{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e1f5fb7-1b71-4b2c-ad60-8aeda213adbe",
   "metadata": {},
   "source": [
    "# 0d - Check and aggregate the different lowess fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eaa47596-c890-4f0a-a7f5-4d57cf220424",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import os\n",
    "import glob\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import ast\n",
    "import moepy\n",
    "\n",
    "from help_functions import get_glob_temp_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df9336aa-f816-4ad7-9ae3-ac71bd8501fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "qs = ['0.05', '0.17','0.25', '0.5', '0.75', '0.83','0.95']\n",
    "col_drops=[ 'fit_to_median', 'avg_over','fit_opt', 'shift_years_2020', 'y', 'add', 'min_0.5_diff', 'min_0.5',\n",
    "           'min_0.5_diff_above_zero', 'median_absolute_deviation', 'rmse',\n",
    "           'algorithm_sel', 'temp_ch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1255b4c-c09a-467c-b619-ff9e90ccc926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152767463429916.1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATE = 'Feb12_2024'\n",
    "approach = '_via_5yravg'\n",
    "\n",
    "pd_rgi_stats_w_hugonnet = pd.read_csv(f'../data/3_shift_summary_region_characteristics{DATE}.csv', index_col = [0])\n",
    "pd_rgi_stats_w_hugonnet = pd_rgi_stats_w_hugonnet.set_index('region')\n",
    "vol_2020_globally = pd_rgi_stats_w_hugonnet.loc['Globally'][f'regional_volume_m3_2020{approach}']\n",
    "vol_2020_globally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e5e65f9-edc3-450f-8afd-895167723cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adapt_pd_r(pd_r):\n",
    "    pd_r = pd_r.loc[pd_r.y.isna()]\n",
    "    for c in col_drops:\n",
    "        try:\n",
    "            pd_r = pd_r.drop(columns=[c])\n",
    "        except:\n",
    "            pass\n",
    "    pd_r = pd_r.rename(columns={'x':'temp_ch'})\n",
    "    if reg in rgi_list[:-1]:\n",
    "        pd_r['region'] = reg[3:]\n",
    "    return pd_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2def61e-d8fa-4457-9b33-5335519296a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_r_l = []\n",
    "pd_r_l_100 = []\n",
    "pd_r_l_500 = []\n",
    "pd_r_l_reg_temp = []\n",
    "rgi_list = [f'RGI{str(i).zfill(2)}' for i in range(1, 20)]\n",
    "rgi_list.append('global')\n",
    "for reg in rgi_list: \n",
    "    # global mean tmep. change steady state lowess fit \n",
    "    pd_r = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_{DATE}{reg}_ipcc_ar6_likely_range.csv')\n",
    "    pd_r_l.append(adapt_pd_r(pd_r))\n",
    "    \n",
    "    ### same for after 100 simulation years\n",
    "    pd_r = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_100_{DATE}{reg}_ipcc_ar6_likely_range.csv')\n",
    "    pd_r_l_100.append(adapt_pd_r(pd_r))\n",
    "\n",
    "    ### for after 500 simulation years\n",
    "    ### same for after 100 simulation years\n",
    "    pd_r = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_500_{DATE}{reg}_ipcc_ar6_likely_range.csv')\n",
    "    pd_r_l_500.append(adapt_pd_r(pd_r))\n",
    "    \n",
    "    ### regional temp. change steady state lowess fit\n",
    "    pd_r = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_reg_glacier_temp_ch_5000_{DATE}{reg}_ipcc_ar6_likely_range.csv')\n",
    "    pd_r_l_reg_temp.append(adapt_pd_r(pd_r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "340beb7e-6168-4a4b-8235-b3c1096cf12f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"['01', '03', '04', '05', '07', '09', '17', '19']\"]\n",
      "[\"['02', '08', '10', '12', '16', '18']\"]\n",
      "[\"['13', '14', '15']\"]\n"
     ]
    }
   ],
   "source": [
    "# in the raw files, the global uncertainties are not computed correctly \n",
    "# (as we here did the fit over the medians of the individual RGI regions)\n",
    "# we will get the global uncertainties by aggregating the quantiles from the five \n",
    "# larger regions with the same glacier models to aggregate the uncertainties,\n",
    "# we aggregate the quantiles over five larger regions\n",
    "list_aggregates = ['only_reg_w_global_models','only_reg_w_global_glogemflow3d',\n",
    "                   'only_reg_w_global_glogemflow3d_kraaijenbrink', \n",
    "                   '06', '11']\n",
    "pd_global_complex_agg_global = pd.DataFrame(index=np.arange(-0.1,6.9,0.05).round(2),columns = qs)\n",
    "pd_global_complex_agg_global.loc[:,qs] = 0\n",
    "pd_global_complex_agg_global_100 = pd.DataFrame(index=np.arange(-0.1,6.9,0.05).round(2),columns = qs)\n",
    "pd_global_complex_agg_global_100.loc[:,qs] = 0\n",
    "pd_global_complex_agg_global_500 = pd.DataFrame(index=np.arange(-0.1,6.9,0.05).round(2),columns = qs)\n",
    "pd_global_complex_agg_global_500.loc[:,qs] = 0\n",
    "\n",
    "# March 2025: For the regional warming lowess fits, it does not makes sense to aggregate to global uncertainties as we have to add them up via the composites. The reason ist the the different regional warming ...  \n",
    "#pd_global_complex_agg_global_reg_temp = pd.DataFrame(index=np.arange(-0.1,6.9,0.05).round(2),columns = ['0.17', '0.5', '0.83'])\n",
    "#pd_global_complex_agg_global_reg_temp.loc[:,qs] = 0\n",
    "for agg in list_aggregates:\n",
    "    if agg in ['06','11']:\n",
    "        pd_low_agg = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024RGI{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_100 = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_100_Feb12_2024RGI{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_500 = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_500_Feb12_2024RGI{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_reg_temp = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_reg_glacier_temp_ch_5000_Feb12_2024RGI{agg}_ipcc_ar6_likely_range.csv')\n",
    "\n",
    "        agg_regs = agg\n",
    "        vol_2020_reg = pd_rgi_stats_w_hugonnet.loc[agg][f'regional_volume_m3_2020{approach}']\n",
    "        \n",
    "    else:\n",
    "        pd_low_agg = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024_{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_100 = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_100_Feb12_2024_{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_500 = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_21yr_avg_period_lowess_added_quantiles_added_current12deg_500_Feb12_2024_{agg}_ipcc_ar6_likely_range.csv')\n",
    "        pd_low_agg_reg_temp = pd.read_csv(f'lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_reg_glacier_temp_ch_5000_Feb12_2024_{agg}_ipcc_ar6_likely_range.csv')\n",
    "\n",
    "        regs = pd_low_agg.region.unique() # it is a list of one item\n",
    "        print(regs)\n",
    "        agg_regs = ast.literal_eval(list(regs)[0])\n",
    "        vol_2020_reg = pd_rgi_stats_w_hugonnet.loc[agg_regs][f'regional_volume_m3_2020{approach}'].sum()\n",
    "    # here the values are in % rel. to the regional estimates,\n",
    "    # but we want them to be in % relative to the global 2020 estimates\n",
    "    pd_low_agg.loc[pd_low_agg.index,qs] = pd_low_agg[qs].values*vol_2020_reg/vol_2020_globally\n",
    "    pd_low_agg.index = pd_low_agg.x.round(2)\n",
    "    pd_low_agg = pd_low_agg.loc[pd_low_agg.y.isna()]\n",
    "    pd_low_agg = pd_low_agg.loc[np.arange(-0.1,6.9,0.05).round(2)]\n",
    "    ## same for 100\n",
    "    pd_low_agg_100.loc[pd_low_agg_100.index,qs] = pd_low_agg_100[qs].values*vol_2020_reg/vol_2020_globally\n",
    "    pd_low_agg_100.index = pd_low_agg_100.x.round(2)\n",
    "    pd_low_agg_100 = pd_low_agg_100.loc[pd_low_agg_100.y.isna()]\n",
    "    pd_low_agg_100 = pd_low_agg_100.loc[np.arange(-0.1,6.9,0.05).round(2)]\n",
    "    ## same for 500\n",
    "    pd_low_agg_500.loc[pd_low_agg_500.index,qs] = pd_low_agg_500[qs].values*vol_2020_reg/vol_2020_globally\n",
    "    pd_low_agg_500.index = pd_low_agg_500.x.round(2)\n",
    "    pd_low_agg_500 = pd_low_agg_500.loc[pd_low_agg_500.y.isna()]\n",
    "    pd_low_agg_500 = pd_low_agg_500.loc[np.arange(-0.1,6.9,0.05).round(2)]\n",
    "    ## same for reg_temp\n",
    "    pd_low_agg_reg_temp.loc[pd_low_agg_reg_temp.index,['0.17', '0.5', '0.83']] = pd_low_agg_reg_temp[['0.17', '0.5', '0.83']].values*vol_2020_reg/vol_2020_globally\n",
    "    pd_low_agg_reg_temp.index = pd_low_agg_reg_temp.x.round(2)\n",
    "    pd_low_agg_reg_temp = pd_low_agg_reg_temp.loc[pd_low_agg_reg_temp.y.isna()]\n",
    "    pd_low_agg_reg_temp = pd_low_agg_reg_temp.loc[np.arange(-0.1,6.9,0.05).round(2)]\n",
    "    \n",
    "    for q in qs:\n",
    "        pd_global_complex_agg_global.loc[pd_low_agg.index,q] += pd_low_agg[q].values.astype(float) # we have to add up the 5 components\n",
    "        pd_global_complex_agg_global_100.loc[pd_low_agg_100.index,q] += pd_low_agg_100[q].values.astype(float) # we have to add up the 5 components\n",
    "        pd_global_complex_agg_global_500.loc[pd_low_agg_500.index,q] += pd_low_agg_500[q].values.astype(float) # we have to add up the 5 components\n",
    "    #for q in ['0.17', '0.5', '0.83']:\n",
    "    #    pd_global_complex_agg_global_reg_temp.loc[pd_low_agg_reg_temp.index,q] += pd_low_agg_reg_temp[q].values.astype(float) # we have to add up the 5 components\n",
    "\n",
    "pd_global_complex_agg_global = pd_global_complex_agg_global.astype(float)\n",
    "pd_global_complex_agg_global_100 = pd_global_complex_agg_global_100.astype(float)\n",
    "pd_global_complex_agg_global_500 = pd_global_complex_agg_global_500.astype(float)\n",
    "#pd_global_complex_agg_global_reg_temp = pd_global_complex_agg_global_reg_temp.astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cb1bd4a7-d5db-4a0f-99c3-d5f7fe7b01ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we are only interested in the uncertainties (not in the median quantile). \n",
    "# The median quantile was estimated by taking the medians over the regions, \n",
    "# summing them up, and then applying a lowess fit. \n",
    "# The fits of the median globally are in pd_fits_all_glac_models_5000\n",
    "# That means: we now just replace the uncertainties of pd_fits_all_glac_models_5000 for \n",
    "# the \"All\" values with these new aggregated uncertainties:\n",
    "### steady_state\n",
    "pd_glob_median = pd_r_l[-1]\n",
    "assert 'All' == pd_glob_median.region.unique()\n",
    "pd_glob_median.index = pd_glob_median.temp_ch\n",
    "pd_glob_median.index = pd_glob_median.index.round(2)\n",
    "\n",
    "for q in ['0.05', '0.17','0.25', ## '0.5', DO not replace the median!!!\n",
    "          '0.75', '0.83','0.95']:\n",
    "    pd_glob_median.loc[pd_global_complex_agg_global.index,q] = pd_global_complex_agg_global[q]\n",
    "pd_glob_median[qs] = pd_glob_median[qs].astype(float)\n",
    "\n",
    "### after 100 sim years\n",
    "pd_glob_median_100 = pd_r_l_100[-1]\n",
    "assert 'All' == pd_glob_median_100.region.unique()\n",
    "pd_glob_median_100.index = pd_glob_median_100.temp_ch\n",
    "pd_glob_median_100.index = pd_glob_median_100.index.round(2)\n",
    "\n",
    "for q in ['0.05', '0.17','0.25', ## '0.5', DO not replace the median!!!\n",
    "          '0.75', '0.83','0.95']:\n",
    "    pd_glob_median_100.loc[pd_global_complex_agg_global_100.index,q] = pd_global_complex_agg_global_100[q]\n",
    "pd_glob_median_100[qs] = pd_glob_median_100[qs].astype(float)\n",
    "\n",
    "### after 500 sim years\n",
    "pd_glob_median_500 = pd_r_l_500[-1]\n",
    "assert 'All' == pd_glob_median_500.region.unique()\n",
    "pd_glob_median_500.index = pd_glob_median_500.temp_ch\n",
    "pd_glob_median_500.index = pd_glob_median_500.index.round(2)\n",
    "\n",
    "for q in ['0.05', '0.17','0.25', ## '0.5', DO not replace the median!!!\n",
    "          '0.75', '0.83','0.95']:\n",
    "    pd_glob_median_500.loc[pd_global_complex_agg_global_500.index,q] = pd_global_complex_agg_global_500[q]\n",
    "pd_glob_median_500[qs] = pd_glob_median_500[qs].astype(float)\n",
    "\n",
    "### steady_state fit over reg. glacier temperature change\n",
    "pd_glob_median_reg_temp = pd_r_l_reg_temp[-1]\n",
    "assert 'All' == pd_glob_median_reg_temp.region.unique()\n",
    "pd_glob_median_reg_temp.index = pd_glob_median_reg_temp.temp_ch\n",
    "pd_glob_median_reg_temp.index = pd_glob_median_reg_temp.index.round(2)\n",
    "\n",
    "for q in [ '0.17', ## '0.5', DO not replace the median!!!\n",
    "          '0.83']:\n",
    "    #pd_glob_median_reg_temp.loc[pd_global_complex_agg_global_reg_temp.index,q] = pd_global_complex_agg_global_reg_temp[q]\n",
    "    pd_glob_median_reg_temp.loc[:,q] = np.NaN \n",
    "\n",
    "pd_glob_median_reg_temp[['0.17', '0.5', '0.83']] = pd_glob_median_reg_temp[['0.17', '0.5', '0.83']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "46163669-87c4-4779-9575-290c6dda8639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.17</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.83</th>\n",
       "      <th>0.95</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_ch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>44.7</td>\n",
       "      <td>48.1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>68.4</td>\n",
       "      <td>85.4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>30.6</td>\n",
       "      <td>35.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>52.9</td>\n",
       "      <td>62.9</td>\n",
       "      <td>80.1</td>\n",
       "      <td>87.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>21.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>36.5</td>\n",
       "      <td>51.4</td>\n",
       "      <td>56.7</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>12.2</td>\n",
       "      <td>18.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.4</td>\n",
       "      <td>39.1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>57.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>10.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>35.3</td>\n",
       "      <td>39.6</td>\n",
       "      <td>52.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>5.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.4</td>\n",
       "      <td>24.6</td>\n",
       "      <td>26.3</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0.05  0.17  0.25   0.5  0.75  0.83  0.95\n",
       "temp_ch                                          \n",
       "1.2      38.0  44.7  48.1  60.9  68.4  85.4  94.0\n",
       "1.5      30.6  35.8  38.0  52.9  62.9  80.1  87.9\n",
       "2.0      21.6  24.4  27.7  36.5  51.4  56.7  66.3\n",
       "2.7      12.2  18.3  19.3  24.4  39.1  46.0  57.8\n",
       "3.0      10.4  15.1  16.6  22.6  35.3  39.6  52.3\n",
       "4.0       5.2   7.4   9.4  14.4  24.6  26.3  36.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_glob_median.loc[[1.2,1.5,2.0,2.7,3.0,4.0],qs].round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6c63c8-f115-4baa-aacc-88776c7e2f5b",
   "metadata": {},
   "source": [
    "new aggregated uncertainties:\n",
    "- uncertainties are much smaller than the conservative regional sum over the quantiles that is currently given in the main manuscript (and e.g. Extended Data Table 1, Fig. 1, Fig. 2)\n",
    "- As expected, uncertainties are very similar and only slighly larger than if we just use the global models (and directly estimate uncertainties from the global aggregated estimates of these four global models, see Suppl. Fig. S7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd097c62-0e7c-4e22-bc3f-cbd80a68febc",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0.05</th>\n",
       "      <th>0.17</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.83</th>\n",
       "      <th>0.95</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_ch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>77.4</td>\n",
       "      <td>79.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>83.8</td>\n",
       "      <td>89.9</td>\n",
       "      <td>90.9</td>\n",
       "      <td>92.3</td>\n",
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       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>70.5</td>\n",
       "      <td>74.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>86.8</td>\n",
       "      <td>87.3</td>\n",
       "      <td>89.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>57.5</td>\n",
       "      <td>63.5</td>\n",
       "      <td>65.5</td>\n",
       "      <td>71.7</td>\n",
       "      <td>78.8</td>\n",
       "      <td>80.1</td>\n",
       "      <td>83.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>42.3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>71.5</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>36.3</td>\n",
       "      <td>47.4</td>\n",
       "      <td>50.4</td>\n",
       "      <td>55.3</td>\n",
       "      <td>66.4</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>23.4</td>\n",
       "      <td>34.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>44.5</td>\n",
       "      <td>57.8</td>\n",
       "      <td>61.4</td>\n",
       "      <td>68.2</td>\n",
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      ],
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       "         0.05  0.17  0.25   0.5  0.75  0.83  0.95\n",
       "temp_ch                                          \n",
       "1.2      77.4  79.5  80.0  83.8  89.9  90.9  92.3\n",
       "1.5      70.5  74.2  75.0  80.4  86.8  87.3  89.4\n",
       "2.0      57.5  63.5  65.5  71.7  78.8  80.1  83.3\n",
       "2.7      42.3  50.0  53.5  59.8  69.4  71.5  76.0\n",
       "3.0      36.3  47.4  50.4  55.3  66.4  69.0  75.0\n",
       "4.0      23.4  34.1  37.8  44.5  57.8  61.4  68.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_glob_median_100.loc[[1.2,1.5,2.0,2.7,3.0,4.0],qs].round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ba9e7a4-0dbd-491e-ae49-e18595d5bf39",
   "metadata": {},
   "outputs": [
    {
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       "      <th>temp_ch</th>\n",
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       "      <th></th>\n",
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       "      <td>48.7</td>\n",
       "      <td>54.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.7</td>\n",
       "      <td>78.2</td>\n",
       "      <td>81.5</td>\n",
       "      <td>89.4</td>\n",
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       "      <th>1.5</th>\n",
       "      <td>41.8</td>\n",
       "      <td>46.5</td>\n",
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       "      <td>60.2</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.1</td>\n",
       "      <td>81.9</td>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>31.1</td>\n",
       "      <td>34.6</td>\n",
       "      <td>36.7</td>\n",
       "      <td>48.4</td>\n",
       "      <td>59.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>70.8</td>\n",
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       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>19.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>34.1</td>\n",
       "      <td>46.5</td>\n",
       "      <td>49.3</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>16.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>42.4</td>\n",
       "      <td>45.3</td>\n",
       "      <td>52.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>8.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>30.4</td>\n",
       "      <td>33.6</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         0.05  0.17  0.25   0.5  0.75  0.83  0.95\n",
       "temp_ch                                          \n",
       "1.2      48.7  54.5  57.0  67.7  78.2  81.5  89.4\n",
       "1.5      41.8  46.5  48.9  60.2  70.9  74.1  81.9\n",
       "2.0      31.1  34.6  36.7  48.4  59.7  62.7  70.8\n",
       "2.7      19.3  22.0  24.2  34.1  46.5  49.3  57.2\n",
       "3.0      16.0  18.7  21.0  31.5  42.4  45.3  52.8\n",
       "4.0       8.1  10.9  12.9  20.2  30.4  33.6  40.3"
      ]
     },
     "execution_count": 18,
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   "source": [
    "pd_glob_median_500.loc[[1.2,1.5,2.0,2.7,3.0,4.0],qs].round(1)"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd89dc55-b672-4993-8efa-e4ae61d67dd9",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2.0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.3</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.1</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "         0.17   0.5  0.83\n",
       "temp_ch                  \n",
       "1.2       NaN  64.5   NaN\n",
       "1.5       NaN  61.2   NaN\n",
       "2.0       NaN  52.7   NaN\n",
       "2.7       NaN  41.7   NaN\n",
       "3.0       NaN  38.3   NaN\n",
       "4.0       NaN  31.1   NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# March 2025: For the regional warming lowess fits, it does not makes sense to aggregate to global uncertainties as we have to add them up via the composites. The reason is the  different regional warming ...  \n",
    "# regionally, we can not easily calculate global uncertainties\n",
    "pd_glob_median_reg_temp.loc[[1.2,1.5,2.0,2.7,3.0,4.0],['0.17', '0.5', '0.83']].round(1)#.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e38479d-dcda-449b-83df-7b6f33cc87b7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>44.6</td>\n",
       "      <td>62.2</td>\n",
       "      <td>86.4</td>\n",
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       "      <th>1.5</th>\n",
       "      <td>35.4</td>\n",
       "      <td>53.7</td>\n",
       "      <td>79.6</td>\n",
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       "      <th>2.0</th>\n",
       "      <td>24.3</td>\n",
       "      <td>39.1</td>\n",
       "      <td>55.8</td>\n",
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       "      <th>2.7</th>\n",
       "      <td>18.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>44.6</td>\n",
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       "      <th>3.0</th>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>7.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>26.5</td>\n",
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      "text/plain": [
       "         0.17   0.5  0.83\n",
       "temp_ch                  \n",
       "1.2      44.6  62.2  86.4\n",
       "1.5      35.4  53.7  79.6\n",
       "2.0      24.3  39.1  55.8\n",
       "2.7      18.1  28.7  44.6\n",
       "3.0      15.0  27.0  37.9\n",
       "4.0       7.7  18.7  26.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### steady state lowess fit w. global mean tmep. changes but only with the four global models (here we can directly use the global fit!)\n",
    "#pd_r_only_global = pd.read_csv('_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024_only_global_models_ipcc_ar6_likely_range.csv')\n",
    "#pd_r_only_reg = pd.read_csv('_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024_only_global_models_ipcc_ar6_likely_range_wrong_global.csv')\n",
    "#pd_r_only_global = pd.concat([pd_r_only_global, pd_r_only_reg.loc[pd_r_only_reg.region!='All']])\n",
    "pd_r_only_global=pd.read_csv('lowess_fits_scripts/_raw_fits/fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024_only_global_models_ipcc_ar6_likely_range.csv')\n",
    "pd_r_only_global = pd_r_only_global.loc[pd_r_only_global.y.isna()]\n",
    "for c in col_drops:\n",
    "    try:\n",
    "        pd_r_only_global = pd_r_only_global.drop(columns=[c])\n",
    "    except:\n",
    "        pass\n",
    "pd_r_only_global = pd_r_only_global.rename(columns={'x':'temp_ch'})\n",
    "\n",
    "## just to print it out... \n",
    "pd_r_only_global_reg_all = pd_r_only_global.loc[pd_r_only_global.region=='All']\n",
    "pd_r_only_global_reg_all.index = pd_r_only_global_reg_all.temp_ch.round(2)\n",
    "pd_r_only_global_reg_all.loc[[1.2,1.5,2.0,2.7,3.0,4.0],['0.17', '0.5', '0.83']].round(1) #.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77181a60-042a-4c80-855c-ecc58eed40fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d269bf00-e068-4485-b394-06159a3497b1",
   "metadata": {},
   "source": [
    "### Save the aggregated lowess fits in ../data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d6f9c30-94b9-4549-981b-4bc8abd1e139",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_r_l_reg = pd_r_l[:-1]\n",
    "pd_r_l_reg.append(pd_glob_median)\n",
    "# frac is only valid for the median values in case of the region=='All'\n",
    "pd_fits_all_glac_models_5000 = pd.concat(pd_r_l_reg)\n",
    "pd_fits_all_glac_models_5000 = pd_fits_all_glac_models_5000.reset_index(drop=True)\n",
    "pd_fits_all_glac_models_5000.to_csv(f'../data/lowess_fit_rel_2020_101yr_avg_steady_state_{DATE}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c1c0ca2-6150-45cd-869d-db5e29db7d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_r_l_reg_100 = pd_r_l_100[:-1]\n",
    "pd_r_l_reg_100.append(pd_glob_median_100)\n",
    "# frac is only valid for the median values in case of the region=='All'\n",
    "pd_fits_all_glac_models_100 = pd.concat(pd_r_l_reg_100)\n",
    "pd_fits_all_glac_models_100 = pd_fits_all_glac_models_100.reset_index(drop=True)\n",
    "pd_fits_all_glac_models_100.to_csv(f'../data/lowess_fit_rel_2020_21yr_avg_after100yr_{DATE}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1392fae5-f366-44ea-a94f-71bfe2f30a9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_r_l_reg_500 = pd_r_l_500[:-1]\n",
    "pd_r_l_reg_500.append(pd_glob_median_500)\n",
    "# frac is only valid for the median values in case of the region=='All'\n",
    "pd_fits_all_glac_models_500 = pd.concat(pd_r_l_reg_500)\n",
    "pd_fits_all_glac_models_500 = pd_fits_all_glac_models_500.reset_index(drop=True)\n",
    "pd_fits_all_glac_models_500.to_csv(f'../data/lowess_fit_rel_2020_21yr_avg_after500yr_{DATE}.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "26b75d30-7534-4388-9ee2-b67c37a6e4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reg. temp change files\n",
    "pd_r_l_reg_temp = pd_r_l_reg_temp[:-1] # remove the global part and instead add the NaN global uncertainties\n",
    "pd_r_l_reg_temp.append(pd_glob_median_reg_temp)\n",
    "# frac is only valid for the median values in case of the region=='All'\n",
    "pd_fits_all_glac_models_5000_reg_temp = pd.concat(pd_r_l_reg_temp)\n",
    "pd_fits_all_glac_models_5000_reg_temp = pd_fits_all_glac_models_5000_reg_temp.reset_index(drop=True)\n",
    "pd_fits_all_glac_models_5000_reg_temp.to_csv(f'../data/lowess_fit_rel_2020_101yr_avg_steady_state_{DATE}_rel_regional_glacier_temp_ch.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce53a8ad-3a06-4758-9023-b4f9e1ba9f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add only global model files\n",
    "for reg in ['01', '03', '04', '05', '07', '09', '17', '19']:\n",
    "    pd_fits_all_glac_models_5000_r = pd_fits_all_glac_models_5000.loc[pd_fits_all_glac_models_5000.region==reg].drop(columns=['0.05','0.25','0.75','0.95'])\n",
    "    pd_r_only_global=pd.concat([pd_r_only_global,pd_fits_all_glac_models_5000_r])\n",
    "assert len(pd_r_only_global.region.unique()) == 20\n",
    "#pd_r_only_global = pd_r_only_global.drop(columns='Unnamed: 0').reset_index(drop=True)\n",
    "pd_r_only_global.to_csv(f'../data/lowess_fit_rel_2020_101yr_avg_steady_state_{DATE}_only_global_models.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ec94753-9663-4597-a556-0242442ed8e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73b81d1e-700a-4e23-b381-8f69d2a94df0",
   "metadata": {},
   "source": [
    "### Same for the per-glacier-model lowess fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ff91ba31-72d7-4ad3-94fd-4f25fff2088b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_r_per_glac_model = pd.read_csv('per_glac_model_lowess_fits_scripts/_raw_fits/fitted_per_model_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_current12deg_5000_Feb12_2024_ipcc_ar6.csv')\n",
    "pd_r_per_glac_model = adapt_pd_r(pd_r_per_glac_model)\n",
    "pd_r_per_glac_model['region'] = pd_r_per_glac_model.region.astype(str)\n",
    "pd_r_per_glac_model.to_csv(f'../data/lowess_fit_rel_2020_101yr_avg_steady_state_{DATE}_per_glac_model.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "44c07955-9115-4e56-b28d-17a559d3d76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_ch</th>\n",
       "      <th>0.5</th>\n",
       "      <th>model_author</th>\n",
       "      <th>frac</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>it</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>88.462133</td>\n",
       "      <td>GLIMB</td>\n",
       "      <td>0.20</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>88.021433</td>\n",
       "      <td>GLIMB</td>\n",
       "      <td>0.20</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>87.525810</td>\n",
       "      <td>GLIMB</td>\n",
       "      <td>0.20</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.05</td>\n",
       "      <td>86.936452</td>\n",
       "      <td>GLIMB</td>\n",
       "      <td>0.20</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.10</td>\n",
       "      <td>86.235724</td>\n",
       "      <td>GLIMB</td>\n",
       "      <td>0.20</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21114</th>\n",
       "      <td>6.65</td>\n",
       "      <td>10.041927</td>\n",
       "      <td>PyGEM-OGGM_v13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21115</th>\n",
       "      <td>6.70</td>\n",
       "      <td>10.023944</td>\n",
       "      <td>PyGEM-OGGM_v13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21116</th>\n",
       "      <td>6.75</td>\n",
       "      <td>10.005590</td>\n",
       "      <td>PyGEM-OGGM_v13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21117</th>\n",
       "      <td>6.80</td>\n",
       "      <td>9.986850</td>\n",
       "      <td>PyGEM-OGGM_v13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21118</th>\n",
       "      <td>6.85</td>\n",
       "      <td>9.967739</td>\n",
       "      <td>PyGEM-OGGM_v13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp_ch        0.5    model_author  frac region  year  it    N\n",
       "2        -0.10  88.462133           GLIMB  0.20    All  5000   2  500\n",
       "3        -0.05  88.021433           GLIMB  0.20    All  5000   2  500\n",
       "4         0.00  87.525810           GLIMB  0.20    All  5000   2  500\n",
       "5         0.05  86.936452           GLIMB  0.20    All  5000   2  500\n",
       "7         0.10  86.235724           GLIMB  0.20    All  5000   2  500\n",
       "...        ...        ...             ...   ...    ...   ...  ..  ...\n",
       "21114     6.65  10.041927  PyGEM-OGGM_v13  0.27     19  5000   2  500\n",
       "21115     6.70  10.023944  PyGEM-OGGM_v13  0.27     19  5000   2  500\n",
       "21116     6.75  10.005590  PyGEM-OGGM_v13  0.27     19  5000   2  500\n",
       "21117     6.80   9.986850  PyGEM-OGGM_v13  0.27     19  5000   2  500\n",
       "21118     6.85   9.967739  PyGEM-OGGM_v13  0.27     19  5000   2  500\n",
       "\n",
       "[13440 rows x 8 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_r_per_glac_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44a1c56f-f869-4f62-84b5-8c7e34e05dc0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a097abc8-d588-436d-b602-a475c26cc4c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "54f05fd8-c367-42a9-98cf-63b1b6f3eb3a",
   "metadata": {},
   "source": [
    "### Some comparison on the different approaches (can be probably removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51d38ef4-6f2a-45a6-921d-c54af09f24d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_low_normal = pd.read_csv(f'fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_{DATE}_ipcc_ar6.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a871b35-bfff-41cf-b2a3-1264375b3e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"['01', '03', '04', '05', '07', '09', '17', '19']\"]\n",
      "[\"['02', '08', '10', '12', '16', '18']\"]\n",
      "[\"['13', '14', '15']\"]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "qs = ['0.05', '0.17','0.25', '0.5', '0.75', '0.83','0.95']\n",
    "\n",
    "pd_global_complex_agg_new = pd.DataFrame(index=np.arange(-0.1,6.9,0.05).round(2),columns = qs)\n",
    "pd_global_complex_agg_new.loc[:,qs] = 0\n",
    "for agg in list_aggregates:\n",
    "    if agg in ['06','11']:\n",
    "        pd_low_agg = pd.read_csv(f'fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024RGI{agg}_ipcc_ar6_likely_range.csv')\n",
    "        agg_regs = agg\n",
    "        #pd_low_agg = pd_low_normal.loc[pd_low_normal.region==agg]\n",
    "        vol_2020_reg = pd_rgi_stats_w_hugonnet.loc[agg][f'regional_volume_m3_2020{approach}']\n",
    "        # here the values are in % rel. to the regional estimates,\n",
    "        # but we want them to be in % relative to the global 2020 estimates\n",
    "    else:\n",
    "        pd_low_agg = pd.read_csv(f'fitted_lowess_best_frac_shift_years_rel_2020_101yr_avg_period_lowess_added_quantiles_added_current12deg_5000_Feb12_2024_{agg}_ipcc_ar6_likely_range.csv')\n",
    "        regs = pd_low_agg.region.unique() # it is a list of one item\n",
    "        print(regs)\n",
    "        agg_regs = ast.literal_eval(list(regs)[0])\n",
    "        vol_2020_reg = pd_rgi_stats_w_hugonnet.loc[agg_regs][f'regional_volume_m3_2020{approach}'].sum()\n",
    "\n",
    "    pd_low_agg.loc[pd_low_agg.index,qs] = pd_low_agg[qs].values*vol_2020_reg/vol_2020_globally\n",
    "\n",
    "\n",
    "    \n",
    "    pd_low_agg.index = pd_low_agg.x.round(2)\n",
    "    pd_low_agg = pd_low_agg.loc[pd_low_agg.y.isna()]\n",
    "    pd_low_agg = pd_low_agg.loc[np.arange(-0.1,6.9,0.05).round(2)]\n",
    "    for q in qs:\n",
    "        pd_global_complex_agg_new.loc[pd_low_agg.index,q] += pd_low_agg[q].values # we have to add up the 5 components\n",
    "    #print(pd_global_complex_agg_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb5f1f96-5483-4707-87f6-a23551ded8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "_sel = pd_global_complex_agg_new\n",
    "pd_global_complex_agg_new = pd_global_complex_agg_new.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df70868-c068-45af-8f9c-c69c9f1a4903",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea4b9dc9-466a-47cf-813d-d64fb984efcc",
   "metadata": {},
   "source": [
    "### CHeck influence of median on how it is aggregated ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ff83dab-11d6-4a0f-80ba-18c22087bdfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/users/lschuster/mambaforge/envs/oggm_gmip3/lib/python3.11/site-packages/pyproj/__init__.py:89: UserWarning: pyproj unable to set database path.\n",
      "  _pyproj_global_context_initialize()\n"
     ]
    }
   ],
   "source": [
    "DATE = 'Feb12_2024' \n",
    "fill_option = 'repeat_last_101yrs' \n",
    "path_merged_runs_scaled_extend = f'../data/GMIP3_reg_glacier_model_data/glacierMIP3_{DATE}_models_all_rgi_regions_sum_scaled_extended_{fill_option}.nc'\n",
    "ds_reg_models = xr.open_dataset(path_merged_runs_scaled_extend)\n",
    "\n",
    "approach = '_via_5yravg'\n",
    "ds_reg_yr_shift= xr.open_dataset(f'/data/GMIP3_reg_glacier_model_data/all_shifted_glacierMIP3_{DATE}_models_all_rgi_regions_sum_scaled_extended_{fill_option}{approach}.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b11fd787-3063-4bf5-a5ff-e592d62c3573",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ipcc_ar6 = True\n",
    "pd_global_temp = get_glob_temp_exp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "afd3bcf4-47a2-4fa4-bc55-099e408a0c1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PyGEM-OGGM_v13', 'GloGEMflow', 'GloGEMflow3D', 'OGGM_v16', 'GLIMB', 'Kraaijenbrink', 'GO', 'CISM2'] ['model 1', 'model 2', 'model 3', 'model 4', 'model 5', 'model 6', 'model 7', 'model 8']\n"
     ]
    }
   ],
   "source": [
    "# Let's take the median estimate from all glacier models for every RGI region and then do the sum:\n",
    "\n",
    "from help_functions import pal_models, model_order, d_reg_num_name, model_order_anonymous, compute_steady_state_yr\n",
    "hue_order_anonymous = []\n",
    "\n",
    "pal_models_l = []\n",
    "hue_order = []\n",
    "for m, p in zip(model_order, pal_models):\n",
    "    if (m!='OGGM-VAS') and (m!='OGGM_v153'):\n",
    "        hue_order.append(m)\n",
    "        pal_models_l.append(p)\n",
    "for m in hue_order:\n",
    "    hue_order_anonymous.append(model_order_anonymous[m])\n",
    "pal_models = pal_models_l\n",
    "\n",
    "print(hue_order, hue_order_anonymous)\n",
    "# select the right models:\n",
    "ds_reg_models = ds_reg_models.sel(model_author = hue_order)\n",
    "pal_models = sns.color_palette(pal_models)\n",
    "\n",
    "dict_model_col = {}\n",
    "for c,m in zip(pal_models, hue_order):\n",
    "    dict_model_col[m] = c\n",
    "    \n",
    "# select the right models\n",
    "ds_reg_models = ds_reg_models.sel(model_author=hue_order)\n",
    "ds_reg_models_vol = ds_reg_models.volume_m3\n",
    "\n",
    "glac_models = hue_order\n",
    "\n",
    "num_dict = {0:'(a)', 1:'(b)', 2:'(c)', 3:'(d)', 4: '(e)', 5:'(f)', 6:'(g)', 7:'(h)', 8:'(i)', 9:'(j)', 10:'(k)', 11:'(l)', 12:'(m)'} \n",
    "\n",
    "ds_reg_models_vol = ds_reg_models_vol.stack(experiments=['gcm','period_scenario']).rolling(simulation_year=101, center=True).mean().dropna(dim='simulation_year', how='all')\n",
    "ds_reg_models_med_vol = ds_reg_models_vol.median(dim='model_author')\n",
    "\n",
    "experiments_l = get_glob_temp_exp(region='global').index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9cf723a8-91aa-4f08-a569-0bf5b1877687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe051a1f890>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( get_glob_temp_exp(region='global')['temp_ch_ipcc'], ds_reg_models_med_vol.isel(simulation_year=-1).sum(dim='rgi_reg').sel(experiments=experiments_l),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "61f8b234-6c11-42c1-8a7a-1e57cd12fe40",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_reg_models_vol_ss = ds_reg_models_vol.isel(simulation_year=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15c3a5d5-5b59-4eaf-92ae-a43a7f9c7216",
   "metadata": {},
   "outputs": [],
   "source": [
    "_sum_l=[]\n",
    "_sum_0l=[]\n",
    "for agg in [['06'],['11'], ['01', '03', '04', '05', '07', '09', '17', '19'],\n",
    "            ['02', '08', '10', '12', '16', '18'],['13', '14', '15']]:\n",
    "    _sum = ds_reg_models_vol_ss.sel(rgi_reg=agg).dropna(dim='model_author').sum(dim='rgi_reg').median(dim='model_author')\n",
    "    _sum['reg_agg'] = str(agg)\n",
    "    _sum_l.append(_sum)\n",
    "    ###\n",
    "    _sum_0 = ds_reg_models.volume_m3.isel(simulation_year=0).sel(rgi_reg=agg).dropna(dim='model_author').sum(dim='rgi_reg').median(dim='model_author')\n",
    "    _sum_0['reg_agg'] = str(agg)\n",
    "    _sum_0l.append(_sum_0)\n",
    "    \n",
    "sum_med_sum = xr.concat(_sum_l, dim='reg_agg').sum(dim='reg_agg')\n",
    "med_sum = ds_reg_models_vol_ss.median(dim='model_author').sum(dim='rgi_reg')\n",
    "sum_med_sum0 = xr.concat(_sum_0l, dim='reg_agg').sum(dim='reg_agg')\n",
    "\n",
    "med_sum0 = ds_reg_models.volume_m3.isel(simulation_year=0).median(dim='model_author').sum(dim='rgi_reg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bdc5cfd9-7f15-4a89-8a60-91b26f2bc1ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe05194f9d0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ds_reg_models_vol_ss.sel(rgi_reg=['01', '03', '04', '05', '07', '09', '17', '19']).dropna(dim='model_author').sum(dim='rgi_reg').median(dim='model_author'),\n",
    "         ds_reg_models_vol_ss.sel(rgi_reg=['01', '03', '04', '05', '07', '09', '17', '19']).dropna(dim='model_author').median(dim='model_author').sum(dim='rgi_reg'),\n",
    "         '.')\n",
    "plt.plot([0,sum_med_sum.max()], [0,sum_med_sum.max()], color='grey')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "512eb5ef-2e1e-45ef-98b9-170a329cffd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_allclose(sum_med_sum0/med_sum0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fe877711-797a-4e70-a8bc-d85284fe9837",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this here works for the mean .... (but it does not work for the median... )\n",
    "#np.testing.assert_allclose(sum_med_sum/med_sum,1, rtol=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cee61ade-a67d-4901-a48e-d872154ab0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe051871fd0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sum_med_sum,med_sum,'.')\n",
    "plt.plot([0,sum_med_sum.max()], [0,sum_med_sum.max()], color='grey')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0d90ceaa-e1f2-436c-99f7-afbf98b4d79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_sum_med_sum =sum_med_sum.to_dataframe()\n",
    "pd_med_sum = med_sum.to_dataframe()\n",
    "pd_sum_med_sum.loc[pd_global_temp.index, 'temp_ch_ipcc'] =  pd_global_temp['temp_ch_ipcc'].values\n",
    "\n",
    "pd_sum_med_sum.loc[pd_med_sum.index, 'sum_over_reg_medians'] = 100*pd_med_sum['volume_m3'].values/vol_2020_globally\n",
    "pd_sum_med_sum.loc[pd_med_sum.index, 'sum_over_agg_reg_medians'] = 100*pd_sum_med_sum['volume_m3'].values/vol_2020_globally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f889feae-8414-4251-b13e-be0a6555a77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_sum_med_sum['ratio'] = pd_sum_med_sum.sum_over_agg_reg_medians/pd_sum_med_sum['sum_over_reg_medians']\n",
    "pd_sum_med_sum['diff'] = pd_sum_med_sum.sum_over_agg_reg_medians - pd_sum_med_sum['sum_over_reg_medians']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "59597052-4501-428c-8755-c6620d4eca63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y8NBU0Px0NQI2ToYN78GOj9P+3DglrVxEBOjZsyevv/46kZGRmEwmKlSoAFhO0xw5ciRPPPFEhr4hISGMHTvW/HnBggUEBQXh6OhIYGAgn332Wa5i+eqrr6hbty5ubm6UKlWKrl27cunSJXP9pk2bMJlM/Pzzz9StW5dixYqxdetWrl+/Trdu3XBxcaF06dLMmDEjwzTTxMRE3nzzTcqWLYuLiwv16tVj06ZN5vr0aZirVq0iODiYYsWKcfbs2QwxZhWDYRh88MEHBAQE4OTkRM2aNfn+++8t+q5cuZLKlSvj5OREs2bNWLRoESaTiWvXrlnEcLvZs2fzyCOP4ODgQNWqVfnyyy8t6k0mE/Pnz+e5557D2dmZypUrs3LlSnN9dHQ03bp1w8fHBycnJypXrsyCBQty9VxERERERKTgUmItvyTEwK45EH1HEi36NOyam1YvIgVSbHwSx/6MYdfpKxz7M4bY+KR8O9dHH33Eu+++S7ly5YiKimLPnj0Z2nTr1o1du3Zx6tQpc9nRo0c5fPgw3bp1A2DevHmMGjWKiRMnEh4ezqRJkxg9ejSLFi3KcSyJiYlMmDCBQ4cOsWLFCiIiIujZs2eGdm+++SaTJ08mPDyckJAQhg4dyvbt21m5ciVr165l69at7N+/36LPyy+/zPbt21m6dCm//vorL7zwAk899RQnT540t4mLi2Py5MnMnz+fo0ePUrJkySxjvTOGd955hwULFjB79myOHj3KkCFDePHFF9m8eTMAZ86c4Z///Cft27fn4MGD9O3bl1GjRmV7P5YvX86gQYMYNmwYR44coW/fvrz88sts3LjRot348ePp2LEjv/76K23atKFbt25cvXoVgNGjR3Ps2DF++uknwsPDmT17NiVKlMj2vCIiIiIiUnhoKmh+uRaZMamWLvp0Wn2pGg82JhG5q8grN1mw/QyRV+PMZf7ezvRsUIHy3i55fj4PDw/c3NywtbWlVKlSmbapXr06ISEhLF68mNGjRwPw9ddf89hjj1GlShUAJkyYwLRp0+jQoQMAFStW5NixY8yZM4cePXrkKJZevXqZvw8ICODjjz/m8ccf58aNG7i6uprr3n33XVq2bAnA9evXWbRoEYsXL6Z58+ZA2si5MmXKmNufOnWKJUuWcP78eXP58OHDWbNmDQsWLGDSpEkAJCUl8dlnn1GzZs27xnp7DDdv3mT69Ols2LCB+vXrm+Pftm0bc+bMoUmTJnz++edUrVqVDz/8EICqVaty5MgRJk6cmOU5pk6dSs+ePenfvz8AQ4cOZefOnUydOpVmzZqZ2/Xs2ZMuXboAMGnSJGbNmsXu3bt56qmniIyMpHbt2tStWxfAPCJRRERERESKBo1Yyy8JsfdXLyIPXGx8UoakGsDZK3Es3HEmX0eu3U23bt34+uuvgbR1xZYsWWIerfbXX39x7tw5evfujaurq/nrvffesxjldjcHDhygXbt2+Pv74+bmRtOmTQGIjIy0aJeeJAI4ffo0SUlJPP744+YyDw8Pqlatav68f/9+DMOgSpUqFvFt3rzZIj4HBwdCQkJyFOvtMRw7doyEhARatmxpcfz//Oc/5uOfOHGCxx57zOIYt8ecmfDwcBo2bGhR1rBhQ8LDwy3Kbo/ZxcUFNzc38xTa1157jaVLl1KrVi3efPNNduzYkaPrExERERGRwkEj1vKLo/v91YvIA3c+Oi5DUi3d2StxnI+OI9jJOpuPdO3albfffpv9+/cTHx/PuXPn6Ny5MwCpqalA2nTQevXqWfSztbXN0fFv3rxJaGgooaGhfPXVV/j4+BAZGUmrVq1ITEy0aOvi8vfIPcMwgLS1xm6XXp4en62tLfv27csQz+0j4ZycnDIcJyu3x5B+/atXr6Zs2bIW7YoVK2aOJ7sYs5JZnzvL7ty91WQymWNq3bo1Z8+eZfXq1axbt47mzZszYMAApk6detdzi4iIiIhIwafEWn7xLA9eAWnTPu/kFZBWLyIFyvWE5Puqz0/lypWjcePGfP3118THx9OiRQt8fX0B8PX1pWzZspw+fdo8ii23jh8/zuXLl5kyZQp+fn4A7N279679HnnkEezt7dm9e7e5X2xsLCdPnqRJkyYA1K5dm5SUFC5dukSjRo3uKb7spG92EBkZaT7nnQIDA/nf//5nUXa36wsKCmLbtm10797dXLZjxw6CgoJyFZ+Pjw89e/akZ8+eNGrUiBEjRiixJiIiIiJSRCixll8cPaDeq7BrLkmXfychKYXkVAN7n0o41H0FB0frjHoRkay5OWb/I/Fu9fmtW7dujBs3jsTERGbMmGFRN27cON544w3c3d1p3bo1t27dYu/evURHRzN06NC7Hrt8+fI4ODgwa9Ys+vXrx5EjR5gwYcJd+7m5udGjRw9GjBhB8eLFKVmyJGPHjsXGxsY8sqtKlSp069aN7t27M23aNGrXrs3ly5fZsGEDNWrUoE2bNvd2Q26LYfjw4QwZMoTU1FSefPJJYmNj2bFjB66urvTo0YO+ffsyffp03nrrLXr37s3BgwdZuHAhkHFUWroRI0bQsWNHHn30UZo3b86PP/7IsmXLWLduXY5jGzNmDHXq1KFatWrcunWLVatW5ToxJyIiIiIiBZfWWMtPxSty5bEhnKw2mIiqfThdYwj/dXuRD3cnEHnlprWjE5E7lPNyxt/bOdM6f29nynllXvegvPDCC1y5coW4uDjat29vUdenTx/mz5/PwoULqVGjBk2aNGHhwoVUrFgxR8f28fFh4cKFfPfddwQHBzNlypQcj6qaPn069evX55lnnqFFixY0bNiQoKAgHB0dzW0WLFhA9+7dGTZsGFWrVuXZZ59l165d5lFu92vChAmMGTOGyZMnExQURKtWrfjxxx/N11+xYkW+//57li1bRkhICLNnzzbvCpo+XfRO7du356OPPuLDDz+kWrVqzJkzhwULFpjXnssJBwcHRo4cSUhICI0bN8bW1palS5fe9/WKiIiIiEjBYDJysshMERcbG4uHhwcxMTG4u+fd2mex8UnMWPtbpms2+Xs7M7hFFdyd7DPpKSL3IiEhgYiICCpWrGiR1MmNyCs3WbjjDGevPJhdQYuimzdvUrZsWaZNm0bv3r2tHU6WJk6cyOeff865c+esHYoUYNn9XMmv9wfJW3pOIiIiklu5eX/QVNB8VJAXQheRzJX3dmFwiyqcj47jekIybo52lPNyVhI8GwcOHOD48eM8/vjjxMTE8O677wLQrl07K0dm6bPPPuOxxx7D29ub7du38+GHHzJw4EBrhyUiIiIiIoWYEmv5qCAvhC4iWXN3slfSO5emTp3KiRMncHBwoE6dOmzdupUSJUpYOywLJ0+e5L333uPq1auUL1+eYcOGMXLkSGuHJSIiIiIihZgSa/mooC+ELiKSF2rXrs2+ffusHcZdzZgxI8OmDyIiIiIiIvdDmxfko4K+ELqIiIiIiIiIiNw7JdbykbuTPT0bVMiQXEtfCF1rNolIuitXrlCyZEnOnDlj7VAeKj179syww2pOzJ07Fz8/P2xsbJg5c2aex1XULFy4EE9Pz/s+zqZNmzCZTFy7du2+jwXwz3/+k+nTp+fJsURERETk4aS5iPlMC6GLSE5MnjyZtm3bUqFCBWuHIncRGxvLwIEDmT59Os8//zweHg/Penzjxo1jxYoVHDx4MFf9OnXqRJs2bXLVp2nTptSqVcsicdmgQQOioqLy7J6PGTOGZs2a0adPH+0WKSIiIiL3RIm1B0ALoYtIduLj4/niiy/43//+Z+1QCozExEQcHBysHUamIiMjSUpK4umnn6Z06dL3fJykpCTs7QvHL1kMwyAlJeWe+zs5OeHk5HTfcTg4OFCqVKn7Pk66kJAQKlSowNdff81rr72WZ8cVERERkYeHpoKKiFjZTz/9hJ2dHfXr1zeXpU95W79+PXXr1sXZ2ZkGDRpw4sQJi74//vgjderUwdHRkYCAAMaPH09yctqOw8OGDaNt27bmtjNnzsRkMrF69WpzWdWqVZkzZ06mcaWkpNC7d28qVqyIk5MTVatW5aOPPrJokz6Vcvz48ZQsWRJ3d3f69u1LYmKiuU3Tpk0ZOHAgAwcOxNPTE29vb9555x0MwzC3qVChAu+99x49e/bEw8ODV155BYD//ve/VKtWjWLFilGhQgWmTZtmcf6vvvqKunXr4ubmRqlSpejatSuXLl2yaHP06FGefvpp3N3dcXNzo1GjRpw6dcqizdSpUyldujTe3t4MGDCApKSkTO/JwoULqVGjBgABAQGYTCbz9N3Zs2fzyCOP4ODgQNWqVfnyyy8t+ppMJj7//HPatWuHi4sL7733XqbnMJlMrFixwqLM09OThQsXAnDmzBlMJhPLli2jWbNmODs7U7NmTX755ReLPtu3b6dJkyY4Ozvj5eVFq1atiI6OBtISZR988AEBAQE4OTlRs2ZNvv/+e3Pf9L9/P//8M3Xr1qVYsWJ8+eWXjB8/nkOHDmEymTCZTOaYpk+fTo0aNXBxccHPz4/+/ftz48YNi/t2+1TQcePGUatWLb788ksqVKiAh4cHnTt35vr160Da36vNmzfz0Ucfmc915syZTKeC7tixg8aNG+Pk5ISfnx9vvPEGN2/eNNd/9tlnVK5cGUdHR3x9ffnnP/9pcZ+effZZlixZkumzEBERERG5GyXWRESsbMuWLdStWzfTulGjRjFt2jT27t2LnZ0dvXr1Mtf9/PPPvPjii7zxxhscO3aMOXPmsHDhQiZOnAikJbS2bt1KamoqAJs3b6ZEiRJs3rwZgAsXLvDbb7/RpEmTTM+dmppKuXLl+Pbbbzl27BhjxozhX//6F99++61Fu/Xr1xMeHs7GjRtZsmQJy5cvZ/z48RZtFi1ahJ2dHbt27eLjjz9mxowZzJ8/36LNhx9+SPXq1dm3bx+jR49m3759dOzYkc6dO3P48GHGjRvH6NGjzckcSBvZNmHCBA4dOsSKFSuIiIigZ8+e5vo//viDxo0b4+joyIYNG9i3bx+9evUyJx8BNm7cyKlTp9i4cSOLFi1i4cKFFue4XadOnVi3bh0Au3fvJioqCj8/P5YvX86gQYMYNmwYR44coW/fvrz88sts3LjRov/YsWNp164dhw8ftniW92LUqFEMHz6cgwcPUqVKFbp06WK+roMHD9K8eXOqVavGL7/8wrZt22jbtq151Nk777zDggULmD17NkePHmXIkCG8+OKL5r8b6d58800mT55MeHg4oaGhDBs2jGrVqhEVFUVUVBSdOnUCwMbGho8//pgjR46waNEiNmzYwJtvvplt/KdOnWLFihWsWrWKVatWsXnzZqZMmQLARx99RP369XnllVfM5/Lz88twjMOHD9OqVSs6dOjAr7/+yjfffMO2bdsYOHAgAHv37uWNN97g3Xff5cSJE6xZs4bGjRtbHOPxxx9n9+7d3Lp16x6egoiIiIg89AwxYmJiDMCIiYmxdigich/i4+ONY8eOGfHx8dYOJVfatWtn9OrVy6Js48aNBmCsW7fOXLZ69WoDMF9fo0aNjEmTJln0+/LLL43SpUsbhmEY165dM2xsbIy9e/caqamphre3tzF58mTjscceMwzDMBYvXmz4+vrmKtb+/fsbzz//vPlzjx49jOLFixs3b940l82ePdtwdXU1UlJSDMMwjCZNmhhBQUFGamqquc1bb71lBAUFmT/7+/sb7du3tzhX165djZYtW1qUjRgxwggODs4yvt27dxuAcf36dcMwDGPkyJFGxYoVjcTExEzb9+jRw/D39zeSk5PNZS+88ILRqVOnLM9x4MABAzAiIiLMZQ0aNDBeeeUVi3YvvPCC0aZNG/NnwBg8eHCWx7293fLlyy3KPDw8jAULFhiGYRgREREGYMyfP99cf/ToUQMwwsPDDcMwjC5duhgNGzbM9Pg3btwwHB0djR07dliU9+7d2+jSpYthGH///VuxYoVFm7Fjxxo1a9a86zV8++23hre3t/nzggULDA8PD4vjODs7G7GxseayESNGGPXq1TN/btKkiTFo0CCL46bHFR0dbRiGYbz00kvGq6++atFm69atho2NjREfH2/897//Ndzd3S3Oc6dDhw4ZgHHmzJlM67P7uaL3h8JBz0lERERyKzfvDxqxJiJiZfHx8Tg6OmZaFxISYv4+fT2v9KmO+/bt491338XV1dX8lT7CJy4uDg8PD2rVqsWmTZs4fPgwNjY29O3bl0OHDnH9+nU2bdqU5Wi1dJ9//jl169bFx8cHV1dX5s2bR2RkpEWbmjVr4uz89+7H9evX58aNG5w7d85c9sQTT2AymSzanDx50mLdrjtH7YWHh9OwYUOLsoYNG1r0O3DgAO3atcPf3x83NzeaNm0KYI7x4MGDNGrUKNu1zKpVq4atra35c+nSpTNMJ72brGINDw+3KMtqZOK9yO7vRvqItcwcO3aMhIQEWrZsafF35z//+U+GKbI5jXfjxo20bNmSsmXL4ubmRvfu3bly5YrFlMw7VahQATc3N4tryO1937dvHwsXLrS4jlatWpGamkpERAQtW7bE39+fgIAAXnrpJb7++mvi4uIsjpG+9tud5SIiIiIiOaHNC0RErKxEiRLmta/udHtCKD0xlT61MzU1lfHjx9OhQ4cM/dITdU2bNmXTpk04ODjQpEkTvLy8qFatGtu3b2fTpk0MHjw4y7i+/fZbhgwZwrRp06hfvz5ubm58+OGH7Nq1K0fXdXsiLSdcXFwsPhuGkeEYxm3rst28eZPQ0FBCQ0P56quv8PHxITIyklatWpnXeMvJgvl3Jt1MJpP5HudGZrHeWXbnNWZ1nNuvE8h0zbfs/m5kd93pbVavXk3ZsmUt6ooVK5breM+ePUubNm3o168fEyZMoHjx4mzbto3evXtnuVbdnfGnX0Nu73tqaip9+/bljTfeyFBXvnx5HBwc2L9/P5s2bSIsLIwxY8Ywbtw49uzZY17z7erVqwD4+Pjk6twiIiIiIqDEmoiI1dWuXZuvvvoq1/0effRRTpw4QaVKlbJs07RpU7744gvs7Oxo0aIFAE2aNGHp0qXZrq8GsHXrVho0aED//v3NZXeOaAI4dOgQ8fHx5mTOzp07cXV1pVy5cuY2O3futOizc+dOKleubDFS7E7BwcFs27bNomzHjh1UqVIFW1tbjh8/zuXLl5kyZYp5/a29e/datA8JCWHRokX5vgNnUFAQ27Zto3v37haxBgUF5fpYPj4+REVFmT+fPHky16OpQkJCWL9+fYa17iDtvhYrVozIyMi7jli8k4ODQ4bdQffu3UtycjLTpk3DxiZtIPyd6/Ddi8zOdadHH32Uo0ePZvvfQPrf/RYtWjB27Fg8PT3ZsGGDOSF95MgRypUrR4kSJe47ZhERERF5+GgqqIiIlbVq1YqjR49mOWotK2PGjOE///kP48aN4+jRo4SHh/PNN9/wzjvvmNs0btyY69ev8+OPP5qnSTZt2tQ8wis4ODjL41eqVIm9e/fy888/89tvvzF69Gj27NmToV1iYiK9e/fm2LFj/PTTT4wdO5aBAweakywA586dY+jQoZw4cYIlS5Ywa9YsBg0alO31DRs2jPXr1zNhwgR+++03Fi1axCeffMLw4cOBv0ckzZo1i9OnT7Ny5UomTJhgcYyBAwcSGxtL586d2bt3LydPnuTLL7/MsLvq/RoxYgQLFy7k888/5+TJk0yfPp1ly5aZY82Nf/zjH3zyySfs37+fvXv30q9fv1wnBUeOHMmePXvo378/v/76K8ePH2f27NlcvnwZNzc3hg8fzpAhQ1i0aBGnTp3iwIEDfPrppyxatCjb41aoUIGIiAgOHjzI5cuXuXXrFo888gjJycnm5/Dll1/y+eef5/q6MzvXrl27OHPmDJcvX850NNtbb73FL7/8woABAzh48CAnT55k5cqVvP766wCsWrWKjz/+mIMHD3L27Fn+85//kJqaStWqVc3H2Lp1K6Ghofcdr4iIiIg8nJRYexASYuDCYTizPe3PhBhrRyQiBUiNGjWoW7durkf5tGrVilWrVrF27Voee+wxnnjiCaZPn46/v7+5jYeHB7Vr16Z48eLmJFqjRo1ITU2962ilfv360aFDBzp16kS9evW4cuWKxei1dM2bN6dy5co0btyYjh070rZtW8aNG2fRpnv37sTHx/P4448zYMAAXn/9dV599dVsz//oo4/y7bffsnTpUqpXr86YMWN49913zbt++vj4sHDhQr777juCg4OZMmUKU6dOtTiGt7c3GzZs4MaNGzRp0oQ6deowb968PB+91r59ez766CM+/PBDqlWrxpw5c1iwYIE5mZkb06ZNw8/Pj8aNG9O1a1eGDx9usYZdTlSpUoWwsDAOHTrE448/Tv369fnhhx+ws0sbqD5hwgTGjBnD5MmTCQoKolWrVvz4449UrFgx2+M+//zzPPXUUzRr1gwfHx+WLFlCrVq1mD59Ou+//z7Vq1fn66+/ZvLkybm+7jsNHz4cW1tbgoODzdN87xQSEsLmzZs5efIkjRo1onbt2owePdq85pynpyfLli3jH//4B0FBQXz++ecsWbKEatWqAZCQkMDy5ct55ZVX7jteEREREXk4mYw7F3J5CMXGxuLh4UFMTAzu7u55e/CrEbBrDkRH/F3mFQD1XoXi2f8DxhxffBLno+O4npCMm6Md5byccXfKvylNIoVVQkICERERVKxYMcvNAAqq//3vfwwfPpwjR45YjPQq6Hr27Mm1a9dYsWJFlm2aNm1KrVq1mDlz5gOLSyQnPv30U3744QfCwsKybJPdz5V8fX+QPKPnJCIiIrmVm/cHrbGWnxJiMibVAKJPw6650OxtcPTI9hCRV26yYPsZIq/+vb6Ov7czPRtUoLz33ReVFpHCoU2bNpw8eZI//vjDvF6YiOQve3t7Zs2aZe0wRERERKQQKzzDIgqja5EZk2rpok+n1WcjNj4pQ1IN4OyVOBbuOENsfNa7rYlI4TNo0CAl1UQeoFdffdVivTURERERkdzSiLX8lBB7X/Xno+MyJNXSnb0Sx/noOIKdsh/xJiKSnxYuXHjXNps2bcr3OERERERERKxBI9byk+Nd1vG4S/31hOT7qhcRERERERERkfyjEWv5ybN82kYF0acz1nkFpNVnw80x+8dzt3oRERERKZyio6O5ePEiMTExeHp6UrJkSby8vKwdloiIiNxBI9byk6NH2u6fXgGW5V4BUK/vXTcuKOfljL+3c6Z1/t7OlPPKvE5ERERECq9z587RuXNngoKCeOKJJwgMDKRz586cO3fO2qGJiIjIHTTkKb8Vr5i2++e1yLQ11Rzd00aq3SWpBuDuZE/PBhX49fdISvMX9sk3SLJzJQofQiqVx93J/gFcgIiIiIg8KNHR0fTp04ewsDBzmYuLC4899hgnT57k/PnzFC9eXCPYRERECggl1h4ERw8oVeOeupY3XaL05QUkXPyd5FQDOxsTIaUqY1+5H1Axb+MUEREREau6ePFihqTakiVL+Oijj5g4caK5PDQ0lPnz52s3aRERESvTVNACLD72Kje3fsqNqJMAuBazw83RHvtrZ2DXXEiIsW6AIiIiIpKnYmIs3+8GDx7MRx99xPr16y3Kw8LC6NOnD9HR0Q8yPBEREbmDEmsFVOSVm0T8Hs7J44c5eyWOU3/dJOLyTeIT/38n0OjTadNLRUTkvvTs2ZP27dubPzdt2pTBgwdbLZ6cmjt3Ln5+ftjY2DBz5kxrhyMiecTDw3K5kCeeeCJDUi1dWFgYFy9efBBhiYiISBY0FbQAio1PYsH2Mzznbfkby7jEFM5Fx1OxhAv2tjZpa7aJiEieWrZsGfb2BXsNy9jYWAYOHMj06dN5/vnnM/xDXEQKL19fX0JDQ83TQRMSErJtf+cINxEREXmwNGKtADofHUfk1TiS7Fwz1MUlppCQlJL2wdH9AUcmIpK3EhMTrR1CBsWLF8fNzc3aYWQrMjKSpKQknn76aUqXLo2zs3aJFikqvLy8mD9/PqGhoQA4Ojpm216JdREREetSYq0Aup6QNt3zyE0PHEtWylCfnGqAV0Da7qIiUuh9//331KhRAycnJ7y9vWnRogU3b94EMp+W2L59e3r27Gn+XKFCBd577z26d++Oq6sr/v7+/PDDD/z111+0a9cOV1dXatSowd69e7ONIzIy0tze3d2djh07mqcYnThxApPJxPHjxy36TJ8+nQoVKmAYBgDHjh2jTZs2uLq64uvry0svvcTly5fN7Zs2bcrAgQMZOnQoJUqUoGXLlpnGkj49c9KkSfj6+uLp6cn48eNJTk5mxIgRFC9enHLlyvHvf//bot8ff/xBp06d8PLywtvbm3bt2nHmzBlzfUpKCkOHDsXT0xNvb2/efPNNc+y3x3j7Pf/qq6+oW7cubm5ulCpViq5du3Lp0iVz/aZNmzCZTKxfv566devi7OxMgwYNOHHihLnNoUOHaNasGW5ubri7u1OnTp1sn0d2z2LhwoXUqJG2IU5AQAAmk8niGkWk8PPz82Pp0qWEh4cTEBBgTrLdKTQ0FF9f3wccnYiIiNxOibUCyM0xbYbujj+SuFKtZ4bkmr1PJajXN223UREp1KKioujSpQu9evUiPDycTZs20aFDhwzJnruZMWMGDRs25MCBAzz99NO89NJLdO/enRdffJH9+/dTqVIlunfvnuVxDcOgffv2XL16lc2bN7N27VpOnTpFp06dAKhatSp16tTh66+/tui3ePFiunbtislkIioqiiZNmlCrVi327t3LmjVruHjxIh07drTos2jRIuzs7Ni+fTtz5szJ8po2bNjAn3/+yZYtW5g+fTrjxo3jmWeewcvLi127dtGvXz/69evHuXPnAIiLi6NZs2a4urqyZcsWtm3bhqurK0899ZR5ZNy0adP497//zRdffMG2bdu4evUqy5cvz/beJiYmMmHCBA4dOsSKFSuIiIiwSGymGzVqFNOmTWPv3r3Y2dnRq1cvc123bt0oV64ce/bsYd++fbz99ttZTje927Po1KkT69atA2D37t1ERUVpV0CRIsjLy4vAwECCg4MtRrClS98V1MvLy0oRioiICACGGDExMQZgxMTEWDsUwzAMIyYu0Ri/8ojRa8Fu49X/7DEWrj9o7N252Ti0bbVxdP82Iy7mirVDFCmQ4uPjjWPHjhnx8fHWDiXH9u3bZwDGmTNnMq1v0qSJMWjQIIuydu3aGT169DB/9vf3N1588UXz56ioKAMwRo8ebS775ZdfDMCIiorK9DxhYWGGra2tERkZaS47evSoARi7d+82DMMwpk+fbgQEBJjrT5w4YQDG0aNHDcMwjNGjRxuhoaEWxz137pwBGCdOnDBfT61atbK6HWY9evQw/P39jZSUFHNZ1apVjUaNGpk/JycnGy4uLsaSJUsMwzCML774wqhataqRmppqbnPr1i3DycnJ+Pnnnw3DMIzSpUsbU6ZMMdcnJSUZ5cqVM9q1a2cuy+ye32737t0GYFy/ft0wDMPYuHGjARjr1q0zt1m9erUBmP8uurm5GQsXLrzrdRtGzp7FgQMHDMCIiIjI0THl3mX3c6WgvT9I5orKc7p69aoRHh5u7Ny50wgPDzeuXr1q7ZBERESKrNy8P2jEWgHk7mRPzwYV8Pd2JjnFYEtkIp+FO7Hiqj+u5Wvh5F7c2iGKSB6pWbMmzZs3p0aNGrzwwgvMmzeP6OjoXB8nJCTE/H36tKD06YK3l90+hfF24eHh+Pn5WYx8Cg4OxtPTk/DwcAA6d+7M2bNn2blzJwBff/01tWrVIjg4GIB9+/axceNGXF1dzV+BgYEAnDp1ynzcunXr5uiaqlWrho3N3/+b8vX1tbgmW1tbvL29zde0b98+fv/9d9zc3MznL168OAkJCZw6dYqYmBiioqKoX7+++Rh2dnZ3jefAgQO0a9cOf39/3NzcaNq0KZA2XfN2tz+D0qVLA3/f76FDh9KnTx9atGjBlClTLO7HnXLyLETk4ZM+gq1evXoEBgZqpJqIiEgBoV1BC6jy3i4MblGF89FxXE9Ixs3RjnJezrg7Feyd6kQkd2xtbVm7di07duwgLCyMWbNmMWrUKHbt2kXFihWxsbHJMH0zKSkpw3Fun1ZoMpmyLEtNTc00DsMwzG2yKi9dujTNmjVj8eLFPPHEEyxZsoS+ffua26amptK2bVvef//9DMdJTzQBuLi4ZBpDdteUfg2ZlaVfU2pqaqbTVQF8fHxydM473bx5k9DQUEJDQ/nqq6/w8fEhMjKSVq1aZdh4Ibv7PW7cOLp27crq1av56aefGDt2LEuXLuW5557LcM6cPAsRERERESkYNGKtAHM3xRFsE0k9m+ME20Tiboqzdkgikg9MJhMNGzZk/PjxHDhwAAcHB/O6Xz4+PkRFRZnbpqSkcOTIkTyPITg4mMjISPN6ZZC2EUFMTAxBQUHmsm7duvHNN9/wyy+/cOrUKTp37myue/TRRzl69CgVKlSgUqVKFl85Tabdj0cffZSTJ09SsmTJDOf38PDAw8OD0qVLm0fcASQnJ7Nv374sj3n8+HEuX77MlClTaNSoEYGBgVmO+rubKlWqMGTIEMLCwujQoQMLFizItF1On4WIiMjdREdHc/z4cXbt2sWJEyfuaVS8iIhkz6qJtS1bttC2bVvKlCmDyWRixYoVFvWGYTBu3DjKlCmDk5MTTZs25ejRoxZtbt26xeuvv06JEiVwcXHh2Wef5fz58w/wKvLJ1QjYOBk2vAc7Pk77c+OUtHIRKTJ27drFpEmT2Lt3L5GRkSxbtoy//vrLnED5xz/+werVq1m9ejXHjx+nf//+XLt2Lc/jaNGiBSEhIXTr1o39+/eze/duunfvTpMmTSymSnbo0IHY2Fhee+01mjVrRtmyZc11AwYM4OrVq3Tp0oXdu3dz+vRpwsLC6NWrFykpKXke8526detGiRIlaNeuHVu3biUiIoLNmzczaNAg8/8XBg0axJQpU1i+fHmO7mf58uVxcHBg1qxZnD59mpUrVzJhwoRcxRUfH8/AgQPZtGkTZ8+eZfv27ezZsyfLJFlOn4WIiEh2zp07R+fOnQkKCuKJJ54gMDCQzp07W/ziRkRE7p9VE2s3b96kZs2afPLJJ5nWf/DBB0yfPp1PPvmEPXv2UKpUKVq2bMn169fNbQYPHszy5ctZunQp27Zt48aNGzzzzDMP5B9x+SYhBnbNgeg7kmjRp2HX3LR6ESkS3N3d2bJlC23atKFKlSq88847TJs2jdatWwPQq1cvevToYU6sVKxYkWbNmuV5HOm/3PDy8qJx48a0aNGCgIAAvvnmmwzxtm3blkOHDtGtWzeLujJlyrB9+3ZSUlJo1aoV1atXZ9CgQXh4eFislZZfnJ2d2bJlC+XLl6dDhw4EBQXRq1cv4uPjcXd3B2DYsGF0796dnj17Ur9+fdzc3DKdjpnOx8eHhQsX8t133xEcHMyUKVOYOnVqruKytbXlypUrdO/enSpVqtCxY0dat27N+PHjM22f02chIiKSlejoaPr06UNYWJhFeVhYGH369NHINRGRPGQy7ly8x0pMJhPLly+nffv2QNpotTJlyjB48GDeeustIG10mq+vL++//z59+/YlJiYGHx8fvvzySzp16gTAn3/+iZ+fH//73/9o1apVjs4dGxuLh4cHMTEx5n98WdWFw2kj1LLyj3egVI2s60UeUgkJCURERFCxYkUcHR2tHY6IFAHZ/VwpcO8Pkik9J3kYHT9+PNvlA8LDw80bDImISEa5eX8osGusRUREcOHCBUJDQ81lxYoVo0mTJuzYsQNI2wEuKSnJok2ZMmWoXr26uU1mbt26RWxsrMVXgZJwl3juVi8iIiIiIg+tmJjsZ7jcrV5ERHKuwCbWLly4AICvr69Fua+vr7nuwoULODg4ZNhu/PY2mZk8ebJ5IWsPDw/8/PzyOPr75HiX36berV5ERERERB5aHh4e91UvIiI5V2ATa+lMJpPFZ8MwMpTd6W5tRo4cSUxMjPmrwC3g6VkevAIyr/MKSKsXERERERHJhK+vr8WsntuFhoZmGLwgIiL3rsAm1kqVKgWQYeTZpUuXzP8jKFWqFImJiRkW37y9TWaKFSuGu7u7xVeB4ugB9V7NmFzzCoB6fdPqRUREREREMuHl5cX8+fMzJNdCQ0OZP39+hhk/IiJy7wpsYq1ixYqUKlWKtWvXmssSExPZvHkzDRo0AKBOnTrY29tbtImKiuLIkSPmNoVW8YrQ7O20jQoavJH2Z7O3oXgFa0cmInJfKlSowMyZM/PseE2bNmXw4MF5drwHYeHChXh6elo7DBERKcL8/PxYunQp4eHh7Ny5k/DwcJYuXVrwlsERESnkrJpYu3HjBgcPHuTgwYNA2oYFBw8eJDIyEpPJxODBg5k0aRLLly/nyJEj9OzZE2dnZ7p27QqkrQ3Qu3dvhg0bxvr16zlw4AAvvvgiNWrUoEWLFla8sjzi6JG2+2eFhml/3jFSLTY+iWN/xrDr9BWO/RlDbHySlQIVkfuxZcsW2rZtS5kyZTCZTKxYsSJDm4sXL9KzZ0/KlCmDs7MzTz31FCdPnsz2uOPGjcNkMmEymbCxsaFMmTJ069Yt0+nvv//+O7169aJ8+fIUK1aMsmXL0rx5c77++muSk5PN7bKKz5qWLVvGhAkTrB1GrnTq1InffvvN2mGIiEgR5+XlRWBgIPXq1SMwMFAj1URE8oGdNU++d+9emjVrZv48dOhQAHr06MHChQt58803iY+Pp3///kRHR1OvXj3CwsJwc3Mz95kxYwZ2dnZ07NiR+Ph4mjdvzsKFC7G1tX3g1/MgRV65yYLtZ4i8Gmcu8/d2pmeDCpT3drFiZCKSWzdv3qRmzZq8/PLLPP/88xnqDcOgffv22Nvb88MPP+Du7s706dNp0aIFx44dw8Ul6//mq1Wrxrp160hNTeXUqVMMGDCAjh078ssvv5jb7N69mxYtWlCtWjU+/fRTAgMDuXHjBseOHePzzz+nevXq1KxZM1+uPS8UL178vvqnpKSYk4/ZMQyDlJQU7Ozu/3+dTk5OODk53fdxRERERETEuqw6Yq1p06YYhpHha+HChUDayIhx48YRFRVFQkICmzdvpnr16hbHcHR0ZNasWVy5coW4uDh+/PHHIj+8OTY+KUNSDeDslTgW7jijkWsihUzr1q1577336NChQ6b1J0+eZOfOncyePZvHHnuMqlWr8tlnn3Hjxg2WLFmS7bHt7OwoVaoUZcqUoVGjRrzyyivs3LmT2NhYIC1Z1LNnT6pUqcL27dtp27YtlStXpnbt2nTr1o2tW7cSEhKS42u5fv063bp1w8XFhdKlSzNjxoy7TtWcPn06NWrUwMXFBT8/P/r378+NGzcs2mzfvp0mTZrg7OyMl5cXrVq1Mq+veefxExMTefPNNylbtiwuLi7Uq1ePTZs2mevTp2GuWrWK4OBgihUrxtmzZzPEtWnTJkwmEz///DN169alWLFibN26FcMw+OCDDwgICMDJyYmaNWvy/fffW/RduXIllStXxsnJiWbNmrFo0SJMJhPXrl2ziOF2s2fP5pFHHsHBwYGqVavy5ZdfWtSbTCbmz5/Pc889h7OzM5UrV2blypXm+ujoaLp164aPjw9OTk5UrlyZBQsWZHnfRURERETk/hXYNdYka+ej4zIk1dKdvRLH+ejM60SkcLp16xaQ9ouEdLa2tjg4OLBt27YcH+fChQssW7YMW1tb86jegwcPEh4ezvDhw7McsXW3nZhvN3ToULZv387KlStZu3YtW7duZf/+/dn2sbGx4eOPP+bIkSMsWrSIDRs28Oabb5rrDx48SPPmzalWrRq//PIL27Zto23btqSkpGR6vJdffpnt27ezdOlSfv31V1544YUMU2fj4uKYPHky8+fP5+jRo5QsWTLL+N58800mT55MeHg4ISEhvPPOOyxYsIDZs2dz9OhRhgwZwosvvsjmzZsBOHPmDP/85z9p3749Bw8epG/fvowaNSrbe7B8+XIGDRrEsGHDOHLkCH379uXll19m48aNFu3Gjx9Px44d+fXXX2nTpg3dunXj6tWrAIwePZpjx47x008/ER4ezuzZsylRokS25xURERERkftj1amgcm+uJyTfV72I3EVCDFyLhIRYcHQHz/JW3Y03MDAQf39/Ro4cyZw5c3BxcWH69OlcuHCBqKiobPsePnwYV1dXUlNTiY+PB+CNN94wTx9NX+eratWq5j6XLl0iIODvXYk/+OAD+vfvf9c4r1+/zqJFi1i8eDHNmzcHYMGCBZQpUybbfrePNqtYsSITJkzgtdde47PPPjOfv27duubPkDbFNTOnTp1iyZIlnD9/3nze4cOHs2bNGhYsWMCkSZMASEpK4rPPPsvRFNd3332Xli1bAmnTdqdPn86GDRuoX78+AAEBAWzbto05c+bQpEkTPv/8c6pWrcqHH34IpN3bI0eOMHHixCzPMXXqVHr27Gm+z0OHDmXnzp1MnTrVYsmEnj170qVLFwAmTZrErFmz2L17N0899RSRkZHUrl2bunXrAmmbRIiIiIiISP5SYq0QcnPM/rHdrV5EsnE1AnbNgeiIv8u8AqDeq2m79VqBvb09//3vf+nduzfFixfH1taWFi1a0Lp167v2rVq1KitXruTWrVv88MMPfPfdd5kmeG4flebt7W3eVKZp06YkJibmKM7Tp0+TlJTE448/bi7z8PCwSNplZuPGjUyaNIljx44RGxtLcnIyCQkJ3Lx5ExcXFw4ePMgLL7yQoxj279+PYRhUqVLFovzWrVt4e3ubPzs4OOR4imt6ogrg2LFjJCQkmBNt6RITE6lduzYAJ06c4LHHHrOov/2eZCY8PJxXX33Voqxhw4Z89NFHFmW3x+zi4oKbmxuXLl0C4LXXXuP5559n//79hIaG0r59+8K/Q7Y89GbPns3s2bM5c+YMkJZUHzNmTI5+/omIiIg8CMrAFELlvJzx93bm7JWMUz79vZ0p5+VshahEioCEmIxJNYDo07BrLjR722oj1+rUqcPBgweJiYkhMTERHx8f6tWrZ5H0yYyDgwOVKlUC0v5BevLkSV577TXz+l2VK1cG4Pjx49SqVQtIm2aa3ic3C/UbhgFknDqaXp6Zs2fP0qZNG/r168eECRMoXrw427Zto3fv3iQlpa0XmZtF/lNTU7G1tWXfvn0ZNrFxdXU1f+/k5JTjKa63bw6RmpoKwOrVqylbtqxFu2LFigFp15ube5Ausz53ltnb22fokx5T69atOXv2LKtXr2bdunU0b96cAQMGMHXq1LueW6SgKleuHFOmTDH/TFq0aBHt2rXjwIEDWY5cFREREXmQtMZaIeTuZE/PBhXw97ZMoKXvCuruZJ9FTxHJ1rXIjEm1dNGn0+qtzMPDAx8fH06ePMnevXtp165drvqPHj2aJUuWmNc9q127NoGBgUydOtWcoLlXjzzyCPb29uzevdtcFhsba7G22Z327t1LcnIy06ZN44knnqBKlSr8+eefFm1CQkJYv359jmKoXbs2KSkpXLp0iUqVKll8lSpV6t4u7Dbpmx1ERkZmOH76xjmBgYHs2bMnw3VmJygoKMN6eTt27CAoKChX8fn4+NCzZ0+++uorZs6cydy5c3PVX6Sgadu2LW3atKFKlSpUqVKFiRMn4urqys6dO60dmoiIiAigEWuFVnlvFwa3qML56DiuJyTj5mhHOS9nJdVE7kdC7P3V36MbN27w+++/mz9HRERw8OBBihcvTvny5QH47rvv8PHxoXz58hw+fJhBgwbRvn17QkNDc3WugIAA2rVrx5gxY1i1ahUmk4kFCxbQsmVLGjZsyMiRIwkKCiIpKYktW7bw119/ZRj5lRU3Nzd69OjBiBEjKF68OCVLlmTs2LHY2NhkOTrskUceITk5mVmzZtG2bVu2b9/O559/btFm5MiR1KhRg/79+9OvXz8cHBzYuHEjL7zwQobF+atUqUK3bt3o3r0706ZNo3bt2ly+fJkNGzZQo0YN2rRpk6v7ldk1Dh8+nCFDhpCamsqTTz5JbGwsO3bswNXVlR49etC3b1+mT5/OW2+9Re/evTl48KDFbteZGTFiBB07duTRRx+lefPm/Pjjjyxbtox169blOLYxY8ZQp04dqlWrxq1bt1i1alWuE3MiBVlKSgrfffcdN2/eNK9xKCIiImJtGrFWiLk72RNcxoN6Ad4El/FQUk3kfjm631/9Pdq7dy+1a9c2r9E1dOhQateuzZgxY8xtoqKieOmllwgMDOSNN97gpZdeYsmSJfd0vmHDhrF69Wp27doFwBNPPMG+ffuoWrUqAwYMIDg4mAYNGrBkyRJmzJjBa6+9luNjT58+nfr16/PMM8/QokULGjZsSFBQkMWOprerVasW06dP5/3336d69ep8/fXXTJ482aJNlSpVCAsL49ChQzz++OPUr1+fH374IctpqgsWLKB79+4MGzaMqlWr8uyzz7Jr1y7ziLL7NWHCBMaMGcPkyZMJCgqiVatW/Pjjj1SsmLYGX8WKFfn+++9ZtmwZISEhzJ4927wraPp00Tu1b9+ejz76iA8//JBq1aoxZ84cFixYQNOmTXMcl4ODAyNHjiQkJITGjRtja2vL0qVL7/t6RawtfROWYsWK0a9fP5YvX05wcHCW7W/dukVsbKzFl4iIiEh+MRk5WfiliIuNjcXDw4OYmBjc3fPnH87WFhufpNFtUuQlJCQQERFBxYoVs0zkZH+AGNg4JW3a5528Aqy6xlphdfPmTcqWLcu0adPo3bu3tcOxmokTJ/L5559z7tw5a4ciuZTdz5WH4f2hIEhMTCQyMpJr167x3//+l/nz57N58+Ysk2vjxo1j/PjxGcr1nERERCSncvOep6mgRUhWybPIKzdZsP0MkVf/3uwgfT228t4u2RxR5CHj6JG2++euuZbJNa8AqNdXSbUcOHDgAMePH+fxxx8nJiaGd999FyDXa8EVdp999hmPPfYY3t7ebN++nQ8//JCBAwdaOyyRQun2TVjq1q3Lnj17+Oijj5gzZ06m7UeOHMnQoUPNn2NjY/NsxKq1REdHc/HiRWJiYvD09KRkyZJ4eXlZOywRERFBibUiI6vkWdd65Vm6+5xFOcDZK3Es3HGGwS2qaOSayO2KV0wbmXYtMm1NNUd38CyvpFouTJ06lRMnTuDg4ECdOnXYunVrhrXQirqTJ0/y3nvvcfXqVcqXL8+wYcMYOXKktcMSKRIMw+DWrVtZ1hcrVizLadeF0blz5+jTpw9hYWHmstDQUObPn1/oE4YiIiJFgRJrRUBsfFKGpBqkJc/mbT1NOS/nTPudvRLH+eg4gp2UMBCx4OgBpWpYO4pCqXbt2uzbt8/aYVjdjBkzmDFjhrXDECn0/vWvf9G6dWv8/Py4fv06S5cuZdOmTaxZs8baoT0Q0dHRGZJqAGFhYfTp04elS5dq5JqIiIiVKbFWBJyPjsuQVEt38uINqpfJOnF2PSE5v8ISERERuS8XL17kpZdeIioqCg8PD0JCQlizZg0tW7a0dmgPxMWLFzMk1dKFhYVx8eJFJdZERESsTLuCFmYJMXDhMGWuHaB/UDyNyztgZ2uyaGJnYyIxOTXLQ7g5KrcqUhBcuXKFkiVLcubMGWuHIvfpzJkzmEwmDh48mGWbVatWUbt2bVJTs/75LCLwxRdfcObMGW7dusWlS5dYt27dQ5NUg7QNF+6nXkRERPKfEmuF1dUI2DgZNryH7c6Psd00kXpn59K9qmGRXHN0sMXT2SHTQ/h7O2c5TVREHqzJkyfTtm1bKlSoYO1Q7lnPnj1p3769tcMoFJ555hlMJhOLFy+2digiUoB5eGQ+68DFxYVRo0bh5OTErl27OHHiBNHR0Q84OhEREQEl1gqnhBjYNQeiIwBwtLfF2cGWhEu/4310IQ3K/r0ZQSUfV+r6e/JckCv9g+IZVPkv+gfF81yQKz0bVNDGBSIFQHx8PF988QV9+vTJ1/MkJiZmKEtJSXloRk0VtGt9+eWXmTVrlrXDEJECzNfXl9DQUIsyFxcXlixZwq5du6hZsyZPPPEEgYGBdO7cmXPnzlkpUhERkYeXEmuF0bVIc1INwN7WBj8vJ3NyrbpL2rQAf29nejaogL/NX7S6vIAqR2fiFz6PKkdn0urKQsqbLlnrCkTkNj/99BN2dnbUr1/fovzo0aM8/fTTuLu74+bmRqNGjTh16hQATZs2ZfDgwRbt27dvT8+ePc2fK1SowHvvvUfPnj3x8PDglVdeYeHChXh6erJq1SqCg4MpVqwYZ8+eJTExkTfffJOyZcvi4uJCvXr12LRpk/lY6f1+/vlngoKCcHV15amnniIqKgqAcePGsWjRIn744QdMJhMmk8mi/+3WrFnDk08+iaenJ97e3jzzzDPm64K/p1IuXbqUBg0a4OjoSLVq1SyOt2nTJkwmE6tXr6ZmzZo4OjpSr149Dh8+nCHmO681Ojqa7t274+XlhbOzM61bt+bkyZPmfleuXKFLly6UK1cOZ2dnatSowZIlSyyuITU1lffff59KlSpRrFgxypcvz8SJEy3anD59mmbNmuHs7EzNmjX55ZdfLOqfffZZdu/ezenTpzO9TyIiXl5ezJ8/3yK5NnjwYD7++GPWrVtn0TZ9QwONXBMREXmwlFgrjBJiLT/b2OMU8ASPNOxAtSfbUsMrhQlP+TG4RRXKuyTDrjnYx5zFzdEeL2cH3Bztsb92BnbNTRv9JiJWtWXLFurWrWtR9scff9C4cWMcHR3ZsGED+/bto1evXiQn527DkQ8//JDq1auzb98+Ro8eDUBcXByTJ09m/vz5HD16lJIlS/Lyyy+zfft2li5dyq+//soLL7zAU089ZZFwiouLY+rUqXz55Zds2bKFyMhIhg8fDsDw4cPp2LGjOdkWFRVFgwYNMo3p5s2bDB06lD179rB+/XpsbGx47rnnMowmGzFiBMOGDePAgQM0aNCAZ599litXrmRoM3XqVPbs2UPJkiV59tlnSUpKsoj5zmvt2bMne/fuZeXKlfzyyy8YhkGbNm3M/RISEqhTpw6rVq3iyJEjvPrqq7z00kvs2rXLfNyRI0fy/vvvM3r0aI4dO8bixYvx9fW1iG3UqFEMHz6cgwcPUqVKFbp06WLx/Pz9/SlZsiRbt27N8fMUkYePn58fS5cuJTw8nJ07d9KxY8cMSbV06RsaiIiIyIOjlesLI0f3v7+3sYfKLeDIMmwvHcMWoERlypSqBfVehcSbFqPbLESfThv9VqrGAwhaRLJy5swZypQpY1H26aef4uHhwdKlS7G3T5uyXaVKlVwf+x//+Ic5+QWwbds2kpKS+Oyzz6hZsyYAp06dYsmSJZw/f94cx/Dhw1mzZg0LFixg0qRJACQlJfH555/zyCOPADBw4EDeffddAFxdXXFycuLWrVuUKlUq25ief/55i89ffPEFJUuW5NixY1SvXt1cPnDgQHPb2bNns2bNGr744gvefPNNc5uxY8eaFzJftGgR5cqVY/ny5XTs2NEc8+3XevLkSVauXMn27dvNib+vv/4aPz8/VqxYwQsvvEDZsmUt7tnrr7/OmjVr+O6776hXrx7Xr1/no48+4pNPPqFHjx4APPLIIzz55JMW1zV8+HCefvppAMaPH0+1atX4/fffCQwMNLcpW7asNqwQkbvy8vIy7/55e5I/M9rQQERE5MHSiLXCyLM8eAWkfV+mJhxZRurFoySnGCTZOpFAMZIu/542Ii35VvbHunP0m4g8cPHx8Tg6OlqUHTx4kEaNGpmTavfqzpFwAA4ODoSEhJg/79+/H8MwqFKlCq6uruavzZs3W0zRdHZ2NifVAEqXLs2lS7mfUn7q1Cm6du1KQEAA7u7uVKxYEYDIyEiLdrdPjbWzs6Nu3bqEh4dn2aZ48eJUrVrVos2d1xoeHo6dnR316tUzl3l7e1v0S0lJYeLEiYSEhODt7Y2rqythYWHm+MLDw7l16xbNmzfP9jpvP2/p0qUBMtwvJycn4uLisj2OiMjtstrQIKf1IiIikrc0Yq0wcvRIG422ay44eZFy4SjxSSlg70yiYxnO/xWPo30ifim/4RQUf5djuWdfLyL5rkSJEhnWxHFycsq2j42NDYZhWJTdPgUynYuLS4YyJycnTKa/dw9OTU3F1taWffv2YWtra9HW1dXV/P2dST6TyZQhhpxo27Ytfn5+zJs3jzJlypCamkr16tUz3VzhTrfHnZM2d15rVvEahmFuN23aNGbMmMHMmTOpUaMGLi4uDB482Bzf3Z5NutvvV/qx75zuevXqVXx8fHJ0PBER+HtDg7CwsAx1oaGhGaali4iISP7SiLXCqnhFaPY2qe7liHcrT7JXJaKd/Dl/A1INiEtM4Vx0PCnJyX+PbruTV0Da6DcRsaratWtz7Ngxi7KQkBC2bt2aabIMwMfHx7xxAKSNsjpy5Mg9nz8lJYVLly5RqVIli6+7Teu8nYODAykpKdm2uXLlCuHh4bzzzjs0b96coKCgLBfa3rlzp/n75ORk9u3bZzGN8s420dHR/Pbbbxna3C44OJjk5GSLqVRXrlzht99+IygoCICtW7fSrl07XnzxRWrWrElAQIDFWnOVK1fGycmJ9evXZ3utd5OQkMCpU6eoXbv2fR1HRB4umW1oAGlJtfnz55unjIqIiMiDocRaYebowQ2Hkvx+3YEzN2y5Ep9K6m2DMeISU7hp6542uu3O5JpXANTrmzb6TUSsqlWrVhw9etQiwTRw4EBiY2Pp3Lkze/fu5eTJk3z55ZecOHECSFs7bfXq1axevZrjx4/Tv39/rl27dk/nr1KlCt26daN79+4sW7aMiIgI9uzZw/vvv8///ve/HB+nQoUK/Prrr5w4cYLLly9nmhT08vLC29ubuXPn8vvvv7NhwwaGDh2a6fE+/fRTli9fzvHjxxkwYADR0dH06tXLos27777L+vXrOXLkCD179qREiRK0b98+yxgrV65Mu3bteOWVV9i2bRuHDh3ixRdfpGzZsrRr1w6ASpUqsXbtWnbs2EF4eDh9+/blwoUL5mM4Ojry1ltv8eabb/Kf//yHU6dOsXPnTr744osc3ytISwoWK1Ysw26wIiJ3c+eGBuHh4SxduhQ/Pz9rhyYiIvLQ0VTQQu6ynS+OJSuRcOn3DHWOJStx2c4X9+JlodnbaRsVJMSmTf/0LK+kmkgBUaNGDerWrcu3335L3759gbR1vzZs2MCIESNo0qQJtra21KpVi4YNGwLQq1cvDh06RPfu3bGzs2PIkCE0a9bsnmNYsGAB7733HsOGDeOPP/7A29ub+vXr06ZNmxwf45VXXmHTpk3UrVuXGzdusHHjRpo2bWrRxsbGhqVLl/LGG29QvXp1qlatyscff5yhHcCUKVN4//33OXDgAI888gg//PADJUqUyNBm0KBBnDx5kpo1a7Jy5UocHBzueq2DBg3imWeeITExkcaNG/O///3PPHVz9OjRRERE0KpVK5ydnXn11Vdp3769xYLgo0ePxs7OjjFjxvDnn39SunRp+vXrl+N7BbBkyRK6deuGs7NzrvqJiIDlhgYiIiJiPSbjXhbIKWJiY2Px8PAgJiYGd/fCteZY+J8xXD3/GyWOLcQ59jRutknYGCmYfKpysfqrJHs9QmAZJdDk4ZCQkEBERAQVK1bMsBlAQfe///2P4cOHc+TIEWxsHu7BxGfOnKFixYocOHCAWrVqZdpm06ZNNGvWjOjoaDw9PR9ofHnhr7/+IjAwkL1795o3b5CCKbufK4X5/eFhouckIiIiuZWb9weNWCvkyno588MhV/5Z71U8Yg5hxF0l1bYYsdF/YXfoS8q3fB1QYk2koGvTpg0nT57kjz/+0FSeh0BERASfffaZkmoiIiIiIoWcEmuFnLuTPa885s31NRM4/cdv5nJnB1v8vJxw2DsvbRqopn2KFHiDBg2ydgjygDz++OM8/vjj1g5DRERERETukxJrRYB38kXcjQt4+7iQnGpgZ2PC0d4We1sbiD6dtrZaqRrWDlNEJEcqVKjA3VYpaNq06V3biIiIiIiI5Dcl1oqChFjsbW3SEmlZ1MfGJ3E+Oo7rCcm4OdpRzssZdyf7BxuniIiIiIiIiEgRosRaUeCY/UJ6N21c2Pzr75ThL5ySbxBv58qWcz6EVCpPeW+XBxSkiIiIiIiIiEjRosRaUeBZHrwC0qZ93iGpeCWuxkPlY7NIuHSKVMAWqFSyElcd++DpXF0j16TI0RRBEckr+nkiIiIiItnJYu6gFCqOHlDv1bTk2u28Akis2YO4X+aRcOmURVXCpd9x3D+fGzFXHmCgIvnL3j4tSRwXF2flSESkqEhMTATA1tbWypGIiIiISEGkEWtFRfGKabt/XouEhNi06aGe5bn1x+/EX/w90y4Jl36n5M0/gFIPNlaRfGJra4unpyeXLl0CwNnZGZPJZOWoRKSwSk1N5a+//sLZ2Rk7O70yiYiIiEhGekssShw9Muz+aZ98w/y9YRikGgapBtiYwMZksqgXKQpKlUpLFKcn10RE7oeNjQ3ly5dXkl5EREREMqXEWhHn6OqJs4MtN24lE5+YQkrq32vFuDvZY3OXjQ9EChuTyUTp0qUpWbIkSUlJ1g5HRAo5BwcHbGy0coaIiIiIZE6JtSLO3rsC5R6pxm/hv1ok1VyK2eFbIYiwPx1o6JVASXdHK0YpkvdsbW21JpKIiIiIiIjkKyXWijpHDxLr9KHEzc8ocfU0KakGBpDiUZH9pTrx4cYLpDp48FgFL8p7u1g7WhERERERERGRQkOJtYfAb4klCPftRXDFa6TExRBncuGXK86s3h5HYkoq1+ISWbjjDINbVMHdyd7a4YqIiIiIiIiIFApKrD0EXIvZsSUykdUJ9pz+ywlIBf7etMDBzoazV+I4Hx1HsJOH1eIUERERERERESlMtBrvQ6CclzP+3s4k37bGWroqpVy5Fp+2wPv1hOQHHZqIiIiIiIiISKGlEWsPAXcne3o2qMCe42fwSkygWOpNbtm6cCqpOO4e3mw4cQkAN0f9dRARERERERERySllUh4S5U2XKBW9kCvnTlDMlIqjKZH6XhWJ83+ZRD9nIm/aUc7L2dphioiIiIiIiIgUGkqsPQwSYmDXHBxiI/Eplkri5QiSEm7AxRM4RJ/jqartcHr0H7hq4wIRERERERERkRzTGmsPg2uREB0BKUnYxUbiSAIuDnY4OdjiFH2C4s62uB78d1oCTkREREREREREckSJtYdBQmzan8nxkBSHjcmEna0JB1sb7GxN2KbcgujTaQk4EREReShcu3bN2iGIiIiIFHpKrBURsfFJHPszhl2nr3Dszxhi/3+nTwAc3dP+TMli10+7Yml/pifgREREpEh5//33+eabb8yfO3bsiLe3N2XLluXQoUNWjExERESkcNMaa0VA5JWbLNh+hsirceYyf29nejaoQHlvF/AsD14BcCuTxFnJYIiPTvs+PQEnIiIiRcqcOXP46quvAFi7di1r167lp59+4ttvv2XEiBGEhYVZOUIRERGRwkmJtUIuNj4pQ1IN4OyVOBbuOMPgFlVwd/KAeq/Czs8g5g9I+v+2JYOhegc4uS4t8eZZ3gpXICIiIvktKioKPz8/AFatWkXHjh0JDQ2lQoUK1KtXL9u+hmGwefNmtm7dypkzZ4iLi8PHx4fatWvTokUL83FFREREHkaaClrInY+Oy5BUS3f2Shzno/+/rnhF+Mc70GEuNB0FTd+Gio3SkmoeflCvLzh6PMDIRURE5EHx8vLi3LlzAKxZs4YWLVoAaUmzlJSUTPvEx8czadIk/Pz8aN26NatXr+batWvY2try+++/M3bsWCpWrEibNm3YuXPnA7sWERERkYJEI9YKuesJGddNs7M1Ub2sB55O9pyLjgegnJdz2si18k9AyaC0jQoSYqHKU2kj1ZRUExERKbI6dOhA165dqVy5MleuXKF169YAHDx4kEqVKmXap0qVKtSrV4/PP/+cVq1aYW9vn6HN2bNnWbx4MZ06deKdd97hlVdeydfrEBERESlolFgr5NwcLR+hna2JplVK8uOvf/DbhRsE+Ljg5mhvueaaoweUqmGliEVERORBmzFjBhUqVODcuXN88MEHuLq6AmlTRPv3759pn59++onq1atne1x/f39GjhzJsGHDOHv2bJ7HLSIiIlLQmQzDMKwdhLXFxsbi4eFBTEwM7u6FawH/2PgkZq77jbNX0qZ81irvyS+nLvPbhRs4OdhS0ccFe5u0Gb/+3s7/v+Zaxt84i4iISO4U5veHh4mek4iIiORWbt4fNGKtkHN3sqdngwos3HGGs1fi8HSyNyfV/Io7m5Nq8Peaa8FOmvYpIiLysPntt9/YtGkTly5dIjU11aJuzJgx2fZds2YNrq6uPPnkkwB8+umnzJs3j+DgYD799FO8vLzyLW4RERGRgkyJtSKgvLcLg1tU4Xx0HOei4wnwccHRwdYiqZYuszXZREREpGibN28er732GiVKlKBUqVKYTCZznclkumtibcSIEbz//vsAHD58mGHDhjF06FA2bNjA0KFDWbBgQb7GLyIiIlJQKbFWRLg72ZtHork5Zj3V88412URERKToe++995g4cSJvvfXWPfWPiIggODgYgP/+978888wzTJo0if3799OmTZu8DFVERESkUMk4pEkKtXJezvh7O2da5+/tTDmvzOtERESk6IqOjuaFF1645/4ODg7ExaWt57pu3TpCQ0MBKF68OLGxsXkSo4iIiEhhpMRaEZO+5tqdybX0XUG1cYGIiMjD54UXXiAsLOye+z/55JMMHTqUCRMmsHv3bp5++mkgbd22cuXK5VWYIiIiIoWO5gUWQbevuXY9IRk3RzvKeTkrqSYiIvKQqlSpEqNHj2bnzp3UqFEDe3vLd4I33ngj2/6ffPIJ/fv35/vvv2f27NmULVsWgJ9++omnnnoq3+IWERERKehMhmEY1g7C2rQNu4iIiORWYXp/qFixYpZ1JpOJ06dPP8BoHqzC9JxERESkYMjN+4NGrBUVCTFwLRISYsHRHTzLg6OHtaMSERGRAiAiIuK+j5Gamsrvv//OpUuXSE1Ntahr3LjxfR9fREREpDBSYq0ouBoBu+ZA9G0vzV4BUO9VKJ71b6hFRETk4ZM+WcFkMuW4z86dO+natStnz57lzskOJpOJlJSUPI1RREREpLDI1eYFMTExLFy4kN69e9O8eXPq16/Ps88+y9ixY9mxY0d+xSjZSYjJmFQDiD4Nu+am1YuIiMhD7z//+Q81atTAyckJJycnQkJC+PLLL3PUt1+/ftStW5cjR45w9epVoqOjzV9Xr17N58hFRERECq4cjViLiopizJgxfP3115QqVYrHH3+cWrVq4eTkxNWrV9m4cSNTp07F39+fsWPH0qlTp/yOW9Jdi8yYVEsXfTqtvlSNBxuTiIiIFCjTp09n9OjRDBw4kIYNG2IYBtu3b6dfv35cvnyZIUOGZNv/5MmTfP/991SqVOkBRSwiIiJSOOQosVazZk26d+/O7t27qV69eqZt4uPjWbFiBdOnT+fcuXMMHz48TwOVLCTEZiyzsYcyNcHJC6LPppWlr7mmtdhEREQeOrNmzWL27Nl0797dXNauXTuqVavGuHHj7ppYq1evHr///rsSayIiIiJ3yFFi7ejRo/j4+GTbxsnJiS5dutClSxf++uuvPAlOcsDxjt0pbOyhcgs4sgwuHYMSlaGYO3hXgZodSdr3JQkXfyc51cDOxoRjqcrYP9FPa7GJiIgUYVFRUTRo0CBDeYMGDYiKirpr/9dff51hw4Zx4cIFatSogb29vUV9SEhInsUqIoVHdHQ0Fy9eJCYmBk9PT0qWLImXl5e1wxIReaBylFi7W1LtftvLffAsn7ZRQfTptM9lav6dVLN3BjuntHInd1LWvcuVmJtcuPH3AsPOMYfxS/4Mp9B3NHJNRESkiKpUqRLffvst//rXvyzKv/nmGypXrnzX/s8//zwAvXr1MpeZTCYMw9DmBSIPqXPnztGnTx/CwsLMZaGhocyfPx8/Pz8rRiYi8mDlaldQwzBYt24dO3bs4MKFC5hMJnx9fWnYsCHNmzfP1e5SkkccPdJ2/9w1Ny255uT1d1LN0x9s036jnOLoRfyZPTh6VQJszd3jElM4d+ooFa+cwb5sTStdhIiIiOSn8ePH06lTJ7Zs2ULDhg0xmUxs27aN9evX8+233961f0REFuu5ishDKTo6OkNSDSAsLIw+ffqwdOlSjVwTkYdGjhNrf/zxB8888wyHDx+mevXq+Pr6YhgGO3bsYMKECdSsWZOVK1dStmzZ/IxXMlO8IjR7+/83MjibNv3TzsmcVANITUogJdXAxkjh9sQapCXXEm5cwx4REREpip5//nl27drFjBkzWLFiBYZhEBwczO7du6ldu/Zd+/v7+z+AKEWksLh48WKGpFq6sLAwLl68qMSaiDw0cpxY69+/P8WLF+fcuXOULl3aoi4qKooXX3yRAQMGsGLFiryOUXLC0ePv3T+LuWeoNmwcAEg12WaoA0iyc8230ERERMT66tSpw1dffXXP/U+dOsXMmTMJDw/HZDIRFBTEoEGDeOSRR/IwShEpDGJiYu6rXkSkKMlxYm39+vVs3749Q1INoHTp0kydOpVGjRrlaXByD+5cc+3/pcZdxc6vLlevxwGpFnWOJSuR4KKRhiIiIkVJbGws7u7u5u+zk94uKz///DPPPvsstWrVomHDhuZZC9WqVePHH3+kZcuWeRa3yMOmMG4A4OGR/drMd6u/H4XxfolI0WaT04ZOTk5cvXo1y/ro6GicnJzyJCi5D+lrrnkFWBTbJl0nofG/iHO3LHcsWYmER/vg6uH9IKMUERGRfObl5cWlS5cA8PT0xMvLK8NXevndvP322wwZMoRdu3Yxffp0ZsyYwa5duxg8eDBvvfVWfl+KSJF17tw5OnfuTFBQEE888QSBgYF07tyZc+fOWTu0bPn6+hIaGpppXWhoKL6+vvly3sJ6v0SkaDMZhmHkpOHrr7/ODz/8wPTp02nZsqX5txAxMTGsXbuWYcOG0b59ez766KN8DTg/xMbG4uHhQUxMzF1/Y1toJMSkrbmWEAuO7uBZnsibdvz6eySl+Qv75Bsk2bkShQ8hlcpT3tvF2hGLiIgUKgX9/WHz5s00bNgQOzs7Nm/enG3bJk2aZFvv6OjI4cOHM+wg+ttvvxESEkJCQsJ9x5tfCvpzkodXdHQ0nTt3znStstDQ0AK/AcCD3hW0sN8vESlccvP+kOOpoNOmTSM5OZlu3bqRnJyMg0Paml2JiYnY2dnRu3dvPvzww/uLXPLO7Wuu/b/yjuDpXInz0WW4npCMm6Mdjb2ccXfStgUiIiJFze3Jsrslzu7Gx8eHgwcPZkisHTx4kJIlS97XsUUeVoV9AwA/Pz+WLl1qnpbp4eGBr69vvsVc2O+XiBRdOU6sOTg4MHv2bN5//3327dvHhQsXAChVqhR16tTRbwALCXcne4Kd8m/NAxERESl4fv3110zLTSYTjo6OlC9fnmLFimXZ/5VXXuHVV1/l9OnTNGjQAJPJxLZt23j//fcZNmxYfoUtUqQVhQ0A0qeVPwhF4X6JSNGU48RaOnd3d5o1a5YfsYiIiIhIPqhVqxYmkynLent7ezp16sScOXNwdHTMUD969Gjc3NyYNm0aI0eOBKBMmTKMGzeON954I9/iLui0iLrcD2tuAFAY6X6JSEGV480L0p0/f54bN25kKE9KSmLLli15EpSIiIiI5J3ly5dTuXJl5s6dy8GDBzlw4ABz586latWqLF68mC+++IINGzbwzjvvZNrfZDIxZMgQzp8/T0xMDDExMZw/f55BgwZlm7AryrSIutwva20AUFjpfolIQZXjxFpUVBSPP/44/v7+eHp60qNHD4sE29WrVzWSTURERKQAmjhxIh999BG9e/emRo0ahISE0Lt3b2bMmMG0adPo1q0bs2bNYvny5Xc9lpubG25ubg8g6oIrOjo6w6LtkLbOU58+fYiOjrZSZFKYeHl5MX/+/AzJovQNADT60ZLul4gUVDmeCvr2229ja2vLrl27uHbtGiNHjqRp06asXbvW/EMshxuMioiIiMgDdPjwYfz9/TOU+/v7c/jwYSBtumhUVJS57tFHH2X9+vV4eXlRu3btbEem7d+/P++DLsC0iLrklQe9AUBhp/slIgVRjhNr69atY/ny5dStWxeARo0a0alTJ/7xj3+wfv16gId2KoCIiIhIQRYYGMiUKVOYO3eueWf3pKQkpkyZQmBgIAB//PGHxVSqdu3amTc0aN++/QOPuSDTIuqSlx7kBgBFge6XiBQ0OU6sxcTEWPwAK1asGN9//z0vvPACzZo146uvvsqXAEVERETk/nz66ac8++yzlCtXjpCQEEwmE7/++ispKSmsWrUKgNOnT9O/f39zn7Fjx2b6vWgRdREREflbjhNrAQEB/Prrr1SuXPnvznZ2fPfdd7zwwgs888wz+RKgiIiIiNyfBg0acObMGb766it+++03DMPgn//8J127djWvl/bSSy9ZOcrCI30R9cymg2oRdRERkYdLjjcvaN26NXPnzs1Qnp5cq1WrVl7GBUBycjLvvPMOFStWxMnJiYCAAN59911SU1PNbQzDYNy4cZQpUwYnJyeaNm3K0aNH8zwWERERkcLM1dWVfv36MX36dGbMmEHfvn1zvAmBl5cXxYsXz/Dl7e1N2bJladKkCQsWLMjnKyg4tIi6iIiIpMvxiLWJEycSFxeX+UHs7Fi2bBnnz5/Ps8AA3n//fT7//HMWLVpEtWrV2Lt3Ly+//DIeHh4MGjQIgA8++IDp06ezcOFCqlSpwnvvvUfLli05ceLEQ79jlYiIiEi6L7/8kjlz5nD69Gl++eUX/P39mTFjBgEBAbRr1y7bvmPGjGHixIm0bt2axx9/HMMw2LNnD2vWrGHAgAFERETw2muvkZyczCuvvPKArsi6tIi6iIiIQC4Sa3Z2dri7u2dZb2trm+luU/fjl19+oV27djz99NMAVKhQgSVLlrB3714gbbTazJkzGTVqFB06dABg0aJF+Pr6snjxYvr27Zun8YiIiIgURrNnz2bMmDEMHjyY9957j5SUFCBt5NXMmTPvmljbtm0b7733Hv369bMonzNnDmFhYfz3v/8lJCSEjz/++KFJrIEWURcREZFcTAUFOH/+PKNGjaJZs2YEBQURHBxMs2bNGDVqFOfOncvz4J588knWr1/Pb7/9BsChQ4fYtm0bbdq0ASAiIoILFy5YDMMvVqwYTZo0YceOHVke99atW8TGxlp8iYiIiBRVs2bNYt68eYwaNQo7u79/r1q3bl0OHz581/4///wzLVq0yFDevHlzfv75ZwDatGnD6dOn8y5oERERkUIgx4m1bdu2ERQUxPLly6lZsybdu3fnxRdfpGbNmqxYsYJq1aqxffv2PA3urbfeokuXLgQGBmJvb0/t2rUZPHgwXbp0AeDChQsAGRaI9fX1NddlZvLkyXh4eJi//Pz88jRuERERkYIkIiKC2rVrZygvVqwYN2/evGv/4sWL8+OPP2Yo//HHHylevDgAN2/e1DIcIiIi8tDJ8VTQIUOG0KdPH2bMmJFl/eDBg9mzZ0+eBffNN9/w1VdfsXjxYqpVq8bBgwcZPHgwZcqUoUePHuZ2JpPJop9hGBnKbjdy5EiGDh1q/hwbG6vkmoiIiBRZFStW5ODBgxmW7fjpp58IDg6+a//Ro0fz2muvsXHjRh5//HFMJhO7d+/mf//7H59//jkAa9eupUmTJvkSv4iIiEhBlePE2pEjR/jqq6+yrO/bt6/5xSqvjBgxgrfffpvOnTsDUKNGDc6ePcvkyZPp0aMHpUqVAtJGrpUuXdrc79KlS9luc16sWDGKFSuWp7GKiIiIFFQjRoxgwIABJCQkYBgGu3fvZsmSJUyePJn58+fftf8rr7xCcHAwn3zyCcuWLcMwDAIDA9m8eTMNGjQAYNiwYfl9GSIiIiIFTo4Ta6VLl2bHjh1UrVo10/pffvnFIrmVF+Li4rCxsZytamtrS2pqKpD229dSpUqxdu1a8/SGxMRENm/ezPvvv5+nsYiIiIgUVi+//DLJycm8+eabxMXF0bVrV8qWLctHH31k/gXm3TRs2JCGDRvmc6QiIiIihUuOE2vDhw+nX79+7Nu3j5YtW+Lr64vJZOLChQusXbuW+fPnM3PmzDwNrm3btkycOJHy5ctTrVo1Dhw4wPTp0+nVqxeQNgV08ODBTJo0icqVK1O5cmUmTZqEs7MzXbt2zdNYRERERAqj5ORkvv76a9q2bcsrr7zC5cuXSU1NpWTJkrk6zqlTp1iwYAGnT59m5syZlCxZkjVr1uDn50e1atXyKXoRERGRgs1kGIaR08bffPMNM2bMYN++feZt2m1tbalTpw5Dhw6lY8eOeRrc9evXGT16NMuXL+fSpUuUKVOGLl26MGbMGBwcHIC09dTGjx/PnDlziI6Opl69enz66adUr149x+eJjY3Fw8ODmJgY3N3d8/QaREREpGgqTO8Pzs7OhIeHZ1hjLac2b95M69atadiwIVu2bCE8PJyAgAA++OADdu/ezffff5/HEeedwvScREREpGDIzftDrhJr6ZKSkrh8+TIAJUqUwN7e/t4iLSD0wiUiIiK5VZjeH5o1a8agQYNo3779PfWvX78+L7zwAkOHDsXNzY1Dhw4REBDAnj17aN++PX/88UfeBpyHCtNzEhERkYIhN+8POZ4Kejt7e/s8X09NRERERPJH//79GTZsGOfPn6dOnTq4uLhY1IeEhGTb//DhwyxevDhDuY+PD1euXMnTWEVEREQKk1wl1qKioli/fj3FixenRYsW5umYADdv3mTatGmMGTMmz4MUERERkXvXqVMnAN544w1zmclkwjAMTCaTeYmPrHh6ehIVFUXFihUtyg8cOEDZsmXzPmARERGRQiLHibU9e/YQGhpKamoqSUlJlCtXjuXLl5sXq71x4wbjx49XYk1ERESkgImIiLiv/l27duWtt97iu+++w2QykZqayvbt2xk+fDjdu3fPoyhFRERECh+bnDb817/+RYcOHYiOjubixYu0bNmSJk2acODAgfyMT0RERETuk7+/f7Zfd5O+S3vZsmW5ceMGwcHBNG7cmAYNGvDOO+88gCsQERERKZhyPGJt3759fPrpp9jY2ODm5sann36Kv78/zZs35+eff6Z8+fL5GaeIiIiIWIFhGPz555/MmzePCRMmsH//flJTU6lduzaVK1e2dngiIiIiVpWrNdYSEhIsPr/55pvY2NgQGhrKv//97zwNTERERESszzAMKleuzNGjR6lcuTIBAQHWDklERESkwMhxYq169ers2LEjw65Rw4cPxzAMunTpkufBiYiIiIh12djYULlyZa5cuaIRaiIiIiJ3yPEaa927d2f79u2Z1o0YMYJ3331X00FFRERECpiUlBQ2b95MdHT0PR/jgw8+YMSIERw5ciQPIxMREREp/EyGYRjWDsLaYmNj8fDwICYmBnd3d2uHIyIiIoVAYXp/cHR0JDw8nIoVK95Tfy8vL+Li4khOTsbBwQEnJyeL+qtXr+ZFmPmiMD0nERERKRhy8/6QqzXWpOiKjU/ifHQc1xOScXO0o5yXM+5O9tYOS0RERPJAjRo1OH369D0n1mbOnJm3AYk8hKKjo7l48SIxMTF4enpSsmRJvLy8rB2WiIjcJyXWhMgrN1mw/QyRV+PMZf7ezvRsUIHy3i5WjExERETywsSJExk+fDgTJkygTp06uLhY/v/9br+J7dGjR36GJ1LknTt3jj59+hAWFmYuCw0NZf78+fj5+VkxMhERuV+aCsrDPUUgNj6JGWt/s0iqpfP3dmZwiyoauSYiIpKJwvT+YGPz97K6JpPJ/L1hGJhMJlJSUqwR1gNRmJ6TFE3R0dF07tzZIqmWLjQ0lKVLl97TyDWNgBMRyT+aCio5dj46LtOkGsDZK3Gcj44j2MnjAUclIiIieWnjxo3WDkHkoXXx4sVMk2oAYWFhXLx4MdcJMY2AExEpOJRYe8hdT0i+r3oREREp+Jo0aWLtEEQeWjExMfdVf6fo6OgMSTVIS9L16dPnnkfAPcw0+k9E7ofN3ZtktHnzZtq2bUulSpWoXLkyzz77LFu3bs3r2OQBcHPMPrd6t3oREREpHLZu3cqLL75IgwYN+OOPPwD48ssv2bZtm5UjEynaPDyyn/1xt/o75WQEnOTcuXPn6Ny5M0FBQTzxxBMEBgbSuXNnzp07Z+3QRKSQyHVi7auvvqJFixY4OzvzxhtvMHDgQJycnGjevDmLFy/OjxglH5Xzcsbf2znTOn9vZ8p5ZV4nIiIihcd///tfWrVqhZOTE/v37+fWrVsAXL9+nUmTJlk5OpGizdfXl9DQ0EzrQkND8fX1zdXx8noEXEEXHR3N8ePH2bVrFydOnCA6OjpPj53d6L+8PJeIFF253rwgKCiIV199lSFDhliUT58+nXnz5hEeHp6nAT4ID/uitpFXbvLr75GU5i/sk2+QZOdKFD6EVCqvXUFFRESyUJjeH2rXrs2QIUPo3r07bm5uHDp0iICAAA4ePMhTTz3FhQsXMu3XoUOHHB1/2bJleRlunipMz0mKrrxcE+348eMEBQVlWR8eHk5gYOA9x1qQ5Pdacg/TvRSR3MnXzQtOnz5N27ZtM5Q/++yz/Otf/8rt4aQAKG+6ROnLC0i4+DvJqQZ2NiZCSlXGvnI/oKK1wxMREZH7dOLECRo3bpyh3N3dnWvXrmXZL7dT1EQkc35+fixdutS8jpeHhwe+vr73tI5X+gi4rHYZze0IuILqQawl97CN/hOR/JHrxJqfnx/r16+nUqVKFuXr16/XDjSFUUIM7JqDfcxZ7B3t/y6/dgZ2zYVmb4OjXqpFREQKs9KlS/P7779ToUIFi/Jt27YREBCQZb8FCxbkc2QiDw8vL688WRDfy8uL+fPnZzmSq6gsup8fu6neKa/XvxORh1OuE2vDhg3jjTfe4ODBgzRo0ACTycS2bdtYuHAhH330UX7EKPnpWiRER2ReF306rb5UjQcbk4iIiOSpvn37MmjQIP79739jMpn4888/+eWXXxg+fDhjxoyxdngikkt5OQKuoHoQo8keltF/IpK/cp1Ye+211yhVqhTTpk3j22+/BdLWXfvmm29o165dngco+Swh9v7qRUREpMB78803iYmJoVmzZiQkJNC4cWOKFSvG8OHDGThwYJb9oqKi+OSTT5g4cSIATz75JHFxceZ6W1tbVqxYQdmyZfP9GkTEUl6NgCuoHsRosodl9J+I5K9cJ9YAnnvuOZ577rm8jkWswfEui/jerV5EREQKhYkTJzJq1CiOHTtGamoqwcHBuLq6Ztvns88+s1iD7dChQ/Tq1YvixYsD8NNPPzFjxgymTp2an6GLyEPoQY0mexhG/4lI/rLJbYeAgACuXLmSofzatWvZrtEhBZRnefDK4rl5BaTVi4iISJHg7OxM3bp1efzxx++aVAP48ccfad++vUXZoEGDGDt2LGPHjmX8+PH89NNP+RQtTJ48mcceeww3NzdKlixJ+/btOXHiRL6d70GLjo7m+PHj7Nq1ixMnThAdHW3tkEQKjPTRZKGhoRbl+TGazMvLi8DAQOrVq0dgYKCSaiKSK7kesXbmzBlSUlIylN+6dYs//vgjT4KSB8jRA+q9mrZRQfTpv8u9AqBeX21cICIiUgQ899xzmEymDOUmkwlHR0cqVapE165dqVq1qkX9mTNneOSRR8yfW7ZsiYuLi/lz1apViYjIYq3WPLB582YGDBjAY489RnJyMqNGjSI0NJRjx45ZxFEYnTt3LsvpZ9oQTCSNRpOJSGGQ48TaypUrzd///PPPFnPaU1JSWL9+fYadpqSQKF4xbffPa5Fpa6o5uqeNVFNSTUREpEjw8PBgxYoVeHp6UqdOHQzD4MCBA1y7do3Q0FC++eYb3n//fdavX0/Dhg3N/ZKTky0WCF+2bJnFcaOjo7GxyfUEiBxbs2aNxecFCxZQsmRJ9u3bR+PGjfPtvPktOjo6Q1IN0nY67NOnD0uXLlXiQOT/FbW15KKjo82JQk9PT0qWLFmkrk/kYZTjxFr6NACTyUSPHj0s6uzt7alQoQLTpk3L0+DkAXL00O6fIiIiRVSpUqXo2rUrn3zyiTkRlpqayqBBg3Bzc2Pp0qX069ePt956i23btpn7Va1alR07dlC7du1Mj7t161aqVKnyQK4B/t4FMH2Nt8zcunWLW7dumT/Hxha8jZguXryY6bpRkJZcu3jxov6hLVIEaaSqSNGU418xpqamkpqaSvny5bl06ZL5c2pqKrdu3eLEiRM888wz+RmriIiIiNyDL774gsGDB1uMLrOxseH1119n7ty5mEwmBg4cyJEjRyz6de7cmTFjxvDrr79mOOahQ4cYP348Xbp0yff4AQzDYOjQoTz55JNUr149y3aTJ0/Gw8PD/FUQ/7F6+yjAe6kX0fp8hc/dRqrqGYoUXrleYy0/19EQERERkbyXnJzM8ePHM4wuO378uHntXEdHxwzrsA0ePJhVq1ZRp04dWrZsSdWqVTGZTBw/fpy1a9dSv359Bg8e/ECuYeDAgfz6668WI+oyM3LkSIYOHWr+HBsbW+CSa7cvqXIv9fJw06inwkkjVUWKrlwn1kRERESkcHnppZfo3bs3//rXv3jssccwmUzs3r2bSZMm0b17dyBto4Bq1apZ9LO3t2ft2rVMnz6dpUuXsmnTJgAqV67MhAkTGDJkCPb29vke/+uvv87KlSvZsmUL5cqVy7ZtsWLFKFasWL7HdD98fX0JDQ3N9B/ZoaGh+Pr6WiEqKQy0Pl/hpZGqIkWXyTAMw9pBWFtsbCweHh7ExMTg7u5u7XBERESkEChM7w8pKSlMmTKFTz75hIsXLwJpyZ3XX3+dt956C1tbWyIjI7Gxsblr4upBMgyD119/neXLl7Np0yYqV66c62MU1OekUUdyL44fP05QUFCW9eHh4QQGBj7AiHLuYV+0vzA/O5GHUW7eHzRiTURERKQIS05O5uuvv6Z3796MGjXKvJj/nS+J5cuXz/Wxo6KimDhxIp988kmexHqnAQMGsHjxYn744Qfc3Ny4cOECkDZV0snJKV/O+aD4+fmxdOlSc6LBw8MDX1/fhyrRILlXWEc9KZGskaoiRVn+7Y8uIiIiIlZnZ2fHa6+9Zt4p093dPVcjt44dO8ann37K3LlzuXbtGgCXL19myJAhBAQEsGHDhvwIG4DZs2cTExND06ZNKV26tPnrm2++ybdzPkheXl4EBgZSr149AgMDlVSTuyqM6/Np0f40Xl5ezJ8/n9DQUIvy9ASj/vsXKbw0Yk1ERESkiKtXrx4HDhzA398/V/1WrVrF888/T1JSEgAffPAB8+bNo2PHjlSvXp3vvvsuX3eF14olIpbyYtTTg56SqUX7/6aRqiJFU54l1lq0aMHp06c5ffp0Xh1SRERERPJA//79GTZsGOfPn6dOnTq4uLhY1IeEhGTab+LEifTr14+JEycyd+5chg8fTr9+/fjvf/9L48aNH0ToInKb9FFPWU2rvFuCxhpTMgvr9NX84uXlpUSaSBGTZ5sXfPrpp1y+fJmxY8fmxeEeqIK6qK2IiIgUXIXp/cHGJuPqHyaTCcMwMJlMpKSkZNrP09OT3bt3U6VKFZKTk3F0dOTHH3+kdevW+R1ynilMz0kkp24fdZbTUU/R0dF07tw5y9Fu+bWjqBbtF5HCyCqbFwwYMCCvDiUiIiIieSgiIuKe+sXGxuLp6QmkrdXm5ORElSpV8jAyEbkX9zLqyVpTMrVov4gUdVpjTURERKSIy+3aarc7duyYeTdOwzA4ceIEN2/etGiT1VRSkaLoQa9RllesNSXzfqeviogUdDlKrHXo0CHHB1y2bNk9ByMiIiIi+efYsWNERkaSmJhoUf7ss89m2ad58+YWmwikb1aQk6mkIjlRmBJV1lijLK9Yc0dRLdovIkVZjhJrBXHbZhERERHJmdOnT/Pcc89x+PBhc0IM0pJjQJaJsXudQiqSU4UpURUdHZ0hVkibRtmnT598W6Msr1h7SqYW7ReRoirPNi8ozLSorYiIiORWYXp/aNu2Lba2tsybN4+AgAB2797NlStXGDZsGFOnTqVRo0bWDjHfFKbn9LCx1mL696ooLMJfmBKZIiLWlO+bFyQnJ7Np0yZOnTpF165dcXNz488//8Td3R1XV9d7ClpERERE8scvv/zChg0b8PHxwcbGBhsbG5588kkmT57MG2+8wYEDB6wdojyErLWY/r2y1hpleUlTMkVE8l6uE2tnz57lqaeeIjIyklu3btGyZUvc3Nz44IMPSEhI4PPPP8+POEVEREQeesnJycyZM4dNmzaRkpJCw4YNGTBgAI6Ojtn2S0lJMf/ys0SJEvz5559UrVoVf39/Tpw48SBCF8mgsCWqrLlGWV7SlEwRkbxlk9sOgwYNom7dukRHR+Pk5GQuf+6551i/fn2eBiciIiIif3vjjTdYvnw5zZo1o0mTJixevJiXX375rv2qV6/Or7/+CkC9evX44IMP2L59O++++y4BAQH5HbZIpgpboip9jbLMPIg1ykREpGDK9Yi1bdu2sX37dhwcHCzK/f39+eOPP/IsMHk4xMYnERUTz1/Xb3ErORUf12JUKOGCu5O9tUMTERGxuuXLl/Pcc8+ZP4eFhXHixAlsbW0BaNWqFU888cRdj/POO+9w8+ZNAN577z2eeeYZGjVqhLe3N998803+BC9yF9ZeTD+3vLy8mD9/fpZrlGkUmIjIwynXibXU1NRMd446f/48bm5ueRKUPBwir9zk4LlrLN0TyW8XbgDg5GBLo8ol6NfkEcp7u1g5QhEREev64osvWLRoEZ9++illy5bl0UcfpV+/fjz//PMkJSUxb948Hnvssbsep1WrVubvAwICOHbsGFevXsXLy8u8M+jdpKSkcPnyZWxtbSlRosQ9X5NIusKYqNIaZSIicqdcTwVt2bIlM2fONH82mUzcuHGDsWPH0qZNm7yMTYqw2Pgktp28bJFUA4hPTGHrycvM3xZBbHySFSMUERGxvlWrVtG5c2eaNm3KrFmzmDt3Lu7u7owaNYrRo0fj5+fH4sWL7+nYxYsXz1FSbfXq1TRu3BgXFxfKlCmDr68vnp6evPTSS0RGRt7TuUXSpSeqwsPD2blzJ+Hh4SxdurRA71Dp5eVFYGAg9erVIzAwUEk1EZGHnMkwDCM3Hf744w/+8Y9/YGtry8mTJ6lbty4nT56kRIkSbNmyhZIlS+ZXrPlG27A/eMf+jGHv2Wimh/1GqmFgYzJhYwM2pL3gB/i4MO7ZagSXKVhra4iIiKR7kO8P165dY8SIEfz666/MmTOHWrVq3bVPhw4dcnz8ZcuWZVr+5ZdfMmDAAHr37o2joyMLFizg5Zdfxt/fn6VLl3L06FF27NhB5cqVc3yuB03veSIiIpJbuXl/yPVU0LJly3Lw4EGWLl3Kvn37SE1NpXfv3nTr1s1iMwOR7Px1/RbRcYncTEw2l9namHCyt8XWZCI51eB6QnI2RxAREXl4eHp6Mm/ePLZs2cJLL73EU089xbvvvpvtu1deLPw+adIk5s2bR6dOnQB4/vnnee6554iMjKRfv3507tyZt956K8vEnIhYT3R0tHnKqqenJyVLltTouvug+ykiWclVYi0pKYmqVauyatUqXn755RztQiVyp9j4JC7fSMT2juknKakG8UkpODvYYmdjws0x13lfERGRIuXcuXMMHz6cY8eOERISwtSpU9m3bx/vvfcetWrVYubMmbRu3TrTvgsW/B97dx4WZbn/D/w9G8wMyzCgDCq7qIBbmYVZp3LDyrSy7JBaaaG2qtWpU6eTZYuWLZo/T6VRmqdjtFnfNgszs9QkS8tUcGN1ARSGQZgZmO33x8TIOMDMwAwzA+/XdXklz/08z3zGrYf33Pf9Wdvp1y8tLUVGRobt65EjR6KiogKnTp1C37598dBDD9nt30ZE/qG8vLzNvev8eZmtv+KvJxG1x6091iQSCRobG13e5JaoNcfVWpzU6NBkNGN4bDj+PjQcq8ZKkHNVE167SoJbhigwpJ8CsUq57Zo6nQEHT2qQX1SNgyc13H+NiIh6hNtvvx0CgQAvvfQSoqOjMW/ePAQFBeGZZ57BZ599hqVLl+KWW27x2usnJibi119/tX29Z88eCIVCW7fGyMhIGAz8fzKRP1Gr1Q4hEGDtKpydnQ21Wu2jygITfz2JyBm3pwQ98MADePHFF5GTkwOxmDOKyH1n9UbsP6FBZlo0ZqaEI3j36zDuL4BJIEKtQYyU6EFQjZ+PUJkEgLV76NodJSir0drukRAlx6zRiewcSkRE3dqvv/6K33//Hf3798fEiRORlJRkG0tLS8OPP/6INWvWeO3177vvPmRnZ2P37t2QSqXIycnBbbfdBpFIBADIz8/HwIEDvfb6RP7MX5cGVlZWOoRAzfLy8lBZWekXdQYK/noSkTNuJ2P5+fnYsmUL8vLyMHToUISE2Acb3GODnGle4nlBqBrhPz0Dwcm9AACLxYKI4BAIDBJIfn8HUD6GOovcIVQDgNJqLdbtLMHC8QMR/lcAR0RE1N2MGDECixYtwh133IHvvvsOQ4cOdThn7ty5Xnv9++67D0KhEO+99x4aGxsxa9YsPPnkk7bxSy65pMNdSYkCmT8vDdRoNJ0aJ3v89SQiZ9wO1iIiInDTTTd5oxbqIWKVckweGILetbvRWPqr3ZjIXA+5qByQBAO1ZThujncI1ZqVVmtxXK1FuoydQ4mIqHtav349Hn74YTz44IO44IILsHr16i6v4Z577sE999zT6pg/dwMl8hZnSwNzc3O9OoPJ2Uw5Z41LPNHYpCfhrycROeN2sOaJjXCpZwuXSTCmjx6WIjVMLY43dwUVGrSAUQfo63DW3H5nUHYOJSKi7iwhIQEff/yxR++p1+shlUo9ek+insSXSwNdmSmnUqmQmZnZao2ZmZm2PRLJNfz1JCJn3GpeQOQpIWYt5DIZwoIECJNYEBYkQEiQECLhX40xTEZAGu60Myg7hxIRETlnNpvx7LPPol+/fggNDUVRUREA4Mknn8Tbb7/d4fv+8ccftv3WiHoKXy0NdHUTfaVSiZycHGRmZtqd1xzAcT8w9yiVSqxevZq/nkTUJqYS5BtCEYSnD8ESmQRLxZ8AAItQBEjkEAhFQHQqEBGPWIscCVFylFY7LgdNiJLbdQ4lIiKi1j333HN49913sWzZMsyZM8d2fOjQoVi+fDnuuuuuDt/bYrF4okSigOGrpYHuzJSLi4tDbm6ubcmoQqGASqViCNQB5eXlWLBgAS6++GI88MAD0Ov1iIyMRP/+/X2+nx4R+QcGa9T19BqgqgCmmmKYB14LmM1AxX7AbITQrIUg+QoIL30AkCoQDmDW6ESs21liF641dwVl4wIiIiLn1q9fjzVr1mDcuHG4++67bceHDRuGwsLCNq+bOnVqu/fVaDQQCAQeq5MoEHhiaWBHOoq6O1NOqVQySOuklrMEP//8c7uxzMxMr++nR0SBgcEadb3aMhgObYY2+RoIC7+EqNdgiAdNgsBsgFkWhabYUTCFJiH8r9Pjo0LwyJV9YKgugVlXB6EsHJKoPpCFh7T7MkRERGR14sQJpKSkOBw3m80wGAxtXvfFF19gwoQJbQYFJpOp1eNE3VnzUsu29jpzFrR0tKMoN9Hver7cT4+IAgeDNep6+jroG/Uo3f01+o26BWGhoRA01cMSFIqz9fU4daIKQnHSuW6fNcWQ5a+GTF187h7KZCBjLhCZ5Jv3QEREFEAGDx6Mn376CQkJCXbHP/roI1x44YVtXpeWloabbrqpzaWiv//+O7788kuP1koUCDq61NLdjqItZ7aFhYVhzZo1ePDBB9HQ0GB3PTfR9w5f7adHRIGlw8HaqVOn8MADD2Dbtm0wmUy47LLL8NprryE5OdmT9VF3JA2HUSBGv4vGI2jf/6CrPGAbClINRsxVi1DU+Fe3T70GyF8NtAzVAEBdBOSvAcY8Bkj56RwREXVvDQ0NeOGFF7BlyxZUVVXBbDbbjTc3I2jLU089hdtuuw0nTpyA2WzGxo0bcejQIaxfv77dYOyiiy7Cnj172gzWgoODER8f7/4bIo/qyLJC6ryOLLV0ZwZUWzPbvvzyS1x33XW2cI2b6HsPZwkSkSs6HKzdeeedyMjIwDPPPIOmpiasWrUK06dPx65duzxZH3VHEfGQpU2E4Y9PYG4RqgGAoLYEhj3/Q+yYJ6wHasscQ7Vm6iLreMxQLxdMRETkW9nZ2di2bRtuu+029OnTx+19zSZPnowPPvgAS5YsgUAgwKJFizBixAjbUs+2vPnmm+0u90xLS0NxcRv/n6Yu0dFlhd7GsK91rs6Aam9mG2DtyHvmzBk2JfAyT+ynR0Tdn8vB2oIFC7BkyRKEhFj3tTp69Cg2btwImUxmG7/iiiu8UyV1L1IFJH0Gw/zjq2j5ebsoOARNYXGorziGRFMlgBhAX9f+vZyNExERdQObNm3CV199hcsuu6zD95g4cSImTpzo1jXBwcEdfj3yPneXFXYVfw37/IGrM6CczWwzGAzIyMjweH1kr7P76RFRz+BysNavXz9cdNFFWLZsGaZMmYK///3vyMjIwLXXXguDwYCNGzdixowZ3qyVuhGRxQRx9ACYdA2wmIwwC0RQmyTQNwoQq5RBYqi3nigNb/9GzsaJiIi6AaVSicjIyA5fP3v2bMycORNjx45lF89uxB83VvfXsM9fuDoDint7+Q9X9tPz9gxNzgAl8m9CV0989NFHsWnTJrzxxhuYOnUq7r77bjz//PMwGAwwmUxYtmwZ/t//+3/erJW6E2k4goKCIQ2NgECuhCU4DBGhMiT2CoEsSHwuMIuItzYqaI0y2TpORETUzT377LNYtGgRtFpth66vrq7GpEmTEBsbi4cffhh79+71cIXkC/4YvrgS9vVkzTOgMjMz7Y6fPwOKe3v5F6VSidTUVGRkZCA1NdUu1CovL0dWVhbS0tIwatQopKamIisrC+Xl5R55bW/fn4g6z6091pKSkrBp0ya89957uOqqq7BgwQK8/PLL/OST3PdXYCZRF0EiOi/fbRmYSRXW7p/5a6x7qrU8J2MeGxcQEVGP8Morr+DYsWNQqVRITEyERCKxG9+zZ0+713/++eeora3Fhx9+iA0bNmDFihUYNGgQZs6cienTpyMxMdGL1ZO3+GP44o9hn79xZQYU9/YKDN6eockZoESBQWCxWCzuXFBdXY2oqCio1Wo89NBDKCgowJo1azBs2DBv1eh1dXV1UCgU0Gg0CA/n0sIuU1PcdmAWmWh/rl5jbVSgr7POZouIZ6hGREQ+1ZXPD4sXL253/KmnnnLrfsePH8f777+Pd955B0eOHIHRaHQ4Z9++fRgyZAiEQpcXOPil7vycp1arkZWV1Wb44otvugsLC5GWltbmeEFBAVJTU7uwosDFver8n7f/vPPvE5HvuPP84PKMta1bt+LWW29FVVUV+vbti48++ghr167F999/j6ysLFx33XVYvHixrZkBkVORScCYx1wLzKQKdv8kIqIey93grD0GgwG//vor8vPzUVJS0ubMlwsvvBCnTp1CdHQ0kpOTsXv3bkRFRXmsDuo8f9xYnTOtPMeVmW3kW96eockZoESBweWPIO+991488sgj0Gq1WLVqFRYuXAgAGDt2LPbu3QuxWIwLLrjAS2VSt9UcmCVeZv0vZ6ERERF5xdatWzFnzhyoVCrccccdCAsLwxdffNHmPj0REREoLi4GAJSUlMBsNrd6HvlWc/hSUFCAXbt2oaCgALm5uT6b0eTqHmLkmvb29uop1Go1CgsLkZ+fj0OHDkGtVvu6JBtvL8f2x+XeROTI5RlrJ0+exKRJkyCVSnH11VfjoYceso0FBwdjyZIl7ApKRERE5CGRkZE4fPgwevXqBaVS2e6etjU1Ne3eKzY2FtXV1Zg4cSJWr16NyZMnQyqVtnvNTTfdhCuvvBJ9+vSBQCDAyJEjIRKJWj23qKio1ePUNZRKpV8FLpxpRZ7i78thvT1DkzNAiQKDy8HalClTcPPNN2PKlCnYvn07rr32WodzBg8e7NHiiIiIiHqq5cuXIywsDACwYsWKTt1r0aJFmDZtmlvBxpo1azB16lQcPXoU8+fPx5w5c2z1EDnT8s+aRqOxBcMM18hVgbBxv7eXY/vjcm8icuRy84KmpiasXr0ahYWFGD58OO68806IxW41FfVb3XlTWyIiIvKOnvT8MHv2bKxcuTIgg7We8vukVqttM8QiIiIQHR3t02+6/X2mEXWdjv7ZDKSN+1u+R2/M0PT2/YnIkVeaFwQFBeGBBx7odHFERERE5H1Tp07FunXrEB4ejqlTp7Z77saNG9sdX7t2re3nx48fh0AgQL9+/TxSJ3Wev4VYgTDTiLpGZ/5sBtLG/d5eju1vy72JyJ5LzQt+/vlnl2/Y0NCAAwcOdLggIiIiIuo8hUJhW34XHh4OhULR5g9nzGYznnnmGSgUCiQkJCA+Ph4RERF49tln2dTAx5yFWL7Y6L2ysrLVPaEAa12VlZVdXBH5Qmf/bHLjfiIKFC7NWLv99tuRmJiIOXPm4Nprr0VoaKjDOQcPHsR7772HtWvXYtmyZdxvjYiIiMiHbrzxRluDgnXr1nXqXk888QTefvttvPDCC7jssstgsViwY8cOPP3009Dr9Xj++ec9UDF1hCshllKp7NKlooE004i8x9U/m23hxv1EFChcCtYOHjyI1atXY9GiRZgxYwYGDhyIvn37QiqV2tofNzQ0YOrUqdi8eTOGDBni7bqJiIiIqB033ngjKioq0Lt3b4hEIpw6dQrR0dEdute7776LnJwcTJkyxXZs+PDh6NevH+69914Gaz7kSojV1UtFOdOIgM4HrNy4n4gChUtLQSUSCe6//34UFhYiPz8fc+fOxZAhQ9CvXz9cddVVWL16NU6cOIH//e9/DNWIiIiIvOTo0aP49ttvodPpAADt9aDq3bs3du3aZTuveVloR9TU1LS6SXhqaipqamo6fF/qPGchVWhoKO6///4uXSraPNOoNZxp1HN4ImCNi4tDbm4uCgoKsGvXLhQUFCA3N5cNMIjIr7jd1nPEiBEYMWKEN2ohIiIiolZUV1fj73//O77//nsIBAIcOXIEycnJyM7ORkREBF555RWHa+6++25cf/31EAgEEAgEiImJafP+JpOp3dcfPnw4Vq1ahZUrV9odX7VqFYYPH96xN0Ue0d5yuXHjxmHnzp3Izs7Gli1b0NDQYDfuynK8juBMIwI8t5STG/cTkb8TWNr7qLOH6Clt2ImIiMhzuvL54fbbb0dVVRVycnKQlpaGP/74A8nJycjLy8ODDz7YZuOowsJCHD16FFOmTMHatWsRERHR6nnXX399u6+/bds2TJo0CfHx8bj00kshEAiwc+dOlJeX4+uvv8bf/va3zr5Fr+kJz3mtLfUcN24cFixYgFtvvRWjRo3CqFGjWl2yu2vXLmRkZHilrpb7uikUCqhUKgYkPYy/dawlInKVO88PDNbQMx64iIiIyLO68vkhJiYG3377LYYPH46wsDBbsFZcXIyhQ4eivr6+3esXL16MRx55BHK5vMM1nDx5Ev/5z39QWFgIi8WC9PR03Hvvvejbt2+H79kVespz3rFjx1BQUAC9Xg+pVIpdu3ZhxYoVtllqX3zxBSZPnuxwXUFBQavLfIk8hQErEQUid54f3F4K2tVOnDiBf/7zn9i0aRN0Oh0GDhyIt99+GxdddBEA654hixcvxpo1a6BWq5GRkYH//Oc/7EpKRERE3UZDQ0OrodiZM2cQHBzs9Pqnnnqq0zX07duXTQr82JkzZ1oNzprp9XqHY9zvjLoCl3ISUXfnUvMCX1Gr1bjssssgkUiwadMmHDx4EK+88ordMoZly5bh1VdfxapVq7B7927ExMRgwoQJOHv2rO8KJyIiIvKgK664AuvXr7d9LRAIYDab8dJLL2HMmDE+rIz8hbON4CMjI+2+5n5nREREnuGRGWu1tbVt7tnRGS+++CLi4uKwdu1a27HExETbzy0WC1asWIEnnngCU6dOBWBtB69SqbBhwwbMmzfP4zURERERdbWXXnoJV111FX799Vc0NTXh0UcfxYEDB1BTU4MdO3b4ujzyA842ik9LS0NBQQGX4wWAlksnIyIiEB0dzd8rIiI/5vaMtRdffBEffPCB7etbbrkFUVFR6NevH/744w+PFvf5559j5MiRmDZtGqKjo3HhhRfirbfeso0XFxejoqLCrp13cHAwrrzySuzcubPN+zY2NqKurs7uBxEREZG/Sk9Px759+3DJJZdgwoQJaGhowNSpU7F3717079/f1+WRH2juxNnyuRg4NzOtT58+SE1NRUZGBlJTUxnU+Kny8nJkZWUhLS0No0aNQmpqKrKyslBeXu7r0oiIqA1uB2urV6+2dXDZvHkzNm/ejE2bNuGaa67BI4884tHiioqK8MYbb2DAgAH49ttvcffdd2P+/Pm2pRAVFRUA4LA3hEqlso21ZunSpVAoFLYf7EhDRERE/i4mJgaLFy/Gl19+ia+//hrPPfcc+vTp0+b5kZGROHPmDADgzjvv5DYZPUBcXBxyc3NRUFCAXbt2oaCgALm5uXzWDRBqtdqhgyYA5OXlITs7G2q12keVERFRe9xeCnrq1Cnb/5y//PJL3HLLLcjMzERiYqLHW3WbzWaMHDkSS5YsAQBceOGFOHDgAN544w3cfvvttvMEAoHddRaLxeFYS48//jgeeugh29d1dXV84CAiIiK/tXbtWoSGhmLatGl2xz/66CNotVrccccdDtc0NTWhrq4OvXr1wrvvvosXX3wRYWFhHXp9nU4Hi8Via6BQWlqKTz/9FOnp6Q4zpMi3uFF84KqsrGx1KS9gDdcqKyv5e0tE5IfcDtaUSiXKy8sRFxeHb775Bs899xwAa5hlMpk8WlyfPn2Qnp5udywtLQ2ffPIJAOsnt4B15lrLT2yrqqra7XAUHBzsUgctIiIiIn/wwgsv4M0333Q4Hh0djblz57YarF166aW44YYbcNFFF8FisWD+/PmQyWSt3v+dd95p9/Wvv/56TJ06FXfffTdqa2uRkZEBiUSCM2fO4NVXX8U999zTsTdGRDYajaZT40RE5BtuLwWdOnUqpk+fjgkTJqC6uhrXXHMNAOD3339HSkqKR4u77LLLcOjQIbtjhw8fRkJCAgAgKSkJMTEx2Lx5s228qakJ27Ztw+jRoz1aCxEREZGvlJaWIikpyeF4QkICysrKWr3mvffew7XXXov6+noIBAJoNBqo1epWfzizZ88e/O1vfwMAfPzxx1CpVCgtLcX69euxcuXKzr05IgLgvLOrs3EiIvINt2esLV++HImJiSgvL8eyZcsQGhoKwLpE9N577/VocQ8++CBGjx6NJUuW4JZbbsEvv/yCNWvWYM2aNQCsS0AXLlyIJUuWYMCAARgwYACWLFkCuVyO6dOne7QWIiIiIl+Jjo7Gvn377LqjA8Aff/yBqKioVq9RqVR44YUXAFg/jPzvf//b5rnOaLVa2zLSvLw8TJ06FUKhEKNGjUJpaWmH7klE9px1dm1vRQ4REfmO28GaRCLBP/7xD4fjCxcu9EQ9di6++GJ8+umnePzxx/HMM88gKSkJK1aswIwZM2znPProo9DpdLj33nuhVquRkZGBvLy8Du8hQkRERORvsrKyMH/+fISFheGKK64AAGzbtg0LFixAVlaW0+uLi4s79fopKSn47LPPcOONN+Lbb7/Fgw8+CMC6/UZ4eHin7k3dm1qtRmVlJTQaDSIiIhAdHc19wtrQ3Nn1/AYGzZ1d+etGROSfBBaLxeLOBe+++y569eqFSZMmAbAGW2vWrEF6ejref/992zLNQFJXVweFQgGNRsOHQ7Kp0xlwXK3FWb0RYVIxYpVyhMskvi6LiIj8RFc+PzQ1NeG2227DRx99BLHY+rmo2WzG7bffjjfffBNBQUFO77Ft2za8/PLLKCgogEAgQFpaGh555BHbEs/2fPzxx5g+fTpMJhPGjRtn+6Z/6dKl+PHHH7Fp06bOvUEv4nOe75SXl7cZErFxWNtahpEKhQIqlapHhGoMYYnIn7jz/OB2sDZo0CC88cYbGDt2LH7++WeMGzcOK1aswJdffgmxWIyNGzd2qnhf4AMXna+sugFrd5SgrEZrO5YQJces0YmIjwrxYWVEROQvfPH8cPjwYfzxxx+QyWQYOnSoyx9ovvfee5g9ezamTp2Kyy67DBaLBTt37sSnn36KdevWubSFRkVFBU6dOoXhw4dDKLRu0/vLL78gPDwcqampnXpf3uTvz3ndNUxQq9XIyspqc1ljbm5ut3if5BkMYYnI33g1WJPL5SgsLER8fDz++c9/4tSpU1i/fj0OHDiAq666CqdPn+5U8b7g7w9c1LXqdAYs33zYLlRrlhAlx8LxAzlzjYiIAur5IS0tDXPnzrUt4Wz26quv4q233kJBQYGPKvM+f/596s5hQmFhIdLS0tocLygo8OtAlroOQ1gi8kfuPD+4vcdaaGgoqqurER8fj7y8PNsDmlQqhU6n61jFRH7kuFrbaqgGAKXVWhxXa5EuY1cmIiLqOiaTCevWrcOWLVtQVVUFs9lsN/7999+3e31RUREmT57scHzKlCn417/+5fT1Gxoa8MILL7T5+kVFRS68C2pJrVY7hGqAtTlEdnZ2wIcJGo2mU+PkX7w5s7KysrLVUA2w/n2orKwM6L8LRNT9uR2sTZgwAdnZ2bjwwgtx+PBh215rBw4ccOhURRSIzuqNnRonIiLytAULFmDdunWYNGkShgwZAoFA4Nb1cXFx2LJlC1JSUuyOb9myxaWZUdnZ2di2bRtuu+029OnTx+3XJ0fdPUxQKNr/ENLZOPkPb8+sZAhLRIHO7WDtP//5D/7973+jvLwcn3zyia1t+2+//YZbb73V4wUSdbVwqRgXJSpxYW8h+liqIDHVQycMwc/VcmwvNyBM6vZfGyIiok7Jzc3Fhx9+iGuvvbZD1z/88MOYP38+fv/9d4wePRoCgQDbt2/HunXr8Nprrzm9ftOmTfjqq69w2WWXdej1yVF3DxNUKhUyMzPbXN6nUql8UBW5qytmVjKEJaJA53ZCEBERgVWrVjkcX7x4sUcKIvK1ILEQw+RqSH56C2UVRwEAoVIxMpMGY9jw2xGrlPu4QiIi6mmCgoIcZpu545577kFMTAxeeeUVfPjhhwCs+6598MEHuP76651er1QqERkZ2eHXJ0fdPUxQKpXIyclpc6ZTIM/G60m6YmYlQ1giCnQdnnqj1WpRVlaGpqYmu+PDhg3rdFFEvlKnM+Bg8XEkH3gHtVXHbMfr9UacKjmIQbINkA38N4DAftglIqLA8vDDD+O1117DqlWrOrwM88Ybb8SNN97YoWufffZZLFq0CO+++y7kcn7A5Ak9IUyIi4tDbm6ubW8uhUIBlUrFUC2AdMXMSoawRBTo3A7WTp8+jVmzZuGbb75pddxkMnW6KCJfOa7Woi9Ow3C6CPIgEcwWC8wWQCgALBYLjGeOAbVlQMxQX5dKREQ9yPbt27F161Zs2rQJgwcPhkRi351648aNXn39V155BceOHYNKpUJiYqLD6+/Zs8err98d9ZQwQalUdpv30hN11cxKhrBEFMjcDtYWLlyI2tpa7Nq1C2PGjMGnn36KyspKPPfcc3jllVe8USNRlzmrN0JmrIcZgEAggEgggKjFuNFsAfR1viqPiIh6qIiIiA7PNvOEG264wWev3Z0xTCB/15UzKxnCElGgcjtY+/777/F///d/uPjiiyEUCpGQkIAJEyYgPDwcS5cutXUJJQpEYVIxdOJQiGCdpRYmlUAiEthmrUlEQkAa7usyiYioh1m7dq1PX/+pp57y6et3ZwwTyJ/1lJmVRESd4Xaw1tDQgOjoaABAZGQkTp8+jYEDB2Lo0KFcBkABL1Ypx/bj0Rhx8Q0IDxbB0KhFkyAI6jOVOHM4H02KRDSKVYjydaFERNTjGI1G/PDDDzh27BimT5+OsLAwnDx5EuHh4QgNDfV1eUTUTfW0mZVqtdr2XiMiIhAdHd1t3ysReYbbwdqgQYNw6NAhJCYm4oILLsDq1auRmJiIN998E3369PFGjURdJlwmwRVxIoh2HoTxyBYY9fUQAoiOGYzeE+7AQcFAvJ9/Bg+MVSJcJnF6PyIiIk8oLS3F1VdfjbKyMjQ2NmLChAkICwvDsmXLoNfr8eabb7p1P5PJhD///BMJCQkufcNoMpmwfPlyfPjhh602r6qpqXHr9YkosPSUmZXl5eVtzs6Li4vzYWVE5M+E7l6wcOFCnDp1CoB1WcA333yD+Ph4rFy5EkuWLPF4gURdSq9B6O9vw1B7AjWh/RGccSfCJvwTIemZkApMEAZJUVajxXG11teVEhFRD7JgwQKMHDkSarUaMpnMdvzGG2/Eli1bnF6/cOFCvP322wCsIdmVV16JESNGIC4uDj/88IPT6xcvXoxXX30Vt9xyCzQaDR566CFMnToVQqEQTz/9dEffFhF1kFqtRmFhIfLz83Ho0CGo1WpflxTw1Gq1Q6gGAHl5ecjOzuavMRG1ye0ZazNmzLD9/MILL0RJSQkKCwsRHx+PXr16ebQ4oi5XWwaoi2ESihE67GpYCj6DrvKAbXjIoGtw+6C7UN9o9GGRRETU02zfvh07duxAUFCQ3fGEhAScOHHC6fUff/wxZs6cCQD44osvUFxcjMLCQqxfvx5PPPEEduzY0e71//vf//DWW29h0qRJWLx4MW699Vb0798fw4YNw65duzB//vyOvzkicgtnVXlHZWVlq00aAGu4VllZ2SNm7RGR+9yesXY+uVyOESNGMFSj7uGvjp+y+BEIKvgM5soDEMDatCBILIS45jD6F29AvNzg2zqJiKhHMZvNMJlMDsePHz+OsLAwp9efOXMGMTExAICvv/4a06ZNw8CBA3HXXXfhzz//dHp9RUUFhg4dCgAIDQ2FRqMBAFx33XX46quv3HkrRNQJnFXlPc3/rnV0nIh6LrdnrFksFnz88cfYunUrqqqqYDab7cY3btzoseKIutxfHT8lYb1gPH3QGqqJhWgymmE0W2AxCHDs0D5EDDiGJnEY4qNCfFsvERH1CBMmTMCKFSuwZs0aAIBAIEB9fT2eeuopXHvttU6vV6lUOHjwIPr06YNvvvkGr7/+OgBAq9VCJBI5vT42NhanTp1CfHw8UlJSkJeXhxEjRmD37t0IDg7u3JsjIpdxVpX3KBSKTo17ExsqEPk3t2esLViwALfddhuKi4sRGhoKhUJh94MooEXEA8pkiEyNkElEdqGaKDgEtUYRZEEimHQarNtZgjodZ64REZH3vfrqq9i2bRvS09Oh1+sxffp0JCYm4sSJE3jxxRedXj979mzccsstGDJkCAQCASZMmAAAyM/PR2pqqtPrW+7ltmDBAjz55JMYMGAAbr/9dtx5552de3NEXSyQ9yfjrCrvUalUyMzMbHUsMzMTKpWqiyuyKi8vR1ZWFtLS0jBq1CikpqYiKysL5eXlPqmHiBwJLBaLxZ0LIiMj8d5777n06WigqKurg0KhgEajQXh4uK/LIV+rKQbKdgGbn4TBZEFDkxHCIDmawuJxvN56iumqJ/B6gQwPZw5Eel8GykREPVFXPz/odDrk5ubit99+g9lsxogRIzBjxgy7Zgbt+fjjj1FeXo5p06YhNjYWAPDuu+8iIiIC119/vVu17Nq1Czt37kRKSgqmTJni9nvpSnzOo5YCfX+ywsJCpKWltTleUFDgUljuLYE+s8rf/nyo1WpkZWW1OksxMzMTubm5AfXrSxRI3Hl+cDtYS0pKwqZNm3z6D7an8YGLHJytALY+D8OpAjRYJDDHj4YwtBcExkYIQqKwP3gE3v1ThzsvS0JGchQAoE5nwHG1Fmf1RoRJxYhVyhEuk/j4jRARkbd01fODwWDAoEGD8OWXXyI9Pd1rr9Nd8TmPmnWHkMKf34O/hVId1TIcVCgUUKlUPvs19fcglag7c+f5we091p5++mksXrwY77zzjsufkBIFnLAY4PKH0LhjDUyKJFsjA1FwCJrC4hAl34Xb02chXGr9K1RW3YC1O0pQVqO13SIhSo5ZoxO5DxsREXWKRCJBY2MjBAJBp+6zZcsWbNmypdU9ct955x2H8z///HNcc801kEgk+Pzzz9u9t7/PWiMCusf+ZEqlEjk5OW0GWL6q31lThUAILZsplUq/qZVLf4kCg9vB2rRp0/D+++8jOjoaiYmJkEjsZ+Ts2bPHY8UR+VRkEoIufwCC756FyaCDJWoA1CYJ1PVmmM8eRV/xesSkLkadzuAQqgFAabUW63aWYOH4gZy5RkREnfLAAw/gxRdfRE5ODsRitx/fsHjxYjzzzDMYOXIk+vTp41JId8MNN6CiogLR0dG44YYb2jxPIBC02rGUyN84CyECZa+1uLg45Obm+s2sKqB7hJb+yJ8bKhDROW4/mc2aNQu//fYbZs6cCZVK1elPT4n8WVBjNUwNVThhkEHbZAJg/YRfHiRCjPEkZNoTKDbH46RGhyvigzAkRAOJsR4GcSj2Nyiw84QOx9VapMv4Pz0iIuq4/Px8bNmyBXl5eRg6dChCQuxnQzvryv7mm29i3bp1uO2221x+zZaz2s6f4UYUiJyFEDqdDuXl5T5ftujKPmX+NKsK4Mwqb2luqNDW0l9fNVQgIntuB2tfffUVvv32W1x++eXeqIfIv+jrIAsSI6lXCPQGE4xmC8RCAaQSESQiIaCvQ73FiNsHWRB1YDX0VcdgBiACkBGdgpTBs1DfaPT1uyAiogAXERGBm266qcPXNzU1YfTo0R6siCjwtBdSjBs3Dt9//z1efPFF7lPWAZxZ5R3+uvSXiOy5HazFxcVx41fqOaTWP+sSkdAapLUyHgsD6g6shb7qmN2QvuoolFgL6fgnkF9UzYYGRETUYWvXru3U9dnZ2diwYQOefPJJl69ZuXKly+fOnz+/I2URdam2Qopx48ZhwYIFuPXWW9HQ0OCzZYuBvE8ZZ1Z5jz8u/SUie24Ha6+88goeffRRvPnmm0hMTPRCSUR+JCIeUCYD6iLHMWUyEBGP3tUlqK8tcRg2WyxoqjqGIHUZ1uyx/lVjQwMiIuooo9GIH374AceOHcP06dMRFhaGkydPIjw8HKGhoe1eq9frsWbNGnz33XcYNmyYwx65r776qsM1y5cvt/v69OnT0Gq1iIiIAADU1tZCLpcjOjqawRoFjLi4OCxfvhxFRUXQ6/WQSqXYtWuXLVQDfLdsMZD3KePMKu/yt6W/RGTP7WBt5syZ0Gq16N+/P+RyucODWU1NjceKI/K1Oosc5mGzIPjlLQjVReeWgCqTgYx5gFQBiaEecUoZytW6v/ZhAywWC4QCAXqHBkNdrwbQGwAbGhARUceUlpbi6quvRllZGRobGzFhwgSEhYVh2bJl0Ov1ePPNN9u9ft++fbjgggsAAPv377cba2u/3OLiYtvPN2zYgNdffx1vv/02Bg0aBAA4dOgQ5syZg3nz5nXinRF1PaFQiMmTJ7c57qtli4G+TxlnVhFRT+V2sLZixQovlEHkf8qqG7B2Rwmqzupx0+B56D+wGmJjPeRhSkh7JUEWHmk9URoOWZAYCVFyaJtMMJgsEAuBJqMZp+sb0SSyn0VQWq1lQwMiInLLggULMHLkSPzxxx+IioqyHb/xxhuRnZ3t9PqtW7d26vWffPJJfPzxx7ZQDQAGDRqE5cuX4+abb8aMGTM6dX+iruSvyxa7wz5lnFlFRD2R28HaHXfc4Y06iPxKnc6AtTtKcFKjw1UDo5Gz+wQOV9QDAGRBGoxJPYnsy4OtSzoj4qELjUfZ0QPQGawz1gwmM0RCAfolDUaeTgGgye7+Z/VsaEBERK7bvn07duzYgaCgILvjCQkJOHHihNdf/9SpUzAYDA7HTSYTKisrvf76RJ7kr8sW/TXwIyKi9rkdrBH1BMfVWpTVaHFBfAS+OXASg5VAdooQweYGNIpCcKSxCf/dVYoHxg4AIEdRwnRIq3OAqqMAAKEAsEQko3bwTIy26DBcUgODOBT7GxTYecKAMCn/6hERkevMZjNMJpPD8ePHjyMsLMyle+zevRsfffQRysrK0NRk/4HPxo0b27123LhxmDNnDt5++21cdNFFEAgE+PXXXzFv3jyMHz/e9TdC5Cf8cdmivwZ+RK5Qq9W2v08RERGIjo7mn1nqMfjdPVErmmeURcqDcHOyCX0K34V2/1GYYP1LMzp2EOqG3YkTai0sAN7YZ8LohLkYkq6BxFgPkzgEQfJw1P/8FlRN5Thb3wQRgIzoFAwfkY1YpdyH746IiALNhAkTsGLFCqxZswaAdV+0+vp6PPXUU7j22mudXp+bm4vbb78dmZmZ2Lx5MzIzM3HkyBFUVFTgxhtvdHr9O++8gzvuuAOXXHKJbX9do9GIiRMnIicnp3NvjshH/HHZoj8GfkTOlJeXtxkIx8XF+bAyoq7BYI2oFWFSMcQiAS7pbUDUL+uBpkMwR4mhbhKhst6EptNHEXVgHYL6/AunDVKH63vJRRBX/YzSqiKYFeeaFAhrizGodANkg/4NwP/3ySAiIv+wfPlyjBkzBunp6dDr9Zg+fTqOHDmCXr164f3333d6/ZIlS7B8+XLcd999CAsLw2uvvYakpCTMmzcPffr0cXp979698fXXX+Pw4cMoLCyExWJBWloaBg4c6Im3R0Qt+GPgR9QWtVrtEKoB1k622dnZyM3N5Z9n6vYYrBG1IlYpxz3DRIhV74L20CYAgABA7+AQKCLjcFIrgL7qKPoaK9Eo7Y/bB1kQdWA19FXHYAagCDVBJA7CoMunQlj0nTWoEwqsXUXry4DaMiBmqE/fIxERBY6+ffvi999/R25uLn777TeYzWbcddddmDFjBmQymdPrjx07hkmTJgEAgoOD0dDQAIFAgAcffBBjx47F4sWLXaojMTERFosF/fv3h1jMx0jq2bj0jQiorKxsdV9AwBquVVZW8u8FdXtCd042Go0Qi8UObdqJuptwgRaDSjdAoK2BWCiwHTc3NkDWcBxRMiHkQSLILA1IDDWh3+H10Fcdg8VigclsBsxGmCoPQHr4M4QljYRSHoQwqQQS0V9/5fR1PnpnREQUKEaMGAG1Wg0AeOaZZ2CxWDB79mysWrUKr7/+OrKzs10K1QAgMjISZ8+eBQD069fP9ixXW1sLrVbr9HqtVou77roLcrkcgwcPRllZGQBg/vz5eOGFFzry9ogCWnl5ObKyspCWloZRo0YhNTUVWVlZKC8v93VpRF1Ko9F0apyoO3ArWBOLxUhISGh181yibqW2DLL6MkikcsiDxAgSCSESCiASCmBuakCExIg4pQwSeQRk2hNQGU8iWCyEtsmEhkYTjBYRBABEpwsBWSuf0EjDu/wtERFRYCkoKEBDQwMAYPHixaivr+/wvf72t79h8+bNAIBbbrkFCxYswJw5c3Drrbdi3LhxTq9//PHH8ccff+CHH36AVHpuC4Tx48fjgw8+6HBdRIHI2dK35kCcqCdQKNrf3sbZOFF34PYc/n//+994/PHH8d577yEyMtIbNRH53l8zykR6NUR9BkNUeQBmM2CGBUIIIJRYIFQNBCLigYr9EIuECJeKESYVw2wBJMGAzBAGk74BxkY9TEYTgsUi672VydbriIiI2nHBBRdg9uzZuPzyy2GxWPDyyy8jNDS01XMXLVrU7r1WrVoFvV4PwBqSSSQSbN++HVOnTsWTTz7ptJbPPvsMH3zwAUaNGgWB4NxM7vT0dBw7dsyNd0XkPzq6lJNL34jOUalUyMzMbPXvRGZmJlQqlQ+qIupabgdrK1euxNGjR9G3b18kJCQgJCTEbnzPnj0eK47IZ5pnlJ38AxgyFUIAwqqDsO60BiA6DciYB0gVgDQceoMJ1Q1NiJQJ0TvIhKZGA8yhfWAxnUCDSYSq0w1IipJDphp47joiIqJ2rFu3Dk899RS+/PJLCAQCbNq0qdV9zQQCgdNgreWHoUKhEI8++igeffRRl2s5ffo0oqOjHY4379VGFGg608WQS9+IzlEqlcjJyWnz7xNDZuoJ3A7WbrjhBi+UQeRnIuKtM8vURcCR74CkvwHpUwBjIyCLBPqPAcJibOdalMmI1f8JWUMZmtT1MJotMAoEkKdcAXH8xWgUJ6A4NAJJ/dMgC+dMTyIicm7QoEHIzc0FYA3DtmzZ0mq41Za6ujqEh4fbft6e5vPacvHFF+Orr77CAw88AAC2MO2tt97CpZde6nJNRP6gs10MufSNyF5cXBxyc3NtM0AVCgVUKhVDNeox3A7WnnrqKW/UQeRfpAogYy6Qv8Yarh3/1XpcmQwMmnQuVPvrXNEld0KU9yRQq4XRAgSHRiKo3zCYh/0d5tOHcFY+HG/ub8QClQjp3F6NiIhcMGLECGzZsgVKpRJPPfVUm8tA26JUKnHq1ClER0cjIiKi1ZllFosFAoHA6f65S5cuxdVXX42DBw/CaDTitddew4EDB/Dzzz9j27ZtbtVF5GudXcrJpW9EjpRKJYM06rE61Ce9trYWH3/8MY4dO4ZHHnkEkZGR2LNnD1QqFfr16+fpGol8IzIJGPMYUFtm3XNNGm6dydbKMs4gGFHd+0KEDboaIdIwCPW1aDrxBxo/XQiBsREXpF0L0aC7UN9o9MEbISKiQNTcvECpVOKZZ57BPffcA7lc7vL133//vW0J6NatWztVy+jRo7Fjxw68/PLL6N+/P/Ly8jBixAj8/PPPGDp0aKfuTdTVOrOUs3lftqeeegqPPfYYtmzZghUrVqChoYFL34iIeii3g7V9+/Zh/PjxUCgUKCkpwZw5cxAZGYlPP/0UpaWlWL9+vTfqJPINqQKIcf4Ng8RwFooze9EovghN+z6BueIgAOuObCKhADh9CFHadQi/xvkG0UREREDnmxdceeWVrf68o4YOHYp333230/ch8rWOLuVsa1+23377DQBcbn5ARETdi9vB2kMPPYRZs2Zh2bJlCAsLsx2/5pprMH36dI8WRxQwpOGQBYkRpOwN7e5CmAQCQGBrdQCzQARhbTF6myoBxLR3JyIiIgCdb16wb98+l19r2LBhLp1XVVWFqqoqmM3mDl1P5A9aLuUMCQnBwoULMWrUKOj1ekRFRbUarLW3L9v8+fOd7stGRETdl9vB2u7du7F69WqH4/369UNFRYVHiiIKOH81OxCZmyAPEkFnMMFktgAARMEhaEAQ4pQySAz1Pi6UiIgCRWebF1xwwQUQCAS2fdTa42yPtd9++w133HEHCgoKYLFY7MZc2aONyJ80dzG8//77kZ2djddeew3PP/+8bby17qCd3ZeNAlfz8l+NRoOIiAjOTCQiB0J3L5BKpa12ljp06BB69+7tkaKIAk5zswNZJERCAeRBIoQEiSEPDUdQ7yREhodAFiS27tNGRETkJrPZ7FaoBgDFxcUoKipCcXExPvnkEyQlJeH111/H3r17sXfvXrz++uvo378/PvnkE6f3mj17NgYOHIidO3fa7tn8o6ioqKNvyyU//vgjJk+ejL59+0IgEOCzzz7z6utRzxAXF4c333wTq1atwpYtW+zGmruDqtVq27HO7MtGgau8vBxZWVlIS0vDqFGjkJqaiqysLJSXl/u6NCLyI27PWLv++uvxzDPP4MMPPwRg/ZSyrKwMjz32GG666SaPF0gUMCKTAIkMKLsGwqpCCEViQCwDRBLruDLZOrONiIjIBZ9//jmuueYaSCQSfP755+2eO2XKFIdjCQkJtp9PmzYNK1euxLXXXms7NmzYMMTFxeHJJ5/EDTfc0O79i4uLsXHjRqSkpLj3JjygoaEBw4cPx+zZs/msSR6l0WhcnoXW0X3ZKHC1t/w3Ozuby3+JyMbtYO3ll1/Gtddei+joaOh0Olx55ZWoqKjApZdeajeFmqhHCosBLn8IyF8DqFt8gq9MBjLmtdpRlIiIqDU33HADKioqEB0d3W7w5cpSzD///BNJSUkOx5OSknDw4EGntYwbNw5//PGHT4K1a665Btdcc02Xvy51f+7MQmu5L9v5MjMzoVKpPF4f+RaX/xKRq9wO1sLDw7F9+3Z8//332LNnD8xmM0aMGIHx48d7oz6iwBOZBIx5DKgtA/R11uWfEfEM1YiIyC0tGwSc3yzAXWlpaXjuuefw9ttvQyqVAgAaGxvx3HPPIS0tzen1OTk5uOOOO7B//34MGTIEEonEbry1GXO+0tjYiMbGRtvXrW1hQgS4NwuteV+21rqC5uTkMGDphrj8l4hc5Xaw1mzs2LEYO3asJ2sh6j6kCiBmqK+rICIiAgC8+eabmDx5MuLi4jB8+HAAwB9//AGBQIAvv/zS6fU7d+7E9u3bsWnTJocxf2tesHTpUixevNjXZVAAcHcWWlxcHHJzc20b2SsUCqhUKoZq3RSX/xKRqwSW81s7tWLlypWYO3cupFIpVq5c2e658+fP91hxXaWurg4KhQIajQbh4dxcnoiIiJzrqucHs9mMdevWYePGjSgpKYFAIEBSUhJuvvlm3HbbbU47fjbTarV47733UFhYCIvFgvT0dEyfPh0hISFOr01MTMR1112HJ5980qdL3gQCAT799NN2l8a2NmMtLi6Oz3nUqvLy8jZnobXsCko9j1qtRlZWVpvBK/dYI+re3HnOcylYS0pKwq+//oqoqKhW9+ew3Uwg8HpnKG9gsEZERETu6ornB4vFgsmTJ+Prr7/G8OHDkZqaCovFgoKCAvz555+YMmVKl3TJDAsLw++//47+/ft7/bXa40qwdj4+55EzarWas9CoVQxeiXoud54fXFoKWlxc3OrPiYiIiMh71q1bhx9//BFbtmzBmDFj7Ma+//573HDDDVi/fj1uv/12p/f673//i9WrV6OoqAg///wzEhISsHz5ciQnJ+P6669v99qpU6di69atPg/WiLxBqVQySKNWcfkvEbmiw3usEREREZF3vf/++/jXv/7lEKoB1v1uH3vsMfzvf/9zGqy98cYbWLRoERYuXIjnnnvOtieaUqnEihUrnAZrAwcOxOOPP47t27dj6NChDs0LvLkVSH19PY4ePWr7uri4GL///jsiIyMRHx/vtdclIgICL3htOQMzIiIC0dHRAVU/USByaSloSzfffDNGjhyJxx57zO74Sy+9hF9++QUfffSRRwvsClwiQERERO7qiueHmJgYfPPNN7jgggtaHd+7dy+uueYaVFRUtHuf9PR0LFmyBDfccAPCwsLwxx9/IDk5Gfv378dVV12FM2fOtHu9L7cC+eGHH1oNFu+44w6sW7fO6fV8ziOinoJLV4k8x+NLQVvatm0bnnrqKYfjV199NV5++WV3b0dEREREbaipqWm3WYBKpYJarXZ6n+LiYlx44YUOx4ODg9HQ0ODS9b5y1VVXwc3PgYk8jrOAyN+p1WqHUA0A8vLykJ2dzWYLRF4kdPeC+vp6BAUFORyXSCSoq6vzSFFEREREBJhMJojFbX8OKhKJYDQand4nKSkJv//+u8PxTZs2IT09vTMlEnV75eXlyMrKQlpaGkaNGoXU1FRkZWWhvLzc16UR2VRWVrbawRSwhmuVlZVdXBFRz+H2jLUhQ4bggw8+wKJFi+yO5+bm8sGMCECdzoDjai3O6o0Ik4oRq5QjXCZxfiEREdF5LBYLZs2aheDg4FbHGxsbXbrPI488gvvuuw96vR4WiwW//PIL3n//fSxduhQ5OTmeLJmoW+EsIAoUGo2mU+NE1HFuB2tPPvkkbrrpJhw7dgxjx44FAGzZsgXvv/9+QO6vRuRJZdUNWLujBGU1WtuxhCg5Zo1ORHxUiA8rIyKiQHTHHXc4PceVjqCzZ8+G0WjEo48+Cq1Wi+nTp6Nfv3547bXXkJWV5YlSibolV2YBMVgjf6BQKDo1TkQd53awNmXKFHz22WdYsmQJPv74Y8hkMgwbNgzfffcdrrzySm/USBQQ6nQGh1ANAEqrtVi3swQLxw/kzDUiInLL2rVrPXavOXPmYM6cOThz5gzMZjOio6M9dm+i7oqzgChQqFQqZGZmthoEZ2ZmtrtfJxF1jtt7rAHApEmTsGPHDjQ0NODMmTP4/vvvGapRj3dcrXUI1ZqVVmtxXN36GBERUVfq1auXW6Ga0WjE4sWLuZ8U9UicBUSBQqlUIicnB5mZmXbHm7uCcmYlkfe4PWONiFp3Vt/+5tHOxomIiLyluroaixYtwtatW1FVVQWz2Ww3XlNT0+a1YrEYL730kkvLUom6G84CokASFxeH3NxcWwdbhUIBlUrFUI3Iy9wO1kwmE5YvX44PP/wQZWVlaGpqshtv78GMqDsLk4ohFgkwup8EQ0I0kBjrYRCHYn+DAjtPGBAmZY5NRES+MXPmTBw7dgx33XUXVCoVBAKBW9ePHz8eP/zwA2bNmuWdAon8VPMsoPMbGHAWEPkrpVLJP5dEXczt7/QXL16MnJwcPPTQQ3jyySfxxBNPoKSkBJ999plDp1CiniRWKcc9w0SQ7lkNfdUxmAGIAGREp2D4iGzEKuW+LpGIiHqo7du3Y/v27Rg+fHiHrr/mmmvw+OOPY//+/bjooosQEmLfkGfKlCmeKJPIL3EWEBERtcftYO1///sf3nrrLUyaNAmLFy/Grbfeiv79+2PYsGHYtWsX5s+f7406ifxeuECLQaUbUF5bYndcWFuMQaUbIBv0bwDch4OIiLpeamoqdDpdh6+/5557AACvvvqqw5hAIIDJZOrwvYkCAWcBERFRW9xuXlBRUYGhQ4cCAEJDQ22dcK677jp89dVXnq2OKJDUlkFWX4akXiHo3zsECVFy9O8dgqReIZDVlwG1Zb6ukIiIeqjXX38dTzzxBLZt24bq6mrU1dXZ/XDGbDa3+YOhGhEREfVkbs9Yi42NxalTpxAfH4+UlBTk5eVhxIgR2L17N4KDg71RI1Fg0Fu/MZGIhJCIWsms9c6/cSEiIvKGiIgIaDQajB071u64xWJxe8aZXq+HVCr1dIlEREREAcntYO3GG2/Eli1bkJGRgQULFuDWW2/F22+/jbKyMjz44IPeqJEoMEjDOzdORETkJTNmzEBQUBA2bNjQoeYFJpMJS5YswZtvvonKykocPnwYycnJePLJJ5GYmIi77rrLS5UTERER+Te3g7UXXnjB9vObb74ZsbGx2LlzJ1JSUrhxLfVsEfGAMhlQFzmOKZOt40RERD6wf/9+7N27F4MGDerQ9c8//zzeffddLFu2DHPmzLEdHzp0KJYvX85gjYiIiHost/dYO9+oUaPw0EMPMVQjkiqAjLmAMhkGkxln9QaotU2oD4lH08g51nEiIiIfGDlyJMrLyzt8/fr167FmzRrMmDEDIpHIdnzYsGEoLCz0RIlEREREAcmlGWuff/65yzdkwEY9WmQSqi9+EJVlR2DUaWAQh2J/gwLHf9HjtlENiI8K8XWFRETUAz3wwANYsGABHnnkEQwdOhQSicRufNiwYe1ef+LECaSkpDgcN5vNMBgMHq2ViIiIKJC4FKzdcMMNLt2M7dapp6vTGbBq52mU1QQB6P3X0SYATVi3swQLxw9EuEzSzh2IiIg87+9//zsA4M4777QdEwgELjcvGDx4MH766SckJCTYHf/oo49w4YUXer5gIiIiogDhUrBmNpu9XQdRt3BcrUVZjbbVsdJqLY6rtUiXcUkoERF1reLi4k5d/9RTT+G2227DiRMnYDabsXHjRhw6dAjr16/Hl19+6aEqiag9arUalZWV0Gg0iIiIQHR0NJRKpa/LIiLq8dxuXkBEbTurN3ZqnIiIyBvOn2nmrsmTJ+ODDz7AkiVLIBAIsGjRIowYMQJffPEFJkyY4KEqiagt5eXlyM7ORl5enu1YZmYmcnJyEBcX58PKiIjI7WBt5cqVrR4XCASQSqVISUnBFVdcYbexLVFPESZt/6+Us/E6nQHH1Vqc1RsRJhUjVinn0lEiIvILEydOxMSJE31dBlGPo1arHUI1AMjLy0N2djZyc3M5c42IyIfcDtaWL1+O06dPQ6vVQqlUwmKxoLa2FnK5HKGhoaiqqkJycjK2bt3KT0+ox4lVypEQJUdpteNy0IQoOWKV8javLatuwNodJXZLSROi5Jg1OpFND4iIyKdmz56NmTNnYuzYsRAIBL4uh6hHqaysdAjVmuXl5aGyspLBGhGRDwndvWDJkiW4+OKLceTIEVRXV6OmpgaHDx9GRkYGXnvtNZSVlSEmJgYPPvigN+ol8mvhAi0eHt6Ex9JrcG+aDlfEB0EsEtgCsrZmn9XpDA6hGmDdl23dzhLU6dhxjYiIfKe6uhqTJk1CbGwsHn74Yezdu9fXJRH1GBqNplPjRETkXW7PWPv3v/+NTz75BP3797cdS0lJwcsvv4ybbroJRUVFWLZsGW666SaPFkrk92qKYfj5DZjPlKJfvwuREBKJYSEi3Hp5OkxRyZCFtz3rzN2mB20tGeVSUiIiOp/JZML27dsxbNiwDs9q+fzzz1FbW4sPP/wQGzZswIoVKzBo0CDMnDkT06dPR2JiomeLJiIbhaL9xlfOxomIyLvcDtZOnToFo9FxA3aj0YiKigoAQN++fXH27NnOV0cUKPQa6La/jvLiw4i6cBKCCj6DofIAREIBZPIwBKVeDYy6F4hMavVyd5oetLZkNLl3CG68sB8++vU4l5ISEZEdkUiEiRMnoqCgoFPLxSIiIjB37lzMnTsXx48fx/vvv4933nkHixYtavXZkIg8Q6VSITMzs9XloJmZmVCpVD6oioiImrm9FHTMmDGYN2+e3RKAvXv34p577sHYsWMBAH/++SeSkloPEIi6I0N1CcqOHoA04SIEFXwGc+UBAIDJbIFOexbmiv1A/hpA3/pUfVebHrS1ZDRcJsHLeYdw7Ey93XEuJSUiIgAYOnQoioqKPHIvg8GAX3/9Ffn5+SgpKeE39UReplQqkZOTg8zMTLvjzV1Bub8aEZFvuR2svf3224iMjMRFF12E4OBgBAcHY+TIkYiMjMTbb78NAAgNDcUrr7zi8WKJ/JW+vhY6gwnhyt62UK2ZyWyB2WgA1EVAbVmr1zc3PWhNy6YHbS0ZjZBJsK9cA32TyWGseSkpERH1XM8//zz+8Y9/4Msvv8SpU6dQV1dn98MVW7duxZw5c6BSqXDHHXcgLCwMX3zxBcrLy71cPRHFxcUhNzcXBQUF2LVrFwoKCpCbm8tmcUREfsDtpaAxMTHYvHkzDh06hEOHDsFisSA1NRWDBg2ynTNmzBiPFknk7wziUACAwNTY6rhZILL+RN/6Ny/hMglmjU7Eup0ldh1Fz2960NaS0SajGUEiIcYkBuMSZQMkxnoYxKHY36DAzhMGp0tNiYioe7v66qsBAFOmTLHr6mmxWCAQCGAyOX4w01JsbCyqq6sxceJErF69GpMnT4ZUKvVqzURkT6lUcnYaEZEfcjtYazZo0CC7MI2oJ9OH9IM0OgUWUTAE542JgkNgFsusX0jD27xHfFQIFo4f2G7zgTCpGGKRAKP7STAkRGML0AxhAjxxmQwJR3Ng/KMIZgAiABnRKUgZPAvhTpaaEhFR97Z169ZOXb9o0SJMmzaN39RTj6RWq1FZWQmNRoOIiAhER0fz7wIREdkE1HfbS5cuxb/+9S8sWLAAK1asAGD9pHXx4sVYs2YN1Go1MjIy8J///AeDBw/2bbHUo4QqolA1Ihu66l0IVQ22LQcVBYfApIhHsFgCKJOBiPh27xMuk9h1/zxfrFKOe4aJIN2zGvqqY7YALenSmyCs+B0lZ+z3z9FXHUVfyXrEpC7u7FskIqIAduWVV3bq+rlz59p+fvz4cQgEAvTr16+zZRH5vfLyctx///0YOnQoRo0ahfLyckRFRSE5ORkJCQm+Lo+IiPyA23us+cru3buxZs0aDBs2zO74smXL8Oqrr2LVqlXYvXs3YmJiMGHCBHYlpS4VLpMgMnYg9oaOQeNViyAcNBGWqAFoCE1EkDQEkl4pQMY8QOqkHbpeA1T8CZTssP73vGYH4QItBpVugLC2BAAgFAAKmQQyMRBy4ieoQkSwWCy28+VBIsQYT0KmPeHpt0xERAHmp59+wsyZMzF69GicOGH9/8J///tfbN++3em1ZrMZzzzzDBQKBRISEhAfH4+IiAg8++yzMJvN3i6dyCvUajUKCwuRn5+PQ4cOQa1WO4zff//9yM7Oxq5duzB58mRMmzYNY8eORXZ2NkpKSnxTOBER+ZWAmLFWX1+PGTNm4K233sJzzz1nO26xWLBixQo88cQTmDp1KgDg3XffhUqlwoYNGzBv3jxflUw9TJ3OgIZGI/rFqHDM1BtRly5CtKkKkZYGSOQR1plqzkK1mmIgfzWgLj53TJkMZMwFIv/qsltbBll9GZJ6hUBvMMFkseBkrR5Nei0M9XUIjzYCCimCREIEiYWQSkSQiIRt7u1GREQ9wyeffILbbrsNM2bMwJ49e9DYaN0T9OzZs1iyZAm+/vrrdq9/4okn8Pbbb+OFF17AZZddBovFgh07duDpp5+GXq/H888/3xVvg8hjysvLkZ2djby8PNux5i6bzQ0BKisrMXToULz22mvYsmULACAkJAQLFy7EqFGjsH//fmi1WvTp04dLQ4mIerCAmLF23333YdKkSRg/frzd8eLiYlRUVNi1ng4ODsaVV16JnTt3tnm/xsbGDnXDImpNWXUDlm8+jJfzDmPl90exZlsRNvyhQW34IEiS/wbEDHVtptr5oRpg7SSav+bczLW/AjKJyBqaVdWbEdr/Ush6J0EWEY0ggRFGgwE1DU3nQjWg3b3diIio+3vuuefw5ptv4q233oJEcm7vztGjR2PPnj1Or3/33XeRk5ODe+65B8OGDcPw4cNx77334q233sK6deu8WDmR56nVaodQDQDy8vKQnZ1tm7mm0WgwatQou1Dt/ffft81emzx5MgYPHoysrCx2xyUi6sFcmrG2b98+l294/lLNzsrNzcWePXuwe/duh7GKigoAgEqlsjuuUqlQWlra5j2XLl2KxYu55xR1Xp3OgLU7SlBWo7U7XlqtxbqdJVg4fqBd84E21ZY5hmrN1EXW8ZihdgGZ3ixE1IWTEFTwGYzGCkiC5ED1UShFoWgKi4PeYLIGay7s7UZERN3boUOHcMUVVzgcDw8PR21trdPra2pqkJqa6nA8NTUVNTU1niiRqMtUVlY6hGrN8vLyUFlZCaVSCYVCYReYLVy40G72WstrsrOzkZuby5lrREQ9kEvB2gUXXACBQGC3d1NLzWOutGt3R3l5ORYsWIC8vLx2W7q3bBsPnGsd35bHH38cDz30kO3ruro625RvInccV2txUqPDFfFBdl069zcosPOEDsfV2nabEdg4W6rZPB4Rbw3K1EUQ9rsQQQWfwVx5AOYzRyH52z0wHt4M08kDCEI5BMEDAOUg1/Z2IyKibq1Pnz44evQoEhMT7Y5v374dycnJTq8fPnw4Vq1ahZUrV9odX7VqFYYPH+7JUom8TqPRuDSuUqlw8uRJ2/FRo0a1uey5ZSBHREQ9i0vBWnFxGzNpvOy3335DVVUVLrroItsxk8mEH3/8EatWrcKhQ4cAWGeu9enTx3ZOVVWVwyy2loKDgxEcHOy9wqnHqG804vZBFkQdsO/SmRGdggFDZsNoNOPgSQ3O6o0Ik4oRq5S3PoPN2VLN5nGpwrrnWv4aBIVGQdvcfVQsxtmCLdDGjELogMkwmRohT7gI6DeMoRoREWHevHlYsGAB3nnnHQgEApw8eRI///wz/vGPf2DRokVOr1+2bBkmTZqE7777DpdeeikEAgF27tyJ8vJyp/uzEfkbhaL9Z6PmcaVSif79+2P8+PH47rvvoNfr273OWWBHRETdk0vBmq9aSY8bNw5//vmn3bHZs2cjNTUV//znP5GcnIyYmBhs3rwZF154IQCgqakJ27Ztw4svvuiLkqmHiZUZUHdgLfRVx+yON50+iv5F/8P2pmx8feTcMtGEKDlmjU5EfFSI/Y1azERzcP5SzsgkYMxjEJX+DFGvZBgtIqhNEqg1Bphrd+AMrN1AkxIvYqhGREQAgEcffRQajQZjxoyBXq/HFVdcgeDgYPzjH//A/fff7/T6K6+8EocPH8Z//vMfFBYWwmKxYOrUqbj33nvRt2/fLngHRJ6jUqmQmZnZ6nLQzMxMuw/oExIS8NZbb2HevHntrqABnAd2RETUPXW4K+jBgwdRVlaGpqYmu+NTpkzpdFHNwsLCMGTIELtjISEhiIqKsh1fuHAhlixZggEDBmDAgAFYsmQJ5HI5pk+f7rE6iNrS21SJ+toSAIBAJIEy5RKEK3sjCAY0CYIwQqlFnkgAo8m6jLrNvddazESzC9eUya0v5ZQqIFT0g0geiZNqHbRN55Zgy4NEiFPKrN1IiYiI/vL888/jiSeewMGDB2E2m5Geno7Q0FCXr+/bty+7f1K3oFQqkZOT02ZX0POXcyYmJiI3Nxc1NTUuB3JERNRzuB2sFRUV4cYbb8Sff/5pt+9a855mntxjzRWPPvoodDod7r33XqjVamRkZCAvLw9hYWFdWgf1TBJDPeKUMhyvMyLyAmsjAfO+AxCKhRAYzeg1aCJuHzQH6w/Zh2ut7r3210w01JZZ91SThltnqrU16ywiHjLVQCSJjkJvMMFotkAsFFi7gfZKYcMCIiKyeffdd3HzzTcjJCQEI0eO9HU5RD4XFxeH3NxcVFZWQqPRQKFQQKVStblHmlKpdDuQIyKinkFgaasjQRsmT54MkUiEt956C8nJyfjll19QXV2Nhx9+GC+//DL+9re/eatWr6mrq4NCoYBGo0F4uJO9rohaqvgT+P45GPteBEvxj7BUHoQQAphhga7JBEvUAFTIByE/YS5+LDs3u3PuFcnISI7q/OvXFLc9yy0ysfP3JyKiNgXS80Pv3r2h1WoxefJkzJw5E1dffTXE4g4vXAgogfT7RIFBrVa7HMgREVFgcuf5we0nqp9//hnff/89evfuDaFQCKFQiMsvvxxLly7F/PnzsXfv3g4XThRw/tobTRwSCZwphFkogNlsHRIEhUBjEkNfdRRD0jX4ETLbZWFSD30zE5kE3WWPwFBdArOuDkJZOCRRiZCFR3rm/kRE1C2cOnUK33zzDd5//31kZWVBJpNh2rRpmDlzJkaPHu3r8qibahlARUREIDo6ulsEUM2z14iIiIAOBGsmk8m2H0evXr1w8uRJDBo0CAkJCbYunUQ9RvPeaMd+gMlsgc5ggslsQZAsFAZ5LE7UGBEsEUJirAf+CtYSouSIVcqd3rpOZ8BxtbbdjqJl1Q1Yu+MUympMAEIAmJAQdQqzRgc7NkggIqIeSywW47rrrsN1110HrVaLTz/9FBs2bMCYMWMQGxuLY8eOOb8JkRvKy8vbXDIZFxfnw8o6p7uGhURE1HFuB2tDhgzBvn37kJycjIyMDCxbtgxBQUFYs2YNkpOTvVEjkX+LTIKxoQa68GRYTEZYBCJUmyUQiOXoO3Q4QhS9IA414d40HU6hN4alxDsEZOezBmYlKKtpu6Nonc7gcA7QToMEIiIiAHK5HBMnToRarUZpaSkKCgp8XRJ1M2q12iFUA4C8vDxkZ2cjNzc3IMOo7hoWEhFR5wjdveDf//43zH+tdXvuuedQWlqKv/3tb/j666+xcuVKjxdIFAiqJP1wQjoQRQ1BKKkXobpRBFnaRESf2YXQ316HaPdbGHhgBSZWr0O8oKrdezkLzOp0BgDAcbXW4ZyW5xafacDBkxrkF1Xj4EmN7ToiIuqZtFot/ve//+Haa69F3759sXz5ctxwww3Yv3+/02urq6tx3333IT09Hb169UJkZKTdD6KWKisrW+2cCVjDtcrKyi6uqPOchYVqtdpHlRERka+5PWNt4sSJtp8nJyfj4MGDqKmpgVKptHUGJeppjuskMAyehSisg77qKJQpl0BS8BlMtSVoCouDACIopRKgtsTabGDMY212+3QWmDV3FD2rN7ZZj85gwv4TGnxfeC7EO3/GGxER9Ry33norvvjiC8jlckybNg0//PCDW3urzZw5E8eOHcNdd90FlUrFZz5ql0aj6dS4P3IlLAzEWXhE3sbl09QTdGoH9fLycggEAsTGxnqqHqKAFBosxmuHBBidMBdD0jUID2mE6cSPOCtLgLrejKReLb4BURcBtWVAzNBW79VeYNZyvK0GCAazGeU1WpjPa/jLJaJERD2XQCDABx98gIkTJ3aoG+j27duxfft2DB8+3AvVUXejULT+4aGr4/6oO4aFRN7G5dPUU7i9FNRoNOLJJ5+EQqFAYmIiEhISoFAo8O9//xsGA5eaUc8Uq5SjX4QMP5Y14fUCGco0BuuSUJ0ZUokIUonI/gJ9XZv3ctYxtHk8VilHQpRjEwR9kwlxkTLUtrL0s3nGGxER9SwbNmzApEmTOhSqAUBqaip0Op2Hq6LuSqVSITMzs9WxzMxMqFSqLq6o83wVFqrVahQWFiI/Px+HDh3iklMKGFw+TT2J28Ha/fffjzVr1mDZsmXYu3cv9u7di2XLluHtt9/GAw884I0aifxeuEyCWaMTbUGXQWztnCsPEiFOKYNEdN5fNWl4m/dqKzAD7DuKnv+azeIj5Zg8rB/2n2j9k1NnM+KIiKh72rZtGyZPnoyUlBQMGDAAU6ZMwU8//eTSta+//jqeeOIJbNu2DdXV1airq7P7QdSSUqlETk6OQ7jWPFMlEJeB+SIsLC8vR1ZWFtLS0jBq1CikpqYiKysL5eXlHn8tIk/rjnstErVFYLGct17MCYVCgdzcXFxzzTV2xzdt2oSsrKyAnAZdV1cHhUIBjUaD8PC2Aw8iZ+p0BhxXayE11SN6zwoE15U4hmrK5Hb3WAOsXUH3HS1DH5yGxFiPRnEoyoxRSIrtA5lEhH5KuW05p66uBobqEph1dRDKwqGV98Wz352A3mBu9d4PZw5Eet9zr91c81m9EWFSMWJb3JuIiNoWSM8P7733HmbPno2pU6fisssug8Viwc6dO/Hpp59i3bp1mD59ervXHzlyBLfeeiv27t1rd9xisUAgEMBkMnmz/E4JpN+n7qbl3koKhQIqlSogQ7VmXbmsTa1WIysrq9VgIjMzM2A7q1LPkZ+fj1GjRrU5vmvXLmRkZHRhRUTucef5we31AFKpFImJiQ7HExMTERQU5O7tiLqVcJkE6TIFAAUQcq+1UYG66NwJymQgY167oRoAxAuq0OfMWugrj6LJaEaTyYw+EUkoC7oNqwuBfhEyayMCQRVk+ashUxfbrpVFJOIfF89EpTEUMeZKSEz10AlD8HO1HGUNEtuMN8Aa4J3fgZRNDoiIup/nn38ey5Ytw4MPPmg7tmDBArz66qt49tlnnQZrM2bMQFBQEDZs2MDmBeQypVLZrcKfuLg45ObmdklYyGYJFOi6416LRG1xO1i777778Oyzz2Lt2rUIDg4GADQ2NuL555/H/fff7/ECiQJWZJJ1ZlptmXVPNWk4EBHvNFSDXgPkr4ZEUwpIRDhZq4fOYALqChGpX4vRCXPxY5nWOqPtzFrreS0Yq46iV8NqCBVpKN35CQAgVCrG1f0HQ3b5vQj9azZanc7gEKoBbHJARNQdFRUVYfLkyQ7Hp0yZgn/9619Or9+/fz/27t2LQYMGeaM8ooDRVWEhmyVQoGtePt3WrMtA3GuRqC0uBWtTp061+/q7775DbGysrTPUH3/8gaamJowbN87zFRIFMqmize6fbaotA/6agaY3mKyh2l/0VUcxJF2DHyFDH5yGvvIoJNJz4ZfBZIamQYfQyu8RPmowQoJFMFusS3Vqyg8heW8OoPwXIFXguFrrEKo1a25yYJ19R0REgS4uLg5btmxBSkqK3fEtW7a4tIRt5MiRKC8vZ7BG1EU424cCXfNei20tn+aMS+pOXArWzv+H+6abbrL7mq1yiTyoRcdQo9lxC0SJsR6ADBJjvcO43mCCFE0wNTZAaGqESChEcz9SncEEfcURSGrLgJihTpsYsMkBEVH38fDDD2P+/Pn4/fffMXr0aAgEAmzfvh3r1q3Da6+95vT6Bx54AAsWLMAjjzyCoUOHQiKxn9E8bNgwb5VO1CNxtg91B125fJrIl1wK1tauXevtOoioWYuOoWKh4x42zR1HDeJQh3Gj2QKRxQQLAIso2OFao9liC+7CpO3/9Xc2TkREgeOee+5BTEwMXnnlFXz44YcAgLS0NHzwwQe4/vrrnV7/97//HQBw55132o4JBIKAaF5AFIg424e6i+621yJRazr8nfPp06dx6NAhCAQCDBw4EL179/ZkXUQ9V0S8tcmBughSiQjyIBG0TdZvWKTRKchvUABowin0xrCYAUBtie1SsVAAk0AEkWow1OrTDrcWCwW24C5WKUdClByl1Y7LQROi5HZNDoiIKPDdeOONuPHGGzt0bXFxsfOTiMijONuHiCgwuB2sNTQ04IEHHsD69ethNpsBACKRCLfffjv+3//7f5DL+c04UadIFUDGXCB/DSTqIsQpZShX62COSMKZwbOx85ABCVFyDEuJh2TA3XadR6USETQh6UDSaKh/fM/utvIgEaQxA6zBHawdTGeNTsS6nSV24VpzV1A2LiAiomYJCQm+LoGoR+JsHyIi/yewWCyOmzi1Y968efjuu++watUqXHbZZQCA7du3Y/78+ZgwYQLeeOMNrxTqTXV1dVAoFNBoNAgPD3d+AVFX0GtsHUUNklCcFqlwXCdBaLAYsUr5ueCrxXmQhqNarMLxkycR/Ntb0FcdBWAN1eL6D4bs8vuAyES7l6nTGXBcrcVZvRFh0vPuTUREbepJzw/r169vd/z222/vokrc15N+n4iIiMgz3Hl+cDtY69WrFz7++GNcddVVdse3bt2KW265BadPOy4/83d84KJA4ywMq9MZUK+phrThBCTGekhDIyCJSrTOhiMiIo/oSc8P58+YMRgM0Gq1CAoKglwuR01NjY8qc64n/T5Rx6jVattyy4iICERHR3OWGBFRD+fO84PbS0G1Wm2rXWiio6Oh1Tru1UREnlVW3YC1O0pQVuO4fDM+KgQAEC7QIhynAZEeCIm0Lv9sJVTjbDUiInKFWq12OHbkyBHcc889eOSRR3xQEZFnlJeXt9kgIC4uzoeVERFRoHB7xtq4ceMQFRWF9evXQyqVAgB0Oh3uuOMO1NTU4LvvvvNKod7ETzLJ7/213NOgrcVRjRC/qEOx84QBRtO5v74JUXIsHD8Q4brjQP5qQN1io2llsnXftsgk2yFXAjoiImobnx+AX3/9FTNnzkRhYaGvS2kTf5+oLWq1GllZWXahWrPMzEzk5uZy5hoRUQ/l1Rlrr732Gq6++mrExsZi+PDhEAgE+P333yGVSvHtt992uGgiakNNsS0o0+sNaDzdgIzoFKQMnoX1hwS2cK20Wot6TTXC954XqgHW5gb5a4AxjwFSBep0BodQrfke63aWWAM6zlwjIuo2LBYLPv74Y2zduhVVVVW2BlTNNm7c2KH7ikQinDx50hMlEnW5ysrKVkM1AMjLy0NlZSWDNSIicsrtYG3IkCE4cuQI3nvvPRQWFsJisSArKwszZsyATCbzRo1EPZdeYzf7zGi2hmj6qqOIwjqMTpiLH8uabKdLG044hmrN1EXWJgcxQ3FcrXUI1ZqVVmtxXK1Fuoz7sRERdRcLFizAmjVrMGbMGKhUKggEAreu//zzz+2+tlgsOHXqlF0zK/Id7hHWMRqNplPjREREQAeCNQCQyWSYM2eOp2shovPVltkFZWLhuW+E9FVHMSRdgx9xLtCWGOvbv5++DgBwVm9s9zRn40REFFjee+89bNy4Eddee22Hrr/hhhvsvhYIBOjduzfGjh2LV155xQMVUkdxj7COUyja/xDR2TjZY8BLRD2VS8Ha+Z9StmfKlCkdLoaIzvNXENZMKhFBHiSCtskEoDlIswZrCVFySEOD2r+f1Lo2PExq/asvFgkwup8EQ0I0kBjrYRCHYn+DAuHSDmXuRETkpxQKBZKTkzt8/flLR8k/qNVqh1ANsC5jzM7O5h5hTqhUKmRmZra5x1prDduodQx4uyeGpUSucem75/M/pWyLQCCAyWTqTD1E1JLUfpNEiUiIOKUMdQ06BKMJUEhwb5oOp9Abw1LiIQkxWhsVqIsc76VMtnYHBRCrlCO5dwiu6t2AqAOroa86BjMAEYAr+g1Ev6EPAvD8p7TsQkpE5BtPP/00Fi9ejHfeeccjW3eYTCb8+eefSEhI4DdZPsQ9wjpHqVQiJyenzUCIv3auYcDbPTEsJXKd0JWTzGazSz8YqhF5WES8NRBrQYYm9G4qR4hcCknFbxh4YAUmVq9DvKAKkCqs3T/Pu8baFXSedRxAuEyCuzN6od/h9dBXHbOdJg8SIcZ4EkG/vmXd382DyqobsG3fUejK/4DsVD505X/gx31HUVbd4NHXISIiR9OmTYNarUZ0dDSGDh2KESNG2P1wZuHChXj77bcBWEO1K664AiNGjEBcXBx++OEHL1dPbeEeYZ0XFxeH3NxcFBQUYNeuXSgoKEBubi6DAze4EvBSYHEWlqrVah9VRuSfuN6LyJ81B2X5a6yz0EwGoLYUwsgkCIdMheTId4BUAtSWnOv6GZlk/W9tmXUpqTTcGtBJ7WegRRmroIiNQ7+UYbAY9RBIpBDo1BBX7LNrdOAJdToDao4fxoCDb9nNjkuJTkGNNBsR8iGcuUZE5EWzZs3Cb7/9hpkzZ3aoecHHH3+MmTNnAgC++OILlJSUoLCwEOvXr8cTTzyBHTt2eKNscoJ7hHmGUqnkjKpOYMDb/XA2LJF7XA7WdDodtmzZguuuuw4A8Pjjj6OxsdE2LhKJ8Oyzz0IqlXq+SqKerGVQVncC0BwHdGrgyHeA2XDuvJZhmFThPBRraoC4dDtQdfDcseh0YMhU673P29+tM+o11ZDuectudhxgbcAg3ZMDTe9/47haziWiRERe8tVXX+Hbb7/F5Zdf3qHrz5w5g5iYGADA119/jWnTpmHgwIG46667sHLlSk+WSm7gHmHkDxjwdj8MS8nf+dv+fy4Ha+vXr8eXX35pC9ZWrVqFwYMH2/bpKCwsRN++ffHggw96p1Kinqw5KNPXAUc2t32eq2GYXgP8/p59qAZYv94PIOlvDvu7dYa04QSqzgvVmmkrj0CuLsMru8+tTE+IkmPW6ETER4V4rAYiop4sLi4O4eEd/3ddpVLh4MGD6NOnD7755hu8/vrrAACtVguRSOSpMslN3COM/AED3u6HYSn5M3/c/8+lPdYA4H//+x/uvPNOu2MbNmzA1q1bsXXrVrz00kv48MMPPV4gEbXgLOxyNQyrLQM0JwCJ3HGs6iAQHmtrdOAJ1u6ljiwWC3RNJogMZ+2Ol1ZrsW5nCep0hlavIyIi97zyyit49NFHUVJS0qHrZ8+ejVtuuQVDhgyBQCDAhAkTAAD5+flITU31YKXkLu4RRr7WHPBmZmbaHWfAG7iaw9LWMCwlX/LX/f9cnrF2+PBhDBw40Pa1VCqFUHgul7vkkktw3333ebY6IrLX3MzASdfPZm124dTXASIJEJEA1JYCBu25iyRyIKS3w55snSENjYA8SARtk32DE7PFAlmQCDqh48y00motjqu1SJfxEzEios6aOXMmtFot+vfvD7lcDonEfrl9TU1Nu9c//fTTGDJkCMrLyzFt2jQEBwcDsG4F8thjj3mtbnIN9wgjX2sOeJuXZikUCqhUKv65DFCcDUv+yl/3/3M5WNNoNBCLz51++vRpu3Gz2Wy35xoRecH5zQyandf1E7B24Vy7owRlNedCM9sSy+aZbUFyICoFMOoAkxEQiQGxDAjz7KdQkqhExPUfjPJjB+zCNZlEhKi4NOTVyAE0OVx3Vm/0aB1ERD3VihUrOn2Pm2++2eHYHXfc0en7ElH3wIC3e2FYSv7IX/f/czlYi42Nxf79+zFo0KBWx/ft24fY2FiPFUZEbXCh62edzuAQqgHnllg+cmU/yJpnvokk1h/NWpn51mlSBWSX34sk8ZvQVxyB0WyBWCiAsFd//NHn79he2PqSzzDpuX+i2px9R0RETjEAIyIidzEsJX/jr/v/uRysXXvttVi0aBEmTZrk0PlTp9Nh8eLFmDRpkscLJKJWOOn6eVytdQjVmpVWa1FSL0KaizPfPCYyCZKx/4KkRSCok/fD1p8qYDQ1OJyeECVHrNK6B1xZdQP+OFqGvjgNmbEeOnEofizvjWEp8WxwQETkgrKysnbH4+M9/IEKERGRn/K3jpLkOn9tliKwWCwWV06srKzEBRdcgKCgINx///0YOHAgBAIBCgsLsWrVKhiNRuzduzcgNzKsq6uDQqGARqPpVMcsIn+RX1SNNT+2sg/bX+ZekYyM5Chrd9B2Zr51hbLqBuw7WoY+OA2JsR4GcShO4VxoVqczoOjwfkj3vAV9i86i0ugU6EdkI3ngEM5cIyKfCKTnB6FQCIFA0Oa4yWRqcyzQBdLvExEReZc/dpQk93TV76E7zw8uz1hTqVTYuXMn7rnnHjz22GNozuOaO0O9/vrrARmqEXVHEXIJrhrUG01GM4IlQqi1Buw/oYHRZP17a1ti6WTmW1eIF1Shz5m10FcetS0RHRYzAJIBdwNIQr2m2iFUAwB91VFI9+SgXvUkwmUxvimeiChA7N271+5rg8GAvXv34tVXX8Xzzz/vo6qIiIi6jrOOkrm5uZy5FgD8cf8/l4M1AEhKSsI333yDmpoaHD16FACQkpKCyMhIrxRHRO4rq27A//LLsLWwCrq/GgUMjAnF5GH98MPhKvSLkNmWWPqcXgPkr4ZEUwqJtMWss9oS6zLVMY9B2nACVeeFarbLq44iuuEEAAZrRETtGT58uMOxkSNHom/fvnjppZcwderUdq/fs2cPJBIJhg61fhjzf//3f1i7di3S09Px9NNPIygoyCt1ExEReYq/dpQk9/nb/n/CjlwUGRmJSy65BJdccglDNSI/0ty04IRah7hIOWRBIgDA4Yp6fLHvBK4c2BuzRif6z9LJ2jJAXdz6mLoIqC2DxFjf7i2cjRMRUdsGDhyI3bt3Oz1v3rx5OHz4MACgqKgIWVlZkMvl+Oijj/Doo496u0wiIqJO89eOkhT43JqxRkT+rWXTAplEhKTeIdA3mWA0W2A0WTC4r8K/NvvX1zkdl4ZGQB4kgrbJcf8feZAI0tAI79RGRNSN1NXZ/3trsVhw6tQpPP300xgwYIDT6w8fPowLLrgAAPDRRx/hiiuuwIYNG7Bjxw5kZWVhxYoVXqiaiIjIc/y1oyQFPgZrRN3IWb3R9nOxSIAr4qUYEqKxNQUIQwOACJ/V50DqZBNpaTgkEfGI6z8Y5ccO2IVr8iAR4voPhiQq0bs1EhF1AxEREQ7NCywWi22fEmcsFgvMZjMA4LvvvsN1110HwLrPyZkzZzxfMBERkYf5a0dJCnwM1oi6keamBGKRALcPsiDqwGroq47BDEAEIDp1GBByLxCZ5NM6bSLiAWWyddnn+ZTJti6lssvvRZL4TegrjtgaHEhjBkAy6p4u72JKRBSItm7dave1UChE7969kZKSArHY+ePgyJEj8dxzz2H8+PHYtm0b3njjDQBAcXExvxEhIqKAoFQqkZOT02ZHSX/as4sCC4M1om4kVilHQpQcCSFGW6jWTB4kQnBdia0pgF8EUlIFkDHXWlPLcE2ZDGTMO1djZBIkY/8FSW2ZdfmoNNwWuhERkXNXXnllp65fvnw5ZsyYgc8++wxPPPEEUlJSAAAff/wxRo8e7YkSiYiIvM4fO0q2Ra1W2+qMiIhAdHS0X9ZJgMBisVh8XYSv1dXVQaFQQKPRIDzcydI0Ij9XVt2A+rLf0ZT3jO2YPEiEOKUMsqC/svSx/wZihvqowlboNdZGBgESmtXpDDiu1uKs3ogwqRixSrn/NIQgoi4TSM8P7777Lnr16oVJkyYBAB599FGsWbMG6enpeP/995GQkNCh++r1eohEIkgk/vtvYCD9PhEREQFAeXl5mzPr4uLifFhZz+HO8wODNfCBi7ofY/mvsJzYC4tRD4FECoFODXHFPsBssJ4wej6QeJlviwxQZdUNWLujxNYkAgASouSYNTrRvxpDEJHXBdLzw6BBg/DGG29g7Nix+PnnnzFu3DisWLECX375JcRiMTZu3Nju9bNnz8bMmTMxduxYh73a/F0g/T4RERGp1WpkZWW1uRdcbm4uZ651AXeeH7gUlKi7qSmGeO+7wKFN545FpwNDpgJHvrOGa86aBlCr6nQGh1ANAEqrtVi3swQLxw/kzDUi8kvl5eW25ZufffYZbr75ZsydOxeXXXYZrrrqKqfXV1dXY9KkSYiKikJWVhZuu+02W5dQIiIi8pzKyspWQzUAyMvLQ2VlJYM1PyP0dQFE5EF6DZC/GtCcACTyc8erDgL7NwJ9h59rCkBuO67WOoRqzUqrtTiubn2MiMjXQkNDUV1dDcD6UD5+/HgAgFQqhU6nc3r9559/joqKCjz11FP47bffcNFFFyE9PR1LlixBSUmJN0snIiLqUTQaTafGqesxWCPqTmrLAHUxIJIAEQmO4Vp4rH1TAHLLWb2xU+NERL4yYcIEZGdnIzs7G4cPH7bttXbgwAEkJia6dI+IiAjMnTsXP/zwA0pLSzF79mz897//tc2EIyLqKLVajcLCQuTn5+PQoUNQq9W+LonIZxSK9r9XczZOXY/BGlF3oq879/MgORCVAvQaACiTrP8N7wtEJvqsPG+p0xlw8KQG+UXVOHhSgzqdwSuvEyZte/W8wWyGBfB6DUREHfGf//wHl156KU6fPo1PPvkEUVFRAIDffvsNt956q1v3MhgM+PXXX5Gfn4+SkhKoVCpvlExEPUR5eTmysrKQlpaGUaNGITU1FVlZWSgvL/d1aUQ+oVKpkJmZ2epYZmYm/7/rh9i8ANzUlrqRij+B759re9zfuoF6QFc2E6jTGfD/vj+CWJkBQ0I0kBjrYRCHYk9dGHZXGDG0XwR+L6v1ag1E5D962vPD1q1bsWHDBnzyyScwmUyYOnUqZsyYgbFjx0Io9N/Panva7xNRIOEm7UStY1dQ32NXUDfxgYu6Db0G2PoCoC5yHFMmA2Me61bLQOt0BizffLjVfc8SouReaSZQf+oIzmxZidoThwEAFosFUtUABI++B6/82gi9wez1GojIP/j780NZWRni413fU/PEiRPo169fq2OxsbGorq7GxIkTMWPGDEyePBlSqdRTpXqVv/8+EfVkhYWFSEtLa3O8oKAAqampXVgRkf9Qq9WorKyERqOBQqGASqVi0NyF3Hl+8N+PF4nIfVIFkDHXGqK1pEzulnurdXkzAb0Gob+/jX6WCvTvHYKEKDkSe4VAerYUhp/fwCV97JeKsqEBEfnSxRdfjDlz5uCXX35p8xyNRoO33noLQ4YMwcaNG9s8b9GiRTh58iQ+++wzTJs2LWBCNSLyb9yknahtSqUSqampyMjIQGpqKkM1P9b2hkFEFJgik6wz02rLrHuuScOtXUDdCNXqdAYcV2txVm9EmFSMWKXcL2dddXkzgb+aQ0hEQkhE1s8l1NomaHQGQHcUQ9I0+BEy79ZAROSigoICLFmyBFdffTUkEglGjhyJvn37QiqVQq1W4+DBgzhw4ABGjhyJl156Cddcc02b95o7d24XVk5EPQU3aSei7oDBGlF3JFV0eC+1rtyzrLPaaybgyrjbWjaH+ItYKLD9XGKsB84L1jxeAxGRiyIjI/Hyyy/jueeew9dff42ffvoJJSUl0Ol06NWrF2bMmIGJEydiyJAhvi6ViHqo5k3a29pjjZu0E1Eg4Hd8RGRTpzM4hGqAdUnjup0lfrdfWKxSjoQoOUqrW99jLVYp9+wLSh3X1kslIsiDRNA2mWAQh3q/BiIiN0mlUkydOhVTp071dSlERHaUSiVycnLa3KSdS9+IKBAwWCMiG1f2LEuX+c+U/HCZBLNGJ2Lf0TL0wWlbl85T6I1hKfGeDwEj4q371bVoDiERCRGnlKFC3Bc/NigANAE4N8uvq4LIQFm+S0RERNRSXFwccnNzuUk7EQUsBmtEZNOZPct8FezEC6rQ58xa6CuPwmi2QCwUYFjMAEgG3A0gybMv1twcIn+NXbgmUw1E3IhZuFGnxfUxDRDKwiGJ6gNZeNcsnQ2k5btERERE51MqlQzSiChgMVgjIpuO7lnWXrATIQ/yXuCm1wD5qyHRlEIibXHP2hJr+DXmMc93Qm2tOYREDvGvaxFefeTcecpkawgX6eFw7zyBtnyXiIiIiIioO2GwRkQ2HdmzrK1g50StDr+X1+LXUjVOqHV29+nITKpWZ8RprF06W6UusoZfHWzi0K6WzSH0GmDrUsc61EXeC/daCLTlu0RERERE5Bq1Wm1bJh0REYHo6GjO7vRDDNaIyKZ5z7J1O0vswrX29gtrK9gZ0k+B3N1lMJosCGsxm6wjM6namhH3SKr6vB6c52mli6fH1foo3PtLZ5bvEhERERGRfyovL2+zsUdcXJwPK6PzMVgjIjvxUSF45Mo+MFSXwKyrc7pfWFvBTYRMgsMV9YiPOjfLTSwSYEg/BSJkEuwuqUEfhdTp0tD2ljoW14swwGSGRCRs/eJWunh6nLPwzsvhXkeX7xIRERERkX9Sq9UOoRoA5OXlITs7G7m5uZy55kf4HRcR2asphix/NWQtZ2G1s19YW8FNk9EMABALBdb/igS4amA0vth3wha4KeVBTpeGtrfUcbc6FPHhiZA0lDkOKpOtXTy9zVl45+VwL1YpR3LvEMTKDBgSorF1Rt3foMBxnaTV5btEREREROS/KisrHUK1Znl5eaisrGSw5kfamOZBRD3SX80A2twvTK9xuKQ52LkiPgj3pumwYMBp3JumQ3okoJBJIA0SAbAuDW0O1YBzgVvz0tA6naHVktpbyrjzhAG6C++yhmgtKZOBjHle3dvMJiLe8fVb1uHlcC9cJsGCERJceTIHoh+eh3n7axD98DyuOJmDBSMkbFxARN3C66+/jqSkJEilUlx00UX46aeffF0SERGR12g0jt93uTNOXYsz1ojonA7sF9Yc7JzZ8gZqTxyGGYAIQFJiOpaNuw1r9pthNFlsS0MBQBYksgVuQPub7Le3lNFosuCMuA96n9+lMyK+a0I1wPo6GXOtwaO66Nzxrgr39BqE/v42gi0ViOodAqPZArFQAKmlApLf3wGU3m2eQETkbR988AEWLlyI119/HZdddhlWr16Na665BgcPHkR8fBfMTCYiIupiCkX7z+/OxqlrMVgjonM6sl9YW8FO03Eoyt/H5IF34tOCetvSUFmQCHGRckiE9hNm25qZ5mypYz+lHJBKvNogwKnIJGv3T1+Ee3+FoRKR0HGvuS5onkBE5G2vvvoq7rrrLmRnZwMAVqxYgW+//RZvvPEGli5d6vJ9GhoaIBKJnJ9IRETkY2FhYbj22mvxww8/OIxdddVVCAsLQ0NDQ9cX1oO48+vLYI2IzunIfmHtBDuS+jJMvKgJyXEDUdPQhOTeIZAGiRxCNaDtmWltzYi7ot9A9B43H6H+stRRqvBNgOXj5glERN7U1NSE3377DY899pjd8czMTOzcudOte61cuRJSqdST5REREXnNJZdcgksuuaTVsZycnC6upufR6/Uun8tgjYjOad4vrOWSxmZt7RfmJLiRGOqRnqhAnc6A4XER7m+yz6WO7fNx8wQiIm86c+YMTCYTVCqV3XGVSoWKiopWr2lsbERjY6Pt67o6fsBARERE3sNgjYjO6ch+YS4GOx2eecalju3rSBhKRBRgBAKB3dcWi8XhWLOlS5di8eLFXVEWEREREYM1IjqPu/uFtRfsRA0EQnoDFX8CxiaE7lkHmfm4ezPPuNSxfb5unkBE5EW9evWCSCRymJ1WVVXlMIut2eOPP46HHnrI9nVdXR3i4uK8WicRERH1XAzWiMiRO/uFtRXsRA0Eht8CbF9h7TQ6YAJwaBNEEjnCIhIAaYuln+3NPONSR+d82TyBiMiLgoKCcNFFF2Hz5s248cYbbcc3b96M66+/vtVrgoODERwc7HB8/vz5CA/v3v/PqK2txenTp3H27FmEh4ejV69eiIiI8HVZREREAaeurg4vvPCCS+cyWCOizmst2AnpfS5UAwDjX/vdGLRAbSkQlQKIWiz/1NehTmfAcbUWZ/VGhEnFiFXKEe5kqaNO3g/FJzX21/hLQ4Ou5KvmCUREXvbQQw/htttuw8iRI3HppZdizZo1KCsrw9133+3WfUJCQhASEuKlKn2vvLwc2dnZyMvLsx3LzMxETk4OZ+wRERG5yWQyuXwugzUi8ozzg52KP8+FagAgbjF7wKAFjDq7YK1BKMfyzYdRVqO1HUuIkmPW6ETEt7HUsf6Cu7DixwoUn2lwvCaq+37zRETUk/z9739HdXU1nnnmGZw6dQpDhgzB119/jYSEBF+X5jfUarVDqAYAeXl5yM7ORm5uLpRKpY+qIyIi6t4EFovF4usifK2urg4KhQIajabbLxEg6jIlO4CdK899HTsSKP4JqDpo/VqZBMgjAQCGiER8EzULnxXUO9wmIUqOheMHIlygtZsRp5P3wyvnhWoO1/TEmWtE1GX4/BAYesLvU2FhIdLS0tocLygoQGpqahdWREREFNjceX4QtjtKRNRR5+99dvIPYMhUIDrd+rXorwmzymTUD78LXx52DMgAoLRai+Nq7bkZcYmXATFDUVwvajVUs7uGiIioB9BoNJ0aJyIioo7jUlAi8o7z90YzG4Aj3wFJfwOG3wqE97XOWIuIx5FTRhhNbT/0n9UbXTrmzjgREVF3oVC036zG2TgRERF1HGesEZF3NHcLVSafO2Y2AA01QPylQPKV1hloUgVCg9vP+MOkjuOtHXNnvLup0xlw8KQG+UXVOHhSgzqdwdclERFRF1GpVMjMzGx1LDMzEyqVqosrIiIi6jl61neeRNS1WusWGhFvDd1aiFXKkRAlR2m14/LNhCg5YpVyh+Mduaa7KqtuwNodJa03fmATByKibk+pVCInJ6fNrqBsXEBEROQ9bF6AnrGpLZG/K6tuwLqdJXZBmbNwqCPXdDd1OoNDN9VmbOJA5F18fggMPen3Sa1Wo7KyEhqNBgqFAiqViqEaERFRB7jz/MAZa0TkF+KjQrBw/EAcV2txVm9EmFSMWKW83VCoI9d0N8fV2lZDNeBcE4d0GffWISLqCZRKJYM0IiKiLsZgjYj8RrhAi3RhGSCsA4ThgCAeQPuhUEeu6U7YxIGIiKjrtJwVGBERgejoaIaZREQ9nF83L1i6dCkuvvhihIWFITo6GjfccAMOHTpkd47FYsHTTz+Nvn37QiaT4aqrrsKBAwd8VDERdVhNMbB1KfD9c8DOldb/bn3BetyT13QzbOJARETUNcrLy5GVlYW0tDSMGjUKqampyMrKQnl5ua9LIyIiH/LrYG3btm247777sGvXLmzevBlGoxGZmZloaGiwnbNs2TK8+uqrWLVqFXbv3o2YmBhMmDABZ8+e9WHlROQWvQbIXw2ozwvE1EVA/hrruCeu6Yaamzi0pqc1cSAiIvIWtVrt0BwCAPLy8pCdnQ21Wu2jyoiIyNf8eirDN998Y/f12rVrER0djd9++w1XXHEFLBYLVqxYgSeeeAJTp04FALz77rtQqVTYsGED5s2b54uyichdtWWOAVkzdZF1PGaoy9cYzhyF7tQxFFgSuv2+a+EyCWaNTsS+o2Xog9OQGOthEIfiFHpjWEp8t33fREREXamystIhVGuWl5eHyspKLgklIuqh/DpYO59GY52BEhkZCQAoLi5GRUUFMjMzbecEBwfjyiuvxM6dO9sM1hobG9HY2Gj7uq6uzotVE5FTeid/B1sbb+MaXZMRZTU6NJ6swJojJgDdv1NovKAKfc6shb7yKIxmC8RCAYbFDIBkwN0AknxdHhERUcBr/j6ko+NERNR9+fVS0JYsFgseeughXH755RgyZAgAoKKiAgCgUqnszlWpVLax1ixduhQKhcL2Iy4uznuFE5Fz0vbbF7c63soxg8mMshoddAYTDOJQ2/HSai3W7SxBnc7Q2Ur9z19LYiWaUoRJJVDKgxAmlUBSW9KjlsQSERF5k0LRfmMkZ+NERNR9BUywdv/992Pfvn14//33HcYEAoHd1xaLxeFYS48//jg0Go3tBzccJfKxiHhAmdz6mDLZOu7CNXqDCTqDCdLoFOxvsH/ALa3W4rha66mK/Ycry2iJiIioU1Qqld0qmZYyMzMdPugnIqKeIyCCtQceeACff/45tm7ditjYWNvxmJgYAHCYnVZVVdXu/9yCg4MRHh5u94OIfEiqADLmOoZrymQgY5513IVrjGYLpNEpODN4NnaecJyddlZv9HTlvteRZbRERETkFqVSiZycHIdwLTMzEzk5OdxfjYioB/PrPdYsFgseeOABfPrpp/jhhx+Qimz4dwAAIOlJREFUlGS/V1BSUhJiYmKwefNmXHjhhQCApqYmbNu2DS+++KIvSiaijopMAsY8Zp1hpa+zLvWMiG89VGvjGpFFhvxiEXYeMsBosjicHibt+D95dToDjqu1OKs3+ldDhI4soyUiIiK3xcXFITc3F5WVldBoNFAoFFCpVAzViIh6OL8O1u677z5s2LAB//d//4ewsDDbzDSFQgGZTAaBQICFCxdiyZIlGDBgAAYMGIAlS5ZALpdj+vTpPq6eiNwmVTh2/3TnGp0BpfsPw2hqcjgtIUqOWKW8Q2WVVTdg7Y4SlNWcW0rqNw0RmpfEqoscx9paRktEREQdolQqGaQREZEdv14K+sYbb0Cj0eCqq65Cnz59bD8++OAD2zmPPvooFi5ciHvvvRcjR47EiRMnkJeXh7CwMB9WTkS+EC6TYNboRCRE2QdozSFYR2aY1ekMDqEa0HpDhDqdAQdPapBfVI2DJzVd0yyhI8toiYiIiIiIyCMEFovFcb1UD1NXVweFQgGNRsP91oi6AU8u2zx4UoNX8g63Of5w5kCk91X4flabXuPeMloi6jQ+PwQG/j4RERGRu9x5fvDrpaBERB0RLpMgXeaZUMlZw4OzeqPTWW0Lxw/0/n5sHVlGS0RERERERJ3i10tBiYh8zVnDgzCpGMfVWodQrVlptRbH1a2PERERERERUWBjsEZE3Y9eA1T8CZTssP5Xr+nwrWKVcoc925o1N0RwZVYbERERERERdT9cCkpE3UtNMZC/GlAXnzumTLZu8B+Z5PbtmhsirNtZgtJqx/3TwmUSl2a1ERERERERUffD7/aIqPvQaxxDNQBQFwH5a4Axj3VoQ//4qBAsHD+wzYYIzbPaWgZvzZpntREREREREVH3w6WgRNR91JY5hmrN1EXW8Q4Kl0mQ3leBjOQopPdV2DUjaJ7Vdv6S0Zaz2oiIiIiIiKj74Yw1Iuo+9HWdG2/3Wo01mNPXAdJwICLebvZbfFQIHrmyDwzVJTDr6iCUhUMS1Qey8BC3X6pOZ2hzdhwRERERERH5DwZrRNR9SMM7N94WV/ZtqymGLH81ZG2c42pYVlbdgLU7Suy6jDbPfIuPcj+kIyIiIiIiIu9hsEZE3UdEvDXMUhc5jimTrePucmXfNqDdc6ovfhCrdp52GpbV6QwOoRoAlFZrsW5nCRaOH8iZa0RERERERH6Ee6wRUfchVVhniCmT7Y8rk4GMeR1qXODSvm3tnGM4cxSVZUfaDMvqdAbbseNqrcN5Lc8/rm59jIiIiIiIiHyDM9aIqHuJTLLOImtnPzS3uLBvm8Fshl5vgNFsgVgogFQigkRk/dxCbzDBqNMA6O1waXNYli6z1nZWb2z3pZyNExERERERUddisEZE3Y9UAcQM9dC92t+XrUEoR2mNFk2nG2zH5EEixCllkAWJYTRbYBCHtnl9y7AsTNr+P8nOxomIiIiIiKhrcSkoEVF7mvdta4UhIhHfn5Jid20opNEptuPaJhPK1ToYTGZIeqdgf0Pbs+VahmWxSjkSouStnpcQJUessvUxIiIiIiIi8g0Ga0RE7Wln37b64Xfhy8MN2HnCgOrBsxzCtcbwRASNvgfHda03HDg/LAuXSTBrdKJDuNbc6ICNC4iIiIiIiPwL1xURETnTxr5tR04ZYTRpAADrDwkwOmEuhqRrIDHWwyAORVXf/kju3Q+3jWrAup0lKK127Ap6flgWHxWCheMH4rhai7N6I8KkYsQq5QzViIiIiIiI/BCDNSIiV7Syb1tosAZikQCj+0kwJOSvQA2h2NvYDzuLDFgQZ91bLT4qBI9c2QeG6hKYdXUQysIhieoDWXhIqy8VLtAiXVgGCOsAYTggiAfQweYLRERERERE5DUM1oiIOihWKcc9w0SQ7lkNfdUxmAGIAGREp2D4iOxzyzxriiHLXw2Zuvjcxcpk6xLTyCT7m9YUA/mrAVfOJSIiIiIiIp/iHmtERB0ULtBiUOkGCGtL7I4La4sxqHQDwgVaQK9xDMoAQF0E5K+xjjdz51wiIiIiIiLyOc5YIyLqqNoyyOrLkNQrBHqDCUazBWKhAFKJCJL6MuuebIBjUNZMXWQ9p3mJaW2Z6+cSERERERGRzzFYIyLqKH0dAEAiEkIiamUC8F/jrtzDpfNduR8RERERERF1GQZrREQdJQ3v3Pj553jifkRERERERNRluMcaEVFHRcRbGwu0RplsHXflHHfuR0RERERERH6DwRoRUUdJFdZuneeHYcpkIGOeddyVc9y5HxEREREREfkNLgUlIuqMyCRgzGPWxgL6OutyzYh4+xDMlXM6ci4RERERERH5FIM1IqLOkiqcd+t05ZyOnEtEREREREQ+w6WgREREREREREREHcAZa0REPUydzoDjai3O6o0Ik4oRq5QjXCbptq9LRBTo1Go1KisrodFoEBERgejoaCiVSl+XRURERGCwRkTUo5RVN2DtjhKU1WhtxxKi5Jg1OhHxUSHd7nWJiAJdeXk5srOzkZeXZzuWmZmJnJwcxMXF+bAyIiIiArgUlIiox6jTGRzCLQAordZi3c4S1OkM3ep1iYgCnVqtdgjVACAvLw/Z2dlQq9U+qoyIiIiaMVgjIuohjqu1DuFWs9JqLY6rWx8L1NclIgp0lZWVDqFas7y8PFRWVnZxRURERHQ+LgUlIuohzuqNEIsEGN1PgiEhGkiM9TCIQ7G/QYGdJww4qzd67XU7M05E1FNpNJpOjRMREZH3MVgjIuohwqVi3D7IgqgDq6GvOgYzABGAjOgUpAyehXCpd/6XECYVtxvohXnpdYmIAp1CoejUOBEREXkfv5shIuohEkNNkB1ej9qqY3bH9VVH0VeyHjGpi73yurFKOe4ZJoJ0j2OgN2xENsRCAfKLqtkplIjoPCqVCpmZma0uB83MzIRKpfJBVURERNQS91gjIuohZNoTUBlPQh4ksjsuDxIhxngSMu0Jr7xuuECLQaUbIKwtsTtuqSlC7OH1yC8sxpofi/BK3mGs+O4wyqobvFIHEVGgUSqVyMnJQWZmpt3x5q6gSqXSR5URERFRM85YIyLqKfR1kAWJkdQrBHqDCUazBWKhAFKJCBKRENDXeed1a8sgqy+ze12hQIB6vQGNFYW4bnANhkt0fy0PDcJ/d5XigbEDOHONiAhAXFwccnNzUVlZCY1GA4VCAZVKxVCNiIjITzBYIyLqKaThAACJSGgN0toY97i/AruWr3tWb4BM0IQgbTlMFX/AvHuT3X5vJ9RahMu4dxAREWCducYgjYiIyD9xKSgRUU8REQ8ok1sfUyZbx72hlcBOaDEh6Gw5TI0NsIiCbcf1VUcRdWAdgk313qmFiIiIiIjIgxisERH1FFIFkDHXMVxTJgMZ86zj3tBKoBdkaYSpsQFC1WDUqU/bjemrjqKXsdI7tRAREREREXkQl4ISEfUkkUnAmMeA2jLrEk1puDX48laoBpwL9PLXAOoiAIAIRkj6DIZu0A1Q//K53enyIBFkFjYwICIiIiIi/8dgjYiop5EqgJihHrlVnc6A42otzuqNCJOKEauUt9504LxATwgLxEU7UPP7V7CYDLbT5EEixCllkMgjPFIfERERERGRNzFYIyKiDimrbsDaHSUoq9HajiVEyTFrdCLio0IcL2gZ6Ok1CCr4ConKIOhDRfYdSnuleG+/NyIiIiIiIg9isEZERG6r0xn+f3v3HxTVfa9x/FlhYUF+KQhIQCFiolVMFKKFxLZJWizNtXE6tbG1qRnD3KiYamz+SJO51nZi6LTTDEmrtCgh8faHdibRpJmo0MSirTc1amj9VQNJDcQKREQQFAxw7h+G1ZNFhQX5Lsv7NXNmwtnlzON3dObJZ89+jzb934dKDuvQf8VdkLOjRZ8Ehulwa5D+9+0P9eg9E3u+c63bp18Pdf69SM5Pvx4q6cbv9wYAAAAAA4jBGgCgz042ntfdsa2KPlKitvr31SUpQNKs2FSlTnlIJxvPKyLkOsMxE/u9AQAAAMAAYrAGAOiz4M4W91DtSm31VYrWiwoa+6SkXgzIBnC/NwAAAAAYbCNMBwAADD0xHXUeQ7VubfVViumoG+REAAAAADD4GKwBAPosxGpVaFBAj6+FBgUoxGod5EQAAAAAMPgYrAEA+swZGqWkUSEew7XQoAAljQqRMzTKTDAAAAAAGETssQYA6LuocQqJu0UpAVVq+6RTHV2WAkc45HIGyBmTeukhBENY84VP9FHjeZ1r61C4K1CJo0Kv/ZRTAAAAAMMSgzUAQN+5IqVZ/y3n34vkbPzg8vlRN0uzHhnST/asbmjVP6qqlaCPFdLRoguBYdpdM0bTUsdpXPRI0/E8MAQEAAAAzGGwBgDwzugU6e4npLPVUluz5Iq4dKfaEB6qNV/4RGc+ek8Tj25QW/376pIUICk1NlVnXLmKCp3qU0OroTYEBAAAAPwNgzUAgPdckVJ8mukUA6alqUGugxs8nnjaVl8l18GNaon7H0WExBtKZzfUhoAAAACAP+LhBQAAfMrVetJjqNatrb5KrtaTg5zo6q47BGxqMJQMAAAAGD4YrAEA8ClnR0u/Xh9MQ2kICAAAAPgrBmsAAHzKFRal0KCAHl8LDQqQKyxqcANdw1AaAgIAAAD+isEaAACfckYnK2nCFI/hWmhQgJImTJEzOtlMsB4MpSEgAAAA4K94eAEAAN1ckQq5a5lSAn+tttpKdXRZChzhkCt+opyfX+pTTzztHgLWvH9E5y92us/74hAQAAAA8FcM1gAAuNLoFDnveVLOs9VSW7PkipCixvnUUE3SkBoCAgAAAP6KwRoAAJ/lipTi00ynuK7mkES1zHhMrtaTcna0yBUWdelONYZqAAAAwKBgsAYAwBBU3dCqf1RVK0Efy9nRok8Cw3TqbJCmuQI1zmU6HQAAADA8MFgDAGCIab7wic589J4mHt2gtvr31SUpQFJqbKrOuHIVFTpVESFO0zEBAAAAv8dTQQEAGGJamhrkOnhpqHaltvoquQ5uVEtTg6FkAAAAwPDCHWsAAAwxrtaTqv/MUK1bW32VYltPSoof3FA+pvnCJ/qo8bzOtXUo3BWoxFGh3MUHAACAAcdgDQCAIcbZ0dKv1/3dlfvPhXS06EJgmHbXjNG01HEaFz3SdDwAQ1RjY6Pq6urU1NSkqKgoxcbGatSoUaZjAQAMY7AGAMAQ4wqLUmhQgM5f7PR4LTQoQK6wqMEP5SPYfw7AjVBTU6Pc3FyVlpa6z2VnZ2vjxo1KSkoymAwAYBp7rAEAMMQ4o5OVNGGKQoMCbOdDgwKUNGGKnNHJZoL5APafAzDQGhsbPYZqklRaWqrc3Fw1NjYaSgYA8AXcsQYAwFDjilTIXcuUEvhrtdVWqqPLUuAIh1zxE+X8/FLJFWk6oTHsPwdgoNXV1XkM1bqVlpaqrq6Or4QCwDDGYA0AgKFodIqc9zwp59lqqa1ZckVIUeOG9VBNYv85AAOvqampX68DAPwbgzUAAIYqV6QUn2Y6hU9h/zkAAy0y8tofWFzvdQCAf2OPNQAA4DfYfw7AQIuLi1N2dnaPr2VnZysuLm6QEwEAfAmDNQAA4D+695+7NU0TxozU+OhQTRgzUim3pinkrrxh/1VZAH03atQobdy40WO41v1UUPZXA4Dhja+CAgAA/8L+cwAGWFJSkjZv3qy6ujo1NTUpMjJScXFxDNUAAAzWAACAH2L/OQADbNSoUQzSAAAe+CooAAAAAAAA4AUGawAAAAAAAIAXGKwBAAAAAAAAXmCwBgAAAAAAAHiBwRoAAAAAAADgBQZrAAAAAAAAgBcYrAEAAAAAAABeYLAGAAAAAAAAeMFvBmvr169XSkqKXC6X0tPTtWfPHtORAAAAAAAA4Mf8YrC2ZcsWrVy5Uk899ZTeffddzZ49Wzk5OaqurjYdDQAAAAAAAH7KLwZrzz77rB5++GHl5uZq8uTJKigoUFJSkgoLC01HAwAAgJfWrl2rrKwshYaGKioqynQcAAAAD0N+sHbx4kUdOHBA2dnZtvPZ2dnau3dvj7/T3t6u5uZm2wEAAADfcvHiRc2fP19Lly41HQUAAKBHgaYD9Nfp06fV2dmpuLg42/m4uDjV1tb2+Dv5+fn68Y9/PBjxAAAA4KXuvvbiiy+aDQIAAHAVQ/6OtW4Oh8P2s2VZHue6/fCHP1RTU5P7qKmpGYyIAAAAuMH4ZgIAABhMQ36wFhMTo4CAAI+70+rr6z3uYusWHBysiIgI2wEAAIChLz8/X5GRke4jKSnJdCQAAODHhvxgLSgoSOnp6SorK7OdLysrU1ZWlqFUAAAA6MmaNWvkcDiueezfv9/r6/PNBAAAMJiG/B5rkrRq1So9+OCDysjIUGZmpoqKilRdXa0lS5aYjgYAAIArLF++XAsWLLjme5KTk72+fnBwsIKDg73+fQAAgL7wi8HaAw88oIaGBv3kJz/RqVOnNHXqVL3xxhsaP3686WgAAAC4QkxMjGJiYkzHAAAAGBB+MViTpGXLlmnZsmVe/a5lWZLE5rYAAKDXuntDd4/AwKuurtaZM2dUXV2tzs5OVVRUSJJSU1MVFhbWq2vQ8wAAQF/1pef5zWCtP86dOydJbG4LAAD67Ny5c4qMjDQdwy+tXr1aL730kvvn6dOnS5J27dqlL33pS726Bj0PAAB4qzc9z2HxMau6urr0n//8R+Hh4XI4HMZyNDc3KykpSTU1NTypVKzHZ7EenlgTO9bDjvXwxJrY9Xc9LMvSuXPnlJCQoBEjhvzzoPwWPc83sR6eWBM71sOO9fDEmtixHnaD2fO4Y03SiBEjlJiYaDqGW0REBP8QrsB62LEenlgTO9bDjvXwxJrY9Wc9uFPN99HzfBvr4Yk1sWM97FgPT6yJHethNxg9j49XAQAAAAAAAC8wWAMAAAAAAAC8wGDNhwQHB+tHP/qRgoODTUfxCayHHevhiTWxYz3sWA9PrIkd64HBxN83O9bDE2tix3rYsR6eWBM71sNuMNeDhxcAAAAAAAAAXuCONQAAAAAAAMALDNYAAAAAAAAALzBYAwAAAAAAALzAYA0AAAAAAADwAoM1H7F+/XqlpKTI5XIpPT1de/bsMR3JmN27d2vu3LlKSEiQw+HQtm3bTEcyKj8/X3fccYfCw8MVGxurefPm6fjx46ZjGVNYWKhp06YpIiJCERERyszM1Pbt203H8hn5+flyOBxauXKl6SjGrFmzRg6Hw3bEx8ebjmXUyZMn9d3vflfR0dEKDQ3V7bffrgMHDpiOZUxycrLH3xGHw6G8vDzT0eCn6HmX0fPs6Hme6HpXR8+j5/WEnmdnoucxWPMBW7Zs0cqVK/XUU0/p3Xff1ezZs5WTk6Pq6mrT0YxobW3Vbbfdpl/96lemo/iE8vJy5eXl6e2331ZZWZk6OjqUnZ2t1tZW09GMSExM1E9/+lPt379f+/fv1z333KP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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.subplot(121)\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='sum_over_agg_reg_medians',\n",
    "                label='sum over medians of\\nfive larger regions\\n(new approach for uncertainties)', alpha = 0.7)\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='sum_over_reg_medians',\n",
    "                label ='sum over medians of\\n19 RGI glacier regions\\n(current approach)', alpha = 0.7)\n",
    "\n",
    "plt.ylabel('Global glacier mass (% rel. to 2020)')\n",
    "plt.xlabel(r'$\\Delta$T (°C)')\n",
    "plt.subplot(122)\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='diff', color='black')\n",
    "plt.axhline(0,lw=3, color='grey')\n",
    "plt.ylabel('Difference in %\\n(sum over medians of five larger regions\\nvs sum over medians of 19 RGI regions)')\n",
    "plt.xlabel(r'$\\Delta$T (°C)')\n",
    "plt.savefig('approaches_to_compute_the_median.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dcf6aad6-ef66-4af5-9068-4e7c0f22b4d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 87.79559  ,  75.59363  ,  90.198975 ,  67.31606  ,  52.131798 ,\n",
       "        57.899414 ,  51.93683  ,  50.52314  ,  41.33349  ,  36.871334 ,\n",
       "        52.61757  ,  31.660711 ,  27.22839  ,  45.612827 ,  27.073183 ,\n",
       "        21.23657  , 108.15851  , 112.14426  ,  86.9002   ,  65.36223  ,\n",
       "        39.50067  ,  41.254578 ,  37.463593 ,  33.67183  ,  25.400024 ,\n",
       "        22.33712  ,  30.978495 ,  15.440788 ,  11.942964 ,  29.769627 ,\n",
       "        10.625323 ,   6.952806 , 102.3264   , 104.16589  ,  96.16344  ,\n",
       "        68.075424 ,  56.675987 ,  55.542423 ,  54.634827 ,  53.74283  ,\n",
       "        39.838444 ,  41.20093  ,  50.12051  ,  32.326653 ,  30.41977  ,\n",
       "        51.81985  ,  28.208038 ,  25.51837  ,  98.24293  ,  95.85801  ,\n",
       "        83.762276 ,  68.083954 ,  38.72     ,  43.154785 ,  41.901062 ,\n",
       "        35.765053 ,  26.31411  ,  25.115862 ,  37.48006  ,  19.680897 ,\n",
       "        18.037195 ,  37.39487  ,  16.373022 ,  12.002929 ,  90.60567  ,\n",
       "        91.74645  ,  93.57783  ,  66.67959  ,  35.476826 ,  35.678913 ,\n",
       "        33.484688 ,  24.447317 ,  18.494785 ,  16.062935 ,  24.90643  ,\n",
       "         8.97272  ,   7.2312946,  22.272928 ,   5.0622363,   4.203675 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "10eb1ddd-fba9-409d-ac78-46ffc4a0450d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>quantiles</th>\n",
       "      <th>0.5</th>\n",
       "      <th>frac</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>it</th>\n",
       "      <th>N</th>\n",
       "      <th>fit_opt</th>\n",
       "      <th>y</th>\n",
       "      <th>min_0.5_diff</th>\n",
       "      <th>min_0.5</th>\n",
       "      <th>min_0.5_diff_above_zero</th>\n",
       "      <th>median_absolute_deviation</th>\n",
       "      <th>rmse</th>\n",
       "      <th>algorithm_sel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>62.373483</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>52.514136</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>37.960147</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>26.030015</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>24.381455</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>16.399218</td>\n",
       "      <td>0.14</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>4.659843</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>2.514961</td>\n",
       "      <td>4.07416</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "quantiles        0.5  frac region  year  it    N     fit_opt   y  \\\n",
       "x                                                                  \n",
       "1.2        62.373483  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "1.5        52.514136  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "2.0        37.960147  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "2.7        26.030015  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "3.0        24.381455  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "4.0        16.399218  0.14    All  5000   2  500  lowess_fit NaN   \n",
       "\n",
       "quantiles  min_0.5_diff   min_0.5  min_0.5_diff_above_zero  \\\n",
       "x                                                            \n",
       "1.2              0.0481  4.659843                   0.0481   \n",
       "1.5              0.0481  4.659843                   0.0481   \n",
       "2.0              0.0481  4.659843                   0.0481   \n",
       "2.7              0.0481  4.659843                   0.0481   \n",
       "3.0              0.0481  4.659843                   0.0481   \n",
       "4.0              0.0481  4.659843                   0.0481   \n",
       "\n",
       "quantiles  median_absolute_deviation     rmse                algorithm_sel  \n",
       "x                                                                           \n",
       "1.2                         2.514961  4.07416  non_negative_and_decreasing  \n",
       "1.5                         2.514961  4.07416  non_negative_and_decreasing  \n",
       "2.0                         2.514961  4.07416  non_negative_and_decreasing  \n",
       "2.7                         2.514961  4.07416  non_negative_and_decreasing  \n",
       "3.0                         2.514961  4.07416  non_negative_and_decreasing  \n",
       "4.0                         2.514961  4.07416  non_negative_and_decreasing  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_t.index = sel_t.index.round(2)\n",
    "sel_t.loc[[1.2,1.5,2.0,2.7,3.0,4.0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9deb34d7-0177-4f83-9ce9-42fb16239dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>0.05</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.95</th>\n",
       "      <th>frac</th>\n",
       "      <th>region</th>\n",
       "      <th>year</th>\n",
       "      <th>fit_to_median</th>\n",
       "      <th>...</th>\n",
       "      <th>fit_opt</th>\n",
       "      <th>shift_years_2020</th>\n",
       "      <th>y</th>\n",
       "      <th>add</th>\n",
       "      <th>min_0.5_diff</th>\n",
       "      <th>min_0.5</th>\n",
       "      <th>min_0.5_diff_above_zero</th>\n",
       "      <th>median_absolute_deviation</th>\n",
       "      <th>rmse</th>\n",
       "      <th>algorithm_sel</th>\n",
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       "    <tr>\n",
       "      <th>x</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>1.2</td>\n",
       "      <td>52.570248</td>\n",
       "      <td>57.545283</td>\n",
       "      <td>60.289471</td>\n",
       "      <td>60.139074</td>\n",
       "      <td>66.641965</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>41.779585</td>\n",
       "      <td>49.221156</td>\n",
       "      <td>52.798979</td>\n",
       "      <td>54.120481</td>\n",
       "      <td>61.484724</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31.163602</td>\n",
       "      <td>32.289840</td>\n",
       "      <td>36.260625</td>\n",
       "      <td>41.861909</td>\n",
       "      <td>44.642124</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>2.7</td>\n",
       "      <td>19.854313</td>\n",
       "      <td>22.034278</td>\n",
       "      <td>24.439460</td>\n",
       "      <td>29.038282</td>\n",
       "      <td>33.131111</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>17.168672</td>\n",
       "      <td>19.897759</td>\n",
       "      <td>22.414929</td>\n",
       "      <td>28.163813</td>\n",
       "      <td>31.523450</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10.852475</td>\n",
       "      <td>12.460028</td>\n",
       "      <td>14.638504</td>\n",
       "      <td>21.285306</td>\n",
       "      <td>27.332475</td>\n",
       "      <td>0.23</td>\n",
       "      <td>All</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>lowess_fit</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>4.808715</td>\n",
       "      <td>0.087584</td>\n",
       "      <td>2.613016</td>\n",
       "      <td>4.206593</td>\n",
       "      <td>non_negative_and_decreasing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       x       0.05       0.25        0.5       0.75       0.95  frac region  \\\n",
       "x                                                                              \n",
       "1.2  1.2  52.570248  57.545283  60.289471  60.139074  66.641965  0.23    All   \n",
       "1.5  1.5  41.779585  49.221156  52.798979  54.120481  61.484724  0.23    All   \n",
       "2.0  2.0  31.163602  32.289840  36.260625  41.861909  44.642124  0.23    All   \n",
       "2.7  2.7  19.854313  22.034278  24.439460  29.038282  33.131111  0.23    All   \n",
       "3.0  3.0  17.168672  19.897759  22.414929  28.163813  31.523450  0.23    All   \n",
       "4.0  4.0  10.852475  12.460028  14.638504  21.285306  27.332475  0.23    All   \n",
       "\n",
       "     year  fit_to_median  ...     fit_opt shift_years_2020   y  add  \\\n",
       "x                         ...                                         \n",
       "1.2  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "1.5  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "2.0  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "2.7  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "3.0  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "4.0  5000          False  ...  lowess_fit             True NaN  NaN   \n",
       "\n",
       "    min_0.5_diff   min_0.5  min_0.5_diff_above_zero  \\\n",
       "x                                                     \n",
       "1.2     0.087584  4.808715                 0.087584   \n",
       "1.5     0.087584  4.808715                 0.087584   \n",
       "2.0     0.087584  4.808715                 0.087584   \n",
       "2.7     0.087584  4.808715                 0.087584   \n",
       "3.0     0.087584  4.808715                 0.087584   \n",
       "4.0     0.087584  4.808715                 0.087584   \n",
       "\n",
       "     median_absolute_deviation      rmse                algorithm_sel  \n",
       "x                                                                      \n",
       "1.2                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "1.5                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "2.0                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "2.7                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "3.0                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "4.0                   2.613016  4.206593  non_negative_and_decreasing  \n",
       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_norm_all = pd_low_normal.loc[pd_low_normal.region == 'All']\n",
    "sel_norm_all = sel_norm_all.loc[sel_norm_all.y.isna()]\n",
    "sel_norm_all.index = sel_norm_all.x\n",
    "sel_norm_all.index = sel_norm_all.index.round(2)\n",
    "sel_norm_all.loc[[1.2,1.5,2.0,2.7,3.0,4.0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3f5dd3c9-046e-4d54-9335-be000bf4e53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.subplot(121)\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='sum_over_agg_reg_medians',\n",
    "                label='sum over medians of\\nfive larger regions\\n(new approach for uncertainties)', alpha = 0.7, color='C0')\n",
    "plt.plot(sel_t.index, sel_t[0.5], color = 'C0', label='median lowess fit' )\n",
    "\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='sum_over_reg_medians',\n",
    "                label ='sum over medians of\\n19 RGI glacier regions\\n(current approach)', alpha = 0.7, color='C1')\n",
    "plt.plot(pd_low_normal.loc[pd_low_normal.region == 'All'].x,pd_low_normal.loc[pd_low_normal.region == 'All']['0.5'],\n",
    "         color = 'C1', label='median lowess fit' )\n",
    "plt.ylabel('Global glacier mass (% rel. to 2020)')\n",
    "plt.xlabel(r'$\\Delta$T (°C)')\n",
    "plt.subplot(122)\n",
    "sns.scatterplot(data=pd_sum_med_sum, x='temp_ch_ipcc', y='diff', color='black')\n",
    "plt.axhline(0,lw=3, color='grey')\n",
    "plt.ylabel('Difference in %\\n(sum over medians of five larger regions\\nvs sum over medians of 19 RGI regions)')\n",
    "plt.xlabel(r'$\\Delta$T (°C)')\n",
    "plt.savefig('approaches_to_compute_the_median.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c910268-2290-48f2-b45b-f64a8da1bff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09689b36-29bc-4118-b4aa-9ca4197f09bd",
   "metadata": {},
   "outputs": [],
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