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    "# Normalized Mean Square Error\n",
    "\n",
    "This notebook computes the normalized mean square error of atmospheric surface pressure.\n",
    "It is compared to ERA5 observations, as well as the CESM2 large ensemble and CMIP6 model output."
   ]
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   "source": [
    "import glob\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "from nmse_utils import nmse\n",
    "from averaging_utils import seasonal_climatology_weighted"
   ]
  },
  {
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   "source": [
    "## Parameters\n",
    "\n",
    "These variables are set in `config.yml`"
   ]
  },
  {
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     "parameters",
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    "CESM_output_dir = \"\"\n",
    "case_name = \"\"\n",
    "start_date = \"\"\n",
    "end_date = \"\"\n",
    "base_case_output_dir = None\n",
    "base_case_name = None\n",
    "base_start_date = None\n",
    "base_end_date = None\n",
    "validation_path = \"\"\n",
    "regridded_output = False\n",
    "base_regridded_output = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7628b86",
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    "tags": [
     "injected-parameters"
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   "source": [
    "# Parameters\n",
    "case_name = \"b.e30_beta02.BLT1850.ne30_t232.104\"\n",
    "base_case_name = \"b.e23_alpha17f.BLT1850.ne30_t232.092\"\n",
    "CESM_output_dir = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing\"\n",
    "start_date = \"0001-01-01\"\n",
    "end_date = \"0101-01-01\"\n",
    "lc_kwargs = {\"threads_per_worker\": 1}\n",
    "serial = False\n",
    "regridded_output = False\n",
    "base_regridded_output = True\n",
    "validation_path = \"/glade/campaign/cesm/development/cross-wg/diagnostic_framework/nmse_validation/fv0.9x1.25\"\n",
    "subset_kwargs = {}\n",
    "product = \"/glade/work/richling/cesm3_0_beta04/tools/CUPiD/examples/key_metrics/computed_notebooks//atm/Global_PSL_NMSE_compare_obs_lens.ipynb\"\n"
   ]
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   "source": [
    "# Want some base case parameter defaults to equal control case values\n",
    "if base_case_name is not None:\n",
    "    if base_case_output_dir is None:\n",
    "        base_case_output_dir = CESM_output_dir\n",
    "\n",
    "    if base_start_date is None:\n",
    "        base_start_date = start_date\n",
    "\n",
    "    if base_end_date is None:\n",
    "        base_end_date = end_date\n",
    "\n",
    "    if base_regridded_output is None:\n",
    "        base_regridded_output = regridded_output"
   ]
  },
  {
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   "id": "74c7803f-a8c5-445d-9233-0aa2663c58bd",
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   },
   "source": [
    "## Read in the current case"
   ]
  },
  {
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   "source": [
    "def fix_time_dim(dat):\n",
    "    \"\"\"CESM2 output sets time as the end of the averaging interval (e.g. January average is midnight on February 1st);\n",
    "    This function sets the time dimension to the midpoint of the averaging interval.\n",
    "    Note that CESM3 output sets time to the midpoint already, so this function should not change CESM3 data.\"\"\"\n",
    "    if \"time\" not in dat.dims:\n",
    "        return dat\n",
    "    if \"bounds\" not in dat.time.attrs:\n",
    "        return dat\n",
    "    time_bounds_avg = dat[dat.time.attrs[\"bounds\"]].mean(\"nbnd\")\n",
    "    time_bounds_avg.attrs = dat.time.attrs\n",
    "    dat = dat.assign_coords({\"time\": time_bounds_avg})\n",
    "    return xr.decode_cf(dat)"
   ]
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   "source": [
    "if regridded_output:\n",
    "    file_path = f\"{CESM_output_dir}/{case_name}/atm/proc/tseries/regrid\"\n",
    "else:\n",
    "    file_path = f\"{CESM_output_dir}/{case_name}/atm/proc/tseries\"\n",
    "\n",
    "dat = (\n",
    "    fix_time_dim(xr.open_mfdataset(f\"{file_path}/*PSL*.nc\", decode_times=False))\n",
    "    .sel(time=slice(start_date, end_date))\n",
    "    .PSL\n",
    "    / 100.0\n",
    ")\n",
    "\n",
    "# Ensure all datasets have the same coordinates as the output data\n",
    "# (Avoid round-off level differences since all data should be on the same grid)\n",
    "lon = dat.lon.data\n",
    "lat = dat.lat.data\n",
    "\n",
    "if base_case_name is not None:\n",
    "    if base_regridded_output:\n",
    "        base_file_path = (\n",
    "            f\"{base_case_output_dir}/{base_case_name}/atm/proc/tseries/regrid\"\n",
    "        )\n",
    "    else:\n",
    "        base_file_path = f\"{base_case_output_dir}/{base_case_name}/atm/proc/tseries\"\n",
    "\n",
    "    base_dat = (\n",
    "        fix_time_dim(\n",
    "            xr.open_mfdataset(f\"{base_file_path}/*PSL*.nc\", decode_times=False)\n",
    "        )\n",
    "        .sel(time=slice(start_date, end_date))\n",
    "        .assign_coords({\"lon\": lon, \"lat\": lat})\n",
    "        .PSL\n",
    "        / 100.0\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "073a2ad0-81e6-4817-9024-4b9b718fabb4",
   "metadata": {
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   "source": [
    "# --Compute seasonal and annual means\n",
    "dat = seasonal_climatology_weighted(dat).load()\n",
    "\n",
    "\n",
    "if base_case_name is not None:\n",
    "    base_dat = seasonal_climatology_weighted(base_dat).load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0527e3e-cd26-46b5-8c1e-08882109e12e",
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    "tags": []
   },
   "source": [
    "## Read in validation data and other CMIP models for comparison (precomputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "126e65b3-2b8c-400c-af02-2ad0b0f82e6e",
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   "source": [
    "# ---ERA5\n",
    "era5 = xr.open_dataset(f\"{validation_path}/PSL_ERA5.nc\").assign_coords(\n",
    "    {\"lon\": lon, \"lat\": lat}\n",
    ")\n",
    "era5 = era5 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CESM2\n",
    "lens2 = xr.open_dataset(f\"{validation_path}/PSL_LENS2.nc\").assign_coords(\n",
    "    {\"lon\": lon, \"lat\": lat}\n",
    ")\n",
    "lens2 = lens2 / 100.0  # convert to hPa\n",
    "\n",
    "# ---CMIP6\n",
    "modelfiles = sorted(glob.glob(f\"{validation_path}/CMIP6/*.nc\"))\n",
    "datcmip6 = [\n",
    "    xr.open_dataset(ifile).assign_coords({\"lon\": lon, \"lat\": lat}).mean(\"M\")\n",
    "    for ifile in modelfiles\n",
    "]\n",
    "datcmip6 = xr.concat(datcmip6, dim=\"model\")\n",
    "datcmip6 = datcmip6 / 100.0"
   ]
  },
  {
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   "id": "22cc331d-413c-4a87-bd89-812ad118cf8c",
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   "source": [
    "## Compute the NMSE"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "nmse_dat = []\n",
    "nmse_cesm2 = []\n",
    "nmse_cmip6 = []\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = []\n",
    "else:\n",
    "    nmse_base_dat = {key: None for key in [\"AM\", \"DJF\", \"MAM\", \"JJA\", \"SON\"]}\n",
    "for ivar in era5.data_vars:\n",
    "    nmse_dat.append(nmse(era5[ivar], dat[ivar]))\n",
    "    nmse_cesm2.append(nmse(era5[ivar], lens2[ivar]))\n",
    "    nmse_cmip6.append(nmse(era5[ivar], datcmip6[ivar]))\n",
    "    if base_case_name is not None:\n",
    "        nmse_base_dat.append(nmse(era5[ivar], base_dat[ivar]))\n",
    "nmse_dat = xr.merge(nmse_dat)\n",
    "nmse_cesm2 = xr.merge(nmse_cesm2)\n",
    "nmse_cmip6 = xr.merge(nmse_cmip6)\n",
    "if base_case_name is not None:\n",
    "    nmse_base_dat = xr.merge(nmse_base_dat)"
   ]
  },
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   "source": [
    "### Set up the plot panel"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def plotnmse(fig, cmip6, cesm2, cesm3, cesm_baseline, x1, x2, y1, y2, titlestr):\n",
    "    ax = fig.add_axes([x1, y1, x2 - x1, y2 - y1])\n",
    "\n",
    "    cmip6 = cmip6.sortby(cmip6, ascending=False)\n",
    "    binedges = np.arange(0, cmip6.size, 1)\n",
    "    ax.bar(\n",
    "        binedges,\n",
    "        cmip6,\n",
    "        width=1,\n",
    "        bottom=0,\n",
    "        edgecolor=\"black\",\n",
    "        color=\"gray\",\n",
    "        label=\"CMIP6\",\n",
    "    )\n",
    "\n",
    "    ax.plot(cmip6.size + 1, cesm3, \"o\", color=\"blue\", label=\"THIS RUN\")\n",
    "    if cesm_baseline is not None:\n",
    "        ax.plot(cmip6.size + 1, cesm_baseline, \"x\", color=\"red\", label=\"BASELINE\")\n",
    "\n",
    "    ax.fill_between(\n",
    "        np.arange(0, cmip6.size + 3, 1) - 0.5,\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.min()),\n",
    "        np.arange(0, cmip6.size + 3, 1) * 0 + np.array(cesm2.max()),\n",
    "        color=\"salmon\",\n",
    "        alpha=0.5,\n",
    "        label=\"LENS2\",\n",
    "    )\n",
    "\n",
    "    ax.set_xlim(-0.5, cmip6.size + 2 - 0.5)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_ylabel(\"NMSE\", fontsize=14)\n",
    "    ax.set_title(titlestr, fontsize=16)\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    return ax"
   ]
  },
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    "execution": {
     "iopub.execute_input": "2024-12-02T20:46:43.556256Z",
     "iopub.status.busy": "2024-12-02T20:46:43.556021Z",
     "iopub.status.idle": "2024-12-02T20:46:44.542084Z",
     "shell.execute_reply": "2024-12-02T20:46:44.541431Z"
    },
    "papermill": {
     "duration": 0.993423,
     "end_time": "2024-12-02T20:46:44.544833",
     "exception": false,
     "start_time": "2024-12-02T20:46:43.551410",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s/jcLK7ZkVvhYW/paytPItPCxwMR+9vKXEEw7EVOA/WfyUcPVFL3b22BAVzvYWYux5WAO/rcyDaXSsnqTlDDcipfA08NUbeCidSMrlJTyXNv6EG6YGaP9R//Hg0aGpMR6VupUN0NGthSxicpB4HsPJcjIkeINDfmOkx7zJwc2RWTjyIV8jQFeITjdppHqdto8zS195a5hPSZ/35mJ0H3ZaFTXAgO68MDgun3Zantlr9mVhS//eILER6Xo9oY1BnWzg7mZCH/8m4Vv/nqi1/6y86T4L14CS3M+3ojgxv1ilJQCbRpaqoxz4eJggro1zXArXgJJiWE31Cb2s4eHs4n838Krc3PdT3mYPI1Zmej5K+ns9UIAQJ0a+j8pYcxzQx0hyKquzQg3NtW1maKnaXda+FrgzS4VezqibN+qdS3s++rdsmuItvbdsI45bK1E8mUA41+nDPUsPi+0qapzRpf6tcwx9GmKRO9aZkrnlfA+SEoY5v6UhvXhObA0F2FYgD06t7DClbvFmPPjIxy/rN93lYq2X0NcuVsMSwsRmnqrpkBr09hSvkxVeN3bMCGEEOOh9DuEEEJeGkmPSxG6N0vtvCdZ+vfg7drSGpsP5mDtniykZpSiQ1Mr+HjyXrXGUiyRyYPWzdT8aDSGif0ccPB8PkL2ZsG/tXWFB5TcfjQHeQX69ybu09FW795q6dlS1K5misUz3OWBrBE97DF9YSp+3pqBTs2tdPYejE8pwcp/MuFdyww/zHJX6ikmPAL/b1QuRvYsl0O9QIbtR3PAGM8He+5GIdIypZjYz14pVUlSGv/hWtNN/THVdOfTE9NKK5zi5PYDCf78vJo8ncek/g6Y+cMj7D2Zh57trOW5tQ05ViEvdcTZfI2pXHq1t8GIHqoDJ64Pz0bo3mxEXSyQ5yZOelwCGdNcD7We1kNSWinaNNJ+vP/FFyM+hecft9WQ4kOT8FN5OH+DB0olJQwPH5Xi3I1C+HiaYeog1WN83t4b5oTPf3uMmcseoUsLK7g9zc9+MqYQzbwt8OFYZ7XrHblQgCMXyoJcFmYiBL3pgNHlcrknpvHeouranp21GA62Ynkb1tfdBxKs+aK6PDf8hH5STAhOxr/HcjGxv4O8vUT/xweqbNfEEvPfcoXl057ijDGs2JKJPSfycPxyAbq9Ya20/dT0Uhw4kwcZA9KzpDh9rRB5BTLMGeOsdL4LQSPhHCuvppspYhNLkPykVCW/tTaT3nTElbvFeJQhNShtVkGRDMcvF8DcTIRm9dVft0/GFOBeogTFEob4lBJc+K8I1V1MMFnP/VTm3NCXtmua4vWsvD/DspCeI8WSmeoHpNdr308HHFXXo1i+b4Vgobb2LRKJUNPNDLcfSFAkkcHSXGzU61RFPIvPC637r6JzRpf6nuYYbi3Gzshc1K9lrva82nooBzfjJOjZ1hqfTSpLKTOsux3eW5KKZX9noE0j3U8MVLT96quwWIb0bCnq1jBTe2OtlpvZ03KUqMwzhte9DRNCCDEeCuoTQgh5aSQ/LsX68JxKb8e3tjk+nuCCX7dnYtexPOw6xh+DdnM0QauGlhjib2dw+p3jlwvwIJX/AMzMleHstUI8zuKD2HVtWTU5750dTDA8wA4b9+dg1/FcDA9QHYRYHzuO5ho04FtLX8MeQZ/8pqNSmqPa1czQp5MNdh3Lw6krhejVXvOAjQCw90QupDJg5ggnlUe/R/eyx/YjuTgaXaAa1C+UKbUXUxNg+hBHjOypPCBz/tNH3G2s1AcabJ4GIPILK55GZai/nVJ+bitLMSb0c8CCNU9w8Gy+PKhf0WPVxM1R/fs02M8WoXuzcfFWkTyon1/Ie3baaqgHaz3rIa9QhsXr0iEW8fo2VPhp1Sc4HG3FCOxgC3enF+/R/xa+lljxoQcWrHmi9NSMu5MJ+nS0kQfOBQ62Ykwf4oiOzazg7myCvAIZYu4UY3VYFlb/mwUbSxEGdC1ro8L7YmOp/qadMBitIcb3c1Aql4OtCTq3sEbE2Xw8fFSCejX59S/s6bXxwzHO8oA+wIOtbw12xN6TeTgana82qK947llZiPDxBBeVc11oS1V57hnix80ZyMyRYfKbDnCwVd/WTl0tRMTZsjbaoLY55k11UXoCQZPKnhv6Eq5p6s5lKwsxxGLVOr1ypwhhx/Lw/nAnvY5F474LZXC0U1936t5PXW3A2kr0dDkGS3PjXacq6ll8Xmjd/wt2zig6cDYfpibAW4OVU8p41zJHnw622HNSv8/8irRfQ+jd5oqqJrXh696GCSGEGA8F9QkhhLw02ja2xOIZ6nsQ3owr1jkwmqLe7W3g38oa0f8V4npsMe48kODGfQkizubj0Ll8zBrlhIHd7HRv6Knjlwtx/HKh0rSuLa0w/y1XlUfkjWl0L3vsPZGHvw/koG9HW40/0rTZ/G3NKigZZ2oCNK6reoOkubcFdh3Lw71Eic4f+DfjeVqT8zeLcPGW6iP3JiYiPHik2qOumospjv5aG1IZw+NMKY5G52PtnizcuF+M4GmuavPuVxV1vX6FafcSy8pe0WPVhDGGA2fyceBsPuKTS5BfKINiGvR0AweE1kVSwhC8+jEePCrF1IEO8qcJDLHqfx7ygXIlJQxJj0uwcX8OVm3LxMNHJfhgtPqe78/LuRuF+PavJ+jQzArfveOGai4meJQhxd8HsrFkQwbuJ5XgveFlAxHWrWGuNEizpbkYPduZwruWGd5ZlIrQfdno39lWbW56Y/FVk8bI9Wn+9DyFQM5/cTxFRfhp9XntLcxEeJCqekOhpa8ljv5aG6VShtT0Uuw9mYdF69NxK6EYM0e+WO+fYM2uLBy5UIB2jS0xto/mm2afTHTBJxNdkFcow72HEvy1OwvvLErFgrfd0KqB5vZujHOjqhQWy7BkYwYa1zXHYD/jDkr8MjpwJk8+LomgSwtrlXEzqoq6p+cMeULuecgvlCHlSSm8qpnCzUm1nC18LbDnpH6f+a8C4YkeRS19LZ/Zef+82zAhhJBn48X9ZkAIIYRUMXMzETo1t0an5ryXqaSEYevhHITsycYv2zPRpYW13oOSzZvigoA2NpBKGR48KsEfO7NwIqYQIXuzMWWAo9KyQoxfw5iUSvN03Q+wthRjXB97/LI9C1sO5cgHFnxR2NuI1QYnnZ6mOspXM1hrebn5fJm/D1TsKQ0TsQjVXEwxNtABYrEIq//Nwt5TeRj09KaNrl5punq16UNdz1UnOzHEIuX9VvZYy1v5TybCjuXB3ckEnZpbwdneBGZPv/2tD89RGovA5mnvxDwN9VCgox4kJQxf/fEYl28XY2ygPcb1cVC7nCHMzUSoW8Mcn09ywa0ECfacyMOoXvYvTHArJ1+K79Y+QU13M3wW5CJv67WrifFpkIs8b/4gP1vUdNOexqBuDXM0rGOBa/eKkfS4FJ4efHkbHb1GC4pk8jasL3XvoZBDXqbw9ufkyyCVQesTUuUHqlZkaiJCLXczvDPUCcUShn+j8tCuiRXaN7FSKkdVnnv6WLcvG5sicvBGAwsseNtVr1z3tlZitPS1xKL33RG0IBmL1qVj0zc11A6+XhXnhjZCe8grlKk8cVBYLINMplynf+3ORnq2FAvfd6v0zSQbK7FB76euNlDwtFez9dMnVSp7ndJHxNl8lXzq1VxMUd/T/Jl8Xqh7ek54Qu5FOWfKE+rdScNTGs4GfOYb2n4NpW+b0/R0lD7KP9EjaOlr+Vq0YUIIIc/Gi/GLiBBCCHkBmJuJMKGvAy7+V4Sr94px/X6xSloJXUxMeBDy6+lumPptCv4+kIMuLayV0vkIP5Ry8jX/uBUGzdTnR9XAbnbYGZmLHUdzMcRP/6cLBFWZUz8nXwaZjKkEijJznh6fHsFIIZizd3ktnbl4dWnbyBKr/wWu3CmWB/WF/LGaBoUT8s/W0pDDWB9ZuVLU9lAO6mbm8l7ziu+xMY81M1eKXcfzUK+mGVb9z0MpfUpGtlQlUFvTzQxikeZ6EHIYq8vlLClh+PKPx7hwswije9lhmpFz35uYiODjaYaUJ6W491DywgT1r8cWI6+QoYWPhUobF4lEaOlriZtxEtx7WKIzqA8ADk8H8S6WlAXweU7jQiSmlaikBcstkCE7T4Ym9aqm96XQNsOW1qr0tto0ssSu43m4cqdIHtQXcjprGhMg6XEpxCKgRiXSweiybl821u3LRksfC3z3rhsszA2/QdKorgVOXSlEUlqp0iDcQNWfG+rUdDfF7QcSJD0uVQmKqruexSbygVUnLUhRu701u/h4HkO722HGCCe1y8j37WaKm3ESZGRLVW6Ky/etkMtbyNmdqCZ/OWP8SR0XBxP5gJ6VuU7p68c5HhrnPYvPC21Pz70I54w6wudVZq76p78M+cw3tP0ayspCDBcHE6Skl0IqYyo38RIflzwtR8XzyQtP9KjzOrRhQgghzwbdfiWEEELKsbSofNoLczMR3hnqCMb44IOKbK3EcHc2QeKjEnnwvryb93kPK++aun9UmpmKMHmAI4okDOvCsw0u646juVgfnqP3q/wj3dqUSoGbcRKV6Vdj+fHVr6U7GNmoDk/FcjOuWMeSuj15mm7GRCFOUMvdFC4OJrgeW4zCYuWbG5IShqv3iuHiYKJxUDl9XLunWnZhWv1aZe+xoccqFnpXq+nEnfKkFIwBrRtaKgX0gbL6V2RuJkLDOuZ4+KhU7Xt88b9CmJmWlVGgGLQc2dMObw/RHvSrKOEmmLYnXJ610qenb5aGQFbW0/PbTI+mI5Uy3H0ogUgEuDuXNdAWPry+o/9TTccUfbPw6TJVk9KhYR1z5OTL1AZcDSU/9xQCaI3rWsDMFIi+VQTGlN/Y9Gwp4pJK0LCOOczNDL8mC+eGVEuDCd2bhXX7stHCxwLfv++mcp7oK13NdQV4dudGedrazIWn0xTbTPumVujXyUbl1fxpirAGXubo18kGTdSkUlPdt+XTfReqzBP23dzHQmF5zWW9FS+R3zQTVPQ6ZSzP4vNCm6o8Z3QRTl1155SNlRjVXU2R9LgUj9WM8SH0GvfW4zPf0PZbEc19LFBUzHBdzWdh9E1hH1XThl73NkwIIcR4KKhPCCHktXM0Oh+Xbqv+IAZ4z9srd4phIgYa6RHA0KZzC2v4eJrh4q0iXL2n/OO0d3sbSGXAH/9mqZTjcWYpth7OhVgM9GirX+7ZHm2tUb+WGcJPqeZR1WXztzVx9Nfaer8MzQkbsjcLpdKyY3yQWoIDp/NhYyVC5xZlgwiXShkepJYg6bFyAHGQny1MxDyVTFqG6rHlFchw92HZjYN7DyVqH2vPyZfir11ZAIB2jcv2KxKJ0L+zDQqLGTaUuymyKSIbuQUy9O9sU6mxEXZG5SoNZlpYJJPvSzG/sKHHamfNv8o9zlQNKns48x/kN+4XQ6YQhHmcWapyo0nQvwvPp/3nLuV2uf9MHhJSS9G9tY3SkwWSEoZ5v/Og5YgednhnqO6gZXq2FA9SSzSmHlDnzgMJrsfy87JJvaoJdFREo7rmEIv5mBqx5fInxyVLcDS6AGamymW+cb9Y5ZyXShl+/zcLjzKkaNvIUmmQ5FYNLFHd1RRHLuTjnsJ7X1Akw4b9OTARA4EdlK8T2Xm8jjXdNNTXUH/+NMvSDRlqt5WRLUVCStn5+l98MSQlqtfV1PRSbI7gT4a0a1J2/bCxEqN7axukPCnF7hNlefsZY1izKwsyVtYmDaXt3AB4QH99eA6a1bfA9+9pD+hLSpjGG237z+ThVrwENd1M5b3OhXWexbmhjn9rG9hYifBvVK7SdSQ9W4odR3NgayWCX6uyp9BG97LH3PEuKq8+HXm76trSCnPHu6B7G92fR3062sBEDGw8kKN0HHHJEhw6l48abqZKYw94epiheX0LxNwpxtnrZTcCSqUMa/fwa2T/zsptwNDrlDE9i88LbarynNHFzloMkQh4ouGcCuxgg1IpsCZM+X2JS5bgwFn+md9F4TNfE0Pbb0W8+bRNrd2TrZSG7tKtIlz4rwjN61vIU6BVhde5DRNCCDEeuv1KCCHktXMzToKdkblwdTRB8/oWcHc2RWkpQ0JqCS7+VwQZA94a7Ag3x8p/TAb1d8C8358gdE82ls8pC2SMC7THpVtFOHAmHzfvF6N1I0tYW4rxKKMUp68WorCY4Z2hjnr/qBSJRHhrsCM+WfVYJRfv8+TiYIK8Ahne+j4VHZpYIr+I4Wh0PiSlDB+Nc1FKMfMkS4pJX6fAw9lEKf1A3Rrm+GC0M1ZsyUDQghS0b2qJGq6myC9iSHlSiit3i9Cngy3mjOUDcB44m4/w03lo6WMBDxdTWJmL8ChDirPXeb12e8MKPdoqBwRG9bLH6auF2HIoF3cf8jQnsUkSnL9RhPq1zDCql+aBM/XRoLY53vo+Ff6trWFuKsKJmAKkpkvRv7ONUo9DQ4+1djUzuDiYIPJiASzNRXBzNAFEPCWTi4MJur1hheOXC/HO4lS0amCJzBwpzl4vwhsNLJDyRPWmQe/2Noi6WIDI6AKkPilFC19LpDwpxYnLBXB3MsHbgx2Vlv9xcwai/yuCs70YVhYihO7NUtlm+XRNa3ZlIeJsPj6e4Iw+HVWDT+Gn8nD+Bg/ulZQCyU9KcfpqAUqlwOQBDnBRM87FsUsFePhI/c2sgDbWaNtYdyBJsCkiW76tG0+fmNl8MEeeH7lfJxs0q8/fMzdHU4wNtMfG/Tl4b0kqOje35gPlZkpxMqYAJaXA9CGOSikkvl37BCIRD/S7Opggr1CGq/eK8fBRKdydTTB7jPJAsiYmIswd54xPVqXhg+WPENDGGtaWYpyMKUBKuhRTBjioXCf+jeJP30zsZ49JbzrqfezltWtihQl97bFhfw4mBCejbRMreDibIidfiuS0UlyNLcaUAQ7wqs5zxG86kINrscXy66qJmL9/528UoqQUGB5gJ687wVuDHRFzpwg/b83EpVtFqOVuhmuxxbgeW4y2jS0RWMFBNd9oYInjlwuxYM0TtG9iCXMzEerVMEeHZlY4cCYP68P5DZGGXubYekh1zADFQS0lJQwzlj5C3Rpm8K5lBldHU+QXynArvhh3H5bAykKEjycov2/GPDey86T4fWfZ+lIpkJ0vw+L16fJpiqk+7KzFmDXSGQvXpWP6olR0b20NsQiIvFSAzBwZPgtykd/0MDZPDzME9XfA2j3ZmPZdCvzesEZRMb/ul0oZPhrrrDJI+ewxzpj5QyqCVz+GXytruDqY4PzNItxPKkG/zjZ4o9wAxIZepwDDzmtdKvJ58duOTPnTRveT+I2w33dmytMKjeltj9rV9Pu8r8g5o9hWMp4+WaI47Z2hjiqpbsqzshSjgZc5rt4rxuL16ajlbgqRiHc+8HA2xehe9jh7vRCHzhfgQWop3mhoiexcKSIvFaBUyvBpkKteaeUq0n6v3StC+Gn+XgpPTl2LLZYfo6cHH1dH8EYDS/TrbIPwU/l4eyH/fpKZK0PkxXxYW4pUrsMAsO9Unrxn//0kfoM1/HRZ7vrOza3QpaV+Nxte9zZMCCHEOCioTwgh5LUzsocdariaIvq/QtxOkOD0tUJIpQxO9ibo+oY1BnS1VepJWBmdmlujQW1zxNwtxqXbRfLtWpiLsXy2B3Ydz0XkxQIcPJuPIgmDg60YbzSwxFB/O5VAhi5tG1vhjQYWuHy78mlqjMXUBFg6yx2rw7Jw8Fw+8gplqFfDHOP72aNzc/172r3ZxRb1a5lh29FcXL1bjNNXC2FjJYa7kwmGB9gr9VT2e8MK+YUy/BdXjGv3ilEkYbC3EaOZtwV6tbdBQBtrlR5oVhZiLJ/jgfX7snH8cgGu3C2Cs70JhgfYYWJ/B/mP1op6f4QTjl0swL7T+XiSVQo3J1O8PdgRI3qqjoFgyLGaiEX4+m1XrA7LwqHz+Sh4OphqQBsb2FqJ8ckEF3g4Z+NETAH+jcqFh7Mphveww5je9ug986HKvk3EInwz3Q1bDuXg0Ll83ivSWozADjaYMtBRJUe28FRIRo5M42CqwgCP+hICMwBP92BrzQckHdjNVmObufuwBHcfqk8R413LzKCg/oWbRSoDDF64WfakTQsfCzSrXzZvygBH1Ktpjr0ncnHxVhHyi2SwtRKjhY8lhvjboWMz5X0P7GaHCzcKEXOnGDn5UpiIRajhZopxfewxsqe92mDrGw0s8dNHHli3NxtRlwpQWgrUqW6GyQMc0bNdxYLe+po8wBHNfSyxMzIXl28VIa9QBnsbMaq5mCKovwN6KjxN1K+zLczNRbidIEH0rSKUljI42pmgfRMr9O9iK8+lr8jFwQS/fFwNa3dn4eyNQpy9Xgh3J1NMetMBo3vZV3jg1jc72+JReimOXizA3wdyIJXxnsQdmlnJ261UBmw7kqtxG0JQ39JChElvOiDmdhEu3y5Gdl4BTE1E8HAxxbAAOwwPsJM/GSMw5rlRWMxUBt0sKjetfP7uXu1t4GArxqaIsqCfj6c5Pp1ob9D5UBHj+zqgmospdhzNxe7jeTB9+rTKpDcd0FBNSpE61c3w68fV8NfuLJy/UYTCYhlquplhxggnDPZTvfFn6HUKMPy81qYinxfHLxeo3HA/frnsyYTADjZ6B0Qrcs6oG7RVcVpQfwc46NHB/7MgF/y6PROnrhQgv4iBMZ4SyMPZFOZmIiz7wB1bDuYg8mIBdhzNgYWZCM3rW2BcoL3eAWfA8Pab9LhU5RiTH5ci+Wne+BY+FkpBfQD4cIwz6tUwx96TedgZlQsrCzE6NrPC1IHqO1Rcjy1W2cf1pzdTAMDD2UTvoP7r3oYJIYQYh4ipyz1ACCGEEEIIIYQQQgghhJAXDuXUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQgghhBBCCCGEEEJeEpRTnxBCCCFET/ceSnDySoHO5aq5mKodBJYQUjExd4oQc6dI53L1a5nrndeaEEIIIYSQlxUF9QkhhBBC9HQvUaJx0EtFLXwsKKhPiBHF3CnS69wL7GBDQX1CCCGEEPLKo4FyCSGEEEIIIYQQQgghhJCXBOXUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQsiLKz4eEImASZOed0leLnXq8FdlvCp1L5EA8+YB3t6AuTk/pqio510qQgghhBBCCKkwCuoTQsjrSAjWlX/Z2ADNmwMLFgB5edq3sXZt2XrXr2tf9tQpYMQIoGZNHlRzcgIaNgTGjgXWrVNeNipKfdkUX4MHK68jEvHt6SJs+513lKdPmlS27QsX1K/r78/np6aWTdNUj4qvli11l0tx+8JLLAYcHYHOnYE//gBkMtV1QkP5svPna96upmXq1OHTq1UD8vPVr6tvvb6qYmKAL78EOnQA3N0BCwugXj3gvfeApCTV5RkD/vc//l7WqAFYWgIeHkCnTsBffwElJc/6CF58x48Dc+cC3bsDDg66byKUP0/UvTZsUF7nhx+A774DatcGPv4YCA7WfMNDOF/UnYtduwIhIarrCNeBPn1U5wnnmb6v+Hi+3p49wMyZ/Py3sdF9ngNAcjLwwQdA48Z8HQ8PoEsXXh9Sqery2sqxaJH6faSmAtOmAdWr8/bt6wt8/TW/cfKiqOh5WFzMj8XXl69TvTo/VsVrvqHmz9d8E6mkBNixg7f3Ro34e2ZnB7RvD/z6q/r37MoVYPJk/jnt4sLL6e0NjBwJREerLp+eDqxeDQwcyK9dFhaAqyvQty8QEVGxY9q0CWjXjpfXyQno10/9vhWX79wZsLXl67Rty8+z8hgD9u8H3n2XH5+DA2BtDbRoAXz/PVBUZHhZL1zg5XNy4vtu146XR5OcHODDDwEvL15XXl78/zk52o9P3/qoyHmtjUwGrFrF68vKCnBz423h7l31y2/cCEyfDrRpw49PJFL/XujL0HNm5cqy9mtqWvEbrMa4aa2vu3d5nbq58Tpu3pzXubrvZLGx/P0cOJB/3xWJ9CtnRAS/Xtnb82uAv7/m89PQz0x9vM5tmBBCXnKmz7sAhBBCniNvb2D8eP5vxoDHj/mP6vnz+Q+KEycAExP16wpBfcZ4oOTHH9UvFxoKTJnCf8D16wf4+ACFhcD9+0B4OP+BEhSkul7r1sCbb6rfZlUGmj/5BDh61LB1FOuxvGrVDNvWRx/x4IdUCiQkADt38psQly8Dv/9u2Lb08egRsGwZ8NVXxt/2y+6dd4Dz53kQavRo/gPy3Dngt9+Abdv4+aHYFqVSHrRo0wbo35//OM3MBA4c4IGObdt4mxdTnwq5tWv5jT1rax501xY8A3jwwt9fdXpJCbBwIa/bHj2U54WH83Pq4EHAzEy/cvXowQPiAFBaCjx8COzaxa9lt24Bixfrt53Zs4GsLOVpK1YA2dn85kJ5jo7877JlwLFjPMhTowZw7572/dy/z4PB6elAYCAwYACvy7AwYOJEfk1Td0PCy0t9QEg4dkWpqXwfDx/yG6u+vsDJk/w4zpwB9u17Mdp2Rc5DmQwYNIh/7rVvDwwdygN0ISHAoUP8vDf0Wq5LbCwwfDgP4gUE8EBgdjYPmr3/Pi/vrl38c1Zw4QIve8eOgJ8fD6rdv8/X2b4dWL9e+bNo2zYeJK9Zk++jZk0gMZHfTDhwAFi6lAcI9fX998AXX/Bz9Z13+M3/LVt4gE8ITCr66CNg+XJed+PG8fMvPJwHdq9f5zfcBMXF/DuChQXfTmAgD+RHRPB9hoXxc8LKSr+yRkXxbZib8+u3gwP/PB03jt88+/xz5eXz83mdxsQAvXoBY8bwmyg//ghERvK2bmNTufow9LzW5Z13gD//5DfyZs7kn+dbt/Jr3enTfLqiefP49wpXVx6AT0io+L4rcs7MmsX/Vq/Oz8vK3DB7Fm7e5DcDCwp4oLlmTf4deeZM4OpVfsNM0YkTvFOMiQm/UafP8f39Nz9nXV35d2GRCPjnH36TeONG3l4VGfqZqcvr3IYJIeRVwAghhLx+4uIYAxgLDFSdV1TE2Btv8PlHj6pf/9YtPn/ECMbq1GHMxYWx4mLV5fLzGbOzY8zenrFr11TnSySMHTyoPC0ykm97+nT9jwdgrEED3ctp2nZQEJ/u7c3/7t+vuq6fH5+XklI2TVs9Gkrd9hlj7O5dxmxsGBOJGIuNVZ4XEsLXCQ7WvF1Ny3h5MWZmxljt2vw9SktTXVffeq1KQh0HBT37fa9cydi9e6rTFy3iZerXT3VeYaHqtJISxrp35+vs3Wv8cqrj5cVflfEs6v7CBcauX2estJSxM2cqvr/t2/m6AwaozqtbV/+6EM6XhQtV592/z89FCwvGCgrKpht6HfDy4strc/w4Y3fuMCaTMbZ5s+7z/N13+TI//aQ8PTOzbH/x8crzAH7d0dfEiXydX38tmyaTlV0/167Vf1tVzdDzcO1aPn30aH5M5adPnFixcgQH8/UjI1XnJSbyuszPV56el8dYmzZ8vX/+UZ6n7rgY4+eQpSVj7u7K5T9yhB+rVKq8/K1bjDk48M+ApCT9juXOHcZMTRnz9WUsK0t539bW/POzpKRs+oULZZ+r6enKx9e2LZ93+nTZdImEse++421WkUTCz2uAsSVL9CtrSQnfr4UFY5culU3PyWGsSRN+HHfuKK/z1Vd8Hx9/rH76V19Vrj4YM/y81uboUb5+1678e5vg8GH+faFbN9V1Dh0quw4sXMjXDwmp2P4rcs7s3Vv2HWf6dM3nhi7G+HzTR7duvIz79pVNk0gY69FD/Xfk2Fj+OSZ8PlhYaC9nRgZjjo6Muboy9uBB2fTkZMaqVePzMjKU1zHWZyZj1IYJIeQV8AJ0pyGEEPJCsbDgj/UCvOe+On/9xf9OnMh7GKWn8x6F5V2/DuTm8u01bao638yM94h7UQQH8ycKPv2UP4HwIqhfn/ceZAy4dMm42xaLea+y3Fzgm2+Mu+2qcP06Txvh4MB7iQ0YwHvSGYIx3tOtc2e+DWtr3qN37VrVZWfM4E9hlDd3Ll/v2DHVeZaWqtNMTctSRlW0V1tkJO8l3qAB73Vua8vLXb6noDaKqUD+/BNo0oSXt3Zt4LPPtKe3uH+f9yoW0lj07Ml7sVa2nG3a8HJoeiJIX2vW8L9Tp5ZNE443Lo735hNSy6jr6a+PunX5cRUX83OmKnXtyp9qUuylrc39+/xvv37K04UUXoDm67k+cnN578l69ZTTl4lEZU9I/PmnftsSUhyFhgJHjvCnAmxseDqZoCD+eaLO1au8x3X16rz3tZcX79mpbnlDz0Oh7IsWKdf55Mm8x+3WrYa/5/7+/PoK8M9Aof0J6Thq1uS96K2tldezseEpXwDVa4y64wL4OdSoEZCWptxzNyCAP61Q/gmKBg2AUaP4Ey6nT+t3PCEh/KmVL77g12DFfU+cyHtpKz7lFhbG/374IeDsrHx8X3zB/6349JmZGe89Lzytojj9s8/4v9Vdc9U5epSXZ+xY4I03yqbb2fGUaqWlyk+uMMavIba2qk+tffYZv+799Zfy9wJD6wMw/LzWRmiz337Lv7cJevTgTygcPw7cuaO8Ts+e/LwxhoqcM/37V+6JFyHVWUKC8jVdXQqYdet46jzhc6hDB9V0j9rcucPrsHt35euqmRlP5waoXvPq1eP70fdpkm3b+JNcM2cCnp5l06tXL3vKa9s25XWM9ZkJUBsmhJBXAAX1CSGEKJNIynLPq8sHX1rKH/F3c+OPB0+cyKcLgX5Fwg/5uDj1+UdfND4+wFtv8WDl338/79KUEQIJplWQNW/iRH7D5Y8/ygKDL6L793lwsrSU57Tv3ZuncejcGfjvP/22wRi/CTV1KvDkCQ/4TJvG0y5Mnap/GgqRiP+g1vf9kMl4qgtA/c0tfSxezH/gtm3LbzaMH8+PYfp0nuLCEMuW8UBbx448B7uDAw/MDB6s/mZWfDxPr/D4MQ/Y9+rFg7Hdu/NH5auqnPpKTOSP6lerxoNGAn9/fqPOwYG/goP5q6L5hx88AG7fBmrV4mMsvEiaNOF/hXYmyMnhY5p4eKimMQB40GjNGp6C4c8/NecxPnOG38zo1Us1mFO9OtCsGU+3YUje8z17eLCsWjUe3Pb25p8tgwapLrt7N8/5vGcPf19nz+b7XLWKt+PMTN3703QeFhXxsjdooD5Y1Ls3P/azZ/U/NoC3Mz8//u+goLL2N3u27nWFNFH6XmNiY3nb9PRUDs4Zcx9C7vPevVXnBQbyv4pBd+HaULeu6vLCNH1T3RmzrMI0xbLevcvHpBDyhCuytAS6dePjqCjeDDK0PowtKoqXVbhp9yz3X1XnjC6Ojuqv6cHByjdr58zh519iIv9snzaNv3+TJpXdMNNF2/vbrh0vS2Xr90VoQ897/8+rDRNCyCuCcuoTQsjr7N69st5NjPHgW0QE//GzZAnPmVze3r38x/qsWfwHto8PD6ocOsRzLSv2NvL2Blq14j3MAwL4D6r27fl2dfUyio7WPPhWnz68N1RV+OorHlj68kueQ9XcXPc6ivVYXocO6gfQ1Nft2/xHjZkZ/yFpbGIxD+gNHMhzlWobRFCbrCyeK9wQhgyuduIEL5/iEwXr1/Ng2YwZPMisy5o1/PimTuU9RIUAkUTCe6EvW8bzKLdurX0727fzHogjRmheRji2J0942W7d4u2/fL53ff32m2pwrLSUB0V/+okH52vX1m9bhw/z80sIBH/3Hd9ORATP4TthgvLyx47xoP8nn5RN+/JL3rsuJIQ/2VIV5dRXSAgP2E6apBz08/fnL2EQPUPa2+HDZQHq0lIeHNq9mwcgXsRB+f73P16+Dz7gOZ+bNeMB/V27+LVjxw7VHuEAv4H51ltl/xeJeA7nP/5QXl4I9vv4qN+/jw/f1v376m8eqLN7Nw/qCAEdqZT3woyK4sFA4Rqfns7bpJsbv0Gh2H42b+Y35776iufRL0+f8zA2lrcfbccG8Dow5MmySZP4DbFjxzSPBaGJ8OSQumAbwPO+h4XxnvYJCbwuAf3HXcnN5dcxS0ve81Yfd+/yHs/qelor1pHAzY3/jYtTXV6YlpjI85Wra5uKdNWHurIqlkuRkxPPx61YVn3at7Cc4r8NqQ9jys8HUlL4zSl136Wqev9Vdc7o4ujIz2lt1/QTJ/h3kUaN+M1I4SbXggX8mvLjjzz/v7pxQxRpaxMiEX+KMjpav/ZbkX1U9XsobPt1bcOEEPKKoKA+IYS8zmJjy9IDKBo4ULnHqyKhR75i4G/iRP7jKSRE+dF1kYg/Ojx+PA9sCD1urK35jYDx4/l21H2hv3iRv9RxdKy6oH61aryX17ffAr/+ql+vSk31CPAgmyFB/R9+UB4od8cO/qNx6VI+KFlVGDCAB3a2bOHBQcV0BfrKytJcB5oYEmR1clIOHgO87Sxdynt7lr+hpM6qVTwou2qVcvDX3JwHtvfs4UFCbUH9hw/5DS0rK+0pixTrQiTiTwEsXKi9fNqo6+1qaspToRw6xNPeqBtwWp0JE8oC+sJ2vv+eb2fdOtWgft26vF0omjqVnyMXLlRdOfXBWFkaDcXUO5V15IjqjSJTUz6AaZs2xtuPsVSrxq/B48bxJ1jCw/l0S0vg44/Vn9Nz5/IbU0IqhcuXefqTjRv5jYzNm8uWzc7mfzX1Are3V15OH2PHKvfQNDHhbSMqircr4Rq/fj2/QfHLL6o3hMaM4dfMLVvUB/X1OQ+r4tgqY/VqfmMmIEA1nZIgJkb52Dw8eD3pG/R+5x1+c/7rr3naI31kZ2t+QkVdHfXty+t6xQr+XgtpdQoKlN+D7GztQdEDB/hNpkaN9D/H9XlPExMNW15xOeHfhtSHMT3vNvu896+NYsBfsXxCz/4xY/gyuoL6hhxjRYP62vZhY8OviVVZh69zGyaEkFcEBfUJIeR1FhionK4hLY0HsmbNAjp14o9XK/bWT0nhwYaGDZUDW6NG8eB3SAjvwauYnqFePZ6zNyaG9369cIH/XwiarV/Pt6mYTxPg6Tr07XVobB9/zIMI333H040IPy40KV+PlbFsmeq0FSv4zYGqtHgxf88/+YSnMjFUnTpVOw7BG2+opkUQifgP8+vXeS9hbUH9ggLg2jV+Y2TRItX5JSX8761bmreRkcGDbGlpvN02aKB5WcZ4T8bkZP50y2ef8aBreLju9qRObi4PXoaF8ZtI+fnK85OT9d+Wup65bdrwGxUxMarzWrRQzcldqxb/m5VVdeXUx9GjvNevnx/vOWksCxeW3USSSvnTSxs28OubEHQWUoK8CGJjgTff5DcEjx/n50tWFn8y5Ysv+PXp5EnlMi9dqryN7t35NblFCx4knzdP+eaPsbVqpTpNXbsSUnicPat+TIqiIt4T/8kT3gNbkbHPw6q2bx9/8sjLi99c0WTSJP4qKuI9WZct40H0xYt1pxH7/HPeLvr04f+uKl278huEGzbwpzcGDuTtLzyc3zRycOABO21P7UVH8+8XDg68g0D57wmvsqiosvQogpYty8aFqGphYaqfB8LTTy+yy5f5X3XlFKap+5x7FQlP9CiqU6fiKegM9bzbMCGEvAYoqE8IIaSMuzvvxVRYyHvELVqkPIDounU8wFW+J6+TE+/tvX07D7KpSzHSsqVyjv6oKN5TPzKS94ifM6cKDqiC7Ox4IGz2bJ6G6Ntvn92+U1J4r9vCQn5TRcj13rBhWY5RgRBo1TZegTCvfFC2vI4d+Q+tsDAe2Ktompiqoqk3mYcH/6urN1dmJg/wJSVpf6KgfBBacf2ePYEbN3iKmfHjdZdZLOZBynfe4b1hR47kN4oWL9a9riKJhAcjLl3iwdoJE/j2TE15eo9163j+Yn1pqkt3d14/5anrSSc86SCVVl059SEMkDttmnG3q8jEhPcQ/+ILng5rwwbei10YT+RFMHkyf7Ln/v2yVAq2tvwGZUYGb3MbN/LltLG25p8B33zDU90IQX2hDWg6z4TBWfXN565pWXXtKiOD//3lF+3by89XDeoDus/Dqji2ioiIAIYN49e0o0f5WAW6WFryVEuhoXzMi08+4cF6TWN3LFjAb1gFBAA7dxo22KYQhFdHUx2FhvIbhn/9xf9tZcU/x5Ys4W3L1JR/f1Dn8mX+5IFIxOvGkBtM+ryn5Xtx61pecTnh31XZZqKiVD+rgoL45/SzaLNhYeoHlvX3f3HOGU37FovL0j8p8vDg8/Tp/a3vMVbm5qDiPso/MZOfz6+DlanD8k/0APwGuBDUf9XbMCGEvAYoqE8IIUSVkLv90iXl6UKA/4sv+Eudv/7SLyDs788DR1Om8ADGixTUB/jAjT/9xPOvzpjx7PdvZcXraN8+oHlzXk937yo/5i382ElP17ydJ0+Ul9Vm4UKeguaTT1TTquhS1Tn109LUTxcGY9R1fMIP79atee9PQ2Rk8ID+5cs8sDh9umHrA2VpMcr3WtPHrl38XJw2jQ9mqmjLFvWBF2001WVaWuV+QBu7nLpkZgL//svTegwbZtxta9KuHQ/qX7r04gT1c3N5HulWrdTnRg4I4AHsixd1B/WBssB4QUHZNF35je/e5cGyevUMK7s+hHP32rWKDzQtUHceenvzsms7NkBz/nBjOHAAGDKE131kZMXqURg8/MQJ9fW0YAG/5vr78+u8lZVh2/fx4U85pKaqtjNNdSQW8yf/Zs1Snh4fD+Tl8Tar7omXS5d4LnaplD851rat4WUVylU+nVpmJv9c7NRJ/fLqqDu+itSHIebP1/wZaWPDb/rExfE6Kn9zxhj7Dw3VPH7Ii3DOaGJvzzszPH6segM7LY3P0ycQr61NMMafGqpRQ/UJQkP4+PDvI3fvqgb1jVGHwhM92vb/KrdhQgh5DejotkcIIeS1JPSMVOwBfvw4/5Lt7c17j6t7OTvzIFtmpn77qcyPoapmbs5vOhQUGJ4r3pgaNuR5vJOTVYPmzZrxv2fOaF5fmNe8uX77mjyZB//++cewcgo59Q15GeLyZfW96E+d4n9btNC+vp0dz8n833+qKWO0UQzor1wJvPee/usqEtLOmFagP0VsLP87cKDqvBMnDN+eunWio/nTIYpP0xjK2OXUZeNG3vN/3DjDA5QVpe7a+LxJJPyvcAOvvMeP+V99U5ecO8f/1qlTNq1DB77+oUOqabZSUnjAvX173nPc2Nq353+1Xef0pe48tLTkN2tu3+ZPO5R38CA/dqEchhACVYpPHpR34ADvuerkxAP6FU0jpe0aIwTX/Pz4jeKK5AD38+N/1aVni4hQXkaXv//mf0ePVp136RK/5paU8LqpSL1rK6swTbGsPj48QHvqlOrnTFER//5To4bye2PM+qgIPz9eVuEz8FnuvyrPGX2YmGg+p4TxQ9TdQBfGddLnc05I1aPu/T1/nn+PqGz9vght6Hnv/3m1YUIIeUVQUJ8QQogymaxswEHF3NvCALnz5vGUF+peQUH8B7Dwgz0ujg9Kmpurup/8fN4THtA9YNnzMnYs//G3Zg3vWfi8fPopD1r+8EPZI8kA783ZpQsPOKvrUXf4MO+RWaeO+jzq6ixYwPc1b55hZRRy6hvyMkRmpmou/PXreTAxIEA5n352Ns+Nn5KivPysWfwmzVtvqb9BEBen/D5nZPCnTi5f5m1V1xMbt26p7wVfUAB8+CH/d9++2rehjpcX/3vypPL0Y8dUe8TrY8MGnkZIUFpallu7MoPYGrucugjXpIoMkKupjehaRzjP9D2fngUXFz6+w4MHZemIBDk5PNUJwHPmCy5fVu6JL9i2jacWcnXlgVWBvT3PbX7/vvJYJ4zxPPUyGT+vFBUU8Dp+8KByxzd5cllKNMV2q7gfIe8+ULHz8O23+d9PP1W+NoWE8BuBo0ZVLM2GszP/qzgoq6LyAX1dvVJPneLna3kxMfx9MTXlPdwVBQfz63rXrvoF9DWdG5Mn8+1/951yyowbN/i12NubX4sVKX5eCU6c4E+FeXnxlEiKFAP6+/fztHAV0aMH/3zctEk5f3puLr9Zb2qq3INZJOJPGOXl8cGDFS1cyD9/pk1THi+oIvVhTEKbnTev7MYewNPnRUQA3bopj4lUVfs39jmjD2dnfhOzqEh1nvAZtmCBcvvLySnrTKDP55yvL6/DyMiygccB3jaF70flr3mGGjmSPx23ciXw8GHZ9JQU3onD0ZEPZl5VXvc2TAghrwBKv0MIIa+ze/eUH419/Jj/gPnvPx4kFX645OTwfPm2ttp/YEyezNPV/PUXD4BmZwMzZwL/+x8PKDRpwgPGSUl80MKMDP5o/MyZqtuKjtb82K6jI893ryglRfNjxrVrq/5Q14dIxAPJffqo740mKF+P5RmSZkYdDw+eDmj5cl6/wcFl89as4T98Jk/mAcd27XgvtqtXecDI2poHcfXtIV6jBh+UV91gss9T167Azz/z4F3btsCdO/ypEAcHfuNI0b//8voIClK+2TF9Ol9/3ToeHOvZkx/vo0c8iHXuHA8CCT2Uhw7lAaGGDXlbVfc+zp7N2yPA6/uTT3gPv3r1eNmSknhwKj0d6Ny5LKhoiAEDeJmWLOGDAjdtyntI7t3LA4I7dhi2vZ49ec/r0aN5cCQ8nG83MFC/sQKMWc6TJ8sC0UKP8pMny87lhg3LBqxVdPEiHxy5VauynpmG0NRGBIcPlwWMZDL+Pu7ezQNJvXqpT/dz7Zrma1CrVqopSLQJCysb4DAurmyacNOpSxflcQRWrOD1/9ZbPCjfqhXvSbpnD2/fb74J9O9ftvxPP/Ht9ejBr4+M8YDqiRO8F+66dfx6r2jRIv758P77vH58ffnyp07xtlM+UHb+PL+R4OdXsbRTAjc3fkwjRvAncvr04e2iqIhfl48d46lUhMHKK3IeTpwIbN3K00TFxfF179/nbdbT0/BxMATdu/PPkS++4NcYBwf+evdd/v/Bg/nTJv7+/BjLKz+o5fvv8/Okc2f+vpWW8nPs4EH+Hi5frvyERWgo/+wzNeWfDeUHRwZUBz/VdG74+vJr4Lx5/Mmv4cP5zdHNm3mg888/VT9nhg/nTwA1b84DvNeu8ffB2Zm3Pzu7smWFp6IyM/l7fOgQfylS99mvjqkpv64EBvLPjjFj+P537uTv77ffqgYLP/6Yn+NLlvCbXq1b82vM/v385v7HHysvX5H6MPS81qZ7d77smjX8Gti/Pz/Xt27lx/rbb6rrrFlTdtP12rWyacL5OXiw/oOYVuScWbSobDB64cmbRYvK2tm0afp18ggI4N8RBwzg76+5OV+vSxf+fWjmTB4ob9qUX6sZ4+/9w4f8Otytm37H+Ntv/NoyZAgPwNeowa8vV6/ysireKAX454PiQNUlJXya4jmseE45OfHvLxMm8Gv26NE8rdHWrfy93LBBdcyJin5mqvO6t2FCCHkVMEIIIa+fuDj1factLBhr0ICxDz9k7PHjsuV//53PnzpV97Zbt+bLXrzIWFERYzt2MPb224y1aMGYqytjJiaMOTkx1qULY8uXM1ZYqLx+ZKTuPt5eXsrr6Fq+RQvlbU+frrx+UBCffuaM+mMKCCjbVkqK7nos/9KHn5/q9hWlpjJmbc2YgwNjGRnK85KT+XvWsCFjVlb8faxXj9f73bvqt+flxZdTJyuLMWdnXp4GDfQrf1UR6jgoiLGrVxnr04cxOzvGbG0Z69+fsevXVdcJCSlbR52tWxnr2ZO3QzMzxmrWZMzfn7Fly5TbvZeX7vc2Lq5s+WvXGHv3XcaaNePbNjVlzMWFv7e//cZYSUnF6+H+fcaGDWPMzY23g7ZtGduypaxNBwcrL+/lpXqeBAfzZSMjGfvjD8YaN+ZtoFYtxj79lLGCAuXlFeteHYAfW2XKKbxXml7lty94910+/9df1c9XpK4uNLURTeWxtWWsXTvGfvyRMYlEeR19rgODBimXR9d1QXivNL3UvSfR0YyNHMlY9eq87dnY8PpfsUK17e3cyctUpw5/n8zNGatbl1/j//tPc7mSkxmbMoUxDw++Tv36jC1YwK/15Qnvefn3UKjjkBDN65RvJ4wxdusWL5+XF9+3kxM/12bNYuz8+bLlKnoeFhXxY6lfn2/fw4Mfa3Ky5vrQR2goL4uFhfLnlz6fdeXrbv16xgYP5tuwsuLl9PJibOxYxk6fVt23rnak7ZzUdN5v3MhYmzZ8/w4O/JqsWP+KfvmFt0EHB15Wb2/GPviAf56Vp895VP481uXcOV4+Bwde3jZtePk1ycpibM4cxjw9+WeDpyf/f1aW5nUMqY+KnNfaSKWM/fwzY02a8Pbl4sLY8OGM3b6tfnnhu46+bUEXQ88Z4XuOppe6a4I6ubmMvfUWv9aJxerLvnYtb3vW1mWfRWvXGnZ8jPG6HD6c162FBa/rn3/mdV+ePm1Ynf37GevWjX/O2Nryfx84oH7Zin5mavM6t2FCCHnJiRhj7HnfWCCEEEIIIVVs/nyefiAyUrlnLiGEEEIIIYSQlwrl1CeEEEIIIYQQQgghhBBCXhIU1CeEEEIIIYQQQgghhBBCXhI0UC4hhBBCyLMWFsYH4dWl/CCWhJDnJz5e/cDK5ek7oCshhBBCCCEVRDn1CSGEEEKetUmTgHXrdC8XHMxz4RNCnr+oKKB7d93LeXnxGwCEEEIIIYRUEQrqE0IIIYQQQgghhBBCCCEvCcqpTwghhBBCCCGEEEIIIYS8JCioTwghhBBCCCGEEEIIIYS8JCioTwghhBBCCCGEEEIIIYS8JCioTwghhBBCCCGEEEIIIYS8JCioTwghhBDykomKioJIJML8+fOfd1EIIYQQQgghhDxjFNQnhBBCCKlikZGRGDVqFDw9PWFhYQFnZ2d06dIFP/74I4qKitSuU6dOHdSpU+fZFpRUqbS0NCxcuBDDhw9H3bp1IRKJIBKJtK4jk8mwatUqtGrVCtbW1rC3t4efnx92796tdnlhm9peDx8+VFonMzMTc+fORf369WFhYQE3NzcMHz4cN27cqNBxpqamYtq0aahevTosLS3h6+uLr7/+GhKJxCh1osvdu3cxcuRIuLm5wcrKCs2bN8eqVasgk8lUlo2NjcX8+fMxcOBA1KxZEyKRqELn3bO60RYTE4Mvv/wSHTp0gLu7OywsLFCvXj289957SEpK0rievnXCGMP+/fvx7rvvonnz5nBwcIC1tTVatGiB77//Xu31KiEhAe+88w5at24NNzc3WFhYwMvLC/3798eRI0cqdIyff/45AgMD4ebmBpFIBH9/f6PXiSaLFi1C79694enpCSsrK7i4uKBNmzZYvnw5CgoKVJavijZMCCGEEKKLiDHGnnchCCGEEEJeRaWlpXj//fexevVq2NjYoG/fvqhfvz6ys7Nx8OBBxMbGwtfXF/v27UP9+vWV1hUCi/Hx8SrbjYqKQvfu3REcHEy99V8iwvsmEong4+ODxMREFBQUQNPXccYYRowYgR07dsDb2xt9+/ZFcXExdu3ahbS0NKxcuRIzZsxQWkdTe7h37x7+/vtvNGrUCDdv3pRPT09PR8eOHXH37l107NgRHTt2REpKCnbs2AFTU1McPXoU7du31/sYU1NT0b59ezx8+BCDBw+Gr68vTp48iVOnTqFPnz7Yt28fxOKyfkWG1okuN2/eRKdOnVBQUICRI0eiZs2a2L9/P65du4a33noLq1evVlo+NDQUkydPhomJibxuPD091Z532jyrc7JDhw44f/482rZti/bt28PCwgLnzp3DiRMn4OrqihMnTqBhw4ZK6xhSJ0VFRbCysoKFhQX8/f3RrFkzFBUVISIiAnfv3kXbtm1x7NgxWFlZydc5fPgwRowYgY4dO6Ju3bqwt7dHUlISdu3ahZycHHz33Xf4/PPP9T7G+fPnY8GCBTA3N4evry+uX78OPz8/REVFGa1OtKlbty5cXV3RrFkzuLu7Iy8vD1FRUbhx4wZatGiB06dPw9raWr68sdswIYQQQoheGCGEEEIIqRJz585lAFjbtm1ZYmKi0rzS0lL21VdfMQCsfv36LDs7W2m+l5cX8/LyUrvdyMhIBoAFBwdXUclJVUhNTWXHjh1jOTk5jDHGGjRowLR9Hd+2bRsDwDp37swKCgrk0x8/fsy8vLyYhYUFi4uL02vfM2bMYADYsmXLlKa///77DAD78MMPlaafPn2amZiYsMaNGzOpVKrnETI2ceJEBoD9+uuv8mkymYwFBQUxAGzt2rVKyxtaJ7p069aNAWD79u2TT5NIJKxHjx4MADt69KjS8rGxsezMmTPy+rWwsNB43mnzrM7JlStXsnv37qlMX7RoEQPA+vXrpzLPkDqRSCTsu+++Y5mZmUrbkEgkbMCAAQwAW7JkidK84uJitW0kKSmJeXh4MDMzM5XtaXP9+nV28eJFJpFIWEpKCgPA/Pz8NC5fkTrRprCwUO30CRMmMABs1apVStON3YYJIYQQQvRB3zYIIYQQQqrAnTt3mFgsZs7Oziw1NVXjcmPHjmUA2JdffskYYywuLo4BUPsSAoaKAcSLFy+y3r17M1tbW2Zvb88GDx6sMdB7//59NnXqVObp6cnMzc1ZtWrVWFBQEIuPj1dZVgikJSYmsqCgIObh4cFEIhGLjIzUetx+fn4MACsqKmJffvkl8/b2ZqampvKyC/PVEQK/iuUPCQlhAFhISAg7fPgw69y5M7O2tmbOzs5s4sSJ7MmTJyrbOXr0KOvTpw+rXr06Mzc3Z9WrV2d+fn7szz//1Fr2Z01X8G/cuHEqwVjBihUrGAD21Vdf6dxPYWEhc3JyYubm5iwtLU1pXs2aNZlYLGa5ubkq6w0ePFhtIFyTnJwcZmFhwerVq8dkMpnSvOTkZCYWi1nHjh21bqMyAdHbt28zAKx79+4q886ePcsAsDFjxmjdRkWC+sHBwRrPWcW2/OTJEzZ79mxWp04dZm5uztzc3NjIkSPZjRs3DNqfOqWlpcza2prZ2NgoTTdGnQhOnz7NALD+/fvrXa4hQ4YwACwmJkbvdRTpE9TXRFOdVNSuXbsYADZ79myty1FQnxBCCCHPgqkxe/0TQgghhBAuNDQUMpkMb7/9Njw8PDQu9+WXX2LTpk1Yu3Ytvv76azg6OiI4OBgrVqwAAMyePVu+bPm80tHR0Vi6dCn8/f0xffp0XL58GWFhYbh27RquX78OS0tL+bLnzp1DYGAg8vPzMWDAANSvXx/x8fH4+++/sX//fpw5cwb16tVT2r6QmsXZ2RmjRo2CRCKBvb29Xsc/dOhQXLlyBYGBgXB2dlbZtqH27NmDvXv3YsCAAXj33Xdx/PhxrF+/HrGxsTh58qR8uX379mHAgAFwdHTEoEGDUL16dTx+/BgxMTH4+++/MW3atEqV41l69OgRAJ4OpDxh2tGjR7FgwQKt29m5cycyMzMxfPhwuLm5qezD1dUVtra2WvfRvXt3neU9c+YMiouL0atXL5Wc4tWrV0ezZs1w7tw5FBUVKbVNYxHSs/Tu3VtlXrt27eDo6Ihjx44Zfb/+/v6Ij4/HunXr4Ofnp3SeOjo6AuDnUocOHXDv3j34+/tj9OjRiI+Px/bt27Fv3z4cOnQIHTt2rHAZRCIRTExMlFIbAcatEzMzMwCAqal+PyHT09Nx7tw5WFtbV/r8rwhNdVJR+/btAwA0bdrUKNsjhBBCCKkMCuoTQgghhFSB06dPAwB69OihdbmGDRuiRo0aSEpKwsOHD+Hp6Yn58+cjNDQUgOYc6QAPMm3ZsgWjRo2ST5s4cSI2bNiAsLAwjB49GgBQUlKC0aNHQyaTITo6Gi1atJAvf/LkSfj7++ODDz7Anj17lLZ//fp1TJ48GX/++SdMTEwMOXwkJyfj6tWrcHZ2Nmg9TXbv3o2oqCh07twZACCVStGzZ09ERUXh7Nmz6NChAwBg7dq1YIwhKioKzZs3V9pGenq6XvsKCwtDTEyM3mXz9/fXOpBnRQkB+Li4ODRq1EhpXlxcHADgzp07Orfz119/AYDaGxpubm549OgR8vLyVAL7huwD4IOxAoCPj4/a+T4+Prhy5Qru37+Pxo0b67VNQ2jbv0gkQv369REdHY2CggKlnOiVJbz369atg7+/v9pz9uOPP8a9e/fw2Wef4fvvv5dPnzRpEvr06YOgoCDcunWrwgHo7du3Izc3FyNGjFCabsw6Wbt2LQD1NwgAPv5HaGgopFIpkpOTsXv3bmRlZeH333+HnZ1dRQ6rUjTVib5WrFiBrKwsZGVl4dSpU4iOjkbv3r0xceJEI5eUEEIIIcRwFNQnhBBCCKkCqampAABPT0+dy3p6eiI5ORkpKSl6LS/o1q2bUkAfAKZMmYINGzbgwoUL8qD+3r17ER8fj2+++UYpoA8AXbp0waBBgxAWFoacnBylnvjm5uZYsmSJwQF9AFiwYIHRAvoAMHbsWHlAHwBMTEwQFBSEqKgoXLhwQR7UFygO5ClwcXHRa19hYWFYt26dQeWriqB+3759sXnzZixatAgBAQHy3u3p6enyJzmysrK0biMuLg6RkZGoXbs2evXqpXYfa9euxYIFC7B06VL59PPnz2Pv3r167UOQnZ0NAHBwcFA7X2hbwnLGZsj+jRnU10UikWDz5s1wcXHBvHnzlOYFBgYiMDAQEREROH36NLp06WLw9h8+fIhZs2bBysoK33zzjdI8Y9XJgQMH8Mcff6BRo0aYOnWq2mXi4+OVnhqxtbVFSEgIxo8fb9DxGIO2OtHXihUrkJCQIP//+PHj8dtvv8mfWCCEEEIIeZ4oqE8IIYQQ8pwxxgBAJWWJLq1atVKZVqtWLQDKgdizZ88CAG7duqW2F3FqaipkMhnu3LmDNm3ayKfXrVsXrq6uBpVJ0K5duwqtp4m+xzpy5Ejs3LkT7du3x5gxYxAQEICuXbvC3d1d732FhobKn5R4nsaMGYOQkBBERkaiWbNm6NOnD0pKShAWFiZP6aTrhovw5MLkyZPV9gJfsGAB9u/fjx9++AFnzpxBhw4dkJKSgu3bt6Nx48a4evWq0j6E3suKJk2ahDp16lT6ePWhrv3Onj1bnubmRXTr1i0UFhbC399fbeDc398fERERiImJMTion5GRgX79+iEtLQ3r169HgwYNjFVsuejoaIwaNQoODg7Ytm0bLCws1C7n7+8PxhhKSkoQHx+P1atXY+LEiTh//jx+/vln+XKhoaGIj49XWnfw4MFo2bKlUcqrq070bcNCGVNTUxEZGYmPP/4Y7du3R0REhPzaQwghhBDyvFBQnxBCCCGkClSrVg23bt3Cw4cPdQbaEhMT5esYQl3vWyHftVQqlU/LyMgAAPz9999at5efn6/0f21jAehSmXXV0fdYR40aBTMzM6xYsQJ//PEHfv31V4hEIvj7+2P58uVGCxw+C6ampti/fz8WLVqETZs2YfXq1XBwcMCQIUMwd+5c+Pr6quTIVySTyRAaGgqxWIwpU6aoXaZWrVq4cOECgoODsX//fpw/fx6enp74+uuvUadOHYwePVppH+V7LwM8mFunTh35e6SpJ35OTg4Azb3G9aFu/IBJkybB0dFR7/3rOy6EsQj71XROCOe9oU8wZGZmomfPnrhx4wZ+++03tT3iK1snly9fRu/evSESiRAREYEmTZroLJeZmRl8fHywdOlSFBQUYOXKlejbty/69u0LgAf1y+fxr1OnjlHOTX3qRFsbVqdatWoYM2YM6tevj3bt2uGjjz7C1q1bK11WQgghhJDKoKA+IYQQQkgV6NSpE6KionDkyBH07NlT43K3bt1CcnIyatasaVDqHUMIAbs9e/bgzTff1Hs9Q58c0Gddobd4aWmpyoCbxkrLMnToUAwdOhQ5OTk4ffo0du7cib/++guBgYG4ffu2zl7dL0pOfQCwsLBAcHAwgoODlaYLA6AqPllR3oEDB5CYmIjAwEDUrl1b43I1a9bEmjVrVKYLveIV91G+h7UiIW+7kMe9vLt370IsFldq0FThqRZD988Yw71791CjRg3Y2NhUeP8VIZx/wsDH5QnTDbnZkJGRgZ49e+Ly5cv45ZdfMH36dLXLVaZOLl26hF69ekEqleLgwYNo27at3uUT9O7dG7/++iuioqLkQX2h7RqbvnWirQ1r07ZtWzg5OVVZ+QkhhBBCDEFBfUIIIYSQKhAUFIRFixbhzz//xIcffqixR/V3330HACo9qU1MTCCRSIxSlvbt2wMAzpw5Y1BQvyo4OTkBAJKSkuDl5SWfLpPJcOXKFaPuy97eHn369EGfPn0glUqxdu1anDt3DoGBgVrXe1Fy6msjPHUhjJugjrYBcnWRSqXYsmULTE1NMWzYML3W6dChAywsLHDo0CEwxpRu7KSkpODatWto3769fGwAYxPeg4MHD+LTTz9Vmnf+/HlkZWXJA8vGJqQoUnxqRNCwYUNYWlriwoULagekFXqt69tTXTF4vXLlSrz33nsal61onVy6dAk9e/ZEaWkpIiIi5NcQQyUnJwOAyg08YzOkTioqLy8P2dnZBj9RRQghhBBSFVQTaxJCCCGEkErz9fXFBx98gPT0dAwYMAApKSlK82UyGb755hts3LgR3t7emDt3rtJ8Z2dnPHnyBEVFRZUuy6BBg1C7dm0sX74cx48fV5lfUlKCkydPVno/+hB6fZfPWb98+XLExcVVevtHjhxRW2dpaWkA1A+gW15oaCgYY3q/1OV5NxYhPYqi7du3Y+3atWjbti2GDh2qdr3Hjx9jz549cHV1xcCBAzVuv6SkBIWFhUrTZDIZ5s6di9u3b2PmzJmoUaOGXmW1t7fHqFGjcP/+ffz+++/y6YwxfPbZZ5DJZHjrrbf02lZF+Pr6olu3boiMjER4eLh8eklJiXyA2qravzAotJBKS5G5uTnGjBmDJ0+eYOHChUrzDh8+jP3796N+/fpKA0FrkpGRgR49euDy5cv46aefMGPGDK3LV6ROhIB+SUkJ9u/fj44dO2rdx/nz59WecwkJCfLjraqbKYDhdaJNQkKC2p78JSUlmD17NmQyWZUeCyGEEEKIvqinPiGEEEJIFVmyZAmys7Oxdu1a+Pj4oH///vD29kZOTg4OHjyIu3fvwsfHB+Hh4SqpNwICAhAdHY0BAwaga9euMDc3R5cuXQweSBPgKVy2b9+Ovn37ws/PDz169EDTpk0BAA8ePMCJEyfg4uKCW7duGeW4tZk8eTKWLFmC+fPnIyYmBt7e3oiOjsb169fh5+enkmvbUB999BEePHggz5EtEolw8uRJnD9/Hp06ddIrcFqVJk2aJP+3cKNHcdoPP/ygNDhx+/bt4enpiUaNGsHS0hLnz59HVFQU6tWrh23btmkcKHf9+vUoKSnBxIkTYW5urrE8jx49QpMmTdC7d2/UrVsXEokEERERuHXrFvr3768ShNZl0aJFiIyMxPvvv4/Dhw/D19cXJ06cwKlTpxAYGIigoKBK14k2v/32Gzp16oQhQ4Zg5MiRqFGjBg4cOICrV69i2rRp6N69u9LyT548UbqhVlJSgidPnijtX59Bkxs2bIgaNWpgy5YtsLa2Rq1atSASifDuu+/CwcEBixcvxrFjx/Dtt9/i9OnTaN++PeLj47F9+3ZYW1sjJCRE7UDG5Q0dOhQxMTFo2LAhMjIy9Bo42JA6EXq8Z2Zmok+fPjh06BAOHTqktH1HR0fMnj1b/v/vv/8eJ06cgJ+fH2rXrg1TU1PExsYiPDwcEokEc+bMMei6devWLSxatAgA5Decbt26JX9PXF1d8cMPP1SqTjS5fPkyhg0bhq5du8LHxweurq549OgRDh8+LB8fRXi6SpEx2zAhhBBCiF4YIYQQQgipUocOHWIjRoxgNWrUYGZmZszR0ZF17NiRLVu2jBUUFKhdJzc3l7311lusevXqTCwWMwAsODiYMcZYZGSk0v8VxcXFMQAsKChIZV5iYiL74IMPmI+PD7OwsGD29vasUaNGbNq0aezIkSNKywJgfn5+Bh+rn58f0/UV89KlS6xHjx7M2tqa2dvbs0GDBrG7d++yoKAgBoDFxcXJlw0JCWEAWEhIiMp21NXDli1b2MiRI5m3tzeztrZmDg4OrGXLlmzJkiUsLy/P4OMxNgBaX4rHzhhjwcHBrFmzZszOzo5ZWlqyRo0asXnz5rHs7Gyt+2nUqBEDwG7evKl1uZycHDZhwgRWr149Zmlpyezs7FjHjh3Zn3/+yaRSaYWOMTk5mU2ZMoV5eHgwc3NzVr9+fbZgwQJWVFSkdnlD60SX27dvs+HDhzMXFxdmYWHBmjRpwn7++We1xyOcL9pe+jp79izz8/NjdnZ2asv++PFjNmvWLObl5cXMzMyYq6srGz58OLt27Zre+/Dy8qpQfelbJ/rUh5eXl9I6e/bsYaNHj2be3t7MxsaGmZmZsZo1a7IhQ4aw8PBwvY9NIJzX+u6/onWiTkJCApszZw5r3bo1c3FxYSYmJszBwYF16NCBLV68WOM1xNhtmBBCCCFEFxFjWkabIoQQQgghhBBCCCGEEELIC4Ny6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCGEEEIIIYQQQgghLwkK6hNCCCHktTJ//nyIRCJERUUpTReJRPD396/0doxp0qRJEIlEiI+Pr7J9GIu/vz9EItHzLgYhhBBCCCGEvPIoqE8IIYSQF8aYMWMgEomwZcsWrculp6fDwsICrq6ukEgkz6h0xhcaGgqRSITQ0NDnXZQXwst0E0MdofzaXmFhYfLlhfdf8WVlZQVfX1/MnDkTqampWvd3//59iMViiEQirFq1SuNy8fHxWsuk63xTdPz4ccydOxfdu3eHg4MDRCIRJk2apHWdxMRETJ8+HbVr14a5uTlq1KiByZMn4+HDhyrLqquT8q8ePXqorHfu3DkMGjQIrq6usLCwgK+vL7766isUFhbqfWyKIiIi4O/vD3t7e9jZ2cHf3x8RERFGqxNdNm3ahHbt2sHGxgZOTk7o168foqOj1S67Z88ezJw5E507d4aNjQ1EIhHmz59v8D6fxflXUlKCHTt2YNKkSWjUqBFsbGxgZ2eH9u3b49dff4VUKtW4rr51kp6ejtWrV2PgwIGoV6+e/LOib9++Gt/DnTt3Yvjw4fDx8YG9vT1sbW3RpEkTzJ49G0lJSQYf58aNGzF9+nS0adMGFhYWWq/zlakTdRISEvDOO++gdevWcHNzg4WFBby8vNC/f38cOXJE7TpV0YYJIYQQUrVMn3cBCCGEEEIEU6dOxZYtWxASEoLRo0drXG7jxo2QSCSYMGECzM3NjbLv//77D9bW1kbZlrEsXLgQn376KWrWrPm8i0IMMHXqVNSqVUvtvIYNG6pM69GjB7p06QIAePLkCY4ePYpVq1YhLCwMly5dgpubm9ptrV27FowxiEQi/PXXX5gxY4bWcrVo0QKDBw9Wmd60aVMdR6S8z3Xr1sHa2hq1a9dGTk6O1uVjY2PRqVMnpKWloVevXhg1ahTu3r2LdevWITw8HKdPn4a3t7d8+ZYtWyI4OFjttrZv344bN24gMDBQafrOnTsxatQomJiYYNiwYahWrRpOnTqFb775BkePHsWRI0dgYWGh9zH+/fffGD9+PFxdXREUFASRSIR//vkHffr0wcaNGzFu3LhK1Yku33//Pb744gvUrl0b77zzDvLy8rBlyxZ07txZfrNB0bJly3Ds2DHY29ujRo0auHfvXqX2X5ViY2MxfPhw2NnZISAgAAMHDkR2djb27NmD999/HwcOHMCuXbtUnvoxpE62bduGd999FzVr1kRAQABq1qyJxMRE7NixAwcOHMDSpUsxd+5cpe3/+++/uHLlCtq2bYvq1asDAGJiYvDzzz9j3bp1OHnyJJo0aaL3cc6bNw8JCQlwdXVF9erVkZCQYPQ60eTu3bvYunUrOnbsiA4dOsDe3h5JSUnYtWsXwsPD8d133+Hzzz9XWsfYbZgQQgghzwAjhBBCCHlByGQyVqdOHSYWi9mDBw80LteiRQsGgF27ds3gfQQHBzMALDIyshIlNc52QkJCGAAWEhJSqbK8CPz8/Fhlv1oGBQUxACwuLs44hXrGhPKfOXNGr+WF93/hwoVK06VSKevXrx8DwL766iu165aWlrKaNWuy6tWrs3HjxjEA7OLFi2qXjYuLYwBYUFCQQcejzoULF9j169dZaWkpO3PmjM7t9u/fnwFgP/30k9L0f/75hwFggYGBeu23uLiYubi4MFNTU5aamiqfXlBQwFxdXZmZmRmLjo6WT5fJZOz9999XW7/aZGRkMEdHR+bq6qp0DUpOTmbVqlVjjo6OLCMjQ2kdQ+tEmzt37jBTU1Pm6+vLsrKy5NOvX7/OrK2tmbe3NyspKVFa5/jx4+zOnTtMJpOxzZs3MwAsODjY4H0/i/MvMTGR/frrryw/P19pel5eHmvTpg0DwP755x+leYbWyZEjR9jevXuZVCpV2s6tW7eYg4MDMzMzY0lJSUrzCgsL1ZZ3zZo1DAAbPny4Qcd56NAhFh8fzxhjbOHChVqv8xWpE22Ki4tVjp0xxpKSkpiHhwczMzNjmZmZSvOM2YYJIYQQ8mxQ+h1CCCGEvDBEIhEmT54MmUyGdevWqV3m4sWLuHLlCtq1a4emTZsiOTkZwcHB6NChA9zd3WFhYYE6dergvffeQ1pamkH7VpdT/+HDhxgzZgycnZ1ha2sLPz8/HD9+XO02JBIJVq5cicDAQHh6esLCwgLu7u4YOnQoLl++rLTspEmTMHnyZADA5MmTldKLKC6jKR3GunXr0KFDB9ja2sLW1hYdOnRQW2dRUVHydByXLl1CYGAg7Ozs4ODggCFDhhicauPkyZPw8/ODjY0NXFxcMGrUKLVpVAAY9N7UqVNHXv66devK60LxPfn3338xZswY1K9fH9bW1nBwcEDXrl2xY8cOg47hRScWi+WpLy5evKh2mYiICCQlJWHs2LHydvTXX39VednatGmDJk2awMTEROeyRUVFiIiIgIeHB2bOnKk0b8SIEWjZsiUiIiJw//59ndv6999/kZ6ejjfffBMeHh7y6adOncKTJ08wePBgtG7dWj5dJBLh22+/BQD8/vvvYIzpdXzbtm1DVlYWZs6cCU9PT/n06tWrY/bs2cjKysK2bduU1jGkTnQJCQlBaWkpvvjiCzg4OMinN2nSBBMnTkRsbCyOHj2qtE7Xrl3h4+NTqTEt9Dn/AOD06dPo378/nJ2dYWlpiYYNG2L+/PkoKCjQaz81a9bEu+++q/JUlI2NDT788EMAwLFjx5TmGVonAQEB6N+/P8Ri5Z+6DRo0wKhRo1BSUoLTp08rzbO0tFRb3hEjRgCAwU8/9OzZE15eXnotW5E60cbc3Fzl2AGgRo0a6NSpE0pKSlSeHDBmGyaEEELIs0FBfUIIIYS8UCZPngyxWIzQ0FC1gbiQkBAAPMUJwHMBL1u2DB4eHhgzZgxmzpwJb29v/Pbbb+jYsSOys7MrXJaUlBR07NgRW7ZsQbt27TBr1iw4OzujV69eOHv2rMryGRkZmD17NoqLi9GvXz/MmTMH/v7+CA8PR6dOnXDhwgX5soMHD8agQYMAAIMGDUJwcLD8pcucOXMwadIkJCYmYurUqZg2bRqSkpIwadIkeRCovOjoaHTt2hWmpqbyXM9hYWHo2bMnioqK9KqPI0eOICAgAOfOncPw4cPx9ttvIy4uDp07d0ZmZqbK8oa8N7Nnz0aLFi0AAB988IG8LhTzOn/22We4ceMGunTpgg8++AAjRozA7du3MXz4cKxcuVKvY3hZCG3f1FR9tkwhgD9x4kR0794dnp6e2LRpk9b3Mjk5Gb/99hsWLlyIdevWITEx0fgFV5Ceno7S0lJ4eXmpDTjXrVsXABAZGalzW8LxTps2TWn6o0ePlLalyNHREU5OTkhISNDrxgEA+cDXvXv3VpknpP0xJMBqqOe1f33Ovx07dsDPzw9RUVEYPHgwZs+eDVtbWyxYsAA9e/ZEcXFxpcpgZmYGQLXNG7NONO1Dk3379gEwLEWVMRlaXm3S09Nx7tw5WFtbo169epXeHiGEEEKeL8qpTwghhJAXiqenJ3r16oWIiAgcP34cfn5+8nnFxcXYtGkTrK2t5Tn3AwICkJqaCltbW6XtrF+/HkFBQVi1ahW++OKLCpXls88+Q1JSEr799lulbaxevRrTp09XWd7JyQkPHjxQyYF/48YNdOjQAZ9//jkOHToEgAf1s7KysGvXLgwePFjvQQlPnDiBFStWoFGjRjhz5oy85+qCBQvQoUMH/Pjjjxg6dKg8R7tg37592LJlC0aNGiWfNnHiRGzYsAFhYWFaxzAAAJlMhrfffhulpaU4fvy4fPuMMYwfPx6bNm1SWceQ92b27NmIiYnBlStXMHv2bNSpU0dle+Hh4SrBqLy8PHTq1Alffvklpk6dqte4CKGhoQY9oTB48GC0bNlS7+XXrFmDAwcOqJ336aefauwVLJBKpVi7di0AqLyPAPD48WPs2bMHzZo1Q/PmzQEA48ePx8KFC7Fjxw6VnO+CQ4cOydsfwAOFs2bNwtKlS9X27K0sJycnmJiYICEhQZ77X1FcXBwA4M6dO1q3k5CQgCNHjqBmzZro06eP0jxhvAFhW4qys7PlN5vu3LmjlLtfk7t37wIAfHx8VOYJ04RlqsLdu3dha2uLatWqPdP96zr/cnNzMW3aNJiYmODMmTPydqd4/i9duhTz5s2rcBmENl8+eG+sOsnNzcX27dthaWmJrl27ql0mLCwMMTExKCgowI0bNxAREYG6devi66+/NvRwjEJTnegjPj4eoaGhkEqlSE5Oxu7du5GVlYXff/8ddnZ2xi4qIYQQQp6155n7hxBCCCFEna1btzIAbOLEiUrThXzR+uT6lclkzN7envn7+ytN15QLHwDz8/OT/7+4uJhZWloyd3d3lXzLUqmU+fr6GpRTf8CAAczc3JxJJBL5NF059dXluJ4yZQoDwLZu3aqyvFA/U6dOlU+LjIxkAFi3bt1UlhfmffjhhzrLf+zYMQaADRgwQGVefHw8MzEx0Tunvqb3pqI5vZctW8YAsKioKL2WF/L/6/vSd8wDofzaXoq5rIX3v0ePHiw4OJgFBwezGTNmsAYNGjAArEOHDiwvL09lPz/88AMDwJYuXSqf9t9//zEArHv37irLP3r0iAUHB7OYmBiWk5PD0tLS2O7du1mjRo0YAPbpp5/qdXzl6ZN7OyAggAFgq1atUpq+Y8cOeZ28/fbbWvcjnLPz5s1TmZebm8vs7e2ZmZkZu3TpktK8mTNnyvexadMmvY7Jx8eHAVDJWy8wMTFhvr6+GtevbD5yMzMzVrNmTbXzHjx4wACw3r17a1y/qnLqr1+/ngFg7777rtpymZqaMm9vb4P3Kfjjjz8YABYQEKAyr7J1Ihg7diwDwL7++muNy5Q/h9u0acPu3bun/4GooSunviba6kQfwvVdeNna2rINGzboXI9y6hNCCCEvB+qpTwghhJAXzuDBg+Hi4oLt27dj1apV8l6FQq/FKVOmKC2/c+dO/PHHH7h06RIyMzMhlUrl85KTkytUhtu3b6OoqAgBAQEqPavFYjE6deqktodxTEwMlixZgpMnTyI1NRUlJSVK8588eYLq1atXqEwA5Ln51eX/F6bFxMSozGvVqpXKtFq1agEAsrKydO73ypUrAKC2h6uXlxc8PT3V9n435nuTlpaGRYsWYf/+/UhISEBhYaHSfH23J6TzqCpnzpxBhw4d9F7+yJEjOHLkiNK0jh074ujRo2p79a9duxZisRhjx46VT2vYsCHatm2LqKgo3L9/X+mJBnd3d8yfP1/+fzs7OwwYMABt27ZF06ZNsXz5cnz88cdwcnIy4Cj1s3z5cnTp0gUzZszAnj170Lx5c9y7dw+7du1C8+bNcfXqVa15vGUyGUJCQiASiVTOewCwtbXF8uXLMW3aNHTs2BHDhw9HtWrVcPr0aVy8eBENGzbErVu35PsQei8rcnR0xOzZs4152BrFxMQgLCxMaVqdOnX0flLnedF23fH09IS3tzdu376N3Nxcg3uB79u3DzNmzICXlxc2btxojOKq+Pzzz7Fp0yb06dMHn3/+ucblQkNDERoaiuzsbFy+fBlffPEFWrdujZ07dyIgIAAAv16uWLFCZV3Fc6yytNWJvm3Y398fjDGUlJQgPj4eq1evxsSJE3H+/Hn8/PPPRisrIYQQQp4PCuoTQggh5IVjbm6O8ePH46effsI///yDqVOn4uHDhzhy5Ah8fHzQrVs3+bLLli3D3Llz4ebmht69e6NWrVqwsrICAKxYsaLCeZ6FfO/u7u5q5ysO1ik4ffq0PPDTu3dv+Pj4wNbWFiKRCGFhYbhy5Uql807n5ORALBbL046UL5NYLFY7joDiAJMCIU+zYqBdE33qo3xQ35jvTUZGBtq2bYsHDx6gc+fO6NmzJxwdHWFiYoKYmBjs2rWr0nX7vCxcuBCffvopZDIZ4uPjMX/+fGzYsAFvvfUWNmzYoLTs2bNncfPmTfTq1Qs1atRQmhcUFIQLFy4gJCQE33zzjc79VqtWDf369cOGDRtw4cKFCqX40KVFixa4cOECgoODERkZicjISNSvXx9//PEHsrKy8L///U9tWxYcOnQIDx48QI8ePdTmzQf4+Bo1atTAkiVLsGvXLkilUrRp0wZHjhzB4sWLcevWLfk+4uPjsWDBAqX1vby85AFR4TzJzs6Gi4uL0nL5+fmQSqVqzyV9xcTEqOzfz89PHtR3cHDQOA5ITk6OUhmfJWHf6q57AG9Lt2/fRk5OjkFB/YiICAwbNgweHh44evSo2huela2TBQsWYOHChQgICMDOnTv1GgzWwcEB/v7+2L9/Pxo0aICJEyciLi4OZmZmyMrKUnkPAeMF9XXVia42XJ6ZmRl8fHywdOlSFBQUYOXKlejbty/69u1rlPISQggh5PmgoD4hhBBCXkhTp07FTz/9hLVr12Lq1KkIDQ2FTCZT6q1bWlqKb775BjVq1EBMTIxScJAxhiVLllR4/0KQKC0tTe18YYBORd999x2Ki4tx8uRJdO7cWWne2bNn5b3dK8Pe3h4ymQyPHz9WCbCnpaVBJpPB3t6+0vspz9D6MPZ789dff+HBgwcq4xsAwKJFi7Br1y69t1XVOfUrSiwWo169eli3bh0SEhKwceNGDBs2DIMHD5YvIwwYe+jQIbWDzwL8+BYsWKBXnnxXV1cAQEFBQeUPQIOGDRti69atKtOFQHabNm00rqtpgNzyNAUpJ0yYALFYLH9SRei9rImPjw+io6Nx9+5dlaC+tnz7+po0aZLWXvk+Pj44c+YMUlNTVXLIG2P/FSVcU9Rd9xSnG3LtOXDgAIYMGQJXV1dERkZqHLy1MnWyYMECzJ8/H/7+/tizZ4/8pqK+7O3t0aFDB4SFheHevXto1KgR6tSpo7UNVYY+daKrDWvTu3dv/Prrr4iKiqKgPiGEEPKSo6A+IYQQQl5IzZo1Q9u2bXH69GncunULoaGhMDExQVBQkHyZJ0+eIDs7Gz169FDp7RsdHa2SnsUQDRo0gKWlJaKjo1FUVKSUBkUmk+H06dMq68TGxsLZ2VkloF9QUIBLly6pLC/0GNWnp7zgjTfewOXLlxEVFYWRI0cqzTt27BgAVEkAukWLFgD4QL3/+9//lOYlJCTg4cOHStMq8t5oq4/Y2FgAwMCBA1XmnThxwoAj4UFvoa70UadOnWcS1BeIRCL89NNPaNWqFT777DMMGDAAJiYmyM/Px9atW2FtbY0xY8aoXffs2bPyAT71CdqdP38eANQOTFyVcnNzsWfPHjg7O6NXr15ql0lPT8euXbvg7OyMIUOGGLyPU6dOIT4+Hv369dO7d7ufnx82b96MgwcPqqRQioiIkC9TVfz8/HDmzBkcPHgQEydOfKb713b+vfHGGwCg9rqTlJSE2NhY1KtXT+9e+gcOHMDgwYPh7Owsf3pDk4rWyfz587FgwQL4+flh3759eg2irY6Q1kt4sqmqGFInFfWsjoUQQgghVU939x1CCCGEkOdk6tSpAHgv3fv376Nfv35KqQjc3d1hZWWFS5cuKfU0zszMxMyZMyu1b3Nzc4wcORJpaWlYtmyZ0rw1a9aozafv5eWFzMxM3LhxQz5NKpVi7ty5ePz4scryzs7OAIDExES9yyXc1FiwYIE89QTA01AIKRkUb3wYS5cuXVC3bl3s3bsXJ0+elE9njOHzzz9XCQRW5L3RVh9eXl4AoLRvANi0aRPCw8MNOpaoqCgwxvR+PY985y1btsTgwYNx69YtbNq0CQDwzz//IDc3FyNGjMCaNWvUvr7//nsAZT3cAR64Lz+2A8Dz3Z86dQqNGzeW37QxtsLCQpSWlipNKy4uxtSpU5GRkYHg4GC14wYAwIYNGyCRSDB+/HhYWFho3IfieSBITk7GtGnTYGpqqlcqIsHIkSPh4OCAlStXKt2oSklJwYoVK+Do6IgRI0bovT1DTZ48Gaampvjuu++UUs7cuHED69evh7e3tzzFl7FpO/8GDRoEBwcHhISEKF3fGGP47LPPUFJSovd5IgSvnZycEBkZqfPJg4rUSXBwMBYsWICuXbvqDOgXFxfj7NmzaueFhITg/PnzqF+/fpU+IWFonWhz/vx5FBUVqUxPSEjAwoULAYB66RNCCCGvALpFTwghhJAX1pgxY/Dhhx/i1KlTAMqC/AKxWIz33nsPy5YtQ4sWLTBgwADk5ORg//798PLyUsk5bqhFixbhyJEjmDdvHk6ePIk33ngD//33H8LDw9G7d28cPHhQafmZM2fi4MGD6NKlC0aOHAlLS0tERUUhKSkJ/v7+KgO0duzYEVZWVlixYgVycnLkPdo//fRTjWXq1q0bZs6ciZUrV6Jp06YYNmwYGGPYuXMnHj58iFmzZimNOWAsYrEYq1evRr9+/dCzZ0+MGjUKNWrUwNGjR5GSkiIf9FRxeUPfm4CAAPzwww+YPn06RowYARsbG9SuXRtjx47FhAkTsHjxYsycORORkZHw8vLC1atXcfjwYQwdOhQ7d+40+jFX1Jo1a3DgwAG18/z9/dUONqrO/PnzERYWhq+//hpjxoyRB+rVDRgr6NevHzw8PLB79248fvwYbm5u+Pjjj3Hr1i34+fnB09MThYWFOHPmDC5fvgwnJyds2LBBYyqf8k6ePIk1a9YAgPxG1cmTJ+UB3YYNGyq134sXL2Lo0KHo1asXPD09kZOTg3379uHBgwd46623tN580zf1zs8//4yNGzeiS5cucHd3x8OHD7Fr1y4UFBTgr7/+UjtItCZOTk5YtWoVJkyYgFatWmH06NEQi8XYunUrHj16hA0bNqgMKGxonWjj6+uL+fPnY968eWjevDmGDx+O/Px8bN68GSUlJfjzzz9VelmHhYXJB9+Ni4uTTxNSTHXp0kVnHQLazz97e3v8+eefGDNmDNq3b49Ro0bBzc0NR44cQXR0NNq1a6fyBI86t27dwuDBg1FcXAx/f39s3rxZZZnyAwcbWiehoaH4+uuvYWpqinbt2mHp0qUq+1A8DwsLC9GxY0c0bdoULVu2RM2aNZGdnY3z58/j0qVLsLW1RUhIiM5jU7RmzRr5Dchr167JpwmfAYMHD5an1apInWjz/fff48SJE/Dz80Pt2rVhamqK2NhYhIeHQyKRYM6cOejSpYvSOsZsw4QQQgh5RhghhBBCyAts4sSJDADz8PBgJSUlKvMlEgn77rvvmI+PD7OwsGC1a9dmH374IcvNzWVeXl7My8tLafng4GAGgEVGRipNB8D8/PxUtp+QkMBGjRrFHB0dmbW1NevatSs7duyYxu1s376dtWrVillbWzNXV1c2cuRIFhsby4KCghgAFhcXp7T8vn37WNu2bZmVlRUDwBS/nmlahzHG1q5dy9q2bcusra2ZtbU1a9u2LVu7dq3KcpGRkQwACw4OVpkXFxfHALCgoCCVeZocP36cdevWjVlZWTFnZ2c2YsQIlpCQwPz8/Fj5r5aGvjeMMbZkyRLm4+PDzMzMVN6TmJgY1rt3b+bk5MTs7OyYn58fO3z4MAsJCWEAWEhIiN7HURWE90vbS/F9EMq9cOFCjdscNmwYA8B+//13BoB5e3vrLMdHH33EALBly5Yxxhj7888/WZ8+fVitWrWYpaUls7S0ZA0aNGAffPABe/jwoUHHKJRZ06v8OZSQkMBGjBjBPD09mbm5OXN0dGQBAQFs+/btWvdz7tw5BoC1a9dOZ5mOHDnCevbsydzd3ZmZmRmrVq0aGzVqFLt06ZJBx6Zo//79rFu3bszW1pbZ2tqybt26sQMHDqhd1tA60cfGjRtZmzZtmJWVFXNwcGB9+vRh58+fV7uscC3S9DLk/NZ2/jHGz/++ffsyR0dHZm5uznx9fdmXX37J8vLy9Nq+cD2qSH3pWye66qP8eSiRSNiCBQuYv78/q169OjMzM2PW1tascePGbPbs2SwhIUHf6pPTdS1Q3H9l6kSdPXv2sNGjRzNvb29mY2PDzMzMWM2aNdmQIUNYeHi42nWqog0TQgghpGqJGKuiUX4IIYQQQgghhBBCCCGEEGJUlFOfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQQgghhBBCCHlJUFCfEEIIIYQQNerUqQORSASRSISwsDCNy/Xs2RMikQihoaFK00NDQ+Xrm5ubIz09XeM2SktL4e7uLl9+/vz5apeLiIjA0KFDUbNmTZibm8PR0RENGjTAgAEDsGzZMsTGxiotHx8fL9+mrld8fLyeNaPbzZs3MX36dPj6+sLKygo2NjaoW7cu/P398eWXX+L06dMq6wj1Xb4eNZk0aZLKMZiZmaF69eoYOHAg9u/fb7TjKe/ixYvyfY4bN07n8oplXLlypdZl58yZI1+2Tp06RioxIYQQQgh5lZg+7wIQQgghhBDyops/fz4GDRoEkUhUofVLSkqwdetWvPfee2rnR0RE4PHjx1q3MWPGDPzyyy8AABsbG/j4+MDa2hoJCQnYu3cv9u7di5SUFPzwww9q12/Tpg0sLCw0bt/S0lLPo9Hu77//xpQpUyCRSGBmZobatWvD2dkZaWlpOHbsGI4dO4b9+/cjOjraKPtzd3eHj48PAKCoqAh37tzBnj17sGfPHnz22Wf4/vvvjbIfRRs2bJD/OywsDLm5ubCzs9N73ZkzZ6qdJ5VKsWXLFqOUkRBCCCGEvLqopz4hhBBCCCFamJiY4MqVK9ixY0eF1vfx8YFIJFIKBJcnzGvQoIHa+Zs3b8Yvv/wCsViMn376Cenp6bhx4wYuXLiAtLQ03LhxA1988QXc3d017mPbtm04efKkxle1atUqdHyK4uPjMXXqVEgkEkyZMgWJiYm4d+8ezp8/j/j4eKSkpGDVqlVo3Lhxpfcl6Nu3r/wYoqOj8eTJE8ydOxcAsHDhQpw5c8Zo+wL4UxWbN28GADg6OqKgoAA7d+7Ua90GDRrgwoULuH37ttr5hw4dQmpqqsZ2QAghhBBCCEBBfUIIIYQQQrQaM2YMAGDBggVgjBm8fu3atdGtWzecPXsW9+7dU5mfm5uL3bt3o27duujcubPabaxbtw4AMGXKFMyaNUulx33jxo3x7bff4uOPPza4fMa0ZcsWFBcXo0GDBvjzzz9VbjJUq1YN77//PtavX19lZTA3N8eSJUvQsmVLeZmM6eDBg0hLS4Onpyc+++wzANB6w0bR+PHjAQAbN25UO1+YPmHCBCOUlBBCCCGEvKooqE8IIYQQQogWU6ZMQZ06dXD9+nX8888/FdqGtmDu9u3bUVhYiHHjxmlM73P//n0AkAeqX1RCOZs1awax+Pn91BCJROjSpQsA4O7du0bdthDAHz16NMaOHQuxWIzIyEgkJibqXHfYsGGwsrLCxo0bVW4Q5efnIywsTH4TiBBCCCGEEE0oqE8IIYQQQogWZmZm+OKLLwDw3voymczgbYwYMQKWlpb4+++/VeYJgX4h8K+Ovb09AOD8+fMG7/tZEsoZExODkpKS51qWijxVoUtOTg527doFABg7dixq1aqFbt26QSaTYdOmTTrXt7Ozw6BBgxAfH49Tp04pzdu5cyfy8/O13twhhBBCCCEEoKA+IYQQQgghOk2aNAn16tXDf//9V6F0Lg4ODhgwYADu3bunlOM9MTERUVFRaNeundY86n369AHAe4m/++67uHDhAqRSqeEHUsWEct67dw99+/bF/v37UVBQ8MzLwRiTB83r169vtO0KT1U0btxY/tTEuHHjAOifgkdIrVN+eeH/2m7uEEIIIYQQAlBQnxBCCCGEEJ1MTU3x5ZdfAgC+/vrrCgXUhWCtYjD377//hkwm0xnI/eSTT9C6dWswxvD777+jXbt2sLe3R5cuXfDpp58iOjpa5/7r1q0LkUik9mWstD49e/bE22+/DQA4cuQI+vXrBwcHB7Ro0QLvvPMO9u7dW+U3IyQSCT7++GPExMQAAEaOHGm0bQvv3dixY+XThg8fDnNzc1y/fl2+T2169+4Nd3d3/PPPPyguLgYApKSk4OjRo2jVqpVRBxEmhBBCCCGvJgrqE0IIIYQQoocJEybAx8cHt2/fVptGR5e+ffvC1dUV//zzjzw1zcaNG2FqaorRo0drXdfOzg4nT57EsmXL0KhRIwBAQUEBTp06hcWLF6Nt27YYPHgwsrKyNG6jTZs26Ny5s9rXG2+8YfDxaPLHH39gx44d8PPzg4mJCUpLS3H16lX88ccfGDBgAFq0aIFr164ZbX/79+9Hly5d0KVLF7Rp0waurq744YcfAABz5syR59avrIcPH+LYsWMAygZPBgBHR0f069cPgH699YX3OysrC/v27QMAbNq0CVKplAbIJYQQQggheqGgPiGEEEIIIXowMTGR99b/5ptvUFpaatD6ZmZmGDlyJNLT0xEeHo6YmBhcv34dgYGBcHNz07m+paUlPvzwQ9y8eRNJSUnYuXMn5syZg7p16wIAdu3ahaFDh2pcf9u2bTh58qTaV0hIiEHHosvQoUMRFRWFjIwMHDp0CN988w3atWsHALhx4wZ69uyJx48fG2VfaWlpOHXqFE6dOoWYmBhYWlqib9+++Pfff7F8+XKj7AOAfHDbDh06oF69ekrzhBQ8QnBel/JPbWzYsAEmJiZKNwsIIYQQQgjRhIL6hBBCCCGE6Gns2LFo0KAB7t27p3cOdUVCT+yNGzfKB8itSO/sGjVqYMiQIVi+fDnu3LmDjz76CAAQGRmpMgDr82Rvb4+ePXti3rx5OHfuHLZt2waxWIy0tDSsXr3aKPsICgoCYwyMMZSWliItLQ3h4eEYPHiwUbYvUJd6R/Dmm2/C3t4eqampOHz4sM5ttW3bFg0bNkR4eDiOHz+OK1euoFevXvDw8DBqmQkhhBBCyKuJgvqEEEIIIYToycTEBF999RWAivXW79ChA3x8fLBnzx5s3LgR9vb2GDhwYKXKZGpqiiVLlqBatWoAgPPnz1dqe1Vp+PDhGDZsGIAXu5zlRUdH47///gMAzJo1S2VMAisrK+Tk5ADQf8Dc8ePHQyKRyG/qUOodQgghhBCiLwrqE0IIIYQQYoDRo0ejcePGiIuLQ2hoqMHrjxs3DsXFxXj06BGGDRsGKyurSpdJLBbDy8sLAB8o9kUmpK550cupSAjUW1tbw8PDQ+1LSKH077//Ii8vT+c2x48fD5FIhAcPHsDW1tboTxYQQgghhJBXFwX1CSGEEEIIMYBYLEZwcDAA4Ntvv5UPequvCRMmoEePHujRowfeeustvdZJS0vTOj8rKws3b94EAPj4+BhUHmPSVU4AOH36NIDnW05DlJaWYsuWLQCAX375BampqRpftWrVQkFBAXbu3Klzu15eXpg+fTp69OiBuXPnwtrauqoPhRBCCCGEvCIoqE8IIYQQQoiBRowYgWbNmiEhIcHgHPb16tXD4cOHcfjwYXTs2FGvdfr164dx48bh6NGjKjcRYmJiMGjQIOTm5qJ69eoIDAw0qDyaTJo0CSKRCJMmTdJ7ne+//x5du3bF5s2bkZubqzQvJSUF77zzDk6cOAGRSISgoCCjlNNQderUgUgk0vspi4iICKSlpcHKykqeOkgdsVgsH+hW3xQ8v/32Gw4fPiy/SUQIIYQQQog+TJ93AQghhBBCCHnZiEQiBAcHY/jw4ZBKpVW+P6lUik2bNmHTpk2wtLRE/fr1YWlpieTkZCQnJwMAHB0d8c8//8DGxkbtNkaMGAELCwuN+1i4cCG6du1aqXKKRCKcPHkSJ0+ehFgshre3N5ycnPD48WM8fPgQpaWlMDExwfLly9G6dWu125g5cybmzp2rcR9RUVFo2rRppcppCCFAP3jwYNjZ2Wlddvz48Vi6dCmOHj2KpKQk1KxZ81kUkRBCCCGEvGYoqE8IIYQQQkgFDB06FC1btkRMTEyV7+vgwYOIiIjA/v37cf36dSQnJyMrKwu2trZo3749AgMD8f7778Pd3V3jNqKjo7XuIz09Xen/qampAICWLVvqXc7vv/8evXr1wv79+3Hu3DkkJSUhPj4eFhYW8PX1Rbdu3fDuu++iefPmGreRl5enNSe9oYMTK5JKpXjy5AkAoEWLFjqXz8nJwe7duwHwgL0uzZs3R7NmzXDt2jVs2rQJ//vf/ypcVkIIIYQQQjQRMcbY8y4EIYQQQggh5MUhk8ng7OwMxhgSExN19lB/WVy6dAmtW7dG9+7dcfTo0eddHEIIIYQQQiqEcuoTQgghhBBClNy4cQPZ2dl4++23X5mAPgD5+AcfffTRcy4JIYQQQgghFUdBfUIIIYQQQoiS06dPw9TUFLNmzXreRTGq06dPo2HDhujXr9/zLgohhBBCCCEVRul3CCGEEEIIIYQQQgghhJCXBPXUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQgghhBBCCCGEEEJeEhTUJ4QQQgghhBBCCCGEEEJeEqbPuwCEEELIq0wmkyE5ORl2dnYQiUTPuziEEEIIIaSKMcaQm5uLGjVqQCymvpSEEEKMj4L6hBBCSBVKTk6Gp6fn8y4GIYQQQgh5xh4+fIhatWo972IQQgh5BVFQnxBCCKlCdnZ2APiPOnt7++dcGkIIIYQQUtVycnLg6ekp/x5ICCGEGBsF9QkhhLwyfv31VyxduhQpKSlo0qQJVqxYga5du+pc79SpU/Dz80PTpk0RExOjNG/Hjh348ssvERsbC29vb3z33XcYMmSI3mUSUu7Y29tTUJ8QQggh5DVCqRcJIYRUFUruRggh5JWwdetWzJ49G1988QUuX76Mrl27om/fvnjw4IHW9bKzszFx4kT06NFDZd6ZM2cwatQoTJgwAVeuXMGECRMwcuRInDt3rqoOgxBCCCGEEEIIIUQrEWOMPe9CEEIIIZXVvn17tGrVCr/99pt8WqNGjTB48GAsXLhQ43qjR4+Gj48PTExMEBYWptRTf9SoUcjJycH+/fvl0/r06QMnJyds3rxZr3Ll5OTAwcEB2dnZ1FOfEEIIIeQ1QN//CCGEVDVKv0MIIeSlJ5FIcPHiRXz66adK03v37o3Tp09rXC8kJASxsbHYuHEjvv32W5X5Z86cwZw5c5SmBQYGYsWKFRq3WVxcjOLiYvn/c3Jy9DwKQgghhLwOpFIpSkpKnncxSCWZm5tDLKbkB4QQQp4PCuoTQgh56T158gRSqRQeHh5K0z08PJCamqp2nbt37+LTTz/FiRMnYGqq/uMwNTXVoG0CwMKFC7FgwQIDj4AQQgghrzrGGFJTU5GVlfW8i0KMQCwWo27dujA3N3/eRSGEEPIaoqA+IYSQV0b5wcgYY2oHKJNKpRg7diwWLFgAX19fo2xT8Nlnn+HDDz+U/z8nJweenp76FJ8QQgghrzAhoO/u7g5ra2saRPUlJpPJkJycjJSUFNSuXZveS0IIIc8cBfUJIYS89FxdXWFiYqLSgz4tLU2lpz0A5ObmIjo6GpcvX8aMGTMA8B9njDGYmpri4MGDCAgIQLVq1fTepsDCwgIWFhZGOCpCCCGEvCqkUqk8oO/i4vK8i0OMwM3NDcnJySgtLYWZmdnzLg4hhJDXDCWAI4QQ8tIzNzdH69atcejQIaXphw4dQqdOnVSWt7e3x7Vr1xATEyN/vfPOO2jQoAFiYmLQvn17AEDHjh1Vtnnw4EG12ySEEEII0UTIoW9tbf2cS0KMRUi7I5VKn3NJCCGEvI6opz4hhJBXwocffogJEyagTZs26NixI1avXo0HDx7gnXfeAcDT4iQlJWH9+vUQi8Vo2rSp0vru7u6wtLRUmv7BBx+gW7duWLx4MQYNGoRdu3bh8OHDOHny5DM9NkIIIYS8GihNy6uD3ktCCCHPEwX1CSGEvBJGjRqF9PR0fP3110hJSUHTpk0RHh4OLy8vAEBKSgoePHhg0DY7deqELVu2YN68efjyyy/h7e2NrVu3ynvyGyImJga2trYGr6fI1dUVtWvXrtQ2CCGEEEIIIYSQ/7N373FVVfn/x98HkIsXMDnKJUEoNRQrFfNGdA/UqbG00Uwpxksymok2ftVMU7Q0M0XLG42JjkVomlmZSjOZktaUI93UMm+YQIaNIF5A8fz+4MepI6BcDgfO4fV8PPYj9tprf9Zng49He33OOnvDvhlMJpOptpMAAMBR5eXlycvLyyqxPDw8dODAAQr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cJ+WSqnGclIvT4tiVy79/+FGhq10qLi6W3++PO04wGNSxY8dUVLws7hhRPp9PR44c0fjx4y3F8fv9mjRpkuV8ACDdGIP4iMyVRX1JKi8vV3l5eczf9u/fP+CYacb/iR4AuJ2VlwN2LyJsNY7TWhPxYgAAAADx8vv9ysvLi/v6cDgs0zRteznwxBNPxB0jKjs7W+fOnaOwDwAJ5NqiPgAgPTmtNRGtgAAAAJBsTnk5EA6HdfToUTU2NmratGlxx4lEIvL54l+zIIqvBgCkKor6AABXckJromgroB9//JGiPgAAAFzP6suBrq4uGYah5cuXW8rDMAxbOi7QUghAqqKoP0hew1Cm4v+HJSvD0EhvpnyGUiaOk3JJ1ThOysVpcZyUS6rGcVIuiYgzZsRwjRg5Iq4YPkMa6c3UlX+3K2MQq9TH0nPjhjzDrP+znJ2drVGj7rAcB4CkG93JzgAAAFe5du2a5Rn/58+fV0NDAy2FAOB3UNQfpExDGm6hiDTK41HO8GyNyDBSJo6TcknVOE7KxWlxnJRLqsZxUi5Oi3Ojp0c5w7P1yckP487DbhkZGfrzgj9r+PBsS3F8vqy4X3YAKcPiyzoAANKVlRn/4XDYcoxoHCe1FJKY8Q/APhT1B6nuarcmjcmK+/rW9v+o7sTHWj7ubuUOH5MScZyUS6rGcVIuTovjpFxSNY6TcnFanNa2C6o78bEWL16ssWPHxp3LhQtfqanplOU4bW1t+vAfH8o8/lHcMaK8Xq/27Nmj3NxcS3G6uyPyeq31Qx07dqwmTJhgKQYQD09nZ7JTwBDZsWOHXn/9dYVCId1///2qra1VYWHhLc8/efKkAoGAPv/8c+Xl5emll15SWVlZ3+/79+/XM888M+C6n376SVlZ8Y8lAACD55SWQpI97YBYbwCARFF/0K7eNPWDMuK+/sqNHrV3/aDOm6ZGpkgcJ+WSqnGclIvT4jgpl1SN46RcnBYnGmPYnWM0clz8D+bmpcu2xIlcuqzQ1S7bPlUuKl4Wd4woO/qh0gsVyWJYb+ULFzh8+LBWr16tHTt2aP78+Xr77be1aNEiffHFFzH/zrhw4YIWL16sVatW6Z133lFTU5PKy8t11113admyX/7evOOOO/Tll1/2u5aCPgC4jx0thST72gGx3gAAiaI+AAApxymfKtvRD9XOXqgMXADEsmXLFq1YsUIrV66UJNXW1urYsWPauXOnNm7cOOD8Xbt2adKkSaqtrZXUu+D6p59+qs2bN/cr6huGoXHjxg3JPQAAEs8Jz9isNwAgiqI+AACIyY6Bi9U4dr1gYOACIJbu7m6dPn1aa9as6Xe8qKhIp06dinlNc3OzioqK+h1buHCh9u7dq+vXryszM1NSb7uG/Px89fT06IEHHlBNTY1mzZp1y1wikYgikUjffiftnwAgJaXiegPhcJhnY2CIUdQHAACO57SBCwulAakhHA6rp6dnwPohubm5am9vj3lNe3t7zPNv3LihcDis8ePHa+rUqdq/f79mzpypzs5OvfHGG5o/f77Onj2rKVOmxIy7ceNGrV+/3p4bAwDgNlh9xo5qbW21JReejYHbR1EfAACkDSctlMaM/+Tp6ZEaG6VQSBo/XioslDyeZGeFZDIMo9++aZoDjv3R+b8+PmfOHM2ZM6fv9/nz5+vBBx/Um2++qW3btsWMWVlZqUAg0Lff2dmpiRMnDu5GAAAYQk5bRFji5QDSB0V9AACA22TXQml8qpw8R49KL7wg/etfvxz705+kN96QiouTlxeSw+/3y+PxDJiVf+nSpQGz8aPGjRsX8/xhw4YpJycn5jUZGRl66KGHdP78+Vvm4vP55PP5BnkHAAAkj9MWEZbseTnAiwG4AUV9AACAQbLrU2UMraNHpSeflH6eVN2nra33+N/+RmE/3Xi9XhUUFKi+vl5Lly7tO15fX68lS5bEvGbu3Ln64IMP+h07fvy4Zs+e3ddP/7dM01RLS4tmzpxpX/IAADiEU1pl2vVygK8G4AYU9QEAAJDyenp6Z+j/tqAv9R4zDGn1amnJElrxpJtAIKDS0lLNnj1bc+fO1e7duxUMBlVWViapty1OW1ubDhw4IEkqKyvTW2+9pUAgoFWrVqm5uVl79+7Vu+++2xdz/fr1mjNnjqZMmaLOzk5t27ZNLS0t2r59e1LuEQAAN3DCywE7vxqg3SaiEtH+k6I+AAAAUl5jY/+WO79lmtI33/Set2DBkKUFBygpKVFHR4eqq6sVCoU0Y8YM1dXVKT8/X5IUCoUUDAb7zp88ebLq6upUUVGh7du3Ky8vT9u2bdOyZcv6zrly5Yqee+45tbe3a/To0Zo1a5Y++ugjPfzww0N+fwAApBsrLwfs+mog2m6zsbFR06ZNizuOxIx/t0tU+0+K+gAAAEnS2tpq6fpIJGJLD+50GCiEQvaeh9RSXl6u8vLymL/t379/wLFHH31Un3322S3jbd26VVu3brUrPQAAMMSsfjVg5yLCzPh3r0S2/6SoDwAAMMTsesg3DENmrH4yg5QOA4XbbYlqsXUqAAAAYNsiwnbO+LdjQlA6TAayS6Lbf1LUBwAAGGJ2POSfP39eDQ0Ntg0UwuFwSj+gFxb2fuba1hb7wdowen8vLBz63AAAAJCanDTj344JQekwGcguiW7/SVEfAAAgSaz2+7QaI514PL19K598sreA/+vxjGH0/re2lkVyAQAA4Bx2zfi3Y0IQ6wQMTqLbf1LUBwAAQFoo/uc6/V+JRws/rhqwUNX/zq/R9H/2SMXrkpQdAAAAEJvViTx2TAhinYDBSXT7T4r6AAAASA8ej6a/t1YX10kfPVqlUKj3Ifq/TtYoY91aqbo62RkCAAAAjmT3OgG0/7TW/pOiPgAAANJDVZUkKWPtWi2o/nm/pkaKFvR//h0AAABAbLT/vD2Jbv9JUR8AAADpI1q4X7tW+utfpe5uCvoAAAAAbJfI9p8ZdiQIAAAAuEZVleT19hb0vV4K+gAAAADsF23/ubJGDQ3SoUNSQ4P09YoaTX9vbfzT9EVRHwAAAOmmpuaXgn53d+8+AAAAANipqkqqrlbGurVa0Fijv/xFWtD4q/W8LEwuov0OAAAA1NraajmG3+93/mJXNTW9rXeiD9HRfYkZ+wAAAADslaD2nxT1AQAA0lhXV5cMw9Dy5cstx8rOzta5c+ecW9j/bUFf6v+Q/et9AAAAAAljx6SiSCQin89nOU7CJydVVf1S0Lep/SdFfQAAgDR27do1maap4uJi+f3+uOOEw2EdPXpU4XDYuUX9np7Ys2Ki+z09Q58TAAAAkEbsnFRkGIZM07QcJ+GTk2K1/2SmPgAAAKzy+/3Ky8uzHMfRbXzWrbv1b8zQBwAAABLOrklF58+fV0NDg/MnJyWo/SdFfQAAAFiWVm18AAAAAFhidVJROBy2JU5CJbD9J0V9AAAAWJZWbXwAAAAA4I8ksP0nRX0AAADYxtEzZQAAAABgqCSw/WeGpasBAAAAAAAAAMCQoagPAAAAAAAAAIBLUNQHAAAAAAAAAMAlKOoDAAAAAAAAAOASFPUBAAAAAAAAAHAJivoAAAAAAAAAALiEa4v6O3bs0OTJk5WVlaWCggI1Njb+7vknT55UQUGBsrKydM8992jXrl1DlCkAAAAAJ0vE2OLIkSOaPn26fD6fpk+frvfffz9R6QMAACDNuLKof/jwYa1evVqvvPKKzpw5o8LCQi1atEjBYDDm+RcuXNDixYtVWFioM2fO6OWXX9bzzz+vI0eODHHmAAAAAJwkEWOL5uZmlZSUqLS0VGfPnlVpaameeuopffLJJ0N1WwAAAEhhrizqb9myRStWrNDKlSs1bdo01dbWauLEidq5c2fM83ft2qVJkyaptrZW06ZN08qVK/Xss89q8+bNQ5w5AAAAACdJxNiitrZWjz/+uCorKzV16lRVVlbqscceU21t7RDdFQAAAFLZsGQnMFjd3d06ffq01qxZ0+94UVGRTp06FfOa5uZmFRUV9Tu2cOFC7d27V9evX1dmZuaAayKRiCKRSN/+999/L0lqb2+3lP/ly5clSaFQSN3d3SkRx0m5pGocJ+XitDhOyiVV4zgpF6fFcVIuqRrHSbk4LY6TcrEzTkdHhySpq6tLnZ2dccexQ/TPN00zqXkgcRI1tmhublZFRcWAc36vqM/4w525pGocJ+XitDhOyiVV4zgpF6fFcVIuqRrHSbk4LY6TcrEzjmvHH6bLtLW1mZLMpqamfsc3bNhg3nfffTGvmTJlirlhw4Z+x5qamkxJ5rfffhvzmtdee82UxMbGxsbGxsbGlubbN998Y8+DLBwnUWOLzMxM8+DBg/3OOXjwoOn1em+ZC+MPNjY2NjY2NjY26fbGH66bqR9lGEa/fdM0Bxz7o/NjHY+qrKxUIBDo279586a+++475eTk/O6fAwAAgNRgmqauXr2qvLy8ZKeCBEvE2GKwMRl/AAAApLfBjD9cV9T3+/3yeDwDPkO9dOmScnNzY14zbty4mOcPGzZMOTk5Ma/x+Xzy+Xz9jt15553xJw4AAADXGT16dLJTQAIlamxxq3NuFVNi/AEAAIDbH3+4bqFcr9ergoIC1dfX9zteX1+vefPmxbxm7ty5A84/fvy4Zs+eHbOfPgAAAIDUl6ixxa3OuVVMAAAAYDBcV9SXpEAgoD179mjfvn1qbW1VRUWFgsGgysrKJPV+uvr000/3nV9WVqaLFy8qEAiotbVV+/bt0969e/Xiiy8m6xYAAAAAOEAixhYvvPCCjh8/rk2bNuncuXPatGmTTpw4odWrVw/17QEAACAFua79jiSVlJSoo6ND1dXVCoVCmjFjhurq6pSfny+pd9XjYDDYd/7kyZNVV1eniooKbd++XXl5edq2bZuWLVuWrFsAAAAA4ACJGFvMmzdP7733nl599VVVVVXp3nvv1eHDh/XII48M+f0BAAAg9RhmdFUnAAAAAAAAAADgaK5svwMAAAAAAAAAQDqiqA8AAAAAAAA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      "text/plain": [
       "<Figure size 1600x1600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))\n",
    "\n",
    "vert_coord = 0.99\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"THIS RUN = \" + case_name + \" \" + start_date + \" to \" + end_date,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    "    color=\"royalblue\",\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "if base_case_name is not None:\n",
    "    fig.text(\n",
    "        0.5,\n",
    "        vert_coord,\n",
    "        \"BASELINE RUN = \"\n",
    "        + base_case_name\n",
    "        + \" \"\n",
    "        + base_start_date\n",
    "        + \" to \"\n",
    "        + base_end_date,\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=14,\n",
    "        color=\"red\",\n",
    "    )\n",
    "    vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Other runs = 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.015\n",
    "\n",
    "fig.text(\n",
    "    0.5,\n",
    "    vert_coord,\n",
    "    \"Validation data = ERA5 1979-01-01 to 2023-12-31\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    fontsize=14,\n",
    ")\n",
    "vert_coord = vert_coord - 0.03\n",
    "\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"AM\"],\n",
    "    nmse_cesm2[\"AM\"],\n",
    "    nmse_dat[\"AM\"],\n",
    "    nmse_base_dat[\"AM\"],\n",
    "    0.3,\n",
    "    0.7,\n",
    "    vert_coord - 0.16,\n",
    "    vert_coord,\n",
    "    \"NMSE, SLP, AM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"DJF\"],\n",
    "    nmse_cesm2[\"DJF\"],\n",
    "    nmse_dat[\"DJF\"],\n",
    "    nmse_base_dat[\"DJF\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, DJF\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"MAM\"],\n",
    "    nmse_cesm2[\"MAM\"],\n",
    "    nmse_dat[\"MAM\"],\n",
    "    nmse_base_dat[\"MAM\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.57,\n",
    "    0.72,\n",
    "    \"NMSE, SLP, MAM\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"JJA\"],\n",
    "    nmse_cesm2[\"JJA\"],\n",
    "    nmse_dat[\"JJA\"],\n",
    "    nmse_base_dat[\"JJA\"],\n",
    "    0.05,\n",
    "    0.45,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, JJA\",\n",
    ")\n",
    "ax = plotnmse(\n",
    "    fig,\n",
    "    nmse_cmip6[\"SON\"],\n",
    "    nmse_cesm2[\"SON\"],\n",
    "    nmse_dat[\"SON\"],\n",
    "    nmse_base_dat[\"SON\"],\n",
    "    0.55,\n",
    "    0.95,\n",
    "    0.37,\n",
    "    0.52,\n",
    "    \"NMSE, SLP, SON\",\n",
    ")"
   ]
  }
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