centerline_elevation_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code to work with SK elevations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import os\n",
    "import fiona\n",
    "import rasterio as rio\n",
    "from rasterio.mask import mask\n",
    "from shapely.geometry import mapping\n",
    "import geopandas as gpd\n",
    "from rasterstats import zonal_stats\n",
    "from scipy.ndimage import gaussian_filter\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import cmocean\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Choose centerline file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "centerline_n_WGS84_500m.shp\n",
      "centerline_s_utm07_250m_squarebuffer.shp\n",
      "centerline_n_utm07_250m_circlebuffer.shp\n",
      "centerline_n_utm07_250m.shp\n",
      "centerline_n_utm07_250m_squarebuffer.shp\n",
      "centerline_s_utm07_250m_circlebuffer.shp\n",
      "centerline_s_utm07_500m_circlebuffer.shp\n",
      "centerline_n_utm07_500m_circlebuffer.shp\n",
      "centerline_n_utm07_121pts.shp\n",
      "centerline_n_utm07_500m.shp\n",
      "centerline_n_final_wgs84.shp\n",
      "centerline_s_final_wgs84.shp\n",
      "centerline_n_final_utm07.shp\n",
      "centerline_s_final_utm07.shp\n",
      "centerline_s_utm07_250m.shp\n",
      "\n",
      "centerline_n_utm07_250m_circlebuffer.shp CHOSEN\n",
      "centerline-north\n",
      "250m\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>100.0</td>\n",
       "      <td>120.762720</td>\n",
       "      <td>POLYGON ((560860.191 6672605.735, 560854.073 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>350.0</td>\n",
       "      <td>120.762720</td>\n",
       "      <td>POLYGON ((561075.015 6672477.864, 561068.897 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>600.0</td>\n",
       "      <td>120.762720</td>\n",
       "      <td>POLYGON ((561289.838 6672349.993, 561283.720 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>850.0</td>\n",
       "      <td>111.317912</td>\n",
       "      <td>POLYGON ((561512.949 6672239.084, 561506.831 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>111.317912</td>\n",
       "      <td>POLYGON ((561745.843 6672148.199, 561739.725 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>None</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>119.116146</td>\n",
       "      <td>POLYGON ((578501.133 6654291.947, 578495.015 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>None</td>\n",
       "      <td>29350.0</td>\n",
       "      <td>119.116146</td>\n",
       "      <td>POLYGON ((578719.542 6654170.301, 578713.424 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>None</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>118.610460</td>\n",
       "      <td>POLYGON ((578938.435 6654049.534, 578932.317 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>None</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>118.610460</td>\n",
       "      <td>POLYGON ((579157.909 6653929.821, 579151.791 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>None</td>\n",
       "      <td>30100.0</td>\n",
       "      <td>118.610460</td>\n",
       "      <td>POLYGON ((579377.383 6653810.108, 579371.265 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  distance       angle  \\\n",
       "0    None     100.0  120.762720   \n",
       "1    None     350.0  120.762720   \n",
       "2    None     600.0  120.762720   \n",
       "3    None     850.0  111.317912   \n",
       "4    None    1100.0  111.317912   \n",
       "..    ...       ...         ...   \n",
       "116  None   29100.0  119.116146   \n",
       "117  None   29350.0  119.116146   \n",
       "118  None   29600.0  118.610460   \n",
       "119  None   29850.0  118.610460   \n",
       "120  None   30100.0  118.610460   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((560860.191 6672605.735, 560854.073 6...  \n",
       "1    POLYGON ((561075.015 6672477.864, 561068.897 6...  \n",
       "2    POLYGON ((561289.838 6672349.993, 561283.720 6...  \n",
       "3    POLYGON ((561512.949 6672239.084, 561506.831 6...  \n",
       "4    POLYGON ((561745.843 6672148.199, 561739.725 6...  \n",
       "..                                                 ...  \n",
       "116  POLYGON ((578501.133 6654291.947, 578495.015 6...  \n",
       "117  POLYGON ((578719.542 6654170.301, 578713.424 6...  \n",
       "118  POLYGON ((578938.435 6654049.534, 578932.317 6...  \n",
       "119  POLYGON ((579157.909 6653929.821, 579151.791 6...  \n",
       "120  POLYGON ((579377.383 6653810.108, 579371.265 6...  \n",
       "\n",
       "[121 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the centerline file ########################\n",
    "clinepath = '/Users/jukesliu/Documents/TURNER/DATA/shapefiles_gis/centerlines/'\n",
    "for file in os.listdir(clinepath):\n",
    "    if file.endswith('.shp'):\n",
    "        print(file)\n",
    "cfile = 'centerline_n_utm07_250m_circlebuffer.shp'\n",
    "########################################################\n",
    "print(); print(cfile, 'CHOSEN')\n",
    "\n",
    "if cfile.split('_')[1] == 'n':\n",
    "    northline = True\n",
    "    newfolder = 'centerline-north'\n",
    "elif cfile.split('_')[1] == 's':\n",
    "    northline = False\n",
    "    newfolder = 'centerline-south'\n",
    "else:\n",
    "    print('Check filename:')\n",
    "    print(cfile.split('_'))\n",
    "print(newfolder)\n",
    "spacing = cfile.split('_')[3]\n",
    "print(spacing)\n",
    "\n",
    "# read with geopandas\n",
    "cline_gdf = gpd.read_file(clinepath+cfile)\n",
    "geoms = cline_gdf.geometry.values\n",
    "cline_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Extract elevations at 250m / 500m spacing (choose corresponding centerline file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dem_path = '/Users/jukesliu/Documents/TURNER/DATA/STRESS_BALANCE/stress_maps/'\n",
    "# dem_outpath = '/Users/jukesliu/Documents/TURNER/DATA/STRESS_BALANCE/stress_maps/centerline_basal/from2014/'\n",
    "dem_path = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/final-DEMs/'\n",
    "dem_outpath = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/'\n",
    "prof_count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pull DEM elevations\n",
    "for file in os.listdir(dem_path):\n",
    "    if file.endswith('err.tif'):\n",
    "#         ds = file.split('_')[0]\n",
    "#     if file.endswith('.tif') and 'dtau_b' in file and '20140318' in file:\n",
    "#         ds = file.split('_')[4]\n",
    "#         print(ds)\n",
    "        \n",
    "        # open DEM\n",
    "        src = rio.open(dem_path+file); elev = src.read(1); affine=src.transform\n",
    "        \n",
    "        # extract mean elevations\n",
    "        dists = []; mean_elev = []\n",
    "        for idx2, row2 in cline_gdf.iterrows():\n",
    "            geom = row2.geometry\n",
    "            dist = row2.distance\n",
    "            dists.append(dist)\n",
    "            try:\n",
    "                out_image, out_transform = mask(src, [mapping(geom)], crop=True)\n",
    "                crop_array = out_image[0,:,:]\n",
    "                crop_array[crop_array < -1e3] = np.NaN\n",
    "#                 mean_elev.append(np.nanmean(crop_array))\n",
    "                mean_elev.append(np.nanmax(crop_array))\n",
    "            except Exception:\n",
    "                mean_elev.append(np.NaN)\n",
    "                pass\n",
    "        mean_elev = np.array(mean_elev); dists = np.array(dists)\n",
    "        if northline: # flip distances for northern centerline\n",
    "            dists = np.flip(dists)\n",
    "        mean_elev[mean_elev == 0] = np.NaN # differences should never be exactly 0\n",
    "        \n",
    "        # write centerline data to csv\n",
    "#         new_df = pd.DataFrame(list(zip(dists, mean_elev)), columns=['dist_m','mean_elev'])\n",
    "        new_df = pd.DataFrame(list(zip(dists, mean_elev)), columns=['dist_m','h'])\n",
    "#         new_df.to_csv(dem_outpath+'centerline_'+cfile.split('_')[1]+'_'+spacing+'_'+ds+'.csv')\n",
    "        new_df.to_csv(dem_outpath+'centerline_n_'+spacing+'_herr.csv')\n",
    "        new_df\n",
    "        \n",
    "        prof_count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b073f220>]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_df.h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate per-pixel st. dev in the 2020-06-05 OIB LIDAR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# laspath = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/Surface/Lidar/ILAKS1B_2020_157_Turner.csv'\n",
    "# lidar_df = pd.read_csv(laspath, index_col=0)\n",
    "# lidar_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.imshow(elev, vmin=0);\n",
    "# plt.colorbar()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge centerline elevations from acquisitions close in time\n",
    "\n",
    "Currently combining the 3 ASTER DEMs from 20161019 (2) and 20161020 (1) as well as the 2 WorldView DEMs from 20180117 and 20180128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cfile' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df2016 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(glob\u001b[38;5;241m.\u001b[39mglob(dem_outpath\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenterline_\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[43mcfile\u001b[49m\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39mspacing\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m*2016*.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m      2\u001b[0m df2016\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cfile' is not defined"
     ]
    }
   ],
   "source": [
    "df2016 = pd.read_csv(glob.glob(dem_outpath+'centerline_'+cfile.split('_')[1]+'_'+spacing+'*2016*.csv')[0])\n",
    "df2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_20180117.csv',\n",
       " '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_20180122.csv',\n",
       " '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_20180128.csv']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files201801"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(df\u001b[38;5;241m.\u001b[39mdist_m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m1000\u001b[39m, mean_elevations, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     23\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlim(\u001b[38;5;241m30\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistance from terminus [km]\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[43mfs\u001b[49m)\n\u001b[1;32m     25\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mElevation [m a.s.l.]\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39mfs)\n\u001b[1;32m     26\u001b[0m plt\u001b[38;5;241m.\u001b[39mtick_params(labelsize\u001b[38;5;241m=\u001b[39mfs)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'fs' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dem_outpath = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/'\n",
    "\n",
    "### 2018 #####################################################################################\n",
    "files201801 = glob.glob(dem_outpath+'centerline_'+cfile.split('_')[1]+'_'+spacing+'*201801*.csv') # change glob search pattern!\n",
    "files201801.sort()\n",
    "files201801 = [files201801[0], files201801[2]]\n",
    "\n",
    "# plot the individual elevations\n",
    "legend_entries = []\n",
    "fig, ax = plt.subplots(1,figsize=(8,6))\n",
    "elevation_lists = []\n",
    "for file in files201801:\n",
    "    df = pd.read_csv(file)\n",
    "    ds = file.split('/')[-1].split('_')[-1][:-4] # grab date\n",
    "    ax.plot(df.dist_m/1000, df.mean_elev, 's', alpha=0.3)\n",
    "    elevation_lists.append(np.array(df.mean_elev))\n",
    "    legend_entries.append(ds)\n",
    "mean_elevations = np.nanmean(elevation_lists, axis=0)\n",
    "legend_entries.append('merged')\n",
    "\n",
    "# plot merged result and original result\n",
    "ax.plot(df.dist_m/1000, mean_elevations, 'k')\n",
    "ax.set_xlim(30,0)\n",
    "ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "ax.set_ylabel('Elevation [m a.s.l.]', fontsize=fs)\n",
    "plt.tick_params(labelsize=fs)\n",
    "plt.legend(legend_entries, fontsize=fs)\n",
    "plt.show()\n",
    "\n",
    "# export result to csv\n",
    "merged_dem_df = pd.DataFrame(list(zip(df.dist_m, mean_elevations)), columns=['dist_m','mean_elev'])\n",
    "# merged_dem_df.to_csv(dem_outpath+file.split('/')[-1][:-12]+'20180122.csv')\n",
    "\n",
    "# # ### 2016 #####################################################################################\n",
    "# files201610 = glob.glob(dem_outpath+'centerline_'+cfile.split('_')[1]+'_'+spacing+'*201610*.csv') # change glob search pattern!\n",
    "# files201610.sort()\n",
    "# files201610 = files201610[:-1]\n",
    "# df2016wv = pd.read_csv(glob.glob(dem_outpath+'centerline_'+cfile.split('_')[1]+'_'+spacing+'*201607*.csv')[0])\n",
    "\n",
    "# # plot the individual elevations\n",
    "# legend_entries = []\n",
    "# fig, ax = plt.subplots(1,figsize=(8,6))\n",
    "# # elevation_lists = []\n",
    "# # for file in files201610:\n",
    "# #     df = pd.read_csv(file)\n",
    "# #     ds = file.split('/')[-1].split('_')[-1][:-4] # grab date\n",
    "# #     ax.plot(df.dist_m/1000, df.mean_elev, 's', alpha=0.3)\n",
    "# #     elevation_lists.append(np.array(df.mean_elev))\n",
    "# #     legend_entries.append(ds)\n",
    "# # mean_elevations = np.nanmean(elevation_lists, axis=0)\n",
    "# legend_entries.append('20161019 (merged)')\n",
    "\n",
    "# # plot merged result and original result\n",
    "# ax.plot(df.dist_m/1000, mean_elevations, 'k')\n",
    "# ax.plot(df2016wv.dist_m/1000, df2016wv.mean_elev, 'r-'); legend_entries.append('20160717 (WV)')\n",
    "# ax.set_xlim(30,0)\n",
    "# ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "# ax.set_ylabel('Elevation [m a.s.l.]', fontsize=fs)\n",
    "# plt.tick_params(labelsize=fs)\n",
    "# plt.legend(legend_entries, fontsize=fs)\n",
    "# plt.show()\n",
    "\n",
    "# # export result to csv\n",
    "# merged_dem_df = pd.DataFrame(list(zip(df.dist_m, mean_elevations)), columns=['dist_m','mean_elev'])\n",
    "# merged_dem_df.to_csv(dem_outpath+file.split('/')[-1][:-12]+'20161019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>dist_m</th>\n",
       "      <th>mean_elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>2351.1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>29350.0</td>\n",
       "      <td>2326.4883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>2290.5947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   dist_m  mean_elev\n",
       "0           0  30100.0        NaN\n",
       "1           1  29850.0        NaN\n",
       "2           2  29600.0  2351.1406\n",
       "3           3  29350.0  2326.4883\n",
       "4           4  29100.0  2290.5947"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>mean_elev_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29500.0</td>\n",
       "      <td>10.400649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29000.0</td>\n",
       "      <td>2.552529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28500.0</td>\n",
       "      <td>6.368925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28000.0</td>\n",
       "      <td>3.609815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>-7.934313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>-7.410291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>-6.107293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>500.0</td>\n",
       "      <td>-10.178942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.885960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dist_m  mean_elev_diff\n",
       "0   30000.0             NaN\n",
       "1   29500.0       10.400649\n",
       "2   29000.0        2.552529\n",
       "3   28500.0        6.368925\n",
       "4   28000.0        3.609815\n",
       "..      ...             ...\n",
       "56   2000.0       -7.934313\n",
       "57   1500.0       -7.410291\n",
       "58   1000.0       -6.107293\n",
       "59    500.0      -10.178942\n",
       "60      0.0       -4.885960\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_df = pd.DataFrame(list(zip(df2016.dist_m, merged_dem_df.mean_elev - df2016.mean_elev)), columns=['dist_m','mean_elev_diff'])\n",
    "diff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_df.to_csv(dem_outpath+'differenced/profile_20160717_20180122.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot centerline elevation profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_s_250m_20131207.csv\n"
     ]
    }
   ],
   "source": [
    "######## Change path to folder with centerline elevation CSVs ###################\n",
    "dem_outpath = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/'\n",
    "filelist = glob.glob(dem_outpath+'centerline_s_250*20*.csv') # change glob search pattern!\n",
    "filelist.sort() # centerline elevation files\n",
    "################################################################################\n",
    "# ref_elev_file = filelist[9]\n",
    "ref_elev_file = filelist[1]\n",
    "print(ref_elev_file)\n",
    "ref_elev = pd.read_csv(ref_elev_file)\n",
    "# ref_elev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eKv/QNdT9UvfeNJrTfbe3tzd1CA1i5syZdOvWjX79+gGQmpoK6P+2VuXh4VHv8+vazJ49m08//ZSCggL69u1b55eWq/XZZ59x7tw5HnroocumuXDhAuXl5bVe199//13rMT/++CPFxcVMnjzZqDga4rPdZApwIcQg4GGgR0Uzg1GklEuBpQDBwcGytk4OTUFoaGitHTSUS1V2UGmo+6XuvWk0p/t+uYVBjK2t9ezZ01Abr09jLac7a9YswsPDCQ8PR6vVVttXszVUSnlVQzlfeOEFHn/8cc6ePcvcuXOZNGkSGzduRAhBp06dOHv2LACDBg1i48aNV5z/qlWreOGFF1ixYgX+/v4AhIWFMWbMGEOaJUuWMGzYsCu+rmXLlnHnnXfi7u5uVCwN8dluMgU4sAT4CjgnhHCq2GYGaCveF0opi00Um3IDUauQKUrDeu6551ixYgXbtm2jVatWhu2enp6Avibu6+tr2J6enn5J7dUYbm5uuLm50bZtWzp06ICvry/h4eEMGjSIDRs2UFpaCuib6a/UqlWreOihh/j+++8NCx4BBAcHExUVZXjv4eGBpaUlWq3W0MJQ33VFRUWxf/9+3nnnnSuO61o0pYlcOgBPoR9+VvkaAPSt+Pd/TBeaoihK8zRz5kx+/PFHtm7dSvv27avtCwwMxNPTk82bNxu2FRUVERYWRv/+/a/pvDqdDoDiYn29zN/fn6CgIIKCgvDx8bmivH755RcmTZrEt99+yz333FNtn7W1tSHfoKAg7O3tsbCwoGfPntWuC2Dz5s21XtfSpUsJCAhgxIgRVxTXtWpKNfBhtWz7P0ALPA2cri+DcjU2+KawdOlSoPGaEhXlZjF9+nR++OEH1qxZg7Ozs6FGamdnh52dHUIInn32Wd5++23at29P27ZtmTdvHnZ2dkycONGQT2pqKqmpqcTExAAQHR1NdnY2fn5+uLi4sHv3bg4cOMDAgQNxcnIiNjaW119/nYCAAAYOHFhnjNHR0ZSUlHDhwgXy8/MNtelu3boBsGLFCh566CE++OADBg8ebLgGCwuLOlcEmzVrFg899BC9e/dmwIABfPHFFyQnJ/PUU09VS1dQUMDy5ct58cUXr/8MkMZ2V2/IF3BPxetz9MPI/lPxfkjFfn+gDHijnnxCuYJhZF5tOsuycl29XfxvRM1pSE1jq/hMNVh+6t6bRnO67011GFnl/6War5CQEEManU4nQ0JCpKenp7S0tJSDBw+WR44cqZZPSEhIrfl88803UkopDx48KIcOHSpdXFykhYWFDAgIkE899ZRMTEysN0Z/f/9a8640ZMiQWvcbM+Tss88+k/7+/tLCwkL26NFDbt++/ZI0X3/9tdRqtTIpKane/Co11DAyIU1QKxVCXO6k26WUQyumVo0D5kop59SRTyhgJqWs+ytahRZBneV7P/7Jo71bXmHEptecOvQ0tsqad2VN/Fqpe28azem+R0ZGGt0JTWn+6vo8CCEipZTBxuRjkiZ0KWWd7QxSynig3rYIKeXQKz33gaQ82sdn0S/g8mMAlaatoQpuRVGUG1lT6sR2zQT6iv/yAymk5akO64qiKErTdVMV4N4OVpSWlSOE4L2tZyir6OWoNC/JyckkJyebOgxFUZRGdVMV4FqN4OlB/pSX6yjVwftb4kwdktIIfHx8rniYiaIoSlNzUxXgAJ087RndzhUpJcl5xfx+JM3UISkNzMvLCy8vL1OHoSiK0qhuugIc4I4unvg6WiKE4M+TFzivnoc3K6oJXVGUm8FNWYADPDskEKTETKthwTbVlK4oiqI0LTdVAV5QVGL4t6WZhsd6+yClpLBcsjKq9uX3FEVRFOVGdFMV4LGJ50nLyDW8797SkdYu+knxt57OJDm3yFShKQ3oSlZuUhRFaapuqgK8tLScB1/4itLScsO2GYP8Eeib0j/cFocpZqZTGtaBAwc4cOCAqcNQFEVpVDdVAW5upmXnwVhmL1z9zzathil9fZFSUqKDHyNV56embv/+/ezfv9/UYShKkzd//nx69eqFg4MD7u7ujB8/nqNHj1ZLI6Vkzpw5eHt7Y21tzdChQzl27Fi1NEuXLmXYsGE4OTkhhCA+Pv6y5ywqKuKWW25BCGHU/+MjR44wZMgQrK2t8fHx4c0336xWEdu+fTv9+/fH1dUVa2tr2rdvzwcffFBvvjt27GDChAn4+PgghODbb7+9JE1+fj5PP/00LVu2xNramnbt2vHRRx/Vm3dDuakK8Fa+bpibafl8xXaWr9tr2N7Zy54O7rYAhMdnczaz0FQhKg1ANaErSsMIDQ1l2rRp7Nq1i61bt2JmZsaIESPIzMw0pHn//ff58MMPWbRoEREREbRo0YKRI0eSl5dnSFNQUMCoUaOYM2dOved8/vnnadnSuPUqcnNzGTlyJB4eHkRERPDJJ5+wYMECFi5caEhjZ2fHM888w44dO4iOjua1114jJCSExYsX15l3fn4+nTt35uOPP77s+uOzZs1i/fr1/PDDDxw/fpxXX32Vl156iR9++MGo+K+ZsaueNIdXz5495VerwqVVt+nSqc+zMvLYWcMKMGXl5XLm6mg5Y3W0fPa3aFlWXn7Z1WJMoTmtzNTUqHtvGs3pvjfV1chqysvLkxqNRq5du1ZKqV+JzNPTU86bN8+QpqCgQNrZ2ckvvvjikuMjIiIkIOPi4mrNf82aNbJjx44yOjpaAjIiIqLOeBYvXizt7e1lQUGBYdtbb70lvb29pU53+ZUn77rrLvnAAw/UmXdVtra2hpXTqurUqZN84403qm0bPHiwnD59ep35NdRqZE1pPfAG8di/BnAgOoGvVu3kgf8uY/dPs3F1skOr0fD0ID8+3B4PGg2f70pkxkB/U4erXIXKb/nGfNtXFFOx7j7DJOctPPjpVR+bl5eHTqfD2Vm/GFRcXBypqamMGjXKkMba2prBgweza9cupk6danTe586d4z//+Q8bNmy4bI23pt27dzNo0KBq6UePHs3rr79OfHw8gYGBlxxz8OBBdu3a1SB/HwYOHMi6det44okn8PX1ZdeuXURFRfHCCy9cc97GuKma0Ct9+OI99O4SQGJqFrM//Od5eGs3W/r5OSKl5ET6RfadzTZdkMpVmzt3LnPnzjV1GIrS7MycOZNu3brRr18/AFJTUwHw8PCols7Dw8Owzxjl5eU8+OCD/Pe//6Vbt25GH5eamlrruavGVqlly5ZYWloSHBzMtGnTeOqpp4w+z+V88skndOvWDT8/P8zNzRkyZAjvvfce48aNu+a8jXHT1cABLC3M+eqth+l1/3yW/7GPB27vxYh+HQCYFOzDwaRcSqXgfwdSCHKzwcXWwsQRK1ciJCTE1CEoSr2upSZsCrNmzSI8PJzw8HC0Wm21fUJUX/1ZSnnJtrq88847mJubM2vWrMum6dSpE2fPngVg0KBBbNy48bLnrm17WFgY+fn57Nmzh9mzZxMYGMhDDz1EWFgYY8aMMaRbsmQJDz74oFFxL1q0iJ07d7J27Vr8/f3ZsWMHzz//PAEBAdx2221G5XEtbsoCHCDIvwWvTr2d1z/5nRnzVhD56yvkFOlwtbdg9vBWvLrhFNaWZvxf2FneGBWEmcb4D6NiWqrpXFEa1nPPPceKFSvYtm0brVq1Mmz39PQE9LVdX19fw/b09PRLasZ12bJlC2FhYZibm1fb3rdvX+6//36WL1/Ohg0bKC0tBTA0mXt6el5S005PTwcubRWobE7v0qULaWlpzJkzh4ceeojg4GCioqIM6YyNu7CwkJdffpmVK1cyfvx4ALp27UpUVBQffPCBKsAb28xJw1n5534OxyQxd/F6jtOCotJy3ry7M2M7uLEpJpOswjK+i0hici8fVYgrinLTmTlzJitWrCA0NJT27dtX2xcYGIinpyebN2+mV69egH4YWFhYGAsWLDD6HN988w0XL140vE9OTmb06NEsX76cAQMGAODvf2mfpH79+jF79myKioqwsrICYPPmzXh7exMQEHDZ8+l0OoqL9WtgWFtbExQUZHSslUpLSyktLb2kNUKr1aK7TktV39QFuLm5lsVvTGTwwx/w+ard3DJ6NMk5JTz4+V7u6OGFpZU5ZVISlZxHTthZnujTEgerm/qWNQmRkZEAaiiZolyj6dOn88MPP7BmzRqcnZ0NtV07Ozvs7OwQQvDss8/y9ttv0759e9q2bcu8efOws7Nj4sSJhnxSU1NJTU0lJiYGgOjoaLKzs/Hz88PFxeWSzmZ2dnYAtG7dus4hZRMnTmTu3Lk88sgjvPbaa8TExPDuu+8SEhJiaEJftGgRgYGBtGvXDtCP7/7ggw+YNm1andeen5/P6dOnAX2Bn5CQQFRUFC4uLvj5+eHg4MCQIUN46aWXsLOzw9/fn+3bt/P999/z/vvvX8ltvnrGdldvDq+ePXvW2m1/9oerpFW36bLXffPl/208Kdu9sFEGPrdedn/tL3nXsn1y6sqjcsbqaPnKhhh5NrOg1jwaW3MaUtPYAKn/aDcMde9Noznd96Y6jKzy/1LNV0hIiCGNTqeTISEh0tPTU1paWsrBgwfLI0eOVMsnJCSk1nxqG5olpZRxcXFGDSOTUsrDhw/LQYMGSUtLS+np6SnnzJlTbQjZRx99JDt27ChtbGykg4OD7N69u/zss89keT1Dhbdt21ZrzJMnTzakSUlJkY888oj09vaWVlZWsl27dnLBggV1DmGTsuGGkQkpb56pQ4ODg2VtM/tcLCwm+N53iE/K4M2nJ3DfhAHMWR3N9hPnMTPT0NrfCW9XW2wszTDTCCb28KKXr+N1jT00NJShQ4de13M2VZU178qa+LVS9940mtN9j4yMVC1CikFdnwchRKSUMtiYfG7KYWQ12Vpb8ulrDwDw9pINlFzM5+sng/n04e44WJpxOj6LpPP5ZOUXU6aTfL8/ma/3nSM9v6SenBVTiIyMbLDCW1EU5UalCvAKt/btwKTxfSguKeOpuT8ipeT2bl78NL0PzjbmnD6bzYXsQlKyCtBJycGkPOZtjmXFwRRyikpNHb6iKIpyk1EFeBXv/fdfeLjas+tgLEt+CQOgjac9K6b3xc3OglPxWVy8WMLp1FyyLhajk5Kd8dm8vvE0X+xKoKz8+vQ8VBRFURRVgFfh4mjLx6/om9Jf/+R3ziZnANCqhR0/Te+Lp6MVJ+OysCrTcYunHTn5xeQWliKBY2kXeWHdSVLVmuIm5+3tjbe3t6nDUBRFaVTXvQAXQrQUQiwSQuwWQhQIIaQQIsCI44KFEEuFECcqjksQQiwXQlw62e01uGP4LfxrZHcuFpYw/a2fDLP6BLrb8tP0vng7W3EkPpudR9JYdHcn5o4OopO7DeXlOsokzP0rlm0xFxoyJOUKpaSkkJKSYuowFEVRGpUpauBBwH1AFhB2Bcc9AHQCPgHGAC8BPYD9Qgjfug68Ugtn34uLow1b9pzgh7V7DNv9XG1YMb0vvi7WHE7MYdIX+3CwNOOpgf48OzgApMRMq+GXw2ks3BpLqWpSN4mkpCSSkpJMHYaiKEqjMkUBvkNK6SGlvB1YeQXHvSelHCClXCyl3C6l/BG4DXAGnmzIAD1cHfjghXsAePGD1SSnZxv2tXSxYcWMvgS42xCdlMvExXs4n1dM2xa2vDu2LU5WWsy0Gs5kFzPrt2iKSssbMjTFCKoJXVGUm8F1L8CllFdVLZVSnq9l21ngPOBzrXHV9MDtvRgzqBM5+YX8+/kvSb2Qa9jn5WTNiul9CfKwIyY1n4mf7SEtpwhbSzPm3taGLp76GYrQapm76RQ301h7RVEU5fpo0p3YhBAdgBbA8UbIm09efQDvFk7sOxJPv3+/y86DsYb9LRys+HFaH9p52RObfpEHPt1DbFo+GiGY0s+Xoa306+Xml0o+2BbX0OEpdZgyZQpTpkwxdRiKoiiNyqQzsQkhngCWAYFSyvgrPNYM2AJ0ANpJKbMuk24KMAXAw8Oj54oVK64oxszcIt79fg9HYi+g1QiemNCVOwYHGebZzS+RfLi7iLM5EkstPN7dgmBv/Xzpu7NtSJb2ALQQ+QxyunjZ89QnPz/fMD+wUrdhw4YBsG3btgbJT91702hO993e3l7NxKYYREZGkpeXV+u+YcOGGT0Tm0nnJgeeQD+/bMBVHPsFUAqMMvaYy82FXp/S0jL50sLV0qrbdGnVbbp8aPbXMj0j17A/v6hUPv39ARn43HoZ+Nx6OW9NtCwp08+z+3+hZ+T0VcfkjNXR8n/7k67q/FI2r3mhG9uSJUvkkiVLGiw/de9Noznd96Y6F/o777wjg4ODpb29vXRzc5Pjxo27ZJ7zyrnQvby8pJWVlRwyZIg8evRotTRLliyRQ4cOlY6OjhKQcXFxl5zL39//knnHZ8+eXWd8hYWFcvLkybJLly7SzMxMDhkypNZ0oaGhskePHtLS0lIGBgbKzz//vN5r//TTT2WXLl2kvb29tLe3l3379pV//PFHtTSrVq2So0aNkm5ubhIw+jPbUHOhN8kmdCHEfPS16seklH819vnMzLTMf+4ulr//OHY2lqzcFEmbMa/z+GvfE3E0HltLMz6e1I037uyImUbw1fY4Jn2+l/TcIp4ZHICLlRlSSvYk5PDRjnh2nMkk46KahrWxqCZ0RWkYoaGhTJs2jV27drF161bMzMwYMWIEmZmZhjTvv/8+H374IYsWLSIiIoIWLVowcuTIajXMgoICRo0axZw5c+o83xtvvGEYBpqSksJrr71WZ/ry8nKsrKyYMWMGY8eOrTVNXFwct99+O/379+fgwYO8/PLLPP3006xatarOvFu2bMl7773HgQMH2L9/P8OHD+fOO+/k8OHDhjQXL16kf//+LFy4sM68Go2xJX1jvLiKGjjwasUxT1/p+a62Bl7V8dgUedfTi6V19xmGGnn/ie/J5ev2yvLychlxJkP2DflbBj63XvYN+VseT8qRxaXlcsbKI4aaeOUr5M9T8rfDqTK7oKTe8zan2khTo+69aTSn+95Ua+A15eXlSY1GI9euXSul1Ne+PT095bx58wxpCgoKpJ2dnfziiy8uOT4iIqLOGviCBQuuOrbp06fXWgN/8cUXZVBQULVtjz/+uOzbt+8Vn8PZ2bnW6zp//rxJauBNanFrIcQzwDzgVSnlIlPE0L6VJ6s/+Q9x5y6w7NdwvluziwPRCTz++vcs/2MvS998iLX/HciM7w4QcSaL+z/dw7LHg5l7e1ve3xzL6YwCbC3NsLUyJ6OglC2nM9l+Jos7O7dgUCtnNBXP1pWrt27dOgDGjx9v4kgU5fKs//WVSc5buPrxqz42Ly8PnU6Hs7O+k25cXBypqamMGjXKkMba2prBgweza9cupk6dekX5f/DBB8yfPx9fX1/uvfdeXnjhBSwsLK46XoDdu3dXiw9g9OjRfPfdd5SWlmJubl5vHuXl5axcuZL8/Hz69+9/TfE0JJM0oQsh7hFC3ANU9uoYU7FtSMV+fyFEmRDijSrHPAD8H/AnsFUI0bfKq+P1vobAlm688+ydnP5zHl+EPIibkx1b956k933vEL43mu+n9ua2rp7kFZXx8JJ97Dudybt3dOCbibfwcLA3ntZaUjILyCsspUwn+fVwGm9vjuVcjpqK9VpNmDCBCRMmmDoMRWl2Zs6cSbdu3ejXrx8AqampAHh4eFRL5+HhYdhnrGeeeYaffvqJbdu2MWPGDD766COmTZt2zTGnpqbWGl9ZWRkXLtQ9a+aRI0ews7PD0tKSp556it9++40uXbpcc0wN5bI1cCHEVRXu0rhx3jUncFlc8XM7MBQQgJbqXzBuq9h+W8WrqsrjrjtrKwsm39mP0QM78dSc/7FpZzQTX/iKhyb04f3n78HN3oL/7Uxg+ncHmPuvTkwa4M+t7dy5tZ07JeU6Nh9PZ8XBFGytLUi/WMp7W+Po7evAxB7eaDWqNn41xo0bZ+oQFKVe11ITNoVZs2YRHh5OeHg4Wq222j5Ro+VQSnnJNmPyr9S1a1ccHBy4//77ee+993B1daVTp06cPXsWgEGDBrFx40aj864tvsrtYWFhjBkzxrBvyZIlPPjggwC0a9eOqKgosrOzWbVqFZMnTyY0NJTOnTtf0bU1lrqa0K9mjUwphOgtpTxQZyIp6/zNSv2QMlFj2yPAI1cR03Xh6ebAb4v+w5JfdvDyR2v4Ye1e9h89y9ZvZtHCwYqFG2N4Y9Ux9sZmcl+flvRv44aFVsPYzp4Mb+fOisgktsVm4mBtwb7EXM7lFPPMQD9sLZvUU44bQmUTuqIoDeO5555jxYoVbNu2jVatWhm2e3p6Avparq/vPzNap6enX1LrvVJ9+vQB4PTp07i6urJhwwZKS/XFkrW1tdH5eHp6XtIakJ6ejpmZGa6urjg6OhIVFWXYVzVuCwsLgoKCAAgODiYiIoKPPvqIr74yzeOPmuoqHQTwFXDOyLy06DuY3bSEEDx1/xCG9mrHA88v4/iZVJ4M+R8/f/gE7vaWvP7rUdZHpbA+KgUvJyv+FezD3b1aEuBuy6N9/bijiycfhcZxvrCM5Nxi3tsWx3/6++HlYGnqS1MU5SY1c+ZMVqxYQWhoKO3bt6+2LzAwEE9PTzZv3kyvXr0AKCoqIiwsjAULFlzTeSsLVS8vLwD8/f2vKp9+/fqxZs2aats2b95McHAw5ubmmJubGwrp+uh0OoqLi68qjsZQX/VumZRynzEZCSG0QN19/m8S7Vt5svrjpxjw4Pv8EXqYBV//xewnbmNgWzdWRZxjVcQ5EjML+ezvWBZvieW1Ozrw6OBAXGwtCLmtDS/8fpy8kjIAPtwez6O9GnymWEVRlHpNnz6dH374gTVr1uDs7GyoydrZ2WFnp58y+tlnn+Xtt9+mffv2tG3blnnz5mFnZ8fEiRMN+aSmppKamkpMTAwA0dHRZGdn4+fnh4uLC7t372bPnj0MGzYMR0dHIiIieO6555gwYQJ+fn51xhgdHU1JSQkXLlwgPz/fUPB369YNgKeeeopPP/2UZ599lqlTp7Jz506+/fZbfvrppzrzfemllxg7diy+vr7k5eXx448/Ehoayvr16w1pMjMzSUhIIDs7G9C3Fjg5OeHp6WlonWhUl+ueDjwIuBjbnb3KMU5Xcsz1fDXEMLIrsXHHUWndfYa07j5Dbgo/ZtheXq6Tu09dkLOWRxkmf/luR5xhf+bFEvnA1/vlQ/+LkjNWR8unV0fLLzfuua6xN2VUTALRUJrTcKampDnd96Y6jIwaE6tUvkJCQgxpKidy8fT0lJaWlnLw4MGXTPYSEhJSaz7ffPONlFLKyMhI2adPH+no6CitrKxku3btZEhIiLx48WK9MdY2AUzN//+hoaGye/fu0sLCQgYEBBg1kcvkyZOln5+ftLCwkO7u7vLWW2+Vf/75Z7U033zzTb33pzYNNYzMpFOpXm/BwcFy//791/Wc85du5M3P1+PsYMPO5S8S2NKt2v7/7TzLG6uOAfDWPZ14sL++mSgmPZ+Zvx7FwcaCFo7WCCQv39paNacbobLDSkN9tkNDQxk6dGiD5KUYrznd98jISDWVqmJQ1+dBCGH0VKpNcia2pmT2E6MZO7gzWbkFPPDfZRQUVp+BbdIAf0Lu0o+Ce/3XY/y8JxGAti3smDW8NRfyism+WIxEsOpwWoMVSs1Z5bdTRVGU5uyaC3AhxCQhxMMNEUxzpNFo+GreZIL83Dkck0TPe9/m9U9+5+DxREMhM3lQAK/e0QGAV1YeYeU+fSE+sr07d3fzIi2nCJ1Ox8nzFzmckm+ya1EURVFuHA1RA/+m4qVchqO9Nb8snIKvpzPxSRl88M1m+k98j853zCXk07VkZOfz+JBAXhrXHinhpZ+P8Os+fef/qQP8ae1mQ1rFBC+/HUmjtPyqllRXFEVRmpGGKMBvBYY3QD7NWofWXhz/Yy5/LZvJ1PsG0cLFnjOJF3j/q78IvvcdNu+KZsrwVrwwth1SwuyfD/PL3kS0GsGMwYFkXSyhuLScjIJStp7OrP+EN7Hx48eraVQVRWn2rnmWECnljoYI5Gag1WoYFNyGQcFt+PDFewk/cJo3P1/ProOxTJi+mKn3D+admXcigPfXn+Sln48gJdzf15dOjuXEZRcS4G7HXycv0NvPEWfr+ufwvRn98ccfpg5BURSl0alObCai1WoY0qstfy2byVvP3IG5mZYlP++g77/fpaeHlpfH6ydMePmXI/y0O4FbPcsoL9eRW1BCSblk7dF0E1/BjWvt2rWsXbvW1GEoiqI0qrrmQn/jcvtqIaWUbzVAPDcdrVbD84+OZGT/Djz26ndEx6Zw66MfsWHJ07wyoT3vrD3BqyuPMvkWCx7o6c2P+5OxtzZn/7lcBgQ6E+RmY+pLuOGo5nNFUW4GdTWhz7mCfCSgCvBrcEu7luxc/iLPzv+F737fzcTnvyR8+Yto7ujAvN+Ps/xICZvHurLxWDoX8opxd7Diq73neGaQvxobriiKchO6bBO6lFJzBS/t5fJRjGdlac6nrz3ArX3bcz4rn/tnLeWB3j7cfosnZTpYFnqGKQP8uZBbRFFJGfkl5SwKP0tq3o0zN++NYOnSpSxdutTUYSiKojQq9Qz8BmNmpuWH9x6jta87USfOMXXO/5g5ug0C+GVPIq1drOnkZU9cej6WWkFecTmLwhNIzy+pN++bxdSpU5k6daqpw1AURWlUqgC/ATk72LDyoynY2Vjy618HWLNhN/1aainTSRZtPs0zQwKxMNMQlZCFnYWG3KIyFoWd5bwqxAF48sknefLJJ00dhqI0efPnz6dXr144ODjg7u7O+PHjOXr0aLU0UkrmzJmDt7c31tbWDB06lGPHjlVLs3TpUoYNG4aTkxNCCOLj42s936ZNm+jXrx82NjY4OTlx66231hvjkSNHGDJkCNbW1vj4+PDmm29Wm4nxkUceQQhxycvW1rbOfHfs2MGECRPw8fFBCMG33357SZra8hVCMH369HrjbggNMRPbZiHEloYIRvlHh9ZefPP2ZABCPl2HvyYLM43gt/1JCCl547a2CCAiLhNHSy3ZRWUsCj/L2axC0wZ+A1BN6IrSMEJDQ5k2bRq7du1i69atmJmZMWLECDIz/5mL4v333+fDDz9k0aJFRERE0KJFC0aOHEleXp4hTUFBAaNGjWLOnDmXPdeaNWt44IEHeOihhzh48CC7d+/mscceqzO+3NxcRo4ciYeHBxEREXzyyScsWLCAhQsXGtJ8/PHHpKSkVHu1atWK++67r8688/Pz6dy5Mx9//PFl1x+vme+6desA6s27wRi76snlXsBp4My15nM9Xtd7NbKGMH/pRmnVbbpsMeA5+eKP+tXLnv7ugJRSyk3R6XL4J7vkiEW75BsbYwwrl608lCILS8pMHHnz0ZxWxWpKmtN9b6qrkdWUl5cnNRqNXLt2rZRSvxKZp6ennDdvniFNQUGBtLOzk1988cUlx0dEREhAxsXFVdteVlYmfX195dKlS68onsWLF0t7e3tZUFBg2PbWW29Jb29vqdPpaj0mPDxcAnLnzp1Gn8fW1tawclpdnnjiCdm2bdt60zXUamQNMZGLcSuhK1dl9hOj2Rh2lH1H4rHKTcXCTMMfUSn8Z0RrRnVwJ6eolC/Cz7LnTAZ33uLNifMX2R6bxaGkPO65xZNbvO1NfQnXXXJyMgDe3t4mjkRRLq/VrA0mOe+Zhbdf9bF5eXnodDqcnZ0BiIuLIzU1lVGjRhnSWFtbM3jwYHbt2mV0X5TIyEgSExOxtLSkR48eJCcn07VrV9577z26d+9+2eN2797NoEGDqtWQR48ezeuvv058fDyBgYGXHLNs2TI6depE//79jb1so+Tn57NixQpCQkIaNN+6qGfgNzghBK//ZywAX/60hXt76Qul//vzFAD3dvfmgR7elJVL1h1OoZO7DS0dLckuKuPLvef4au85ikrLTRa/Kfj4+ODj42PqMBSl2Zk5cybdunWjX79+AKSmpgLg4eFRLZ2Hh4dhnzHOnDkDwOuvv84rr7zC+vXradmyJUOGDDF8Ia9NampqreeuGltVOTk5rFy5slH6yPz4448UFxczefLkBs/7coyqgQsh3AAbKWVClW1Tgc7AJimlmruyEd3atz0dA12JjstAm5mMtYWWzUfTOHQ2m1v8nXiivx+5RWVsiE7n5wPJ2Jhr6dfahYzCMqKS87hwsYSn+vviaHVzTL3q5eVl6hAUpV7XUhM2hVmzZhEeHk54eDhabfWRw0KIau+llJdsq4tOp1+g6dVXX+Wee+4B9H1Z/v77b3744Qdmz55Np06dOHv2LACDBg1i48aNlz13bdsB/ve//1FeXs5DDz1k2BYWFsaYMWMM75csWcKDDz5odOyVli1bxp133om7u/sVH3u1jK2Bfw28VPlGCPE68DkwEfhdCHF/I8SmVBBCMOk2/ZrhS3/cyr/76GuXT30TyZHEHIQQzBreigV3dqRHS0cKSsvZcuI8Mcm5mGsE53KKWbj9LKm5N8d48eTk5Dq/tSuKcmWee+45fvrpJ7Zu3UqrVq0M2z09PYFLa7vp6emX1IzrUvmlu2PHjoZtZmZmtGnThoQEfb1xw4YNREVFERUVxZdffmk4f23nhktbBUBfyN599924uLgYtgUHBxvyjYqKYsKECUbHXSkqKor9+/df99EvxhbgwUDVnuZPAe9IKV2Bz4BZDR2YUl23Ni0Y0L01WbkF6NIS6dPahbTcYu7/dDd/Hk5FCEEPX0cW3NWRxfd1YXCQC4Wl5Rw9l42ZgMyCUj7aEc/pCwWmvhRFUZqQmTNn8uOPP7J161bat29fbV9gYCCenp5s3rzZsK2oqIiwsLAresbcs2dPLC0tOXnypGGbTqcjNjYWf39/APz9/QkKCiIoKMjwiKxfv36EhYVRVFRkOG7z5s14e3sTEBBQ7Rz79u3j0KFDlxSy1tbWhnyDgoKwt7/yfkNLly4lICCAESNGXPGx18LYAtwFSAMQQnQGPIHvKvatAdo1eGRKNVWfhX/+41YWTezCPb1bUlSqY9q3B/h8S6yh6aidhx0hY9rxyb2dsTLTcORcNlZaQUGpjs92JhAel0WZTtZ1OkVRFKZPn84333zDTz/9hLOzM6mpqaSmppKfnw/o/y49++yzvPvuu6xevZqjR4/yyCOPYGdnx8SJEw35pKamEhUVRUxMDADR0dFERUUZhqM5ODjw1FNPERISwqZNmzh58iQzZ84kKyuLSZMmXTa+iRMnYmNjwyOPPMLRo0dZvXo17777LrNmzbqkCX3p0qW0adOGIUOGGHXt+fn5hlq5TqcjISGBqKgoQ4tApYKCApYvX84TTzxxRY8NGoQxXdWBJOCBin8/AyRW2TcayDW227spX01xGFmlyiE1Ix//SFp1my7f/mKD1Ol08ostp2WrWetl4HPr5fM/Rsni0vJqxx1JypG3f75HDv9kl3zljxNyxupoOWN1tHxtY4zcEnOhWQ4369Gjh+zRo0eD5dechjM1Jc3pvjfVYWTo17m45BUSEmJIo9PpZEhIiPT09JSWlpZy8ODB8siRI9XyCQkJqTWfqkOzSkpK5AsvvCA9PDykvb29HDJkiIyMjKw3xsOHD8tBgwZJS0tL6enpKefMmXPJELLc3Fxpa2sr33vvPaOvfdu2bbXGPHny5Grpvv76a6nVamVSUpLReTfUMDIhZf01MSHEd0Af4FPgBWCtlPLpin0zgSeklF2M+cIghGgJzEbfLH8LYA0ESinjjTjWCv2iKZMAJyAKmC2NXJM8ODhY7t+/35ikN5zQ0FCGDh1K2P5TjHryYxztrDmxYS5O9jZsOpzKrB8PUVhSzmNDAnjtjo7Vjj14LodX1h6npFwyor07OiFIzdPP2mZtrmFQoDPDg1ywtbzmUYU3hMpvwcZ8to1Ree+V66s53ffIyEh69uxp6jCUG0RdnwchRKSUMtiYfIxtQn8RSATmA7HA3Cr7HgTCjcwHIAi4D8gCwq7gOICvgCeBN4BxQAqwSQjR7QrzabIGBbdhSK+25OQX8vrHvyOlZHRXT76b2gszjeDr7fFsi66+Vnj3lo7MHdsOc43g7xPncTQXPNHbh9au1hSW6vgrJoO5m2MJPZ1JeTNoWt+/fz9N9YuaoiiKsYwqwKWUaVLKkVJKeynlcCnlhSq7RwAzr+CcO6SUHlLK24GVxh4khLgFfa/356SUy6SUW9B/EUgA3ryC8zd5bz49AXMzLV+u2sms91ai0+kIDnThuTFtAXjhp8Ok5xZVO6a3vzOvj2mLViP47XAaX+1O4N/dvZg12J927rYUlupYdSSN+VvOcCw13xSX1WB69uypajuKojR71zyRi5QyV0pp9CoaUkrdVZ5qAlAK/FwlrzJgBTBaCHHTLIrdu0sAKz58AgtzM774eQdPv70CnU7H1GGtGNDWlcyLJcxafghdjdr0gFYufHhXR9ztLDiWksfUnw6RnFPE9AG+TOnbEndbc9LyS/hidyKf70ogp7DURFeoKIqi1KchFjOZJIR4uCGCqUcnIE5KWXMc1DHAAn3T/E3j9sFdWPXxVKwszfl69S6mzlmOlJIPJ96Cq50Fu05lsGTbmUuO6+LtwJIHutI3wJm84nLeWH+Sz3bE09rVhldGtObOzi2wMtMQnXaR97bFNclhZ3PmzKlz0QRFUZTmoCGmUv2m4tXYXNA/N68ps8r+m8qIfh347ZOnsLGy4H/r9vLYa99hrYUF/+4KwMKNMRyMv/SWOVqbM29cO/4z0F+/wtnhVO7+MoJ5f8ZgBrw4LIA2bjYVa42fZdvpzAbrEHY9zJ07l7lz59afUFEUpQkzqhd6nRkIMbgin+1XcewTwDKM6IUuhNgM2Ekp+9XYPhL4CxgspbykU5wQYgowBcDDw6PnihUrrjTMG0J+fj52dna17jsae543lu2ksLgMa0szRvUJwLxlG3amCJytBFN7WtDWVVvrsckFgs0pZiQU/PNdzlwjaeegw9fZirOl+jVzfc0L6WGbi9l1HuZ4NSrX7X3kkUcaJL+67r3SeJrTfbe3t1f9MhSDyMjIasutVjVs2DCje6FfcwF+La6wAP8Z6CalbFdj+33on4t3llIeq/XgCs1hGNnlHIhOYPaHqwk/cFq/QWhoPXgYBRp914B/9/Nl9rj2OFjXPh96el4x209nEHoqgxNp+k5s9pZa7u3pw8HkfIrLdLjZmjOktQt9/ByxNq/9C0Fz1JyGMzUlzem+q2FkSlXXexjZjeAYECiEsKmxvSNQgn5d8ptWj45+bP7qWfaseInJd/TD0lxD7I5t5MWdQiPgp92JjHpvB5sO175CUAt7S+7t7s1n93Xhh4e708ffibzicr7elYCzhYYWdhZcuFjKqsNpvLbxFD9HpZByk8ytriiKciMyugAXQlgIIe4QQrwohHijxuv1xgyywlrAHLi3SkxmwP3AX1JKVZoAt7RryRdzHuT0n/N4/pFbyY8/RdqeHbSwhvTcYv7z7QEe/zKCkym1N98AeDta8fb49jw9JBALrWBrzAVOJucwuq0rbdxsKCmXhMdl886WMyyPTL7hxo5HRkYSGRlp6jAURVEalbHLiXqjn6wlAP10cpVPQqv+5X7L2JMKIe6p+GdlG8IYIcR54LyUcrsQwh/9hDFvSinfBJBSRlU0o/+fEMIciAP+AwSin0xGqcLN2Y63nrmDwJZuzJi3gkMbNjD2X6M4WWDJtujzbD9+nruCfXj2trb4OFtfcrwQgju7etKtpQPzN53i9IUCPgk9w7SB/tzT1YOwuCz2JuSwJyGHwjIdj/TywUxzYzwgDw7Wtz41pY53iqIoV8rYGvgC4Dzgh77w7gO0At5G33Td6vKH1mplxeupiveLK95Xdh0WgLaW+B5F3+N9HrAe8AVuk1IeuMLz3zQe+9cAvpv/CGZmGtav/ou+1llM6u+HRghWRSRx6/ztzF97nLyi2sd8B7jYsOi+LtzdzYtynWTRjni+33eOOzu34OmB/libaziUnMeyPYmUlF/tEP+G1aNHD3r06GHqMBSlyZs/fz69evXCwcEBd3d3xo8fz9GjR6ulkVIyZ84cvL29sba2ZujQoRw7Vr070tKlSxk2bBhOTk4IIYiPj7/kXDExMdx55524ublhb29P3759+fPPP+uN8ciRIwwZMgRra2t8fHx48803q315T0lJYeLEibRv3x6tVmt059b6rr20tJTZs2fTtWtXbG1t8fLyYuLEiZcsdtKYjC3ABwEfApWLLOuklPFSyjeAX4FPruSkUkpxmdfQiv3xFe/n1DiuUEo5S0rpKaW0klL2kVKGXsm5b0b3ju7JyoX6MeP/+y2cfX9t5c3bfBnXzYuSMh3LQuOY/EUE+UVltR5vodUwbVAAr45ug6WZhs0nzjPz12PYmGl4eqAfthZaotMusmR3IsVlpi/EVRO6ojSM0NBQpk2bxq5du9i6dStmZmaMGDHCsIoYwPvvv8+HH37IokWLiIiIoEWLFowcObJaL+uCggJGjRpV5/wM48aNo6ioiC1btnDw4EEGDhzIHXfcQWxs7GWPyc3NZeTIkXh4eBAREcEnn3zCggULWLhwoSFNcXExbm5uvPTSS/Tp06fBrr2goIADBw7w6quvcuDAAX7//XcSExO57bbbKCur/W9pgzNmxRPgIjCw4t95wIgq+24FcoxdPcWUr+awGtm1CNt/SnoMfF5adZsurbpNl30fmC8/WLFTDnxziwx8br184NPdsqC47tXJTp/Plw9+GymHf7JL3rV0n9xwLE2eTMuTr6w/KWesjpYfhsbJ1Nyia471RtKcVsVqSprTfW+qq5HVlJeXJzUajVy7dq2UUr8Smaenp5w3b54hTUFBgbSzs5NffPHFJcdHRERIQMbFxVXbfv78eQnIrVu3GraVlpZKjUYjV65cedl4Fi9eLO3t7WVBQYFh21tvvSW9vb0vWZFMSinHjh17yWpixqp57bU5duyYBOThw4frzKuhViMzdvmpc4Bbxb9jgVHA3xXvewNFtR2k3FgG9gzi2LoQlq0M5/MV24k6cY6od3/Ex9cTq7bd2BubyeNL9/HNU72xNKt9mFhrN1sW39+FdzadIiIhhw+2xKIR0NHLHktzM+IyC5n39xm8HCzp5m1Pdx8HPO0trv86uYpyg7t10W6TnHfL0/3qT3QZeXl56HQ6nJ2dAYiLiyM1NZVRo0YZ0lhbWzN48GB27drF1KlTjcrX1dWVDh068MMPP9CrVy+sra1ZunQp9vb2DBgw4LLH7d69m0GDBmFt/U8/ntGjR/P6668THx9PYGDgVV7ppWpee21yc3MB6kzTkIwtwLcBQ4A1wBLgs4oVwErRrwe+pDGCUxqeq5MdLz15G88+fCs/rY/g/37YQkx8KtqMMFy792XPmSxuefoX7mhjxQuPjsTN+dKJNByszHl7fAf+PJ7OjtMZRJ3L5WhyHmZaQQsHa5xszUnJLSYlt5iNJy7g7WDJv7p40K6F7XW5Rm9vbwCSk5PrSakoypWYOXMm3bp1o18//ZeA1FT9sFQPD49q6Tw8PEhKSjI6XyEEmzdv5q677sLBwQGNRoOLiwsbN27Ey8vrsselpqbSsmXLS85dua8hC/Ca115TSUkJ//3vfxk/fvwlMTUWYwvw16iYqlRK+XmV4Vs2wPvcZKuBNQdWluY8+q/+TL6zL7sOnmH7/hg2HzhLvPCmxNqR7yOT+emPeSx86T7uHtn9khq0ViMY28mDsZ08uFhSxv6zOeyKy2TnmUySswqwszKjlZstWjMtybnFfLozgV6+jtzVpQX2jbzueEpKSqPmrygN4VpqwqYwa9YswsPDCQ8PR6ut3kJX8++DlPKKWt2klEybNg1XV1fCwsKwtrbmyy+/5O677yYiIgIfHx86derE2bNnARg0aBAbN2687Llr2345YWFhjBkzxvB+yZIlPPhg9YFNdV07QFlZGZMmTSI7O5u1a9cafd3Xyqi/pFK/fOiFKu8XAYsaKyjl+tFoNAzsGcTAnkG8CuyPvcDkpfvBw5vc/Fwemv01v/zZlY9fvh8vd8da87C1MGNIG1eGtHElp7CUX6NS+O1QCofP5QDQzdcJnRBEJOZwLDWfu7q0oI+fY6M1q1/JN39FUer33HPPsWLFCrZt20arVv8MOvL09AT0tV1fX1/D9vT09Etq5XXZunUr69atIzMzEycnJwAWL17M5s2b+eabb3jttdfYsGEDpaX60TKVTeaenp6GVoCq54ZLWwUuJzg4mKioKMP7msdd7torlZWV8e9//5sjR44QGhqKq6urUedtCE1pJjblOghu7cZ3T/VmZGcP3nqwF/a2Vqzbdpged89j0fJtxMSn1Tm+2tHanMf7+bF8cg8mBvtgba4hKjGbrNwiApytKCgtZ/mBFD4JSyApp3G6Tnh7exua0RVFuTYzZ87kxx9/ZOvWrbRv377avsDAQDw9Pdm8ebNhW1FREWFhYfTv39/ocxQU6Fc91GiqF0kajQadTj+yxd/fn6CgIIKCgvDx8QGgX79+hIWFUVT0z9+SzZs34+3tTUBAgFHntra2NuQbFBSEvb29UdcO+qFk999/P4cPH2bbtm2GLzTXS+O2ZSpNUnCgC8GB+sXdxg3uzDNvr+DP8GO8+MEqXvxgFZ5uDgzsEcSgnm24tW97Wvu5X5JHZUF+Z1dPXll3gtPnL5JfUsbkvn7sis/mdEYB722NY0CgE+M6uGPbyM3qiqJcuenTp/PDDz+wZs0anJ2dDbVdOzs77OzsEELw7LPP8vbbb9O+fXvatm3LvHnzsLOzY+LEiYZ8UlNTSU1NJSYmBoDo6Giys7Px8/PDxcWFfv364eLiwqOPPsobb7yBtbU1y5Yt48yZM4wbN+6y8U2cOJG5c+fyyCOP8NprrxETE8O7775LSEhItRa+yhp2bm4uGo2GqKgoLCws6Nix41Vfe1lZGffeey8RERGsW7cOIYQhjaOjY7WOdY3G2O7qzeF1sw8ju1o6nU7++leknPjCl9Jv+EuGYWiVry53zJUvfrBKbtt7QhaXlF5yfH5xqXx+9TE5/JNdctzne+WuM5ly5aEU+cxv0XLG6mj54roTMvR0hiwrv3TYx9V48skn5ZNPPtkgeUnZvIYzNSXN6b431WFk6GfbvOQVEhJiSKPT6WRISIj09PSUlpaWcvDgwfLIkSPV8gkJCak1n2+++caQJiIiQo4aNUq6uLhIe3t72bt3b/nHH3/UG+Phw4floEGDpKWlpfT09JRz5sy5ZAhZbef29/e/pmuPi4u7bJqq11WbhhpGZtLVyK635rwa2fUipeRkXBphkafYvv8UW3YfJzuv0LDfwc6Kpx8cxnOTR2BrbWnYXlKu4/3Np9l2KgMzjeCZoYHc4uPImqPpnDx/EQAPOwvGdXTnFm/7a3o+XnlsQ322b5R7f7NpTvddrUamVNVQq5GpdkvligghaN/Kk/atPHny3kGUlZWz51AcG8OPsnHHMY6fvcDbSzbyzW+7eevpCTxwezAajQYLrYZXRrfBxdaCVVEpLNx6Bnc7C8Z1asG/u3my+VQGafklfLUvCT8nK8Z3akH7qxx2tmTJpaMadVJy4WKpYXhbSl4xxaU6nKzNKl7mOFmb4edkjY3FzbNUqqIoTZcqwJVrYmamNfRi79nrFh75aBuuXCQ59hSPv/49i1dsZ8Hzd9OvWys0QvCfgf60crVhRWQSidlFfLP3HOYaweAgF4a1dibyXC4J2UV8tjOB1q7W+DpZY2epxd5Si52FGdbmGoQQaIT+y4RAP6TNrMrrvoceJT2/hNDTmSTlFJGUW0xqbjGlRqyaZqYRdPGyo5+/E+1a2KJRE9AoinKDMroAF0J0AO5Bv4CIVY3dUko5uSEDU5qeyNPnKS2XpGKDc8ee6DLOEXnsLMMfXch/HxlJyLRxmJtrua1jC0Z1cOdAYg6/H05ld1wWW2Iy2BKTwcBWzgwMcCIyKZfYjEJiMwrrP7GRnKzN8HKwxMveEi8HS6zNteQUlZJdWEZ2YSkXLpYSn1nIwaQ8Dibl4WRtRh8/R7Q6NVhDUZQbj7HLiT4MfI3+AX06UFIjyc3zIF25rLcf7s3YXv48/9VuDp7JAGtPfLv7knT8KB9+u5nwA6f5bv6j+Hu7oBGCYD8ngv2cSMkpYvWhFNYdTSP8TBY7z2QxOMiF4UFumGs15BeXk19cRl5xOUVl5UhZ0VNESnQSyqWkXCcp00nKyiUn9mzB3tKMUWPG4u1oiY+DFV4OlkY1jWcWlLIvIYe9CdlcuFjKppMZaHGj9Fg6I9q4quZ1RVFuGEZ1YhNCnAIOA49LKbMbO6jGojqxXR/l5Tq+2xpDyPJILuQWoRFgXZLDxeSzONtZsmTOJMYP63rJcefzi/lpfxIbjqVTqpNoBIxq787DfXzxsLes5Uy1a4hObDopic0oIPR0FodT9KsqWZtrGNnWlSGtXLAwU7XyxtaUPvP1UZ3YlKoaqhObsX+FPIHFTbnwVq4frVbDYyPbc/jTe/jP7R1BCC6aO2IR2IUsnRX3zVrKjHk/kZyeXe04dztLnhnaiu8e7s7YTi0QQvDn8fNM/v4gi8PiyS6sfc3ymsaNG1fn2FFjaISgjZstT/ZtyTD7DNq42VBYqmPtsfPM3RzL9thMSm+Q9c8VRbk5GfsMfCfQAdjSiLEozYyznSULn+jHoyPa8dyXu9gZnQbu/ggHN7764yDL/9jH43cP4PlHR+Hp5mA4zsPeklnDW/NATx++3ZPIlpgLrIpKYcOxNMZ38aSLlz1B7ra429W+ytm6devqjKuwtJzMiyXkFZdTXFZOUamOkjIdxeU6WthZ0raFLVbm/zSVu5iVcddAP06ev8jaY+dJzC7i18Np/B2Twch2rvTzd8Jcq2rkiqJcX8YW4DOA1UKIDOAvIKtmAimlqo4oteoS4MLmt8ayYkcsr3y3j9RswLsdRYV5fLbmAF+u2smUewbyzEPDaenxzzJ83o5WvDK6Dff18Obr3QnsPZvNLweS+aViv6OVmaEgN9dqMNMKzDUCrUZDSbmOwpJyCkvLKSzVcbGkjMyLpWRcLKGorO6PqlYjCHKzoZOXPZ287Ckqrxg+18KOdu62HE7JZ+OJ8yTlFLPykL4gH9HWld6+jtUKfkVRlMZk7DNwK/RLhk66TBIppbzhh6SpZ+Cml1tQwoLVh1j653FyCyqaxEuLIOc8oiCLkX3a8fCdfRk3pAuWFubVjj2anMvOuCxOn7/IqfMXySsqu6oYdDpJebkOnU4/m5FOVzkjIZiba7CsMa2rVkj6BrowvK0bfQOcsTLXopOSw8l5bDhxgZTcYgCszDT09nNkUKAzng7GP7NXatdcPvOgnoEr1V3viVyWoV8+dA1wgkt7oSuKURxsLHhrUi9e+NctfLslhs/+OEbCecDNF6nz4a9TOfw1ZxXO5j/zwJhgHr97AJ2C9AuTfLrpFGk5RXg5WdPDzQZHWwuEVoNE3xNdV9ErffbotgAMfW87F/KKkfKfQtvByoxO3g509nXEy8mKwpJyCorLKSgp50JeMX9EpVBarsPayoxurVxwtLPgRHo+Oyt6x1uZaxgQ6MKt7dwI9nOiq7c9h5PzCI3NJDajkB1nsthxJou27jYMbuVMZ097tBo1llxRlIZnbAF+B/CClPLjxgxGuXk42FjwzPjOTLu9I2v2xLNo7WH2ncoAO2ewcyarvIzP/47j8w3RdPBzZvLYHhxJLCElu4hDCTlGneNsaj4uthYM6+TOsA4t6ObvhJeTVZ3TtP739rZ8uvk0K/eeY9exdMy1gqEBZgzv25Gw2EyOp+WzJeYCW2Iu4GRtzrC2roxs587MQf4k5xYTdiaLiMQcYs4XEHO+ABcbcwYFOtMvwAlbNQRNaWLmz5/P6tWrOXnyJJaWlvTt25f58+fTuXNnQxopJXPnzmXp0qVkZWXRp08fPvvsMzp16mRIs3TpUn766ScOHjxITk4OcXFxl6wWduDAAWbPnk1ERARarZa7776bhQsXYmdnV2eMR44cYcaMGezbtw8XFxemTp3K66+/Xuv/8/DwcIYOHUr79u05evRonfl+9tlnLFmyhPj4eAA6derEa6+9xtixY6/o2huTsT1vLgLRjRmIcnMy02q4Z0Artr93Jye+uI+3JgXTxsMatGZg7wquLTl+0ZaXfjlJ1OF4Lp7PoCAzh7ysXC7mXqSooIjiwmJ0paVYoMPOXNDrtU1M+nQnC//dhT1zhrPg37dwezcvvJ2t651j3cvJmrfv7cLmlwZzV7APZTrJ5thSlv51msnBPvzwcHce6+uLr5MV2YWl/HYolWm/HGHKT4eJu1DA/d08mTemDXd38cDd1pzMglJ+P5bO63+eYsXBFNLzVeOV0nSEhoYybdo0du3axdatWzEzM2PEiBFkZmYa0rz//vt8+OGHLFq0iIiICFq0aMHIkSPJy8szpCkoKGDUqFHMmTOn1vMkJyczYsQIWrVqxd69e/nzzz85duwYjzzySJ3x5ebmMnLkSDw8PIiIiOCTTz5hwYIFLFy48JK0WVlZPPzww9x6661GXXvLli157733OHDgAPv372f48OHceeedHD58+IquvTEZ+wz8HcBLSvlo44fUeNQz8KZBSsnWfdH8uDmKo3EXOJerIbvEDJ3Wolo6DRIrCy0l5foJXGrjam9Jn3Yt6Nvegzv7BtDG2/GKYjkQn8XTX+8hJV+f/0MD/HlhbDtsLbXEpF/k75Pn2RqTYRji1t7Djkf7+tLT1xEJHE+7yPbYTI6n6xds0QgI9nXktnZuuNtZXO60Cs3rM99cnoHn5+fj6OjImjVrGD9+PFJKvL29mTFjBq+++ioAhYWFtGjRgg8++ICpU6dWO37//v306tXrkhr40qVLeeWVV0hLS0Or1bdUHTlyhK5du3Lq1CmCgoJqjefzzz9n9uzZpKWlGZbvnDdvHp9//jnnzp2r9oX9X//6F7fccgtSSn799dd6a+C1cXFxYf78+UydOvWKr72q6/0M/CzwbyHEZuBPau+F/rWReSlKnYQQ3NqnE7f26UR+fj4HDhwgMjKSI2eSSbpoTeZFLUdjEtEBBRXH9Atux8hBt9A6wJuECxfZf/oCe0+kkZpdyIb9iWzYn8gb/9tPt0BX7hnYinsGBOLfwr7eWHoEODNniBVHSnz4YkssP+w8y5boNN69vysD27rRzsOOJwf4s/5oGsv3J3EiLZ/Zvx/nFh8HHgz2obuvI5087UjLK2bLqQz2JuSwLyGH/Yk59PJ1ZLQqyG9aT/923CTnXXRXh6s+Ni8vD51Oh7OzfrRIXFwcqampjBo1ypDG2tqawYMHs2vXrjoLsaqKi4sxNzc3FN6V+YC+2ftyBfju3bsZNGhQtbW3R48ezeuvv058fDyBgYEALF68mNTUVFauXMlbb711ZRcNlJeXs3LlSvLz8+nfvz/QcNd+LYwtwD+v+OkP1Nb+INFPtaooDcrOzo7BgwczcOBATp06hRCCtm3bknohl9V/7uGrnzcRk1LC7v0n2b3/JK5OtlilbcHP25Uzm/8k4Xw+u0+k83fUOdbtO0tUXAZRcRm89kMEAzt68tydXRjT07fOpnVzrWDWmLaM6erJ7J8Pc/RcLg9/sY+J/f14eXx7bC3NuOsWL27r2II1h1JZcSCJQ0m5HErKxcvBkjEdWzC6Qwsm9vBmVDs3Np28UDFdaw4RiTkMaeXCbe3d1DStyg1v5syZdOvWjX79+gGQmpoKgIeHR7V0Hh4eJCUlGZ3v8OHDmTVrFu+++y6zZs3i4sWLvPTSSwCkpKRc9rjU1FRatmx5ybkr9wUGBnLkyBHmzp3Lnj17qn1BMMaRI0fo168fRUVF2NnZ8dtvv9GlSxdD/lXPV/X8V3Lt18LYAjywUaNQlHpoNBratWtneO/p5sDYQe2wKEogNi6R6HPFRJ0tIj37IkXHI4k9DhNf+IrnJo/ggcGteWBwa4pKyvjr4DlWhp9hfUQC4dGphEen0tnfmf/edQv3DAjErI4JWTr4OLB6Zn+WbDvDJ5tO8eOuBHacOM/7D3Slb5Ar1uZa/h3sw/guHqw5nMr6Y2mk5Bbz9Z5Evt2bSN8AZ/4d7MODPbwZ3c6NP0/oC/JtsZlEJOYwrqM7/QKc1ApoN4lrqQmbwqxZswgPDyc8PPySgrDmF2ApZb39Tarq1KkT3333HbNmzeK1115Dq9XyzDPP4OHhYThXp06dOHv2LACDBg1i48aNlz135fbi4mIeeOABPvjgA0NtvKawsDDGjBljeL9kyRIefPBBANq1a0dUVBTZ2dmsWrWKyZMnExoaWq0T37Ve+7UwqgCXUp6ta78Qou5ugtXT+gIfASMBAfwNPCulTDDiWD/gLWAY4AacA34B5kspLxobg9I8+Pv788QTT3D+/HkOHDhAVFQUcSl5bLW4k4QLJazZEsWaLVH0796aZx+6ldsGdmJCnwAm9Akgr7CErzef5OO1Rzl6NotH/y+UuT9FMvvuW3hoWBu0lynIzbQapo8IYnjHFrzw02Gik3KZuHgvE/v7MXN0G9ztLbGzNGNSr5b8u6cPkYnZbDiWzq64LMOrf6Azj/fzY1JPb4a0dmHV4VRiMwpZEZVKeFwW/+rqQRu3q1sLXVEaw3PPPceKFSvYtm0brVq1Mmz39PQE9LVRX19fw/b09PRLaqb1mThxIhMnTiQtLQ1bW1uEECxcuNBQ8G7YsIHSUn1fk8omc09PT0NNuOq5QV8TTklJITo6mkcffZRHH9V34dLpdEgpMTMzY8OGDQwaNIioqCjD8VXjtrCwMDTfBwcHExERwUcffcRXX33VoNd+tYxdjewTKeUzl9lnB2wCBhiRjw2wFSgGJqNvep8HbBNCdK2rEBZC2KIv7M2B14EEoBcwF2iDfpy6chNyd3dn9OjR3HrrrZw4cYJ+3SOxsHEiOgWWrQxn18FYdh2MxdXJljuG38I9o3oyqGcQMyd04akxHflp+2k+/O0wp1Ny+c/icBavj+a9R/swrKv3Zc/ZwduB357tz+K/Y/ls82l+3JXAb/uTeGRQAFOGtcLRxhytRtDb35ne/s5kFpTw26FUVkelsCsui91xWYxo784jfVoyc5A/B5Ly+P1oGudyivkkLIEgVxtGtXOlfQvb6/ZtXlFqM3PmTFasWEFoaCjt27evti8wMBBPT082b95Mr169ACgqKiIsLIwFCxZc1fkqC7+vv/4aKysrRo4cCei/sNfUr18/Zs+eTVFREVZW+lWuN2/ejLe3NwEBAZSVlXHkyJFqxyxevJjNmzfz22+/ERAQgLW19WWfsdek0+koLi5utGu/YvoZqOp+AXnAK7VstwXCgQQj85kJlANBVbYFAmXArHqOHYW+wB9VY/u7Fcfb1Hf+nj17yqZq27Ztpg6hSSkvL5dSSpmbXyhfeOc76TPwGWnVbbrh5Tf8JTntzeVy1V8H5IWsPFlWVi5X7Dgt205ZIa3u+lJa3fWlvPvtv2RMUna99/54Uo584ssIGfjcehn43HrZ9eVN8tO/TsmcgpJL0mbkF8uPt8XKUZ/ulsM/2SVHfrpbvv1njDyRmieLS8vlxuPp8oV1J+SM1dFyxupo+f62M/JQUq4s1+ka4zbd0JrTZ37//v2mDuGqTJs2Tdrb28stW7bIlJQUwysvL8+Q5t1335X29vZy1apV8siRI/L++++XXl5eMjc315AmJSVFHjx4UC5fvlwCcv369fLgwYMyIyPDkGbRokUyMjJSnjx5Un766afS2tpafvzxx3XGl52dLT08POT9998vjxw5IletWiXt7e3lBx98cNljQkJCZKdOneq99tmzZ8sdO3bIuLg4efjwYfnSSy9JIYTcsGHDFV17ber6PAD7pRHlqZTS6Gfg9wK/CyFSpJTfgKE2/WdFATzYyHwmAHuklKerfIGIE0LsRD9ZzKWD9/5R2VU3t8b2bPTj2VU1RQH0Q1IApkyZgr2tFfeP6oSn5QViz2VxIqmY40nFpGfm8fXqXXy9ehdCCLq1b8nQ3u1Y/Fg39icW8sHqw6zfn8Cmg4nc3tmBLj2LcLW3qvV87b0dWPZ4MAfjs/hgYwy7T2Xw4cYYPvnrFP2CXBnVxZMRnVvQwsEKF1sLnhnainu6e/Pd3kS2VkwKsyXmAl287bmnmxchI1uz62w2W09lkpBVxLK95/C0t2Boaxd6+TlioRZOUa6TxYsXA1wydjokJMQwpvvFF1+ksLCQ6dOnGyYz+euvv7C3/2eUxxdffMHcuXMN7ysnQ/nmm28MY7337dtHSEgI+fn5tG/fniVLlvDQQw/VGZ+joyObN29m+vTpBAcH4+zszH//+19mzZp1rZdOamoqkyZNIjU1FUdHR7p27crGjRsZPXq0IY0x196YjBoHDiCEeBj9lKp3o1+V7E/0TddDpJSnjMwjFfhdSjm1xvbFwL1SSvc6jrVCvyZ5CvAf9E3ovYHlwG9Symn1nV+NA7851LYeuJSSc+fOER0dzbFjxziVmMmZtBLS8s2ISy2gpPSfedV9PZ0Zd2sPUsts+X3/OXQ6iaONBbPv6ca0sR2xrGfBkp0xF1j8dyx7YzPQVfnv1d3fiYcH+nNHTx/DtrTcYn47nMKGY+lcLCkHwNPBkrGdPBjezpUTaQVsOZVBdsW873YWWgYEOjG4lQsOVjf88gPXpDl95pvLOHClYTTUOHCjC/CKjF8E5gBHAT9gqJTyxBUcXwIslFK+VGP7POAlWc+CKEKIFsAqYGCVzV8CU+VlVkMTQkwBpgB4eHj0XLFihbHh3lDy8/PrnVJQ0fvggw8AeP7552vdL6UkLy+PzMxMbGxscHByIToug92HEtgRlUhu4T8fJR9vd3TOXqQU6AttTwczHu3vysA2tvX2Fs8tlhxKK+dgajnH0ssp1cHdHcwZ28b8krTF5XAoS8u+DC1ZJfp8NULS3kFHD+dytBaWnCq2Ibtcf6wGib9FIe2sLmKrbZ4LATanz7y9vb0qwBWDyMjIy87WNmzYsGsvwIUQl2un+z/gAfTjwY9VbrxcAVojzxLgQynlyzW2vw3MrqsAr6iBbwS80fdEr6yBvwEsl1L+p77zqxq4UpdTp07x+9q1HI/PJPpcMSeSiikqrRiSYuOAjU8gF8v0/y06+jrxwt3d6h16VqmguIwdJy/QxdcRH2fry6bTSUlkQg7rjqayOy7LUIMPcrfl4d4+eNhbsS02iyMpeUhAK6CPvxMj27riZtu8JoRpTp95VQNXqroeM7GVoe80Vus5gKgq72U9eVXKAlxq2e5MLbO71fA4MBR9B7jYim07hBA5wFIhxBdSykNGxKAotWrTpg3/nTWLhIQEjh49yuEjxzgSl82xxCJi03K5eOoQ2LsinL2ITszm0f8L5dXv9vLSvd2YfGs7LOpoWrexNOO2rp71xqARgl7+TvTyd+J8fjEbjqXzx9E0Tp+/yBvrY2jXwpbJfXwZ19GNv05mEHkul13x2ew5m00fP0fGdHDH2frSGr6iKM1PXYXum1y+AL9ax4DalmnpSP2LpXQBsqoU3pX2VfzsAKgCXCE5ORkAb+/LDwO7HCEE/v7++Pv7M2bMGOLi4ti8eTOePv6kF9rxvz/2EnHkGNi5gJMHyVnwzNLd/HfZTtq6mjGsgxs92nnSqqUb5mZaCopKKCoupaCoBJ1O0rWtD4Et3YwaGuZuZ8nkPr480NObP46m81NkEifTL/LKuhN08LDjif5+3NbejU0nM9ifmMPusznsP5fL8CAXbm2jn1hGUZTm67IFuJRyTiOcby3wgRCilZTyDIAQIgD9GPKX6joQSAWchRBBVXuxA30qfl6fueuUG56Pj76T2JX076iNRqOhdevWJCYmGppyp9w3iNDwvXzx/RrOXjjD6Txb8i3dKbWw5tgFHcfC0uGvWMjLgMJc0JVfkq+Hqz39uwfRr1srhgS3oWu7lpekqcrSTMvd3bwY26kFa4+k8fOBJI6n5fPf36LpF+jMkxUF+bpj6UQl57HpZAY747K5vYM7/QOc1HrkNwidTodGo0YQ3Ox0uobrs3K9u7EuA2agH5L2Gvoa/ltAIrCkMpEQwh+IBd6UUr5ZsflbYBawoeKZeQIQjH5Sl0hg53W6BuUG5+Xl1aj5D+jTg0BfTxISEkhISCA6Jp5DyZkcz7UntdQOnbU9WOuHkWhkGea6YixlMRa6YvKysknLyOO3vw/y298HAejR0Y+n7h/MPaN6YG1V/Tm2Tic5mZJHe297rMy13NfDm/GdPfg1KoUVB5LYHZfF3vgsxnby4OE+LRkW5MKao+nEZRbyy6FUtp7OYGhrF/r6O2FppgoPU7GwsKCgoKDZdMpTrl5BQQEWFg3TX6WuTmxvAF9KKZONykjfJvg6sFRKmVpHOj+qT6W6Bf1UqvFV0gQAccDcqi0BQoiO6HvB90M/lWoi+lr921LK+p6hq05sylWp797rdDry8/PJyckh5UI2h9LN+DH0NEfOZnKxqOyS9I7mpbiIfFytzYiNSyYrR7+mmrODDZPv7MfU+wYR4OMG6JczveeT3fi6WDOqiyejunjQI8AZrUaQebGE7/adY8OxNHQSLM00DG3jythOLSgpl6yLvsD5i/r1x23MtQxq1bSGnzWnz3xmZiaJiYm0bt0aGxsbVRO/Cel0OgoKCoiNjcXX1xcXl9q6gzXQMDIhRDnQV0oZYVRGQmiBEqCXlPKAMcdcb6oAV67G1d57nU5yLuMiJ89lc+JcNgdiL/DXgXNk5hcb0rg5WNLD146Yw4eITzgPgEYjuGdUD55/dBTnCjWErD7GhbySf46xt2BkZw8mDwqgrac98ZkFfLkrgd1x/3yHbeVqw7jOLXC3t2L32Wzis4oAMNMI+vg5MqIJ9Fpvbp/5zMxMkpKSKCkpqT+x0ixZWFjg4+Nz2cIbGq4A1wHrgfNGxiaAh1EFeKNobn/MmpKGvPdl5Tr2nEjjj4gE/ohIIDbln4kFPS0LMSvIICnxguH5/ZhBnXhu8kisnF3460gafx1JJTGz0HDM6C4ezBgZRKeWjpzLLmT9sXQ2RaeTU6Xm7+NoRSt3W4RGcKFAv10APVs6MLKtK96Otc8wZ2rqM2866t6bTkMNI0sAOtex/3LHFNebSlEaUeX4ysjISBNHcikzrYaBnbwY2MmL+ZN7E3HqPF9vPsnK8DOkFluDtiU2QV64lp0nOSGVjWHH2Bh2jEE9g3j9qbG8MmEoJ5LzWLEnkZ/3JrLpSBqbjqQxrKM7M0YEMXWAP4/29SU8NpON0ekcTc4lKaeIpBx9DdzCTIOPszXWlubsP5fL/nO5dPa04/YO7vg63ZgFuaIotaurF3rAdYxDURrMgQM3ZAPQJYQQ9G7bgt5tW/DeI334OSyWZZuOc/RsFgV4Yt/aDXfdBdJTMgmLPM2oJz9mSK+2vDb1dube3YnpI1vzZWgcy3clsC36PNuizzOgrStPj2zD8LZuDG/rRlm5jjMZBZxIy+dEWj6Hk3KJO38RM63A1c4KFzsLjqbmczQ1n+4+9ozr4E4Le0tT3xpFUYzQNHqzKMoVaIqPSRxtLZhyWweeHN2etXvPMu/nAxw9m0Uenvh1as0wD3O2b9vD9ogYtkfEMLRXW16dejuvTOjA1OGt+Hp7PD+En2VnTAY7YzLo3cqFGaOCGNDGlbYt7Gjbwo4JXfRD66JT8/nrxHlCT13gQl4RbvaWONtZcjApj6ikPPr6OzGmg5uaEEZRbnBXNBd6U6eegStXwxT3XqeTrN4dx7wVBziZlAOAr5sNVtlnOBuXSkmZ/v/toJ5BvDr1dgYHtyGnoJRvw+L5Niye3EL9s+4eAU48PbINg9tfOnlMSZmO3fFZrD+axqHkXNztrXCytUAIgUbAoEBnRrVzM1mvdfWZNx11702noZ6BK4piIhqN4J4BrbirbwC/hJ9h/sooTiXnAJ44tXbDr/w8CfFphEWe5rYpnzCge2teevI2Zo5uz2NDAvlh51m+3h7HgfhsHl0WQVdfR54eFcTwji0MBbmFmYYhQa4MCXIlPrOA3w6lEnoqA0dbCxxtLNh+Joud8dkMbe3CiDYu2FqqPxeKciNRgxGVZmfOnDmGtYqbOq1Ww7+HBHHg43/x9cwhBHk5kF1ixulyL6xad6FVkDeWFlp2Hoxl/LTP6H73PP63ZicP9fVhx2vDeGlce1ztLDicmMOTX0UyYeFODp3NvuQ8AS42PDesFT883I0x7d1Iyyogr7CUMp3k71MZvL7pNOuPn6eg5NKZ5RRFMQ3VhN5EqCYt49W2Hvi1uJHufVm5jl/CYnl/1SFD07q1VkegXQkZSamkndePBbe1tmDi2N5MvrMf7Vt7s2LvOZZuPcP5vGJsLLR8+UQwfYNcL3uegpJyVh9KYc3hVOxtLLCz0j8Pt9AKRrZ1ZUhrl0afa/1Guu83G3XvTUc1oSs3tZCQEFOH0GjMtBomDm3DA4OD+CPiLB/+dph9MeeJzrHCyrk1g3s6cj7+NMdPnGXZr+Es+zUc7xZOjB3cmdeGd+Lvs2Wsi0rh0aURfPFoT4Z0cK/1PDYWWib1asmELh78ciCFjdHpONpagJU5649f4O+YDEa2c2Noaxc1RauimMgV1cCFEJ6AH3DJgFEp5Y4GjKtRqBq4cjVu5HsvpWRndCoL1xxhY2SiYbubRTG2JZlkpJwnv+ifZm9bawsCe/clQ2OHmUbwycPduK1r/XPHZxeWsioqhT+jz2Nva2F4Hm5roeWerh70bOlg1AprV+JGvu/Nnbr3ptPgNXAhhA/wP2BwbbvRL0qi1i5UlOtMCGGYGCY2JZcv/zrB91tiuJAPF/DCwseTVtYXsSzOJiM9m/ScEo5u34FDUAdsfQOZ9s0ButkVMn1sJ4YEt8XMrPb/xk7W5jzez4/7unuz+lAyG6LPY2+jn4r1u/3JbI/N5L5unvg6WV/Py1eUm5qxTeifo5+V7UXgCGq2NeUGVjkDW+WMbDeL1l4OzJ/cmzce6MGqXXEs23ScfTHnOXPRDrDDpoUPw/ra0j3QnbOxCexIjMfMK4CD+dY89PletDnrGN/Tl3tH9WBQz6BaC3N7KzMm9/Hj3u4+fL8vkb9jMnB3sCI+q4j3t8XTT40hV5TrxtgCfBDwjJTyh8YMRlEaQnCwvvXpZuqgWZW1pRmThrVh0rA2nEnN5dfwM/wSfoZjCVlsiylgW8xZvF1sGNrZmbjUcySZeWPl2gJcW7A+tYiV7/2JTUEmD43pwSN39iPIv8Ul57Cx0PLUwACGtnFjwd+nySmXuNhZsvtsNvsSsukf6Myotq44qYJcURqNsQV4IZDemIEoSkPp0aOHqUO4YbTydODFe7rx4j3dOHY2k18qCvP4tDxWZhYAWnycM/F21pJZbkWBpTX2AW2QOh1Lw+JZ+L9QBnUL4JG7+nPn8FsuWa+8vYcdnz/QlR8jklgZlYKrvSUO1uaEnclid3w2AwKcGKEKckVpFMYW4MuAh4BNjRiLojSIG3ERkxtBJ38X5vq7MGdiT/bFnGfFjlhW7TxDUlYRSVmlWJqX0Ku1oBQdCUXW2Pm1wsbbl8i4U+x47XtecLThyXsG8eS9A/Fu4WTI10Kr4ZG+vgwKcuHr3YkcPJeDm4OVYTKY8Lgsevs5cmsbVzzUPOuK0mCMLcCTgIeEEFuBDUBmzQRSyq8bMjBFURqHEII+7VrQp10LFjzWh42RiSz98zh/RyURfkI/jrxbkAUOLg6cSi/AoU1H7H0DyD59gne//JMPv/2Le0b3ZMbEYfTo6GfIt7WbLW+Pb8+JtHy+35dI1Llc3B2ssLc2Z/fZHHafzaGrlx0j2rgS6GpjqstXlGbD2AL8i4qfAcDQWvZLQBXgitLEmGk1jO/tz/je/pxOzmHZphN8vzWGqNPngfME+bpgaWdDJjY4d+6BtiCLtGPH+Gl9BD+tj2BwcBteeGwUt/ZtbxhG1t7DjnfGd+BEah7fR5wj6lwuLnaWONpacDgln8Mp+QS6WDOqrSsdPe3QNPDwM0W5WRhbgAc2ahSK0oC8vb0BSE5ONnEkTUuQtyPvPdqHN/7dg2+3xPDRmiOcTswEMmnp7Yy5jTVFNs649RqAzcVUko4eZ8f+U+zYf4oeHXz576OjuGP4LWi1+old2nva8874DiTnFPHH0TT+On4ec3MtznaWxGUWsmTPOZyszRjbwZ1evo5oNaogV5QrYVQBLqU829iBKEpDSUlJMXUITZqtlTnTx3biyVHt+WlHLB/+dphTyVkITTYeLZzQWltRYOuFex8PWlvmEx1xmAPHE3nwxa8I8nNn7owJ3DWim6FG7u1oxZQB/jzS15fw2EzWHkklKbsEVztLsgvLWH4ghZWHUhnRxpWR7dwwUwW5ohjliqZSFUJ0BoYALkAGsENKebQxAlOUq5WUlGTqEJoFC3Mtk29ty6ShQazZe5YPVh0iKi4DrZkWFzdHsLLkZLEDfkNGcLebZNXKTZxOOM+DL35FcCc/5j/3Lwb2DPonP62G4W3dGN7WjaTsQjZGpxMWl4WluRmgZcOJC/x58gKDAp25s/OlQ9cURanO2JnYzIBvgX+jn3mtkhRC/Ag8IqVUyxQpN4TKJnSlYWi1Gu7uH8i/+gXwd1QSC1YfIuxYKhZWFji5Olb0Yod2QwbRIeEg+4+dZ/+xBEY+8X+MGdSJt565g05B1X8nPk7WPNHfn0f7+nEgMZvVh9PIKCzD0lzL9jNZhMZm4iGsGVCuw1yr5lpXlNoY+z8jBLgPeAP983Drip9vAPdX/FQUpRkTQjCye0v+emss2+aPY3jHFqQnnScnIwep03E6R8tpp2AGjRlIvw6OmGthY9gxet33Dv9+/kuiTiRekqdWI+jl78z88e2Ze1sQfg4WlJbpEEKQjgPPrjnOhmg1BYWi1MbYAnwS8JaU8m0p5VkpZXHFz7eBecDDjReiolyZKVOmMGXKFFOH0az1befBb6+NZvu74xkY5ELauXQu5l6kXCc5lGNPuu9Abh/Tg+6B1mgErNkSRb9/v8fdM79g7+G4WvP0crDihVtbs/COdnT1tKWsXIdGo2HjyQxmrTnO4eTc63yVinJjM2o1MiFEMTBWSvl3LftGAOullDf8DA1qNbKbQ3NeD/xGtedkGm//fJDQY2k4uDhgaa3/c+Bpp2GASzaizJyvVu2koKgEgN5dApg4rjd3j+yBm7NdrXn+uSWUQ/iSkF2MRiOQUuJqbcaUfr74qEVTGpX6zJvOlaxGZmwNPBkYcJl9/Sv2G0UI4SuE+FUIkSOEyBVCrBZC+NV/pOH4DkKIlUKIC0KIQiHESSHETGOPV5q/JUuWsGTJElOHcVPp286DdW/cxsY3RtHexZzMtEzKSstIzdexKsGBOAsvVi75L9MfGIClmWDfkXienf8LgSNf4Z6ZX7Bq8wEKKwr3SlZamH1ra14cFoBtxZrjmUXlvLMljve3xJJbWGqKS1WUG4axvdCXA68KIXQV/04BPIEHgFeB94zJRAhhA2xFv5rZZPQTwMwDtgkhukopL9ZzfHDF8aHAE0AO0Aao/Su8clNSzeemM7CTF3/PG8vfUUmELI/k5IUc7B3tiYzP5olvs+nt58VTd7bhTFwKx84VE5dewvodR1m/4yi21haMH3YL94zqwYh+7Q15+rvY8O74duyOy2JFVApoBIm5JcxeH0MXTzse79sSy8ssg6oozZmxTehmwPfoC+yqBwjgJ+BhY3qhV9SUFwLtpJSnK7YFAqeAF6WUC+s4VoN+KdMYKeVd9QZdC9WErlwNde+vjpSSPyISeH/VIU6eL8LWwRahEQigj58Ng92zSTh5hMPx+RxLLCY1u8xwrJO9NX06evDK9Hvo1dnf8FgEYP2xNDaezDBsKyvXcWsbF+7u6lktnXL11GfedK6kCd2oArxKxp2AwejHgWcC26WU0Vdw/BbASko5oMb27QBSyiF1HDsc2AIMllKGGR10FaoAvzmsW7cOgPHjxzdIfureXxspJbtOpPHer4fZl5CLtZ21oaD1cTDnjg5WeJUlUFBmRnaZEys3HeDIqX/G8ndt58OT9wzi/jHB2NtaAVCu0/FjZDJ7EnP/mYpVSib18KJPgPN1v8bmRn3mTafRCvBrJYRIBX6XUk6tsX0xcK+U0r2OY98A5gKjgLeAnkAWsAKYLaUsrO/8qgC/OahObDeu44lZfLDmCH8fv4DGwhJRMeuaRldOv1ZOzBrbgW6tXPnjr3A+/HIthxOKKCzR/x7tbCx5cFxvZkwcZlijvKCknCW7EjidWYhG6Du62ZlreaJvS4LcbU12nU2d+sybToMU4BUdy1KklKXGdDKTUiYYEVgJsFBK+VKN7fOAl6SUl30mL4T4ApiKvtD+FP2z8GDgTWDT5ZrVhRBTgCkAHh4ePVesWFFfmDek/Px87OzUo35jvPLKKwC88847DZKfuvcNr1wn2RNfyB8nS0gtNkNo9J3UdOU6zMqK6eUp6eKQje7iBQ6fyeFgfBHnMvSd1oSAPp28uGtIW7q0dkMIQXaphh3Z9pRoLRFCoJMS87Ii+jvm0cLy+lVSmgv1mTedYcOGNUgBXg70k1Luq+i8Vuf/Aillvb1IKgrwD6WUL9fY/jb6WnRdBfhS4ElgkZTymSrbZwPvAp3qa85XNXDlaqh737hyC0r4cP0Jfj+QTG6xzrC9uKgYb1szxnW2x6s8kcPHYoiILeRYYjHlOv2fo+4dfHn24Vu5e2QPtFoNh5Jy+F9kCkXl+v06ncTJSsujvVWN/Eqoz7zpXEkNvK5e6I8BsVX+3RBfY7PQPz+vybliX10yKn5urrH9L/QFeDfA6OfxiqLcGBxsLJh7b1fm3NOFA/HZ/N/GE+yJzcLSypKMcvjmQD4lhdYE+/ZnwpgC5nf0YdexDJb8HMbB44lMfvlb5n72B7MeGcGk8X1YMKE9+xOyWRGVSjGQW6Jj4Y6zuNuY8VR/P7wcrUx9yYrSIC5bgEspv6vy728b6HzHgE61bO9I/YXvscpwamyv7HaqQ1GUJksIQc9AZ36Y1o+8olLeW76N7UlakrKLsbKz4WiW5ECqlu+PJPP48Nbs+vkVPlz8Iz9vjubMuQvMmLeCeV9s4JmHhvPE3QP5YEJ79p3N5pdD+oI8s6icNzfHEuhsxVMD/HCwMjf1JSvKNTFqIhchxFYhRPvL7GsrhNhq5PnWAn2FEK2qHB+AfpKYtfUcuxH9+PHbamwfXfGzabaNKw1OCKGGEzVx9lbmjGhtQdgbt/LHfwdyVw8vLLQCC0sLSi2s+Gx7Ar1f2cSJUg/uGxnEhGB73B20pF7I5ZWP1tDmttd4Y9Fa/Gw1fDChPfd09UArwEyrITG3hBf/iOGj0DjOZdXb91VRbljGzsQ2FHC4zD579EuMGmMZEA/8LoS4QwgxAfgdSAQMU2cJIfyFEGUVPc8BkFJmAPOBp4QQ7wghRgghXkK/kMp3lePKFUVpXjr6OPDhpO4cfHsU79zbmZbOVmi1WqwdbDlZYMHKlJacsO7B6CEd+FcfB1q6mpOTX8SCr/+i/dg3mDHvJzxECR/d0Z6RQS4gJeZaDWeyipi35QxP/3qUXw4kkVWgZnZTmpYrWQ/8cs/AWwP5RmUg5cWK8dwfAT+gb/7eAjwrpayahwC0XPoF400gD5gGPI9+RrgF6IeVKQrQcMPHlBuLtYWWB/r5cX9fXw6ezeaLLafZGn0eK1sr8oCNF8wx1znTp1c+A/LjySx35+89p/hq1U6+WrWTdoEe3D64C/cM6Mg5Ycv+c7lQsVTp9vgc1kWfJ8jNhsf7+tJSzbWuNAF19fp+FHi04q0Elgoh8mokswY6oy+EjVIx3OzuetLEU33d8crtEv1MbpedsU1RlOZNCEGPAGeWPt6L9NwivgqN44fws2BlAViwK9+askI7Hhvailf+cyff/xbOig17ORmXxsm4ND767m+cHWwYNaAjT0+9gz9PZZKaV4KTrSUXCst5bUMMbVxteLyfL54OqsObcuOqqwldB5RXvESN95WvDOBz4PHGDVNRFOVSLRyseHlCB/a/NYKQuzribmeBmbkZVg72/BCRyr8+3oN08eaxES349wBHerW2xsVOS1ZuAbsPnqKtixmvjmjNGyNbE9xS/5TQ3tqC1IIyXl4fw7ubT1NQUu8s0YpiEvX1Qv8OQAixDfiPlPLE9QpMUa5W5RSqlVOqKs2fjaUZkwcFMGmAP5uOpPLB+pPEXyhA62DHxtP5FOW3oZO7GQNbXWREfjwZeSXkF+lYuHAhkyZNonXr1kzu5cOdnVvw+9F09p/Lxc7KnKT8Up7//Tjzbm+Li62FqS9TUaox6hm4lHJYYweiKA3ljz/+MHUIioloNYLbb/FiTFdPwmMusOCPExxNysPGwY4zRZLjMWCr6cBt7WzoaZ9FbnoiLVu2NBx/YE843uXlzAzuSFiKjv3nctGaaXl5/UleHxlES2f1bFy5cVxJJzaEELcA7YBLHgxJKb9vqKAU5VqsXVvfiESluRNCMKidO4PauXMwPoulW2P5+1g6wsaKcmBtbDm/XDSni3dXfHclcEffAKwtNERERFBYWMjOnTtxdXWlR+ueHCh0xMLcjDc3x/LsYH86etqb+vIUBTCyABdCOAHrgb6Vmyp+Vu3uqwpw5YbQUKuQKc1D9wBnPn8smKyLJfy6N5Gvd8SRlluCraMdZy7CS6uieX75QYa2d2NCtxHYlSRz4vhxMjIyyMj4C3cXX85798HSXMvHO84yuZcP/QPVimeK6RlbA38HcEW/lGgYcBeQg36K1X7o1wlXFEW5YTnbWvDk8NY8MawV+2Iz+WVvIhsPpQLmmFuYsze5iO2n47HXSh4dciujgqw4cWgvCQkJOOVnk9tuNBbmWr6PTOZCfjETunia+pKUm5yxE7mMRl+I76l4f05KGSqlfBj4G5jZGMEpytVYunQpS5cuNXUYyg1KCEGfIFc+fLAbB98ZyZLHejK0nRtaAVY2VpRaWvPZ9kTG/V8Uf6T40L73SPp1bce829thZSYw12r4MyaDJTvPqjkHFJMytgD3As5IKcuBIvSzr1VaDYxt6MAU5WpNnTqVqVOn1p9QuelZmmkZ2dmDr6f2Zu/cEcwcFYS9lRnmluY4ujmxP7WY//wUz+rTtuTkFzFvTBsczUGr0XA47SLzNp2mrFwtw6CYhrEFeCrgVPHvs+ibzSsFNWRAinKtnnzySZ588klTh6E0MS52Fsy8rS375t7K/Pu6EOhug9ZMi5ObE5tisuk+63feXnEQ75Q9kJWMRgjSCkp5ce0J8ovLTB2+chMy9hl4OPpC+w/0U6CGVCxCUgZMpv6FSBTlulHN58q1sDTXcn9fX+7t3ZK1B5J5Z+1xLgAWlhYs23EW8zJL7mhzDk1ZITr31pQCz/4WzeRePgxq7Wrq8JWbiLE18LnAnxX/XgB8hr7Z/N/oC++nGz40RVEU09FoBHcG+xD66lCeHhWEuVZgbWeN1sGRFTFWRETnoUvUr3JsbWnOjwdTeWndCVJzikwcuXKzMKoAl1LGSinDKv5dKqX8r5SypZTSRUo5sWKlMEW5ISQnJ5OcnGzqMJRmwsbSjOdua8vWV4YytpsXQiOwd7bnXLktv0QUkxIVSXlhHmZaDRfLJHM3x/JZeDyl6tm40siMXQ98ghDiiiZ9URRT8fHxwcfHx9RhKM2Mj7M1ix7uzvL/9CHAzQYzczPs3Z3ZlWrDn1tO4lyUhQb9muMnzhfy3Jrj7IrLNHXYSjNmbBP6GiBFCLFICNGrEeNRlGvm5eWFl5eXqcNQmql+bVzZNHswL49vj6WZBitbK6SDC0u2JHFg72n8bM0oK9chNBp+PJhKyIaT5BSqtcaVhmdsAd4PWAHcD+wRQpwUQrwqhPBvvNAU5eqoJnSlsZlrNTw5rBXbXhnKhB7eCCGwdbAlsUjw/ooDHAyLJC/jAgCZxTpeXn+S73edVuPGlQZl7DPwvVLKpwFv4E7gEPAqcEYIsV0IoZYTVRTlpuPpZMX/TerG+ucHMridGxqNBnsne1JxYf3OZKIiYygsKkar1RKRVsq0nw/x867jpg5baSaMrYEDIKUsk1Kuk1LeB3gATwKtgCWNEZyiKEpT0MHbgW+n9ubnGX3p6uuIRqvB3tmepIvm/LHtFKfi0ikr12FmaUl4mn7YWXhsBoWFhapWrly1q+qYVtF0Pgl4CPBBP9GLotwQevbsCUBkZKSJI1FuNr1aufDbs/3ZfTqDJVvPEHbyAjZ2tsScu8jJ+Gw6t3XHz8sJNIIVh9L4fk8BfnknuGPALXTq1AmN5orqVMpNzugCXAjhCNyHvtAeABQCv6OfB31zo0SnKFfhwIEDpg5BuYkJIejfxo3+bdyITcvnq+1xrIo4RynmHDuTTczZbDoFueHtboeljS3JVj34v73ptAldxsj+wdxyyy2YmalBP0r9jF1OdCUwDjAHtqNfhWyVlDK/EWNTlKuyf/9+U4egKAC09rDjnfu68Pzt7VixJ4HvwuI5n1dC1MkLnE7MpnOQG66O1ti4enC23J0lEQm02raI3j1uoW/fvtjY2Jj6EpQbmLFf8zoAc4DlUspzjReOoly7yiZ0RblRuNhZMG1EEFOGteLvY+l8sz2OiLgs9hxOxd3Fio6BrtjZWGDmEUCcsw/Jx07Sq1e5qcNWbnBGFeBSys6NHYiiKEpzZ6bVcFtXT27r6snx5FwWbTrFn0fS2J6ZhK+XPe38nLG0MEe27MxLmxKY3MuHni3tWbp0KX5+frRr146AgADVxK4AV/YMXADjgcGAKzBHSnlWCDEEOCWlVANvlRvCnDlzqv1UlBtRB28HFj/akxPJufx/e3ceX0V1NnD89+Rm3xOyQBYIYQmLyBbZRVwQVAQVpVqXor7g8lZb37Zvazdc2tfWpdpaq4BaN4qtdQMVBGWRIlQB2cK+BkggZCEh+8J5/5hJeolZbkhyb3J5vp/P/cCdOTPzzNybPDlnzpzzx0/38um2Exw7fpq+PaPp0TUMX4cPb2zM4pW1FQQUQE7OBjZs2IC/vz99+vRh7NixOmDRec7Ve+BRwCfASKAIaz7w57GmFp0F5AMPtlOMSrXIo48+CmgCV51Dv4RwXrxzODuzipj7+X6WbD3OgSOFDOzTha7RwfgHBWL6TyCzvJKa/BN0zd1ERkYGGRkZ9OvXj6lTpxIUFOTp01Ae4GoN/CkgGav3+ddApdO6z4CfuHpAEUkGngUmAmJv/0NjTKar+7D38zDwf8BaY8y4lmyrvNucOXM8HYJSLdY/IZznbh/K4+VVLNt2gvc3HGPd1ix6d48iJiKQ4EB/SEjmdLckjhWVEXtiA7m5uQQEBHg6dOUhribwacCPjTHrRMRRb10mVnJvlogEAyuACqx5xA3wG2CliFxojClxcT+pWCPB5bgYvzqPaM1bdWZhgX5MvyiJ6RclkVNUzrJtJ/h8Zw7ZpdXEdwkmLMiPyIhgqiLGs6u6ikMF5aR2CaawsJAlS5YwZswYunfv7unTUG7gagIPBY41si4QqybtitqR29KMMfsARGQrsBe4B/iDi/t5EVgApHGOg9EopVRHFxceyG1je3Db2B6cOWPYmV3Ea2sPc7i4itiIIHx8/Xj2i8PEhfjRtzqT3bt3s3v3brp3787YsWPp06cPVvcl5Y1cHfZnN3BlI+suAba5uJ+pwPra5A1gjDkIrMWq5TdLRL4LDAMedvGY6jyzceNGHYVNeR0fH2FgYgRPzbiQed+5gMOZeeScKuPMGUNOSRVryrsSl34lgYGBZGZmsnDhQl566SW2bNlCTY0+kuaNXE3gLwA/FJFfALVtM5EicifwfXu9KwYC2xtYngEMaG5juzPds8D/GmN0ol3VoPT0dNLT0z0dhlLtJjIkgI8euphJ3cNYvy2LvKJyRIQdlZE40qdz+RUTCQsLIycnhw8++IBPPvnE0yGrduDqc+DzRaQX8CjwmL14OXAGeNIYs8DF40UDBQ0szweiXNj+KWAP8JqLx0NEZgOzAeLj41m1apWrm3YoxcXFnTZ2d+vTpw9Am10vvfaeode9eakO+NlwwxMrD5LYN5HucWEcLqpiz4lgrr1gCCX5ORw5cgRjTN21rKqqwtfXt8mmdb32nYO0ZCYcexKTiUAckAcsN8YcaMH2lcAzxpiH6y3/LfBTY0yjf1CIyMXA58AwY8x2e9kqwNfVXujp6emmsw6zuWrVKiZMmODpMM5Leu09Q6+764wxzF+2m4XbcuiX0gVfhw/lldWMTw7n1lHJdZOkGGN46623qKys5NprryUuLq7B/em19xwR2WiMcakJsUUdwIwxh4GXzykqSwFWLby+KBqumTubC7wCHBWRSHuZL+Cw35cZYypaEZtSSnVKIsLsSf2YMa4ns/+6gfDYcIL8ffn38RIWv7qJ1GAf/ufqfgRIFTk5ORQXFzN37lzGjh3L+PHjdWS3TqqpGm+LnkNw8TnuDKz74PUNAHY0s21/+3VvA+sKgIeA51yIQSmlvFJkSAD/+P5Y3vn3Yd7blUeXsEDiu4Rw+oxh5hubKcst5I5xk+lZdpBtmzeyZs0aduzYwS233EKXLl08Hb5qoab+7DqE9Zy2q+o/H96QRcDTIpJa2/QuIilYA8T8rJltL21g2XP2cR8A9jWwXp2HEhISAMjK0tF91fnpppE9uD49mb+uy2Tt4ULCgv3plRxFVbcI/r6zkD17KhnVfSTJZw5icnN45ZVXuOWWW0hOdmlID9VBNJXA76JlCdwV87F6rX8oIr+09/84cASriRyou9e+H3jMGPMYgDFmVf2dicgprHvg31qnzl/Z2dmeDkEpj/N1+DBrXAq3jazh5S8z2XK8mCB/X/p0j6JXciQnC8r4eO8ZKvJCmRyTya5duzSBdzKNJnBjzGttfTBjTImIXIb1KNibWAPAfI41lKrz3OKCVbN29TE3peocO9bYmENKnX+C/Bw8cElPCkorefPrY2zNLibQ30F8dDDxI3tSVJrIis1RjPbv6elQVQu1uueCiPgAka4+l23fK5/eTJlDuDC6mzFmgivHVOeX2iZ0pdR/RAX78+AlPak5Y/hs90k+3nmSagPhwf5MGJ3KW1tyWJ1xnN/deiG7du0iMDCQ5ORkunbtisPhyh1S5W5NdWLLB64wxmyy3wvwIVZt2fnRsYuAL3HtHrhSSikPcvgIk/rHMal/HPtzS3hm5UEcvg56JUdRWl7FpCc+Z6xPLidOfAqAr69vXRKPjIzkuuuuq9vXggULCAgIIDw8nIiICGJjY+nRo4cmfDdpqgYeWW+9DzAFeKQd41Gq1WbPng3AvHnzPByJUh1br5gQ/nzjQOb+6zCbs4sJDvRjxIg09mV15fCug/QJKiLRN4/qo0cBKC0trdu2qqqKffu+3Xc4ICCAPn36MHr0aG0Na2f68J/yOvPnzwc0gSvlCh8R7rs4hYO5JTy+bB/BgX6kJEaSGD+YvYfzWPFNJvGhDob3jMA/Kop/ZWQzKKULoYEOZs6cSWFhYd0rMzOTkydPsn37doYMGVJ3jMOHD1NRUUFycrLOXd6GNIErrzN37tzmCymlztIzJoT5N1/Ii2sOsjm7mKAAPwb0iiUlKYqdB/JYcSCPT3cV8eTHB6iuqqZrRAA948PoHhtqv+IYd9VokiKE3bt3k5KSUrfvdevWsXv3bgBiY2MZPnw46enp2tTeSprAldepbUJXSrWMw0f4/iWpLP98Fbv9UsjIKSE4wJfh/eMZ1CeG0vJqTpdVUXi6goKicvYWlpORm0/1thxqqmqorKhkfL9YfnbjkLOSc1JSEmVlZRw7doyTJ0+ydOlS1q9fz6WXXsqgQYN0ytNz1FwCTxSRVPv/Dqdlp5zKJLV5VEoppTzGzwHfH59CSUU189YdYX9+Gf6+DvxDHUSGBpAcGwpAeWUNRaWV5BeVkZNfRmlpFd+cKOea335Ges8oHr5pCJcPTmTcuHGMGzeOmpoa9uzZw4oVK8jNzeX999+nvLycESNGePiMO6fmEvg/G1j2Qb33QtsP+KLUOVu8eDEA1157rYcjUapzCwnw5aEJ1qNnR0+VszmriH0nSzlRXEFp1RkC/R0E+gcRFxlE32RDSXk1x04Wc/hYELsKypj+5EpSugQxfUxPrh/dkwt6RNG/f3/S0tLYsmULGzZsOOte+fHjx4mJidGx2V3U1FW6021RKNWGpk6dClgzLymlWs/hI/SIDqJH9H86oFXVnGHPyRLWHzrF3txSSqpqCAvyo1/3KFK6hZOdW8L+zFPkF5fzp2V7efL9baTGhXL96BTuuao/Q4cOZciQIXXN51VVVbzxxhs4HA5GjhzJ8OHDtcNbM5oaie11dwaiVFuZMmWKp0NQyuv5OXwY2DWMgV3DACiuqObTXbmsOVhAoJ+Dnt3CSY4L5WRhGYePFZF3qpz8ikpeWL6PFz7ZwU9vGMwD1w4k0N9KQ4WFhYSFhZGTk8Pnn3/OmjVrGD58OKNHjyYsLMyTp9phaTuF8jq1TehKKfcJDfBl+uCuXDconlX781my8yTgQ7foELpFh1BSXsWxnBIOHTtFRWUNT368i1c/283vZ47g2hE9iImJ4d5772X//v2sW7eOAwcOsG7dOr766isuvPBCJk+ejL+/v6dPs0PRBK6UUqrNOHyEy/t04bLe0WzNPs2H23I4UVJJSKAffbtH0js5gqzcEjL25VFWUc3dL/2bIR/t4NZLejFxaBK9e/emd+/eZGVlsXbtWnbs2EF2djZ+fn6ePrUORxO4UkqpNiciDE4IZ3BCOCUV1by75ThfHy3CRwRfPwfJyRGUllZRFOrP3qJKfvr3rTz4yr/p1y2MycOSmTaqBzfddBN5eXmUlZXV3SsvKChg9erVjB8/nujoaA+fpWdpAldep/YHXTuxKdUxhAT4cseIJO4YAZuOFLJ8dw6r9lRCsB/BwX5UVdVQUhJCaWklx0sqeGnFAf6weDvpqV148NoLmDqyR92+1qxZw5YtW9i6dSuDBw9mzJgxxMbGevDsPEcTuFJKKbcZlhzBsOQI7h2bwpIdOSzadoITpyuIjHQQGRlIdfUZSkurKCmpZG9BCXe9sJa417/iv68ZwJ1XpHHxxRdjjGHLli1s3ryZzZs3k5KSwogRI0hLS8PH5/yZhVoTuPI6WvNWquOLCPLj5uGJ3DQ0ge3ZRaw9UMC/9udx4nQl4eEBhIcH0KVLEIWFYZwqKOXx9zJ48t2tPDTtAu67+mouvvhivvzyS7Zu3cqhQ4c4dOgQo0aNYtKkSZ4+NbfRBK6UUspjHD7C4MQIBidGcN+4HhzILeWL/Xl8tD2HU0BsrC9RUUEUFYVQeKqc3y3ayfOLt/OjGwYz68rJXHHFFWzZsoWvv/6awYMH1+23urra6weE8e6zU0op1WmICL1iQ+gVG8KtFyWxYncu73yTxaH8MqKjg4mICKQgPICC/FLm/GMLz324jafuGsX0MSMZMWLEWf1f3nzzTSIiIrjyyisJDQ318Jm1D03gyuvUDqGqz4Mr1Xn5O3yYPCCOSf1j2ZBZyFtfH2V79mliYkIICwsgLzSA4qJy7nx+De99eZDnZo0mNsIauS0nJ4esrCwyMzPZu3cvEydOZOjQoV43acr5c7dfnTc++ugjPvroI0+HoZRqAyLCRT0ieW76QH45qQ+xof4EBPiSkBBOQlIkcYkxLN+Zy/AfvMf76w4CEB8fz/3330+vXr0oLy9n8eLF/O1vf6OoqMjDZ9O2NIErr7No0SIWLVrk6TCUUm1IRLi0bwyv3TaE2y9Kws8hhIb6k5QUQdfEaAgLY+bz/+L2Z1aQebKYqKgobr31Vm644QYCAwPZt28fL774Itu2bfP0qbQZbUJXXkdnIVPKewX6OZg5KplJ/WP546qDfJ15itjYEEJC/AkI9GP5rnw+evA9Zl+Zxv/eOJhBgwaRkpLC4sWL2bt3L6dOnfL0KbQZTeBKKaU6nW4RgTwxtR/Ldp3kL18cAiApKYL8YD+KioJ4bW0mr6/cy0NTL+D7UwZyyy23sGvXLtLS0ur2YYzp1PfFtQldeZ158+Yxb948T4ehlGpnIsKk/nG8etsQxvSMwsdHiIkJITExnJj4CIK6RPGHpXsZ/tAHZGQW0L9//7qBXoqKinj11Vc5ceKEh8/i3GkCV17nnnvu4Z577vF0GEopN+kS4s9j16Txy0l9iLM7uXXtGka3bmFExYRT7h/E5XOW1nVyA1i9ejVHjx7ltdde48iRIx6M/ty5PYGLSLKI/FNECkWkSETeE5HuLmyXLiLzRGSXiJSKSKaILBCRnu6IW3Ues2bNYtasWZ4OQynlRrWd3F6/fSj3jO1BaICDoCA/EhPDiYsPJSQ6gv96cT2PLdzImTOGq666irS0NMrLy3nzzTfZt2+fp0+hxdyawEUkGFgB9AO+B9wO9AFWikhIM5vfDAwE/gRcBfwMGAZsEJHkdgtadTrahK7U+cvf14cZwxJ4846hzBjaDT+HEBYWQEJCOF26RvHC5weY8bvllFaeYcaMGQwZMoSqqioWLlzI9u3bPR1+i7i7Bj4LSAWuM8Z8YIz5EJgK9ACaa/P8vTFmrDHmL8aY1caYvwGTgSh7v0oppRQA4YF+3DMuhZduvpCkyEACAnxJTAgjJj6CL48UM+nXSygsrWLq1KmMGjWKM2fO8O6777Ju3TpPh+4ydyfwqcB6Y0xdW4Ux5iCwFpjW1IbGmJMNLDsMnAQS2zhO1YllZWWRlZXl6TCUUh1ASnQwL8wYZHVyc/gQHx9K126RHC2Dq+YsoaC4kkmTJjFx4kQAampqPByx69ydwAcCDbVRZAADWrozEekPxAE7WxmX8iKJiYkkJurfdEopS2iAL49ek8Zdo5LxESEqKoiEpEiOVQiT53xC3ulyxowZw6xZsxg7dqynw3WZuxN4NFDQwPJ8rKZwl4mIL/ASVg38ldaHprxFt27d6Natm6fDUEp1ID4i3HpREr+b1p8gPx9CQ/1JSIwku9KHyXOWkFtUTkJCQt1z4QUFBbz33ntUVVV5OPLGiTvnThaRSuAZY8zD9Zb/FvipMcblgWVE5CXgbuAaY8yyJsrNBmYDxMfHD3/77bfPKXZPKy4u9toZdTo6vfaeodfdc7z92h8rFd466EflGaG4uJKsY6cIrTrN767rRkSQA4DNmzdTWFhITEwMAwYMcNuAL5deeulGY0y6K2XdPRJbAVYtvL4oGq6ZN0hEnsBKyt9rKnkDGGPmAfMA0tPTzYQJE1wOtiNZtWoVnTX2zk6vvWfodfec8+HaDz9+mh9/sANC/UlMiuTYUfjN8iI+ffRqosMCGDBgAK+++iq5ubmUl5czefLkDjdqm7ub0DOw7oPXNwDY4coOROQXWI+Q/cAY82YbxqaUUuo80a9rGE9fN4BgPwchIVYSP17pw9WPLKGguIK4uDhuvvlmHA4HX331FevXr/d0yN/i7gS+CBglIqm1C0QkBRhrr2uSiDwI/Ab4hTHm+fYKUnVuw4cPZ/jw4Z4OQynVwfXrGsZT1w8g2P8/SfxYhXDNo0spLKkkJSWFadOsB6SWLVtGRkaGhyM+m7sT+HzgEPChiEwTkanAh8ARYG5tIRHpISLVIvJrp2U3A88BS4EVIjLK6dXiHuzKe23atIlNmzZ5OgylVCfQLz6Up68bQIhTEs8sNUx5bClFpZUMGjSIK664AoD333+fggKX7/a2O7feAzfGlIjIZcCzwJuAAJ8DPzTGFDsVFcDB2X9gTLaXT7ZfzlYDE9opbNXJbNiwwdMhKKU6kbT4UJ6+fgA/fn8H4E9SchQHMwuY+vgyPnlkMmPGjKGoqIjIyEiioqwHpsrLy6mpqSEkpLlBRNuP26cTNcZkAtObKXMIK1k7L5sJzGyvuJT30OZzpVRL9Y2rl8S7R7HvSD53PreKhT+5nKuuuuqs8hs3bmTFihWkpaWRlpZGamoqYWFhbo1Z5wNXSimlsJL4MzcM5EfvZQD+JHePZvW+k/zyza/4v++NPKtsUVERxhh27tzJzp3WWGJxcXGkpqbSu3dvUlNT273XuiZw5XUeeeSRs/5VSilX9YkN4dkbBvJQbRJPiWHuigP06hbB3Vf2qyt31VVXMW7cODIyMti/fz+HDx8mJyeHnJwcsrOzSU1NbfwgbUQTuPI6jz76KKAJXCl1bnrFhvD7af35wbsZhIcHkNwzlh+//jU94kK5YkhSXbmwsDBGjRrFqFGjqK6u5ujRo+zfv58hQ4bU1b7Ly8sJCAhol9q4JnDldebMmePpEJRSnVz/rmH8/MrePL50L9HRwVR0j+PWZ1bxwS8mMrJvHD4+ZydkX19fUlJSSElJqVtmjOGdd96hqsqa9SwmJqZNY9QErryO1ryVUm1hQp8YMgvKeP3fR+naNYyqimou+8VHhAf5MTQ1hmG9YxjWK4bLBycSFRrwre2LiorIycmhuLiY+fPnM3PmzDadp0ETuFJKKdWI2y9K4mBuKV/szycxORLBUJBfzNrdJ1m9PRsAf18fJg1L5jvje3H18GSCAqzUGhERwf3338+iRYvYtWsXCxYs4O677657FK213D2Qi1LtbuPGjWzcuNHTYSilvICI8PCVfUjtEoSvrw8JyVF0T42ja/c4Uvskkta7Gz7+fiz+6jC3Pb2CHnf9jXv+/AUZh/MBCAoK4sYbb6Rnz56UlJSwYMECSktL2yQ2TeDK66Snp5Oe7tJkPkop1Sx/Xx+eun4gKdFWEo+KCiI5OYKwqCAqfX2JioumX79k+vaMo6SihjdW7CX9ofeZ8bvP2LjvJA6HgxkzZhAfH09eXh4LFy5sk2lKtQldeZ1hw4Z5OgSllJeJDPLj5e8O5pujRSzadpy1B/IJCfEnJMSfMzVnyM0ro8Q4SOrZlfgQP3bsO87irw6z+KvDTBySyM+/M5Rbb72Vl19+uc2matUErryONp8rpdqDiDAsOYJhyRHkFleyZEcOi7cfJ6+kiri4EHwlhBO5pRw7XUF4XDT9wv3Zc/AEyzcf47Mtx/jZjUN44M67iAgPw8en9Q3g2oSulFJKtVBMqD+3j0jire8N48eX9yI5MpBqA126BNMnNZouUYFkn64ktEsUQwd1xy/Ajyfe2cwtf1hLTmE5ADU1Na2aHEVr4EoppdQ58nf4cNWAOCb1j+XLA/ks3JjFrhPFhEcGEdclmJy8Uo4XlNOlawymspI1O08w6kcf8MLsERzd8jmVlZXce++9+Pn5tfjYWgNXXichIYGEhARPh6GUOo/4iDCuVxf+fNMF/N+1/egVE0x5jSE8MogL+nYhIiwA8fenW3I8p2uEm55azfL9kJeXz8qVK8/pmFoDV14nOzvb0yEopc5TIsLIlCgu6hHJqr15/HV9JlmFFUTHBBMbE0xuQRniEMrDglmWWUhORCSybj0DBw4kMTGxRcfSBK68zrFjxzwdglLqPOcjwmV9YxjfK5qlO0/y7uZsMgvKiIoKIjIykJKSKoJDAtie5UtZphD9/ofcd+/sFh1DE7jyOtp8rpTqKHwdPky5IJ5rBsbxzdEiPtx6nC8P5hMa6k9IiB/Bwf4czvRl7oYc+q5c3bJ9t1PMSimllLI5P4J24nQFb288xqJtJ4iODiI4uBtHjwTwwFt7WrRP7cSmvM7s2bOZPbtlTVFKKeUu8WEB/GBCKr+f1p+wAAeBgb6k9orFP6llc4hrAldeZ/78+cyfP9/TYSilVJPSu0fyxh1DGdE9Ah8fIb5reIu21yZ05XXmzp3r6RCUUsol4YF+PDFtAB9vP84fVx1s0baawJXX0eZzpVRnc80FXbmkTwxhD7i+jTahK6WUUh1AaEDL6tSawJXXWbx4MYsXL/Z0GEop1a60CV15nalTpwJgjPFwJEop1X40gSuvM2XKFE+HoJRS7c7tTegikiwi/xSRQhEpEpH3RKS7i9sGishTIpItImUisk5Exrd3zKpz0SZ0pdT5wK0JXESCgRVAP+B7wO1AH2CliIS4sItXgFnAr4EpQDbwqYgMaZeAlVJKqQ7K3U3os4BUIM0Ysw9ARLYCe4F7gD80tqGIDAa+C9xljPmrvWw1kAE8Bkxt39CVUkqpjsPdTehTgfW1yRvAGHMQWAtMc2HbKuDvTttWA28Dk0QkoO3DVZ2RiCAing5DKaXalbsT+EBgewPLM4ABLmx70BhT2sC2/kDv1oenlFJKdQ7ubkKPBgoaWJ4PRLVi29r13yIis4HaobmKRWS3C3F2RDFArqeD6EzasBau194z9Lp7jl57z0lztaAnHiNr6OFcV37Tyrlsa4yZB8xzYf8dmohsMMakezqO85Fee8/Q6+45eu09R0Q2uFrW3U3oBTRcU46i4dq1s/wmtq1dr5RSSp0X3J3AM7DuZdc3ANjhwrY97UfR6m9bCez79iZKKaWUd3J3Al8EjBKRulnLRSQFGGuva25bP+Amp219ge8Ay4wxFW0ebcfS6W8DdGJ67T1Dr7vn6LX3HJevvbhzvGh7sJYtQBnwS6x72o8DYcCFxphiu1wPYD/wmDHmMaft3wYmAT8BDgL3YQ3oMsYYs8ltJ6KUUkp5mFtr4MaYEuAyYA/wJrAAKxFfVpu8bQI4GojvTuCvwG+Aj4FkYLImb6WUUucbt9bAlVJKKdU2dD7wDkBEbhSRd0XksD1Jy24ReUJEwuqVixKRl0UkV0RKROQzERnkqbi9gYgkicjz9sQ4pSJi7H4ZzmVS7OUNvSI9E3nn58q1t8vp976diciERr7fpzwdm7drzQRfmsA7hh8DNcDPgcnAi1j395eLiA+AWKOSLLLXPwBMx+rUt1JEkjwRtJfoDczAeoxxTTNlnwBG13udbtfovFuz116/9273IGd/v6/wbDjerbUTfOl84B3DtcaYk07vV4tIPvA6MAHrA54KjMPqL7ASQETWYfUh+F+sHzzVcl8YY+IBROS/gCubKHvAGLPePWGdF1y59vq9d6+d+h13q3Oe4Au0Bt4h1Evetb62/020/50KZNX+ErO3KwQW0/xEMKoRxpgzno7hfOXitdfvvfJmrZngSxN4B3aJ/e9O+9+mJoLpLiKhbonq/PaEiFTb96oW6X1Yt9DvvXstEJEaEckTkb+5ei9WnbPWTPClTegdkYgkYs1x/pkxpnZc3GjgUAPFa4eQjQKKG1ivWq8CmAssA05i3a/6OfCliIwwxuxsamPVKvq9d49C4BlgNVAEDMX6jq8TkaHGmBxPBufFWjPBlybwjsauUXwIVGM99163inOfCEa1gjEmG7jXadEaEVmK9VfyL4DbPBLY+UG/925gjPkG+MZp0WoR+QL4CqufwS89Etj54Zy/35rAOxARCcTqcZsKXGKMOeq0urnJXJqbDEa1IWPMERH5F3CRp2Pxcvq99xBjzCYR2YN+x9tTayb40nvgHYWI+AHvAiOAq40x2+oVaWoimMx6I9kp92isdqjajn7vPUu/4+2rNRN8aQLvCOxnvRcAlwPTGnmMYxGQKCKXOG0XDlxL8xPBqDZmd+4ZC/zb07F4Of3ee4iIpAN90e94e2rNBF86lGpHICIvYt1j/S3wUb3VR40xR+0k/y+s8d9/gtW88jBwITDYGHPEjSF7FRG50f7v5Vifw/1YndVOGmNWi8gzWH/srrOXp2Fd+whgpDFmt/uj9g4uXHv93ruBiNTOS7EJOIXVie1hoBQYZozJ9Vx03svVCb4a3V4TuOeJyCGgRyOrHzXGPGKXiwaeBq4DArESyv8YY7a0f5TeS0Qa+yFYbYyZICJ3YY2M1xvrBysXa3CdRzV5t05z194uo9/7diYiDwO3YP0eCgaOA0uAOXYnTtVO7Na8Z4GJWLcsPgd+aIw51Oy2msCVUkqpzkfvgSullFKdkCZwpZRSqhPSBK6UUkp1QprAlVJKqU5IE7hSSinVCWkCV0oppTohTeDK7URkpogYp1eJiBwSkfdFZIY9eIdz+RS73MwWHGOCiDxSf1+dkYiMFpF/29fJiMgQT8fUEBH5oYjc4Ok46rO/Bx3ueVn7O+r8c5DitO6QiLzVzsff53Ts37TnsVT76PS/3FSndhMwGrga+BXWtJ0LgWUiEuRULtsu93EL9j0BmIN3fMdfwZp46Fqs67DHs+E06odAh0vgwMtY162j+m+s+Nw9YMqNdOzropqhs5EpT9psjNnn9P5NEXkHeAd4EngAwBhTATQ0PrzXs1sQ0oDfGmNWNFM2wL5WXqMtzsme1e9oswU9Z0cj8x+0K2PMZgARnZm1s/KG2onyIsaYd7HmQ58lIsHQcBO6iFwkIstFJE9ESkXkgIj8xV73CFbtG6CqtpnQadtHRWSTiBSKSK6IrBCRUc5xODVvThWRP9vlTorIWyISWa+sr4j8VER2iEi5XW6piPRzKhMjIi+KyDERqRCRXSIyu6lrYZ9vDdbP6a/seA7Z614TkaN28/qXIlKG9UcPIpJm3444JSJlIrJeRCbX2/cj9v76icindvN8pojcaa+/3Y6xWERWikivZmI9hDUM561OzbKvOa0fLCKLRKTAjmmtiFxcbx8NnpPT53+viDwhIsdF5LT9WQSLSG/7HIrtZuHvNXSu9ZYZEfmNiDwoIgft/a0WkYH1yh1yPo962z/i9L6vfc1z7O9Apoi8IyJtUkkSEYeIzBORIhG53Pm82uozVJ2P1sBVR/QJ1rjX6cAX9VeKSCjwKfAVMBM4DaQAY+wiLwNJwN3AOKwk6CwRa+zho0AIcBvwhYikG2O21iv7R6wJZr6LVRN+0t6fc5J42473OeAzrPG6xwPdgF1izZ61FggCHsGaNGIS8KJYNcznG7kOH9vx/wurGf1lrNsMtSLsYz8N/BwoE5EEu/xp4PtAIVYT7cciMsUYs6TeMd4B5tv7uB94VUT6YN2C+BngZ1+DvwEjG4kT4Hqsz22LfY5gTUqCiAwD1gDfALOwJsi4F/hMRMYYYzY2dU5O6x4GVmFd+wFYn8UZrIk3as/hPuCvIrLBGJPRRLxgfe67gR8A/sBTwIci0s8YU93MtvV9hDUJyH1YY+UnYt0aanUlSazbSQuxmrsnGGM21SvSVp+h6myMMfrSl1tfWEnXAL0bWT/JXv8d+32K/X6m/T7dfn9hE8d4xC7j20wsDqw/ZHcDf3RaPsHe/vV65f8MlPOfeQQus8s92MQxfmVv06fe8vlYv+wbjdGOzQCP1Fv+mr18Wr3lTwPVztfWPsfdwKYGrs8dTsui7G3zgHCn5Q/aZXs0cy0PAW81sPxzYCfgXy+mncAHLpxT7ee/ot7y9+zltzVwDnPqn2u9bQ2wF/BzWnajvXxMvXN6rYFzqvtMgBj7/dQW/hzUfscmNHYt7fNZA+yn3s9LW32G9vLftCR2fXWMlzahq46o9qZcYz2H92LVduaKyG0iktyinYtcYTcp5mH9sqvCmvc4rYHi9TvObQMCgHj7/ZV2nPObOORkrDmVD9rN7b520+qnQBes2uS5qObb08+OB9Ybp74FxpgarBrcELs1wNkSp3IFQI69fZFTmV32vy26zlBXe7wEq5Z4xuncBau1YrwL5/StWOvF9WkD5+BKrMuNMVVO77fZ/3Z3YVtnecAB4HciMsuu/baFBKzkHQqMNWf3F3HWrp+h6rg0gauOqPaXTIO9co0xhcClQBbwFyBTRLaLyPTmdmw3534CFGM1sY8CLsJq+g1sYJP8eu9rm7Bry3YB8o0xZTQuDitRVdV7veO0j3ORYydnZ9E0fN2OYyXNqHrLC+q9r2xkGTR8fZoTjVXb/hXfPv/vA1Fy9qN+DZ1TU7E2ttyVWJv7bF1irGrsRGAD8ASwR6w+Gfe1ZD8NuBAYCLxtjDneRLn2/gxVB6X3wFVHdA1Wk/PGxgoYqwftdLs2l451f/QfIjLYGLO9iX1Px6rl3eBc+xKRKKxafUvlAtEiEtREEs/DqhX9oJH15zqneEMtFPlA1waWd7XL109a7e0U1n3qF4A3GipgjDnj/NYNMbVEOdb98TpizU9+FmPMAeAOERFgMNYfJ38RkUPm2/0OXLUU6w/LJ0Wk3Bjzx3Pcj/JSWgNXHYpYA4FMBV4yxpQ2V94YU22sR3B+hfV97m+vqq1NBdXbJBirE5pzr/TLaHmzaa1lWDXb/2qizFKgH5BpjNnQwOv0OR67IauBUXL2oCAO4DvAN218rPoqqHe9jTElWM3Ag7HuwX/r/NsxnrZwGLig3rIpjRU2ls3A/9iL6m/bIsaYp+x9PSciD7VmX8r7aA1cedIQEYnBquF0x/rFeBOwHKtG3SARmQLMBj7A6tEdgtVJ5zSwzi62w/73RyKyBKixk8VSrAFHXhORv2Ld+/4VcOxcTsAYs1JE3gX+YN+LX4HV63c88LExZhVWj/fvAGtE5FmsGncIVlK/2Bgz7VyO3YhnsToJLheROUARVs/kvlgtG+1pB3Cx/fkcB3KNMYewEtAXwKci8gpWE38MMAxwGGN+1s5xtcbbWL26n8W6Nz8Y6/rWEZELsXp5/x3Yh3XLYCZWS0+Tz+67whjzrIjUYCVxhzHm6dbuU3kHTeDKk2rvAZdjNTFvAm4G/mnfV2zMXqzHi36F9ajWaeBrYKKxBu0A65ftX7CS16+xaslijPlURB7ESirTge3AHcAvW3EeNwM/xXq86YdYj259jfXYF8aYQhEZY8fxU6xHjE5hJfJ3W3HcbzHGZInIOOD3wItYHe42A9cYY5a25bEa8DBWZ75/YNXEX8d6cmCTiFyE9Wz+n7AeFTuJ9Xm/1M4xtdbrWH0y7gbuwWpNuB4rUdc6DmRifaeSsL7P24Ap5uxH5M6ZMeZPdhJ/XkR8jDFPtsV+VecmTf+eVEop1R5EZAKwErgCWG1a/ux5a4/vwPrDtgprpL/W/BGrPEDvgSullGd9hjViYIqbj7sbK3mrTkpr4Eop5QEiEsbZYw9sNcZUNla+HY5/Af95rCzLGJPlrmOrtqEJXCmllOqEtAldKaWU6oQ0gSullFKdkCZwpZRSqhPSBK6UUkp1QprAlVJKqU7o/wGBLeDKJ6d52wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# intialize plot\n",
    "lw = 2\n",
    "a = 1\n",
    "# colors = plt.cm.viridis_r(np.linspace(0,1,9))\n",
    "# colors = plt.cm.Reds_r(np.linspace(0,1,5))\n",
    "colors = plt.cm.Blues_r(np.linspace(0,1,9))\n",
    "# fig, ax = plt.subplots(1,figsize=(6.5,4))\n",
    "fig, ax = plt.subplots(1,figsize=(7,5))\n",
    "fs = 16\n",
    "# ax.set_xlim(30,0)\n",
    "# ax.set_xlim(22,6)\n",
    "# ax.set_ylim(0.375,1.2)\n",
    "ax.set_xlim(22.5,0)\n",
    "ax.set_ylim(0,1.5)\n",
    "\n",
    "# ax.set_xlim(30,22)\n",
    "# ax.set_ylim(1.2, 2.4)\n",
    "ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "ax.set_ylabel('Elevation [km a.s.l.]', fontsize=fs)\n",
    "\n",
    "plt.tick_params(labelsize=fs)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "legend_entries = []\n",
    "legend_objects = []\n",
    "c = 0\n",
    "w = 0\n",
    "# legend_entries.append('2019-04-30')\n",
    "legend_entries.append('2013-12-07')\n",
    "ax.plot(ref_elev.dist_m/1000, ref_elev.mean_elev/1000, '--', lw=lw, color='gray') # reference profil\n",
    "for elevprofile in filelist:\n",
    "    elev_df = pd.read_csv(elevprofile)\n",
    "    ds = elevprofile.split('/')[-1].split('_')[-1][:-4]\n",
    "\n",
    "    # choose which ones\n",
    "#     if not ds.startswith('2016') or ds.startswith('2015') or ds.startswith('2017') or ds.startswith('2018'):\n",
    "    \n",
    "    # QUIESCENT:\n",
    "    if (((not ds.startswith('20180117') and not ds.startswith('2012') and not ds.startswith('2013')\n",
    "    and not ds.startswith('20180128')) and not ds.startswith('20180507') and not ds.startswith('202'))):\n",
    "        \n",
    "#         # SURGE:\n",
    "#     if ds.startswith('202') and not ds.startswith('20211025') and not ds.startswith('2022'):\n",
    "        \n",
    "        if ds.startswith('2012'):\n",
    "            ax.plot(elev_df.dist_m/1000, elev_df.mean_elev/1000, '-', color='orange', linewidth=lw, alpha=a)\n",
    "        else:\n",
    "            # fill between standard error\n",
    "            if ds.startswith('201'):\n",
    "                st_err = 2\n",
    "            elif ds.startswith('2020'):\n",
    "                st_err = 3\n",
    "            else:\n",
    "                st_err = 3\n",
    "#             ax.fill_between(elev_df.dist_m/1000, \n",
    "#                             elev_df.mean_elev/1000 - st_err/1000,\n",
    "#                            elev_df.mean_elev/1000 + st_err/1000, color=colors[c], alpha=a-0.4)\n",
    "            \n",
    "#             ax.fill_between(elev_df.dist_m/1000, \n",
    "#                             ref_elev.mean_elev/1000,\n",
    "#                            elev_df.mean_elev/1000, color=colors[c], alpha=0.3, label=str())\n",
    "            # plot the line\n",
    "            leg_obj = ax.plot(elev_df.dist_m/1000, elev_df.mean_elev/1000, '-', color=colors[c], linewidth=lw)\n",
    "#             legend_objects.append(leg_obj)\n",
    "            c+=1\n",
    "            legend_entries.append(ds[0:4]+'-'+ds[4:6]+'-'+ds[6:8])\n",
    "#         plt.legend(legend_entries, ncol=1, fontsize=fs, facecolor='white', framealpha=1)\n",
    "# #         plt.savefig(dem_outpath+'figures/DEMs_cline_north_'+ds+'.png',dpi=200)\n",
    "#         plt.savefig('/Users/jukesliu/Documents/TURNER/paper1/figures/elev_profiles_north_')\n",
    "\n",
    "#         plt.axvline(x=16, color='k', ls=':', lw=2)\n",
    "#         plt.legend(legend_entries,fontsize=fs-2,framealpha=1)\n",
    "#         plt.title('Quiescence (southern)',fontsize=fs)\n",
    "#         # plt.title('Surge (northern)',fontsize=fs)\n",
    "#         plt.tight_layout()\n",
    "# #         plt.savefig('/Users/jukesliu/Documents/TURNER/paper1/figures/elev_profiles_south_quiescence_legend.png',dpi=300)\n",
    "#         plt.show()\n",
    "\n",
    "\n",
    "plt.axvline(x=16, color='k', ls=':', lw=2)\n",
    "plt.legend(legend_entries,fontsize=fs-2,framealpha=1)\n",
    "plt.title('Quiescence (southern)',fontsize=fs)\n",
    "# plt.title('Surge (northern)',fontsize=fs)\n",
    "plt.tight_layout()\n",
    "# plt.savefig('/Users/jukesliu/Documents/TURNER/paper1/figures/elev_profiles_south_quiescence_legend.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Calculate surface slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-08-26 0.06492442366754242\n",
      "2016-07-17 0.05491503311658456\n",
      "2018-01-22 0.05640990054626966\n",
      "2019-04-30 0.05032091890109889\n",
      "2020-06-05 0.06769050993541324\n",
      "2021-07-17 0.04715046400415747\n",
      "2021-10-25 0.04870066879431146\n",
      "2022-05-22 0.05001616395178818\n",
      "0.055016010364645734\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###################\n",
    "dem_outpath = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/'\n",
    "outfigpath = '/Users/jukesliu/Documents/TURNER/DATA/figures/'\n",
    "filelist = os.listdir(dem_outpath); filelist.sort()\n",
    "# spatial extent for calculation of surface slope [km from terminus]:\n",
    "EXTENT_START = 0\n",
    "EXTENT_END = 7\n",
    "###################\n",
    "\n",
    "# plot fontsize and colors\n",
    "fs = 12 # figure fontsize\n",
    "colors = plt.cm.viridis_r(np.linspace(0,1,len(filelist)))\n",
    "col_count = 0\n",
    "\n",
    "date_list = []; slopes = []\n",
    "for elevprofile in filelist:\n",
    "    if elevprofile.startswith('centerline_n_250m') and elevprofile.endswith('.csv') and '20' in elevprofile:\n",
    "        elev_df = pd.read_csv(dem_outpath+elevprofile)\n",
    "        ds = elevprofile.split('_')[-1][:-4]\n",
    "        \n",
    "        if 'me' in ds or 'xx' in ds:\n",
    "            day = '15' # replace the day with a generic half point in the month\n",
    "        else:\n",
    "            day = ds[6:8]\n",
    "        ds_leg = ds[0:4]+'-'+ds[4:6]+'-'+day\n",
    "        \n",
    "        # EVALUATE SURFACE SLOPE AND PLOT\n",
    "#         if True:\n",
    "        if ds_leg != '2015-10-31' and ds_leg != '2018-05-07' and ds_leg != '2013-12-07': # only for upper glacier\n",
    "            if ds_leg != '2018-01-17' and ds_leg != '2018-01-15' and not ds_leg.startswith('2022-04')  and not ds_leg.startswith('2022-02') and ds_leg !='2016-10-19' and ds_leg != '2018-01-28': # incomplete\n",
    "                surface_slope_df = elev_df[(elev_df.dist_m > EXTENT_START*1000) \n",
    "                                           & (elev_df.dist_m < EXTENT_END*1000)] # between 1 and 16 km\n",
    "                nanmask = ~np.isnan(surface_slope_df.mean_elev)\n",
    "\n",
    "                if len(surface_slope_df.mean_elev[nanmask]) == 0:\n",
    "                    continue\n",
    "\n",
    "                slope, intercept, r, p, std_err = stats.linregress(surface_slope_df.dist_m[nanmask], \n",
    "                                                                   surface_slope_df.mean_elev[nanmask])  \n",
    "                print(ds_leg, slope)\n",
    "                slopes.append(slope)\n",
    "                date_list.append(ds_leg)\n",
    "\n",
    "            col_count+=1\n",
    "        \n",
    "plt.figure(figsize=(7,3))\n",
    "plt.plot(pd.to_datetime(date_list), slopes, 'mx-', markersize=10)\n",
    "plt.ylabel('Surface slope', fontsize=fs)\n",
    "# plt.ylim(0.044, 0 .075)\n",
    "# plt.title(str(EXTENT_START)+' - '+str(EXTENT_END)+' km from the terminus with 250m spacing of mean elevations')\n",
    "plt.grid()\n",
    "plt.tick_params(labelsize=fs)\n",
    "plt.tight_layout()\n",
    "\n",
    "ss_df = pd.DataFrame(list(zip(date_list, slopes)), columns=['datetime','slope'])\n",
    "ss_df.to_csv(outfigpath+'surface_slopes_'+str(EXTENT_START)+'to'+str(EXTENT_END)+'km.csv')\n",
    "\n",
    "print(np.nanmean(slopes))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ds1</th>\n",
       "      <th>ds2</th>\n",
       "      <th>slope_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20120826</td>\n",
       "      <td>20131207</td>\n",
       "      <td>-0.003663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20131207</td>\n",
       "      <td>20140318</td>\n",
       "      <td>0.000355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20140318</td>\n",
       "      <td>20151031</td>\n",
       "      <td>0.007040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20151031</td>\n",
       "      <td>20160717</td>\n",
       "      <td>0.000954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20160717</td>\n",
       "      <td>20180122</td>\n",
       "      <td>-0.001704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20180122</td>\n",
       "      <td>20190430</td>\n",
       "      <td>0.002866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20190430</td>\n",
       "      <td>20200605</td>\n",
       "      <td>-0.006130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20200605</td>\n",
       "      <td>20210717</td>\n",
       "      <td>-0.006678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20210717</td>\n",
       "      <td>20220522</td>\n",
       "      <td>0.004767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ds1       ds2  slope_change\n",
       "0  20120826  20131207     -0.003663\n",
       "1  20131207  20140318      0.000355\n",
       "2  20140318  20151031      0.007040\n",
       "3  20151031  20160717      0.000954\n",
       "4  20160717  20180122     -0.001704\n",
       "5  20180122  20190430      0.002866\n",
       "6  20190430  20200605     -0.006130\n",
       "7  20200605  20210717     -0.006678\n",
       "8  20210717  20220522      0.004767"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1s = ['20120826','20131207','20140318','20151031','20160717','20180122','20190430','20200605','20210717']\n",
    "ds2s = ['20131207','20140318','20151031','20160717','20180122','20190430','20200605','20210717','20220522']\n",
    "slope_changes = [0.041809 - 0.045472, # 2013-2012\n",
    "                 0.042164 - 0.041809, # 2014-2013\n",
    "                 0.049204 - 0.042164, # 2015-2014\n",
    "                 0.050158 - 0.049204, # 2016-2015\n",
    "                 0.048454 - 0.050158, # 2018-2016\n",
    "                 0.051320 - 0.048454, # 2019-2018\n",
    "                 0.045190 - 0.051320, # 2020-2019\n",
    "                 0.038512 - 0.045190, # 2021-2020\n",
    "                 0.043279 - 0.038512, # 2022-2021\n",
    "                ]\n",
    "\n",
    "ss_td_df = pd.DataFrame(list(zip(ds1s, ds2s, slope_changes)),columns=['ds1','ds2','slope_change'])\n",
    "ss_td_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Calculate elevation differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.listdir(dem_outpath+'for_differencing/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "centerline_n_500m_20131207.csv\n",
      "centerline_n_500m_20140318.csv\n",
      "centerline_n_500m_20151031.csv\n",
      "centerline_n_500m_20160717.csv\n",
      "centerline_n_500m_20180122.csv\n",
      "centerline_n_500m_20190430.csv\n",
      "centerline_n_500m_20200605.csv\n",
      "centerline_n_500m_20210717.csv\n",
      "REFERENCE FILE DATE: 20131207\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grab reference elev profile, grab each date after reference OR use ss_td_df to grab elevprofile1 and elevprofile2 along centerline\n",
    "filelist = glob.glob(dem_outpath+'for_differencing/centerline_n_500*.csv') # change glob search pattern!\n",
    "filelist.sort() # centerline elevation files\n",
    "for file in filelist:\n",
    "    print(file.split('/')[-1])\n",
    "# filelist = filelist[1:] # grab subset of all files\n",
    "# print(filelist)\n",
    "\n",
    "# set gradient spacing:\n",
    "grad_spacing = 500 # m\n",
    "SCL= 1500 # m\n",
    "smoothing_size = SCL/grad_spacing*0.34\n",
    "\n",
    "# grab the reference profile\n",
    "ref_elev_df = pd.read_csv(filelist[0]) \n",
    "ref_dhdx = np.gradient(gaussian_filter(ref_elev_df.mean_elev, smoothing_size), grad_spacing)\n",
    "ref_dhdx = -ref_dhdx\n",
    "ref_date = filelist[0].split('/')[-1].split('_')[-1][:-4]\n",
    "print('REFERENCE FILE DATE:',ref_date)\n",
    "\n",
    "for file in filelist:\n",
    "    ds = file.split('/')[-1].split('_')[-1][:-4]\n",
    "    elev_df = pd.read_csv(file)\n",
    "    dhdx = np.gradient(gaussian_filter(elev_df.mean_elev,smoothing_size), \n",
    "                       grad_spacing) # calculate surface slope from SCL smoothed elevation\n",
    "    dhdx=-dhdx # convert to positive gradients\n",
    "    \n",
    "    # calculate 1D gradient in slope at spacingx2 for each, add slope back into csv and save\n",
    "    elev_df['dhdx_1500m'] = dhdx\n",
    "    elev_df.to_csv(file)\n",
    "    \n",
    "    # calculate CHANGE in elev, CHANGE in slope from reference, and CHANGE in driving stress\n",
    "    delta_dhdx = dhdx - ref_dhdx\n",
    "    delta_h = elev_df.mean_elev - ref_elev_df.mean_elev\n",
    "    delta_taud_kpa = delta_h*delta_dhdx*9.81*917/1000\n",
    "    \n",
    "    # Plot \n",
    "    fig, ax = plt.subplots(1, figsize=(8,5))\n",
    "    fs=14\n",
    "    ax.plot(elev_df.dist_m/1000, delta_h, 'k-')\n",
    "#     ax.plot(elev_df.dist_m/1000, elev_df.mean_elev/1000, 'k-')\n",
    "#     ax.plot(ref_elev_df.dist_m/1000, ref_elev_df.mean_elev/1000, 'k--') # plot refrence elevation\n",
    "#     ax.plot(elev_df.dist_m/1000, gaussian_filter(elev_df.mean_elev, smoothing_size)/1000, 'k--') # plot smoothed elevation\n",
    "    twinax = ax.twinx()\n",
    "    twinax.plot(ref_elev_df.dist_m/1000, ref_dhdx, 'b--') # plot reference slope\n",
    "    twinax.plot(elev_df.dist_m/1000, dhdx, 'b-')\n",
    "    ax.set_xlim(30,0); ax.set_xlabel('Distance from terminus [km]',fontsize=fs)\n",
    "    ax.set_ylim(-120, 120); ax.set_ylabel('Change in elevation [m]',fontsize=fs)\n",
    "    twinax.set_ylabel('dhdx',color='blue',fontsize=fs)\n",
    "    ax.tick_params(labelsize=fs); twinax.tick_params(labelsize=fs)\n",
    "    plt.title(ds,fontsize=fs)\n",
    "    \n",
    "\n",
    "    \n",
    "    # plot driving stress as 3rd y-axis\n",
    "    twin2 = ax.twinx()\n",
    "    twin2.spines.right.set_position((\"axes\", 1.2))\n",
    "    twin2.plot(elev_df.dist_m/1000, delta_taud_kpa, '-',color='orange')\n",
    "    twin2.tick_params(labelsize=fs); twin2.set_ylim(-15,15); twin2.grid()\n",
    "    twin2.set_ylabel(r'$\\Delta \\tau_{dx}$ [kPa]',fontsize=fs, color='orange')\n",
    "    \n",
    "    # export to csv in form of profile_ds1_ds2_cumulative.csv (elevation and driving slope)\n",
    "    if ref_date != ds: # only write out the files with differences\n",
    "        dummy_ds1 = list(ss_td_df[ss_td_df.ds2 == ds].ds1)[0] # grab dummy ds1 from ss_td_df\n",
    "#         print(dummy_ds1)\n",
    "        diff_df = pd.DataFrame(list(zip(elev_df.dist_m, delta_h, delta_dhdx, delta_taud_kpa)), \n",
    "                               columns=['dist_m','delta_h','delta_dhdx', 'delta_taud_kpa'])\n",
    "        diff_df.to_csv(dem_outpath+'for_differencing/profile_'+dummy_ds1+'_'+ds+'_cumulative2013.csv')\n",
    "    plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ds1</th>\n",
       "      <th>ds2</th>\n",
       "      <th>fname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ds1, ds2, fname]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_diff_path = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/differenced/'\n",
    "\n",
    "# grab differenced DEM info:\n",
    "ds1s = []; ds2s = []; fnames = []\n",
    "for file in os.listdir(dem_diff_path):\n",
    "    if file.endswith('.tif'):\n",
    "        split = file.split('_') # split filename\n",
    "        ds1 = split[0]; ds2 = split[1]\n",
    "        \n",
    "        if ds2.endswith('.tif'):\n",
    "            ds2 = ds2[:-4] # remove extension\n",
    "        ds2s.append(ds1); ds1s.append(ds2); fnames.append(file)\n",
    "        \n",
    "dem_df = pd.DataFrame(list(zip(ds1s, ds2s, fnames)), columns=['ds1','ds2','fname'])\n",
    "dem_df = dem_df.sort_values(by='ds1'); dem_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20151031_20131207.tif\n",
      "20140318_20131207.tif\n",
      "20160717_20131207.tif\n",
      "20200605_20131207.tif\n",
      "20220522_20131207.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mr/7f69f7y50zj85n420pyvh8cr0000gq/T/ipykernel_53426/4089570575.py:18: RuntimeWarning: Mean of empty slice\n",
      "  mean_diffs.append(np.nanmean(crop_array))\n"
     ]
    },
    {
     "data": {
      "image/png": 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D/ZVk2aeVMky0VhaD/lu0mjSunRTLcEA/EP5LUB++YpB/i1YdzO2kWIYD+oGyDwp3vWfzqZbhgH4g/FG4dmfzq14xzFh8oE8o+6Bw7b5X4ANvfwNhD/QJ4Y/C0dkODB7hj1Kgsx0YLGr+AJAgzvy7VIdZLwGkq6czf9v32D5l+wXbE8t+tsf2s7ZnbE/21sxyYeoBAFXXa9nnGUnbJX1m6ULbr5d0r6Q3SNoi6S9sD7307dXEt4EBqLqewj8iTkdEq8TbKukTEfGDiPhPSc9Kur2XdZVJWWa9BIBu9avmPybpC0ten82WvYTtnZJ2StK6dev61Jzutartr/RtYABQBdc887f9r7afafFv60pva7EsWv1iRByIiImImBgdHb3edg9Eu9r+nbeOMgMogEq75pl/RLy1i889K+mWJa/XSjrXxecUql1t//GvXdLe7RsZ7dMFRkkB5dCvss8RSR+z/WFJayRtkPTFPq2rb1aq7XNTUudaTd2859BJSXylIjBovQ71fIfts5LeJOnTto9KUkSckvSwpK9K+mdJ746I65uzt0TK8I1WdcIoKaA8eh3t88mIWBsRPxYRN0fE5JKffSgiXhcR4xHxT703dfD4dq98MUoKKA+md1jBtk1j2rt9I9MK54QrKaA8mN7hGqjt56fd1M1cSQGDR/hjYJi6GSgPwh8DxZUUUA7U/AEgQYQ/ACSI8AeABBH+AJAgwh8AEuSIlpNtFsL29yTV+V7/GyV9s+hG9BHbV2113r46b5skjUfEqzt5Q9mGes5ExMS1f62abE+xfdXF9lVXnbdNWti+Tt9D2QcAEkT4A0CCyhb+B4puQJ+xfdXG9lVXnbdN6mL7StXhCwAYjLKd+QMABoDwB4AElSb8bW+xPWP7Wdu7i25P3mx/3fZJ2093MyyrbGw/aPui7WeWLLvB9jHbZ7LHVUW2sVtttu0B281s/z1t+21FtrEXtm+x/bjt07ZP2b4/W16X/ddu+yq/D22/3PYXbX8527YPZss73nelqPnbHpL075LulnRW0lOS7ouIrxbasBzZ/rqkiYioxY0mtn9R0vOS/jYi3pgt+2NJ346IfdkBfFVEvK/IdnajzbY9IOn5iPiTItuWB9urJa2OiC/ZfrWkE5K2Sfod1WP/tdu+X1fF96FtS3plRDxve1jSZyXdL2m7Otx3ZTnzv13SsxHxHxHxf5I+IWlrwW3CCiLiM5K+vWzxVkkHs+cHtfAfrnLabFttRMT5iPhS9vx7kk5LGlN99l+77au8WPB89nI4+xfqYt+VJfzHJH1jyeuzqsnOWiIk/YvtE7Z3Ft2YPrk5Is5LC/8BJd1UcHvy9h7bX8nKQpUsiSxne72kTZKeVA3337Ltk2qwD20P2X5a0kVJxyKiq31XlvB3i2XF16PytTkifk7SL0t6d1ZaQHX8paTXSbpN0nlJf1poa3Jg+1WSHpH03oj4btHtyVuL7avFPoyIyxFxm6S1km63/cZuPqcs4X9W0i1LXq+VdK6gtvRFRJzLHi9K+qQWSl11cyGrty7WXS8W3J7cRMSF7D/dC5L+ShXff1m9+BFJD0XEoWxxbfZfq+2r2z6MiFlJT0jaoi72XVnC/ylJG2y/1vaPSrpX0pGC25Qb26/MOp5k+5WSfknSMyu/q5KOSNqRPd8h6dEC25Krxf9YmXeowvsv6zT8qKTTEfHhJT+qxf5rt3112Ie2R22PZM8bkt4q6WvqYt+VYrSPJGXDrj4iaUjSgxHxoWJblB/bP6mFs31pYSbVj1V9+2x/XNKbtTBV7gVJH5B0WNLDktZJek7SPRFRuY7TNtv2Zi2UC0LS1yX97mKNtWps/4Kkf5N0UtIL2eL3a6EuXof912777lPF96Htn9FCh+6QFk7eH46IP7T9E+pw35Um/AEAg1OWsg8AYIAIfwBIEOEPAAki/AEgQYQ/ACSI8AeABBH+AJCg/wdFga2FZxnLPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n",
      "201801me_20131207.tif\n",
      "20170712_20131207.tif\n",
      "20190430_20131207.tif\n",
      "20210717_20131207.tif\n",
      "20200605_20140318.tif\n",
      "201801me_20140318.tif\n",
      "20170712_20140318.tif\n",
      "20190430_20140318.tif\n",
      "20210717_20140318.tif\n",
      "20151031_20140318.tif\n",
      "20160717_20140318.tif\n"
     ]
    }
   ],
   "source": [
    "for idx, row in dem_df.iterrows():\n",
    "    print(row.fname)\n",
    "    ds1 = row.ds1; ds2 = row.ds2\n",
    "    \n",
    "    if not os.path.exists(dem_diff_path+newfolder+'/profile_'+row.ds1+'_'+row.ds2+'.csv'):\n",
    "        src = rio.open(dem_diff_path+row.fname); elev_diff = src.read(1); affine=src.transform # open it\n",
    "        \n",
    "        dists = []; mean_diffs = []\n",
    "        for idx2, row2 in cline_gdf.iterrows():\n",
    "            geom = row2.geometry\n",
    "            dist = row2.distance\n",
    "            dists.append(dist)\n",
    "            try:\n",
    "                out_image, out_transform = mask(src, [mapping(geom)], crop=True)\n",
    "                crop_array = out_image[0,:,:]\n",
    "                crop_array[crop_array < -1e30] = np.NaN\n",
    "#                 print(dist, np.nanmean(crop_array))\n",
    "                mean_diffs.append(np.nanmean(crop_array))\n",
    "            except Exception:\n",
    "                mean_diffs.append(np.NaN)\n",
    "#                 print(dist, 'no overlap')\n",
    "                pass\n",
    "        mean_diffs = np.array(mean_diffs); dists = np.array(dists)\n",
    "    \n",
    "#         # calculate change in driving stress from elevation differences\n",
    "#         slope_change = float(ss_td_df[(ss_td_df.ds1 == ds1) & (ss_td_df.ds2 == ds2)].slope_change)\n",
    "#         rho = 917 # kg/m3\n",
    "#         g = 9.81 #m/s2\n",
    "#         driving_stress = rho*g*mean_diffs*slope_change\n",
    "        \n",
    "        if northline: # flip distances for northern centerline\n",
    "            dists = np.flip(dists)\n",
    "        mean_diffs[mean_diffs == 0] = np.NaN # differences should never be exactly 0\n",
    "        plt.plot(dists/1000, mean_diffs, 'o')\n",
    "        plt.xlim(0, 30)\n",
    "        plt.show()\n",
    "        \n",
    "#         # write centerline data to csv\n",
    "#         new_df = pd.DataFrame(list(zip(dists, mean_diffs, driving_stress)), \n",
    "#                               columns=['dist_m','mean_elev_diff', 'change_td_Pa'])\n",
    "        \n",
    "        # write centerline data to csv\n",
    "        new_df = pd.DataFrame(list(zip(dists, mean_diffs)), \n",
    "                              columns=['dist_m','mean_elev_diff'])\n",
    "        print(len(new_df))\n",
    "        new_df.to_csv(dem_diff_path+newfolder+'/profile_'+row.ds1+'_'+row.ds2+'.csv')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot all elevation difference profiles colored by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem_diff_path = '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/differenced/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # grab the profiles in order\n",
    "# filenames = []\n",
    "# nofiles = 0 # number of files\n",
    "# # for file in os.listdir(dem_diff_path+newfolder): # Sequential elevation differences\n",
    "# for file in os.listdir(dem_diff_path+newfolder): # Sequential elevation differences\n",
    "#     if file.startswith('profile') and file.endswith('csv'):\n",
    "#         filenames.append(file)\n",
    "#         nofiles+=1\n",
    "# filenames.sort()\n",
    "# filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filenames = filenames[1:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(10,6))\n",
    "# fs = 18\n",
    "# alpha = 0.7\n",
    "# # background_col='xkcd:pale grey'\n",
    "# background_col='xkcd:light grey'\n",
    "# # ax.set_facecolor(background_col)\n",
    "# colors_cold = cmocean.cm.ice(np.linspace(0,1,6))\n",
    "# colors_warm = cmocean.cm.matter(np.linspace(0,1,6))\n",
    "\n",
    "# legends = []\n",
    "# a = 0\n",
    "# b = 0\n",
    "# for filename in filenames:\n",
    "# #     if filename.startswith('profile_2013'): # only use this conditional for total quiescent\n",
    "#         [pre, ds1, ds2] = filename.split('_') # grab dates\n",
    "#         print(ds1, ds2)\n",
    "#         ds2 = ds2[:-4] # cut off the .csv\n",
    "#         timeperiod = ds1+' to '+ds2\n",
    "#     #     legends.append(timeperiod)\n",
    "#         if ds2.startswith('2018'):\n",
    "#             ds2 = ('2018 late Jan.')\n",
    "#         legends.append(ds2)\n",
    "#         slope_change = float(ss_td_df[ss_td_df.ds1 == ds1].slope_change)\n",
    "#         df = pd.read_csv(dem_diff_path+newfolder+'/'+filename)\n",
    "\n",
    "#         if ds2.startswith('202'): # surge\n",
    "# #             ax.plot(df.dist_m/1000, df.mean_elev_diff, 'o-', color=colors_warm[a], alpha=alpha) # Elevation\n",
    "#             ax.plot(df.dist_m/1000, df.mean_elev_diff*9.81*917*slope_change/1e3, 'o-', color=colors_warm[a], alpha=alpha) # Driving Stress\n",
    "#             a += 1\n",
    "#         elif ds1.startswith('2012'): # end of the last surge\n",
    "# #             ax.plot(df.dist_m/1000, df.mean_elev_diff, 'o-', color='white') # Elevation\n",
    "#             ax.plot(df.dist_m/1000, df.mean_elev_diff*9.81*917*slope_change/1000, 'o-', color='white') # Driving stress\n",
    "#         else:\n",
    "# #             ax.plot(df.dist_m/1000, df.mean_elev_diff, 'o-', color=colors_cold[b], alpha=alpha) # Elevation\n",
    "#             ax.plot(df.dist_m/1000, df.mean_elev_diff*9.81*917*slope_change/1000, 'o-', color=colors_cold[b], alpha=alpha) # Driving stress\n",
    "#             b += 1\n",
    "    \n",
    "# ax.set_xlim(27,0)\n",
    "# ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "# ax.plot([30, 0], [0, 0], 'k-', linewidth=0.5)\n",
    "# ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "# ax.set_ylim(-10,10)\n",
    "# # ax.set_ylim(-70, 140)\n",
    "# # ax.set_title('Driving stress difference from 2013-12-07', fontsize=fs)\n",
    "# ax.set_ylabel('Driving stress change [kPa]', fontsize=fs)\n",
    "# # ax.set_ylabel('Elevation change [m]', fontsize=fs)\n",
    "# ax.legend(legends, loc='lower left', ncol=3, fontsize=fs-6)\n",
    "# ax.grid()\n",
    "# ax.set_facecolor(background_col)\n",
    "# plt.show()       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot cumulative elevation change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['profile_20120826_20131207.csv',\n",
       " 'profile_20131207_20140318.csv',\n",
       " 'profile_20140318_20151031.csv',\n",
       " 'profile_20151031_20160717.csv',\n",
       " 'profile_20160717_20180122.csv',\n",
       " 'profile_20180122_20190430.csv',\n",
       " 'profile_20190430_20200605.csv',\n",
       " 'profile_20200605_20210717.csv',\n",
       " 'profile_20210717_20220522.csv']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the profiles in order\n",
    "filenames = []\n",
    "nofiles = 0 # number of files\n",
    "# for file in os.listdir(dem_diff_path+newfolder): # Sequential elevation differences\n",
    "for file in os.listdir(dem_outpath+'differenced/'): # Sequential elevation differences\n",
    "    if file.startswith('profile') and not file.endswith('cumulative.csv'):\n",
    "        filenames.append(file)\n",
    "        nofiles+=1\n",
    "filenames.sort()\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['profile_20131207_20140318.csv',\n",
       " 'profile_20140318_20151031.csv',\n",
       " 'profile_20151031_20160717.csv',\n",
       " 'profile_20160717_20180122.csv',\n",
       " 'profile_20180122_20190430.csv',\n",
       " 'profile_20190430_20200605.csv',\n",
       " 'profile_20200605_20210717.csv']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2013-12-07\n",
    "filenames = filenames[:-1]\n",
    "filenames = filenames[1:] \n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(2,1,figsize=(15,12))\n",
    "fs = 15\n",
    "alpha = 0.7\n",
    "lw = 3\n",
    "# background_col='xkcd:pale grey'\n",
    "background_col='xkcd:light grey'\n",
    "# colors_cold = cmocean.cm.ice(np.linspace(0,1,6))\n",
    "# colors_warm = cmocean.cm.matter(np.linspace(0,1,6))\n",
    "colors_warm = plt.cm.Reds_r(np.linspace(0,1,3))\n",
    "colors_cold = plt.cm.Blues_r(np.linspace(0,1,9))\n",
    "ax.set_xlim(30,0)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_ylim([-40,130])\n",
    "ax.set_ylabel('Cumulative elevation change [m]', fontsize=fs)\n",
    "ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "\n",
    "ax2.set_xlim(30,0)\n",
    "ax2.set_ylim(-6,6)\n",
    "ax2.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax2.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "ax2.set_ylabel('Cumulative driving stress [kPa]', fontsize=fs)\n",
    "\n",
    "ax.grid(); ax2.grid()\n",
    "# ax.set_facecolor(background_col); ax2.set_facecolor(background_col)\n",
    "# ax.set_title('Elevation accumulated since Dec. 2013',fontsize=fs+2)\n",
    "# ax2.set_title('Driving stress accumulated since Dec. 2013',fontsize=fs+2)\n",
    "\n",
    "legends = []\n",
    "a = 0\n",
    "b = 0\n",
    "c = 0\n",
    "ds1s = []; ds2s = []; max_increase_driving_stress = []\n",
    "for filename in filenames:\n",
    "    [pre, ds1, ds2] = filename.split('_') # grab dates\n",
    "    ds2 = ds2[:-4] # cut off the .csv\n",
    "    timeperiod = ds2\n",
    "#     legends.append(timeperiod[0:4]+'-'+timeperiod[4:6]+'-'+timeperiod[6:8])\n",
    "    legends.append(timeperiod[4:6]+'/'+timeperiod[0:4])\n",
    "    \n",
    "    # grab the slope change\n",
    "    slope_change = float(ss_td_df[ss_td_df.ds1 == ds1].slope_change)\n",
    "      \n",
    "#     df = pd.read_csv(dem_diff_path+newfolder+'/'+filename)\n",
    "    df = pd.read_csv(dem_outpath+'differenced/'+filename)\n",
    "    if c == 0:\n",
    "        elev_diff = np.array(df.mean_elev_diff)\n",
    "        elev_diff[np.isnan(elev_diff)] = 0\n",
    "        cumulative_elev_diff = elev_diff\n",
    "#     elif ds1 == '20180128':\n",
    "#         print(elev_diff)\n",
    "    else:\n",
    "        elev_diff = np.array(df.mean_elev_diff)\n",
    "        if not (ds2.startswith('2021') or ds2.startswith('2022')):\n",
    "            elev_diff[np.isnan(elev_diff)] = 0\n",
    "        cumulative_elev_diff = cumulative_elev_diff + elev_diff\n",
    "    max_driving_stress = np.nanmax(elev_diff*9.81*917); \n",
    "    max_increase_driving_stress.append(max_driving_stress); ds1s.append(ds1); ds2s.append(ds2)\n",
    "    nan_cumulative_elev = cumulative_elev_diff.copy() # copy the cumulative_elev_diff\n",
    "    nan_cumulative_elev[cumulative_elev_diff == 0] = np.NaN\n",
    "#     ax.plot(df.dist_m/1000, cumulative_elev_diff, 'o-', color=colors[a])\n",
    "\n",
    "    if ds2.startswith('202'): # surge\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color=colors_warm[a], linewidth=lw, alpha=alpha) # \n",
    "        ax2.plot(df.dist_m/1000, nan_cumulative_elev*9.81*917*slope_change/1000, '-', linewidth=lw, color=colors_warm[a],alpha=alpha)\n",
    "        a += 1\n",
    "    elif ds1.startswith('2012'): # end of the last surge\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color='white') # # Elevation\n",
    "    else:\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color=colors_cold[b], linewidth=lw, alpha=alpha) \n",
    "        ax2.plot(df.dist_m/1000, nan_cumulative_elev*9.81*917*slope_change/1000, '-', linewidth=lw, color=colors_cold[b],alpha=alpha)\n",
    "        b += 1\n",
    "    c += 1\n",
    "    ax.legend(legends, loc='upper left', ncol=1, fontsize=fs-2) \n",
    "    ax2.legend(legends, loc='upper left', ncol=1, fontsize=fs-4)\n",
    "\n",
    "    \n",
    "    new_df = pd.DataFrame(list(zip(df.dist_m, nan_cumulative_elev)), columns=['dist_m','cumulative_elev_diff'])\n",
    "#     new_df.to_csv(dem_outpath+'differenced/'+filename[:-4]+'_cumulative.csv')\n",
    "    \n",
    "#     plt.savefig(dem_outpath+'figures/cumulative_dh_dstress_'+ds2+'.png', dpi=200)\n",
    "ax.plot([30,0],[0,0], 'k-')\n",
    "ax2.plot([30,0],[0,0], 'k-',alpha=0.3)\n",
    "ax.axvline(x=16, color='k', ls=':', lw=2)\n",
    "ax.set_title('Reference 12/2013', fontsize=fs)\n",
    "fig.savefig('/Users/jukesliu/Documents/TURNER/paper1/figures/elevation_taud_deviation_20131207.png', dpi=300)\n",
    "plt.show()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/differenced/centerline-north/profile_20131207_20140318.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# grab the slope change\u001b[39;00m\n\u001b[1;32m     39\u001b[0m slope_change \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(ss_td_df[ss_td_df\u001b[38;5;241m.\u001b[39mds1 \u001b[38;5;241m==\u001b[39m ds1]\u001b[38;5;241m.\u001b[39mslope_change)\n\u001b[0;32m---> 41\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdem_diff_path\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mnewfolder\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m c \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     43\u001b[0m     elev_diff \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(df\u001b[38;5;241m.\u001b[39mmean_elev_diff)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m    310\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:586\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    571\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    572\u001b[0m     dialect,\n\u001b[1;32m    573\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    582\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    583\u001b[0m )\n\u001b[1;32m    584\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:482\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    479\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    481\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 482\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    485\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:811\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwds:\n\u001b[1;32m    809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1040\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1036\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1037\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown engine: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mengine\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (valid options are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmapping\u001b[38;5;241m.\u001b[39mkeys()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1038\u001b[0m     )\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;66;03m# error: Too many arguments for \"ParserBase\"\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py:51\u001b[0m, in \u001b[0;36mCParserWrapper.__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     48\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musecols\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39musecols\n\u001b[1;32m     50\u001b[0m \u001b[38;5;66;03m# open handles\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open_handles\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;66;03m# Have to pass int, would break tests using TextReader directly otherwise :(\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py:222\u001b[0m, in \u001b[0;36mParserBase._open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_handles\u001b[39m(\u001b[38;5;28mself\u001b[39m, src: FilePathOrBuffer, kwds: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m    Let the readers open IOHandles after they are done with their potential raises.\u001b[39;00m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m        \u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    226\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    228\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    229\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    230\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/newautoriftenv/lib/python3.9/site-packages/pandas/io/common.py:701\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    696\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    697\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    698\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    699\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    700\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 701\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    702\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    703\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    704\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    706\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    707\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    709\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    710\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/differenced/centerline-north/profile_20131207_20140318.csv'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(2,1,figsize=(10,13))\n",
    "fs = 16\n",
    "alpha = 0.7\n",
    "lw = 3\n",
    "# background_col='xkcd:pale grey'\n",
    "background_col='xkcd:light grey'\n",
    "colors_cold = cmocean.cm.ice(np.linspace(0,1,6))\n",
    "colors_warm = cmocean.cm.matter(np.linspace(0,1,6))\n",
    "ax.set_xlim(30,0)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_ylim([-25,135])\n",
    "ax.set_ylabel('Cumulative elevation change [m]', fontsize=fs)\n",
    "ax.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "\n",
    "ax2.set_xlim(30,0)\n",
    "ax2.set_ylim(-0.6,0.6)\n",
    "ax2.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax2.set_xlabel('Distance from terminus [km]', fontsize=fs)\n",
    "ax2.set_ylabel('Cumulative driving stress [MPa]', fontsize=fs)\n",
    "\n",
    "ax.grid(); ax2.grid()\n",
    "ax.set_facecolor(background_col); ax2.set_facecolor(background_col)\n",
    "ax.set_title('Elevation accumulated since Dec. 2013',fontsize=fs+2)\n",
    "ax2.set_title('Driving stress accumulated since Dec. 2013',fontsize=fs+2)\n",
    "\n",
    "legends = []\n",
    "a = 0\n",
    "b = 0\n",
    "c = 0\n",
    "ds1s = []; ds2s = []; max_increase_driving_stress = []\n",
    "for filename in filenames:\n",
    "    [pre, ds1, ds2] = filename.split('_') # grab dates\n",
    "    ds2 = ds2[:-4] # cut off the .csv\n",
    "    timeperiod = ds2\n",
    "    legends.append(timeperiod[0:4]+'-'+timeperiod[4:6]+'-'+timeperiod[6:8])\n",
    "    \n",
    "    # grab the slope change\n",
    "    slope_change = float(ss_td_df[ss_td_df.ds1 == ds1].slope_change)\n",
    "      \n",
    "    df = pd.read_csv(dem_diff_path+newfolder+'/'+filename)\n",
    "    if c == 0:\n",
    "        elev_diff = np.array(df.mean_elev_diff)\n",
    "        elev_diff[np.isnan(elev_diff)] = 0\n",
    "        cumulative_elev_diff = elev_diff\n",
    "#     elif ds1 == '20180128':\n",
    "#         print(elev_diff)\n",
    "    else:\n",
    "        elev_diff = np.array(df.mean_elev_diff)\n",
    "        if not (ds2.startswith('2021') or ds2.startswith('2022')):\n",
    "            elev_diff[np.isnan(elev_diff)] = 0\n",
    "        cumulative_elev_diff = cumulative_elev_diff + elev_diff\n",
    "    max_driving_stress = np.nanmax(elev_diff*9.81*917); \n",
    "    max_increase_driving_stress.append(max_driving_stress); ds1s.append(ds1); ds2s.append(ds2)\n",
    "    nan_cumulative_elev = cumulative_elev_diff.copy() # copy the cumulative_elev_diff\n",
    "    nan_cumulative_elev[cumulative_elev_diff == 0] = np.NaN\n",
    "#     ax.plot(df.dist_m/1000, cumulative_elev_diff, 'o-', color=colors[a])\n",
    "\n",
    "    if ds2.startswith('202'): # surge\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color=colors_warm[a], linewidth=lw, alpha=alpha) # \n",
    "        ax2.plot(df.dist_m/1000, nan_cumulative_elev*9.81*917*slope_change/10000, '-', linewidth=lw, color=colors_warm[a],alpha=alpha)\n",
    "        a += 1\n",
    "    elif ds1.startswith('2012'): # end of the last surge\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color='white') # # Elevation\n",
    "    else:\n",
    "        ax.plot(df.dist_m/1000, nan_cumulative_elev, '-', color=colors_cold[b], linewidth=lw, alpha=alpha) \n",
    "        ax2.plot(df.dist_m/1000, nan_cumulative_elev*9.81*917*slope_change/10000, '-', linewidth=lw, color=colors_cold[b],alpha=alpha)\n",
    "        b += 1\n",
    "    c += 1\n",
    "    ax.legend(legends, loc='upper left', ncol=1, fontsize=fs-4) \n",
    "    ax2.legend(legends, loc='upper left', ncol=1, fontsize=fs-4)\n",
    "    \n",
    "    plt.savefig(dem_outpath+'figures/cumulative_dh_dstress_'+ds2+'.png', dpi=200)\n",
    "\n",
    "# fig.savefig(dem_outpath+'figures/cumulative_driving_stress_quiescence_wslope.png', dpi=200)\n",
    "plt.show()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # driving stress\n",
    "# max_ds_df = pd.DataFrame(list(zip(ds1s, ds2s, max_increase_driving_stress)), columns=['ds1','ds2','max_increase_ds_kPa'])\n",
    "# max_ds_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(10,5))\n",
    "# for idx, row in max_ds_df.iterrows():\n",
    "#     d1 = datetime.datetime.strptime(row.ds1, '%Y%m%d')\n",
    "#     d2 = datetime.datetime.strptime(row.ds2, '%Y%m%d')\n",
    "#     ax.plot([d1, d2], [row.max_increase_ds_kPa, row.max_increase_ds_kPa],'k-',linewidth=3)\n",
    "\n",
    "# ax.grid()\n",
    "# ax.tick_params(axis='both',labelsize=fs)\n",
    "# ax.set_ylabel('Driving stress increase [kPa]', fontsize=fs)\n",
    "# fig.savefig(dem_outpath+'figures/increase_driving_stress_quiescence_noslope.png', dpi=200)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot one driving stress profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prof_df = pd.read_csv(dem_diff_path+newfolder+'/total_quiescent/profile_20140318_20190430.csv')\n",
    "fs = 16\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "twin1 = ax.twinx()\n",
    "\n",
    "ax.plot(prof_df.dist_m/1000, prof_df.change_in_driving_stress_Pa*(0.053484 - 0.039550)/10000, 'o-')\n",
    "ax.plot([30, 0], [0, 0], '-', color='gray',linewidth=0.5)\n",
    "# plt.plot([30, 0], [0, 0], '-', color='gray',linewidth=0.5)\n",
    "ax.set_ylabel('Change in driving stress [MPa]',fontsize=fs)\n",
    "ax.set_xlabel('Distance from terminus [km]',fontsize=fs)\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "plt.title('2014-03-18 to 2019-04-30 (quiescent period)', fontsize=fs)\n",
    "ax.set_xlim(30,0)\n",
    "\n",
    "twin1.plot(prof_df.dist_m/1000, prof_df.mean_elev_diff,'-')\n",
    "twin1.set_ylabel('Elevation difference [m]',fontsize=fs)\n",
    "twin1.tick_params(axis='both', which='major', labelsize=fs)\n",
    "fig.savefig(dem_outpath+'figures/total_driving_stress_quiescence_wslope.png', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot bed elevations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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>sIPR_UTMx_proj</th>\n",
       "      <th>sIPR_UTMy_proj</th>\n",
       "      <th>sIPR_dist_km_proj</th>\n",
       "      <th>Pick_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570661.311252</td>\n",
       "      <td>6.658280e+06</td>\n",
       "      <td>0.357101</td>\n",
       "      <td>-357.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570662.688259</td>\n",
       "      <td>6.658279e+06</td>\n",
       "      <td>1.936953</td>\n",
       "      <td>-356.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570664.025658</td>\n",
       "      <td>6.658278e+06</td>\n",
       "      <td>3.471361</td>\n",
       "      <td>-356.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570666.759973</td>\n",
       "      <td>6.658277e+06</td>\n",
       "      <td>6.608462</td>\n",
       "      <td>-356.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570668.075655</td>\n",
       "      <td>6.658276e+06</td>\n",
       "      <td>8.117954</td>\n",
       "      <td>-356.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sIPR_UTMx_proj  sIPR_UTMy_proj  sIPR_dist_km_proj  Pick_depth\n",
       "0   570661.311252    6.658280e+06           0.357101     -357.03\n",
       "1   570662.688259    6.658279e+06           1.936953     -356.68\n",
       "2   570664.025658    6.658278e+06           3.471361     -356.68\n",
       "3   570666.759973    6.658277e+06           6.608462     -356.68\n",
       "4   570668.075655    6.658276e+06           8.117954     -356.00"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sIPR\n",
    "sIPR_df = pd.read_csv('/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/bed/sIPR/sIPR_proj_data.csv')\n",
    "sIPR_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mr/7f69f7y50zj85n420pyvh8cr0000gq/T/ipykernel_1221/3192313712.py:15: RuntimeWarning: Mean of empty slice\n",
      "  mean_depth = np.nanmean(depths)\n"
     ]
    },
    {
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       "      <th>dist_m</th>\n",
       "      <th>mean_sIPR_depth</th>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>8850.0</td>\n",
       "      <td>-323.240333</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8600.0</td>\n",
       "      <td>-312.423000</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8350.0</td>\n",
       "      <td>-306.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dist_m  mean_sIPR_depth\n",
       "82  9600.0      -350.032653\n",
       "83  9350.0      -347.036812\n",
       "84  9100.0      -352.672121\n",
       "85  8850.0      -323.240333\n",
       "86  8600.0      -312.423000\n",
       "87  8350.0      -306.310000"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract bed elevations along centerline\n",
    "from shapely.geometry import Polygon, Point, MultiPolygon\n",
    "dists = []; mean_depths = []\n",
    "for idx2, row2 in cline_gdf.iterrows(): # NEED 250m centerline\n",
    "    geom = row2.geometry\n",
    "    dist = row2.distance\n",
    "    poly = Polygon(geom) # square polygon\n",
    "    \n",
    "    depths = []\n",
    "    for idx, row in sIPR_df.iterrows():\n",
    "        depth = row.Pick_depth\n",
    "        point = Point(row.sIPR_UTMx_proj, row.sIPR_UTMy_proj)\n",
    "        if poly.contains(point):\n",
    "            depths.append(depth)\n",
    "    mean_depth = np.nanmean(depths)\n",
    "    dists.append(dist); mean_depths.append(mean_depth)\n",
    "dists.reverse() # reverse the distances\n",
    "bed_df = pd.DataFrame(list(zip(dists, mean_depths)), columns=['dist_m','mean_sIPR_depth'],dtype=float)\n",
    "bed_df = bed_df[~np.isnan(bed_df.mean_sIPR_depth)]\n",
    "bed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>mean_elev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30100.0</td>\n",
       "      <td>2398.708500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29850.0</td>\n",
       "      <td>2375.599000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29600.0</td>\n",
       "      <td>2347.267800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29350.0</td>\n",
       "      <td>2323.523000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29100.0</td>\n",
       "      <td>2287.253400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>97.178566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>850.0</td>\n",
       "      <td>84.588480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>600.0</td>\n",
       "      <td>68.902440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>350.0</td>\n",
       "      <td>66.976230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>100.0</td>\n",
       "      <td>56.794110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dist_m    mean_elev\n",
       "0    30100.0  2398.708500\n",
       "1    29850.0  2375.599000\n",
       "2    29600.0  2347.267800\n",
       "3    29350.0  2323.523000\n",
       "4    29100.0  2287.253400\n",
       "..       ...          ...\n",
       "116   1100.0    97.178566\n",
       "117    850.0    84.588480\n",
       "118    600.0    68.902440\n",
       "119    350.0    66.976230\n",
       "120    100.0    56.794110\n",
       "\n",
       "[121 rows x 2 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2020 = pd.read_csv(dem_outpath+'centerline_n_250m_20200605.csv', usecols=[1,2],dtype=float)\n",
    "df2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>mean_elev</th>\n",
       "      <th>mean_sIPR_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9600.0</td>\n",
       "      <td>639.02893</td>\n",
       "      <td>-350.032653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9350.0</td>\n",
       "      <td>625.77924</td>\n",
       "      <td>-347.036812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9100.0</td>\n",
       "      <td>611.35425</td>\n",
       "      <td>-352.672121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8850.0</td>\n",
       "      <td>601.32150</td>\n",
       "      <td>-323.240333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8600.0</td>\n",
       "      <td>598.22620</td>\n",
       "      <td>-312.423000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8350.0</td>\n",
       "      <td>598.39380</td>\n",
       "      <td>-306.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dist_m  mean_elev  mean_sIPR_depth\n",
       "0  9600.0  639.02893      -350.032653\n",
       "1  9350.0  625.77924      -347.036812\n",
       "2  9100.0  611.35425      -352.672121\n",
       "3  8850.0  601.32150      -323.240333\n",
       "4  8600.0  598.22620      -312.423000\n",
       "5  8350.0  598.39380      -306.310000"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2020.merge(bed_df,on='dist_m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>mean_elev</th>\n",
       "      <th>mean_sIPR_depth</th>\n",
       "      <th>bed_elev</th>\n",
       "      <th>tau_d</th>\n",
       "    </tr>\n",
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       "      <td>288.996277</td>\n",
       "      <td>128156.698845</td>\n",
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       "      <th>1</th>\n",
       "      <td>9350.0</td>\n",
       "      <td>625.77924</td>\n",
       "      <td>-347.036812</td>\n",
       "      <td>278.742428</td>\n",
       "      <td>127059.837882</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9100.0</td>\n",
       "      <td>611.35425</td>\n",
       "      <td>-352.672121</td>\n",
       "      <td>258.682129</td>\n",
       "      <td>129123.081615</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8850.0</td>\n",
       "      <td>601.32150</td>\n",
       "      <td>-323.240333</td>\n",
       "      <td>278.081167</td>\n",
       "      <td>118347.284721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8600.0</td>\n",
       "      <td>598.22620</td>\n",
       "      <td>-312.423000</td>\n",
       "      <td>285.803200</td>\n",
       "      <td>114386.757844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8350.0</td>\n",
       "      <td>598.39380</td>\n",
       "      <td>-306.310000</td>\n",
       "      <td>292.083800</td>\n",
       "      <td>112148.618364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dist_m  mean_elev  mean_sIPR_depth    bed_elev          tau_d\n",
       "0  9600.0  639.02893      -350.032653  288.996277  128156.698845\n",
       "1  9350.0  625.77924      -347.036812  278.742428  127059.837882\n",
       "2  9100.0  611.35425      -352.672121  258.682129  129123.081615\n",
       "3  8850.0  601.32150      -323.240333  278.081167  118347.284721\n",
       "4  8600.0  598.22620      -312.423000  285.803200  114386.757844\n",
       "5  8350.0  598.39380      -306.310000  292.083800  112148.618364"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df2020.merge(bed_df, on='dist_m')\n",
    "new_df['bed_elev'] = new_df.mean_elev + new_df.mean_sIPR_depth\n",
    "# calculate driving stress from thickness and slope \n",
    "slope_a = 0.0407 # 06/2020, 0-20 km\n",
    "new_df['tau_d'] = np.abs(new_df.mean_sIPR_depth)*9.81*917*slope_a\n",
    "new_df\n",
    "# new_df.dropna()\n",
    "# new_df.to_csv('/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/sIPR_tau_d.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>h_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>9600.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>9350.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>9100.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>8850.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8600.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8350.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dist_m  h_err\n",
       "82  9600.0   56.0\n",
       "83  9350.0   56.0\n",
       "84  9100.0   56.0\n",
       "85  8850.0   56.0\n",
       "86  8600.0   56.0\n",
       "87  8350.0   57.0"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Millan_herr_df[(Millan_h_df.dist_m > 8300) & (Millan_h_df.dist_m < 9700)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82    46093.034551\n",
      "83    40024.879344\n",
      "84    39044.722168\n",
      "85    42989.540940\n",
      "86    50555.653281\n",
      "87    54254.053373\n",
      "Name: h, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((Millan_h_df[(Millan_h_df.dist_m > 8300) & (Millan_h_df.dist_m < 9700)].h-56)*9.81*917*slope_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dist_m</th>\n",
       "      <th>h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30100.0</td>\n",
       "      <td>222.366490</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29850.0</td>\n",
       "      <td>221.125150</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29600.0</td>\n",
       "      <td>214.023300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29350.0</td>\n",
       "      <td>212.245940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29100.0</td>\n",
       "      <td>226.235300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>83.256370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>850.0</td>\n",
       "      <td>94.044890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>600.0</td>\n",
       "      <td>109.037130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>350.0</td>\n",
       "      <td>104.761890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>100.0</td>\n",
       "      <td>72.052986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dist_m           h\n",
       "0    30100.0  222.366490\n",
       "1    29850.0  221.125150\n",
       "2    29600.0  214.023300\n",
       "3    29350.0  212.245940\n",
       "4    29100.0  226.235300\n",
       "..       ...         ...\n",
       "116   1100.0   83.256370\n",
       "117    850.0   94.044890\n",
       "118    600.0  109.037130\n",
       "119    350.0  104.761890\n",
       "120    100.0   72.052986\n",
       "\n",
       "[121 rows x 2 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Millan\n",
    "Millan_surf_df = pd.read_csv('/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_20180122.csv',\n",
    "                            names=['idx','dist_m','surface_elev'],usecols=[1,2],skiprows=1)\n",
    "Millan_h_df = pd.read_csv('/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_h.csv',\n",
    "                         names=['idx','dist_m','h'],usecols=[1,2],skiprows=1)\n",
    "Millan_herr_df = pd.read_csv('/Users/jukesliu/Documents/TURNER/DATA/ICE_THICKNESS/surface/centerline_elevations/centerline_n_250m_herr.csv',\n",
    "                            names=['idx','dist_m','h_err'],usecols=[1,2],skiprows=1)\n",
    "# Millan_surf_df\n",
    "Millan_h_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>dist_m</th>\n",
       "      <th>surface_elev</th>\n",
       "      <th>h</th>\n",
       "      <th>h_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30100.0</td>\n",
       "      <td>2406.212891</td>\n",
       "      <td>222.366490</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29850.0</td>\n",
       "      <td>2381.678711</td>\n",
       "      <td>221.125150</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29600.0</td>\n",
       "      <td>2355.566895</td>\n",
       "      <td>214.023300</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29350.0</td>\n",
       "      <td>2329.444092</td>\n",
       "      <td>212.245940</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29100.0</td>\n",
       "      <td>2290.409668</td>\n",
       "      <td>226.235300</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>112.488480</td>\n",
       "      <td>83.256370</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>850.0</td>\n",
       "      <td>100.550964</td>\n",
       "      <td>94.044890</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>600.0</td>\n",
       "      <td>87.110077</td>\n",
       "      <td>109.037130</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>350.0</td>\n",
       "      <td>76.554176</td>\n",
       "      <td>104.761890</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>100.0</td>\n",
       "      <td>58.823116</td>\n",
       "      <td>72.052986</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dist_m  surface_elev           h  h_err\n",
       "0    30100.0   2406.212891  222.366490   57.0\n",
       "1    29850.0   2381.678711  221.125150   57.0\n",
       "2    29600.0   2355.566895  214.023300   57.0\n",
       "3    29350.0   2329.444092  212.245940   57.0\n",
       "4    29100.0   2290.409668  226.235300   57.0\n",
       "..       ...           ...         ...    ...\n",
       "116   1100.0    112.488480   83.256370   40.0\n",
       "117    850.0    100.550964   94.044890   40.0\n",
       "118    600.0     87.110077  109.037130   47.0\n",
       "119    350.0     76.554176  104.761890   47.0\n",
       "120    100.0     58.823116   72.052986   40.0\n",
       "\n",
       "[121 rows x 4 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Millan_merged_df = Millan_surf_df.merge(Millan_h_df,on='dist_m').merge(Millan_herr_df,on='dist_m')\n",
    "Millan_merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eyY3Qx+Xg4MC6desYO3YsixYtiv0ZeHl50aVLFz7++GM8PT2TbSc5hQoVsvuaqKgoGjVqxF9//UXlypXp2LEj+fPnj50XMGbMGCIjI2PPt37i4Ovrm2zb1tSqo0ePMmbMmETPu3Hjht39FplDAvgM1K9fP5566inGjBnDqlWrWLduHbVr16ZMmTK8+uqr5MmThwMHDnD06FHc3NwoWLAgYEY4ypUrR+3atalSpQqPPfZYiv5DCiGEeHiUL18eSDyX21osIW6+efny5Vm9enXsJMgHWQP8W7duxe6z5n4nlT5jTQ+1WCwEBgZSs2bNdOuz1d27d+nUqRMLFy6kU6dOfPvttwly9QE8PDzw9fXl7NmznD9/PkEefFL3sCV37txMmTKFKVOmcOzYMTZu3MisWbOYMWMG165d47vvvgOInZR79+5dm+2EhYUl+qYhNYN2S5Ys4a+//qJLly7Mmzcv3rHz588nCLytr3ncUfnEWPvZrl07fvvtN7v7JrKeBPAZrEaNGqxYsYLg4GC+/PLL2EoAo0ePTvK6gwcPsmTJktjHr7zyCj/++GMG91YIIUR20bBhQwBWr15NTExMvKou4eHhbN26FTc3t3jBdOPGjfn888/Zv39/gvYiIyNjg9u4kyUXL15M4cKFE03zWLduHa1bt8bZ2ZnAwECefvrpdO0zmOopHTp0YMmSJbz++usEBAQkqGITV6NGjfjuu+9YtWoV3bp1i3ds5cqVsefYq0yZMpQpU4ZOnTpRoECBeH+HrW9+Tp8+neC6Y8eOce3atRSN+qeUtWpQ+/btExzbuHFjgn0VKlQgV65c/Pvvv5w7dy7JNBrruTt27CAqKip2VF/kHDKJNZOUKFGCiRMncvbs2dj0Gn9/f1q1asXAgQOZMGECs2bN4vPPP6dHjx5UqVIl3n+on376iZkzZ2bhMxBCCJGZSpcuzXPPPUdQUBBffPFFvGOjRo0iIiKC119/HQ8Pj9j9zZs3p1SpUgQGBrJmzZp414wbN46wsDDq168fm9IREhLC1q1badmypc1R4tWrV9OqVStcXV1Zu3ZtksF7avscGRlJu3btWLJkCT169Eg2eAfo27cvYCqpXL16NXa/9b4uLi4JAntbTp48yX///Zdg/9WrV4mMjIyXhlShQgW8vb1ZsmRJ7GRhMJ9mDBw4MNl72cv6JmvDhg3x9p84ccJmupODgwP9+/fn1q1b9O3bN156DZg3SSEhIYApy/nmm29y/vx5Bg4cGO8TGavz589z4MCB9HkyIt3JCHwmc3NzY+DAgSn6zx4VFcXBgwdZsGABEydOZMCAASxYsIDFixeTL1++TOitEEKIrDRz5kxq167NwIEDWbt2LRUrVmTnzp2sX7+ecuXKMWHChHjnOzs7M3/+fJ577jmaN29Ou3btKFGiBLt27WLTpk3kz5+f2bNnx56/ZMkSoqOjad26dYJ7Hz58mDZt2nD79m1atGjBkiVL4o1IWz34ibK9fe7bty8rVqwgX758+Pr6Mnbs2AT3aNCgAQ0aNIh9XLt2bYYOHcrkyZN54oknePHFF7lz5w4///wzoaGhfP755ymqZ793717atWtH1apVqVy5MkWKFCEkJIQlS5YQFRUVL1B2cnJi0KBBjBs3jipVqtCuXTvu3r3LmjVrKFKkSLITR+31/PPPU6ZMGSZPnsy+ffuoUqUKp06dYtmyZbRs2ZJTp04luGbUqFHs3LmTpUuXUq5cudja86dPn2b16tVMmjSJrl27AqYU5d69e/nqq69YunQpjRo1wtfXl0uXLnH06FG2bt3KhAkTeOyxx9L1eYl0Yu/KT4/CZs9KrJklICBAOzg4aEB7e3vrY8eOZXWXsi1ZzTPnktcuZ5OVWDPGqVOndNeuXXWhQoW0k5OTLl68uB44cKC+cuVKotf8999/ukOHDjp//vzayclJFy1aVPfu3VufPn063nktWrTQuXPn1qGhoQnasK48mtyW1j7Xr18/2XsktvrpvHnzdLVq1bS7u7v29PTU9erV00uXLk3ipxnf6dOn9bvvvqtr166tCxYsqJ2dnbWvr69u1qyZXrFiRYLzY2Ji9Mcff6xLlSqlnZycdLFixfRbb72lIyIikl2JNTFdunRJdAXcU6dO6U6dOukiRYpoV1dX/dhjj+lPPvlER0VFaUDXr19fax1/JdaoqCj9+eef66effjp25dsyZcroXr166aNHjyZ4Pt9++61u1KiRzp07t3ZyctJFihTRderU0RMmTNCnTp1K8c9SJJSRK7Eq/YjM5rdH+fLltXUxiuxk9erVsaMhLi4ubN68OdmPMx9FGzZsiDdSI3IOee1ytvR8/Q4ePEjFihXTpS1hW3h4OPnz56djx47MmDEDLy+vrO6SSKXw8HB5/bKhlP4eU0r9rbWuZk/bkgOfgzz33HNs376dfPnyERkZSa1atfjrr7+yultCCCFyoBUrVsTmnwshchYJ4G3QGk6fhtu3s7onCfn7+/P3339TunRpoqOj6dmzJ1FRUVndLSGEEDlMx44d0VrTtm3brO6KEMJOEsAnIioKgoLgwgV4YCJ3litevDhbtmyhcOHC7Nu3j7feeiuruySEEEIIITKJBPCJcHUFb2+IiDCB/JkzcOuWGZ3PDgoVKsSvv/6Ko6Mj06ZNo3v37lndJSGEEEIIkQkkgE+Gmxt4ecGdO3DqlAnmw8MhJiarewa1atVi2rRpAAQEBMSuFieEEEIIIR5eEsCnkKurCeQtFjh/Hk6eNIF8Vo/I9+vXj06dOgHQrVs3smP1HCGEEEIIkX4kgLeTkxN4epqvZ8+aUXkbC5hlGqUUc+fOpWLFikRHR1O7du0Eq68JIYQQQoiHhwTwNly96pxsBRpHR5MjHxMDwcFmVD6risG4ubmxcuVKPDw8CA0NpVOnTty5cydrOiOEEEIIITKUBPA2XL7sQr168NtvyZ/r4mIC+Zs34cQJk1aTFUqUKMHvv/+OUorffvuNJ554giFDhnDx4sUUtxETJ7E/Ojo6I7ophBBCCCHSSAJ4G3x9b5I3L2zden9fcqPrbm7g7g7nzmVd/fgmTZqwatUqypUrx+HDh5k6dSq+vr706dOH0NBQoqKiYoP0ffv28eWXX/L222/TsmVLSpUqhbu7O8OHD+ell16iRo0a+Pn50b9/f/744w/CwsKy5kkJIYQQQoh4HLO6A9mRu3s0y5ffD8T/+Qd69oSuXeHVVyFPHtvXOTiYEfmzZ6FECZNmk9mee+459u3bx/Tp0xk9ejQRERHMnj2buXPnorWmR48eREZGsnPnTo4cOZLg+v/7v/+L9/jLL7/kyy+/xGKxUKtWLTZs2IBjVjwxIYQQQggByAh8oiwWM6IOZsJqhQrwySfw9NPw7rsm790WZ2fz9fz5rCs16ezszPDhwzl27FhsffiYmBi01sydO5fvvvsuXvDu6elJ1apV6dy5M2PHjmX+/PkMGzYMf3//2HNiYmLYunUrvXv35tq1a+isLr8jhBBCCPGIkqHUFHj8cfjxRzh0CObMgZ9+ghUr4K+/zIj7g9zc4MYNuHwZChTI/P5aFSpUiK+//pp+/foxb948XF1dKVCgAPnz56dAgQIUKFCAIkWKUKRIEZRSNtsICQlh/fr1/Pnnn3z77bcEBASwZMkSihUrxh9//EHx4sUz+VkJIYQQQjzaJIC3Q4UK8H//B2+9BQcPmuA9JgaWL4cWLUwKjZWHB1y5cn9F16xUrVo1qlWrlqpr8+fPT4cOHejQoQNDhw6le/fubN++ndDQUMqUKcOUKVPo379/om8AhBBCCJG0BQsW8Omnn3Ls2DFu3LjBoEGDmDp1alZ3S2RjkkKTCoUKQcOG5vt166BvX2jaFLZsuX+OUmbhp3PnsrZOfHqqUKECmzdvZvLkybi4uBAVFcUbb7xBpUqV2Lx5c1Z3TwghHkpnzpyhe/fuFClSBBcXF/z8/Bg8eDBXr15NcG5UVBTTpk2jW7du+Pv74+zsHLteSEoMGDAABwcHQkJCiIqKYvHixfTo0YPKlSvj7e2Nu7s7jz/+OB9++CHhSZRdy4w+X7p0iREjRlC5cmW8vLzImzcvVatWZdKkSUn2LbvZvn07nTt3Jjw8nH79+jFq1CiaNWuW1d0S2Z3WWrYHtrJly+kzZ7Q+ezb57cwZrWfN0rpYMa1B6+nT4x8PCtL66FGtb9/WD5WjR4/qunXrakADOl++fHrmzJk6PDxcx8TEZGnf1q9fn6X3F6knr13Olp6v34EDB9KtrZzs2LFjukCBAhrQbdq00W+//bZu2LChBnT58uX15cuX451/9erV2N/LBQsW1MWKFdOAnjNnTpL3uX79uo6JidG+vr66Xr16WmutDx48qAHt4eGhW7VqpUeMGKH79++vS5curQFdrlw5HRISkiV9PnnyZOw9GjRooIcPH67feOMNXa5cOQ3oJ554Qt+8eTOlP+YsNXLkSA3orVu3prqN69evp2OPRHpJ6e8xYLe2M1bN8mA5O25ly5bThw6ZwDulgfzx41rXqqW1q6vWK1fGP3bypNaHD2sdEZGi1zHHiI6O1gEBAbG/zAHt6empixYtqr///vss+4UiQWDOJa9dziYBfPp77rnnNKCnT58eb/+QIUM0oPv06RNvf2RkpF6xYoU+d+6c1lrrUaNGpTiA37Fjhwb0lClTtNZanzlzRn/xxRf6xo0bCe7RsmVLDeg33ngjS/rcv39/DejRo0fH23/37l3dqFEjDej58+cn+Zyzi27dumlAnzx5MtVtSACfPWVkAC8pNDYoBaVKmRSYGzdMCoxOpuiKqyvMmgXlyplFneJydjbHT52C69czrt+ZzWKx0LVrVw4fPswvv/xClSpVuHHjBmfOnOHVV1/F29ubIkWK0LVrV/NuUQghRIqdOHGC1atX4+fnx4ABA+IdGzNmDB4eHnz33XdERETE7nd2dqZ58+YULlzY7vv9dm/1wrZt2wLg6+tL//798fDwiHees7Mz7733HgAbNmzIkj6fOHECgNatW8fb7+DgQMuWLQFThCElwsPDGTduXGyakJeXF6VLl6Zjx478/fffsedt2LABpRSjR4+22Y6fnx9+fn7x9s2bNw+lFPPmzWPVqlU0aNAAHx+f2H1KKQICAgAoWbIkSimUUgQFBQGwfv16evfuzWOPPYa3tzdubm5UrlyZMWPGcDuRRWeio6P56quvqFOnDj4+Pri5uVGmTBl69uzJ0aNH45179+5dZs6cSc2aNWNTpKpUqcKMGTPiLe4osh8J4BPh5AQFC4Kfn5mQeuMGREQkXRoyb15TnaZmTfM4bszq6AieniYnPjQ0+TcEOYmDgwMvvfQSf//9N6tWraJu3bqxx86fP8/8+fPx9fWlc+fOtGvXjsOHD2dhb4UQImdYt24dYNb3sFji/7n28vKiTp063Lx5kx07dqTL/RYvXsxTTz2VIAi1xcnJCSDBuiCZ1edKlSoBsHz58nj7Y2JiWLlyJRaLhUaNGiXbjtaaZs2a8eGHH+Lt7U3Pnj3p168f1atXZ9OmTWzfvj1N/bRatGgRrVq1wsvLi759+9KhQwf8/f0ZNWoUTz75JACDBg1i1KhRjBo1ily5cgHwySefsHr1avz9/enTpw89e/bE2dmZ0aNH07x58wSrpt+5c4dmzZrRr18/Tp8+TadOnRg4cCBVq1Zl8eLFbI2zQmVUVBStWrViwIABXLt2jU6dOtG7d29iYmJ488036dKlS7o8d5ExpApNMlxczKTVfPnM6HloKERHm1KRttYzUsoE51Onmio048ffP2axmFH9S5fMyq7585tztTZvDGJizPcWi6lo4+Bg2ssplFI0bdqUpk2bcvLkSdauXcu6detYu3Yt58+f58cffwRg2bJlfPXVV/To0SOLeyyEENmXdbCjXLlyNo+XLVuW1atXc+TIERo3bpymex08eJCjR48ybty4FJ3/zTffACSYbJlZfR4xYgTLli3jgw8+YP369Tz11FPcuXOH1atXc+HCBebOnUuVKlWSbWf//v1s27aNtm3bsnjx4njHYmJi0m0V8hUrVrBixYoEPy9/f3+CgoLYu3cvgwcPTvDmaebMmbEj83F98MEHjB8/nkWLFtGxY8fY/aNHj+bPP//k+eefZ+HChbjEqXUdGRnJ9ThpABMmTCAwMJA33niDqVOn4nCvlF50dDS9e/fmm2++4cUXX6RNmzbp8jMQ6UsC+BRydDQrsObKdb/G+61bZnT+gUEGlDLnBARApUrwyivxj3l7Q1iY2eLS+n7Abg3kHR1NCo67O/j4JLxXdlWyZEl69uxJz5490Vrz33//sWzZMsaNG8fNmzfp2bMnixcvZvHixbEjOUIIkVINGjRIsK9Dhw7079+fmzdv0qJFiwTHu3btSteuXbl8+TIvvvhiguP9+vWjY8eOnD59mtdeey3B8WHDhvH8889z+PBh+vTpk+D4+++/T5MmTdizZ0+8hfBSyxo8+vj42Dxu3X/t2rU032vp0qUAtGvXLtlz//jjD2bNmkXRokUZMWJEvGOZ1ecCBQqwY8cOunfvzuLFi2NH/pVS9OrViyZNmtjVnpubW4J9FouF3Llzp6mfVm3atElVZZlSpUrZ3D948GDGjx9PYGBgbAAfHR3NzJkzcXNz46uvvooXvAO4uLiQP39+wLw5mTFjBoUKFWLKlCmxwTuYT9X/7//+j4CAAH744QcJ4LMpCeDtZLGYANzT04zIh4SYoNvNLf5o+bvvwoED5mvZsvBgGXZPz5TdLzrajNaHhJiAv3Bh24tHZWdKKSpXrkzlypV55ZVX6NGjB2vXrmX58uWULFmS3bt3U6hQoazuphBC5CjWuUXpsQ7H0qVLKVu2bGxqSmK2bdtGp06d8PDw4Ndff7U7wE2vPgcFBdG6dWtu3brFihUrYlNzlixZwrBhw1iyZAnbt2+nZMmSSbbz2GOP4e/vz4IFCwgODqZNmzY888wzVKtWDWfr0urpoHr16qm6LiIigmnTprF48WKOHDlCeHh4vDllZ8+ejf3+0KFDhIWFUaNGDYoUKZJku0eOHOHKlSuULVuW8XFTBeJwc3Pj4MGDqeq3yHgSwKeSxWJG4z09TVpNaOj9yapgRs5nzoRWreD11+Gbb+7nxtvDmkrj7AyRkRAUZFZ3zZUrZ6XXWJUoUYI1a9Ywd+5cBg4cyNmzZ6lUqRI///yz3SMmQohH14OTJ+Nyd3dP8ni+fPmSPF6sWLEkj5cvXz7J4+kx+g73R6sTS+OwpkMkNtqdUsHBwezdu5e33347yfO2b99O8+bNsVgsrFy50mZQmll97tq1K/v27WPv3r088cQTAHh7e9OnTx9u377N4MGDGTNmDPPmzUuyHQcHB9atW8fYsWNZtGhR7M/Ay8uLLl268PHHH+OZ0hG3JKRmkCoqKopGjRrx119/UblyZTp27Ej+/PljP7UeM2YMkZGRsedbP9Xw9fVNtu0rV64AcPToUcaMGZPoeTdu3LC73yJz5JCEjOzL0dEE1CVLmomvcf+t584NP/1kJsNevJj2e7m4mJSdS5fgzBkzMp8TWT/iPHz4MM899xyhoaG0bNmSJUuWZHXXhBAi2yhfvjxgRkttsVYUSSzfPKWsud9Jpc9s3ryZpk2bopRi9erV1KlTx+Z5mdHn8PBwNm7cSJ48eWKD97ga3ltpMW4FmaTkzp2bKVOmcPr0aY4ePcrcuXOpUKECM2bMoF+/frHnWSfl3r1712Y7SeXLp+YThyVLlvDXX3/RpUsX9u3bx+zZs5kwYQKjR4+2mcJlnfgad1Q+MdY3UO3atUuyVOHJkyft7rfIHFkewCul8iqleiqlFiuljimlbimlwpRSW5RSPZRSdvVRKVVUKfWNUuqcUipSKRWklJqqlEqfRLZEuLhA0aImlSbuyqvFikFgIFhTyI4cSVsFGutE2Dt34OTJnL3Ka/HixVm1ahUvvfQSd+7coX379uzcuTOruyWEENmCNRBdvXp1gpJ+4eHhbN26FTc3N2qm5uPdOBYvXkzhwoUTTfNYt24dzZs3x9HRkTVr1iR5v8zo8507dwAzmm/9Pi5r+cjUpMCUKVOGHj16sHHjRjw9PeMNLFnThU6fPp3gumPHjqXLXIQH2wRo3759gmMbN25MsK9ChQrkypWLf//9l3PnziXZtvXcHTt2EJVTRwMfcVkewAMvAXOAGsBOYCrwK1AZmAv8olL41lUpVRr4G+gG/AVMAU4Ag4DtSqm86d35+Pc3OepgAmwr6++Q//6DZ5+F9983ue1p4eZm3jScPQuJDAbkCEopvv/+e6pXr050dDT169fn+PHjWd0tIYTIcqVLl+a5554jKCiIL774It6xUaNGERERweuvv56gTrs9QkJC2Lp1Ky1btrQ5Srx69WpatWqFq6sra9eu5emnn87yPufNm5eKFSty9+7dBFVzbt++HZvTnZIqNydPnuS///5LsP/q1atERkbGm9xaoUIFvL29WbJkCZcuXYrdf+vWLQYOHJjap5Moa0UaW7X2baU7OTg40L9/f27dukXfvn3jpdeAeeNjfXPj6OjIm2++yfnz5xk4cCC3bIwGnj9/ngMHDqTPkxHpLjvkwB8BWgPLtdaxb9eVUu9hgvD2wAuYoD45M4ECwECt9edx2poMDAEmAH3Tr+sJOTqCry8EB9/PX7eqWBF69DALPp07B40bm8oyzz9vjo8ebUpP+vqa0fxixcyCUsWK2b6Xk5MJ3i9ehCJFcmZOPJhRkj///JMqVapw/PhxqlSpwsmTJ8mbN0PfbwkhRLY3c+ZMateuzcCBA1m7di0VK1Zk586drF+/nnLlyjFhwoQE10ycOJFDhw4BsGfPHgACAgLYsmULAM888ww9e/YETJpGdHR0ggWRwJSEbNOmDbdv36ZFixYsWbLEZqrjgwsbZXSfAaZPn07Lli0ZP348a9asoXbt2ty6dYuVK1cSHBxMmTJlks3pB9i7dy/t2rWjatWqVK5cmSJFihASEsKSJUuIioqK14aTkxODBg1i3LhxVKlShXbt2nH37l3WrFlDkSJFkp04aq/nn3+eMmXKMHnyZPbt20eVKlU4deoUy5Yto2XLlpw6dSrBNaNGjWLnzp0sXbqUcuXKxdaeP336NKtXr2bSpEl07doVMKUo9+7dy1dffcXSpUtp1KgRvr6+XLp0iaNHj7J161YmTJjAY489lq7PS6QTe5duzcwNeA/QwOcpOLfUvXNPApYHjnkBN4AIwCO5tsqVK5eipW+Tcv261gcPan3mjNZnz8bfxo3T2mIxFeCfeur+/oYNtS5aVGsHB2t1eK2bNr1/PCgoYVtnz2p96JDWV66kuctZ7vz58zpfvnwa0E8//bSOiopKVTvpuZy7yFzy2uVs6fn6pXQJ8kfBqVOndNeuXXWhQoW0k5OTLl68uB44cKC+ksgv/vr16+t7fw9tbl26dIk9t0WLFjp37tw6NDQ0QTvr169Psh3rltl9ttq7d69+9dVXdbFixbSTk5N2dXXVjz32mH733Xf11atXk/25aq316dOn9bvvvqtr166tCxYsqJ2dnbWvr69u1qyZXrFiRYLzY2Ji9Mcff6xLlSqlnZycdLFixfRbb72lIyIidIkSJXSJEiXinR8QEKABHRAQkGgfunTpogF98uTJBMdOnTqlO3XqpIsUKRL7/D755BMdFRWlAV2/fn2ttdbXr1+PvSYqKkp//vnn+umnn9YeHh7a3d1dlylTRvfq1UsfPXo0wfP59ttvdaNGjXTu3Lm1k5OTLlKkiK5Tp46eMGGCPnXqVIp+jsK2lP4eA3ZrO2NkpbPxkqBKqbeAT4GpWushyZzbE5OKM1trnWB2h1IqEHgOaKK1XptUW+XLl9fpsVro5ctmRN3Ly/axqChTkvLBTxKto+qnT5uqNv7+sH+/Gb2fOBHupRjG0hrCw6FECZNak5MdPnyYGjVqEBYWRq9evZg1a5bdk382bNhgs0a0yP7ktcvZ0vP1O3jwIBUrVkyXtoRt4eHh5M+fn44dOzJjxgy8bP2xEjlCeHi4vH7ZUEp/jyml/tZaV0v2xDiyQw68TUopR+D1ew9XpeCS8ve+2p76DkfvfU3bdH075M1rykzammiaL5/Jl7eVBmhNw6lZ0wTvYNJj3N3h1VfhnXfg5s3751vr0J87l7Pz4cFUMFi5ciWurq7MmTOHZ555huz8JlMIIXKqFStWEBkZmaLFm4QQ2Uu2DeCBiZiJrCu01oEpON9aVDaxOk7W/bnS2K8UUwoKFTJf01otplIlWLkS+vSB7783k2F37bp/3MnJjMRfvJi2KjfZQa1atfjll19QSrFt2zaaN2+e1V0SQoiHTseOHdFa07Zt26zuihDCTtlhEmsCSqmBwDDgEJBwPetUNnvvq83wVinVG+gNkD9//iQX6UiNO3dMYG1J41um9u2hbFkfJk2qwPz5V3B3PxbveEwMHDoUf/JsTuTl5cVbb73Fp59+SmBgII0aNeLDDz9M0bU3btxI99dPZA557XK29Hz9fHx8CA8PT5e2RPKio6Pl552DyeuXPd2+fTvD/qYlGcArpU6kwz2maq2np/RkpdQAYBpwAGistQ5N4aXWEfbElnfzfuC8eLTWs4HZYHLg0zsPNyYGLlwwueqenmmrGFOpkgnknZ2LAkXjHbPmw+fObdJ0cnIg36BBA/Lly8eIESNYv349FStWTFCWzBbJo8655LXL2dI7B15yejOP5FDnbPL6ZU+urq5UqVIlQ9pObjzYD8iNGb1OzVYCO1JWlFKDgRnAfqCh1vpCSq8FrLNOE8txL3vva2I58hnKYjE573nymAA7rWku1trye/eanHjrehlKmYmx4eEQFJR06o7WcPu2WT326tX7E2dPnDDbxYvmmPXTg6zw1ltvMXz4cABmzZrF1q1bs6YjQgghhBDZREpSaKZorcempnGlVEzyZ8We+zYm730P8KzW+rKdt1t/7+tzSimLjl9T3guoA9wCdtjZbrpRCvLnN5NUL140FWbABN/3C0eaYN9iuV9HPqnR+v/9D777DooXh/797+93dzdVboKDzUh8njymTWvQHh4OYWH3A/+493N2NufduGHO0drsd3c3baVicbs0+fTTTwkJCWH+/Pm0atWKlStXpnnlQSGEEEKInCpbTGJVSn2ACd7/xqTNJBq8K6WclFIV7q26GktrfRxYjfnUYMADl40BPIBvtdYR6dn31Mid21SZcXAwk089PMyoed68UKAA5MplgvuYGDOCHh5uNlurt3bpAi1bmvKScSe1gmnbywtCQ00gf+kSHDsGp06Z9tzczHEvL9MHV1dzjTWYd3Mz6T5eXub7W7fMtQ8s7pbhlFLMnTuXtm3bcu3aNerUqUPPnj2lOo0QQgghHknJjcA/DZxLQ/vJXq+U6gKMBaKBzcBAG3W/g7TW8+597wscBIIxwXpc/YFtwHSlVON759UAGmJSZ0am8nmkO2vgnBLR0aZs5IULJriOW+tdKfjsM1Mnvn9/CAw0o+1xj3t6mjQYa9Cemom01lKVd+6YNwPFimVuzXlHR0cWLFhA+/btWbFiBV9//TXHjx9n9erVODk5ZV5HhBBCCCGyWJKhnNb6b631+dQ2nsLrS9776gAMBkbZ2Lqm8H7HgWrAPEzgPgwoDUwHammtr9j1BLIJBwcT7Pv5mVHy69fvp76AGb3/6iuzONScObbbcHZOffD+YDuurmYk/saNtLVlL1dXV/744w/69esHmAlzjz32GNeuXcvcjgghhBBCZKEsLyOptR4NjLbj/CDul4S0dfw00C2t/cqOnJygSBETwF+4YB5b8+ifeAIWLry/8FNGcnQ0+fBnzpiJuT6J1f3JAA4ODnzxxReUKlWKt956i2PHjlGnTh327t2Lo2OW/3MWQgghhMhwEvHkMEqZgNnNzQTxN2+aYBqg2r1FeM+fh/fegzFjzOTWjODgYFJzrKu/5sqVeSUrlVIMHz6c4sWL8+qrr3LgwAGee+45bt26xb///ovWGicnJywWC46OjgwbNozcuXOzadMm9u3bR8GCBZk7dy4lSpTInA4LIYQQQqSjdA3glVLFgPmA1lo3Ts+2RXzOzmYibHCwyUuPWxnm6FHYvh2aNIGxY6Fjx7TVnU+MxWLSd65cMVuuXGbLrCo1HTp0wNfXl9atW7N+/fp4x27FqZ/57rvvxju2b98+qlSpwrFjx8gTd8KAEEIIIUQOkN4j8O5AAxJZ7VSkLwcHk1ITFGTSWqz57fXqwdq1MHgwDBtmJrZOmmRKQCbnxg04edKM3KckNcY6SVZrk9pz9aqpaJMnj/mUICPeOMRVp04d/vnnHxYvXszdu3dRSnH8+HHKljVl/2/dusW1a9e4evUq165d49q1a2zYsIGrV6/i7+/PkSNHcLXmIQkhhBBC5ADpHcCfwlR8EZnE1RUKFTLpNN7e9/f7+sLPP8PcuabE5Gefma/Hj8OECSbIdnc3o/dnz8Lbb0PVqrBxI/TubQLvChWgRg2oXh0aNzaBemKUup/KExlpFoRSClxcTCDv5mZy9p2c0j/VpkSJEgwePDj2cXKrQS5ZsoR27dpx+vRpPvroI8aOTdUyB0IIIZIxevRoxowZw/r167NkleWgoCBKlixJly5dmDdvXqbfX4iMkq514LXWt7TWG7XWG9OzXZE0Hx+z3bwZf7/FYoLxlSvNaq1garmfPg3//GNG5jdtMgs7WWu7P/00zJplRu7z54dffjHlKUNDzfG4iz8lxsXFVM3x9DRBfHi4ycsPDjZvIK5eTb6NjNSmTRu+/fZbAMaNG8fMmTOzrjNCCJEOunbtilIqQZBq3R938/DwoHLlyrzzzjtcvXo12fPd3d157LHHGDZsGCEhIZn4rIQQiZFJrA8BpcwCULby4QHKl7//feXKsGZN4m0VKACtWt1/fPcuHDxo6r4DvPGGKVf53ntQt27yfXN0NJtVTAyEhJggvlCh+6P2me3VV1/l+vXrDBgwgAEDBrB9+3a+++67rOmMEEJksDZt2uB/r0zZhQsXWLp0KZ988gmLFi3ir7/+SrCeRtzzL168yIoVK5g8eTK//vorf//9N3nz5s3kZyCEiEsC+IdEYvnwaeXoCI8/br7XGlq3Nvn0L78M9eubQL5y5ZS3Z7GYkfmoKFNL3sfHjPRnRQXI/v37c+3aNUaOHMn3339P4cKF+fTTTzO/I0IIkcHatm1L165dYx9/9tln1KhRgwMHDvD5558zdOjQJM+/ffs2NWvWZO/evcyYMYNRo0ZlUs+FELYkGeYppdalclubWU9A3OfqCgULmomoOgOmESsFL71k0m4+/BD27oWmTWHZMvvbcnIyOfsREXDihJkAmxXee+89hg0bBsCkSZPo3r171nRECCFs+OOPP2jcuDGFCxfGxcWFIkWKUL9+/TSn/nl6etKlSxcA/vrrr2TPd3V1pXPnzgDs2rUrVfecP38+VapUwc3NjQIFCtC9e3cuXLhg89zQ0FDeffddKlasiJubGz4+PjRu3JjVq1fbPD88PJyhQ4dStGhRXF1dqVChApMnTyYmK/M1hchAyY17Nkhlu1KFJovkygXR0SbNxTpxNL25ukKfPmYUfvZsqF3b7N+wwdy3deuUl5J0dzf9PXvWjMrnyZPxlWseNGnSJO7cucPnn39OQEAAZ86cITAwEJXZHRFCiDhmz55Nnz59KFSoEM8//zz58uXj0qVL/PvvvwQEBNC/f/80ta/vjfSk9Hed9fwH021SYsqUKaxevZqOHTvSrFkztmzZQkBAABs2bGDnzp3kz58/9tzg4GAaNGhAUFAQdevWpVmzZkRERLBs2TKaNWvGrFmz6NWrV+z5kZGRNG7cmF27dvHkk0/SuXNnrl27xrhx49i4UabkiYdTkgG81jpdJ7mKjKeUKRfp4WEmjt65Y77PCD4+8NZb9x8vXAi//26q3HTqZCrYVKoEyaVKOjiY0fiQEJNznz9/+qUApYRSiunTp1OqVCmGDBnCmjVr6N69O7NmzcI5s4raCyHEA6y/g/bu3UuBAgXiHbt8+XKa2r5x4wbz588HoEaNGsmef+vWLb7//nsAnnnmGbvvt3LlSnbu3EmVKlVi9w0ZMoSpU6fyzjvv8PXXX8fu79KlC8HBwSxYsICXX345dv+1a9do0KABAwcOpHXr1hQsWBCA//u//2PXrl288MILLFy4EMu9PyDvvPMOVatWtbuvQuQEkgP/kHJzgxIlzIj41atmpDuj88xnzIAOHUzpyqlTzb5GjcA6N3TmTLNabPXqCa9VygTxYWHmTUeRIpm3sqvV4MGDKVWqFK+88grz5s1j+/btrFq1Cj8/v8ztiBAiSWpMzvh0TI9K+4fRjo6ONke886VkYY84fv/9d4KCggAzKfWPP/7g3LlzlC5dmjfeeCPJ8y9dusSyZcs4ffo09erVo1+/fnY/j9deey1e8A6mxGRAQAA//vgjM2fOxMXFhb1797Jx40ZefPHFeME7QK5cuRgzZgxt27bl119/jf0EIiAgAIvFwqeffhobvAOULFmSgQMHMmbMGLv7K0R2JwH8Q8zBweTEe3qa0fjISBPYZ9TotlJmYmv9+qbs5IED91Npbt+G//s/8/WFF+CDD0zFmwd5et4vdenrmzEpQElp3bo1mzZtolmzZhw+fJjy5cuzbt066tSpk7kdEUI88jp37sywYcOoVKkSHTt2pH79+tSpUydeuklKLVmyhCVLlgDg5uaGn58fnTt35p133iF37tyEh4cner7Vs88+y/Lly1OVQlO/fv0E+3x8fPD392fjxo0cPHgQf39/tm/fDkBYWBijR49OcI21jOXBgwcBk/t+7NgxihUrRunSpROc36BBAwngxUNJAvhHgIcH+PmZiaKhoSbn3MUl5XnqqZEnD8T9lNXVFfbvN6P0M2fCn3+axaNeey3hSLubmwn0g4NN+UoXl4zrpy1Vq1Zl9+7dsTmY9erV49dff6Vt27aZ2xEhhE3pMbKdEwwdOpR8+fIxc+ZMpk+fztSpU1FKUb9+fSZNmkS1atVS3FZAQEC8qjIpPT86OpoTJ07wwQcf8PPPP9OvXz/mzp1r93Oxprs8qFChQoAJ2AGuXLkCwJo1a1iTRM3jGzduxLsuufaFeNik61isUqq8UipaKXU3PdsVaefoaILqUqXMyLZ1gaWbN81244Z5fONG/O3mTTO5ND24uZmc+T//hCefhE8+Mek9tri6mj6fPn1/kanMVKJECfbs2UPVqlWJiYnhhRdeYNasWZnfESHEI+31119nx44dXLlyheXLl9OjRw82bdpE06ZNuXTpUobf38HBgbJly/Ljjz9So0YNvv76a/744w+727l48aLN/dYqND4+PvG+Tps2Da11oltAQEC885NrX4iHTUYkU6gMalekA2sd9hIlzJYrF+TODYULm9HuEiWgdGkT6Bctao7B/YA+IsKkuERFpb5UZenSsGABrFplJtzGxMDq1Qnbc3bO2iDex8eHzZs306RJE7TWDBo0KMkRISGEyCi5cuWiRYsWzJkzh65duxIaGsrmzZsz7f4Wi4Vp06YBMGLECKKjo+263lY1mLCwMPbs2YOrqysVK1YEoGbNmgApfm5eXl6UKVOGs2fPcvz48QTHN2zYYFc/hcgp0jXQ1lof1lpbpHpNzuDqagLovHnBy8tMdHVxMSktjo7mcd68JqgvUwaKFzerp3p7mxH8uCP39gbYSpl2AZYuhW7d4J13TBWauJydTX9OnzaTWzObm5sbK1eupHPnzkRGRtKyZUu++OKLzO+IEOKRs2rVKu4++EsRYkfe3TN5KesaNWrQqlUrDh8+zLfffmvXtd999x3/+9//4u0bPXo0YWFhvPLKK7jcy5WsVq0adevW5bfffuObb76x2da+ffviffrQrVs3YmJiePvtt+PVfT958iTTp0+3q59C5BSSAy9SxGIxAb+rqwn2wYyYR0eb4P3KFRPIOzqac+wpof7882bC64wZcO4cfPml+ZTAysXF3OPUKfMmIrMrOzo6OvLtt9+SP39+pk6dyhtvvMGff/7Jb7/9JrXihRAZ5uWXX8bV1ZVnnnkGPz8/tNZs3ryZXbt2UbVqVZo0aZLpfRo7dizLly9nzJgxdO7cOcWldps3b06dOnXo0KEDhQsXZsuWLWzZsgU/Pz8mTpwY79wff/yRRo0a0aNHD6ZPn06NGjXIlSsXZ86c4d9//2X//v1s3749trTmsGHD+P333/n111956qmnaNq0KWFhYfz888/Uq1cvVSk/QmR3MlIuUk0pE7B7eJjA2s/PBN7WVBsbA0c2WSzw7rsmJ37jRlOl5sG0ResnA6dOZc1IvMViYfLkybFVEX7//Xdq165tc3RMCCHSw8SJE6lVqxb//PMPM2fOJCAggKioKD755BPWr1+fqmowaVWlShXatWtHcHCwXfOChgwZwsyZM9mzZw9Tp07l0KFDdO3alW3btiWocV+0aFH+/vtvJkyYgIODAz/88APTp09n27ZtFC9enFmzZvH444/Hnu/i4sKff/7JkCFDCAkJYdq0aWzYsIH333+fKVOmpNtzFyI7UdqORGallB/wGLBRax1xb58j8AHQFogAJmmtF6d7TzNR+fLl9eHDh7O6GznW3btmNP769fipNRaLCfiTGrBZvx7efBPmzIFatRIej4w0o/5JVafZsGEDDRo0SNNzSMoXX3wRWze5bNmy7NmzJ9M/yn5YZfRrJzJWer5+Bw8ejM2LFhkvPDwcL+vHqyLHkdcve0rp7zGl1N9a65SXlcL+EfhRwHdA3Izn9zEB/ONATeAXpVRNO9sVDxFHRzP5tUQJKFvWjMz7+pp8eovFBPaJDVw3bAg7dtwP3u9VCIvl4mLaDw42k2mzwoABA1iwYAEWi4WjR4/y7LPPyki8EEIIITKNvQF8LWCt1vougFLKAvQHDgHFgeqYUfgh6dlJkXMpZUbcPTxMUF+smAnm79wxaTa2PgCy5r8vWQK1a8O+ffGPOzubPPtTp0y6TlZ4+eWXWb58OS4uLmzbto0OHTpw8+bNrOmMEEIIIR4p9gbwBYHgOI/9gXzAF1rrM1rr3cAS4On06Z542ChlJsGWLGlKWN64kXgFm6pVzbkvv5wwiLdWyTlzxozoZ4VmzZqxdu1acuXKxeLFiylQoABbtmzJms4IIYQQ4pFhbwDvBMQdM61z7/G6OPvOAIXT2C/xkHNwgPz5TZqNxWJy5h8cjS9aFBYuNKP3L79sVnJ9sA1PT1O55upVkxsfp4JYAlqbLalz7FWnTh02btxI3rx5iYiIoEGDBlIrXgghhBAZyt4A/gzwRJzHLYDLWuuDcfYVALJoTFTkNK6upoJNnjwmiH8wuC5WDBYtMqPtHTtCSEj84xaLGaW/dAmOHTNbZCQcOXL/8bFj5vHRo2Y7dgwuXkz9QlQPeuKJJ9i5cydFixYlOjqaZs2asXTp0vRpXAghhBDiAfYG8MuAZ5VSnymlxgPPAg8WWK1A/DQbIZKklBmNL1jQpNQ8uMBf8eImiB861Jxn63ovL7N5et5fbdbN7f7m6Rl/u3bNBP3pFcSXLl2aHTt2ULp0aWJiYmjTpg3Lli1Ln8aFEEIIIeKwN4D/FDgJDAXeA85jKtMAoJQqAdQGNqVXB8WjI3dukzZz8yZERcU/VqIE9Ohhvt+zx1SqSY5S97cHeXmZID49R+J9fX3Ztm0blSpVQmtNv379OHbsWPo0LoQQQghxj10BvNb6EqZcZOt722Na63NxTvHEBPdz062H4pHi6WmC9agouH074XGtYexY6NwZVq9O2728vEyZygsX0i+It05krVWrFmfOnKFOnTrMmzcvfRoXQgghhCAVK7FqrW9prZfd28IfOPaf1nqa1vpQ+nVRPGqsefFKJaz1rhTMnQsVKkDPnvDzz2m7l5eXyb1PzyA+V65crF69mkaNGnHp0iW6devG0KFDsWfRNCGEEEKIxNgdwAuRGZydzQRWiyVhEJ8njwnca9c2efFffZW2e3l63g/iH8y/T32bnixfvpxmzZoBMGXKFKpWrUpERET63EAIIYQQj6x0DeCVUt5KqdeVUq+nZ7vi0eTomHgQ7+kJ8+dDy5awc6epXnP+vJnsumlTftasgU2bzHb1qrnm1Cn46Sf4/feEOfaenmYCbXCw7dSd1HB1dWXp0qWMHDkSgP/9738ULlyY3bt3p88NhBBCCPFIckzn9goD8zC14b9N57bFI8gaxJ86ZYJ4N7f7x1xc4MsvTTBuscB//8GgQQCV4rXx009Qty78738wbJjZt2wZzJxpRvqtPDzMCrFBQaYiTq5ctifA2td/R8aPH0/Dhg1p3749YWFhNGvWjB9++IGmTZumrXEhhBBCPJLSO4UmDBO4S/Au0o01iLeVE+/gYHLmAerUga1bYdasXaxYAb/9BosXwxP3Vi5o0sSM1o8aBStXQt++JmCPy9n5fl35M2cSjtSnVuPGjTl8+DCNGzfmypUrNGvWjBdeeIHo9MrZEUIIIcQjI10DeK31Ba11V611t/RsVwgnp8SDeCs3N/Dzg5IlI3jySahRA6pXBx8fc9zDw5Sp7N0bJkyAwEAzOv8ga135O3fg5EmTH58eChYsSGBgIOPHj0cpxeLFiylXrhzXr8u6Z0KIjNOgQQPUAx8nbtiwAaUUo0ePTvZckbj0/nkdOXIEZ2dnJk2alG5tZidaa/z9/albt67d13bt2hWlFEFBQenfsRRI7P9MVpFJrCLHsAbxtnLi7dW1KyxcCK++mvg51kWgzpyBy5fTp0qNg4MDI0eOJDAwEGdnZ06cOEHx4sWlXrwQwialFEopLBYLx48fT/S8hg0bxp77qJeunTdvXo79OQwdOpS8efMyYMCA2H1aa1atWsWbb76Jv78/uXPnxtXVlfLlyzN48GAuXryYaHuhoaEMHjwYPz8/XFxcKFKkCN27d+fMmTMJzj179iyff/45zZs3jz0/b968PPvss/z2228229+zZw+jR4+mTp06FC5cGGdnZ3x9fXnllVf4559/EpyvlGLMmDFs2bKFRYsWpeInJKwkgBc5ijWId3Q0Cz6lRe3a5s1AcDAMH2578qqDA3h7w5UrcO4c3L2btntaPfvss+zZs4e8efMSFhbGY489xp9//pk+jQshHiqOjo5orfn6669tHj969CgbN27E0dH2tLZvv/2WgwcPZmQXRTrYtm0by5cv580338Td3T12f2RkJM2bN2f27Nnkz5+fHj160K9fP1xdXZk2bRpPPvmkzUGgK1euUKtWLaZNm0bp0qUZMmQI1atXJyAggKpVq3LixIl453/++ecMHDiQw4cP07BhQ4YOHUrTpk3ZvHkz7du3Z+jQoQnu0bdvX8aMGUNkZCQvvPACQ4YMoXLlyvz000/UqFGDxYsXJ7imTZs2VKxYkZEjR0p55TRI1SRWpZQL8DTgC7jYOkdrLXnwIkM4OppUmPPnTeUYT8+0tffPPyaVJigIPv8cCheOf9yaUnPrlplM6+trJtCmVcWKFTl06BD16tXj4MGDPP/88/zyyy88//zzaW9cCPHQKFiwIIULFyYgIICxY8cmCNTnzp2L1ppWrVrx+++/J7i+ePHimdRTkRZffPEFFouF1157Ld5+BwcHxo8fT//+/cmdO3fs/piYGPr378+sWbN47733WLlyZbzr3nvvPY4cOcKQIUOYPHly7P7p06czaNAg+vfvz6pVq2L3V69enQ0bNlC/fv147Rw8eJCaNWsyZcoUOnfuTNWqVWOPde7cme+//54yZcrEu+aHH37g1VdfpVevXrRs2RLnuBUjgC5duvDOO++wdu1amjRpYudPSkAqRuCVUt2Bs8BG4Ecg4IFt3r2vQmQYBwcoUuT+QkxpeRPfrh1Mnw579piJrsuX2z7Pze3+iH165cXny5eP//3vf3Ts2JHbt2/Tpk0bhgwZIqMSQoh4evXqxYULF1i2bFm8/VFRUcyfP5/atWtTqVIlm9emNU/7zp07zJgxgxYtWlCiRAlcXFzIkycPTZo0SRA0Wvn5+eHn58fNmzd56623KF68OC4uLpQpU4ZPPvnE7t9xoaGhvPvuu1SsWBE3Nzd8fHxo3Lgxqx9YkrtBgwZ062am4XXr1i02rSiludPz5s2jffv2lCpVCjc3N7y9valTpw7ff/+9Xf211/Xr11m0aBG1a9emWLFi8Y45OTkxcuTIeME7gMVi4cMPPwRgy5Yt8Y5FRETw3Xff4eHhwZgxY+Ide+ONN/Dz8yMwMDDeKPwLL7yQIHgHM9jUsWNHwOSBx/Xmm28mCN7BBPZly5blypUr7Nu3L8Hxl19+GSDRT5WSEhMTw+TJk6lQoQKurq4ULVqUIUOGJDqf7MyZM7zxxhuUKlUqNi2odevW7Nq1y+b5Fy9epEePHhQsWBA3Nzf8/f2ZP3++3f3MaHYF8EqpZsBc4DwwHFDAEmAksObe44VA9/TtphAJWSxQqJAp9xgebiadRkWZYD462mwxMfe/v3vXHL9zJ2HA/8ILZlJriRJmkmtiqXnOzumfF+/i4sKCBQsYO3YsWmumTp1KhQoVCA0NTXvjQoiHwiuvvIKHhwdz586Nt/+PP/7g4sWL9OrVK8PuHRoayqBBgwgPD+fZZ59l6NChtG7dmv/973+0aNEiQZ+soqKieO655/j1119p3rw5PXv25NatW7zzzjuMHTs2xfcPDg6matWqTJw4kfz589O3b186duzIwYMHadasGXPmzIk9t2vXrrRp0wYwqRqjRo2K3XLlypXsvfr160dQUBD16tVj8ODBvPzyywQHB/Paa6/xwQcfpLjP9tq0aRN37tzhmWeeses668j2g5/KbN++nVu3blGnTh28vLziHbNYLDz33HMArF+/PkX3cXJysnmf1F5TokQJfH19+fPPP+1+MzdkyBDGjRtH/fr1GTRoEPny5WPq1Kk0atSI2w/kwv7zzz/4+/szc+ZMypcvz5tvvsnzzz/Ppk2beOaZZ1ixYkW8869cuULt2rX55ptvKFeuHIMHD8bf35++ffsyZcoUu/qZ4bTWKd4wQXoI4HXvcQzwYZzjPYAo4Bl72s1uW7ly5bTIOWJitA4N1frMGa1PndI6MHC9PnFC6+PH728nTmgdFKR1cLDZDhzQ+vRprc+ejb8FBWn9wQdaHz1qHp88mfCcs2fNvQ4dMl/v3k2/57JgwQLt6OioAe3q6qoDAwPTr/EcYP369VndBZEG6fn6HThwIN3ayskA7evrq7XWukePHtrBwUGfPn069njTpk21t7e3joiI0CNHjtSADggIiNdG/fr1tflzf9/69es1oEeNGqW11vr69euJnnv79u1497S6du2arlSpks6dO7e+efNmvGMlSpTQgG7evHm8YxcvXtQ+Pj7ax8dH37lzJ0U/g/r162ullF6wYEG8/VevXtVPPvmkdnV11RcuXIjdHxAQYPPnkBLHjh1LsC8yMlI3atRIOzo66jNnziTo24M/r9R4++23NaAXLVpk13UTJ07UgG7fvn28/TNmzNCAfuONN2xeN2nSJA3oESNGJHuPsLAwXbBgQa2USvH/yx07dsT+272byB/Jtm3bakD/999/KWqzS5cuGtB58+bVQUFBsfujo6P1Cy+8oAE9duzY2P1RUVG6dOnS2sXFRW/YsCFeW2fPntVFihTRhQoV0rdv347d36tXLw3owYMHxzt/165dsX+brf9nUiKlPy9gt7YzVrU3heYpYKnWOm4CQWwbWuuvga2YEXkhMoVSkDu3yU0vVsyMkpcsCaVK3d9KljSj68WLm83X14zax8TEb8vJydSHd3c3Oe/Nm8NXXyUcaY+bF3/6dMJ68qn18ssvs3v3bgoUKMDt27dp2rQpI0aMSJ/GhXiINGiQcJs50xy7edP2cWtRksuXbR//+Wdz/PRp28eXLjXHDx+2fdw6D33Pngx4wpg0mujoaL755hvAjEyvWbOGzp07x5v0mN5cXFwoWrRogv0+Pj50796dq1evJpqOMH36dNzirMBXoEAB2rRpQ1hYGIcPH0723nv37mXjxo20b98+Nu3CKleuXIwZM4bbt2/z66+/2vmsbCtdunSCfc7OzgwYMIC7d++ydu3adLnPg06dOgVA4QcnYSVh165djBkzBi8vrwSfDoSFhQHmNbLFuv/atWtJ3kNrTc+ePbl48SL9+vWjYsWKyfbr6tWrsXn8kydPxsHBweZ5hQoVAu4/95QaNGgQJUqUiH1ssViYNGkSFosl9v8GwPLlyzl+/DhvvvlmgtSgIkWKMGLECC5cuBD7mkZFRfHDDz/g5eWVoFRktWrV6Ny5s139zGj2TmL1wKTPWN0GvB84ZzeSQiOyOe97/2rPnTOTYC023srevWuC/3HjYP9+mDQp/kqwYAL927dNXnzRogmPp8aTTz7Jvn37aNOmDTt27GDq1Kn4+/vTqVOntDcuhMixatSoweOPP84333zD+++/z9y5c4mJicnQ9Bmr//77j0mTJrFp0ybOnz+fIFXh7NmzCa7x8fGxmR9tzfG+evVqsvfdvn07YAJSW/W3Q0JCANKtys6pU6f45JNPWLt2LadOneLWAzWLbT3P9HDlyhWABHnuiTly5AjPP/88UVFR/PTTT5QqVcqu++l7o1LJzY0YNmwYCxcupG7duvEmwiYmIiKC1q1bc/ToUUaMGEGHDh0SPTdPnjwAXL582Y6eYzNPv1SpUhQrVoygoCCuXbtGrly5Yv/tBAcH2/y3c/ToUcD822nRogWHDh3i5s2b1K1b1+YbnwYNGmSrXHh7A/gLQP44j88D5R84xwew/XZLiGwkbhDv4WEmxsbl5QWzZ5vKNJ9+CkeOwNy5ZgQ/LldXk1sfHGwm1no/+JY2FQoUKMDGjRt54403mDNnDq+++ire3t60atUq7Y0L8RB4YC5dPO7uSR/Ply/p48WKJX28fPmkj/v7J34srXr16sXAgQNZtWpVbDnAKlWqZNwNgR07dtCoUSPu3r1L48aNad26Nd7e3lgsFvbs2cOSJUuIjIxMcF1iOefWnOiUrERtDWzXrFnDmjVrEj3vxo0bKXgmSTtx4gTVq1fn6tWr1K1bl+eeew4fHx8cHBwICgpi/vz5Np9nerB+SvHgGyNbjh49SsOGDQkNDeWnn36idevWhD9QWcEagFpH4h9knfCZ2Ag9wFtvvcWUKVOoV68ey5cvxyWZ8msRERG0bNmSLVu2MHToUD755JMkz7e+OXKzc+SrYMGCNvcXKlSI4OBgwsLCyJUrV+y/nYULFybZnvXfjvVnlVT72Ym9Afx/xA/YNwMvK6Xqaq03K6UqAx3unSdEtuftbdJhzp61HcQrBQMHQuXK8MYb8O678MMPCdtxcjLXWmvF585trk0LZ2dnZs2aRb58+fj4449p3bo1e/bs4Yknnkhbw0KIHOu1117j7bffpk+fPpw9eza2CklGGj9+PLdu3WL9+vU0aNAg3rGPP/6YJUuWZNi9rQHmtGnTGDhwYIbdB0y6x5UrVwgICKBr167xji1YsCBDR18LFCgA3H/DkpiDBw/SuHFjrly5wsKFC2Mn7D6ofHkTqh05csTmcevoc7ly5WweHzJkCFOnTqVhw4YsW7Ys2RSt8PBwWrZsyebNmxkxYkSywTvcf67W555SFy9ejH1+cV24cAG4/2/G+nXJkiW0bt062Xat5ye2MJa1/ezC3hz4lUAdpVSRe48/BaKBDUqpEGAv4AWMT78uCpGxvLxMTvzNm2Yk3ZZGjUx5yU8/NY9v306YF2+xmLZCQsyWHhVqrKvWPf7442itadq0KTEPJu4LIR4ZuXLl4sUXX+TMmTN4eHjwyiuvZPg9jx07Rp48eRIE7wAbN27M0HvXrFkTgM2bN6f4GmvOdUpG+OOyLobUvn37BMcy+nlaB2YOHTqU6Dn79u2jQYMGhIaG8uuvvyYavIP5ubm5ubF169YEo/MxMTGx5TcbNmwY75jWmgEDBjB16lSeffZZli9fnmzwHhYWxnPPPcfmzZsZOXJkioJ3MM/VYrHw+OOPp+h8K1uvxYkTJzh9+jR+fn6xn/zY+2+nQoUKuLu7s2fPHpufXDxYQjOr2RvAz8Is3nQZQGt9AGiMCewvA6uB5lrrFYm2IEQ25OVlPja/e9dMTLWlZEkT6EdHQ58+MHhwwtVblTI59VevwoULCSfJpoaTkxM///wzjo6OXLhwgf79+6e9USFEjjV+/HgWL15MYGBgghKBGcHPz4/Q0FD+/fffePu//vprAgMDM/Te1apVo27duvz222/xJijGtW/fPi5duhT7OG/evID9kyP9/PyAhIFaYGBgoqUyE3Po0KEkg/EHWd8c7dixw+bxPXv20LBhQ8LDw1myZEmy6ZSenp689tprREREJMj/njFjBkFBQTRt2jRe7rzWmt69ezNz5kyaN2/OH3/8kWx6y9WrV2nSpAk7duxgzJgxjB+fsvHbyMhI9uzZQ5UqVVJU3jOuadOmERwcHPs4JiaGt956i5iYmNg1AMCUES1dujRffPFFgnKRVtu3b+fmvWXdnZyc6Ny5M+Hh4Ql+Zrt37+YHWx+/ZyG7Umi01lHAxQf27QAkMVfkeO7uplJNSAiEhdlOqQEz0u7vD599BidOwNdfQ9xPAK0VasLDzRuCIkVst2OPihUr8sknnzBs2DBmzZpFjx49ePrpp9PWqBAiRypevHimrq46ePBgAgMDeeaZZ+jQoQM+Pj7s3r2bLVu28OKLL7IosYUz0smPP/5Io0aN6NGjB9OnT6dGjRrkypWLM2fO8O+//7J//362b98em4pRq1Yt3N3dmTp1KqGhobE5zW+++WaSOd/9+/cnICCAl156ifbt2+Pr68v+/ftZtWoVHTp04GdrmaIUsFZr0Sn8KLZy5cqUL1+etWvXEh0dHa9yy9WrV2ncuDGhoaE0btyY7du3x07QtIqMjOTtt9+OFwx/9NFHbNiwgcmTJ7Nnzx6qV6/OwYMHWbJkCQUKFOCLL76I18bYsWOZO3du7OJFEydOTNBPf39/2rZtG/v4hRdeYPfu3ZQuXZqYmBibk0Xbtm2L/wMTQzZs2MCdO3dsftqRnDp16uDv70/Hjh3x8fEhMDCQvXv3UrVq1XhV25ycnPjtt99o2rQpLVu2pHbt2vj7++Pu7s7p06fZtWsXJ06c4Pz587GfMnz00UesXbuWqVOnsnv3bp555hnOnz/Pzz//TIsWLfjjjz/s7m+Gsbfu5KOwSR34nC2ttahjYrQOCzN13o8ft10H/uxZrWfP1trNTevChbUODLR9zrFjppZ8CssdJyk6OlrXrFlTA7patWo6JiYm7Y1mM1IHPmeTOvDpjzh14JOTUXXgtdZ66dKlukaNGtrT01P7+PjoZ599Vm/cuDHRmuslSpTQJUqUsNnPUaNGacCufy/Xr1/XEyZM0E899ZT28PDQrq6u2s/PT7do0ULPmjVL37hxI975K1eu1DVr1tQeHh4a0IA+efJksvfZunWrbtiwoc6VK5f29PTUderU0YsXL07w87JK7Odlvac9pk6dqgG9YsWKePtPnjwZ215Sm63nd+XKFT1w4EBdvHhx7eTkpAsVKqS7detms66/tc56UluXLl3iXWOt95/UZqse/yuvvKKdnZ31xYsXU/zzsfbv+PHj+rPPPtPly5fXLi4uukiRInrQoEE6LCzM5nUXL17Ub7/9tq5UqZJ2c3PTHh4eukyZMrp9+/b6u+++01FRUfHOP3/+vO7WrZvOly+fdnV11U8++aQOCAhI9N9AUjKyDrzS6ZGo+5ApX768Tkl9WpE9bdiwwWaupr3u3IHz5yEy0ozG25qUun8/dOtmJrFu3Gi+PujWLZNKU6SIaSctgoKCePzxx7lx4wbfffcdr776atoazGbS67UTWSM9X7+DBw+mqOa0SB/h4eGZko4jEnf9+nVKly5N7dq17Z4YnJNev0uXLuHn50enTp3sTk3KaVL6e0wp9bfWupo9bdubAy/EI8PZ2eTF585t0mFszYeqXBlWrDDlJp2czMTVB98Tu7mBi4tZHCYkJG158X5+fkybNg2APn362DWxSwghRPbl7e3NmDFj+OOPP9i9e3dWdyfDfPTRRzg4ODBu3Lis7kqOZm8ZSSEeKRYL5M9vAvDz581XZ+f45+TPbzaAiRMhIgLGjImf9+7oaPLir10z1W4KF07YTkp169aNX375hcDAQF566aVsV9pKCCFE6vTp04dr164lWsowp9NaU7hwYb777ju7Vp0VCckIvBAp4O1tJrhGRydepUZrM2k1IAD69zepN3EpZVJooqMhKAjuraNhN6UU8+bNw9nZmYsXLzJp0qTUNSSEECJbcXBw4L333qNly5ZZ3ZUMoZTi7bffjjcRVqROlgfwSqkXlVKfK6U2K6WuK6W0Uur7VLQTdO9aW5sMUYo0c3U1Qbyzs0mpeTBVRin44AOzLVsGr71mzrPVjpubWfQptfXiCxUqxKhRowD48MMPuXPnTiqekRBCCCFyoiwP4IH3gTcAf+BsGtsKA8bY2D5LY7tCACYVpmhRyJPHBOe28tn79oXp02HnTnjlFdu58w4OJqXm6lU4c8aM3Ntr+PDhFCxYkNu3b9OrVy/7GxBCCCFEjpQdcuCHAGeAY0B9YH0a2rqmtR6dHp0SIjFKmZx3Z2eTF+/mlrD6TPv2Jsi/fj3xGvDWRZ9u3YLgYLNIlKtryvvh7OzMrFmzaNu2LQsXLuSzzz4jvzUZXwghhBAPLbsDeKWUO9ADM2JeFLBROA+ttW6ckva01rEBu7JVp0+IbMrHxwTxZ86YUfYHg++4K1T//LNJl+nXL2FA7+ZmSlYGB5vJrd7eKe9D69atadq0KYGBgXzwwQd89dVXqX9CQgghhMgR7ArglVJPAKuB/EBS0XZWFZd3UUq9ChQHIoB/gU1aaxtJDEKknZsb+PnB2bOmusy9xdwS2LnTBPEbNsC0aWa0PS5nZ5Oec+6cGZHPly9lq7cqpZgyZQqPP/44s2bNolOnTtSrVy+tT0uIbEFrLQM7QogcKaPXWbI3B34qJngfBfgBTlpri40tjQvHp1oh4Dtgwr2+rgOOKqXqZ1F/xCPAycnUi3d3tz25FeD//g8mT4a9e+G558wk1wdZLCYvPjwcTp60PQHWlooVK/LKK68A0Llz5wz/pSFEZnBwcCAqKiqruyGEEKkSFRWFQ0pG4lLJrpVYlVI3gWVa6w4Z0hmlGmBy4H/QWtu1xKRSahSwGfgPCAdKYSbH9gZuA7W01nuTuL73vXPJnz9/1V9++SUVz0BkBzdu3MDT0zNL7n33rtkS+z979qwbEydW5PBhb2bM+Jty5WxH6VqbCbIODmZkPrlByPDwcNq3b09UVBSDBg3KsSW6svK1E2mXnq+fq6srvr6+5MqVK13aE0mLjo7O0GBDZCx5/bKfa9eucfbsWW7fvp3suQ0bNrR7JVZ7A/hLwHyt9Vv23MSO9huQygA+iTY/A4YBv2ut26XkmvLly+vDhw+nx+1FFkjP5dxTIzQULl0yE1QtNj7jioqCzZuhUSPz+M6dxBd1unXL5NcXKpR8bvzEiRN59913cXd35/Lly7i5uaXtiWSBrH7tRNqk5+sXGRnJqVOnyJ07N97e3jg5OUk6TQYKDw/Hy8srq7shUklev+xBa01UVBTXr1/n6tWrFC9eHBcXl2SvU0rZHcDbO4l1HVDDzmuy2leYAF4Sg0WmyJPnfj67h0fC0Xgnp/vB++7dZtGnGTOgevWEbbm5mQD+3DnzOKkg/q233mLy5MmEhIQwYcIExo8fnz5PSIgs4OLiQvHixQkNDSUoKIhoW/VYRbq5ffs2rvaUwRLZirx+2YeDgwNeXl4pDt5Ty94A/j1gp1LqHeATnTOSbS/d++qRpb0QjxRvbxO4nz0LLi4Jy0zGPc/ZGV56CcaOhddfT5gu4+Bg3ghcuGAq3SQ2Wu/g4MAnn3xC9+7d+eqrrxg5cmSOHIUXwsrFxYXChQvLkuuZYMOGDVSpUiWruyFSSV6/R49dk1i11ieAZ4C3gGNKqV+VUt/Y2L7OkN6mTq17X09kaS/EI8fDA4oXNykyiaXAlSsHy5dDvXrw3nvw1lsmbeZB1lz4CxeSXrm1a9euVK1alStXrjB79uz0eSJCCCGEyFbsCuCVUkWBJUBuoCTQDuiayJbulFJOSqkKSqnSD+yvpJTKY+P8EsCMew+/z4g+CZEUV1coUcIE4IlVqPHxgfnzYdAgWLDAlJtMrK1bt8zqrYlRSjFq1CgAJkyYwC1b7waEEEIIkaPZm0IzFSgHfAPMB84BqVgE/j6lVFug7b2Hhe59raWUmnfv+8ta6+H3vvcFDgLBmDKWVi8B7yil1gMnMVVoSgMtAVdgBfBZWvopRGo5O5uR+KtXzeRWW2kwFguMGGEWf7J+CvrPP1CqFMQtwuHpadpwczObLc2bN8fR0ZGQkBDmzJnDwIEDM+R5CSGEECJr2BvANwICtdY907EP/kCXB/aVureBCdaHk7T1QHmgCiZlxgO4BmzB1IX/Lofk64uHlFJmcqu7O5w/DzdumBSbB/Pdn37afL1zB3r1MhNYx42DVq3MuUqZwP38+fsj+w9ydHSkSZMmrFq1io8++ojevXvL5CYhhBDiIWLvQk4WYF96dkBrPVprrZLY/OKcG/Tgvnv7N2qtX9FaV9Ba59JaO2mt82utn9VafyvBu8gurCk1uXPD9eumZrwtzs4mraZQIejbFwYOvJ9+4+RkasRfumT7WoAxY8YAcPHiRebMmZPOz0IIIYQQWcneAH4HUDkjOiLEo8Jigfz5TSAfGZn4BNfKlc2KrYMGwW+/wTff3D/m7g5hYeZNgC1PP/00xYoVA0x9+JQsJCGEEEKInMHeAH4k0EAp9XJGdEaIR4m7uwniLRaIiLB9jqOjqUzTpAkcORL/mIeHGYW3VR5bKRWb+37u3Dnmzp2bzr0XQgghRFaxNwe+JWYxpx+UUn2Bv4EwG+dprfW4tHZOiIedszMUK2YC8bAw8PJKmBevFMyeberJx+XgYNJqQkPNiP6DevTogYeHB/3792fixIn07NlTcuGFEEKIh4C9AfzoON/XI/HVTTUgAbwQKeDgYHLdXVxMIO/ubkbe47IG7wcPwsaNJi8ezLlXrpgFoR4M8HPnzk2fPn346quv+Pfff/n6668ZMGBAxj8hIYQQQmQoewP4hhnSCyEecdYqNa6ucO6cqULj5pZwNH7hQpg1C8qUMWk1SplR/EuXzEj+g0JCQmJH3SdNmkS/fv2wWOzNnBNCCCFEdmJXAK+13phRHRFCmBH1kiXh8mVTN97NzVSdsRoxAjZtgqFD4c8/oUABE/Rfv27y6D084reXJ08eTp48ibu7O8HBwaxdu5Znn302c5+UEEIIIdKVvSux2hjjs3le89R1Rwjh4AAFC5oJrtHRpma8tYSkqyvMnGmC9aFDTTlJMIH+xYv3H1s5OTnx6quvEhkZCcDXX3+dic9ECCGEEBnB3s/SVymlfJI6QSnVBPg19V0SQoAJyv38TGpNePj9ajPlysEHH8D69SalBswo/d27ZiLsg7p160b0vYsXL17MlStXMucJCCGEECJD2BvAlwb+UEo52zqolKoL/I5ZBVUIkUYWC+TLB0WKmJF4qy5dYOpUaNPm/j53dwgJgaio+G08/vjj1KtXDycnJ+7cucMPP/yQKX0XQgghRMawN4DvDjwDfP/gAaVUTWA5cBNokvauCSGsvL3N6q03b5rHSsFLL5mUGuvIvMVitsuXE17/xRdf0Lp1awDmzp2LLE4shBBC5Fx2BfBa6x+Bd4EXlVJTrfuVUlWBlUAU8KzW+kB6dlIIYWq9WyzxR9j37oW6dU15Sbi/Quu9lPdYlStX5scffyRfvnzs27eP3bt3Z17HhRBCCJGu7K4np7X+FPgCeFMpNUwp9TiwGlBAM6313nTuoxACM7m1cGG4ffv+pNZixUzA/uGH9/c5Opqc+Qc5OztTv359AGbNmpVJvRZCCCFEekttQeiBwBLgE2Aj4AK00lrvSq+OCSEScnMzI/HWfPg8eeCtt2DbNlixwuxzdTUlKB+sSAPQrFkzAL777jtuWvNxhBBCCJGjpCqA1yaB9hVgByZ4f15rvSU9OyaEsC13bpMqc+uWefzqq1CxIowda/ZZLCZ4tx6Pq2fPnuTLl487d+4wbdq0zO24EEIIIdJFkgG8UupEYhtwEFOVJgb4+oHjxzOj80I8ipSCQoXM5NXoaJMyM2YMnDkDv/9uznFysl1SEuC9994DYOLEicTYGqYXQgghRLaW3Ai8BZPbnth2G7hsY7+s1S5EBnJyMvnwERHmcZ068Ntv8PLL5rGrq8mDv3s34bU9e/bE2dmZ69ev8+WXX2Zep4UQQgiRLhyTOqi19sukfggh7OTlBR4eZlKrqyvUqGH2X79uyk4qZQJ8H58Hr/OiU6dOzJs3j1OnThETE8OtW7fw8PDI/CchhBBCCLvJSLkQOViBAnDnzv0KNNu3w9NPQ1DQ/cmstvTs2ROAb7/9lsDAQHx9fRkyZAhHjx7NnI4LIYQQItUkgBciB3Nxgbx57y/wVKKEGXVftMjkxkdGJqwJD1C7dm0qVKjAhQsXmDZtGjVq1GDGjBmUK1eOpk2bsnXr1sx9IkIIIYRIseQmsXZQSj2W2sbTer0QInl58ph0mehoKFLELOy0aJGpROPgYLsmvFKKUaNGoZQiMDCQ1atXU6lSJdq2bcu+ffvo2LEjt2/fzvwnI4QQQohkJTcC/xPwYhraT+v1QohkODiYVBrrKPxLL8Hp07Bjh0mjCQu7n2IT18svv8yRI0cYPnw4efLkYe/evfz+++9ERETQoUMHXFxcMveJCCGEECJFUpJCY+NPvxAiO/HyMuk0d+5A8+bg6QkLF5qa8Hfv2q4JD1CmTBkmTZrEmTNnmDdvHtWrV+f69etMmTKFH3/8kW+++UYWfBJCCCGymZQE8KOVUtGp2ZDgX4hMoRQULGgq0ri5wcSJ0L27OebkBNeuJX29m5sbXbp0YefOnbGlJfv27UvPnj15/fXXpV68EEIIkY0kWUYS2ETag/CgNF4vhEgBNzdTMvLmTWjX7v5+Fxe4ccPkyDs4JN9Onz59WLFiBUuXLqVs2bL8+uuvfPjhh4wfPz7jOi+EEEKIFEuuDnyDTOqHECId5MsHJ06YCaz//mvy4Pv2NcciIkx9+OQopZgzZw6VK1fm6NGj1KpViwkTJlChQgVeffXVjH0CQgghhEiWlJEU4iHi5GTKSt6+DevWwfjxcPasGYUPDbU9mdWWggULMmfOHAD++ecfqlevTt++fbmaWGF5IYQQQmQaCeCFeMh4eZl0mRdfNAH7okUmsI+MNKu0plTbtm3p1q0bkZGR3L59mx9++IHcuXNnXMeFEEIIkSISwAvxkHFxMVvhwlCrlqlGozV4eMDFixAVlfK2pk6dip+fH//++y+7d+8GYOHChSxbtiyDei+EEEKI5EgAL8RDKE8eM+L+0ktw8iT8/bcpKWmxwOXLKW/H29ub+fPno5Tio48+YsuWLUyePJkXXniB33//PcP6L4QQQojESQAvxEPI3d2MurdoAUWLwrlz9/eHhd1f9Ckl6tWrx/Dhw4mJiaFjx44EBARQtWpVXnrpJRYuXJgxT0AIIYQQiZIAXoiHkKOjyYV3cjKVaFq3vn/MzQ0uXDCValJq/Pjx1K1bl3PnztGtWzf++OMPatasycsvv8z333+f/k9ACCGEEImSAF6Ih1SuXGYVVqVMsB4aavY7OZn91scp4ezszKJFiyhevDg7duxgxIgRrFy5kgYNGnD8+PEM6b8QQgghbJMAXoiHlKuryXmPiYGXX4YOHUzgDmZC65UrJk8+pQoUKMCSJUtwc3Nj3rx5zJ07l1WrVjFixAgAFi9eTPv27Tl8+HAGPBshhBBCWKUqgFdKFVBKtVBKdVZKvW5rS++OCiHsY7GYUfjbt6FrVzh4EObONceUMiPxly6lvDY8gL+/P/PmzQNg2LBhbNiwATc3NwBCQkJYvXo1lSpVok+fPmzatIlQe4b5hRBCCJEidgXwSiknpdTXwFlgKfAtEPDANu/eVyFEFvPyMiPwzZtDkybw2WdmYScwI/QREXD+PNy6lfJAvkOHDowcOTJ2UuuxY8cA6N27N8ePH6d///4EBARQv359OnfuHHvd+PHj+f7777luTzF6IYQQQiTgaOf544BuwHHgB+A0cDe9OyWESB8uLmakPToaJkyABg3g/fch4N5bbC8vM0J/6tT9VVw9PMwk2KSMHTuWffv28ccff9CyZUu+/PJLGjZsSIECBZg+fTrvv/8+//zzT+zo/J07d5g4cSIRERG4ubnRvn17unXrRoMGDbBYJJNPCCGEsIe9AXwn4AhQRWt9KwP6I4RIZ3nymAWcihaFYcPgl1/g2jWTXqOUGYl3dTX58RcvmpH4XLkgf36ThmOLxWLhu+++o06dOuzfv5/GjRtTs2ZN3n//fVq0aEGBAgVo1qxZ7PnOzs6EhYWxa9cu5s+fz4IFC/j+++/56quv6NOnT2b8GIQQQoiHhr0BfAFgpgTvQuQcHh73v+/VC3r0AGfnhOc5OoKnpwngw8JMQF+4cOJBvLe3N1u3bmXGjBlMnjyZHTt20KpVK/z9/Rk5ciSlS5cmODiYoKAggoKCCA4OJiwsjLJlyzJy5EiuXr1K7dq10VqzZMkS/vvvP55++umM+SEIIYQQDxF7A/hTgHdGdEQIkTEcHc0CTpGRJqUGIDwctm6FOIPksZQygfyNG2ZEvlAhs88Wb29v3nvvPQYNGsSsWbP47LPP2LNnDy+99FKi/Vm/fn3s9x9//DHe3t7kz5+f48eP4+vry5w5c2jevHlanrIQQgjxULM3gJ8HDFBK+WitwzKgP0KIDJA7N5w5cz+AnzLFVKRZsQIqV7Z9jacnXL9uRuALFEg8iAfw8PBg6NChsRNYv/zyS7TWlChRAj8/v9iv3t7eHDlyhAMHDnDgwAH+++8/rly5EjuxNTQ0lBYtWtCmTRumTp2Kn59f+v4ghBBCiIeAvQH8ROBJ4E+l1Ajgb621lJQQIptzczOTVKOizNc334RFi+Cdd+CHH8DHx/Z1Xl4mXx6SD+IBXF1d6devH/369Uv0nKZNm8Z7fPr0aSZPnsyXX37JrVsmO2/58uXUrVuXYcOGcenSJbTWFCxYMKVPVwghhHio2Vv+IQp4EagK/AlcVUpF29ikMo0Q2YjFAgULmnKRYEbkx42D//0PnnrKTG69m8j/Wi8vuHoVLl/OmL4VK1aMKVOmcPz4cdq2bYuTkxN3795l+PDh1KhRg/bt21O4cGFq1arFxIkTOXr0aMZ0RAghhMgh7A3gNwObgI33via2bU7HPgoh0oGHhxlptwbxbdpAYCC0bw8hIfdLR27aZOrDx+XlZVZuDQmxb+Ene/j6+jJo0CCOHTtG7969cXZ25q+//mLLli1orfnrr7949913KVeuHNWrV48drRdCCCEeNXal0GitG2RQP4QQmSBfPjh50izuZLGY/PdPP70flF++DK+9BiVKwK+/mlKSYFJnvLwgNBTu3DETWx0cMqaPxYsXZ9asWfzf//0fmzZtYs2aNaxZs4b//vsv9pxdu3aRK1cuqlSpgpOTE61ateKNN97AI27JHSGEEOIhJSuoCPEIcXIyqTQPjrBbc9vz5jWLPJ07By+/bAL2uOd4eZkR/OBgE8hnJE9PT1q0aMGUKVPYv38/Z86cYd68efTs2ZNKlSoRFRXFzp072bJlC++88w6FCxdm+/btGdspIYQQIhtIdQCvlHJSSj2ulKqrlHpCKeWUnh0TQmQMb29TjSYyMuExpaBRI/jmGzNS37mzqUQTl7u7OS8oCG7ezJQuAybFpkuXLsyZM4f9+/dz9epVVq9ezbvvvku+fPkIDw+nTp06fPjhh5nXKSGEECIL2B3AK6W8lVJfAdeAPcAG4H/ANaXUV0qpXOnYPyFEOlPKpMBERiaez16vHsyeDQcOwNKlCY+7uJjVW0+dMhNcMyovPik+Pj48++yzfPTRRwQHB9OiRQu01owbN47XXnuNiAc/ZhBCCCEeEnYF8Eopb2Ar0Bu4i5ms+su9r1H39m+5d54QIptydTXpMkmNoDdpAmvXmlF4W6wrt166ZEbrw8NNbn1WcHd3Z/ny5cyYMQMXFxe+//57qlevzrZt27KmQ0IIIUQGsncE/l2gEvAlUEJr3UBr/cq9ya0lgC+Ax+6dJ4TIxvLkMRNZo6ISP6dMGfN1/34YNChh3rvFYvLiHR3h/HkTyIeFQXR0xvU7KQMGDGD37t1UqFCBAwcOUKdOHapWrcratWvRWfExgRBCCJEB7A3gXwB2aK0HaK2vxT2gtQ7TWr8JbAfap1P/hBAZxMEBChc2wXZ4uJmcmliMu2+fWfjp5ZdNOckHWUfjnZ3h4kUTyF+6ZNJrwsPNpNlbt0zaTkaP0leuXJldu3bx/PPPA/DPP//QpEkTChYsyPjx4yWQF0IIkePZG8AXx+S8J2UjUCylDSqlXlRKfa6U2qyUuq6U0kqp7+3sl7Wtokqpb5RS55RSkUqpIKXUVKVU7tS0J8TDzt0dSpaE4sVNnfgbN8z24Aj6K6/AF1/A3r3QogXEqegYj4ODCeRdXU3gfuWKGZk/exZOnzbVa44fN8F9Um8Y0srT05M//viD7du3U6dOHQBCQkIYN24cc+fO5e7duyxevJhz585lTAeEEEKIDGRvAH8TKJDMOfnvnZdS7wNvAP7AWTv7E0spVRr4G+gG/AVMAU4Ag4DtSqm8qW1biIeZUuDmZia2li5tar9HRsLt2/HPa9sWfvvNrNjapg38+2/ibVospk13dxPQx93c3U1wf+oUnDhhas/bqoiTHmrWrMnmzZtZtWoVVapU4c6dO/Tu3ZsiRYrQvn17fH19qV27NpMnTyYoKChjOiGEEEKkM3sD+F3AS0qpsrYO3guiO9w7L6WGAOUAb6Cfnf2JaybmzcVArXVbrfU7WutGmEC+PDAhDW0L8UhwdITcuc2IvNYJA+snn4QVK8zE1ooVU3cP6xsGLy9TzebaNZNyc/FimrufyP0UTZs2Zffu3fz0009UqFCBkJCQ2FSaXbt2MWzYMEqWLMn06dMBiIiIICQkJGM6JIQQQqSRvQH8JMAT2KWUGqeUaqSUqqiUaqiUGoMJ3D2Bz1LaoNZ6vdb6qE5DYqpSqhTwHBCEmUgb1yggAnhNKSXLNAqRAs7OUKyYSaV5cOJqwYIwZoxZFOrKFRg+3ExyTQ2LxYzIe3mZUfnISBPQZ0SevMVioWPHjhw4cIB//vmHt99+m5IlS3L37t3YcwYNGkTevHl58sknKVCgAAULFqRZs2ZMnjyZs2dT/QGhEEIIka7sCuC11muB/oAr8B6wBtgP/Al8AHgAb2it/0znfian0b2vq7XW8f70a63DMaUv3YGamdwvIXIsaxAfFZV4pZqdO02d+KZN4dVXYfv21OW1K2UCeYvFjMRn5CJRSimqVKnCxIkTOX78OLt27WL48OE8+eSTeHh4EBoayvHjxwG4dOkSgYGBDBs2jKJFi1KwYEEaNWpEt27d+PPPP2VCrBBCiCzhaO8FWutZSqmVwGtAFcAHCMMs5vS91jo4fbuYIuXvfT2SyPGjmBH6csDaTOmREA8BFxcTxJ86ZR47PbDecosWUKcOfPstzJ0LL74I1arBwoXmDUBqeHmZNwynT5tUm1y5zARbB4c0PRWblFJUq1aNatWqAaC15uLFixw7doyjR49y9OhRDh06xJ49ezh16hSXLl3i0qVLAMybN4/8+fMzcOBAhgwZgoeHfMAnhBAic6jsNIKklGoArAd+0Fq/asd1s4FeQC+t9VwbxydgPjF4T2v9cSJt9MYsREX+/Pmr/vLLL3b3X2QPN27cwNPTM6u78VDR2qTSKGU2WyIjLaxeXYgzZ9zo18+MYHfrVp1btxzw8rqLt3cU+fNH0qjRRapXD7XZxu3bN3B1vf/aaX1/RN9iMUG8xe71o9NHdHQ0ly5d4vTp0wQGBrJ582ai7n004eDgQK1atejZsydFixbFISPebeQA8n8v55LXLmeT1y9na9iw4d9a62r2XGP3CHwOZQ05En23orWeDcwGKF++vG7QoEEmdEtkhA0bNiCvX/q7eRPOnDGj8C4uts956inrd6aS7CuvmDKSV6+6EBpqKteULFmQbt1MnvvWrVCrlpk8C/DffxuoVKlBgnatE2rv3jUBfN689xeQygojRozgzp07zJo1i08//ZQzZ86wZcsWtmzZgqurK76+vtSoUYPKlStTqVIlSpYsia+vL7lz50Yl9g7oISD/93Iuee1yNnn9Hj1J/vlTStW79+1fWuvbcR4nS2u9KU09s0/Yva8+iRz3fuA8IYSd3N2hRAlT0/3WLZPekpwhQ+I/vnvXXAvw119mYah8+eD116F//8TbUcrUlgcT+F++DCEhpmKOj0/q03XSwtnZmTfffJM333yTnTt3Mn36dDZu3MjZs2c5fvx4bB59XG5ubhQpUoSiRYtSsmRJnn32WZo2bUrevFLlVgghRMolN361ATNqXRGTX259nBKZ+Rny4XtfyyVy3Fr2MrEceSFECri4mCD+/HlTNcbTM/GUGlscHc3IOYC/P8yZY/LlJ0+Gn36C7t3z89hjSbdpsZiceK0hLAxCQ8Hb24zKZ0UgD1CjRg1++OEHAC5cuMD8+fP57bff+N///kdUVBQWiwU3NzciIiJig/uNGzcyb948LBYLNWvWpGXLlrRo0YInn3zyoR6lF0IIkXbJBfBjMQH75QceZzfr7319TilliVuJRinlBdQBbgE7sqJzQjxMHBzA19eMgl+5YoL41OSlu7qaSbAtWphqNu+/D199VZquXVM2um+tXKO1Se8JDzf9yuq5pIUKFeLtt9/m7bff5ubNm6xcuZJ169bx+eefExERQd++fdm1axclSpTg+vXr/PPPP2zbto1t27YxcuRIypcvz9ChQ3nttddwS8kPQgghxCMnyQBeaz06qceZTSnlBJQGorTWsZ9Pa62PK6VWYyrNDAA+j3PZGEx5y1la64jM7K8QDyulzIqtLi5mNN7RMWVBd2Jq1ICVK2Hdur24udUgMhI+/9xUtfHzS74vbm4mPef0aShc2KTVZAfu7u60b9+e9u3bA+Dl5UWDBg04deoU69atIyYmhnLlylGrVi0cHR1Zvnw5hw8fpk+fPrz//vsMGDCA/v37kz9//ix+JkIIIbITu8bNlFLFlVLeyZzjpZQqbkebbZVS85RS84B37u2uZd2nlIq7KJQvcBDbpSD7A5eA6Uqp35VSHyul1mFWej0CjExpn4QQKePtbQJsNzczAn7rVurqwIN5E+DraxLkt2+HadNMicq2beG778wCT8ld7+lp3lCEhKS+HxmtV69ebN68mfPnz/Pll1/i6+uLi4sLc+fOJTg4mF69evHEE08QEhLC6NGjKV68OL1792b+/Pls376dK1euZPVTEEIIkcXsreFwEhgNjEvinIGYVJuU5sD7A10e2Ffq3gYQDAxPrpF7o/DV7t27GdACOA9MB8ZorW3XrRNCpImLCxQpYspMXrtmNovFpMiktuRjgwYmreb332HRInjnHfjwQ9i0ydSlT4zFYnLsQ0PNiHzBgllXdjI5BQoUoG/fvvTt25eYe0vPHjp0iDlz5uDi4kLTpk2JiIhgy5YtzJkzhzlz5sRemydPHsqWLUvRokXx8vLC09MzdvPy8qJo0aKUKlWKkiVL4pNdPo4QQgiRbuwN4BX3SzKmi3tpOaNTeG5QUvfXWp8GuqVHv4QQ9nF2hgIFTGWYsDATyMfEJDwvpcF9kSKmMk2/fvDff7BuHRQtao7NmGFSeNq1SzhxVSkTxN+4Yd5UFChg7ped54Va7v0wnnjiCfbt28fMmTP59ttviYiI4PHHH6dBgwaEhIRw9OhRjhw5QmhoKDt37mTnzp3Jtp0nTx5KlizJU089xYcffkhR6w9RCCFEjpURVZQLApJrLsQjysnJlIbMl8+kscTExN9u3jTB/d27Ju3FxSXpYF4pqFzZbGDaWLUK/vc/+PRT6NULOne+X93GysMDbt82q8g6OZkVXT09s65STUpVrlyZmTNn8vHHH/Ptt9/y008/8emnn+Lq6srbb79NdHQ0pUuXJm/evOTJk4cCBQrg4ODAjRs3uHHjBmFhYZw6dYoTJ05w8uRJQkNDCQ0N5e+//2bBggVMmDCBAQMGPLKLTQkhxMMg2QBeKfX6A7v8bewDkzJTHHgN2JcOfRNC5HBKmao1cWNFNzfIk8cE1+HhZrQ+Jiblk2AtFli6FDZsgJkzYdw4ky8/ZQo0axb/XFdXs0VHm4o5ISHmPrlymTcOzs5Jj8xHR5s3GtZ8+rjnOjrGf17pzcfHJ7bOvFXp0qUpVKgQO3fu5MyZM4CpenP+/HkAfv75Z5ydnalXrx558+ZFa83Fixc5fvw4kydP5rfffmPQoEF8//33zJ49G39//4x7AkIIITJMSkbg53G/dKQG2tzbHmT903YTU/lFCCFsslaOcXMzI/UREXDpkgnktU4+3UUpaNjQbHv2mIo1FSokfr6Dw/3yklFRcOHC/Xbc3c3IvHV12Tt3zKcEN2+acxPri1Km7z4+mZdn37t3b3r37g3AtWvX+PfffwkNvT+9Z/To0Rw6dAilFNWrV6dp06a0adOGOnXqUKdOHZYsWcIbb7zBrl27qFatGkOGDGHUqFGyBLsQQuQwKQngrTnlCvgG+B1YYuO8aOAKsF1rfS09OieEePhZJ566u8PJkyZ33Z6ylP7+8PXX5nut4ccf4aWXEk+VcXIym/X8O3fMmwetzWaxmIDfyen+6q+2xMSYEf3QUJOPn9p6+KmVK1cu6tWLvzj2P//8w549e1i9ejWrVq1i/PjxXLhwgVmzZgFQt25dDhw4wPvvv8/nn3/OZ599xuzZs3n55Zfp3r071atXl0WkhBAiB0g2gNdaz7d+r5TqAvyutf42Q3slhHjkODiYwL1kSRMYX79uRsWtI+MpsWMHjBgBixfD3LkmVSYpStl/DyuLxQTt0dFmRN/R8X4gn1UxsJubG7Vq1aJWrVqMGjWKK1eucOuWKc25ceNGWrZsyciRI/n000959dVXGTJkCFu3bmX27NnMnj2bxx57jO7du9O5c2cKFSqUNU9CCCFEsuyaxKq1bphRHRFCCDAj576+JoXl8mWTJ2+xmCDbMZnfWLVqmXSaYcOgdWtTP75EiYztr4ODCdrv3oVz58wnB9bKN1ktb968sd+XKFGCZ599lvfee4+AgACmT5/Oli1bOHDgAAEBAXz77bccOHCA4cOHM3z4cLy9vSlSpEi8rXDhwhQqVCje5uPjI6P2QgiRyTKiCo0QQqSZuzsUL27y0G/eNJNQb90yAbObW+Kj3C+8YN4AdO8Ozz4LI0dClwdXmsgAjo4mFSgyEoKCTDnNvHmTf9ORWfz8/Fi8eDGBgYEMHDiQ5s2b8/rrrzN//nwmTZrERx99xIoVK/jmm29YvXo1169f5/r16xw6dCjJdl1cXGKDeQcHBypXrkyhQoUoVqwYFSpUoGLFivHeSAghhEg7u/+0KKU8MKueNsWsjGrrw2ettS6dxr4JIQROTmaiqI+PCY7Dw03eOZgg31beeY0asGyZGY0vfm9d6KAgWLgQXnzRpOnYQ2uziNSePfDvvyZXv3VrU8LSOjnWylrdJjzcpAEVKGBWrM0ug9RNmzZl3759TJ06NXaRp6ioKNasWcPzzz9PmzZt0Fpz9epVzp07F7udPXuWixcvcuHChXhbeHg4wcHBBAcHA7Bt27YE98yfPz8VK1akYsWKVKpUiccff5zKlSuTL1++TH3uQgjxsLArgFdK5QK2AI8B1wFvIAxwBqxTzs4BUenXRSGEMKz56rlzm+D4yhWTg+7mlnCku2RJmDz5/uOdO025yalTTcWaGjXM1rRp/HSXa9dg3z4TqDs7myBdKRg61OS6lyplct0nTTJpM8NtrBNtrW4TE2OuCQkxQbyHh7lXVpdgd3Z2ZsSIEbGPFy9eTMeOHSldujRvvvkm3bp1I0+ePOTJk4fK1gL8iYiIiIgN7P/880/y5cvHhQsXOHnyJAcPHuTQoUOEhIQQEhLCpk2b4l1bsGBBKleuzJNPPkmTJk2oX78+7u7uGfKchRDiYaK0tcBxSk5W6jNgKNADU14yGrOK6jigBjADs4hTU6317XTua6YpX768Pnz4cFZ3Q6TShg0baNCgQVZ3Q6SCva9dTIypWnP5skm1SW4C6fnz8PvvsGUL7NplKtAcPGjeAHz3HXz1lRmpt6pd24zagznP19cE4gB//22C+dy5Yds2k//erp3t4Dwmxnx6cPeueezmZj5RcHPLHgtLRUVF8dtvvzF9+nS2bduGg4MDBQsW5Pjx47i6uvLLL7/w77//kjdvXjw9PfHy8iJXrlw0u1d4/+LFi0RHR/PPP//QrFkzHOO8m4qJieHMmTMcOnSIAwcO8N9//7F//37279/PjRs34vXDxcWFunXr0rRpU5o2bUrlypUlvz6TyO/NnE1ev5xNKfW31rqaXdfYGcAfAc5prRvcexwDjNZaj733uABmEafZWusP7OlIdiIBfM4mv8hyrtS+dlqb0fjLl1NezvHuXZMKU7aseTx4sKlH/8QT8OSTZuXXPHlSdv9Bg2DRIqhYEcaONYF/UqKizJsH6wJWuXObEfusHpkH2LVrF7///jtXrlzhq6++AqBfv37Mnj2bmJiY2PNy584dW4P+xRdf5Ndff4095uzsTPny5fn3338BWLZsGTExMZQrV46yZcvi4OCA1ppTp06xf/9+tm/fTmBgIH///Tdx/yb5+vrSokULWrZsSePGjaVefQaS35s5m7x+OVtmBPC3gC+11kPvPb4LfKK1HhnnnHlAHa11WXs6kp1IAJ+zyS+ynCutr11YmBlld3fP3MmjMTEm5/6jj+D0aXj+efjgAzNin5w7d8wGZnTfOjKf3URHRxMeHh67RUZGUqVKFQDWr1/P0aNH2bNnD4ULFyYiIgJ3d3c+/PBDAKpVq8bff/8N3K9f/8ILL9DlgdnFly9fZs2aNQQGBhIYGMgF64pbmDcFDRo0oEWLFjRp0oTHHntMRufTkfzezNnk9cvZUhPA2/sn7iYmbcYqDHiwWPBFzORWIYTIVD4+ZtLr6dP3J5NmBovFTGp99lmThjNjBtSsCV27Jn+ts7PZtDafAFy7ZqrX5M2buQtDJcfBwYFcuXKRy0Zx/YYNG9KwYcNEg4g1a9Zw9OhRDh48yObNm9mwYQPe3t506dIFrTU9e/akUqVKPPPMM7z44ou88soraK3Zs2cPy5cvZ/ny5ezcuZPVq1ezevVqAAoUKEDDhg1p1KgRjRo1onTp0hLQCyEeGfYG8KeBYnEeHwDqKaUctNbWwP4Z4EKCK4UQIhO4u4OfH5w5Y8pOZuZotpsbDBkCHTpAwYJm37JlEBwMDRuaFJvEYkylzPWuriaIj4iAwoVTt8hUdpM7d26qV69O9erVY0fdIyMjAQgNDWX9+vV88803gFmMqkaNGgwfPpyWLVtSpUoV3n//fUJCQli1ahWrVq1i/fr1nD9/np9//pmff/4ZgKJFi1K3bl3q1atHvXr1qFixogT0QoiHlr0B/Eagg1JKaZN78zMwHViulFoKNABqAl+may+FEMIOLi6mfOS5c6aco6OjCYzTGs9pDbdv35+M6uRke8GmuKkzCxfCn3+a9JpChUwg36wZNGli+x5KmWo11nryhQplrzKU6cXl3juTvHnzcuLECc6dO8fWrVtjt9u3TR2EgwcP8umnn9KuXTtefPFFXnvtNbTWHD58mHXr1rF+/XrWr1/PmTNnWLBgAQsWLAAgX7581K1bl2bNmvH8/7d33uFRVN0f/9xNNp0WOtJrFAFBOkgRpEuTalfsKMqrP7uvYAMbr70roohKLwYQ6cVCFxXpJYTeCRACSe7vj7ObLGE3jbSF83meeWZ35s6dO3N3ku89c+45N95I2bJl8+1aFUVRcpqsCvgxSMjI8og1/hPgeqAn0MFVZhnwfA61T1EUJVs4nSLiz5yRkJMnToivelBQ1q3aiYlSD0DRouKqY62EhzxxQkS8L3edMWPEL3/hQpg/Xyzyx46lCvjkZO+uMsHBcg1790qkndKlC05SqNygXLly9O3bl759+563fePGjUydOpWvv/6asLAwOnToQIcOHbjtttt46KGHeOihh0hOTmb9+vUsWbKExYsXs3jxYvbs2cOUKVOYMmUK999/P40bN6Z79+50795do9soiuL3ZGkSq89KjLkWqA7sAFZYa5PTP6Jgo5NY/RudzOO/5GbfJSWJS82xY5LZ1VoRxE7nhcI4KUkixSQlicB2OqFECbGMp40UEx8PBw6kuus4nem349w5aUPJkrBxI9xzDzz7rFjlfWlKd3vLlJFsrwWV3Oq/c+fOsWjRIqZOncqMGTPYu3cvR48eJTw8nOnTp3P69Gnatm1LaZffkrWW7du3M3/+fGbMmMEvv/xCfHx8Sn1Vq1alf//+9O/fn7p166qYR/9u+jvaf/5NXkxi9Yq1dhWwKifqUhRFyQ0CAiTEZESEWNTPnhXRHRcnC6QK6MBA8aUPDU1NHuVL44WGiqX/9GnYv18s9eHhviegOp0i3kHKOp0i4lu2lBCUtWpdeExYmAwmdu8WAV+qVMYDhUsJp9NJ+/btad++Pe+//z579uwh3JUC98MPP0yZ2BoVFUWrVq3o2LEjvXv3pmrVqtxzzz2cPn2auXPnMn36dGbMmMG2bdsYMWIEI0aMICoqigEDBjBgwABqebv5iqIoBZAsxTgwxrxujLkytxqjKIqSF7gFevHiMuG1enUR4RUrQrVqkqCpTBlxlcmM77zbb71KFTkuPj7Vap4e9erBnDnwyiuS/fWGG2DECO/HBQSIL/yZM+Ibf+JExvVfihhjuMJjkkF0dDS///47r7/+OlWrVuWHH37giy++SNn/+uuvs3TpUjp16sQXX3zBnj17WLhwIQ888AAlSpRgw4YNDBs2jKioKBo2bMh7773HwYMH8+PSFEVRMk1Wg5T9H/C3MWa5MWawMSaTaU4URVEKLgEBqRFgLiaZkjEi+qtUEUt5XJxMRk2PwEC46y7JDtuvn9SR3oDB3c69eyXSzsmTqZNqL0cCAwNp0qQJTz75JNHR0Rw5coQxY8YAcPz4cV5++WU6duxIiRIl6N+/Pz/88AN169bl448/Zs+ePcyePZu77rqLIkWKsGrVKh599FHKlStHz549mTJlCmfdQfoVRVEKEFkV8DcDPwP1kegze4wxE40xNxpjCkAOQUVRlPwnMFAmnVauLAOCuDixyKcntCMj4a234Kmn5PuyZfDdd76t8YUKSX27d8O2bRATI4ms8lpvnjkjIS/PnJG2eiRrxVrx+T91SrLk7twpbd21S+YNHD8u+xISUuccXOxbhYCAAEq6fJSKFCnCwYMHmTFjBgMGDGDRokXceuutKZFqEhISaNSoEV999RX79u3jxx9/pEuXLlhrmTZtGr1796ZMmTIMGDCA0aNHs2fPnotrnKIoSg6RJR94a+0PwA/GmNLAbcAdQG+gF3DIGPMd8I21dm1ON1RRFMXfCAmBChVE3MbHi2B1R7NxOr1Hw3Fb38ePh4kTYcECeOMNEfhpcfvngwj3AwdSJ90WLixuQsHBF/dWwRvWyoDk8GFZu/39z56FLVtkABMQIN+tTZ0wHBgo0XqSkmRQc/x4qmB3r42RYwMC5P4VLeo9VGdmCQ0NpVu3bnTr1o1PPvmE5cuXU6OGJAr/4YcfePDBB2nfvj19+/alY8eO9OvXj3379vHdd98xZswY/vrrr/PizdepU4dOnTrRuHFjKlWqRMWKFSlVqpROhFUUJU/J1iRWa+1+4C3gLWNMfeBOYCDwGPCoMeYva+01OdRGRVEUv8WdoCk0VET4uXNicT5+XPzYIyK8T3j93/8k8dPIkeIb/+abcP31vs/jzugKIpCPH4cjR+R7aKgIend22uxmeE1KEpedw4flOoKDpV43DodcT3KylA0L8+4O5HCkPwk3OVmWU6fkOkJDZb6Cr/oyi8PhoGnTpinfW7RowdChQ5kwYQKDBg0CoEqVKqxfv57HH3+cO+64g0OHDjF37tyUBFJ//fUXf/3113n1BgcHU7FiRSpVqkS9evVo0KABDRo0oEaNGgTk9OhJURSFHIhCY61dA6wxxjwOPAq8BtS52HoVRVEuRZxOWSIiRMDv3SsW5rSC1uGABx6AFi1gyBC47Tb48UeJVpMRbp9+N27rvNvKHRIiLjghIWIVd4fLTEpKjdCTmJgqpD33GyPHpWcVdziyP0jwPN4d3vPsWfH3d4fz9DXoySpXXnklb7zxBq+//jpr165l0aJFbN++nRDXxd15550sWLCAKlWqUKlSJW677TbCwsJwOp1s3LiRnTt3EhMTw5EjR9i8eTObN29m7ty5KfWHh4dzzTXX0KxZM3r06EGzZs1U0CuKkiNctIA3xhQB+iPuNE0BAxy/2HoVRVEudQoXFov47t0imD1Ft5s6dWD2bHGnad5ctu3efX6214zwtM5bK+c6fPh8f3VInUDrFtDu705n6vf8wN3+xETYt0/aUayYDEKympTLG8YY6tevT/369c/bftddd1GjRg127NjBjh07+O2336hTpw6LFi0CoHXr1tSvX58GDRpQqVIlihQpwrZt21i9ejWrV69m165dKZll33rrLUqWLEn37t3p2bMn7dq1I9RbhyuKomSCbAl4Y4wD6IiI9u5AMGCBeUi21sk51UBFUZRLmZAQqFQpNeNqePiFQjk4GG65RT7v2wft2kGrVvDyyzJZNiu4BXlOxZE/dUranBH798Mnn8igpWJFueZKlcSi7r7e06dlsuuhQ+L+U7euxLx3ExiY6qJz7JiUCwsT16SwsJyxynty0003cdNNN523LcEVVshaS926dVm2bBmjRo0iKSkJgCFDhjB16lQAtm7dypYtW/jll1+YMmUK27Zt48svv+TLL78kPDyc9u3b06VLF7p06UL58uVztvGKolzSZEnAG2PqALcDtwClEWv7JuAbZPJqbI63UFEU5RInMBDKlxdBeviw94yvbooXh8GDYdQomDULGjcWH/l+/bxPdM0NrIVFi+DTT6Xt334r20eMuJJSpWTi7tmzsHYttG8PgwbJgOGbb1In8bp59VW480749Vfo2/f8fRERMHYsNGp0/naHQwQ7yHl275b7VbKkWOVzWsh7Euwy+RtjeP/99wE4ffo0q1atYtmyZVx99dUAxMbGUrNmTRo0aMANN9zA2LFjUzLHTp06lVWrVjFt2jSmTZsGQN26denSpQtdu3aladOmBKZND6woiuJBVv9C/OlaHwe+AL621v6Ws01SFEW5/DBGBGhIiFjZHQ7vLjVOJzzyCHTpApMmSSKol1+Grl1FwK9bJ5bsa6/N+WytZ87AlCnw+eewcaNY/597TvZZC4cOBfPvv9L+gACIikr1Y4+MhE2bZPJrbKyElIyJSXULqlZN6ipePHXC6qRJ4NLDxMd7vx9u95qkJLHyHzyYN0Lek7CwMK677jquu+66lG2BgYH897//Zd68ebzxxhuMGDGC0qVLM2HCBJ5//nliY2OZNWsW0dHRzJ07l3Xr1rFu3TpGjhxJ0aJFueGGG+jSpQudOnWiTJkyeXMhiqL4DcZmIeiuMeZnYDQwxVqbQXoS/6VWrVp248aN+d0MJZssXLiQNm3a5HczlGygfSecOyci+PRpscZnJET37IFy5eTzvffCzJlivW7RAlq3hjZtxF0lO8THy/mDgyWc5bvvwlVXwf33Q/fuqb71AP/8s5DatdukxITPKRfv+Hix5LduLXHyixTxXTYpScoHBIh7Tl4KeV8cPXqU2bNnM23aNN555x3KlCnD6NGjmTRpEl27dqV9+/bs2LGD6OhoZs2axaZNm847vkGDBvTq1Ytbb72VypUr50ob9dnzb7T//BtjzCprbcOsHJPVOPAds9YkRVEUJas4neJSc+yYWJVDQs4Xymlxi3eAt9+G3r1h4UJxc/n5ZwlH6Q6OMneuiPnq1S/0tU9OFt/zbdskkdSyZbBqFXz0EXTuLJFwmjeXgUF6E1ovJm67N6wVv//RoyE6WtoxcKD3ibwBATJ4SUqSQdChQ6kW+fyahFusWDEGDhzIwIEDU7adO3eOf//9l+joaACuvvpqbrzxRjZs2MC2bduYNWsWs2bNYsGCBSmTYl944QVatmzJbbfdRt++fSlWrFj+XJCiKPlOlizw5x1oTBRwJRBhrf02R1uVz6gF3r9RS4T/on13IWfOiIU9vbjqvrBWxPiRI+JHnpgoYv70aYnicu214uLSoQP07y/ncfubGwO1a0vYyr59xR0mI9wW+Nzir7/kLcCCBdK+OXPketIjMVEs8k6nCPmIiPwT8mmx1rJx40Z++uknoqOjOXv2LMuWLQNg2rRpNGnShCJFijB//nzGjRvHlClTiI+PByAoKIhu3brRp08funTpQpH0XktkAn32/BvtP/8m1y3wrpNcg/i/e8bb+ta1rzUwC+hvrZ2R1boVRVGU83FHqTlyRCa4ZmSN98QY8S2vVk2+BwSIRX7FCli+XKzrAQESSQZE4L78slj/GzbMu0mx3nDHofe81jp1ZMJsTAz89FPqoOKDD2QgcscdF7rtBAaK9T0xUQYowcFynZmJnJPbGGOIiooiKiqKJ554IiWSzYkTJ+jfvz+JiYl07dqVu+++m9GjR/PJJ58wefJkvv32W+bPn8/kyZOZPHkyTqeTdu3a0atXL3r06EHprIYmUhTF78iqD3xNYDkQAHwO1AQ6W2sDXPsNsAuYZ629I+ebmzeoBd6/UUuE/6J9lz7x8RJuMjHRe7jJ/MCd7CkgANav926BT0wUv/6kJHkr4I41HxCQuiQlSUSZ5GQp447vfvZsxtc6aJDEyi9ZEh56SFxsfPnfnz0rbzWKFz8/hGVBY+PGjYwePZoxY8awb98+ChUqxJgxY+jVqxcAu3fvZsKECUyZMoWlS5eS7Arq745p36hRIxo2bEjDhg2pXbs2zgxmNOuz599o//k32bHAZ3Vqz4tAENDYWvsfYIXnTiujgd+ARl6OVRRFUS6C0FCoXFks4ydPihjNLxITIS6OlAmr8fEivuPiZDl5MnVJTpb47+XKSQz4cuXEhSckRPadOiXrYsUkBGX16nKdFSvKtrg4Efi++PJLiY5TsyYMHy4++vPmeS8bFCQW+SNHUl2TCiK1atVi5MiR7Nq1i59++okBAwYQ5XrlMHnyZHr37s3x48d5+eWX2b59O1988QVdu3bF6XSyevVqPv30U+69917q169PoUKFaNq0KQ8//DBjxoxh/fr1KdZ+RVH8k6y60LQDJltr/02nTAxwQ/abpCiKovjC4RDLcUSETHCNixMrsme21dwkIUEGDk4nlC0r7XBHeYmNhSpVRBS7rfJBQd6jwEREZHwuhyM1tOaePVKXr8yrjRvD+PHw228ykbdsWdl+9Kicy9MAbYyI+FOnYNcumQyb0yE3c4rAwEC6du1K165dz9tmjGH48OEMGzaM4OBgmjRpwk8//YQxhpUrV7Jq1SpWrlzJypUr2bJlC3/88Qd//PFHSh0RERE0aNCAJk2a0LZtW7I7H05RlPwhqwK+KJBRsiYHYqVXFEVRcgm3b7zbJeT48VQxHxgoQjcn3UPi48XqHhoqVvLQUO/158YgolAhscjv3i0TcN1JnLzRrBlMnJj6/bnnJKHU449Dz57nJ8gKD5friokREZ/T0XNyi+7du9O9e3eOHDnC0qVLWbRoERs3biQiIgJjDF999RXr16+nRYsWvPLKK9SuXZt9+/alCPoVK1YQExPD4sWLWbx4MW+++SYOhyNFzLdt25YWLVoQmlNxQBVFyXGyKuAPANUzKFMb8YNXFEVRchm35b1wYbF8nzmT6sKSlCQiOzg4exZmt2tMYqLEXne7veQHwcEyYDlwAE6ckEFKSEjGg5SbboItW2DIEPjwQ3jySejYMfW40FAZBO3cKa49hQrl/rXkFJGRkSli3pP69euza9cuPv/8c9577z0AbrjhBubMmQPAkSNHSExMZMWKFSxbtoz58+ezYsUKfvvtN3777Tdee+01QkNDuf766+nSpQtdunTJtfjziqJkj6wK+PnAQGNMLWvtBbM8jTGNEDebD3OicYqiKErmCQgQq3J4uIjvs2dFgJ84IYLeWnFLcTpl8SV+k5NlIJCUJKK9aNG8cc/JiIAAcY2JjJQ3DsePp0549TVAadcO2raVqDVvvikTXu+/H/7739QyQUFS9549Yt0vVcq3q44/MHToUIYOHcq5c+dYt24dy5Yto5BrZJKYmEiVKlUoVaoU119/PR07duT5559nwYIFGGOYP38+8+bNY+3atURHR6fEqb/yyivp0qUL3bt3p0WLFgR4vspQFCXPyWoUmlrAauAkMAy4BrgHqAu0Qia5hgB1rbUxOdzWPEOj0Pg3Ohvff9G+yz3cUV7OnBEx7wonDqRGhnETGCgW/SJFsma5z+v+S0oSP/YjR8Q33+lM/w1BYqIkgmrf3ncYyTNn5D4VLy4DhUtNp8bHx/Ppp58yf/58Fi1axIkTJwgPD+fhhx9m5MiRKeX27t3L7NmzmTlzJnPmzOHEiRMp+0qUKEG3bt3o0aMHHTp0ICw9nyYlT9C/nf5NXmRi3WiMuQn4HvjAfV5gnWt9DOjtz+JdURTlUiQgQNxFQkPFqp6cLKEdExPPD+nocBTc0IppCQiQgUbhwjIgOXRI3Id8TXYNDIQePeTzyZPw6afiWuM5SAkJkWOPH5dMuKVKiVuNt4m4/khoaCiPPfYYjz32GImJiSxevJjx48enuMj8+uuvfPDBB/Tu3Zu+ffty1113ce7cOX799Vd++uknpk6dypYtW/j666/5+uuvCQ0NpVOnTvTr149u3boRkZnZyYqiXDRZ/pNkrZ0NVAH+A4wH5gKTgf8Dqltr5+doCxVFUZQcx+EQoRoeLqLe7UbiL+I9Le7JtRUrilA/cUKs8r6YPx9GjYKhQ2Uw44kx4koTEiKRfrZtk8FBevX5I4GBgVx//fV88sknKSEqd+7cyZw5c+jbty8lSpSga9eujB49miZNmvDmm2+yadMm/vnnH1577TUaN25MfHw8U6ZMYeDAgZQqVYq+ffsyYcIETrmzgymKkitky6ZgrT1mrX3XWjvQWtvBWtvXWvu2tfZITjdQURRFUTKLW8hXqiQDkhMnvMfL794dnn5a4sc/+6y4EaUlIEBCUIaEiDV+xw5ZTpzIfPz4pKTUGPn+wMCBA9m3bx8LFy7koYceYsOGDTzzzDMEBsoLe7c//TPPPMMff/xBbGws7777Li1atCA+Pp6JEyfSr18/SpYsSbt27XjxxRf55ZdfiIuLy+crU5RLi6xOYlUURVGUAk9oqFjjT5+WyDVxcSLEPd1lHnlEtn/4ofj7P/OM97ocjtTQlefOwb598jkoSAS++w2G0ykDgYQEEe1xcalzDQIDU+P3F3S/+sDAQFq3bk3r1q15++232bt3L4GBgVhrueOOO9i2bRstWrSgX79+9OnThyFDhjBkyBB27drFxIkTGT9+PL///jvz589n/nx5Ke9wOLjmmmuoW7cuxYsXJzIyMmUdGRmJw+HAWnveEhkZSf369XFcKv5LipKDpCvgjTGtsluxtXZxdo9VFEVRlJwgLEys8Z5CPjRUBDWIaD9xAiZMkOg0kZHp1+eO4AMyf+D4cZlEa60Ic2vF2u5wpGZ9BbHE798vbSheXPz2A/3AhGaMoVy5cimfZ82axYQJExg/fjyPPvoojz32GC+++CIvvvgi5cuX57HHHmPo0KEcOHCAZcuWsXTpUpYuXcrq1atTlqxQunRpbrzxRnr06EG7du00Nr2iuMjoz8dCILvp2Qq4jUFRFEW5HDBGfP0rV5bJqwcOiPh2x5F/7TXxhY+MlCg0P/wAN9+ccejMwMDzRXhystTnbR6B2x0nORkOHxaf+pIlZUKxP1GzZk2ee+45nnvuOTZs2MCECRNo1qwZABs2bKBDhw507dqVbt260blzZ3r16gXAqVOnWL58Odu2bePw4cMcOXIkZX306FGSk5Mxxpy3bNmyhZ07d/LFF1/wxRdfEBYWRocOHXjggQfo2LFjft4GRcl3MhLwL5F9Aa8oiqIoBQZjxCIeHCy+7ImJIsAdDihdWsrMni3ZW7/4AoYNkzjymZ3YmxlPD4cjNU7//v3yvUiR7F5R/hIVFcULL7yQ8j0xMZHGjRvz3Xff8emnnxIeHk7nzp156623qFSpUkqW18xirWXdunVMnz6dadOmsWrVKqZOncrUqVPp3Lkzo0aNSpl8qyiXG+kKeGvtsDxqh6IoiqLkCUFBknU1NlYEvadA79lT3FuGDYM77oBWreDuu+GGG3K2DcaIRX7v3lTrvL9Tp04dJk2aREJCAosWLWLKlCnMnDmTokWLAjBlyhRiY2Np0qQJ9erVIziDbFnGGOrVq0e9evV44YUXiI2NZezYsYwYMYJZs2bxyy+/8PDDD/Pf//6XYv72KkNRLhKdGaIoiqJcdkREiAvLyZMX7rv+epg3D158Ef79Fz77LHXfgQM51wa3NX7PHnHduVQIDg6mQ4cOfPzxx+zYsYMirlcM48aNY8iQITRp0oRChQrRqFEjnn766UzXW758eZ5++mk2b97MvffeS1JSEu+88w41atTgk08+ISmzoYEU5RIgQwFvjGlljKmY2QqNMfWMMbdfXLMURVEUJXeJjBQh75mV1o3TCffdBytWwHvvybYDB6BRI+jdGz7/HNaulag0abEWtmwRN5wnn0y/DQEB4tKza5f3cJf+jvF4vTF+/HhiYmKYOHEiQ4cOpVChQmzYsCFl/wMPPMDrr79ORpnQS5UqxWeffcbq1atp3bo1hw8f5sEHH6RZs2asXbs2ty5FUQoUmbHALwDu9NxgjHnKGHPYR/mewOiLa5aiKIqi5C7GiO+7Mb7Fs9MJZcvK56AgEeSHDomLTdeuEBUFc+fK/jVrZH+TJtC6tVjwf/st1cr/+uviY582JrzTKb74sbHil3+pYoyhQoUK3HTTTbz++uvMnz+fqVOnApCQkMDq1at5+umniYqKIioqiuHDh3P8+HGf9V1zzTUsWLCACRMmUL58eVasWEHDhg154oknOOnt1YqiXEJkRsB7m74TAhTNyYYYY8obY74yxuwxxiQYY3YYY94xxmTasc11jPWx7MvJ9iqKoij+T2Cg+MMnJGScbKloURg8GBYvFsv8J5/ArbdCzZqyf/NmmDYN6tSBkSPh999hyRKx8p8+DdOnw6BB0LatRLrxFOvBwWK5j42VNwLeEktdygQHB7N8+XJiYmL44IMPKF++PMOGDWP06PTtgcYY+vTpw/r163n00Uex1vL2229Tu3ZtfvrppzxqvaLkPQUiCq0xphrwK1AKmAZsABoDjwKdjDEtrLW+LP5pOQ6842W7DscVRVGUCwgJESv7nj3yOaPwkSCiv1w5uPHG1G09ekCvXucni3ITFgaLFkF0tCSOevxxEfj/+1/qJNrQUPGFj4mRgUWRIiL+g4MzHwnH36lQoQKDBw9m8ODBrF27NiXKzLRp04iLi+Pmm2/2mtipUKFCvPPOO9x6663cd999rFmzhhtvvJFOnToxePBgOnfuTEBBz6ClKFmgoExi/QgR70OstT2ttU9ba68H/gfUAl7NQl3HrLXDvCxv5UbDFUVRFP+ncGFJ+ASS7Ck78yGDg72LdzeBgSLyf/5Z4s4vWSKhJD0JCUkNdXnsmIj5bdvg6NGM3xBcalxzzTWEhIQAMGbMGG677TYaNGhAdHQ01scrioYNG7J8+XJGjRpFeHg4s2fP5sYbb6Rq1aq8/PLL7NmzJy8vQVFyjXwX8MaYqkAHYAfwYZrdLwKngNuMMeF53DRFURTlMiI0VER82bLiUnPqVO64shgjFvhffoEyZbyXcTjEah8RIW8EDhy4dCe6ZoaJEyfy/fffExcXR7du3WjatClLly71WjYwMJChQ4eybds2Ro4cSdWqVYmJieG///0vFStWpFevXnz88cesWbOGxEt50oFySZPvAh643rWeY609z75grY0DlgFhQNNM1hdsjLnVGPOsMeZRY0xbY4y+N1MURVEyxBixxlepIllST54U//Wctn4bI1FwkpJg+HBYuNB32YAAsconJUkCqri4nG2LP+BwOBgwYAAbNmzgiy++YP/+/ex3vb5ITEz0apEvVaoUTz31FJs3b2bOnDncdNNNAEydOpWHHnqIBg0aULhwYa677jr+7//+j+nTp3P69Ok8vS5FyS6ZFfC5OZ2mlmu9ycf+za51zUzWVwb4FnG7eQeYD2w2xrTObgMVRVGUy4uAAChRQoR80aLimx4XJ5b5nOTMGVi6VEJW/v13+mVDQuQtwe7d4npzOYY9dzqdDBo0iE2bNtGrVy8AXnvtNVq2bMno0aM5ceLEBcc4HA5uuOEGJk6cyK5du/jggw+49dZbqV69OvHx8SxdupS33nqLHj16UKJECXr16sWYMWM4fDizU+8UJe/JrIAfZoxJci/AfwE8t6XdlwXcSaR9xYpyby+aibpGA+0QER8O1AE+BSoDs4wx9bLYNkVRFOUyJihIhHzVqlC+vPi4x8WJe01CQvouNmfPSrm4ON+RZcLD4ZtvxOp/xx0iztMjIEDKnjgBO3fC8eNS9+XmCRIUFJQymbVChQrs37+fu+++m9KlSzNgwABmz57t9biyZcsyePBgvv32WzZv3syhQ4eYOXMmL7zwAo0bNyY+Pp6pU6dy5513Urp0adq2bcvEiRN9+twrSn5hMvpRGmOy8+LQWmsz5bZijPkMuBe411r7hZf9rwHPAM9Ya0dmoy0YY94CHgemWmt7+ShzH3AfQMmSJa8dP358dk6lFABOnjxJxKWQl/wyRPvOv7lc+s9acalxL75wOFKXpKRUi7mXICps3x7O0KH1KVQokUce2UTjxkcy1Y60/8IdDnHPcUet8Yxe47k9Lf7ed9Za/v33X+bMmcOCBQu45pprGD58OAC7du2iQoUKmarn4MGD/PrrryxdupQ1a9akZHetXbs2Dz74ILVr1861a7gY/L3/Lnfatm27ylrbMCvHZCjgcxtjzJvAE8AT1tq3vez/ABgMPGSt/Tib56iOuOIcsdYWz6h8rVq1bEaZ4JSCy8KFC2nTpk1+N0PJBtp3/s3l2H/WSjbWs2fFV949+TQk5EKhfu6cWMyPHpXjwsLOL7NiBbz2miSDatYM/vkHFiyQUJXuCDkZtSUxURa3uPf8F2+tuOAULSrnDvQIJJ1R3507J9eXkCADkeTk8wcl4eGyBAVJvfkZ9vLs2bMcPXqU0qVLs2nTJmrVqkW9evW46667uOWWWyhRokSm6jl27Bjjxo1j+PDhHDhwAIC+ffsyYsQIqlWrlpuXkGUux2fvUsIYk2UBXxAmsbqVsi8f9xqutS8f+cxwwLXWSDaKoihKjmGMiNaICChVStxt0gpzN05nqjtOyZIyQdbTgt+oEUyZIuIdYNkyGDECmjeXBFJe3LsvaIvTKSI9LEwEdURE6lKokIj4fftg61aJe3/qlO/INklJ0kZ3KMsDB8Qd6MwZEfTWilgPCJDtu3dLua1bYe9eEfz5QVBQEKVLlwagdOnSfPDBBwQGBvLYY49Rrlw5evXqxY4dOzKsp2jRojz00ENs2bKF559/ntDQUCZMmMCVV17JkCFDUEOfkp8UBAG/wLXuYIw5rz3GmEJACyAe+P0izuH6c8i2i6hDURRFUS6agACJcFOunAhkXy/C77sP/vgDhgyBGTOgQwdYterizu10pgr6M2dEdG/fLpb1LVskVOWBAyLyt22T/cnJIv7Dw2VwEBwsgxanU64lMFC2uwcJISHilx8TI8fnZ+jLIkWKMHjwYFauXMm6det4+OGHWblyJUWKyPS7H3/8kVdffZU///zTp597oUKFePnll9m0aRN33nkniYmJvP/++0RFRdGyZUu++uor4i7H0EBKvpLvAt5auxWYg0w0HZxm93DEav6NtfYUgDHGaYyJcmVvTcEYU9sYE5m2fmNMJeAD19exOdx8RVEURckWhQuLRf5kOnnCy5eHp54SyzzArFk5c25jRGi7RbfDId+Tk1Mn6YaFyb7MZKb1xF1X4cIySNi+HQ4dyv+oOXXq1GHUqFHExMRQrFgxABYtWsTzzz/PNddcQ+XKlXnyySdZvXq11+PLly/P6NGjWbNmDYMGDSIiIoJly5YxaNAgypYty1133cX333/PihUrOHIk4zkMinIx5LsPPIBLjP+KZGOdBvwLNAHaIq4zza21h11lKwPbgZ3W2soedQwDnkYs+tuBOKAa0BUIAWYCvay1GdoC1Afev1FfQP9F+86/0f7LOm6Xlrg4EdPpceKECOOgIPjrL3HD8ZUIKqv8889CatdukzOVpcHaVHeaUqVE2Oenj3xa9u3bx8yZM5k0aRJz5syhefPmLFq0CEh/AuzJkyeZNGkSX331FYsXL75gf9GiRalevTo1a9akbdu2dOrUifLly+fKNeiz599kxwc+MOMiuY+1dqsxpiHwEtAJ6ALsBd4DhltrMzOUXYDElK+PuMyEA8eApUhc+G9tQRitKIqiKIoLY6B0aZl4Gh8vrii+KFxY1snJ8PDD4mdety5ceaUsDRpAVFTetDsrGCPuN0lJMlg5cUIGHk5nfrdMKFOmDHfffTd33303hw8fTkkQdejQIapWrUpUVBT9+/enf//+1KhRI+W4iIgI7rjjDu644w42b97MuHHjWLduHVu3bmXr1q0cO3aMlStXsnLlSsaNGwfA1VdfTefOnenUqRMtW7YkKKuvNxTFRYGwwBc01ALv36glwn/RvvNvtP+yT2Ki+IwbIz7mGbFlC3z2GaxfDxs3ioV74EB46y0Ryi1bimgOCxPLfpEi0KcPtGvnvb7MWuCTkyXh1MmTqVlqy5WTgURISOauNT5e2limTOqgpCBy4sQJxowZw48//siyZcsAqF+/Ph999BFNm6afHN5ay8GDB9myZQt//vkns2fPZt68eZw6dSqlTOHChenWrRs33XQTHTt2JDw8+3E29Nnzb/zWAq8oiqIolzOBgeLvvnOniPmQEJkg6ovq1eGNN+RzcrJMPnWTmCgRbU6fFpF94gRs2gTXXiv74+Jg5Uq47rrzQ0l648gRmDhRfOEHDhR3mM6dLyx3zz0wfLhEp5k/X84fecGsNCE0VAS8OwpOyZIZtyM/KFy4MI888giPPPIIu3btYsKECfz444+ULFkSgIkTJzJx4kRatmxJy5YtqVOnDgGuTjPGUKpUKUqVKkXz5s158MEHSUhIYNmyZcyaNYtZs2bxzz//MG7cOMaNG0doaCidO3emd+/e9OrVi7CwsPy8dMUPUAu8F9QC79+oJcJ/0b7zb7T/Lp6zZ0V0HzsmYjggQCzy6Yn5zOCOVx8UBOPHw9ChIpzbt4cqVcDh+Ifbb69NeLgMCJYuhe+/h9mzpU033giffCJ1zZmTatkPC4MdO2TwcdVVsHYtdO0q7b39dvjPf3wLeUjNUFu2rNRVkHzjM+LTTz/llVdeITY2FpBoNe3atUsR5BmxefNmpkyZwuTJk/njjz9StkdGRnLfffcxePDgTPvM67Pn32THAq8C3gsq4P0b/UPmv2jf+TfafzmHtali/vhx+RwefvFCHiRk5Pz5MGmShKl0B0xZu1ZE/TvvwJtvSsKnm24Sy/uVV2a+7j//hMmT4bvvxEVm6FC44w7fPu9u///gYJnk6m/G55iYGJYuXcrixYvZsWMHs2fPBmDEiBEULVqUnj17UrZs2XTr2LVrF1OnTmXs2LEsX74cgICAAPr06cOjjz5K06ZNMemMbvTZ829UwOcQKuD9G/1D5r9o3/k32n+5g7Ui5PfuFVeTzPqaZ5a4OFiwYCU33tgQY2Dq1FRXmYs514YN4lazfTssXJhxXWfPStjJ8HAZSOT0deYl1loaNWrEqlWrcDgcDBgwgKeffpo6depkeOzvv//Ou+++y4QJE0hyxd5s0KABffr0oXfv3tSqVeuCY/TZ82/8NROroiiKoig+MEZ80CtXFgt8esmfskOhQlCt2skU95WePaFXr4sX0FFRMG4c/PRTanKn//s/cAV5uYCgILHYJyaKW87u3fkfOz67GGNYsWIF//zzD//5z3+YPn06devW5aOPPsrw2KZNm/L999+zY8cOnnnmGSIjI1m9ejXPPvssUVFRXHXVVTz33HOsXLmSZM9UvsplhQp4RVEURfEDgoKgQgXxKY+LE6Fb0DFGklUB/POPuNa0awfR0b6PCQ4WIX/6tISd9FdHAWMMV111FW+++SY7d+7kpZdeorNrBvCaNWuYPHky8fHxPo8vX748r732GrGxsUyZMoXbb7+dYsWK8e+///Laa6/RqFEjSpQoQdeuXfn222+ZN2+eZoS9jFABryiKoih+gsMhgrhiRZmU6s6aeuZMwbdWN2wIP/8sbb/vPhgyRCLk+CI8XN42HDqUd23MLSIjI3nhhReoUqUKAO+88w433XQTJUuWpF+/fowfP56TPlLyhoaG0rNnT8aMGcP+/fuZO3cugwcPpkKFChw9epSZM2fy1Vdf0b59e4oWLUrjxo2ZPn066iJ9aaMCXlEURVH8jLAwcampUEEmfoaGiqB3x2f3trgjvlwMyckyUEhMFJ/1hARZMuvJUb06TJsmE1unToXHH0+/fEQEHD6cvtD3R7788kvmzp3L7bffzuLFi+nfv/95seW3b9+e4v/uidPppF27dnzwwQfs3LmTnTt38sMPP9C7d28aNWqEw+FgxYoV9OjRgzZt2rBixYq8vCwlDymAkVcVRVEURcmIgIDUiC1Fisg6OVnEdXKyiHVrUz+fOiUWe2slIkxmEkaBCHa3+Hc6xS3G4ZDPDodsP31ayhkj7QoK8h0xx+mEJ56A669Pbff+/dLuK644v6wxIuL37pU6/XliqyeBgYG0a9eOdu3a8f7777Ns2TKOHTsGQFJSEvXr18daS/Pmzbnuuuvo0KED11577XmRaIwxVKxYkYoVK1K6dGnatGnD6dOn+eqrrxg2bBiLFy+mcePG3Hzzzbz66qtUrlw5fy5WyRXUAq8oiqIolwgOR6rQDQ1NjddeqJBkPq1WTTKnOp0i5j0t82mt6GfOSJlz56B0aTm2alWJG1+pkojtsmWlPve+K64Q//Vz58Rqfu6c77Y2aCDHAYwcCa1awYgRF1rbHQ65ntjY9OvzVwICAmjVqhXdu3cHIDk5mQ8//JCBAweya9cunnvuORo1asSrr76aYV1hYWE8/PDDbNmyhaeeeorg4GDGjRtHVFQU999/P/PnzyfRHyZPKBmiAl5RFEVRLhMcDhH05cuLeC5TRgS3Malx50+eFOHunjRbpYpYytPLlmqMDArcISCrVhVf9+RkqSsj//zHH4cuXeCDD6B5c/jyS2mPG7flf8+ezLvr+CtOp5NbbrmFTz75hL///ptDhw7x8ccf07t3b0DCTD7yyCOsX7/eZx1FixZl5MiRbNy4kVtuuYWEhAQ+++wz2rVrR7ly5bj//vv55ZdfOHcpjoguE1TAK4qiKMplSGCgiPeSJUUgV60qPupVqsjn8uUvLjuq20+/TBmx5p865dsHv3x5eP99yfxauzb897/w1FPnlwkNFVG/d+/54v5Sp3jx4jzwwANcddVVgESw+eyzz6hduzb169fnhRdeYP369V4nrVaqVImxY8fy119/8eyzz1KjRg0OHjzIZ599RocOHShTpgy33XYbP/zwA0ePHs3rS1MuAk3k5AVN5OTfaEIL/0X7zr/R/vNfcrvvkpLg6FGJKBMcnL7/vbXw229Qv76I9rTEx0t9JUpItljHZWiKPHjwIN988w3Tp09n6dKlFClShEOHDuFwOPj9999JTk6mQoUKlC1blkCPVyfWWv766y8mTpzIhAkT2LBhQ8q+gIAAWrZsSdeuXenVqxfVq1fPj0u7LNFEToqiKIqiFDgCAkRwV6kiFv30klEZI240oaHifvPjj+fvDw0VV53DhyXh06lTud78AkfJkiV5/PHHWbRoEQcPHuSVV17B4XBgraVfv360aNGCihUrEhwcTPny5RkyZAggE1+3b99Oly5dWLZsGf/++y9vvfUWbdq0wRjDokWLePLJJ6lRowbdunVj3rx5Go6ygKJRaBRFURRFyROCg8U3/sgRscaHhor7ji9Gj4bXX5fP/funbjdGRHxiIuzaJX79pUunX9elSmRkZIp7DUB0dDSxsbHExsaya9cuYmNjqVSpEiARbvr168dZlw9SyZIladu2LU8//TRNmjRhzpw5TJ8+nYkTJxIdHU10dDR169Zl6NChDBw4kODMhi5Sch0V8IqiKIqi5BnuZFTh4an+7OHh3ss+9BAsWwZPPy1++Y0anb/f7ccfHw8xMeJLfzlrTGMMderUoU6dOj73//nnn2zevJnNmzezbt065syZQ6NGjejYsSM33HADq1at4rvvvuPPP//ks88+Y926ddx11108/fTT3HLLLfTr14/GjRufF9JSyXtUwCuKoiiKkueEhko4ykOHxD8+JEQi33gSGAiffALdusG990J09IWx4t11JSSkinhvvvMKOBwOoqKiiIqKStlmrU2xyK9du5b//e9/nDt3jqCgIBo3bkzTpk3ZvHkz69evZ9SoUYwaNYqKFSvSp08f+vbtS5MmTVTM5wPqA68oiqIoSrokJCbkSr0BAeL64vLwIC4ODp06zKr9vzNh0zckJCVQrJi40sTHiyXeF8HBMgCIiREfeyVzGGNSXGPatm3L4cOHiY6OZsiQIcTHxzN9+nQmTZrEsmXL6NmzJ4ULFyYmJoZRo0bRrFkzqlatyjvvvENcXFw+X8nlhVrgFUVRFEVJl8EzBzNn6xxaVWrFdRWv47pK13FliSuzbXm11rLz+E7W7V/HX/v/YuPhjWw+vJmNhzdx9MyRlHLXlGxEjWJXUrOmiPiMkom6s8PGxkqCqcKFs9W8y5pChQrRpUsXunTpAsDRo0cpWrQoxhiioqKYOnUqAEFBQTgcDnbs2MHQoUMZNmwYDzzwAI888ghXeHtNouQoKuAVRVEURUmXdfvXsevELr776zu+++s7AEqElaBlxZb0iurFgKsHEBQQ5PP4xOREpvw7hQU7FrBu/zrW7V9H3FnvFtuIoAiqFK5BhbCaJCamDhCaN5d1cjJ8+incfrt33/mAAJnUumePTHK9XENN5hTFihVL+TxixAgee+wxFixYwPz585k3bx7x8fFUr16dJUuW8Prrr/P6669TqVIlevXqxT333MNVV12lLja5gMaB94LGgfdvNBa1/6J9599o//kvGfVdsk3mnwP/sHjnYpbELGHxzsXsPbk3ZX+FwhV4vNnj3NPgHsKDUlV1XEIcX675kv/9/j9ijsecV2fp8NLULV2XOqXqcGXJK6lZvCY1ImtQJqIMxhji42HnThHjngJ81Sro1UvixH/7rW8ru7XiShMYKMmqChXKflKqgk5+PnsnT54kIiKCP/74g/vuu49169adt/+qq65izpw5XHHFFcTHxxOqExQuIDtx4NUCryiKoihKujiMgzql61CndB0GNx6MtZZtR7cxd9tc3v3jXf499C+P/fwYLy9+mUcaP8JNV93E2HVj+WTlJxxPOA5AzeI1ufuau2lQtgF1S9eldETpdM8ZGipuMHv2nC++r70WPv5YItQMGADffQceRuIUjJHjEhMl2s2hQyLkIyIuXSGfH0RERADQpEkT/vzzT7Zt28b777/PmDFjOHr0KOvXr6dixYq0adOGX3/9ldatW9OvXz969uxJZGRkPrfef1ELvBfUAu/fqBXQf9G+82+0//yXi+m7ZJvMjI0zGLF0BH/s/uOC/ddVvI4nmj9Bt5rdcJis+7IcPChx4wsVOn/7nDlw//1QvTr88AMUL55+PYmJMhHW6RTB73SKu43n4q8UxGfv3LlzzJo1i2+++YYZM2akRLpxOBwkJycTGBjI9ddfz6uvvkrDhlkyPl9yaCZWRVEURVHyFIdx0COqB78N+o2FdyykU/VOhDnD6HtVX34f9DuL71pM91rdsyXeITVmfHz8+ds7dICvvxYL/ZYtGdcTGCiDgIAAyeK6Z49ErNmxQ47fskWSQh0+LNldz571nS1WyRin00n37t2ZOHEie/fu5cMPP6R+/fokJycDkJiYyJIlS/j1119JSkpi1apVTJw4kTNnzuRzy/0DdaFRFEVRFOWiMcbQunJrWlduncP1Qtmy4g+fkHB+oqbWreH331Ot88ePQ5Ei6dfndHrP2GotnDsnMemTkmSbwyE+9iVK+LeFPr+JjIzkoYce4sEHH2T58uV89NFH/Pjjj8THx/Poo4/y5ptvUqpUKVavXk2RIkXo27cvt956K9dddx0OnYHsFb0riqIoiqIUaAICJEHTuXPiCuOJW7xPmQItWsgk1+xgjMSRDwuTOgsVks9xcWKlP336oi5BQQZ5TZo0YcyYMezevZs333yTatWqERsby+rVqwEJT/ntt9/Spk0bWrVqlc8tLriogFcURVEUpcATFCRZWE+f9u7a0qCBWN/79YO5c3PmnMaIiA8MFHebgwcljOXFkpQkg5HLmeLFi/PEE0+wadMmFi5cyJ133kl4eDgHDx4kISGBoKAgnE4nW7ZsISkpiT59+vDVV19pwigXKuAVRVEURfELwsMlkoy3TKuVKsHUqVCjBtx9N/z4Y86d1+kUi/zRo+LKc+aMiPCEBBlQHD8u4n7PHjhwQMrFxYkvfXy8rI8ehd27Yds22LpV1tu3y7Fp3ypcTjgcDlq3bs3o0aPZt28fo0ePplWrVpw9e5aFCxdSs2ZNbrzxRlasWMGgQYMoX748zz33HAcPHszvpucrKuAVRVEURfEbIiPFKp52UiuIuJ84UZI+/ec/IphzCmNSQ1Du2CEifOdOyfp64ACcOCGCPi5OJsLu3Svn37VLyhw+LBNjg4KkHveE2v37pa49e3y/XbhciIiI4M4772TRokX8/fff3HXXXQQGBjJr1ixiYmKoV68ederUYcSIEVSqVOmCmPOXEyrgFUVRFEXxG4yBMmVE6HqzXEdEwDffwBtviMsNwM8/i7DOCYKCZGJrRETqEh4uceuDg2UdFnb+frc/fXDw+UmpAgNTy5w5I2J/+3YZDOSEq44/U7t2bb766it27NjBU089RZEiRfjzzz9ZtmwZZcuWpXHjxpQqVQqA6dOns3Pnznxucd6iAl5RFEVRFL/C6ZQkT74s1kFBcMst8nnfPrj3XmjaFD76yLvlPr8xBkJCROgHBkqbVcgL5cqVY+TIkezatYtRo0ZRrVo19uzZw6JFi6hUqRI333wzgwYN4sorr+Sll14iviB2cC6gAl5RFEVRFL8jLEzCO3rzh/ekTBmYMQPq14dXX4VGjWSi67Ztsn/7dli4UNxiCoL7itsqHxSUKuSPHy8YbctPChUqxNChQ9m0aROzZs2ie/fuJCYm8v3333Po0CEiIiJ48cUXqV27NtOmTeNST1SqAl5RFEVRFL+keHHf/vCe1KsHY8dKqMm2bcVdJSRE9kVHi7W+RQu46iro2xdeeinnXG6yS0BAqpDfu1fE/OVujQeZ9NqpUyemTZvGtm3bePbZZ4mMjOTgwYMEBAQQFxdHz549+fPPP/O7qbmKCnhFURRFUfwStz98cnLmIrk0bgzvvw/Tp4sLDsDNN8PkyeIz36OHuOX88IP4sgMsWwaHDl18W8+dg02bUt8YnDoFR45kfFxAgPjcnzwpk2Iv54g1aalUqRKvvvoqmzZt4p577iEpKYlDhw5RqlQpdrtmME+fPv2SDD2pAl5RFEVRFL/F6ZQkT2fOSJSXrBIZCU2aiBV+5EixyP/1l7iyxMfDQw9Bu3YwZ07W6j12DL77DgYPhvbtJbxl27bw22+yf+JEuPZauP9+ceFxZ3/1RXi4RLmJicnedeYUiYlyr0+elIHN7t0SSefYMRmUJCRkfC05TfHixfn8889ZunQpderU4cCBA3Tr1o3OnTtz0003UbNmTb799ttLyq1GBbyiKIqiKH5NaChUrJgqLi+WgIDUen/8EUqXhrvugscf9+1zn5gIK1bA+vXy/cABePJJ+P13sfbfdx+8+y7UrSv7mzeH224TC/8tt8gg4o030k/wFBYm65iYnLlOX3iK9KNHxX1n507YvFlCXsbESNjL48elvadOSRz82Fgpt3WrzCnIzTZ6o0WLFqxatYq33nqL8PBwZs+eTVBQEMHBwdx+++20bNkyJeOrvxOY3w1QFEVRFEW5WEJCRMTHxorl3O0Cc7FERcFPP8GoUfDhh/DrrzBrFhQtCq+8Alu2iGh1i+o+fUSo16gBCxbI2pgL661RQ3ztn3sOfvlFBgrLl8sbBRDxHBHh/TrPnpVzlisnPvI5xdmzItiPH0/d5nDIEhgoAwhv1+Krrh075A1H8eKpg6Lcxul08vjjj9OvXz8efvjhlBCT1atXZ8OGDbRo0YLt27dTpkyZvGlQLqECXlEURVGUS4KgIBHxu3eLL7vbYp0T9T79tLjSLFgg4h1g8WLxv69SBVq3hgYNZA0idGvWzLju4GDo1k0Wt3/7gQNw3XWybfBgqFr1wvY4HDK51VpxW9m2LTUOfUiIDATcg4GMSEgQ4X7smBwTHp55oe6LoCCp6/hxmRBcpozUm1dUqFCBqVOnMmnSJB555BG2bNlCYGAgAwYMoFixYgDs3LmTSpUq5V2jchAV8IqiKIqiXDIEBopP/N69YsXOCTHqplEjWdxk1S8+IwJdqszhkFCX48bB+PHQv79Y+92Rc9xl3RZ6h0ME89mz8vbBHa3G4ZDrDw8/X8xbmxqW8sQJWQIDZbJsTmKMnDsxUZJUFS4MpUqlXmduY4yhT58+tGvXjqeeeorPP/+csWPHsnz5cvr168fIkSMZNmwYTz31FIF51agcQn3gFUVRFEW5pAgIEPeSyEgR8adO+Vcc9RIl4OWXxX9+0CD4/nvxk09v8qrDIRZ4zyywISEi6PfvFxefXbtkiY2VtxTuNxWFCuWcy5E33IOD+HiJa5/XQWGKFSvGZ599xsKFC6lVqxabNm3ilVdeoWTJkjz//PO0bt2arVu35m2jLhIV8IqiKIqiXHI4HCKEq1aFIkVEyPvK3FpQKVkShg0T3/sWLbLu7+5wiIgPDxeR7hb2nkt2hbtnTPozZzJ3X93uPbt3yxuSvI5W07p1a9atW8fbb79N4cKF2bt3Lw6Hg1WrVlG7dm2ee+65lLIJCQl527gs4l/vCxRFURRFUbKA0yluG8WKSdz1Y8fEtcNzcqb7c17gjlmflCQW88yct2fP1M9r14qQv+qq3GrhhWzdKufdvFkm7W7eLAOiVatk/5AhsGSJzAWoWlXWPXpA9eoX1uWOa3/qlExyLVs25+YqZIagoCD+85//cMstt/D888/z5ZdfkpCQQGhoKGvXruXPP//kiiuuoHz58lx77bW0bduWjh070rRpU5yZnVSQB6gFXlEURVGUSx6nU8JBVqkiorFYMbEIW5saMjEuTpb4+JxLmJSQkFr3yZNyLqdTrN9nzsj2zBp7rZXQlL17w9KlOdM+T5KTxa//hRdg4MDUMJDffisi/aOPJBlV9eoSbcd9j7p0gV69RJivWAH/+5/Et0/PKh8WJoOnmBgJQZle+MzcoHTp0nz++eesWLGCFi1aEB8fz8yZM7nmmmto2bIlTZo0IT4+npEjR9KqVStKlChBdHQ0QIGIJ68WeEVRFEVRLhuCgry7oiQni4g8d04E/OnTIriTk0VsZ9VCn5wsVubgYPHHDwwU4e4ZTrFkSTnX0aMi5N0uL75CLhoDo0dL/Phbb4UnnhAf+YslOVkSWL37Lvz7r7jcVK8ubyzKlZNz3HwzVK7s/d717Hn+W4KDByWSjjFyH//+W7LgpsXplPty7Jicq1Ch1IFVXnHttdeyZMkSli1bxnfffcf48ePZuHEjGzduBKBhw4bUqFGDhIQEatWqBcDYsWN566236NWrFz179qRevXqYnJopnUnUAq8oiqIoymWPexJoRIQI60qVJFZ7uXIiQrOSlCghQcS7ux73hNK0wtwdJaZ8eXE9KVFCJqrGxfl+A3DFFTBlClx/PYwYAS1bwpEjFxcMft06eOABOfd770kyqpkz5doBKlSQkJiZ9cEvWRJq15bPn34q1vknnxShnhZ3pJpCheQex8SIa4178JQXGGNo2bIlH3/8MXv37mXGjBkMGDCA0NBQVq5cyffff8/kyZNp27Yt999/P5s2baJQoUK89NJL1K9fn6pVqzJ06FDO5eFrBLXAK4qiKIqieMEYcQsJCZFspHFxIjZ9WePdVveQELFWBwdn/lxOp1if3RNuDx1KTUiVNsJhkSLw1Vfw22/w889QrJiEp9myBapVyzhs5tGjMGmSnOexx+CaayTSTYsWOZ9w6f775Tyffy7uOW++CTfc4L1sSIgs587JRNeAAIkkVKhQ5mPaXyxBQUF069aNbt26cfLkSebOnUt0dDTR0dHExsby2WefAVCkSBFuvfVWrrjiCv7++2+WLFmS4iP/3HPPYYyhdevWNG/enPBcCIBvCoIfT0GjVq1a1v3qRPE/Fi5cSJs2bfK7GUo20L7zb7T//Bftu4yxVizIBw6kJilyT0b1jKZSsqQkerpYjwr3YODgQbGMp+fG888/CylWrA0tW8KVV0oSqGuvlSUyMrX9y5fD2LHiLpOQAE2bSpz5vMiS+vffMHSoWPeffx4efDDjY5KTxSqflCQivmhRGdDksbcKIH7va9asITo6milTprBmzZqUfdWqVeOWW26hR48eFC1alHvvvZdFixaRlJREYGAg3bp1Y+jQobRq1cpr3caYVdbahllpj1rgFUVRFEVRMsAYsZCHhUkIxLNnU7OWugV9VrKfZoTDkRr68cQJOad74qc33CEnv/9eJpu6BxUffCAuLO+9B2+8IXUOHCg+7W43l7zg6qvhp59kgqvbAp+cnP7cAocjNUKN270mKEj6ISIi8/faWhlsnT0r9Zw5I/cxKEjWAQGyeBsYGCPnMcbQoEEDGjRowAsvvMDff//Nt99+y9ixY9m6dSsvvfQSL730kpfjDT/99BN79uzhmWeeoVmzZgQFBaVkg80uKuAVRVEURVEySXCwuMfkFcaIy4zTKUmYgoO9+6I7nXD77bLEx4tf+6pVYoUH6N4dypSRdU5NErVWBgrGZM6KHxwMTz+deuxDD0HFivD44xm7G7nda5KSxL3owAG5jshIWQcEpLYnMVGWc+fkLUZ8fGr2WXfYUHfs+oz87K0VN6rixc+/71dffTWvv/46r732GgsWLOCbb75h9erVnDp1ipMnT3Lq1Cni4+NT/OKXL19Or169ABH1bdq04Y033qBhwywZ3lNQAa8oiqIoilLACQuTCbGxsSI6Q0J8lw0NhSZNZHFTpYos2cFtwXa7DLkxRkRtYqKIZM/tAQHptzExUYTxhx/CtGniKz9gQMYx4QMC5K0HiEV9924R5E6nCHa3IHfH+g8MlDovxu3m9Gl5C1KsmCyelv+AgADat29P+/btLzguKSmJ48ePs3LlSpYsWcKSJUv47bffOHv2LAsWLCAmJsb/BbwxpjzwEtAJKA7sBaYCw621R/O6HkVRFEVRlIJESEiqiD99OvcSICUknB+X3Rg5d5Eisg4MTHU9cQvj5OTzrd/HjonoDQ/3bp13OsWlp2tXGDVKYs+PGgVffnn+wCM93CFB3Zb3ixXqvnDnCzhxQiYAFy8u/vi+3JncBAQEEBkZSYcOHejQoQMAZ8+eZdWqVSxZssSnT3xmKBAC3hhTDfgVKAVMAzYAjYFHgU7GmBbW2sN5VY+iKIqiKEpBxOmUsI5794p7SE4GOHEnsAoPF9cUt2+/L/9wT9zZbN3W6YgIiT6zf7989zXYaN1alhUr4IsvwBVqnbVrpQ5v2VzTYkzGYvpiMUauwVoR8W4hX6RI1iYBBwUF0axZM5o1a3ZR7SkQAh74CBHdQ6y177s3GmNGAUOBV4EH8rAeRVEURVGUAklgoMSDd4e2vBjcmWjdLi2RkVkLf+kLY2TCbGgoHD4sgjc01PfE00aNZHHz/POwZo2EtrzjDujQIWcmCG/cCAsWwPHjspw4IetHHpFkU2vXymRgN1ddJfvKlk29rvBweeNw+LAs2RHyF0u+J3IyxlQFOgA7gA/T7H4ROAXcZoxJd4yZU/UoiqIoiqIUdBwOEZVFi4r7SHaigsfHi5U8PFz848uWzRnx7klgIJQuLZNVk5LkfJlp6+jRMuF1xw647z5xq/n+++y14cABqQdEZL/8skTnmTZNBgmHD6cm6goIkHsQHCxtHzdOkmVt2nR+ne4kXO4ByrZtkk02Pv58X3xPkpPFPenkSSnrK1lXZigIFvjrXes51trzLtdaG2eMWYYI86bAvDyoR1EURVEUpcBjDJQqJUIzLk4s3pnxAbdW3G9CQ8UdJ7MZVi+GsDCJ3uO2WqdnjQcJi/nIIxKpZv58+Oab1H1Hj8KiRdCxo++IOtaKW87XX0tW2RtukGRS1aqJFT483Pu9qlMHfvwx9XtMDEyYIFl5Af74Q8q4XYLcQt5tkfccnAQGykDAHfXGPa/A02c/u64/BUHAu7yd2ORj/2ZEeNckfeGdU/UoiqIoiqL4BW7/79Klxd88vYRPIMLx1Clx+yhePP2yOY3DIcI8PFx8+M+dy1zUmRtuOD976+zZ8MQTMmDp3l3i3Dud4A7o8txzMHGiWLoLF4Y775TwmiD3KyIi8212h7kEcbe57TZp//DhcOONqYMAt5D3JCkp1RrvdJ7/duPkycy3wRsFQcAXca2P+9jv3l40j+pRFEVRFEXxK4oVE7G7Z4+IxaCgC32yExIk9OIVV4j4zS/c1vgDB8T/3FekGl/07y9vDiZMgMmT4bvv5Jo3b5Z1tWrQty/UrQvduuVctJ7CheVcL74omWRnzIDXXpNBiTfcCaJyg4Ig4DPC/YIjG95dma/HGHMfcJ/ra4Ix5u+LPJ+Sf5QADuV3I5RsoX3n32j/+S/ad/6NR/85XFHYHQ75DCJ9DJBs4ey57HnM5xYBHrFrMkqr5Jtz57wn2Bo6NLs1ZszMmbJkjwAHJJyVPknxIsk0BUHAuy3jRXzsL5ymXK7UY639DPgMwBiz0lqbvcj6Sr6j/ee/aN/5N9p//ov2nX+j/effGGNWZvWYfI9CA2x0rWv62O+aNuDTtz2n61EURVEURVGUAktBEPALXOsOxpjz2mOMKQS0AOKB3/OoHkVRFEVRFEUpsOS7gLfWbgXmAJWBwWl2DwfCgW+stacAjDFOY0yUK+tqtuvJgM+yeBlKwUL7z3/RvvNvtP/8F+07/0b7z7/Jcv8ZWwDmMbjE+K9IFtVpwL9AE6At4vLS3Fp72FW2MrAd2GmtrZzdehRFURRFURTFHykQAh7AGFMBeAnoBBQH9gJTgeHW2iMe5SrjQ8BnpR5FURRFURRF8UcKjIBXFEVRFEVRFCVj8t0HPi8xxrxujJlnjNlljIk3xhwxxqwxxrxojCnu45jmxpiZrrKnjTHrjDGPGWNyKTS/khZjTHFjzD3GmCnGmC2uvjtujFlqjBnkZdJyZWOMTWf5Ib+u5XIkq/3ncZw+ewUAY0wfY8z7xpglxpgTrmdorI+y+uwVMLLSfx7H6LNXwDHG7EjnOduX3+1TMsYYU94Y85UxZo8xJsHVp+8YY4pl5viCEAc+LxkKrAZ+AQ4gE1ubAsOA+4wxTa21u9yFjTE9gEnAGeBH4AhwI/A/JKpN37xs/GVMX+BjxB1qARADlAZ6A18AnY0xfe2Fr5P+RNyn0qJJuvKWLPefPnsFiueBesBJIBaIysQx+uwVHLLUf/rs+RXHgXe8bD+Zx+1QsoiXOZsbgMbAo0AnY0yLDOdsWmsvmwUI8bH9VSRN2Uce2wojIj8BaOhZh+umW2BAfl/T5bAA1yP/QBxptpdBxKAFbvLYXtm17ev8brsu2eo/ffYK0IIEAaiBpHFs47r/Y32U1WevgC1Z7D999vxkAXYAO/K7Hbpku/9+dj1Pj6TZPsq1/ZOM6risXGistWd87BrvWtfw2NYHKAn8YK1NyZDlquN519cHc7yRygVYa+dba2dYa5PTbN8HfOL62ibPG6Zkimz0nz57BQhr7QJr7Wbr+u+i+BdZ7D999hQllzHGVAU6IIOwD9PsfhE4BdxmjAlPr57LzYXGFze61us8tl3vWs/2Un4xcBpobowJttYm5GbjlHQ551onetlXzhhzPxKN6DDwm7V2nZdySv7hrf/02fN/9NnzT/TZ8y+CjTG3AhUR0bcOWGytTcrfZikZ4H7O5ngxbMUZY5YhAr8pMM9XJZelgDfGPAFEAEWAhkBL5Ic/0qNYLdd6U9rjrbWJxpjtQG2gKhJvXsljjDGBwO2ur97+4dzgWjyPWQjcYa2Nyd3WKRmRTv/ps+f/6LPnn+iz51+UAb5Ns227MeYua+2i/GiQkil8PmcuNiMCvibpCPjLyoXGgyeQ1xSPIeJ9NtDBWnvQo0wR1/q4jzrc24vmQvuUzDESuBqYaa392WP7aeBl4FqgmGtpjUygbAPMy+jVlJIn+Oo/ffb8F332/Bt99vyH0UA7RMSHA3WAT5F5KLOMMfXyr2lKBuTIc3ZZCnhrbRlrrUF++L0Ra8IaY0yDLFRj3NXldPuUjDHGDAEeR2Zu3+a5z1p7wFr7X2vtamvtMdeyGBnR/gFUB+7J80YrKaTXf5k53LXWZ6+Aoc/eJY8+ewUEa+1w1/yi/dba09bav621DyCTIEOR6HqKf5Kp5+yyFPBuXD/8Kcg/l+LANx673SOgIhccKBROU07JI4wxg4F3gfVAW5vJDLvW2kQkbCFAq1xqnpIBmeg/ffYuMfTZ8xv02fN/3IEB9DkruOTIc3ZZC3g31tqdiJiobYwp4dq80bWumba8y3e3CjLxblueNFIBwBjzGPABEk+6rSuSSVZwu0npa/x8IJP9p8/epYk+ewUfffb8nwOutT5nBRefz5kLd0REXz7ygAp4T8q51u7Z2/Nd605eyrYCwoBfdSZ+3mGMeQpJJrIWEX8H0j/CK01da/0HlMdkof/02bs00Wev4KPPnv/TzLXW56zgssC17uAlk3whJGFaPPB7epVcNgLeGBNljCnjZbvDGPMqkg3rV2vtUdeuicAhYIAxpqFH+RDgFdfXj3O52YoLY8wLyKTHVUA7a+2hdMo2McYEedl+PZKNFyDdVOJKzpKV/kOfPb9Fnz2/R589P8AYU9sYE+lleyXkDSfoc1ZgsdZuBeYgE44Hp9k9HHl78o219lR69ZjLJTeH69X9m0gs261IbOLSSISEqsA+RFis9zimJ/IH7QzwA5JSujsSAmgi0E+Tm+Q+xpg7gK+RtyPv490vbIe19mtX+YVIqLOFSOpwgLqkxl59wVr7CkqekNX+cx3TE332CgSuvujp+loG6IhY95a4th2y1j7hKrsQffYKFFnpP4/y+uwVYIwxw4CnEUvudiAOqAZ0RbLmzgR6WWvP5lcblfQxxlRDshuXAqYhYVmbIJmTNwHNrbWH063jcnkOjTFXIxnkWgDlkfA8p5AbFQ28520ypDGmBfAc8loqBNgCfOUqr8kS8gDXH6sXMyi2yFrbxlV+ENALCVFYAnAC+4HfgA+stUt8VaLkPFntP4/j9NkrAGSi/3Zaayu7yuqzV8DISv95HKPPXgHGGNMaeACoT2oYyWOIe+K3wLc6yCr4GGMqAC8hLmvFgb3AVGB4ZoJzXDYCXlEURVEURVEuBS4bH3hFURRFURRFuRRQAa8oiqIoiqIofoQKeEVRFEVRFEXxI1TAK4qiKIqiKIofoQJeURRFURRFUfwIFfCKoiiKoiiK4keogFcURVEURVEUP0IFvKIoOYYxZqExRpNLZBNjzBBjzHpjTLwxxroySCv5hKsPFuZ3O7KDMeZOV/vdy+w0+4e5trfJh7Y1TNO2HXndBkXxdwLzuwGKohQsvAjws8AJYBewGpgEzMmNjIzuf+RpM0NeDhhjBgDvAmuAd4AE4Pf8bNPFcjn3ZwFiGpKhc0s+t8OTPcBw1+fH8rEdiuK3qIBXFMUX7n+wAUBRoDZwGzAIWGmMucVauynNMbcDYXnWwkuLbu61tXZPvrZEcXMlcDq/G3GRTLXWfp3fjfDE9fseBvKmIF8boyh+igp4RVG8Yq0dlnabMaY08D7QF5hrjGlorT3gcUxM3rXwkqMcpIgbpQBgrd2Q321QFEXxhvrAK4qSaay1+4EBwEKgAvCs535vPvBGuMMY86sx5qAx5owxZpcx5mdjTH9XmTau4yoBldL4x37tUVdPY8xYY8wmY8wpY8xJY8wql+/4BX/PjDFfu+qobIy53xjzl+v8+40xnxljini7TmNMeWPMe8aYza7yR4wxy40xL/go+4ExZpsxJsEYc9gYM90Y0ygz99Ttiwy0dX1PuXaPMtZ1b8sYY74wxuw2xiR5Wi+NMf2MMYuNMcddPvR/GWOeMcYEeznnDtcSYYz5n6s/4o0xa40xPV1lAo0xz3rcg63GmIczeU2Z6k9X2ShXP+1y3b/9xphxxphaXup192dVY8wjxph1rnYvTLO/ijHmYSPzCc64rvVZY4xxlevr6s9TxpgDrv4L8XK+C3zgjYfvuDGmj6ue067fyA/GmCt83W8f98qrL7ox5jpjzAxjTKzrvuwzxvxujHkxvXt/sRhjKhpj/jHGnDXG3Jr2GnLrN6MoStZQC7yiKFnCWptsjHkFaAMMNMYMtdamN3H1VeAZYDswHjgOlAUaIZb8H4EdiMvOY65j3vE4fq3H55FAMvAHsBsoAlyP+I43Qlx8vPEG0BGYAcxBxPK9QHXX8SkYYxoCPwORwGJgMuIWdBXy2v9lj7INXPVFuo6ZDJQAegJLjTG9rLUzfbTJzULX+k5E8A73US4S8Yk/6TpPMrDf1Y7XkHt8CBjnKtMZeA3oaIy5wVp7Lk19TuAXV73TgCBgIDDJGNMBeAhoAsxC/PH7Au8bYw5aa3/M4Jp2kIn+NMZ0cl2LE+mbLUB5oDfQ1RjT1lq72kv97wLXAdHATCDtfIy3kN+nu7+7I7/DIGPMEeR3NBVYAtwADEZcxR7M4Lo8echV73RgEXKv+gP1jDHXWGsTslDXebjuSzQy92Q68luPRFx6HsL3b+SiMMbUQ+5nIaCLtXZumiK5+ZtRFCUrWGt10UUXXVIWwMqfhnTLBAPnXGWreGxfmPZY4DAQC4R5qadEmu87gB3pnLeal20OYIyrLU3S7PvatT0GqOixPRAR5xZo7LE9CBloWOBmL+eqkKaOLcAZoHWacuUQ0bUXCM7kfb/g3qXtE+AbIDDNvmYe11gmTftmuPY96+U+W9f+YI/t17m2HwFWAEU99lVFJjSvycJvyWd/AsWAo8ig46o0+2ojg5DVPvpzt+fvzsv+HcAVHtuLus5zCjgIXJnmt7weEZylvNz3hWm2DXNtPwHUSbNvnGtfvyzcB3d9bTy2TXJtq5fRM5POvb/TVcedmTkv0B4ZXO/xcd5c+c2kd2900UUX34u60CiKkmWsWBcPu76WzMQh57jQSoq19lAWz7vVy7ZkxCILYmX3xkvWwz/fWpsIjHZ9bexR7kagMjDdWjvOy7l2eXztClQD3rfWLkpTbg9i9S8DtEvnkrLCWeAJV9s9udu1fsVau8+jDYnA44il/h4fdT5mPSzF1tolyACmGPCUtfaYx75twDKgjjEm4CKvBWTCc1HgRWvtes8d1tp/gM+B+saYq7wc+4a1dns6db9srd3tUd8xxJIdBnxsrf3XY18C8hYoCLFwZ5b3rLV/pdn2uWvdOG3hbBKfdkNWn5nM4HKVmYkMjJpaa/9Mp3h+/mYURXGhLjSKomQX41pnFPf9O+AR4B9jzATE3eA3a+3xLJ/QmOLA/wFdEOteeJoiF/gfu1jpZZtbjBfz2NbUtZ6VieY0c60rGWOGedlfw7W+EhFHF8sO6zFh2IMGrvX8tDustZuMMbFAFWNMUU9xBRzzNiBCLLBVgFVe9u1GXE3KuD5fDO77V8/H/avpWl+JWMg9WZ5B3d762z052Nd1gbjvZJbM/qayw3eIG9EfxpgfgQXAMmtt7EXW641HgR6I0O5urT2aTtn8/s0oiuJCBbyiKFnGNeEv0vX1YAbFhwJbEUvx064l0RgzE3jcWpup+NTGmKLIK/oqiID7Bnltn4hYch9F3CG8cczLNrcl29MyWNS1zozQKO5a982gXEQm6soM+3xsd0/E3etj/16goqvcMY/tvgZQiQA+Bljue+b02crM475/92ZQztv983Uv3KTX9py6rmPp1HNR1mZr7WRjTDfkDcrdwP0AxphVwDPW2l8upv40tEIG4/MyEO+Q/78ZRVFcqIBXFCU7tET+fuy31u5Ir6CVhE/vAu8aY0q5jh2ACN/axpjaNnMT/u5BxPtwmybEpTGmGSLgL5ZjrrUvS74nbrHSw1o7PQfOnRG+3nS421EGGSilpWyacgUFd3vqWWvXZfFYf8v2m4y46HijqLeN1tpoINoYE45MDO2GTLL9yRhTP63b0UUwCBlUv2iMCbDWXhBpSVGUgof6wCuKkiWMhGt8zvX1Aj/x9LDWHrDWTrbW9kNcPqoBV3sUScK39bK6az3Jy77WWWlHOrgzn3bOQtnrcujc2WWNa90m7Q5jTHXELWR7GveZvCK9/iwo9y8vOAqUNsZ4s0I3TO9Aa+0pa+18a+1/kKhCQWTu95lZjiGReJYAzxtj3sjBuhVFySVUwCuKkmlcFvQfELEYgwiK9MoHG2PaueNve2x3kuqC45np8jBQ0hgT6qW6Ha51mzR11UdCKOYEM1zn6W6MGZh2Z5oY39MQi/dgY0wXb5UZY5oZY3I7M+1XrvXzxpiUCcWuSYNvIX/nv8zlNvgivf4cjYjHF40xF0z6NMY40sZG92OWI2+s7vLcaCSOf4u0hV3PjLd7Vtq1ztHssNbaOKATMA/4P2PMuxkcoihKPqMuNIqieMVjYqEDec1fG3F/CUIEyS2ZiIgRCswFdhhj/gB2AiGIxe9KJNrLvx7l5yHx3GcbYxYjof3+tNbOQHze/w94xxjTFtiMTBTthsQS738x1wtgrT1rjOmLxA4fZ4y5H7EUh7ja2w7X301r7TljTG8k/nu0MeZXJMb5aSTJVSNkom1ZclhwpWnzry6r6ZPA38aYiUi4xM7I242lwJu5df4M8Nmf1trDxpg+wBTgd2PMPOAfxN2kIjLJtThy7/2d9xHx/rExph0y2bUe0Bz4CfkNe/I2UNmVRGoHEoHoWiRnwU5kEJ2jWGtPu/zuJwFDXPNcHrDW+pu7kqJcFqiAVxTFF+6Mj2eBOEQ4fIP8g5/jCt+YEaeAp5DESc2RBEdxiOX6QVKtx25eQQYLNyKWyQAkxvsMa+0eY8x1SBKelkjIyA1I8pi55ICAB7DWrjTGXIP4BXd2tTsOifn+Ypqy61zJb/6DiLC7EAG6F3FteRGJP56rWGufMsasAR5GwjM6kXv8PPC2tfZsbrfBBz77E8BaO88YUxd4AunP65Df2x7Excqbu5TfYa1db4xpj7yxuhGZ2LkEGaT05kIB/xrQC3GvaY/8ptxvvN7JxGTT7LbzjDGmFzJAuA8INsbcnclnXVGUPMTo4FpRFEVRLi1c7jmjgbustV/nb2t8Y4zZAWCtrZy/LVEU/0J94BVFURTl0mW0McYaY2bnd0PcGGMautpkgUr53R5F8UfUhUZRFEVRLj3WAsM9vmcq30IesYfz23Ysn9qhKH6LutAoiqIoiqIoih+hLjSKoiiKoiiK4keogFcURVEURVEUP0IFvKIoiqIoiqL4ESrgFUVRFEVRFMWPUAGvKIqiKIqiKH6ECnhFURRFURRF8SNUwCuKoiiKoiiKH/H/ur7UMaI2kuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 20\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "# twin1 = ax.twinx()\n",
    "\n",
    "# sIPR transect begins at 9.5 km from terminus\n",
    "ax.plot(df2020.dist_m/1000, df2020.mean_elev/1000, 'k-', linewidth=2) # surface 2020\n",
    "ax.plot(Millan_merged_df.dist_m/1000, Millan_surf_df.surface_elev/1000, 'k--') # surface 2018\n",
    "\n",
    "ax.plot(new_df.dist_m/1000, new_df.bed_elev/1000, 'g-', linewidth=2)\n",
    "# ax.plot(9.5-sIPR_df.sIPR_dist_km_proj/1000, sIPR_df.Pick_depth, 'g.-') # sIPR\n",
    "# ax.plot([30, 0],[0,0],'g-',alpha=0.5)\n",
    "\n",
    "ax.plot(Millan_merged_df.dist_m/1000, (Millan_merged_df.surface_elev - Millan_merged_df.h)/1000, 'b--') # bed elev\n",
    "# error bars on bed elev:\n",
    "ax.fill_between(Millan_merged_df.dist_m/1000, \n",
    "                (Millan_merged_df.surface_elev - Millan_merged_df.h - Millan_merged_df.h_err)/1000,\n",
    "        (Millan_merged_df.surface_elev - Millan_merged_df.h + Millan_merged_df.h_err)/1000,color='blue',alpha=0.1)\n",
    "# ax.plot(Millan_merged_df.dist_m/1000,\n",
    "#            (Millan_merged_df.surface_elev - Millan_merged_df.h - Millan_merged_df.h_err)/1000,'--',color='gray')\n",
    "# ax.plot(Millan_merged_df.dist_m/1000, \n",
    "#            (Millan_merged_df.surface_elev - Millan_merged_df.h + Millan_merged_df.h_err)/1000,'--',color='gray',)\n",
    "\n",
    "\n",
    "\n",
    "# # Millan:\n",
    "# twin1.plot(Millan_merged_df.dist_m/1000, Millan_surf_df.surface_elev, 'b--') # surface\n",
    "# twin1.plot(Millan_merged_df.dist_m/1000, Millan_merged_df.surface_elev - Millan_merged_df.h, 'k--') # bed elev\n",
    "# # error bars on bed elev:\n",
    "# twin1.plot(Millan_merged_df.dist_m/1000, \n",
    "#            Millan_merged_df.surface_elev - Millan_merged_df.h - Millan_merged_df.h_err,'--',color='gray')\n",
    "# twin1.plot(Millan_merged_df.dist_m/1000, \n",
    "#            Millan_merged_df.surface_elev - Millan_merged_df.h + Millan_merged_df.h_err,'--',color='gray',)\n",
    "\n",
    "# plot params\n",
    "ax.set_ylabel('Elevation [km a.s.l.]',fontsize=fs)\n",
    "# twin1.set_ylabel('Elevation [m a.s.l.]',fontsize=fs)\n",
    "# twin1.legend(['01/2018 surface','Millan et al. (2022) bed'],loc='lower left',fontsize=fs)\n",
    "# twin1.tick_params(axis='both', which='major', labelsize=fs)\n",
    "\n",
    "ax.legend(['06/2020 surface', '01/2018 surface','sIPR bed','Millan et al. (2022) bed'],\n",
    "          loc = 'upper right',fontsize=fs)\n",
    "ax.set_xlabel('Distance from terminus [km]',fontsize=fs)\n",
    "ax.tick_params(axis='both', which='major', labelsize=fs)\n",
    "ax.set_xlim(30,0)\n",
    "ax.set_ylim(0,2.5)\n",
    "# for axis in [ax, twin1]:\n",
    "#     axis.set_ylim(0,1200)\n",
    "ax.grid()\n",
    "\n",
    "# twin1.plot(prof_df.dist_m/1000, prof_df.mean_elev_diff,'-')\n",
    "# twin1.set_ylabel('Elevation difference [m]',fontsize=fs)\n",
    "# twin1.tick_params(axis='both', which='major', labelsize=fs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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