiJJy7RB1Rp4u4JtXgLaVi2cmrFkyRKOPPJIlixZkutQRESkZiXa48LMBpvZO2Y2w8xeNbMeSQesnCUikt8ybVB9QcWNpdbA4qqFUzP+8Y9/MGnSJEaPHp3rUEREpGYNIaEeF2a2M3A9cJi7dwOuAR5NLNKIcpaISH7LaJQ/M7se+ClwsLuvTlPeEPgX8J67n514lBsfL+sRk9yd9u3b07ZtW+bPn89HH32EWc4GmRIRqbNyMcqfmf0A7OruC8rZph1hRNumFeyrGOjk7k9Fz1sC/wU2dfdVse2Us0REClyVR/kjnHVrBbxlZmeY2e5m1s7MupvZ2cAMYAfgykQirkYvvfQSq1atYt9992XFihW8/PLLuQ5JRERqTmI9Ltx9fqwxZcCNwOPxxlSpHj16rFtGjRqVcbDKWSIi+S/jeajMbHPgL8AJQOlZOwP+B4wDrnT3GunyV5WzfSeccALffvstvXr14rXXXqN58+Y89NBDCUcoIiIVydEVqsR7XJhZU+A+wonFw9z9m5Ry5SwRkQKXxBUq3P1rdz8DaAF0BvYFdgZauvv5NdWYqoxjjz0WM9tgmTx5Ml26dAGga9euTJ48eaNtjj322BxHLiIi1STRHhdm1hp4FVhLGOjim2wDU84SESlMGV+hyieZnu2bM2cOAwYMoHnz5vzsZz9jk002KXPbVatW8fTTT/Ptt98yYcIEOnXqlGTIIiKSIhdXqKLjJtLjwsw2A94B/uHuZTbAlLNERApfIleoClGnTp2YMWMGO++8M/fddx9ffvll2u0WLVrEfffdxy677ML06dOVmEREarEEe1ycB7QBjo6GTS9dtswmLuUsEZHCVKuvUMXdcccdXHrppVxwwQUbld10001cd911nHnmmQlFKCIiFcnVFaqappwlIlL46uwVqrjdd9+d5s2bpy37yU9+wh577FHDEYmIiKSnnCUiUjjqTINq2rRpbL311gDMmDGD22+/nRkzZgCw9dZbM23atBxGJyIisp5ylohI4agzDaqpU6fSvHlznnzySebMmcOoUaOYPXs2TzzxBM2bN2fq1Km5DlFERARQzhIRKSR1pkH15ptv8tJLL7Hzzjszffp0BgwYsO7m35deeok333wz1yGKiIgAylkiIoWkzjSodt55Z/72t7/xwAMP0LRpGCW3adOmjBkzhr/97W906NAhxxGKiIgEylkiIoWjYBtUo0aNqtT2jz32GKecckraslNOOYXHHnssgaiqR2XrWujqUn1V19pJdZWqyiZn1bXPoi7Vty7VFepWfetSXaH21rfONKgKWV2qK9St+qqutZPqKrlQ1z6LulTfulRXqFv1rUt1hdpb34JtUImIiIiIiOSaGlQiIiIiIiJZUoNKREREREQkS2pQiYiIiIiIZMncPdcxVJqZFV7QIiKyEXe3XMdQ3ZSzRERqh7JyVkE2qERERERERPKBuvyJiIiIiIhkSQ0qERERERGRLOVdg8qCf5jZxbF1PzGzmWbWI7aui5m9YmbvmNl0M+sbKzsi2v4DMxtvZs1quh6ZyLSusbLNzWyemR0XW1er6mpmu5nZ92Y2I7bsHJXVtro2NrMR0ff3QzP7Tays1tQ1ej4jZVlrZr+OyguirlCpz3ZbM3s6+n2aZWaDY2UFUd9K1LVr9Fs828xeNbMDY2UFUdd8lvo5mFlzM3s4er/fNbPfxrbd1cxejv6PTTezn8XKTo22/4+ZjTSzhrmoT0UqWd8+ZvZ29B173sy6xsryvr7p/o/Fyh41sxGx5zuZ2YtRnd4ws11iZXlfV6hcfWPrTzWzJ9Ksy+v6VvKz7Rn9hs5Iky/yvq5Q6fr2MbNpFvLjVDP7aaysIOqblrvnzQJ0BJ4DfgAujtYdDnwArAR6xLadCQyIHncGvgM2AbYCvgR2isquA/6W67pVpa5RmQGPA0uA46J1ta6uwJnAqDT7qI11vRUYC9QHmgPzgV61sa4przsfeAFoWCh1zeKzvQ+4Knq8XfT71KpQ6lvJus4HToket4q2KZi65vNSxudwK3Bz9Lhp9P7vFT2fApwaPd4dWAo0IOTIT6LPpB4wDrgk1/WrSn2j38yvgYOisl2i716jQqhvurrGyi4BvgJGxNa9AQyKHvcFZhP+Lsj7umZZ3y2Av0e/nU/G1ud9fStT1+gzXAgcHD3fvvR3sxDqmkV9N4nqt3v0/Ejgg0L5bMtb8u0K1bnAXcD42LqhwGDg85Rt9wAmRo/bA98Aa4FDgTfd/T9R2UjgZDPLt5GkKlNXgMuBWdFSqjbWdW+gY3TW8Q0zOyZaX6vqGsX9c+AP7r7W3ZcCfYD3qWV1jTOzHQnf5Z+7+2oKp65QufrWB5pH9WgCrAF+pHDqm+n3uAWwAzAawN2/IJzsOozCqWs+S/c5/AooPQu8DaEBsTR6Xh/YPHq8GbAietwfeNzdv3L3H4E7CJ9lvqlMfXcClrr7swDu/j7wLaGxVQj1TVdXzKw34f/P32PrtiM0GB8EcPfJwKaERnMh1BUqUd/I8cBnrP/sSxVCfStT10bAle7+bwB3/y+hAbI9hVFXqER93X0VsJ27T49yQTvChQIonPqm1SDXAcS5+3kAZnZobN1h0brUbddElxjnAsXAr9x9rZntQGjhlvov0IyQXL6t1gpUQmXqamaHAAcAPwOejRXVuroSznCMI/xH6gC8YGYLqX113YoQ+8FmdhfwE+Bed7+lNn6HY64FbnP3hdHzgqgrVLq+vwNeAgYSPuuL3P3L2vbZuvtiM/sY+CVwj5m1A/YD3gacAqhrPivjc3BgjZk9ABwHTCBcmYHwh81zZnYh0BI4McqVOxCu7JT6L+EPtrxSyfo2BZqa2aHu/oyZ9QQ6ERpdeV/fdHU1s22BWwh/hJ4Z23wH4LPoj8xSpXXK+7pCpeuLu/892mZIyq7yvr6Vqau7rwDujm1XQviNnAqcQJ7XFbL6bFeb2daEPNGCUE8ogM+2PPl2hapSPGgP7AhcaqHvfj1CIk+1tkaDS4iZtQZuBAa7e2odalVdAdz9HHf/W3TV5j3gIeAoal9dGxLOJrcHDiQ0ls8yswHUvroCEP1R9zPCj2ypWllXYAzwF3ffFtgV+G3UT7w21rcfcJyZzQKuAp4CVlE765o33H0w4Y+RLYA/mFkR4fdyiLtvD+wP3BH9v0v9LIwC+xxS6+vu3wIDgMvM7B3gF4RuR+m+e3lf3+hekXHAhe6eesU73f+l0joVXF2hwvqWp+Dqm2ldzexS4ErgKHdfTgHWFTKrr7svcvftCFeU7zWzDhRofUvl1RWqTJnZJsAxwP+5+4/u/rGZ/Ztw+XshsGds8+2Ar939hxyEmoSBhC5D/4zODO8IXB91talVdTWz+sClwK3u/l3pamA1tayuhEv6q4HR0VnHRWb2JOHHZRa1q66ljgMmxD5bqH2fa2k3uH2BgwDc/T9m9i/CH7i1rr6EJNjP3dcAmNkzhPs9N6P21TXnLAw0McvdP3P3781sHHAs4f6DJu7+JIC7TzWzOYTPYCGwbWw32xLO/ua9suprZvWA7929d2zbD4GPCPd0FFp9exC6P90Y5fpWQP2ooTwc2MbMLLpiB+vrVKifbZn1dffTy3ldIda33LqaWSPCfbe7Eu6HnB+9rhDrCuV/ly8CDnT3CQDu/nZ0QmQ3Cre+QIFeoYr6YF4DnAjrLi32Idzo/gzQy8x2ijY/i/X3WhUcd/+ru7d3927u3g2YBvwmuhxe2+q6lnC2uwTAzNoQ/lB4hNpX11XAE4SuUpjZpsAhwJvUsrrGHMCGXVahdtZ1CSEJHAfrGlj7A69TO+s7inClADPbm9Dt6t/Uzrrmg+OBK6Iu742i588RGhLNo88AM2tP+ANtOqGB28/MWkb3LZQAj+Ui+CyUVV8HJtn60URPINwzNpMCrK+7v+buO8Ry/d+Bh9z99Oi+mo+IukZFjcwfCSffCq6uUH59K3hpwdU3g7o+QOgOvXesMQUFWFeosL5rCd3D9wEws06E+wNfp0DrW6ogr1BFjgZuN7NLCD8sv3H3aQBmdgrwcHQlay6hK0CtE92TUdvqejLw96jfdH3ggqjrX238XM8AbjGzdwl1HevuD0OtrCuEm8jnx1fUxu+wu7uZ9QNuM7PfE36f/uTuL0Gt/GxLgLvM7Arge8LVqh+AH2phXfPBRYQ/UEoHKJoA3OLuP5rZ0YTflCLCQCgl7j4XwMyuIjREGhL+eLmuxiPPTln1dTMbBNwZfb8+J4z868DMAq5vWU4i1PVyQsNxYNS7oTbWtUzuXqvqa2Z7EU6+fQi8YuvvUf2tuz9dm+oKEF1lHgDcHHUNXEkYvfK/wH8Lub62/uqxiIiIiIiIVEZBdvkTERERERHJB2pQiYiIiIiIZEkNKhERERERkSypQSUiIiIiIpIlNahERERERESypAZVLWZmPzGz681snpktM7MPzOzSaKjKvGNmbmYHV/I1U6LXlbX0rp5oM45vFzN7JXo8xMw2mqTOzEZEn89+FeyrZVSnA1PWPxSt75ay/hUzuymBaiTGzDaNhsTP5rW9o3pmNN2DmR1kZm+a2ffRd/+0lPI+ZjYzeu+nmNmOKXGOMLNPzex/ZvaomW2X5hiNzGx2/HtrZp3M7AWLjX8rIunVdJ4q63c4Vj7FzK7JYr91JhdF2/ZOU8fvzezlaCjwTOM6zsxaRY+Hm9nL5Wyb1WdTxr7am9kTZva1mf3XzP4aTTdQWt7GzJ4xsx/M7D0z65vy+vLyh5nZFdF+vzaz8Wa2dRJxZ1CvZmZ2r5ktiXLXKAvzXJaW72Jm/44+qwVm9ptYmXJXFahBVUuZ2RaEMfx7EeY76gRcBpwP3JvD0JJ2DLBNtFxEmFB1m9jyau5CA2AEcFVZhRbm7jkDOLp0nqKyuPuXwAfAnrHXG9Ab+Aw4MLa+IbAHMCX70KvFRUBFEzdWmYXJZJ8kzFnTjfAZ3G5mR0XlOxAmEXyAMKv7F8BEMyv9TbyZ8L4OJEzK2xh4LFZOlHzHEf5vrePuc4AFRJM2i0h6eZqnjgH+nOXr6kQuSrE96+u4B/Ap8JSZNa/ohWbWBhgPlP7BfwPQrxLHzoqFecueIMyBtDdh/ssBwLVRuREmIV8C9AT+ATxiZm2j8oryR0m0DAb2A1oB91R3vSJ/I/w/Ohg4lPD3wk1R3A2BycBCQl48F/i9mZ0Myl1V5u5aauEC3AnMBopS1h9AmGF+z1zHmCZmBw6uwutPB+bnuh6xePYGPoo9HwL8N/b8LMLkm8dWYp+jgAmx552Bb4E/AE/G1v+UMCP55rl+H1LiHw68nOVre0ffkQYZbHs58Fqa9+7B6PFV8TiAJtH7eDBhQsFlwM9i5dtGx94ler4rMAN4J933lpDE/kM0158WLVo2XnKRp1J/h6upXnUhF6X9PSY0HpwwyXJF+yiOtt0xw2NOAa5J4P3YF1gFbBpbNwj4Inp8ILAc2CxW/u/SY5eXP6LnE4GbY+VHActr4HM24C6gZ2zdUODD2Pv9ENA4Vv4oMCr2XLkry0VXqGohM2sEnAiMcPcV8TJ3f4HwYzEr2tbN7Awz+8jMvjOzcWbWLCq7Orpk3CJ6vpeZrSmv64KZHWlmb5vZ8ugy+cBY2RQz+72Z/TO6TD7LzA5Ps48To8vkm8TWHRZdwq5UNxAz297MHosu2y80sz+X7jfq9vCymV0WHe8LMxtkZsdH235tZn+M7Wu+mV1oZjOi/U02s23LOfzZwGNlxDWQcMbwNHd/pBJVepHQWCp1IPAS8Cywv63vDtcLmOnuX5vZZmZ2l5l9aWarLHSpOTYWi5vZL6LuC9+b2fNm1j5W3t3Mpkaf6atmdpWZTSkrQDMbYGZzos/4bTM7LFo/BLgC2MfM0s4oHnVHmBx9F1dEn0+ndNtW4P+A81LWOVDapaMX4b0MBe7LgLeBvQhJ6WjglZTXEnv9fsAz0fbpvEE463pIFrGL1Hq5zFMVxLWuW5kFvzezRWb2lZmdH+WBSnVNj/ZV23JRWdZE/640s+Los4t3h4t36/s4+vc/0Xuwrix6PtXMHjazpWa2Qc8GC13yPjOzG6Ln5ea5FB8Ah7v797F1DjSKHvcCprv7d7Hyl1n/e19e/oBwZatv9Jk3Bk4C3irrDbPQ1e65KMd+ZGYXma3vdmdmZ0bfg6Vm9sfoO7pRTw8PTnf3N0vfI0JD8dmofL67n+Duy6Pv9j6EHhjPxnaj3JWtXLfotCS/EM6eO9A9g20d+Bw4knDpehbwUFTWCHifcGZ/E2AOcHs5+zqQcNbnAqADcCGwGvhpVD6FcOZ/CNCRcKn/M6B+LJaDCWd7vgOOjO37HuCOCuqywVlBwh/GbxK6juxC+CN4NvDXqHwI4ZL/fcCOwK3A94Qfyt0IZ+0c2C3afj6wFPg54crQ88DUMmIx4Cs2vMoxhNAN5KDouP+XxWfbOopp++j5Y8DFhKsqPwC9ovXjgJuix3cRGgfdgJ0IZ4X/BzSKve9zo7h6AO/FvgPNo3qMjN7D86LPdEoZ8XWNPrufA+2j93B5dOzGhC4drwOtynjPPgT+Hr12D+A1YFJU3psMr1Cl2ffW0Wf36+j5LOC8lG0eKus7RugCtBhoWMb/oY2urBK6g9yU698DLVrycSF3eWoI5VyhInYVBDgH+JrQDW336LdrVbr/7yn7qAu5aKPfY2CLqI5fAT8hzRUoYr0UCN3pnNBAaZxSNiQq+xOwM+E3fApwDbAlIU/dHdtvuXmugrrUi177ePT8NuDhlG3OBj6IHpebP6J6z4ziXxO9163LOHZj4BPgj1HcfaPn50flh0ef0RBCV76nouenV1CnMdHxPyZ9vv00Kn+C6G+wWJlyVxZLzgPQUg0fari8n9Fl9Gi7C2PP+xC6im0RPd8v+kF4MPqPuWk5+3qUKMnF1j0IjI8eTwEeiZV1iY7fOhZL6SXzB4B/RI83ISS13hXUJTWJHUQ4U1Q/tu6A6MeoQfQDtYbosj4hcTnQJ7b9N8DA6PF84JZYWdto+25pYimO1y1aN4SQJL8lJMBV6V6bwWc2n9Bfvx4hYfSI1j8DXBY9/hjoHzvubrHX7xzF1jb2vp8fKx8KzIselxD6W8eT5jjKblDdH3+PonX3EiU+yunyBzQFLmHDbhhnAguix73JokEV7fcVQgJuEq2bC5yRst1o4L40rz+W8H/ilHL+D6VrUF1WVl21aKnrC7nLU0PIvEH1NnBlrKxzWf/fU/ZR63NR7Pf4+2hZFj3/D3BQyrHLalBtUE76BlW8290U4K+E+9EeTnk/h1BOnqugLjdH8XeKnt8NjEnZ5tTSz5QK8gfh+/gG4b6sPYGnCY3xTdIc+zTgnZR1vwTmRo8fJfpbKHregnCSsqIGVSfCFbOXgOlAvZTyHoQTBZ8Ct6aUKXdlsajLX+20OPp38wy3j98sO43wh/rOAB5uTr0POAE416NL5Gb296h7WOnSmnDV6fU0++4Yez439vjb6N903fjGAv2jLhGHEn7sXkyzXXk6Es6SLS2NE5hEaKC1ibZZ7Osv6y+P/l0Q28cK1ncDgNh75e4fExo08fqValm6/5T1TQk3jR5E6AJwn1V+NKuXCN3+did8VtOj9VOAfS2MJtSa9e/XaGAnM7vVzJ6J1aF+bJ+pn0tpTF0IXR/WxMpfKye2jsBZ8e8GoctBh4oq5e4/EN6bn5vZ3RZGpLo5Jc5KsXBj9D+BdoQrnsuiotTPlej5svgKMzuR0ID8q7vfW8nDL2H990BENpSrPFUZuxDulSQ6zmzCFfjKqq25CKA74apQZ6CFu+/k7s+W/5KMLfENu91BGEihF/Cpu6+Nrc8kz20g6vZ2S7TPkzwMygAV54cyy6OueqUnFh9z99eB4wmfTboBNzoCnVJy5kigOPr7J/U7uJjQaC2Xu89x99cI/ye6Err2xcunufvjhAFUzrTYLRYod2VFDaraaS7hx/Wn6QotDLM9ILYq/sdy6Y/Pj9G29Qg/lGsJZwVL/YHwI1q6fEb4kUlVnw1/0FalCynNumcIXcsOIYy09pC7/5hmu/I0IPzwxOPsSris/km0zZo0ryvvOKnb1y9je4/+Tf0/9oW7XxrV5QxCt5dh5RwvnRcJXSV6Ay/GksoUwhmpvYjun4rWjyac1fuG8EN9RJp9pn4upZ/JGjb+fMobUrUBoVtft9jSidCoKpeFoV3fJIyM9D7hfqvflPui8vfXgnD2tR3h6ma80fgp4ebpuFaEbkWlrz+d0G3iVne/JIsQ6hP+34jIxnKVpypjGRv/3q2s5D6g9uYiCFdSPnL3ee6+pIxjx2U07UUk3d8Uswg9NM61DacKySTPrRN9Z+4hdOU7wd0nxooryg/llW9FaCTPLi1w96XAR4RclKoBIXd3iy1dCCcL1lCJ76CZNbYwDH3T2LE/I7wnLcxsOzNLbdS9S2jYN4utU+7KghpUtVD0B/Y44DwLN/6uY2Z9CGdLvoqt7hZ73IPQkHk/en4eoTvBscAFZrZHdIwvox/R0mUNoUvVnmxoL8INoJWtwxrCJf3+wGFRfSrrA2AHwlmuj9z9I8KP3p/I/rvfrfRBdKNtc0Jf6VRfRP+2SFm/7kcqOtv5Z+CylMRQkRcJyXgfQoOh1JuEK0sDiYZLt3Dj9iBgkLv/wd0nEPq5Q/kNo1JzgK5mFm8Udy9n+w+AdvHvBqGf/9FReboEW6o34fPq7e7Xu/u/CVfaKj0nRnS27UnC+7+/u6d+B6cSRnoq3b4J4Yrf1Oj50YR7Mv7s7hdX9viRLYFFWb5WpFbLYZ6qjHcJJ69K49qBjX/TM1Fbc1FFSk/Uxf9YjzcqyssHZXna3R8jDFv+t+gqUzZ57q/Ra45x90dTyqYC3eINE0K+mBorLyt/LCE0eLrEyhsTujfGT+qV+oDQg2N+7LvRDfht1NhN/Q42pfweH2OBn8W2b0e4Cvwe4WrYo2YWv/rUHfgquvJVSrkrG7nuc6ilehbCWZKPCX989yHc5P9LQoK6J7adE86Q9SY0huaw/n6XHQjdv34ZPX+A0Kc87T0srE9yFxDOvF1AOMNySFQ+hdiQp2zcf3qDvumEfsjLiQ33WkGdU/ut1yeczZpEaIDsRUjApTczD2HDoWN3jGIojq37AhgcPZ5PONPTn/Bj+SLwbBmxGOEHqV9s3QbHi9Y1IvxgvkM04EH02ZV5D0Asru9J6fdO6Kv9A+vvn2pI6KJyffR+HwrMi+rZuYz3fV2chNF+FhNu0t05eo9XAc+XEVev6DO/MHo/S4fjPSIqv4iQVDbq1x59Pj8SuigUR8daRugKAyn3UBESZvMy4vgt4bt4EOEPl9Kl9J6L4ui7NYxwZnYc4YxiPUJXmK8ICbtVypKuD3xZ91A9DNyQ698CLVrydSE3eWpItP/DUpZ9ovIprL+H6qjo9/N4wpX2f5X1/z3lGLU+F5HBPa3R7+nC6LewHeHk2lLW3ye1ZbSPYwm5Zjgb3kOVGmP8s2lL+A0/lQzyXMp+ekVll5LyGx/7vOYQBs7qRMgnP5R+HpSTP6LyEYTumgcSGjHjCAMubTRABrBZ9PncF217SPT8uqi8GyHnnkXo/vdAFHvae6gIV+fmEk649iDchjEhKmtIaHRPio51ZHSsX6XsQ7kriyXnAWipxg83zJ1zByERLY9+LC8iNlJZ9B/zakJSWxr9ZyyKyh4nDBVq0fNW0TaXlHPMgYQzISuj/7jHxMrW/RhGz4spv0FlUexXZ1jfjeb+IPzoPhH9GC4mjPxTeuPvECqfxG4mJMbvCaP6bFFOPKOB62PPNzhebP0+hIbEVbHjDK+gruMJ3WVSbzS9NNrX5rF1/QndDUq/A6cRRh36eRnve+r7shfhPq2V0ffhHsJZwrJiOyE6zkrC2befp3zm/yE0lFqmee0VwJeEP5BeYf2Nya3ZuEE1hTSDSERl06JtU5f43CF9CX/ULAOeA9pH648s47VlNZw2Wk/47n5G7KZyLVq0bLxQw3kq9puSusyOyqewYZ46n9CV6xvgV2X9DqQco9bnotTf43KOfUj0O7uCMKnsxSm/w/cScsWvqESDKnp+NaFxvAUV5LmU/dxQxncgnl92BF6I4p4DHJqyj7T5IyorIlzxW0AYUOtxyhjlL9p+96huywl54zo2HAjqeEID8fvYfstqUDUGbom+L0uj97dZrLx1FM+3hK6LvyM25xTKXVkvpT9AUkdZmA/oEA/dq/JKdBl9EWHY9ffyIJ75hB/zuzLcfl/CPTjFXon/aFF3lz3d/c9ZBZogCzPDb+fuL8fW3Q40dfchOQssz0Wf4d+Bjl75e/9EJCaf8lQ+xFIXc5GsV9nPv5L7Vu7Kku6hkrxkZscR/lO/kw+NqWxEjZD5hHkkKmMo4UxmPmgOPBvd6NrGzI4hdNsYn+O48t1ZhC4bSkgiklO1JBdJzVDuylJlRlsRqUl/JHw/++c6kCo6hzDh4FOVeM3x7r66muKpFHefYWbnEm6e3oHQH/7X7l6Z+tQpZtYZ2I7Q1UJEJB8UdC6S6qfcVTXq8iciIiIiIpIldfkTERERERHJkhpUIiIiIiIiWVKDSkREREREJEtqUImIiIiIiGRJDSoREREREZEsqUElIiIiIiKSJTWoREREREREsqQGlYiIiIiISJbUoBIREREREcmSGlQiIiIiIiJZUoNKREREREQkS2pQiYiIiIiIZEkNKhERERERkSypQSUiIiIiIpIlNahERERERESypAaViIiIiIhIltSgEhERERERyZIaVCIiIiIiIllSg0pERERERCRLalCJiIiIiIhkSQ0qERERERGRLKlBJSIiIiIikiU1qERERERERLKkBpWIiIiIiEiW1KASERERERHJkhpUIiIiIiIiWVKDSkREREREJEtqUImIiIiIiGRJDSoREREREZEsqUElIiIiIiKSJTWoREREREREsqQGlYiIiIiISJbUoBIREREREcmSGlQiIiIiIiJZUoNKREREREQkS2pQiYiIiIiIZEkNKhERERERkSypQSUiIiIiIpIlNahERERERESypAaViIiIiIhIltSgEhERERERyZIaVCIiIiIiIllSg0pERERERCRLNd6gMrMBZvZdVbcRERERERHJNXP3mjuY2U7AZKCVu29ahW1qLmgREak27m65jqG6KWeJiNQOZeWsBjUVgJk1AR4Afg2MzXabUjXZEBQRkeSZ1fq21DrKWSIiha28nFVjDSrgjmiZWcVtAOjRo8e6xyUlJZSUlFQ1PhERERERkUqpkXuozOwcYI2731OVbeKmTZu2bsmmMbV8+XI6dOjABx98UOnXioiIJGnhwoXsvffeFBUV0a1bN5544gkOOuigXIclIiIZqKlBKYYAPc1sBjAJaGxmM8xs20puk5jGjRtz2WWXcfrpp6srhoiI5NQtt9zCjBkzeOihh7j77rs56qij+PHHH7nzzjtzHZqIiFSgRgelADCzYmB2WQNOZLKNmXkSca9Zs4Y2bdowYsQIjj766CrvT0REMmdmdWZQiopy1imnnMIbb7zBnDlz1q2bPHkyQ4YMYeHChTRq1Ki6wxQRkXKUl7NyOg+VmfWIrkhVi1tvvZWddtqJRo0a0alTJx566KENyhs0aMCxxx7LDTfcUF0hiIiIlKu4uJj77ruPd999FzPjvvvuA+CQQw5h7dq1jB49OrcBiogUuOHDh1fr/mu8QeXu80uvPLn7NHfvVt422bryyiu56KKLOPHEE3niiSc45JBDOOmkkxg/fvwG2x177LG8+uqrfPLJJ1U5nIiISFYmTJjA4YcfTrt27Xjttdc44ogjgHDS76ijjuLBBx/McYQiIoXtyiuvrNb95/QKVXX55ptv+POf/8xvf/tbrr76ag499FBuvvlmTjnlFC699NINtt1jjz0AeP7553MRqogkaMWKFVx99dVsu+22XHPNNaxYsSLXIYlUaPfdd2errbaicePG9OrVi6222mpd2R577MErr7zCqlWrchihiIiUp1Y2qKZOncqKFSs44ogjWLNmzbqlb9++zJs3j48//njdtpttthmbb7458+fPz13AIlJlkyZNonPnzkyfPp3x48fz9ttv07lzZyZPnpzr0ESy1qZNG1auXMkXX3yR61BERLKyfPlyxo4dy9VXX83YsWNr5cnOmpyHqsYsWbIEgL333jtt+eeff07btm3XPW/SpAlLly6tkdhEJFnz58/nggsuYM6cOdx222307dsXgH322YfJkyczdOhQOnXqxM0330xxcXFug5W8Y2bnAWcDDswFznD3L2PlvyBMNl+qObA9sL27LzKzxcB/Y+XXu/uYpOJr0qQJgHKUiBSkN998k/6H9qfLmi70+KEHo5uO5uJzL2biMxPp2bNnrsNLTK1sUDVv3hwI/dK33377jcp33nnnDZ5//fXXbLnlljUSm4gkq0+fPgwaNIgHH3yQoqKiDcr69u3LrFmzuOqqq+jTp88GV6elsJnZwUBfoCfQElgLLAKmAU+5+wsZ7KM7cDHQ1d2XmtkNwNXAmaXbuPtoYHS0fUPgReDPUWNqZ+B/6e4FTsrXX38NoBwlIgVn+fLl9D+0PyO/GUl/+oeV38NEJtL/0P7M+3zeRnm7UNXKLn977rknDRs25Msvv6RHjx7rltmzZ3PVVVdtMO/UN998w7Jly2jdunUOIxaRbO2222507NixzB/loqIiOnbsSJcuXWo4MqkOZvZLM/uQ0MhpCUwGbgRGAM8BbYGxZvaemf2yvH25+1vATlFjqgjYDlhSzkt+C3zp7ndEz/cG1prZS2Y208z+YGb1q1TBFJ9++imNGjVi6623TnK3IiLVbsKECXRZ02V9YyrSn3DF6tFHH81RZMmrlQ2qrbbaiqFDh3LRRRdx3XXX8fzzz3PTTTdxzjnn0LRpU5o1a7Zu29deew0z04z0IgXq+OOP32j0zlTjx49n4MCBNRSRVBczmwL0AQa7+7bu/nN3/5O7j3L3ke5+jbsPdPftgHOAvmb2Ynn7dPfVZjaA0G1vf+DeMo7dArgIuDC2ugHwb+Cw6LU/A85P9/r4yb1Ro0ZlXOfXXnuNPn36UL9+ou00EZFqN3fuXHr80CNtWfcfujNv3rxEjzd8+HDMLO0ClFlmZlUeVj3jiX3NbDfSdK1w93erFEEWMpkk8ccff+SGG25g1KhRfPLJJ2yzzTYMGjSIK664YoMJEi+88ELeeOMNXnnlleoOW0SqwdKlS2ndujULFy5c1903Xfknn3yywckUyb3KTuxrZrtWNueYWSd3n1PxlmBmZwC/A3Z09x9Tyi4DOrj7kHJefyww1N0PSFlfYc4aMmQI06ZNY/bs2evWrV69mu22245rr72WM844I5MqiIjkjbFjxzL6zNH88/t/blR22KaH8Ys7fsGgQYNqJJYo3ySxj7Q5q9wGlZltDpxL6E/eCphH6A5RH9gSKAY+AUYBI939mypFmqFMklMmVq1axXbbbcedd97JgAEDqh6YiOREv379OP744xk8ePBGZffffz8PP/wwEydOzEFkUp7KNqjSvL4Y2NTdZ0fPTwOedff5Gb5+R6CVu78cPa8PrAJauvuSlG3fITSWXoit+znwjrvPjJ4fB5zl7genvDarnPXoo49y7rnn8vHHH9ea+wxEpO5YsWIF7bZpt+E9VIR7qM7+ydk1eg9VdTeoyuzyZ2anE27u3QY4g5C0dnb3vd19T3ffEfgJYXSknYGZZlZSpUhr2OjRo2nXrh39+/eveGMRyVvldftTd7/aycyOBN4F+sVWnwDMMrMDM9zNNsCDUXc+gJOB2WkaU5sDOwKvpry+M3CVmdU3s8bAecBDlatJ2W666SZ+//vfqzElIgWpqKiIic+ExtNhmx7GMBvGYZsextk/OZuJz0ysVb9tZV6hMrM/EUYyymis1ighXeLulyQYX1nHqvIVquXLl9O5c2eefPJJOnbsmFBkIpILS5cupUOHDnz55ZcblbVs2ZIPP/wwbXdAya2qXKEys1nAne5+a8r6XwG/dPc9MtzP2YSeGGuAz6LHWwJ3lY7eZ2Y9gXHRicT4a5sQBsPoBTQExgPDUhNUNjlr4sSJ3HTTTTz//PPr+v+LiBSi5cuXM2HCBObNm0e7du045phjarwxldMuf/kqqS5/IiKSO1VsUP0AdHH3uSnr2wOz3L1JEjEmQTlLRCS3qrtBVe48VGbWlNCFYi/CRIaNgGXA58BU4EF3/6FK0YmIiFTeu8BJwDUp648FPqr5cEREpK4q7x6qHoRBKC4lNLzeAV4CZhO6NvwW+MjMulV/mCIiki/GjBlHcXFn6tWrT3FxZ8aMGZeLMH4H/N7MXjSzm6JlCnAlUO1dz0VEREqVd4VqJDDW3S8sawMzuwn4O6H/uIiI1HJjxoyjpGQYy5bdDezLggUvU1JyGgAnn3xSjcXh7v82s67AqcCuhNH5XgeGZDrKn4iI1A1XXHFFte6/vEEpfgD2cPcPynyxWUfgrZruq67+6CIiuVFc3JkFC24jzK9b6nnatDmf+fNnl/WytKo6bHo5+62XOo9ULilniYgUvqyGTSd08atoJsGzgfeyDUxERArLwoXvAfumrN03Wl+9zGwTM7vezD4zsyVmNsHMdknZZmtgdbUHIyJSBxS3aoWZpV2KW7XKdXh5o7wuf2cDk6KZ318hDCe7kjAwRStgb2BT4IjqDlJERPJD69YdWbDgZTa8QvUyrVvXyPQTfwQGABdFz88DppnZie7+ZGw7jTMuIpKABYsWUdb1dVu0qEZjyWdlXqFy93eAnYCrgR+AXQij/e0KLAeuBXZy97dqIE4REckD1147jCZNTgOeJ1wIep4mTU7j2muH1cThjwdOcfdx7j6OcKnsLuARM4vP0K7+dSIiUmM0D5WIiFTKmDHjGDbsWhYufI/WrTty7bXDshqQorL3UJnZ/4Be7v5hyvpbgDOBo4G3gc/cvX6lA6omylkiUqjMrOwrVFDluZ0KSdYT+5rZucDJQBHwAvBHd/8qVt4CeNvdWycbcvmUnERECl8WDarHCT0khrj78pSye4ETgSuAP6lBJSJSdWpQrZfVoBRmdikwHHgC+D+gHzAzmp+qVH1gu+RCFRERKdMFwG7A12Z2YErZqYRpPP5c00GJiEjdVt6w6XOB8919UvS8CBgDHAz8zN2nRqMp1XjXCp3tExEpfNkMm25mDYGfAh+4++I05b2AY939NwmFWWXKWSJSqHSFar2suvyZ2beEeag+iq2rR7hadVC0fIoaVCIikoWk5qEys/2BFsC77v5+1SNLlnKWiBSq4latWFDGaH5ttt6a+V98UcMR5U6281C9RRiSdp1oosSTCLPR/wtI7XIhIiJSLcysqZndYWbvm9kIM2tpZm8Thhy8E5hjZg+b2aY5DlVEpFaY/8UXuHvapS41pipSXoPq18CJZrbQzPYuXenuqwnzgLxA6AIoIiJSE24E9gRuB7oDrwHfA9u7+5aEqT62B27OVYAiIlL3VDTK3+bA4cAUd/80TfkJwHHuPrD6Qkwbl7pPiIgUuCxG+VsMHOrub5tZG+BjYC93fz22TQ/gX+6+efIRZ0c5S0Sk8JWXsxqU90J3/xoYY2bbmdmeQCNgGfC5u3/q7g8BDyUesYiISHprANx9gZmNBb5NKd8EWFHjUYmISJ1V3rDpZmaXmNkCYCGha8UU4A1goZl9YmYX1UyYIiIiPAGMMrPuAO4+2N3fAzCzxmY2APgH8GDuQhQRqf3mzp3LhReew9ZbN6N+/XpsvXUzLrzwHObOnZvr0HKivHuo/gIMBS4D2gFNCPNONQHaR+svMDPN+SEiIjVhKPARcGaassMJPSaeAS6tyaBEROqSyZMn06tXFxo3votXX/2OlSudV1/9jsaN76JXry5Mnjw51yHWuPKGTV8CHO3uL5b5YrMDgPHu3jLjA4YziPe7+2ZpygYDvwGc0LVwqLtPS7Od+qOLiBS4pIZNj/ZVBODuedfdTzlLRGqLuXPn0qtXFx5/fBl77bVx+WuvQb9+TZg6dSbt27ev+QCrUbbDpq8Bfqxg3/UIV60yDWQn4AbCXGCpZTsD1wOHuXs34Brg0Uz3LSIidZe7r8jHxpSI1KwVK1Zw9dVXs+2223LNNdewYoV+FpI0YsRfOeOM1WkbUwB77QWnn76a22+/qWYDy7HyGlR3AePM7BQz29HMmphZ/ejfdmb2C+AB4J5MDmRmTaLtf13GJiuB09398+j5NKCVmW2SWVVEREREpK6aNGkSnTt3Zvr06YwfP563336bzp0718kuaNVl7NgHOO201eVuc/rpqxk79v4aiig/VDRs+q+BXwE7ELrhxX0CjASujyb8Lf9AZvcTJl98Dpjt7mVOvGhmBtwPFLn7cWnK1X1CRKTAJdnlL58pZ4lUr/nz53PBBRcwZ84cbr31Vvr27buubPLkyQwdOpROnTpx8803U1xcnLtAa4H69euxcqXToJxxwlevhsaN67FmzdqaC6wGZNvlD3e/0d3bANsBPYEDCJMqbu/uxe5+XYaNqXOANe5e4dUsM2sK/B+wI3B6Wdv16NFj3TJq1KiKdisiIiIitVCfPn3o1KkTs2bN2qAxBdC3b19mzZrFrrvuSp8+fXIUYe3RosWmLFhQ/jYLF4bt6pJy56Eys8bAfkARMNXdv0wpLwIGZdBQGgI0MbMZhDlCGkePD3f3z2L7a00YFvc9oI+7Ly9rh9OmbTRWhYiI1BFmdmqm25aXo8zsPOBsQi+MucAZaXLdX4GBwP+iVR+4+wlmVh/4K3AYIZ/e4O5/r1RFRKTKdtttNzp27EhRUVHa8qKiIjp27EiXLl1qOLLaZ9Cgwdx991388Y9ld/u7666GDBr08xqMKvfKG+VvF+CfwJaEQSQaAH9096ti22wNfObulRmYopg0Xf7MbDPgHeAf7n5lBftQ9wkRkQJXlS5/ZvY80IvQEPoP4T7cDsBmhLkTS7m7tytjH92BR4Cu7r7UzG4ANnP3M1O2ew24yN1fTVl/DnAk0C867mvAL9z9jZTtlLNEqtEDDzzA+PHjmThxYpnb9OvXj+OPP57BgwfXYGS1j0b5q3yXv9sI9zxtATQHLgAuMbMxZlZuV8FKBNYjulIFcB7QBjjazGbEli2TOJaIiNQqrwLPAju4e1d3/ymhe/o4YJy7t42WtI0pAHd/C9gpakwVRa9fEt/GzBoBuxPy3ywzeyTqTQFwNHCvu69x968JEwrrrzWRGnbUUUcxZcoUli5dmrZ86dKlvPDCC/Tr16+GI6t92rdvz+jRD9OvXxN+97uGzJ0b7pmaOxd+97uG9OvXhNGjH651jamKlNcw+inhitRqd18bdWP4GeFs3P3RwBGV5u7zS69Oufu0aIh03P1P7l7f3bulLEvK3aGIiNRF5wC/iecId/8BuJpwgi4j7r46mh/xv8D+wL0pm2xLGEzpcqALMBWYGOXAHQgDNJX6L7B9pWsiIlXSvHlzDjjgAJ544om05Y8//ji9e/emWbNmNRxZ7dS3b1+mTp3JypUl7LNPMxo3rsc++zRj5coSpk6dudF9bHVBeQ2q/wGt4yvc/RWgP+Gs3D0VvF5ERKS6fAv0SLO+N/BVZXbk7o+5ewtgOPB0vBeGu3/s7oe7++yo394NQHugmJAD4335DEg7rJUGUhKpXscffzzjx49PWzZ+/HgGDhxYwxHVbu3bt+fGG0fwxRdLWbNmLV98sZQbbxxR565MlSrvHqqrCINJXAVMdPevYmWHAw8Ds4HulbmHKgnqjy4iUviqeA/VmcAthC5+MwiNmZ7AscBgd384g33sCLRy95ej5/WBVUDL0itfZtaFcI/V/dFzIzTmOgL3Abe7+4So7ArgJ+5+YcpxlLNEqtnSpUvp0KEDX3755UZlLVu25MMPP6R58+Y5iExqi2zvoboSGAX8FugUL3D3SUAfoGlSQYqIiGTK3e8AjgGaASXAycAaYM9MGlORbYAHzaxF9PxkwqBJ8a7mPwK3mlnb6PnZwEx3/y8wETjVzBqY2U+AE4HHsq+ViGSrefPmLFq0CHffaFm0aJEaU1Ktyp3YN6MdmHVy9zkJxZPpMXW2T0SkwCU1sa+ZNQDWZpMYzOxs4FxCY+yz6PGWwF2l9/ia2WDgUqA+4T6p09x9YXTcG4BDCFOC3OHuN6Q5hnKWiEiBKy9nVblBlQtKTiIiha+qDSozOxe4kHC/7y6ERs9i4PJMJp2vKcpZIiKFL9sufyIiInnJzH4NXEK4z7d0hslngFOBa3IVl4iI1D26QiUiIjlRxUEpPgTOd/enzew7wsAR88zsEMLcUHkzfLlylohI4dMVKhERqW22B/6TZv0nwOY1HIuIiNRhVWpQmVlDM9s/qWBEREQy9BpwSuy5R/NH/RZ4PTchiYhIXVTVK1RbAM8nEYiISCFZsmQJRx55JEuWLKl4Y6kOQ4Ffmtl0oAi4A5gHHEYYqEJERKRGVLVB9RXQLolAREQKyT/+8Q8mTZrE6NGjcx1KnRRN19EBuA24GZgD/BnYyd3fyWFoIiJSx2hQChGRSnJ32rdvT9u2B6mkXwAAV5tJREFUbZk/fz4fffQRZlWeTqnOSWoeqnynnCUiUvjKy1kNynlRxqdd3f0X2QQmIlKIXnrpJVatWsW+++7L+++/z8svv8x+++2X67BEREQkB8rr8re2EouIZGjM2LEUd+xIvfr1Ke7YkTFjx+Y6JKmk22+/nd1224169eqx2267MWLEiFyHJCIiIjlS5hUqdz+lrDIRyc6YsWMpueQSll10Eey2GwtmzaLkkksAOHnQoP9v707DpKjOv49/7wGRRUENqLiwqqCgoGBcYyAaFVcWjUaMUaPjhsbEmMQYiYokMWokSmLEDRcwPiibCjF/FxKIGygIEldwwJ1FwYVN4H5enGoomp6mp7tnunv697muvqarzqnqu6pn6sypOkuBo5NUBgwYwNixYzdZt+2223LRRRcB0K1bN+64447Nmvz179+fxx57rM7iFBERkcLIqA9VNBRtf6AL0CCxGtga2N/dv19rEaaOR+3RpSS123tvFlRWwv77b1w5cyZtR4yg6o03CheYVGvu3Ln07duXFi1acMwxx9CoUaNq865Zs4annnqKL774gnHjxtGlS5c6jLT05KMPlZk1BE4iDJA0D3jC3b/JR3z5ojJLRKT05WNi3+HAg8AxwG+AXsC5wBWA/gsUydDCt9+GfffddOW++4b1UpS6dOnCrFmz6NSpEyNHjmTRokUp83366aeMHDmSzp07M3PmTFWmaoGZ7Whmj5vZ12b2bzPbDZgG3Af8ELgbmBOtFxERqROZVqhOBQa6+6HAu8BFQBvgYaBpLcUmUu+02WsvmDNn05Vz5oT1UrSaNWvGqFGjGDx4MKOr6fM2evRoBg8ezEMPPUSzZs3qOMKyMRxoAZwGfAg8D3wO7O7uPYDdgJlRPhERkTqRaYWqOTA9ej8HOMjd1wF/APrURmAi9dHQa66h6S23wMyZsHYtzJxJ01tuYeg11xQ6NMnA/vvvT4sWLVKmbbfddhxwwAF1HFHZOQq40N2fAH4CtAYGu/sXAO6+Erge0JCLIiJSZ6odlCLJPOAA4H3C5InfBu4lVMia105oIvVPYuCJq4cMYeHbb9Nmr70Y+qc/aUCKEjFjxgx22mknAGbNmsV///tfDjvsMLp3785OO+3EjBkzOPDAAwscZb32NfCt6P0OhD69Oyfl2Rn4oi6DEhGR8pbpoBTnAH8l9Jt6DXgVGAkcAnzs7nX6lEodfEWkEM466yw++eQTPv/8c5YvX86f/vQnrrzySlq0aMEOO+xA69atuf/++wsdZsmo6aAUZvY74DxgDKF1xBpgJTCMcLNvP+AG4GF3/03eA86SyiwRkdKXrszKqEIV7eQwYKW7v2pmxxAKtaXAte7+Sd6izSwWFU4iUuf23ntvqqqqGDBgAHfeeSfNmjXj66+/prKykrFjx9K+fXv+97//FTrMkpHNKH9mdgGhMrWU0LyvDfAA0BZYTRiY4gp3X5PncLOmMktEpPTlpUJVTFQ4iUgh9O3bl5NPPplzztl8mr777ruPCRMmMH78+LoPrETlY9j02L5aAUvdfX0+9pdPKrNEREqfKlQiIlJ0sq1Qmdn3CE3OdyPMh7gC+Bh4wd2fzW+UuVOZJSJS+lShEhGRopNFH6qOwARgd0Jf3k8Jzfy2JgxGsT+wADjZ3d/Lf8TZUZklIlL68jGxr4iISKHdRZi6Y2d37+3up7v7j6OfvQjDqM8FRhQySBFJb9Soh2nXrisVFQ1o164ro0Y9XOiQRHKSUYXKzO41s21TrN/ezMbkPywREZHNHARcF803tRl3X0EYqOKQOo1KRDI2atTDVFZezYIFt+O+igULbqey8mpVqqSkVdvkz8wOB/aKFu8Cfg58mZStM3CBu6ee6bKWqPmEiEjpy6LJ32vAA+5+S5o8VwFnufve+YgxH1RmiWzUrl1XFiy4HegdW/scbdteSlXV64UKS2SLsupDZWbdgHGAEYaj/QBYF8viwFfAcHev0+YVKpxEREpfFhWq3oQ+VLOA/wAfsWkfqsMJT7H6u/tTeQ84SyqzRDaqqGiA+ypgq9jabzBrzPr166rbTKTg0pVZDavbyN1fAzpEO3iOUEB9XjshioiIpOfuz5nZPkAloeLUGmgKrAI+BKYC57j7gkz3aWaDgIsINwnnAee7+6KkPGcCV0Z5VgCXufuMKG0J4YZjwk3uPiq7IxSp/9q02ZsFC6ax6ROqabRpUzQPlUVqrEaj/EWT++4FPEp4avWOu6+u0Qea9QUedPdUfbKOB/5AuNs4G/iJu3+RIp/u9omIlLh8zkOV5ef3AB4Durn7cjO7GdjW3S+I5ekETAEOcPePzew44O/u3iZKe9zd90q1/9g+VGaJRBJ9qFasuIfwUHkaTZv+hBEjhjJw4A8LHZ5ItXIe5c/MWpnZy8C/CP2pWgFDgTfMbI8aBLIncDOhGeFmnwHcBwxw907AfOCPme5bRETqPzNra2bXm9n/mdkbZjbfzF6PloeYWZtM9+XurwB7RpWpxsCuwNKkbKuB89z942h5BrCzmTUCDgXWmdlUM5ttZoPNrEEeDlOk3ho48IeMGDGUtm0vxawxbdteqsqUlLyMnlCZ2SOEStCPgUVAN2AJ8ADQyN2Py2AfTYHnCBWx0e6+TVL6QOAMdz8+Wm4HvAZsl3xrT3f7RERKXxZ9qI4lPFF6HpjG5vNQJfpQ9XX3Z2qw377A3dG+ern7O9XkM+BBoLG7n2Jm5wP7Ab8mdAh5Ehjj7sOStvMePXpsWK6srKSysjLT8EREpAjkPLFv1Eb8CHf/n5l9SWgeMd/MOgMvZTLKn5k9SKhQPQu8nqJC9WugnbtfGC03BL4BWiQ3+1PhJCJS+rKoUM0ljPJ3Y5o8vwLOdPd9s4jnfOAqYA93X5+U1gwYSZhU+Fh3X5Zi+wGE/lXfTVqvm4AiUpZGjR7N1UOGsPDtt2mz114MveYaBp5xRqHDykpWg1IkWU/o+JusNaEz8JYCuBhY6+73Rk+eUqkgdPhNlnLIlxkzZmzpY0VEpH5pSxjlL53Hgd9lsrOoyfrO7j4tWnUv8Hdge2JN/6JmhI8DbwC9E/NgmdmPgNfcfXYiK+FGoIhI2Rs1ejSVv/wlK664AvbdlwVz5lD5y18ClGylqjoZ9aECRgO3R0OpO9DczL4P3Ak8ksH2ZwMHmtksYBLQxMxmmdkusTwLgfjyrsDn7v51hjGKiEj99jzw26gJ+WbMrAlwLfBShvtrDfzDzFpGywMJLSjilaltCYNSjHX305MmFe4KXG9mDaLPHkRmZaKISL139ZAhoTK1//7QsCHsvz8rrriCq4cMKXRoeZdpk79GhNH3LgEaRavXEtqc/9zDhAKZfWB4QpWqyd+OwBzgcHd/x8z+QLhzeE6Kfaj5hIhIicuiyV8bYCKwJzCTzeeh6g4sAE529/kZ7vMiQtm2NtrfJcC3gLvdvXs0UfANhPIp7khgJTAcOJjQh2oMcLX6/YqIQEWDBvhTT4XKVMLatdgxx7B+XenNOZZzH6rYjpoQ5qZqCMxz96+yCKYdUYXKzHoSFVpR2nGEilsjwnwgZ7n7Zyn2ocJJRKTEZTtsupl9j9TzUL0I/Du5/1OhqcwSkXLUbu+9WVBZGZ5QJcycSdsRI6h6443CBZalvFWoioUKJxGR0ldb81CZWUUxVapUZolIOUruQ8WcOTS95RZG/OlPJdmHKh+DUoiIiBSUmW1NaH43kNDM7z/AVe7+ZizPToSme5oPSkSkgBKVpk1G+SvRytSW6AmViIgURBZ9qG4G+gLXRKsGEeZFPN3dn4jy7AR87O6ZDrpU61RmiYiUPjX5ExGRopNFhWohMNDdp0bLBtwKXAT8wN0nJJ5QuXvRPKFSmSUiUvqyavJnZucCo9x9dfS+Wu5+b44xioiIbMk2wKeJhaiWcnlUsXrEzPoBrxYqOBERKU/VPqEys/eAnu6+NHpfHXf3DrUSXTV0t09EpPRl8YRqImGo8rOT5oPCzO4DTidM6vsHPaESEZF8UpM/EREpOllUqDoATxCm7zjO3Z+NpRnwZ+CnhBt9qlCJiEjeZFWhiub5yIS7+3PZBpcNFU4iIqUvm2HTzWwr4NvAW+6+JEX6wcAAd78yT2HmTGWWiEjpy7ZClekcHnV+J1CFk4hI6auteaiKjcosEZHSl9WgFMU05KyIiIiIiEgxqrbSZGYdMny1r8uAReq7a6+9ttAhiIiIiEiGttTkz4EtNcdQkz+RPIoeKRc6DJFapyZ/IiJSKrJq8gfoyZOIiBStaICKXYDETT0Dtgb2d/dRBQtMRETKSo2GTTezw4C9gEeBtsA77r66lmJLF4fu9km9pSdUUi5yeUJlZv2BEcD2KZI/dPc2OQWXRyqzRERKX7oyK6OBJ8xsRzN7GfgXcBfQChgKvGFme+QtUhERkcz8HngM2Af4HDgUOBGoIkzuKyIiUicyHcnvdkIh1ZIwSz3Aj4DZwG35D0tERCSt9sCf3P0t4BVgZ3efBFwEXFHQyEREpKxkWqE6ErjW3ROVKdz9C+DXwGG1EZiIiEgay4Cm0fs3gf2j92+hPsAiIlKHMq1QrWdjwRXXGliVv3BEysO1116LmaV8AdWmmZmGVRcJHgf+ZmZdgCnAj8zs28DFwIeFDExERMpLRoNSmNkw4CDgQmAqcAShH9VfgX+6+2W1GGOqeNTBV+otDUoh5SLHQSm2AW4llEkPRq8fAl8DA9398bwFmiOVWSIipS9dmZVphaoR8AfgEqBRtHotcDfwc3ev06dUKpykPlOFSspFvuehMrNtCf18t3H3Zfnab65UZomIlL6cR/kDehFGTdoe2JfQVn0Hd7+4ritTIiIiZvan5HXu/iVwJqEflYiISJ3ItEL1MNDO3Ve6+1x3f83dv6rNwERERNIYaGYjLOp4aGb7mdl/gb8BdxY2NBERKScNM8z3GvAd4PVajEVERCRThwKTgUfM7ENgEDAR2MfdqwoZmIiIlJdMn1B9CQw3s0Vm9rKZ/Sf+qs0ARUREkrn7AuBwYFfgUmCAuw+oaWXKzAaZ2Vwze93MJpjZjinyHG9ms83sLTMbY2bNo/UNzGyYmb1pZu+a2YV5ODQRESkxmQ5KkXbWeXe/Lm8RZUAdfKU+06AUUi5qOiiFmV2fYnVTwoBJs4GnEivdfXAG++sBPAZ0c/flZnYzsK27XxDL0wqYCxzm7u+Y2Y1RnovN7GLgBOAkYFvgBeAsd3856XNUZomIlLh0ZVamTf7eAx5x99VJO24GnJtjfCIS87vfpb1/IVLOvlPN+heT0jOqvbj7K2a2p7t/Y2aNCU+73kvKdjQw3d3fiZbvAF4zs0uAfsAId18LfG5m/yAMivEyIiJSNqp9QhU1e9gmWnwHOBhYmpStGzDa3ZvUWoSpY9PdPhGREpfjPFTnAk+4+6I8xNGXMA3IaqBXrPKEmf2aMCjThdFyQ+AboAWh4nS2u78YpZ0HHOfu/ZP2rzJLRKTEZfuE6jvAGDbe6XspluZAYocjcw1QRESkhm4B/gPkXKFy9/HAeDM7H3jKzPZw9/VRcgWpn3itS5Fm0frN9OzZc8P7yspKKisrcw1bRESKRLUVKnd/zMzaEQqM+cC3gcXxLMBX7v5ZrUYoIiKyuaeBs83sD+7+dTY7MLM9gJ3dfVq06l7g74Q5FxMtMhYCB8U22xX43N2/NrOFwC6xtF2AD1J91owZM7IJUURESkDaUf7cfaG7V7l7hbvPAN6PtvkQ+ESVKRERKZDdgN8AX0Qj0C6MvzLcR2vgH2bWMloeCLzu7vHm7f8CDjazPaPlC4EJ0fsJwLlm1tDMtgNOB8Znf0giIlKKMhqUImoz/gfC0LQNgb2AP5rZWuD8TO8Omtkg4CLC06150baLkvL0A64D1gOfRXnmZXY4IiJSJv4evbLm7lPNbCgwJSrPPgL6mllP4G537+7ui8zsHOBRM2tEKLvOinZxB9CRMFdjI+BOd/93LjGJiEjpyXTY9D8AxxMmTpwE7Edo2nAX8O9EZ90t7COT4WmbAEuiPO+a2c+Ao9z9+KR9qYOviEiJy2VQilKiMktEpPTlY9j0HwID3f2/ZuYA7j4tGmVpAqEJRFoZDk/bgNCpt0W0vA2wKsMYRUSkHosmkj/J3ZeZ2VTSDI/u7kfUXWQiIlLOMq1QtST1SEpfAxkPmR5VpvqycXjawUnpX0UzzT9vZksJFazDMt2/iIjUa88Aa6L3TxcyEBERkYRMm/yNJzTFOx/4gtDk7zNgNLDW3U+u8QeH4WmvAjYMT2tm+wLjgGPcfZ6ZXQb8BOgeby9hZt6jR48N+9IQtCIipSfHeajOIs2E8+5+ez5izAc1+RMRKX3pyqxMK1S7Eio67QnDyb4N7E4YTv0kd1+QwT42GZ7WzBoQ7jTumBhRycx+Aezr7j9OyrOTuy+J7UuFk4hIiatphaqYJ5xPR2WWiEjpy7kPlbt/CHzbzI4EOkfbvQX8Kzb54Za0Bh42s+5R5SjV8LSvAoPMbCd3/xToC7wXr0yJiEjZ0oTzIiJSdNI+oTKzbYHehP5OL7j7Fzl9mNlFwCVAYnjaS4BvEQ1PG+W5hDCa4BpCs8JB7j43aT+62yciUuKyafJnZm0osQnnVWaJiJS+rJr8mdnBwBPADtGqxcBp7j6lNoKsCRVOIiKlT8Omi4hIqci2QjUV+BD4KbAOuAk41N071VagmVLhJCJS+nIclOI/6dKLadh0lVkiIqUv2z5U3YFzor5MmNkVwBIz297dP89/mCIiIhl7Jmm5IdCBMAn99XUfjoiIlKt0T6jWE0blWxRb9zXQxd2r6ia81HS3T0Sk9NVGkz8z+xFwursfn8/95kJllohI6UtXZlXUcF/rs9hGRESkrkwjDKYkIiJSJ9I1+XOgwswSFShLsS5kzHzodBERkZyZWYcUq5sDvwGq6jYaEREpZ+kqVEYYlCJ53Vsp8jbIW0QiIiJb9i4b56NKMOB94Ny6D0dERMpVugqVmkyIiEixap+07IT5Cz9VhyUREalLaSf2LVbq4CsiUvqynNg3MeH8KuDFXCecrwsqs0RESl+2w6aLiIgUjVQTzptZUUw4LyIi5Usj9omISKm4CXgaaA3sCEwC7ixoRCIiUvbU5E9ERAqipk3+zOxLYH93fzda3gFYAnyrmCecV5klIlL68jkPlYiISKE0Azb0mXL3z4CVQIuCRSQiImUvpz5UZtYMuILQOfhxd38jL1GJlIFmDRqwYn3qKdyaVlTw9bp1dRyRSEnShPMiIlJQuRZCTYBzgJeBoWam+ahEMrRi/XocUr6qq2iJlLkNk8tHrwYp1m02+byIiEhtyqgPlZndDfwx0W690NQeXeoDM9tsVtINaYB+x6W+y6IP1XpST+a72R+LuxfNDT6VWSIipS8fw6b3B4bmLyQREZEa04TzIiJSdDJ9QvVb4DvAMGABoc/UBu4+vzaCSxOP7vZJydMTKil32UzsW4pUZomIlL50ZVamFarkDh2JjaL/++q2aYUKJ6kPVKGSclcMFSozOxO4kqj7InCZu8+IpZ8F/Dy2SQtgN2A3d//UzJYAH8TSb3L3UUmfoTJLRKTE5aNC1TZdursvyDK2rKhwkvpAFSopd4WuUJlZJ2AKcIC7f2xmxwF/d/c21eTfCvgPMNLd74y2f9zd99rC56jMEhEpcTn3oYpXmGKjJxmwNbA/oRmgiNRA04oKLM2w6SJS61YD57n7x9HyDGBnM2vk7mtS5P8VsMjd74yWDwXWmdlUwpOrR4Gh7q45D0REykhGFSoz+w5wB7B3iuRvgMb5DEqkHGieKZHCcvcqoArAzAz4MzAxVWXKzFoS5l3sEVvdEHga+DWwFfAkYeLhYbUYtoiIFJlMb4P/BXgXOA74GhgAXAosBX5cO6GJiIjUjJk1M7PBZvZLM0t1EzDlNsD/A/YAzqsmWyUwIT4Ik7vf5e6XuvvX7r6MUCHrl2rjnj17bniNGDGiJockIiJFLtNh0/cBznD3N83sFWC1u//NzBYR7sw9UmsRioiIZC4x4fw5hAnnT03XBM/M2gCPA28Avd19ZTVZTwMuS9r2R8Br7j47sYrQamMzM2bMSLVaRETqgUyfUK0AEp093gS6R+9fBjrlOSYREZG0zOxuM9sjeb27L3H39u4+xd37b6EytS1hUIqx7n56dZUpM9ue8PTq+aSkrsD1ZtbAzJoAg9ANRhGRspNpheoZ4I9mtiuhQDndzHYkNG34rLaCExERqUZ/INeOiIOAtkA/M5sVex1kZrNi+fYAPnb35KdP1xHKwDnAbEL5eHeOMYmISInJdNj0nYEHgYnA3wmdcL9DKMwucvc6LUA0BK2ISOnLZdj0YptwPh2VWSIipS/neahS7NAI/aqWufuHOcaXzeercJKSMm/ePIYPv4XRox9iyZKvaNlyG84440wGDbqCjh07Fjo8kYLIsUJVVBPOp6MyS0Sk9KUrszKe7MbMWpjZxWZ2O3AzcDBq7ieyRZMnT+bgg/ejSZO7ef75L1m92nn++S9p0uRuDj54PyZPnlzoEEVKUfukV4folXgvIiJSJzJt8ncgoZnfYmAWoSJ2AGHejePc/bVajDFVPLrbJyVh3rx5HHzwfkycuIJDDtk8/YUX4KSTmvLii7P1pErKTi5PqJL2s9mE8+7+31z3my8qs0RESl/OTf7MbDbwL+DKRKlgZg2A24Ge7v7tPMabSTwqnKQk/OxnF9Okyd38/vcpR1IG4KqrtmL16kr+/OfhdRiZSOHl2OQv7YTz7l40E86rzBIRKX35qFCtBLq5+9tJ6zsBM929aYaBDAIuIrR1nwec7+6LkvLsS6iotSAMenGBu7+SlEeFk5SEnXZqzvPPf0m6h0/z5sFhhzXnk0+W111gIkUgxwrVq8BCQqVqDHAWsAtwLXCpuxfN8OUqs0RESl8++lBNAs5Isf4U4LkMg+gB/AI41N27Au8AQ5LyNCU8CfuTu+8fpY/KMEaRorNkyVe0bZs+T5s2IZ+I1Mg+wK/d/Slgw4TzwMXAlQWNTEREykrD6hLM7IHYYgNgsJkdD0wnTPK7L3AoMDKTD3L3V8xsT3f/xswaA7sC7yVlOxqY5+6TouWJKfKIlIyWLbdhwYL0T6gWLgz5RKRGUk04PxlNOC8iInUs3ROqdbHX58D9wOtAE6AZMB94iFDZykhUmeoLfAAcAdyXlGUv4BMzu8fMZgD/RzWVvp49e254jRgxItMQROrUGWecyT33bJU2z913b8UZZ/yojiISqTc04byIiBSFTPtQVdsA3Mw6uvu8Gn+w2fnAVcAe7r4+Wnc1cDXQ291fMrOTgTuBtu6+Orat2qMLK1euZNy4ccybN4+OHTvSv39/Gjcumn7ogEb5E0knxz5URTXhfDoqs0RESl8++lCNjkb1i+90azO7FpiTYRB7mNnhsVX3Am2B7WPrPgLecPeXANx9AuEJmOYUkU1Mnz6djrt05IELHmD171bzwAUP0KF1B6ZPn17o0DbRsWNHHnjgUU46qSlXXbUV8+bBN9+EgSiuumorTjqpKQ888KgqUyI15O6fuPv33f12d/8G6EVoit6umCpTIiJS/2VaodoPmBj1fcLMjgP+B1wIDMpwH62Bf5hZy2h5IPC6uy+N5ZkMtI8GsMDMjiCMCKh+VLLBypUrOfnok7lj2R3886t/coPfwD+/+id3LLuDk48+mVWrVhU6xE306dOHF1+czerVlRx2WHOaNKngsMOas3p1JS++OJs+ffoUOkSRkqQJ50VEpBhk2uRve0KzCoAlwHHAcOA6d/8i4w8zuwi4BFhLeBp1CfAt4G537x7lOQK4idBPazXwU3eflrQfNZ8oY6NHj+aBCx7gn1/9c7O0Y7c5lrPuPIszzkg1KKWIFJMcm/wV1YTz6ajMEhEpfenKrGpH+Ytz98/N7CjCEOZ9gSPd/d81DcTd7yDMGRL3HmF0pkSe/wAH1XTfUj7mzZtHz697pkzr8XUP5s+fX8cRiUgB3APcReoJ5+8C6nTCeRERKV+ZDpuesBr4BrjHzJ5PrHT3s2ohNpGUOnbsyAPNHoAUUze90uwVzuqgX0eRMrAncEr80Y+7rzOzvwAzCxeWiIiUm0yHTU+81gD/AKYmrRepM/3792d2w9lMYMIm6ycwgdkNZ9O/f/8CRSYidSjnCedFRETyIaM+VMVG7dFl+vTpnHz0yey3dj96fN2DV5q9EipZ/5rAgQceWOjwRCQDNe1DldRyojlwEvAKKSacd/fz8xlrLlRmiYiUvnRlVrUVKjN7BLjG3d/O8EP2IQxScWrWkWZIhZPAxnmo5s+fT4cOHYpyHioRqV4WFarkyeCr4+5+bpZh5Z3KLBGR0pdthep7wG2E0fjGA/8HvBvr/GtAF8JEij8EdgIucfen830AKWJT4SQiUuJyHOUv7xPO1xaVWSIipS+rClW0YUPgB4T5pg4lDHe+jND3antCE4sXCCMq/cPd66Q/lQonEZHSl2OF6mHgzHi5Y2ZbA1cBv3T3pnkKM2cqs0RESl/WFaqknTQH9gd2JEy2+wkwuybzUOWLCicRkdKXY4VqLlAFDHD3VdGE87cT5jD8jbvfm79Ic6MyS0Sk9OWlQlVMVDiJiJS+HCtUeZlwvi6ozBIRKX2qUImISNHJpUIVbb81OU44XxdUZomIlL50ZVa1E/uKiIgUE004LyIixUgVKhERKRWpBj5aR5hwXkREpCBq1OTPzCrcfb2Z7UwYLn2Wu79Ta9FVH4eaT4iIlLhcm/zlKYYzgSsJgy2tAC5z9xlJeW4BTgU+i1a95e6nmVkD4BbgWMINypvd/e8pPkNllohIiUtXZlVkuIODzex94LtmthMwA7gHeN3M+uUvVBERkdTM7BEz26sG+fcxszFp0jsBNwHHunt34AZgbIqshwKnu3v36HVatP4CYC+gK3AgcLmZfTvT+EREpH7IqEIF/BmYAEwHfkKYj2pH4GJgSO2EJiIisok7gbFm9i8zu9jM9owmmQfCZL9m1tXMLjKz/wDjom2qsxo4z90/jpZnADubWaPYPrcmTBnySzObY2aPmVmbKLkfcJ+7r3X3zwlND8/M29GKiEhJyLRCtT9wk7t/BZwEjHf3VcDTQMfaCk5ERCTB3Z8FugMjgdOBN4CVZvaJmS0C1gCvAKcRKlL7uPvTafZX5e5PQqiMEW4eTnT3NbFsuwDPAr8F9gNeBCZE+XcH3o/l/QDYLdVn9ezZc8NrxIgRNT10EREpYpkOSrEUaBMVIAcC10TrewIfV7uViIhIHrn7WmA0MDpfE86bWTNCJW13Qn+o+Oe9R5jjKpH3ZkIZ2I5wUzLeOcpIPXAGM2bMSLVaRETqgUwrVPcC4wl3/2YDz5jZxYS251fXTmgiIiLViypOOc09FTXfe5zwtKu3u69MSt8P6ObuD8ZXE4ZqX0h4gpWwC+EplYiIlJGMmvy5+2DgPOBG4Gh3Xw9UAae5+7Bai05ERKSWmNm2wBRgrLufnlyZiqwHbjOz9tHyRYSnYB8Q+hafa2YNzWw7QjPE8fmOc+nSpZxwwgksXbo037sWEZE8yLQPFe4+DrjN3RdHw6Y3A96qtchERERq1yCgLdDPzGbFXgeZ2SwAd38duBR43MzeIAxE8cNo+zuAecBrhEGb7nH3nJ6YpXL//fczadIkHngg1bzGIiJSaBnNQ2VmBwNjgLOA/xE6/TYHtiYMJTuuNoNMEY/m9BARKXHFMA9VXcilzHJ3OnbsSPv27amqquLdd98lNrChiIjUkZznoULDpouISBEys4ro585mdqqZ7VnomPJp6tSprFmzhsMPP5xVq1Yxbdq0QockIiJJNGy6iIiUnHKZcP6vf/0r++67LxUVFey7774MHz680CGJiEiSTCtUiWHT2xGGTX8yWq9h00VEpBDqXcuJAQMGYGabvCZPnsx+++0HQLdu3Zg8efJmeQYMGFDgyEVEylumfaiuBy4hDJv+CdADuJBo2PS6HulPfahEREpfLn2ozGwl0NndF5jZi8CL7n65mbUF3nT3JnkNNgeZlllz586lb9++tGjRgmOOOYZGjRpVm3fNmjU89dRTfPHFF4wbN44uXbrkM2QREUmScx8qDZsuIiJFpt61nOjSpQuzZs2iU6dOjBw5kkWLFqXM9+mnnzJy5Eg6d+7MzJkzVZkSESmwmg6bPgboamZNgFfc/Ylai0xERKR6iQnnX2DTCecfAG4rYFw5adasGaNGjWLw4MGMHj06ZZ7Ro0czePBgHnroIZo1a1bHEYqISLKGmWQys22A+4ABhEkO9wJuNbNWQF93T30bTUREpBa4+2Azm0mYR2qUu683sypCy4mSv9m3//7706JFi5Rp2223HQcccEAdRyQiItXJ9AnVLUBLoD2QmEn+CsAp4TuBhbZy5UpGjx7NkCFDGD16NKtWrSp0SCIiJaM+Tzg/Y8YMdtppJwBmzZrFX//6V2bNmgXATjvtxIwZMwoYnYiIxGU6KMXHwHHuPtPMvgS6uft8M+sGPOfuO9R2oEnxlPygFNOnT+f444+nVatWtGzZkiVLlrB48WKefPJJDjzwwEKHJyJS63IclKKoJpxPJ5sy66yzzuKTTz7h888/Z/ny5fzpT3/iyiuvpEWLFuywww60bt2a+++/v5YiFhGRZOnKrIya/AFNCCP8JdsayLgwNLNBwEWEJ1vzgPOray5oZn2BB91920z3XypWrlzJ8ccfz5FHHknnzp03rH/zzTc5/vjjWbhwIY0bNy5ghCIiRS8+bPplbBw2fSBh2PSiqVBlY/r06VRVVTFgwADuvPNOmjVrxve//30qKysZO3Ys7du3L3SIIiISybTJ3wTgD2a2XbTs0Wz0twMZtVU3sx7AL4BD3b0r8A7VzBUS7ftmalBZKyXjxo2jVatWm1SmADp37kyrVq0YO3ZsgSITESkZ9XrC+U6dOvG3v/1tk4EnEgNW/O1vf2OvvfYqcIQiIpKQ6ROqSwmDUiwlVHJeI7RVfwr4aSY7cPdXzGxPd//GzBoDuwLvJeczs6bAQ8DPgdRDHJW4efPm0bJly5RpLVu2ZP78+XUckYhIyUkMm26EYdOvidaX7LDpcePHj6827ZxzzuGcc86pu2BERCStTJ9Q7Q6cAuwJnAj8COji7se5+2eZflhUmeoLfAAcQaikJbszes3OdL+lpmPHjixZsiRl2pIlS+jQoUMdRyQiUnLq5bDpIiJSejIdlGIR0MfdX8nbB5udD1wF7BFNFExUGB7o7udEkzW+7u7bpNjWe/TosWG5srKSysrKfIVW61atWkWbNm1S9qF65pln1IdKRMpCLoNSRNv3Y+Ow6YvN7DigotiGTa8PAymJiJS7dGVWphWqV4Fr3X1iDkHsAezs7tOi5QaEgS52dPel0bqXgaaEzsWNgE7AHMIIgx/F9lXyhZNG+RORcpdrhSrax66EuRFfBJq7+6d5CS6P6kOZJSJS7vJRoRpJGDlpJlAFbDJhkruflcE+vgM8DHR39yVmdhZwhbt3qyZ/O9I8oaoPhdPKlSsZN24c8+fPp0OHDvTv319PpkSkbOQ4bHrKCeeBoptwvr6UWSIi5SwfFapUfZ02cPeMesea2UXAJYQnUB9F778F3O3u3ZPytqOeV6hERMpZjhWqOwmVqLOB14FuhH7B9wPvu/vp+YozVyqzRERKX84VqmKjwklEpPTlWKEqqgnn01GZJSJS+tKVWdWO8mdmDczsajN71cxeMLNfm9lWtRemiIhIxvIy4byIiEiu0g2b/kfgV8BLwAzgl8Df6iIoERGRLch5wnkREZF8qLbJn5m9D1zo7k9Gy98FJhNGUVpbdyGmjE3NJ0RESlyOTf6aEwal6Et4IvUVGyecP7MmcyTWNpVZIiKlL6s+VGb2DdDG3T+OliuA1UB7d/+gtoLNhAonEZHSl2OFqgvwP6A9sDfQEHjL3d/MY4h5oTJLRKT0pSuzGqbZrgGwLrHg7uvNbBVhfigREZFCeo6NE87PL3QwIiJSvtL1oRIRESlWHwC75roTMzvTzF4zs1lm9ryZ9axJHjNbEq1PvAbmGpOIiJSWdE+oAH5lZl/HlhsBPzOzz+OZ3H1w3iMTERGp3mzgMTPLZcL5TsBNwAHu/rGZHQeMBdpkkidK+yx5HkURESkv6SpU/wEOSFr3PNA1aZ0ahouISF1z4KEc97EaOC/RV5gwou3OZtbI3ddsKQ9wKLDOzKYCLYBHgaHuvg4RESkbmthXREQKIpdBKfLNzAx4EGjs7qdkksfMzgf2A34NbAU8CYxx92FJ26nMEhEpcVlN7CvFa/ny5fTr14/ly5cXOhQRkTpTWxPOm1kz4P8BewDnZZrH3e9y90vd/Wt3Xwb8GeiXavuePXtueI0YMSLXkEVEpIhsqQ+VFKGJEycyfvx4Hn/8cc4888xChyMiUlf+CFwAjALWEiac7wicn+0OzawN8DjwBtDb3VdmmsfMfgS85u6zE1mBb1J9zowZM7INUUREipyeUJWge++9d5OfCaNGjaJdu3ZUVFTQrl07Ro0aVYjwRERqy+nAD939Ine/lPA0aKCZZXVz0My2BaYAY9399GoqU+nydAWuj56cNQEGAY9kE4uIiJSujPpQmVmDYupkW27t0Y866iieeeaZDcuNGjVizZo1G34mVFRUsH79+g3LTZs2ZcSIEQwcqFF8RaT41LQPVb4nnDezq4AbgDlJSRcAd7p79zR5jgRWAsOBgwl9qMYAVycXUOVWZomI1EfpyqxMK1RLCAXFaHefmuf4aqzcCqfnnnuOE044gRUrVlSbJ/qSN1vftm1bqqqqajE6EZHsZFGhWg/s7O6LYuu+BLq5e9FO7ltuZZaISH2Uj0EpziTMQTXBzN43s5vNrEfeIpS0evfuzRNPPEHTpk1Tple3HmDhwoW1FZaIiIiISNmr0bDp0WhKxwCnRD+/BEYDD7v7W7USYeo4yvJu3xNPPMGpp57KqlUb569s3LgxY8aMYdCgQSxYsGCzbfSESkSKVZZPqG4F4hPO/woYARTthPPlWmaJiNQn6cqsGnXkdfdvzOyfwHpCu/UfAecCPzezWcBF7j43x3ilGsuWLaNhw4ZUVFSw9dZbs3r1aho2bMiyZcsYOnQolZWVmzQLbNq0KUOHDi1gxCIieaUJ50VEpOhkVKGKRlA6GjgVOJkwLOyjwNHuPs3MmgJ/ByYQ5uiQWnDPPfewYsUKunXrxo033sivfvUrXnvtNe69916effZZAK6++moWLlxImzZtGDp0qAakEJF6w917FToGERGRZJkOSvEZ0ACYSGji96/kUf/M7BRgqLt3qo1Akz6rLJtP9O3blyOOOILLL7+ciooK1q1bx7Bhw5g6dSrjx48vdHgiIjVS0yZ/papcyywRkfokH6P8nQZMTDVHRyGocBIRKX2qUImISKnIqkJlZt/L9APc/dksY8uKCicRkdKnCpWIiJSKbCtU61MmbM7dvUG2wWVDhZOISOlThUpEREpFVqP8uXumc1SJiIjUqWJuRSEiIuUl43mozMyAXYGtgHjtzN39vVqILV0sutsnIlLicnlCZWbvAa0Jk85/SZjKoyUbh0xP7LfOW1EkU5klIlL60pVZGT2FMrM+wPvAAmAe8C7wTvTz3TzFKSIikqlbgFnAvu7ewt13BNoCzwCXu3tF9CpoZUpEROq/TEf5mw28CQwBvkhOd/cF+Q8tbTy62yciUuJyfEL1KWEuxNeS1ncBprr7DvmIMR9UZomIlL6s+lAl6Qic6u5v5S8sERGRrDmwG/Ba0vp9gFV1H46IiJSrTAee+A/w7doMREREpAb+CjxgZoPN7GQz62tmQ4G7gaEFjk2kVlRVVdGrVy+qqqoKHYqIxGTa5O+3wK+ByYQ+VGvi6e4+uFaiqz4eNZ8QESlxuQ6bbmYXAz8BOgMrgP8BN7n7E3kKMS9UZkm+9O3bl7fffptOnToxbty4QocjUlby0eTvSGA6YQSllklpKiVERKTOufvfgL8VOg6RujBp0iTmzp3LK6+8wgEHHMDkyZPp06dPocMSEWowbHpePsxsEHARoRI2Dzjf3Rcl5TkTuDLKswK4zN1nJOXR3T4RkRJX0ydUZnY98Ed3XxG9r1Zdt5xIR2WW5GrVqlV07dqV4cOHc+yxxzJ58mQuu+wy5syZQ+PGjQsdnkhZSFdm1WQeqp6Eis7eQAPgLWB4phMmmlkP4DGgm7svN7ObgW3d/YJYnk7AFOAAd//YzI4D/u7ubZL2pcJJRKTEZVGheg7o5+7LovfVcvfeOQeYJyqzJFdDhgxh5syZjB07dsO6fv360aNHD377298WMDKR8pFzhcrMTgFGEypEzxMqVIcCfQmj/03IMJCt3P0bM2sM3Ae85+6/iaW3A7q4+5PR8o7AB8A27r4mlk+Fk4hIicu1D1WpUJkludpll10YM2YMhx122IZ106ZN47TTTuPDDz8sYGQi5SMfFaq5wF3uPixp/eXA2e7evQbB9CWMwrQa6OXu71STz4AHgcbufkpSmvfo0WPDcmVlJZWVlZmGICIiRaA2KlRmdiihZcN++dxvLlShklzpCZVI4eWjQrUC2M/d301avwcwx92bZBHU+cBVwB7uvj4prRkwEtgdONbdlyWlq3ASESlxtVShOgaY5O4N8rnfXKjMklwl+lDdfvvt9OnTh0mTJvHTn/5UfahE6lC6MivTeajeAFINJXM88F6GQexhZofHVt0LtAW2T8rXhtCscB3QO7kyJSIiIlJOGjduzO23385ll13G8uXLueyyy7jttttUmRIpEpkOm/474LGoKcVL0bqDgP7AGRnuozXwsJl1d/clwEDgdXdfmshgZtsSBqW4392vy3C/IiIiIvVanz596NKlC4cccgj77ruvhkwXKSIZPaGKJknsAzQCKoGzgPXAoe7+WIb7mEqYvX6Kmc0CTgf6mlnPaBlgEOGpVT8zmxV7fasGxyQiIiJS7wwbNowdd9yRW2+9tdChiEhMjeehMrPdgY/cfV3thJRRDGqPLiJS4rIYNv19tjyZfGPgW5n2ocpw7sPjgT8AWwOzgZ+4+xdm1gC4BTiW0OLjZnf/e4rPUJklIlLi0pVZmTb5i5sLdAfm5xKUiIhIDeV1OLNo7sOb2HTuw7FAm1ieVoRpPg5z93fM7Ebgj8DFwAXAXkBXYFvgBTN71d1fzmecIiJS3LKpUNX7OUNERKT4uPv9qdab2W6ElhPrU6WnsRo4z90/jpZnADubWaPY3IdHA9NjU3zcAbxmZpcA/YAR7r4W+NzM/gGcCahCJSJSRjId5U9ERKRY/Q9oV9ON3L0qNpG8AX8GJsYnkidM3/F+bPkDoDnhiVSqtN1SfVbPnj03vEaMGFHTUEVEpIhl84TqIeCLfAciIiKSpZxaTiTPfZiUXEHqflvrUqRZtH4zM2bMSLVaRETqgYyeUJnZEYn37n5RNOw5ZlZhZr+oreBERERqUwZzHy4Edokt7wp87u5fp0jbhfCUSkREykimTf6eMbPbzKxpYoWZdQemA1fXRmDlatWqVQy57nfsstP23HD9taxatarQIYmIFLusWk7E5j4c6+6nu/vKFNn+BRxsZntGyxcCE6L3E4BzzayhmW1HmA5kfE3jEBGR0pZpheoI4HvAHDM7Nhrl6GVCu/XOtRVcuZk0aRJdO7dn5j9vZsyFy3h18k107dyeyZMnFzo0EZGikqeWE9XNfXhQYn5Ed18EnAM8amZvAPsCV0Tb3wHMA14j3GC8x93/nYfDExGREpLxPFRmthXwAPADQtOIAe7+eC3Gli6WejWnR1VVFZcPOp+5s57ntjNW0Kf7xrTJs+Cy0U3p0v1Qhg2/i3bt2hUoShGR/KrpPFRJ235DqND82t1XROu6A/cAHdx9+7wFmqP6VmaJiJSjdGVWpn2odgBuB04B7gVeAUZEEyJKjnofcTBdtnqOOTdsWpkC6NMd5tywgn0aPkfvIw4uRHgiIsVILSek7Cxfvpx+/fqxfPnyQociIjGZNvl7h1B49Xb384FDCbPG/9XM1LwhR/t22Zu9W6+jcaPU6Y0bwd6t17Ff133qNjARkSLl7i8A+xMqUU8CPyO0nPiRu39a0OBEasnEiRMZP348jz9ekAZCIlKNTCtUfwW6u/s0AA9uI8wOv6yWYisbPxj4E8a8uk3aPGNe3YZTzzi3jiISESluajkhxWjlypWMHj2aIUOGMHr06LwPLHXvvfdu8lNEikPGfagAolGMOgHrgTfd/ctaimtLcdSr9ujLly+nzW47sfDW1bRomiJ9BbT52da8/+EimjdvXvcBiojUghz7UC0FPgUq3X1aNDHvpcAQYJa7fzePoeakvpVZktr06dM5/vjjadWqFS1btmTJkiUsXryYJ598kgMPPDCrfR511FE888wzG5YbNWrEmjVrNvxMOPLII3n66adzPgYRqV66MiujCpWZbQPcRbgT2CBavYYwSMWgpFnla119LJxO6tObH7SfwpmHb5724FR4dEFvJkx6tu4DExGpJTlWqK4Hbkguf8xsd2C4u5+cjxjzoT6WWbKplStX0rZtW4488kg6d97Yhe/NN9/kmWeeYeHChTRu3LjG+33uuec44YQTWLFiRbV5mjZtypNPPkmvXr2yCV1EMpTzoBTACKAL8H2gObA9cAKhL9Wf8xFkuUvX7E/N/URENuXug919jZltFw1zfqCZbevu7xdTZUrKw7hx42jVqtUmlSmAzp0706pVK8aOHZvVfnv37s0TTzxB06Ypmq+gypRIsci0QnUCcLa7T3H3r9x9ubs/DfwEGFh74ZWPE088kRfnNcQGstnrpfkNOfHEEwsdoohI0TCzbczsYWAx8ALwErDYzEaYWTVD/IjUjnnz5tGyZcuUaS1btmT+/PlZ77t379488sgjmz3haty4MY888ogqUyJFINMK1afATinWNwY+z1845atFixZ8uvhz3H2z16eLP6dFixaFDlFEpJio5YTUmVWrVjHkut+xy07bc8P112422ETHjh1ZsmRJym2XLFlChw4dcvr8ZcuW0bBhQyoqKmjSpAkVFRU0bNiQZcuW5bRfEcmPTCtUNwJ3m9llUbOK/c3sHEIfqgfN7HuJV+2FKiIisoFaTkidmDRpEl07t2fmP29mzIXLeHXyTXTt3J7JkydvyNO/f38WL17Mm2++ucm2b775JosXL6Z///45xXDPPfewYsUKunXrxoQJE+jWrRsrVqzQaH8iRSLTQSnWZ7g/d/cGW86WG3XwFREpfTkOSvEOcJm7T05a/13gPnfP7ZFAHqnMKk1VVVVcPuh85s56ntvOWEGf7hvTJs+Cy0Y3pUv3Qxk2/C7atWtXK6P8JfTt25cjjjiCyy+/nIqKCtatW8ewYcOYOnUq48ePz2nfIpKZnEf5KzYqnERESl+OFarzgOsILSheANYC3YFrgZHAhknn3b2gQ6SqzCpN7dvszBk9lnDNyetonKJX3qo1cP34Bjz8akveW/gJEEb7GzduHPPnz6dDhw70798/q9H9RKT45FShMrMDgTnuviq27iRgkbu/mNdIM6TCSUSk9OVYoSqqlhPpqMwqTemmM0nQtCYi5SOrYdPNrKGZPQi8CByUlHwG8F8zu8vMClpQiYhI+XH3igxfKqMkK+mmM0nQtCYiAukHpbgC6A30dvd/xxPc/XTgKOBkwsz0IiIidSIaHKlx0rqTzOzgQsUk9c+JJ57IlLnfsLyaOXWXr4B//+8bTjrppLoNTESKTroK1dnApe7+n1SJ7v4ccCVwXi3EVauqqqro1asXVVVVhQ5FREQypJYTUpdatGjBdw8/hMdfTZ0+8RXo9Z1Dad68ed0GJiJFJ12Fqg1QzWVkg6lA+/yFUzcuv/xyFi1axM9+9rNChyIiIplTywmpU+ma/am5n4gkpKtQfcKWK0ttgNQz2RWpSZMmMXfuXF588UXmzJmzyTwSIiJS1M6mnrackOJ04okn8uK8hthANnu9NL8hJ554YsrtRo0aRbt27aioqKBdu3aMGjWqjiMXkbpU7Sh/ZnYT8G3gKHf/JkX6VsD/AW+4+0W1GuXmn53ViEmrVq2ia9euDB8+nGOPPZbJkydz2WWXMWfOHA1rKiJSx2o6yp+ZfQ3s4+4L0uTpQBiZtlk+YswHjfJXXkaNGkVlZSUrVmzsfNW0aVNGjBjBwIGac1qkVGU1bLqZtQBeBlYDtwMzgOXA9oSK1iCgMXCYu39SC3FXK9vCaciQIcycOZOxY8duWNevXz969OjBb3/723yGKCIiW5BFhWoe8BN3n5ImTy/gfndvm3OAeaIKVXlp164dCxZsXudv27at+m6LlLCs56Eys+2BPwGnAYm7fQZ8BjwMXOfudd7kL9vCaZdddmHMmDEcdthhG9ZNmzaN0047jQ8//DCfIYqIyBZkUaEq2pYT6ahCVV4qKipI9X2bGevXZzp9mogUm5wm9o120AjoCGxH6DM1z90LdlXQEyoRkdKXRYWqaFtOpKMKVXnREyqR+imriX3j3H2Nu7/h7i+4+zvZVqbMbJCZzTWz181sgpntmCLP8WY228zeMrMxZpa38UivvPJKZs+evWEgikmTJvH666/zi1/8Il8fISIitcTdlwMHAy8BtxAqVO8A04EhwLPAQcVUmZLyM3ToUJo2bbrJuqZNmzJ06NACRSQitS2jJ1R5+SCzHsBjQDd3X25mNwPbuvsFsTytgLmEu4vvmNmNUZ6Lk/aV9d2+xEAUM2bMoEePHtx+++306dMn6+MSEZHs1PQJVdK2RdVyIh09oSo/o0aN4uqrr2bhwoW0adOGoUOHakAKkRKXc5O/PAaylbt/E81wfx/wnrv/JpY+EDjD3Y+PltsBrwHbxUujXAunvn378vbbb9OpUyfGjRuX9X5ERCR7uVSo8hyHASMJowPenJR2FvDz2KoWwG7Abu7+qZktAT6Ipd/k7qOS9qEKlYhIicu5yV++RJWpvoTC5whCpSpud+D92PIHQHNg23zGMWzYMHbccUduvfXWfO5WRERKjJntDTwDnJIq3d0fcPfu7t4dOJAwR+OgqDLVCfgskR69NOGQiEiZqdMKFYC7j3f3lsC1wFNmFo+hAkh1G29d8oqePXtueI0YMaLaz0uV1q5dO6ZMmUK7du1qGH1pS3eeyo3ORaDzsJHORVCG5+ES4G5gTAZ5fwUscvc7o+VDgXVmNjXq+zvYzBpk8qH19TzXx+PSMZUGHVPpqI/HVZd9qPYAdnb3adFyA2ANsKO7L43WnQmc6u4nR8ttgZnuvkPSvjJuPtGzZ09mzJiRvwMpYToXG+lcBDoPG+lcBHV5HoqlyR+AmY0EXk9u8hdLb0kYAKOHu8+P1p0P7Af8GtgKeBIY4+7DkrbdrMyqr79v9fG4dEylQcdUOkr1uNKVWQ3rMI7WwMNm1j2au2ogofBaGsvzL+AWM9vT3d8BLgQm1GGMIiIiqVQCExKVKQB3vyuewcz+DFwGDEveuGfPnht3VFlZa0GKiEjdq7MKlbtPNbOhwBQzWwt8BPQ1s57A3VHb80Vmdg7waDSC0zzgrLqKUUREpBqnESpLG5jZj4DX3H12YhWw2YTDwGZ3Y+tjkxcRkXJVl0+ocPc7gDuSVr8HdI/lmQRMqsOwREREqmVm2wN7AM8nJXUFBpjZAKARYWJhDUohIlJm6nxQChERkWJmZj3NbFZs1R7Ax+6e/PTpOuAzYA4wm1DhurtOghQRkaJRp/NQ5YuZlV7QIiKymWIZlKI2qcwSEakfimJiXxERERERkfpETf5ERERERESypAqViIiIiIhIlkqyQmXB/Wb2i2i5hZk9amavm9n/zOxXsbw7mNkoM5tpZm9Gw9wm0s6N8r9jZneY2VaFOJ5cZHouzGwfM5sVe80xMzez/lF62ZyLKG0fM5sWnYuZZnZMLK2kz0UNz0NvM3vVzGab2XNm1i2WVtLnATY/F0lpY81seGx5TzP7T3TML5tZ51haSZ+LmpyH2PpzzezxFOtK9jzUtvp6DcrjcT1mZu/axnLo1kIcTxRLvbtO5vGYivZ7SkoryWt4Ho+paL6nKJ6Mjyu2vv6UM+5eUi9gb+BZ4GvgF9G624Bh0ftmQBVwSLQ8Ebgxer8b8Hn0syvwPtCKULF8GPhloY+vNs9F0ra3AKOj92V3LoApwLnR+/2B5YRpBEr6XNTkPAAtor+HI6O0zsBbwNalfh6qOxextF8Ci4HhsXUvA2dE7/sArxPmFSrpc5HFedgB+DvwJfBEbH1Jn4dCnOf6cA3K13FFyx8Bu5TSd1Uq18l8HVOxf0+xtJK8hufrmIrpe8ryuOpdOVOKT6guIQxLOya27qdAokbcmnChW25mOwDfJwxti7t/ABxEGOb2ZGCiuy929/XAncCZdXIE+ZPxuYhvZGbfAU4BLoxWleO5aABsH73fFlgVvS/1c1GT87AnsNzdnwFw9zeBLwgFbqmfB0h9LjCzXsCxhIt5Yt2uhH8q/gHg7pOBbQj/EJb6ucj4PER+QCiok+8ylvp5qG319RqUl+Mys/bR8l0WWkjcF5XRhVAfr5N5OaYS+J5K/Rqel2Mqsu8JVM6UXoXK3Qe5++ikde7ua83sIULtfQrhbssewMfAz83sv2Y2AzjA3VcAuxNqwQkfEJ5clYwanou4m4Cr3f2LaLkcz8UlwFVm9gHwNHCRu6+lxM9FDc/D20AzMzsawMwOBLoQCt6SPg+Q+lyY2S7AX4CBwLpY0u7AR9EFPCFxzCV9Lmp4HnD3v7v79cDqpF2V9HmobfX1GpTH49oxWr4Q6A58BdxbJweRpD5eJ/N4TEX9PZX6NTyPx1Q03xOonIESrFCl4+5nAi0JjxIHA1sB7YEv3P0w4HTgVjPrQTj2+JjxRtIXXspSnAsAzOxQwqPU+C9+WZ0LM2sMPAKc7e67AUcAd5rZ7tTjc5F8HqIKdV/gN2b2GnAW4ZH9GurheYjaYT8M/MzdP05KTj5e2HjM9epcbOE8pFOvzkNdqq/XoJocl7u/5O793P19d18HXAscb2aNChV/KvXxOlmTYyrm76k+XsOzPaZi/p6gPMuZhoUOIB8sdHqd4+4fuftXZvYwMAAYGWW5D8Dd3zWzacC3gYXALrHd7EKoCZe0NOci4TTggaQ7HuV2LroCTd39CQB3f9HM5hKag9a7c1HdeTCzCuArd+8Vy/s28C6hPXS9Og9AT6AD8GczA9gZaBD9E3gt0NrMzN0TF/PEMde334lqz4O7n5dmu/p2Hmpdfb0GZXNcZtYO2N7dJyZ2A6ynSP5Zqo/XyWyOKeoSUKzfU328hmd1TEX+PUEZljP15QnVD4DfRSOMbB0tP+vu7wGvAj8GMLOdgEOBGYTBKk4ysx0tfNuVwPhCBJ9nKc9FLP27wDNJ25TbuXgXaBE9rcPMOgL7ADOpn+eiuvPgwCQz6wlgZqcR+jvMph6eB3d/wd13d/fu7t6d0Kb7EXc/z0P/yncJNxwS/4isB+ZQz85FuvOwhU3r1XmoI/X1GpTNcW0D3G4b+3lcCTwa3V0vBvXxOpnNMRXt91Qfr+E5HFPRfk9QnuVMvXhCBVxB+LLmRMvjCO02AfoBfzWziwgVyOvdfTqAmV1PuLhsBbwE3FiXQdeSdOcCQmfUqvgG7j67nM6Fu683s37AX6K7QGuBSnefB/Xy96K68+BmdgahU2sjQn/DvtGdsPr6O5HODwnn4reEfy5OjZ7kluO52Ew9vk7Upvp6DcrmuOaZ2W3Af6MnJHOA8wsQe3Xq43Uym2OaXOTfUzr18Rpe3TGV8vdUrVIuZxJDL4qIiIiIiEgN1ZcmfyIiIiIiInVOFSoREREREZEsqUIlIiIiIiKSJVWoREREREREsqQKlYiIiIiISJZUoapHzGw7M7vJzOab2Qoze8vMfm1hxuqiY2ZuZkfVcJsp0XbVvXrVTrQZx9fZzP4bvT/bzDabkM7Mhkffz3cy2F+vFMf4lZlNM7NDahDXKWa2c82OZsO27aLP3SNa3vC9mVmVmW1pXonq9ts9k3NQzbYjzeyhDPM2NLPro1i/MLOnzWzvWLqZ2VAzW2Rmn5vZzWbWIJZ+QPR796WZvWdmV0XD1CbSu5nZC9F3+oqZHRhL+6OZlfxQtiK1pa7Lrequy7H0KWZ2Qxb7VdmUY9lkZtea2bQ0ebP6bqrZV0czezy65n9gZrdYGOo/kd7WzP5lZl+b2Rtm1idp+95mNjs6X1MS5WOUZmb2u2i/n5vZGAvzoNY6M2tuZveZ2VIz+8zMRpjZNrH0zlEZ+JWZLTCzK2NpXczs32ZhJl6pGVWo6gkLk7u9BBxMmIugC/Ab4FLgvgKGlm/9gdbR6wrCDNqtY6/nCxcaAMOB66tLNLPfEb6ffu4+tQb73Y2Nx3gA8CHwpJm12NKGZtYWGEOYCDDfDgRGZbntOKBTHmOpzq+BcwkTBB5I+J35p5k1i9J/BpwNnEqYt+6HhEkSE39XkwlzfPQg/D1dAVwSpTeL0l+M0qcSvpdto33/EbjKzL5Vq0coUoKKtNzqT/i7zWY7lU25lU03AyfV4LOzYmHurceB1cChwECgLzA0SjdgArCUUGbcDzxmZu2j9N0JE9A+BPQEPgEmxG60VUavM4HvADsD99b2cUX+Rvg7Ogo4GjgIuDWKeytCebUQ6E4ox64xs4EA7j4XWAD8uI5irV/cXa968ALuAl4HGiet/y5hVvSDCh1jipgdOCqH7c8Dqgp9HLF4DgXejS2fDXwQW76QMNHlgBrss1d0nhomrd85Wt83g320i/LukeVxbbJ9rt9bbL9VwHlZbjsSeCjDvO8CF8SWGwFfAcdGywvjcRAKwfdj7z8AKmLpvwFejN6fSyiAKqJlA95J2t8dwDWF/v3US69iexWi3Eq+LtfScalsqoWyCZgC3JCH83E4sAbYJrbuDOCT6P33gJXAtrH0pxOfTaiYToulNQW+SJSLhMrYsFj6icDKOvieDbgbODC27jLg7dj5fgRoEksfC4yILR8UlWFW6N/bUnvpCVU9YGZbA6cDw919VTzN3f9NuDjMifK6mZ1vZu9GTZgeNrPmUdqQ6BFxy2j5EDNbm66pgpmdYGavmtnK6LH4qbG0KWZ2jZn9M3osPsfMjkuxj9Ojx+KNYuuOjR5Z16jZh5ntZmbjo8f0Cy00uWoUpZ0dNUf4TfR5n5jZGWb2gyjv52b2+9i+qszsZ2Y2K9rfZDPbJc3HXwSMryauUwl3CH/i7o/V5JiqsTb6uXpLxw28F/18JzoH15rZRDN7LjrmY81sazMbZmaLo/P+/zJpomBpmvyl26eZTQHaEmaAH1nN9udEv1NrzGyJmd1hZg0zPkMbVbLp97KeUPA0jr7P3YH/xNKnAbtFdyH/DZzuYWb6BAcSTUMOBv6bSPdQIv0XiDd5mQhcELt7KVL2CllubSGuDc3KLLjGzD6NrmOXRte8GjVVj/ZVdmWTJTUXjz4v3qwvVdk0Lcp3tpm9aGaPmtny5HLGQpO8j8zs5mh5WzO720LT7TUWmo4OqCbGt4Dj3P2r2DoHto7eHwzMdPcvY+nT2HhdP5hYmeHuK4BXY+lLgT7Rd96E0OrhlepOmIWmds9a+D/qXTO7wmxjszszuyD6PVhuZr+Pfkc3K3c9OM/dpyfOEaGi+EyUXuXup7n7yuh3+zDgiER65GXCE8PvVxevpKYCvn7oSPgDmJ4q0d2fi/7gE64HLgd6A10JdwkBbgAWAb+PLvR3A3e6+5RU+zWz7xHubjwAdANGAKPN7NuxbFcB/yA0h3oTuNti/VMiE4GGhMfTCT8AHnX3b6o96s3jMUIzss+jzxsInAD8IZbtQGCv6Of/i2IeBBwfxXqVme0by38tcAvhrk3j6Hir++xjgf9LkXYkoWnAWHe/P9PjqY6FZjI3AUuAFzI47sT3cQjh7hSEO2aPEe4E/xf4fZR+QrSuAngiflHPQrp99ic8+bkC+GmKYzyc0HThamBPwh3Uc6LtasTdn3X3T2OrzgO2IjTTax2t+yiWnsi7m7u/7+4b2vRHheP5bCxMWydtm9h+t9jyc4S7tvsiIgkFKbdq6CLg54S/+aMJT6zTVVxSKteyKYNNUpVNcYmnJd8mNNFLfM63gH8Ck939F9HqW4G9Cd9TF8I1+q6o4r4Jd1/s7k/H9ldBONeJpo5buq5vKf16ws3O94EvCZWWM1IcX6JM+SehPNqP0Nz18igeLNyEvg0YTHjS2I1Nb9ilZGajCK1AdgKuS5HlA0Il8QXg0cTK6KbgM0CfFNtIOoV+RKZX7i/CH1lGj82jfD+LLfcG1gE7RMvfIdxh+gfh7tE2afY1Fngkad0/gDHR+ynAY7G0/aLPbxOLJfGI/CHg/uh9I0LB02sLx7JJswrgSMKdoQaxdd8lXNgaEpo5rCV6jE/4B9eB3rH8y4BTo/dVwF9iae2j/N1TxNIufmzRurMJTcu+IPxTvSbVtls4xl7Rfr+KXiui5XeAIzM87kRsiSZ71wJLYnmbRnn3j61rEn3Wd1JsH//eqkjRbG9L+0y3bZTWAzgjad0LwHXR+5Fk2OQvaR+HAV/H9vOd6Hi2iuWpiNb1Stq2AaES+imwc7TuGWBoUr7rgSlJ696u7lj10qscXxSu3DqbNE3+iDUrIzx1uC6W1jV+/UuzD5VNm372HrHtriVqLpecnpR2dpQWb3Y3hVCJfJ5QCWiQdEz7xpY7Rdu3z+BYhkXxd4mW7wFGJeU5N/GdAvOA85PSHwBGxn4fXyb0yzoIeIrQV7BRis/+CfBa0rofA/Oi92OJ/jeKllsSmiOmLU8IlcpDCJXEmcSarUfpPQn91T4EbktK+w2xJo16ZfbKpvmMFJ8l0c/tM8wf7xw7g/APZCfgBXefaqEJ1k+A4z16JG5mfyfcnUvYh3A36C429TyhiVXCvNj7L6KfqZrxjSY83WpEuMO0gk2bYWVib2A7YHn8aTmhgtY2Wl7iGx/jr4x+LojtYxUbH/tD7Fy5+3tm9ln0ObOSPnvHxP6T1jcDbiRcoP4LjDSzA70GT94iPQj/QKwHlrv70ljalo57XYr9VcXed4jyTk16INWYcMf0/RrGmsk+03Z6dvdXouYP1xEKhn0JT6qeSbddOlEToImEO52JO3aJpkZbA9/E3kP4HUxs24jwO3o04Z+pT2LbJ98B3Tq+bWQpG39HRKRw5VZNdAaGJBbc/XUz+zJN/uqUa9mUq6W+abM7CAMpNAJud/d42fYA0NfCqKqdo7gg3AhLKXp6Nwy4GDjFw6AMEM51i6Ts8et6ddf9pdE+HwSudvfx0ef8gFCOnkTsaVBkb6CLmcWbH1YAW0flTmdi/2e5+xIze6e6Y4rlmxt99mmEp1FHECqkifQZUXpT4H4z+4W7r4mSVV5lQU3+6od5wGdsfHy+CTN7xMz6xlatjb1PXGzWR3krCHfh1hHuAiYMJowKk3h9xMZ/RuMasOkFbE2KPKmakf2L8A/t9wmjrT3im/ZbyURDwt2xeJzdCP+IJyoFa1Nsl+5zkvM3qCa/Rz+T/6Y+cfdfR8dyPqFAvzrN51Vnnru/6+7zUxRYmRx3svh3l7ix8t2kfezF5hf/TOW0TzM7hnB3uDWhOcQphEI/K1GzicnAJODM2O/Wh9HP+JDyifcfR9s2IVTEvk8YyOKlWN4Pk7ZNbP9x0roGpK7YipSrQpVbNbGCzcur1TXcB5Rv2eQp8tfkRn6q/zHmEJp+X2Jm3WPrHyA8vVpGGAjo+HQ7jn5n7iU06zzN3SfEkrd0XU+X3opQSX49keDuywmDI3VIEUpDQkWne+y1H+FmwVpq8DtoZk0sDEOfGMEWd/+IcE5amtmuZpY8iuL/CBXU5rF1Kq+yoApVPRDdpXkYGJTcXtjMehP6Iy2Ore4ee9+TUJF5M1oeRGg+MAC43MwOiD5jUXTRTLzWAm8QHmfHHULo8FnTY1hL+Ef7ZEJ774druo/oc3cn3NV6193fJVzk/kD2v+vdE28sdKxtAcxOkS/xxKJl0voNFyV3f50wHO9vkgqCXG3puFMVanHzojhbxrZfDPyZjXdPayqTfaaL63xCM4dKd7+b8LvWkdSV8bTM7CBCU70xwMDody0EEAqbhYRRnxIOBz5y98Q/OqMIv+dHuXtype5F4NBEX7Po56HR+rhvsbFvlkjZK2C5VRP/I/RpSsS1O5tf4zNRrmVT4oZq/J/1eKViS2VTKk9FT34mAn+LBldoTuijdIa7D3b3ccAOUf7qyoxbom36u3ty/7MXge7xigmhXHgxlr6hzIie8uwfrV9KqPDsF0tvQmjeGG+xk/AW4UZjVex3ozvwq6iym/w72CzKX53RwDGx/B0IT4HfIDwNG2tm8adPPYDF7h5/gqnyKguqUNUf1xGaU/2fhQnnOprZjwmdW+9L+kfwWguT8h0E/AV40N2XR4XFDcAvo7s1jxAGkajujtKfgf5mdrmZ7WlmlxPuHP01y2MYDfwI+NKjUWpq6F/AfGCUhclWDyF0UF7vSaNI1cClZnayme1HuJv1rLu/kSLfB4SO0fulSIsbSrhTdb9FIxiaWSuLTbyXhS0dd6IpQbdUnxM1qbgLGG5m3zOzzoR5N/Yj3FWtsQz3+RXQOerInGwpcIiZ7WdmXQh9plqzeTMLzKyBme1ssVEiY2lG+N7mEuajahXl3Tkq5CDczfxDFOd3Cf/k/CXa/jTC3FSDgPdj27aKtn2U0LH+djPbh/A30ZzQlyMRw7aEwnTGFk+cSHkpRLkFoTnVsUmvw1Lku4lQ4ftBdB3Kdi6hci2bPiU8gfuNmXUwsx+x6ZOjtGXTFlxBqMScQ2gi+TXh/5F2ZnY0YeRCSF1mHEwY+OF3wIzYdT3x1OnfhOaWIy2MwPcrwsh+iaZ39wIHmdnV0XX/HsKNuWeiGwV3AzdEZcreUf4lwBMpjuOhKMa7zWxvM/s+4X+oz6L0PwMDzOzCqBy9k00rqBu4+8oolpvM7DAz60m4aTE+agL4b0IFbWT0WScQKtJDk3bVDZVXNVfIDlx65fdFGH3oTsIFbCXhD+cKNu1w74Q24e8Bywn/TDaO0iYSRn2xaHnnKM8v03zmqYQ7H6sJd8f6x9KmEJszgi3MZ0S4k/Q+MCTD491srg/CXcrHCRfXJYQLYKKj79lsOvfGHlEM7WLrPiE0CYPQz2gYoYnBV4SCeoc08TwA3BRb3uTzYusPIzTNuD72OddWs89epJjrI0W+ao87Sr8v+o5+Sqzjbyy9CaEAWkwYlegpYK8tfW+kH1ii2n1G6RdE6x9LsW3rKP/XhGYU90b7eiZKH0k0KEUsvl4p9pPoRJ7qdV6UpwHhbuXn0bm7iY3zSj1azbbx36MDCc0TVxE6IvdIiuFYYEGhrw966VWML+q43GLjYAfJr9ej9ClsWm5dGl2DlkXXz03KrWo+Q2XTxnzfJzxJXEVodv0LNp3DKWXZlCrGFN/NEEL5sgOhdcu7sd+hnxAqkz9KEdPN1fwObDie6Dv4dxT3XODopH30iY5rBfAs0DGW1phQUVlAKFcmEhsUJEU8+0fHtpLQLPXG+HklPK2dH33Xif2mK3f/Ev2+LI/Ob/NYepsoni8ITRevIjbnFOH/sI+IDYiiV2avxAVIyoSZOfB9jw0ZWiyix+afAt/21Hfa6jqeKsLF++4M8x9OaB7WzmvwhxU1bznI3f+YVaBS1MzsfsLEisl3AUUkA8VUbhVDLCqbyltNv/8a7rs38Hdgb695P/aypiZ/UhTM7BTCH/FrxVCZyoaH+YqqgM0mL96Cy4jNsSH1h4XJRo8k3FEXEalzKpukBi4EblRlquY0bLoUi98Tfh9PLnQgObqY0H76yRps8wOv+VC1Uhp+RbiT+NkWc4qI1B6VTZKWmXUFdiU0E5QaUpM/ERERERGRLKnJn4iIiIiISJZUoRIREREREcmSKlQiIiIiIiJZUoVKREREREQkS6pQiYiIiIiIZEkVKhERERERkSz9f2C8zVk/3PDgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2, figsize=(12,15))\n",
    "\n",
    "\n",
    "#### Thermobarometry - Cpx only\n",
    "x='T_Put_Teq32d_Peq32b'\n",
    "y='P_Put_Teq32d_Peq32b'\n",
    "ax1.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "# plt.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], '.k', mfc='yellow')\n",
    "# plt.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '.r')\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_TAS[x], Cpx_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_TUL[x], Cpx_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_HI[x], Cpx_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_RI[x], Cpx_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_Cape[x], Cpx_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_SUNY[x], Cpx_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_RNAS[x], Cpx_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_UBC[x], Cpx_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_ANU[x], Cpx_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax1.plot(Cpx_B1038_Calc_Corr_Houston[x], Cpx_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "\n",
    "ax1.set_ylabel('Cpx-only Press (kbar, Putirka 2008 eq 32b)')\n",
    "ax1.set_xlabel('Cpx-only Temp (K, Putirka 2008 eq 32d)')\n",
    "ax1.plot()\n",
    "\n",
    "## Cpx-Liq, Liq and Cpx correctd\n",
    "x='T_K_calc'\n",
    "y='P_kbar_calc'\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Meas[x], Cpx_Liq_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_MIT[x], Cpx_Liq_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_TAS[x], Cpx_Liq_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_TUL[x], Cpx_Liq_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_HI[x], Cpx_Liq_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_RI[x], Cpx_Liq_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_LaMere[x], Cpx_Liq_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_Cape[x], Cpx_Liq_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_SUNY[x], Cpx_Liq_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_RNAS[x], Cpx_Liq_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_UBC[x], Cpx_Liq_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_ANU[x], Cpx_Liq_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax2.plot(Cpx_Liq_B1038_Calc_Corr_Houston[x], Cpx_Liq_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax2.set_ylabel('Cpx-Liq Press (kbar, Neave & Putirka, 2017)')\n",
    "ax2.set_xlabel('Cpx-Liq Temp (K, Putirka 2008 eq 33)')\n",
    "ax2.plot()\n",
    "#### Thermobarometry - Cpx only\n",
    "x='T_Wang21_eq2'\n",
    "y='P_Wang21_eq1'\n",
    "ax3.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "# plt.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], '.k', mfc='yellow')\n",
    "# plt.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '.r')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_TAS[x], Cpx_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_TUL[x], Cpx_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_HI[x], Cpx_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_RI[x], Cpx_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_Cape[x], Cpx_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_SUNY[x], Cpx_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_RNAS[x], Cpx_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_UBC[x], Cpx_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_ANU[x], Cpx_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax3.plot(Cpx_B1038_Calc_Corr_Houston[x], Cpx_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax3.set_ylabel('Cpx-only Press (kbar, Wang et al. 2021)')\n",
    "ax3.set_xlabel('Cpx-only Temp (K, Wang et al. 2021)')\n",
    "ax3.plot()\n",
    "\n",
    "\n",
    "## Cpx-Liq, Liq and Cpx correctd\n",
    "x='T_K_calc'\n",
    "y='P_kbar_calc'\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Meas[x], Cpx_Liq_B1038_31_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_MIT[x], Cpx_Liq_B1038_31_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_TAS[x], Cpx_Liq_B1038_31_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_TUL[x], Cpx_Liq_B1038_31_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_HI[x], Cpx_Liq_B1038_31_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_RI[x], Cpx_Liq_B1038_31_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_LaMere[x], Cpx_Liq_B1038_31_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_Cape[x], Cpx_Liq_B1038_31_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_SUNY[x], Cpx_Liq_B1038_31_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_RNAS[x], Cpx_Liq_B1038_31_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_UBC[x], Cpx_Liq_B1038_31_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_ANU[x], Cpx_Liq_B1038_31_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax4.plot(Cpx_Liq_B1038_31_Calc_Corr_Houston[x], Cpx_Liq_B1038_31_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax4.set_ylabel('Cpx-Liq Press (kbar, Putirka 2008 eq 31)')\n",
    "ax4.set_xlabel('Cpx-Liq Temp (K, Putirka 2008 eq 33)')\n",
    "ax4.plot()\n",
    "\n",
    "ax3.set_title('Average Cpx-Only PT \\n (Krawczynski et al., 2013, Exp B1038)')\n",
    "ax4.set_title('Average Cpx-Liq PT \\n (Krawczynski et al., 2013, Exp B1038)')\n",
    "ax1.set_title('Average Cpx-Only PT \\n (Krawczynski et al., 2013, Exp B1038)')\n",
    "ax2.set_title('Average Cpx-Liq PT \\n (Krawczynski et al., 2013, Exp B1038)')\n",
    "\n",
    "# ax5 - Another example, using equation 31 and Petrelli \n",
    "#### Thermobarometry - Cpx only\n",
    "x='T_Petrelli21'\n",
    "y='P_Petrelli21'\n",
    "ax5.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "# plt.plot(Cpx_B1038_Calc_Meas[x], Cpx_B1038_Calc_Meas[y], '.k', mfc='yellow')\n",
    "# plt.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '.r')\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_MIT[x], Cpx_B1038_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_TAS[x], Cpx_B1038_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_TUL[x], Cpx_B1038_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_HI[x], Cpx_B1038_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_RI[x], Cpx_B1038_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_LaMere[x], Cpx_B1038_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_Cape[x], Cpx_B1038_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_SUNY[x], Cpx_B1038_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_RNAS[x], Cpx_B1038_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_UBC[x], Cpx_B1038_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_ANU[x], Cpx_B1038_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax5.plot(Cpx_B1038_Calc_Corr_Houston[x], Cpx_B1038_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax5.set_ylabel('Cpx-only Press (kbar, Petrelli et al. 2020)')\n",
    "ax5.set_xlabel('Cpx-only Temp (K, Petrelli et al. 2020)')\n",
    "ax5.plot()\n",
    "\n",
    "## Cpx-Liq, Liq and Cpx correctd\n",
    "x='T_K_calc'\n",
    "y='P_kbar_calc'\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Meas[x], Cpx_Liq_B1038_32c_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_MIT[x], Cpx_Liq_B1038_32c_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_TAS[x], Cpx_Liq_B1038_32c_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_TUL[x], Cpx_Liq_B1038_32c_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_HI[x], Cpx_Liq_B1038_32c_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_RI[x], Cpx_Liq_B1038_32c_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_LaMere[x], Cpx_Liq_B1038_32c_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_Cape[x], Cpx_Liq_B1038_32c_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_SUNY[x], Cpx_Liq_B1038_32c_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_RNAS[x], Cpx_Liq_B1038_32c_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_UBC[x], Cpx_Liq_B1038_32c_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_ANU[x], Cpx_Liq_B1038_32c_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax6.plot(Cpx_Liq_B1038_32c_Calc_Corr_Houston[x], Cpx_Liq_B1038_32c_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax6.set_ylabel('Cpx-Liq Press (kbar, Putirka 2008 eq 32c)')\n",
    "ax6.set_xlabel('Cpx-Liq Temp (K, Putirka 2008 eq 33)')\n",
    "ax6.plot()\n",
    "\n",
    "ax1.annotate(\"a)\", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax2.annotate(\"b) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax3.annotate(\"c) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax4.annotate(\"d) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax5.annotate(\"e) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax6.annotate(\"f) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "fig.savefig('Lab_Corrections_B1038_31_Supplement.png', dpi=200)"
   ]
  },
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