Monthly Sea Ice Concentration Climatology.ipynb

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  {
   "cell_type": "markdown",
   "id": "0a565264",
   "metadata": {},
   "source": [
    "## Compute monthly sea-ice concentration climatologies from EUMETSAT OSI SAF data\n",
    "\n",
    "We compute monthly cliamtologies (1981-2010, 1991-2020) from EUMETSAT OSI SAF SIC CDR v3 data. This uses xarray.\n",
    "\n",
    "**NB :** This software is for demonstration purpose only, it is not finalized for production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2724be23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "\n",
    "from datetime import date, datetime, timedelta, time\n",
    "from dateutil import rrule, relativedelta\n",
    "\n",
    "import cftime\n",
    "\n",
    "import json\n",
    "import uuid\n",
    "from copy import copy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db3e79c",
   "metadata": {},
   "source": [
    "Configuration of the run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "000192ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "area = 'nh'\n",
    "outdir = '.'\n",
    "indirs = None\n",
    "\n",
    "# climo : years to include in the climatology run. **Both ends are included**.\n",
    "climo = (1981, 1990)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59061b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input monthly SIC files\n",
    "fn_patt = 'ice_conc_{a:}_ease2-250_{c:}_{d:%Y%m}.nc'\n",
    "fn_patt_src = {'cdr': 'cdr-v3p0', 'icdr': 'icdr-v3p0', 'icdrft': 'icdrft-v3p0'}\n",
    "\n",
    "if indirs is None:\n",
    "    inpdir_cdr = 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/'\n",
    "    inpdir_icdr = 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_cra_files/monthly/'\n",
    "    # access through THREDDS/OpenDAP\n",
    "    sources = {'cdr':inpdir_cdr,\n",
    "              'icdr':inpdir_icdr,\n",
    "              'icdrft':inpdir_icdr}\n",
    "    jsond = json.dumps(sources, sort_keys=True, indent=4)\n",
    "else:\n",
    "    # load json file with path to input directories (if the daily SIC files are downloaded to a local disk)\n",
    "    # an example json file (prepare_monthly_osisaf_sic_opendap.json) is provided to demonstrate the format\n",
    "    #   expected for the json file (but the effect will be the same as setting indirs to None: read from\n",
    "    #   THREDDS/opendap)\n",
    "    with open(indirs, 'r') as f:\n",
    "        sources = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ff91ac7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: could not find OSI SAF SIC v3 file for nh 1986-04-15\n",
      "WARNING: could not find OSI SAF SIC v3 file for nh 1986-05-15\n",
      "WARNING: could not find OSI SAF SIC v3 file for nh 1986-06-15\n",
      "WARNING: could not find OSI SAF SIC v3 file for nh 1987-12-15\n",
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'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198907.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198908.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198909.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198910.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198911.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1989/ice_conc_nh_ease2-250_cdr-v3p0_198912.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199001.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199002.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199003.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199004.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199005.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199006.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199007.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199008.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199009.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199010.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199011.nc', 'https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_450a_files/monthly/1990/ice_conc_nh_ease2-250_cdr-v3p0_199012.nc']\n"
     ]
    }
   ],
   "source": [
    "def find_one_monthly_sic_file(dt, area):\n",
    "    \n",
    "    found_one_file = False\n",
    "    for cdr in ('cdr', 'icdr', 'icdrft'):\n",
    "        fn = fn_patt.format(a=area, d=dt, c=fn_patt_src[cdr])\n",
    "        fn = os.path.join(sources[cdr],'{:%Y/}'.format(dt),fn)\n",
    "        try:\n",
    "            # this url exists, append it and move to next date\n",
    "            ds = xr.open_dataset(fn)\n",
    "            found_one_file = True\n",
    "            return fn, cdr\n",
    "        except OSError:\n",
    "            # no valid file at this url, check the next rule\n",
    "            # print(\"Failed with pattern {}\".format(fn))\n",
    "            pass\n",
    "        \n",
    "    # no file found. Add a warning (but we can continue)\n",
    "    if not found_one_file:\n",
    "        print(\"WARNING: could not find OSI SAF SIC v3 file for {} {}\".format(area, dt))\n",
    "        return None, None\n",
    "\n",
    "def find_climo_monthly_sic_file(year_start, year_end, area):\n",
    "    \n",
    "    files = []\n",
    "    for year in range(year_start, year_end+1):\n",
    "        for month in range(1,13):\n",
    "            dt = date(year, month, 15)\n",
    "            fn, src = find_one_monthly_sic_file(dt, area)\n",
    "            if fn:\n",
    "                files.append(fn)\n",
    "    \n",
    "    return files\n",
    "\n",
    "climo_fns = find_climo_monthly_sic_file(climo[0], climo[1], area,)\n",
    "if len(climo_fns) == 0:\n",
    "    raise ValueError(\"Found no files for building the climatology by month.\")\n",
    "\n",
    "print(climo_fns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2105ae",
   "metadata": {},
   "source": [
    "## Climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cbdb63ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A routine to reconstruct the unfiltered, unthresholded SICs from a OSI SAF SIC CDR file\n",
    "def reconstruct_sic(ds):\n",
    "    ice_conc = ds['ice_conc'].to_masked_array()\n",
    "    raw_ice_conc_values = ds['raw_ice_conc_values'].to_masked_array()\n",
    "    status_flag = ds['status_flag'].to_masked_array().astype('short')\n",
    "    \n",
    "    # combine ice_conc with raw_ice_conc_values using the status_flag\n",
    "    new_ice_conc = copy(ice_conc)\n",
    "    raw_100_mask = np.array((ice_conc==100) * (~raw_ice_conc_values.mask))\n",
    "    new_ice_conc[raw_100_mask] = raw_ice_conc_values[raw_100_mask]\n",
    "    \n",
    "    new_ice_conc[(status_flag & 4) == 4] = raw_ice_conc_values[(status_flag & 4) == 4]\n",
    "\n",
    "    # re-enter \"full\" ice_conc into the xarray dataset\n",
    "    ds['ice_conc'][:] = new_ice_conc\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d22d531f-f9f6-4006-9df4-f617b3393ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open all the monthly files for the climatology period\n",
    "climo_ds = xr.open_mfdataset(climo_fns, engine='netcdf4')\n",
    "climo_ds = reconstruct_sic(climo_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0fb39c8d-760e-4348-99ad-1982daf80ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove some variablea and attributes before computing the climatology\n",
    "_grid_mapping = climo_ds['Lambert_Azimuthal_Grid'].copy(deep=True)\n",
    "_lat = climo_ds['lat'].copy(deep=True)\n",
    "_lon = climo_ds['lon'].copy(deep=True)\n",
    "climo_ds = climo_ds.drop_vars(['lat', 'lon', 'raw_ice_conc_values', 'status_flag', 'Lambert_Azimuthal_Grid', 'time_bnds'])\n",
    "\n",
    "del climo_ds['ice_conc'].attrs['valid_min']\n",
    "del climo_ds['ice_conc'].attrs['valid_max']\n",
    "del climo_ds['ice_conc'].attrs['ancillary_variables']\n",
    "del climo_ds['ice_conc'].attrs['comment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f8a01bdc-10ba-4714-b807-e6c602e21ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 197MB\n",
      "Dimensions:            (month: 12, yc: 432, xc: 432)\n",
      "Coordinates:\n",
      "  * xc                 (xc) float64 3kB -5.388e+03 -5.362e+03 ... 5.388e+03\n",
      "  * yc                 (yc) float64 3kB 5.388e+03 5.362e+03 ... -5.388e+03\n",
      "  * month              (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
      "Data variables:\n",
      "    ice_conc_mean      (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_sdev      (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_median    (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_min       (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_max       (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_5pctile   (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_10pctile  (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_25pctile  (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_75pctile  (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_90pctile  (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_95pctile  (month, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "Attributes: (12/43)\n",
      "    title:                     Monthly Sea Ice Concentration Climate Data Rec...\n",
      "    summary:                   This climate data record of sea ice concentrat...\n",
      "    topiccategory:             Oceans ClimatologyMeteorologyAtmosphere\n",
      "    geospatial_lat_min:        16.62393\n",
      "    geospatial_lat_max:        90.0\n",
      "    geospatial_lon_min:        -180.0\n",
      "    ...                        ...\n",
      "    algorithm:                 SICCI3LF (19V, 37V, 37H)\n",
      "    references:                Product User Manual v3 (2022),Algorithm Theore...\n",
      "    contributor_name:          Thomas Lavergne,Atle Soerensen,Rasmus Tonboe,C...\n",
      "    contributor_role:          PrincipalInvestigator,author,author,author,aut...\n",
      "    source:                    FCDR of SMMR / SSMI / SSMIS Brightness Tempera...\n",
      "    product_status:            released\n"
     ]
    }
   ],
   "source": [
    "# group the monthly fields by month, and compute the statistics\n",
    "climo_ds = climo_ds.chunk({'time': -1})\n",
    "climo_grpby = climo_ds.groupby('time.month')\n",
    "\n",
    "# compute mean, sdev, and all quantiles (include median, min, and max)\n",
    "quantiles = np.array([50, 0, 100, 5, 10, 25, 75, 90, 95])/100.\n",
    "ds_mean = climo_grpby.mean(skipna=True, keep_attrs=True)\n",
    "ds_mean = ds_mean.rename_vars({'ice_conc':'ice_conc_mean'})\n",
    "ds_mean['ice_conc_mean'].attrs['cell_methods'] = 'time: mean within years time: mean over years'\n",
    "\n",
    "ds_sdev = climo_grpby.std(skipna=True, keep_attrs=True)\n",
    "ds_sdev = ds_sdev.rename_vars({'ice_conc':'ice_conc_sdev'})\n",
    "ds_sdev['ice_conc_sdev'].attrs['cell_methods'] = 'time: mean within years time: standard_deviation over years'\n",
    "\n",
    "ds_climo = xr.merge([ds_mean, ds_sdev], compat='override')\n",
    "\n",
    "ds_quantiles = climo_grpby.quantile(quantiles, skipna=True, keep_attrs=True)\n",
    "for q in quantiles:\n",
    "    lname = None\n",
    "    if q == 0:\n",
    "        suff = 'min'\n",
    "        meth = 'minimum'\n",
    "    elif q == 1:\n",
    "        suff = 'max'\n",
    "        meth = 'maximum'\n",
    "    elif q == 0.5:\n",
    "        suff = 'median'\n",
    "        meth = 'median'\n",
    "    else:\n",
    "        qpct = round(q*100)\n",
    "        suff = '{:d}pctile'.format(qpct)\n",
    "        meth = 'percentile_{:d}'.format(qpct)\n",
    "        lname = '{}% Percentile'.format(qpct)\n",
    "\n",
    "    if lname is None:\n",
    "        lname = meth.capitalize()\n",
    "                    \n",
    "    ds_quant = ds_quantiles.sel(quantile=q)\n",
    "    ds_quant = ds_quant.rename_vars({'ice_conc':'ice_conc_'+suff})\n",
    "    ds_quant['ice_conc_'+suff].attrs['cell_methods'] = 'time: mean within years time: {} over years'.format(meth,)\n",
    "    \n",
    "    ds_climo = xr.merge([ds_climo, ds_quant], compat='override')\n",
    "\n",
    "ds_climo = ds_climo.drop_vars(['quantile',])\n",
    "print(ds_climo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f91ec9d5-21f1-4531-b551-4c158ba1ef59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the time coordinate\n",
    "mid_year = int(round(0.5 * (climo[0] + climo[1])))\n",
    "mid_time = [cftime.datetime(mid_year,m,16) for m in range(1,13)]\n",
    "mid_time_da = xr.DataArray(mid_time, dims=\"month\", coords={'month': range(1,13)}, name='time')\n",
    "\n",
    "# Assign the time dimension and swap the dimension from 'month' to 'time'\n",
    "ds_climo = ds_climo.assign_coords(time=mid_time_da)\n",
    "ds_climo = ds_climo.swap_dims({\"month\": \"time\"})\n",
    "ds_climo = ds_climo.drop_vars(('month',))\n",
    "\n",
    "# add climatological time bounds\n",
    "bound0 = [cftime.datetime(climo[0],m,1) for m in range(1,13)]\n",
    "bound1 = [cftime.datetime(climo[1],m+1,1) for m in range(1,12)] + [cftime.datetime(climo[1]+1,1,1),]            \n",
    "bounds = np.column_stack([bound0, bound1])\n",
    "bounds_da = xr.DataArray(bounds, dims=['time','nv'], coords={'time': mid_time, },)\n",
    "ds_climo = ds_climo.assign(climatology_bounds=bounds_da)\n",
    "\n",
    "ds_climo['time'].attrs['climatology'] = 'climatology_bounds'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4bda9dc6-1516-41dd-9357-c81aec6d569e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 199MB\n",
      "Dimensions:                 (xc: 432, yc: 432, time: 12, nv: 2)\n",
      "Coordinates:\n",
      "  * xc                      (xc) float64 3kB -5.388e+03 -5.362e+03 ... 5.388e+03\n",
      "  * yc                      (yc) float64 3kB 5.388e+03 5.362e+03 ... -5.388e+03\n",
      "  * time                    (time) object 96B 1986-01-16 00:00:00 ... 1986-12...\n",
      "    lon                     (yc, xc) float32 746kB dask.array<chunksize=(432, 432), meta=np.ndarray>\n",
      "    lat                     (yc, xc) float32 746kB dask.array<chunksize=(432, 432), meta=np.ndarray>\n",
      "Dimensions without coordinates: nv\n",
      "Data variables: (12/13)\n",
      "    ice_conc_mean           (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_sdev           (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_median         (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_min            (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_max            (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_5pctile        (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ...                      ...\n",
      "    ice_conc_25pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_75pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_90pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_95pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    climatology_bounds      (time, nv) object 192B 1981-01-01 00:00:00 ... 19...\n",
      "    Lambert_Azimuthal_Grid  float64 8B nan\n",
      "Attributes: (12/34)\n",
      "    title:                     Monthly Climatology (1981-1990) of Sea Ice Con...\n",
      "    summary:                   This climate data record of sea ice concentrat...\n",
      "    topiccategory:             Oceans ClimatologyMeteorologyAtmosphere\n",
      "    geospatial_lat_min:        16.62393\n",
      "    geospatial_lat_max:        90.0\n",
      "    geospatial_lon_min:        -180.0\n",
      "    ...                        ...\n",
      "    Conventions:               CF-1.7,ACDD-1.3\n",
      "    references:                Product User Manual v3 (2022),Algorithm Theore...\n",
      "    contributor_name:          Thomas Lavergne,Atle Soerensen,Rasmus Tonboe,C...\n",
      "    contributor_role:          PrincipalInvestigator,author,author,author,aut...\n",
      "    source:                    FCDR of SMMR / SSMI / SSMIS Brightness Tempera...\n",
      "    product_status:            under development\n"
     ]
    }
   ],
   "source": [
    "# transfer some variables back from the original files\n",
    "ds_climo = ds_climo.assign(lat=_lat)\n",
    "ds_climo = ds_climo.assign(lon=_lon)\n",
    "ds_climo = ds_climo.assign(Lambert_Azimuthal_Grid = _grid_mapping)\n",
    "ds_climo['Lambert_Azimuthal_Grid'] = ds_climo[\"Lambert_Azimuthal_Grid\"].isel(time=0).squeeze()\n",
    "print(ds_climo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8850a9b5-2f1a-4bf6-a48e-549a6516b9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clip the ice_conc variables to 0 - 100%\n",
    "for v in ds_climo.variables:\n",
    "    if v.startswith('ice_conc_'):\n",
    "        ds_climo[v] = ds_climo[v].clip(min=0, max=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd8e626",
   "metadata": {},
   "source": [
    "### Write Climatology to a netCDF file\n",
    "\n",
    "Xarray datasets can be written to netCDF files with the to_netcdf() method.\n",
    "\n",
    "**Note: this is still preliminary format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b956aa64-2f9f-43d7-b902-513c4674b28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_first_last_date_climo(climo, dt):\n",
    "    first_day = date(climo[0],dt.month,1)\n",
    "    last_day = date(climo[1],dt.month,1) + relativedelta.relativedelta(months=1)-timedelta(days=1)\n",
    "    return first_day, last_day\n",
    "\n",
    "def get_first_last_date_month(dt):\n",
    "    first_day = date(dt.year,dt.month,1)\n",
    "    last_day = first_day + relativedelta.relativedelta(months=1)-timedelta(days=1)\n",
    "    return first_day, last_day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "af6c4ecf-056a-4b0f-b02e-ec1cd0ccb305",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'doi'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[115], line 13\u001b[0m\n\u001b[1;32m      9\u001b[0m     ds_climo\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m ds_climo\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMonthly\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMonthly Climatology (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m) of\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(climo[\u001b[38;5;241m0\u001b[39m], climo[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m     11\u001b[0m ds_climo\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mproduct_status\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munder development\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[43mds_climo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdoi\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m ds_climo\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnaming_authority\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m ds_climo\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mproduct_id\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "\u001b[0;31mKeyError\u001b[0m: 'doi'"
     ]
    }
   ],
   "source": [
    "# Amend some attributes\n",
    "now = datetime.utcnow().replace(microsecond=0)\n",
    "ds_climo.attrs['history'] = now.isoformat()+'Z' + ' creation'\n",
    "ds_climo.attrs['date_created'] = now.date().isoformat()\n",
    "\n",
    "ds_climo.attrs['tracking_id'] = str(uuid.uuid4())\n",
    "\n",
    "if ds_climo.attrs['title'].startswith('Monthly'):\n",
    "    ds_climo.attrs['title'] = ds_climo.attrs['title'].replace('Monthly','Monthly Climatology ({}-{}) of'.format(climo[0], climo[1]))\n",
    "\n",
    "ds_climo.attrs['product_status'] = 'under development'\n",
    "\n",
    "del ds_climo.attrs['doi']\n",
    "del ds_climo.attrs['naming_authority']\n",
    "del ds_climo.attrs['product_id']\n",
    "del ds_climo.attrs['product_name']\n",
    "del ds_climo.attrs['algorithm']\n",
    "\n",
    "#ISO 8601 time attributes are not straightforward for a monthly climalogy period. We remove them.\n",
    "del ds_climo.attrs['time_coverage_start'] \n",
    "del ds_climo.attrs['time_coverage_end']\n",
    "del ds_climo.attrs['time_coverage_duration']\n",
    "del ds_climo.attrs['time_coverage_resolution']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "382a766d-856c-4eb5-8d47-c97c92735060",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open one of the original files to extract information like dtype, _FillValue, time units, etc...\n",
    "dso = xr.open_dataset(climo_fns[0],mask_and_scale=False,decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f2f2559c-2852-4047-a27d-ecf59d6ff9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding\n",
    "encoding = {'time': {'dtype':dso['time'].dtype, 'units':dso['time'].units, 'calendar':dso['time'].calendar,'_FillValue':None},\n",
    "            'climatology_bounds': {'dtype':dso['time'].dtype, 'units':dso['time'].units, 'calendar':dso['time'].calendar, '_FillValue':None},\n",
    "            'lat': {'_FillValue':None}, 'lon': {'_FillValue':None},\n",
    "            'xc': {'_FillValue':None}, 'yc': {'_FillValue':None},\n",
    "            'Lambert_Azimuthal_Grid': {'_FillValue':None, 'dtype':dso['Lambert_Azimuthal_Grid'].dtype}\n",
    "           }\n",
    "climo_vars = []\n",
    "for v in ds_climo.variables:\n",
    "    if v.startswith('ice_conc_'):\n",
    "        encoding[v]: {'dtype':dso['ice_conc'].dtype, 'scale_factor':dso['ice_conc'].scale_factor, '_FillValue':dso['ice_conc']._FillValue}\n",
    "        climo_vars.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b326f477-6050-4cb7-864f-0a64e88e67af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reorder the variables in the dataset before writing to disk.\n",
    "ordered_variables = ['Lambert_Azimuthal_Grid', 'time', 'climatology_bounds',\n",
    "                     'xc', 'yc', 'lat', 'lon',] + climo_vars\n",
    "if len(ordered_variables) != len(ds_climo.variables):\n",
    "    raise ValueError(\"Missing some variables!\")\n",
    "\n",
    "ds_climo_2 = xr.Dataset({var_name: ds_climo[var_name] for var_name in ordered_variables}, attrs=ds_climo.attrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "a019efed-e710-43ba-a28a-5ab41c12f1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 199MB\n",
      "Dimensions:                 (time: 12, nv: 2, xc: 432, yc: 432)\n",
      "Coordinates:\n",
      "  * time                    (time) object 96B 1986-01-16 00:00:00 ... 1986-12...\n",
      "  * xc                      (xc) float64 3kB -5.388e+03 -5.362e+03 ... 5.388e+03\n",
      "  * yc                      (yc) float64 3kB 5.388e+03 5.362e+03 ... -5.388e+03\n",
      "    lon                     (yc, xc) float32 746kB dask.array<chunksize=(432, 432), meta=np.ndarray>\n",
      "    lat                     (yc, xc) float32 746kB dask.array<chunksize=(432, 432), meta=np.ndarray>\n",
      "Dimensions without coordinates: nv\n",
      "Data variables: (12/13)\n",
      "    Lambert_Azimuthal_Grid  float64 8B nan\n",
      "    climatology_bounds      (time, nv) object 192B 1981-01-01 00:00:00 ... 19...\n",
      "    ice_conc_mean           (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_sdev           (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_median         (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_min            (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ...                      ...\n",
      "    ice_conc_5pctile        (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_10pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_25pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_75pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_90pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "    ice_conc_95pctile       (time, yc, xc) float64 18MB dask.array<chunksize=(1, 432, 432), meta=np.ndarray>\n",
      "Attributes: (12/34)\n",
      "    title:                     Monthly Climatology (1981-1990) of Climatology...\n",
      "    summary:                   This climate data record of sea ice concentrat...\n",
      "    topiccategory:             Oceans ClimatologyMeteorologyAtmosphere\n",
      "    geospatial_lat_min:        16.62393\n",
      "    geospatial_lat_max:        90.0\n",
      "    geospatial_lon_min:        -180.0\n",
      "    ...                        ...\n",
      "    Conventions:               CF-1.7,ACDD-1.3\n",
      "    references:                Product User Manual v3 (2022),Algorithm Theore...\n",
      "    contributor_name:          Thomas Lavergne,Atle Soerensen,Rasmus Tonboe,C...\n",
      "    contributor_role:          PrincipalInvestigator,author,author,author,aut...\n",
      "    source:                    FCDR of SMMR / SSMI / SSMIS Brightness Tempera...\n",
      "    product_status:            under development\n"
     ]
    }
   ],
   "source": [
    "print(ds_climo_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0ebafc31-2c63-4e2e-973d-27128788e956",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_320772/3156398317.py:6: SerializationWarning: saving variable Lambert_Azimuthal_Grid with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_climo_2.to_netcdf(outname, encoding=encoding)\n",
      "/home/thomasl/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/coding/variables.py:652: RuntimeWarning: invalid value encountered in cast\n",
      "  data = data.astype(dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./ice_conc_nh_ease2-250_climatology-v3p0_19811990.nc is ready.\n"
     ]
    }
   ],
   "source": [
    "# write to netCDF/CF\n",
    "\n",
    "## Note : this filename is still just a proposal\n",
    "outname = './ice_conc_{}_ease2-250_climatology-v3p0_{}{}.nc'.format(area, climo[0], climo[1])\n",
    "\n",
    "ds_climo_2.to_netcdf(outname, encoding=encoding)\n",
    "\n",
    "print(outname, \"is ready.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176921ed",
   "metadata": {},
   "source": [
    "# BELOW THIS POINT, THE NOTEBOOK DOES NOT YET WORK.\n",
    "\n",
    "## Anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "63deff8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read monthly mean field\n",
    "month_ds = xr.open_dataset(fn, engine='netcdf4')\n",
    "month_ds = reconstruct_sic(month_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "78698e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pylab as plt\n",
    "from cmocean import cm\n",
    "\n",
    "cmap = copy(plt.colormaps['PuBu_r'])\n",
    "cmap.set_bad('grey')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.imshow(month_ds['ice_conc'][0].to_masked_array(),\n",
    "          interpolation='none', vmin=0, vmax=100, cmap=cmap)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.text(0.01,0.99,'Monthly Mean SIC {:%B %Y}'.format(dt,), va='top', transform=ax.transAxes, color='white')\n",
    "ax.text(1.01,0.01,'EUMETSAT OSI SAF data with R&D from ESA CCI', rotation=90, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax.text(0.01,0.01,'Source: OSI SAF SIC CDR+ICDR v3', fontsize=12, va='bottom', transform=ax.transAxes, color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "df7c9df4",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataArray' object has no attribute 'drop_dims'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[221], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# compute climatological anomaly\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m month_anomaly \u001b[38;5;241m=\u001b[39m \u001b[43mmonth_ds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mice_conc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop_dims\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m-\u001b[39m climo_mean[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mice_conc_mean\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdrop_dims(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m month_anomaly \u001b[38;5;241m=\u001b[39m month_anomaly[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mice_conc\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mexpand_dims(time\u001b[38;5;241m=\u001b[39mmonth_ds\u001b[38;5;241m.\u001b[39mdims[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m#month_anomaly = month_anomaly.transpose('time', 'yc', 'xc')\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#print(month_ds.dims)\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m#print(climo_mean.dims)\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m#print(month_anomaly.dims)\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/common.py:285\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m    284\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 285\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    286\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    287\u001b[0m )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataArray' object has no attribute 'drop_dims'"
     ]
    }
   ],
   "source": [
    "# compute climatological anomaly\n",
    "month_anomaly = month_ds['ice_conc'].drop_dims('time') - climo_mean['ice_conc_mean'].drop_dims('time')\n",
    "month_anomaly = month_anomaly['ice_conc'].expand_dims(time=month_ds.dims['time'])\n",
    "#month_anomaly = month_anomaly.transpose('time', 'yc', 'xc')\n",
    "#print(month_ds.dims)\n",
    "#print(climo_mean.dims)\n",
    "#print(month_anomaly.dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "a0ee19a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FrozenMappingWarningOnValuesAccess({'xc': 432, 'yc': 432, 'time': 1})\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"No variable named 'ice_conc'. Variables on the dataset include ['xc', 'yc', 'lat', 'lon', 'Lambert_Azimuthal_Grid']\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1474\u001b[0m             \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1476\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_virtual_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'ice_conc'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1573\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_dataarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1574\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1575\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   1576\u001b[0m                     \u001b[0;34mf\"No variable named {key!r}. Variables on the dataset include {shorten_list_repr(list(self.variables.keys()), max_items=10)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1474\u001b[0m             \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1475\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1476\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_virtual_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(variables, key, dim_sizes)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0msplit_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit_key\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'ice_conc'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_327118/228516380.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonth_anomaly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m ax.imshow(month_anomaly['ice_conc'][0,:,:].to_masked_array(),\n\u001b[0m\u001b[1;32m     11\u001b[0m           interpolation='none', vmin=-100, vmax=+100, cmap=cmap)\n\u001b[1;32m     12\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/py311/lib/python3.11/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1573\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_dataarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1574\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1575\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   1576\u001b[0m                     \u001b[0;34mf\"No variable named {key!r}. Variables on the dataset include {shorten_list_repr(list(self.variables.keys()), max_items=10)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1577\u001b[0m                 ) from e\n\u001b[1;32m   1578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"No variable named 'ice_conc'. Variables on the dataset include ['xc', 'yc', 'lat', 'lon', 'Lambert_Azimuthal_Grid']\""
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pylab as plt\n",
    "from cmocean import cm\n",
    "\n",
    "cmap = copy(cm.balance_r)\n",
    "cmap.set_bad('grey')\n",
    "\n",
    "print(month_anomaly.dims)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.imshow(month_anomaly['ice_conc'][0,:,:].to_masked_array(),\n",
    "          interpolation='none', vmin=-100, vmax=+100, cmap=cmap)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.text(0.01,0.99,'Anomaly {:%B %Y} wrt {}-{}'.format(dt, *climo), va='top', transform=ax.transAxes)\n",
    "ax.text(1.01,0.01,'EUMETSAT OSI SAF data with R&D from ESA CCI', rotation=90, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax.text(0.01,0.01,'Source: OSI SAF SIC CDR+ICDR v3', fontsize=12, va='bottom', transform=ax.transAxes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbb5bb5",
   "metadata": {},
   "source": [
    "### Plot the monthly mean SIC and the outline of the climatology median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "643e1c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/thomasl/Downloads/test.tiff\n"
     ]
    },
    {
     "data": {
      "image/png": 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jJsjXx3+/+B8SvB68fuc1UZ2Njm2z0KFNJgBgX1kFavwBDOrRCV63yxyTmpSAF26+/JDsJQiCIAiCaM20yDAwe47Kyq0FWLJxJzbuLsL+8uhVo+KNb2cuwtaCIlw+aSLycjKwfON29OrYDscM6YOf5yzDKUa/kEMlGJLx17ufx0/P3oHpr9yPPxatwpqtuwEA+TkZGDugJ0oqKtHn/FvrPU6PDrn4+vGb4XG7MGPGYpx//OiIMTv2FOO9n2earzft2osH3/wKj0w5D8s/eAJf/bkQCV43zj9+NCRRxN/+/UZY1azLThmP+646GyFZxqJ1W3DrRZMizjFj6VqzQabX7cLcNx/Gqi07sWLjDhQUlyIl0YdhvbtgSM/OqKyuxVWPvHZI942xeN1WLF7XuMIDr33zB0b06YrLTp2A0f16YOriVSgsLkNOegp6dMjFiD5dcfH9/8WOvfuhaRpe+fp33HLhJCz74HH8OHspkhO8OGnUQOzYux8FRrEEgiAIgiCOLupq8Ei0QGclPT0dY8eObTE5KjzPAQCCISvZ2x8I4fgbH8WzN12CY4b2wcg+3TB/zSZMvO4hnDpm0GE7K4D+wD3o0ttx60WTcfKogRjbvwcCIRkFxaX4buZifPp73bkYjDbpKfAYCoC9/K6d6UvXhjkrAPDv977Fjj3F+Md5J2PKWX9BMCRj3qqNuP+NLyKcgA5tswAAkijilgsjHRUAePDNL01npbo2gPvf+AITB/fGsUP7IjM1CSFZxvY9+/H8pz/j+U9/xq6ikgavrSm58pFX8cu85bj6tGNx6pjBSPR6sK+sHJt27cVt//kIfyxabY696+VPUVpRhctOmYDrzjoeRaXl+Oz3eXjgzS+w8qOnjqjdBEEQBEHEnrysVFx/mh5tU1tbd/j50QqnaVqD5ZYqKiqQkpKCO+64Ax6P50jYFRW3240TTzyxRZUnzklPQeFPr2La4jU4/sZHYm0OQRAEQRAEESfkZaXii3uugM/nQ0VFBWbOnIlAIL7SGvx+Px5//HGUl5cjOTn5iJ+/RWlOqampkCQJ1dXVLcJRAfTGhYCV9E4QBEEQBEEQeVmpePOWi+PaUYkHWlQYWEpKCgAgEAjEvaNyx2Wno2/n9jj32JGoqvHj9W/+iLVJBEEQBEEQRJzw/i3nIyMjBRUVFZg1axY5KnXQYpyVDh06oG/fvgCAwsKD759xpLn1wkkQBB7TFq/Gva9/jh1798faJIIgCIIgCCJOSEpKAgAsWrQIfr8/xtbELy3CWeF5HgMHDgTHcfhk2iI8/slvsTapQTJPvCbWJhAEQRAEQRBxyKjenSCK+mM4NYKsnxaRsyIIgvmGPv05hVMRBEEQBEEQLZNB3drj+RvOBc/zKCgoQE1NTaxNimtahLPSp08fALrnqaiH1pmcIAiCIAiCIGLNoxcdB49Lwp49e7BgwYJYmxP3xL2z0qtXL3Tp0gWqquHBD35Bw4WWCYIgCIIgCCI+EQQBALB79240ooPIUU/cOyvt27cHADz12W/4cf7qBkYTBBHPTH3pXijzPglbd9kp46HM+wSXnTI+RlY1H6352giCIAjiSBDXCfaiKEKSJADAup17Y2zNoaPM+wTrdxSgz/m3xtqUg2ZAtw4497iRGNKjEwb16ISstGRMX7oWx93wcJ37pCYl4K7Lz8Dp44eifXYGKqprMXPZOjzw5pdYu2131H0mDu6NWy+ahL5d8pGZmoTC4jIsXLsZT37wPVZu3hk2tnO7bFx80jgM7tEJg3t2QrusdGzfU4wuZ/3joK8vNysN5xw7EqeMGogeHXLRJiMVpRVVmLtyA5768AcsXLsl6n5JPi/uv/psnDVxONpkpGJvyQF8PX0hHnzzK1TWhHef9bpdmHLW8RjSsxMG9eiI7u3bgud5dD7zxnqrxHXNa4NHppyHiYN7I9HnwaZde/HGd1Pxyle/N3om5r6rzsb9V58DAHji/e9w1yufRh339D8uxj8vOBUAcOfLn+DJD75v1PFbI2/fMwWXnTqh3jFn3f4Mvpu5+AhZRBAEQRBHL3HrrIiiiDFjxsDj8SAYDGJzQXGsTToqOX38UNx52RkIBEPYuGsPstLq71yanpyIOW88hO75bTF31UZ8P3MJ2mam4qyJw3HSqAH4y98fiXAAbjjnRLx4y+Uoq6jCNzMWobisAt3z2+KcY0fg7GNGYNItT2DqIktVGzegJ+6/+hzIsoJ1OwrQJj31kK/v7+eciNsvPR2bd+/FH4tWYV9ZBbrltcHp44fi9PHDcNH9/8EXU+eH7ePzuPHnK/dhUPeO+H3BSnz6+1wM6NYB/7zgVEwc0gfj//YAavxWrfTstGQ8/Y+LAQDb9xSjrLIaGSlJ9drVq2M7zH79Qfg8bnwxdT4Kiktx0qiB+M8tV6B/l3xMeeLNg7rOkCzjkpPH457XPoOqhjs6oiDgohPHIiTLkMQj/yvhmxmLMH/NZuzZX3bEz10fb30/Dbv3lUbdtn5H/JdPJwiCIIjWQNw6K927d0dmZiYqavyY8tzHqKylRjmx4MtpC/DDrCVYtWUXMlISUfjTq/WOf+Cac9A9vy2e/fgn3PafD831I/t2w4xX7sebd0/BgIv/ZSoDoiDg4b/9FeVVNRh4ye1hD4enjx+Kr5+4BXdcekaYszJz+XqMvvperNi8A/5ACNXT3zvk61u0dgsmTHkAs1dsCFs/dkAP/P6fe/DSbVfiu5mLEQzJ5rbbLp6MQd074skPvsedL1shTfdffQ7uu+ps3HbxZDz45pfm+v3llTjxH49hyYatKKuoxs/P3YETRw6o166X/nUVUpMSMOnmJ/DLvOUAgHtf+xw/PXc7rjnjOHz6+1xMX7q20df567wVmDxuCE4eNRA/zVkWtm3y2MHITk/B9zMX47TxQxt9zKaioroWFdW1DQ88wrz1/Z9YsGZzrM0gCIIgiKOauM1Z8fl8AIB3fp2HNdv3xNiapuXte6ZAmfcJOrTJjNh231VnQ5n3CSYM6mWumzCoF5R5n+C+q87GoO4d8ctzd+DAH2+j9Pe38NXjN0c9DgB0bJuF1++8Btu++Q9qZryP3T+8jLfvmYL8OsZHY+223Vi2cTtkRWnU+NPHD4WiqHjgzS/C1s9fvQk/zlmKPp3zwq4tIyURKYk+rNqyM2IW++e5y6CqKrIdas62wn1YsGYz/IFQo6+jLr6ZsSjCUQGA2Ss2YPqSNchISUK/Lvlh26467RhUVtfi4be/Dlv/+PvfobSiCldOnhi2vro2gD8WrUJZRXWjbOrWvg0mDOqFaYvXmI4KAMiKgntf/QwAcPXpxzbqWIxvpi9EWUUVrpg0MWLbFZMnYl9peYQTYycrLRnP3HQJNnzxHGpmvI+iX17HF4/9H/p0zos6fkz/Hpj28n2omPYO9v36Oj55+B/Iy06POrauvI4zJgzFRw/eiA1fPIfKP99F6e9vYfor9+OsicMjjtGhTSaUeZ/g7XumoFNuNj5/9P+w/39voGLaO/jfi3ehf9f8iH2akrTkBLz8r6tQ+NOrqPzzXcx/6xGcMaF+x+/aM47Dyo+eQvX097D92//i8RsuhNslQZn3Caa+dG/E+ESfB/dffQ5WfvQUqqa/h5Lf3sTPz92BMf17NNdlEQRBEETMiUtnJSEhAdnZ2QCAyhrq6MkY2qszZrx6P2RFxevfTcXi9VtxxoRh+O0/d8PtksLGDu/dBUve+zcuPWU8lqzfihc//xWzV2zAhSeOwfy3HkGn3OxmsTEnPQX7yytRHUUJ21a4DwBwzNA+5rqi0nIUl1WgX5d85GalhY0/edRA8DyPP5esaRZbGyJkOGh2R61b+zZol5WOuas2hoV6AUAgGMKsZeuQl52BrnltDvm8Ewf3BgD8vnBlxLaFa7egrKIK420OX2PwB0P47I95OHXMYGSmWiFobTPTcOKIAfjof7MRkuWo+3Zul41F7zyGf/z1JGzZXYT/fvk//DJvGU4cOQBz33gYw3t3CRt/7NA++OO/92BE7y746s+FeOO7aeiYm4VZrz2ItKSERtv86HXno3fnPMxZsQEvfv4rvpw2Hz3y2+KLf/8TN5xzYtR9OrTNwrw3H0ZmWjLe+XEG/li4Cn8Z1g9TX7oX2WkpjT73weB1uzDtpfvwtzP/gi0FRXjx81+xcecefPLwTTj72BFR93ngmnPwyu1XIy0pAW98Nw1fTpuPc44dgc8euSnq+LTkBMx5/SHcd9XZKCmvxKtf/46vpy/EkJ6dMfWle3B6DBQxgiAI4tCQjb+3HTp0AM/H5aN4XBF3YWButxvjx4+H1+vFlsJi/Lqo8aEurZ1TxwzGBfe8gM9tORTv3ncdLjl5PM4YPxSf/TEPgB5a9fHD/wDPcxh+xd1hCepj+vfAtJfuxfP/vBSn3/Z0k9tYXFaJnPQUJHjdEQ4Lc5C6t28btv6mZ9/Fe/dfj+UfPIFvZyxC8YFKdMtrg0ljB+Ob6Qtx72ufN7mdDdE+JwPHDe2LPfvLsGqLdf+6GbZv2hW94MOm3XuNcW2wefehFYXo2l53dDbXcY7Nu4swrHcXeN0u1AYa3/X2nR+nY8pZx+Oik8bihU9/AQBcesp4iKKAd36cjqE9O0fd7937rkebjBSc/H+P449Fq8z1j77zDRa+8yheu/NaDLrkdgAAx3F49fZrIAo8Jv79EcxZaalWHzxwAy48cWyj7Z1085Omg8tI8L6P2a8/hIeuPRdv//BnxPVPHNwbd7z0MZ768Adz3YPXnot7rjgLl0+acFCFA6467Zg6w/We+OB7BIK6snfbxZPRv2s+3vh2algu0Qe/zMSvL9wVsW+39m1w56VnYMeeYgy9/C6UVlQBAO5/4wvMfSN64YoXb74cfbu0x1WPvIp3f5phrr877TMsePsRvHr71fh1/grTJoIgCCJ+ueP93/H6zRchOzsbo0aNwpw5c2JtUlwTd+5c27Zt4fP5UFVVhauf/oiUFRszlq0Lc1QA4J0fpgMAhvayZrcnjR2ETrnZeOrDHyMqac1ZuQHfz1qMk0cNQpLP2+Q2/jp/OQSBx31XnR22fljvLjh1zCAAQGqiL2zbZ3/Mw6k3PwFZUXHVacfijktPx9nHjsDm3UV476eZEdW1mhtREPDe/TfA43bhjpc+DktIT0nU71l5VfRusyz3IsVxjQdDSoK+b3l1HeeoObRzLF63FSs37wwLBbvslPFYuHYz1myNXqVtYPeOGNO/B97/eWaYowLoDtub3/2J/l3zzXCwsQN6oEteDn6asyzMUQGAu1/9DLLcuHBCABGOCqCH1L330wykJiVgmEPRAYCtBUV4+qMfw9a9/f2fAIBhvaI7Y3Vx1WnH4v6rz4n6z2NTMi85eRwCwRDufyM89PH3hasi7hkAnH/8GIiigGc/+cl0VNi1PfbuNxHjM1KS8NfjRuGPRavCHBUA2FdWjqc/+hHZ6Sn4y7C+B3V9BEEQRGxYta0Q17/wCVRVRZs2bZCQ0Piog6ORuFNWXC4XAODAgQMorWxcjP/RwrIN2yLW7S7W8zxSk6wH1xF9ugEAenbIjXAaACAnIxWCwKN7flssWb+1SW184I0vcdLIgbj1oskY2bcbFqzZjLYZqTj7mBFYu60AA7p1gKKqYftcfuoEvPyvq/DK17/jv1/8D3tKytAjPxePXXc+vn3qVtz07Lv47xf/OyR7UhJ9uOm8kyPWP/TWV1HHcxyHt+7+GyYM6oU3vp2KD3+dfUjnjVfe/Wk6nr3pUgzt1Rkel4QeHXJx/ZNv1Tl+ZJ+uAIA2GalRP0s9O+SayzVbd6N/1w4AgFkr1keM3bl3P3btK2l0CGJWWjJuv+Q0nDRqIDq0yYTP4w7bnpuZFrHPys07I8o6s+9IykGEoAHA6KvvbTDBPtHnQed2OVizdTeKSssjts9evgF/GdYvbN2Abnr+zLxVmyLGz121MWLdsF6dIYoCPC4p6nvQzVDienTIrTfviCAIgogflm3eDVmW4XK54HK5UF1Nz7x1EVfOSk5ODnr31uP1KyoqYmxN/BFtNp/NVAu2mMf05EQAwEUn1R9yk+B117v9UCgoLsWIq+7GA1efg5NGDcTw3l2xq6gE97/xBXbsKcYnj9yE4gOV5vju+W3xyu1X46c5S3HLCx+Y61ds2oGz7ngWaz99Bo9OOR/v/Dg9ah5MQ6Qm+sw+I3bqclZev/MaXHzyOHz4yyxcF+UhvryqflUjOaF+5aUxMEWFKSwR5zAUsUOpoPXRr7Px+PUX4opJE+FxSagNBPHp73PrHJ9mfJZOHTMYp44ZXOe4BK9Ht9m4L8Vl0b+/RaXljXJW0pITsOCtR9ChbRZmr1iPqYtW40BVNRRFxcBuHXD6hGFwS5G/vqLdd0XRnWOhGeKC2Xu0ryzSUalrfZLxGSk+EHmPojk87Ps8dkBPjB3Qs05bEjyehg0mCIIg4oby8nJkZWVhxIgRmDlzJmpqDv3ZoTUTN86KJEkYOXIkBEHAtGUbcNtr38XapGZDZWV7RSFi2+GEDzHYQ+xptz4Zk5nWwuIyXPvvNyLWs1nhxessNef44f3hkkT8uSQyNykQDGHeqo244IQx6Nmh3SGpQDv27ocw6oIGx3EchzfuuhZXTJqIT36bgyseeSVq48VNu/TKdGw220k3I7G+rpyWxsByVbrWcY6ueTkoKC6NSPBvDPsPVOKnOUtx/vGjIQoCvpm+sF7Hin2WbnzmHbz85W8NHp8dq65+PDnpjUtyv3LyMejQNgv3vPoZ/v3et2Hb/nXJaTh9wrBGHae5YfenruT9aOsrjX2yUpOx09EUNNr9YWF/z3z8I/71n48Oy16CIAgifrjwqU/w1q0XIz87HcOGDcOMGTMa3ukoJG5yVhISEiCKIgKBAG577WvIitrwTi0UVsK2XVZkKdeB3Tse9vEXrtVDV0b27XbYx2oqeJ7DecePQkiW8fX0heZ6lzE7npUWvUliVqr+0BsINV/isN1R+ez3ubj0wZciGicyNu3ai4LiUozu1z0iLMntkjBuUC8UFJcecnI9ALN/yvHD+0dsG967C9KSEzFz2bpDPv47P05HalICEn0evPPj9HrHss/SqEZ+llZu3gFAb9zpJL9NJtpnZzTqOF3a5QAAfpi9JGJbtGPHisqaWmwtKELXvJyojsbYgZFlhVds0vPIRvWLvKej+3WPWLdo7RaoqhpX32eCIAji8NlXVonbXtNzFZOT62+6faTgOA65ubnIz883/wlC5OT6kSRunJU2bfRZZFmWW7WjAsBUCJx9Jc4+ZrhZtvZw+G7mYuzYU4x/XnAqxg2MfLATBaHZejOIggCPO7yMMsdxeOrGi9GzQzs9J8XWqXyukYR9zenHRThvxwzpg4mDe2NvyQGs3RY9Afxw4TgObxqOyhdT5+OSehwVxlvf/4mkBC/uvfKssPV3XHo60pMT8ZaR0H2obNq1FzOWrcOxQ/vg5FEDzfV6A83zAABvfjftkI//y7zlOPNfT+PMfz2NaYvrLwu9aO0WzF+9CecfPxp/PW5kxHaO48LKKM9esQFbC4pw6phBEZ+xR6ecF1VNjMYOQ3EY6zjGBSeMxilGoYZ44cNfZ8PtkvDgNeeGrT9+eL+IfBUA+OyPuVAUFf+84FQzxAsAfB437rz8jIjxRaXl+GLqfIzp3wO3XDQpqg3DjepwBEEQRMuCKfSiKCIzs/F98JoDnucxZswYjBo1CsOGDTP/sXzyWBEXYWBdu3ZFnz56743Nm1t/x+hvZy7C1oIiXD5pIvJyMrB843b06tgOxwzpg5/nLDvsh7FgSMZf734ePz17B6a/cj/+WLTKrPaUn5OBsQN6oqSiEn3Ov7XBY/XokIvbLzkNAMyHoZ4dcvH2PVMA6N3Z7aEpOekpWPXxU/h94UpsKyyGSxJxwoj+6NWxHX6asxR3vfJp2PEXrNmMD3+ZhYtPHofVnzyFb2csxt6SA+iR3xaTxg4BAPzfc++FORAZKUl46saLzNeSKCIzJcm0CQCufOTVRt2re688C5dPmojK6lps3LkHd19+ZsSY72YuxopNO8zXT334AyaPG4J/XXIaBnXviCUbtmFA13ycPHoQlm3cHlY2l/HkjRchM0VXj/p2aa8f58aLUVWrV7t74oPvsWFHoTn+hiffwuzXH8RXj9+ML6bOR+H+Mpw4cgAGdOuAN7+bdlDd652oqobvZ0UqFnVx0X3/wdSX7sUnj9yEf5x3MpZu2AZ/MIT8nEyM7NsNWalJSJh4GQBA0zRMeeJN/PjM7fjtxbvwuWH7MUP6oG1GKlZs2oEB3To0eM4Pf5mFf108GS/cfDkmDumDHXuL0b9LPo4b1g9f/7kAZx0TvX9JU1Jf6eIZS9dihqFuPfXhDzhz4jBcc8Zx6N05D7OWr0f77Ayce9xI/DRnaUSuz8ade/DEB9/hrsvPxIoPn8SX0+ZDVlScOXEYVm/ZhX5d8iMc5huefhvdO+Tiyb9fhItPGof5qzeivKoW7bMzMLhnJ3TPb4vcU6ccVClrgiAIIvYUlpRjz549aNu2LcaMGYOdO60qrjU1Ndi8eTOURjbmPliysrLQrl07cBwHAEhJSUFGRgZkWcb+/VaYcnOdv7HEhbPCkupf/n4mXvthVoytaVp4Xv8ABEPWG+0PhHD8jY/i2ZsuwTFD+2Bkn26Yv2YTJl73EE4dM6hJZo4Xr9uKQZfejlsvmoyTRw3E2P49EAjJKCguxXczF9ebVG2nTXoKLjt1Qvi6jFRz3fY9xWHOSnl1Db6ftRij+/XAqWMGIyTLWL11N6597HW8/eP0qHkglz/8CmavWI9LThmPMyYMhc/tRklFFX6YvQTPfPwj5q4Mr5CU6HVH2JTo84Sta6yz0rFtFgA96fnuKyIdFQDYsac4zFmp8Qdw7PV6g76zjxmBCYb689wnP+Ght76Kmkty9jEjzHOZ62wNA9/7aUaYs7JuewFGXnUPHplyHk4aNRCJXjc27y7CP555Fy9/1XDuSFOyfU8xhlx2J/55wSk4ffxQXDFpIhRFxZ6SA5i1fB2++nNB2Pipi1bj+BsfwUN/Ow/nHDsCtYEgpi1eg/Pufh7v3nd9o85ZUFyKY65/CE/ccCGOG9YXosBj6YbtOOn/HkP77Iwj5KwcW+e2B9/80nRWavwBHHP9Q3jsuvNxxvhhGNyjE9Zs3Y0L7n0BKQm+qIUJ7n3tcxTsK8UN556Iv535F+wrq8Bnf8zFi5/9isnjhqDSUba6rKIaY6+9DzeccyL+etwoXHjCWPA8h70lB7By8048+s7X2F9eGXEegiAIIv457aH38PwN52JM3y7o3Dm8zH5WVhbmzZvX5A5DXl4ehg8fbjoqDFmWcfWzH2PZpl3mOk2ObQ8vTov29OigoqICKSkpuOOOO+BphoozZ555Jniex/G3vYh9B1rXH9yc9BQU/vQqpi1eg+NvfCTW5hAEEcccN6wvfnvxbjz5wfe48+VPYm0OQRAEcYRwiQImj+qPzBS9zL4oCLjoL8OQ4HGjqKgIc+fOhaoeWpqEy+XCwIEDkZRk5QenpKSA4zhMW7YB63fqebaKqmHasg3Yuie8+Ismh6At/grl5eUxya2JC2WlNXP6+KEArERlgiCIzNQklFZUORqO+vDolPMB6KGHBEEQxNFDUFbw1azwCq5z12zBGzdfiJycHIwePbrRDgvHcaZiIkkSxo4di9TU1Ihx38xejgff/wkNyxaxhZyVZuKOy05H387tce6xI1FV48fr3/wRa5MIgogTLjxxLG658FT8uWQNCovL0DYzDSeOHICc9BS8++N0zF8d2TCSIAiCOLpYtnk3rnn2Y7x80wWNdlhYHrgohj/i7y+vwmMf/YraoB7SVV5dizXb9zSr/U0FOSvNxK0XToIg8Ji2eDXuff1zs7oRQRDEvFUbsXR9bxw3tB/SkxOgqCrWbS/EI+98jVe++j3W5hEEQRBxwrLNu3H9C5/gzVsuMh2W9evXRx2blZVl5oHbqaqqwlVPf4jte0ua29xmIebOSkpKCnijs7Qa7zrUQZB54jWxNoEgiDhl0dotOPP2Z2JtBkEQBNECWLZ5N65+5iNTYcnJyal3/Mvfz8RHf1g97Wr8wRb9jB1TZyUlJQXjxo0DABQXF2N/eVUszSEIgiAIgiCIuGPZ5t2Y8vzHePbqSXX2PdE0Ddu3b291lXVj6qz07t0bbrcbK7bsxvUvfNrwDgRBEARBEARxFLJiSwGOu/O1WJtxxIlpB3vmGb7323xU1Ub2piAIgiAIgiAI4uglps6KScsNoyMIgiAIgiAIopmIqbMiCEIsT08QBEEQBEEQRBwTM2elX79+SEtLAwDsKCqNlRkEQRAEQRAEQcQpMXFW2rdvj+7duwMAHvnwF2wuLI6FGQRBEARBEARBxDExcVaCwaD5c15WaixMIAiCIAiCIAgizomJs1JUVIQ1a9YAAC4/cRR65beJhRkEQRAEQRAEQcQxMXFWPB4POnbsCACoqqqinBWCIAiCIAiCICKIibPSqVMnJCQkYOe+Upz50NuoCQQb3okgCIIgCIIgiKOKmDgroigCAGas2IR9ZZWxMIEgCIIgCIIgiDgnJs5KbW0tAOCc8YMwuFv7WJhAEARBEARBEEScExNnZevWrdi7dy+8bhfeuPlCZKcmxcIMgiAIgiAIgiDimJg4K6qqYt68eaisrIQoiujXKTcWZhAEQRAEQRAEEcfErIO9qqrw+/0AgK552bEygyAIgiAIgiCIOCVmzgoAFBYWAgCuP208zps4JJamEARBEARBEAQRZ8TUWdm8eTM2bNgAALjropPIYSEIgiAIgiAIwkSMtQGrV68GAPTo0QN3XXQShvfsiIoaP76cuRRrtu+JsXUEQRAEQRAEQcSKmDsrQLjD8pchPQEAp43qi2ue/RhLN+2KpWkEQRAEQRAEQcSImIaB2Vm9ejXmzJmDlStXYt++fRBFEW/cfCH1YSEIgiAIgiCIo5S4cVYAYO/evdi0aRPmzJmDoqIiiKKIl286nxwWgiAIgiAIgjgKiStnhaGqKubOnYuioiKzcSQ5LARBEARBEARxdBGXzgpgOSx79+4lhYUgCIIgCIIgjkLi1lkBrE73e/fuhdftwss3nguOi7VVBEEQBEEQBEEcCeLaWQF0h2Xx4sUAAK/XC568FYIgCIIgCII4Koh7ZwUAZFmGqqoAgHPGD46xNQRBEARBEARBHAlahLOiKEpYp/s3rz0hxhYRBEEQBEEQBNHctAhnBQDWrl2LzZs3AwA6d+4cY2sIgiAIgiAIgmhuWoyzAgAlJSUAAJ5vUWYTBEEQBEEQBHEItJin/pSUFAwaNAgAUF5eHmNrCIIgCIIgCIJoblqEs8JxHEaPHg2Xy4UVW3bjwme+iLVJBEEQBEEQBEE0My3CWRFFET6fDwBww4ufodofjLFFBEEQBEEQBEE0Ny3CWbFT4w/E2gSCIAiCIAiCII4ALc5ZIQiCIAiCIAji6ICcFYIgCIIgCIIg4pIW56xIohBrEwiCIAiCIAiCOAK0CGdFlmUEAnquyhPXnEkOC0EQBEEQBEEcBbQIZ0XTNCxcuBCKomDiwO749JZzYm0SQRAEQRAEQRDNTItwVgBg3759WLZsGQAgIyMjxtYQBEEQBEEQBNHctBhnBQD8fn+sTSAIgiAIgiAI4gjRopwVgiAIgiAIgiCOHlqUs6IoCgAgKSkJ/TrlxtgagiAIgiAIgiCakxblrJSUlKC4uBiiKOKd2y5G13ZZsTaJIAiCIAiCIIhmokU5K5qmYc6cOSgpKYEkSThjzIBYm0QQBEEQBEEQRDPRopwVQA8FKyoqAkANIgmCIAiCIAiiNdPinBWCIAiCIAiCII4OWrSzkpuRCo6LtRUEQRAEQRAEQTQHLdJZKS4uhqZpGN+/K+684KRYm0MQBEEQBEEQRDPQIp2V/fv3Y/HixdA0DecdMwTDe3aMtUkEQRAEQRAEQTQxLdJZAYCdO3di3759AIDs1MQYW0MQBEEQBEEQRFPTYp0VQC9lTBAEQRAEQRBE66RFOysEQRAEQRAEQbReyFkhCIIgCIIgCCIuIWeFIAiCIAiCIIi4hJwVgiAIgiAIgiDiEnJWCIIgCIIgCIKIS8hZIQiCIAiCIAgiLmmxzkpiYiJSU1MBAEFZia0xBEEQBEEQBEE0OS3SWfF6vRg3bhw8Hg8qKiowe9WWWJtEEARBEARBEEQT0yKdlfz8fPh8Pmzbsx+nP/AWagLBWJtEEARBEARBEEQT0yKdFZ7XzV6+ZTdKK2tibA1BEARBEARBEM1Bi3RWqqurAQCnje6Pk4b1jrE1BEEQBEEQBEE0By3SWdm5cye2bdsGgefx+DVnoGtuVqxNIgiCIAiCIAiiiWmRzgrHcZAkCQCgqiqCshxjiwiCIAiCIAiCaGpapLOSn5+PvLw8AMBtr3+DnfvKYmwRQRAEQRAEQRBNTYt0VkpLSxEKhQAA504YDFFokZdBEARBEARBEEQ9tMin/MrKSsyZMweyLGNU784Y07dLrE0iCIIgCIIgCKKJaZHOCgCUlJRg//79AIBknyfG1hAEQRAEQRAE0dS0WGcFAPx+PwBgyuRxaJOeHGNrCIIgCIIgCIJoSlq0s7J27VpUVVUhLysNX95zOQSei7VJBEEQBEEQBEE0ES3aWamtrcXMmTOhKAqSkpLQLjM11iYRBEEQBEEQBNFEtGhnBdAdFkVRYm0GQRAEQRAEQRBNTIt3VgiCIAiCIAiCaJ2Qs0IQBEEQBEEQRFxCzgpBEARBEARBEHEJOSsEQRAEQRAEQcQl5KwQBEEQBEEQBBGXkLNCEARBEARBEERcQs4KQRAEQRAEQRBxCTkrBEEQBEEQBEHEJa3KWenTMTfWJhAEQRAEQRAE0US0Cmdl165dAICHr5iMCQO6xdgagiAIgiAIgiCaglbhrKxYsQI7d+6EJAp47PJTYm0OQRAEQRAEQRBNQKtwVjRNw/r16wEALpcrxtYQBEEQBEEQBNEUtApnhSAIgiAIgiCI1kercVYEQYi1CQRBEARBEARBNCGtwlnxeDwYPnw4AKCysjLG1hAEQRAEQRAE0RS0Cmdl+PDhSEpKQsH+A7j4qU9ibQ5BEARBEARBEE1Aq3BWUlJSAAC3vfY1CkvKY2wNQRAEQRAEQRBNgRhrA5qSyhp/rE0gCIJoIjR092kYmaqhV6KKjh4N6S7Ay2tYVM7ju30CZpZxALhYG0oQBEEQzUarclYIgiBaOn0TVfy1jYLz26ro4tOijhmTpuD/OiqYWsLjtV0C/izlURoip4UgCIJofbR4Z6Vjx45mb5WaQCjG1hAEQRw8bd0aLmqr4OJcBf2SLAfFrwCLKzgsr+CxtZZDcVB3SI7LUHFeGwXHZag4LkOFqgErKjnMLuPxUzGPqSU81HoUFw4a2nmAQUkqBierGJqiob1HQ5qkIUEAJA7YWsthZSWHOWU8fivhsb22VUQNEwRBEC2MFu2sdOzYEUOGDAEAvPe/+dhfXhVjiwiCOHrR0NGrwcUBKoBEo5r6tloO5bLTcdDQwaPhmHQVk7JVTM5SIRq+QEAFfinm8WWRgO/28ahRIp2Oj/cIeHSLgL93UHBcuoq+SRoGJWsYlKzgxg4KCv3A/HIeW2s4BFQgXQJ6JGjIdmlIFjW0cQOuBnyP/kka+idpuDhXd4a+38fjrd0Cft1fvyNEEARBEE1Ji3VW7I7KB78vwLNfTo2xRQRBHI24OA1Xt1dwTZ6C/knRw7YqZaAoyEHR9JyTbBfgcbSGmlPG4f1CAV/sFaI4N5FsreVx83rd42jj0jA2TcWxGSrObaMg1wOc5VHr3V9WgbXVHJZVcFhUzmNTDYeyEIdKGdAAdE/QMDRZxcR0FePTNZyRo+KMHBXbaji8uVvAe4UC9gTIaSEIgiCaF07TtOh/XW1UVFQgJSUFd9xxBzwez5Gwq16cjsrTn/8RY4sIgjjayHZpODNbwc2dFHQ1ckuCKlCtAAIHVMn6MscdfX9ZBRaUc5hWyuOrvQJWVTVNmJWL0zAuTUWfJD20S+R0mzZU8yjwA5UKh70BDnsDQEhrnLPRO0HFlXkKLmunIF3S1ykasOAAh99LeMw7wGNhOd8oJ4sgCIJoWWhyCNrir1BeXo7k5OQjfv4Wp6zYHZUP/1hIjgpBEEcYDU92l/HPjgp449l8TwB4bKuIjwsFHHA8sCcIGnLduprCAQhowL4AsCfAIdhIZ+FgCGocppYKmFradMdcW83j1g087tkk4q9tVFyZJ2NcmobRaRpGpykAFDNvZlkFj221uoqULumhZAEVeGKbiHkHKO+FIAiCODhalLOSmJhIigpBEDFD5DT8p5eMa9srAIBF5Rw+2yPg9d0CqqPklgBAtcJhUw2HTTVH0tLmwa/qoWrvFwrI82g4KVPB+DQVI1M1dPFZeTPRmJwdxIGQHhL3vxIBj2wRsctPSgxBEARRPy3KWUlISACgh6WRo0IQxJFkQpqC//SW0SdRg6oBN6wV8fruFvUrtEnZ7efw5m4Rb+7WX7O8mZ6JGjp6NGgAqhRgXRWHYSkaLs5VkCoBqRJwdZ6CS3IVfFQo4PFtArbUkOJCEARBRKdF/aVt164dACAYDMbYEoIgjhaSBA1P95RxdZ6uGBQHgatWS/ipWGhgz1YKF0UN0TTsDXL4skgAiiI3v74buGWDiFy3hjyPhn910ssuX5mn4Py2Cv6yyIUF5eSwEARBEJHEvbPStm1b5OXlwePxIDs7G5qmYdOmTbE2iyCIVo5P0HBVOwW3dZLRzqgr8spOAfdsEiPyUoiGqZA5VMgc1lcDf5QIGJWq4vHuIYxN0/BR/xAGz3Ohgu4rQRAE4SCunRV7Mj0AaJqGu9/6Hj8tWB1DqwiCaM2kSxpu6iDjuvYKMvR+s9hSw+Hq1RJmlh3Fs//RFJW6tjVcZBLzDvCYvNSFJaOC6OzT8GLPEKasleBXyWEhCIIgLOLWWbE7Kj/MW4l1O/ZixZYCrN5eGGPLCIJojfgEDZe0VfBAVxnZRrnhLTUcnt4m4N0CoVkqd8Ut9TkmTUiFzOGq1RL+HB7EJe1UnJYdwPpqDm4eeKdAwH93CgA1oCQIgjiqiUtnhfqoEARxZNAwPk3Dle1knJmjItH4jbimisP9m0R8t4+6tTc3M8t4XLlKxH1dZXT0AiNSdVXmhWQZA5M0XLdWbHQ/GIIgCKL1EXfOCjkqBGHAR0ngrivcRlMbFXpDWAxIUvFW3xAGJVv3bUsNhxd2CHh9lxD7B+TGqBuH8543p3rCcQdl23uFIj4oFPCXDBUpItDJp+GRbjKuyFPAccBVq0WQwkIQBHF0ElfOit1R2bRpEzkqBEE0Axr+2UHBv7vLkHi978cne/RQrwXlHOihODao4PBbieWgr6/i8OWgEC5vp6A4CNyxkRwWgiCIo5G4cVZIUSEIHS6zAwBACxhdBEN+famE6t5JlQHVaMZHCku9XJqr4umeMgDgq708rl8rYX8oTh6CD1btOEK5JYdEXbY18vP5fbGA69ZoeL2vjNs6KeiZoOGujSKCGrC9loN8pJUvdj30/SIIgjiixNRZSUpKQl5eHtxuN7p06QIgUlHpld8Gxw7qYU6oqaqG/y1ai6179sfCZIIgWjC5bg3P9dSdvoe3CHhgM83WxzNvFYiQeODZnjImZ6uYnK332PqjhMekJVLsQ/UIgiCIZidmzkp6ejrGjh0LSZLMdR/+sRBPffa7+Xps3y548e/nQhDCY/fPGtEDx9/9xhGzlSCOJFpNuf6DaJSk8iToy0ANIBsNUVk+C1uqgqW8sGW8zADbZ9htNomchgFJ+uu1VRxqm7lkrYfX8FH/IFIlYGE5h4e3xNBRiRdFhKujFLOmNuM567n2KJ/ZV3eJmHuAx6u9Q+ibqCFBBP6SoWL68CDOXe5CYaAJ7uXBqECksBAEQRxRYuKsJCcnm47Kyq0FWLW1AOt3FeH7uSuRn52Gl6dMhtvtRkZGBgRBwL59+1BRUQGfz4fc3NwwB4fhdUn48J9nITk5GaqqYuvWrbjunT9jcHUE0Ti8bgnz//uveseUlZVh4t1v6y9cPoAzHBH2kMmL4UvAFg6mNKG1TcMJmSqmtJfRzq2hZ4JmVt9SNOCLvTyuXC0h0CxOi4YP+ocwPl1DeQi4cpUEJVaz8vHuqDS0zU5TOzV1OAIrK3mMXqA77ydlKviwfwgjUzV8OTCIYxa5Du4zczD3/2B6ywDkwBAEQTQDMXFWsrKyTIfjP99Mx8L12wEAHXLS8eYtFyM7Lckcu3v3bixcuBCapiElJQW5ublIS0vDBccOxSfTFgPQHZUXb/wrunbtaO6Xk5ODk9YU4deFa4/YdRHEwTCkW36DY9LS0o6AJc2PwGl4sIuMOzvLYetLQ4CsAtlu4Py2KkQuhAtWSE1eLvisHBVn5agIqMDpy1xYV30UN3ds4fy6X8CI+RwWjAxiRKqGLweGcN1aCbv9ceIEEgRBEE0Kp2kNTwVVVFQgJSUFd9xxBzwez2GfVBAEjB49GtnZ2QiFQrjy6Y9woKoGn99zBbxeL8rLy7FhwwYEg0EUFRWF7dunTx/07NkTACDL+oMPx3EQBAGhUAirVq1CRkYGOnToAE3TcNkT72HFloLDtpkgmoOL/zIcw3roCfUQjXbpbMZWVfDOH0uxfKvRCFXyWMn2LPyLhYrxnB4mBgBBY6mEOwaxguOAj/uH8Nc2utLzToGA7/YJ2FrLYW0loIHDCRkKvh0cgpsH7tkk4t9bm24epZNXxZJRQaRIwBNbBdy1KVKZPSI0paJyMOFbjVVJDofmChuz/3ly3L8JaQp+GhyEVwBUDZhRxuPjQh5fFgmokO1j2TG4qMdpVkhpIQiiFaDJIWiLv0J5eTmSk5OP+Plj4qwA4Q5LVW0ANYEgslOTUF5ejlmzZiEQCNS5b+/evdGzZ09wtj86gUAAc+fORWlpKQBg7NixyMnJwcvfzcBrP85uEpsJollJzNCXovEwzZwP9hVtoc7K491l3NZJRkAFrlwt4dO9NkfE9uvn8lwZb/WTUSEDXWa6UdoEFbo4aPhjWBAT0zXMKeNw3CJX7JKyyVk5hOPW7awAwNgUBQ91C2FCujWuVgF+KuZRowAdvRr6JGqQeOC9AgHPbRewIxClf1FzQc4KQRCtgFg7KzFLsFcUBXPnzjUdlkSvu1GOCgCsXbsWW7ZsgSha5tfW1kJVrT+Y1dXVzWY7QTQ1XHqe+bOmGk6G26cvFVvuCVNfjBwVzig+oWnqkXkoPUj+r4PuqADAVaslfFrk0ie4ozzcvlco4O8dFAxK1vB/HWTct/nwFJAMScNrfUKYmK6hWgYuW3UEq0c1a8PFJsg1aWqinZe9xwdjk/Nz0cB9nF0u4NjFAtp7VFzQRsHFuQr6JGo4p03k5+vGDgquaKfgtGUuzCgTGm/b4ThiB9kckyAIgogkpqWLmcMyZMgQuFwuLFq0qEFHhREIBBo9liCII8917WU8Y/QzuXujiE/2ivUW39LA4bGtIr4YGMI1eQoe2SIieBDOhZvXcGKGirFpKsalqxiSrEHggJAKXLNGwrba+HPmiKZhl5/Hk9t5PLlNwKAkDZOzFfhVDrv8HNZVcchyaXigq4yRqRp+GhzEDeskfLJHQDDWhhMEQRANEvOmkIqiYOHChU1+XKayjOzdCe/9Nh/+YHyExBBEGF5dTtVqK2wVvlhZYuPrGVa6OzyERWOzvopiVQGLA/6SoeDFXvp37rEtAh7fJjSqSvB3+3gU+IF2HuDydgpe393wryifoOHOTjKuaa8gyxW+bXkFh2vWSFha0cyOSmOUFPb+2mfq41ANazIO5doO9X7YVJxlVcCyqshQr5mLBXwxIIhTs1S83TeEp3uE8OR2Cc/tkOpvMBmL8s4EQRCESav9S7l9+3aEQiEM7paPF274K/h4KRdKEK2cPI+GD/qFwHPA27sF3HsQjRcVjcOT23QH5cGuMpLF+kNoeiWoWDwqiLu66I7Kzlrg9V0CLl0pocMMN4bMcze/o0K0CAIqh7OXu/DQFhG7/UC6BDzeLYQFI/zonUCOB0EQRLzSav+Kl5eXY86cOZBlGSN7d8KArnkN70QQRxolpP+Tg9bP7J8q6/8UxVBONOtnOaT/C/r1fyE/IAf0f5oWszj53gkqZg4PINsNLKvgcOM6h6OiqQ3OSL+6S8D6Kg7Zbl1dqYtct4b/DQ2iR4KG3X7gr8sldJ3lxnVrJXy0R2j6UrYcZyko9p/rHM+H/4u2vjXjvEfsdWP+RTtOvf/4Rv0LQcCDW93oNMuHK1a7UBIEBiapmDe8Fn9tIzfepkO5D011PIIgiKOMVv3XsqSkBFVVVQD0XiwEETdIHv2faoRvqbLNSTHWmY6M4YQEa+r+F6q19muEQ9DU8NBwWa6MmSOC6OAFNlRzOHOZC/5DaPAoaxxe2qWH8VySq8AqPcvQMDJFxY9DgmjnAdZUcRg8142vioSmbfRY18NltAdw87X94djYxguN/3cwD/QH88DflBzs+eu7R7wY/s957xpyROo6RwN2qxyP9/e60HdBIv4oFZAoAp/0C+DbAX4MSdYc9gnIcPMYlAJ0Tohi1+HeR4IgCKJeYp6zQhBEy2ZMqoqXe4fQN0l3KuaUcThjmeuwSg9/sVfAMz1kDE7W8GxPGbesF+HmdaXlhnwFvRP1c+0PAqcvlVDSBGWOiaOPfUEepyz34YFOAfyrQxCTs2RMzpIxo0zAiioBqaKG8akyOnoth3lJBY+Ht7rwQ/ERLIFMEARxFHPUOCsZyQmxNoEgLJSQvgxTQJTwdc5ZW/tYFubF1sUg7IuDhge7yrizswKe07vR/3uriP/uEKJX8ToIG4uDHG5aJ+KVPjJu6qDgslwFEgckGL+xahTgy708HtgsYoe/CQXiQ53pZu+VGSbGW4USzG22Mc57Yb6PtvezLnWs0b1UDlNda8z7dVjKAh/lfjvuFRA5xv7ZdxYtiPb+Oe+//X3hOCgA7t3pwUf7ZdzRvgbnZwcwIU3BhLTwEMSiIId0UcOQZBWf9/fjxGU+zDwght/miNLLjbk/Ub7XBEEQhEmrd1ZKSkqQmpqKuy86GXtKyrF4485Ym0QQLR4Pr+G9fiGzn8U7uwXcukHEAbnpFI7Xd4uoVTm80SeEVCOKc0ct8Mx2Ee8XCKhUSE0hmo71NSIu35CMu7cpuDAngGRBQ0DjsKhSxOxyCdUKhwxRxfs9y3FShozvB9Tg+GUJWHQg1pYTBEG0bmLWwf5IwfM8xo0bh8zMTPj9foz6x7NQafaKiBVs5rdRs9b1NSWJ3Wc4XdLw+cAQjklXEVCBa1dL+HBP84XE5Lg0tPNokDVgbRUH2Z5qF23muqF7XF8+T9TZ/oOAlZtmOSgAIEiObbbjq4aNrBEoKz/N8piAw/+sMBpznDD1ro775CwWUJcdDZ2vseWenepJY9Qnu13sfRDd+lLS61tzghR5bOP+s5LgXJTPl0sJ4vtexfhLagC7AjwGLEhEebAOhZPltjhtsqPKcaGUEgRB1EWsO9i36gR7AEhNTUVKSgoAoLS0lBwVgjgMeieoWDAyiGPSVVTJwMmLm9dRAfTwm6UVPFZW8vX3wyCII0BA43DW+kxsrBHQ3q3ihW61sTaJIAiiVdPqw8D69OkDSZKwYN02/OO/n8faHOJo52Cc5ThzrDt5Vfw+LIg2bmBrDYczl0lYXXUE5jsiStna1I9o+SB15frYZ6/rm5F35p9EI9oMOhCurBgz+Zwxk2/O7PM2544pKSyHyVhqoQCgCtHtt69j1JUfE2azQ5mIdj0OhSFsXbR7bb9e+zZNtY7hPF9jcm6c+SUAwBxVc3cV4ByOcrTGqkxJcfn0de5E/bXkscYxRSXk17ex9yOK0sbJQdQAuGoHML1nAS5pG0KqxOHZ3V7MKuOggeUkaUiRgIFJQJakYWalhH0hIfJaWcU/57UBcfc7gCAIIha0emfF5dL/UL37P+piTxCHSo5Lw69DQ2jj1rvCH7/48Kp9HRL2B2L7wygQ7izwdSg99gfxqAUOHMd2PpxHfch3PMibIUcSOOacGA/HcHmMoaJtd+N3Ush4WGUPy7wAzXRgovSaUR3rHM4SJ0p1O1s2x4SFO0EORR5XqaPQAzuuIAGSV1/lNhwB3uZgyUH9R3ZtZoibLeTN6UBFK1RghvVFS753jLdfPwCILsBwUjiP8T64jWIrot1Z0W3imNOgsBA8WxK/w7GcF/Tg3n0cHsvZjcmZQUzODGJPUMCuoIDObhlpogrB8RbMqHDj1t1tsKzWYx1XDkBjPYGM99+691GKMRAEQRxltPowMIIgDg8Pr+HbwUF09WnYWsPh1CUxcFQIIg55siQX/dd3xZvFySiXebR1KRieGESmZDkq2/0CllfpztOE5ADm9dqB29uUxNBqgiCIlkWrV1YIgjh02ro1vNI7hOEpGkqCwElLJOwNRmmQ2Byzv3WFevGClbTOlsZMOmdXVpyKgG1GX5PDw65MeMGanRfqOA5gKgERidj2ZG42o89UB6a0CNavXY7N4BvXoTHVRZCsUKSIMCotMvnernawc7Gf60pQVxVLSeANFSRYa26rU1Fh4W2eRCAh3foZ0BudMrtCxrGChloQ0Bv0MvVAC9RY4U91huXZQs0YRoECjhejfA6MkDvJsFGQdAUFAAwbma2JHhGS4VH4Q/r5awLGfTXDslSYTUmZsmIoRpxxretkH6aUZuHGUhXjXaVI5BVsq+VQLIuoCMqoVvX7mOcBnmxXhL+mVeDRdvtRwKfiw4os/f44w+/qCqEjCII4CiFnhSCICDIlDfd0kXFNngKPAARV4NzlErbUkBhLENEIgcfUaqNKTpizo7M7JOHC7XlYHyrDfdl78HTWdvxUlYqyGNhKEATRkmjVzoooimbOCkEQjeOkTAUf9A8h3ZisnlXG446NIuaXCzBnmSOS3g8yLOxglBinwiJI5kw6J7mtdY5tEdhyJjhDAdGU8PyWqLP10RL82Y8Rie6GKuP2WSqDS8/riKasmLkiIaYMGfYobiufxYmmWvs5lRWmMInuyOR3hi33wlRSUBVuDxeK3I/dc2+S/jo5G5yhrDCFJclr3fsqv2F/baW+9FfoJhuvOa4EWq1q2hKGqVSJVoECu2oEAC6PlRfkfM/sSouRV5Pk04+TlqBvS3CLEAyVJqQwZUW/N9UBfWxQVhFS9HscCLH7pisrVp6P11LBDNWF5SBpgWpLBTNsfawyC2elVqCvqxrvttuOCwp7oMauNuk3R19SugpBEETrzVkRRRFjx46Fz+dDMBjE6m2FsTaJIOKe07MVfDtId1SWV3A4fpGEiYtchqNCEMThooDH34u7I6BxONW3H1PzViJTpOIvBEEQddFqlZWuXbsiIyMD5dW1uPbZj1FR44+1SQQR10xpL+OFnjJEHvhkj4ArVksIOfua1KMyNIhdhWiMEuPMPbFXALPldgCwqRZS3Tkr9rwAMy/FocLYlRVW8tbMmWAVp2wKU4TNxljJY1ad4gxlJcGt2yXwHBSjGWRNUJ+tV5nawpaKDM5Z8auuawFs6pNVjQyCy2avDTP3wg/O2I9VHuPEoPXacY85j3E9ydn6+sRMpKSlAgDapevXmGmoFyIHlBjKSmGZvq7YWLJ3XpMDQCgYfj3sPWN5MW6f7T4auT9MqZI8lnplKEqSpC/don4cl8gj0aNfR4pP35bi1l8nigJEQ1mR2fthKCyVRuVIf0hFtZHHUm2oLoGQvj9TXIKyalVuY/k5Qq1xOYJ1bebnyoM5WgJOLPHhq/SFGOapxJweAZy/Ix9Lq53loTXY6hmHQ1XCCII4Smi1yorXq/9B+2TaYqzfuTfG1hBE/CJwGp7pEcJLvXVH5d0CAZdFc1QIgmgy5gQzMGH/WGwLedDFHcS8bpvxY59SnJAaiLVpBEEQcUWrVVYYqkrVVAiiLsakqnihVwiDkvVZ2rs2iXhimwhr/ttBtD4YzvXOBoOmotKIuRG74uLMubDlI0TkMYTlrAjGodj5wq9F0xRAMcazfAIzH0aw8iCcvVzM10Ld12/Ywbu9SDVm8tnMvkdidgGyMSvPZu2r/ILxWlcKNFW23UfnDLpW96y6LddDMs4nCeH3PSjr74fsF60EcJfxgGwkhnP2KlwePUfFzE9JygAAtM1IQqcMXe3olKTbnWPkrAg8h321IfNn/Rr1a62ttRQf1g/FzM9hOTcePVGd86WaVdXgDm/qmORzmffW59Kv1eti16yfU+J5JEr69ae49LHJxtIn8hCN91E1lLKg8b5UGfkp5UEZBwz1q8J4r2qN1+w+BmQVtUH9mFWsqpvgUP7sP7MeMIqCDfBg+J6ReD15Kc5MrcDJ6UGcnB7EA1vdeHiby/ic1aEQEgRBHCW0WmeFO9iEX4I4ihiVquLx7iGMTdMfzkpDwPVrJXxRVM+vBHuDPmcYVrTQKxYaY28CWN+xGXanwH4uFpYlSFbYF1uaIVsSwEdxoOynggsQ62pGyJtlb83zio5wKsEF3nAAmCPgMsKO2MNysldEsvHgnmw4DfaQo6ARdlRtPGwnesIfhFmIEQBohmOiatZr9vuNZz4TC+cyxgo8Z9rElsy/YaFnBwQOtcZKzniPNHvxABZi5U3VbUzVl+0NB6VLqtd0UrKMa2UOAQBIhnEF1boDJAoO59EWcse5jXWsu3xCmv7alwrOqzsuLDE+LcFKlE83QroSRP0ee4xzsHvNcxw8huOSaLwPPmOsi+dMG9n9U4z74TcckaqQgjIjJOyA4ayUG6+rjHLHVUHZTMz3GI5RbVA/R0D2WfdDYO+Dfo7aWj1U7IAq4dxteejuDuAfqYWYkluLBzoH0NWn4ur1CZbCyd5AsxiBzWmhkDCCIFoxrTIMrE2bNsjPzwcAFJVVxtgagogvLs1V8OewIMamaQiqwBu7BfSa7anfUSEIolnZGHDjhs1JuGZjEkIqcHGbEH4ZUIU0kZQUgiCOblrd00liYiJGjhwJQRDw+5J1+HH+qlibRBBxw+W5Mt7qp88Mf7aHx80bXOFNHoG6k+btoUFm8z09N8wMy+J4q2EiS55mzQGjHdeZPB9FvWGhQuY5bQ0PzdLFdjWGd5Qc5h0z+nalxVn6lxfMpG2mLDC1hM2au0TeVCtYaJfb2JZkKAupLgEJjpl81TiXXVkJsJK5hsJSw0K0NC1MSWH7MdgsvViHgsxzgIupPo4k8oqQpQIUGfvXGEsziZ0XzPCv/Ex9XVdDUemRqi9zvBJSjetlqoXHuC+KqqEqZIRmORQeVkSAc3msMgUsdI+d3wg5S01NRk6K/h5nMUXFUFOyPZKp5LDzChEqmqXw+IwxbuO+eATevEes2zyzJ8SUr5Bi3q9yQ1lhr0uZ0hKQUeHR17HEfBYipqia+RFj778ZDudnzTptqggv4u19SSgI8Pi0VwWOSZPx66AajFqZY4U0s++TqVzWo1gSBEG0AlqdspKeng5BEHDgwAHc8ca3kBWalSIIAEgRNTzZQ39Qena7gItWSpGOCkEQMed/ZW6MW56GcpnD0MQQzsyobXgngiCIVkqrU1YYfr+fHBWCsHFZOwUZLmBNFYfbN0rQos3K25UNhj3BnCkYTFFhSc8sv4EXzJlejjca3bHECtnW+M+RmM4JNmXFmbNilnw1zim66h4jiDaVho1xqjfRHDTOPDZL2k72GvkQxkw+U08kgTdzI1jydpIUrjAkS4I5289gM+uKBrN0cdCYLfezMrjG7yz7by6miMjG/jwiVROeJYrbZumd25iaU2HM/idKgqkMlRnXyhQBl8gjJ0V/T3unMUVFv/9tjfLESS7ROgdbGueukVVzW6Jo5fEAQImRPA+OB+cxPhPsvTHKFGen6bkr+RledEjU7cg1zyuY9lu5KuHXqtjuA1NbmD1uW56Rx8w9YvYbKpQtd6XaUFIq3OG5K0zVKXfLqDDygPyKGrZ/SFFt9z1cASkxFEPV1rjSvB8A1gD4z34R97TZhze7luGsrATMq/Li1V0eyJqtkAUpKwRBtHJanbJCEER02CP65moOal3VvgiCiBte3J+J9X43kgUV56dX4oX8ffijbwlyXeSgEARx9NCqlBWO49CuXTsAgKLQL3OCsLOiUndQ+iRFqRxkr5wVrVQvYJQMZtW3DCWFVW8ylRXeLIersbwSpnrIgcjqW86KXxxnqzDGclZYrgpbL1nNE50lYgXRzFnhDEWFlc5ls+eiwJtVtBhsjFsSTAUgjVXzYmVxjVl8keMs1cBQJlh+SoJo5UM4K02ZVb1gK+zEZuBVLew1YFX/YuuYGiPw1vklh3rCiOaKsln/akMpSnfLyPLo11iZ7A4bmyAK6GBU+mIVv9oZOSMphvLkFnhrct9R+lfRNFP9STXO1zbZE3Yd1QGPqX6zfBZWQa29oep0S/Ga501zS4ZtRuU1kYeXlWd23Ad2Dvsnnb3nok1hcTuUFcHxwZAVzVRWnNXEEiRdYUl2CaYiZr2PMO6Hikpj//3+UNi9qqjV9y8JJIGTjWacLiNfyvh+lPEihu3JwrHeMvTny3BrRiHGpQSxZNB+XLk+Ab+UuvVqe0xdoapgBEG0QlqVszJ8+HDk5uYiEJJx38fTYm0OQRxZ2ANrHQ8syYZfUGaLxnL2BwkL9XI6DZIVrgLWzZy9Zt3SOc5M/DUf0lmIl+yKTGi39TfRX4qRjozgSLAXXGapYuaYiCwcSLKS30XHg6i9zLBgOhII25YgCUg1nJMMT7izIhnODw/rodZr7OczH6CtsCSJr1u4ZuFazoTuaGMUh9Mi2MrxWgni4Q4Rx3Hmz+aDMws5k1nomRThJLHjJEkCMg3HIcsIv0o3nBSX8dDO8xxUhSX/G8c0yvnKqoYkYxxziHoZYWQZxnECtjBdViiAOSR5hoPS1ucyyyInsv4oRuidR7ScDdG41+z9ZPdOVa1CBQyzlgLHme8jC2NzvtY0K/yPFQpwi+Hvebqimu8jOxcL3auWFTP8i32O2HvGxvIcUMz6ywQcuSk8D7+q4mdk4ueaCnwRzMdnGUswwOvHj/0q8GxBIv61QYBGoWAEQbRiWo2z4vP5kJeXB03T8H8vfYHFG3fG2iSCOMJo6JmgYXSKgs4+DckisLWGw3uFAspCwLXt9QeajdUUAkYQLZEtsg+jt/TA420KcWNmMW5uVwVFduOODRTRTRBE66XVOCuSpM9MhUIhzF2zNcbWEMQh0IAyUh89EzW83CuECemRRSUe7SajXAZy3IBfAR7fJkUJwzKWoju8+SJbZyzNhHq2zsWUFWOsptqaMjL1grf2cV6bs3FjtAR7M6nfCDWT3PC6dRu9js7lHkmwKSuG+iGy8B1rRpyFC7GZdDazn+wSkFZHOV6mOtiVFZagzWbdmdLisZ2jvrfVUk/CN0ZTRlRjKl7kOXN23gxfcpzLfj42y+9UUVRVMxP5WelepgYlSAKS3MZ9c4crKuy+apqtHLOh1og8KxCgIaSwPy/6+5ZivEcdWVnfKEnwphpjqCnpXsnqOG/s72ZKl8jbGk2y+xZ+7YqiWSoLW2krCc0sYLeNc4STcZx1vYKjEShTeOxvnfOeV4UUs5yxM4yMfR6TJQF7EvXPc3mNrljaSx9XGw0nZRZqFwrg5rIULK/k8FanfbitQwCyKuH+rS4oTK2icDCCIFoRrcJZcbvdGD58OACgqqoqxtYQxJGDh4ZbOyl4sKsMFw8EVGDeAR6rqzhUyhxOzlQwMFmDRwCCKvCP9RLWVvPRkxoIgmgxvFeSghxJwWN5JbizUwgT0hSctdyFYipHThBEK6NVOCvDhw9HcnIyisoqcPUzn8baHII4NOqaDbVPF9umziVOwzcDQzg5S59N/alYwN/Xu7DTbDan4p7NIvonahA4YEsthwrZ0RjRrqjAKEVsKhhGsi9TT0SXtU20VA79OMY5VQUIOUJSWAlhaIjwkjgucukoa+zMWfG6XUg1chqc5YUTJcHM52CKSrJklboFdPXDnrcAWDP7CSJvzuSzRG6XY/ae56z9WKI7S/T2mHkxnFkGN1qlZGeCfYTgZNvHqboIHGcmlDMVyanicOAs1YOVQ45WV8FMOjdUA1aeWYzM/REcBQMAK5FdFvRz8LyVO8Gu32fcmwxDLWH5LbKqmcdiSe9MoWJJ+YleCV7j/bArKvq5OFuhBPYDU1GMV5qtKaMarrAoqmYqEc77z9tymlSzsEG4suJVRXMMg41l99wX5M1rYp8N9rlin8dMj4R9RhGD4lo90f6AkedywB9CaZW+bq+iN+lESC8JjkAynjyQjJ01Gl7qXIrRqSp+GxLAMYu9OMDy0uwX5vwgkvpCEEQLoVUEuqampgIAbn31a+zcVxZbYwjiCPFgVxknZ6moVoCr1rhw2nK35aiYcFhZxWNZJW85KgRBtBo+2Z+AEYuSUBjg0D9Jw8+D/ejspR5jBEG0Hlq8spKamgpRNJqNVVTH2BqCaAacFbQA9ElUcVtHPRb+0lUufFviAZgCYJZEUsKXdnhHyV9WXcvli6z0xdQUlw8wSquyHDHWVJDlV9QEFVtjQiZfyNZ5nVXAwIWvt2MqK6JholE61ychLUE/f7aPlbW1lBWfY+Y6JYpSEj4PbykrXpE38ybcjlwV+yWwdW5bjgpgVY7ieS5MgQjb33ZeVk1Ldcxy8xznFAus/XlL7WDLCGXF1jNQUaOfg4OlIDiPJwqc1egxSj4MoB9fYI0qIwQyDoKhcCUo+v2UFUvRAHTFiB2SKSssL8ZnVAzzukW4DIVMcJQX1u1z3uNwZQWaFqayADaFRdWgGNfrbCDM2a/ZEAbNnCHRytlx4lRWRMFSqLwSy3XRD8jUozZeCQcSwssb7zMUloLqoNXU08hjKQ0a5cK9RuWwYA02VQVw8ioJ0weUYkSKioUjA7hqjRtTSzhUKVx4s1f2vWJlxFWFVBaCIOKaFq2spKamYty4ceB5HsXFxSjYfyDWJhFEs+PmNTzZPQSeA74u4vHtPqHhnQiCaNWsrhExfFEi5pcLSJOArwcGUHKMH+/1DSLHRUoLQRAtlxarrDBHxeVyYdnmXbj+ecpVIVo5moop7VXc3zWEbEMI+fc2lzFrylt9SJhqwmZOjb4nUEKReSCGesK5jCpfngTrZ3OdPpPr9XqR4gtXVNhkba0RY89xHKqN6k8qy4dhyo59at5ZBQy216xXCG/1RQGs/JT0RBdyE/VcmXYJ+jLV1rgxyZGjwraxxo2SLQeF5YOwNW6RN6s1uYRwZcE0nbNVEWMVosToSof9Eu2qgzN/IlrOCldHzgvHWZWpBEd/EFMRiHLsaOqNmbvjUFF43lJWrLSQcEM0TYPCSpWZPT2t47B7ojjUI3btms0ewXEfPWZ+imBW4+IdvVTs98h2d/SxYOfgwJvnta4bABROA8dp5rEagu3H7I6mRbBrsytWkqm6GZ9Lw3Fg6otfUVFhVPxKqjY+e7b3w55jA1i9bGrkgH5S2Q/O+HlrrYYTVnrwRKdynJwho6NXw8W5CiZl+bGqRsSGGhEv7/ViRbWkN5MEADlo/Y7QqJoYQRDxR4t0VhITEyMclZpAMNZmEUSzkSRoeLNvCOe00R8mdtRyeHSriKVVohHPI1hJ8kaoFkNjDyJBv/UwYk+oBwBPsrFvAuDWw8DYMiVJH5OZ5EaGkdjOHuRZV3TWjduObDykyVEyu0XHQ7b9YZs9DJqNF40H13SjvGuHJA/aG85KuhFKY5a3FXkzoT7J2JbkcFZEKyvbfABkSAJvOjO84HhYN7A/yLOxghD5sO+ErbGHhzkdCitB3hGK5LSBbYviZDDMh+o6HCL7WN7h7PBcuJ12rK71Vl0FwLE/bzlkmhj+cB/NeWLviehM6hciG3haZ+Qi3xz7NsMqpimYNSCM0wqc3fGpO8jArHhsOinhTot9DG+GqlnXLKmGs+JyOI+spLSsIkGSTXt1G1l4HWeW1Wawz+xWOR0AIKsKNGMdB6AGwN+38sBWYEhiCK91r8SgRBnjUkIYlxLC1W1r8VVZEv62NR0HFAFaoMYWEsYuiJpMEgQRP7RIZyUzMxMuI379nV/mkaNCtHr+00t3VIIqcPtGCS/tEqBoXAsP5CQIojlZUiVh5LI0DEvl0T6Bx2mpVfhrWiXOTqvEkL61uHRLNuYEYm0lQRBE/bRIZ2Xnzp3Izc1F27Zt8cLfz4WiWLNAe/fuxdn//gj+YORML0G0REakqLgoV5/ynLzUhT9KBSP0C2HNHTnRCO1y62FbLNSLY2FYrlor3IOFgTFFxQj1gjcZ8OglUrOSdfUiJ0VXanITXchwS2G2VYb0Y/O2KW6mNgRkVhbWSnpmk/lmIncdCos+Rv85yUi2ZiFfnZI8aGOUwc3w6EufmcQsmOFfTFExmxqK1mw9s4klfdtLGQumohKZtK4vuYgwMPs2RsSkv6maNCLmyCYaRAocXMT5rDAua5SmRQ9bsis8TvUmLOSN/Wj2UnQk6NsqabOyvsx31njNFu4VfX/7S66Oz0EUgSoq0dPs9Q3ss2kqLPYxZvShI9TPWKq20scw1xn7m1IRoNX1ZgNW5e46bJQV1Xz/Mo1YtZBZLpkzQ8LYPWHnZ+rLdmRBdhzTbOgKQAEwX/NgfsCLL4qA5yuq8XHuRnRxBzCzdyFu3ZaM57bXEbJJ4WAEQcQBLXJeVlVVzJ8/H4WFheA4DqIomv/y8vLw4t//Co+rRfphBGFDwwkZCr4aFATPAZ/sEXRHhSAI4hBZEkjEsB398XFpCgDgwfxKZEqUgE8QRPzSYp/oVVXFvHnz4PV6zVmxpKQkjBgxAiN6dcIP91+ByQ++QwoL0SLplaDi6R4yTjIaPq6s5DBlrRQ50J4wbyTEc4YyYjZsZFO7aghQ5PD9WMNHYx9fYiKyDUUl11BUOhj5ITk+l1n+l+WhlAaMWHummPAcKo34ezlKWV6zCaOZ8xH+WuQ4U20xm+gZSkn3FN3WvAQ3sgxlxVRPjKR4r8TDZ6zzGIoKS2xmic4Cz5kTxlbDRKawcFET6gFrrlxvRhieo1GfWBKRAs5Fbo1MorfpLxG5GtYOVsngyONoWtgekXbZ8lKilSc2FRGnwqJZapSzrLOlonDguehFBOzHddofcV/tCgUi77V1j6IrI5zNfp7ljnNW8j1nqhQRh9Zfa+HXpK8LT9iPlm5vt9V5jQx7npLboQJmKlZJaHaPmTsRMD6zsi3xfoeWGTaGffc1I4me40WrAayqoBIuXLorH909mzHUV4vrcgN4eLvHpqiAIAgibmiRyoqd2tpa1NTUoKamBkVFRZg9ezZCoRCys7NJYSFaHB5ewzM9Qlg+OoiTslQEVOD57QKOXeTS+yUQBEE0CRxeLckAAByfHmpgLEEQROxodU/ypaWlmD17NsaOHYsRvTrhxb//Ff/47+eksBBxTxefii8GhjAgSZ/W/LaIx782ithSawv9suWoADDLFXMur5Wr4qjmZZY05jir6o9xHLdbn4HNTNLH5KR4TCWlQ5KurGQbKkaqS4RkTBOzKmBuR+6JR+BRaSgZQTU8tMRe2ciZq2Jfz2L0We5JppGX0s6oRJbtcyHNyGNhqgvLR3FJgqmkuBzbzFwUW8UxlnOiNSY235ZzEi1HRacxU9KR+ztVFLt6Ys8xMYdFyaMJO07EC9tg2+Zoio6FVWEKAFRWFphjryOxUh00qCzvRHWOtx83mmwU/ZiW2GLtWb/9li0AoJoKETugZp7QbFHq2F3ToqguztQbTbPygxyXEU29YhtZTo89B8os3cwaSMoCAqJ+55iqySrgtTUaotbIqtkwskDRK4Sx0mdcyF58hslIRl6KpmJmpa5WDktWkONSUeQHQRBE3NHilZVoMIclFAphRK9OeOu2S3D/padi0qh+sTaNIKLCQcNnA3RHpSgATFoi4ezlLmypaZVfUYIg4oCtQRfmV0hw8cCdHchTIQgiPml1ygrDrrD07ZiLvh1zccaY/vhjyTpSWYi4Y1yahkHJGspDwIj5buzyR5ti5m2KipGPYjR1hDvB6pliKCpSgp6Hkmpr5MhmcN3GzG2GMTvLZmk7JLqRY/ycZSgayazhoiSYM8a1sj47y5QWtvSJPGpkq9mdHZHjzP4skjHzK3DWNkBXWtjPTDVhyk62YVe6RzRj+535KKLIh1X9YscEENbkkM2Es6aAbLo7XGCpWyWJqB7Fhe+vL8OPHVZoK0I1caznIvezJuYjkzbqOl59Nut2anVuc65jjRaZQsFHkUFYxS/9/oZLEs4qaJoWed2RNtjvifN+RFOaIo/DBD5rE1N2OKv3Cxc950TjYOa1WKpR+GdHVa06Z06Fzp57VLcKpEWsY3up0CIULJbDwirgpboEVBiqI2sYWYJUY3CNvpQDevNHwNZTRf8OP7YrCd/3KcWN7YPwq8Cdm93QmIJL/VYIgogDWq2zAugOy7Rp09CuXTv07dsXPM9DFAQA5KwQ8cWpWfpDwXf7+EhHxZ5Ez0K6DMeEM0K/OG+K2Wme9+pLlijPmimmuEWzYSIr+cscgVzDEcjxuZDhZU6K/uuBhaTYu2lXBcIfYuwORq3Myq+y5HV9jMRzcBkOhNtZqpY5KxzMMcxZSTUck3TDrgSPaCbPmyWQzUR93mz+5+x4HrWrfJSkb+uBs+4coahhV7C5J5z9oTZyX2defn0P4s7wofosi+YQNYb6nBbn8ezNFTUr+9w4ju14DYTWhTkiEeewQqfqu4qGnJ3wMZFjnUn/zsIFmsZFuKzRHJJIIhtxOqsBc7Z752wUKbMSxooGv/F9YkUt2CFZKGWCJJjfDRYOxiitMsIcA5zlpLA7akx8/FTuwwPbA3igYzVu6xBEvkfDxatcetgflTAmCCIOaPUxJlVVVdi0aVOszSCIehmVqj9I/F5CpYkJgjiScHh4ZwIuXZ+MoAqclxPCnZ0o4Z4giPihVSsrBNEy0NArUZ+5XF0VNjWuL+3J9ExRMZo5wmuUKfYkmgn2mUm6osKUlRyvTTUxFAmmrOQ4QqyyfBJSjJASl1EOmM2kaxoQMkK72Hy3rIbPyPskwRwTVMNnY0WeMxPyWRK9aJtBB3SlRbLNGANAgtso62rY7HEJZvJ8RIgXV3fyujUBbgtRiux8GDURvU4cu1vhYFodoV2oI3k+cqyzVK9lfuMVE/vwaJPjdnud+0SW2g2/Zzw0q0GhqSRY5YobmoyP9l5FNIG0vdai/NAY9YiNYQUWzL1tNmqO989+2DC1CLZCA7apvgg7bI04zaacEffDWsEUS8W4oaw8cVBRETR+DqjhpaCZmpkoCeb3kMFCIVlp8P2VnKnWQDRa1kt6AQ3OFYCmqfioRIKwUcM7PSvxQOcgFlWI+G0/hYMRBBF7Wr2yQhDxzpBkDekSUCUD66sO7kGUIAiiqXi/yIO39njAc8CHfWvRxkXNIgmCiD1HhbKSnp5u/qyqFHtLxBdX5+mzlv/bzyOo2eLEmaIiGM0gRTc4llDPZkXNRHsPJKMMMUuob5egv27Pmjp6pYhclUxDdUljTRYTJCQYYwTHNLeiauBDuq3se5RizPKyWeagopmzvGwGmKkmIm8l2Lv48JLH7FSiwJsz4EzZYQ3zPMZSEHhLUWEz4bbk+cYRniSt2SQSR869Y49wnL9N7OoH51hpV3o4x8ZoSfTRSvU2dF57o0E41tVHfTkzzjHR8jEicj+0SLUm6nmNZb3NLdlYplDY7ke0620sdmWn/vwkww4z1yT8dX07aZplP1sqNtVFX2phihRgFQVQYfv+GHaw/C6W25WMcJUFAFJYk0lWgELgUWAe22gUqeihXpoSsu6tquDvW1IwMCGEIckKPuoXwElLPQjpSVj1XDBBEETz0eqVlfT0dIwePRoAUFhYiJpAsIE9COLIMSZVxbXtdQfgpZ2Ur0IQRGwJahwuXetDhQxMTFfx0yA/evhIYSEIIna0emWlT58+kCQJ89duw00vfR5rcwjCxM1reK2PPrv51m4BMw6IxrS7rfpX2FKwTX3z4UtwkIyZVq8xq5plqCV5hsKSm+BCmpGzkmXkqCT7rApbAOB1i2aFLZYHYs1e22aAVSFsG1M6ZEWtc9Zf4HmIxvSw5DiHmVfAc6aywsaIrCyxbR++EUpAfTP7zhl1syxvWD5LnTtZq8ybY5zTdtTIPIxI1SCiHHGUvJaIPA7UpwM0nsaWN66PiEpZ0cY4VlrvuV1Jceb1RHk/mY2HI6ccBpGlhus+v1VdzabaaeEai7OSGmCpKCzXxC3wcBs/ew2l0V5dDwBCAo9EiVUR05eprJIf+85wVr7ZXsXIc2MKi2Z9ryHrv4/W+0O4YG0SvupTieMyVKwYHcC1ayW8XyiSwkIQxBGn1TsrLpf+UPbeb/OpvwpxZKkns1niNLzfL4ReiRr2BoB/bZTqcVL0JceLNuck8lxmDxXjASXFeGBhIV9tElxIN0K8Eo11PsNJsYdYRfTXsD0tM4dBEq2HMX294dgA0BxJ9+xZTeA5c39nYrw9wZ6FdDnHCA7Hxn5++8Ot5niYdN5+jov2nMucFsve+p7JzLc2igPiPGa00sl1hphFDRVznrvhh3TN9gAa2ROm/v0YL731Grp16waO41BeXo5zJp0R5Vjh4XTOx/lo57Wi27jG+Rta9JeqZvXpaUzp5aYiWn+Ug3l+jwiZg81JMRxzrxG+FVI0MzSMhX95jTEs8T6kalCYr2HEj1UbJYzNSQRVQ7URwsl6sRxQZNMQs0CzrCffa6Fa/HrAi/4LNDzf3Y9TMmW82SeE/UEOPxe3+oAMgiDijFb9W6dTp05ITU0FAJRUVMfWGIIwyJA0/G9oEOe0URFUgYtWSjggH9lZYoJoiO7du6Ndu3bIzc1Fr169EJKpnO3RxpZaHpNXePFugQCBA57qEUJ9zVIJgiCag1arrHTq1AmDBw8GALz/23xs2FUUY4uIFs2hNEdzjJU4Dadnq3i8u4xOPg0VMnDBShemlwm6YmIm0huNH5miItoUFuc63vgKC6JZrtTnWCbZmismGWFfXkcHeL6eBHV26fosrdHU0dhmNWU0ShpzXJ1N8wSei1BSzP50bMnztp/DlZRozRHNc0R9FVmOV9/K1alsaJxmNnOsi6hhXPXsEtFAMlo4Wd2bDkktiPY+OIm22Qrz08ymmgxF0aAKjpLMdZXltUXTRZgf5W107m+qY/VcAq/Z92MqWrji15xKS7Qy1RHXYSs0oDqW1oEsRcVKsLf+NLMiFD4jDMxe1hjQS4SzEK+AIbGIvK6isLCwTI+EciOyoCZR3xYI6WXQazXN7GYPl74O/ip9aXS9v2mDG2fn1KBngoZzc1R8UUT5dQRBHDlalbIiCAISEhLQpUuXMEflmS+m1rlPdloS2mWmwuuSjpSZxFEGDw035MvYOSGAzwaG0MmnYUsNh9HzXfh1P/3RJ+KTysrKWJtAxAlVCofndugO1H1dZStsjCAI4gjQapSVjIwMjB492sxRARp2VD666XT07dsXABAMBnH9i59j0YYdzW4r0cKwl+1sjMJim83N96j4uH8Qo1L18YV+4J0CAU9tl1CpcOF5KkxRMcoRc5Lb2gbo20XHOmMM7/Ig2at/nVMM1cRrqB4eY9bWLQqmksJUGDMPJIpc4Ux+BgcI5jYjcdeYuWVJ8NGTFfQFHy2hmg2xKS7OHI+DKeEbdkw2pkElxhrD1ZdgbzOD3a9I1SDK5yOK3dGOqS+bTgmo61jRPrqsFHWQ5TUEZIwfdRxKy/chEPIDAALBANwuV+NKIcN6/yKS8I0VKjTrPtZdjNnK8XBusakWvPNzZVO8mltlaVDFaqDUMcdxZulv9v20NlrfrQSWo2Lko9TalrUh1rRRX4ZU/Vo9NpU1ychhSzTy1JKMvDV/yAstVGsYYDSdNX6vaMZ7D1XGs9tF3JQvo3eihknZGn7YR6GrBEEcGVqFspKRkYExY8bA5XJBURQEg0GsXbu2XkcFANLS0gDof/BcLhf+c+N5GNo9/0iYTBwF9EpQMWt4AKNS9ZCvG9aK6DjTjfs2G44KQcQxPMcjPSXbfL17XyFq/LUxtIiIJZUKh9d2647ObR2pWA1BEEeOVqGsDB48OKw88cFW/Vq5ciXy8/ORlpaG56ecgbE3v9hMlhItEk076JyVXLeG/w0JoJ0HWFPF4dSlLuzy87qSYi/1ZFdWWD6KZDV6NLcBgMtjNYV0J4Qt0xJcyEjUlZlMQ1lJsDWEAwBBsHJGBGOds2JXfXCAmVwiGLeBd9bV1SJnwO05CnVXk7KO58ztqDefI4qd5kx8PYpGnZdre68jxkS5jrom69VGFhc+ErkVdc36a5qVIxEM6jPyZZV+YxlAaqIbshLel2rl+tUY1FsPsWUlpu1E+5qw80czg1URi8xHsnJnrJyPsE0AF57rBFi5H1YlOeugznwW/eemue91lTXWNM363Djea/PabTlU7NPvlqyxLlFXUGQjH4UpK1xA/zsnqxr8jutQHGoOx3FwGffELIts3CuXyCPAG797jN81mlPBVUIAp+GFnRL+r4OMMWkqeiWoWFfdKuY7CYKIc1rFbxqPR3+oe+zjXw+pPHH79u3NqmF79uxpStNaHY9eeRr+ffXpsTYjrkkQNPw4WHdU1lZxmLDQrTsqBNGCkJUgavwlAIDqar2aIvtdSxyd7A3yWFWlOzudfZS3QhDEkaFVKCuyLMPlcuHS40fgkY9+aXTBplBIL8WZnp4OAPh8+hI89vGvzWVmi8frkjBpVD8AwKMf/Yqq2kCMLTqCNCpQX/8jfm4bBQOSNOwNcJi03IsyldenBTjeUlJ4IXwpSJGKirHkXIaaIrpsikoiACA1WX/dNtWD9kbzx0yjlwqrAuYyZlIFnrPlqDivz1hwVv6Ao4cdOJ6zNS9seEbaOcSe62GtZNssuw5lsjt8hrz+SlJRj29TY5yKkPPlwdpXV1rLwRJNIalPGXAqGs6qVIqimTkq5VW6glJSoSsrFQE/FLkckiRh3759WLp0KU466SS4XC4cqDyApIRkCIIARQnPdTEVEQ1QHDkW5sfJdh0Cz6rJOfKjDFRVQ1A2KlvJRuND47iKqpmf5wRH3yC30TcIGmc2/LTui3XvzJyXaJ03mwCOM5QTwJwaNC/fOKeq2W0yeqrwTPUAFLMqnn7dLL/I3reH9VcJstwjsweLce9t95x3fCLDqpPV1euJF8yKYdtqOAxJ1vBINxnLKngUBiiklSCI5qVVOCurVq3C8OHDcc6EwThhUFfIsq6uhEIh3PLWz1i1rTBsfK/8NnjmypPwwo8L0CFnByRJwI69JfhpwWpqzlsPXrdVMc0tiUeXs9IQHGf+oe+RoH/+vih2YUfIDUjG14wXopcjdmyDEerFuTxhr+Hymc5KaqLumLRN1cfkJ7mRb6xjTSBZoj0reSqKvPlwZ3Vcd5T31axU54jE9kY08bM7I9ES45s62qm+h/XGlBO2sJoaNtQg3b6v04EIe+6rw47Ghh41VHq4vn1UDVBV9nCrb3M6D4GQglojlKiyJghAwb69q+Hz+ZCanAxR1B2VuXPnQlEUbNy4Ed27d0dJ+X5s2rbRPGdNTQ0G9xsBQRBR49cngGRFM1Xu2oCRtG+c1ytZ4UcsHJGFKjqrTiuq5VD5jSVLLA+pmtEgUcOuZdPQpk0bALo6fuYpZ+jOiKpBCf/Ih8Wqsa8Du0fOIYcbJqbvbwv30n9gW3UzVC3MgTKM04dyHBRbSBxgOWsB2bqvrGQxK2vsVxzOi6qZPzMHht3PUCgEqEYPHc1xI0xTLWX4gS0SxqQF0D9Jw4ZxAfxSzGNRBY95ZTxmH+CAyKkQgiCIw6JVOCu7d+8Gx3EYNmwYkpOTw7a9+s8Lcf0Ln2DFlgIAQJ+ObfHx3VcCAMb174p73v7hiNvbEpFEAY9dZYV/BUOUYFkXHT36Q8G2Wgr9IuIf3cFRwKECeXl55nq7owLok0Icx6Fbt26mGg3oynRJeREyUnJsao5mc7aiL+1jnB3onevrsByABrdYi969e4fZU1SyDzkZ2XXvShwy66p5jF3oxif9gxieouHsNirObqM7OR8W8rh2jYSASg4LQRBNR6twVgBg165dKCsrQ2JiormuW7duyM7Oxvt3XB51nzZpyVHXExbHDe6Bp/92VkSDONk5FXm0EqVDYI4hiOxRPIDktSkkHjN8ywztEuzNHY0HM0EM32Y0avN6PUhN0JPo0xL0bbmGmtIuwY1sr74ty1imskR7t1Wu2Ax3iciCrxsziZnj6hxvn7U+mHCppk4sb4waEe2c0Rr8NSbEynrtOB7qn51vqF5DY1UVhWWds9AuNvsuq6aCorAQIeO131BTQrKKrduWoH379uZ7XF1djZUrVyIUCmH//v0RdqxcuRI7d+40c1d4nkf//v2RkJCAfWUFUW00KuaayzD7FWt54MABZGZ0gaaFUFNbDLfbjWAwiEo5CbWyZCoqfllFmkdGjrcWkiSZ92vlypXgOA79+vVDeVU5lq1chmPHHGfebBY2ZTUbhbnN2dwzmrrWmI9q9CIC4WpJZKK99R1THSGYqqaZSoqpMBnFEGpCrHSxYqpWbMlUFHsDyQAbY+xXbSheCPqBkK6Sa7KhlqtRJqM463fH9loeoxa4MTJFxZhUFcNSVJyZreLiXBV5niBOWOyC0kBzVYIgiMbSapwVAKiqqkJVVZX5uri4GCNGjEDbtm3DxukPJByG9eyI/zv7GExdtgEFxeUoraw+0ibHPSkJ3ghHhaifVFF/UCiV6Y81ET+oqgJVlaEYuQeyUoX27dub2ysqKjBnzhzU1NTUe5wDBw5EvB4zZkyEqn2wpKamQlbKAKhwu3Un3OVyIV0K4IDfeoBPlBS0TZIB6I6KLMtYsmQJdu/eDQDw+/0YNmwYOnfujJLy/UhPyWzWamtHLxzmlwuYf0D/+3BMuoKvBoYwMV3DtXkKXtnVqh4vCIKIIa36t4miKJg7d25Yo0hAbwDZtWtXDBgwAFecNBpXnDQaqqri1le/xtRlG2JkbXzy9azl6N+5Hc4cOzDWpsQXEYkIvKGOAD4jt9cv+sB5vIA3RR/iSzGVFZZ7Irr0hzJJ4MzSrGyWlTVuTDZyUFITJGQYyfNphmrC8lNyvC7z5zQjyTjVWHqMsZIoNFyqmIumCHC2bdF3tK89nOdCe//NxlBvzshhJ+ofOlHLLDdqZr7hi9c0Sy1hCe5MUQkZeQw1ftlUVniOQ0iuRY2/JFIhlWXMnz8fZWVlCAbDyxQ3lpqaGvz+++8Rv2cPBpfLhdGjRyMpKQkAsGPHDqxatQp9+vRBp06dkOYNIM0bvs+mTZuwfv16yLJs5ucAusoOAMOGDUNlTSUUVUNKYrqZT8VKLgsCD9HIleGdH3WmwkCzqW7h5z/Y97Ou/a2slnBFBdBzf4IhpqxYSgoA1MhWDg/LUQkwZU0NV2j0nJXwXJVaQ6FByA8Ejf45fmPCTjY+C4ZjC01t8Iv5Z6mAuzZpeKm3jAe7yni3QEAthYMRBNEEtGpnhRHtj/DmzZshyzK6desGSZLg9XrxxLVn4rbXvsafyzdGOcrRhSjwmDigO5Zt3oUH3vsJfxnQxXyQIOom16Wis0f/A789cFR8vYg4wB+oRVAOQlVVeIywQVVVoKh+QFMRlKvA8zxqa2vNHJRgMIhVq1Zh//79TWLDoTo7bN+ZM2di8ODBqKysxKpVqwAAS5cuhd/vD1OANE3Djh07sGFD3RNLdoelxl8FVVPhEt1wiS54BW+d+xGHxxu7BdzSUUFnn4azc1R8uEeItUkEQbQCjuqnqe3bt2P79u0A9D9q+fn5+PeVkzDyH8/G1rA4YGzfrnjmurMBAP967Wt4vfQHPiq2UsRtvDy+6lMFgQNmVXhQIGYCIsAlZupjEtOQmqwrK5lJ+ix0kqF+SLYZbzbL6zFmfRONEsQZHslUVBIM1YWVJ05zi8gylJV0Q33xGWPdxhiBjygcHFFKmLP/74jjjzZH6sxP0WxlWOubh7XV0zL2t2avG5qx1s/hKMsb9dh17a8dloJS32y5nYYaR9Z1zLqOy8bYK2RpmobK6nIcqCozx+0L7IXPm2XmfTB27NiBxYsXN2xMjPD7/Zg7d27E+rVr12Lt2rUHfTy7w+IP1MAf0MPb3LUpcIkJEEUOXiORRjK+I6yEspnbxXFgX836c5icrxv3GTHHOPZjlb8UVTV/Vs1tMLaxMsWaqayYiorj+DzHmTk75neG5aXIAWhBI/TPWGpMaVGMKmFMYQm/yIhVisbhnQIBD3eTcXMnGd/s41GtkLpCEMThcVQ7K3ZWr16N/Pz8wwplaE2kJfnMn5/821kxtCRO4cwnGADA6Tka3u5ZhlRRw35ZwM1F7cElpOljEvXKSW0ykpCXrjt9bX365yzZKGvsETiIxgOSy3g68hgOSYK5FJBs9I9IMHuoWA4NC/tKMpwWr+GsiGa3epv5dTgkgNXlnnc8bGsAOOdDtX0jHD9q0R+cwnHUqrWtc/a+iBYi5XzI42w21Rnp1oz5C40teevsfRJ9TLhDFmLJ0kHFLAv8+5/fY/BgvaP83r174fP5kJycDEWtgNvtRkVFBQ4cOIDy8nJs3Hj0Kca7du1CMBhE+/bt4fF4kJOTg0CwHHPnTcOQISeiStQfxj2G05Lk0787rE8LK60MIMJpaWy4Yl3FFOyOdl2fA57jbGFr+lJ0fD8BvYyzvjTKVZvV1fTtHoEzJz9YeKnV+EXVQ8EAaEaivRkGptRT0riOC3tzt4BbOsoYkKThpyFBnLzYReFgBEEcFpQ5bcDingVBwA2nT4i1OTGnvoeoUChkxsYTAKDhpW5VSBU1LKmSMGZzNyyr9TW8G0EcJt27dwcAbNiwAXPmzMH06dNRVqarLGVlZZg+fToWLVp0VDoqjKKiIixevBizZ8/Gpk2bAABDhgwB4I+tYa2UfUEOJy9x4UAIGJem4b+9ZTQ0ZUEQBFEfpKwYhEIhrFq1CgMGDMC1k8bi4mMHIxQK4d4P/ocZKzbF2ry44KcFq7F6awFWbi00y2kedThnzHkRuS4Vbd0aZA2YuLEj/EnZ4LwwE+szUvXQr/x0H7ql6MpKG2MGlzVslHhLWfE5wr/YMsElmONdoj7WbZstdRnjPMassMsW/qWbbis9HKV8KntdZxlXABHVSOtRO6Kt0CJqJoerB+CsjuMs3MV5y/X+HNHPr6FhRcV+vKZqAltfQn20rux1YVdc7EnWAFBrNFysrAmZPUpEUf8VziphhUIhzJw5Ezk5OSgqKjIb5BI6K1euBKCXtQeqcKC8CIFAAMkpHQBIqDbucXqyXprZ5xEhGd85ln3BO74f+s8NKwd1lUXmOUBzqIgimBpqNWllYV9emS31CSN3kINk7G8Eb0Uk7AMwx7BQtzCDWJiXPaHevmzMhdlOuLCcx7nLJfw6NITL2ylYXcnhuR30uEEQxKFByoqNzZs3Y8WKFdA0DT6fDykpKXhmytk4blCPWJt2xNmxrzTs9f8Wr8Vdb36Hj6ctxurthTGyKj7pmaD/gd/kd6FWo68U0fxomoZgqBwejweKooSVG5ZlGQUFBeSo1MHKlStNhSUhIQHp6ekQ+EoAodga1sqYVirg/9brDsr9XWVMylLQP0kFqSwEQRwsNNXhYPPmzSgoKIDH40HXrl2Rn5+Pp6ecddSVNV62aRde+nYGbjhDD4nLTqVKYGGY06IC8jz6Q84u2Wj66NGVFNGn3zOWTN8+0Y32RhPHLCO/xGtTVlzGzKfXUESSDIUk0cxTESFJujMksZKrttlSFl/PlBTBMVvLwZar4rgMu+pQl3iiQqszQT/q44dDxbErIqbC4phl1su4OnJloiWbRztfHURTj8zjHKK0UtduYfexrjLP9ZRnNhOsNc0sSxww8lOqakPm663bliA/Px+qqmLhwoWHVYnraGTlypXYunUrJElC3759kZ2dDU0txYGaVMiqaCrH6cke+DxW6W8gMgk/TI10nKc+FY99PgSeA8cZ7ztb8vqSV7VI1c5R3jioaAgoLGfFWBqfGaaLyKpmNYiUmWoSJZTX9nst/Jy20sVMbXGqMFF4eZeIy9spGJKs4bvB+ud3STmHF3eI+HQvD5kaRxIE0QhoGjgKtbW1KCsrw6JFi7Bz507wPI8nrj3zqFNYXv9ptvlz7w5t6xl5dOM1/q5XqfR1IpoXTdOgqBWmo7JgwQIUFpLSeShUVVWhrKwMc+fOxb59+yBJEpLc1XAJQQBBkALQFHC4bq0Lf5TwWFbBwa8AQ1I0vNc/hKJjArigLeU+EgTRMKSsNMCiRYsA4KhVWBjBEIWURIUXoBo+vygI4DyJgNHnIpFV52JNGl0CUg2VJNMoL8yqekkCb8aUew31hG1z25aSUclHFFkzu4YVkcY0KIw2v2mlVhizvNFGcpEPdNHLIbOThuejWDkr7Do0s9CaZT+rvtTwwyNnO199pYMjSwVbr+tSRA5VhXHCcVydpYrZUlE0BEJWg0d2/kDwADQtQI5KE8KaB48ePRrZ2dlIdOuZH5WVlXCJHVFr9EtyGcqKx218Z0WmZPK2Cnr6Me2fWcXRoJF9vJjyKYq8VVbYodDwPCBxgrHOUQXMpliqdXyeg6zsMezfZ4bxRRMks0mt+dl35LBoqgwY/XnMCmFywHrNxkf57iyp4HHWMgnn5Cg4JUvFOW30L3+qBHzYP4RF5Rw219BED0EQdUO/IRqBU2GZMKBbrE06Yjz8wc8AgNte+zrGlsQvqaL+x7dcJd+faB50R6UcsuInR6UZYA7L1q1bUVJSAr/fj6SkJMjKAWjRwqWIRnNxWxnFxwbwdj/ZdFQYPxfzKAxQKBhBEPVDT1eNZPHixcjIyEBCQgLuv+A4HHuUVAj7cuYyfDlzWazNiB/MJpCiucz36FOWBVoCIHkBl15JyGuoKKy/gUfkzZ+ZapLkFs0xkhELz3qnsJlb1hdBECJ7oNhncutSC8KUAi7ih/DL03eo8/LrUmus3BWbQmH+YNgKK4aexehH6zfCVBbNoew4bYiw2zhXXebbZ7sPRiSJpoJEXL9jxcH2cmFnsCqAqfAHdEWloioITVOgGbWeNm3aRI5KM6AoCpYt03/X+Xw+jB8/HgkJCfAHSlETSkTI6BXCcsmY0iJJlrLC3kj2WlZUWwNPfRv7aCQZvZYSvJJVocuAqSCcbTxbuiSmqurqrMBzEIwmMOx3h9s4nmTLW/MbuSqpRiXC9CQ9f65UTgUniOwmIOxCjHwUTlMBxVD4/FX6ttpy/XVNORAy6pBFaR75UFcZbtu06B8lPN4rEPDbfh77Q+SoEATRMOSsNJJ+/fohISEBiqrisc+nx9ocIl7gBXT3GdXA1GRAlCBJ+sOAx3ioYA6KxPOWIyKEb/NKPEQ+PEHensAL6A5BRFd520OxM1wq4oHZFipmlU11hEpxkV3u7Qeoy0mxFvU/fFihWeHhX/aEe7OhnekjRIZzsR+dDSDtie3Oe1RfGNfBOBf1DT3cfpNmMQNVgz+of652V/gBaBB5DtkJGrp27Yo9e/agpKTk8E5G1ElNTQ1mzpxpOiyCWoSaykoAQCmAwsJC9OwzETwno3DPZvA8j+rqaiRl9w8rQ1EZlKHnvmvwFyxETk6OuW2vpmHLli0YOnyS+Z3PTtNLm7OJClnRIsrEs9LkrHGlJAlmA1hWDCDZLM6hv06UAkgyJkjYtmTj9e4kNw7UpAKwyiM7i3NomobqgBGWWGU4K5XF+lLYA63K+CwazSXtTsvTOyT8p6dVAOKYdBWLyjn8sI8COwiCaBzkrDSSzp07AwDue/dH/LF0fYytIeKJnl59xnGjnBBjS4jWC4ddFRyyfBoEQUBOTg45K82M02Hxer3mtuzsbChqNXguhDZt2pjrQ0o1yv0Jjrp5GrJ8IaT26xdxDv04AQCeZryS2PLyLgnv7OaRJgEPd9X7rtzZWcFV7RQ8tlXEG7sF+KnDPUEQ9UDOSiNhM6/z126LsSVEPJEsqMhz67OI65QUgBfM5o5sdtJjU1HYzy4WpmEsRZ6HIDgUAeMcTBHQbC0PzcaCfPhYO42a5XeoD/ayus72jfaWi5YSE+X8zpAo297WkHCFhc2xNiY8K2pUm+24dTVjPJhE/cZSX1nk+vczf4JhlPGaFSDQzPc4pKiQeBV902vBcW6UlZWZfUKI5qWmpgZTp05FVlYWeEP5TE5ORq9evSDweqJ5QUEBCgsL0a9fP3g8HqR7y6EaFSTS3PpnQhAEaJqGtWvXospQJtq0aYMOHTpA4KuweG8NygMcxip6H5JAzQ5dyVcUuN3pACxHKdUI3/IaRTokgTfVFrZkCktita5opHtFtK3VJ1U6GOXTOyXpDtK+2hDKg+EFVFjzWfY7LKSqKDUKPWwo10uyryvQJ2dqBcn8QGvVB/QDOMLCajUOtUENV63l8WOxjEe7hdAjQcPzvWTc2VnGD8UC5hzg8UeJEJ7D0oTfVYIgWi7krBDEYdDXpz+w7A65UK65YmwN0ZrQc2tkeEQFmT4/BF53VGbNmoVQiBoYHilCoVBEjlAgEED//v2xe/duLF68GJqm4cCBAxgzZgx8Pp/p2DBYPsyOHTvMdUyp4TgOkm04x8lISUkBAIiiCKAauhPra47LO8Jw+GafgB+KeVzZTsHtnWR09Gq4Ok/B1XkKgBAWlnO4Zo0Lq6soTIwgCB1yVgjiMOifqM82rgxYIWBsdp0lxnuNpU/k4WFJ846EWFG0kmSt44SfS9PCGzQCgCIbygTPRSTdM8y8FHCmbdY657msBnUaK7nKzg/OVEAimuDZjhepyERi2mEcUFXt2+rZEfWrF/U1Y2xKDlVRqQuzGSTLGRCAwr3rkJ6ejhy9xyg5KnHEli1bsH37diiKlZtRUVGBX3/9NSxcjBEMBiHLlnqRnp6O/v37AwDeX3UAS8r09dur/OiVzmNMroiqqirs2LEDffr0AVCDmiAPRRPNxHx76WORlU83FBGXxJQWo3y6T0KmoYzUGrknHY0CDgcCstkwUnSowey1rGoo8+ufu94Vel5KG6+u7MwReZSzBH1pr76s0S9ICxo5LErIKnmsypABvF4g4O1CCSekyxibpmJiuoJhyRqGp2hYMjKAH4p5fF8s4Pt9Ag7IHKksBHEUQ87KEWZAl3YorajBruKyWJtCNAHjk/U/xosDiTG2hGgtKKqCopI9SE9Ph6IoCIVCKC0txeLFi8lRiSPsjgpD0zTU1NQ0uK/f70coFIIkSeiT6cbK8gBCKtApmceotkbi++7dWL9+PTIzM5GTkwOeU6BoretPtqxx+Hm/gJ/3CwAktHFpeKV3EKdlqzgzR/9XrYTwQaGAF7YL2Ej9WAjiqKR1/eaLcy49fgRu+etfIMsy/vbcJ1i8cWesTSIOFV5/oBiXpDdGm1abDkiseZyhpBjx4+lGpZ50t4hEY8aTNX40mzwKtsZwjhlE+6y9orBKXzDG6ktV1aycF8Gp0FgqgDPXxIw1N8uqRs5eWhWErcLEZhWvaDkr5pjwWq1RtQeHGqRCs663HvWiodLBTUm0YzeVoqIYM9qsvG1IlrGvdC9CchB+vx8zZ85EpVGFimg91NTUYPbs2Rg7diyGtPXCJfGYvqsWx+Tpvwe2b9+ONWvWAICpyPgVFTUhBYoaXh2Md0qpAHjj9wzbJkk8fEaOC/vMpRufubYh1VT02HjRzJ+zfiex5qRtEvScl2RDtUmQBMw1cmQK3MakDasOVluhLwNV0PzV+s+smaRZOUw2c1v2BjmcudyN/okqzsxRcGa2gn5JGqa0VzClvYKfinm8tFPEbyW8VcSAFBeCaPXQNMVBcqiPJsxRAfQ45P/+43wM69Gh6Qwjjjgpoopcl/5HdgUpK8RhoqoqOSpHEaWlpZg9ezZCoRD6Zbpx46BU8DyP7du3Y8mSJbE2L6asrOLx4BYJA+e5cewiF77fx0PVgFOzVPw8JIjN4wJ4vkcQg5LUhg9GEESLh5SVRhIKheB2uzFl8jg88tEvBzWZY3dU1q1bh9TUVLRt2xav/t/5mPL8p1i0YUcDRyDiBrNBiYAeCfofysKgiCreA4jh/VVSjZnHNLe+PtUtms0gPayhnKGs8Ly9Q0l0RYG3KyzGTCjrScJzVsPIiM9muFCh42y4GE0hcMo3sFUm07iw186+J9GIuolVHmO31dapha9DvThSeSl10RTnZzPZQaNRnz8gwx+oQUgOIhAIkKNylMAclrFjx0KSpHodFUXVEFLUiHwpTbMqxzkbT7I8OE7kzBwXRTXy5YzfRZrRB0bfL1xRsX/KE336Z5VVGmPqcILII9vIX1mQqlcY21yUDAA4UGH0ZKkpB+fXP89atZHPUlOubwvWWGqLWYrQbB+LGWUCZpQJ6OZTcV17GZflKujo1XBjBwU35Ct4u0DAXRtFlMg8qSwE0UohZ6WRrFixAsOGDcM5EwZjcPtUnPX4J436vfjBjZPNRMq1a9di3bp14HkeI0eONB2W8x5+G5sLi5v5CogmgXWw53gMStRVldV+d9gQ1vCRJdazZmw+STAT7FnIFnNAnE3Y7NvsYR4sDMwMsbIl3JsPLHx4irtV+tg6DkvQ552OQNj/js7tUV9FL1Osv3CUOa4Hy9mJfGByHO6QHQXL0Tqk3Zvs/KqqIWQ4KcEgC/9SETJyIMrKyshROYooLS3FtGnTkJKSgoKCgjrHyZqGoKqZYVxmaWtZNT9P7JPJJkHMME9oYNXOnY4Nz3Nm9bKIJrE22O8sUdAdEubsJLhEZBlJ/+2NELFVKXrlsrVl+nJ3aRKKD+hhYBwLFRP1sVrlfuskZhK+4+Saik01PG7e4MLdmzQcn6Hi/DYKzmurVxE7O0fBPZslvLZL0H/PkdNCEK0KCgNrJLt27cKiRYugaRo6d+6Mey46ucGHsEuPHxHhqAB6uMf8+fNRUlICURQxcWD35jafaAaGJOl/WJfWtN6GbkTzoaoqVE3/JytB1Pj1mebGJGgTrYuqqqp6HRXColbl8H2xgAtXuTBuoQvLKzikScBLvUL4qF8IUpS8O4IgWjakrBwEu3btAoBGKSz20C+7o8JQVRXl5eXIyMhAdmpSs9tOHCbmtLqlrAw1nJUFNT49bMGYGQ8ZM59m8rvtA6Lamv4BgGyMBcdbpUgFPmxp7ms/TpQwLOf5zKRZNrtqH2SqLuGXF3apxg9mPm9YOJhjVZT9rdfWijobMtrCTiL3jzzO4cA1cuK1qZP4QyFDTZEVfP/rt+jXr1/EMcvKyrB69erDOg/RWlHhl1Wz9LDfWIqiBr9RhpipsByn/2lnYWCqCsiG+hIwGkCyUFK3S4DLCEuFEF3N1DTr8y+J+jLRUFNckoBEnx4Glm0s842Gkz1S9TLO61JrsKpEX7dpr66oBIwiJeA4aFXGiQKGo24qLEbFNc32pdUs2WXuAQHD5vO4rr2Cp3uEcF5bBamShr+ucKFKIYWFIFoL5KwcJHaHpXPnzrj/0lPxy8I1YWMGdW2P604bDyC6o8IoKSlB586dcd4xQ7B97358PG1x8xpPNCEaunv0P/qra90AiSvEQZCdnR3hqOzbtw/z58+n8sREGKWlpWjXrh2yfQqCMgVDOFHB4aVdIjbWcPhqYBAnZqqYMyKAM5e5sLUmdrltBEE0HeSsHAJ2h+XMsQNx5tiBUcfV56gAwM6dO5GUlISePXvi9gtOxJw1W7GjqLQ5TCaamBxRhlcAFA3Y5ecBl6KX4IQtaVoJX1YGZLgNtSRk5J6wvBaPJMBrlDh2SeHlQxmyAmhGiINgzqBa25358GxO0UzC12BTMBz5G6ZCYj0MmbUEeEthqUsZaUwjyMZg37/pFRVLjarvkE2dvM9mtKtrdSekqjZobluyZAl27tRLmKsqVTYiItm4cSMSExPRqVMn5CUHUROqRkh1IalaT0r3uUVTbWF5JSyfBKaSq5q5LgGjZDH7KrskIeKLG6GcQouSvK//4HWLVrl2I/k+2VBdcoxlW58LmUbpZDZ2nXGcWgCQjLw/e9I9bE0lQ7WWyhIlnwUAfi8RcNwiN74eGEDfRA3zRgRwyhIXllSQg0cQLR1yVg6RXbt2QVEU9OjRw0xOZGiahh07dmDLli0NHmfNmjXIzc1FcnIy2qankLPSQkiX9L/c5QoPGdxhP6QTRyeqqpKTQjTI0qVLAQCdOnWCT6pFTQgAfBHjZDkAWQlA01gYmDHRoOmJ+YIgQdOkmFbTa04WVfAYvsCDbwcGMDRFw9RhQVy1WsLXRXxYgRGCIFoW5KwcBoWFhSgsLDzs49DDSgvATAjR1ZNUwz8tV3hACUGTg+CMbQymaFQbM+v7akOmyuI1YsQTWJljt4RM4wHCbTSTdJYo1b+sRilSLjwvRVcLwv8YRxNBrMJiB1liGOGKQ8QYewlixwysmVdTX1njKOdprgeqI/2gFpL1GWGmqFTWUJgXcfDYHRZeKUVphZ4PEjJKCitKLUJyOTiOgz9Y52HgEn3wuFPMSTbBVjZddUgq7FeIoqiQlfBfKEy9kUQekvGzIISXRWZKS6JLMFVlMzfP+B5uEHmUlyfoB/XoJY8RMBpI1upKi1ZbbuWzyMbFmb9vbZOFmoo9AQ7HLXbj64FBHJeh4vOBIayr4nDfZhFfF/E4fP2XIIgjDTkrBHEwGH/Eu3n0P5hbawVockh3VIwQKrfxh9plPAyEDGe0JCBjtxG6wUIiWBfokKLBZSSuetxGLxbDaTF7JQhchJOisNdRPBPmmJglkDku4kHdWbpY0zSzh0q0RPcGH/Q1zey1Em2kPRQr2jlaEyzsJmQmNutOi1lUgSAOknXr1qFTp05wuVworAoA0PunLF3yK4YOHQqO47B3796oFeUEQUB+fj6Ccg32FO1CMKj/DpNlGSMHj4Lb5bb6NzmW/qBsdrBn393kBD3Ei/dK4LlwZ8Vr/PJhryXRVkDEWLKS7u0SXNhQpjtehWVGf5b/Z++94+w6yvv/z5xy+92uLWqrXixbxZIsy7JkbHAMCS1AKAmhhJJvyvdHM4EQYohJ8g0lkIQSSEjokFCDCQkGY4y6LcmWZMlWX9Xt9e5tp87vj5k5t+yutLuStj5vXodzT5+71p05z3yeMijaz7Jim+UqwVMd4vnKVcyRLmIlk0TieWnPx8ueCeEjSxz8nwUeVic4vrfewS+6Nfxdi44nesloIYjpBBkrBDEOlkTFC+eZPP2ECIKYGBzHged50HUdVREb/fkQGPKBoXL27FkcPnx4xOs7OjqwefNm1NbWluxv7b6Cprq5MI3wCFdOPyyf4UOnTfztOQMPLnbxZ4s93F/n4/46H08NMLzjmIljaYpnIYjpAL1pTSHikdBkN4G4FjKYs8kUs+RXLE0EfnIOyNnFsAwgNWWwqy3dJ7pcG7acqeyUwdaLK0QaMVNjSFoyOFXOwCeiUn0wlDJSUEv8oGBkwQ2sPABWBcYPV1xyyNeS6+HVkGH2qTNLVwAbGr8zvPvY2N3Qxkt5UoCJcgNTRfhUIc/yWWuCGCuu6+LIkSO4/fbbkQw7CLMMGDNHZagAItYylUqhqqoq2Nfc3Iw5c+bgcselkkx0bW1tuH3tNjDGkMk7OPrsLtTV1WFu43JoWiHuxXH94N92sWtY8XYiaqKhJlZyrFZWvV+YCGNllTh2uiYHADiXUsUkhXtYV18SzBDnQxdrnpbxnY64RvTD3pDvnPYYPnrGxDdadby72cNb5nm4o5Ljqa02/uG8jo+dNZDzSWUhiKkMTStMAVKpFADgw298CZbOrZvk1hCjYVVcGC0X8vo1ziQIgrhxtLS04Omnn4bv+wiFQmCM4cyZM9c0VBQDAwO4cOFCsOzZswednZ3QNA3hcDhYFi1ahMFsDzzPgeOmsGLFCtTU1CBv98H3p1/c1dmshv/7vImlO8P4704NYQ34wBIPz9xlY0sluWYSxFSGlJUpwOHDh5FMJlFTXY1v//mb8Xv/7+s409o12c0iRoCBY2NSzOAdSGnCZ9p3EaQJlTP5ubIUxlnXh1UWr9CRFX7jFaaOjEwpaqu1vD4kM49pmlYUfK7SEasZQT4koL5QIK4Qg1I4W30XlJyjaaxwXdn3Lr49GyEupTSV8vAF5so/A6XFKW+U8jFZiko5QYIEpbh5NEdEXB8tLS24cuUKwuEwXNdFLpcb9708z8OuXbuQSCSC30gymcTmzZsB5NGfFrEhvu8jm80ikUigu/cCKhOrwRhDznKDeBStLFaFF6mIKjV7o1RYkrKAZF3MRL1McbwwIdzQlleKGJaTSRG78lwihJOt4hzGyrJvZvrEBztbVDBSnSO3i/qCTpvhFc+YePkcH5+9xcHyuMgatuOpEJ6mNMcEMSWhX+YUwHEc7Nq1C319fYhEIvjX9/0eKSxTGA6gzxWD8tLoULeDqxE3GOYn6GdHEMT1Yds2BgcHr8tQKSadTmNwcBCDg4NobW3F3r17kU6n4TgOstksnnrqKTzxxBMAgEQiAV40feFzH7aTH7EO09SD4ZEuHbftCePn3RqiOvAf6xxUGNOl/QQxuyBlZYqgDJbt27cHCsvv/r+v4Wxr92Q3jShGzur9oNPE/11g451NefxPf1zsl7N6ShFJydgTn4sMYFET+Os7qwAAnziUQkfWR8oR98t7fqCspOU6Ka+PFKUyLo9DCWJGGCu8OpTFrlwtFXAQ31KkrIwkQBSHWhQUmaHnXU1RGYkbXQDyRt9rrBS/tKlZ5rjMAKczjoqKiiHnEcRUoqurC48++mjJvuKaYqm0DcY0OJ6PU6f2Y8mSJbCcNJrqmoJMeHaRWlxQXcQ6LmNWdF1DRGZFrJP7GuOFYpIAUB81EZP94LOXRBtcGSOo+mSeRlBMEp5yUytTWIASlSXlMrzhiImDW20sjXF8fIWLP3rOHP0fiSCICYGmeKcQ5QrLl9/3RnzwDb+Bd/7W3YiGqAOdSnzxShiuD7ys1sK9ldY1z6+LaHj/7RXBdtKkn95sxPNdZPPdiMViyGQyaG9vn+wmEcR1owL2c1YOXX2lLsyu5yKdG4DnucNcOfn0uwxveVaMr2+f72F9kuJXCGKqQcrKFKNcYXnDfZsBAFe6+/HTJ49NcutmMZqcxZOZaHKRGH4wqOF1lQN4aF4vfvWsBjZnCQBRMwUA0rYYnNsGXNxzWwUqQwUDJaQxRHQNMVkcUmMsiGfJOmKds8T1KntOLAKYhqpnUKqWiEnLqysaImZl+HOKM4WNpEhoGJpxbDSMRjyY7vVWymuqeJ4ffG9VFJLBQyrdBZ97yGQy2LlzZ0n2JYKY6nDO4bouDMNAT7ofGSeMiO4jmUwG5wxmBxGLVMAwTOTyFvoHO+FzD7l8GtXJBpimGdwLEEUi41HxKqL6urisNZWQ2cTihoa46vtkX/WsXFtF7mg8LT8ohcWXbro+CvEshWq1wXW7+zV8t03Da5t8/GCDjR1PhnHFmuadEkHMIMhYmYIog2XRokVYvHgxkskkomFSViYVZawwhub6KnzvQ29CwskA//Mp3F3DUW/66NbFz8mSrg856cbVk7bByt7YK0IGaiMaamXQaajIWFBGS07eJyzXmsaK3LXUwK2axYa6hpV/h+IUwmW7rladvmCgjOwiVuzONJqK9TMF5eaSlokS0rI6veP6wd+BQ/xNenrPIplMBobKcIX7CGIqwznHsWPHsH79esRDNpxsJ6KhOEzTRFdXFyorKxEKhZDJ22DMRUdXC2IxEVDv+R4ud5wr+Xd/5coV3HnH/dBYoXgkACSiwv1LuVBGDA1ReSwI5pe92EHZX/q+GxgkXAXhu1L19pwiw0XFGZYG3/9/J0xsqLCxPM7xi802fvsZEyczpIATxFSAfolTFMdxcPr06SCtMTF1WLt4LsKmASdWiXxFPTQAG5NXnyHvyJa6QAw6FKswm+DcRjKZhGVZZKgQ05qzZ8/iyJEjAIT7lzJU9uzZg3RaSBu5fD/SWeHumEql8Mtf/hLpdBqRSAQ1NTXBctttt8F2BqdE7FaXzfAbB0O4kANWxjkObrXxxqaxJVAhJheRTXKyW0HcDEhZIcbNy7behg//7m/goa/9Dx49+PxkN+fmwVgQxAnPwfnWzuCQE6tEJNWJpqgOyOBTVSCtLyMMmJzt4V8O9eJEr4P1TQn0WRxN0SgqTKBGKiuGpkGXvawrr1cKiyPdwkzDhyP3qRlH6IWA+0Kq4tF8peFPGj518MgvEsWqi9geqrCMpx1THVXYMS9d9fpSYga3Ly3WypUPELPCumYjbAC5XI4MFWLac+bMGXR0dCAej8PzPHR3d4NzjkOHDmHHjh0IiwzESKVS2LlzJyzLwmOPPYa6urrgN19ZWYlbb70VjpuB7XjgiAUeBGHl/hWR7mG6hrBeOrfqyN9gVqqbx7ymgppsyAZYwnjiVhZwRArmQG0JfqIFheVinmHbk2F89TYHL6r18bW1DlbEfTx0hjwbpjLrls7DF/+/1yIWi8H3fTz4pR/il0+fnOxmETeQMRkr1dXViEajoz7ftu1gpoWYWbzirrV4+K0vAwA8sPmWmW2slHHicif6+/sRiURwpjuDDQAS+sgv5guSBqqjOnpyHk71i4G1afQ/I2KaozEXIV0YKH19fZPcGoK4Mag0x8Uo4+Suu+6CbdvYs2cPLEsYB57noaOjIzi3vb0dtm3j9ttvB2N5gAOcV0z6BEabxfCSgyY+vNTFR5Z5+IulHg4OaHikiwoATyVMQ8eqBQ2or07i4be8DLGoMFA1TUNFLDLJrSNuNGMyVnbs2IFEIjGmB5w4cQLHjx8f0zXE1KbYUAGAgUx+ElszwRhhOADuef/nAQBfv83GhrmAYYYCmUEpI07exe2NEXzpJXODy399ycPFQY7FSdGZhuRsoevzIYqILQP1lZrieTxQbVThyUDZKCrmeDXG8x5wtZeH0agnw50yTQWVAPXfwZX/bVSckkpXPWi5cr6WI6b1gZkRdHZ2Bu4zBDFTSaVSePTRR0fVN7S0tABAYLBk8xyuH0VSxqyo9MaxiBEkFVnClfIs1oMykYnt+jglPduZKV9Wc9KYyvYXikeqQPtgPbRdPhgePmsioQPvW+zhX2518OivNVj+NO+4ZhCP/OWbMHfu3CH7v7/zGfxoN/WzM40xGSuZTGZMN08kEli1ahUAjNpg0TQN8+fPRygUKnluW1vbmJ5N3BxecddafPTNLy3Z99jTJyapNZOPGrpGSna5uLLUfSBlT75vNjFxaAyIRMSL0759++B55ANPzHzGEoNSbLAYukoDHxr5ggnkL04beG2jhwVR4MV1Pn7cSerKVKG/v3+IsTI4OIiPfeN/JqlFxM1kTMbK/R/8ZzBj9L6bb7hvEz74hgdGbbBomoatW7eisbFxyLEzZ87MyllJ3xevwS+8fSUe2XsUtjt5LzuvuGst/uotLy2Zaf/Lr/wEe46dnbQ2TQicF2bhUDpYNUjX6DzXgywzKn0tYwx3L4gF5/7XGRtRPYz6KBCTPtkRmeHGdn14coBX8RCWvI9aRzw/OMbVuqgtIxVWLH5xuJaLxVgDXYfJAhp85uU7Si4o3ZxuBBnPpLmqMhQZRV/I9TkYODjnYIxh3rx5uHDhwsQ3liCmOOUGS86yAOhBdrCKeAghs7TvXST7xX6prKQcL1A8L3SLc12Zap4xFhSK5KpgpFoz1WENHVsdzvC9Dh3vXeThvYtc/KRTgz80zyIxCbzhMz/Ch343h9fduzHYR5NBM5ebGmD/nccPAkBgsHiehxMnRp6FV4ZKzrLx+OFTAOcImQbu37gay5YtQ0VFBfL5PC5dujRriqmdOXMGTU1NuGvNUjzy0Jvx8oe/NqEGy8oFDfjEm14E0zQxd+5cMMaQSqVQUVGBVCqFR/YenbC2TAm8Qtav5TEf99aIAfOX/gLAFelrIVMYc99HRagwsHXmgFsH92DBggVATlSIrlx+j7ikqM6KcvvKyf/OGVnlPubqgduRV2a0CINgpKD5QvD7tdIKj9VffLjA+nJ4yYdSK2W6pzlWBb1VBe6KsHRJ8X3A9gAw9OcNVEc9bNy4Ea7r4sqVK5PUWoKYurS0tGDlypWIx+MAfHDoQb+g6xpM6TLLZFX7OdLlsjkv+uR+K4ZcMFEk7nlBrh3PBSzpGWKVVbkvrr8yTD/2uQs63jHfw93VHB9e6uLhsxRsP1X4px/9qsRYIWYuNz118XceP4hP/ucvAAArVqwY8bxwOBwoKj/eexQf+vKP8aF/ewQPfvGH+OjX/hsAUF9fj4ULF+Kuu+4SL3yzgN7eXuzduxeu66KpqQl//0evQciYGCn6tsVz8W8PvhGLFi3CvHnzwBjD2bNncfjw4Ql5/lTnw0tdaAz4724TZ53hI+b7c4WUxVubdGzduhULFizAggULsH79elwt0xYxM+jKmhjIixev5cuXT3ZzCGLKooLxNS2HqdI3XshrePfzYjLiI8s8fGKFAzZF2jbbsRwXrlsYY23bnsTWEDeTCUld/PNDz+P9r7sfpmnigQceQEtLC06dOlVyjmVZeP7557F69Wq8/t5NWBzz8M4vPwYA+NHuIzjT2oU1zXOxbuk8/OaWW7F582asWbMGlmXhmWeeQX9//0R8lUmhq6sLe/fuxV133YUda5dh9z+8p+QHCoiUqO/87A+Qzln4xvteh0QiAd/3cfToUfzxV58Y9bNqK+L49vtfj2QyiUgkAl3X0d3djcuXLyOTyaC9vR1z5sy5wd9wGqBSF4fjAIDmuIY3NF0CADzctwiAX1BWDOVvzbD7wiB2LKoAANxWV2pkapqGhngIAEPG9tAv0+CqYpC2VE1UUUjb9eHJoFKlrBQH3I+luvyNVjRUcL/wWiz3Axt2s6w9hbOmg8oSzPhKaSUqK267vvhvX/xdsw6Qc3VUwoOuk887QYzEkSNHcPfdd8M0gda2ZzF/7V0AgJCpB66WioQMvp8jlZYFCRfpoO8UaoktlZaLVhJMpjFGXgTd83JlBSjyYS0tGPnVVh0NYeBvV7h432IPDgf+4jQpLJON43p408e/jtsWz4PPOXYePY2HXrwKD/9s9sbRzlQmxFgZSOdgWRbC4TASiQRuu+02mKaJM2fOwHXdwM/wueeeQzgcxpIlSzBv3rySezx7rhXPnmvFfz5xEOmchde+YCPi8Tji8Ti2b9+OvXv3Ip1OBzMzM41igyUcDiOsEtlL4vE4vvy+NyJr2WiYUx3sX7hwYfC5Jhm/6otsIhrGP/zJ76CxsS7Y19nZib1795IvaBnzQh50BvR7Go7kosAIqYhVZrDhyGQyiERrblILiamCqfmoi4kXI0rlThAj09vbi3379mHHjh2YP39+EO81+TB8vMVA2gP+abWL1zX6+IvTk90mAgCOn2/D8fMiAdPv3HM77r9/GzrNOfjiT3ZNcsuIG8mEGCuW4+KFf/Y5LKyvxh2rFuHdr74Pq1atwqpVq+C6Lg4cOIDW1lYAonL71eAc+Jtv/QzffOwpVMQieN9rX4QNyxbgBS94AQBRx6A4t/tMoqurC//zP/8zJH00Ywzr169HTXU1ahBHOp1GKpUqyZTxyIffiObm5lE9J5vN4uDBg7BtGwMDAzf0O0x7pMJyIBdFt6ujzvDwosQgHs2FwDT5czKlIem5gBsvuZxzwOcxxKNRxCINMKVLXyjnICtjVFSxs7yMT1HF0PKuj2SQsrh07fvFcSM3Jh5lPGgak+oKUNAXWMmq7OO0RE3yquKc4VCpYsJ9DsfjYPAArw+GFsXAwACeeeaZiW4qQUwrit8BQqYGxhg0VlBvDRl0H5MFdSulwlIbNdFkCZUl45amEu/LxDGYT4qbJsS7geqDuOoXXXtowciyoPs9fTLg3+AQ/dt078lmFoloGNFoFEmqszLjmLAK9oPZfGABp3MW/uy1L4RpmjAMA1u2bMGzzz4LwzCwcuVKAEBPTw/uWrMEYdPA5a5+nL7SWXK/Cx29AIA//of/wA//4o1obGwEYwzV1dXYsWNHSeaxTCYzY166HccZtrDcrl27sGXLFpimic7OziADW1dXFwCgtrYWwLWzPQ0MDGD//v1jTlM923DB8K2eCryroQ8PN7bjV+ercXUzW8AYAM6ha1MjNSdx82DwEDYHoYWEobJz507yqSaIq5BMJrFt2zYAQtVf0LBochtUxrkcQ9oFakPAaxt9fLed3DqnEl/52T4sQB8+SW5gM44JM1aK+d6vn8b3fv00NMbw8FtfipdtXYt169YFx7/26H64nod/fvcbgn2f/M9f4JuPPTXkXlnLxosf+ncAwML6anz5wTeioboCW7duLTnv2LFjOHny5E36RpOP4zjYvXs3wuEwfuu3fgsA8KPdh/HRr/0vFjfWBm5jv/rVr667inZdnXATG2ua2xmBTE/MXQufvBLHG2sHsDGWw5OLj+N1V1bglB0Fk/7OPJ8GzwmjpLW1FX19fVizZg10LQdDj6AiXhW45fk+oDExq+fKv6vKbJORftj5otTFCrXFOQ+OMZmKczRFIm8G6rnqn0fQLmC4ZDvy3GIVZvh4muH+vU2Wi0j5LG95SuuIayB16TQaGhrIUCGIUbJhwwZEIhGYRghrb9lYFJNXeFlRvz2VyjgeEcpKXdREWiopOU+lNRbbAzUuTtti3AqiPQ2pgKvCkbkB8Lx007RlxrCy2JWUy/DJ8wb+apmLz612cCLDcHTwpucpIsYAxavMTCbFWFH4nOOhr/w3llWZQdB2a2srPv39X+Kv/0BUSM/n84hEInj/6+7H8nn16B0UM/45y8F/7TmCzv7B4H4XO/vwtk9+E1/6k1cEL+eapqGmpga33norOOdDAvtnGpZlob+/H9XV1bhn7XJ84p2/jQc23wJAFFG63kQEK1aswC23iPup3PizlTbHwOvPNuE7S9pwW9TCt+adxdaWWzBSdI9K271mzRoMpPsAMFQmKiesvcTEEo2KQKYjR46QoUIQo0D9ZioTNdA1fUpOiH2yRcdL6jzcWcXxi0027noyhLNZMlgI4mYyqcYKIAyW13/6h0P2Z2Xu9Hw+jytXrmDp0qV45d3rSs555d3r8PZPfROtPQUXr0tdffjNj3615Lw/eMldeNer7sUtt9yCM2fOBIUWZyq7du3C9u3bUVNdHRgqALB///7r6vxXrFiB2267DQDwhUd24kuzKYDNl/NxyqdZzrg93qVh/cAcHN3QhQ3RLL7VdBKf6pqDo7kILCsP2HUltyk2WHoHulBbWS18sjXxWwBEbApQmB0MFRWH9PxS1aFYPFH/ZcvVl8lSWFQbC4Ujh/7bK89gxkvqxfBhzy3dNzkqUiEbmGyH/KDLv72ha0HGvsWLF6O7u3tKvngRxFRC/Way+TSi4UgQI6hrbMhvXMWLxaPiNabKMjFHqtCqZpWK+7O9wpjfKjP3DaRFlkYWSYkD4TgwIAK1g0xhUkkvjl2xfIbfPBTCk3faWB7n+KMFHh48ScYKQdxMpuwv7Fu/fAq9qQyqqqpKKtqfOXMGp0+fRjqdxry6Knz/L9+KimsEU33jF08CAHRdx/bt27F9+3Zs27ZtxqbgdRwHBw4cGLL/jjvuAGMM4XAYGzduLMkUdi0WL148ew0VQAxavicKiXmOMFpcC3ByaMvYeOPzSdg+8OrqQexbcQ6X15zAS6uGj/s5fVqkkdH1wsyh53O4cvG4WBxfLK7vi4Vz8f5eUhCeFRYUnKg4CkUgi4tBTgaafNEQ7SxNraza6nOxcF5ot++LhfNSQ6X4O5Xfb7Io+e8gv2dj/RIAwIIFC7Bhw4ZJbiFBTH3y+TwAYaw8sfeXiIYNRMMGTEMb8hvTdbGYhg7T0BELm6iJiKU+FkJ9LISFiTAWJsJYXR3DpoYkNjUksWF+JTbMr8TK+dVYOb8aVfWNqKpvBJJ1QKxaLEZYLJpeWIo6mwGX4Ued4vXJmAL9D0HMdKassXKhoxdv//tvIp/Py4q2glAohKNHj+LXv/41stks4vE47rp1yVXv5Xl+kB2srq4O9fX1aGxsxLZt20oMoZmEiisZHBzE2z75Dbiui5qaGjQ0NGDHjh1YtGgRNm/ejKVLl47qfk1NTQCAnlQa3/3VoZvW7unKo30hvOxUIx7tj8LxgWrDx98t6B32XN8v/Hvs7OukGfcZiq5HEDKqAACLFi2aIilYCWJqsnr16pLxuKmpaUr3jSlX/J63VfvQqEgkQdxUJt0N7Gqcbe3GA3/+BWxa0YzGmgq8+9X3YeHChUENFk0WZDO0q9tcPuf47Y9+GWsWNQX7XnLHGty3YSW2bdsWxHE8+eSTM6YOgvrb9Pf34+Cpi8jn80gkEkGmlXTOQiIaxvr165HNZtHW1nbV+x09ehT19fWorUjgic+8B4CY4V7/zr+9uV9kqqECLpVXgSoEyRge667GY93VaNBjaN3ag1VRB7H40JgUzjkOHjyIrVu3IpNL4/TFAbiui4iewIBVKDSms6HXKVex8kGcMQSzfuWvxIXA+4IKMRkvzsOlNebDJAzgKP8e5d+VDft5MigUwywkEQCE24quMzBmwnYLystUfvkiiMmiubk5iIVMxiqRiMcQDUdgyiD6YhewwPVT/vYN2VFGwjpiEfFKUy3dvjx5rsEYEvJejbKIZHtCxLWejOQAAM86HnI56RIWEROkgTuYawFcZUMRLmFfvqzjwUUubq/geOcCD1+8NKVfpwhiWjNllRVFfzqHx54+gW8+9hT+7F9+BNd1oes6dF0HYwye5+H0la5r3udKdz9+fvD5YHn/l34YpDOuqqpCVVUVduzYgbq6OiSTyZv9tSac8tTNr/vYl/HT/c8CAObPn4/KykpUVlbCMIbvcIcLEJ7sF8WpSoejIytdnBN8+MDq9vZ27Nu3D47jQNd1hMNh1MUdVEVGkwCZmA743EPeEpn3UqnUjI+VI4jxUl9fDwBIRCtQlaxBRTwJ05jaFeK7bIaPnBHj5WdWubi3hgonE8TNYlpNBTz+zEnc/e5PozJeKBc+mM0jkx85085DLxb1RlQ6O7X98V+eQWVlYdb7fHsPFjXW4p577gEAdHd3Y8+ePUHA33Tn9Z/4D3znw3+A+bK6/ZXuflzpFgbMwoULg/gVx3Gwb9++oD6LYv369dB1HalsHg9//ado70vh2XOtE/slpgLBzHjZi6cZKcgWnoOQChiPCMM3Ho9D07SSF9b29nb89Kc/RSgUwqJFi3DLLbegNuagJqIjZTPIuNGgKKSYmZf3LWsWY4Vge2VEFgpHyhYXzeprMr2xClKdKApKhNgeNvi+POr+KveZKpQrVoau4fSZZzBv3jxks1ns27dvEltHENODcMiEaWhBX3a137n6zak+LGTqiEtlxfd4yTlhXQuUld6QGNOTSrVRcSh5Byczoh4ZbBFvyFSq+jwrqOhFY8DnLxm4u9rH7zT6+MF6B5v2MZzLTfk5YIKYdky7X1XOctDemwqWqxkqI6FpGr7/0XcE29994hDe8vGvo729HblcDp7noa6uDnffffeISsN0I5XN4w1//e9obW3FuXPnwDnwxJFTGBwcRC6XQy6Xg23bME0Td911VxDzYhgGFi5cGCQjqIhFoOva7DRUxsAJmcoy0XkWruuisrISd911V+Cep/A8D7lcDs8//zw6O0Xh05hJrkIzAaXQPv300zPGvZQgiAIcDG9+1sSePoZKE/jOOgchRv03QdxoZsab+FUoV1Qe/tkJvOSONfjt+prgnOPn29CXzuKBv/w3AMDmlc348oNvRG1tLZqbm3H27NmJb/h1ombwa2pqMKcyga6BNFLZPF7yka8E5xw/34a73/tPwXbI0PGfD70dS5rqsG7duiAFclVVVcm9P/bm38KjB54bscDfjGSIoiKNDkNWog/FglMfuq8ZJ3kfPtcdw/ezIRz4VTc+8YJ6NDQ0YOHChTh//vywj1Cudo1xwPF09MksyaZWUEpc+d/VlemMrxYDUXDTk4XV/ELhSB4cE/eZLIVF4Qe11/iIikp5KuSpRPl/Bs93EIuJfxOOQ659BHE11HiVs3Koqawa07VBkUhDQ0wWiFS/R03Gs4R1FyH5WZ2vuiCV3nhxZRTdNcLboscRk3PcE8oKAwoFI1XcorzO8hnecCSEp++ysKmS4ycbHfzOYTMIwCcI4vqZ8cbKcBw4eQEtLS2orKzEwMAALMfFR9/8W2AyPFYF4luWhY6OjhHvU1FRgSVLlkDXdaTTaZw+fXrK+KVfuXIFK1euRDwex2Ofehd2P3sWz5y5hK/9fD8cd3jf2tfs2IAlTUJRaW9vDwyVfD6Pnp6eIO3xpUuXZpehMg6OsWr8W1oMeM92WXj8Yha/tTQRFCsdjnPnzmHu3LmoCAPzkw76LJWMmJhOWI6Fjp42GIaB7u5u9PYOnxWOIAhBS0sL5s+fj2w+i9audixomDutYiKvWAyvO2LixxscvKjWx94tNl71jIlTVCySIG4Is8ZYKVdYXvm33w6O7fnMu5BIJErOtywLu3btGtF9o7q6GnfffTdCoVCwr66uDvv27ZsSBott23j22Wdx5513AgDuvm0p7r5tKdYtnY/3/vP3A4PloRevCv427/ptEa9z9uxZNDY2oqqqCrlcDq//26/hfHvP5HyRqUYQNCINvuIBVRYwe/jXwkXuIfHnxOfsRaO6dVdXF/bt24etW7eiMgIsqPCxt5UHPtUZ14fOCrOIABAOCb9rw9ChPMz0IH4iaFiwzaSLQpBVzFPb8tphiq9NBOVtLd03dQmUKl74uw6kB+BzHz09PdizZ89kNo8gpgW9vb04d+4cVqxYgUwuA9t1YRommH9t5Vh1V7quIVQWk6/LztDUNeiBQi2OOVIZqZNqzNLKCHotEc+SyonYFccRajf3vaJMkKpQZOn2E7067nmK4b822Fid4Hhqq433nDDwlSs6aNKJIK6PWWOsKNSLeTG6Ll74zpw5g1wuB845rly5gmw2i2XLlqGhoQFHjhwJDJdYLBYYKs+cuYR9z7XgrQ9sRWNjIzZt2oSnnnpqQr/TcMRiMWzcuHHI/h1rl2Hn3/9/cBwHFy5cwO9/9icAxEuqis9pbm6GYRjo6k/j7X//TTJUrkZgvHDAEP+O4Iu/48eeFdtzb9NhjNIAUFnCtm7diuYKDV73OfzRIWEQr6iP4965VQAAt2wQ1zUWGDVMGjJ6mdsDY4AvDSr1ku2ptafcyhh0maJzIlzDhhopvOhY6d9sqgXVF+MX/TvgcrKitbV1xiToIIibSVNTE5YvXw4ASMQqwKDBdT0w2acON3FR7haqaQyGSkYiz9EDFzE92GlLF9qca8i12J4TMdGcFMp3V41I4tNiiYQ0zJfFgAFwt6y6vV/wVDg8qGHL/jC+vc7GC2o4vnyrixfW+njzsyY8PnX7L4KY6pBGWcT58+dx6tQpnD59GtlsFqtXr8a6devQ2NiIHTt2oKKiAoZhIJlMBorKY4dO4HtPHMK5tm4AIkZkKtDY2AjTNDEwMIBXfeRLJX7zsVgMlZWVWLt2LT74ht9AY00FPvmHrwqMFcMwkMvlyFC5DmoiBlbEPdTrY49XaG9vDwzeBQsW3OimEQRBTCmqq6sD4yMeiV/j7KlNh81w/4EQPnDSgO0Db2jy8bnVLobmcCQIYrTMOmXlakQikaAeybx584IiVZ39g6ivSuL+++8X252daGlpweLFi/H+192P97/u/uAe8XgckUgE+Xz+prWzqakJmzZtQltbGw4dOlQSZF1dXY2tW7ciGhUzQ6lUCmdbu2FZFkzTxN69e1FTU4NVq4Q73Bvu24w33Le55P6kqIwDxsA08XP6nz97CebOnQsAuHjxIj543AzctkaLKlSq6zrsgT705z1kLRfNiQiAguuCWZTWuFHNNEbFMU0rnZVkjBWpLTK1p1cIvgcA8GLXplJ3Ru0muIiVJwEYzg1sKvquq79R+ZpiuQhi7Jw8eRK1tbWor6/H5Y6LWDxvKTRNhy59W5lWKKgauH8NUyxW9WtqHpYFqjJDXPaZtiOUkIQj+reMTGFcETLQGBWTkE0Vop8dyAr38F7HBlOKiiOKSHKVylgVjuQFhcUHw6fOGzibZfjuegfvXODhfI7h4y30ykUQ44GUFQA9PeKl/I477ggyXymF5Md7j+B1D385OAcQBawqKirQ19c37P127NiBSCRyU9ra1NSEO++8E6FQCM3NzdiyZUvQeVdXV2P79u2BoQIAplnqxFtVVRXI7SPxvi9+nwyV60C5FQII6teMlWw2i2w2C03T8KVXLsPWhUlsaoygOamhOamhKuyjKuwjrLtgI8zYOa4L1yM3JIIgpjae52Hv3r3I5/MwTROOOzMy6P2oU8e7TggD5aPLXNwSn/x4VoKYjpCZD+B3/u47+MK7X48NyxZg+/bt2LNnT2C0dPen0TuYxX0f/CJCho7l8+vxz+9+A2pra4fc51xbN8KmgXl1VdixYwd27tx5QxUWZahomoanTpzH+qXzMW/ePGzZsgWnT5/Gtm3bYJomnj13BbctmQdAuIOtXNAQZKFSatGvDp/Ch778YwCigN2f/+4D+M0tt4rvPEA1IcZMUQDm7/zjT/HoR34f0bAwFPOOF8SGjBbOOXbv3o3t27djRV0UX3j50vIHynUe2WwWttOEdE48Lx4zkcllcKn9Mnzfx8rFyxCLREuuDooXGqpIYyFQvBB+IfepNvk3L25kKqclLkf8jUoVFS9IVODDljOuV0srTRBEKZ7nDYnxcj2VJrjwW1OxdKahVGVxCkOR6qKUYxUnorNA3Y6GxWtPwhJKSMaRyoqpo1oea06I8TJVLdqTsSph2VlxL1P2pdqgXMvJKe4PK61+4aKO+2t9vLzex8dXunjZ06Eh5xAEcXVIWQGQtWz88T/8B7q7uxEKhXDvvfeivr4enufhF4cKAfm26+H4+Ta88++/hc7OTvT395e8kPztt36Gt3/qm8hkMkgmk9ixY8eQVLVz587FqlWrsGLFiqAOw2hobGwMDJVLly7h/3zm23jPF74Pz/Mwb948vOAFL4Bpmujq6kJLmSry3YfeXqKwtLW1YdfRM3jrS7bi1TvWgzGGv/i3R3Dy5Em0tLQEle2J8TGQtfDAX30DFy9eRPu+X+Dj7i8RcoXROmfOnFGrLYODg9i5cyfa29vR398/7JLP5xGLxaBrg7BssXT396Ctqx26rsM0TZxvvYRsPnczvzIBYah09rbDdmw4joO2trbJbhJBEJMOw/tPCiPogTof9SGaxCCIscL4KKb/UqkUKisrwTa9Gswwr3X6tCUWDmHf594fbH/iP36Ob/3ywFWvefer78NbX7wVAPDk8y34w898G6sWNOI//vJtAIBjx47h5MmTAIBbb70VK1euDK61LAs7d+5EKpW6Ztvuu+8+VFdX40JHD171kX8JZpze/Bt34r2/88LgvEf2HsXL71oLzjnOnj2LhQsXlqRXbu9N4fj5Vrzw9lXBvn/4weP4ys/2XbMNxFUwI2CJ0uQKLN2Do1sGsTru43ykCZmXvBNcF4PWhQsXcPDgwet+bDgcDpI/lHPlyhWEQiHMmTMHjuMMq7BcjfK0vMInfBpIHzeZ4uKaKgW463Fkcml093fCcRzs3r2b6qsQxBh50YtehMrKSsQjCVQla4P+xvcBT2bZi4REHxqWKkhJ1BsvjR0LUrRzDttRhSeFWtKfFlV3u+W6K+ugIytU0cty33P9Qk053DaIS1c6xb16L4l13xXxECsj1p5TSGc8zGvVni0W7qzi+FSLjg+cmrnvUcTMhLsO+MEfYGBgYNj3jZsNGStl/NaWW/G3b39FsP3yD/8zLnSUvnTUJGP46rtehaqqKiQSiZIYhd7eXoRCISQSCeTzeTzxxBPIZDJYtWoV1qxZAwD46ZPHsGJePZbPr4dlWTh16hSi0Siee+45AMCaNWuGqC5NTU3B5+effx6v//QPsXRuHf7zw38wJC4FAJ577jlEo1H09/cjFArBtm2sX79+2GDl3/zzz+NKd//Y/1hEAcYAU8YpReQPmfu4M2Hhp6s7UWUCB0MLcXDT72NrYxi6xnDu3Dk888wz1/3ocDiMZcuWlRil6XQaZ86cgaZp2LZtW2CwrF6yApGrFKYkro3v82CyIC9ffDgH0tkU+gZ7cOXKFezfv38ym0gQ0xLl1swYw6lTp7Bj64sAiBTrKg1xVAbKh2Rg/NWMFQ7lrllIz27JAPtBaZgMpMW6K2ujJydiZdoyYt+ZlFCk97an8OwF+R7QU2qs8JycbHTyw6YzVryi3sMPN4j7v/SQif/t1oecQxBTlck2VihmpYy23lIXqK23LBlirNy2ZB6am5uD7dbWVrS2tmLjxo1BYH4+n8fOnTuRyYhZl/nz5wMATl/uxEe/+t/YumYJ/ulPX4twOIzbbrsNAFBbWwvO+TXTH6t7bb1lSWCo+L4PTSt49S1atAixWAye52Hfvn3o6OiA4zjYvHlzYLDYto3f+di/k6FyE9mfDuP9p0z86xoHt+Qu4k+eS+NYj4P/c1sSixcvxpEjR667iKhlWTh+/PiwxzzPw549e7B9+3bU1taif3AAjeH663oeQRDEzeDKlSs4dOgQNm3ahGXLloFzPiWzAY6HH3fq+OeLPv5ooYf/WOfghQcYDqbIE58gRgMZK2U8fVrEgzRUVyCds7Dz6Okh5+x+9kxJEPv58+fR09ODfD6PaDSKw4cPg3MeBL5fuHABhw8fxrZt27B8fj0O/PMHh9wzbzuorhYFqPrTWXzuv34N2xmayYkxhkOnLgIAfrjrMF60cRU2LFtQYqgAopZK3nYQCZnYunUrHn/8cXR0dARtBMTsO2X9ukFwLmbW1GcAiFYCAF5QJwbbH/ZEkavw8WRbDv/ntiQYYzBNE5Zl3dSmeZ6Hrq4u1NbWwvN8OI4XBKmSW9f4KA+w930Ozxs6m0oQxNhQsV6apsF2PWhMJCkOSyXFUOnXZdcV+IZwPiQvIoNK3MEDCUbZPqqApLpfWNcQkfuSIfGsKulyVh0xkYgKRTodlnVgZP8OX47TxVXuWXnjBO89YWB5jONFdT5+cruNjfvCaLWoDyaIa0HGyjDse67lqsc9n+Mtn/g6/u4dr8T9G1djy5YtyGazgRGwZMkSJBIJaJqGTZs2gTGG8+fPY+/evdixY8eQ+/37/+7FT/cfwzc/+PtgjOEdf/8tnLrcOeyzK2IR3Lp4LhbUC8PmH3/4K3zyrS9BbW0tPvK1n6I3lcE//slrkMlk8Jq/+Ro+9Yevwrql89Hc3Iz6+vqStMaqpgxx87i3IoffaxAuBZ9rjQOLAdsDMpkM4vE47rrrLuzevbukaCcx/bDsHAaz4vc0mhg0giCGx3Ec5HI5RKNRDGa6URGvA9jMUCBszvDqwyZ+fYeN9RUcn17l4PVHKDsYQVwLMlbGiev5+OC//hcA4P6Nq5FMJtE9kIZp6KiU/nxnW7uwdO4cbNy4EX19fSWZwy529uLdn/8eBrMWOvtFCsQd7/1HMMbwgRcuA26twcM/O1HyzHgkhF3/+L4hbXn8mZN46GNfw2BOzNDveM9nkLMc+Jzjclcf1i2djxUrVgAQBR//7F9+iJ5UZoh7G3GdqP++qkiYa+EDc/sBAI+nEziQCYPt/DkA4P84L8A//UYENTU1WL9+PQ4cuHoihxuF7TrgKKQENUAKy2jhRcG6hfTOgO976OhthWEYaG9vx4kTJ65yF4IgrgbnHHv37sX27duBEHDoyB7ccfuOQAkJCkVK9aIkxXqZtFKstQTp0VF6XeG5RaqL/BCV6Y7nREzMqZDKSlZMFLKyopDcda4as6JIewxvPWbiwJ02fqfRx79f9vDzHopfIYirMTOmKyYJZbC0tLSgr68Pb/vUN/HOT38b/f39OH36NF7z0X8NikmuWLEC27ZtAwB0dHTg/372u1i1oBHxSGFWxXY9WMO4finy9vCz78fOtwaGCgBk8naQBeWXz5xEPp+H4zjo6+vD2//+m3j69CUyVCaILkf8xO5LpPE/t3Rjvin+G57td/CJ/d0AgMrKypvejo6ODnDOkcll0N7dgVQ6Bcu+ue5nswXGtMCVLxaLDZvwgiCI0dPf34/Dhw8DmJj+caI5Oqjhi5eEgfKNtQ4WRiidMUFcDVJWrhPX8/HKv/12yb573v/54LNti9kXVVtj7/GzePfnv49/etM9uPPOO3H06FH8/md/AgB46MUinbBSVB568aoSdcXzOda942+u2abi+/zy6ZPY8vTJ8X494nrgPv7sXBxNEeDeijweqLbw16wfb7m8GLn0IHoHJm6A6u7uxsGDB7Fp0yakMimkMilwzjF3TiOqkpVwXNEWVWiNKDBsfEpZ6uKmhiXCZaWiAqtXrw5etAiCGB9q7IzH49CYXwgDkceD32OR4jkWgvTGqu5kkWIaZB4zhEFRHzXRXCVcqDOymGSnIyd7HFnDKp8CbBUYo/rRkVMZ/9kpA3dW+dhUyfFvtzq4/6CJQm4zgiCKoTeTm8ypU6fQ1taGjo4OnDp1Cu/+/PdhOS4sy0JHR0eQLYyYmbTZOl70fCPecrYOAHBHdPL+e1+8eBH79+9HW1sbent7wRhDW3cH+gcpdul60TUDkXACABCJRCa5NQQx/enq6sLAwADC4TC6+9vhuDMrrs/yGd5wxETWA+6r9fGO+ZSggyBGguqsEMSNRk0ByrorLF6NOsND64YL0Biw5UACB60KrFkwB99+32vg+35gREwk69evx9KlS8E5R23lHCRiSXBeqF8QlhlxZms8i6rL4Hmii1RF6TyPD6nV4LoeLKcXnLs4f/48Dh06NAktJoiZRXHR28pEFaoraoLYFUXhFYbhalmOlRpqy99uWv52U7KmSk/GQb8lDKK0Lc7pl+vuvIPzgyLb49EeMeF09KKY5OHdF8S69xJ4pk88zLVU4+R6ZIXlXc0uPr3KRbsFNP86DJfPzv6WmNpMdp0VUlYI4kbDuVg8B/AccCuLroyFb/ckAQD/uDIPLdOD4ydO4n+ePAZN03DnnXeirq5uQpt5+PBhnD17Fowx9Ax04cjzT+Pg0f3oHxxEJmdjMGNhMGPBdrxggJ/p+D6HbXuwbQ+5vItc3kU6ayOdtTGQFkvfYB6ZnIN01sbBp3+Fi5ePo2/gAjh3kc/ncerUqcn+GgQxI7AsC1euiOKLrufBcfygP7JssTiuD8f14XMuk1/woAseDo2JxdC1kiViaDA0sTDGwBiDzgCdAXFDE0H2EROVYQOVYQMRU0PE1ABNH2YxxMKYXDS5DDVEPn9RR5sFNIaBV9RfX80tgpipkLFCEBPEhy7XIu8Dd1a4uL9ODEof/vdH8Osjp6FpGhYsWDDhbTp8+DDOnDkDxhiqq6vR0NCA3lQnbMcKBn5iKJxz5Kx+rFq1CjU1NYhGo0Eh2MHBwcluHkEQ0wSXM3ztilCxX980OyaFCGKsUIA9QdwsVPpKOwsAuJLy8f2uCN7YkMedNToeTUXhATh6oQP3rFs+abEOR44cwYULFxCJRLBixQrMmTMHvakO9KaAnp4ebF67CYZhwPf5jHUJU0aZ5/mwZdB8Ni9cQjJ5kaFPuX55ro8LF5/GwoUL4fs+jhw5gkwmg97eXqqXQxA3Cd/nsF0PXP7EVBC8cgvTGIevqfTEQ8tDBp9Y6XWGTCoS0hlMrbTgZPE16nkRVURSusvmTJHSGOEEmEpjbIk+P0hjr9Z+8H8l0s8POnR8cImHB2p9xHWOjDcz+1mCGC+krBDEBGJJf2SjaCw6fqEDADB37lzccsstk9Es9Pf3o729HXv27EF7e3uwv7a2Fpc6LiObzyKbzyKTy8KSWXpmC5z78DwbnMsFg4Gh8uSTT+LcuXPo6OggQ4UgiHHxdIrhZIYhbgB/tIDUFYIoh5QVgrhZFBWJbAxxfHRpHm9rFC/6h50KsHgCvK8V+46ewKd+fhwP/sYarF69Gp2dneju7p6UJnuehz179sA0TUQiEdx9992IxWK43HGl5LzayhrUVdfBl0HnxbOVN1p9UYHuftFkqXpCuQs4Y2xczy8u8qgC6i3HA+cc/alLCIVKq0wrQ6W1tXXMzyIIYuzYro90rjAhEA2L1xddKh0+59BUyEeQ5ngkpaXQdyjFRNe04LOm+rOivkTN7Kp0xlUxkWyoPyqCjZljgavCkzJ1caCwBAH2fiH3cpCLmQNg+LtzBr5ym4P3LHLxqfM6KI0xQRQgZYUgbjJzQhy/3GzhHfOEobJ3wMDPBmIl53xzv5idB4BEQqTAra+vn7SCaI7jYHBwEDt37kRHRwfS6XSwAEDPQC/auzvQl+qb0cUlOfcDQ0V9/76+Puzfv58MFYIgbhjfadPgcRFo3xi69vkEMZsgZYUgbhIbK3y8dI6Ht8zzsTDKcTHP8Puna7E7FQIz5TyBLn6CzSvXBIaJ53lYuXIlbr311klLa6zIZDLYvXt3yb7ly5dj7dq1GEiL1J2+34W66kZEwzFojCESFjOPpnF9qY+DYoxKoJIKC5czkUBhclLNhDKtcJ16LrtKPtPiQo+ASE+sCj2mcw449+RxH48++ui4vgdBEONHqbeua8F1bei66FdCMtbE98XahR8oI+o3H3Q9w3QB6pyCssIQlfeMybXliXVE54jLGJX6iFBU5leJGMOMJbI8dvl+oOBwFaOiCkeyonlh9ZmXZv5yOMP5HMPSGMeL6zx8tZVezwhCQb8GgrjBNEd8/MsaFy+qKwxGpzMMLzsSxWkWD/RMXWP49nteiZqaGiQSCUQiEaRSKVRUVGDVqlUAEKQ1Lp7Fz2azOHHixKTFSJw+fRq5XA6NjY2Ix+Ooq6tDd187Wltb4TgO7tq8HaZhBgaAIY0WQ2dXNRzKUdeXZyTjHGBMHZMuIPIlgfk8cP3w/TLXD2XQFN0z8NSTL0SO68N21csRB8CvWruBIIiby4ULF7BkyRJEo1EMDLQgllgEgMGwpKuV/A1rGgsMGDVRwjXZLzAWTGiUJzgMjBa9YKwkQ+LVSPXgxS4oTkzcIC8nTzIy8UbG8pCVyVRgyKB7aViVwEdOT/yVKzr+ermLT61y8Xivjot56nwIAiBjhSBuKA/UefjabQ7mhADHB37cqeHn3Rq+265jEBoQL5y7oLYCK1euDLZTqRQ6OjoCQ2XXFRu1EQ231BqYP39+yXPq6uqwe/fuSTNYLl++jMuXL4Mxhs2bN2PBggVBGzt629BQ0wjTmO6V3DkA8fJhz7KkAgQxVcjlcti5cyfuueceVFZWwvUccMxMP6lPtuh4Rb2HzZUcj2+2cd+BEBksBAEyVgjihvH+xS7+boVIc/t0iuG1h0205Irm5DQepLDkAFrae/Hjfcfwiq23AhDuDsuXLwcA/HdLDrtabTAACc1FTURHyNBQFTXwwMIIampqcPfdd0+qwQIIheLAgQO4fPkyIpEIFi9ejKqqKrRcPovF85fCNEKwHTGTGA7pMOXMZbmLVrHSEaQRLnfRKgq0H6n+C2NsiGqitpVCUjzLqtxD1DM0JmZnOee4dOmZIOvXM888cx1/JYIgrod0Oo1UKoVIJIKM48HyXDgyEUbElimIdS0Iuo9KYUP1N7qGQB4p9A+l/QtjLEhnHDNln8P14BxHnpeR96wIiWPV8pkVUQNZU0zQMJXOWAXaj/J7upzhNYdDeGyTjeVxjsc227j3qRCuWGSwELMbCrAniBvAHy8sGCpfuKjj7idDpYbKCDz09f/Ft391CABQVVUFAHB8jlP94l4cwN7WPP77XAY/O5/Fr69Y+OzRQViWFRgspmnelO80WjjnaG1txblz57Br1y709/cjEomgo7cdjjv1FAnf9+B6bsniycX1XHi+i4F0T0l6YgqmJwhiIricZ3jhgRDOZkX8ys4tFjZXUmV7YnZDygpBXCfbqnx8drUwLv7qjI6Hz45gPHAfcEXAJYuJYHqe7sbH/+MXgO/jd1+4GQBgagx/uiaMe79zGTmXB37YVlhHZczE2QEfX3zOwdtuNaeMwqKwbRu7du3C9u3bA4WlIjkPGjMQj5pIxoT7RkgGqzKtEEcCCMPHDwo0irXjqngS4Ruet73gGEdBEQGEeiLDT4L7KAydIW+lkLcHRxU7Q4YKQUw9HN/FgMWQl/1CyBa/5bipF5JrlAXYa0wfJk6ttH/QNRYUiAzLQ6482fI0aPI5rrzO9kqfFTF1wJDuabpZumZjmxe+YgmD5bHNNpbFOHbeYWPHUyEcGKD5ZWJ2Qv/yCeI6efM88RL93TYND58dn/3/8f/4Oc6cORNsh0IhNCVGvld71se/HctOKYVFoQwWpbDYTh987k5ae3zfhevlkbP6YTlpMMbg+/5Vl3w+T4YKQUwhOjs7AQC1UQfJ0OT1JxPFpTzDHftCeKxbQ0gDPrnSwegdyghiZkHKCkFcJy+uE8bKly+PopCXVFZ4pg8AwCobxHZfK179z0/gA6+08Lvb14hTB7rA+3OwokKFiYYqg9lBQ2PoznP89KKGlyzmqKmpwcqVK3Hs2LEb/fXGRbnCkhq8grzdgLwt6stUxqXCIv2+taLYFTXjqRSVbF4oRoNZsc7bHixPFaMUzwvryjedgZWkNc6D81Il5dChQzh//vxN+NYEQdwsTp48iXg8jsWLF2NONIeLKR0+Z2ByzjXPfERUJj8pr3IUsnEFMWwoVViC4pA6C4o5qt7Ck2mRQ3phXlfFrqhsYIpExEAiJmJW0jmZSSUsamZBFYf0i4ysQPn1y7YLDLgMbz1m4tR2C9urOdYnOQ4PUvwKMfsgZYUgroOwxjFPJr16evD6f04f/699ePbZZ3H27Flc6Mtd8/xei+NolzCWotHodT//RlKusDCk4Ps3w1WNQ2N5aCwLhgwQLGkAQknp7+9HZ2cn9u/fT4YKQUxTnn76abiuC13XobHZoTK0WgwpaeOQmULMVkhZIYjrYJ50bs56QN+13sM5B3xhWMBKy11SIahdEJz2xq8/JfZJf2dWv0weqQwKJDYnRbaZBfEw4obYWV9fj2QyicHBwev5SjeUcoUlm++B51cE8SRhs6zAGwdsWbcgZ4kROivX/Xmxzrt+4EtuMKA2loeueSO24dy5c5TNiyBmAIsWLYJhiNeWmkgIHFpBGWEMplRAzCKlFQA0DQhe9YO4FrGpMoDp+tDaTJ4v+qeo6yOsiwtC8sKE7LtqIwX3W1Wj6YxdCwDwXTko+IWEKVC1WFThSCXQ8JH6MI4q+Yhx1tcliGkPGSsEcR0sjIrB7WKOYVTzXkrqD4yWjNjtOYVCYposaKZSasrBrT+VRkVU/GRNMfqiPhqC53PYnotIJIJ77rkHqVQK6XQazz777JQMuvfcTly8JNpYN3c9AIaIUVCllBuYMEg4Oi8/g2QyGRwvrt4SDocRMyvgeR5aWlqCateKVCqFCxcu3LwvRxDEhLBo0SJs3LgRAGAacTTVCFer4hTlKnGHSmFs6MpY0YpSmKsikAUjRV3vqsQdZenTo4aGCnnPOVFhOXjyHFXtvsLUEdHVpIs4dsarF89UsSZMA0/3is9ywiooEsnYsK5gAMN/d2p4daOPP1rg4e3HySGGmH2QsUIQ10GFHOjSI0/s33R8ztCZiSLhX0FVVRXmzJmDOXPmoLKycspmCVNtzNgefD5SN8QRNdJYsmTJVe/teR727duHjo6OG99wgiAmnebmZtx+++0AANOIIWQmr3HFzOLvzxt4daONN83z8NmLOo7cAJdjgphOkLFCENfB2ZyYpVsaG6P/dHlwpWsX1BapsLAq4UrAbRG7wnqv4KJ0K1iwQVSLr5OzfBUhAzFzMTzPAWMAYxnU1NRgy5Yt2L1793i+2g3Htm088cQTqK+vx8aNGxEOh9GWtjBoO0jIQPvKsI7+K4ewZMkShHQNmqbBtm0cPXoUrjt8BqC+vj5ks9mJ/CoEQUwQzc3N2Lhxo1REIgiZFWCMISH7PqWQMBRUExakLC4u/qrSGUuFpcynioNB9cecq0KT4tyQqaMqIl6XVIC9Lu+dskW/lDS9QFmxpcKbs0WffsWrFs90bcDJi2c4145JVDw5oOF77Rp+p9HHp1c5eOGBECiChZhNkLFCENdBQ0jm4Z+gml0aA5ZUR5CQFZZNGashxl0NHGrgZgA4QqHQxDRslHieh7a2NvT09GDu3LlornRwfsBESLprJEMO6lesCM7P5XLYu3cv+vv7J6nFBEFMFuWGChAfVY2kmcj7T5p46RwLL6jheE2Dj+936Ne+iCBmCGSsEMR18OoGYaX8tGucA0exwsLVdKC8V0ik+WWmjGWJV+MHv1mJRYsWoZBvf+TZuUwmg/3794+vXTeZQ4cOIRaLoaqqCstrHAAFVzXOOQ4dOoTOzk5YljUkDoUgiJlPIpEIDJWMbYIhAsBHNFyaelipJ7rGAkUF5QZNUSxIufpSSql7lSvTE5uGhkRIvC6p7kgpKyptetTwgrb022KSqFWmaO8Ii0yNrhEeWjhSMwptHDHIXtRd+XiLgY8uc/F3K1z8oEOTahBBzHzIWCGIcRLXOX53rhhc/rNtYnyIKyoqAAiXKs8beWBLp9M4ePDglHWPUjEsmzZtQlVVVbDfdV0cP34cV65cmbzGEQQx6ViWBcuyEIlEYOqeDH6f3S/nn2rR8e5mF4tjHHdXc+zqm91/D2L2QMYKQYyT+2p8VBhAS5bh8d6rGCvFU3jl2V6Kj8mCZGrGLVBUpMISrawJTj1w4ADa29vH3fapgG3b2Lt372Q3gyCIKYjjONi3bx/uvfdehHQftpdH1gkj6YpJGpWpixdVdyyoJqzkGEdBiw7OCbYLzyx3MTMNoXK7HgeXQkhSKTqyuzY1pbAw2FKJqZIqjIrFi4fFeiAUASKiUCQrzgQJiKxgfOQCkQCQ8xn+q1PHW+Z5eHm9h119FGhPzA7oXzpBjBNbjicpDxMuxy9fvvyaWbIIgiCmK4wx3HbbbQDExIbl0twqADwhJ8buqCT3WGL2QL9+ghgnl/PCQFkQGUMmMDVzN8LM2bDnSizXh23bAEQByPr6elRWVlLBQ4IgZhx1dXWoq6sTCkuHicaoD8BHXUzWOZHBI4zpcs2GxKOoGo+cFzmQsZJViZqiSf1FFZM0ZQ0Vzy8UnwwyjWnqGqniAKiURSErpKKiarJ0VwiVfDBfBV9mfSxXengWBWVFZYYcZpx4sl+cf0clR1OYo80iVzBi5kPGCkGMkxaZtrjGBKpNjj5nDIPGcNGdZQMVdy1xqhyU/YFOvOephdi+oA+/vbwWjXEfS5YsgWVZeO6558b/RQiCIKYYuqzWODg4iGe6Y9g8R7z4N7vKSCl1+dIYK5oLKnUR40CRsTGSG1ihTy4YJNLFy9SD4PnytcLjHHlXtLkxJoLobWktFScBOC8D8i15HVdV7h1bpLAHCsbKMJzKatjVx7C9muP/a3bx56fMEc8liJkCuYERxDhxfLEAQHyCski2Zzx878QgLqV0XBgQP98FCxZMzMMJgiCISecfz4t55lc1kCsYMTsgZYUgxsntFRymBgw4wOX8VU4sCgC96j6FL4sf5mUApkphHE6gXroTXEhbGHSBRVUaTNOEYRgjFk0kCIKYbkSj0eBzr+XAksHrSqUISRctPSjuyIO0wgVlZeh9R1JUhuuK1a25xgKXMKW2FFQcGUTvc1ieeKVy/dIH+7IhOdfDQFYoKe2ZiLiPSqSi6+CB/CPnkUdIZfzLXg0+B5bFyBWMmB2QskIQ44Lj4eXCOPhFj4bJSKmZskXgaTgcxrZt22AYNPdAEMT0p6GhAevWrQMAdHd3T3Jrph4pl+GZlBhzXlhD6gox86G3G4IYBy+o8fGiWh95D/jgKWOkCmMCzkvXwAgxK/K4SmVppcWpCZmy2IygY0BIOP97sVec6lfgVcs56urqsGLFCopdIQhiWtPQ0ICtW7dC13U82ZbHZ05VYFOTgZqIiM1IygB7pXSovtT3eaGrlffiwxSDLDBUUQkE77LtoVcBuow9MeVbVMjnSMh4GscTF+bldlymQNbAYMt9kDGJ3BaFfbnrFI0V1zZAft6jYWOlhxfVevhmG1WzJ2Y2ZKwQxDi4o1IMKj/u1NCS0yatVtnqGhH4yTlHT0/P5DSCIAjiBlBfXx8YKleuXMFnntHhjiHZ4mxiX78GwMO6CvoDETMfMlYIYhzcXSVmvo6kNTE1x4bxqBzF7NiwKH9rWwbCpIWKguwABqrnAQAu6nOxMGlgTZ0OzjkOHDiAjo6O8T2PIAhiCnDrrbdC13Uc6LDw+eMhrKgTcR3ra+NYUS1i96qTIsZDKRtKPRHKSvmL+1D1pDwFcTHqar8s5sXzOBwZM+OXxaOo+xgaC+JoInJtyPgWT94o5Xjoy0jlPJ+W60Gxdq0xjRlHBsUz1iY5/nC+iy9dptc5YuZC/7oJYoxsrPDx4jliUPmvrpBIuK8ZRdWH1XoMNy02dniZm0A+JU7RTfBW4ebV3XsJ8xbUAmhCJpPBpUuXhr1tOBzGLbfcgo6ODrS2tl61CbW1tWhubsbp06cxODg4hsYTBEFcP6YpXLxO9wG31iawtjYOALijsQLz54jPIVPWVZHXFGqp8KKq9mIfY1yu2RAXL12/thyujB/b9WA7pcZKeaC9uHfpPl3VUJHHc64XpKRX68Dtt5hgPBi5ov3lPMNnzut4zyIPX1jjYl6E46EzlMaYmJlQgD1BjAEGjs/f4kBnwH+26TiZnZyfUDxs4D0PiOrOlmUNe044HMb27duxZMkS3HnnnWhubh7xfnPmzMHdd9+NxYsXY8eOHUgmkzel3QRBECOh+rJ75hsI0dvJNXnwpIHPXhDG2x8uGLk2C0FMd0hZIYgx8NZ5HjZXcqRc4N2nwkJRAQBNB7gqm6zOHmbwKPc9YNrQ/eVVjK2s2G1Gg7TG3/+DLWhqqoNt22hra8MDDzyAUCgU3CKdTiORSAT7GGPYtGkTGGM4f/58SRMSiQTuuusukf7Y54hEInjhC18Izyttv+u6OHHiBFpaWq76NyIIghgPhw8fxvbt2zEvEcIf32bC83J4/vnnsXjdSxENi742qPguxQaldHg+h+cVPotzCoqESnHMTb3kPkoh4ZwPH1Evn+VKNzB1b81XxSHFOY7nB65itmyHKgrplLiOSfVFjh3cUKmLrYLKMmp1vjBu/KybrDti5kL/uglilPxGrYfP3SKMhY+dC6HTnrzc9k1NTQCA8+fPY/Xq1YFhopaampoiQ6UagKhZcPvttw9RWOrq6mAYBvr7+/HDMx56e3uh63rJ/UKhEGKxGG6//XYsXbp0Qr8rQRCzg/7+fuzatQuWZcEwDITDYSxcuHCymzWlWZsUhs3PuikjGDFzIWWFIEbB0piPH25wENaAH3Ro+IeLpgyslwaLpgNcFfJSztFlfsfF+8qLfw0XoK9QCovnAGao5NCKFSsAAMd7HJzpE/dcVxvH/EoLKrOnrpvg3IDnAYzlsHHjRvT19SGVErEwiUQCAJDNZhHTq/Bcdg4iNmDLWcJBWzx/QQVwS62G9evXo7e3F319fdf6sxEEQYyJ/v5+/O///i+am5uxYcMGAEDY1AMFhZcFoatgeMf1A/WjXAXhPg8UlLi8Tm2bTAXqF4QMpbqworiUQjpjcZZbpt5Yjo+ULSazBi2xVttKWdE1hnBEJA2wIqLfhWeLZ/guuOrrh6S7Hz52JaJxrEmIfb3DhL4QxEyBlBWCGAV/t8JFVAee6NXwe0dD8CcrV7Hk/PnzsG0btm2jpaUF3z2dR8oWhSItT8P5vgg6OzsDly/GGBji6OvrA2MMFRUVAIBly5Zh5cqVAID29nZ5d4a8y5B1xDLoABkXqJR2kuu6sG17gr8xQRCzBc/zkM1mJ7sZU55/XO2iLgT02MBTA/Q6R8xcSFkhiGtQZ3K8qkHMbL3nhAmHM5EBDChVRMpzYmpSludFhk25khKcawxVW9QMYvG2nKF75Zd2lzyDLVyPFfNF8cimmLAqmisWARVATs7yOR6H4xSm35YtWxZUie7LGahavA1hlyMkU4KqmcxwjmFNnY+aKIfrutizZw8ymcw1/moEQRDjRykW8XgcrudCza2quJDhYlaKP4u1OJcxFrzsuGUpiP2rxIWoeBRD1wrZv2SsiqfiU2SRxwHLRb/sa3vzop/tk9s5VygmMUNHTUL0z235SnE/GRvI7Tyg0tW7wydNKebNc128fb4HnwOvP2Kiz5ncCTSCuJmQKU4Q12DQK0jscyMzowDX8uXLA0Pl+eefR1/exHCVLRn4EEOlu7t7gltLEMRso6urCwMDAwiHw+geaIc7XIrfWcrtFT6+IOMn/+qsgcd7KV6FmNmQskIQ18DyGf6jTccfL/TwF0tc/KJHg1deUwVFg4Uuc91rZT+v4Qp+qXN1E8xQ18l7lcWxcDsPFoqUHlPXpzrRnxXphs8Nitm5vJz5yzhi5k4DUOuKAb+mRqgwPVkDev0GKEOlImQgLINdGDgqwjmEDTJUCIKYWHzfx65du7B9+3ZUVlaif7ALlck5yEsPVK5iRoLzeSHmRK4NqRJrRQUbTVVMEqX3YRoLYlVQVhSSAdClmq6x0mN5qaxkHQ8pqaQMyDi/nFfa51eYOmqlspLOiyKXg5ZUqXXz6rGLkqjG8c21DiI68JNODX9zlgwVYuZDxgpBjIJPtej4vSYPd1X5eLDZxccvKBevokBIZWSUGxvFblzlBos0aJgZAkJi8IIZlftkSks1gHpOkMYYTr70+UYIOTlAHukRg19nzilZA0B9tBG9XT6ihoZBG2DQAHAkzdJHARze4GmE441kqBAEMSlYllVisHR0Xxzigur7Ps6cOYNt214GTXZghiz4aEoDRde1wFgJ3Lnk9cpljHEeXF8ItEdwTVBEUnanflmgvVtkLCmTw5A3qAoZ8prCJFJPWlhdg2q88L0gNf1IKZQB4K+WuVgZ57icB97yrAk+yfGTBDERkBsYQYyCC3kND54UA87b5k/f4ltpBzjew3G2n6EzyzCc6xfAURnOobGRDBWCICYXZbAMDAwgFAqhurq6ZKmtrcWWLVvg+7nJbuqEcG+tMHb+9qyBfpcMFWJ2QMoKQYySH3bo+NdbXSyNcTSHXVzIa4WZMMYKiop052Kq2NcolBWYEbCIrBoflsk1Q1F5jgyidy1A3pNnB8QxFYiZH8RgXy8A4KicleuuFtefMsXzF1VEMD8urk/IwmhqtkLNNvqcwx88Q4oKQRBTBsuy8Pjjj6Ourq7gqiVpamrC0qVL4fuD0E0NITMOXS+dh2UYmg3Y9UsD9QEE6okqIKkmc3RNg675JccKqotYh3QNcbPUJUslK1FqiqEx9Mt0xhFTtbHo+wSpi4dxGZYPvZJnuL2CI0LeX8QsgpQVghgl/S7DLlnL5HOrHYyivPC0goFjTixPigpBEFMO3/fR2dmJjo6OkuXw4cM4ffo0ACBnDcB2Znamwl/3ite2P1vsosqYWWMQQYwEKSsEMQb+6LiJg1tt/OYcH29q8vD1tqLUwxKmgt6NspgTYGgcizyXmeFCzIpUWMyYUFhUwHvG8sDldUzOvPG89LF2LLCMKNKYlrNzZ+xqAEBznbhvLuYHqThN+cGQa50xVIQdRE2PDBWCIKYVR48eBSCyHOasARi6jlgkUUjb7haUCpUS2StLc6xpDJGQkCuiYdGfh5QCrTGE5WfbUSqJ6IvDKtW7WejqVb8a1sW5KnUx57ygapdLM8XqfDBOqEQuhe/6+UsG3jbfw+oEx/sWufjLM+YY/lIEMT0hZYUgxsDzGQ0fOSMGsk+sdFBjzpyZLUO6OZw6dYoMFYIgphVHjx4NFJbBbC9yVnqSW3RzsDnDZy+KMWhtcuaMPwRxNUhZIYgx8g8XdPz+XA+3Jjne1+ziL86UpRxWKotetl/XwbTyc1WcSyjIAqZFhBJSHRcpLpVvczTkoctPiPNlzQEmVRTuWOCeyC7DbJExzHfEfWxXpDvWmChKBhRiVnQ5uycmAsXnhoYGnD59Gq7rjv2PQxAEMUkUKyy9A50wjAYwxpCxvEKKYaly5OS2LeNJEqaO+RWir2RQWcVkNjGDBXEwkbDoOx15H88fGjxSiI9RmcLkfRhDSKou0ZAaF6S6HooFcYpc1ZQp74I5B7iPs1lxj82VPiIaR96nQHtiZkPGCkGMEZczPHTGwA83OHj7fBcfO2cgz9iIRkqQglgzCq5hpjBEArlfMwB5XjIi7lMRFWs1qJm6howlzsnaMvjekQH23AdjZcaSNIzi0qWhMmQgKgdc5aagnCMs10dfTkfUcFBbW4sXvOAFGBwcRE9PD86cOTPOvxRBEMTEcvz4cSxfvhyGYaA36wBg6Mk76MiKyRxVZb5HrpUb2KJkGJeP/xwLFy5EG2Po6enBK1782wBEDRZlgag6Laac+LFkHSudscCjqxCEP9SIiMjrK2S/HIuJvjybrwS4uBczhdHEc4PiIkdmOnPygA/sGmC4mHOwMMrxnkUe/l+LedV0xwQx3SE3MIIYBz/p1NCSZagLAe9fNDMUCNvTcDEVhm3bqKysxPz587Fu3Tps2LBhsptGEAQxKnzfh+MIZSJm5jHaRCgNcY677roLCxYsCPo+z5+aaeotn+FDp4Wx8+dLXCyMjJA9jCBmCKSsEMQ48MHwl2cMfHOtg4eWungm5+KnORUcqaIsyyrZm5Giwo9lwfeMwTTF+TE546YUlkpZUCxsaMjI2cBsViozhlgz7hdUGvmMqoR4Rp10J6sJG0EwqCpkNijTaCp3sJCu4ZKfRDzkoyqiIaxbWLJkCfL5PJ5//vlx/rUIgiAmBs45nnnmGWzevBkR0wa8FEzTwUknhGM9XqGIo1QiEoaO1TU6bqn1wRhDa2sr5s6dCwAwDBa4gvnS1UrXuVxLF9qiVMYaSgtOlmNqLHDBnSf75VS9SKRySW/AgBofYlXiPrmU+E4yeQrPDgDSzfc7ncA7+jzcU+3jX9c4eMXTJrmDETMWUlYIYpx8p03DFy8Z0Bjw1VVpLAw5175oGuD6DGlLh+MZQXXkZDI5ya0iCIIYHZcuXcKBAwfg+z4ikQiSySReujiMDXMMJE1Wstxaq+Mli0wwxnDx4kUYhpgcyufz0LWpXMyE4U9ORJDzgBfV+nh0kz2jEr4QRDGkrBDEuGF498kQNlT42FLp42/m9+KNZ+eAqfTCKoZExamE42ARESAfD5cOgp7PEZIpitWxaqmw1EaE4pL0dORl2sxUTiosKmYFAHRxPosKw6K+Qjy3KRaS9zODWBU1u+jJsU1n4gMHkMicxOLFi4PbDgwM4PDhw2P6yxAEQUwmly5dQmdnJ6LRKJqbm7Fs2TI80BzCA82hYc+/ePEiIpEI6uvroTGGVUtWQNdlP815Idu8VFRUf61iWGzmFwvlAAqzwWo7pGuokEr5oqRKfCIOVkUMdFaKfX2ZSgBAV3+VuD4sE6uEE0CmRzQpn8HzHvCbRzT86LY07q7mOLXdwhcv6fj0eQO9DqksxMyBlBWCuA4czvDuE2Lwe2l1Fiab/jNbtbW1AIQ7RVdXF3bu3Anbtie5VQRBEGPDsiz09/fjyJEjOHHiBFzXhe/7JYvrujh37lxgqDiOg4VN8xGLRCe7+aNiZ7+Be55O4niaodoE/nyJh9PbLXxgsYuEPv3HI4IASFkhiOuD+zgwAHTYDA0hji3RDPa4cXlQzoap2blQBNXx0gxdCsv1gwwyKvuXilVpjApjyAcPMteo2JULjlBRfNsMFJzGKjE7N18qKwtk7EpVSEfMKKTQLEYVi3T8wuC2a9cudHV1jenPQRAEMRU5fvw4jh8/PuyxFStWYMmSJdCYhpWLliIcioBzHvSLPhhUoL5SQgzVl0plRWMM5cm/VDyLysKoodDXqplilR2sKRZCX4Xo11XmspaE6Psv9Yg+PRuKARE5vmT6AQA804NjANYf0PHy6hz+cqmD9Ukff7vCxR8ucPG2YyZ+1TuV3dkI4tqQskIQ1wkHw9MpMRisis6MzGAEQRCzhUhEGAM1lVWIRWOT3Jrx4YPhv7oMbN4fwZuPmmjJMjRHgZ9udLC5krKFEdMbUlYI4gZwXqbBX2DagCtmxbgvDBcmgzRN00RCZvhSa0WsSNGolKpLTRCzItbFakhWFjRTSksqZwaxLguqhfvCYukT3RgVak5dxERMZqIpn6Ww5X0GbDK2CIKYnfg+h+14QX0UpYKA86DAo+qpVeFIvSgbmEL11aY8ppuqVhaD6YneNySLskSlQpMwdVSFSuMV4/I6pcRfCuvoGhD9O9NLY2+46wC+Bx/AN9s5ftSl49KOPCpN4E8WuHjLwPCxOgQxHSBjhSCuBzmAOXIE07gHLoPemTJAZOriWEgPXLwS0sUrpBcC3lXw+xwZUF8j11Xy3IihBYOfCpBX1/RabuDipYyUZrlWAfY1URORMjcwdZ+sLGxme/6whcwIgiBmOo7nI5d3EJJGgnLxAoapuajcuZRBojMYykUsMGBKp4V8riHkiYkmVclejQER3UdO9s8hXfXTKLlfRNcQkW27JGvAMFUwMjcALtMaw/fgcA5btvln3eQGRkxvyA2MIG4AOU8MJnXTPHVkY8IL0hTncrlJbg1BEAQxHl7f6GJOCLiUA77fQa96xPSGlBWCuAEcGBDGyp2VbhBQz1ThR1MoHBVRMyj0WCEVFhVcqWssmGmrk4qKcgVQCkvE0IKijsoDWbkSpBwPETkN1yAD8lVgfb0sPlYZNhA2SgctV+Yu1hlDSLeRCIvZumeffRbpdHq8fw6CIIhpRy7vwnXzqEqKvjMq+2BNK6jN6pNy/1IpjMOmDtctdRXzpByjFZWJtLRS9SVwC9N9hKR7b+A+FrijFZTwHhl8D80saxGCwsBrEx7+Yomo+/WFSwZcTmo5Mb0hY4UgbgBZ8Y6P8DQeE0xdxKucO3cOp06dmuTWEARBEGOhyvDxscVZ/OFcCzoD2i3gy5fJBYyY/pCxQhA3AJVt5cCgAaaKQJoiEDIWE+uquIm6sCzwKJWVmKEUFoa4/FwlZ/PqVcyK3I6aWhCMqWbeYlJpyXo+wmXKTKNUVmqkmhMO6dClEqOCRXVNtNvnPJgpzGQy1/nXIAiCmH5Yjo10Pl+IQ1HqiakH+4Lge6WQqDgTU4fjlmbd8mU/q8IAOS9SVKQSbks1xtRYcEwvm/SyiuJclBoOGavCbbHeEBrEf946gCVRce732nW85wQVhyRmBuTISBA3gJVxMYA8m6FZLIIgiOlEW1sbOOfQNQeGng0mc6YLr0j2YvdtHVgS9dGS0/DCAyG8/mgIbRYZKsTMgJQVgrgBLJazWS15HdClL7EpYkUqouJn1hALoUFm5lKZuwprHXH5WSkpNTLlcGWRspKQAfyRonSXgJh5M/XS66tUrItMaaxrLJgV9GUWMDXzZ+pacKyxsRFnz56F53nX+VchCIKY+nR1deHAgQPYvHkzDM1GNp8DYyZiUpU2DK0k7gQoRIqoflPXGAzZBytTp9zo4RzQZd+r+0pFEWOHVjR1rPpnlaLeKUot35eRMSt5EVN4GzrwrbnnEdaAR7p0vPV4BP22rxpXeDBBTGNIWSGIG8CiqBgMWnLTV1nhPALHcTBnzhxs3bp1sptDEAQxYVy6dAl9fX1ya3oUUYwwD99efAURjeN/e0J49ZEI+l1SU4iZBykrBDEeymqR1EkxpcdlgCnnAGR9FVVbpTJkoE7O1CWlIhI1VOyKFsSsqExhQVyLvF84pAezeME9w2JQLa7TEpHHVCYbo6hWgCf9nT0urlMTbmIGUIeDajC3Hw0NDaipqUFvb+9Y/zIEQRDTEtsWqoXG8giZ0UCt1hgr7/IDihUWXQab8CD7ltyWW77Pg/sURA/xweOFZ6givf2WSHrSmhG1u86n8uhNyVoqVgavSHRidcRGm63jLc9F4HOv6GkjUK62lH8xUmGIKQgZKwQxFkYYsc7lGFbGOZZFOS4Go1EhLTGgKhSrqvTCulGuX8VpiVWVeVW9OCSNmJCpB4OnwvUKRod6rCpoZsp7qxa7Pg+MlXI3BVW/0vN15J08EokEdH36qkQEQRBj5dixY6ipqUEoBFy8fAwLN901pEiuV/aSr44bOgv6XJVqWJ2q3G19xuHJzpbzsmB8v1Cctysn0g6fT+cBAM/0iKQnLZ0ZICPUH54bwEvntAMAvtYRRbd9lS82mkK/ZKQQUxhyAyOIG8CxtPgp3Z50J7kl1wNHWM8hkUiAc05ZwQiCmFUMDAxg165dAID6+np4/tR1B2s2bbyqYgAA8F89kUluDUHcXEhZIYjrQaonewYMvLrBw91VHj5liVTF0MXPKxQEw2uoCJQVsVYqSrGyotbqOtMsbCvVRM3cqRm7YmVFKTkqtWZhwsyHd5XpCY1ZMHQxPff0008jm82O5S9BEAQx7env7w8+m4YGw9DheX4Q9K7gZWmJdU2D7N7habzkHNVPi6KR0nVX5i9Rt826HgYssfOiVFQOdYkg+uOXUwAAq68TPC1ccz9cdxkhjeOxPlMUJeajNKzK3b9IUSGmAaSsEMQN4IRMWTw3NH0zaDEmVKHTp0/j/Pnzk9sYgiAIYliazTx+v24QAPBQS3ySW0MQNx9SVghiPEhFRc1OZZn4KcUNBhZNAgBicTGINMZFuuLKkFEUPC/OV2mJI6YWBMLrZQXJVNExQ9cKAZ9aqQ9y8dxYuXcyLwrkLC92xqAUGh8GhKGVz+dH9zcgCIKYYdTU1BQ2imJPeBDXJ9USv1Q9ASvqV4NOmJVcA/iQYYbBdargY8b20J4TyvapAVHo8VSHUFasvk5xzUA7kO3HuxZ2wWDAL/pCeLIfAJyxf1FSVIhpBBkrBHEDyMuc+WE2vQYAzi0ALkK6BU3z4DgOWltbJ7tZBEEQE0YoFEJzczNM08SyZcsAiEKRty5bNcktG0qN7uFtdcIt7FOX47hm9i+CmAGQsUIQ14NMT5xhIsCxxvChxyrhQcP8GhG7srRCrJtiIVRLJSUpCzXG5ToSMgrKil6qjaj9hl6kjMh9JUleeMlqmCrMGkw1K2hw+L4LDjHoaRrgOA52796NdDo95j8DQRDEdCQSiWDHjh1IJpPBvlgkihfcuR22Ld16GcBRFofiF+IFAXF8JLNBCwpHatC00rTxSlkZdDx0qixgKaFu9w6IvphnZf2XTC/eWteJuM7xzKCOx7p5IVaF82vHrZCaQkxTyFghiPGgrARNGBun3Dh6PAO1uot7klns8RuxsFIYMAsSYQBAfdREhapGb6j0wmJt6FoQSF/uBqYGOk0rGCvB41m520EhYDNwCCsaoHhR6mNXRnh6nodz587h/PnzSKVS4/hjEARBTD8MwwgMFUM3EI/GYRgGapJVKHeoDbrRskmhQpITXjBg5LHyKveMFfpshSPTyacdL0hZ3D1YWqUeORGfwq0s3jBHuIh98bIp8h370qAabYA9QUxDKMCeIG4AHhj25ysBACu0/sltzBhxHAdHjx4lQ4UgiFlFfX09kskk8vk85tXPQ0NtPWora6BpU/fV6Ja4MEoe76O5ZmL2QP/aCeJGUFzhWNMRMjREZWC8KvwYNfRgX6EysriEozArp5UVG9OKUhEHxwqPLTkXKJ6BULN86vqie3EGVhakTxAEMZtQRkkqlYLva0EfHLjZopB6+FoeVJwXVG2ltgRDguyUi9MfB5mD5bbjc2Sl2m27UiXxZN0u15I38JD2gBoNCMETDyxWVMjNi5ihTN3pA4KYZnR5IuvXGtY7yS0hCIIgZiKX8uK1bVWM3L6I2QMZKwQxHlQuS+4D3Af3HPxwsBYA8HLtHDKWg0Hbw6DtIev6yLo+HN8Hh5xJYyhxifZ8DtvxYDseXM+H6/ngnA8NklfXMVYWXV+aXrP4ZHUqY6wk7oUgCIIAotEoLNsJ+mDP8+F5PlyPw/U4HMcP+mWfc/icF7piFLpy1Wf7vljUNY4rFs/nQT+t+uKQLhZTY4joGiK6BlNnMHWm5HARGynjI59Ni9e22ys8UlWIWQMZKwRxg/hlrhoeB+ZqOTSy3GQ3hyAIgrgKPT09cBwHyWQSfYNd8Kd4kHpc47izUriKtVs06UTMHihmhSDGQjB7pXyKZTEux0Y+m8Y5O4zlYQsv9U/jF/11AIDzFSIr2JyoiYQsBhmRWcC4/AUyXijeOBrYcJ/Krh9OlCEIgiAEuVwOu3fvxt133w0A6EsNIBpOwg0qN4qV4/lB/6mK9Ko4weKsYKr4o+Oq9MS85FyNsSDBsS4DWeKmGAuqwwYaYsKVuFoWEm43Rdp7yPV7F+axLMZxKc/wzTb9hvwNCGI6QMoKQdxA/rVXuIJ9InkUS3jfJLeGIAiCuBq9vb24cOECAMBXaYCnIDo43tGYBQB88HQIKZemn4jZAykrBHE9KPnCyQH5QXzmchQPVFbjhbE+vC27D2/MvQDHk6LOypxoCFVSWakIyWKQ4UKdFVUM0pQzd4WaKkOzgZXHnZSrKqUEHtXBHtv1oSYPI5EIFi1ahPPnz4/pqxMEQcwEgkKPvBBnAgCerIFiu17Qi8aiJgAgIhURHtRZKWT7UtnA1PWMyUyPGhuSyTEkM48lQwZqZB2uavkMhGJyHcHKUBbzwj6yHvCDrhAA9wZ9e4KY+pCxQhDjodwdzPfA82lwAH92eQ4OrejDy42LqMq040KvcAO7kIxgnpT36+W6Qt5G1xnCcvBTRotyEyguDlleFDIY+oqMlcA0KbNRfBS5LnAOjekAIgDy2LhxI2zbRmtr63j/NGFyMgAA9QxJREFUIgRBENMaU9cQCxvI2cIQUEaL7RQUF9UHq/Tzhf6ZF/rnMoGmJGVx2TPVBJSpMYTlPROyQLAh3YVdpqPdE+NITAdCGuCoG1BgPTELIDcwgrjBHMlHcdYKIcx83GpO9UKLcQBC+Vm4cOHkNoUgCIIYlj7fxBVLGC9bK6euuxpB3AxIWSGI8TAkmt0vBNvbWdhqJs3Joz9jAwA6cjb6LTFjl5EzdUEgJwozduWKCitzBxuxSWVtK5wtXRDAgntGpDtayDTg+SF4nnXN+xMEQcxkNI3BMDRE5KsRL1K+FUaZolLspqtceAPXMKm8+0qA93mJui2uL3q+eoZSW+RzXbn9s/4I3taQwUtqXTzWRQH2xOyBlBWCuMGYjGNRWBglnV54kltDEARBzARO5oQRNcck1y9idkHKCkHcCIr8htdFLUQ1jm7PxAknCk2msUw7PtJSUcnKdXGKSzXDFvhAl83cDSd8qDSYjLMgRiUI4FTnBBcWfKrVOmzqsF0NHnkVEAQxy+EQXbnqMZVSEg0bgaJSntzEl7IJL9KyTRlrovru4oD9IJgfSmEpPHvEKi/yPvWmOKPTIRWcmF2QskIQN5jVUeH2dcROgKqbEARBEDeCWkMYK102jSvE7IKUFYK4UTBh+zeExFRZlCnVRBx2fB95OcOWlYqK5RZ8mlVmr/IYlWEVFXlPdYyDFzKDqXOGXFTISsN9ciMgCIIoxvdF6uKcVZoNLBY2EJLZGpWwogpABn0xGGS4YfBixVipGuMyP7iOlfXBns/hyn1qnFDb6iH1ppDAex0QxKyCjBWCuBEwFlgOP05V4iNNXbgrMoC3VbThK2wBADHwlA9Cau35flG+/mD0k9ujN1pKdpZtul7hGYqc7QZuDARBELMZy/bQ71uBsaLqpTiuH/TVKmWx6kuLE6OoCSYVkK/qqzAVaK+x4J4K5crr+D4yrjBG+m3pJmwLlZ77oj2rosJKOZ0lZYWYXZAbGEHcYE5bYfxlWwMA4C8rz4IN1TimDJx78H1RFdmyrEluDUEQBDEcTbqFpVFhxDybpkxgxOyClBWCGAvl8oaU+cE0QJM/J93EF/oa8JdN3ZhnWLjL7MFTfj2Mogr0QYVjFWzp88A9QFU9VhTiOdmIlerZECewgqLiBQGggK5r8LmPvQd/jfr6epimCdM0kclk8Pzzz4/2r0AQBDHjSOUdZOws8tI9V/W3sZyLRFaoGiEZdK/cwuJR0e9Hwww6K5v/lZucF3pn1c87SmX31DYP3IMzUtmBa8u1g1ckuwAA+/o19FCAPTHLIGWFIG4CNtfwRL4WALBR65rk1hTwuY/27nYsWrQIsVgMpmkinU5j586dyOVyk908giCICSedTgMAooaNkD41A0JWhvIAgJ39pKoQsw9SVgjiRsA0QJODiFyf92MAgGY9ixA0RA0dUVVQrEwicX0OW/orF4qNacGtAeEbPUTYUXrKVSba/KJAzkefeBTLly+H67o4cOAAMpkMUqnUkFgWgiCI2cK5c+dQU1OD5uZmJEMZtKR0dGYZ4lJFiZs6YvJzRKYlro6I1yfVJ4cMHXpRXw0AMmQFvl/ooP1AQZfpjGXf63IOS+7LyZgVOPnCWsatBMI79dnELIKMFYK4SZxwkgCAW7UeYIrUMamsrAQAHD16FK2trZPcGoIgiKnBwYMHAQDNzc1YO4fjdB/AuTAewjoQ1jk8H+DQMBkp6btd8brWFCYjhZh9kLFCEGNh2PRbcrs4fgXAPrcOALCFdeC2UApVoWokpJ9zxNBKbud5flAg0tClv3RZAUfxmNLnFsJZivaXz7jJY2omDwAcZ2q6OhAEQUwWxQbLyhqOQgL4Qt/ZkXFwoodhwAoBKKjkkZAOQ/breqB4yz58mDT0QTHIou46SF3syOd5MnbFc9AnPy6K+KSqELMOMlYI4iZx3E3iV1Yd7g134yv8UXzYfxOA5GQ3iyAIghiBgwcPIpvNoq6urmQ/Yww1NTVoiGvgALoyHBOpsGxPiKyNh1IUs0LMPshYIYjxEKgorLCtsdJjmonfHdiKg/W/whI2gLdm9+G4+dsAAFOeq/yVLceHrpfGrBh6qfpSPJk2rKJyrSYzBtcV03OLFy9GW1sbPG+K+KcRBEFMEZ577rlh98+dOxdbtmxBY1zDU20ZDNhASPbXyaiBWMQUJ0p7Iuie5VpjhayNgape1LG7Kn5FqeCeVMBdCy9IZAAAP+pgAKfaWMTsgrKBEcRYKCr+CADQTbEYYTDdFIsZBjPDgBlGt1GBj8XuBwDcl3oac+wehHQNnIvByvE5HJ8j63qwbLHYjg/bEW5hjuvD98XCOQ+WAHWjYeDyf8XnbLxtExhjqK+vx5133nlT/kTNzc3YtGkTIpHITbk/QRDEZNDa2hpkTTyVyuKprkFcyVi4krEwkHOCPls5kDG5aEwsxW68qn/2uFgcX0TDaAB8Lhb4HuB7qGE25pjCQDmaptc2YvZB/+oJ4ibzlLEAu41m6PCx+covJ7Ut4VAEDTVN4JyjsbHxhhsUy5cvx6ZNm9Dc3IwdO3aQwUIQxIxhzZo1iMfj4JwjZU+cuvGyamkgZTVkPaqxQsw+yA2MIMaDLuV+IwwAYJE4EBd1VRBJAAASMfGiXh0x8Q19O+7uu4DlPUdxcN4LYYebAAB5ma6YATA08dl0VNExuVZBmzqHJouL8SB+c5iBSwVz8tJtz/WgaQwhMwzP82AYBjRt/PMV8XgcW7ZsCTKMAQjul3c5kskktm3bhl/+cnINNIIgiPEyd+5cbNiwAaFQKOjf/uFAL86nhLHSKws4Zh0/cN/iQxKxFKUulodsmVBFFaD0fB6E8RflVAEDx7uaUgCAf7ti3sivRhDTBlJWCGICOGM24Ex8MRg45qXOTXZzxkU8HkdVVRWqqqpQV1eH7du3o7q6GpqmBYvv+3j8koV/flb4V1dVVSEUCk1yywmCIMaOilGJRCJB//bpp3rwvROpCWvDb8Z6sDbuIOUyfLmV+lJidkLKCkGMBVX40RCDBovK7F7JOrBolfgcrwYA1CWF6lIZEte0+k1YlmlBVbYNLXIGzpBTaCGdFwXSiw++nGbz5fZw4SnFuoqaxAvOkdsqYN/XtbHE45ewdu1aLF++fMj+nAM836OjNy9UIY9r8BHBqqrw+B5EEAQxBVCGiqZp+O9TA/jcoT5kHB+LGpJY2pDAksooACAmi0QarFC0d0iK+aBv5oH6kpOKStYRfWfG9WF7Qzv491ZdAgD8S2cS/e4N/5oEMS0gY4UgJohKR8zG5Y34JLdkbKxbtw7Lli0DAGSz2WD/4OAgLnhz4fgM+aKkYjrptQRBTGOKDZULFy7gI09kwfWJVzXekWzFjmg/sh7DF9qTAAYnvA0EMRUgY4UgRoOaGtPkT8YUs2qIingNFqkEYuJzVUIcS0bFuQZjiPl5LB88AwA4E18MS86uJWWRSENjQTpjLVirRw+VQwJP6GLf5mCjVFpR/s8aE37RY6GyshLLli0D5xwD+SgiplCNfA4kq4ElMuZGLyp6poHJNgyM6VkEQRBTgdtvvx2apuF/z6bxsT0+6muS2LqgCgBwV0MFAKBWpiluSggjZm5VBLGw2BcU8g1UcbH2fA5bKikpGevSlRPpiTtyNnry4nPe8bGc9eOTtacBAB+6WIkLeY1SFhOzFpoDJYibDef4/f5fIebn0RWZg8vxhZPdooC1a9di3rx5Ix4Ph4U7VyqVQs6l4E6CIGY+qt/77MEejHF+58Y8Hy6+HHoCcc3HL7PV+FxbYuIbQRBTCFJWCGI0BIUehRLCTBmzEoqJ/WYI0MXPSU2qKRXjpQP78cLMUfgAHp37Eli8IIeYuvgcMTSEpe9zSK7V7FzgBw2MGHNSrL6UKyzFftSeDISxbRuGYWDevHmYN28eDh8+jLNnz5bcU9d1rFq1CgBgWRbiFXoQ/xKVGcpqK8SgHpFxObqmBbVgelOkrBAEMX25Y1E1Mg7HXQ0V2D5PKOfzq4RyHpZ9XiRkBNuqkK/qg70iRQUAbMdD1hF9cJ9UVi6mLQDA831ZnOnLwuAe/ll7HOu1HvR4Bt7SugTcb7vp35UgpjJkrBDEaAjcwGSAvVb20/FcwBGDTionFIhoyMEW3oo38Z0AgP+s/Q20JJeBoRCUGZNuYAlTDwY/01Api5UBUGq0jLftmlaonlxTtQCebyOVyyNqOli/fj2WLl1acplpmohEIuCcoaZ2GXTNQCQs2x0R3z8RFd81LAdsXWOwZSHL3olLmEMQBHHDeeHcSjg+w+bGCqyQxorq+xgrn0wqTk+ssqXIbb9gtCgX4G7p/nV6QNRQebYrjTPtaXwttguvDl+CzRle3zIfbWmrUMl+hALABDHTIWOFIG4iK9ELADhmNOGXVZvRPMntUWiaCU0zkbYBnzPEQzaSyeSQ8xzHAdNqca2ugnMOn/sAZ/A8D119nQCEIuM4zs34CgRBEDOKTXo3fjd0Di5neE3XBvwqbU92kwhiSkDGCkGMBTWz5QsJn9siOxYDAFcMLK50tUqFqnFCiwMmEPMthDQWBNQnQqXraNgIXKmUwqLrauYu8OMqNKOsOcPXhpSKijpb14J7V8SFG9sCnwOIwfUccPjw/UJgv65riIQNhE2jsF3m+mCaOizbwomWEzDN0pgW13Wxf//+QoE0giCIacSSqjBsT0dNPBQo3rp09dK10k7X50XFINUwUZR2Xq0dqbKkZKB9y2AeAHC2I4Ov6ocAAN/pr8L/dHDwrHSl9YomfKg/JWYhFGBPEDeRw34dXGhY4veiyeqa7OaMCGMGNBYCYyY0FoLGQtC1EBi7dhdh2dYQQyWXy2HPnj3o7u6+WU0mCIK4KXR1ib66IW7B0CYmA5cBH78ZE/3ll3rnTMgzCWK6QMoKQVwLxoqmxsTAxW0xGxaEsttZQAbbMxnPYruVaEUYe5NLsMM+g1f078LT898EMIbKsIz5kApFJGQEqodRNnOnKd/oqzSRcz5simPRfLFfZwCTcTDxqCxGKZUez1M+1YWBWR2LyLZ6nh/MKqo2ahoL9mWzWfzqV7+CZVmkphAEMW3Zv38/duzYgcrKSkSsi/D8W5CRMSaBoiL7x+H63SHFe4vEcRVsn5HKSo909drqnEWV5qLTNfBkZx4cFiCVe6XkU+piYrZCygpB3GS+Fb0DLjSsTT2HpV1PT3ZzbjiJWAy5XA6xWAx33303DIPmQAiCmL7Yto1nnnkGABCLxSbkmS+JdAAAHh2sgH/VqSmCmH3QWwVBXAvOC7KGL0u1O0JZ4cqXWDfB1HRaXBROVGmJL8fm4lv+XXjz4G7cfukX6J67EQk5K6fUC10v5JIpH6Z42bpkYxRjWnHq4iCpmVaaecwvKl5WHgejZg5Dph6oKMUYuoHVS1fgfOslVFZWYsGCBTh37ty1G0YQBDFFaW4W6VBc14Xr+YGyovpM1S8qlRmsELMyXKyKWjtSvc7IgrrpvFBN7g8JY+VnfWHAERnChmQBI8WamKWQskIQ5TA2NGKd+2LxXbF4jlhcq7Coc5gGMA2GLpaooePRijuQMRKI5/uwtOsZhHSGkM6gsUJdFi4Xn3Ox+IDvI6hbUtpGjMpQEV+HFaXZFJ81TSyGocEwNIRMHSFTRzhkBJ9NQyyGzmDobFhDBQAc18GxU88DADKZDDRNw4YNGxAKhUbXQIIgiCnE7bffjsWLFwMA6moXIh4xwTQGpjE4rg/H9eH5HJ7Pg/5aTfRwDqgP6pg61/V8OB6H43Hk5eL5HHE/j9vMQQDAr1NhMSnme4UxRS0EMUshY4UgJgBbM3GocTsAYPnZn4G5MyMlpeM6aLlyEYlEAplMBpcuXcK6deuwZMkS7NixgwwWgiCmFcpQ4ZzD1Cuga5Gb/sw16IXGgPOWgXaHHF4IohwyVghCUa6oqO3ifWrqTCkswQwYDxQVtSjVRINYnm3YgmykGlGrH9UX94tUlyhNQ1y8XBV5Yen1vGRB2f7RolQXtRQrM8U4roNDx47AdpzAUFEV7xljqKysxLZt20b9XIIgiMlk1apVgaISMqtQmaxEIhZCdTKMilgIFbFQoDwrcbu83y5dRN/rer5QVVwflicW1+dwZbD9QiZUlTN5Uyr4RePKqAcFgpi5kLFCEBOEp5m4uEC8vFd0npjk1lwfjuvg5PmzSCQSAIArV64EhkpVsgoLGhYAAGpqakhdIQhiWlAIpmfQ2MQpHJuYiFd5Lmde40yCmJ2QsUIQYyHwH1azXcP4Egf+yqJQmMs5XM7h+By9FQsBALG+C/CKfJyHUz64/N+wzVDH5A2uNqt3tWeMl90H9pdsr1ixAgBQEa9EIloJ23Fv2LMIgiAmgqNHj8raUByDmTZozEUkJBKLmEbpUhDdhb5drGoX4g7FYjsebMdD1vGQChYXKcdF3vHwAlwCAPxiIAJOsSkEMQQyVghiAulPLoDPNITy/Qhnpm/BxL6+vuCzbdvI5/N47rnnUFNRA9dz0dknZgp7enpg2zMjPocgiJmN67pBMdtQKISu/g7YjnVTn7mJdWGhlkHa1/Gr1M2PjyGI6QhFchEEMDT717UIZr+0wrbax0VKSksW/VLFv7KujzyLoLeiGXUDLUh0n4E7Z27hlmXpLkeT7ms4rWRI6uPghiy4YqQCkqPlVb/5cgCA48o0nFkbyxauQm/Kwskzh9DU1ISBgQHs3bv3up5DEAQxkSiD5UUvehHi8TiOnzqKdbdsKsmoWIwv40540WdX9ot5W44BOaE0d2VtXMmIyRu1fgNOAwB+nK5BzuEAvKKxhFQWggDIWCGICScdm4O6gRaY+QHMRGepcDgMADh+/DipKgRBTDuWLFmCeDwOQMTjrbtl00171ouMNgDAI+kaANmb9hyCuNls2LBhVBOhTz899uLYZKwQs5PrVBaGoDK4AEEhr5ycVeuzhEmSdjw4PkfWFEHp4Xwf8sWJxsqbiNKZPI0NcyzYHmUzg4eMXWHhnMNx1Iyh+E6qUFrfoHCVGMg5cByxb9myZejs7ITneaN+BkEQxGSzevVqAEBFvAq/9RuvhOf50PXSvlL1paoApOdzeF6pojKYFZM1XXJ9YdDCyX5hkFzqyWIFenGrMQCPA0/06qJeF0CKCjEteeUrX3nT7k3GCkFcD0GZ96LwLzWKeWKASkkXgB7LRdrx0BGuxy0AIgNXMFB8K7nWpFWiqcLIZdmUS04exti44XaY/D6O4yMtB93+tBhUlXvDoDTIMo4HFl8En+dQX1+PLVu2kCsYQRDTBsYYDEO8GoXNOFxZAFLVxFWuXspIUdsiiF4YGVlLTNj0Z8W6I6OMlTxO94vq9F19aXw0fhiIAD/O1KE7kx1asZ4gphEf+chHbtq9KcCeICaY7sR8AECi/yKYk5/k1tx4bE9HZyYKAGhqaoJpUjpOgiCmHpqmlSy6rmPLli0AROIQxm7eK1Kzlsabw+cAAJ/rn3uNswli6pPP5/HII49gcHBwyLFUKoVHHnkEljW+hBWkrBCzixslO2i6XMufkBEGjFDpM9yy2bWsjV7LRa9WhVykGtF8H0KdZ5FtuqWk8OIQ1yw5yVZcF4yVHxzF9+KcB/ceizuY54lz8raLATlDqBQVzxcziSHpIhHSDTg+B4MGIHfNexMEQUw0iUQCW7duRUVFxbDHGRgWNDXDdQtld1UaeU8qKapftF2ZUMX2CgH1UmnuzRX6fgA4n7bQMSAmqP4m9AzCzMcvBxPY2cMBO19wJQ4C7ElhIaYPX/rSl/DII4/g5S9/+ZBjFRUV+Kd/+idcvHgRf/qnfzrme5OyQhCTQCbRCAAwc33XOHP6weAjaqYBAIODgxRkTxDElCGRSGDHjh0jGiq2baO+tgGxSGzY4zeCEDy8JtoKAHi4owGjjzokiKnLt771Lbz73e8e8fi73/1ufP3rXx/XvUlZIYixoNwCAkVFqCksHANCcnBTqoucFVMpjHstNwi2d1RYi+vBcXz4YQ4ElymfaKm0yG3GAS73cSb3DTPIlU/GFQsb6t6jUViUL7ZST2zHCwJIDUOcF9GFi5cWxO4AJ089hYULF8K2bezZs2dI+wiCICaLTZs2IRqNImSG0FTXhEi40If5nENjWpCSPR4Vx1RxXaCQrl3167Zc5y0PaaWo5EsVlStqnbHQk7bxAr0NybiHDi+Evf0MQE7Eq/iUjISYvpw+fRrr1q0b8fjatWtx+vTpcd2blBWCmASSmU4AgBOtmeSW3HhUwchQKITGxsZJbg1BEEQBlZK4vqYepmFC13Sx6GI9EW6rv2eeAQD8NFsHTqoKMUNwXRddXV0jHu/q6oLrjq9gAykrBDEaWFlqLqWemCKQHKEYWEh+VqpLWQyKDw7b87Escw6JfA84GAarFsLzfficlxVvxJBcxrxk58gDXEEsKT2XsZHjR9SpvKgdSllRM4mux2EY4u8QMsX3V9ta0X3v2/FbyOYHkLMGsX79egDA2bNnR2wvQRDERNDY2IhQSKjhnDO4ng9T9mGcF7p3tU9lZnRdP1BSHG94ZSVruxiQykq3jFW5KLMmnpLpitv68zDyg3hV4gIA4Kv9cwBfBiNTumJimrNmzRo89thj2Lhx47DHf/GLX2DNmjXjujcZK8Ts4Hpmy4ozwgRuYOJlnZkyqN6MANIlKgi0l25hVTGxvy5solaz8Ya2RwAA55q2wA/FwCDcuYKUxaquSmAfldZUAYqMC1a2A8NUtR+mgn3wdQJ3MOV6hsDVyy1zdyge2EOG+P66VmqQcXD4PkdFvAqMAdm8MFg8z8P58+fLW0YQBDEhNDY24s4775RZvyIAdDiuj5BZcIENvFmD8aKQpjioSi8NEsuS7l+yfxy0PfTLY23S7etMSgTTn+oVxkprXx534zISmosO18TedAScFyewJ4jpyx/8wR/gve99L9asWYOXvvSlJcd+8pOf4K//+q/x6U9/elz3JmOFICaQHR07UemkMBitw5FlL8OSyW7QTYIxhlikEpwDOWsQy5cvJ2OFIIhJQRkqwtUrgrBZNWlt2RLqBwD8OpsEBdYTM4l3vvOd2LlzJ17+8pdj1apVWLlyJRhjeP7553Hq1Cm89rWvxTvf+c5x3ZuMFWJ2EOT8vUGDw5BAe1OoKwAQSQIAGquFW9jSWqGw7IgO4s72/QCA06t/G4lYvMjdoKCosDK1olAIcrhg+oJz2MiB9QXVI8h0XHoouI/n+UFhM0tWqVfpOAEgEip1/ypXVgDAlwH5QimKI2cNQtMoPI4giImn2FDxfBOmUQHX44XijnLNtUIQvVfmAms7HrJ50R/mVAFcqayklbJiuYGyotzALkk3sPZ+obDwTB+WxFMAgJNZA7CzgEdB9cTM4Zvf/CZe/vKX49vf/jZOnToFzjlWrlyJv/qrv8JrX/vacd+XjBWCmCDu7j0Anfu4ULUK3XW3THZzCIIgZjTlhorjxRGd5LpPt4QyAIBTVnhS20EQN4vXvva112WYDAcZK8Ts4noVlhEqGjPNCGJVkgmhpCypE1ln1tXGYfoObj9zDADQtuReVMuUmKaM/RBNG6EAWFF64ULmGDkbOOwlpd+NB1UlhzmTle70fQ5HFjlLy9lBW84uhgwNmlSSlKKi66UB9owBnIt9InGA+GyaJkzThOM4w39HgiCIG0ixoZJzDABxAIW4lPJEIp7H4ct9rozbc6TKnMk5SOdEHEpPRvRhg1J5zsr+cdAupKbvket+mcJY9aWw0lgjjZVnUxyw0iJlsWhQofFUDJKYhrS2tuLTn/40HnrooSF1jAYGBvDXf/3XePDBB9HQ0DDme5NvBkFMALcNPo+wZyEbqUFP9dLJbs6EETJCyOfzCIfD2L59O0zTnOwmEQQxwyk2VC5fvoz+fARTIT5koZFHle7B9hlO5qkvJGYWn/70p5FKpYYtuFpZWYnBwUEKsCeIG85wKopKL6nWvlvYlqpDRVSs58WF0jI3Hsa2y88CAHqa70RtPIyYjP3Q9UJxRr8ofbBYqwKQ6tnB/w078TYkxiXYX9T88riW4ngWlMa+lPt0Fz9DKSuGXh5fUyhTyTwAuoalC5ahracV1dXVWLlyJY4dOza08QRBEDeITZs2Qdd1DNoMXcYSVDg+IoZUS9zStVJROABeFquSkcpIKmOjIy2VlVypspKR56YdDz3y/C6pwgzm5Pjg5AAAt2q9AIDn8ibsvNgHV8S1wHcpfTExrfnZz36GL37xiyMef9Ob3oR3vOMd+PjHPz7me5OyQhATQNwRufQHa2Zq/q+RCZkhVCWqAADRaHRyG0MQxIynra0NABA3OZKhqeNSVaHJAHxHv8aZBDH9aGlpwcKFC0c8Pn/+/HFnBSVlhSDGQqCoiLgO7ogZNNg5ML+0Mqsh1YaYoQXKQ0WmHVbj6iBWRRtGDeFDFBZ5gLFh407EsYKTQ1C3suzeHMM4Qqi6ArygjCjVJCzVH1X/JWTqwecgVkVTMSsIrg++f9FcCGUDIwhionj66adhGAbmz5+Pat6OM1YjwjKDYUIVcZRxJSojo8k5ZCLDoLaUijXpTNtol5m9VDyKygKWkeuU46Fd1ldRdVb6Mmp8EHVWohBqiuX5BUVFxaz4HsWqENOaaDSK8+fPj2iwnD9/ftwTlmSsEIRCuX2NRopXA4wacOwsYInAyT4ZgNljiXW/5eJI9Qbcl/sZmo4/gv66VeDhBdd8xI0ethiKXMrKHqLcwBgKhkg0LLqHsCyaZujakMrOyrAJnsGunsOATXImHoIgZj6cc5w7dw7z58+HYRjIpD3Y0t0rJ4Pmw6qooynWGmNBgL1KT9wvjY6urI1Oabj026XGSr/tBfs75Pk90mUsnRUpi2ELI6U+LLa7bAwfWE8Q05gtW7bgG9/4Bnbs2DHs8a9//eu44447xnVvmu4kiAngQP1WdFUvg+7ZWPbkl8Cc/GQ3aULRdaHSzJs3D3Pnzp3k1hAEQUw8i8PCQLlskRsYMfN48MEH8ZWvfAUPPvggOjo6gv0dHR143/veh69+9at48MEHx3VvUlYIopwR0hMDKJIN5LacHeN2NpD6s4MiPuVsrygS2RQT+fT/e/Hr8Prc5xHNdCH5/GPoXPWSosB0DZr0QTAC+aNMtSjaVSjqyILt0YgWpYmPh/larBD0r9IUKzQ2tBhkWTNGnCRMRBPIRDNI59LYsmULHn/8cQwMDFy7wQRBENeBYRiwfR+mdEWNmSqdeqlKDACuJ5UVWQCyK6sC5p1AUUlJJaVXqi8Dcn+/7SKlXMTk9XClG5gntjfHhMJyKFUkQVNQPTFDuPfee/H5z38e73rXu/CZz3wGFRUVYIxhYGAApmnis5/9LO67775x3ZuMFYKYIHJmAmeWvxS3PftNNJz4X6Sa1gL1zZPdrAmBMYY51fU4f+k86urqUFdXR8YKQRA3jcHBQXieh3g8jvX1gOMOG7U3YRjwsToijJdnBg0A7tUvIIhpyB/+4R/ipS99Kb773e/izJkz4JxjxYoVeM1rXoP58+eP+75krBCEQs1wXU1ZKT9XBtrDzgOZPvHZFIrK2Q4RSBYLC8k/ojOYNWtQUb0KzX0nMH/fv6DlBe+DG04CMIOAeD7Sr7IkiL40eH6ssSDlCkvhcgZdKz2rOGCfaeWpiuV9rpJKuRCMz5DL5cbUToIgiPGQz+exb98+bN26FY1xHZbrwfYiqEwIpTshC/MGKjErpDFOK9XEKqgmKkVxoLA4bsl2f94NUhXnpPoSROwDWB7KwdSAjAdctnWZ2x03PjiRICaZefPm4T3vec8NvSfFrBDERMIYdi59DXKRasSzXViy9wvQ3NkVv0IQBDERdHR04NSpUwAAxibX3epl8R4AwL4BE3wKFKgkiOkEKSsEoRiNolKuYCiFxXPA8yIbGDL94lSpsLR0ip9ZZdhAVcgAYOCxNW/D/Ue+gFj/RTQc/Ba6tr0jyLTFy2SKQirjYiVFrQvtGYu4Uj6Zxwo5jIeoNGpbY2zo1+fl20MLSKqJS59mEAmCmGA8TygYhsagm3qgpAR9qNz2fV5IWVyWljjn+sFndWxAxa6oIpE5F/0yxkVlEwuKBmsaXhIXyvv3e6KAbhaygcG7od+XIGYipKwQxCQwGKvHzjVvAWcaGtqeRvLK4cluEkEQBHET0OHj9rBIvPLrgdAkt4Ygph+krBCzk6sVAxmOkc4rjl1RNVestFwLpSWdE/v7827g3zzoeBiMLkDLovuwpOUxzH3627g8byX8SDJQILj84AeP5mAjFFdkrEgdCXaqq4rbO9L3UytWvmtsks2wbZOxOJzDccRs4tKlS3HlyhXk8+QCRxDEzcfnHK7nB6qHymwYYQVlxZVxKaomi6XWPkdWHsvJtYpnGZBqSl/GQTav6m7Jfk2qJxvDacQ0H32ujtNODDBYYbxQcY8+KSzEzMd1XRjG2E0PUlYIYjRwfu3iXdwvTUOp6YCmQ9MNaLqBkKEhpInF0BgMjeHcshdjMNEE0xpE3aHvgkEMqsMufuHYcM3h8n9FO0oWzjlG+l9wHmQa5HH+mRhjRUupncM0hhfteCEM3UAymcSOHTuoSCRBEBOCzwHH5/A8sXAuluIOT3WDPheLKxfH95F1PWRdDylHLOm8i3TeRSonlmw2B+QGxZJPi8W1ANfCb8W6AAC/SCcB3QDTdeF2PBrXY4KYATz33HN473vfi3nz5o3revqlEMQkwjUDz615PQAg2bIf0bbnJrlFNxdDNxCNiCxp8XgcoRC5RBAEMXNh4Hh9UhgrPxmsnOTWEMTEkU6n8eUvfxlbt27F2rVr8dRTT+GDH/zguO5FbmAEcT2omTHGRNAkAIQTJev6CpEqc148hKqw+MlFZTrfsK7Bql2M3mX3oObMrzFn39fQU/8Q/FhhUCukK0aQ3vhqgoQvJZdrFWosOUfKKoyzgjZTVoByrB5zQ9MrA9/50fexdu1acM5x4MABWJY1upsSBEFcD1KNDvq6oKCuclP1hyQ3caUrbtb1kXFLg+6zMsA+KABpZQouwK4sFsw9vDjeiyVmHv2ejv/qS4J7g7I9fumaIGYIu3fvxpe//GX84Ac/wOLFi/Hcc8/h17/+NbZt2zbue5KyQhBTgI7bfhtOsh5Gthc1P/80mD0z65FUV1cDAK5cuQLXddHY2Fiy1NXVTXILCYKYKSSTSSSTyUl7vg6O/1d3AQDwlf565Di9chEzl0984hNYtWoVXv/612POnDnYvXs3jh49CsZYMPaPF1JWCOJ6UMqKboJFK8QupYpExbomIVyd6iIm4jKoM2rqci22zWgE3b/xHtT/9P/B7LmIqn3fQPZFfyQfIVMAa4WijOVRJcOpJ37ZhN1wyshwcS8KbZgLRooxKYlNGfEcFsxczp8/f8Rqtm1tbdi/fz/88i9AEAQxSpYtW4Z169YF2xyADw5PJS4py6Xu84LI4cq+x5HrtOMhJZWUjFVa+JG7oio9HAvczorPMsD+/9Z24NZwFt2ujr9pqwac3NDAeoKYIXzoQx/CBz7wATz88MPQdf2G3pvMfIKYIngVDeh54Z+CgyFyZh/0jrOT3aQbTktLC7q7u9Hb2zvs4nkempqasG3bNtxyyy2YO3fuZDeZIIhpQmVlJVavXo3bb789MFT6+/vR1dUF2zMntC2boxl8rLENAPChtkb0ezQ3TMxsHn74YXzve9/D4sWL8YEPfADHjh27YfemXw9BjIZytUApKpqcPQjHgIh0NwjHAQDJmFBUYiFxznAzA4FOojEYuga/6f9n77zj7CjL/n1NOW17TbIpJCGFJKQQSkhCRywUo/RmoSjiq4IvoiL+aCqCDZDXgoigCFZAQVRQWhJKqAmkkYT0stlNtu+ePW1mfn9MOXPaZnezyabc1372M+fMPPPMM3Pa3M/3LhOJT5xLePUrhJc/R9ewcV4bVfEnFrZnBS1r1zEs3h6+icSeijsq2bEqu+7at2/Prb94+VUAJB3/727H3/vRtzYDUJIwmVGTZMiQIQwZMgSAlStXsmLFgZ14QBCE3aO2tpa5c+dmpEXVtWKGDRlKImkSd75zsotCumqvYZoknVTFMSc9cYdXADJFp6OkuIqKW0ASV1lJxSFuKytlRgd/HrWJiGrxz9YiHtweBqJYyXiusrKrLJOCsJ9w4403cuONNzJ//nwefPBBZs+ezbhx47Asi5aWlt3qW4wV4eCmt/VWstv53L8AFD2EErSzXKHbAfWux1bc+eFrTxrej1+3s0x6RVXSXSdHzyS8+hXUtoaMQ1qkf1gt3ODQ3Irx6eeZQ88sspK/bfbxMtqwe5imRdz5oe/osn+wd7bZ7hJjy+1r1500aE8WEbaSRHQFRYkzefJkamtrSSaTrF27loaGht0ciSAIBxJ+QyWgh9C0AAE9iKqEURQFVTW8uiq6Zn+TBZwkJ25CkmTKpMOpg9Ucz1y2J4wc9y93ieEYK6bh1VW5a0QDh4RSfNCtccn75VjJLqdtMl25XgLrhQOUk046iZNOOomf/exnPProozz00EOcdNJJzJo1i/POO4/rrruuz32KG5gg7GMY5UMB0Fq2HZSzbilTozMRBkqxLFulqqmpoa6ujrlz54prmCAIHn5DJRQIU1U2hNKiSsLB4kGp4/TpqlYuG9KFacEVayrpNOQ2Szg4KS0t5eqrr+b1119n8eLFzJo1izvvvLNffYmyIggDgaKmDQtn5qwr7rgQOBWOG8JJqpzUxdVhW5GpdWbnKg3TC/g0KkdgaQHURBSttQGq63KP5yks3gDSj7I91jyFRSHtPpa/rX9btrZioexWMXvLsjz3ry7H/SvhKEw1Eft6hMtClDiPiyMBIAJWGSkjSTQeJZ6Icuyxx/L666+zbdu2/g9GEIT9Hr+hUhwuoqZyKIqieJXo3a8r07RQVfv7LOAoLKojfacMe30sYdDmfC/t6La/s3fG7GVnwiCWdFzEnGWGooKdpnhKMMrPR9lxKrdtKuWVZguIpVUU0xD3L+GgZNq0adxzzz386Ec/6tf+YvILwr6GppOsOwyA8OJ/DPJgBp9gIERRuITy4mrCwSJUVeXYY4/lkEMOobS0FFWVrzFBOFhQFIXS0lKGDx/uGSr19fXU1dYNipLiomLx0Jh6ilSL/7SEuH1zyaCNRRAGg+bmZrZs2ZKxbvny5Vx++eVccMEF/OEPfyAQ6F+iC1FWhIMb/4+bO/ul9HDz686G+WfKAFLptJWKbgfWp5zlDsdHOqirlDizeq6y0uLM3NXEA0SC9sdRVRU6jjqb6i3LCK5+mcTRn8CsGNrjaVjkxpTkKwqZq5r0PkB/d/FnCnVnN0uL7WukOwZHUVgn6KR1dn3M3YDYoKFSFB5K/c4G4skoxxxzDADd3d28/PLLtLe37/mTEARh0CgqKuKEE06gpCRtCBRHijlx9gloiormqCfu11nSUVgsy/c9omemVHXj6Dq7k7Q5cSmdbmyhs3/SML2Ux4mUq6w4xSANu+2NVZs5qjhOS0rlslVldnyh+/vgj1MRRUU4QPnSl75EXV0dd911FwCNjY2ccMIJDB8+nHHjxnHZZZdhGAaf/vSn+9y3TEkKwj5Iasg4EqOmo1gWwXf/PdjD2WdQFIWSoio++OADYrEYqVSKSCTCCSecMKjF3wRB2LMUFRVx4oknUlJSQiqVIh6Ps2HDBoYPqUPtaYJpL/DJyHZurbEzGn59yxAakgNbY0IQ9gcWLVrEvHnzvOcPP/wwVVVVLFmyhCeffJLvf//7/PznP+9X36KsCAc3+YI3shUWy8xVW9w2zoyZlYihqG32Y6et22O3c4xtikLYUQ0qQ7ayUuMoLDWxFJGQPVOnOUpMdMYZBDe/R3DlAmIzzsCqHIpVQAJRyVVHeprAS+sqhRUW16XC362bjSzb3cKv2ORuS892uopKmaOouPu5s56apqCpmT7lXgI2Z6mqCuPHzwagI56gSO0iHA5z4oknsmDBAjo6OgqfuCAI+x2uoVJcXIym6QytGkFJUZgp46Z4hoqi4LmEaqblrXOXmqZmrHNjVaJx+zu8OZaixVFWupzYupRPDnaVlZSR+d2vmAl+UrUSgJ9uL+e3O0rAtH8LMB31xf29EFVFOIDZvn07Y8eO9Z6/8MILnH322V468Xnz5nHHHXf0q29RVgRhHyVZN4nE8CkoRpLI648N9nD2UVSiyWJaWlo8g2XmzJne/7hx4wZ7gHuFsWPHMmLEiMEehiAMOO7nuri4mI6ODmrKh6Jp+84869xQK4focVoNjRu3VA/2cARh0CgrK6O1tdV7/sYbbzB79mzvuaIoxOPxfvW973ziBWFfw58HP0dtcWbI3JmzZHdao3Azdbkzb86+Uctii6ssOFnBhjsKw9BIgApnnT9bTfvMT1CzbQX6lmVYloXpDENV8qsOPZHRpocJPqVARRV/XEwhhSXvfj7xSnf8xd1ZzvTY7H5UxfdYzezbndnUVIVQ0O3HbqOXTCSRsg2WQw89NGO/yspK3nrrrV2Oc3/liCOO8IyypUuXsnr16kEekSAMHKNHj7YVFVVn9MjDKArbday87xKfAut+17jfoeFQ+hbHS9bofI90ORm/Wp1sjTujCZocZaXViWNJmOnsie5+XvyJUwzykyXbAXiytYRYwtnmKSqipAgHD7NmzeLee+/l17/+NU888QQdHR2ceuqp3vbVq1czatSofvUtxoog9IVCQfim4VUmtrzg+1R6G/ZNeGub7fa13rnZ9lIZh3TPgHFdxQKaihUqt/tIJTBN8BJfeeXl+1ZnvlDhyLy4uQRcg8iyMEkbFQV3y/qBTh9DQcsK+s83nkIGkLs+GNAoLQpmbAvqKqYZpjvRRSLpFG+LJ4FuRo8eDZBhsIwbN47Ro0dnHGvLli2sWrWq8IntQ4wcOZIJEyYQCAQyYnWmTZvGIYcc4r0GsViMJUuW0NXVNVhDFYTdws0eFAqGCeq65+qlZk3U2J/lTLdWN5jesiDuFHzsdtITt3XZxsZ2Z1kfTbDNKVbb4riGdTvuYP4Aey+w3vm+Py3SDMC/dgYgGXPaSOFH4eDju9/9LqeddhqPPPIIqVSKG2+8kcrKSm/7n/70J0466aR+9S3GiiDsy6j2R1Rxf/yEgqiqSklRGd3O7Gg8GceyAhhGq2ewLF68mIkTJzJlypSc/SsqKgiFQixbZqtY2UaXi6IoKIqCae76RiQ7rXJv9ulpf4BDDjmEI4880jO0LMuiOFKFaaWIxTsoLy/PaH/iiSfy8ssvFzRY+jomQdhb1NbWeqqhpu57tyvHBNuYGo6RMBWebw8D/XNxEYQDgSOOOIKVK1fy6quvMmzYMI499tiM7RdddFHe397esO99+gVhMPDfmPYlj6+3n5meTXPTVbrFwtwZOS2A4vzg1qv2jN8yx12hMqRT6Sgr5c4yGFBRsdsplomZSqIG3RzlucHvhfBmHn2tzQJ+YJYFKFnKiCOtWEru8bLdwSyr8OXzu2kUUk8sy99n5jZ3JjUY0HKC/900x7arnL1/KKABRSSSYJjtjB492jNaAEqKyumO2T10xGIUBWNMmDCBCRMmkEwmWbx4MZs3b84Yw4QJEzj88MPRNI14PM6bb75JQ0NDznmoqsqsWbNy4kg2b97MW2+9tUsDQVVVjj766B4l87LiMiKhInQ9QHfclcFCWJZByrAwTBPooqioiI985CMF+4lGo7z++us0Nzf3OCZB2JuEw2GvjoplBQgGSkgZlvfZMS1HYfHt4zfgASyvCK5FzHHtaumwDYrN7bYKstFddsbZ6qgsOx31Jeq6g6V8yorTp2IafKdyLQB/bCmjJZby1JYc9y9xBxMOEmpra/nEJz6Rd9uZZ57Z734lwF4Q9mFMPeQ9VlIya9cfVDWMppaRSNg3IoZhUFZcSVlxBaoaRFWDxFIhuuIRUin7JiUQCHDMMccwbtw4ampqqKmpYfLkyUyfPh1Nsw3IUCjEnDlzGD16NDU1Nd56VVWZO3du3oD3UaNGMXv2bGpqagqmWnaLXhYyVEzTpLykgpqKWiLhIgJ6wLevjqaFUNUgihIEyndphBQVFXH88cczYsQI71yz/8PhcM8XWRAGmIqKCnRdp7OzE4vSQS34mI9ryzdzWlELMVPhzoaawR6OIAw6Z5xxBm1tbd7z22+/PSPgvqmpSZQVQdht+qSo5Au4z5otd5WWZLe9jLb4gvDtthuc5yVhnVonjXGlsywJ6oCCpeooZgor3o0ZKXH2d9MB58aQ5FNSnBUeqvPE7MWMnzs76e8vX8bn7G099tmb42apMO4yoKvptMZktrEsy1FUoDgS8B0ryMgh1fb2kO4lKqit8B8xRGNHACNuURyMEQkkOeKII3LGFUuGSRhBIoFuAhocffTRgK1QLFy4kJEjRzJ0qF3Ec2h1HUUh+0Y/kYyzbWc9dXV11NXVAbBy5UpWrFiR0f+4ceMYPnw4AJpaTllJsX0+zvsCBTRVwzAt7/q724KOwtTVnXJSsmqUV4zHMC3iKSeOxSlqlzBMkqZJbXGMcCCQkbUlG9M0WbRoEfX19QXbCMJA4vq6x+Nx1KDluXcGA24CEjd2xUlXrCne94rhpBdOJu1lRzTBzlb7e3hDm62kfNBmP1/rLNd3xNjhBN13O4pK3Nk/ljTTxSAti9OC27mjYh0A120Zypp4CKwOX4piSVUsHHw8++yzGdm+fvCDH3DxxRdTUVEBQCqV6ndcqBgrgrAvY1lgOW5lqhQa2x3cWBNVVXtww7Irz3Qmwmz44L0MdcSyLNavX8+hk04GoDtZxMb1bzNkyBBCoZBXXbuzs9PbpzPaQUmkCLArbY+oHc67K98jGAxSWlrK5MmTATIMluJi2zhRlSJUNeTdjOWLX+nlmTv/pu85WCiYlkJjZxizdQVVVVV599Y0jeLiYmbPns3q1atJJBJs2bKF7u7ufo4nl6qqKmpq7Nnp7du3097ePmB9C/sf48eP92ZgN2/ezMQp4wd5RGmm6a38uXwRumLxaMdQ7m+q3PVOgnAQkD0J2ZtJyd4ixooguGR/sHqjtPgVFn/8ih+3cGQ8mm7vZApzj7Bme4jhJXaGqzon01VlWEdLxT0VJakGCXrHyEyrZVkKBe9llYyF3d4tsOjduBb+UlF88THZScj2hGtGIdXGn1VM15S82yxLIRCwOyiyMr/e3LgWVUnPzmqqfa1Nb0bWork7ASgMHT2bFBBwjhXRVQ6fNpakYTlKlsJhh81x+oFkqpmioiKKioq8Y3Z1d5IyqgnoAVRVoaSomBOOmQtAU2szDc07MgyWcDjsqTK6rhEMaOhZaZ51La0suSmsLd0ev5uyNaBrFEfs83dnpF2f/VjcXnYlU3TannFEy6ew04C4UygvaZgYvkKfo9UUVRGYNGkSABMnThywApyjRo3imGOO8d5LU6ZM4bXXXssbCyQc+GiaxtSpUwFY2wIN5UdQ1Z1EVzPjUbxCsqrrBmnihpXEnfd6a6c9y7utKcoHLVEAVjtKymqfogLQ1Jkg5igp2QUguxMGqWSCEAZ/Kn+NMjXFS9FyrmqYCGYjzsAG9kIIguAhxoogFKInX6f+YBqQsH8YvZ81JyYl3lHGupYIAFsr7Zn1Q7qTFEebADC0ECk1SDD799A3Nne4aqHg9Xwrc4wWX8MsI8dWJtKPCwyjV7/Z2YGwfSG7/komFppjtYWCmePRvQrWSs5LGnGSGpQWB0i6LiTOMqRn1nQJ6pZnwGm+NKpBvQpF6cayTIJBDcs0CYfCBAOBnGMahklFaQWGabGzdSeTJ0+murqa4uJiiouLUVWNypIyNE0n6Li16b76O9kJBtxHrsGqaYrX3t2/2HX/cpYl8RRlTiBxq+P+0uYaNqriVe82LYv6zgBJ0yCkWRQFTK9Q3+4aLHV1dZ6hYpg6pmUR0GDOnDlisBykaJrmxX+9XG+/H4s7YqQcNTThLF3D3K3ZFEQj6aQabnNSEG/aYWfAe29nJyscY2W5s9zsuIW1dKVdv7LdYl2jxUzGIdHNdSWrmKh3sS0V5PzNE4ibyXRCFcsUg0U4qHG9F7LXDQRirAjCPkzAsH90TT00cEaTsEdQFJXiSAUAlWVhzzjqKS6ossxuv7N1J0OGDAGgq6uL0SMm7FNVukGhuduewY7oUFcSGxCDZfz48Y6hEiRpFNGRMCgLdRPSbYPl1VdfpbGxcSBPRNjHMQyDVCqFruvMGqbyxvZ9I7X2WK2Lb5XY/vbfbJlIi7kvfT4FYfCxLIvLLruMUMiehI3FYlx99dWea3N/q9eDGCuCMDBYZm6hyJ5w/RXcDF+pOF2Oe06bU7wsmjJJ6RUABOLtWPFuCNsZpNSsmXW7mKIzlOyA+CylxE/as8rfX35FqSdbaaBEqHz39Va26xtWj7M1aeXFSWfs7t2D+uPOzhaFA95sanY1bNf4MHzxLtkqVijoFqFL12nxXitfW3eMlgFVZZUE9CCJVAIsGDlkNKqqemNyXd6yi+H5yb4eqqKiOimoDdddxu1PdxSjgEbYGa/rFheO2sv2RMoLxPdml33nvCNaxNDibsLhMB/60Ie8TGtgxxgsXbo0d5BAeXk5Rx11lJddzP1RCwUiREIhLBKYVgkpsxNdg7lz54rBcpBhGAZLlizhqKOOYnKVSqB5HS+3jaaidQnDhw+nMzGCpKFR5lSeL4nYEqqiKJ7L444WWzVZutOOH3t5ezvv7bAfb3O2uUUhvUKO+b7DXdUkFeeHlUuIKCYvdJXxx+YSiNlxVVYyntlWEA5SPvvZz2Y8/9SnPpXT5jOf+Uy/+hZjRRD2YRKBIpKhMgLxdgItW6B88mAPSdgDhEMRwqGIVyNmIAMT9wSmpdAaK0JPbKayspJIJOJtmzhxIsFgkJUrV2acR3FxMXPmzCEYDGb2ZZqg+3+KFGKpYsJ6lxgsByGRSITGxkZWrlzJlClTGD9+PCNTFmF9qtOim4bOSI99DDSnhXbwyeIdpCyFaxvH0rsKV4JwcPHQQw/tsb7FWBGEXdFTpcOecGfpvKXiZfRS3NoYAedHVw9RHLK3lTkxBkXOjH531RgC9e8RaXgfc+wUryt7WTidcL6Uw7scMqD0GBPiHiuzGGT2GHaH3MD63vvAKopCdlHJQoUs7ceZxwwGNMpL7Nl+VxFxlY2MOJvsMbgqiuqqF6qngHhpln37uOKM45qP5aaS9lSY9H5anv0L4U9CoHpvO1eZyQxMNlQT1SlS6gXsO8tAt0p30knf6sTuGFkvroqCEhlNe9z0Yp5UNUVE72bMmDGMGTOmwCh1FKWEolCAcEhDVTUMMzMFdzxpYFnlWFY7mmOw/Pvf/94tNwJh3yYQCHDcccdRXV2dsy2sZ773KwKtFIUqMj4TyaRBq1Pwca2jnrzbZMesvL29nbUN9mOr28k05yoqniKSkYLE2xZRDH46/D0Aft48hJWtSaANK9aV1U8qnUBFEIQBRYpCCsI+TvvwGQCULvsPSmz3sy8JwsChYFqa958wgkSTRXR3d2MYRs5/fX09ilKOogTQtAABPYhWICW3oihYlBKNRtE0jZKSkr18bsLeIhAIcMIJJ1BdbddC8r9n8jEQWeh6h8Uva1YyMdjN1mSA23bkFnoVBGHPI8qKIAwE+eJVvFk252ZM1VECTkX6kB1wphSV209Lyzm00lZZDnFm9mscX+z4sOOJr32RUOsWIu8+Q3zOBT0OJUfdcGIXFMs3c5itXrjLXUze5yonmSvyzf7nc2nqSSXYderinvGrC4WOn5ul2lEUdNWLUclOD9yb4yu+tr2Jq3EVFi8Y3/c2clWGvikq/ueZ41bc65o+gvfqhQJkoKoKwYQ9GDd9qztWf8IA97GbOSxlgmkFSTCMrrij5Cjp1M+VNTVepqeArnrZ1CIhJ6OTc+3dNMud3Uk6O+w4hCFDhtDU1LTLayHsf0yaNMkpAqmgqpXojlug+96PJwxPESwvCVJeMtwr+uoqiF2xFPVOwUc3LbGrrGzcGcXqarYbxpw6SEbKObpfaU0XdaxUk9xRs4FLS3aSsuCz64bR3tWF5WR0JGFnFfPiDvPJyumc6n29JIIg+BBlRRD2dVSVlqlnABBY/Zr88AkHFevW2ZXCp0yZwoQJEwZ5NMKewE24oChFKMpgzaFa1GlxLi5t5Hd1a9g47h0+V7kT04Krt4zkpc6iXXchCMIeQZQVQegNu0p35c8kY+VMYaf3deqqKJEye52zHF4ZZmyp/YNdE7ZnDEudGBZdU0kccgRWIIzasQN99asYk47POJZKbu2Qns/HWRRQWHrdTXacTC8NqZ7aFbzEvcw4llYi8qdB60np0TQlR2XKp2wUGkNfc8p7WcHcc+vh8u3uJK2SpRCpSjp+xVTdGjJOTBW+7GdOdUg3C5p7fMuy0knt3LgWX9vsq+9mSitxZsSLwgEvC5nbyj2Gm7nMNC1OmHsG8WQHiWQHU6ZMYc2aNf27AMI+SyplqxyWFQMrjB6wb01CTvxeJKijO3Erbk2kgPNeTTlZ69o642xst1WPlU4tlU3N9jLZ1eEpKpanrCQpVg2urtrBScWdHFMUpVbPdDtbHA1x89Ya/t0SxEplKSmprCxg+eJVZGJJEAYEMVYEoS/0JUevz6UgY18AzQ2wt42XskiAYvdG0XV9cG4A40kD0Gmfejrli/9GeOEjtA2bhFVcmZHe1g3u7lf6YGdsVi+NnkLeYD2lB+6dG5X/cf7UyZbVc+ribGMk+ybf3jerza6HtkfJNiT89MYA7Ms9UdoI8wffZxpNuq5669LVvDPbAF72Mneb6RWSTMehuO5srrFS5Nxs6j43MM+A8p7bK4rCOp3dSQJ6EYlkB7quZyRREA4M1qxZw/DhwwmHw3RFNxONRtm+fTuTp5wE2Km1ixX7O1PJcu/sdlK97+xMsNGpRr+m3XYD29nhpCdORLFc4yIRRcXiY8Ut3HNII4eGXHcwMCxY3h3k6ZYIz7RGeKVNBxQwY2AknUbOsqdgenl/Cgc5W7du5ZVXXqGxsdHO+Ojjmmuu6XN/YqwIwn5Cx7TTKd7wJnrLFsr+cSft874FpVWDPSxBEITdorOzkwULFnDiiSdSVFREUVER1dXVTkzUQE4lWHxraCPX1DZ5KsqGuM69DRW81hlmaZdOzFLTsStI7RRB6CsPPfQQV199NcFgkOrq6ozJRUVRxFgRhH0S103ASKZn5TzXAddtxqIrlVkUsskpeuZO0iVCOsaJ/8Pw//6YQGs9JU9+n84zr8csG4KiqJ4PkUrhWXpfXcWMpecOZlleGt18u3u7Zc0cpl1+8kgr6VzKBV2r8qVC7ourlbt/byc0++qulXu8Qusz0yYPxLFy97cyXLH63Y+ioLjvGU/acDeqvm2ZKY+9WjCkVRdPhTFyx6NprouZ6jzfdXFLpwkBXcWyZKL6YKCjo4P//Oc/jB49mhkzZqAoCg2dCUChKqwTdFzC3KQOVspedjrfk/WdcdY7qYsb2uxlqtuXXjge5bah2/j2ELteT1NS5eEdRdy6qZROQwUssBwlJlsVN430d7bpKDHeh1DSFQuCn5tvvpmbb76Zb33rW55avrtIgL0g7EekSmvZ9uGvkSquJNC2nfInbkWvXzXYwxIEQdgtysrKKC8vZ9y4cQC0tLQMaP+XVzbx7SENAHxjczV1bw7n+g2VjqEiCMJAEY1GueiiiwbMUAFRVgShf+SLXXFn2LID7XHWG0mslKOsJO2ZP8VZdsZSNHbb29xl0J2tdvqJGyYVhglqBdZHvsWw+T8n3LyR0n/+mOjHvw6jJueOyY+S53EehUVxZy7zFD70JrgLKQueLuNTW3z9pQWcTCWlL+pDT6mIe5MmWVF6F0+zO7P5/n13U1jJwR9zkz5vZ+m2yXPcvNfGK+aY5zjee8J5jbzCl05ciml5ioyhZG6zrPQ2V5FxA6L9RS6tLGnPfe7Fvph28L7lm70uLy+ntbU1d8DCfomqqsyePZu6ujpvXTwFm5NDqNXT76Hs7464G1gftdWQ7dEEW7vs79OWLkchSdqxK2VmFz8YtgWA2zaX8ZPNRekAeZd8H3i/itIbRUUkQEHgyiuv5K9//Ss33HDDgPUpxoog7IcYRZVs+/DXGfHyfYS2LqP46R8T+8Q3MIcfNthDE4QBR1FUGhsbGTJkCMcffzwLFy6kra1tsIcl7CaqqjJnzhyGDRuGYRjEYjG6urrYzggSxsBZ+V+t3EaVbrK8O8T3NpcNWL+CIORyxx13cNZZZ/HMM88wbdo0AoHMQl533XVXn/sUY0UQBppCCotlpmfz3IJiSTt7TWs0SYOjqAzptmcF08qK09QXD2D7b+s0nfYVqv9zD6H6lYT/fifxM67FOvTIvg8Z/9R8pjbS0y2D5bVRfOuy2+D105tUx64S0pdJSrcf07dPdkxEZhxJ/mMWGlO+Y+2K3Y1V6csx+pMdSyEdq5RO1+xTWrJUN09p8TJzK178QPr1TI/HK6qpptNC+58rii+zU5aiYmfAg1gi5R2vomoiltVGKBRiypQpvPbaa30+Z2HfwW+oWBYkzDK6qIRiGOvENxUH0+nb3WKtLnGncGhrzInxi6fY4XyHdna7CnaMYVqcr1ZsBeC2rZXpzETuG8tMZwMrHIxmimoiCL3k+9//Ps8++yyHHWZPnmYH2PcHMVYEYX9GD9L8kWupeek+AhuXEHr6LlJzzid19LyB9z8ShEEgZXRgmlGCzq+VZVls3LhxcAcl9JrS0lLGjx/vVaV3KSkpoaqqilQqRdIqx7ACeC6zA0SRYvBAzXJKNZM3OkP8raUY6B7QYwiCkMldd93Fgw8+yGWXXTZgfYqxIgi7Q091V3IUFjOdUcZVWFxlpStBvVPQrMiZQUw4s8ztzixzRzLgxa8UO5lxisMBIEDXx66lZP5v0N9/mcArf0JpayR12pXpY0Om5FHAjrGsnm0cT3NxZ+Szq0rmm3z0ZxrLUk2y41sA1EIBMT3gKSq7qMECu57ZKaRW+DOOZY8wu8e9bScqWQqJvS7/IPwZyzxFzHsbp3Uw953jqh5uljn3dlJRLG+dd/m9Q6br9bgxKm4MixenhJLzBnCziSWc93w8aWCa0Yzxx+Nxtm3blvfchH2LsrIyTjzxREKhUN7tqVSKutqRgO0mknTiUFyFzcs2pyiEg5m3K+57pNNZtsRTtLuKSirOUXozD9e9xsRgN92mwlUfVGAmE76MjL2JQenlOkEQPEKhEMcdd9yA9inGiiDsTZwfunSBMju1ptLVzMad9p1bwvnBdl0aap2K36NKQt6NZplTWK+8KAiAWRQg/uGrMeomEHzpt+jLXoBICanjL04f2neLnWNkuOt3dZOdFWzt3ev2ZF9kBH/nT3nsxzM8vJvaPpBHbu6pEn0+Chkp/pS93jHcNtnH9xl9+dIy7y7pvvxOdtltMp9nFsW0x5VjfGb2kNVjpsuf3U2mse6zdTw3PK/Qo5r5eviHny9BAEAiadKdLCakd6NgoigQDoeZOHEiq1evzjlnYd/Bb6gEA0HKS+xYEb+bZ3GkmGAg4H1Gkkn7u881RJJOYVxdU9GdfNZJJ8V7ytnW5bTdGUvS3m0bIJfoa/h1xWKCisXmZJDL19WytNMCjLSR0lPl+Wwsa9ftxE1MEAC49tpr+b//+z/uvffeAetTjBVBOFBQFFLTTkMJRgg++wv0N5/CHH4Y1ri+x7AIwr6CYQWIJgN0J02KAnGKg3FGjBghxso+jN9QaW5uZvrkmRSF7YkVr15PDxn8dodJNHuGyhMdVVy1fRytHa1A14AeRxCE/Lzxxhu88MILPP300xx++OE5AfZPPPFEn/sUY0UQBoJd+U+5uLN5SSe1ZmezvTtgOmmNt8ZKAahvLQagstj+kd9RFfFcc4Y6ikpd3J4lTBomQcspmjblBFKN69AXP0PgmV+QuPi7WJXptKCAbwq8D+SLUM+XRjhbPbEyNhbs0rd2l433lJeVZeUWXDSzZv9NX1reXPGhB7fAPUiPrntZKkxf7w3TYlGmqqYoeZIhKORs8wLs8ygr2e8VV41x0xyHAhqabwfTynQlE/Y9FEXh+OOPJxQKoSg6w4dNQkFNp7J2FBItz76q4nw/Oi+vbjptfdky4gk3CYOtdLQ434E7upN0R2PcVfQiQcXiH9FaLtgwAkhh+d2/st2+evOBENcvQeg1FRUVnHPOOQPapxgrgnAAkjrhUtT6Najb1xJ8/PvEL7oNSqoGe1iC0G8CWoqSoB3XtWPHjkEejVCISCRCJBLBNE2KwlUoyt4ruvi90FscqTXRZAT4UvMUoH2vHVsQBJuHHnpowPsUY0UQBopdzar7C4s5xcrcYneKkcSKOW4KcVtZMYNFADRFbV/veLKCSidWZUJ5BIAxCbu/qqThBaDqAJpO6pPfIPDnW1Fa6gn+/YckLvoO6MEeTyEj6LnQ+flX9dBXdvPM5+4sf1aUhOJPh5welX8fO+VugYAMtxP87iVZ++d5fXp0RXED6/1nm871m3v8vUhfFIZ8cS658TmZS7ed09re5saieGvyBPj7lJVsRSUjjaWVuQ1n1l13Og6HNEojAUwzTjDZgaJobN26lWXLlvXupIVBwzRNz1BJpgxShq2lZKe09uOqLmHFdRVLBzO5sSoxJ2Vxo1MMckunHf9X17KWrwRXAHBFw2HUx0iniDeSvoD6AVJUJEZFEHpkx44drFq1CkVRmDhxIrW1tf3ua+9NeQiCsHcpKid5zrewImWoOzaiP//gYI9IEPqMaSYwzTY0zTZUXn/99QGPcxD2bzTL4JbUfADu7x7Hv6LVgzwiQTh46erq4oorrqCuro4TTzyRE044geHDh3PllVcSjUZ33UEexFgRhD2FomamDgYnq4xlz/QZSTt1cTKG1d2B1dWC1dUC7Y3Q3ojl/NPVDF3NdHZ10xRL0hRL0p40aE8aRJMm0aRJImlimPa/aVr2v2VhltWSPPMrWIqCvmI+2gdvePEEiqKQ/ZcPq4f/9IPeYDkxIRamhfNvj9Nbb1reJXL/s9vY663Mf98wCg0p3aflW2dlPc+zLqs/xRGfMq6WfVF7eyF2Sc757aGb88LDzvtq2/u4/07GL1WxZ8n9/5rvX1Xs//R7zteHqmT8u/u42Z/CQY3t2+1A+mg0yhtvvCGGyj6OYdjKh67rmFY3mqZgmhZG1r+Z598l+32EYmdJTKRMWjritHTEWdEcZUVzlFcb2hm34XnGWS00GEFu3DEKK9pq/ydiWImY/V3rfQGYhRUTd1u+NtlfTIIg5OW6665j/vz5/OMf/6C1tZXW1laefPJJ5s+fz9e+9rV+9SluYIKwp8mutwK5QZ5myqu9knatcdr7Umy6dQfiTtrObrceRcLwahTomrN05yJGTcU45hPob/wd/fkHSY6cglJU6hwkczj5vMDyeG8VPtU8DTIC1Qv8yPfcbWYQt6JYOWP0KqEr6QBw01mXW7dF8Y6Y66qWXmH2MCjvcvUhoN6fwrg/N9z+ffZ0gHlmvgjXVSvbrS73tP3jyk5CkO4unZI65zRc1zH3aUph8oSpNLU1UlRUxPTp01myZEnfT0jYa8TjcT744APGjx9PLN7K5i1rSCaTzD16DuFQ2EuikPd7wU3KkPW9ZBgWXU4q941ttgvtWzs6AFi3ZSfnht8GBb62dRitrS1YXa32jnndwLK+e3uDGCeC0Gsef/xxHnvsMU4++WRv3RlnnEEkEuGCCy7gl7/8ZZ/7FGVFEA4CUseejVk9AiXahvaCuIMJ+w+RUBFVZTUAjB07dpBHI/SGd999l7Vr16IoCkOGDGHEiBFs3r6F7vjAV4//TuBNKpQEq5JF/KW1YsD7FwShb0SjUYYOHZqzfsiQIeIGJgj7HNmuBIWqIVtmfvcCLWD/6+5/iICmEtBUDAsMC6Ipk2jKpC2WpL0rQXtXgq7uJF3dSeJJg3jSwDBMDEUn+ZEvYikq2qrXUF/8HSTj6WPl+DXlOx9fux7a+92t/C5fZLlrFTpE9n5pNyj733YXA9N32TJdxbKOb2a7l1k5lzrf+N0HVk6/5FmX6Y7W4znut7O09ovuuo4pftcu599r6XuPeG8VZ0e/G1m6fWY/rvtPJBwgENAoitjJJlRVlbTF+wlLlixhwYIFNDY2AmBaJpvqN2NZJpqmpj1iDdNzDSvkJppIGjS3x2huj/F+Szfvt3TzzvYOtG3vc6X+PgBf3jQMo7MFq7PZVlQS0bS7rZnqnRtYNuLyJQh9Zs6cOdxyyy3EYjFvXXd3N7fddhtz5szpV5/iBiYIBwnWsHEYx1+EvvAPaO/8G3XtO6QuukVSGgv7NIZh0NC0HYD29vb92Ng7+CgpKWHIkCHec8MwMExzYG48LItvpF4F4P7OQ3ixswRoGYieBUHYDe655x5OP/10Ro4cyYwZM1AUhSVLlhAOh3n22Wf71acYK4KwN8kXv+JfD6A5HvuBsL3UQwBEgpqXBrbbSePZHLP9uIsDKoZzExd3UnuWOcUkS4rs6rFhRcE45uOYNaMIPPcblLYGtGd/hXHut/KWYbRyHuTb6D5Nx4Bkp7/1bi5dZcZPntS/2fEgbppiz60976DSHXtxM87Ci53wpezN3t8/RHe8nk991iSsovjbZx7LH6aRPqX+KQEDLSCkzyv9vNB9f75jZ8ewFGpnt+jd4F2VJDetcTp25YW3X2HEiBF0d3fz2muv9apfYfAZO3YsRx55JACVZRVUl1eiqRqapmUE0+fDcra76YpbOmKs3Gmndn+twa6dMrN5KdNKGukwVG7ZWo3V2WgnLAFfAUhfmmKJURGEvcK0adNYs2YNjzzyCO+//z6WZXHRRRdx6aWXEolE+tWnGCuCcJBhjT2C5Hk3EnzoOpQN70FHM5SKuiLsm5SV2XWG3n77bTo7Owd5NEJv8Bsqq1ev5uMfPtOroTIQ6FaK70feAeAnTXXsMAID1rcgCP0nmUxy2GGH8fTTT/P5z39+wPoVY0UQBhO/XJA98+eqLz4VJulkAWt3C6M5yooJtMXtdVVhO+tNXSqzP1uVcQqzVdRhDj8MddsqlNWvYR11ZnoY3tjchS8LVVYRRzNrth4sL4tWhqKSTW4aKaf/Xc9o+gUaf4Yw+3nujL7pDED1NBkLpcDQ/GqDO/ub7XZkx1UUGJtXMdHyXatdn1P+QpXutl3u3iO9cZvKrnHZY8Yvv2pSaGxWnodZ47BQvNfbPYamZXbYHYt5M3GJRKLg+IV9B7+hUhIp45TjPkzKsHA/dF6mL6e9qirpJ84y5X7Pddmv+ZqGThbWtwHw6sYWLlRWM1brYmsqyE82alhmYzpGBfKk+csXL5jVRmKhBGG3CQQCxOPxAY8tlAB7QThIMSbOBkBdtWiQRyIIuXTHYmzYtgld19m5cyctLRKPsK+TraiUl1TugYQIFp9TlwHws7ZRRE25jRGEfYmvfOUr/OAHPyCVSg1Yn6KsCMK+hlNUzZsldJbd8QTNnfZM48ag/dFNObODO7qTVITsdcMitkuEO5cYCtg/5qGghhcLoCkY445Bn/8wav0aUk1boWpEhrRi5puR9xQMMpamP84jS7XwT9bn1kfJbOuf3eyx3ksvyL5HchUixVI8tcXb5lODPGXIeZB9HRQl85wytmWctem0V/I1zRikWwumtwqLv2ZLb8n3cmZNaHuqiZfNyz/uno7lvv4Zq6yMPr317lPT8qbZ3WvtFhTsTnSztXEbpmnS1NTEK6+8UvjYwj6B31AJBoo5euZJxJPuZwBcVVdTXcU4va/72UwZ9rLbUYy3OnEqbzV28OLmVgCOal3K9JomoqbCb7ao0G2v9wo/Qt/iU7xBSJyKIAwEr7/+Os8//zz/+c9/mDZtGsXFxRnbn3jiiT73KcaKIAw0ffHf8QLMzXTxR9dIcQqaWXHbT1+JBtmq2D/4CcfFq9EJno8ENarD9uMJ5bbbTMjxEa8M2x/zSEj3gs0DlgqhcowxM9HXv4P67nOkTv4MKkrewo7Zp5YdPO8P3s7nNgW+9LX0bLQUumr51vfKbcm76S5sCKSNrlwjxe/iZj9PjzI7P4D73LRANTMNCv9Y3ceuS4yp7tpoyUeha+1f39M9WI5B6DdQsgr05T++u19uogSvjXtOXgVSe2FaFpbnqmgXypy/6CVKS0spLi4mEAjQ1NTEyy+/PKAzdMLAM3z48LShohejqSWkDAvTsl83VfG9p/Xsz46F4RgpsYTdvrHZrseyzDFWFtS3sXprC2Dx22JbVblvW4Sm9g6vmK73/VkIMUYEYa9QUVHBueeeO6B9irEiCAcxySkn2sbKpmWDPRThICeRTDBq1CjvuRgq+w+HHHIIAAG9iFCwDGMX2b76yyXBDRwT6aLTVPnhluJd7yAIwl7hqaee4vTTTycQCPDQQw8NeP9irAjCnqI3AZz+NqZzU+bMFFrdHfZublPDQHFSc+7otn+odwTtYnl6MERtmZ3i2J0JH1Zkpy4eHrP7LetOerObxY7aolXaN4dq02a0+Y9gjZ6GNXo6KEqBicjc9Lf5nu9p8ikqmet7VlS8dUqWMmH51YVMJ7V8wfs5cbz+RAOu+uK5n6WH7AX7K/ldpfKR1ysv29UtRw3qPznqV9bBLMvKl3m611hOsT+375TjAhaPx3nttddobm6Wmir7CeGwnWbdMDW6E4b3ugV1zVmf4SAIpFOCJ1Omp6jsaLEVlaWNtpr88nY7TfE7W9qo7NjCHSPeAuD7WyvYEU0CyZ7ffH7Jc3ferIIg9MjZZ5/N9u3bqa2tRdM06uvrM2os7S4SmSYIBzFmaS1G1UgAAu/8k+Df7iTwzM937VIhCHsIwzBoamoSQ2U/4cgjj6S6utoxXvfU/KfFr2tWMFxPsKo7wD3by/fQcQRB6A+1tbUsWmQn67G/CwY2sYYoK4Kwp8mrqOQpDunenLkxK+5qtxvTwEraM49KxK49QdB+ngqV0KZXANDqpDVud2Yrm7vt/iK6mpOOVy0O0nL2LRRtXoy+ZTmB919Ge/8VzCFjSB15pnvk3OFnLXuDRW6hx7ztdhHzs6uvwBxFJU+kvpWjEOXpv0A//nMulJxYydg/c2Cqb5K3x+D7XrC72Ve9yWZXBcpXnLPgvkpuzIy7ry9RQ85G3wp3dl1RLGIJR1EUI2W/YcSIEYwdOxYAXS/HNDXA9JQU3Ymb01TFTlHsw01P3NmdoNFRVJb7YlQAXtjQDMCnu17jE+U7iJsKn1pRTDzamVaie5OWWN5TgrBHufrqq/nEJz7hpPZXGDZsWMG2biKVviDGiiAc7OhBUhPnkpo4F6pGEHz5D6gb3gXPWBGEPUs01klrRxMAmzdvHuTRCPkoLS2lrq4uY11nZyepVApd1zHNGJYVGPAZ1anWDr5ftgKAr22r453O5C72EARhb3Prrbdy0UUX8cEHHzBv3jweeughKioqBqx/MVYEYU+TMW3fix9yLzWVM/vgxrDgy/ak2Zm/FD3o7RZwCurpTpuEM7u5M5b+cU8YmdmmAro98+lmCUuMnEYQULetwUwmQA+iKruaY/efXu5su39v73GBmc6MbGCF2iiK15GSZ2xW+iDOIivWAiuPopL7GnljzElPnEv2DZoCOXE1aTUlfb1zM4XtHr2ZQM6nXGSrP3YKajcdce74e3OsQnE4+dZua9xCOBxm/fr1LF++vOeOhb1OdXU1xx13HIFAZqX4VCrF+++/z6RJk9B1UNUOQoFKup0CtWmRNP2qu+mJuxzFd1tTlHca7Pi8hU6MysubWuz9t2/kp1XzCeoWj+8M88uNFM7+JeqJIAwqkyZNYtKkSdxyyy2cf/75FBUVDVjfYqwIwt4k28UpnwuD6xrmbvO3cbfpzk2DbgfVE4pQ4gTNFzkGSMxxs2iKp7zn7jpdzTRWNMddg4rhmMWVqF0tKJuWkxo9A8tNq+t3X3JPh9zn+dLnpjdm7pexLfthL4yE7L0sS8m5mc6Xijk3HXGPnWcNJF/Afr4m+W/yVUcmL9BVQfzXc3dmr/O9Prk1aRSfsVKoHyDLfSwzjDrz2qaTCmT3AcGgbXSvXLmy9yci7FHC4TBTpkwhFApRW1tLIBAgFAgRCtrfOYlknBj2DYrp+POZZoKNm5dyyKhpOe9Rw/nucQ2ZLY7L15vbO3hhWytgV6cH2LG9gTIlyfzKlxird7OmW+d/VgYhGRUjRRD2cW655ZYB71OMFUEQMrBCxdDVgtLWMNhDEQ4S4vE4kUiE6dOn88Ybb0jcyiATiUQ48cQTKSkpSa8LRairqUPX7AxfimKxpXEbXd3RjH1ra2vpuWJS77irfClTw91sTQb46IoadjrxeoIgHHyIsSIIg0FfCkf6cZQVRXU+upq91PUAQUclcavatzqKSspxBysJaF519rKgvZ9b9T4ctG9Aij54A615CwDxUTMwDRPVdJQVVUFTM2WCQgHmmaeaPlclj9iS0caHmnWzk1GwsMfjZQyxV22zx5PRQVaH+d3ActbktunDzduubtZ7U8F+V+5aPe5rWbsMsO/pWJZlZTwGMLIGYhiWF2Q9tHYU7Z07GTlyJPF4nCVLlvTiqMJAoqoqM2fOZNiwYei6jq7rBPQA1eWVaJpGaXEJqqJmBMqPHj6Sjq5OTzUxTJNQIETKsL+L3GKPmqZ4ikp9k62ovFFvu3z9a3Mzr663FZXOndsBOM9ayWeLNmNacNHqaja2x9KJRzzFWQxaQThYEGNFEAQPravZe6y2N2KW1Q7iaISDBV0NoCoapmVkzOYLe55gMIiu656h4tLZ2ckRk6Z5cSr5jFtVUSkvKfOep1K2IdHeFe/naCy+XLKBH5WvA+DH9WW82hkG+tufIAgHAmKsCMJg0hsZwAu4T3mzi1bKXiqGo56kknTG7JuKpoC9zVVRulL2jGbCDBBxYlPctMbusiRhfxW0TPoIesMHRDa/S+m/7qL5o9eRGDEZsNOQWpqrsqgZw86IVSgUGG/lix7Jt4/dqZnVWvXpOFbWgdOXL+1+UujS2uv7MCubpajkf6n6oJrkeTLACZTsrndj4tnOYZA/rsZ/09qTAuQpKs4b0U2b7T43DJOkc3O7duMKKisr6ezs5K233ur/wIU+MWXKFCZNmuRLoa1QUzGEcChIcGQQXdewLFsZ6UucVFHY/i5ylbNYPEVji+0u5ioqT2+yJ0bmf9BEcucWwOInxYu5psLOCvfbxmK+vVoHOuzvPVFUBOGgRYpCCoKQRtNpPvV/iI2ajmIkqXzuXpRY52CPSjjAKS8vB+DVV18lFosN8mgObEpKShg5ciQzZsxg8uTJKIpCKpWis7OTIVXDKI4UEwqGBjwFcc9Y/KhyNdfUNmFa8PUNFVy5thpzN+NeBEHYu5imyYMPPshZZ53F1KlTmTZtGvPmzePhhx/erVhEUVYEYV/Dy/6VlRXMNLASTlHIuGNABCP2Mh6lpcuezXTjStx7DV21My2ZluXFDbhpjbudme2YU0hSVRV0VWHr3C8w8pnbCbVtI/zKo+ycewWBgEYoYMe26M43h5p1Q5O3/mWP30/5NmZn6MpUWhQUlGxJIiPzcFbqZKvwDU9OWEoPI8xORZy5LmufPKfl6kqKv2KiohRs37uYm/wXt783mhkZwrL6yi7qZ2/LbO3PFpd+7L6e9tJ1FYonDa9ooEsyKTU09iQjRoxg1qxZnjIKENBLqXaMxVAwgGlZORnseno/+eOT/BnvIB2zsqO1m3e22+mJ/+UoKi+tsRWU1I6NXBNYylfLdwDwhQ/KeXCLgqeogJ0BTBQVQdinsSyLefPm8a9//YsZM2Ywbdo0LMti5cqVXHbZZTzxxBP8/e9/71ffYqwIgpCDpQXYefTFDH/hbkrXvUayYgTRGWcM9rCEA5wpU6awefNmduzYMdhDOWAoKSlh9OjRBINBxowZg6qqtLa2Eo/H2bhxIyefOG9Qx3dUsJ07htuv99fWlfFgQzEgmb8EYX/jt7/9LQsWLOD555/nlFNOydj2wgsv8MlPfpKHH36Yz3zmM33uW4wVQdgXsKwepukdZcVIgmorGyRtVxlPaQmESQXCAHQn7DZu8TUXXVHQ3BlPR1mJObPcbU7mMMOyvDatJWMxpp7DIUsfo+qdx4hbOh2HfwiAkJM9LKjbS0/NURVfwUPvBLxT3BWKkq9d3mouWdv8rdN1YfK3KawQ5BtjHkGhT+T0qVh9ygzWv2PuuibLriT53ogz2TVbvJl1FJ+Tsf3APZruvJ2TRrp+UCqVIhgMMnbsWMaMGcObb74plewHgLKyMk488URCoZC3TlHCVFdPIKCpjB41HVXNjUfJyZJnWXnapGOR3PaGU28l7ii1O5w4lSX1HTy10VZS/rNqJwCpxg3MCHbw1LDFBFV4YkeIe9ZbQGe6lorEqQjCfsMf//hHbrzxxhxDBeDUU0/lhhtu4NFHHxVjRRD2O3q6S852B1PwfsS9AHu3mrORBCfY3sgKZDbdmwrnHyDprIsm7f46nMD5uGGm4/kti4Yhc0iN3smhG1+ibvGf2JKI0zjhNCIh20gJBzNTH4cCGmg5JRKdU7W8oP9s8hkEVo6Nkm/n3HzGPTa3t6Tb5jlub9IQZ48x/419YXe07DTMezI8YG/ULMl1kbO8hAhKltGSRvdubofVjCKe7CaZSpBIxjjmmGMYN24ciUSC5cuX09bWtmdPYJAZO3YsNTU1LF26NCdmZ/jw4YwfPz7DdQvs13XdunUFjTq/oaIoOooSRFF0dDWMoigEHZfOgK6iZ31m3VTEqudSquS8j/IlSog7CTsaW+xJlGVOZfp/bmrmedftq3EDAKemVvHYmHpKNYu321U+tyIIZp5CuIIg7Be89957/PCHPyy4/fTTT+fee+/tV99irAiCUBhFYdWEeZiqzvj1zzFy+d/Qkt20HvGJPXuHLRxUhIJhQsEwqZRBR7SFWKKL6upqAKqqqli4cOEBa7AcdthhTJ06FYCKigoWLlzoGSyjRo3imGOOKaiO1dTUEAgEWLduXcZ6v6HS0tLC0CETcY3FfeFje3y4lafqthFS4YWWAOe9F6YttQ8MTBCEftPc3MzQoUMLbh86dCgtLS396luMFUEYTPyzlQVz7bqzjYov2N6ewcwIQHVUl7gzy5lwg+edWdKEYZI0048BOh1lxUVVFC8IP+nMnGoK1A/7EFFLZ/qGZ6hb/Qyhtq18MPNTGL4aC2C7g2lO0LQXfO8sTAvUXriEeYUi3UfZEe75cv+6s/i+dVa2apU3+j/PqgLN/c/T48+fDAByg47zHbe3hS57S29d7frXd+FClOl1/tcj87VxhQHdvWkGDDOzL11TCQVrSabKMEyDji7bQDnttNO841uWxcaNG1m8eDGWZaHrOrNmzcqoEeKSTCZ577332L59O8ceeyw1NTVYlsWWLVt4++23Mc3BmcEfNmwYRx55JOFw2HftFM/IWLBgAbW1tZ6h0tilkDLtBBpB5/MVUFOUh1PMnDmTrq4umpubmT17NrW1tV6fAT3I5PHTiCUsuh1XT/d7wW0TCmqeyuImPMh+OW1XLydBgpGZlKOrO0lrh63wbnOWqxz3r9ca7DTFL61vJrpjGwB10c38dfR6Qio8uTPIRe8GSDiqsCAI+y+GYaDrhc0KTdNIpfr3WRdjRRCEXaMorBz9IdRIKZNX/Y2qhqVMX/BDGmacR8eIIwZ7dMIBhKIohIJ2/FVxuIg333uDIUOGpGuBKApjx44lEAiwZs0apk2bRk1NTd6+gsEgRx11FNFolOLiYm//Qw45hEAgwKJFi/aawaJpGmVlZZSWlnLkkUeiabaBYJomAb0MVQ3R1r6Z0tJSTjnlFCKRCIqisL1LZX2rSk3E/rk2Lceg0NMTDUVFRRx++OFUVlZ663bu3MnUw45AVTVg3zEG7h7ZQI1u8HaHziUry0hYkqpaEA4ELMvisssuy4iR8xOP97+4q2L1wqG5vb2d8vJylKPPRdED/T6YIAh9IGdqXwXN+fw5wfRKkZ1yVCkbAhH7McUVANSWFwEwtNxue0hpiFEl9pdItVO0rdgJkNfVdKB0MiuwxA2eLw1olAU0Kju2cNL7v6coZsu5GyZ9gui00wEoKwlSFLZvqvRshYV0/IxLPoXCU1ay2iq++BQ3xiV7ll9RFJTeqBXZ+xXeVKCn/ApRfvUlc5t/jKrvBjz/sftG/pTJWePIM9bekD3GfApLWv3IXecWhfTHRGWP11Vf0qqcHSthmIaX+CEa72ZrQz3+EqOqqjJiyAg0JwGF4VZSj7bR1tnmdKVSWlyDYRh0ddvxE/X19XvFYCkpKeGEE06gqKjIWxcKRigrrnLiQexzM40ULR2NmJZtiHQmdLa264BCWLcvTmlQR1MMyiN2+vK1a9dSVVVFZWUllqVgmKXUVBSjKCrFzuc8kTRodlSPaMw2XIJOf1XlYcqL7fTmAUdh8fAC5y0vHqXF6afeiUvZ2B5jTZv9+P1WW1FZ2Wwv12y3xxhvacRqrWdMIM4Hhy3DsODIN0pZ1qVlFnwshATYC8KgYqWSWG89TltbG2VlZXnbXH755b3q66GHHurz8UVZEQShT7SUjmTB7K9z2Np/M3bzQsa8/yRNqXYaZpwLBAd7eMIBhqIo6JruGc1leinqMJX33l9GIBAgFotx5NSZhENhLzDcvfmtLq/htTdeo6amhgmHTkPTAqiKRXGkmvbORurq6pg7dy719fV0dnbS0NDQqzGpqsqIESMIBoMkEgm2bt2aY/BomsbIkSMJBAJMnDiRSCRCIpEgmUxSX1/PcbNO8Qw+1/VS03QqS4ew8oN3aWpqonTE0aTTYvj6Vp3EGB0dVFdXU1FRQSwWQw8MAXRHTdn3OLPUNhwXtgdtQ0UQhAOG/hghvUWMFUHYV8kX9OArEAlgudnA4lEU3ZFek/a6rnjIWdozop2RAJ1Je/+Ikz/WFVFcpcIwLe/WyJ3JDjobVfyz8gEWjfk4hh5i/PrnqP7gRcxYJzuPu9IbqpshTHOmy+0Uqe7+mU7x7syyovhiPXqRhcslY5I/Kx7Ea5OvAzf1bsa27L4LqUF5Ql56MQGcsX/2KfaYXczd5ssqlq0+9RAf01ObXRX9y3xeoONdkfWaqyhYSmYcjKuoqL7Cpvkm1UuKipkzc5a3zW3i7hcMpm+EP/4Ru45IMmV/ZmyFJ0BJUYBtjVsZOnSoFxS6YsUKVq5c2eNpqKrKnDlzMmJkGhoaePXVVz2DRdd1jj/+eC9JAEDCUNgWLWfq8AqG1o73Yrt0TfXOO5kyCegaY0fPYOxoaGjrxnXhcj+PYV31PlOlpaX2+VsKgeBQisO2ihp2FJJ8hTzdVZqTAUz3py72qV5gKzJgqzENjlqydIetlizZaS/f3tnJ6sYuABpb7SVdrfbSKV5rdXegdLfyqfJGAP7VFJCMX4JwgNPS0pLhmgqwaNEiZs+e3ee+xFgRBKF/KAqrx59JW+kojnzvt9RueZPO5g9D0fjBHpkg7JJIqIgFCxZw6KGHEgwGqaurY8qUKVRVVZFIJAruV1JSQlVVFalUivr6eurq6hg6dCgnn3wyHR12qt7y8nLKy8tJJBI0NDQQi8UI1E7HzLFOd59YLEYgOJR9/ef8/KpOjimO02ko/L4hAhS+xoIg7P9UV1czefJkrrjiCr70pS/x1FNPcfnll9PV1dXnvvbtbzdBENJYZnoq3p2VdOqtkIhiOUHJSsJWVKJR21+9LWDPwhaHdMqcGdewM6tqODEr7myraflmXr2ZcEcZMUxvnaqkg3vbKqYwbNhMRmx/h9Kl/2Rb2RcAKArZXy8hXw2WgFtE0pn4zqeweJPBSmaMg3+CPZ1nKrsYS95GOft7rfNM21vZKkN2gTzffjmlYLwD5IvnsJeqYnl75IQM5sl4ljOcXvjv++ti9Fal2VWb7GxguxqGF6uSDkJylr6+e5EHLZ864HbXm/Gbmn1A3YnR8M4DuPpy+71qGCYtHS00tTblzSqWTSqVYmj1CA6pG4dlJtncsInKysqMWcREIsHfN0BFcDQEYE7E/ly6nwuvoKqmpBVONZ2hC6AsFCCRsje6Wf1iKRNdBacbSoqHozuxbEWRgNenv79AQKPE2eYqnm4GsGBA85SUuKOkZMenbGyO8k6jrZLMr28F4K3NtktX884m6Gq2r23UXucpKknbIDm6KMovR9vFIH+0KURjzPBlOfQpLD0XLhIEYT/izTffZOnSpTzwwAPcdddd7Nixg1tvvbVffYmxIgj7Ov4fcK9UtGOkOBX3rGQ3SneHsy7TF7xZTbubuEG1rv9/hdOdnufewA26T3qB0emidO7thetr/07dyYzY/g7V299jRcMWokW1lDupTUucwpFFYZ1wyHFDC2UG4ecLMM+5j+9N2mP7DtZp767MaqT07MCkZLs95esmjwHlP2h2H/ZK/01+QTOnQPucQ/ib5CXbyMhHoW19cQsrRLaR0pu9cpMrpFMg50um0BvcG3ZVzbweiqJ4Qf+pFFSUVGKaGqlUwkvLa1mWV4zRLZyoayqhQISiiD05oKk6Y0ccSjQW9W76O6MJQsEQp49OEXE+czUVEQAiTtC7azwpCliGlTFG16AojgTodNw4XWPFxCJhqhhGCRY6uq57fQVyPk+OW1xA8z5r7jn7L1/COd+OqP290tBqB8yvdZZv7+jg+S2tACzbZBsmNG+1r1F7A1bMmSlN2u3d7ycNk4uHJrl7QjflusX8Fo0frdfATGZnYSADCagXhP2ONWvWADBhwgQAjjrqKI466ii2bt3Kd7/7XcLhMOeee26/+s4uKSwIgtBnWouHsbViIgoWY9e/MNjDEYR+EQqEKY6UoevF9r9WTDBg/4dDpYRDpZREyghkZcUMBoJUlFZQXlJOeUk5ul6Mqu7JuUAFiyD76k/4+IjBrWO7WTW7g99NiVIVsHitTWPeu0XETVFNBOFA5Atf+ALvvvtuxrpf/epX/OAHP+Dpp5/mqquu4uabb+5X36KsCML+gmXhaRqutOEG2MfAMhzXLKdgZLaLVKOiEXDdQ5z7haQzWxvSclUTFzfAviygUe6oJK7akjLtGeCEYfL60BM4p3U1Q7cvYeGYjxNP2TPPMSeNbIVpYjiF7dwZ37CTPMwNGNZUJa1OuK5SrmdUD65R+SiUsjevP1i+oPd87l/kVxbSV9obfK6Dk6swKH5pp5DCotAbLcIi8/VMH2r3ZqZ3f/++7pD1ICOJQOaYekqd3Bvy7ee6TYWd97cb8G5alqd2uO9RV8XQvc+SklMUtNZRUSpLQukCjYG0wmn3l07uoCiZ5+a2iYQ0ihyVJeokCNCyVBNVUby+sl3m/CqSu8l9f/uLO7pFHTc5Ssr7TjD9okZbrX11cyv19XZwvNViF3e02rfbHcW6fMVpUxxdZrDw6G6CzldKYwLu3hjg/zYF6DbN3MB6UVEE4YDgnXfe4cgjj/SeP/bYY3z729/mmWeeYe7cuVRXV3Paaaf1q+99c1pGEIT9ji2lY+gKlBE0YgxtWzfYwxEEYa9jcef4BEEV3m5X+dR7AQ5dEOaHG4J0i6IiCAc0qqrS2GhPajz77LNcd911PPfcc8ydOxewi/QahtFTFwURZUUQ9ie8Wcg8CoszY2llzVx6s/2aTmN7ZqHGmJPK2I1lyUfYmeXtCmrEHN/6ipCjqDgKS8Rp80H5RGbsfIshDe+xscz2W005bQzfDKqWNQMc1N3gGdWLEfBm0N3Jdm9quOBQ8wfR90SevnLd53sX0J5zzNzIeG/pBfEXUFgsTzPpecjutTF7UBt6M2+d71i7Ei56TOHcC6y8e2XH6aTb5Ab2W/1SV/IVrsxWP1ylxV8f1Us34akY7mfJ15eiZrTWNTVdAFRNKyH+Y/ofex8Lp5uArhFxiqwWJ904Gmeb85kL6Kov9ivzHN37AssyMZyT6XZiYNo77eD3nV0JNnXYVeRXtNiKyuuOorLYCaLv2FHvU1TsmxESdlu7qKPd97yaJKdUGcRNOHdxgM0x94QM/+AQBOHA49RTT+WSSy5h7ty5PPbYY3znO9/hiCOO8Lb/8pe/ZMaMGf3qW4wVQRAGjFWVU5mx8y0mNi/lHWMehhbY9U6CIOz3jAqb3DfFNoDu3qD5DBVBEA4G7rvvPr7xjW+gaRqPPfYYl1xyCe+88w4zZ85k4cKFPPPMMzz//PP96luMFUHYHymksPibxJyPtxPoq6gajmc5m5NlADQ5mYmCXjYhJWeWN+L4zHeEdaJO/Ek0ZfdZ4WT1clMiLwuN5iPBCioSrVQ3vc+6yikZhf7cDGO6mqnkmM4xTNNCd1QWV33JLhioqErBHFqKlX++Ppvs26g8E/l9ypSVvUrJd4weM2xlD6CXN3qutGKlFZk+7J0+ft51vQkM6n2mr3QGtZ6yutkr3bezneXZed1zUjHnxvXkVZbyKCn+9W5P/v3d957mG2+6bXZcSHpMqqdiprfljC3PGNV0BzbOBdA0JZ0ZzImncRWSkKNuhoOal4bYPYZTmxLDKR4bTxp0Opm+Gp34lM3ttpqytj3G8pbMgo9rtjuph1vr7WXLVqwOO/UwyZjTufNtYpmMDps8d1SCoSF4t0Phu2vz3FqIoiIIBzQ1NTU8+OCD3vNFixZx22238dvf/pYRI0bwz3/+k5NOOqlffYuxIgj7M36jJdtgcdw0LDV9k6Q4Ny/E7VSj0agdBB/V3Eh3HVxDQrUNGTfNcGkkQDzpGiv2sts1Xpw2VSGdNcXjOCbxNjXtG1lWOomg5qQrNkwv2L4rmXKHZI/ec1vRCDg3Y66bi1dp2633ohZwu/J36F/lXqo867I9Uywsn72Q2VfeW/c8Roq7b85QenGvls5SbRU6Yt72uUaKb99CY8w3NP8Yd3Fzmc8s7Mv96C5TUOO8BFn1XVxjQcXyXS8lY7+e+sxXtyf/SjKOV6g/t1W+3RWU9HXKeq0y2mUbO+5H0FIIOYZIUSTz59pNBhDU1fT5O58dN3FGLO6mJE6wzTFS1jiGybvN9nfAu01drG2wjZO2lla7884mu782O4je6mpJu3253yGOu2mRZvHkzASHFll8EFWY906QmMSnCMJBz5QpU/jzn/88IH1JgL0gCAPKxqJDAJjUuhwlO/OPIAgHDJpi8cfpCaaVWmyPwylvBNki7l+CIOShvr6eL3/5y/3aV5QVQTgQ6CmtcbcTeJ9KeooKAVtRQberaiuhIvu5qoPq+LDottrSHbfbdscidMbsx13FtjLSWWSrLzVG0BvK2yWTOF2LUJFo4dCWFWwfagfUJU3LS3nc7Sg0mmLP0rrKSsowCRr28Q0n2Nl1cXHRLAXT51oGWYpG+mEG+XSSfJ5Z2SpFj0pET216UA56lXq5FzJFTsHIPJ5GhbUmXz89uGb1FBfd8xAdly63TS8uiJL3fNzXyt7oCoWm4nf7ytwxs95glquaT+EoNKRepcm20tuzx51uYuWoX9lvHlVRct5s7lNNVQg4le5LIplj85JUKGA4fl+JlKuoOEH0XXYMyeaOOO+7ikqT/R2wuMEOol+zvROrY4fdt1OJns6sivTJWFpRcagJWPx5RoKTq0y6DThnSYhtcTFUBOFgZsWKFbz44osEAgEuuOACKioq2LlzJ7fffjv33XcfY8eO7Ve/oqwIgjCgJNUAb1TMBGBS28pBHo0gCAPNEaUGr8+Oc3KVSUcKLnovyOttcjshCAczTz/9NDNnzuQrX/kKV199NUcffTQvvvgikydPZsmSJfz1r39lxYoV/epblBVBONCwvOjarOdJLNfv3M3S5S4DtsKCHkqvc5QVJeioLsEioskSAGLO0p3JdWd7g6pCka7yVngcp/AqYzvX8l/DBEUhYZjpmd+s6eq4W5wypRIJOPErTsFJt2/TmaLXNTUdfO+lg7Xb2MXv8m/rSU3JVCJ6jlWxSAd7F6SnQH9fEcFCKlCP8Rw9NLR88RyFYl485aiHSfDeKiy9yUabzjidFdfh26mwiqR4ioo/rbP33FNNMgeSFnN8ykbewJzMbTljU5QCak96PJljyt+vv/ecQH8lNwWzf8xe8UgnhitbqTFNi1jCVj1cRaWj2w5+39RmB8OvbIny1k47LmW5E0S/rtFWWKyOHVgddowKnXYQvRWz23jB9GbK+x65YGiKB6cmiWiwpkvhk4sDvN+lku+6CoJw8HD77bdz9dVXc/vtt3P//fdz/fXXc/XVV/P4449z4okn7lbfMhUiCMKAsz40HICSVBfhVHSQRyMIwkBw9hCDR6fbhsq/dqgc+3rIMVQEQTjYWblyJV/60pcoKSnhmmuuQVVV7rnnnt02VECUFUE4cCg0O+35mhvpx27aUaeIneXOoGoBX8yKq7o4zvKhYhTTnrl1Y0xaVTsFspv6OBLUKHZiTFr1UipSHYS7GmkqPoSQqhBQ7dlZN6wm6fjau6mMw7rlFY8ssqyMYwVSdhtdU9F1xXsM/kJ7oKmZWcQsL77FvSBKQUXAsnLT2OaqHhZmgZn8fGEJ+SJEsiMt3DVKRqsC+BWJrE19iRjwp9zN3eZL65udFU3xt7OXWZmonfWZ+7lt0qpBOvVwoWRcfn0o9+1t5Ug7ORm/LMtT5LLxn1ehTGGKZblZoX2vmavYFY7z6Q2ZxyygzPjHkvWedc8rmTKJO8pKqy9GBfDiVN5o7ODd7XaMyuad9pKuFru/rmaIOo/jzsSCX1FxDnpchcEj0xKoCjywReOLy/V8GdMFQThIaW9vp6KiAgBd14lEIkycOHFA+hZjRRCEPcKW8DAqOjsY1bWZrcWHDPZwBEHoFxafrE3xu6kJwho81ajyxRUBMVQEQchhxYoVbN/upDy3LFatWkVXV1dGm+nTp/e5XzFWBOFAJ2Pq141jcZ87SotfcXHUFlK2smKlbBVGsUwsr8CkvUw66ku7o6ZEgkmaHJVlpTaMqayhIrqdtkQKVcFTTSJOxq+g448fdBSSuKGScmJVUs7McZHhKCtOG11XvIxhqqPUuH79qqqgOesCzjhcpcXNHKb5Cl/2VIOlMHb1jIz9smbkM2Iw8tSEyYzC2P0ijrn95Knzkjkct1VWv+l4lpxMY/nyiuXJPgZ4akTmmDJXWFYeJSWP0pVv3Ont+bOAuaqDYVqkDDOjrb9Iqfs4+3Xzx7f4wlecZaZil+/Fy5edLt1j1tLqKWbHf04455SZ+as7lqIpq9DjCkdReWuHraK8s62d7Y22euKpKG6mr+42rG5HbUl2OwezFZURgRQ/m5xg3hD7WM81qVzyboACYpUgCAc5H/rQhzK+z8466yzA/t60LDs+zzCMQrsXRIwVQTiYyL7z8tb7gvJdY8XKDNC3ACU7ID9uP+9wikoG9XTweze2S0qLobKlK0HCtLwikhHd/rIKOwaIu4zoqlfQLukYJDHdfh52jI9gMh2E7xkpWtog0J12rtuYa7S4S9NU0ymPs29ALcsXoG9vS3s4pU2b7BvObPwuRnmD6AtYEn5Xn/xJeQseMOO4quozLnqwhLKP4RYlzDQ2endTXuhQhW7WFaxcY8k7/12fdU9ubK7rYDJpEHWCzpOpTMNW11XPAM5+H/ivi2v4uH2621wXRCWdwSFPMgd/EoHMc8tTU9Jn9OaevzsO10iJOkH0LV0Jz0hZ3WYbG+84wfTvbGsHYHtDUzp43k1LHLO3kYjlun2ZBh+tNvjTjARlOiRMuHuDxq0f6CSsHt5QgiActKxfv36P9S3GiiAIe4Qq057d7VAjgzwSQRD6wkerDZ44wnb7eq1V4aplAVZIIL0gCD0wevToPda3GCuCcDBSaOZaUdKKSrZTuqJixZ2Upk4QvuLKD86yCdv1BqAmZM/grjaK2dwZJ26YlAftr5yyoOM25lNUAIp0jZjj9hV3lkXOtrizDOmq5yKmZ6Un1lQFPekoKYFMhcWdCQ8E0jPqXgpk3xS94RYfNNPuQvYxfKpUgeuXd865UH7iPO3zpgx2t3krcl2L0qmb3WWuspNe0cM43P1947AKyUg9uHqphS9R/nH06AaVqURk7qbkbWs46lx3IkVH1Fb4Ek4hUnd/XVO994SbjEHPSs7gf6lddzL36mtaWqkplIzBP27XfSuVcl0IrZxxpNM8p3Hfo66yknKUlXjSVifb4imaHfVoa5ftDrbBUVq2tzqKSbQ1rah0OmmKXTXFSKbdQC2Tj/gMlb81qFz0boCUqCmCIAwiMlUiCMIeYZtSCsAZ8eX9T5kkCMJe46xag7/PtA2VJxtULhZDRRCEfQBRVgRBSOM3Kqz0bKuHMxlrZbVX3JlZM0WrYRsp/y81nY+GP2BGahsfbX2Dv6WOpixkf+WUOKpHaSBTaSkLaERT9uNuZ1niBO+XOsuwoRLPij/QnCntgKYQchWUlBO87ygy7qx5IKV68St6lsKiqmnVQnNnu52bNU3N0TgKouRRPzIKDe6yB39fzjJzbf42+Wb2s/vrxTgsJxgS0rEaOcqGhS9A3Q3MVzLa9kieFMw9pR7Ol97YTXTgnog/nS/Yaor72H3PmO4+ScOL0XEVOt1VWLTMOKd856QZuS+MktXWMC1PUTEcpTDpBfzjHTPkvLezlT5NUzLG4Pbp39+0IOmsc1XJmKO6kIo7y0Q6eD6VcDpy0pebBlgmcysM/jojQVCFx7erXPpegKQYKoIg7AOIsiIIwh5hi1XCLcmjAfi29RonJ9cO8ogEQcjH0KDFn6fbhsrfGlQuFkNFEIR9CFFWBEHoGW+aOJmbIcxJa+zN0qYSKEl7Nrc7WcJPGcrkqvFcGfqAH3f/i/tiW/kpR0IwDEBJ2P4KKncUl+pwgBrnccxZJtwsSM6y2FC9VMeuouLNjKtpZcXLHmaYGW1CKTWttmTHrqhKOluUs81ta2lufE7uJcrOIqUofkUkWxOxvLS3fbsdTMdFFOrbTz6VIrMfq2AWLH97bybfzFQxFMDMykLmZr+yx5jVZ5bsY+UZY1pFcVWdwuOwMvomox9XTTF92d3c1999HyUNi7iTQtM9NbdN2PceUrOuUTamlZve2H0eN0zihjsWe13KO74bQ6N4xw1qmWMt0jXCjuriZrlzlRpXPYmlDLpT9uP2hB270hV3lRX7c2mlEliekpLO+GVvNPnRxATDw7C8U+GzSwMYYqgIgrAPIcaKIAi9w7LSNziue4nz3LsRSnZDwq5qrzhtvhwfjVod5fLSbXyJJXzcWM0XmubyXGo4RWE79XFlsb1sLQ7SFrEf1zrLmrB9c+YG5xfraWPFKWTvPQ+oihe0n220uEH1Qc0k5Abdq5k3h6qipNs5rmqWlVn3RSH3xtW9oVV9/bgpbV1XJS9dsq9Oi0ehHLzkxueripJj5eQYJiaYbkC34xqUU8ldSbtxuQHlaVc3xRcQbmbu7xuHkmUtuM81VUH16ttknoe/BoobCO+6SBlZY04ZZjqw3G3rc4PKvu7+FNT+fv1j89IzW5bnGhZzlm4dIK+mj6p4BnF2OmV//olUlpHlN1YSWamP3V7cBBIxw/T2c4/lvmeLdY3ykJOMQtcyjt/uBNVv7oyzxklZ/IGz9ALr3fTE8U47RTFkGCkAR5YaXDrcfnzZ0gBdhhgqgiD0n4aGBq6//nqef/55Ghsbc747pc6KIAj7HClUrmo6nKcSI7mrYjljtS7+WfIc34tN5xfMpFu+hgRhUAgqFg9MtScaHt2m8k67eIYLgrB7XHbZZWzatImbbrqJurq6gqp0X1CsXlTfam9vp7y8HOXoc1GcitWCIBzEZH/5OKmM0QIQsOuqKI6rFwFnGSqhKBjgJ7Vr+Xz5dgAajSAXNh/Ny6qdn72oqIjqErvAZJWzdBUWV1mpCGoUObPMAc99xi0uqXgz0G7K40iWwhLWfMpMVnFITVG8diXO8UJO8L8bfG2RViS808+jUPiD9v1tFF8Qf08pb72+1UzVw04CYD92VQZXRTF8KoSriMQS9iyW6xrltlGUdAB50HE1cl3e7CrDmSlyXbzgb1XxxubiJTxQVe9aZFeJ96fejTtj63bcl7odtaDTWR83zBz3qZR7rlb69XPfB1rW9TSt3AQB7vOEaXnHcd2nokZmvm7LSqstrjuXex7usTQlHeDuuSw6bWOG6Y07G7dtZ9KgK5XpjhZ0zqcyFGCo8/4vDmQpK86YN3XGeb/Zrmm0ZrudWjzVXG+Ptb3BXnbshJhTpd5IoikW86qTfHtcipllFjsSMP2VEI0JUVUEQcjFSiWx3nqctrY2ysrKemxbWlrKwoULOeKIIwbs+DKNIgjCXqPb0vifxol8unEq65NhhmgJnqx+g5la82APTRAOAiyuPSTJ2uO7eWxmkpllFu0p2/1LDBVBEAaCUaNG5bh+7S6irAiCsPt4kdqqT6ZwvitUx81LD6IEQs7jEGHF5J8Tt3JScSc7jADHbT2a9foQCJcAUFJkKzKVxbbC4gbjl4Z0TzVxA6FDrhqia1SE3FTHdvtshSWiq54y4wU0+1Ifu8pMiVu4MisY36+CuAqHWxvTbaNrao6y4o85ScevkNEmH5qWeXxNVXJUioSjfkRjjkIQT3qKSocTbN3hzMTHfYHd7rmVOSpSxInTUfApGc5PRNBN/eyOR1fQ1Mxr4p5zMKDlnL+r7MQc9aQrlqIlZrsgNUXt5XangOMOZ31XyswItod06mHTSr9uWoHrZ1mWpwS5M3P+IPguLzDdXnam0ooOQMKwiDrjdoPY3V/MoFfINF2k1FVqupxzjCXT4/fG4RzfVbi6EwZRZz93nXs+pWGd8iL7cxRx3o/u8budfVqjSVrbHNXEKfhotfkUFYBEFFJxrh6Z4udT7Gu7MwH3b9a4d5PODjFUBEHogb4oK//5z3/4yU9+wq9+9SvGjBkzIMcXZ3FBEAaFmKXyyU0TeH7MKo6MRHm2bjEfaz6edZQM9tAE4YDkwjrbwLl3o8YNq3XiphgpgiDsPpWVlRmxKV1dXYwbN46ioiICgUyRo7m5754UYqwIgrD7eGmofPENbtYhV3ZIdmPFna8cR3Vp7w4yr7OMF6clmRCO8VrNi3xl5yT+0jWEzmgxAJ3tRfY+QTsWpiikE3YUALeYnqusFIU0r/BklbN0Y13cwpMlAc0rMJkd31KsaxhZWrM7y+6Ph3AzSrmZrtKqg72zrps5QYV+FcWLeSlwr6iAp1roTsozr3Cgpnqz9a6S0t5pKxINnXYGtsZogp3d9gx6szPL3+YoK+2OCqOSLrhZ5su0Bnamq+xYC7dthdO2JKh5cRSuQuPG+4R01SvC6eKm6m1zxrwjmmBrlz3eDR32cnlLFwBb2u3nXfFUjtrgkpmNzD1GZppjw3cOAS0zY5h/eyKVGZ/jFZA0TDDcIoqpzIPptuKnBoLpt3/CKbyYiKb3KVSx04m9wUj60gm7hSttmgMhmnVHjVS1rP1T3jGsuB2rQleLPY7O5qxxJBkZMplTbvf8042aGCqCIOySGeNGcPfnP86LL77IbW89XrDdPffcs0fHIcaKIAiDSn1C45RVh/C38Vs5pjjGo0OX8+loPVe0HsUOMzTYwxOE/ZrjylNcOyrO4cUWARVealbY0C3hqoIg5Ke6rJgLTj6KsqIw8+ZOpyQSYtKkST3u89nPfnaPjkmMFUEQ9gw9qS1eEUl7Br0+leDEt0v4xpggN47s4GNFzTyjLOS0deNoDlQAoGj2THZUDxF1Y+ey42ICQYrCtoFTFnEUFSebkhvzUhHUqAy5NVzsde7zypBO0nTjFexlIGtG31/Ez90WyqrtEkj5Zu/dmAvT3T+35kf2pLumKF72MTdTVyLgjEdXvfiP9i571n9Tu11DY02rPbO/ujXK+g57Xb2jurhxFG6sg2nhKVTFjgrlZTyz0kqIqwK517OuyH4dRhaHqA1n1sSpdPopCmhePJF7bq5a4So+mzrjrHLqgizdaSsDaxtsZSXe0WrvlOhOKwjua5wvPsorXOoqFG4BRDP34vqfpytmOu2duBR3f8NIv2fdcbhKoZPtztTTBrUVd5QMn6Lhtc9RVtKfBcvIrH3i7qNoGrj9Z5+r108Ky62hknSX9nUdoSf5+4w4Vc7HxLTgpjUSdyoIQn6GVJTywPWXMnpotbeusbGRZcuW9boPTdOor69nyJAhGeubmpoYMmSI1FkRBGEfJ9uAMdPLBPC9NRqPbS/jvzPamR6J8d/Rq7h2QyUL28PgGiiBcPrGVcs0WhQ9SNRxF4s6KZO3O89V5+ayLBKgwglarim1b7yHRexlXVGQaucG3HWJ0rLS4upK2ijx3MlcdzRfSuTsSuWJrMKB4EsDrGQ+11XF66so4AZy28dQFeh2bvzrHbev1Y6RsqTJvtlf0tDBpib7hjkZdVyEXBclt6AnpK+j62LkLi3T58bnDM5JSa1G7JiiEZUR6srtazquzF4Od5IhVIcDlLmV153r1+W4WG13jJUP2rpZ4Yx3db09RqvVTmltdeywjxlrz3Un9Cy9PD9fWdXZrZ5+FC0z3adrJHhLX3XN7HXu8V0jwpd0xko61zaVyD1GtrHhjtWy0sfIHqLfICt4HlZmXwCmQXXA4k8zElQF4P1Ohe+s1VkTVaSWiiAIeRlSUcoTN19OaWkpXV1dbN68mVgsxoYNG/pkYBTK2xWPxwkGg/0amxgrgiDsU7wf1fnIu2W8eEQ7M0pSvDR1B4s7A/ynvZg/NpWwLBUe7CEKwj7N9FKTJ45IMrbIoisFn1wcYE1UjBRBEPLjKiquobJgwQKi0Wif+rj33nsBe9LtgQceoKQknSzHMAwWLFiwS3eyQoixIgjC4ONTWDCSrEzA0YvC3HhoksuHp5hZkmRmSSvfHN7K0zsDfH1dCau79RxFwFJ1lIAzcxN0AvND9tJ0lIHWQJjWkB28v8VRCVyFpbI4SKXj0lSWFbzvujWVBTQvWL/CC97PTJOskFZSXEUh5igsyTxFAl1PM9flKqgq3vGyg9gBWhyXrq2OG5gbmL60wVYoNmxvBUedsJyg6wzXJPdau9fPnf13n5tG7my/c/3MonIANreXs7m4yn5cZV/jugrbkBxRHGSIo1C5QfetTgplL6i+pZuNO+0xWe2N9tIds/OcRDT93nDJyB2ddQOerYL412WTT1nxtvWiRoDjcmWpWn61JN948x2j0PgKka3wZI31YzUGfz0iSZEGH0QVLlgihoogCIWpLS/JUFT6Y6gA3H333YCtrNx3331oWjopSDAYZMyYMdx33339GqMYK4Ig7JNsiav8z8oQt6wN8tFqg08MMZlXk+SsmiQfrmrhB5uK+OHWUrolq5EgADC3wvQMlWd3qlz6XoCWpHw+BEHIT215Cb/5+qd221ABWL9+PQCnnHIKTzzxBJWVlQM2TikKKQjCfsOEIpN7Jqf4WI09G709rvDDDQF+sSVA0lLsmXJXHXDjWZzYgnRByiCEbEVFcRQWfEvFiXFxi/BlB6GXFwUYUuQGltuKTLaKYFqWp6R4BQfdtLiG6RU4dAsumllfw7rqK07pLMNOYEvCtGhyiibWO0UUtzTb8Sg7mtrsDtobsdqdwoBRZ50bR+Gf/d9VPIQfL4mBrZ4oReUopTX2uqIKZ529rCoJegH5bhFJN7C/xVGDOju70kUM3ZS77b5YFbBVoN2phNyTarG7FZbzXbt8fe5KWRkggorFhpPiDA3Bv3aonL04QMoSQ0UQhMI8850rqKur26WhEovFuPPOO3tVFHJPIMqKIAj7DWuiKme+E+TcoRY/mJhgbMTirsMSfHFUkl9uCTC/JcCSqErBAiaCcIByeq3J0BBsi8EFS8RQEQRh17hxJe+8806/FRWA6667ju9+97sUFxdz3XXX9dj2rrvu6nP/YqwIgrB/YcHj2xWebAjy2eEGt01IMaHI4q6JCSDB2+0Kt68N8OROHVC8WA3Ln/lKs4vmWa7qEnaUlUDEi3GJOspM1EmZ3OwoM5tDxZQU2+pLTam9rtRREUKesgIxV0lJ2rP7UUdZSKRMrxhhylFfvOfOUlUULwuZq+y4zxMp0+srGXMyfMU67GW3rUhYHTvTsSpuOtt88RR9QXHT+dpLKxVPx8F0O8d3VJym9ghNXlppLWM/L9YjEU0XMXT3z46r6SFTVq8YYPWiX33vyTH4+OQQ+z3x5+2auEYKgrBLZowbQSRi/5Ylk8nd6mvx4sVeH4sXLy7YLrtYcm8RY0UQhP2SlKXwm606f96ucdUog1OrTE6qMjmqzOKJmQn+tSPFF5YH2BZ3vhzdGhyK4ksn66TMdd2OVB3Lvcl2AvUV77n9pU64mM4Oezaqs8U1cpwMZV7At2XX6ICcdLr20q0LUqAN6RvcqFe53A2oNtMGQ1bFdMu92Y91pVMUm1k1PPxk30j39EPiNvUFk1tu327K3qhjIOmh3OB9I8vYSXSnDSl/0H+hseYd094xBPYHjq+0r8UzOyWYXhCEnpl+6Ah+87VL0XWdxsZGWlpadqu/F198Me/jgUK+1QRB2K/pNBTu2qBz1jtBxswPccc6jbgJZ9SaLDsuzrWjU+iK3NQKBy6lmsWhRfZ7/PVW+VkXBKEwM8aN4JdfvZhAIEBjYyOvvvrqgPb/3//+l+7u7gHtU5QVQRAOGJqSCv9vTYBHt2k8MDXJ7AqLuyaluGKEwWeXBljSoTquRVnpcPOpLnEnHXJ2Wl895LmKeemRXfXFr0wUUgn8akC2ouKqDoYvdXC+9LzZVdUzVBtnvXf8PhhqPbVVfMoO2EpLljLiKTvZY87Yz5fmt1D63r6k/hWoCTppslPQYYgLmCAI+RlRU8FvvnZphqHSn4ryPXHuuecSj8c56qijOOmkkzj55JM57rjjMuqu9BWZghEE4YBjZZfK8a8H+fwyncY4TC21eG12gs8OTw320ARhwJlRahsrm2NiqAiCUJi5hx9KIBCgra1tjxgqAC0tLbz00kvMmzePxYsXc/7551NVVcXs2bO54YYb+tWnKCuCIByQWCg8uFXnyUaN+w9P8smhJr+YkmJNVOXVQq4yftWlUDHCZAwrbse6pItS5vkqLaSM+NUbt41PGRkWNPlIZQJNsWhOKvynKU/AdLaqkEeZULAo0SBlwaRiiw9Vm1QFLFIWbIsrfBBVWBtVaE4qVAcsJpdYHFFqMSZiURO0WBdVWNapsDmmsKZLYWMs3zVzjmtknUc++pJG2K+miIKSFxWL84eZXDbC4CNOKu//NMn8oyAIu6ajo2OPGCoAmqYxZ84c5syZww033MCyZcv48Y9/zKOPPsqbb77JnXfe2ec+xVgRBOGApimpcO6SAH+bmWTeEJN/HJlg3jtBXtnHfPsPKzK4dXQXZ9cmCfiG1pGC/zZrvNuh0ZhQiJvgJBijRLMYFrIYHzEZGrIo1y3CKhRpMCJkEdbyH6s/fBBVWNCs8mabwj92aNTHZRZ/MCjSLD4z3OCa0QaHFacNua4UPLR1AF9wQRCEfrBy5Urmz5/PSy+9xPz58zEMg+OPP56f/OQnnHTSSf3qU4wVQRAOAhQueTfAv49OcEKlxfPHJLhyWYBH6/twc+fFVxiFVZdeDUXNaF+iWdw4JsF1o9NGyhttCo1xhSklduD0OUMMzhnS/1mwzhS80KyyNqoQVOGQsMW4IotDI7ZB05WCDTGFd9oUVkdVmpMwsdhicrHF8JDFYcUW44ssxhcZXDES7jFT/H6bxvfXapmKS3YsUH8RNSUvH681uO/wJMOc+qbNSbh3o84zO1WWdypEJV5FEIRB5vDDD6e2tpavfvWr3HTTTRx++OG73acYK4IgHBR0mwpnvR3kgalJzh9m8vD0JF8dk+LBLRp/3a6xM7m3b/QsPjUsxQ8mJBgWsm/On96hctOaAO91KF6b2eUWcytNppaYVOgQUkFXAMU2QpqSCmuiCttiCm0piBoQMxW2xqAxoaAqEDPByFMkUMEipNrte6JEszi5yuTYcpNTq01mV1h8bqTBZ4Yb3LdZ46Y1Op1yo7zHqAtZ/GBikkuH25LauqjCTzdq/G6rJgH1giDsU1xzzTUsWLCAW2+9lb///e+cfPLJnHzyyZxwwgn9DrJXLGvXU1jt7e2Ul5ejHH0uih7o14EEQRD2BRQsvjchxf+OMQg5okDKhJdbFf67U2N+i8rbbQqJPDf3KhYlOnQZ+W/+e0uxZvGLKUk+5dx8rulSuH6VztM79g83nuMqTG4el+I0J1big6jC1ct1FrSou3VdDlZKNIvTa0zmVpqMi1hUBS2SJrSnFMIazCo3KdPtYqM/3qBxyxo97/tTEAShJ84/6Uj+36dOZ8uWLbz++uu93i8Wi3HnnXfS1tZGWVlZr/ZpbW1l4cKFzJ8/n/nz57N06VKOOOIIFi1a1Odxi7IiCMJBhYXCt9cEuGuDzqXDDS6uM5hVbnFylcXJVXa2sLhpGxCbYwqmBbUhi9Fhi9ogOIXkiRq2ymFYtjvOuqjCojaVhriCYdntOlKwsEVlZZcthWiKxcdrTe6alGR0xDaSblur86P1Gsn96ObzlVaVj74d5ENVBr+emmR8kcVzxyTpTNnq0HXvB2hI7D/nM5gcXmLy1JEJxkTybU3PJb7eqvCVlQHebt+3Yq0EQRDyYZomqVSKRCJBPB4nmUyyYcOGfvUlxoogCAclTUmFezfq3LtRZ0zE5GM1JqdVmxxXYTIkZKc7nlpaWHguckQQTYFhIRgWsphbmT9mozMFW2IKo8IWxc637sZu+Mx7QV7exwL9+8LzzRpHvqryo8NSnD3UoDIAF9WZnFwV5/wlwcJZ1wQAzqgx+P30JBUB2BaDJxo0lnYq7EgoBBSoCFhEDYX6OLzUrGIhBqAgCPs21157LS+99BLLly+nqqqKE088kauuuoqTTz6ZqVOn9qtPMVYEQTjo2dCtct9mlfs2A9gqyuQSi7qQfXvYnFTY0K1QH1doTUGpBiW6RdxU0BWoDlhMLbGYVWFSolloiu2yMzxsu0yV6DCpxDZ8WpLwy00ad6zXD4iA6NaUwueXB7hquc6scotfHZ5kWqnFP49K8LX3dR7aqslNdhYVusUdE1NcNco2bhe2KJy9OEjLXo+bEgRBGFi2bt3K5z//+d0yTrIRY0UQBCEDhY0xhY2xwi3iJhkB+ZtjCks64JE82cWCil27ZGTYYltcYVWXckDevFsovN6mMGdRkH8cleSUKpNfT01xzlCTy5YGBiGBwb6HplhcO9rgxkNTVAZsg/anGzW+vUYnvoskB4IgCPsDjz322ID3KRq9IAjCHiRh2emAX2jWeL/rwHfl6TYVPvZWgOvf1+k24PRakzfmxDmtes8UINtfqAlYPHt0kh8dZhsqSzsUTnszwPWrAmKoCIKwVzi0rmawh9AvxFgRBEEQBpSUpXD3Rp3Zi4Ks6VIYHYFnj07ypxkJDgkffDVUTqg0eWtOnFOqTDpS8LllOke+GmR+y/6R/U0QhP2fK06fyyUfOgaA+vr6QR5N3xBjRRAEQdgjLOtUOea1ID/dqGFYcP4wkyVz41w5IoXKwWG0fLTG4D9HJxgVgVVdtpvcQ1t1zANcYRMEYd+hqrSYa885BYB169axadOmQR5R3xBjRRAEQdhjdBgK170f4KhXg7zSolAegPunplg0O8HxFeZgD2+PckSpyV9mJAmq8LcG23Bb2SU/u4Ig7F2aO7poaGgAYNSoUVRUVAzugPqIfGsKgiAIe5ylnSqnvBnka+/rtCXhqHKL+ccm+POMBB+uNtCVA0tpOabc5JmjE5To8FyTykXvBug6ALK/CYKwfzLvO7/lzfc3EAgEGD9+/GAPp09INjBBEARhr2BYCvds1Hm0XuO28Sk+P9LgvGEm5w0z6UzBolaV55pVXm5RebtNKVilPaJajI5YdBoK7SkoUu2aJDUBu0DnqqiC0acimxaTiy2OrzSpDNiFPjtTENLgvKEGhxZZLO1Qea5J5dXWnsdWGbD4+pgUXxtjoKvwVpvC+UsCpPajop+CIBx4xBIpnn1rJcdMGoOm7bl4OcMwuPvuu/nLX/7Cpk2bSCQSGdubm5v73KcYK4IgCMJeZUdC4X9WBLhvs8YXRhqcPdRgaAhOqzE5rcZ2DYub8F6HQmtSwbDsujZlOlTqFiPCoPZw7x81YEO3wtaYQkMC2lJ2PZyoAdviCkvaFV5uURkZtjhnqMlVowzGFfWs7NSFTD7ijC1mwBttCovaVJa0q8RNGBK0OKbc4oJhBiXOL+sf61W+tCJAe0oMFUEQDg5uu+02HnjgAa677jpuuukmvv3tb7Nhwwb+/ve/c/PNN/erTzFWBEEQhEHhvQ6VL61U+cpKncklFqdWmZxUZXJ8pUltEI4pt6BAIH5bEiIaBFVbCWlPQVNSYVjQokSHKSUWU0p671oWNWxlZ2O3gq5YlOoQVuE/TSpvtKkcU25yYqXJ3AqToSE4scrixCoDyE3J/G6Hwq1rdJ7aIdm+BEHYd6gtL9njx3j00Uf59a9/zZlnnsltt93GxRdfzLhx45g+fTqLFi3immuu6XOfYqwIgiAIg4qJwvJOheWdKv+3CcBiXJHFtBKLYg00BToN6EhBS1JhQ7fiFJm0CCqQsAAnu5aKxaFFFoeEbQVmSNCiXLdIWgolmr3+hCqTuhAkTFjUqvDwNo0/1Wt091Dv5LVWlXs32mMbX2RxQqXJrHKLySUmKtBp2IrNC822uxiS7UsQhH2IM4+dyufOPA7onytWb9m+fTvTpk0DoKSkhLa2NgDOOussbrrppn71KcaKIAiCsI+hsDaqsDa663aJLPHEROGDqMIHPe5rMTZiUR9XiPW5IKPbv8pDW/u4qyAIwiBQVVrM9674OKqqsm7dOtasWbPHjjVy5Ejq6+s55JBDGD9+PP/5z3848sgjefPNNwmFQv3qU7KBCYIgCAcZCuu71X4YKoIgCPsfQypKUFWVWCzG4sWL9+ixzj77bJ5//nkArr32Wm666SYmTJjAZz7zGa644op+9SnKiiAIgiAIgiAc4Jjmnq9tdeedd3qPzzvvPEaNGsUrr7zC+PHjmTdvXr/6FGNFEARBEARBEA5UlL2nIi9YsIC5c+ei67aJceyxx3LssceSSqVYsGABJ554Yp/7FDcwQRAEQRAEQTgACQd1vnb+hwCIx+N7/HinnHJK3gD+trY2TjnllH71KcqKIAiCIAiCIBxgRIIBnrrlMoYMGUIymWTJkiV7/JiWZaHkUXKampooLi7uV59irAiCIAiCIAjCAUQkGODer1zgGSovv/zyHk1ZfM455wCgKAqXXXZZRuYvwzB47733mDt3br/6FmNFEARBEARBEA4gHr3uXMaNG7NXDBWA8vJywFZWSktLiUQi3rZgMMjs2bP5/Oc/36++xVgRBEEQBEEQhAOIsrIyAN577709bqgAPPTQQwCMGTOG66+/vt8uX/kQY0UQBEEQBEEQDhCGV5dTWloK7J2gej+33HLLgPcpxoogCIIgCIIgHAAMry7nges/RTgcpqOjg8bGxr0+hscee4y//OUvbNq0iUQikbHtnXfe6XN/krpYEARBEARBEPZzXENlRE0FHR0dLFiwAMMw9uoY7r33Xi6//HKGDBnC4sWLmTVrFtXV1axbt47TTz+9X32KsSIIgiAIgiAI+zElkRCP3XR5hqESi8X2+jh+8YtfcP/99/Ozn/2MYDDIN77xDf773/9yzTXX0NbW1q8+xVgRBEEQBEEQhP2YoyeOpri4mO7u7kEzVAA2bdrkpSiORCJ0dHQA8OlPf5o//vGP/epTjBVBEARBEARB2I+pLrOzb3V1dQ2aoQIwbNgwmpqaABg9ejSLFi0CYP369ViW1a8+xVgRBEEQBEEQhP2UWZNG8/ULPwxAS0vLoI7l1FNP5R//+AcAV155Jf/7v//Lhz/8YS688ELOPvvsfvUp2cAEQRAEQRAEYT8kFND5+VcuIBgMUF9fz7JlywZ1PPfffz+maQJw9dVXU1VVxcsvv8zHP/5xrr766n71KcaKIAiCIAiCIOyHlBdHCAaDWJbFokWLPENhsFBVFVVNO25dcMEFXHDBBbvVpxgrgiAIgiAIgrAf8rFjpgCQSqUGzVB57733et12+vTpfe5fjBVBEARBEARB2M94+MtnMWPGDABWr149aOM44ogjUBQFy7JQFKXHtv2p+yIB9oIgCIIgCIKwH3Hph47xDJWVK1fy/vvvD9pY1q9fz7p161i/fj2PP/44Y8eO5Re/+AWLFy9m8eLF/OIXv2DcuHE8/vjj/epflBVBEARBEARB2E+4/3OnceyxxwK2obJixYpBHc/o0aO9x+effz733nsvZ5xxhrdu+vTpjBo1iptuuolPfvKTfe5flBVBEARBEARB2E8YMWIEAOvWrRt0QyWbpUuXMnbs2Jz1Y8eO7fdYxVgRBEEQBEEQhP2Mtra2wR5CDpMnT+Z73/teRmHKeDzO9773PSZPntyvPsUNTBAEQRAEQRCE3ea+++7j4x//OKNGjfJiat59910UReHpp5/uV59irAiCIAiCIAjCfoJlWQDU1NSwbt26QR5NJrNmzWL9+vU88sgjvP/++1iWxYUXXsgll1xCcXFxv/oUY0UQBEEQBEEQ9hM2btzIyJEjGTVqFMlkksWLFw/2kDIoKiriqquuGrD+JGZFEARBEARBEPYTvvS7+XzrgSexLItDDz2034rF/oIYK4IgCIIgCIKwH/HvN5bT3d0NQCAQGOTR7FnEWBEEQRAEQRAEYZ9EjBVBEARBEARBEPZJxFgRBEEQBEEQBKHfPPjgg8Tj8T3StxgrgiAIgiAIgrAfMaSi1ItVcVMZDyaf//znM4pUDh8+nA0bNgxI32KsCIIgCIIgCMJ+Qm15CQ9cfymBQIDOzk7a29sHe0g5BlNHRwemaQ5I32KsCIIgCIIgCMJ+wq++eBajh1bT1dXFwoUL9wllZU8ixoogCIIgCIIg7CeEQiEAVq9eTTQaHeTR2CiKgqIoBZ/vDlLBXhAEQRAEQRCEfmNZFhMnTvQMlM7OTmbOnImqZuoizc3Nfe5bjBVBEARBEARBEPrNQw89tMf6FmNFEARBEARBEIR+89nPfnaXbVKpVL/6lpgVQRAEQRAEQRD2CCtWrOBrX/saI0aM6Nf+YqwIgiAIgiAIgjBgdHZ28sADDzBnzhymT5/O66+/zg033NCvvsQNTBAEQRAEQRD2A4rDQcrKyoB9oxhkNi+//DIPPPAAjz/+OGPHjmXFihXMnz+f4447rt99irIiCIIgCIIgCPs4kVCAX1x7MWVlZSQSCbZv3z7YQ/L44Q9/yKRJk7jooouora3l5Zdf5r333kNRFCorK3erb1FWBEEQBEEQBGEf52PHHM4R40eSSCRYuHAh3d3dgz0kjxtvvJFvfvObfOc730HTtAHtW5QVQRAEQRAEQdjHKS8OA1BfX09ra+vgDiaL73znO/z1r39l7NixfPOb32TZsmUD1rcYK4IgCIIgCIKwDzN6aBWXnjYLgHg8PsijyeXGG29k9erV/P73v2f79u3Mnj2bGTNmYFkWLS0tu9W3GCuCIAiCIAiCsI8yemgVf/72ZQypKKWtrY1Vq1YN9pAKctJJJ/G73/2O+vp6vvjFL3LUUUdx0kknMXfuXO66665+9SnGiiAIgiAIgiDsg4weWsUD13+KSCRCW1sbCxYsIJFIDPawdklpaSlXX301r7/+OosXL2bWrFnceeed/epLjBVBEARBEARB2Ad55BuXeorK/mKoZDNt2jTuuecetm7d2q/9xVgRBEEQBEEQhH0MTVW8miqvvvrqPm2oNDc3s2XLlox1y5cv5/LLL+eCCy7gD3/4A4FAoF99i7EiCIIgCIIgCPsYcw8f5z3elw0VgC996UsZMSmNjY2ccMIJvPnmm8TjcS677DJ+//vf96tvqbOyl5g1ZRzf/MwnOPKwsQytKqe1M8q6rQ28tnQNX/+/R/b6eIzX/sjPH3uWa37y271+7ONnHMaXz/8Yc6dPpLaijO54guXrt/Dovxfy8L8XEo3te1kudoc50ybw4VnT+emf/01bZ3TA+7/4I3OprSzn3j//O2eb8dofue2Bx/jObx4f8OMKgiAIgrBnOH7qOO764rkAbNy4kVQqNcgj6plFixbx0EMPec8ffvhhqqqqWLJkCbqu8+Mf/5if//znfPrTn+5z36Ks7AXOmDuTl+//DqXFEW74+R/42Ffv4H/v/h2vLl3NBafNHuzh7VVu+dx5zL/vVobXVnLL/X/lo9d+n0tuupcX3lrGzZ87j+9+4YLBHuKAM2faRG753HlUlBTtkf4v/shxXHvh6Xm3zf3cTfzmqRf3yHEFQRAEQRh4Tpg2nnu/fD7BgM6WLVt4++23B3tIu2T79u2MHTvWe/7CCy9w9tlno+u2LjJv3jzWrFnTr75FWdkLXP+pj7O+vpHTv3oHhmF66//83Gt882d/GMSR7V3OO/VYbr7yXH7z1AtcdcevM7Y9s+hdfvTIP5gzdcIgjW7fIRwKEIsnB6Sv15d/MCD9CIIgCIKw55l+6Ah++qXz0DSNLVu28MYbb2BZ1mAPa5eUlZXR2trK6NGjAXjjjTe48sorve2KovS7PowYK3uB6rISdrZ2ZBgqLvnegBd8aDbXXHg608cfgmXBq++t4lu//BNLVm/w2hw16VC+dsmZHDt1AkOrymlobmPR0jV865d/ZNP2nf0aZ3lJEf/v8nP45ElHM6K2irbOKG+/v46v3fsIqzZuAyCga3z9U/O49GPHMbZuCO1d3fzzlXf45s//wM7Wjh77/39XnENzeyfX3vW7vNs7ozH++8ZS73koGODmK8/lwtPmMKK2ih2t7Ty54C3+331/znCnWvvEvSxft5mfP/Yfvnf1hUweM4KN23fw40ee5qGnX8o4xvDaSm6+4lw+NucIhlaVs7O1g9eWrubLP36IxpY2AEqLItx05Tmcc/Is77iPvfA6/+++P2e4qLmudG8s/4AbPvtJRg+rYc3m7dz0qz/zz1cWA3Dzledyy+fOA2Dd3/7P2/fU//kO8xev9Mb+0D9e4ttXnMPk0cP56Z//zY2//BNfPPfDXPChOUwaPZziSIh12xp55N8vc8+f/kXKMAB4/uc3cfKRU7zxuGhzLvbWZbuBHX7oSL73hQs54YhJREJB3t+4jZ/++d88/K8FXpuTZk7mhV/czKU3/x+HHzqSy848mZKiEG+sWMtXfvwQqzfV9/haC4IgCILQd06fdTiaptHQ0LDfGCoAs2bN4t577+XXv/41TzzxBB0dHZx66qne9tWrVzNq1Kh+9S3Gyl5g0bI1fO4Tp3LP/36WPzz7Mu+s2uDdbGZzw2c/wXevuoDf/nM+3//t3wkGNL526ceZ/8tbmH3l/2PlBjvt25i6GlZtqufPz71Gc3snddUVfOGcD/P6b77H1Eu+TlNbz4ZDNiVFYRbcdytj6mr54SNP8cbyDyiJhDlh5mTqqitYtXEbiqLw9x9ez/EzJvGjR//Ba0tXM3pYDbd87jxemHIzs664saAiMKy6gmnjDuHP/32V7njvgsT+9oOvcerRh3Pnw0/y8pJVTBt/CLd+7jxmT53AcZ+/mUQy7b85ffxofvSVT/HD3z9JQ3MbV847lQe+/QU+2LKdhUveB2xD5fXf3E5A17jj4b+z9INNVJeX8pFjp1NZVkxjSxuRUJAXf3kzI2urvDZTxo7kts+fz9Rxo/jIV27PGOMZc2dy9ORx3Prrv9IZjXH9pz7O43dex+QLv8b6bY385qkXqSor4SsXfIxzb/gJ9TtbAVixPp2+b+bEsUz68gi+/9u/sX5bI12OQTRuxFD+9N9XWb+tkUQyxfQJo7nxs59k0pjhfO72XwHw5R89yH03fI5xI4Zy7g27LrY08ZA6Xr7/Nhpb2vnq3b+jqa2TSz92PA/d9EWGVJbz40f/kdH+e1dfyKvvreaqO+6nrDjCHf9zMU/+6OscfvHXMM394wtUEARBEPYXVFUBoKmpab8xVAC++93vctppp/HII4+QSqW48cYbqays9Lb/6U9/4qSTTupX32Ks7AW+9Ys/ctjo4Xzlgo/xlQs+RiKZ4s2Va3n65Xf4+WPP0tVt35yOHFLFrZ87j58/9h++endaffjvG0tZ9Ze7ufnKc7n4pnsBePzFN3j8xTe8Nqqq8PQri6n/531c/JG5/Oyvz/ZpjNdeeLp9M37N7Tz/5jJv/d/mv+k9vuBDs/nYnCM474a7Mta/u2YTbzx0O5edcRL3/e25vP0fMrQagPX1O3o1no8cO52Pzp7BN372KD959GkAnntzKVsam/jT967lM6efyANPveC1r6ko5YQv3MLmhiYAFix5n1OPPpyLP3KcZ6zc9vnzqakoZeanv8n7jlIE8NfnF3mPv3LBx5g+7hDmfO4m3n5/HQAvvLWcbTta+Osd/8vHZs/gmUXveu0joSAfueZ2OqMxAN5ZtYEt//gF539oNj/8/VNs3dHMpgZb6Vq8agMb86heQyrLmHrJ9azZvD1j/fX3phMvKIrCwnffp7mtg998+2quv/cRWju6WLlhK22dUeLJVK9cvm658jyCus6HvvRdtjQ2A/Dv15ZQUVLEzVeew/1/f472rm6v/cr1W/nMbT/3nhumyZ9v/yrHTB4nLmaCIAiCIABwxBFHsHLlSl599VWGDRvGsccem7H9oosuYsqUKf3qWwLs9wLN7Z2c/MXbmHX5t7nh53/gqYVvMXFUHXf8z8W8+8gPqS4vBeCjx84goOv8/t8L0TTV+48lkixYvJKTjky/yMWREHf8z8Ws+uvdxBc+QvKVP9Dx4m8pKQozecyIPo/xY3OOYNXGbRmGSjZnHnckLe2d/OPldzLGt2TNBup3tmSMb3c55ajDAfjdP+dnrP/r84vojMY49ejDM9YvWb3BM1QA4okkqzfVM3pYjbfuY7OP4KW3l2cYKtmcedxMlq3bzJI1GzLO8dnX38U0zZxzfOntFZ6hAtDY0kZjS1vGcXfFe2s35RgqAEdMHMPff3g9jc/cT+rVP5B4+VF+d8uX0HWNiaOG9bp/P6ccfTgvvLXcM1RcfvevBRRHwsyZNjFj/T9ezgzqe++DTQB9Oj9BEARBEHaNrqmMH1472MPoN7W1tXziE5/IMVQAzjzzzIwA/L4gyspe5O3313mz9bqmceeXLuZ/Lz6Tr3/q49zw8z8wpKocgDceuj3v/v6Yl0dv+wqnHn0433vob7y1ci3tXd1YFjz9k28QCQX7PLbailI2+W728zG0qpzKshLiL+dPtVxdUVpwX7fvsXW9+xBWl5eQTKXyxsFsb271DDyX5vbOnHbxZIqw71rUVpayZUdzTjs/Q6vKmTCqjsTLj+bdXpN1jk3tueOLJ1N9eg22O65hfkYNrWb+L29h1aZt/O/dD7OhfgexRJJjpozj51+/ol+vMdjxU/VNLTnrt+1o8bb7yXYnjCdsN7/+Hl8QBEEQhFw0VeGHV53N0YeNxjAMtm0rPLF6sCHGyiCRMgy+85vH+d+Lz2TqoSOB9I3h+d+6O6+7kEtZcYQzj5vJd37zBD/8/VPe+mBApyrrZrO37GjtYGRtVY9tdrZ2sLO1gzP+98682zui3XnXA2xvauW9Dzbx4WOnEwkFdxm30tTWSUDXqakozTFYhlVV8NaKdT3un48dLb07x+540osJyd3e3ufj7gqLXJ/UT5x4NCVFYc771t0ZCROOmDB6t47V1N5JXXVlzvrhtfa6nX2MdRIEQRAEYfeZe/g4PnTkJAzD4LXXXqOtrW2wh7TPIG5ge4Fh1RV517vuWtt22rPazy56l2QqxaEjhnoqTPY/gGWBqqrEk5nB7FfOOwVd1/o1xmdeW8Jho4d77lf5+Ocr71BTUYqmKnnHtqsMUbc/9ARVZSX89LrP5t1eHAnx4VnTAHjhLdsd7dKPHZ/R5txTZlFSFPa294VnFi3h5KMOZ+IhdQXb/POVxYwbMYSmto6859iTEVmIeMJOBNAXNcKNqXOVDJcrP3FqTtt4Itnrvl94axmnHH04dTWZBsunTz+Bru4Yi5b1Lwe6IAiCIAj9p7w4DMDOnTtpaGgY5NHsW4iyshf49z3fYmtjE0+//A7vb9yGqirMmDCG6y4+k46ubu79yzMAbNy+k1t+/Rjfu/oCDh0xhGcXvUtLRxdDq8o5Zso4urrj3PbAY3REu5m/eCXXX3oWTa0dbNi+gxNnTuaKs06hJY87VG/46Z//zQUfmsPffvA1fvD7p3hzxVoioSAnzpzMP195h5feWcGfnnuVSz56HE/f9U3u/cszvLliLclUipFDqjn5yCk8tfAt/j7/rYLHeOyF1/nug49z0xXnctjo4Tz0j5dYu7WBonCIWVPGc9UnP8Rfnn+N/76xlP++sZRnF73Lnf9zCWVFRby6dBXTxtnZwN5ZtZ7fP7Owz+d4y6//aset/PIW7vjd31m2djMVJUV8dPYM7v7Tv1jlpPA955RZvPTLW7jnT/9i6dpNqIrCIUNr+PCx07nrD0/zxoq1fTrusrV2nMc1F57Ow/9aQDJlsGrTtoxYl2yee3Mp8USSR7/zFX78yD8IhwJcffaHqSwtzmm7dO1mzjnlWK4++zTeXrUe07Q8wzab7/zmcc48bibP/+z/8b0Hn6C5vZNLPno8Zx53JN/42aMZwfWCIAiCIOxd9qcMYHsLMVb2At//7d+Yd8JRXHvRGdRVVxAKBqjf2cLzby3lzt89mRHw/YOHn2Tl+i185YLTuejDcwkFdLY3t/HWyrX8ypdp61O3/B/3fPWz3PnlS9A1lVffW81Hr/0+//jJN/o1xs5ojBOvvpVbPncen//Eqdx85bm0dHTx1sq1/PpJW/kxTYtPfOPHXHPB6XzqY8dzw2c+QSplsGVHMwsWr2Tp2s27PM6tv36M599cxpfP/yjf/cKF1FSU0h1PsHz9Fu7+0z+5/+/Pe23P/uZPuOXKc7nsrJO48bJPsrO1g0eeeZlv3/enjLTFvWXbjhZmX/n/uPXz5/HNT8+juryUHa3tvPLuKi/mJRqLc9LVt/HNz8zj8588lbF1Q+iOJ9jU0MTzby5lQ33flZX5i1dyx+/+zmfOOJHPzTsVTVO9OiuFWLVxG+ffeDffueoCHrvzOpraOvjjf17l7j/9i3/dfUNG23v/8oxdO+XqCykvKUJVVa/OSjarN9Vz/FW3cPvVF/F/119OJBRk5YatXPHdX/I7X50VQRAEQRD2HuFgYLCHsFu0t/fOTb6srKzPfStWL0y49vZ2ysvLUY4+F0Xfvy+mIAiCIAiCIOwrTD5kGPdfdwllxRE++OAD3n333V3vtBeJxWLceeedtLW1FTQ2VFVFUZSCfViWhaIoGAXqDPaEKCuCIAiCIAiCMAiEAjq//fqlhMNhdu7cyfLlywd7SP3ixRdf3GN9i7EiCIIgCIIgCIPA0MpSwuEwqVSKV155hVSq727u+wL9rU7fG8RYEQRBEARBEIRB4OjD7JIEhmHst4YKgGmamKaJrqdNi4aGBu677z66urqYN28exx9/fA89FEaMFUEQBEEQBEHYy5w1eyo3feoMADZs2DC4g9lNrrzySgKBAPfffz8AHR0dHHPMMcRiMerq6rj77rt58sknOeOMM/rct9RZEQRBEARBEIS9yFmzp/K9K+ahqgrr1q1j2bK+14/bl3jllVc477zzvOcPP/wwqVSKNWvW8O6773Ldddfxox/9qF99i7EiCIIgCIIgCHuJX15+Ct+7Yh6KYhsqixcvHuwh7TZbt25lwoQJ3vPnn3+ec889l/LycgA++9nP9jt5wAHvBmaZBpjmYA9DEISBRlVRVG2wRyEIgiAIfWLMmDEoisLGjRsPCEMFIBwO092dLiy9aNGiDCUlHA7T2dm/wuUHtLFimQbW4n9AsnClcEEQ9lMCYZj5cTFYBEEQhP2KUCgEQGNj4yCPZOCYMWMGv//977njjjtYuHAhDQ0NnHrqqd72tWvXMnz48H71fUAbK5gmJGMoR34S9CAoCiiqb6miqOnHmdudxzjPVT1jP1TFLn6T1V/uYwVw/v3PCz1WNcDtO7sNWe0LHMNZqopiD11RUBVQ/Es1ex3p55Cxr+Ks9z/Pt1Sz2ulOX+4xVLddxrr08VRfP3abzPUKTh8oGUtNVXL6VfAfyxmX/7zI7FPJs7+3zmmrZfWteO3w9i342HccJWdMoGC313Cvt31+iu94OO3c1yv7Mb7HSp5jgfv2UNJ9Z7+tdrGve15uO3eD1we57fHenYrXhqw+MvbJ6dPfzl7Z0dHOqAlj7c+4GCuCIAjCfsI3Lvww1dXVQO+rvu8P3HTTTZxxxhn85S9/ob6+nssuu4y6ujpv+9/+9jeOO+64fvV9YBsrLloARQ/kNSjyGyt5Huc1VnqxX4axoeY3UHptrGQ/zneM9GPvxjLPMtdYyVz2ZJTsylhx99fVwsZKhnGS/VzxGST5jJWsNnovjJX0/r0zVrzHvv3zGSuZ7XpvrOQ8do0V7zXKNVbyGSj9MVbsc/QbK33bN6OdsyFtWBQwVvzH8dr231hxnwuCIAjC/sI3Lvwwl542C4C3336b1tbWwR3QAHLKKafw9ttv89///pdhw4Zx/vnnZ2w/4ogjmDVrVr/6PjiMFUEQBEEQBEEYJL6eZajs76mK8zFlyhSmTJmSd9tVV13V734lG5ggCIIgCIIg7CG+ceGH+dQBbqi4/PWvf+Wcc85h6tSpTJs2jXPOOYfHHntst/oUY0UQBEEQBEEQ9gDZrl8HqqFimiYXXnghF154IStWrGD8+PEceuihLF++nAsvvJCLLroIy7L61be4gQmCIAiCIAjCAHOwGCoA99xzD8899xxPPfUUZ511Vsa2p556issvv5yf/vSnfPWrX+1z36KsCIIgCIIgCMIA8vg3LzxoDBWA3/72t/zoRz/KMVQA5s2bxw9/+EN+85vf9KtvMVYEQRAEQRAEYTcJ6BolkRDfuPDDjB8/Hjg4DBWANWvWcNpppxXcftppp/HBBx/0q29xAxMEQRAEQRCE3eCs2VO59TNnEAgEvHUHi6ECEIlEaG1t5ZBDDsm7vb29nUgk0q++RVkRBEEQBEEQhD5SXhxh9uSxfOq0WXz38nmeoZJMJvn/7d13XNT1H8Dx193BsYeACDgQNffMDZIjU1NLzZ85UzEzUyszzVWZTU0tc7XcmVrONDW0XKho7r3AATKUIZtj3u8P4OJk4yHD9/Px4CHf732+n+/7A6d+3/dZp06demoSFYD27dvz/fff5/n60qVLad++fbHqlp4VIYQQQgghiqBBDSfWTn0NtVqtO3fr1i3Onz+PVqst9spX5dXMmTPp1KkTERERTJ48mfr166PVarl69SoLFizgjz/+4MCBA8WqW5IVIYQQQgghCqlBDSd+mjQEtVpNQkICycnJBAcHc/Xq1dIOrdS4u7vz22+/MWbMGLZs2aL3WqVKldiwYQMeHh7FqluSFSGEEEIIIQqhoaszaz4YhlqtJjw8nKNHj5KamlraYZUJ/fr1o3v37nh7e3Pz5k0A6tatS7du3TA3Ny92vZKsCCGEEEIUwa/v9sHe3r7AciEhIXh9v+cJRCSeBElUCmZubk6/fv0MWqckK0IIIYQQhWRlbkrjxo0LVdbZ2Zm3Xo7n+x2HSzgqUdIaujrz43uDJVEpBZKsCCGEEEIUkrHqv4VUT506lWc5Kysr6tWrx9iXPPHq1oaUlBRmrNrNoQs3n0SY5U6P1g35eGg3LCwsiI6Opv9nqwmLjsv3mj8/Ho6joyNarZaAgABenb+JkpjX3qCGk/SolCJJVoQQQgghiuHu3bv5vq7RaGjatCkmJiaYmJiw4K3+TP5hKwfP33hCEZYPvdo25vNRL6NUKgCwsbHBxtIs32TFwlRN9erVdcd169Zl9oje/LzraK7lU9PSCImM0R1XdbBBqVBy/2EMyalpOcorFFDVwRYXe1sWjH1FEpVSJMmKEEIIIUQJ8PPzIzAwELVaTf369alRowbfjuvPpO+3cOCcJCyQkah88frLKBQK3bmvN+7FLyis0HWcOXOG5s2b08ejGX08muVZLiQkhCHzNvL79GFUrlwZgMTERIZ/vY4b9x7oytlZmbPtYy9sbW115yRRyd/atWsZOHAgJiYmBq9bkhUhhBBCiBKSlJREUlKSbshYjRo1mPfmKyzadoCwqDhOXL1NZGxCKUdZOnq1bcyXo/vonZu9dhdbfc4VqZ67d++SnJxMkyZN9PY9yU6lUuHs7Mzur8ZjZqImPT0drVaLmZkZP00ayndbD5CckopCAaNedMfW1pb09HTS0tJ48OABp06dkkQlH15eXvTo0QNHR0eD1y3JihBCCCFECdNqtXoJy/sDugIZQ8UGf7mGWyHhpRneE2ekUvLpyF565z5Zs4ttR84Vq76goCCCgoLyfN3BwQEPDw/MTNQkJyfj4+NDfHw8HTp0wM7Ojk9G6MeSkJCAj48PcXH5z5sRGUpyE0xJVoQQQgghnoCshCUuLg57e3ssLS2xsLBg+eShjJ7/61OVsKSmpXPjxg2qV69OfHw8d+7cKXaiUhjh4eH4+Pjg5uaGn58f0dHRABw5coRGjRphaWmpK5uUlMSVK1eIj48vsXgqouxD+Qzp6UhW0lLQKhQZs6UUymx/KkGZ7Xu91zO/J/NYmZbLdbnUl+N7BZD5lf04r++VKv1zemV4pHwe98j8U5t5jVahQKsg81zGn+lKRWYxxSOXZb7RFIrMcxl/agvxpzbz+nQFKDPjUpJxTqkg8+vRcxnHyuzHCjLL6J9XkFkHCr0/05SKHPUqyH6vjPNZ5xSZ9WevU5HL9bpzmWVVj9St0JVDd22e32e7jyJHTKAgo7wq809FZvsU2e5HZjndW++R78n2vSKXe2X+WjN/L//dR+9tVcC1We3KKqd7r2TVQc7y6N6dCl0ZHqlD75ocdWYvl3EyNva/SZJCCPEkefVoD0BycnKxrtdqtbqdztVqNZ6entjb2j6VCcvgb7fl+drHPeoD8Olf1wx2v8jISCIjI/XOpaSkcO7cOYPd42k2cuTIAuesbN26tcj1VuhkRW2ixtjGlrgz2/MsU3KdVkKIkmRpY0uKiZqUNPlbLIR4MtZP7EujRo0AuHjx4mPXlzUcKSth2TBjBD1n/kBETOl9or92Qm+9VbbyEhsby9AFm4hNTDLo/Ve82R1nZ2cGfr2R2ASNQesWJcvKygozMzOD11uhkxVTE1NCQ0Oo0n10uX3DW5mbErhzGdVfGldu2wAVox3ShrLDytyU+97Lceo1jpSExNIORwjxlHB1dQXgwoUL3LlzxyB1JicnExAQgK2tLaamptR2cXiiyYoyW8/22Jefo1mzvFfTys7Ozo4f3hvC2G/XGyxhGfp8a1q1agVAm/qu/HPmukHqFU/GokWLZIJ9cZiamqIwUqMwyrmGdnmgMFJjbW1drtsAFaMd0oayQ2GkxtTUtLTDEEI8ZbKGrIaHF32ollKppFWrVvn2Wvz697/8e+1unq8bcmhUHZfKrJkyRG+uRpYrV67w8OHDPK81MjKiefPmNHZzYdP0ofT4eGWh72umNmbLjKHY2NjQddoyEpNSAPBsUocPBnXTlXOwzhlXdpWszAt9T1HySmq+CjwFyYoQQgghRGmxtLTExMSEevXq4ezsnGsZrVbLmr3H+Xbz/nzr6tixI5UqVWL/vU0cueRf7JjquFTm5/eHYmlpoXc+LS2Nixcv4u9fcN0ajYaOHTvi4OBQ6PuaqY1Z9ParVK1aFYBazg5cvhMCgNpYpVf2/Ve7EvAgEt8rt3PU42hrxbJ3BwMQHR1Nenp6oWMQJaOg1cAuXrzIihUrWLhwYZHrlmRFCCGEEKIENGzYkAYNGuiOE5NSmPT9Zi7dDtYrl5KWputhyE9CQgJGRkYkJBVvcj9kJCq/Th+OqakpDx8+xNfXV7d/SNa+IoWRmJgxBFelUuHmZM/t0Ih8y5upjdkxa2Sew4T+OXOdZTsOM+7l5wAwMTZi6TsDGb/otxwJy9pJA3CuYkd8fDzHjh0rVLyiZB04cAA7Ozu9czExMWzYsIEVK1Zw6tQpmjZtWqy6K3SykpSSwuzlm0lKKfgfgLKqIrQBKkY7pA1lR0VphxCi4mrQoIEuUYmLiyM5OZkpK3Zx6kZAses8efIkAGduBhaqvFKhoGvL+lSpZJV5rGRk93a6RMXHx4eUYv47Gh8fT2RkJHZ2diyfPIw1e4/n++l65+b1cHR0JCUlBWNj41zL/LjTh4EejbC3twcyEqGF4wfwzpLfOXH1jq6chUVGj9DZs2dJSHg6N9Qsazp27Kj7/tChQ6xYsYItW7ag0WiYMmUK69evp06dOsWqW6EtxC4uMTEx2NjYoGjVH4VR7m8wIYQQQoiKzGfB21hbW+Pn58f58+fzLGdmZkbPnj0BmPfbPtb9/e+TClFHqVDwmddL9G7fJMdrj5uoZMlaetnW1rZQ5VNSUjhy5AitWrXCysqKX/adYP7vf+uV8f7sdZycnAgODkahUODs7Ex0dDTPTV6iK5P1ezh06FCx5g+JotFoNMyZM4fo6Gisra1zLRMSEsKqVatYuXIl8fHxDB48mCFDhtC+fXvOnz9Pw4YNi33/Ct2zIoQQQghhKF9vPsjno16mTp06pKSkcOXKlRxlatasSbVq1YCMh7wnlagsHOJOrVq1UKky5n6Ympri4OBAeno6QUFBunkdGo2G69evP3aiAv8tvVy3bt0CFz1JT0/H39+f6Ohorl+/TqtWrXjthbZ0b+ZGUlISd+/eZfyaQ8xav58l7wzExcVFt3GjWq1+7FhFyXJzc2PAgAEsXbqUF154AaVSabC6JVkRQgghhCiEnb4XMTNRM3NoD+rWrZsjWXl0jkpxN44sqtb1XPH09MTISP+xLj09nRMnThAcHJzHlY8vOTmZS5cuFemau3fvolKpaNGihW4OS7Vq1eh++QHep66y/9x1urdqiI2NTUmELEqAq6srR44coUaNGri6ulK/fn2D1S3JihBCCCFEIf19+iozh/ZApVKhUql0E9Jr1aqlS1R+2XeCwAcPOX4150pWhlbTyZ4fJg7CyMiI+/fv6yUmERERut6JsubWrVtERUVha2uLg4MD1atXZ+6Yfswe/qJuY8Hbt2/j5uZWypGKwrh+/TpHjx5lxYoVtG7dmrp16zJs2DDg8Zc1lmRFCCGEEKKQYhI0aDQaTE1NcXd359ixY6SlpemW4/1511GWbD/4xOJxb1QLIyMjIiMjOXbsWLlaxjcyMpLIyEhu3bpFamoqbm5umJmZodVqOX/+POHh4ZKslCMeHh54eHiwaNEiNmzYwMqVK0lLS2PcuHEMGTKEvn37Urly5SLXa7gBZWWMkUrFovdHEub9M2HeP/PdpJGoVGW3uWpjI36c9gZ+W74j6u+VXN44H6/enXSvl7f2mJoYc33Tt0TsXa47V97a8FKHlpxe8xUx+1cRuGMZb/brCpSfdrhUrsTWOZN48NdP3N/zE7998S6OlTK61MtqG8b9rxsnVn5BwqG1bJ0zSe+1gmIuq20SQlQsqWnpjP3uN+I1STg6OuLu7o6xsbFuzoZf0IMnGk/WZ9ZxcXHlKlF51JkzZ/D29uaff/5hz549enu9GBkZYWtpVorRiaKwtLTkjTfewNfXl8uXL/Pss8/y4Ycf4uLiUqz6Kuz/5DO9+uHRtB5NhkyhyZApdGhWj+kj+pZ2WHkyUqkIiXhIt3e+wLbrKEZ99gPz3h7KC20yVvEob+2Z/cYA7j2I1DtXntrQvV0zlkzxYtLCtdh2HUWToVM4eCZjbHJ5acfSyaMAcOv3NrX7v4OJsTEL3xsOlN02hIQ95MvV21i+I+fGaAXFXFbbJISoeM763WP8dxtJSUnB0dGRHj16YG1tTWpqKhduBZV2eOVWXFwcUVFRuj1cYmNjSUxMxNjYmJ8mDZWEpRxq0KABCxYsICgoiN9++61YdVTYZMWrdye+XL2N0IgoQiOi+GrNdkZl66koaxI0SXzy82ZuZX4ic+KyHwfPXMGjWT2gfLWnRd2avNi+BXPX/qF3vjy1YfYbA/h85VYOnb1KerqWqNh4rt/NGAdcXtpR08WRTfuPE5+YRFyCht//9qVRrepA2W3DtkMn+ePwKcKjYnO8VlDMZbVNQoiK6azfPd74Zj3xmiTUajWJSSm89d1vBEeUzTki5VF6ejo+Pj5oNBrqVa/CH7NG0bRWVd2clkLsviFK2aFDh9i9ezexsbG88sorxaqjQiYrtlYWVK9iz7mbd3Xnzt24i6tzZawtykdWbqI2pnXD2lz0CyhX7VGplPw4/Q3enr+SpOT/lkUsT20wNzWhZX03rCzMuLxxPkF/fs+Gz96hip1NuWrHwg27+F+XdlhbmGFjac6gbu7sPna2XLUhS0Exl8c2CSHKv7N+93jz2/Xcu3ePcd9t4N9rd0o7pAonNjaWw4cPk5iYiK2tLb9MH4mxsTHR0dFERkYWXIF4IubNm8esWbN0x1qtlh49etC5c2d69+5NgwYNuHz5crHqrpDJiqWZCQBRsf/tahoVFw+AlXn5eHD5efoYbgaGsvXgyXLVnkmDe3HRL4BDZ6/qnS9PbahkZYFSqWRYD09enDiHugMmkpKaxtpZ48tVO45euEHlStZE7F1OuPfPVLK25IvV28pVG7IUFHN5bJMQomK4eCuYXrPXFHpXeUOyMjfVbfqYmpr6xO//pMTGxuLj46MbHhYdHc3hw4elZ6UM2bBhg97Gj5s3b+bw4cP4+PgQHh5Oq1atmD17drHqrpDJSlxiEgA22cY22liaAxCbkFgqMRXFsg9ep24NZ16ZugCtVltu2lOrqiPj+ndjyuJfc7xWXtoAEJeoAWDJpr8ICA0nPjGJT5ZvokurRqRn/sNY1tuhUCjwXjSDYxduYN3FC+suXhw9f509C6eXq99FloJiLo9tEhXDxz0Mt5eAKN+WjujIjg+H4WSX+w7fxeFsZ82OD4fx4+vPA7B8TDf+mDkUU7URKqWCHbO8aOjqjEaj4ebNmwa7b1kUGxvLwYMHOX/+PIcPH35ie9iIwrl9+zZNmzbVHe/evZv+/fvj4eGBnZ0dH374Ib6+vsWqu0ImK1Gx8QTej6D5MzV155o/40pAaDgx8WX7wWXJ5FG0blibHhO/0sVaXtrj2aw+lStZc3H9PIL+/J7NcyZhbWFG0J/f80x1p3LRBoDouATuhoSR2wc20XEJ5aIddtaW1HSuzOLf/yIxKZnEpGSWbPoL9yZ1USmV5aIN2RX0d6C8/B0RFcvwbm3x8PDg+Rb1SjsUUQbUq1cPV1dXvOe+jbMBEhYXextWTHkNV1dX2rZty9w3+tK6dWtq1qzJs8/UoF51J+zs7EhNTcXHx4e4uDgDtKJsS0hIwM/PTxKVMiglJQUTExPdsa+vL+7u7rpjFxcXwsPDi1V3hd1nZfWug0wf2ZejF64DMG1EX1bsPFDKUeVv8WQv3JvWpeuEz4mKjdd7rTy057d/fPnr+HndsXuTuqz4cCzPDp9GRHRcuWhDlp//2M+EAd3xPnGeyJg4PhrVn/2nLhOfmFQu2hERHcvNwBDG/a8bn67YAsC4/t0IvB9BRHRsmW2DSqXESKXCSKVCqVRiojYmPT2dlNS0AmMuq20SFVe9alVwcnLCxUF22Rboli4G2PyRF56TFul64wtdh9qILdOH4uzsjFKpRKFQkJKSgrGxMT3aNNKVs7My1220p9FoiImJMUwjhCimOnXqcPjwYWrVqkVAQAA3btygY8eOutfv3buHvb19sequsMnK5yu3YW9jxeUN8wFYv/coX63ZXrpB5aOGkwPj+ndDk5TM7W2Lded/9T7CuK9XlIv2aJJS0CT9twpKZEwcWq2W+5EZ58pDG7LM/eUP7KwtObt2LgAHz1xm+OylQPlpR78PFvDNu68RuGMpSoWCszfu0veDjJjLahtmjuzHrNH/0x0nHFrLwTNXeH78ZwXGXFbbJCquz3/dQ3rYbX4/eLXgwqLCCwoKon79jGGBSqUSI5WS5NS0Ql9vqjZi0YRXqVatmu5cdHQ0R44c4ZlnnqFOnToolRkDYqYN7k5KZt1ldYd68XR56623mDBhAj4+Phw/fpz27dvrzWHZv38/LVq0KFbdCm0hZifFxMRgY2ODolV/FEbGxbqREEIIIURF5mBjiUqpIDI2QZdMFIap2oids7xwdHQkJSWF48eP6/YYyZLV0+Lh4aHbBfzhw4f4+PiQkpKSV9VCPDaNRsOcOXOIjo7G2jrvIY4rVqzgzz//xMnJiVmzZuHk5KR7bdy4cbzwwgv069evyPeXZEUIIYTIg4ONJZ5N6mCkyvhEW6vVcvzqbe6FReV5TYfGtXG2tyExKZn9Z2+QkCTj60XeTNVGLH57IG3q1yQlJYUjR47kuySvSqXi2WefxcjIiFOnTkmiIkpcYZKVtWvXMnDgQL15K4ZSYYeBCSGEEI/DzcmejTNH6s1FgIyJpKMXrOec/70c18wY0oOBnVvqjq/eDWXMN78Sk6Ap8XhF+dSrbZNCJyoAaWlpnDx58glFJ0TheHl50aNHDxwdHQ1etyQrQgghxCPcnOxZPnkYpqamxMXF6eYFWFpaYmNjw7KJg/j7zDXINjbB0daK9o1qodVqCQ0Nxc7OjgauTvw0aSheX68lMVk+ARc52VlnLLN+79492eRQlFslueeNJCtCCCFENlmJioONJVFRUfj4+OiWSlWpVLi7u+Po6Egf92Y5rtVqtZw+fZq7d+9Sq1YtWrRoQQNXJxq7uXDy+t0n3RRRxrnY29CvQ3MAWY5XlHtZK9QZmiQrQgghRKbsQ78eTVQgYwjO0aNHqV69eq5js8PDw4mMjMTGxka3QdqBczc4czPgibVBlA+maiM2f+SFhYUFsbGx+Pn5lXZIQjyWkSNHFjhnZevWrUWuV5IVIYQQAv2hX7klKlnS09O5ezf/XhIXFxdUKhWnbwQw+YctpKWX3BAJUT41cauKhYUFSUlJHD58GI1G5jWJ8s3KygozMzOD1yvJihBCiKdeQT0qRZU1HOLmvQekpqUbKkxRgWTf1FESFVERLFq0qEQm2CsNXqMQQghRjhS2R0UIIUTuSmq+CkjPihBCiKfciokDsM9lMr0QJalZ7apAya6iJMSTUpLvY+lZEUII8VQzN89YOvbMmTOSqIgnYkiXVkzo2wmAgABZfEGUfwcOHMDOzq5E6pZkRQghhCBj4rwhqNVqXFxcAEiU5Ec8YkiXVkwd3B2Aa9eucfPmzVKOSIjHN3fuXOLj43XHX3zxBVFRUbrjiIgIGjZsWKy6ZRiYEEIIYSBqtRpPT09sbGwIi4pj06GzpR2SKAOa1a7KV6+9gLGxsW4C8rVr17h8+XIpRyaEYXh7e5OUlKQ7njt3LoMHD8bW1haA1NRUrl+/Xqy6JVkRQgghDCArUbG1tSUxMZHRC9YRFB5V2mGJUta6niuL3x6ImYmx7pwkKqKieXTOiiHnsEiyIoQQ4qllpFKiVBpmRLS7uzu2traERcUxesE67oRGGKReUX61rufKDxMHYWRkRGhoKIGBgSQkJBAeHl7aoQlRbkiyIoQQ4qlkpFKyYGx/TExMSE1NJSEhodh1GRsbY29vD8Abkqg81VRKBVUdbHFzdmDuG/10iYqvr6/B5kUJUdYoFIocyxcbajljSVaEEEI8lda925cGDeqSlpaGr68vKSkpxa7L0tJS9/3d+5GGCE+UU3u/GIODg4PuWBIV8TTQarWMHDkSExMTIGOz07Fjx2JhYQGgN5+lqCRZEUII8VSytrYG4MqVKzx48KDY9djY2ODh4QHA/fv3SZd9M55qWROKU1JSCA4O5syZM5KoiApvxIgResfDhg3LUWb48OHFqluSFSGEEE+1x+lRsbGxwdPTExMTEy7eCmLswg0GjEyUN63ruaJSqQDYt28fiYmJpRyREE/GqlWrSqxuSVaEEEKIIlCr1dSuXRsTExOqVauGiYkJERERjF24gbjE4g91EOVbizrVWPz2QBQKBcHBwZKoCGEgkqwIIYQQhZR9eeIsF24F8ZYkKk+9L197ATMTY0JDQzlx4kRphyPEEzVq1KhClVu5cmWR65ZkRQghxFOpqEsWZ09UwqPj2HL4LFFxCWw/eoGEJNmp/mlnZJTxSBUYGChzVMRTZ/Xq1bi6utKiRQuD7rECkqwIIYR4yqiUCnq3a4KTkxNAoYbrZE9UNBoNr8+X5YmFvqylrxs1akRkZCRxcXGlHJEQT87YsWPZuHEjt27dYtSoUQwbNgw7OzuD1G2YnbCEEEKIMq5xTRd8v3uPMz/O4FOvl1AoFPj7+xMaGprvddkTlbCoOAZ+sVoSFZGD16Jt+AeHYW5uTocOHQy2x4QQ5cGyZcsICQlh6tSp7Ny5k+rVq/Pqq6/i7e392D0tkqwIIYSosIyNVDSrXZXnW9Tjh/cGY25uDmTsCXDjxg3OnTuX7/XZE5XExETZmV7kKTI2gdHz15Geno6FhQVmZmalHZIQT5SJiQmDBw9m3759XLlyhUaNGjFu3DhcXV0fq6dRhoEJIYSokBxsLNn2sZduPxWAsLAwTpw4QWpqKmlpafle/2iPiiQqoiCRsQmkp6ejVCqxtbXVDQ0T4mmTtaO9Vqt97DlckqwIIYSoMNRGKr6bMIC61argYJOxq3xycjJJSUlERkZy9uzZApMUkB4VUXyBgYG4ubnRpk0bfH19uX//fmmHJMQTkZSUxNatW1m5ciVHjhyhd+/eLFmyhB49ehR5QZPsJFkRQogK4uMe9QH49K9rpRxJ6Xnr5edwb1Rb79zhw4eJjo4udB3SoyIex//mbuTrMf14/tn6NG3alH379pV2SEKUuHHjxrFx40Zq1KiBl5cXGzduxN7e3iB1K7SFmPUSExODjY0Nilb9URgZG+TGQgghhKHZW1uwf8FEvXMREREcOnSoUJM8JVERhlCnamW2fDIGjUbDrl27SjscIR6LRqNhzpw5REdH6w2rzU6pVFKjRg1atGiR7+ISW7duLfL9pWdFCCFEhRERE0+zN74AYNzLz/HmS57Y29tjaWlJbGxsvtfK0C9hMIbdZkKIMm/48OEltgKeJCtCCCEqnD7uTRnTuwMA/v7+RUpUpEdFCCGKZvXq1SVWtyxdLIQQokLp496UT0b01u2jIssTCyFE+SU9K0IIISqMPu5NmT2y8IkKQKtWraRHRQghHkPnzp1zHQZmY2NDvXr1GD9+PNWrVy9W3ZKsCCGEqBCK2qOSxdbWFoAZK7ZLoiKEEMXQvHnzXM9HRUWxe/dulixZwpEjR/Islx+DJyttGtZm6vA+PFvPjSp2NkTFJXAr6D6+F28yZfE6Q9+uXKjv6sIHr71M55aNcLK3JSY+kZNX/FiyyZu/jp/Ptfz0EX1xb1oXF4dKxCUmEXA/HN+LN5j5/W/EJiQCsPLDsfTv3Bab570KjKFb26ZMGtyLRrWqY29jSUR0HDcCQ/A+fp6vf9mR6zWbv3qPfp3asHSzN+8sWJ3j9Y4tGrB/2ce5Xrtl/wlenbnQIDH5b13E5VuBvDx5nt71VuZmTBjQnb4dW1O3hhMmxsaERERx4PRllm7y5uyNO/neH8Cxkg0TB71IT48WuDk7YqRSEvgggj3HzrF081787oUC8PHr/Zk1+n+66xI0SUTGxHHl1j3+PHqWNbsPEZeg0at75YdjGdGro+44OSWVwPsRbD98ks9WbNX9Hh+Hq5MDt7YtZsridXyzXn/FGTcXR94b3JMX2jSlehV7tFott4PD2H7oJD9s20dw2MNc44xP1BAWFctFvwC2HzrJ+r1HSU5J1av7n6Uf0enZhrpjTVIyt4IfsGHvMeat20FKasH7WBTWT9PfoG2jZ6jmaIeJ2pjA+xHs8DnF17/sJCI6/3kI4ulhY2HGJyN6oVQWLVGxsbFBrVYDEBkjG/gJIURxfPvtt/m+Pn78eGbMmMHu3buLXLdBk5We7i3Y/vVkDp69wrSl6wmJiMLZ3paWDWoxsGv7pzJZ6dexNetmT+BW8AO+WLWN6wHBVLGzYWSvjuz6dhrz1u1k2tL1uvLN69bE58dPuHoniM9XbuVOSBgOtlY0fcaVgV3bs+DXP4v8kPtmv64s++B1tuw/wTsLVhEZE0f1Kva0b1KX/p3b5pqsVK5kTS+PZwEY0s2DKYt/JSk5Jdf6Z3y/kYOnL+udi4iOM3hM2dWq6shfC2fgWMmaH7f/zezlm4lL1FDTuTIDurTj1JqvqNR1FDHxef+sWjeszY55U1AoFCzdvJfjl26QnJJKPVcXhnTvwPEVn+HQ/Q29a16c+BXRcQmojY1wcahEl1aNmTthCJOH9qbPlHlc8AvQK5+gSaLrhM8BsLWyoH/nNrw/pDdNa9egx8Sv8m3j4+jl0YL1n75DeHQsSzd7c+7GHbRaaFy7Ol69O9HTowWtRkzPNU4zEzXVq9jTo11zfpz2Bu8N7kXP9+YQFBapdw//e/d57ZMlQMb75fWXOvPZm69So4o9Y+cuN1hbLExN+fmPf/C/dx9Ncgot69dixsi+vNi+BS1HTDNoYiTKr0pW5iiVSlJSUgqdqFhbW+Pp6YlKpSIiIgL/kLCSDVIIIZ5Sb775Jt27dy/WtQZNViYPe4nbIQ94ceJXpKWl687/9rcvU5esz+fKkmGkUqFFqxfLk1SrqiNrZo3jon8gXcZ/RoImSffa5v0nWDplFFOGvcTZ67f57W9fAN4d+CLp6Vq6jP9M75P6LQf+ZdZPm4oVx9TXXubQ2as5ejrW/XUkz2XmXnvRE7WxEbuOnqGXx7O80qk1G/Yey7WsX2AIJy77lXhMWZRKBVvmvI+DrRUeYz7m8q17utcOn73K2t2H6dGuWb4PsVbmZmyb+z6a5BQ6jJml9yB+6OxVftr+D/07t8lx3elrt/U+zf/tb1+WbvbmwLKP2f71ZOoPnKTXC5GertX72XgfP4+bSxVeaNuUms6VuZPPw9GIns+x8qO3ULUfnO/P41E1nSuz/tN3uBEYwvPjP9NL2A6cvszi3/+iX8fWetc8GifAL3t8WL3rIDvmf8DvX07E4w39XrTEpGS9a/b4nuPS+vkM7/kc7367Js/ktqiGzlqsd3zg9GXiEhJZ+sHrdGhWnwOPJMri6TSkS8Z7Ojk5udDX1K1bFxMTEy7eCmLswg0UYhsWIYQQxWBmZoZGoym4YC4MuhqYvbUl4VGxuSYHj27GpVAomDz0JS5vnE/CobWE7PqB1R+/RdXKdnrl/LcuYuWHY3PU98/Sj/hn6Ue6444tGpDmu4FhPTow7+1hBOxYSuLhtdSp5gRA93bN2Lt4JpH7VhB7YDWXNsxn6vA+enW2rF+L7V9PJsz7Z+IPruHUmq8Y8Hy7Yv88Jg7qiYWZKe9+s1ovUckyedE6HsbEMX1kX905extLYuITcwwpehz2NlaEhj/M9bW8Nknz6t2J0IgoRn76PQmaJEb27mSweIobU5a+z7WmaZ0azFn7h16ikt1fx8+TmJT3Q8voPl1wdqjEtKXrc/QYZNly4N9848hywS+AL9dsx9W5MgO7ti+w/OlrtwCoYmdTqPqL6r3BvbA0N2XCvJV59ixtO3SyUHXt+/ciy3fsp13jZ/BsXj/fsmlp6Zy/eRcTtTG2lub5lj215isOfj8rx3mlUkHAjqVs/uq9fK8Pi4oBIDVNelUE/PZ+fwZ2bolWq+Xy5cInr0ZGGZ/X/XHsAnGJOf+NFkIIYRh79+6lbt26xbrWoMnK8Us3adf4GRa+N4I2DWtjpFLlWXbZB6OYO2EIf/97ib4fzGfWz5vo3rYZR36ajb2NVbFj+OKtQdRwsmfc1yvoM2U+Dx5GM+qlTvy54AOUCkXm+Xks+f0vqjn+lxh1erYhPj9+go2lOeO+XkG/qQs4f+MOGz9/lxE9n9O7h//WRfhvXVRgLF1bNyE0IirPXofEpGT2/XuRJrVr6B5cfS/exKVyJdbNnsBzLRpgamJc7J9FluOXbvJK5zZ8/Hp/mtapgVKZf89F+ybP0NCtGr/s8SEyJo6tB/6lS8tG1HSunGt5pVKJSqX/ZeiYsnuhbRMA/jh8qtDXPKpbmyakpqax88iZYteR3U6f0wAFPtADuLlUJiU1lVvBDwxy70e90Cb/911R/de2BgWWrelcmYcxcbpkIi+r/zyIZ/P6ug8TsnRr05Sqle1YvetQjmtUKiXmpia4N63L7DGv4nPuGkcvXC9CS0RF5ebmBsDZs2cJDAws5WiEEOLps2PHjly/fvnlF9555x0++OADJk+eXKy6DToMbPqyDdRzdeHtV3vw9qs9SE5J5eRVf/48coalm72Jz/zkqp6rC2P6dmXZlr28+81q3fVnb9zh+IrPmTjoRT768fdixeAf9ICBM7/THVuYmTD/ndc4euGGbkw+wP5T+p++LZkyisu379H17c91PUN7T1zAwdaKz8cOYu0eH90n/oX9NLeGkwPnCpjkfTvzgbWGkwP3I6NZsP5PWjWoxeBuHgzu5kFqahoX/APYc+wci37fQ3hU0ScUv/X1crbOfZ9Zo//HrNH/I0GTxLGLN9h+6CQ/b9+foz2jXuoMwKo/DwKwcucBhr3oycjeHfnk58056t/4+bs5ztUdMBH/e/cNFlN2Nao4AP/97IqjupMDYVExufZ4Fcfd0HAAXBwq5XgtK3mzsTBnwPPt6NexDXN/+YOwh/oP9EqlQm8InFKp1Ls+S3q6Nt/ep8K874qiMG1zsLFi7Csv0Lphbd6au5z09Px7x9Z7H+XrCUMZ0es5vb/rI3p1JDQiij2+5/TKt21Uh2PLP9Md7z56lsEfLyrwPqLiMzMxRpX5wVhYWNHmnBgbP/6HQULkR6VSoVKpSJNeYFHB9e3bN9fzVlZW1K9fn9WrVzNgwIBi1W3QZCUyJo5Ob82mZf1adGnViFYNatGxRUO+GjeYMX2fp+2oD4mIjqVz5ipCax759PTkFX+u3L5Hl1aNi52sbD1wQu/YvUldbCzN+WHrvjyvqV2tCg1qVmXyoowFALI/HO45do7eHVpSr4Yz1+4GA1BvQP5DVIoi6+E06+EzOSWV/tO+ob6rC93aNaNV/Vo816IBM7368Wa/rni+OYsbASFFusetoAe0eG0aHZrVo+OzDXV1dm3dhJG9OvHc2E908wsszEwY0KUdRy9c53pmew+dvYrfvVBG9OzI7OVbcjwoT12yPse8gcD7+S//WZSYyoO8ptlYmpuSfORXvXMb9h7N9f19c/N3ufZePXr97OWb+XTFluIHW0R59Xk1rl09R2xfrdnOT9v/KbDOyJg4/jxyhuE9n+Pjnzah1WqxtbLgZc+WLN7knWMo6UX/QNp4zcTcVE2zZ1yZ+lofvL+bQdcJn+c73E9UbGYmxix9ZxBGRkYkJSWRkFD41byaNGmCo6MjANcD8/5gRYjiCI6IJjk5GbVajbu7O8eOHZOERVRo6eklNz+8RPZZOX3tlm5cvpFKxZzxg3lvcC+mDHuJaUvXY5c5zCskPCrHtcHhD3F1cij2vUMj9OusbGsNwL0HeT88Zw3Bmv/OMOa/MyzXMg62VnC3aLEEhIbj5uKYb5msh9NHH+6v3Q3WJUcA7w56kW/eHc6nY15l0IffUVRarRafc9fwOXcNAHNTE5bPGMPAF9zx6t1Jl8wN7NoeKwszNv1zHJts8w42/XOc6SP68kKbJuw9cUGv7tvB93W/75KI6VEB9zM+6XdzcdQlVEUVGBrOM60aY25qYpDeFVenjN9j8CPzcBI0SXR661MAnOxteG9wLwZ38+CCX0COFc/6TJmHSbZPent5tGDW6P/RxmumXrlH7/GowrzvisLVOfe2+d0LZchHi1EoMpZQnjGyH9NH9OWiX4BuwYj8rPrzIP27tNW9pwa94I6piTrHhxiQ8XPMeo/5nLvGv5f98F3xOWP6Pc93G/cYoJWiPFo38RXq1KlBcnIyR48eLfR/lk2aNNGNnf583R4u3AoqyTDFUyghKZnR36zn+4mDcXR0pEGDBly6dKm0wxKiXDLonJXcpKal6T4FblyrGgCRmaspOTvY5ijv4lBJb6iTJjlZ7wEui0Me81oe/dQ/a+x8NUf7PGPMut9Xa7bTxmtmrl/nbhYxUwH+PnkRJ3tb2jaqk+vrZiZqurZpwkX/AO5HRudb13cb9/AwJo5GmT/Dx5WgSWLO2j8AaFzrvx1FvTKHgC18bwSR+1bovqaP6Av8N0SsJOQV06P2Hs9Ilvo816rY9/I+cQEjIxUvdXi22HVk91KHlgAcOnNV73x6ulaXvO86epae783hkn8gH7/eX2/OFMAl/0Bd2dPXbnE3c6Ww7OdOX7tFSAHJyt4TF/J93xW/bVf0zmuSUjh97Ranrt5iy4F/eeHtLwiNiOKbicOxMDMpsF7vE+cJCotkZOYeLyN7deT4pZtcvVPwg+Opa7dIS0unbnXnYrRIVBSWlpYAXLp0iYcP8/97keXRRGXTIcPMWxPiUef9g1iyPePDFyur4s/FFaI86NmzJ9HR/z3LfvHFF0RFRemOIyIiaNiwYS5XFsygyYqTvW2u5xvUrAr898ns/swhQ0O7d9Ar16pBLRq6VdObT3I3JJwmdWrolXumuhP1XAv3kHLs4g2iYuMZ069rnmVuBIRwIyCEZnVcczwYZn0VZ3WuhRt3k6BJ4rtJIzE3zfnwNu/tYdhZW/Llqm26c3n9DJ0dKmFtYV7gp+q5Kezvpb6rC+5N6rJl/wm6jPs0x9ffJy/ysmdL7KwtixxDcWPKzR8+p7jgF8C04X3yTN66tW2KmYk6zzpW7jxASPhD5owfgkvlnHMxgBzL++alaZ0aTB/Rh9vBD/j9n/x7FJJTUnl7wSrMTNTM9OpXqPqLauHG3cQlaFgyZRTWFma5lunbsXCJXtfWTXj95c4cvXC9wMnskTFxzFi2ASd7WyYM6FFg3enpWtbt8aHPc63o0KwerRvWZnXmPKmCdGzREJVKiV8+86JExZfVk+Lo6FjgkueQ8cAoiYp4khKTys9wZiEeh7e3N0lJ/41UmTt3LpGR/622mpqayvXrxVsUx6DDwPYsnE7Qgwj+PHKGa3eDUSoVNHumJpMG9yI2PpFFv/8FZCQHP23/mwkDupOu1fKX7zlqOldm9phXCQgNZ+HG/3a3XLfHh19mT2DJ5FFsPXgCV6fKTB7Wm7CHhZtoHp+YxJTF6/h5xpvsXTyTFX/s535kNHWqOdH0mRq6ndnfmrucXd9MZc+301iz+zBBYZHYWVtSv2ZVnq1XU2/S/vVNGbt0FjR35VbQA0bMXsYvn4znxMrPWbhht25TSK/enXjRvQXzf93J7/8c113z47TR2FhasPXgv1y+FUhaWjr1XV14d1BP0rXpzPtlZ6Hand3F9fPYf+oSf/mexz/oPqZqY9o0qsOkwb0IjYhi5c4DAIx6OaPXZN6vOzl5xT9HPVYWZnRt3YShPTqwOPN3WVyFjSk36ela+k9bwF8LZ3D0p0/5YdvfHDx9mXhNEq5ODvTv3JbeHZ7Fvtto8lqNNCY+kX5TF7Bj3hROr/6KpVv2cvziTZJTU3mmmhNDenSgWZ0aOZb4bVnfjei4BIyNVLpNIYe96MmDyGj6TJlXqA0KD5+9yu6jZxnZqxNz1+7Id6+V4rgTEsaQjxex4bN3OLN2Dks379VNuG/gVhWv3p1QKBRsP/TfampKpULXE2OiNqZGFXt6tG/OgC7tuHo3qNBDD9fu8WHi4F68P6QXyzbvLXAD01V/HmTq8D6sm/02CZqkHMPHenm0YPTLXdh55DR3Q8MxVqlo2aAW7w58kZuBIazYsb8IPxlRnjxT1ZFBnVuiNjbiXthDVv3lS/Ijf7/8/f1xcnKiWrVqmJqaEh8fr/d6SkoKN2/e1M1lMTHJ+NAoNjZWEhUhhDCgR0c2FbQNRVEYNFn5cvU2XvZsybuDeuJsb4uJ2piQ8If8c+oic9b8oTcHY9zXK/G/94BRL3ViXP9uRMcl4H38PDO+30hkzH+7n6/fexTnypV4s29XRvbuyCX/QMZ/vZKPXu9f6LhW7jxIcNhDprz2Mj9NH4NCAXdCwlm7+7CuzMEzV2j3+kfMGNmXbyYOp5KVBRHRsVy5E8TmbMkEkO+SzI/aevBfro4MYsqwl/jo9f5UsbMhNiGRk1f86T1pbo5Vj5Zs8ubVru0Z/XIXqlauhIWZCWEPYzl+6QYjP11WrOVoZyzbQLe2TZk+oi9O9jYYqVQEPohgw96jfLl6O6ERURipVAzr4cnZG3dyTVQAdh87S+D9CEa91Pmxk5XCxJSfW0EPaDVyOhMG9KBvx1aM7ddV937zOXeN58Z+ku/u9ZCxoEOzYVOZOOhFBnRpxwfDXkKlVBL4IIL9py7zzoJVOa7ZszBj13dNUjKRMfFc8g9g2tL1rN51qEi9b9OWredsu7l8OOoVRn/xY6GvK6xdR8/SbNhUJg3pxZv9nqe6oz3pWi23g8PwPn6eJZv0f3/mpia61bYSNEmEPYzhgl8Ab875mV+9jxR6l3itVsuMZRv485upvDvoRT5fuTXf8jcDQzl64ToeTevx619HcvzO/O7dJzk1lZler+jmlt0JCWPlzoPM/eWPAn/HonxqXNOFH94bjJW5qe5cs9rVmLh0k17CMmHtYTqdD+Xbcf1xcHDAwSHnfEdnZ2d8fHz0EhlD/icqhBCiZCm0hfhXOyYmBhsbGxSt+qMwkqUehRBClIzGNV1Y/cEwjI2NCQ8P5/79+9SrVw8jIyPu3btHr9lrclxTv3oV2jZwy3G+X4dmuDk7EB8fj4+PDxYWFnh6ehITE4Pn+4ufRHPEU65fh+Z8MqIXwcHB+PoWvPCIEGWRRqNhzpw5REdHY21tnWsZlUpFaGgolStnLMpjZWXFhQsXdPtg3b9/HxcXl2Ktilciq4EJIYQQRZXVo2JsbExYWBhHjx4lLS2NyMhIPD09qVatGiqlgrRH9te5Fnifa7ksP7zrxCW2zxqFlZUVnp6euh6VR4eLCSGEeDxarZaRI0fqhttqNBrGjh2LhYUFgN58lqKSZEUIIUSpMzdRs2rKUNRqtV6iAhn/6RVHeHQcfWevZPn7Q3FzzhgiFvAgktHfbjJY3EIIIWDEiBF6x8OG5dwKZPjw4cWqW5IVIYQQpa5GFTvUarVuz5SsRMXCwgIPDw8gY+nLR3tVChIeHcfoBb+yacZrKBQKRs9fx/1CLtAihBCicFatyjnP11AkWRFCCFHqmtfOWIY8JSVFb0xzixYtMDc3xz84jDcW/FqsusOj4+g89XuDxCmEEOLJKvFNIYUQQoj89HFvytRB3QAICAjQe83c3ByAr9Z7ExEjc02EEOJpIz0rQgghSk0f96bMHtkbhUKBv78/V65cybVcajFWkBFCCFH+SbIihBCiVPz4+vO0bdtWl6icO3cuz7KF2aFeiLJGKe9bIR6bDAMTQghRKmrVqoVCoeD27dt5JipZK4FN+t/zWJqZPMHohHg8LvY2vN7THSj+inZCCElWhBBClBKlMuO/oNDQ0DzLnD9/nqSkJJrUqsqhbybi/dnrfNyj/pMKUYhiW/ZmL6o62BIbG8vVq1dLOxwhyq1CDQPL2khLm5ZSosEIIYR4ehw8eBClUklSUhLp6em5ltFoNOzbt4927dphamqKRqNBo9GgTZX/j0TZdvz4ceLi4rhx44b0rIhyLWtDx6x84ElTaAtx53v37lG9evUnEY8QQgghhBCijAkMDKRatWpP/L6FSlbS09MJDg7GyspKJjkKIYQQQgjxlNBqtcTGxuLi4qIbvvskFSpZEUIIIYQQQognTSbYCyGEEEIIIcokSVaEEEIIIYQQZZIkK0IIIYQQQogySZIVIYQQQgghRJkkyYoQQgghhBCiTJJkRQghhBBCCFEmSbIihBBCCCGEKJP+D7fXmTbIxGzDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pylab as plt\n",
    "from cmocean import cm\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "cmap = copy(cm.ice)\n",
    "cmap = copy(plt.colormaps['PuBu_r'])\n",
    "#cmap = copy(plt.colormaps['Blues_r'])\n",
    "cmap.set_bad('grey')\n",
    "\n",
    "with_median = True\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "cols = ax.imshow(month_ds['ice_conc'][0].to_masked_array(),\n",
    "          interpolation='none', vmin=0, vmax=100, cmap=cmap)\n",
    "ax.contour(np.nan_to_num(month_ds['ice_conc'][0].values,nan=-10), levels=(-10.,),\n",
    "           colors='lightgrey', linestyles='-')\n",
    "if with_median:\n",
    "    ax.contour(climo_med['ice_conc'], levels=(15.,), colors='coral',)\n",
    "\n",
    "cbaxes = inset_axes(ax, width=\"32%\", height=\"3%\", loc=3, borderpad=0,\n",
    "                    bbox_to_anchor=(0.06, 0.1, 1, 1),\n",
    "                    bbox_transform=ax.transAxes)\n",
    "cbar = plt.colorbar(cols, cax=cbaxes, orientation=\"horizontal\",)\n",
    "cbar.ax.set_title('Sea Ice Concentration',fontsize=12,color='w')\n",
    "cbar.ax.tick_params(labelsize=9,colors='w')\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.text(0.1,0.99,'{d:%B %Y} Mean'.format(d=dt),\n",
    "        va='top', transform=ax.transAxes, color='w', fontsize=14,)\n",
    "if with_median:\n",
    "    ax.text(0.1,0.96,'{d:%B} {c1:}-{c2:} Median Edge'.format(d=dt, c1=climo[0], c2=climo[1]),\n",
    "        va='top', transform=ax.transAxes, color='w', fontsize=14,)\n",
    "ax.text(1.01,0.01,'EUMETSAT OSI SAF data with R&D from ESA CCI',\n",
    "        rotation=90, ha='left', va='bottom', transform=ax.transAxes)\n",
    "ax.text(0.01,0.01,'Source: OSI SAF SIC CDR+ICDR v3', fontsize=12,\n",
    "        va='bottom', transform=ax.transAxes, color='w',)\n",
    "\n",
    "outname = '/home/thomasl/Downloads/test.tiff'\n",
    "plt.savefig(outname, bbox_inches='tight')\n",
    "print(outname)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d5c1c89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}