20240215-multi_GEM-buffering_vs_aridity.ipynb

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    "## SPEI buffering statistics vs. aridity\n",
    "This notebook will compute the change in glacial drought buffering over time, as expressed in the difference of drought frequency and severity in SPEI$_G$ versus SPEI$_N$, comparing future time slices with an early-century baseline of 2000-2020.\n",
    "\n",
    "This analysis builds on a previous notebook that plots the buffering statistics against percent glaciation.  Here, we will compute a historical aridity index for each basin and plot against that.\n",
    "\n",
    "15 Feb 2024 | EHU\n",
    "- 16 Aug 2024: check revised read-in order for basins. \n",
    "- 19 Aug 2024: Read in SPEI for all GCMs\n",
    "- 26 Aug 2024: Compute Spearman's rank correlation coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "42993dcb-71f6-428e-b44b-fa4320f9991d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.patches import Rectangle\n",
    "from scipy import stats\n",
    "import gSPEI as gSPEI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fb8ec6d-4857-4743-8b1f-36d947b61c7d",
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    "## Define the filepath -- new data is streamed from Google Drive\n",
    "# fpath = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/SPEI_computed/Results-scoats-20230901/'\n",
    "# fpath = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/SPEI_computed/Results-scoats-20231023/'\n",
    "fpath = '/Users/lizz/Desktop/SPEI-scoats-20240819/'\n",
    "\n",
    "## Settings in filenames -- slightly different model names and scenarios\n",
    "gmodels = ['GloGEM', 'PyGEM', 'OGGM']\n",
    "integration_times = np.arange(3, 28, 4) # all SPEI integration times used\n",
    "# modelnames_provis = ['BCC.BCC-CSM2-MR',\n",
    "#                     'MPI-M.MPI-ESM1-2-HR',\n",
    "#                     'MRI.MRI-ESM2-0',\n",
    "#                     'NCAR.CESM2-WACCM',\n",
    "#                     'NCC.NorESM2-MM'] \n",
    "\n",
    "## Settings in filenames -- slightly different model names and scenarios\n",
    "modelnames = ['BCC-CSM2-MR',\n",
    "              'MPI-ESM1-2-HR',\n",
    "              'MRI-ESM2-0',\n",
    "              'CESM2-WACCM',\n",
    "              'NorESM2-MM',\n",
    "              'FGOALS-f3-L',\n",
    "              'GFDL-ESM4',\n",
    "              'INM-CM4-8',\n",
    "              'INM-CM5-0',\n",
    "              'EC-Earth3',\n",
    "              'EC-Earth3-Veg']\n",
    "\n",
    "\n",
    "scenarios = ['Ssp2p6', 'Ssp4p5', 'Ssp7p0', 'Ssp8p5'] # climate scenarios\n",
    "cases = ['NRunoff', 'WRunoff', 'diff'] # inclusion of glacier runoff\n",
    "\n",
    "yrs = np.linspace(1900, 2101, num=2412) # time interval over which we have data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de2c12c8-01b9-4abc-a127-6fec25a06b2a",
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   "source": [
    "## We want to confirm that the basins are the same order Sloan used\n",
    "\n",
    "#Load in list from total Basin area data -- following Finn percent glaciation code\n",
    "from scipy.io import loadmat\n",
    "import pandas as pd\n",
    "path_to_area = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/Summary-statistics/BasinArea.mat' ## uploaded by Sloan\n",
    "BasinAreas = loadmat(path_to_area)\n",
    "#Creating indices a dataframe would use\n",
    "basin_areas = BasinAreas['BasinArea']\n",
    "basin_names = BasinAreas['BasinNam']\n",
    "basin_name_list = [name[1][0] for name in basin_names]\n",
    "\n",
    "revised_names = {'ISSYK-KUL': 'YSYK-KOL', 'LAGARFLIOT': 'LAGARFLJOT'}.get ## revise to align Sloan's names with Finn's spelling\n",
    "basin_name_list = [revised_names(n,n) for n in basin_name_list]\n",
    "\n",
    "basin_names=basin_name_list ## just to make sure to get them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39b222bc-77df-4286-a987-727a90804292",
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   "source": [
    "itime = integration_times[0] # select timescale of integration. [0] is 3 months, used in manuscript.\n",
    "scen = scenarios[1] # choose which SSP to load\n",
    "\n",
    "## Create terrible nested dictionary until we overhaul the rest of the structure\n",
    "SPEI_by_GEM = {g: {} for g in gmodels} ## outer organization by glacier model\n",
    "for g in gmodels:\n",
    "    SPEI_by_model = {m: {} for m in modelnames} # create dictionary indexed by model name\n",
    "    for m in modelnames:\n",
    "        norunoff_f_m = fpath+'NRunoff_{}_{}_{}_{}.txt'.format(g, itime, m, scen)\n",
    "        wrunoff_f_m = fpath+'WRunOff_{}_{}_{}_{}.txt'.format(g, itime, m, scen)\n",
    "        SPEI_by_model[m]['NRunoff'] = np.loadtxt(norunoff_f_m)\n",
    "        SPEI_by_model[m]['WRunoff'] = np.loadtxt(wrunoff_f_m)\n",
    "        SPEI_by_model[m]['diff'] = SPEI_by_model[m]['WRunoff'] - SPEI_by_model[m]['NRunoff']\n",
    "    SPEI_by_GEM[g] = SPEI_by_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "78a43a53-a5c0-4e47-aeb5-c46b01d4d578",
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    {
     "data": {
      "text/plain": [
       "12"
      ]
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     "execution_count": 5,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Inspect - looked like some NaNs, so we spot-check a few rows\n",
    "sum(np.isnan(SPEI_by_GEM['GloGEM']['BCC-CSM2-MR']['NRunoff'][9]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e16a530-8996-42ac-b276-9d8a706e50a7",
   "metadata": {},
   "source": [
    "In the original `SPEI-analysis.ipynb`, we use `gSPEI.sort_models_to_basins` to organize by basin.  Here we have a different subset of basins to analyze...so we're doin' it live."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98f631e3-eed9-4ac6-93ea-331ee7ac9b91",
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   "source": [
    "import pandas as pd \n",
    "\n",
    "SPEI_basin_sorted_GEM_raw = {g: {} for g in gmodels}\n",
    "for g in gmodels:\n",
    "    SPEI_by_basin = {b: {} for b in basin_name_list} # create dictionary indexed by basin name\n",
    "    for i, b in enumerate(basin_name_list):\n",
    "        SPEI_by_basin[b] = {case: {} for case in cases}\n",
    "        for case in cases:\n",
    "            tempdict = {}\n",
    "            for m in modelnames:\n",
    "                tempdict[m] = SPEI_by_GEM[g][m][case][i] # pull data from SPEI_by_model dicts into this new dict\n",
    "            SPEI_by_basin[b][case] = pd.DataFrame.from_dict(tempdict)\n",
    "    SPEI_basin_sorted_GEM_raw[g] = SPEI_by_basin\n",
    "\n",
    "SPEI_basin_sorted_GEM = {g: {b: {} for b in basin_names} for g in gmodels}\n",
    "for g in gmodels:\n",
    "    for b in basin_names:\n",
    "        for c in cases:\n",
    "            SPEI_basin_sorted_GEM[g][b][c] = SPEI_basin_sorted_GEM_raw[g][b][c].fillna(-3) # fill negative excursions that were stored as NaNs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a229d7c-bf8c-4aa2-b6d2-698dfe1edf8a",
   "metadata": {},
   "source": [
    "## Glacial drought buffering over time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e243ca0f-4ee1-4de1-978f-555ea62e898c",
   "metadata": {},
   "source": [
    "Now we summarize the number and severity of droughts counted in all basins, for all GCMs, for each GEM.  We contrast the SPEI$_N$ series versus SPEI$_W$ series to isolate glacial drought buffering on each metric.\n",
    "\n",
    "We use another helper function to identify the droughts in each series and summarize them all, storing each in a dictionary per time period such that we can see change over time.\n",
    "\n",
    "Here we will use the early century 2000-2020 as a reference period.  We set up another layer of dictionary to include the GEM differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d53e56c-a52c-4193-805a-5b2eab7c8a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lizz/mambaforge/envs/agua3/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/lizz/mambaforge/envs/agua3/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/Users/lizz/Documents/GitHub/glacial-SPEI/gSPEI.py:479: RuntimeWarning: Mean of empty slice\n",
      "  mean_drtdur_w = np.nanmean([np.mean(drt_dur_w_bymodel[m]) for m in modelnames])\n",
      "/Users/lizz/Documents/GitHub/glacial-SPEI/gSPEI.py:481: RuntimeWarning: Mean of empty slice\n",
      "  mean_drtsev_w = np.nanmean([np.mean(drt_sev_w_bymodel[m]) for m in modelnames])\n"
     ]
    }
   ],
   "source": [
    "basin_stats_bymodel_early = {g: \n",
    "                            {m: \n",
    "                             {b: gSPEI.basin_summary_stats(SPEI_basin_sorted_GEM[g], basin_name=b, modelnames=[m], period=(2000,2020)) for b in basin_names}\n",
    "                             for m in modelnames}\n",
    "                            for g in gmodels} \n",
    "basin_stats_bymodel_midC = {g: \n",
    "                            {m: \n",
    "                            {b: gSPEI.basin_summary_stats(SPEI_basin_sorted_GEM[g], basin_name=b, modelnames=[m], period=(2040,2060)) for b in basin_names} \n",
    "                             for m in modelnames}\n",
    "                            for g in gmodels}\n",
    "                            \n",
    "basin_stats_bymodel_endC = {g:\n",
    "                            {m: {b: gSPEI.basin_summary_stats(SPEI_basin_sorted_GEM[g], basin_name=b, modelnames=[m], period=(2080,2100)) for b in basin_names} \n",
    "                             for m in modelnames}\n",
    "                            for g in gmodels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c0bca5f-387f-4058-8db1-cf92aed8708d",
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    {
     "data": {
      "text/plain": [
       "[(14.0, 15.0),\n",
       " (6.785714285714286, 6.666666666666667),\n",
       " (-7.154211286214286, -7.5489181540666666)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_stats_bymodel_early['GloGEM'][modelnames[0]]['INDUS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5477be46-1573-4c6a-aca3-c557fc70d89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(16.0, 18.0),\n",
       " (6.1875, 6.611111111111111),\n",
       " (-7.2350768066875, -7.493108738611111)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_stats_bymodel_midC['GloGEM'][modelnames[0]]['INDUS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "689519c4-035e-461b-a0e9-d167ea277c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 20.0),\n",
       " (5.761904761904762, 7.0),\n",
       " (-7.077195155809524, -8.141801516249998)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_stats_bymodel_endC['GloGEM'][modelnames[0]]['INDUS']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96478e40-30bf-46ff-9b4f-f97d153299fe",
   "metadata": {},
   "source": [
    "Okay, we can see some changes in buffering over time, as might be expected.  Looks okay.  Not directly comparable to our previous results because these are 20-year periods instead of 30-year...but we can compare the glacier evolution models to each other.  Let's make a new version of the $\\Delta$ frequency and severity plots we made for the previous manuscript. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26267f1d-9ce1-4127-9180-676077410356",
   "metadata": {},
   "source": [
    "### Direct comparison of the three GEMs\n",
    "We've made some single-GEM plots to see change in drought buffering over time.  Now, can we compare the single-GEM results directly?\n",
    "\n",
    "Perhaps let's select just the end of the century and show it with all three GEMs, with their change compared to their own early-century baseline?  This will be similar in format to the highlight figure produced for ESD (`highlight_severity_fig.py`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5920287-b1f4-41d9-a146-ede4c0dcebbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "## bring in Finn's initial volume list\n",
    "## this is for GloGEM for SSP245 for now\n",
    "volume_file = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/Summary-statistics/Mean_Initial_Volumes_ssp245.csv'\n",
    "\n",
    "initial_vols = pd.read_csv(volume_file, index_col=0) ## Lolol just read this in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19e24714-3302-468b-b81a-4cc61c4e23e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1948.3885153846152"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_vols.loc['YUKON', 'GloGEM']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744fad3c-60ef-45c8-906f-c96dd27efd19",
   "metadata": {},
   "source": [
    "Plot with initial volume on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db23a512-08ab-4672-9573-11639c5d6a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Composite of stats over time\n",
    "# color_fam = cm.get_cmap('tab20b')\n",
    "# inc_color=color_fam(5)\n",
    "# dec_color=color_fam(17)\n",
    "\n",
    "## Color defaults per model\n",
    "colorcode = ['RdBu', 'PuOr', 'PiYG']\n",
    "\n",
    "## Create figure with only the end-century severity panel\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "\n",
    "for increment in range(len(gmodels)):\n",
    "    test_GEM = gmodels[increment]\n",
    "    c = colorcode[increment]\n",
    "    offset = increment*0.2\n",
    "# for test_GEM, c in zip(gmodels, colorcode):\n",
    "    this_cmap = plt.get_cmap(c)\n",
    "    base_color = this_cmap(0.5)\n",
    "    dec_color = this_cmap(-0.7)\n",
    "    inc_color = this_cmap(0.9)\n",
    "        \n",
    "    for j, b in enumerate(basin_names):\n",
    "        number_b = []\n",
    "        severity_b = []\n",
    "        number_midC = []\n",
    "        number_endC = []\n",
    "        severity_midC = []\n",
    "        severity_endC = []\n",
    "        \n",
    "        vol = initial_vols.loc[b,test_GEM]\n",
    "\n",
    "        for m in modelnames:\n",
    "            number_b.append(basin_stats_bymodel_early[test_GEM][m][b][0][1]\n",
    "                            -basin_stats_bymodel_early[test_GEM][m][b][0][0])\n",
    "            number_midC.append(basin_stats_bymodel_midC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_midC[test_GEM][m][b][0][0])\n",
    "            number_endC.append(basin_stats_bymodel_endC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_endC[test_GEM][m][b][0][0])\n",
    "            severity_b.append(-1*(basin_stats_bymodel_early[test_GEM][m][b][2][1]\n",
    "                                  -basin_stats_bymodel_early[test_GEM][m][b][2][0]))\n",
    "            severity_midC.append(-1*(basin_stats_bymodel_midC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_midC[test_GEM][m][b][2][0]))\n",
    "            severity_endC.append(-1*(basin_stats_bymodel_endC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_endC[test_GEM][m][b][2][0]))\n",
    "\n",
    "        # ## Count pos vs neg buffering\n",
    "        # if np.nanmean(number_b)>0:\n",
    "        #     count_pbuff_num +=1\n",
    "        # elif np.nanmean(number_b)<0:\n",
    "        #     count_nbuff_num +=1\n",
    "        # if np.nanmean(severity_b)>0:\n",
    "        #     count_pbuff_sev +=1\n",
    "        # elif np.nanmean(severity_b)<0:\n",
    "        #     count_nbuff_sev +=1\n",
    "\n",
    "        ## Color code changes over time\n",
    "        # midC_v_hist_n = np.nanmean(number_midC)-np.nanmean(number_b)\n",
    "        # if midC_v_hist_n >0.1: # buffering on number increasing\n",
    "        #     midC_color_n=inc_color\n",
    "        #     midC_marker_n='^'\n",
    "        # elif midC_v_hist_n<-0.1:\n",
    "        #     midC_color_n=dec_color\n",
    "        #     midC_marker_n='v'\n",
    "        # else:\n",
    "        #     midC_color_n='k'\n",
    "        #     midC_marker_n='o'\n",
    "        # endC_v_hist_n = np.nanmean(number_endC)-np.nanmean(number_b)\n",
    "        # if endC_v_hist_n >0.1:\n",
    "        #     endC_color_n=inc_color\n",
    "        #     endC_marker_n='^'\n",
    "        # elif endC_v_hist_n<-0.1:\n",
    "        #     endC_color_n=dec_color\n",
    "        #     endC_marker_n='v'\n",
    "        # else:\n",
    "        #     endC_color_n='k'\n",
    "        #     endC_marker_n='o'\n",
    "\n",
    "        endC_v_hist_s = np.nanmean(severity_endC)-np.nanmean(severity_b)\n",
    "        if endC_v_hist_s >0.1:\n",
    "            endC_color_s=inc_color\n",
    "            endC_marker_s='^'\n",
    "        elif endC_v_hist_s<-0.1:\n",
    "            endC_color_s=dec_color\n",
    "            endC_marker_s='v'\n",
    "        else:\n",
    "            endC_color_s='k'\n",
    "            endC_marker_s='o'\n",
    "        # ## First column: historical\n",
    "        # ax1.errorbar(pg, np.nanmean(number_b), \n",
    "        #              yerr=((np.nanmean(number_b)-np.nanmin(number_b), np.nanmax(number_b)-np.nanmean(number_b)),), \n",
    "        #              color='k', marker='o', lw=1.0)\n",
    "        # ax7.errorbar(pg, np.nanmean(severity_b), \n",
    "        #              yerr=(((np.nanmean(severity_b)-np.nanmin(severity_b), np.nanmax(severity_b)-np.nanmean(severity_b)),)), \n",
    "        #              color='k', marker='o', lw=1.0)\n",
    "        # ## Second column: mid-c\n",
    "        # ax2.errorbar(pg, np.nanmean(number_midC), \n",
    "        #              yerr=(((np.nanmean(number_midC)-np.nanmin(number_midC), np.nanmax(number_midC)-np.nanmean(number_midC)),)), \n",
    "        #              color=midC_color_n, marker=midC_marker_n, lw=1.0)\n",
    "        # ax8.errorbar(pg, np.nanmean(severity_midC), \n",
    "        #              yerr=(((np.nanmean(severity_midC)-np.nanmin(severity_midC), np.nanmax(severity_midC)-np.nanmean(severity_midC)),)), \n",
    "        #              color=midC_color_s, marker=midC_marker_s, lw=1.0)\n",
    "        # ## Third column: end of century\n",
    "        # ax3.errorbar(pg, np.nanmean(number_endC), \n",
    "        #              yerr=(((np.nanmean(number_endC)-np.nanmin(number_endC), np.nanmax(number_endC)-np.nanmean(number_endC)),)), \n",
    "        #              color=endC_color_n, marker=endC_marker_n, lw=1.0)\n",
    "        ax.errorbar(vol, np.nanmean(severity_endC), \n",
    "                     yerr=np.asarray(np.nanmean(severity_endC)-np.nanmin(severity_endC), np.nanmax(severity_endC)-np.nanmean(severity_endC)).transpose(), \n",
    "                     color=endC_color_s, marker=endC_marker_s, lw=1.0)\n",
    "\n",
    "\n",
    "ax.set_ylabel(r'Glacial drought buffering'\n",
    "              '\\n'\n",
    "              ' [$\\Delta$ SPEI deficit]',fontsize=16) \n",
    "ax.set(xscale='log', ylim=(-2,7))\n",
    "ax.set_yticks([-2, 0, 2, 4, 6])\n",
    "ax.set_xlabel('Initial glacier volume', fontsize=16)\n",
    "ax.set_title('End 21st Cent. (2080-2100), all GEMs, {}'.format(scen), fontsize=14)\n",
    "ax.tick_params(which='both', labelsize=14)\n",
    "ax.axhline(0, ls=':', lw=1.0, color='k')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b67f6b-d16f-4161-af49-8e7ce5fd8a0d",
   "metadata": {},
   "source": [
    "Read in percent glacier cover from Finn's summary stats.  Yay! Using just GloGEM baseline for now; these shouldn't vary much by GEM anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be60921d-727d-41ac-8fe9-366fd46b800e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## bring in Finn's basin summary stats\n",
    "## this is for GloGEM for SSP245 for now\n",
    "summary_file = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/Summary-statistics/MASTER_GloGEM.csv'\n",
    "\n",
    "percent_glaciated = pd.read_csv(summary_file, index_col=0).loc[:, 'GlacierAreaFrac'] ## Lolol just read this in instead of fixing up the copypasta below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "167c596d-db3c-4796-a307-67a4cf27d876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0120027953168836"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_glaciated.loc['YUKON']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ceddfe2-cf44-4a01-a724-c946706d755f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Buffering vs percent glacier cover\n",
    "\n",
    "## Color defaults per model\n",
    "colorcode = ['RdBu', 'PuOr', 'PiYG']\n",
    "\n",
    "## Create figure with only the end-century severity panel\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "\n",
    "for increment in range(len(gmodels)):\n",
    "    test_GEM = gmodels[increment]\n",
    "    c = colorcode[increment]\n",
    "    offset = increment*0.2\n",
    "# for test_GEM, c in zip(gmodels, colorcode):\n",
    "    this_cmap = plt.get_cmap(c)\n",
    "    base_color = this_cmap(0.5)\n",
    "    dec_color = this_cmap(-0.7)\n",
    "    inc_color = this_cmap(0.9)\n",
    "        \n",
    "    for j, b in enumerate(basin_names):\n",
    "        number_b = []\n",
    "        severity_b = []\n",
    "        number_midC = []\n",
    "        number_endC = []\n",
    "        severity_midC = []\n",
    "        severity_endC = []\n",
    "        \n",
    "        # vol = initial_vols.loc[b,test_GEM]\n",
    "        pc_glac = percent_glaciated.loc[b]\n",
    "\n",
    "        for m in modelnames:\n",
    "            number_b.append(basin_stats_bymodel_early[test_GEM][m][b][0][1]\n",
    "                            -basin_stats_bymodel_early[test_GEM][m][b][0][0])\n",
    "            number_midC.append(basin_stats_bymodel_midC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_midC[test_GEM][m][b][0][0])\n",
    "            number_endC.append(basin_stats_bymodel_endC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_endC[test_GEM][m][b][0][0])\n",
    "            severity_b.append(-1*(basin_stats_bymodel_early[test_GEM][m][b][2][1]\n",
    "                                  -basin_stats_bymodel_early[test_GEM][m][b][2][0]))\n",
    "            severity_midC.append(-1*(basin_stats_bymodel_midC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_midC[test_GEM][m][b][2][0]))\n",
    "            severity_endC.append(-1*(basin_stats_bymodel_endC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_endC[test_GEM][m][b][2][0]))\n",
    "\n",
    "    \n",
    "\n",
    "        endC_v_hist_s = np.nanmean(severity_endC)-np.nanmean(severity_b)\n",
    "        if endC_v_hist_s >0.1:\n",
    "            endC_color_s=inc_color\n",
    "            endC_marker_s='^'\n",
    "        elif endC_v_hist_s<-0.1:\n",
    "            endC_color_s=dec_color\n",
    "            endC_marker_s='v'\n",
    "        else:\n",
    "            endC_color_s='k'\n",
    "            endC_marker_s='o'\n",
    "        # \n",
    "        ax.errorbar(pc_glac, np.nanmean(severity_endC), \n",
    "                     yerr=np.asarray(np.nanmean(severity_endC)-np.nanmin(severity_endC), np.nanmax(severity_endC)-np.nanmean(severity_endC)).transpose(), \n",
    "                     color=endC_color_s, marker=endC_marker_s, lw=1.0)\n",
    "\n",
    "\n",
    "ax.set_ylabel(r'Glacial drought buffering'\n",
    "              '\\n'\n",
    "              ' [$\\Delta$ SPEI deficit]',fontsize=16) \n",
    "ax.set(xscale='log', ylim=(-2,7))\n",
    "ax.set_yticks([-2, 0, 2, 4, 6])\n",
    "ax.set_xlabel('Percent glacier cover', fontsize=16)\n",
    "ax.set_title('End 21st Cent. (2080-2100), all GEMs, {}'.format(scen), fontsize=14)\n",
    "ax.tick_params(which='both', labelsize=14)\n",
    "ax.axhline(0, ls=':', lw=1.0, color='k')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc441852-d0fe-4bc6-859c-e10ae00aafa0",
   "metadata": {},
   "source": [
    "This returns the expected relationship between buffering and initial glacier cover.  \n",
    "TODO: eventually compute Spearman's rho over full scatter to compare with single-GEM results reported before"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780aaf13-a2dd-4483-b664-d80b4410b848",
   "metadata": {},
   "source": [
    "## Plot against aridity index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2a489b-16fc-4bc2-acca-000758db6e5b",
   "metadata": {},
   "source": [
    "We need to compute the aridity index from historical precipitation and PET.  This data is now updated for the CMIP6 model realizations and list of 75 basins. Plot with these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85bd4db6-0bc4-44c9-aedc-67cde45ac225",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath_p_pet = '/Volumes/GoogleDrive/My Drive/Runoff-intercomparison/GCM-PET_Precip/'\n",
    "## Settings in filenames -- slightly different model names and scenarios\n",
    "modelnames = ['BCC-CSM2-MR',\n",
    "              'MPI-ESM1-2-HR',\n",
    "              'MRI-ESM2-0',\n",
    "              'CESM2-WACCM',\n",
    "              'NorESM2-MM',\n",
    "              'FGOALS-f3-L',\n",
    "              'GFDL-ESM4',\n",
    "              'INM-CM4-8',\n",
    "              'INM-CM5-0',\n",
    "              'EC-Earth3',\n",
    "              'EC-Earth3-Veg']\n",
    "# scenarios = ['Ssp2p6', 'Ssp4p5', 'Ssp7p0', 'Ssp8p5'] # climate scenarios\n",
    "scenarios = ['Ssp4p5', 'Ssp8p5'] # climate scenarios included for 11 GCM set\n",
    "\n",
    "\n",
    "yrs = np.linspace(1900, 2101, num=2412) # time interval over which we have data\n",
    "\n",
    "PET_by_model = {m: {} for m in modelnames}\n",
    "P_by_model = {m: {} for m in modelnames}\n",
    "for m in modelnames:\n",
    "    for s in scenarios:\n",
    "        example_pet = fpath_p_pet+'{}_PET_{}.txt'.format(m,s)\n",
    "        example_p = fpath_p_pet+'{}_PREC_{}.txt'.format(m,s)\n",
    "        PET_by_model[m][s] = np.loadtxt(example_pet)\n",
    "        P_by_model[m][s] = np.loadtxt(example_p)\n",
    "    \n",
    "    \n",
    "PET_by_basin = {b: {} for b in basin_names} # potential evapotranspiration by basin\n",
    "P_by_basin = {b: {} for b in basin_names} # precipitation by basin\n",
    "AI_by_basin = {b: {} for b in basin_names} # AI by basin\n",
    "historical_avg_AI = [] # list for multi-model average basin aridity index over historical period\n",
    "avg_precip = []\n",
    "for i, b in enumerate(basin_names):\n",
    "    PET_by_basin[b] = {s: {} for s in scenarios}\n",
    "    P_by_basin[b] = {s: {} for s in scenarios}\n",
    "    AI_by_basin[b] = {s: {} for s in scenarios}\n",
    "    for s in scenarios:\n",
    "        tempdict_pet = {}\n",
    "        tempdict_p = {}\n",
    "        tempdict_ai = {}\n",
    "        for m in modelnames:\n",
    "            tempdict_pet[m] = PET_by_model[m][s][i] \n",
    "            tempdict_p[m] = P_by_model[m][s][i]\n",
    "            tempdict_ai[m] = np.nanmedian(tempdict_p[m][0:959])/np.nanmedian(tempdict_pet[m][0:959]) # historical avg(P)/avg(PET) for this model\n",
    "            if tempdict_ai[m]<0:\n",
    "                # tempdict_ai[m] = np.nan\n",
    "                tempdict_ai[m] = np.abs(tempdict_ai[m])\n",
    "        PET_by_basin[b][s] = pd.DataFrame.from_dict(tempdict_pet)\n",
    "        P_by_basin[b][s] = pd.DataFrame.from_dict(tempdict_p)\n",
    "        AI_by_basin[b][s] = np.nanmedian([tempdict_ai[m] for m in modelnames])\n",
    "    historical_avg_AI.append(AI_by_basin[b]['Ssp4p5'])\n",
    "    avg_precip.append(np.nanmean(P_by_basin[b]['Ssp4p5']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67cf3bf8-57a6-472f-a784-30e73d3d1567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7op4kSdi2bRs++OADLFu2DOfPn9fsmzRpks7zuTrXMDYpdfHiRSxcuBDe3t6YPXu2UedQM+YxycrK0mmuXtn4stTN6IUQRq/SaGdnp5OQAkqr2gYNGoT9+/dDoVBoqslMQf1/zs/PT2ffn3/+ibCwMK1tjz/+OPr376+1LS0trcLXxhEjRlSYlJo8eTLmzp2LNWvW4KuvvkJcXBwOHTqEd955p8pKOXt7e51tnp6elR5TGUmS8Ntvv2HixIk4duyYpiLOwcEBjz76KCZOnIhJkybpvO7Z2trqxCpJkqbyqqwOHTroVIC99957+Prrr7F582Z8++23murPGzdu4MMPP8TMmTM1lXiGmDRpkiYhBZRWHH766ac4ePAg1q1bp5WUGjlyJD744AP06NEDLi4uiIqKwvLly7F+/Xo8/vjjuHTpUo0SzEREDRmTUkREZHZDhw6tdAnz8tq3bw8nJyetbdbW1vDx8dFKMoWHhwOAzhs9AHByckKPHj0004qqS6VS4cUXX0RYWBimT5+u91Py6nBxccGwYcOwb98+9OzZE88//zyCgoLQp08fral05qZeMv7atWt6KzaSkpKgUqlw48YNreofQ39GtSEtLQ1WVlZwdnaucqypf44AUFBQgHHjxuHMmTPYtGkThgwZAkmScPjwYbz55pvYu3cvTp8+jbZt2xp1/qNHj+o8b3v06FHhlMfo6Gg89dRTUCqV+Pnnn6ucSlbXeHh4QKlUIjMzE+7u7kaf5+LFi1i6dClOnDiBpKQkKBQKrf2pqanw9fWtabgaFU3pAkqTUp988onWtiZNmui8VnXo0AHXrl2r9rXVFVGbNm3CF198gdDQUM2UtYqMGTMGW7duRZ8+fTB+/HgMHjwYQUFBFSYTq6NNmzY4efIkLl68iEOHDuHvv//GyZMncfjwYRw+fBjr16/H3r17NVVLY8aMwXfffYcuXbpg7NixGDBgAB599FGd6aJq/fr100lY2tvb46GHHsK+fftw48YNdOnSBSqVClOnToWfnx8+/vjjat2HoKAgnW29evWCvb09Ll68qLX9rbfe0vq+e/fuWLduHUpKSrBp0yasXbsWr7/+erWuT0TUWDApRUREdV5FFRrW1tZQKpWa79WVFxW9qTK0kqY8IQSmT5+OjRs3YuLEiVrTBWvi999/x6efforNmzfjP//5DwDA2dkZL774Ij799NMafbKunjYVHx9frePS09MBoMqeMnl5eVrfG/ozqg329vZQKpVVVr4Y8nNU34+Kqniys7O1xgHA4sWLsXPnTuzYsQPPPPOMZvvo0aPh7OyMJ554AosWLcK6deuqdQ11RdHRo0d1qmemTJmiNyl1584dDBo0CCkpKdiyZUuNppKpGfOYuLq6Vmt8WQUFBQBQo+f/yZMnMXjwYACl0xfVSVNJkrB9+3aEh4ejqKjI6PPr4+Pjg2vXriE+Ph4dOnTQ2vfxxx9rkiKhoaGVJouMNW3aNOzcuRPbtm1DaGgoHnnkEXTs2LHC8WPHjoWNjQ1WrFiB77//Ht98842m/9ny5cvRo0ePGsfUo0cPrfMcPXoUEydOxJEjR/DNN9/g7bffBgB8+eWXaNOmDUJDQzWPVZMmTTBmzBgsW7ZMJ7Fa1Wu8+rn35Zdf4vTp0/jzzz+r/Xyq6Bre3t4Gv66+9NJL2LRpE/766y8mpYiIKsBG50RE1GCo3+TK5XK9+/U1wK2KSqXCSy+9hDVr1mD8+PEIDQ3VmqJWE46Ojvjkk09w+/Zt3L59G6tXr8YDDzyAlStXat6sGSsgIADNmzdHXFyc3t49FVEnQnbt2gVRukqv3i9136S6wMvLC8D9hJo+hv4cK+t3VHZ72f5Kf/zxBwD9vYQGDRoESZI0jeuruoYQAjdv3oSfn5+mSmTBggU6j39oaKjOsTExMRg4cCASEhLw66+/4qmnntJ7H6rLmMekffv2yM3N1Uxpq2p8Wenp6XB2dja4948+n3zyCYqKinD48GHs3LkTy5Ytw8KFC7FgwQK9fa5MoW/fvgCAI0eO1Mr5q/LUU0/Bx8cH7777Lm7fvl1hg/Oynn32WYSFhSE9PR179+7Fyy+/jGPHjmHo0KG1UuE4cOBAfPTRRwC0m9nb2Njgvffew+XLlxEfH49NmzYhKCgI69ev15mKDVT9Gq/+XXDx4kUIITT/D9Vf6v+r33//PSRJ0pvgregacrm8woRqeepkWn5+vkHjiYgaIyaliIiowVD3QDpx4oTOvtzcXJ0pF1VRqVR4+eWXsXbtWowdOxYbNmww2YpZ5bVu3Rovvvgijh07BicnJ+zcuVOzT33N6lYcqZsAGzJtpbi4GADQp08fAKjVlaOMvT8VUTcqryhpUp2fY/v27eHn54e//vpLpxqssLAQYWFh8PPzQ7t27TTb1Y9dSkqKzvlSU1M1vcPU1CvxHThwQGf82bNnkZmZWe2knzohFR8fj19++UWzYqMpGPOYqOPXdx/379+vNaas/Px83L17t0aLCAClfes8PDzQr18/nfOX7fllSlOmTIFMJsMPP/yA1NTUWrlGZaytrTFx4kTEx8fDwcEBY8eONfhY9XTiH374AVOnToVcLseZM2dqJc6KpuSp+fn5Yfz48di3bx/at2+PQ4cOaarn1P766y+d6ZIFBQX4559/YG9vj8DAQAClz7GXXnpJ52v48OEASpvHv/TSSxgyZIhOHMePH9fZdu7cORQUFBhcRaZ+DFu1amXQeCKixohJKSIiajD8/f0RHByMiIgInelnn376abU++VdX1qxduxajR4/Gxo0bTZqQSklJwdmzZ3W2Z2RkoKioSKv5sLrRr7qht6HeffdddOjQAevXr8fcuXP1TleKjo7GyJEjceXKFQClzZT9/f2xfPlyncbMQGmTcH1Jv+qo6v7Exsbi2rVrBlcXqJMb+h7P6v4cJUnCyy+/jNzcXCxatEhr3+LFi5GRkYGXX35Zq5+NOvGxcOFCrUSbSqXSLD9ftooqMDAQwcHBOHLkCPbs2aPZrlAo8OGHHwIApk+fbtB9B7QTUj///DNGjRpl8LGGMOYxmTZtGqytrfHJJ59oTeO7fPky1q9fj7Zt22qm15V17tw5KJVKvQkrdZWLIQICApCRkYHLly9rtimVSrz77rt6k4em0KFDB8yaNQtyuRxPPPEEbt26pXdcbfZYe++997Bt2zbs379fayECfQ4fPozCwkKd7eoKobKvQQsWLIAkSQatmBkdHY2vv/5as4JoWXl5eVi5ciWA+73/ioqK8Oeff+okmfLy8pCTkwMbGxud/7PXr1/XrC6p9vnnnyMlJQXjx4/X9OWbNm0aVq1apfP13nvvASh97Vi1apXeqXUbNmzQev6UlJRg7ty5AEoTkGq3b99GYmKizvFXr17VTMuuqEE9ERGxpxQREVnAzZs3K31zY8gbn4r873//Q79+/TB58mRs374d7du3x99//42zZ88iKChI76ff+ixatAihoaFwcnJCYGCg3mqjkSNHan1ifu3aNXz22WcA7vfFuXbtGqZOnQqgdCrHF198AaC011OfPn3QuXNn9OzZE82bN0daWhp27NgBhUKhtWLao48+Cnt7e6xYsQLZ2dma6WoffPBBpffB2dkZ+/fvx4gRI7B48WKsXbsWISEhaNGiBfLz83HhwgX89ddfsLa21sRlZ2eH33//HU888QQGDBiAxx57DF26dAFQmiw6fvw4PD09jWrGrDZ48GD8/vvvGD16NIYPH44mTZqga9euePLJJwGUriR27NgxHDlyRFNVVJnHHnsMzs7OOHToEGbNmqW1z5if4+zZs7Fz504sXboUFy5cwEMPPYTw8HDs3bsXPXr00FnN7j//+Q927tyJ9evX459//sHgwYMhSRKOHDmCS5cuoVWrVnj//fe1jvn222/Rt29fjBo1CmPGjIGfnx/27duHiIgIvPzyy9XqBTVw4EDcuXMHjzzyCCIiIhAREaEzpvz/qVWrVmmSi5cuXdJsUzdUHzlypNaUpuo+JoGBgViwYAE+/PBDdOvWDc8//zzy8vKwefNmKBQK/Pjjj3pXhTt48KDm+mWpExaGJobfeOMNHDhwAP3798eYMWPQpEkTHD16FPHx8Rg4cKDRCx5U5bPPPoNCocDKlSvRoUMHDBgwAN26dYODgwPkcjkuXryIc+fOwcXFBd26ddM5PjU1tdLXv7feeqvSlTl9fHwqbIBf3jvvvIPY2FgMHDgQrVq1giRJOHHiBM6ePYu+fftqVZmpVCoAqHIlP6C0n9Mbb7yB9957D0FBQejcuTPs7e0RHx+P3bt3Iz09HQ899BDeeOMNAKWvlY899hjatGmDPn36wN/fH7m5udi9ezeSkpLw/vvv6yz+EBISgtdeew1//PEHHnjgAZw/fx779+9Hy5Yt8emnnxp0/6vy+OOP45FHHsG4cePg4eGBPXv2IDIyEkOHDsXEiRM149SLJgwaNAht27aFs7MzoqKi8Mcff0ChUGDevHl45JFHTBITEVGDJIiIiMwkOjpaAKjyqywAYsCAAXrPFxAQIAICAnS2X7p0SQwfPlw4OTkJZ2dn8cQTT4hLly6JKVOmCAAiOjq6yljVYyv7Wrt2rdYxR44cqXR82VgzMjLEggULRHBwsPD19RW2trbCz89PDBs2TOzfv18nnj/++EM8/PDDwt7eXu/jVJni4mKxZs0aMWzYMOHj4yNsbGyEs7Oz6Nmzp5gzZ46IjY3VOebu3bti5syZon379sLOzk64uLiIjh07ipdfflkcPnxYa2x1f0YKhULMnj1b+Pv7C2trawFATJkyRbN/wIABAoA4cuSIwfdxxowZwtraWiQnJ2ttN+bnKIQQmZmZ4u233xYtW7YUNjY2omXLluLtt98WmZmZeq+fmJgo3njjDdGuXTtha2sr7OzsRGBgoJg1a5ZITU3Ve8z169fF888/Lzw9PYWdnZ3o3Lmz+PLLL4VSqTT4fgshqv1/ypDHZf78+TV+TIQQYuPGjaJXr17C3t5euLq6imHDhomzZ89WOL5NmzaiR48eOtvDw8MFAPHCCy8Y9qAIIX7//XfRs2dP4eDgIJo2bSrGjBkjbt26pfd1QP1/t/z9Vj8Xq+vcuXPi5ZdfFoGBgcLR0VHY2NgIHx8f8fjjj4vly5eLlJQUnWMM+TmWjVkdW2JiYpXxqO/zqVOnNNt+/vlnMWbMGNG2bVvh4OAgXF1dRY8ePcTSpUtFbm6u1vGjRo0SMplMXL9+vcprFRYWii1btoh//etfonv37qJp06bCyspKuLu7i/79+4vly5eLgoICzfji4mKxZMkSERISIlq0aCFsbW2Fj4+PGDBggPj555+1zl3253Ts2DERFBQkHBwchJubmxg3bpze1zJ91OeZMWOGzr758+drXn++//570alTJ2FnZydatGghPvjgA5Gfn681Pjw8XEyaNEl07NhRuLq6Cmtra+Hj4yOeeeYZva/lRESkTRKikvVriYiIiOqBq1evomvXrvjkk090qpKofvjzzz/x2GOPYd26dZg8ebLWvq+//hpvvvkmLl26hM6dO1sowsbL29sbAwcOxK+//mrROI4ePYpBgwZh/vz5NaqorcyCBQuwcOFCgys1iYioZthTioiIiOq9jh074sUXX8SyZct0mnFT/bBo0SL06NFDa2qU2vHjx/HMM88wIWUBV69eRUpKCubMmWPpUIiIqAFiTykiIiJqED766CP4+fkhJiaGyYt6JjMzEwMHDsTTTz8NmUz3M9NffvnFAlERUJrw5cQKIiKqLUxKERERUYPg4+NTa1N6qHa5ubnxZ0dERNQIsacUERERERERERGZHXtKERERERERERGR2TEpRUREREREREREZseeUvWASqVCQkICnJ2dIUmSpcMhIiIiIiIiokZECIGcnBz4+fnpXZTEWExK1QMJCQlo2bKlpcMgIiIiIiIiokYsLi4OLVq0MNn5mJSqB5ydnQGU/vBdXFwsHE3tOHH2BL48/xre7PkN+vfub+lwqAG7EpuBsV8cwy/vDkAnf3dLh1On4qkolkOXzuCHv6bjX/1+xONd+1gwQiIiIiIisoTs7Gy0bNlSk58wFSal6gH1lD0XF5cGm5RydHSETRMZHB0dG+x9pLrByVkJma0DnJzrxv+nuhRPRbE4Ot37/+nE/59ERERERI2ZqVsKsdE5ERERERERERGZHZNSRERERERERERkdkxKERERERERERGR2TEpRUREREREREREZsekFBERERERERERmR2TUkREREREREREZHZMShERERERERERkdkxKUVERERERERERGbHpBQREREREREREZkdk1JERERERERERGR2TEoREREREREREZHZMSlFRERERERERERmx6QUERERERERERGZHZNSRERERERERERkdkxKEVGjkpOWB9+YNOSk5Vk6FCIiIiIiokaNSSkialRy0vPhdycDOen5lg6FiIiIiIioUWNSioiIiIiIiIiIzI5JKSIiIiIiIiIiMjsmpYiIiIiIiIiIyOyYlCIiIiIiIiIiIrNjUoqIiIiIiIiIiMyOSSkialTyFGkoefgc8hRplg6FiIiIiIioUWNSiogalbySdCh7n0deSbqlQyEiIiIiImrUmJQiIiIiIiIiIiKzY1KKiIiIiIiIiIjMjkkpIiIiIiIiIiIyOyaliIiIiIiIiIjI7JiUIiIiIiIiIiIis2NSioiIiIiIiIiIzI5JKSIiIiIiIiIiMjsmpYiIiIiIiIiIyOyYlCIiIiIiIiIiIrNjUoqIiIiIiIiIiMyOSSkiIiIiIiIiIjI7JqVq2bZt2zBkyBB4enrC3t4erVu3xvjx4xEXF2fp0IiIiIiIiIiILMba0gE0VEIIvPLKK/jhhx/Qtm1bjBs3Ds7OzkhISMCxY8dw584dtGzZ0tJhEhERERERERFZBJNSteSrr77CDz/8gNdffx0rV66ElZWV1v6SkhILRUZEREREREREZHmcvlcLCgoKsHDhQrRp0wYrVqzQSUgBgLU184FERERERERE1HgxM1ILDh48iPT0dEydOhVKpRI7d+7EjRs34Obmhscffxzt2rWzdIhERERERERERBbFpFQtOHfuHIDSaqju3bvj+vXrmn0ymQxvv/02vvjiC0uFR0R1SHZhCry9DyC7sBsAd0uHQ0REREREZDacvlcL5HI5AGDZsmVwcXHB2bNnkZOTg7CwMAQGBmLZsmX49ttvKzy+qKgI2dnZWl9E1DDlFKbC2/sQcgpTLR0KERERERGRWTEpVQtUKhUAwNbWFtu3b8fDDz8MJycnBAUF4ffff4dMJsOyZcsqPH7x4sVwdXXVfHGVPiKypPysAq1bIiIiIiIiU2BSqha4uroCAHr16gU/Pz+tfZ07d0abNm1w69YtZGZm6j1+zpw5yMrK0nzFxcXVdshERBXKyyrUuiUiIiIiIjIF9pSqBR06dAAAuLm56d2v3l5QUKB3jJ2dHezs7GopOiIiIiIiIiIiy2OlVC0YNGgQAODq1as6+xQKBW7evAlHR0d4eXmZOzQiIiIiIiIiojqBSala0LZtW4SEhODmzZtYtWqV1r7PPvsMmZmZGDVqFKytWahGRERERERERI0TsyK15JtvvkHfvn0xffp0bN++HQ888AAuXLiAP//8EwEBAfj8888tHSIRERERERERkcWwUqqWtG3bFufOncPUqVPxzz//4Msvv0RUVBRef/11nD17Fs2aNbN0iEREREREREREFsNKqVrUsmVLrF271tJhEBHVyK0UJaKi3sEtJ6WlQyEiIiIiogaElVJERFQhIQQOXlWgqMgHB68qIISwdEhERERERNRAMClFRI2KpuonpW5U/eRnFWjd1jVhV5IRn1WaiIrPEgi7kmzhiIiIiIiIqKFgUoqIGo26WPWTl1WodVuXCCGwfPtlSPe+lwAs3365TjxuRERERERU/zEpRUSNBqt+qifsSjIi7mRAnYISACLuZPBxIyIiIiIik2BSiogaBVb9VI/68ZJJktZ2mSTxcSMiIiIiIpNgUoqIGgVW/VSP+vFSlUs+qYTg40ZERERERCZhbeyBYWFhBo+1srKCs7MzAgIC4OrqauwliYiMUrbqp2ySRV31E9zJB1K5iqDGTFNVJgH6CqIkCXzciIiIiIioxoxOSg0cONCoNyOdO3fGK6+8gldffZVvZojILNRVP+WVrfoZ0LmZBSKrm0qUAgkZ+XoTUkBpoioxIx/FJSrY2ViZNzgiIiIiImowjE5KBQcHQ5IknDx5EgqFAvb29mjfvj2cnZ2Rk5ODqKgoFBQUwNbWFo888gjy8/MRFRWFyMhIvPHGG9i/fz+2bdsGmYwzCImo9rDqp/psrGXYMfcxpOcUAQD2hJ3AgYSlCPGbjeHB/QEAni52TEgREREREVGNGJ0ROnz4MNzc3ODo6Ii1a9ciPT0dFy9exPHjx3Hx4kVkZGQgNDQUTk5OcHd3x6lTp5Ceno5ff/0VHh4e2L17N9asWWPK+0JEpKO4RGVw1Q/d5+fhgC4B7ugS4A4/Nxns7ePh5ybTbPN1d7B0iEREREREVM8ZXSn1+eefY+fOnQgLC0O/fv109tva2mLy5Mlo27YtgoKCsGTJEsydOxfPP/88nJycMHz4cKxfvx4vv/xyje4AEVFl7GysWPVDRERERERUBxldKbVu3ToEBgbqTUiV1a9fP3To0AHr16/XbBs2bBh8fX0RGRlp7OWJiAzGqh8iIiIiIqK6x+ikVExMDBwdHQ0a6+joiNjYWK1tLVq0QH5+vrGXJyIiIiIiIiKieszopJSHhwciIyORmJhY6bjExERcunQJ7u7uWtuTkpJ0thERERERERERUeNgdFLqqaeeQnFxMUaNGqVTBaUWFxeHZ599FiUlJXjmmWc029PT03H37l20bt3a2MsTEREREREREVE9ZnSj848//hj79u3D2bNnERgYiIEDB6Jbt25wdnZGbm4uIiIicOTIERQXFyMgIACLFi3SHLt69WoIITBs2DCT3AkiIiIiIiIiIqpfjE5KeXl54eTJk5gxYwb27NmDAwcO4ODBg5r94t76608++SS+++47eHl5afZNmTIFY8aMgbe3dw1CJyIiIiIiIiKi+sropBQANG/eHLt378aNGzdw8OBBREVFIS8vD46OjggMDMSQIUPQvn17neOYjCIiIiIiIiIiatxqlJRSCwwMRGBgoClORUTUqNxKUSIq6h3cclJaOhQiIiIiIiKzMrrRORER1YwQAgevKlBU5IODVxWaac9ERERERESNgUkqpbKysnD79m3k5uZW+qYqODjYFJcjImoQwq4kIz6r9DUzPksg7EoyBnRuZuGoiIiIiIiIzKNGSamwsDB88MEHOHPmTJVjJUlCSUlJTS5HRNRgCCGwfPtlSAAEAAnA8u2XEdzJB5IkWTg6IiIiIiKi2md0UurIkSMYNmwYFAoF7Ozs0KpVK3h7e0Mm44xAIqKqhF1JRsSdDM33AkDEnQxWSxERERERUaNhdFJq/vz5UCgUeOGFF7BixQp4enqaMi4iogZLXSUlkySoykx5lkkSq6WIiIiIiKjRMLqs6fz583Bzc0NoaCgTUkRE1aCuklKV68GnEkJTLUVERERERNTQGZ2UsrW1Rbt27WBlZWXKeIiIGjRNL6kKCqEkqbS3FFfiIyIiIiKihs7opFSfPn0QExPDN05ERNVQXKJCQkY+KnrpFAJIzMhHcYnKvIERERERERGZWY16Sg0YMABffPEF3nvvPVPGRETUYNnZWGHH3MeQnlMEANgTdgIHEpYixG82hgf3BwB4utjBzoZVqERERERE1LAZnZTy8/PD8uXL8c477+DkyZN46aWX0LZtWzg6OlZ4jL+/v7GXIyJqMPw8HODn4QAAuOgmg31GPPzcZOgS4G7hyIiIiIiIiMzH6KRUq1atIEkShBDYuXMndu7cWel4SZJQUlJi7OWIiIiIiIiIiKgBMTop5e/vzyXLiYiIiIiIiIjIKEYnpWJiYkwYBhERERERERERNSZGr75HRERERERERERkLCaliIiIiIiIiIjI7JiUIiIiIiIiIiIiszMoKWVlZQUrKyt07txZZ5uhX9bWRrevIiIiIiIiIiKiBsagTJEQAgCgUql0thmquuOJiIiIiIiIiKjhMigpVTYZVdk2IiIiIiIiIiIiQ7CnFBERERERERERmZ3RSanBgwdj+PDhKC4uNmU8RERERERERETUCBidlDp16hTkcjlsbW1NGQ8RERERERERETUCRiel/P39UVhYaMpYiIiIiIiIiIiokTA6KfXcc8/h2rVruHHjhinjISIiIiIiIiKiRsDopNSHH36IHj16YMSIEQgPDzdlTERERERERERE1MBZG3vgv//9b7Rv3x6///47evbsic6dO6Njx45wdHTUO16SJKxevdroQImIiIiIiIiIqOEwOikVGhoKSZIghAAAREZGIjIyssLxTEoRNV7yzAJsCruNCcFt4O1mb+lwiIiIiIiIqA4wOim1du1aU8ZBRA2YPKsQK3dfxePd/ZiUIiIiIiIiIgA1SEpNmTLFlHEQEREREREREVEjYnSjcyIiIiIiIiIiImMZXSlVVlxcHI4fP474+HgUFBRg3rx5mn0KhQJCCNja2priUtSApOfJse/SJgzrOsHSoRARERERERGRmdWoUio1NRVjx45F69atMWnSJHzwwQdYuHCh1php06bB3t4e//zzT40CpYYnPU+OzWe/RHqe3NKhEBEREREREZGZGZ2UysnJwYABA/Dbb7+hefPmmDp1Kpo3b64z7uWXX4YQAlu3bq1RoERERERERERE1HAYnZRaunQprl69iueeew7Xrl3D6tWrERAQoDMuODgY9vb2OHLkSI0CJSIiIiIiIiKihsPopNTvv/8OOzs7rFq1Cvb2FS/xLpPJ0K5dO8TGxhp7KSIiIiIiIiIiamCMTkrFxMQgMDAQrq6uVY51cHBAamqqsZciIiIiIiIiIqIGxuikVJMmTZCTk2PQ2MTERIOSV0RERERERERE1DgYnZTq3Lkz4uLicOfOnUrHXbx4EbGxsXjooYeMvRQRERERERERETUwRielJk6cCKVSiX/961/Iz8/XOyYjIwMvvfQSJEnC5MmTjQ6SiIiIiIiIiIgaFmtjD5w+fTo2b96MgwcPomvXrhg9ejSSk5MBAGvWrEFkZCQ2btyI1NRUhISEYNy4cSYLmoiITC+7MAXe3geQXdgNgLulwyEiIiIiogbO6KSUlZUVdu/ejX/961/45Zdf8Pnnn0MIAaA0YaX+95gxY7B69epqn3/w4MHGhqaXJEk4fPiwSc9JRNSQ5BSmwtv7EHIKpwIItHQ4RERERETUwBmdlAIAZ2dnbN68GXPnzsW2bdtw6dIlZGVlwcnJCZ06dcKoUaOM7iV19OhRSJKkSW7VlCRJJjkPERERERERERHVnEFJqfXr18PHxwdDhw7Vu79r167o2rWrSQMDgC5duuDLL7+s8XneeOMNXL582QQRERERERERERGRKRiUlJo6dSqCgoK0klJt2rRB79698fPPP9dacK6urhgwYIBJzkNERERERERERHWHQUkpSZJQUlKitS0mJgYtWrSolaAAoFu3bmjfvr1JztWuXTvk5uaa5FxERERERERERFRzBiWlmjZtilu3bkGhUMDGxqa2YwIAXLx40WTnWrt2rcnORUTVl5OWB9+YNOSk5QEBXNWNiIiIiIiIDExKDRo0CL/99huCgoIwaNAg2NvbAwBiY2OxaNEigy4kSRL+7//+z/hIiajeyknPh9+dDOSk51s6FCIiIiIiIqojDEpKLVmyBGfPntV8qcXGxmLBggWVHqteQc8USamwsDC4urqie/fuVY6NiIhAZmYmgoODa3RNIqLG4mpcEaKi3sHVNkUYWvXLLBERERERUY0YlJQKCAjAtWvXsH//fly7dg35+flYuHAh/P39MW3atNqOUWPgwIEICgrCsWPHqhw7c+ZMHD9+XKcXFhER6RJCYPvpbBQV+WD76WzMfLL0wwQiIiIiIqLaYlBSCgBsbW3x9NNP4+mnnwYATVJq/vz5tRacPkKIWhlLlpWdX6x1S0TmFXYlGXfkCgDAHbkCYVeSMaBzMwtHRUREREREDZlBSalFixbB398fU6dO1Wxbu3YtmjWru29Y0tLSNL2vqO7LupeMymJSisjshBBYvv0yJAkQApAkYPn2ywju5MNqKSIiIiIiqjUGJaUWLFiA/v37ayWlpk2bhqCgIAwdOrS2YkN2djYyMzO1thUVFSEuLq7CKqiCggIcO3YMkZGRBvWeIiJq7MKuJCPiTobmeyGAiDsZrJYiIiIiIqJaZVBSSiaTQaFQ6Gyv7elx//3vf3VW9zt37hxatWpl0PEvvfRSLURFRNRwqKukZJIEVZnXdJkkaaqliIiIiIiIaoNBSalmzZrh+vXryM3NhZOTU23HpOHm5gZ/f3/N97GxsbC1ta1w2qAkSbC3t0ebNm0wduxYTJw40VyhVmnp0qV4//33AQCnTp3CI488YuGIiIh0q6TUVEJoqqWIiIiIiIhqg0FJqSeeeAKrV69Gp06d0K9fP02vpqioKLz44osGXUiSJKxevbpawc2cORMzZ87UfC+TyfDwww8jLCysWuextKtXr2LevHlwdHREXl6epcMhatTkN3Jh89toyEfnWjoUiyvfS6o8dW+p57ty0QgiIiIiIjI9g5JSS5YswdWrV3Hy5En88ssvmu3JyckIDQ016ELGJKXKW7t2LXx86tdUEqVSiSlTpqB79+4IDAzExo0bLR0SUaMlhMCVvUmQZbjjyt4kiFmiUTfyLi5RISEjX29CCihNVCVm5EOparyPERERERER1R6DklIeHh44ceIELl26hGvXriE/Px/Tpk1DYGAg5syZU9sxakyZMsVs1zKVJUuWIDw8HOfPn8fnn39u6XCIGrXwE9HIjCsAAGTGFSD8RDR6BLWxcFSWY2djhR1zH0N6ThEAYE/YCRxIWIoQv9kYHtwfAODpYofDJ45YMkwiIiIiImqgDEpKqXXt2hVdu3YFULr6nre3d60limJjYwEANjY28PX11dpWHWV7UplbZGQkFi5ciA8//BCdO3e2WBz1QVLBtTK3T1o2GGqQhBDYvDIMkAAIABKweWUYuvdv3airpfw8HODn4QAAuOgmg31GPPzcZOgS4G7hyIiIiIiIqKGrVlKqrCNHjsDV1dWUsWhp1aoVJEnCAw88gMuXL2ttM5QkSSgpKamtECtVUlKCqVOnomPHjvjggw+qdWxRURGKioo032dnZ5s6vDpFCIGI9F0AgIj0XRBiVqNOElDtCD8RjVuXku5vEMCtS0mNvlqKiIiIiIjIUoxOSg0YMMCUcejw9/eHJEmaKqmy2+qDTz/9FOHh4Thz5gxsbGyqdezixYuxcOHCWoqs7rmedAEZxaVVcBnFsbgQexw9A4ItHBU1JOoqKZlMgkp1v4GSTCaxWoqIiIiIiMhCjE5KGbMCXnCw4YmGmJgYg7bVReHh4fj444/x7rvvomfPntU+fs6cOZg1a5bm++zsbLRs2dKUIdYpey5thAQJAgISJGw8tRwP+gcxSUAmo1MldY9KJVgtRUREREREZCFGJ6UGDhxYb6bSmduUKVPQtm1bLFiwwKjj7ezsYGdnZ9qg6rC49JuafwsIRMkjWC1FJqOukpIk6F1lTmJvKSIiIiIiIoswOikVHBxc4Ru4vLw83Lp1CxkZGbC1tcWjjz5qdID1UXh4OACgSZMmeverH49t27Zh5MiR5gqrzhH3MgSSJIMQKs12mSRjtRSZTIlCidSEbL0JKaA0UZWWmIMShRI2tka/JBIREREREVE1Gf0O7OjRo1WO2bJlC2bOnAk/Pz/89NNPxl6q3nnppZf0bg8LC0NUVBSeeeYZeHl5oVWrVuYNrI65nnQBALQSUgCgEipWS5HJ2NhaY8mWqchOzwcA7Ak7gQMJSzDA7W04ZrrjkZAOaNm+KRNSREREREREZlar78Kee+45+Pv7o0+fPnj44Yfx1ltv1eh8O3fuxFtvvYXXX38d77zzToXjli1bhv/973/4+uuvMXz48Bpd0xirVq3Su33q1KmIiorCnDlz8Mgjj5g5qrpFCIE9lzZWuJ+9pciUmvq6oKmvCwDA7bY9hCINdvYyHPohHCHjHoRnM5cKj5VnFmBT2G1MCG4Dbzd7c4VMRERERETU4Mlq+wIPP/wwAgMD8eOPP9b4XOvXr8edO3cwatSoSseNGDECMTExWL9+fY2vSbWjRFmMzPzUCvcLCKTkJqJEWWzGqIh0ybMKsXL3VcizCi0dChERERERUYNilvkqtra2uHnzZtUDq3DhwgV4e3ujTZvKV8lq164dfHx8cO7cuRpfk2qHjbUd3h6yDIt2vYRZIf/FpchEHExYiiF+szE8uD8AwM3eEzbWjafhOxEREREREVFjUuuVUjdv3sS1a9fg6upa43MlJCTA39/foLEtW7ZEYmJija9pSqGhoRBCNPqpe2rujl4AgJYebeFh1xIA4GHXEu28u6Cddxc0dfa1ZHhEREREREREVIuMrpSKjY2tcJ8QAikpKfj777+xdOlSKJVKPP3008ZeSsPR0REpKSkGjU1NTYWdHatsiOqCPEUaSh4+hzzFAEuHQkRERERERHWE0UmpVq1aGdSAWgiBzp0749NPPzX2Uhpdu3ZFWFgYzp07h169elU47ty5c4iJiUH//v1rfE0iqrm8knQoe59HXkm6pUMhIiIiIiKiOsLopJS/v3+FSSlJkuDo6Ig2bdrgiSeewLRp00xStTRhwgQcO3YML7zwAvbu3au3t1R0dDReeOEFSJKECRMm1PiaRERERERERERkekYnpWJiYkwYhmFefPFFrFu3DidPnkSXLl3w7LPPok+fPnBzc0NmZiZOnz6N7du3o6CgAH379sX06dPNHiMZLiOnSOuWiOqmpIJrZW6ftGwwRERERETUYJhl9T1TsbKywu7duzFt2jTs2LEDmzZtwubNmzX7hRAAgFGjRmH16tWwsrKyVKhkgPTcIq1bInNQJ1jSlDdRz14CLUIIgYj0XQCAiPRdEGKWQVO3iYiIiIiIqlLv3pG5ublh27ZtOHfuHHbs2IGrV68iOzsbzs7O6Ny5M0aOHImePXtaOkwiqoPKJlhuKPZDYLiFI6r7LsQeR0Zx6cIWGcWxuBB7HD0Dgi0cFRERERERNQQ1TkolJyfju+++w/79+3Hjxg3k5OTA2dkZgYGBGDp0KGbMmIFmzZqZIlYtvXr1qrTZORFReWUTLNkiHjYt71o4orpNCIGNp5ZDggQBAQkSNp5ajgf9g1gtRURERERENSarycFbtmzBAw88gEWLFuH06dNIT0+HQqFAeno6Tp8+jUWLFuGBBx7Ab7/9Zqp4iYiMUjbBUkpCSZ9zmmm/pOtC7HFEySMgUPoYCQhEySNwIfa4hSMjIiIiIqKGwOhKqRMnTmDs2LFQqVTo2bMnXnvtNXTs2BE+Pj6Qy+W4evUq/ve//+H8+fOYMGEC/Pz80K9fP4PPHxtbWs1gY2MDX19frW3V4e/vX+1jiKjhUSdY7hMQPim4ln4GbTHSUmHVWeoknkySQSVUmu0yScZqKSIiIiIiMgmjk1KLFi2CEALvv/8+Fi9erLWvTZs2eOSRRzBt2jTMnTsXn332GRYuXIgDBw4YfP5WrVpBkiQ88MADuHz5stY2Q0mShJKSEoPHE1HDVFGCBSoJf9z6EcODRjDBUo5uEq+USqg01VLsLUVERERERDVhdFLqzJkz8PT0xCeffFLpuI8++girVq3CmTNnqnV+f39/SJKkqZIqu42IqDoqSrBAJhCXc5UJlnLK95Iqj72liIiIiIjIFIxOSkmShNatW0Mmq7wtlZWVFVq3bo3r169X6/wxMTEGbSMiqkxVCRYwwaJDhRKk5CZU8HiV9pZKyU1EibIYNtZ2Zo6OiIiIiIgaCqOTUj169MDly5ehVCphZWVV4biSkhLcvn0bDz74YLXOHxYWBldXV3Tv3t3YEImIUKIsrjTBAiZYdFhJNlg+djuyCtIBAHvCTuBgwlIM8ZuN4cH9AQBu9p58vIiIiIiIqEaMTkrNmTMHw4cPx5w5c7B06dIKx/3nP/9Beno65s6dW63zDxw4EEFBQTh27Jhm2+DBg9GtWzesWLHC2LCJqJGxsbbTm2DpWPIv3NySimc/GIRhA3sywVKOl7MfvJz9AAAednfu3bZEO+8ulgyLiIiIiIgaEIOSUvpWvevYsSM+/fRT/N///R8OHz6MV199FR07doS3tzdSUlJw9epVfPPNN4iMjMTixYvRoUOHagUmSRJUKpXWtqNHj7JxORFVm74EiyN8IUsFvO3bo6mzb2WHExERERERUS0wKClV2ap3QghcvHgRM2bMqPD4OXPmYO7cudVKKLm5ueHOnTsGjyciotohv5ELm99GQz4619KhEBERERFRA2JQUsoSq949+uij2Lt3L5577jmEhITA3t4eACCXy7F+/XqDzzN58uTaCpGIqMETQuDK3iTIMtxxZW8SxCzBhvBERERERGQSBiWlLLHq3aeffoqTJ09i27Zt2L59u2Z7VFQUpk2bZvB5mJQiIjJe+IloZMYVAAAy4woQfiIaPYLaWDgqIiIiIiJqCIxudF7bunXrhuvXr+Pnn3/GtWvXUFBQgNDQUHh7e2PYsGGWDo+IqMETQmDzyjBAAiAASMDmlWHo3r81q6WIiIiIiKjG6mxSCgC8vLzwxhtvaL4PDQ1F+/btsXbtWgtGRUTUOISfiMatS0n3Nwjg1qUkVksREREREZFJ1OmkVHnz58+Hv7+/pcMgImrw1FVSMpkElUpotstkEquliIiIiIjIJOpdUkotLi4Ox48fR3x8PAoKCjBv3jzNPoVCASEEbG1tLREmEVG9p1MldY9KJVgtRUREREREJiGzdADVlZqairFjx6J169aYNGkSPvjgAyxcuFBrzLRp02Bvb49//vnHQlESEdVf6iqpigqhpHu9pYQQ+gcQEREREREZoF4lpXJycjBgwAD89ttvaN68OaZOnYrmzZvrjHv55ZchhMDWrVstECURUf2mUgqkJmSjopyTEEBaYg5KFErzBkZERERERA1KvZq+t3TpUly9ehXPPfcc1q9fD3t7ewQFBSE+Pl5rXHBwMOzt7XHkyBELRUpEVH9ZWcuwZMtUZKfnAwD2hJ3AgYQlCPF7H8OD+wMAXD0dYGNbr36FEBERERFRHVOv3lH8/vvvsLOzw6pVq2Bvb1/hOJlMhnbt2iE2NtaM0RFRfVKMHJQ8fA55igGWDqVOaurrgqa+LgAAt9v2EIo0uLWwR5vOzSwcGZFlyTMLsCnsNiYEt4G3W8V/ixARERFR1Yyevrdo0SKEhoYaNHb9+vVYtGiRsZfSiImJQWBgIFxdXasc6+DggNTU1Bpfk8xDfiMXNptGQ34j19KhUCOhQC6Uvc8jryTd0qEQUT0Sfu06dh34DOHXrls6FCIiIqJ6z+ik1IIFC7BmzRqDxq5du1anGbkxmjRpgpycHIPGJiYmGpS8IssTQuDK3iTIMtxxZW8SmycTEVGdlZwSD4fAv5CcEl/1YKI6Lj1Pjk2nVyA9T27pUIiIqJEyS6NzlUoFqaJlnKqhc+fOiIuLw507dyodd/HiRcTGxuKhhx6q8TWp9t0Nz0BmXAEAIDOuAOEnoi0cERERUeUiM/bwjTzVe+l5cmw++yWfy0REZDFmSUrFxsbC2dm5xueZOHEilEol/vWvfyE/P1/vmIyMDLz00kuQJAmTJ0+u8TWplgng2PoLgDpnyaXmqZHh1FUi88mQ5+KXr44jQ17z/2+XM/fyjTwRERFRDRnc6DwiIgIXL17U2iaXy7F+/foKjykoKEBYWBju3LmDgQMHGhujxvTp07F582YcPHgQXbt2xejRo5GcnAwAWLNmDSIjI7Fx40akpqYiJCQE48aNq/E1qXZJcS1QcNfm/gYB3LqUhPAT0egR1MZygRHdk5OWB9+YNOSk5QEB7iY9t87U1VnCJFWlRKTfrdtp+O3rv9D2YX/08naydDhEREREjZ7BSalt27Zh4cKFWm+YoqKiMG3atEqPE0LA1tYWc+fONT7Ke6ysrLB7927861//wi+//ILPP/9cU1Ezffp0zb/HjBmD1atX1/h6VLuEELA+0wtCUkES94v2ZDIJm1eGoXv/1nyDThaXk54PvzsZyEnXX51ZE+EnonWmrjIZS1R70nOLtG6JGruMnKL7t94WDoaIiBolg5NSPXr0wJQpUzTfr1u3Dt7e3hg2bJje8ZIkwd7eHm3atMGoUaPQpo1p3mg5Oztj8+bNmDt3LrZt24ZLly4hKysLTk5O6NSpE0aNGsVeUvXE3fAMyOS6fwGpVILVUtTgCSGweWVY6dRVAc3UVSZjiYjIXJioJSIiSzM4KTVixAiMGDFC8/26devQvn17rF27tlYCq0rXrl3RtWtXi1ybak4IgXO/xEBAQILuG3CJb9CpgQs/EY1bl5Lub+DUVaJ6h9UlRERERDVjcFKqvOjoaDRp0sSUsVAjUqJQIi+1SG9CCgCEANISc1CiUMLG1uinKVGdpK6SkskkqFT3m/pz6ipR/cLqEiIiIqKaMfrdfkBAgCnj0BEbG2uS8/j7+5vkPGRaNrbWGLn4Qaw7+j4AoE/TKTiTug4hfu9jeHB/AICrpwMTUg3ErRQloqLewS0npaVDqRN0qqTu4dRVIiIiIiJqTGr8jl8IgYiICNy+fRu5ubmaZuP6TJ482eDztmrVqsaVApIkoaSkpEbnoNrj1LQJhFcaAMDZzxpCSoNbC3u06dzMwpGRKQkhcPCqAkVFPjh4VYF5onGvMKeukpKk0orA8iw5dTWp4FqZ2yfNem0iIiIiImp8apSU2rRpE95//30kJCQYNL46SSl/f/8K35DFx8drkk3W1tZo2rQp0tLSoFAoAAA2Njbw8/Mz+FpkeYXF9T95KM8swKaw25gQ3AbebvaWDqfOCLuSjPis0uxLfJZA2JVkDGjEiccShRKpCdl6E1KA5aauCiEQkb4LABCRvgtCzGrUyUMiIiIiIqp9Rr/j+e233zBx4kQAQLNmzdC9e3d4e3tDJpOZJLCYmBi92//973/jxx9/xJtvvonXXnsN7du3hyRJEELg5s2b+N///ofvvvsOTz31FL766iuTxEK1L7+o/k/rkmcVYuXuq3i8ux+TUvcIIbB8++WyC8xh+fbLCO7k02gTHja21liyZSqy0/MBAHvCTuBAwhKLT129EHscGcWl06YzimNxIfY4egYEmzUGIiIiIiJqXIx+17NkyRJIkoT3338fixYtgrV17b+B+uabb/Dtt99i8+bNGDNmjNY+SZLQvn17rFixAn379sX48ePRqVMnvPrqq7UeFxHpF3YlGRF3MjTfCwARdzIafbVUU18XNPV1AQC43baHUFh26qoQAhtPLYcESbMi5sZTy/Ggf1CjTR4SEREREVHtM7qs6cqVK/Dy8sKnn35qloQUAHz//ffw9/fXSUiVN2bMGPj7++P77783S1xEpEtdJSUrl9SQSRKWb79caf85Mq8LsccRJY+AQOnPREAgSh6BC7HHLRwZERHVpvysAq1bIiIiczM6KeXo6Gj2le1u3rwJLy8vg8Z6eXkhKiqqliMiooqoq6RU5ZJPKiE01VJkeeoqKZmk/etAJsmw8dRyJg+JiBqwvKxCrVsiIiJzMzopNXDgQNy4cQPFxcWmjKdSTk5OuHz5MjIzMysdl5mZicuXL8PR0dE8gZFR+Klcw6XpJVXBzC9JAqul6gh1lZRKqLS2q4SK1VLU4OQp0lDy8DnkKdIsHQoRERERoQZJqY8//hgqlQqzZ882ZTyVGjJkCAoKCvDCCy8gPT1d75iMjAy88MILKCwsxNChQ80WG1UfP5VruIpLVEjIyK90hbnEjHwUl6j0DyCzKNtLSh91bykmD6mhyCtJh7L3eeSV6P8bgoiIqCFKz5Nj0+kVSM+TWzoUIh0GNYMKCwvTu33BggWYM2cOjh8/jmnTpqFt27aVVicFB9dsJadPP/0U+/btw759++Dv74/Ro0ejY8eO8PLyQkpKCq5du4bffvsNeXl58PT0xMcff1yj6xGRcexsrLBj7mNIzykCoF5hbilC/GZrVpjzdLGDnY2VJcNs9EqUxUjJTdD0kipPQCAlNxEqhxIzR0ZEREREppKeJ8fms1+id5vH4eHobelwiLQYlJQaOHBghSswCSFw4cIFXLx4sdJzSJKEkpKavbHx9/fH8ePHMXHiRFy4cAHr1q3Tikv9af6DDz6IDRs2ICAgoEbXIyLj+Xk4wM/DAQBw0U0G+4x4+LnJ0CXA3cKRkZqNtR2Wj92OrILSqpE9YSdwMGEphpRJHrrZe2Lf0fOWDJOIiIiIiBoog5JSwcHBdWZZ8I4dO+Kff/7Bn3/+if379+PGjRvIzc2Fk5MTAgMDERISgscee8zSYRIR1Qtezn7wcvYDAHjY3bl32xLtvLtYMiwiIiIiImoEDEpKHT16tJbDqL7Bgwdj8ODBlg6DiIiIiIiIiIiMYHSjcyIiIiIiIiIiImMxKUVERERERERERGZn0PQ9fSpakU8fKysrODs7IyAgAK6ursZekoiIiIiIiIiIGgijk1KVrchXmc6dO+OVV17Bq6++WmeapxMRESC/kQub30ZDPjrX0qEQEREREVEjYPT0veDgYAQHB8Pa2hpCCDRp0gRdu3ZF37590bVrVzRp0gRCCNjY2CAoKAgPPfQQXFxcEBkZiTfeeAMjR46ESqUy5X0hIiIjCSFwZW8SZBnuuLI3CUIIS4dEREREREQNnNFJqcOHD8PNzQ2Ojo5Yu3Yt0tPTcfHiRRw/fhwXL15ERkYGQkND4eTkBHd3d5w6dQrp6en49ddf4eHhgd27d2PNmjWmvC9ERGSk8BPRyIwrAABkxhUg/ES0hSMiIiIiIqKGzuik1Oeff46dO3di165dmDJlCuzs7LT229raYvLkydixYwd27NiBJUuWQJIkPP/889iwYQOEEFi/fn2N7wAREdWMEAKbV4YB6hnVErB5ZRirpYiIiIiIqFYZnZRat24dAgMD0a9fv0rH9evXDx06dNBKQA0bNgy+vr6IjIw09vJERGQi4SeicetSEqDOQQng1qUkVksREREREVGtMjopFRMTA0dHR4PGOjo6IjY2VmtbixYtkJ+fb+zliYjIBNRVUjKZ9sITMpnEaimiKuRnFVg6BCIiIqJ6zejV9zw8PBAZGYnExET4+vpWOC4xMRGXLl1C06ZNtbYnJSXB3d290muYYnrf5MmTa3wOIqKGSlMlVY5KJVgtRVSFvKxCS4dAREREVK8ZnZR66qmn8OOPP2LUqFH49ddf4e/vrzMmLi4OY8aMQUlJCZ555hnN9vT0dNy9exd9+vSp9BpTp06FJEmVjqmMJElMStVhSQXXLB0CUaOmrpKSJEBfQZR0r7dUl8lNdXcSERERERHVkNFJqY8//hj79u3D2bNnERgYiIEDB6Jbt25wdnZGbm4uIiIicOTIERQXFyMgIACLFi3SHLt69WoIITBs2LBKr+Hv71+jpBTVXUIIRKTv0vqeiMyrRKFEakK23oQUUJqoSkvMgUrpad7AiIiIiIioUTA6KeXl5YWTJ09ixowZ2LNnDw4cOICDBw9q9quTDE8++SS+++47eHl5afZNmTIFY8aMgbe3d6XXiImJMTY8quMuxB5HRvH9PmPZythKRhPpl5uYgUvfH0DXGSFw8q14OnB6nhz7Lm3CsK4TNNuS8+wRJX8Ht5yU5gi1TrKxtcaSLVORnV7a329P2AkcSFiCEL/3MTy4PwDA1dMBe/8+bskwiYiIiIiogTI6KQUAzZs3x+7du3Hjxg0cPHgQUVFRyMvLg6OjIwIDAzFkyBC0b99e57iqklHUsAkhsPHUckiQIO4t95VQdMrCUVF9lJeYgdOLfkWbZx6uMim1+eyX6N3mcQClFUCXUjxRVNQEB68qME+IRluV2dTXBU19XQAAbrftIRRpcGthjzadm1k4MiIiIiIiauhqlJRSCwwMRGBgoClORY3AhdjjiJJHaG3LVyVbKBpqjHJzA5FR2AQAEJ8lEHYlGQOYhCEiIiIiIjIrmaUDqMz69euxf/9+vfuys7ORn59f4bFff/01Zs2aVVuhkZHUVVIyqfxTT9LsJzIleWYBNh27pfleCAG5fCike1V6EoDl2y/zuUdERERERGRmRldKxcZWvweQvhX6KjN16lQEBQVh6NChOvvc3NwQFBSEY8eO6T32l19+wcmTJ7F8+fJqx0m1R1+VVKnShEDpinxPmTUmatjkWYX49fBNtO4E5KTn42aKCgUFLTX7BYCIOxmsliIiIiIiIjIzo5NSrVq1qlYPFkmSUFJSUu3rVFa9wMqG+kVfL6nyLmXshhDvNNr+Pg2V/EYubH4bDfnoXItc37qktJl5Vno+Dl9TAFChbKGoTJKwfPtlBHfy0Xnu5SnSUPLwOeQpBpgxYiIiIiIioobP6KSUv79/hYmDvLw8pKamAgBsbGzg5+dn7GWoASlRFiMlN6HChBQAFCgzUKIsho21nRkjo9okhMCVvUmQZbjjyt4kiFmWayoemaBAfJZA+ZnLKiEqrJaKSEzBNfdBiEhMwSgzxkpERERERNTQGZ2UiomJqXR/dnY2fvzxR3z00UeYMGECPvnkE2MvRQ2EjbUdlo/djqz8NMxa8zduJmbfS08JuNor0dTvGwzxe48JqQYm/EQ0MuMKAACZcQUIPxGNHkFtzB6HEMDWf/IgAXrTopIEnWopIQQOXlWgqMin0a/SR0REdUd6nhz7Lm3CsK4T4OHIVa2JiKj+qrVG5y4uLnjnnXfw888/47PPPsOGDRtq61JUj3g5+yE+1QuXol1QUNgChYUtUFjYEskZrZCbGwgHa3dLh0gmJITA5pVh6j72gARsXhlmkam3QlghLU9ZYZ2eEEBiRj6KS1SabWFXku9VVt1fpY+IiMjS0vPk2Hz2S6TnyS0dChERUY0YXSllqGHDhiEgIAArV67EpEmTavtyVMcJIbB8+2XIJAmqMokJCaUrorFPWMMSfiIaty4l3d8ggFuXkixSLSWTKTH/aXfcTMrDgYSlaK18FtFWWxHiNxvDg/sDADxd7GBnY1Ua6r3nqrqySr1Kn76+U0RERERERFR9tVYpVZabmxuuXbtmjktRHRd2JRkRdzK0ElIAICChoKAlbqaoKjiS6ht1lZRMpp3Akckkk1VL5Wemat1WxdPJCn5uMtjbx8MutQgu2/rCWp6PLgHu6BLgDl93B81Y9XNVHWXZVfqIiIiIiIio5mq9Ukoul+Pq1atwdHQ0+vj169cbtc9S4uPj8dtvv2HPnj24du0akpKS4OHhgX79+mH27Nno06ePxWKzJE3liVQ6VUqXCoevKbCAfXsaBJ0qqXtUKmGyaqm8e8moPAOTUhoCSD+LCpuvV1TRV9kqfURERERERFQ9tZaUSk1Nxd9//40PP/wQxcXFGDFihFHniYqKwrRp03S2S5JU4T6g9E2lpd40fvXVV1iyZAnatm2LIUOGwNvbG1FRUdi+fTu2b9+OzZs3Y8yYMRaJzZKKS1RIyMivICEFADJkFQoUl6g0U6ioflJXSVWUgJTu9Zbq3r+1Wf+fZucXl14/rgWKU0q36Wu+rq6SKq+yVfqIiIjqm6SCa2Vun7RsMERE1CgZnZSysjIsaSCEQLNmzfDZZ59V+xr+/v71shqhd+/eCAsLQ1BQkNb248eP47HHHsOrr76KESNGwM6uca0yZ2djhR1zH0N6ThEAYE/YCRxIWAoA6OgwHlfzN2NU6/eZkGoAShRKpCZkV5iAFAJIS8xBiUIJG9taL9jUyMovhhAC1md6oWyzqLIJsqoq+vSt0kdERFTfCCEQkb4LABCRvgtCzOLvNSIiMjuj3w1W1Q/G0dERbdq0wRNPPIF3330XTZs2rfY1YmJijIzOsp599lm924OCgjBo0CAcOHAAly5dQq9evcwcmeX5eTjAz6O0b89FNxnsM+IBAJ5OCtiLeLja84+hhsDG1hpLtkxFdno+AHUCcglC/N7XNBV39XQwa0JKTX4jFzJ5meWzyzVfr6qir+wqfUygEhFRfXUh9jgyimMBABnFsbgQexw9A4ItHBURETU2Rr8jVKnYkNoYNjY2AABra/O/GScyp6a+Lmjq6wIAcLttD6FIg1sLe7Sx4LQ3IQSu7kuGkFSQxP11HtTN17v3b11hRV9Fq/QRERHVN0IIbDy1HBIkCAhIkLDx1HI86B/EaikiIjKrepEZyc/Px8GDBxEVFQUAaNeuHYYMGWJ083RLiY2NxaFDh9CsWTN07drV0uEQNTryG7nIjCuAVG7h0fLN1/VV9Pm5ydAlwN0SYRMREZnUhdjjiJJHaL4XEIiSR7BaioiIzK7OJ6X++OMPTJs2DWlpaVrbPTw88OOPP2LkyJGWCayaFAoFJk2ahKKiIixdurTSnlxFRUUoKirSfJ+dnW2OEIkaNgFc3Zd8v5dUOZZqvk5ERGRO6iopmSSDStyf+SCTZKyWIiIis5NVPaRyRUVFCA0NxYQJE9CrVy906NABvXr1woQJE7Bu3Tqt5Ep1XblyBc8//zxSU1Nha2uLzp07o1OnTrC1tUVaWhrGjRuHiIiIqk9kYSqVCi+++CLCwsIwffp0TJo0qdLxixcvhqurq+arZcuWZoqUqAFTyVCQodCbkAK0m68TERE1VOoqqbIJKQBQCZWmWoqIiMhcalQpdf78eYwePRoxMTE6jc/Pnz+PX375BR999BF+/fVX9OzZs9rnX7ZsGYqKijBkyBCsX78ePj4+AICkpCRMmjQJhw8fxvLlyxEaGlqTu1GrhBCYPn06Nm7ciIkTJ+K7776r8pg5c+Zg1qxZmu+zs7OZmCKqKSsVBrzVFsW5ShxIWILWJc8h2npLnWi+TkREZA7le0mVx95SRERkbka/+7p79y6GDBmCjIwMNG3aFNOnT0fnzp3h4+OD5ORkXL58GatWrcLt27cxdOhQXLx4Ec2bN6/WNY4dOwY7Ozts3LgRXl5emu3NmjXDTz/9BH9/fxw7dszYu1DrVCoVXn75Zaxduxbjx49HaGgoZLKqi9Ps7OxgZ2dnhgjrjoRMO0Tdfge3nFilQrXHwc0WDm6AUKTBrgQQ1pZvvk5ERGQuJcpipOQm6E1IAaW9pVJyE1GiLIaNdeP6W5SIiCzD6KTU4sWLkZGRgWeffRYbNmyAvb29zpj/+7//w6RJk7BlyxYsXrwYX3/9dbWukZCQgPbt22slpNS8vb3Rvn173Lx509i7UKvKJqTGjh2LDRs2VNpHqjETAjgf64KiIlscvKrAPCH46RwRERGRidlY22H52O3IKkgHULrC7MGEpRhSZoVZN3tPJqSIiMhsjO4ptXfvXjg6OiI0NFRvQgoAmjRpgrVr18LR0RF79uyp9jUKCwvh5uZW4X43NzcUFxdX+7y1TaVS4aWXXsLatWsxevRobNy4kQmpSuTmBiItzxYAEJ8lEHYl2cIRERERETVMXs5+aOfdBe28u8DDrrQ9hIddS822ps6+Fo6QiIgaE6MrpRISEtCtWzc4OTlVOs7JyQkdO3bEpUuXjL1UvbNo0SKEhobCyckJgYGB+Pjjj3XGjBw5Ej169DB/cHWMEIBcPvReZwMJEoDl2y8juJMPq6WoVuVJdwEASQXXADxp2WCIiIiIiIgaIaOTUs7OzkhONqyiJTk5GY6OjkZdRy6XY/369RXuA4ANGzboNFpXmzx5slHXrYmYmBgAQG5uLj755BO9Y1q1asWkFEqrpAoK7jdxFwAi7mQg7EoyBrDPj0HkmQXYFHYbE4LbwNtNf9Ui6ZLL/gYARKTvghCzmAQlIiIiIiIyM6OTUg899BAOHjyIn3/+GePGjatw3ObNmxEXF4eQkBCjrhMVFYVp06ZVOmbq1Kl6t0uSZJGkVGhoaJ1eEbCuEEJALh8KQIWyM0llksRqqWqQZxVi5e6reLy7H5NS1VAopQAAMopjcSH2OHoGBFs4IiIiIsPkpOffv/W2cDBEREQ1YHRS6o033sCBAwcwZcoU/P333/j3v/+N1q1ba/ZHR0fjq6++wjfffANJkvDmm29W+xr+/v5MSjRgN1NUWlVSaiohWC1FtUZTVSkkQBJc/pqIGrzEi1HY/9b/MHTF6/Dt0d7S4ZAJZGcUaN0SEVUmI6fo/i0T2VTHGJ2UevLJJ/H+++9jyZIlWLFiBVasWAE7Ozt4eXkhJSUFRUWlT3whBObMmYPhw4dX+xrqaXDU8AghcPiaAuWrpNQkib2lqHaU9pACIJUmpwQEouQRrJYiogZLfiUaGWFxkF+JZlKKiKgRSs8t0rolqkuMXn0PABYvXoydO3fi0UcfhSRJKCwsRFxcHAoLCyFJEvr164ddu3ZV2FeJGq/iEhUyCwQqegoKASRm5KO4RAUASM+TY9PpFUjPk5s0jto6L9VNQghcytits10mybDx1PIKe9MRERE1RLdSlIiKege3UpSWDoWIiBopoyul1J566ik89dRTyMvLw82bN5GbmwsnJye0a9fO6Obm1PDZ2Vjh1WA7bIteCgDo6DAeV/M3o4/XZJxJWY93hv4XPQI6wc7GCkBp8mjz2S/Ru83j8HA0Xc1pbZ2XzCMzM1PrtipJBdeQURyrs10lVBarllJXblW0CmB6nhz7Lm3CsK4T+BwlIiKTEULg4FUFiop8cPCqAvOEYHU6ERGZndGVUq1bt0b37t1RXFwMAHB0dET37t3Rr18/dO/enQkpqpKrvQz29vGwt4+Hp5MC9vbx8HFRwt4+Hu39msDX3cHSIVIdV92kVGmVlP4/uNW9pcpXS8lv5MJm02jIb+TWIFL9hBCISN8FQL0KoG6lljpxymo+IiIypbAryciOyUOnv+8gOyYPYVcMW1WbiIjIlIxOSsnlctjZ2cHW1taU8VTbjRs3MGPGDDz44IPo3LkzRo4ciZ07d1o0JiKqm/JLMgDon6InIJCSm4gSZfH9bULgyt4kyDLccWVvksmn912IPa6p3FKvAkhERFSV7PxirdvqEkJg+bZINI9Og32+As2j07B8WySnsRMRkdkZnZRq27atwdUJxjpw4AC8vb3x9NNP691/7Ngx9OzZE6tWrUJ4eDiuXr2KnTt3YtSoUfjggw9qNTYiqn9Cmr+HIX6zAQCtSp4FAAzxm43/jttZ+jV2O2ys7TTjw09EIzOudGWjzLgChJ+INlksQghsPLUc0r3KrYoqtYiIiMrLupeMyjIyKRV2JRkxF+PheG9FLsecIsRcjGe1FBERmZ3RSamJEyfi1q1bOHv2rCnj0XLo0CGkpaVhzJgxOvuKi4sxZcoU5Ofnw8HBAe+99x6+/fZbTJw4EQDw+eef4+TJk7UWGxHVPw7W7vCwawkAsEdTAICHXUu08+6Cdt5d0NTZVzNWCIHNK8Puz/aTgM0rw0yWNLoQexxR8ggI6K4CSEREVFvUVVJ+Mema2mEBwC8mndVSRERkdkYnpd555x0MHz4cI0aMwI4dO2rlF9hff/0FSZIwYsQInX3bt29HbGwsZDIZ9u/fjyVLlmDGjBlYv349/vOf/0AIgVWrVpk8JiJqHMJPROPWpSSU/Yv91qUkk1RLqaukZJL2SzBXASSi2lCYk6l1S41b2SqpMp+7sFqKiIgswujV94YMGQIhBFJTU/Hss8/C1dUV7du3r7DBuSRJOHz4cLWucffuXbRt2xYuLi46+/bt2wcAGDhwIPr27au175133sHSpUtZKUVmlSJPQsY/vyJF3hkIcLd0OFQD6iopmUyCSnU/QSSTSdi8Mgzd+7eu0QpF6iqp8iy5CiARNVyFeZlat1T/VbVya0XKV0mV/U1WtloquJMPV+IjakDiMiLL3PatfDCRmRmdlDp69KjW95mZmfj7778rHG/ML7aUlBR0795d775Tp05BkiQMHz5cZ5+rqysCAgIQHx9f7WsSGStFnoys878hRf4agI6WDodqQFMlVY5KJTTVUj2C2hh17rK9pISepuvq3lIP+gfxDQEREenQXbl1lsG/L4pLVEi/kQKfe72kylJXS8lvpKC4RAU7GytThk1EFiKEwKnoXwAAp6J/wUsDpvNvTKpTjE5KHTlyxJRx6CWTySCX6y6Dnp2djRs3bgAA+vTpo/dYd3d3xMbG1mp8ZHr5+fkAgNTUVLTztnAw1CDkpOXBPSXX4PHqKilJAvTNopPu9ZYytlqqRFmMlNwEvQkpQHsVwLJN14mIiAD9K7caWl1ray3Dw9kFiJWgfzFaCeiVXQBba6M7fBBRHXMh9jjkOaXtJ+Q50azIpzrH6KTUgAEDTBmHXq1bt8b169dx9+5dtGjRQrP90KFDEELAzs4OvXr10ntsSkoKmjVrVusxkmlkl5T+cVU2KUVkCjnp+XBPzYfCwPElCiVSE7L1JqSA0kRVWmIOShRK2NhW/yXUxtoOy8duR1ZBOgBgT9gJHExYiiF+szE8uD8AwM3ekwkpIiLSUb7atrrVtSUKJbLkufoTUgAggOyUPKN/xxFR3XL/NUMGARUkyFiRT3VOnf5tM2TIEFy+fBmvv/46fvnlFzRp0gTZ2dlYvHgxJEnC448/Djs73Tdu6enpiI6ORr9+/SwQNRmqbDPn+CL2/6LacTU5B1fadoNvrm4PJ31sbK2xZMtUZKeXJkj3hJ3AgYQlCPF7X5M0cvV0qNEf617OfvBy9gMAeNjduXdbugogERFRRcr3JCy7cqshlQ/m+B1HRHWH7msG+5dS3VOnf+O8/fbbWL16NXbv3g1fX18EBgYiKioKWVlZAIB3331X73Fbt24FACal6jh1k04AyFdVvtKLunKK0/qoOoQQ2BaRhKImDkhOfgKiizDoU6Gmvi5o6lu6wILbbXsIRRrcWtijTWdWXxIRkWWUXblVJVSa7eqVWw2tfODvOKLGwVSvGUS1zaCk1ODBg2t8IWNW32vZsiW2bduGsWPHIi0tTdNIXSaT4aOPPqpwCuHXX38NSZLwxBNP1Dhuqh1CCFzK2F1mS2lzA1HBnKmySSkiQ4VdScadjAIAQEFBS9xMUaG9Nxu3EhFR/cOVW4moOviaQfWFQUmp8ivtlaXOrupLJpTdZ2wWdvDgwbh9+zb27NmD27dvw8XFBSEhIWjfvr3e8WlpaZg2bRokSUL//v2NuibVvrJNOkuVPn9yBFdMJNMQQmD59stlGparcPiaAt3bOQAArO2tYXCjKQvKuLdCUkZOEcAqQaIauZWiRFTUO7jlpDTJ+Uorfp80ybmIKsOVW4moOviaQfWJQUmpilbaO3HiBBYtWgR3d3e8+OKL6NixI3x8fCCXy3H16lWsWbMGGRkZmDdvXo2m0jk7O2Ps2LEGjfX09MTMmTONvhbVvspeJBOU5y0UFdUXOXGpyE/JBgAU3r1/m3z+NgDAwdsVzi08EXYlGRF3MsocKUN8lkBMdumKQrZNbOpFUio9t0jrloiMI4TAwasKFBX54OBVBeYZ+YFZ2Q/hItJ3QYhZ/IOeah1XbiWi6uBrBtUnBiWl9E2T++eff/DJJ5/gueeew9q1a/U2HJ8/fz6mTZuGjz/+GH/99VfNo6UGoaJSUgAoEPV3el6UvBh+zy9HlLzY0qE0WCVFCmzqPRv5yVla2+NW/I1NK0qn9zo0c8OLt7/F8u2XIZMkqMq8gZQA7DyTAycvc0ZNRHVB2JVkxGeVvh7EZwmEXUnGACN66JTth5hRHMvpD2QWXLmViKqj/GvGqRvh+PX8/2FMz4/waGB3AHzNoLpDZuyBCxYsgI2NDVatWqU3IQUAtra2+PHHH2FjY4P58+cbHSQ1HGWrpKoaV58IIbDvah5s3Vvij8hc/HdHJOSZBZYOq8GxsrWGc8umgKyC549MgnMLT/x1Mw0RdzK0ElJA6STRO3IFcnMDaz9YavQSExOxYMECJCYmWjqURk8znffe9xKA5dsvV/t3Tfl+iOrpD/XtdxbVT17Ofmjn3QXtvLvAw64lgPsrt7bz7oKmzr4WjpCI6pKyrxnezq0BAN7OrfmaQXWO0UmpU6dOoUOHDnBwcKh0nIODAzp06ICTJ08aeylqQKoqJVVTCdP0+1DLTczAqQW/IDcxo+rBRgi7koy7maUxJ+aosO7ET7iZHFcr1yovRZ6EjH9+RYo8ySzXsyRJktD3o/GAqoLnj0rg0UXj8d8dV1B2No2jYxTatfsCjo5RkADI5UPB95BU2xITE7Fw4UImpeoA9XRep4x8dPr7Dpwy8hFxJwNhVypf+bW88v0QBYSmWSwREVFdlZ9VoHVLVJcYnZQqLCxEQkKCQWMTEhJQWFho7KWoAVGXkv537A5kJ83EzZtv4ObNN3Hr5puIixuLQOtnAABWMoNmlhosLzEDpxf9irxySSlTrOqn+wm8QHGxN7ILUow+Z3WkyJORdf43pMir9+aqvgoI6QGfXm0hWWm/fElWMvj0agvfwV2RkJFfJukk4OOzF02ayOHjsxcCAgqFa4V5LSJTyc0oQkfPx5CbwX5glqR+jZYBaFF4AVYjfkKLwguQoXrVUhVV+qqX1ma1FBER1VV5WYVat0R1idFJqW7duiExMRHfffddpeO+//57JCQkoFu3bsZeihoYL2c/xKd6ITa1OQoLW6KwsAUKClsgK+shZOUGmDUWUySl1J/Aq9+OCEgoKGiJK3F8I1ob1NVSQqnS2i6UKvT9aDya2Fpjx9zHsOs/pV/jHomGg8NdAICDw128PDwZbdt+BSujX/2IDJObWYROno8jN5OvBZakqZJKz4V1t5MQHpmw7nYSTum51aqWUvdDLF/pW3ZpbSIiIiKqHqPflr333nsQQuDf//43xo8fj2PHjkEul0MIAblcjrCwMEyYMAGvv/46JEnCe++9Z8q4qR4rX1l0nwoXE5rWq2lVmk/gdVZeUmHnmRx+cl5L/sm9jSSbXKhEaWJKJVRIssnFP7mlK/D5eTigS4A7Ovu7Ib54j+Y4CRJuZ2yDjU2W3vM2do7WHrA62xOO1h6WDoXIJO7/vhFoXnwRwqe0glX4pKB58UVIEAZVS1XVD5G9pYiIiIiMY3RSatSoUfjss88gSRJ+/fVXDB48GL6+vrC2toavry8GDRqEn3/+GQDwySefYNSoUSYLmuq38pVF98mQlm9fr5pQq+9L+YbagAx35Ipq9yupz8zV1Hnr1q0YPXo0dhRfhEwqfQmTSTLsVIRj9OjR2Lp1q2asvv4v8pzoWo2vPnO08YT1373gaONp6VCITKK4RIWEjHw4p+fBuuspQHUvqaSSYN31FJzT85CYkY/iElWl56nO0tpERER1za0UJaKi3sGtFNP27SUyhRpNYJk9ezb+/vtvTJgwAV5eXhBCaL68vLzwwgsv4OzZs/jggw9MFa9B4uPjsXTpUnTt2tWs16WqaT61rnDxPVFvmlAbcl+MWd2pvjJHU2elUomZM2dCCIHrkCNWlC5zGyvScU2UJgDfeustKJXKCisbpHsve43l50LUmNnZWGH7nMF42OlGaZWU7N7/e5mA8EnBw043sH3uYNjZWFV6HnU/xEkPr8XNm2/qfE16eC3+O3Y7l9YmIqI6RwiBg1cVKCrywcGrCv4NTHVOjbtJ9+jRAxs2bAAAZGVlITc3F05OTnB1da1xcNWRm5uLLVu2YMOGDTh69Cj/s9VR6k+thQCcM/LR8mYK4tp5IcddvYqjBIXCFVV8aF0nlL0v+kmaT+CresNDhjl+/Dju3r2r+f4PXMYo0R1/4DKA0l+6cXFxOH78OFxayxAlj9A5h0DpkytbGauzj4gaHvnlJMibHy6tkpKVecFWSZA3P4zkyJfgF9y2yvM0dfLFT39eRVFhC616KZkk4ac/SzD6kWamD56IiKiGwq4kIz6r9DdXfJZA2JVkDOjM31lUd5h0iTNXV1ezJqNUKhUOHDiADRs2YMeOHSgoKNAko6SKy1fIguxsrLBj7mNIyy7EF1PXIy9fgTYJV9AiqCWuFfyMDjbP4JbYBBur/1g61Cqp70t6TmkT44Mn/sEHM/+F5157EXLH/ejn/S+8MnI4E1ImVL4KKwopWIpDOuMSEhKwM+lXSJD0T7cRQELRqdoKkwgAkJ6Rhiuph5Ce0d7SoTRaQgis2rAWoque1VDvVUut2rAWXwd9VOXfDerp2uWphNA0TOcf+UREVJeU7eUrAEgoXXk2uJMP3y9TnVEv15+6cOECZs2ahebNm+PJJ5/E5s2bkZ+fj8DAQCxYsADXr1+Ht7e3pcOkCvh5OEAZl4m8xNIlSa1T3WCXUgR7+3gU256qV02o1Q21uwS4o7mbNYrTouFqWwB7+3g0c1XBV1MBRqbg6+tr0DjvZl6V9n+BBBSLHBNGRqQrPSMNV9MPIz0jzdKhNFqK4hIk+B1GRS8FEECC32EoiksqPU9V07UlCY1qujYREdUPuquEo1orzxKZg0krpWrT3bt38dNPP2HDhg24evUqgNI/Elu0aIGxY8diwoQJePDBBy0cJRlCCIHNK8OgTtkLSQV5mAJ4FkhSXbR0eFSHBQUFoUWLFoiPj9f75k+SJLRo0QKDBgxGt17b8PGb63H3ZprWG1JJBqjc0vHAiNaIzF9rxuj1c3Ww1bolItORrFRw8FMgu6iiAYBj8xJIVpXPGa9qurYQ4HRtMitN02InNi0mIv3KrhJedlEmmSSxWorqlDqdlMrNzcXvv/+ODRs24NixY5om6h4eHnj++ecxYcIEBAcHWzpMqqbwE9G4dSlJ870kZChIEpDiWkD4363kSOPlZ6aWuW1TK9cwl8TERHz//feYMWOGwZVDDYWVlRU+/3geZv37FQCSVmJK/Ut16Uf/BysrKySEFyHhlAQZmuqeR94UUkITwM1MgVfC5V4yyoVJKSKTs7G2w4oJO5FVULoowp6wEziYsBRD/GZjeHB/AICbvWeVDcrLTtfeE3YCBxKWAgBCypzH08WOCSkyi/JNi+cJwTeWRKSD086pvqjTSSkfHx8UFhZCCAFHR0c888wzmDBhAoYOHQpr6zodOlVAXSUlk0lQqcqWrwDWZ3pB0fIuUAt/V+XdS0qpb2tDUXbpFB1VUW6tXQO4v8rdM8880+iSUiXFRYj7/WP8q3vFCZy7Wz6BYtwkbF4ZBkmC3soGAYHksBLgaVYoETV0Xs5+8HL2AwB42N25d9sS7by7VOs8fh4O8PNwwBaFQEb482h5MxXWY/PRJcDd5DETVYZNi4moKmWnnev7W1g97ZzVUlQX1OmeUgUFBQCAjh074tChQ/jpp5/w5JNPMiFVj6mrpLQSUgAgAJncG1JcCwBAcW6mSa9bkJmtdVsbinJKP4lXFebV2jUaOysbW7j6tCydg6ePJIOLdwsIWCE1IbvCqTYSJBRlqQCVjBVKRGQwIQQOXimG9w0VrLMccWVPEvtIkVmVbVoM3G9azOchEZVVnWnnRJZWp7M7nTt3xuXLl3H16lX07dsXnTt3xvjx4zFu3Di0bt3a0uFRNamrpCqrXlFXSxWVS0qpk1TGJqvy7yWj8msxKUUVSyq4Vub2SaPPI0kSBk+fj43vPKN/gFBh8PT5sLWzwZItU5Gdnn9vqs0SAECI3/vo2sUXyw68hae6vYotiXX/F3F+VoHWLRFZTtiVZBRnXoLNU7ugOt4XmXdbIPxENHoE1e9p4VR/lJ+OU7ZpMauliEit/Crh6qnnnHZOdVGdrpS6dOkSLly4gLfffhvNmjVDZGQkPvzwQ7Rr1w59+/bFV199haSkpKpPRHVCiUJZZfWKlOsEqHSfluokVflkVWOUkpKidVvXCSFwJXM3AOBK5u4af5rbtvfj8HvgIUgy7V+ikswKfg88hLa9HwcANPV1QZvOzeDWwh7CKw3CKw1uLezRvIMrhFcaHNzrxy/hvKxCrVsisgwhBJZtuwRvv/0QHpkoeeRvqCCweUUYq1TILMo2LS5L3bSYz0MiKqvsKuF+bjLY28fDz02m2cZVwqmuqNNJKQDo3r07li1bhri4OOzbtw/jxo2Dvb09Tp8+jbfeegstW7ZESEgI1q5di6ysLEuHS5WwsbXGki1TsXRr6dfAt9qheMwWFI/ZgubPovTfo7cBVayC1NilpqZq3dZ1F2KPI6WwtI9LSuEdXIg9XqPzqaulhEp7xSGhUmLw9PkmnxevXeVFRI1V2JVkJN8Jg9RUDgAQPilAy7u4FZmE8BPRFo6OGgN1lZSqXPKpbNNiIiKi+sag6Xvr1683ycUmT55s9LEymQwhISEICQlBXl6eZlW+o0eP4tChQzh8+DBee+01PPHEExg3bhyUSi6RWxc19XVBU18XAIDbbXsIRWlzcLsSQFinWTI0qgVCCGw8tRwSJAgISJCw8dRyPOgfVKPkkbpaKuH6+dK5oJIEvw49NVVSZclv5MLqlxcQ164pbjkp8Ug1449I3wUAiEjfBSFmsRkkUSOkrpLy8j0IqCRAJgCVhJI+52B9twU2rwhD9/6t+fpAtYZNi4mIqKEyKCk1depUk/yCq0lSqixHR0dMmTIFU6ZMQUJCAjZu3IiNGzciMjIS27dvx44dOyC4PK5F3bmWgK0Lt+DZ+c8h4AE/S4djMTmpiTi3YzV6jXgJzk1rZ6U8ZXoehqIjlOm122A9MTER33//PWbMmGHwqn8XYo8jSh6h+V5AIEoegQuxx9EzINjoWHR6Swmht0pKCIEre5JgneUI7xsqHPQrxoQnDZ/ecCH2ODKKYwEAGcWxNY7bGKbqx2UIZw8HJAS4w9mD5dxEZamrpLy7yu9vlInSaqkWd3ErUmqQvaWMed2n2lGdpsXsEUNERPWJQUmpyZMn19kEj5+fH2bPno3Zs2fj4sWLWL9+PX7++Wf2mrKw+Fty4JejiJ8YZFBSKkV2zgxRmV9OWhKOrf0EHfo/WbtJKck8SamFCxfimWeeMejNibpKSibJoBL3p2TKJJnJqqUc3FoiPzMODm4t9VZJhZ+IRubd0gbhjjlFSIjJw7mbhj1OtVXlVR3mrtRy9nREYitPOHs61to1iOobvVVSaupqqbiGWS115MgRLFy4EIGBgZgwYYKlw2nUyjct3hy6D1f+iESnJ7tg/NRhANi0mIiI6ieDklKhoaG1HIZp9OjRAz169MAXX3yBAwcOYOPGjZYOiQyUKjtvkevGyC+UuX3eIjE0FOUbsJevklJTCZXJqqVadBmOmwc2okX/4XqrpDavCIOyxV0og07C6nhf+MXYIfRgCmzcqz5/bVV5VUddqNQiauyKS1RIyz8Pj2Zy3Z3qaqmWd5Ga5IgShRI2tnV6YeNquX37ttYtWZafhwP8PBwghEDycTmsspyRfFyOzvPcjE6GOlp7wOpsTzi29zBxtERERIap843OjSGTyTBs2DAmpahSQgj8LT8EAPhbfqjerFpTfG8FwuI6thJh2QbsZauM9FFXHdX0MXfxDoTzxQFw8Q7U2Rd+Iho3IxOhfORvCI9MKB/5Gw45hUiKTEduru74sspWeZWlrvIyx3Ol/GNoqseMiKrH1lqGBzufACp4PQMkNB13G0t+n9KgElJUd4WfiEZmXGkVcGZcQY0a7TvaeML6715wtPE0VXhERETVUmeTUosWLTJZhVZoaCgWLVpkknOReSQVxdX6NS7EHkd6SWlVT3pJSo1XhTOXorwsrVtzKV8JVV5GRobmtkRZjJTcBAjoT6AICKTkJqJEWVwrsaqrpFT+d0urGFC6Upaq5V34xaRDnhxSYV8O4H6VVNlph4B2lVdtU8egfgzLVmoRkfmUKIuRVZAMVPB6BggUIB2uXk3MGRY1UkIIbF4Zdj9HKgGbV4bxAwsiIqq3TPKRnkqlQlRUFNLT06FQKCocFxxs+LSTBQsWoH///pg6dWqN41u9ejVOnjyJefPm1fhcVLXExERs2bIFLWpwjos5p2u1WX1d6BdU35SthNInMzNTc2tjbYflY7cjqyAdAHA+4h9suLIQkzrNR89uDwEA3Ow9YWNtpznelA11NVVSz5/TWilL2eccHH5vAZsEP9xI1t93o/xzozxzPFdqux8XERlO/Xr298lLWLXooM7+l+cNQe++3bRez4hqS/iJaNy6VKZvqgBuXUpqkI32iYiocahRUiolJQUffPABfv31V+Tn51c6VpIklJSU1ORyVE8kJiZi69ateBMPVDm2KKcEds7WKM4rga3j/aejunKptvrn1IV+QQ2dl7MfvJxLm9zHO6YBAHwcW6Gddxe9429eicDChQvxWNCjNUpKaaqkWt6vkgKg6f1SWi1lhwMRfnBtrnt8daq8autNaG334yKi6mnq5ItjPx6AdboXVKoyrw0ScOzHRDwREmK54KjRUFdJyWSS1vNQJpOweWXDa7RPRESNg9FJqbS0NPTp0wd37txBixYtYGVlhZycHPTt2xdxcXGIj4+HUqmEvb09evfubdQ1zp07hzZtav6pD1fiq7uK81SwcwYUBQK2ZRb8qs1qlNqoQlEqlbh+/ToAICE2EVJzltFXV35WmtatsUoUSqQkZkE56JzelbKUfc7BZkcAsvIluAjdaqnyVV57wk7gYMJSDPGbjeHB/QHoVnmZUl2o1KL6KzcxA5e+P4CuM0Lg5GtAR38yiE51ihqrVMiMKnoeqlSCz0MiIqq3jE5KLV26FDExMXjjjTewcuVKBAUF4eTJkzh+vLTfSXp6Or744gssW7YMAQEBRvWHKiwsRExMjLEhauGbt7rNyqbcymm1WLlUVRXK3nO/4cSp05g+4g20Dmhd5fm2bt2KmTNn4u7duwCAPb/uQZMDVvCbfg4hXq1hF9seBfIcoINJ7wZVwMbWGpP+1wHLj+vpfXWvWqrDrGSM8Q3EL1FKvecoW+XlYXfn3m3LCqu8TKkuVGpRzSmVSkTdKk1UR926DqVSCSur2l+qPS8xA6cX/Yo2zzzMpJSJqKtTJAl6e9FJ93r6sEqFahOfh0RUE/IbubD5bTTko3MtHQqRDqOTUrt27YK9vT0++ugjvfs9PDzw6aef4oEHHsC0adPQu3dvvPbaawafPzra+JVEqP6xcdDtuV+2cknd+LymDdAFqq5C2Rr+PZIRjdiEEVUmpbZu3Yrnn39ep8FoYaYSP37+NTrBBU3utkdBCn8BVCU/M1vr1lhCCOy6/l2lP2OVwy20cH26RtepLVqVWkLgpZV/Qp4r4O0kYfXMwYAk1WqlFtVc+UT1198vx/Y/fsXKlSvx7LPPWjg6qq4ShRKpCdkVLo4gBJCWmIMShZKr71Gt4fOQiIwlhMCVvUmQZbjjyt4kiFm117eXyBhG/9a6c+cOWrVqBRcXFwCATFaaVFAoFLCxsdGMmzx5MubOnYvVq1dXKykVEBBgbGhUT7g62Gr+bWWjm5RSVy6dvxOGizmnAdS8AboSyiqrUHIU6YadS6nEzJkzK13xZsmaH/CuCDIq1sbGVEkpQyuNlKLu9rhTV2odu5yE2NTSxlexhUB8qhcGdG5m9ngcXZto3dJ98swCbAq7jQnBbeDtZq+VqPZ2aIvuXk8jPGUX4uNv4/nnn8fvv//OxFQ9Y2NrjSVbpiI7vbR35p6wEziQsAQAEOL3PoYH94erpwMTAVSryj8PT5+5g61LjuDZ9wfhkT6lfzPzeUhE+oSfiEZmXAEAIDOugFN9qc4x+jeXjY0NHBwcNN87OzsDKO3f1LJlS62xvr6+mn47RGouZZJSFSWaJEj4IWwR0ktKp2LVtAG6Nay1+gXtP7gB+9J+wzDP0Rg6ZBIA4NrVKHwf/k6V5zp+/LimEqIiSWmpuA39q9VR7aioJxQATV8oN3tPXI68YckwqySEwPLtlyGhdCF6CcDy7ZcR3MnH7J9uObjaa93SffKsQqzcfRWPd/eDp7OtVqK6c9OhcLHzQeemQ3Ek9htIkoS33noLI0aMMMtUPjKdpr4uaOpb+iGc2217CEVp7zu3FvZoY4FEMTVOZZ+HNxNLP8Bp6u/O5yARVUg99bfsH5Sc6kt1jW55ioFatGiBxMREzfeBgYEAoOkppZaXl4eoqCg+6alSFT0/BASSs+5P2VM3ea6sOqkqXs5+aOfdBe28u8DDxgsA4GHjpdnmbOth0HnKPv8rk41CZGZmGhsuGUHrZ2x3P0mu7gvV1Nn41f3MJexKMiLuZGjqvQSAiDsZCLuSbMmwqJzswhR4ex9AdmGKVqLax6E9PJqUPvc8mrSEj0N7CCEQFxen83vSlHJT07VuiYiIqPHSLJBQ5g9K9cIIRHWF0Ump3r17Izk5WfNm++mnn4YQAu+99x4OHTqEvLw83L59GxMnTkROTg4effRRU8VMDVBBhkLr+2Geo/HfcTsxY8ACrWlWZRugVyUxMRELFiwwOHlUXb6+hiU2XNCkVpNSt3KBDc8Nx61abltVnJupdWsKWclxSLh+AQnXL6AwIwHNHCUUZiRotmXJK69Ea6jUVVKycslamSRh+fbLNUrKkmnlFKbC2/sQcgpTtV5rOno+huS8m4jLvojkvJvo6PmYZl9tvSYBTEoRERFRKXWVlExW7u9JmYTNK8P49yTVGUZP3xsxYgRCQ0Oxa9cuTJo0CYMGDcKIESOwY8cODB06VDNOCAE7Ozt8/PHHJgmYGiZVuUXQPGy80NarM77580PIJBlUQqXZV7YBemUVeNEXrmPVwpUYFPgw8u/e61d0NxvJ528DABy8XWsUc1BQEHx8/ZCcmFDhmKZu7miT2bRG16mMEAIHkyVkuLviYLLAvBr026pKUV6W1m1NlRQX4YeX+yMvQw6VEIjNVqFvc2sc3/4N7vz5HWSSBCcPH7z1+3VY2+pv6u3m5qZ121Coq6TKUwmhqZayRG8pqpw6Ue1q64sziZtRUHL//4q9tStcbX2RVZxocEKbiIiIyFiaKqlyVCqhqZZibymqC4yulHr66acRFxeHESNGaLb9+uuvWLBgAdq3bw8bGxu4uLjgySefxF9//YVevXqZJGBqPC7EHkeUPEIrIQXcb4BeWbVUSZEC5yevwTvSYJyfuAbXVpwCAFxbcQqber1X+tV7NoRCVeE5qmJlZYUP5i8GUPH0w+kjntepdqkJpVKJGzdLk2o3bt7GkUsJiC8sPX98oVSvpnZZ2djC1aclrqapsPJcEdZFKrD1hgLrIhVYea4IV9NUcPFuASsb2wrP0RCTUppeUhU8bSQJrJaqo4KCguDp6Yms4kSthBQAFJRkIas4EZ6enggK4uIHREREVHvUVVKV/T3JaimqK4xOSslkMjRv3lyz+h5Q2vx83rx5uHbtGgoLC5GRkYFdu3ahZ8+eJgmW6hHvNtjw3HBEyvMNPkSoBFKjChD/Ty4uX76JDaeWQYL+V1IJEkLDlmDnguXISpTr7LeytUaTZi5QVfRCK5Pg1MITV6/fRvw/ubhy+RaUSqX+sZV4fNjT8Hr8XXg30658aOJmhenv/Rt9u/Wo9jkrsnXrVrRq1Qr//XY1AOC/367Gk0E9kB9dujKhBFGvkhWSJEHR7jH8eq0Y2cXa+7KLgV+vFUPR7rFG14+uuESFhIz8Spf9TszIR3GJ8QlVqj3Kksr//1W1n4iIiKimShRKpCZkV/r3ZFpiDkoU1X//Q2RqXDeWTE4IAXR7HBnurvjlahqmVzKlTJ2Iuns2F/Ir+SjOLX2jHW71PYZ81Ao2jhU3QE/LTcQfH3+JHs+EwNXXW2u/JElo9eoAXHrjZ/1BqgS+jd6Dvz66DAC4gO8QumY3Vq5cCU9fz2rdX8fWfbD9+/ex8X+f4Yvly+DQ1BqD/tMCD/r3AmLyqnWuipRdZr6swuxUFB5aBq/H34Vj6z71amqXUqnEp1/+WOmYxV+uwivvzWtUK5XZ2Vhhx9zHkJ5TBEC9/PxShNxbORAAPF3sYGfTeB6T+iIsLAyZWZX3csrMSkdYWBgGDRpkpqiIiIiosbGxtcaSLVORnV5aIFD69+QShPi9r/l70tXTATa2TAeQ5fFZSCZ38W4B4NEcAHA7s6jCJEnY0TCc/iYJxTm6FR8qJXBsaRxsnXTfeKsTXAvenIZrytUVxuH+SBvEinT4W3kCqjLJHJmEWFU6/kq9rDU+Pj4ezz//PF59dwbg7Iy/I85gwKMDDLrPVlZW6NChAwDAuokMksy0U/bKLjOvT/qptXAI6AUrK2ss334ZwZ18TFphVH7aoFKp1EoUKZVKREeXruIRHR2ts1+fI4cPI6GKhs/xiQk4cvgwHg8JqeE9qF/8PBzg5+EAADggz4fLtr6wHp2PLgHuFo6MKnP3brxJxxmjMCdT65aIiIgap6a+LmjqWzqrye22PYQiDW4t7NGmHnx4TY2LQUmpRYsWAQCaNm2K1157TWuboSRJwv/93/9VMzyqb4QQ2Hw2HVCpAJlMM6WsfJJk69atmDd3HgCgaWATdH7OE5e3pCH1RqFmTGGmEoWZ+ktKJUnC0i++wVOouN+QJEnYiyuYoeqvvUMlsFdc1hkv7lV0xVifgcw1ByeTt0GlehcymdGzXE2i7DLzFVHmpaEw6Rrs/TqbvFpq69atmDlzpiaG/367Gr/t2o+VK1fi2Wef1dm/Zs0aHDhwQLO/IvLUVIOub+i4hkgIgSt7kyDLcMeVvUkQs2qvkT3VXMuWLUw6zhiFeZlat0RERESuDrZat0R1iUFJqQULFkCSJHTo0EGTlFJvq6p/jXoMk1KNQ9iVZNxKKQbuJXIEJJ0kibryR+2Bpzzg3MwWDzzlgRPLK17JriwhBBKTk5AEnwrH5P5/e3ceFlXZ/gH8e2YGhn0HQUFQ3HI30dzR3FoszX3JNDV967U0K3+VFZhvm2Vm2WpppmmZmrZpWeFui+CGpuKCC6Aisivr3L8/cCYGZmCAgUH4fq5rrtFznnPOM4ezzLnnfp7n0lWkIA0OTbyQE38NEEAUIEmXhhSkwRlqZMM46NWsfVOo/NMAAPkel7FhyzcYde+YiuwCq7N0+PjC60Wjtek7wrZGtpS5ZoP6rLKnn34ab731ltn569evNxuYatiwoUV1sLRcXXRo91mkXbgBAEi7cIOjpNSQpINx+Hn2+xj8zn8R0LG5xcv17t0bgYGBSEhIMHlvVBQFgYGB7OicasS1C4nISi5qTnrt4iXkyHVcu3gJ52NiAQCuft7wDORIkERE9YHbzWCUG4NSVAtZFJSKiIgAUJQpVXIakZ5+1DCVUrK1nGIUJCme+ePbyhEewVoAgEewFndGBOLw2qtGGVNluQ7TmVT5ubnY9PBsjHH2wZnEPWgotwEAFAHgkIQxah9k5+vwRd4lFG882OweV0CXAagE0ClYd+A9jLh7VIWypfSf52rOGQSVETSzlJ+fX/mFABRevwbRFQIqtaEj7Kr0O1RWs0H9tLffftvsfEVRMHv2bKMROovTP8CXlQUWFBRU5Qf4W/UXIf2oKVAACICbo6R06NWE2VLV7Mqxs0jdeQFXjp2tUFBKrVZjyZIlGDlyZKkfbfR/s3feeade9ZFGtpGfm4vXutyPzMvGmabnFv+DnxcvBwC4+fvilfjdsNNqbVFFIiIiIgAVDEqVN43qt53HLuPwudRS03UiRtlSxTN/Wt7jCV2hQKUueoBz8rKrUMaUE8w93AlcQy9CUeUDcg0FhwOgyfZAgXManNufhbMCuOcpUPaj6IEfQPMOoVD7FxvGXSXIcUuscLZUk34NABTicMpv6IhxFi9XVal/roL9+d/wbMRrGDP6nip3hG1Rs8EyRiwUEVy4cAG7du2Coi0dRDE8wI8YAVP5lgqs8wBf1V+EbJXufGj3WZw+cunfCQKcPnKJ2VI1oCL9Ml1Pv2H0Pnz4cKxfv96oSSsABAYG4p133imzSSuRtWjs7eHVuCGyklMgOhNZeyoFnkEB0NjfmkF7IiIiqjts21kO1Rn6LClzCRz6JmUigoCAouYC+iwplVq5Wabo3SNYC99WjuVu0w4KXM0EpTT2Wjj5BBQNg6oAOcEnUOiYiZzgE4BSNAxqWq6gsNh39aIsqRIf4Ga2lE5XujN2U3xbOcLVtyhQkyOJuIB4i5Yry5UrVywveykJcx6djH1RW6u8XUubDVZlPcOHD8c369fDzc7BaLq7vQO+KaPpX02yRbqzPktKVaLDfJVKwdolO8ttNl2XJCUlITIy0mrHoyUq0i9TdnqO0TtQdFzHx8dj5ow5AICZM+bg7NmzteJ4pvpBURTcv+ApkwEpoGjk2/sXPMWsSyIiIrI5BqXIKvIKdEhMvQ5zz8oiMDQp0zfbanmPJ8RErEdE0PIe86OM6b9E50OQinyzZdqPnmUIkhV6pCCr0y4UeqTcnA/8cj7PUN6QJaUq8QGKZUuVR0TQalhDiCg3/69gF/ZCTOYBWU4fxLOEPlgxe/bsMrOYrL3dqqxnxIgR2LXpR9yHBugPH9yHBtj57Y8YMWKEVbZ/K9JnSelKPFDqdGLIlqovkpKSMH/+/BoNSlmDWq1G89CiETmbh7Zkkz2qca0H9UFwl/ZQ1MZf9RS1CsFd2qP1oD5lLp+Xl4e9e/cCAPbu3Yu8vLwyy1PN8nLRGr0TERHdqiodlFq5ciXUanW5o/AtWLAAarUaa9asqeym6BagtVNj8/P98f28/nhzRCO4R72Psd9uxXO3Cb6fVzR987z+0NqpoVarMfeNx+AeZI+U0zeQEJ2Fq3E3DL/oKooCj2AtWvdujGeeeQaBgcYjVQUGBuLhsRMAADkwn8Hkd9sdSM5EqUCZCGDnGoAcl3+DJc3ucYXZ2JHAomypK7qzcA/QQVH0n0OQpUlCSqvMMpcrjz6IZ+kv2sWbzRWXlZSKfZFfIyupdBPL4g4ePIi+ffvC1dUV3t7eZZYtr07e3t4W9QnV7u5+aBEUhGZwRougILS7u1+5y9RV+iypsrIO61u2FBFVXNbFFPSdNBH2hVpo4Wh42Rdq0XfSRGQlXDO77Ny5c+Ho6IgtW7YAALZs2QJHR0fMnTu3pqpP5fBy1Rq9V5arlxMSgz3h6uVkjWoRERFVWKWDUl9//TUURcH06dPLLDd16lQAwFdffVXZTdEtoqGXE9oGe6Kprxaa9ET4paSisTPQNtgTbYM9EeBZ9IVHRPDL/m/x2/wL+GPpJRz4Ihl/LL2E3+ZfQNKh7KKVCTDw0Q544403EB8fjxde+g86PeSLF176D86ePYsObdqWWx9FUfDz+bxSD/eKAoyO+Ajx8eeK1jvRH2q3vKJOjEyuCMizS0NObunO15OvXEJq9DpcuZyEP9K2msxsOXTfxSplS+n7Xqqokpkl2Ump+OPldcguJyh19OhR7NixA8eOHavwNitLURSE3NMN15CHkHu61UiTEjetN9R/3Q43bdmBt5pWkF+Iq4kZZWYdpiRloiC/aplwVPNs0RSR6qeC3Hys6ToXfzz+FUKU1qVefzz+FdZ0nYuC3NLZxnPnzsWbb75Z6ocYnU6HN998k4GpOsbV2xlJId5w9Xa2dVWIiKieqnRQ6ujRo2jYsCH8/f3LLNewYUM0atQIR44cqeymqI75Zv06/LD4b+SkGT9U56QVInr5laLAlAKcTo7Fr3tX4PKpw2jq64hmTbVo6uuIy6cOoyA9Ca4WdPFzOk0HR6/Ghgd8EcAzoAWa3TEQarUarduEolGYIwZ5jsTTXVbg6S4rcK/HXCDVHZ1vPIQDS/Iw0jcCb436Fk6OpX9F3Bt7Ac5Nu+PDbxYBzukm+wBSGuXi57O/V3p/Af92nuzh4WHxMpVtfpeaWhS02r9/P1JSUsosW162TkpKSqmMLXM8WwThGyTBs0WQZRWtInetDzR/h8Fd61N+4RpkZ6/BGxsmY+HGolff2c2QN3oD+s5uZpj2xoZJsLO3aJwKq2AzEfNOJxciLu4pnE4uP0h4+PBhzJ8/H4cPH66BmlFdZUlwU22vgWuQD6AyE+BXKVB7OWLAoIE4ePCgYXJeXh4WLVpU5vYXLVrEpnxERERkNZUOSl2+fBkNGza0qGxAQAAuXbpUfkGq8woLC/HUnKfLLHN0483RgkTw6baX8PHUHjj31Ydove0qzn31IT6Z2gOZ+z7BIx20hqZypiQnJwMAggdPNmRLKQpwz1NvlsrE8fF0RXj3cIR3D0eAbwDgmQ43JzcknElAu5bt0Lxp6WHhRQRb/8mGnXsALuGXMjuUPeEYg8S/TuJyzBlkXiw70GNK+uUL6NamCWZPHQd/Z6XUq3iATlEUBAUFWdRszpS0tDQAQGJi2SPvWSohIcEq66lPfALc0LSNP5q28YdHoCPENwUegY6Gad7+bjVaH8+bzUM8q9hMpK4REWz7Jx+5uQ2w7Z/8coO0cXFxRu/lYWYVmWJJP2uKoqDHgnGAmfsSdILr4b7YsXMHdu/ebZi8dOnScpuq63Q6LF26tFJ1JyIiIiqp0j+1u7u7lztcvF5CQgJcXFwquym6xeSkX0ULlxvA1dLzdu3aVe5xk5NWiJTTOfBp7og8RzVEBSglviMLgIxcQY6Yzk4oLCzEzp07AQBHErPg7OCHvJwrcHBsAN+QVkg8caBoW1cS4ZiajxxVIhJPHIBOp8O+339BwpUs/KX7C8C/wa2Sdh67jGvJV5B/cD0cRyhQzPwiragUuLlpsLbn/0FVqIKTvwemnv0IGq1dmftBryAvF59M64Xs1KJR+GZ0LB0YyMwTLNmfC93NNojvvPNOlTtWdnV1r9LyesnJyQhsGlh+wRrm6euCUTN7wtPXttcmN09Ho3e6dew8dhkJ6UUP/Qnpgp3HLiO8jfnsYX3AV/9enqioKMyfPx8tWrTA+PHjq1pdqiP09yRz9ya94EEd0SAsFFcOnIUU/nsTVdQq+HVqgjP+Rb9LFj8eLc1s3bVrF+bMmVPBmlNt5OfugFlDboOfu0P5hYmIiKpBpYNSnTt3xs8//4xt27Zh4MCBZstt27YNiYmJGDBgQGU3RbeYzPiLaH+9gcl5lv7i77M7FbedzYImVweViR9tFQBR5wtQcoy+pKQkzJ49G7t27TJs69VXX4W3qwsG+GgwesQwLHuktyHAAwCtAZzDh/jkqw8BAPl5gsP7c3FAvgZQ1MHr4MGDjbYjIli84SDuOf0i3o9Nxq54NexdioJA49EZDeAGVbFsLLtMDVSFKkABnPxdoa5A0yu1nT3cGwQhO+0qTA1XqBNBRq6gUICgoEC88847Vhl6PiQkBP5+Prh0xUR0sQJ8fX2rXJfq4OnngjGPVy6bzJr0ncuyk9lbi4jg7U1H4eIcB/+AzbiUNBRvb/JEn9YNrNYn2pkzZ4zezUm/fKHo+gAgM/UCCp3TkZl6wRB8d/b0hbtf7QsMU+UUz7greW8qLuPKRbSc0RWXHzltNF0KdWg5oytOn/2r1DKurq4W1cHSclT7+Xk4Yvb9bWxdDSIiqscqHZR6+OGHsXXrVjz44IP49ttv0aNHj1Jl9u3bh4kTJ0JRFEyZMqVKFaXaLSHmMFLjizKgruyJhSbDE4UOmUg9fBSxG53h1bQxGnZsCz8/P4vW55Wvg1Nawc3/KSg+NJ6iUuO64ozTaVfQvtjoe4WFhZg/fz7WrVtXan0pmVn4OhNonZwDfwsDPHpLlixBnz59jAI9O49dxqELmVCyHXGjAEBaoaGPrCicxgyll+kPJsBFZSsK89+Axt6yplCKouDORyKw+qn7Tc5XKQqU803w/H9H4+UlL1lt6HmVSoWlH3yEkSNHVmk9jRo1sqicg7OH0bu1uGm9oYptBV3b41ZdL9VvO49dxuFz19A8+EdoHa4gwOdHHD7XrNxsKUsVFhbi7NmzAICzZ8+isLDQ5LldMpMSANAB2B+1B/uj3gIAuHg1wOz1Jyy+5lDtZknGneG4uHYFzs49oM52gwIVBDoUOmfg++UTAa0z1CXipxMnTsSqVavKrcPEiROr8AmIiKimGQb46VW7BvghAqrQp9SoUaMwbNgwJCcno3fv3ujZsyeeeuopLFiwAE899RR69uyJXr164cqVKxg6dCjGjh1rzXpTLZKbmYWve7yAbSM/w7aRn+Hq8nNQ57hCneOKSx8cwbaRn+Gr7s8jNzMLhfk6OKjL/oXVUeOGIDc76ERw9YYOKDFynegKcVHVGACQf3Pexo0bERwcjI8//rjMdb+/eR3Cp7xgMiAFFAV4os4XlJo+e/ZsFBYWBZ30GRIqlYKj2s6lyp7AFZyXa9CV2IZAhwLnNFxUEgBVxQJHoV0HoGGrzig5RKCiUkPr2QSt0m/Dbc3blHpozbxwFZdjzuByzBmc2RcLADizL9Ywrbz+rUaMGIGlr70EtxKdyrvZA+++8gICA8vOvqhI31YOLh5G79birvWB+mhrq66T6jf9NcDV5SS0rokAAK1rIlxdTuLtTUdN9i2Vl5eHvXv3AgD27t1bZkfR+uvZ8uXLAQDLly9HcHAwNm7cWKqs2s4ezp4NUGqYUT1FgbNHA6jtLBgZguoMfYYtVCrkNj4J5ebXPQVF/4dKBTi4Gf0AAwB33nlnud0tuLq64s4776yuqhMRUTWorQP8EAFVCEoBwNdff41Zs2ZBo9Fg3759WLx4MSIjI7F48WLs27cPGo0GTz75JL766itr1ZdqITtnJ6jdAIGZjr4hULsWlUtOuQKdmX6gDOtT5UOlFEKlKNh6pgAJmToI/u2p3Kdxa9zIdoAP7OECNTZ8sQYzRoxDZsLlcut6JS0ViblaNGzVGUqJwJBOBAmZOpxOKx2wunDhgqGvjaIMiVToBMjwbG9yO1twDCrF+PTSPwz8fPq6UceyltBnS5kK0Hm1HQEFpR9I9UOCrwl7BmvCnsEfj68EAPzx+ErDNHNDghf32P9F4o0JPTCprT2Gt7DDpLb2eGNCD8x87mUsWbLEbFMlRVEq1LeVn48vOsMdfj7Wbe7n6euCVj1DrLrO+sbL2Q/juj4BL2fLMh2t6cSJE0bvtYE+S8rfawugu3n86xT4e23B4XPXsPOY8bVo84/r4ejoiC1btgAoahLs6OiIuXPnllr3xo0bMWLEiFIDBCQkJGDEiBGlAlMFeXk4vzsDMNfJugjO7U5HAUdLqxNMZdCZYrhniA4FHldR4JwGAChwTkOBR1GmsKp5eKnl1Go1Vq5cWWYdPv/8c6tl5NKtq7CwENu3b8fatWuxfft2s8ciERFReaoUlLKzs8PixYsRHx+PZcuWYc6cOZg6dSqefPJJfPrppzh37hwWLVoEe3v+QluXqVQqdJk3zGRgBAAUKGg5oysyryYiOTkZebrrZa4vI+8G4tN1hgBR1PkCKPpgjAhObkmE398XMAIBaA83fLPkIzSAFg1g2XF26dIl3PlIBERn/AXKXJaUXlJSkiFDQlFwc3kd1HalOwctmS2lz5I6p76C02m6So2mFdK5H3K1XtAZHj4VNGzVGU4N2hqVu3HjBmbOnIl77xuCa7rrZQ4J7hrobdS/lakHHkVRMGBGJELcVWjnq0aIuwoDZkRCURQMHz4c69evL5UxFRQUhPXr11eob6sW7dsgIiICLdpb3reFo68LcgLj4FhGZ+Wefi5o3buJxeuk0ryc/TC+22ybBKVOnz5t9F5d0i8X9cGUeOJAqX6ZEk8cQPqVoubJhr6kXE5A65YIqG6ejyqB1i0RLi4nSmVL/b5jW6kRzXQ6Hd58802jwFRhYSEmTZpUZj0nTZpk9PCnsbeHu1tz6K54QpXpBlVWsVemG3RXPOHm1hwa3odveRs3bkRISIhRBl1ISIjJDDrg3wxbRa1GTvAJFDpmIif4BBS1umi6T1OTyw0fPhwbNmwo1fQ6MDAQGzZssEqfhXRr0x+L/fr1w/jx49GvX78yj0UiIqKyVLpPqeICAgIwdepUa6yqTvn7778RERGBffv2IS8vD23atMHs2bPr5ChKYTPGYe+La6DKcjU0EwBg6L/ij99fQeyhT9FgZKRF69MHpQDgdJoOOldvqDJTkJepQV6aPXQQ/IarOIOyA1ym+Pn5IbTrnWjYqjMST8QAItCJIClLTGZJ6cXFxSGvQIfE1OvIOvMnUvd8hoIbqYb5KijwgCMcbwbHduI0HlS6APg3SyrqQlHQKyAgoEJ13rhxIx5//HEgPQndGmngag80dlNB23qAUabSsGHDsHnzZsP/z8HPfP9WOkGPBeMMy2/cuBFPPPGEIUNj+fLl+Pnnn/Huu+/i3nvvxekbzrielQEnFzcEdexjWM3w4cMxdOhQTJ8+HcuXL8eUKVPwySefVPiXdPcAP9wX+WSFlnH0c0Vu4zg4+lVfp7scHa/uK8jLxScT+uHG1cx/J4YCMd8fQcz3nwAAnHzdMPvHWBQqGiSmZiPAa2tRlpSqWIaSTkGA11YkpbZHXoEOBQXmg9x6ixYtwv/+9z/Y29vjt99+Q1ZWVpnls7Ky8Ntvv2HQoEEAgMK8Arid94BDWnezy9h7OqEwr8DiET+p9tFn0JV08eJFjBgxwmSwqHh/hIUeKcjqdHNkPR0QPuUFLPzsGwCm+yyz1nWd6p6NGzdi5MiRpZopJyQkYOTIkRX+QYqIiKhKmVJk3vbt29GrVy/s2rULI0eOxKOPPoqrV69iwoQJePXVV21dPauz0zrA7vZMo4AUYNx/hZtfIAKDgi1a366LhThzc6h1X19fBPa7B3l5amSdc8EpZOMznK9UQAoo6qD122+/vdm0oWgbKkXB9gtlP0BGRkbix+83Y0a7LCT/+pZRQAoAdBBcw3WEwhtD0Q73oS2ytGkAYMiSOpMuhn6W9BlNgwcPxsyZM3Hjxg2T29U/jCQmJiIxW7DxZD5Wxubj7b9zMfmp+fj70J8AgMWfvmkUkALM92+lgw5JqlTsS/rbaBvmmgw5OjpidUwKNp7Mx+qYFDg5ORlleKjVajRpUpSN1KRJkzr14MLR8eo+0SnQ7G0L18O9zL7Ue9pAdAq0dmq8MsnBOEtK72a21CuTHaC1U2Pnnt/L3bZOp8N7770HABZ1MF2ynNpeA89mAWU2n/YMDajQiJ9UuxQWFmL69Olllpk+fbrJ5lOG/giVf5vAX3IIQf/R08rts6wuX9epcgoLCzFr1iyT/ebppxXvg5OIiMgSVgtK5eTkICkpCefPnzf7qi8KCgowbdo0KIqCnTt3YtmyZXjrrbdw6NAhtGlT1ERJP6RzXaEoCu5+7WUUOKdBYNxkTd9/xZ2PRKBPnz7ldo5d0h133AHXkBY42dkX36Wn4zekwHw+U/mSkpIwYsQIHLqYAa1fUfOEAmdPnEote60iglmzZmHmjIfLLLcTp/EBduFt/I7zPv8Ymkxsv1gU9HrnnXcwYsQIODk54f3338cvv/yC999/H05OThg2bJjRuspqzpN9syuoT778ADlSgJjY/SbLmerfSgUV3FrG4cDHs3E9K7PcJkMlv4CaanpUFmYbUWVUpHPwqhDokK/LLTOwUzRfBxHBxj8Wl+zerXhhbNi3GCKCM2cta3Ko72OuvCwpveLlFEUpyngso/l08YxIuvVs374dKSllD0qRkpKC7du3l5r+b99SRQfsP1cL8PFvxy3us4youF27duHixYtm54uIUR+cRERElqhSUCo3NxcLFixAq1at4OzsjMDAQDRp0sTkq2lT030X1EW///47Tp8+jfHjx6NTp06G6a6urnjxxRdRUFCAFStW2LCG1aN5t0Gw75xVapQfff8VoV0HQK1Wl9k5til//vkndDodYo6n4BxMZxNVxuTJk+HTvT9uuKkRMnAo5s+fX+4yFy9eRG5urkXrz0QuPr2YhL8bb8d5dTJyXRpi/fr1+OKLL0plNOlt3rzZKDBlrjmPBxzRCB5oBA945zlgC44Y/t8IHnDHv8Efc/1b5bknI/V6IZZ+8KHFD8MlLVq0yKJAga2zjZw1XlD/dTucNV422T5V3Ny5cy3uHLyq7LRa6ILtygzs6ILtYKfVIr8wF5fSL5YcCLN4YVxKv4j8wlxoHbQWbV8/2lmvXmaa2pZQslzwoI5oEBZaKqgmEDQIC0XwoI4WrZdqp99/Lz/jrqxyoV0HwNUjCDoRfH+q7IzgyZMnM8uFzLK0P8zK9JtJRET1V6WDUtevX0evXr0QGRmJ+Ph42NvbQ0TQsGFDqFQqiAhEBPb29mjcuDGCgoKsWe9aTf9rpb7Pj+L003bs2FGTVaoRWRdT0O2hmShwKOqXpcAhEzpNHpQMZ9ze5zFkJVwDAEPn2H5+DSxab3JyMo7GnsLZ7ZnlF65IfbOycCZNh2N3+cG5cShCQ0Otun69n07nI+C2u3D2bDzuvvtuswEpvc2bNxua8plqzqOGCrPRD08pdxpeDyidjP7/JPpBXez0Lp4tpQ8WqlRFHbt/9913lf5sxZse1WbOdt7Q/B0GZztvW1eFLDB37ly8+eabFnUObg2KomDIkjnIVWcaMj31BDrkqjMxZMkcKIoCRaeBx9axsPt6uNmX59ZxUHQadOnUzaLtT5w4EQDw+OOPQ6Uq+7asUqmK+pcrUX9T2VLMkqobLM00N1dOURQ0bXsfzl+2x40Ccyl+RTIzMy0OglH94+Nj2VDylpYjIiICqhCUWrx4MaKjozFy5EikpaUhLCwMiqLgwoULyMnJwYEDBzB27Fjk5+dj0qRJhhG96gN907zmzZuXmufp6QkfH58ym+/l5uYiIyPD6AUAR48eNZQ5duwYLly4AKCo6WRMTAwyM4uCNpcvX8ahQ4cMZU+cOIFz584BAPLz8xETE4P09HQARQGfAwcOGNVd/7cqLCxETEwMUlOL+k5KSUlBTEyMoSnX6dOnDSNi5efkYVmbmdg7bRM0OUWdTmtyXOF6pKg/ll2TN2DV7XNQkFvU5qxJkybY/PXPcHH0KH+HAtj28x6LylXUb9v+AADk5eUjOjq6WraRlQ/8cSYZarUaM2fOtGiZZ555BteuXTOZJl8IHdJwvdgofMZ0IkjDdRQWe7jWZ0sBMGRJ6Uc3tDTzy5xff/0VAAwZU/r3xMRExMbGGsplXspDUuIlAP8es/oMrUuXLuHw4cOGssePHzc8YOXl5SEmJsZwHly5cgUHDx40lE25ocP5i0VNUQoKCoyO2atXryImJsZQNiHhHM6cOVO0n3Q6xMTE4Nq1ov1y7do1xMTEGLIEzpw5Y3SeHj18DMnJyQCAtLQ0xMTEID+/6HiOj4/HiRMnDGUPHjyIy5cvAwAyMjIQExNj2M/nz5/HP//88+9nPXoCOelF2QtZWVmIiYkxBCUvXryIi+fP/VuHo0cNzW5u3LiBmJgYZGdnAyj6ZfrIkSOGspW9RoiuAMdiD1n9GiEiiImJMTRDSk1NNdrfZ8+eLRpMIC8PixYtQlneeusto8966NAhw/7OzMxETEwMcnJyAAAXLlzAsWPHDGWPHDli+BU/OzvbsL/bDA5HQWONyX7x0NQBTft0Lfo75uXgrVWP49lXJ2PGM0Nx54PdUHDnDrQdGIKnX34Qb33yNF75bDqOxB5GYwv60HNxcYGnpyfS0tJgb2+PRx99tMzyDz74oOG80R+zOp0OwYM6wqNdoCFbSiBwaR0A59uLmkvr97e+8/WzZ8/i5MmThvUePHgQV65cAfDvMas/l8+fP4/jx48byh4+fBiXLhWdy/pjVr+/L168aLS/Y2NjkZiYCKDox6yYmBhcv17UH2DJa8SxY8cM1zxrXiNOnjxpdMymX8hF9s31lrxGnDp1qtxrhO7mMZt9NR+XEv9tBhcTE1Mt1wit1rKMu8aNG5u9Rnj5tcSB05YNCLFq1apS14jk5ORq/x5h6TVC78CBA4b9nZ6ebrS/z507Z7S/rXGNAIqaORb/Lnb06FHDMavf3/pjNikpyeiY/eeffwzHbG5urtExe/nyZaNj9sSJE4iPjzfa32lpaYb9XfyYPXvujOGY1e/vUsfszQD/mTNncOrUKcOyMTExuHr1qtH+LusaoW9OXZ6tW7festeI8r5HWHKNMPc9orquEYcPHzYcs6a+R5Q8Zqv7ewSvEbXrGhEXF4czZ87A09cFIx7rhvNJp6rtGlEfvkfU92tE8bJWJZXUsWNHsbe3l8uXL4uISK9evUSlUpUq9/rrr4tKpZK1a9dWdlO3nIEDBwoAiYuLMzm/adOmYm9vb3b5iIgIQVGPJUavgIAAQ5l27drJ448/LiIicXFxAkCioqJERGThwoXi6elpKNutWzeZOnWqiIgkJiYKAPnhhx9ERGTp0qVGdenfv7+MHTtWRETS09MFgKxbt05ERFasWCEAJD8/X0RE7rvvPrnvvvtEROTrr7+W2egrb+EBeVsZXuq1SHlAXnK7X3Q6nYiI2Nvby0vP/U9UKrXJz1ry5eTkYFG5ir4a+HvLkCVNZOFbT1XL+vWvJg0DRUTktttus6j8oEGDZNWqVWbnt4Sfyf2sf7WEX6llOmp85U2ngfJym0YS0dNBmnmqJCgoSO64444qfba2bduKiMijjz4qAOTRRx8VEZF58+ZJcHBw0TF6+Yi4+NvJwzMmiojI8ePHBYDs2rVLREReffVV8fHxMRyHYWFhMn36dBERuXDhggCQrVu3iojIkiVLxMnJSUREEo7HSLCbIsPvu1tERFJSUgSAbNiwQUREli1bJgBkb/QF6T7wI+ne80554IEHREQkJydHAMiqVatERGTNmjUCQLKyskREZOTIkTJo0CCJu3xEhixpImq1Wj766CMREdm8ebMAkCtXroiIyKRJk6Rnz56G+ru6usqiRYtERGTbtm0CQM6ePSsiIo899ph06tTJUNbXz0da3uMhcZePyN69ewWAxMbGiojI008/Lf6NGsmQJU1k1Xc/SGhoqDz77LMiInLo0CEBIH/99ZeIFF03GjVqZFhvZa4RR+KvSeCET6rlGpGfny8AZMWKFSIism7dOgEg6enpIiIyduxY6d+/vyxatMii4y4oKMhQJ09PT1m4cKGIiERFRRldfx9//HFp166doWyjRo0kIiJCRET++usvASCHDh0SEZGnRj0kr2kGyiJl2M3r1jB5TTNQjmyJktjY2KJjae9eERFZsGCB+Pv7y6rvfpAhS5pIcNNQeeyxx0RE5OzZswJAHntkdrmfY/ny5QJANm/eLCIiH330kSiKIiqVyqicWq2W2bOL1rdmzRoREcM1IicnR0REHu7xQKnrwLJly0REZMOGDQJAUlJSRERkwoQJEh4ebtgvTk5OsmTJEhER2bp1qwCQCxcuiIjI9OnTJSwszFDWx8dHXn31VRER2bVrlwCQ48ePi4jIk08+Ka1btzaUDQ4Olnnz5omISHR0tACQ6OhoETG+RoiItG7dWp588kkRsd41QkQkPDxcJkyYICIiH365VgDIE88+LyL/XiP07rnnnnKvEcvWrZchS5pIQEcnaVfsXK6ua4Snp6dF58Svv/5q9hqxfdHH4gtni9YzbNgwwzViwYIFhvOtur9HWHqN0LO3t5elS5eKiMgPP/wgACQxMVFERKZOnSrdunUz2ofWuEY8++yzEhoaaijbokULefrpp0VEzF4j9Dp16lTqGrFt2zYREVm0aJG4uroayvbs2VMmTZokIiJXrlwpdY1Qq9VyOjZJRrR4TXr3CJeRI0eKiEhWVlaZ14gHHnhA7rnnHsN2AFToGtG7d2+LjiEHB4db9hph7nuEniXXiJLfI/Sq6xrh7+8vCxYsEBEx+T2iRYsWhrLV/T1ChNeI2nKN0Bs0aFCNXSPqw/eI+n6NCAgIMDrvrKXSQSkXFxdp2bKl4f+9e/cWlUpluIjoFRYWiq+vr/To0aPytbzFVDUolZOTI+np6YaX/kTRX8RERI4ePSrnz58XEZEbN25IdHS0ZGRkiIjIpUuX5ODBg4ayx48fl/j4eBERycvLk+joaElLSxORogtZTEyMoezJkyflzJkzIiJSUFAg0dHRcu3aNRERuXr1qkRHRxsCS6dOnZJTp05JQUGBBAYGlhso2bnsO8N2YmJi5MMlyy36cgNA3N1dLC5bkdew4f1lyJIm8vmqV2Xq1KnVsg0AMrBrdxERmTJlikXl//vf/0pKSors27ev1MOp/lUUBBxmtI/fwjCZjb4my49sqZGIng7yYg+tTGtvL4qiyIYNGyQuLk4URan0Z9MHnF988UUBIC+++KKIiCQkJMiRI0dEpCgoFf5cI9l5YJvRMZuZmSkiIklJSYYbuojIP//8I+fOnRMRkdzcXImOjjZc/C5fviwHDhwo2sbxGJl5u73s+/V7ESn6wlL8mE1OTpbo6Gg5En9NQh75Rn7cvl9Onz4tIkXXpujoaMPNNSUlRaKjo6WgoEBERE6fPi0nT540BKU2bfvacGNITU2V6OhoycvLE5GiLw/6m6mIyIEDB+TSpUsiUvSFKzo62nDDP3funBw7dsxQ9vvf18uAl4Mk7vIRyczMlOjoaLl+/bqIFN0kX1v6viEoFRsbKxcvXhQRkevXr0t0dLThxpaYmCiHDx82rLcy14gj8dckeOpa+fqHKKteI0REdDqdREdHy9WrV0VE5Nq1a0b7+8yZM3Ly5EkZNmyYRcfdgAEDDHU6ePCgYX9nZGRIdHS03LhxQ0REzp8/L0ePHjWUPXz4sOFLaVZWVqn9PT94sNE5taDp3aLT6Qz7W3/MJiYmyqFDhwxBqdff/9BwzObk5Eh0dLS88fI7AkDGjnxIfH19jerv7+8vGzZsMFyTU1NTDfs7OjpacnNz5e677xYA0qdPH8nNzTXs75LHbGFhoWF/vxswQd5WhstHjSfL/v37JTk52Wh/6+/RZ86ckRMnThgds/ofmfTHbG5uruGY/eeffwxlDx06JElJSSIihmNWv78vXLhgtL+PHDkiCQkJIiKSnZ0t0dHRkp2dLSLG1wiRomNW/wXWWtcIEZETJ04YjtnPv90svZ9uKB+t+UpE/r1G6MXFxZV7jVj57WYZsqSJ9HsxUN786GPDstHR0dVyjYiJiSk3MOXt7S0FBQVmrxHbF30sg9DSonNr0aJFhmuEPij1xBNPVOv3CBHLrxHF94t+f6elpRnt7/j4eKP9ba1rxMWLFw1f5EWKHjL1x6y5a4TesWPHSl0j9MfspUuXjI7Z48ePGx4uzF0j9EGpX3/cYzhmy7tGnD592ui7aXR0dIWuES+//LJFx9DTTz99y14jzH2P0LPkGlHye0Tx/V0d14hDhw4ZjllT3yNKHrPV+T1ChNeI2nKNKL6/a+oaUR++R9T3a4Q+qFWrglJdu3Y1/P/uu+8WlUplOMCK69Kli7i7u1d2U7eckSNHCgDZv3+/yfk+Pj7i6+tr8fr0vyJY+49vLfpfFMoLlPz+++9Gyy1e+IHFgY/WbUKtHihSqVTyxZevyZAlTWTN12/Kr7/+avVt6F/rXlssIkUntyXl9RcKEZFnnnnGZBlzQUBTWVJBHvYS0dPB8OrWvIEhwl/WNsp7ubi4GC6s+ocXfSS+OH1gJ+7ykVLzqiLheIxE9HSQhOMxZZbTB6WOxF+r8Daqq+6Wrn/jT3tl6IQRsvGnvSbnW1NV9pO1TJw40aJjb+LEidWy/SNbouQ19UB5Wxkur6mLsqTKog9Krfruh1Lzliz8WADIkoUfS0FBgSEoPWXKFMN5U5ayzilzvpvxqrytDJfvZrxq8TL1SVl/r4quo6rrqQj9L9TmXsWv56ZsX/SxLMQwUSy4L+ofIkQqdwxSzdAHpU7Hlv7eXV1yc3NFrS47w12tVhsdQ0REVHdUV1yi0n1KNWrUyNAWFIBhdL0///zTqFx+fj7OnDljaH9aH+j7kjLVb1RqaiquXr1qsr+pW1XxUVaKd6itp1JU2IJjRscLAPj5WNbRua+vL+4b2rfK9SzpqaeegkajMfy/b9++8PYuuyPs4uUt1RYBcLS3BwA4Ojpi6NChZZYfOnQoHB3/HT1v4cKFeOaZZ6BWq43KnVSSkddAC+g7MVYUNAgLRUz2Wfz3v//FoEGD8N///hfXr1/HmeRsKI6+RcUcfbHr2EUMHz681DZKdrJcXgfJK1euNNTLw8PD6J2so7510q7v9Nta5SqqzeBw3PDKRa7cwA2vXLQZHF7pdTUKbmB4V6vVaNKkCYCiPvVKns/W4tbC2+id6obhw4djw4YNCAwMNJoeGBiIDRs2GF3PzdEoKozpO7DMMk899RTsb96viEqyt7fHnDlzyiwzZ84cHkNERFQhlQ5KdezYEZcuXTJ0NDZo0CCICJ5//nlDB2m5ubmYNWsWrl27hk6dOlmnxreA8PCih5hffvml1Dz9NH2ZuiAgIMDwb32H2jop6jBPJzqcl2s4gStG5QCgS+c7YG/nUO76P/jgA7Rr1wJqrXVGkFKr1XjmmWewcOHCUtM/+eSTMpdds2YNHBzKr7PeHW3aY4rS3Wjapk2bzAamhg4dik2bNpWavnDhQly/fh133323Ydq7r0dizOfPAPoOz0XQY8E4ODk5YenSpfj555+xdOlSODo6QqPRoHnn4VBdd0bzzsNNBtcWLlyIGzduGLZx9913IycnBxs2bECjRo2MyjZq1KjUg5Cnp6fRO1mHl4vW6L2uu/POO+Hi4lJmGVdXV9x5553Vsn1FUdB6eA8k4gxaD+9RpZHrmjQLMnqvKP2PPfp3qt+GDx+O+Ph4TJkyBQAwZcoUxMfHWxSQ0vvPfSNN/gChUqlM3heJSjL3Q5m571ZERETlqXRQaujQocjPz8eWLVsAAPfddx+6deuGf/75By1btoSvry/c3Nzw8ccfQ6VS4aWXXrJapWu7/v37o2nTplizZo1Rz/2ZmZlYsGABNBoNJk+ebLP6WVvv3r0RGBhoeHgrni2lUlTYqvyDoKAg9O7d22g5tVqNpg3blrnuZ555BiNHjoRKpULHCb5lli35y5yrVoXRrewQcXcw/nvfSPREU/z3/lG4fv264UuTs9oVAUcz4awuGpVI/2t0ySCM/tfoUaNG4csvvyyzHmoFuC1AjcVffowXpswwWWbTpk24fv16qYwmUwGp4p+vR48ehv9rNBoED+oITVBREEgT5IngQR3NLt+4RVe4HgxH4xZdLdpGjx49YG9vj+HDh+PcuXN4Y/48AMAb8+fh3LlzFXoQosrzctUavdd1arUaK1euLLPM559/Xm2ZRgDg36IJmiht4N+iSZXWExAQgIiIiFIBeUv169cPERER6NevX5XqQXWHNTLuTP0AcePGDQYTyGL6H8oWL16MmTNnYvHixUbfrYiIiCqi0kGpBx54ALt27UKXLl0AFP26vGXLFkyePBlOTk5ISUlBfn4+WrdujU2bNmHgwLJTxusSjUaDTz/9FDqdDr1798b06dPx9NNPo0OHDjh69CgiIyPRokULW1fTatRqNZYsWQKg6DjQZ0sBMGRJvfPOOya/PHt7NETnKX5wcHY0mq7V2mHdunVGX3ACOjhj9pyHSgWM3F3dAAC7d+9GVFQU1qxZg6ioKMx/YhJ8HBV4th+AUX0HYoTSEaPCBxgFr1w0bmh4NAsuGjfDNH0Qxtyv0frAlZubG4pzgAYeWmBGZ3t0fLgBtA5lBxEcHR1LZTRZ6vYGKnh7ekBRFDjf2x4A4Hxv+zKzOqrSvE6tVqND29YAgA5tW1drQICovODwrRIQDQgIQGRkpCEoVdHMp5LLE1mLqR8giCrC3t4es2fPxnvvvYfZs2fzGCIiokqrdFDKwcEBPXv2ROPGjQ3T3N3dsXz5cqSlpSExMRGpqak4cuQIhgwZYpXK3kr69euH3bt3o1evXli3bh0++OADeHt7Y/Xq1Zg3b56tq2d1w4cPx/r16w0PkT/iKC5JBva5XcT69evLfIgM6OCMQWOHoNtMf9x2f1HWz4gx/TFq1KhSZbve0Q7nzp1Dt9s7AwAawwHbvtqIiIgIBAYGom/fvhg3bhz69u0LR/9m+OBAHrR+FW/6Ut6v0cOHD8dTTz1l+P/QnnfjP+iFtFxg9SUgs0H1ZrWE+Wvg7VW0r+xb+hu9m+Ps4WP0Xhe4evsj/OF5cPUu+7Pfyjx9XTBqZk94+pbdpK2uKS84XFs4uzsYvZelVatWRu9ERJVVX+8NRERU91S812YLqNVq+PvX3YdES3Xt2tXQvLE+GD58OIYOHYrPPvsMr8x8Eln5ufj407fRYfh95S6rqFTwae4Irasa/3yXCpXKfMaPWq2Gj6cXAMAZGjTwb4DIyMhS5TxvZgR5engAaZX4QOXo3r07vL29kZKSgtCAhgDOWH8jVuR7W3N0e2k0fG+rO53su/oEoN/UF2xdjWrl6eeCMY/3Lr9gHVRTnYNXhZO7o9F7WaranI8IYF9jVKQ+3xuIiKhuqXSmFJEparUaYWFh8NTYo5nia9OHyIbBoUbvFVVec7eBAwcami0W18TNOh2yl8fHp2IZTy4BnugeOQYuAeyInMgW2ByPrIEZd0RERFSXWJQp9cUXX1hlYw899JBV1kN1n4OD5f0rmePs6Wt4z8S5Ci9vyWhybdq0QXh4OAL9AnDxZqZUEw8VCipRX0sUD5BVNChVVU1btMZD9/VD0xata3S7VL+FhoYavdc29W10RLI9ZtwRERFRXWJRUGry5MlVGhZbj0EpKkmXZY+TW1LR2t/4gc7Rsfz+WWqDjh07Yvv27djwykfIgAP6NnCCgwbIqqbtlRUgq27N23bEyu9+t9n2qX5q2bKl0Xtt43lzVETPWjI6ooOzh9E71T36jDsiIiKiusCioNRDDz1klaAUUUm6bHuc3JqGjg+7GGUX2cH2QSl9JpIlGUkaD0ccsYtH38Z2iNcUVFtQqiawvxKiW5eDq4fROxERERFRbWZRUOrzzz+v5mpQfaVVO+M2r/5wc/fC9WLT/dx8rbodJw83o3dLtG/fHhEREWjfvn25ZT2aNcIe++Noa68FUPs6Y66Ifv36ISIiAv369bN1VYiIqJrccccdCA4Oxh133GHrqhAREVE9Vi2j7xFZylXrB1/npggM8MSlYtPd3SwPHplTvN+N69lZyAmMg1fzhhVa3tImEi3at8FDk6cAB780ml5eZ+m1UWWbhlQks4yorvBy9sO4rk/Ay9nP1lUhqpCBAwciPj7e1tUgIiKieo6j75FNOWhcAADenu4VWq5xYGMAgHMZh3Dxka4c/VyR2zgOjn6ula9sGQICAjDr8f+Wmn4rBqUqy9fX1+id6FZVkX6ZvJz9ML7bbAaliIiIiIgqwWqZUmWN0NevXz8EBQVZa1NE6BjWGVj5GVxhZ+uqEFEdw36ZiIiIiIhqRoWCUvfeey+ioqLw4osv4rnnnjOaV9YIfYMHD8ZPP/1U+VoSldClV3fccXsYPGMuVmk91d3kzNHXBTmBcXD0damW9ZNl/NwdMGvIbfBzt30H+kRkO84aL6j/uh3Ozb1sXZVbQn3K9iUiIiLbsDgoFRMTgy1btqBNmzb4v//7P5Nl3N3d0blzZ6NpJ06cwM8//4yDBw+iY8eOVaos1X2ay14AEsot17FjR6xbtgKvdh5Spe1VR1DKt3UPXMUpAKj2ZoNkGT8PR8y+v42tq0FENhbk2RCav8MQNNPy/gXrs169eiE8PBy9evWydVWIiIiojrI4KPX1119DURTMnTsXKpXpfnzatm2Lbdu2GU1bv349Ro8ejbVr1zIoVY9czy96d/aoWLDnNs/e+A1HLCrrHuCHeyNmwT2g/L5cXL39Ef7wPLh6+1eoPpXh16Y3/sk7VS3r9vHxgbMd4GJvOiuRiIjM83LVGr1T2Tp27Ijt27fbuhpERERUh1nc0fnu3buhVqsxdOjQCm1g2LBhcHJywq5duypcObp1XS8oeneqYFDKxckF4UFqeHt6lFvWPcAP90U+aVlQyicA/aa+AFefgArVp7Zp3749np45A64MShFVGwcfV1yVRDj4MMORzFPFtoKzhs0AiYiIiKrC4qDUyZMn0aRJE7i5uVVoAxqNBgEBATh1qnoyR+jWN2B0Bzi42AMAXJxd0bexHby9PI3KOKtdEXA0E87q+v2QGBAQgOkzZti6GkR1mn+7UHSLGA3/dqG2rkqFufh4Gb1T9cm4HAb/BhzEhYiIiKgqLG6+l56ejmbNmpmdv3btWrNDwXt7e+P8+fMVrx3VC4PGdsLeM9FAsvkyLho3NDyaBZd7KhYUrYtqsikiAV7OfhjX9Ql4OZefkUfWFxAQgIiICAQE1FyWoz4L81bEoFTNSfV1gau3s62rQURERHRLszgo5eLigoyMDLPzx4wZY3ZeRkYGnJ35xY3Mc3cqypRytFOQb2J+dY+SdyvRN0WkmuHl7Ifx3Wbbuhr1VkBAACIjI21dDSIiIiIiqgYWN99r1KgRTp8+jRs3blRoA9nZ2Th9+jQaNWpU4cpR/eF2MyjlYG/6kGRQioiIiIiIiKhusTgo1adPH+Tn5+Obb76p0AbWrVuH/Px89OnTp8KVI6pubApHREREREREZBsWB6UmT54MEcG8efOQmJho0TIXL17EvHnzoCgKJk+eXNk6Uh3l6euCUTN7wtPXxTDN1a2oI/OayoiqjlH5HBQXnNySCke1u9XWSUREVWfqvlNZPVr6wc/dwQq1IiIiIqq/LA5KdenSBWPHjkVCQgK6deuGTZs2QURMlhURbNy4Ed27d8fly5cxevRodOnSxWqVprrB088FYx7vDU+/YkEpl5oNSlUHB8UFJ7emwUnjYeuqEBFRMabuO5XVo5Uv/DwcrVArIiIiovrL4o7OAeCzzz7DhQsXsGfPHowYMQL+/v7o3r07QkJC4OzsjOzsbMTHx2Pv3r24fPkyRATdu3fH8uXLq6v+VE/cSs3s9J22e7nY31L1ripbjJJGREREREREt64KBaUcHR0RFRWFiIgIvPfee0hKSsLGjRuhKIqhjD57ysXFBTNnzsT8+fNhZ2dn3VpTvXMrjTjn5lR0vHu5OtRIvT29vJF8892WOEoake05B3ii20uj4RzgaeuqEBERERGVq0JBKQDQaDR45ZVXMHfuXPz000/Yu3cvEhISkJmZCVdXVzRq1Ajdu3fHPffcAw8Pj2qoMtVlDq4e9SazyFr0wShbB6WIyPZcAjzRPXKMratBRERERGSRCgel9Nzd3TFu3DiMGzfOmvWhes7RxQPht0hGVG3h5WJv9E5ERERERER0K6h0UIqIjOn7j9K412zGkqerg9E7ERERERER0a2AQSkiK9H3H5WUlMQOv4mIiIiIiIjKwaAUkZWxw28iIiIiIiKi8qlsXQEiIiIiIiIiIqp/GJQiIiIiIiIiIqIax6AUERERERERERHVOAaliIiIiIiIiIioxjEoRbWCm9Yb6r9uh5vW29ZVISIiIiIiIqIawKAU1QruWh9o/g6Du9bH1lUhIiIiIiIiohrAoBRZXUBAAKZPn27rahARERERERFRLcagFFldQEAAZsyYYetqEBEREREREVEtxqAUERERERERERHVOAaliG5xzgGe6PbSaDgHeNq6KkREREREREQW09i6AkRUNS4BnugeOcbW1SAiIiIiIiKqEGZKUbVg9g4REVHt4OThZvROREREVFswU4qqBbN3iIiIagfHm8EoRwaliIiIqJZhphQREQEA/NwdMGvIbfBzd7B1VYiIiIiIqB5gphQREQEA/DwcMfv+NrauBhERERER1RPMlCIiIiIiIiIiohrHoBQREREREREREdU4BqWIiIiILOTuZG/0TkRERESVx6AUERERkYXcbgaj3BiUIiIiIqoyBqWIiIiIiIiIiKjGMShFREREVIc5+rogJzAOjr4utq4KERERkREGpYiIiIgs5Kb1hvqv2+Gm9bZ1VSzm6OeK3MZxcPRztXVViIiIiIwwKEVERERkIXetDzR/h8Fd62PrqhARERHd8hiUIiIiIqrDXL39Ef7wPLh6+9u6KkRERERGNLauABERERFVH1efAPSb+oKtq0FERERUCjOliIiIiIiIiIioxjEoRbWCp68LRs3sCU+ODERERERERERUL7D5HtUKnn4uGPN4b1tXg4iIiIiIiIhqCDOliIiIiIiIiIioxjEoRURERERERERENY5BKSIiIiIiIiIiqnEMShERERFZiANzEBEREVkPOzonIiIishAH5iAiIiKyHmZKERERERERERFRjWNQioiIiIiIiIiIahyDUkREREREREREVOMYlCIiIiIiIiIiohrHoBQREREREREREdU4BqWIiIiIiIiIiKjGMShFREREREREREQ1jkEpIiIiIiIiIiKqcQxKERERERERERFRjWNQioiIiIiIiIiIahyDUkREREREREREVOMYlCIiIiIiIiIiohrHoJSVZWdnY/Xq1Rg9ejRatGgBR0dHeHh4IDw8HGvXrrV19YiIiIiIiIiIagWNrStQ1+zatQsTJ06Et7c3+vfvjxEjRuDKlSvYuHEjxo8fj7179+K9996zdTWJiIiIiIiIiGyKQSkrCwgIwJdffolRo0bBzs7OMP3VV1/FHXfcgaVLl+Khhx5Cly5dbFhLoprl5eyHcV2fgJezn62rQkRERERERLUEm+9ZWYcOHTB+/HijgBQANGjQADNmzAAA7NixwxZVI7IZL2c/jO82m0EpIiIiIiIiMmBQqgbpA1UaDRPUiIiIiIiIiKh+Y1CqhhQWFuKLL76AoigYMGCAratDRERERERERGRTTNmpIS+++CKOHDmCKVOmoG3btmWWzc3NRW5uruH/6enpAICMjIxqrSMR1bzMzCzk5+iQmZmFDAee40REREREVPvo4xEiYtX1KmLtNdYRPj4+SElJsbh8VFQU+vbta3LeJ598ghkzZqBTp07YuXMnXFxcylxXZGQk5s+fX5HqEhERERERERFVq9OnT6Np06ZWWx+DUmY8/vjjyMzMtLj8s88+i1atWpWavmLFCkydOhVt27ZFVFQUvL29y11XyUwpnU6Ha9euwdvbG4qiWFynmpSRkYGgoCBcuHABbm5utq4OUa3G84XIcjxfiCzH84XIcjxfiComPT0djRs3RmpqKjw8PKy2XjbfM+O9996r8jqWL1+ORx55BK1bt8Zvv/1mUUAKALRaLbRardE0a/7Rq5Obmxsv6kQW4vlCZDmeL0SW4/lCZDmeL0QVo1JZt2tydnReTZYvX45p06ahVatW+P333+Hr62vrKhERERERERER1RoMSlWDzz77zCgg5efnZ+sqERERERERERHVKmy+Z2W///47HnnkEYgI+vTpgw8//LBUmY4dO2LYsGE1X7lqpNVqERERUarZIRGVxvOFyHI8X4gsx/OFyHI8X4gqprrOGXZ0bmWff/45Hn744TLLTJo0CZ9//nnNVIiIiIiIiIiIqBZiUIqIiIiIiIiIiGoc+5QiIiIiIiIiIqIax6AUERERERERERHVOAalqEr+/vtv3HPPPfD09ISzszO6du2KNWvW2LpaRLVOSEgIFEUx+frPf/5j6+oR2cTq1asxY8YMhIWFQavVQlGUMvtczMjIwJw5cxAcHAytVovg4GDMmTMHGRkZNVdpIhupyPkSGRlp9p7j4OBQsxUnqmEJCQl45513MGjQIDRu3Bj29vbw9/fHiBEj8Oeff5pchvcXqs8qes5Y+x7D0feo0rZv347BgwfD3t4eY8eOhbu7OzZu3IgJEyYgPj4ezz//vK2rSFSruLu7Y/bs2aWmh4WF1XxliGqBF154AefOnYOPjw8CAgJw7tw5s2Wzs7MRHh6OgwcPYuDAgRg3bhwOHTqExYsXIyoqCrt374azs3MN1p6oZlXkfNGbNGkSQkJCjKZpNPz6T3Xbe++9hzfeeAOhoaEYOHAg/Pz8EBcXh02bNmHTpk1Yu3YtRo8ebSjP+wvVdxU9Z/Ssdo8RokrIz8+X0NBQ0Wq1EhMTY5iekZEhbdq0EY1GIydPnrRhDYlql+DgYAkODrZ1NYhqlW3btkl8fLyIiLz22msCQFasWGGy7EsvvSQAZO7cuSanv/TSS9VdXSKbqsj5EhERIQAkKiqq5ipIVEts2LBBdu7cWWr6zp07xc7OTry8vCQnJ8cwnfcXqu8qes5Y+x7D5ntUKb///jtOnz6N8ePHo1OnTobprq6uePHFF1FQUIAVK1bYsIZERFTbDRgwAMHBweWWExF8+umncHFxwUsvvWQ077nnnoOnpyc+++wzCAcUpjrM0vOFqL4bPnw4evfuXWp679690a9fP1y7dg1HjhwBwPsLEVCxc6Y6MH+XKmX79u0AgEGDBpWap5+2Y8eOmqwSUa2Xm5uLlStXIiEhAZ6enujRowc6dOhg62oR1XpxcXFITEzE4MGDSzWhcHBwQJ8+fbB582acOnUKzZs3t1EtiWqfXbt24a+//oJarUarVq0wYMAAaLVaW1eLyGbs7OwA/NvEiPcXorKVPGeKs9Y9hkEpqpS4uDgAMHlx9vT0hI+Pj6EMERW5dOkSJk+ebDTtrrvuwqpVq+Dj42ObShHdAsq65xSfHhcXx4cGomJKZn4EBARg5cqVGDhwoI1qRGQ758+fx6+//gp/f3+0a9cOAO8vRGUxdc4UZ617DJvvUaWkp6cDKOq42RQ3NzdDGSICpkyZgu3btyM5ORkZGRn4448/cPfdd2Pr1q24//77mRZOVAZL7jnFyxHVdx07dsTKlSsRHx+PGzduIC4uDgsWLEBaWhruv/9+HDp0yNZVJKpR+fn5mDhxInJzc7Fw4UKo1WoAvL8QmWPunAGsf49hphQRUQ0o+UvCHXfcgR9++AHh4eHYvXs3fvrpJ9x77702qh0REdUlw4YNM/p/s2bN8MILL6BBgwaYPn06/ve//+Gbb76xTeWIaphOp8OUKVOwc+dOPPLII5g4caKtq0RUq5V3zlj7HsNMKaoU/a8J5n41yMjIMPuLAxEVUalUePjhhwEAe/bssXFtiGovS+45xcsRkWmTJk2CRqPhPYfqDRHBI488gtWrV+PBBx/ERx99ZDSf9xciY+WdM2Wp7D2GQSmqlOLtq0tKTU3F1atX2e6ayAL6vqSuX79u45oQ1V5l3XOKT+d9h6hs9vb2cHV15T2H6gWdToepU6di+fLlGDduHD7//HOoVMaPv7y/EP3LknOmLJW9xzAoRZUSHh4OAPjll19KzdNP05chIvP+/PNPAEBISIhtK0JUizVv3hwNGzbEnj17kJ2dbTQvJycHO3fuRMOGDdGsWTMb1ZDo1hAXF4fU1FTec6jO0+l0mDZtGlasWIExY8Zg1apVRn3i6PH+QlTE0nOmLJW9xzAoRZXSv39/NG3aFGvWrMHBgwcN0zMzM7FgwQJoNJpSo4wR1VfHjh1DWlpaqem7d+/G22+/Da1Wi+HDh9d8xYhuEYqiYNq0acjKysLLL79sNO+1115Damoqpk2bBkVRbFRDotojMzMThw8fLjU9NTUVU6dOBQCMGzeupqtFVGP02R4rVqzAqFGjsHr1arMP17y/EFXsnKmOe4wiHPKJKikqKgqDBw+GVqvFuHHj4Obmho0bN+Ls2bP43//+h3nz5tm6ikS1QmRkJBYuXIj+/fsjJCQEWq0WsbGx+OWXX6BSqfDRRx9h2rRptq4mUY379NNPsXv3bgDAkSNHEBMTg549exp+kR42bJihM83s7Gz06tULBw8exMCBA9G5c2ccOnQIW7ZsQceOHbF79244Ozvb6qMQVTtLz5f4+Hg0adIEYWFhaNeuHfz8/JCQkIAtW7YgJSUFAwcOxA8//AB7e3tbfhyiahMZGYn58+fDxcUFs2bNgkZTemyvYcOGoWPHjgB4fyGqyDlTLfcYIaqCP//8U+666y5xd3cXR0dHCQsLk9WrV9u6WkS1yvbt22X06NHSrFkzcXV1FTs7OwkMDJSxY8fKn3/+aevqEdnMpEmTBIDZV0REhFH5tLQ0efLJJyUoKEjs7OwkKChInnzySUlLS7PNByCqQZaeL+np6fLf//5XOnfuLD4+PqLRaMTd3V169eolH330kRQUFNj2gxBVs/LOFQCyYsUKo2V4f6H6rCLnTHXcY5gpRURERERERERENY59ShERERERERERUY1jUIqIiIiIiIiIiGocg1JERERERERERFTjGJQiIiIiIiIiIqIax6AUERERERERERHVOAaliIiIiIiIiIioxjEoRURERERERERENY5BKSIiIiIiIiIiqnEMShERERERERERUY1jUIqIiKge+vzzz6EoCiZPnmzrqlRJfHw8FEVBSEhIhZcNCQmBoiiIj483mt63b18oioLt27dbpY5VERkZCUVREBkZWSPb2759OxRFQd++fWtke5WVk5OD4OBgtG7dGjqdzjC9KseDoihQFMWKtax5NX28lGTuurJz504oioIXXnjBJvUiIqLai0EpIiKiaqAPeJR8ubi4oH379njuueeQkpJi62pSBWzfvh2RkZG1IlhV37333ns4f/48XnjhBahUtefrbFpaGiIjI/HOO+/Yuiq1Sp8+fdCnTx8sXrwYiYmJtq4OERHVIrXnLk5ERFQHNW/eHD179kTPnj3RvXt3+Pr64siRI3j99dfRoUOHUlk6NcXd3R0tW7ZEQECATbZvLXZ2dmjZsiVCQ0Otts7GjRujZcuWcHJyMpq+fft2zJ8/v0aDUj4+PmjZsiV8fHxqbJu1XUZGBl577TU0bdoUY8aMsdp6W7ZsiZYtW1ZpHWlpaZg/f77NglK1+Xh5/vnncf36dSxYsMDWVSEiolpEY+sKEBER1WXPP/98qaYsBw4cwJAhQ5CQkIC5c+di3bp1NV6vBx54AA888ECNb9faGjVqhOPHj1t1nV988YVV11cVM2fOxMyZM21djVrlyy+/RGpqKp544gmo1Wqrrdfax5Et1ObjZeDAgWjYsCFWrVqFN954A25ubrauEhER1QLMlCIiIqphnTp1wrx58wAAv/76q41rQ3RrWbZsGQBg3LhxNq4JVYRKpcKoUaOQnZ2NtWvX2ro6RERUSzAoRUREZAPBwcEAgLy8vFLzLl26hPfeew+DBw9GSEgIHBwc4OnpifDwcKxatcrsOmNjYzFhwgQEBQXB3t4eHh4eaN68OcaPH4+tW7calTXXIXHxjq51Oh2WLFmCtm3bwsHBAQ0aNMDUqVORnJxc4c/7xx9/YO7cuQgLC4Ofnx+0Wi2CgoIwceJEHD161OQyxTttTk5OxsyZMxESEgI7OztDvcvr2PrcuXN48MEH4efnBycnJ7Rv3x7vv/8+RMRsXU11dK4oCubPnw8AmD9/vlE/YZMnT0ZaWhocHR1hZ2eHy5cvm133kCFDoCgK3n///bJ3mIl9UFzxv19ubi4iIyPRrFkzODg4ICgoCHPmzEF2drbZ9X777bfo0aMHnJ2d4e3tjSFDhmD//v3l1ufatWuYN28e2rZtC2dnZ7i6uqJbt25YtmyZUYfjADBt2jQoioKBAwea3N8vvfQSFEVBu3btkJuba9H+OHnyJA4cOIDQ0FCLmtqtXr0aYWFhcHJygpeXF0aNGoUzZ86YLGuuo/OUlBQ8/fTTaNWqFRwcHODs7IyQkBDcdddd+OCDDwzlJk+ejCZNmgAoOu5K9idX0o8//oi77roLPj4+0Gq1aNKkCR577DFcuHDBZP2Kd8wfFRWFu+++Gz4+PkbHankdnSckJGDOnDlo3bo1nJ2d4e7ujnbt2uHpp59GXFycUdnKnLPlGTJkCADg66+/rtTyRERUBwkRERFZXXBwsACQFStWmJwfGRkpAKRz586l5i1YsEAAiKOjo4SGhkpYWJg0btxYAAgA+c9//lNqmT///FMcHR0FgLi7u0uHDh2kbdu24u7uLgBk6NChRuVXrFghAGTSpElG06OiogSAhIeHy/jx4wWANG/eXNq0aSMajUYASJs2bSQnJ6dC+yM0NFQAiLe3t7Rt21Y6dOhgqJujo6NERUWVWiYiIkIAyGOPPSaNGzcWtVot7du3l/bt28uUKVNEROTs2bMCQIKDg0stf+zYMfH29hYA4uDgIJ07dzbsx8cee8zwNzp79qzRcuHh4QLAqE49e/aUoKAgASBBQUHSs2dPw+uVV14REZFx48YJAFm0aJHJfXDp0iXRaDRib28vKSkpFu03/T6IiIgwmq7/+40fP1769OkjiqJImzZtpGXLlqJSqQSADBw40OQ633jjDcOxFBAQIJ07dxYXFxfRarWGYy88PLzUcrGxsdKoUSMBIPb29tK6dWsJDQ0VRVEEgIwcOVJ0Op2hfGZmpjRt2lQAyOLFi43W9ccff4harRZ7e3s5ePCgRftCRGTZsmUCQMaNG2dyfvHj4dlnnzX8u0OHDqLVag2fOTk5udSy+n1SXFpamuHY1X/m22+/Xfz8/ERRFHF3dzeUfeWVVyQsLEwAiFarNTpGevbsabRefd0ASGBgoHTu3FmcnJwEgHh6esrff/9dqn764/XVV18VlUolnp6e0qVLFwkMDDQcq+aOFxGRX3/9Vdzc3ASA2NnZSfv27aVt27aG7ZZcpjLnrLnrSvH9qSiKODo6Sm5urskyRERUvzAoRUREVA1MBaUKCwslISFBPvjgA3F0dBRFUWT9+vWllt21a5f8/vvvUlBQYDT90KFDcttttwkA2b59u9G8IUOGCAB5/vnnSz3s/f333/Lll18aTSsvKGVnZycNGzaUP//80zDvxIkTEhgYKADkww8/rMjukJUrV8rp06eNpuXn58unn34qGo1GmjZtKoWFhUbz9Q/YarVaunfvLhcuXDDMu3HjhoiYD0rpdDq5/fbbBYAMHjzYKAi0du1asbOzMwTZLAlKFa+PqQd+EZFt27YJAGnfvr3J+YsWLTIEbyxVXlDKzs5OWrduLSdOnDDM27dvnyH4sGXLFqPlYmJiRK1Wi6IosnTpUkMQKTMzU8aMGSN2dnYmg1JZWVmGIMUTTzwh6enphnlHjx6VNm3aCABZunSp0XJ79uwRtVotDg4OEhsbKyIi2dnZ0rx5cwEgb7zxhsX7QkTk4YcfFgDy1ltvmZyvPx40Go24ubnJTz/9ZJiXlJQk7du3FwDyf//3f6WWNRWUeuuttwSADBo0qFQg8dy5c6WCbWUFSfW+//57Qx1Xr15tmJ6eni4PPPCAAJCQkBC5fv260XL6a4parZb58+dLfn6+iBQd6/ogsbnj5dy5c4aA0kMPPWT0WQoLC+WHH36Q7777zmiZypyz5QWlRERatGghAGTfvn1myxARUf3BoBQREVE10D9Amnt16dJFfv755wqv99dffxUA8sgjjxhNb9mypQAwChaUpbygFADZsGFDqeXeffddASD3339/hetuzoMPPigAZM+ePUbT9Q/YWq1WEhISTC5rLgig30+Ojo4ms2KeeOIJw+e0VlBKp9NJSEiIAJADBw6Umq8PiPzwww8mlzelvKCUoigms2rmzJljCCAVp9/Xo0aNKrXMjRs3xM/Pz2RQSv93f+CBB0zW89ChQ6IoijRt2rTUvOeee04ASMeOHSU3N1dmzJghAKRPnz6lghrlufPOOwVAqSCrnv54MJex9t1335kNHJoKSunrunnzZovqZ0lQqmfPngJAZs2aVWpedna2+Pj4CAD57LPPjObpryn33Xef2XWbO14ee+wxASD9+/c3ymarLHPnrCVBKf359dVXX1W5HkREdOtjn1JERETVqHnz5ujZs6fh1bJlS2i1WkRHR+ODDz5AamqqyeUyMzOxbNkyTJo0CYMGDULv3r3Rq1cvPPvsswCAQ4cOGZUPCgoCAKuN5Ofp6Ynhw4eXmt6lSxcAMNsvT1mOHz+OiIgIDB8+HH379kWvXr3Qq1cv7NixA0Dpz6Q3YMAANGzYsELb+vnnnwEAo0aNgo+PT6n5jz32WAVrXz5FUTBp0iQAwMqVK43mHTx4EIcPH4a/vz/uuusuq22zY8eOCAsLKzXd3N/pl19+AQA8+uijpZZxcHDAlClTTG5n48aNAIr6iTKlffv2CAkJwZkzZ3Dx4kWjefPnz0enTp1w8OBBDBkyBB9//DHc3NzwxRdfQKWq2FfRq1evAgC8vLzKLTt16tRS0yp6/OrPq2+//RYFBQWWVtOsrKws7Nu3DwDw+OOPl5rv5OSERx55BMC/f6uSHnrooQpvd/PmzQCAZ555xmT/VuZU9pwti/5vV5m+6YiIqO7R2LoCREREddnzzz9fqjPxtLQ0zJo1C1988QUGDRqEv/76y+hB8cCBAxgyZAgSExPNrvfatWtG/589ezZ+/fVXPPLII1i0aBEGDx6MXr16oV+/fvD29q5wvUNDQ01O9/PzA1D0cF0Rr732Gl544YVSnWEXV/Iz6d12220V2hZQ1CF2Wcs2b94cGo3GKoGG4h5++GG8/PLLWLNmDd58801oNEVftfRBqgcffBBqtdpq26vI3yktLQ1XrlwBYH6/mJt+5MgRAEWdk7/66qsmy+gDRgkJCQgMDDRMt7Ozw+rVq9G5c2ds27YNAPDuu+8aOvuviJycHACAVqsts5yPjw/c3d1LTa/o8fvwww/jzTffxOeff44tW7bgrrvuQu/evdGvXz80bdq0grUHTp06BZ1OB61Wa3b5Nm3aAPj3GC6poudDZmYmEhISAADdunWzeLmqnLNlcXR0BADcuHGjwssSEVHdw0wpIiKiGubh4YFPPvkEjRo1wv79+w1ZDABQWFiI0aNHIzExEffccw927NiBq1evoqCgACJiGCErPz/faJ333nsvfvzxR/To0QMnT57EkiVLMGrUKPj7+2P06NGGh1JLOTs7m5yuz2yRMkavK2nnzp14/vnnoSgKXnvtNRw9ehRZWVnQ6XQQEcybN8/kZyqvLmXRBx18fX1NzlepVCYzqKoqODgYd955J65cuYItW7YAAAoKCrBmzRoAKBWgrKqK/J2KB2LM7ZcGDRqYnJ6eng4AiI6Oxp49e0y+MjMzAZgONjRr1gyNGzcGALi7u2PEiBHlfTST9Fk2aWlpZZYrb79YqmHDhti3bx9GjBiB9PR0rFy5EtOmTUNoaCi6d+9uyHqyVPHj0lzGkv5voN+fJVX0fMjIyDD821SgzpSqnrNl0QeyquP8IyKiWw+DUkRERDag1Wpx++23AwD++usvw/S//voLp06dQnBwMDZu3Ig+ffrA29vbkF1jbrh4ALjnnnuwZ88eJCcnY9OmTXj88cfh4eGBb775Bvfdd1+lHiCt4csvvwRQ1HTo2WefNQxHr38oL+szVZaLiwsA802EdDodUlJSrL5dAIYmcPrsqC1btuDKlSsICwszZMHYgn6fAOb3iz6TytyycXFxkKI+Sc2++vbtW2r5efPm4eTJk1CpVEhPT8eTTz5Zqc+gz3SqTIZOZd12221Yv3490tLSEBUVhcjISLRq1Qp//PEHBg0ahPj4eIvXVfy4NBfYvXz5MgDA1dW1ynUvuR59cLE81XnO6v925gKjRERUvzAoRUREZCP6ZjHFH7D1D7idO3c22UTJkj5cvLy8MHToULz77ruIjY2Fu7s7Dhw4gP3791un4hWk/0w9evQwOb8y/dKUp0WLFgCK+sQx5dSpUxUO0lnaF8/w4cPh4eGB77//HteuXcPnn38OwPpZUhXl4eFhCOqY2y///POPyemtW7cGAMTGxlZ4uzt37sTbb78NJycnbNu2DR4eHvj000/x/fffV3hdHTt2LLOe1Umr1aJv376IiIhAbGwsevbsiaysLKxdu9ZQprxjpFmzZlCpVMjNzTXbr9XRo0cB/HsMV5Wbm5uhOeUff/xh0TLVdc6KCE6cOAEAhqA8ERHVbwxKERER2UBOTg4OHDgAAEZ9y+j7W9FnSxSXn5+Pd955p0LbadCgAZo0aQIAZfZRVZ3K+ky//PJLtQSlBg0aBAD45ptvTGZEffDBBxVep6V94Tg4OGDcuHHIy8vD0qVL8cMPP8De3h7jxo2r8DatbeDAgQCAjz76qNS83NxcLF++3ORy+k7v33333Qo13czIyMCkSZOg0+nw5ptv4s4778T7778PoKjT9Ip2dt2rVy8AsFmAVU+tVhs6TS9+XpV3jLi4uBgCPe+9916p+Tdu3MCnn34KABg8eLDV6jts2DAAwKJFiywqX13n7PHjx5Geno6mTZtWePACIiKqmxiUIiIiqmGpqal45JFHkJiYCHt7e4wePdowr1u3btBoNNizZw+++OILw/T09HRMmDDB5EMiAIwdOxY//vgj8vLyjKavX78eR44cgaIo6NSpU/V8oHLoAwmvv/46zp49a5j+999/Y8qUKXBwcLD6Nvv3749OnTrh+vXrmDhxotEoh+vWrcOHH35o6ITcUvrg4d69e8vtIF3fhG/BggXIy8vD/fffb9GIcdXtySefhEqlwrp16/DRRx8ZAkzZ2dmYMmWK2WZxM2bMQNOmTREVFYUJEyYgKSnJaH5WVhbWrVuHOXPmGE1/4oknEB8fj0GDBhlGPBw/fjzGjBmDK1euYPr06RWqf48ePeDs7Iz9+/cbOj2vTvPmzcNnn31Wqg+r2NhYw0iXxTN+fH194erqiitXrpjN5vq///s/AEWBUX1fY0BRH1IPPfQQkpOTERISgrFjx1rtczzzzDNwd3fHtm3bMHXqVKPzQafT4aeffsIPP/xgmFZd5+yePXsA/Bs0JiIiYlCKiIioGr366quGYdR79eqF2267DQEBAVi9ejU0Gg0+/vhjhISEGMr7+/tj9uzZAIBJkyYhODgYYWFhCAgIwKZNm7B48WKT29m6dSuGDBkCNzc3tGvXDl27dkXDhg0xatQoFBYW4oUXXqjUaGHWMH36dDRt2hSnT59Gq1at0L59e7Rq1Qpdu3aFu7u7IVhhTYqiYNWqVfDy8sKWLVvQqFEjdOnSBSEhIRgzZgymTZuGRo0aVWidgwYNgqenJ3bv3o3GjRujV69e6Nu3L15//fVSZcPCwtC+fXtD8MrWTff0OnfujP/9738QETz66KMIDAxEly5dEBAQgA0bNuCll14yuZyLiwt+/PFHNGnSBGvXrkVgYCBat26Nbt26oWXLlvDw8MCYMWOwd+9ewzLffvstVq5cCU9PT6xYscJofR9++CEaNmyITZs2lZpXFmdnZ4wdOxbZ2dn48ccfK7cTKuDo0aOYNm0avL290bx5c9xxxx1o3rw52rVrh8TERPTr1w8TJ040lFcUBaNGjQJQFKzq0qUL+vbta9TP1pAhQ/Dss88iPz8fEyZMQOPGjQ1/g/Xr18PT0xPr1q0zZCtZQ+PGjbF+/Xq4urpi+fLlaNCgATp27Ij27dvDzc0N9957r1H2WXWds19//TWAf4O2REREDEoRERFVo7i4OKMRys6ePYtGjRrh4Ycfxv79+00GKxYuXIh33nkHrVq1wqVLl3Du3DkMGDAAu3btwl133WVyOytXrsT06dPRvHlzJCYm4vDhw3BycsIDDzyAHTt24OWXX67mT2qem5sbdu/ejYceeghubm44ceIE8vLyMGfOHOzbt89qHTqX1KZNG+zfvx/jx4+Hk5MTYmNj4ebmhvfeew9Lly6t8Prc3Nzwyy+/4O6770Zubi727duHHTt2mO2fSf+39ff3N/t3s4XnnnsO69evxx133IHU1FScPn0avXv3xu7duw0ZMqa0atUKhw4dwuuvv44uXbogISEBBw8eRF5eHsLDw/HWW2/hq6++AlDU7EufBfXBBx+UaqqlD1QpioJZs2ZVqLNw/Xr1nXFXpxdeeAHPPvssunTpgqysLBw8eBA3btxAeHg4vvjiC/zyyy+lMu6WLFmCWbNmwd/fH4cOHcKOHTuwY8cOozKvvfYavv/+ewwcOBBZWVk4fPgwfHx88J///AeHDh0yNA20pgEDBiA2NhYzZ85EcHAwjh8/jgsXLiA0NBTPPPOMUXCtOs7Zy5cvIyoqCu3bt6+Wz0dERLcmRSrSMQARERERWeTZZ5/FG2+8gaeffhpvvvmmratTpwwaNAhRUVGIi4szyjSk2isiIgIvv/wy1q1bZ8gmIyIiYlCKiIiIyMry8/MRHByMpKQkHD161DB6HVnHgQMH0LlzZ0ybNg2ffPKJratD5UhPT0dISAhatGiBP//809bVISKiWqRiPXwSERERUbneffddJCUlITw8nAGpatCpUycsW7YMV65cgU6ng0rFHilqs3PnzmHWrFkYOnSoratCRES1DDOliIiIiKzg0qVLGDt2LFJSUhAbGwuVSoWdO3eiZ8+etq4aERERUa3En5WIiIiIrCAnJwc7duzAiRMn0KZNG6xbt44BKSIiIqIyMFOKiIiIiIiIiIhqHDOliIiIiIiIiIioxjEoRURERERERERENY5BKSIiIiIiIiIiqnEMShERERERERERUY1jUIqIiIiIiIiIiGocg1JERERERERERFTjGJQiIiIiIiIiIqIax6AUERERERERERHVOAaliIiIiIiIiIioxv0/SRt5WsFOw+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Composite of stats over time\n",
    "# color_fam = cm.get_cmap('tab20b')\n",
    "# inc_color=color_fam(5)\n",
    "# dec_color=color_fam(17)\n",
    "\n",
    "## Color defaults per model\n",
    "colorcode = ['RdBu', 'PuOr', 'PiYG']\n",
    "\n",
    "## Create figure with only the end-century severity panel\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "\n",
    "for increment in range(len(gmodels)):\n",
    "    test_GEM = gmodels[increment]\n",
    "    c = colorcode[increment]\n",
    "    offset = increment*0.2\n",
    "# for test_GEM, c in zip(gmodels, colorcode):\n",
    "    this_cmap = plt.get_cmap(c)\n",
    "    base_color = this_cmap(0.5)\n",
    "    dec_color = this_cmap(-0.7)\n",
    "    inc_color = this_cmap(0.9)\n",
    "        \n",
    "    for j,bname in enumerate(basin_names):\n",
    "        ## correcting small discrepancies between basin name sets\n",
    "        if bname=='TARIM':\n",
    "            b='TARIM HE'\n",
    "        elif bname=='BALKHASH':\n",
    "            b='LAKE BALKHASH'\n",
    "        elif bname=='ISSYK-KUL':\n",
    "            b='YSYK-KOL'\n",
    "        elif bname=='JOEKULSA A F.':\n",
    "            b='JOKULSA A FJOLLUM'\n",
    "        elif bname=='OELFUSA':\n",
    "            b='OLFUSA'\n",
    "        elif bname=='NELSON RIVER':\n",
    "            b='NELSON'\n",
    "        elif bname=='GLOMA':\n",
    "            b='GLOMAA'\n",
    "        elif bname=='HUANG HE':\n",
    "            b='YELLOW RIVER'\n",
    "        elif bname=='LULE':\n",
    "            b='LULEALVEN'\n",
    "        elif bname=='DAULE-VINCES':\n",
    "            continue ## not included in 2024 set, as far as I can tell\n",
    "        elif bname=='KALIXAELVEN':\n",
    "            b='KALIXALVEN'\n",
    "        elif bname=='DRAMSELV':\n",
    "            b='DRAMSELVA'\n",
    "        else:\n",
    "            b=bname\n",
    "        number_b = []\n",
    "        severity_b = []\n",
    "        number_midC = []\n",
    "        number_endC = []\n",
    "        severity_midC = []\n",
    "        severity_endC = []\n",
    "        \n",
    "        hist_AI = historical_avg_AI[j]\n",
    "\n",
    "        for m in modelnames:\n",
    "            number_b.append(basin_stats_bymodel_early[test_GEM][m][b][0][1]\n",
    "                            -basin_stats_bymodel_early[test_GEM][m][b][0][0])\n",
    "            number_midC.append(basin_stats_bymodel_midC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_midC[test_GEM][m][b][0][0])\n",
    "            number_endC.append(basin_stats_bymodel_endC[test_GEM][m][b][0][1]\n",
    "                               -basin_stats_bymodel_endC[test_GEM][m][b][0][0])\n",
    "            severity_b.append(-1*(basin_stats_bymodel_early[test_GEM][m][b][2][1]\n",
    "                                  -basin_stats_bymodel_early[test_GEM][m][b][2][0]))\n",
    "            severity_midC.append(-1*(basin_stats_bymodel_midC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_midC[test_GEM][m][b][2][0]))\n",
    "            severity_endC.append(-1*(basin_stats_bymodel_endC[test_GEM][m][b][2][1]\n",
    "                                     -basin_stats_bymodel_endC[test_GEM][m][b][2][0]))\n",
    "\n",
    "        endC_v_hist_s = np.nanmean(severity_endC)-np.nanmean(severity_b)\n",
    "        if endC_v_hist_s >0.1:\n",
    "            endC_color_s=inc_color\n",
    "            endC_marker_s='^'\n",
    "        elif endC_v_hist_s<-0.1:\n",
    "            endC_color_s=dec_color\n",
    "            endC_marker_s='v'\n",
    "        else:\n",
    "            endC_color_s='k'\n",
    "            endC_marker_s='o'\n",
    "\n",
    "        ax.errorbar(hist_AI, np.nanmean(severity_endC), \n",
    "                     yerr=np.asarray(np.nanmean(severity_endC)-np.nanmin(severity_endC), np.nanmax(severity_endC)-np.nanmean(severity_endC)).transpose(), \n",
    "                     color=endC_color_s, marker=endC_marker_s, lw=1.0)\n",
    "\n",
    "\n",
    "ax.set_ylabel(r'Glacial drought buffering'\n",
    "              '\\n'\n",
    "              ' [$\\Delta$ SPEI deficit]',fontsize=16) \n",
    "ax.set(\n",
    "    # xscale='log', ,\n",
    "    xlim=(-1,25),\n",
    "    ylim=(-2,7))\n",
    "ax.set_yticks([-2, 0, 2, 4, 6])\n",
    "ax.set_xlabel('Basin aridity index (historical)', fontsize=16)\n",
    "ax.set_title('End 21st Cent. (2080-2100), all GEMs, {}'.format(scen), fontsize=14)\n",
    "ax.tick_params(which='both', labelsize=14)\n",
    "ax.axhline(0, ls=':', lw=1.0, color='k')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55119e93-731b-4160-9ecd-14715f15e824",
   "metadata": {},
   "source": [
    "### Statistical metrics\n",
    "We previously reported Spearman's $\\rho$ for the strength of the relationship between basin variables (percent glaciation or aridity index) and drought buffering effect.  Let's compute that for these multi-GEM, CMIP6 results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6079021-89bd-4289-96b3-e637fc3c28ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "## adapted from previous work (2021)\n",
    "## TODO: flatten the number and severity stats from per-GEM into aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "400e838b-4b60-455c-a66c-ab236b364fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Composite of all stats over time - Spearman vs % glaciated\n",
    "all_number_b = []\n",
    "all_duration_b = []\n",
    "all_severity_b = []\n",
    "all_number_midC = []\n",
    "all_number_endC = []\n",
    "all_duration_midC = []\n",
    "all_duration_endC = []\n",
    "all_severity_midC = []\n",
    "all_severity_endC = []\n",
    "percent_glac_all = []\n",
    "\n",
    "test_GEM='GloGEM'\n",
    "\n",
    "for b in basin_names:\n",
    "    pc_glac = np.ones(11)*percent_glaciated.loc[b] # percent glaciated\n",
    "    percent_glac_all.append(pc_glac) ## make the data structures match up\n",
    "    \n",
    "    \n",
    "    all_number_b.append([basin_stats_bymodel_early[test_GEM][m][b][0][1]-\n",
    "                                basin_stats_bymodel_early[test_GEM][m][b][0][0] for m in modelnames])\n",
    "    all_duration_b.append([basin_stats_bymodel_early[test_GEM][m][b][1][1]-\n",
    "                                       basin_stats_bymodel_early[test_GEM][m][b][1][0] for m in modelnames])\n",
    "    all_severity_b.append([-1*basin_stats_bymodel_early[test_GEM][m][b][2][1]+\n",
    "                                           basin_stats_bymodel_early[test_GEM][m][b][2][0] for m in modelnames])\n",
    "    ## multiplying by -1 does not work as expected when working with the lists rather than their means\n",
    "    ## do the multiplication inside the list instead of outside\n",
    "    all_number_midC.append([basin_stats_bymodel_midC[test_GEM][m][b][0][1]-\n",
    "                                        basin_stats_bymodel_midC[test_GEM][m][b][0][0] for m in modelnames])\n",
    "    all_number_endC.append([basin_stats_bymodel_endC[test_GEM][m][b][0][1]-\n",
    "                                        basin_stats_bymodel_endC[test_GEM][m][b][0][0] for m in modelnames])\n",
    "    all_duration_midC.append([basin_stats_bymodel_midC[test_GEM][m][b][1][1]-\n",
    "                                          basin_stats_bymodel_midC[test_GEM][m][b][1][0] for m in modelnames])\n",
    "    all_duration_endC.append([basin_stats_bymodel_endC[test_GEM][m][b][1][1]-\n",
    "                                          basin_stats_bymodel_endC[test_GEM][m][b][1][0] for m in modelnames])\n",
    "    all_severity_midC.append([-1*basin_stats_bymodel_midC[test_GEM][m][b][2][1]+\n",
    "                                             basin_stats_bymodel_midC[test_GEM][m][b][2][0] for m in modelnames])\n",
    "    all_severity_endC.append([-1*basin_stats_bymodel_endC[test_GEM][m][b][2][1]+\n",
    "                                             basin_stats_bymodel_endC[test_GEM][m][b][2][0] for m in modelnames])\n",
    "\n",
    "pcg = np.ravel(percent_glac_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bd39732-b6ed-4575-a673-7d42ea3dc388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.48973781962248986, pvalue=5.548408085976249e-51)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all_severity_endC\n",
    "stats.spearmanr(pcg, np.ravel(all_severity_b), nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28da33b-b67d-4f76-a95c-4146f3b9a4ff",
   "metadata": {},
   "source": [
    "This looks like what we'd expect. Compute full table with `all_` stats rather than means:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "115e7d58-2677-4b76-85bd-832ecc324d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compute Spearman's corrs with basin variables\n",
    "pcg_strictorder = [percent_glaciated.loc[b] for b in basin_names]\n",
    "\n",
    "constructor_d = {}\n",
    "for f,name in zip((pcg_strictorder, basin_areas, avg_precip, historical_avg_AI), \n",
    "             ('percent_glac', 'BasinArea', 'avg_precip', 'historical_avg_AI')):\n",
    "    ## zip does not work well here, because order of percent_glac dataframe is not the same\n",
    "    ## corrected order above for now...double-check other lists\n",
    "    \n",
    "    fn_expanded = [np.ones(11)*fval for fval in f] ## use all 11 GCMs, make other data same shape\n",
    "    fn = np.ravel(fn_expanded)\n",
    "    \n",
    "    d = {'Number-hist': stats.spearmanr(fn, np.ravel(all_number_b), nan_policy='omit'), \n",
    "         'Severity-hist': stats.spearmanr(fn, np.ravel(all_severity_b), nan_policy='omit'), \n",
    "         'Number-midC': stats.spearmanr(fn, np.ravel(all_number_midC), nan_policy='omit'), \n",
    "         'Severity-midC': stats.spearmanr(fn, np.ravel(all_severity_midC), nan_policy='omit'), \n",
    "         'Number-endC': stats.spearmanr(fn, np.ravel(all_number_endC), nan_policy='omit'), \n",
    "         'Severity-endC': stats.spearmanr(fn, np.ravel(all_severity_endC), nan_policy='omit')}\n",
    "    constructor_d[name] = d\n",
    "df = pd.DataFrame.from_dict(constructor_d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f5148c05-b67b-4260-895b-5b5209f0d7e0",
   "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>percent_glac</th>\n",
       "      <th>BasinArea</th>\n",
       "      <th>avg_precip</th>\n",
       "      <th>historical_avg_AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number-hist</th>\n",
       "      <td>(0.47042530113356545, 1.171169273883186e-46)</td>\n",
       "      <td>(-0.26434690963759505, 1.1725113053179143e-14)</td>\n",
       "      <td>(-0.20453858570274608, 3.0514561389708805e-09)</td>\n",
       "      <td>(0.19104074516748193, 3.203830440902402e-08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Severity-hist</th>\n",
       "      <td>(0.48973781962248986, 5.548408085976249e-51)</td>\n",
       "      <td>(-0.33213246728114904, 1.071474861474974e-22)</td>\n",
       "      <td>(-0.29925872785909646, 1.5689692149828412e-18)</td>\n",
       "      <td>(0.12956032322966407, 0.0001903940743515342)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number-midC</th>\n",
       "      <td>(0.5010154268434186, 1.2247328616884547e-53)</td>\n",
       "      <td>(-0.23602363097388762, 6.5925406766003704e-12)</td>\n",
       "      <td>(-0.18651637126090415, 6.792435798352976e-08)</td>\n",
       "      <td>(0.33366629907032636, 6.649227446176536e-23)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Severity-midC</th>\n",
       "      <td>(0.5042800534487255, 2.6816396772483694e-54)</td>\n",
       "      <td>(-0.35527599248521924, 6.852398805302614e-26)</td>\n",
       "      <td>(-0.31563066456285555, 1.7064009136951203e-20)</td>\n",
       "      <td>(0.06802460771851797, 0.05108294670817002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number-endC</th>\n",
       "      <td>(0.4669865063370431, 6.457363750719883e-46)</td>\n",
       "      <td>(-0.20295344265824458, 4.05646218091928e-09)</td>\n",
       "      <td>(-0.14606894473606466, 2.5338965918512574e-05)</td>\n",
       "      <td>(0.3702885861426718, 3.2902162130354046e-28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Severity-endC</th>\n",
       "      <td>(0.5947539942379747, 4.5033327864236945e-80)</td>\n",
       "      <td>(-0.4387526112632949, 3.87263204201872e-40)</td>\n",
       "      <td>(-0.38374475873522, 2.444027231195918e-30)</td>\n",
       "      <td>(0.19428732041381708, 1.847481128044827e-08)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               percent_glac  \\\n",
       "Number-hist    (0.47042530113356545, 1.171169273883186e-46)   \n",
       "Severity-hist  (0.48973781962248986, 5.548408085976249e-51)   \n",
       "Number-midC    (0.5010154268434186, 1.2247328616884547e-53)   \n",
       "Severity-midC  (0.5042800534487255, 2.6816396772483694e-54)   \n",
       "Number-endC     (0.4669865063370431, 6.457363750719883e-46)   \n",
       "Severity-endC  (0.5947539942379747, 4.5033327864236945e-80)   \n",
       "\n",
       "                                                    BasinArea  \\\n",
       "Number-hist    (-0.26434690963759505, 1.1725113053179143e-14)   \n",
       "Severity-hist   (-0.33213246728114904, 1.071474861474974e-22)   \n",
       "Number-midC    (-0.23602363097388762, 6.5925406766003704e-12)   \n",
       "Severity-midC   (-0.35527599248521924, 6.852398805302614e-26)   \n",
       "Number-endC      (-0.20295344265824458, 4.05646218091928e-09)   \n",
       "Severity-endC     (-0.4387526112632949, 3.87263204201872e-40)   \n",
       "\n",
       "                                                   avg_precip  \\\n",
       "Number-hist    (-0.20453858570274608, 3.0514561389708805e-09)   \n",
       "Severity-hist  (-0.29925872785909646, 1.5689692149828412e-18)   \n",
       "Number-midC     (-0.18651637126090415, 6.792435798352976e-08)   \n",
       "Severity-midC  (-0.31563066456285555, 1.7064009136951203e-20)   \n",
       "Number-endC    (-0.14606894473606466, 2.5338965918512574e-05)   \n",
       "Severity-endC      (-0.38374475873522, 2.444027231195918e-30)   \n",
       "\n",
       "                                          historical_avg_AI  \n",
       "Number-hist    (0.19104074516748193, 3.203830440902402e-08)  \n",
       "Severity-hist  (0.12956032322966407, 0.0001903940743515342)  \n",
       "Number-midC    (0.33366629907032636, 6.649227446176536e-23)  \n",
       "Severity-midC    (0.06802460771851797, 0.05108294670817002)  \n",
       "Number-endC    (0.3702885861426718, 3.2902162130354046e-28)  \n",
       "Severity-endC  (0.19428732041381708, 1.847481128044827e-08)  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754e651a-1c21-43fc-bb45-95e60a763297",
   "metadata": {},
   "source": [
    "Note the need to get the percent glacier cover (a dataframe) in the same order as the list variables. Be very careful about this!  Check list comprehensions etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b36262-d02e-49a8-817b-351375112c6c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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