From a600ef4788723b97b1a4d8d7c38543180ebf0618 Mon Sep 17 00:00:00 2001
From: rolfhut <r.w.hut@tudelft.nl>
Date: Sat, 19 Apr 2025 10:49:49 +0200
Subject: [PATCH 1/2] first commit: changed to 2.0

minor details left:
- merging output: update to hydrograph?
- xarray as default?
---
 Case1_Marrmot_Merrimack_ESMValTool.ipynb | 743 +++++++++++++----------
 1 file changed, 414 insertions(+), 329 deletions(-)

diff --git a/Case1_Marrmot_Merrimack_ESMValTool.ipynb b/Case1_Marrmot_Merrimack_ESMValTool.ipynb
index 04b258b..c055d60 100644
--- a/Case1_Marrmot_Merrimack_ESMValTool.ipynb
+++ b/Case1_Marrmot_Merrimack_ESMValTool.ipynb
@@ -48,6 +48,8 @@
     "logger_esmvalcore.setLevel(logging.WARNING)\n",
     "\n",
     "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
     "from cartopy.io import shapereader\n",
     "\n",
     "import ewatercycle.forcing\n",
@@ -56,6 +58,16 @@
     "import ewatercycle.observation.grdc"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d6b2fac4-45e2-4977-815f-e98959343bed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rich import print"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "554b8621",
@@ -68,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "7ceef35f",
    "metadata": {},
    "outputs": [],
@@ -95,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "d7747178",
    "metadata": {},
    "outputs": [],
@@ -120,48 +132,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "51c1b658",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'auxiliary_data_dir': PosixPath('/mnt/data/climate-data/aux'),\n",
-      " 'compress_netcdf': False,\n",
-      " 'config_developer_file': None,\n",
-      " 'config_file': PosixPath('/home/rhut/.esmvaltool/config-user.yml'),\n",
-      " 'drs': {'CMIP5': 'default', 'CORDEX': 'default', 'OBS': 'default'},\n",
-      " 'exit_on_warning': False,\n",
-      " 'extra_facets_dir': (),\n",
-      " 'log_level': 'info',\n",
-      " 'max_parallel_tasks': None,\n",
-      " 'output_dir': PosixPath('/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335'),\n",
-      " 'output_file_type': 'png',\n",
-      " 'plot_dir': PosixPath('/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/plots'),\n",
-      " 'preproc_dir': PosixPath('/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/preproc'),\n",
-      " 'profile_diagnostic': False,\n",
-      " 'remove_preproc_dir': False,\n",
-      " 'rootpath': {'OBS6': [PosixPath('/mnt/data/climate-data/obs6')],\n",
-      "              'default': [PosixPath('/mnt/data/climate-data')]},\n",
-      " 'run_dir': PosixPath('/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/run'),\n",
-      " 'save_intermediary_cubes': False,\n",
-      " 'work_dir': PosixPath('/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/work')}\n",
-      "Shapefile /home/rhut/technicalPaperExampleNotebooks/settingFiles/Merrimack.shp is not in forcing directory /home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/work/diagnostic_daily/script. So, it won't be saved in /home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/work/diagnostic_daily/script/ewatercycle_forcing.yaml.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "experiment_forcing = ewatercycle.forcing.generate(\n",
+    "experiment_forcing = ewatercycle.forcing.sources[\"MarrmotForcing\"].generate(\n",
     "    target_model='marrmot', \n",
     "    dataset=forcing_dataset_name, #ie. \"ERA5\"\n",
     "    start_time=experiment_start_date,\n",
     "    end_time=experiment_end_date, \n",
     "    shape=shapefile, #ie. the shape of the Merrimack basin\n",
-    ")"
+    ")\n",
+    "\n",
+    "# forcing = ewatercycle.forcing.sources[\"MarrmotForcing\"].generate(\n",
+    "#     dataset=\"ERA5\",\n",
+    "#     start_time=\"1989-01-01T00:00:00Z\",\n",
+    "#     end_time=\"1992-12-31T00:00:00Z\",\n",
+    "#     shape=\"data/Rhine/Rhine.shp\",\n",
+    "# )\n"
    ]
   },
   {
@@ -176,22 +167,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "83d6b4fd",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "eWaterCycle forcing\n",
-      "-------------------\n",
-      "start_time=1990-01-01T00:00:00Z\n",
-      "end_time=1990-12-31T00:00:00Z\n",
-      "directory=/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/work/diagnostic_daily/script\n",
-      "shape=/home/rhut/technicalPaperExampleNotebooks/settingFiles/Merrimack.shp\n",
-      "forcing_file=marrmot_ERA5_Merrimack_1990_1990.mat\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">MarrmotForcing</span><span style=\"font-weight: bold\">(</span>\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">start_time</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1990-01-01T00:00:00Z'</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">end_time</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1990-12-31T00:00:00Z'</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">directory</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PosixPath</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">084751/work/diagnostic/script'</span><span style=\"font-weight: bold\">)</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PosixPath</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_0847</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">51/work/diagnostic/script/settingFiles/Merrimack.shp'</span><span style=\"font-weight: bold\">)</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">filenames</span>=<span style=\"font-weight: bold\">{}</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">forcing_file</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'marrmot_ERA5_Merrimack_1990_1990.mat'</span>\n",
+       "<span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;35mMarrmotForcing\u001b[0m\u001b[1m(\u001b[0m\n",
+       "    \u001b[33mstart_time\u001b[0m=\u001b[32m'1990-01-01T00:00:00Z'\u001b[0m,\n",
+       "    \u001b[33mend_time\u001b[0m=\u001b[32m'1990-12-31T00:00:00Z'\u001b[0m,\n",
+       "    \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_\u001b[0m\n",
+       "\u001b[32m084751/work/diagnostic/script'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mshape\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_0847\u001b[0m\n",
+       "\u001b[32m51/work/diagnostic/script/settingFiles/Merrimack.shp'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mfilenames\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "    \u001b[33mforcing_file\u001b[0m=\u001b[32m'marrmot_ERA5_Merrimack_1990_1990.mat'\u001b[0m\n",
+       "\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -209,20 +218,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "5e2b0035",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The catchment area is: 13016500000.0\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The catchment area is: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13016500000.0</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "The catchment area is: \u001b[1;36m13016500000.0\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "shape = shapereader.Reader(experiment_forcing.shape)\n",
+    "shape = shapereader.Reader(shapefile)\n",
     "record = next(shape.records())\n",
     "merrimack_area = record.attributes[\"SUB_AREA\"] * 1e6\n",
     "print(\"The catchment area is:\", merrimack_area)"
@@ -235,96 +250,178 @@
    "source": [
     "### Setting up the model object\n",
     "\n",
-    "To use a hydrological model, supported by ewatercycle, an object from its class must be instantiated. Information on the versions of models can be queried as shown below:"
+    "To use a hydrological model, supported by ewatercycle, an object from its class must be instantiated. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "564402a9",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">MarrmotM01</span><span style=\"font-weight: bold\">(</span>\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">parameter_set</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
+       "    <span style=\"color: #808000; text-decoration-color: #808000\">forcing</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">MarrmotForcing</span><span style=\"font-weight: bold\">(</span>\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">start_time</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1990-01-01T00:00:00Z'</span>,\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">end_time</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1990-12-31T00:00:00Z'</span>,\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">directory</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PosixPath</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">419_084751/work/diagnostic/script'</span><span style=\"font-weight: bold\">)</span>,\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PosixPath</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">084751/work/diagnostic/script/settingFiles/Merrimack.shp'</span><span style=\"font-weight: bold\">)</span>,\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">filenames</span>=<span style=\"font-weight: bold\">{}</span>,\n",
+       "        <span style=\"color: #808000; text-decoration-color: #808000\">forcing_file</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'marrmot_ERA5_Merrimack_1990_1990.mat'</span>\n",
+       "    <span style=\"font-weight: bold\">)</span>\n",
+       "<span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
       "text/plain": [
-       "('2020.11',)"
+       "\u001b[1;35mMarrmotM01\u001b[0m\u001b[1m(\u001b[0m\n",
+       "    \u001b[33mparameter_set\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+       "    \u001b[33mforcing\u001b[0m=\u001b[1;35mMarrmotForcing\u001b[0m\u001b[1m(\u001b[0m\n",
+       "        \u001b[33mstart_time\u001b[0m=\u001b[32m'1990-01-01T00:00:00Z'\u001b[0m,\n",
+       "        \u001b[33mend_time\u001b[0m=\u001b[32m'1990-12-31T00:00:00Z'\u001b[0m,\n",
+       "        \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250\u001b[0m\n",
+       "\u001b[32m419_084751/work/diagnostic/script'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "        \u001b[33mshape\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/rhut/repos/technicalPaperExampleNotebooks/esmvaltool_output/ewcrepcawqgw1a_20250419_\u001b[0m\n",
+       "\u001b[32m084751/work/diagnostic/script/settingFiles/Merrimack.shp'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "        \u001b[33mfilenames\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "        \u001b[33mforcing_file\u001b[0m=\u001b[32m'marrmot_ERA5_Merrimack_1990_1990.mat'\u001b[0m\n",
+       "    \u001b[1m)\u001b[0m\n",
+       "\u001b[1m)\u001b[0m\n"
       ]
      },
-     "execution_count": 7,
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = ewatercycle.models.MarrmotM01(forcing=experiment_forcing)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3040f3d-1dbf-4983-96db-05bd12cc17fc",
+   "metadata": {},
+   "source": [
+    "The model also has a version. This version is retrieved from the BMI container image.\n",
+    "\n",
+    "To run a different version of the model, you can specify the container image:\n",
+    "```py\n",
+    "from ewatercycle.container import ContainerImage\n",
+    "\n",
+    "ewatercycle.models.MarrmotM01(\n",
+    "    forcing=forcing,\n",
+    "    bmi_image=ContainerImage(\"ewatercycle/marrmot-grpc4bmi:2020.11\")\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "To view the model's version, do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78c3f054",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2020.11'"
+      ]
+     },
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ewatercycle.models.MarrmotM01.available_versions"
+    "model.version"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ba428256-1a0c-4f83-8eb0-b7bd60275b38",
+   "id": "4dc2dccc-f153-4dcd-951c-2dcb502d2751",
    "metadata": {},
    "source": [
-    "subsequently a model object is created:"
+    "The model's parameters can be accessed using the .parameters property:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "26f62da4",
+   "execution_count": 10,
+   "id": "1304fc91",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "eWaterCycle MarrmotM01\n",
-      "-------------------\n",
-      "Version = 2020.11\n",
-      "Parameter set = \n",
-      "  None\n",
-      "Forcing = \n",
-      "  eWaterCycle forcing\n",
-      "  -------------------\n",
-      "  start_time=1990-01-01T00:00:00Z\n",
-      "  end_time=1990-12-31T00:00:00Z\n",
-      "  directory=/home/rhut/technicalPaperExampleNotebooks/esmvaltool_output/recipe_marrmot_20210930_125335/work/diagnostic_daily/script\n",
-      "  shape=/home/rhut/technicalPaperExampleNotebooks/settingFiles/Merrimack.shp\n",
-      "  forcing_file=marrmot_ERA5_Merrimack_1990_1990.mat\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">dict_items</span><span style=\"font-weight: bold\">([(</span><span style=\"color: #008000; text-decoration-color: #008000\">'maximum_soil_moisture_storage'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'initial_soil_moisture_storage'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'solver'</span>, \n",
+       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Solver</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'createOdeApprox_IE'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">resnorm_tolerance</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">array</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span><span style=\"font-weight: bold\">])</span>, <span style=\"color: #808000; text-decoration-color: #808000\">resnorm_maxiter</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">array</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>.<span style=\"font-weight: bold\">])))</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'start time'</span>, \n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">'1990-01-01T12:00:00Z'</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'end time'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'1990-12-31T12:00:00Z'</span><span style=\"font-weight: bold\">)])</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;35mdict_items\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'maximum_soil_moisture_storage'\u001b[0m, \u001b[1;36m10.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'initial_soil_moisture_storage'\u001b[0m, \u001b[1;36m0.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'solver'\u001b[0m, \n",
+       "\u001b[1;35mSolver\u001b[0m\u001b[1m(\u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'createOdeApprox_IE'\u001b[0m, \u001b[33mresnorm_tolerance\u001b[0m=\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mresnorm_maxiter\u001b[0m=\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m6\u001b[0m.\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'start time'\u001b[0m, \n",
+       "\u001b[32m'1990-01-01T12:00:00Z'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'end time'\u001b[0m, \u001b[32m'1990-12-31T12:00:00Z'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "model = ewatercycle.models.MarrmotM01(version='2020.11', forcing=experiment_forcing)\n",
-    "print(model)"
+    "print(model.parameters)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8d91ae21",
+   "id": "5d8f9336-c85e-433a-a75b-1443ee02d44b",
    "metadata": {},
    "source": [
-    "Create config file and start up container for Marrmot"
+    "Setup model with maximum soil moisture storage as specified above"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "7b862f10",
+   "execution_count": 11,
+   "id": "0203d607-b226-4e1a-91f0-9904ee20e80e",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running /mnt/data/singularity-images/ewatercycle-marrmot-grpc4bmi_2020.11.sif singularity container on port 54579\n",
-      "/home/rhut/technicalPaperExampleNotebooks/ewatercycle_output/marrmot_20210930_125524/marrmot-m01_config.mat\n",
-      "/home/rhut/technicalPaperExampleNotebooks/ewatercycle_output/marrmot_20210930_125524\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080\">/home/rhut/repos/technicalPaperExampleNotebooks/marrmotm01_20250419_084810/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">marrmot-m01_config.mat</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[35m/home/rhut/repos/technicalPaperExampleNotebooks/marrmotm01_20250419_084810/\u001b[0m\u001b[95mmarrmot-m01_config.mat\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080\">/home/rhut/repos/technicalPaperExampleNotebooks/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">marrmotm01_20250419_084810</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[35m/home/rhut/repos/technicalPaperExampleNotebooks/\u001b[0m\u001b[95mmarrmotm01_20250419_084810\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "cfg_file, cfg_dir = model.setup(\n",
-    "    # No need to specifiy start and end date, using dates from forcing\n",
     "    maximum_soil_moisture_storage=experiment_maximum_soil_moisture_storage,\n",
     "    initial_soil_moisture_storage=experiment_initial_soil_moisture_storage,\n",
     ")\n",
@@ -334,14 +431,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "b5da4787",
+   "execution_count": 12,
+   "id": "3e0f3efc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">dict_items</span><span style=\"font-weight: bold\">([(</span><span style=\"color: #008000; text-decoration-color: #008000\">'maximum_soil_moisture_storage'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1999.0</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'initial_soil_moisture_storage'</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1799.1000000000001</span><span style=\"font-weight: bold\">)</span>, \n",
+       "<span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'solver'</span>, <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Solver</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">name</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'createOdeApprox_IE'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">resnorm_tolerance</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">array</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span><span style=\"font-weight: bold\">])</span>, <span style=\"color: #808000; text-decoration-color: #808000\">resnorm_maxiter</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">array</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>.<span style=\"font-weight: bold\">])))</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'start</span>\n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">time'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'1990-01-01T00:00:00Z'</span><span style=\"font-weight: bold\">)</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008000; text-decoration-color: #008000\">'end time'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'1990-12-31T00:00:00Z'</span><span style=\"font-weight: bold\">)])</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;35mdict_items\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'maximum_soil_moisture_storage'\u001b[0m, \u001b[1;36m1999.0\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'initial_soil_moisture_storage'\u001b[0m, \u001b[1;36m1799.1000000000001\u001b[0m\u001b[1m)\u001b[0m, \n",
+       "\u001b[1m(\u001b[0m\u001b[32m'solver'\u001b[0m, \u001b[1;35mSolver\u001b[0m\u001b[1m(\u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'createOdeApprox_IE'\u001b[0m, \u001b[33mresnorm_tolerance\u001b[0m=\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mresnorm_maxiter\u001b[0m=\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m6\u001b[0m.\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'start\u001b[0m\n",
+       "\u001b[32mtime'\u001b[0m, \u001b[32m'1990-01-01T00:00:00Z'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'end time'\u001b[0m, \u001b[32m'1990-12-31T00:00:00Z'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(model.parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e35fb142-c867-4c95-8e35-d8072852df08",
+   "metadata": {},
+   "source": [
+    "Initialize the model with the config file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e7df84f4-2e21-46df-b4f9-5c8041f149bd",
    "metadata": {},
    "outputs": [],
    "source": [
     "model.initialize(cfg_file)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "66232472-94c8-4681-885f-f1f3bffc8c8e",
+   "metadata": {},
+   "source": [
+    "Get model variable names, only `flux_out_Q` is supported for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "85c9208b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('P',\n",
+       " 'T',\n",
+       " 'Ep',\n",
+       " 'S(t)',\n",
+       " 'par',\n",
+       " 'sol_resnorm_tolerance',\n",
+       " 'sol_resnorm_maxiter',\n",
+       " 'flux_out_Q',\n",
+       " 'flux_out_Ea',\n",
+       " 'wb')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.output_var_names"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bf7db734",
@@ -353,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "id": "6822b99a",
    "metadata": {},
    "outputs": [],
@@ -390,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "e8c99679",
    "metadata": {},
    "outputs": [],
@@ -411,125 +584,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "e2eaedac",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GRDC station 4147380 is selected. The river name is: MERRIMACK RIVER.The coordinates are: (42.6459, -71.2984).The catchment area in km2 is: 12004.7. There are 0 missing values during 1990-01-01T00:00:00Z_1990-12-31T00:00:00Z at this station. See the metadata for more information.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>streamflow</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1990-01-01</th>\n",
-       "      <td>119.214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-02</th>\n",
-       "      <td>136.204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-03</th>\n",
-       "      <td>131.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-04</th>\n",
-       "      <td>133.655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-05</th>\n",
-       "      <td>139.035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-27</th>\n",
-       "      <td>668.276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-28</th>\n",
-       "      <td>512.534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-29</th>\n",
-       "      <td>419.089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-30</th>\n",
-       "      <td>393.604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-31</th>\n",
-       "      <td>399.267</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>365 rows × 1 columns</p>\n",
-       "</div>"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">xarray.Dataset</span><span style=\"font-weight: bold\">&gt;</span> Size: 6kB\n",
+       "Dimensions:              <span style=\"font-weight: bold\">(</span>time: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">365</span><span style=\"font-weight: bold\">)</span>\n",
+       "Coordinates:\n",
+       "  * time                 <span style=\"font-weight: bold\">(</span>time<span style=\"font-weight: bold\">)</span> datetime64<span style=\"font-weight: bold\">[</span>ns<span style=\"font-weight: bold\">]</span> 3kB <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1990</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1990</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31</span>\n",
+       "    id                   int64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4147380</span>\n",
+       "Data variables:\n",
+       "    streamflow           <span style=\"font-weight: bold\">(</span>time<span style=\"font-weight: bold\">)</span> float64 3kB <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">119.2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">136.2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">131.1</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">393.6</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">399.3</span>\n",
+       "    area                 float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.2e+04</span>\n",
+       "    country              &lt;U2 8B <span style=\"color: #008000; text-decoration-color: #008000\">'US'</span>\n",
+       "    geo_x                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-71.3</span>\n",
+       "    geo_y                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">42.65</span>\n",
+       "    geo_z                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.58</span>\n",
+       "    owneroforiginaldata  &lt;U7 28B <span style=\"color: #008000; text-decoration-color: #008000\">'Unknown'</span>\n",
+       "    river_name           &lt;U15 60B <span style=\"color: #008000; text-decoration-color: #008000\">'MERRIMACK RIVER'</span>\n",
+       "    station_name         &lt;U33 132B <span style=\"color: #008000; text-decoration-color: #008000\">'BELOW CONCORD RIVER AT LOWELL, MA'</span>\n",
+       "    timezone             float64 8B nan\n",
+       "Attributes:\n",
+       "    title:          MEAN DAILY DISCHARGE <span style=\"font-weight: bold\">(</span>Q<span style=\"font-weight: bold\">)</span>\n",
+       "    Conventions:    CF-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.7</span>\n",
+       "    references:     grdc.bafg.de\n",
+       "    institution:    GRDC\n",
+       "    history:        Converted from 4147380_Q_Day.Cmd.txt of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2019</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">03</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span> to net<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
+       "    missing_value:  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-999.000</span>\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "            streamflow\n",
-       "time                  \n",
-       "1990-01-01     119.214\n",
-       "1990-01-02     136.204\n",
-       "1990-01-03     131.107\n",
-       "1990-01-04     133.655\n",
-       "1990-01-05     139.035\n",
-       "...                ...\n",
-       "1990-12-27     668.276\n",
-       "1990-12-28     512.534\n",
-       "1990-12-29     419.089\n",
-       "1990-12-30     393.604\n",
-       "1990-12-31     399.267\n",
-       "\n",
-       "[365 rows x 1 columns]"
+       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 6kB\n",
+       "Dimensions:              \u001b[1m(\u001b[0mtime: \u001b[1;36m365\u001b[0m\u001b[1m)\u001b[0m\n",
+       "Coordinates:\n",
+       "  * time                 \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m datetime64\u001b[1m[\u001b[0mns\u001b[1m]\u001b[0m 3kB \u001b[1;36m1990\u001b[0m-\u001b[1;36m01\u001b[0m-\u001b[1;36m01\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m1990\u001b[0m-\u001b[1;36m12\u001b[0m-\u001b[1;36m31\u001b[0m\n",
+       "    id                   int64 8B \u001b[1;36m4147380\u001b[0m\n",
+       "Data variables:\n",
+       "    streamflow           \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m float64 3kB \u001b[1;36m119.2\u001b[0m \u001b[1;36m136.2\u001b[0m \u001b[1;36m131.1\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m393.6\u001b[0m \u001b[1;36m399.3\u001b[0m\n",
+       "    area                 float64 8B \u001b[1;36m1.2e+04\u001b[0m\n",
+       "    country              <U2 8B \u001b[32m'US'\u001b[0m\n",
+       "    geo_x                float64 8B \u001b[1;36m-71.3\u001b[0m\n",
+       "    geo_y                float64 8B \u001b[1;36m42.65\u001b[0m\n",
+       "    geo_z                float64 8B \u001b[1;36m1.58\u001b[0m\n",
+       "    owneroforiginaldata  <U7 28B \u001b[32m'Unknown'\u001b[0m\n",
+       "    river_name           <U15 60B \u001b[32m'MERRIMACK RIVER'\u001b[0m\n",
+       "    station_name         <U33 132B \u001b[32m'BELOW CONCORD RIVER AT LOWELL, MA'\u001b[0m\n",
+       "    timezone             float64 8B nan\n",
+       "Attributes:\n",
+       "    title:          MEAN DAILY DISCHARGE \u001b[1m(\u001b[0mQ\u001b[1m)\u001b[0m\n",
+       "    Conventions:    CF-\u001b[1;36m1.7\u001b[0m\n",
+       "    references:     grdc.bafg.de\n",
+       "    institution:    GRDC\n",
+       "    history:        Converted from 4147380_Q_Day.Cmd.txt of \u001b[1;36m2019\u001b[0m-\u001b[1;36m03\u001b[0m-\u001b[1;36m27\u001b[0m to net\u001b[33m...\u001b[0m\n",
+       "    missing_value:  \u001b[1;36m-999.000\u001b[0m\n"
       ]
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "observations, metadata = ewatercycle.observation.grdc.get_grdc_data(\n",
+    "observations = ewatercycle.observation.grdc.get_grdc_data(\n",
     "    station_id=grdc_station_id,\n",
     "    start_time=experiment_start_date,\n",
     "    end_time=experiment_end_date,\n",
     ")\n",
-    "observations"
+    "print(observations)"
    ]
   },
   {
@@ -544,166 +667,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "b5a9f011",
+   "execution_count": 18,
+   "id": "97d8fbf1-1422-4ce7-bec4-405ef273a268",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>simulation</th>\n",
-       "      <th>observation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1990-01-02</th>\n",
-       "      <td>8.584977</td>\n",
-       "      <td>136.204</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-03</th>\n",
-       "      <td>0.017747</td>\n",
-       "      <td>131.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-04</th>\n",
-       "      <td>0.002347</td>\n",
-       "      <td>133.655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-05</th>\n",
-       "      <td>1.517997</td>\n",
-       "      <td>139.035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-01-06</th>\n",
-       "      <td>0.150256</td>\n",
-       "      <td>131.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-27</th>\n",
-       "      <td>1.074040</td>\n",
-       "      <td>668.276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-28</th>\n",
-       "      <td>0.065816</td>\n",
-       "      <td>512.534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-29</th>\n",
-       "      <td>974.469585</td>\n",
-       "      <td>419.089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-30</th>\n",
-       "      <td>217.303574</td>\n",
-       "      <td>393.604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1990-12-31</th>\n",
-       "      <td>767.973426</td>\n",
-       "      <td>399.267</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>364 rows × 2 columns</p>\n",
-       "</div>"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">xarray.Dataset</span><span style=\"font-weight: bold\">&gt;</span> Size: 18kB\n",
+       "Dimensions:              <span style=\"font-weight: bold\">(</span>time: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">729</span><span style=\"font-weight: bold\">)</span>\n",
+       "Coordinates:\n",
+       "  * time                 <span style=\"font-weight: bold\">(</span>time<span style=\"font-weight: bold\">)</span> object 6kB <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1990</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1990</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">01</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">02</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1990</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31</span>\n",
+       "    id                   int64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4147380</span>\n",
+       "Data variables:\n",
+       "    streamflow           <span style=\"font-weight: bold\">(</span>time<span style=\"font-weight: bold\">)</span> float64 6kB <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">119.2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">136.2</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">131.1</span> <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">393.6</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">399.3</span>\n",
+       "    area                 float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.2e+04</span>\n",
+       "    country              &lt;U2 8B <span style=\"color: #008000; text-decoration-color: #008000\">'US'</span>\n",
+       "    geo_x                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-71.3</span>\n",
+       "    geo_y                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">42.65</span>\n",
+       "    geo_z                float64 8B <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.58</span>\n",
+       "    owneroforiginaldata  &lt;U7 28B <span style=\"color: #008000; text-decoration-color: #008000\">'Unknown'</span>\n",
+       "    river_name           &lt;U15 60B <span style=\"color: #008000; text-decoration-color: #008000\">'MERRIMACK RIVER'</span>\n",
+       "    station_name         &lt;U33 132B <span style=\"color: #008000; text-decoration-color: #008000\">'BELOW CONCORD RIVER AT LOWELL, MA'</span>\n",
+       "    timezone             float64 8B nan\n",
+       "    simulation           <span style=\"font-weight: bold\">(</span>time<span style=\"font-weight: bold\">)</span> float64 6kB nan nan nan <span style=\"color: #808000; text-decoration-color: #808000\">...</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">974.5</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">217.3</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">768.0</span>\n",
+       "Attributes:\n",
+       "    title:          MEAN DAILY DISCHARGE <span style=\"font-weight: bold\">(</span>Q<span style=\"font-weight: bold\">)</span>\n",
+       "    Conventions:    CF-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.7</span>\n",
+       "    references:     grdc.bafg.de\n",
+       "    institution:    GRDC\n",
+       "    history:        Converted from 4147380_Q_Day.Cmd.txt of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2019</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">03</span>-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span> to net<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
+       "    missing_value:  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-999.000</span>\n",
+       "</pre>\n"
       ],
       "text/plain": [
-       "            simulation  observation\n",
-       "1990-01-02    8.584977      136.204\n",
-       "1990-01-03    0.017747      131.107\n",
-       "1990-01-04    0.002347      133.655\n",
-       "1990-01-05    1.517997      139.035\n",
-       "1990-01-06    0.150256      131.107\n",
-       "...                ...          ...\n",
-       "1990-12-27    1.074040      668.276\n",
-       "1990-12-28    0.065816      512.534\n",
-       "1990-12-29  974.469585      419.089\n",
-       "1990-12-30  217.303574      393.604\n",
-       "1990-12-31  767.973426      399.267\n",
-       "\n",
-       "[364 rows x 2 columns]"
+       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 18kB\n",
+       "Dimensions:              \u001b[1m(\u001b[0mtime: \u001b[1;36m729\u001b[0m\u001b[1m)\u001b[0m\n",
+       "Coordinates:\n",
+       "  * time                 \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m object 6kB \u001b[1;36m1990\u001b[0m-\u001b[1;36m01\u001b[0m-\u001b[1;36m01\u001b[0m \u001b[1;36m1990\u001b[0m-\u001b[1;36m01\u001b[0m-\u001b[1;36m02\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m1990\u001b[0m-\u001b[1;36m12\u001b[0m-\u001b[1;36m31\u001b[0m\n",
+       "    id                   int64 8B \u001b[1;36m4147380\u001b[0m\n",
+       "Data variables:\n",
+       "    streamflow           \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m float64 6kB \u001b[1;36m119.2\u001b[0m \u001b[1;36m136.2\u001b[0m \u001b[1;36m131.1\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m393.6\u001b[0m \u001b[1;36m399.3\u001b[0m\n",
+       "    area                 float64 8B \u001b[1;36m1.2e+04\u001b[0m\n",
+       "    country              <U2 8B \u001b[32m'US'\u001b[0m\n",
+       "    geo_x                float64 8B \u001b[1;36m-71.3\u001b[0m\n",
+       "    geo_y                float64 8B \u001b[1;36m42.65\u001b[0m\n",
+       "    geo_z                float64 8B \u001b[1;36m1.58\u001b[0m\n",
+       "    owneroforiginaldata  <U7 28B \u001b[32m'Unknown'\u001b[0m\n",
+       "    river_name           <U15 60B \u001b[32m'MERRIMACK RIVER'\u001b[0m\n",
+       "    station_name         <U33 132B \u001b[32m'BELOW CONCORD RIVER AT LOWELL, MA'\u001b[0m\n",
+       "    timezone             float64 8B nan\n",
+       "    simulation           \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m float64 6kB nan nan nan \u001b[33m...\u001b[0m \u001b[1;36m974.5\u001b[0m \u001b[1;36m217.3\u001b[0m \u001b[1;36m768.0\u001b[0m\n",
+       "Attributes:\n",
+       "    title:          MEAN DAILY DISCHARGE \u001b[1m(\u001b[0mQ\u001b[1m)\u001b[0m\n",
+       "    Conventions:    CF-\u001b[1;36m1.7\u001b[0m\n",
+       "    references:     grdc.bafg.de\n",
+       "    institution:    GRDC\n",
+       "    history:        Converted from 4147380_Q_Day.Cmd.txt of \u001b[1;36m2019\u001b[0m-\u001b[1;36m03\u001b[0m-\u001b[1;36m27\u001b[0m to net\u001b[33m...\u001b[0m\n",
+       "    missing_value:  \u001b[1;36m-999.000\u001b[0m\n"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "simulated_discharge_df = pd.DataFrame(\n",
-    "    {'simulation': simulated_discharge}, index=pd.to_datetime(timestamps)\n",
-    ")\n",
-    "observations_df = observations.rename(\n",
-    "    columns={'streamflow': 'observation'}\n",
+    "# Create a new Xarray data array for the model output\n",
+    "sim_da = xr.DataArray(\n",
+    "    data=simulated_discharge,\n",
+    "    coords={'time': timestamps},\n",
+    "    dims='time',\n",
+    "    name='simulation'\n",
     ")\n",
-    "discharge = simulated_discharge_df.join(observations_df)\n",
-    "discharge"
+    "\n",
+    "# Align the simulation with the existing dataset\n",
+    "observations = observations.merge(sim_da)\n",
+    "\n",
+    "print(observations)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "6ae81fd7",
+   "execution_count": 19,
+   "id": "1bdc643e-b8d2-4dd1-bbd5-96af04bb3d79",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gU5drH8d+mJ4QEAiQhEHqXIqg0RUCqNBE7iqKIHkERxaOix1c8dhRFwXYsoIJixW6kCChSpEgVECkCUqXXtJ33j8lsdpMAKZvMlu/nunJN2cnsvTuT7Ow99/M8DsMwDAEAAAAAAABlKMTuAAAAAAAAABB8SEoBAAAAAACgzJGUAgAAAAAAQJkjKQUAAAAAAIAyR1IKAAAAAAAAZY6kFAAAAAAAAMocSSkAAAAAAACUOZJSAAAAAAAAKHMkpQAAAAAAAFDmSEoBAGCzRYsW6aqrrlLVqlUVERGh5ORkXXnllVq4cGG+bceMGSOHw6F//vnHhkjtVatWLQ0ePNjuMLR9+3YNGzZMDRo0UHR0tBISEtSsWTMNHTpU27dvd21nHSu7OBwOjRkzpsi/d+LECY0ZM0Zz5871ekxz586Vw+Eo1L4XL16syy+/XDVq1FBkZKSSkpLUrl07jRo1ymO7V199VZMnT/Z6rGXtwIEDuvbaa5WYmCiHw6H+/ftLKv5xBADAH4TZHQAAAMFswoQJGjlypFq3bq2xY8eqZs2a2rZtm1555RVddNFFeumll3TnnXfaHSZy7NixQ61atVKFChU0atQoNWzYUIcPH9bvv/+ujz/+WJs3b1Zqaqok6dZbb1XPnj1tjrjoTpw4occee0yS1KlTJ1ti+Pbbb9WvXz916tRJY8eOVdWqVbVr1y4tXbpU06ZN07hx41zbvvrqq6pcubJPJCxL4vHHH9f06dP1zjvvqG7dukpISLA7JAAASh1JKQAAbPLLL79o5MiR6tWrl6ZPn66wsNyP5WuvvVaXX3657r77brVs2VIXXnihjZEW7OTJk4qOjrY7jDL15ptv6p9//tGvv/6q2rVru9b3799fDz30kJxOp2td9erVVb16dTvC9Htjx45V7dq19cMPP+T7uxg7dmyx95uZmSmHw+GxT1+xZs0a1a1bV9dff73doQAAUGZovgcAgE2efvppORwOvfbaa/m+JIeFhenVV1+Vw+HQM888k+93t2/frgEDBiguLk7x8fG64YYbtG/fPo9tfvzxR3Xq1EmVKlVSdHS0atSooSuuuEInTpxwbZORkaEnnnhCjRo1UmRkpKpUqaKbb745375q1aqlPn366PPPP1fLli0VFRWlxx57TC1btlSHDh3yxZedna1q1appwIABRX6uzMxM3X///UpOTlZMTIwuuugi/frrr2d9PzMzM5WYmKhBgwble+zQoUOKjo7WvffeK0lyOp164okn1LBhQ0VHR6tChQpq3ry5XnrppTM+x/79+xUSEqLExMQCHw8Jyb20Kqj5nvU+fvPNN2rZsqWio6PVuHFjffPNN5KkyZMnq3HjxipXrpxat26tpUuXevx+p06dCqxeGjx4sGrVqnXG2Pft26dhw4apSZMmio2NVWJioi655BL9/PPPrm22bt2qKlWqSJIee+wxORwOORwOjyqkjRs3auDAgUpMTFRkZKQaN26sV155Jd/zrV+/Xj179lRMTIwqV66sf/3rXzp69OgZY7Ts379flStXLjB55P4e16pVS2vXrtW8efNcsVrvg9VU8P3339eoUaNUrVo1RUZG6s8//5QkzZo1S126dFFcXJxiYmJ04YUXavbs2R7P9eeff+rmm29W/fr1FRMTo2rVqqlv375avXq1x3bWc33wwQd64IEHVLVqVcXGxqpv377as2ePjh49qttuu02VK1dW5cqVdfPNN+vYsWOu99zhcGjWrFlat26d63WcqYnjmjVrdNlll6lixYqKiorSueeeq3fffdf1uGEYSkpK0vDhw13rsrOzVbFiRYWEhGjPnj2u9S+88ILCwsJ06NChMx8UAABKAUkpAABskJ2drTlz5uj8888/bTVNamqqzjvvPP3444/Kzs72eOzyyy9XvXr19Omnn2rMmDH64osv1KNHD2VmZkoyv+j27t1bEREReuedd5SWlqZnnnlG5cqVU0ZGhiQzMXPZZZfpmWee0cCBA/Xtt9/qmWee0cyZM9WpUyedPHnS4zmXL1+uf//73xoxYoTS0tJ0xRVX6Oabb9b8+fO1ceNGj21nzJihnTt36uabby7ycw0dOlTPP/+8brzxRn355Ze64oorNGDAAB08ePCM72l4eLhuuOEGffbZZzpy5IjHYx9++KFOnTrlimfs2LEaM2aMrrvuOn377bf66KOPNGTIkLN+MW/Xrp2cTqcGDBigH374Id/zFMbKlSs1evRoPfDAA/r8888VHx+vAQMG6NFHH9Vbb72lp556SlOnTtXhw4fVp0+ffMehuA4cOCBJevTRR/Xtt99q0qRJqlOnjjp16uRKgFStWlVpaWmSpCFDhmjhwoVauHChHnnkEUnS77//rgsuuEBr1qzRuHHj9M0336h3794aMWKEq8mfJO3Zs0cdO3bUmjVr9Oqrr+r999/XsWPHCt0UtV27dlq8eLFGjBihxYsXu87rvKZPn646deqoZcuWrlinT5/usc3o0aO1bds2vf766/r666+VmJioKVOmqHv37oqLi9O7776rjz/+WAkJCerRo4dHYmrnzp2qVKmSnnnmGaWlpemVV15RWFiY2rRpow0bNuSL56GHHtLevXs1efJkjRs3TnPnztV1112nK664QvHx8frwww91//336/3339dDDz3kes8XLlyoli1bqk6dOq7X0apVqwJf84YNG9S+fXutXbtWL7/8sj7//HM1adJEgwcPdlWRORwOXXLJJZo1a5br95YuXapDhw4pKirK4zXOmjVL5513nipUqFCoYwMAgFcZAACgzO3evduQZFx77bVn3O6aa64xJBl79uwxDMMwHn30UUOScc8993hsN3XqVEOSMWXKFMMwDOPTTz81JBkrVqw47b4//PBDQ5Lx2WefeaxfsmSJIcl49dVXXetq1qxphIaGGhs2bPDY9p9//jEiIiKMhx56yGP91VdfbSQlJRmZmZlFeq5169ad8fXddNNNp309hmEYq1atMiQZ//vf/zzWt27d2jjvvPNcy3369DHOPffcM+6rIE6n07j99tuNkJAQQ5LhcDiMxo0bG/fcc4+xZcsWj22tY+WuZs2aRnR0tLFjxw7XuhUrVhiSjKpVqxrHjx93rf/iiy8MScZXX33lWtexY0ejY8eO+eK66aabjJo1a3qsk2Q8+uijp30tWVlZRmZmptGlSxfj8ssvd63ft2/faX+3R48eRvXq1Y3Dhw97rL/zzjuNqKgo48CBA4ZhGMYDDzxgOByOfOdft27dDEnGnDlzThuXYZjn1UUXXWRIMiQZ4eHhRvv27Y2nn37aOHr0qMe255xzToHvyZw5cwxJxsUXX+yx/vjx40ZCQoLRt29fj/XZ2dlGixYtjNatW582rqysLCMjI8OoX7++xzlqPVfefY4cOdKQZIwYMcJjff/+/Y2EhASPdR07djTOOeecfM+Z91hce+21RmRkpLFt2zaP7S699FIjJibGOHTokGEYhvHWW28ZklzbPfHEE0ajRo2Mfv36GTfffLNhGIaRkZFhlCtXLt/fLwAAZYVKKQAAfJhhGJKUrxlY3n5nrr76aoWFhWnOnDmSpHPPPVcRERG67bbb9O6772rz5s359v3NN9+oQoUK6tu3r7Kyslw/5557rpKTk/M1H2revLkaNGjgsa5SpUrq27ev3n33XVd/SgcPHtSXX36pG2+80dX8qrDPZcV/utd3Ns2aNdN5552nSZMmudatW7dOv/76q2655RbXutatW2vlypUaNmxYkSqeHA6HXn/9dW3evFmvvvqqbr75ZmVmZurFF1/UOeeco3nz5p11H+eee66qVavmWm7cuLEks2leTExMvvV//fVXoWIrjNdff12tWrVSVFSUwsLCFB4ertmzZ2vdunVn/d1Tp05p9uzZuvzyyxUTE+NxHHv16qVTp05p0aJFkszjeM4556hFixYe+xg4cGCh4qxUqZJ+/vlnLVmyRM8884wuu+wy/fHHHxo9erSaNWtWpNEnr7jiCo/lBQsW6MCBA7rppps8XoPT6VTPnj21ZMkSHT9+XJKUlZWlp556Sk2aNFFERITCwsIUERGhjRs3Fvie9enTx2PZOoa9e/fOt/7AgQOuJnxF8eOPP6pLly6uDvUtgwcP1okTJ1yjdnbt2lWSXNVSM2fOVLdu3dS1a1fNnDlTkrRw4UIdP37ctS0AAGWNpBQAADaoXLmyYmJitGXLljNut3XrVsXExOQbiSs5OdljOSwsTJUqVdL+/fslSXXr1tWsWbOUmJio4cOHq27duqpbt65Hn0l79uzRoUOHFBERofDwcI+f3bt35/viX7Vq1QJjvOWWW/T333+7vuh++OGHSk9P9+iHqLDPZcV/utdXGLfccosWLlyo9evXS5ImTZqkyMhIXXfdda5tRo8ereeff16LFi3SpZdeqkqVKqlLly75+nA6nZo1a+qOO+7Q22+/rY0bN+qjjz7SqVOn9O9///usv5v3WEZERJxx/alTpwoV09m88MILuuOOO9SmTRt99tlnWrRokZYsWaKePXsWqong/v37lZWVpQkTJuQ7hr169ZIkj+OY9xhK+Y/r2Zx//vl64IEH9Mknn2jnzp265557tHXr1iJ1dp73vLX6U7ryyivzvY5nn31WhmG4mjree++9euSRR9S/f399/fXXWrx4sZYsWaIWLVoU+J6VxbHdv39/gX+LKSkprscl8xy1/g9YySorKbVjxw5t2LBBs2bNUnR0tNq3b1/kOAAA8AbfG3oEAIAgEBoaqs6dOystLU07duwosF+pHTt2aNmyZbr00ksVGhrq8dju3bs9qm2ysrK0f/9+j8RNhw4d1KFDB2VnZ2vp0qWaMGGCRo4cqaSkJF177bWqXLmyKlWq5OpDKK/y5ct7LOet1rL06NFDKSkpmjRpknr06KFJkyapTZs2atKkiWubwj6XFf/pXl9hXHfddbr33ns1efJkPfnkk3r//ffVv39/VaxY0bVNWFiY7r33Xt177706dOiQZs2apYceekg9evTQ9u3bPSqWCuPqq6/W008/rTVr1hTp94oqKipKhw8fzre+MJVDU6ZMUadOnfTaa695rC9s5+MVK1ZUaGioBg0a5NGBtjtrRMJKlSpp9+7d+R4vaF1hhYeH69FHH9WLL75YpPc573lbuXJlSdKECRPUtm3bAn8nKSlJkvme3XjjjXrqqac8Hv/nn39s64OpUqVK2rVrV771O3fulJT7+iSpS5cu+vLLLzVv3jw5nU516tRJ5cuXV0pKimbOnKlZs2apQ4cOioyMLLP4AQBwR6UUAAA2GT16tAzD0LBhw/J1ZJ6dna077rhDhmFo9OjR+X536tSpHssff/yxsrKyChyZLTQ0VG3atHGNkLZ8+XJJZlOj/fv3Kzs7W+eff36+n4YNGxbqdViJii+++EI///yzli5d6tFUrijPZcV/utdXGBUrVlT//v313nvv6ZtvvtHu3bvzxeOuQoUKuvLKKzV8+HAdOHBAW7duPe22BSUDJOnYsWPavn27q1qltNSqVUt//PGH0tPTXev279+vBQsWnPV3HQ5HvuTDqlWrXM29LNY2eSuBYmJi1LlzZ/32229q3rx5gcfRSip27txZa9eu1cqVKz328cEHHxTqdZ7ufbaazLm/z5GRkUXqDP7CCy9UhQoV9Pvvvxf4Gs4//3xXJVNB79m3336rv//+u9DP521dunTRjz/+6EpCWd577z3FxMR4JNq6du2qPXv2aPz48Wrbtq0r+dulSxdNnz5dS5YsoekeAMBWVEoBAGCTCy+8UOPHj9fIkSN10UUX6c4771SNGjW0bds2vfLKK1q8eLHGjx9fYNOazz//XGFhYerWrZvWrl2rRx55RC1atNDVV18tyew76Mcff1Tv3r1Vo0YNnTp1Su+8846k3L5mrr32Wk2dOlW9evXS3XffrdatWys8PFw7duzQnDlzdNlll+nyyy8v1Gu55ZZb9Oyzz2rgwIGKjo7WNddc4/F4YZ+rcePGuuGGGzR+/HiFh4era9euWrNmjZ5//nnFxcUV+r295ZZb9NFHH+nOO+9U9erV833x7tu3r5o2barzzz9fVapU0V9//aXx48erZs2aql+//mn3++STT+qXX37RNddco3PPPVfR0dHasmWLJk6cqP379+u5554rdIzFMWjQIL3xxhu64YYbNHToUO3fv19jx44t1HvTp08fPf7443r00UfVsWNHbdiwQf/9739Vu3Ztj4Rf+fLlVbNmTX355Zfq0qWLEhISVLlyZdWqVUsvvfSSLrroInXo0EF33HGHatWqpaNHj+rPP//U119/rR9//FGSNHLkSL3zzjvq3bu3nnjiCSUlJWnq1KmuJpVn06NHD1WvXl19+/ZVo0aN5HQ6tWLFCo0bN06xsbG6++67Xds2a9ZM06ZN00cffaQ6deooKipKzZo1O+2+Y2NjNWHCBN100006cOCArrzySiUmJmrfvn1auXKl9u3b56om69OnjyZPnqxGjRqpefPmWrZsmZ577rnTjphZFh599FF988036ty5s/7v//5PCQkJmjp1qr799luNHTtW8fHxrm0vueQSORwOzZgxw2N0xK5du+qmm25yzQMAYBt7+1kHAAALFy40rrzySiMpKckICwszEhMTjQEDBhgLFizIt601otuyZcuMvn37GrGxsUb58uWN6667zjVCn7XPyy+/3KhZs6YRGRlpVKpUyejYsaPHSG6GYRiZmZnG888/b7Ro0cKIiooyYmNjjUaNGhm33367sXHjRtd2NWvWNHr37n3G19G+fXtDknH99dcX+Hhhnys9Pd0YNWqUkZiYaERFRRlt27Y1Fi5caNSsWfOso+9ZsrOzjdTUVEOS8fDDD+d7fNy4cUb79u2NypUrGxEREUaNGjWMIUOGGFu3bj3jfhctWmQMHz7caNGihZGQkGCEhoYaVapUMXr27Gl89913HtuebvS9gt5HScbw4cM91m3ZssWQZDz33HMe6999912jcePGRlRUlNGkSRPjo48+KtToe+np6cZ9991nVKtWzYiKijJatWplfPHFFwX+7qxZs4yWLVsakZGR+UY93LJli3HLLbcY1apVM8LDw40qVaoY7du3N5544gmPffz+++9Gt27djKioKCMhIcEYMmSI8eWXXxZq9L2PPvrIGDhwoFG/fn0jNjbWCA8PN2rUqGEMGjTI+P333z223bp1q9G9e3ejfPnyhiTXa7FGxPvkk08KfI558+YZvXv3NhISEozw8HCjWrVqRu/evT22P3jwoDFkyBAjMTHRiImJMS666CLj559/zjcK4umea9KkSYYkY8mSJR7rrXNj3759rnWFHX3PMAxj9erVRt++fY34+HgjIiLCaNGihTFp0qQCX2fLli0NScYvv/ziWvf3338bkoxKlSoZTqezwN8DAKAsOAwjZ1gfAAAAAAAAoIzQpxQAAAAAAADKHEkpAAAAAAAAlDmSUgAAAAAAAChzJKUAAAAAAABQ5khKAQAAAAAAoMyRlAIAAAAAAECZC7M7AH/hdDq1c+dOlS9fXg6Hw+5wAAAAAAAAfJJhGDp69KhSUlIUEnL6eiiSUoW0c+dOpaam2h0GAAAAAACAX9i+fbuqV69+2sdJShVS+fLlJZlvaFxcnM3RFF9mZqZmzJih7t27Kzw83O5wEMQ4F+HLOD/h6zhH4S84V+HLOD/h6/z5HD1y5IhSU1NduZTTISlVSFaTvbi4OL9PSsXExCguLs7vTmoEFs5F+DLOT/g6zlH4C85V+DLOT/i6QDhHz9b9ER2dAwAAAAAAoMyRlAIAAAAAAECZIykFAAAAAACAMkefUl6WnZ2tzMxMu8M4rczMTIWFhenUqVPKzs62OxyfFx4ertDQULvDAAAAAAAg4JCU8hLDMLR7924dOnTI7lDOyDAMJScna/v27WftcAymChUqKDk5mfcLAAAAAAAvIinlJVZCKjExUTExMT6bwHA6nTp27JhiY2MVEkLrzTMxDEMnTpzQ3r17JUlVq1a1OSIAAAAAAAIHSSkvyM7OdiWkKlWqZHc4Z+R0OpWRkaGoqCiSUoUQHR0tSdq7d68SExNpygcAAAAAgJeQlfACqw+pmJgYmyNBabCOqy/3FQYAAAAAgL8hKeVFvtpkDyXDcQUAAAAAwPtISgEAAAAAAKDMkZRCodSqVUvjx4+3OwyvCbTXAwAAAACAvyEpBW3fvl1DhgxRSkqKIiIiVLNmTd19993av3+/3aEBAAAAAIAARVIqyG3evFnnn3++/vjjD3344Yf6888/9frrr2v27Nlq166dDhw4YEtc2dnZcjqdtjw3AAAAAAAofSSlgtzw4cMVERGhGTNmqGPHjqpRo4YuvfRSzZo1S3///bcefvhh17ZHjx7VwIEDFRsbq5SUFE2YMMFjX2PGjFGNGjUUGRmplJQUjRgxwvVYRkaG7r//flWrVk3lypVTmzZtNHfuXNfjkydPVoUKFfTNN9+oSZMmioyM1JtvvqmoqCgdOnTI43lGjBihjh07upYXLFigiy++WNHR0UpNTdWIESN0/Phx1+N79+5V3759FR0drdq1a2vq1KleevcAAAAAAEBxkZQqBYZh6ERGli0/hmEUOs4DBw7ohx9+0LBhwxQdHe3xWHJysq6//np99NFHrn0+99xzat68uZYvX67Ro0frnnvu0cyZMyVJn376qV588UW98cYb2rhxo7744gs1a9bMtb+bb75Zv/zyi6ZNm6ZVq1bpqquuUs+ePbVx40bXNidOnNDTTz+tt956S2vXrtUNN9ygChUq6LPPPnNtk52drY8//ljXX3+9JGn16tXq0aOHBgwYoFWrVumjjz7S/Pnzdeedd7p+Z/Dgwdq6dat+/PFHffrpp3r11Ve1d+/eIhxRAAAAAADgbWF2BxCITmZmq8n//WDLc//+3x6KiSjcYd24caMMw1Djxo0LfLxx48Y6ePCg9u3bJ0m68MIL9eCDD0qSGjRooF9++UUvvviiunXrpm3btik5OVldu3ZVeHi4atSoodatW0uSNm3apA8//FA7duxQSkqKJOm+++5TWlqaJk2apKeeekqSlJmZqVdffVUtWrRwxXDNNdfogw8+0JAhQyRJs2fP1sGDB3XVVVdJMhNlAwcO1MiRIyVJ9evX18svv6yOHTvqtdde07Zt2/T9999r0aJFatOmjSTp7bffPu1rBgAAAAAAZYNKKZyWVSHlcDgkSe3atfN4vF27dlq3bp0k6aqrrtLJkydVp04dDR06VNOnT1dWVpYkafny5TIMQw0aNFBsbKzrZ968edq0aZNrfxEREWrevLnHc1x//fWaO3eudu7cKUmaOnWqevXqpYoVK0qSli1bpsmTJ3vst0ePHnI6ndqyZYvWrVunsLAwnX/++a59NmrUSBUqVPDiOwUAAAAAAIqKSqlSEB0eqt//28O25y6sevXqyeFw6Pfff1f//v3zPb5+/XpVrFhRlStXPu0+rIRVamqqNmzYoJkzZ2rWrFkaNmyYnnvuOc2bN09Op1OhoaFatmyZQkM944uNjc2NPTratT9L69atVbduXU2bNk133HGHpk+frkmTJrkedzqduv322z36r7LUqFFDGzZs8IgTAAAAAAD4BpJSpcDhcBS6CZ2dKlWqpG7duunVV1/VPffc49Gv1O7duzV16lTdeOONroTOokWLPH5/0aJFatSokWs5Ojpa/fr1U79+/TR8+HA1atRIq1evVsuWLZWdna29e/eqQ4cORY5z4MCBmjp1qqpXr66QkBD17t3b9VirVq20du1a1atXr8Dfbdy4sbKysrR06VJXc8INGzbk6zwdAAAAAACULZrvBbmJEycqPT1dPXr00E8//aTt27crLS1N3bp1U7Vq1fTkk0+6tv3ll180duxY/fHHH3rllVf0ySef6O6775Zkjp739ttva82aNdq8ebPef/99RUdHq2bNmmrQoIGuv/563Xjjjfr888+1ZcsWLVmyRM8++6y+++67s8Z4/fXXa/ny5XryySd15ZVXKioqyvXYAw88oIULF2r48OFasWKFNm7cqK+++kp33XWXJKlhw4bq2bOnhg4dqsWLF2vZsmW69dZb83XsDgAAAAAoA4tekxa/YXcU8BEkpYJc/fr1tXTpUtWtW1fXXHON6tatq9tuu02dO3fWwoULlZCQ4Np21KhRWrZsmVq2bKnHH39c48aNU48eZjPFChUq6M0339SFF16o5s2ba/bs2fr6669VqVIlSdKkSZN04403atSoUWrYsKH69eunxYsXKzU1tVAxXnDBBVq1apVr1D1L8+bNNW/ePG3cuFEdOnRQy5Yt9cgjj6hq1aqubSZNmqTU1FR17NhRAwYM0G233abExERvvH0AAAAAgMI6eVBKe1D6/n4p/Zjd0cAH+H4bM5S6mjVrevTTVJCtW7ee8fH+/fsX2C+VJTw8XI899pgee+yxAh8fPHiwBg8efNrf//XXX0/72AUXXKAZM2ac9vHk5GR98803HusGDRp02u0BAAAAAKUgZzAtcz7bvjjgM6iUAgAAAAAApS/crRsVR+EH6ULgIikFAAAAAADKgNuo6IbTvjDgM0hKAQAAAACAMuDefI+kFEhKAQAAAACAsnDqsNuCcdrNEDxISgEAAAAAgDLmOPsmCHgkpQAAAAAAQBlw5E6jK9gZCHwESSkAAAAAAFAGcprsOUhFwMSZAAAAAAAASp/VubmDpnswkZQCAAAAAAClz8iplHJmScf/sTcW+ASSUjgjh8OhL774otSfp1atWho/fnypP09BJk+erAoVKtjy3AAAAAAQNKxKKUnKzrAvDvgMklJBbu/evbr99ttVo0YNRUZGKjk5WT169NDChQslSbt27dKll15qc5T5kUgCAAAAAD8TUS533qqaQlALszsA2OuKK65QZmam3n33XdWpU0d79uzR7NmzdeDAAUlScnKyzRECAAAAAAJCTIIUGpFTJUVSClRKBbVDhw5p/vz5evbZZ9W5c2fVrFlTrVu31ujRo9W7d29Jns33tm7dKofDoY8//lgdOnRQdHS0LrjgAv3xxx9asmSJzj//fMXGxqpnz57at2+f63k6deqkkSNHejx3//79NXjw4NPG9sILL6hZs2YqV66cUlNTNWzYMB07dkySNHfuXN188806fPiwHA6HHA6HxowZI0nKyMjQ/fffr2rVqqlcuXJq06aN5s6d67HvyZMnq0aNGoqJidHll1+u/fv3l+h9BAAAAAAUkjXynntTPgQtklKlKeP46X8yTxVh25OF27aIYmNjFRsbqy+++ELp6emF/r1HH31U//nPf7R8+XKFhYXpuuuu0/3336+XXnpJP//8szZt2qT/+7//K3I87kJCQvTyyy9rzZo1evfdd/Xjjz/q/vvvlyS1b99e48ePV1xcnHbt2qVdu3bpvvvukyTdfPPN+uWXXzRt2jStWrVKV111lXr27KmNGzdKkhYvXqxbbrlFw4YN04oVK9S5c2c98cQTJYoVAAAAAFAI2VlSVs53YZrvQTTfK11PpZz+sfrdpes/yV1+rp6UeaLgbWteJN38be7y+GbSiQKqe8YcLlJ4YWFhmjx5soYOHarXX39drVq1UseOHXXttdeqefPmp/29++67Tz169JAk3X333bruuus0e/ZsXXjhhZKkIUOGaPLkyUWKJS/3yqratWvr8ccf1x133KFXX31VERERio+Pl8Ph8GheuGnTJn344YfasWOHUlJSXLGmpaVp0qRJeuqpp/TSSy+pR48eevDBByVJDRo00IIFC5SWllaieAEAAAAAZ3Fwa+48lVIQlVJB74orrtDOnTv11VdfqUePHpo7d65atWp1xqSSe8IqKSlJktSsWTOPdXv37i1RXHPmzFG3bt1UrVo1lS9fXjfeeKP279+v48dPXxG2fPlyGYahBg0auKrAYmNjNW/ePG3atEmStG7dOrVr187j9/IuAwAAAABKg1t1lIN0BKiUKl0P7Tz9Y45Qz+V//3mGbfP8sY5cXfyYChAVFaVu3bqpW7du+r//+z/deuutevTRR0/b51N4eHhuaA5Hgeucztysd0hIiIw8pZmZmZmnjeevv/5Sr1699K9//UuPP/64EhISNH/+fA0ZMuSMv+d0OhUaGqply5YpNNTz/Y2NjZWkfHEAAAAAAMqIVR0VXVGqWNPeWOATSEqVJvfhLu3athiaNGni6tzcG6pUqaJdu3a5lrOzs7VmzRp17ty5wO2XLl2qrKwsjRs3TiEhZkLu448/9tgmIiJC2dnZHutatmyp7Oxs7d27Vx06dChw302aNNGiRYs81uVdBgAAAACUAispRZUUcnAmBLH9+/frkksu0ZQpU7Rq1Spt2bJFn3zyicaOHavLLrvMa89zySWX6Ntvv9W3336r9evXa9iwYTp06NBpt69bt66ysrI0YcIEbd68We+//75ef/11j21q1aqlY8eOafbs2frnn3904sQJNWjQQNdff71uvPFGff7559qyZYuWLFmiZ599Vt99950kacSIEUpLS9PYsWP1xx9/aOLEifQnBQAAAABlwdVyxWFrGPAdJKWCWGxsrNq0aaMXX3xRF198sZo2bapHHnlEQ4cO1cSJE732PLfccotuuukm3XjjjerYsaNq16592iopSTr33HP1wgsv6Nlnn1XTpk01depUPf300x7btG/fXv/61790zTXXqEqVKho7dqwkadKkSbrxxhs1atQoNWzYUP369dPixYuVmpoqSWrbtq3eeustTZgwQeeee65mzJih//znP157rQAAAACA07AqpU7849npOYKWw6CTnUI5cuSI4uPjdfjwYcXFxXk8durUKW3ZskW1a9dWVFSUTREWjtPp1JEjRxQXF+dqGocz86fj608yMzP13XffqVevXh59kgG+gPMTvo5zFP6CcxW+jPPTBrtWSW/kdLVyx0IpqYm98fg4fz5Hz5RDcUdWAgAAAAAAlL7oim4L1MeApBQAAAAAACgLFVKlconmPI22IJJSAAAAAACgrFgj71n9SyGokZQCAAAAAAClz5ktZRzPWaBSCiSlAAAAAABAWfh7uZRx1JynUgoiKeVVTid/VIGI4woAAAAA3uBeHeWwLQr4jjC7AwgEERERCgkJ0c6dO1WlShVFRETI4fDNPzCn06mMjAydOnVKISHkJM/EMAxlZGRo3759CgkJUUREhN0hAQAAAID/sqqjEupIKefaGgp8A0kpLwgJCVHt2rW1a9cu7dy50+5wzsgwDJ08eVLR0dE+mzjzNTExMapRowZJPAAAAAAoCWvEPQffrWAiKeUlERERqlGjhrKyspSdnW13OKeVmZmpn376SRdffLHCw8PtDsfnhYaGKiwsjAQeAAAAAJSUqx8pvl/BRFLKixwOh8LDw3062RMaGqqsrCxFRUX5dJwAAAAAgECTUym1f6O0d52U2NjecGA7auYAAAAAAEDpcx9x7+RB++KAzyApBQAAAAAASl+5KrnzhnH67RA0SEoBAAAAAIDSl9hYqtwgZ4GkFEhKAQAAAACAsmKNvOfelA9Bi6QUAAAAAAAofYaR22yP5nsQSSkAAAAAAFAW/pwl/bPBnKdSCiIpBQAAAAAAyoJHdRSVUiApBQAAAAAAyoJVHZXSSqp7ib2xwCeQlAIAAAAAAGUgpzrKQSoCJs4EAAAAAABQ+qxKKYfD3jjgM0hKAQAAAACA0mf1KbVjifT3cntjgU8gKQUAAAAAAEqf+4h7x/bYFwd8BkkpAAAAAABQ+mKTcufdE1QIWiSlAAAAAABA6avRRqre2py3mvIhqJGUAgAAAAAAZcMaeY9KKYikFAAAAAAAKCuukfeolAJJKQAAAAAAUBZWfyptW2jOUykFkZQCAAAAAABlwZmdO0+fUhBJKQAAAAAAUBas6qga7aUml9kbC3wCSSkAAAAAAFAGcqqjImKkkFB7Q4FPICkFAAAAAABKn6sfKccZN0PwICkFAAAAAABKn9WP1J8zpW2L7I0FPoGkFAAAAAAAKH3uI+4d2GJfHPAZJKUAAAAAAEDpK5+cO++eoELQIikFAAAAAABKX4MeUv3uOQuGraHAN5CUAgAAAAAAZcORk4agUgoiKQUAAAAAAMpMzsh7BpVS8KGk1NNPPy2Hw6GRI0e61hmGoTFjxiglJUXR0dHq1KmT1q5d6/F76enpuuuuu1S5cmWVK1dO/fr1044dOzy2OXjwoAYNGqT4+HjFx8dr0KBBOnToUBm8KgAAAAAAIEla9q70x/fmPJVSkI8kpZYsWaL//e9/at68ucf6sWPH6oUXXtDEiRO1ZMkSJScnq1u3bjp69Khrm5EjR2r69OmaNm2a5s+fr2PHjqlPnz7Kzs52bTNw4ECtWLFCaWlpSktL04oVKzRo0KAye30AAAAAAAS9rFNuC1RKwQeSUseOHdP111+vN998UxUrVnStNwxD48eP18MPP6wBAwaoadOmevfdd3XixAl98MEHkqTDhw/r7bff1rhx49S1a1e1bNlSU6ZM0erVqzVr1ixJ0rp165SWlqa33npL7dq1U7t27fTmm2/qm2++0YYNG2x5zQAAAAAABB2rOqpOJ6nFQFtDgW+wPSk1fPhw9e7dW127dvVYv2XLFu3evVvdu3d3rYuMjFTHjh21YMECSdKyZcuUmZnpsU1KSoqaNm3q2mbhwoWKj49XmzZtXNu0bdtW8fHxrm0AAAAAAEAps/qRKldFCo+yNxb4hDA7n3zatGlavny5lixZku+x3bt3S5KSkpI81iclJemvv/5ybRMREeFRYWVtY/3+7t27lZiYmG//iYmJrm0Kkp6ervT0dNfykSNHJEmZmZnKzMwszMvzSVbs/vwaEBg4F+HLOD/h6zhH4S84V+HLOD/LXkh2pkIlOQ1D2bzvZ+XP52hhY7YtKbV9+3bdfffdmjFjhqKiTp8hdTgcHsuGYeRbl1febQra/mz7efrpp/XYY4/lWz9jxgzFxMSc8fn9wcyZM+0OAZDEuQjfxvkJX8c5Cn/BuQpfxvlZduruWaemkkLWfKqFp+rpn/JN7A7JL/jjOXrixIlCbWdbUmrZsmXau3evzjvvPNe67Oxs/fTTT5o4caKrv6fdu3eratWqrm327t3rqp5KTk5WRkaGDh486FEttXfvXrVv3961zZ49e/I9/759+/JVYbkbPXq07r33XtfykSNHlJqaqu7duysuLq6Yr9p+mZmZmjlzprp166bw8HC7w0EQ41yEL+P8hK/jHIW/4FyFL+P8LHshCzdJO835trXLy9m6l70B+Th/Pket1mZnY1tSqkuXLlq9erXHuptvvlmNGjXSAw88oDp16ig5OVkzZ85Uy5YtJUkZGRmaN2+enn32WUnSeeedp/DwcM2cOVNXX321JGnXrl1as2aNxo4dK0lq166dDh8+rF9//VWtW7eWJC1evFiHDx92Ja4KEhkZqcjIyHzrw8PD/e5kKEigvA74P85F+DLOT/g6zlH4C85V+DLOzzIUX801GxriUCjve6H44zla2HhtS0qVL19eTZs29VhXrlw5VapUybV+5MiReuqpp1S/fn3Vr19fTz31lGJiYjRwoNlLf3x8vIYMGaJRo0apUqVKSkhI0H333admzZq5Ok5v3LixevbsqaFDh+qNN96QJN12223q06ePGjZsWIavGAAAAACAINbiGunPWdLqj3M7PUdQs7Wj87O5//77dfLkSQ0bNkwHDx5UmzZtNGPGDJUvX961zYsvvqiwsDBdffXVOnnypLp06aLJkycrNDTUtc3UqVM1YsQI1yh9/fr108SJE8v89QAAAAAAENQcIebUcNobB3yCTyWl5s6d67HscDg0ZswYjRkz5rS/ExUVpQkTJmjChAmn3SYhIUFTpkzxUpQAAAAAAKBYXAOOUSkFKcTuAAAAAAAAQBBY9Lq08kNznuZ7EEkpAAAAAABQFk4dzp2n+R5EUgoAAAAAAJQFKxFVv7t0wRB7Y4FPICkFAAAAAADKQE6TvQo1pah4e0OBTyApBQAAAAAASp9VKeXq7BzBjqQUAAAAAAAofVZS6tf/SX/OtjcW+ASSUgAAAAAAoPS5j7i3/Vf74oDPICkFAAAAAABKX7kqufOMvgeRlAIAAAAAAGWh3TCp9W05C8YZN0VwICkFAAAAAADKhiMnDUGlFERSCgAAAAAAlJmckfcMKqVAUgoAAAAAAJSFn1+QFr+Ws0BSCiSlAAAAAABAWTi2N3ee5nsQSSkAAAAAgD9IPypNOE9KG213JCi2nOqoxn2ldnfZGwp8AkkpAAAAAIDv+22KtP9PadGrdkeC4rKqo6o0kmKr2BsLfAJJKQAAAACA73Nm2x0BSsrq3NxBKgImzgQAAAAAAFD6rEqpec9Kf8ywNxb4BJJSAAAAAADfV+tCcxqbZG8cKD73zs03zbYvDviMMLsDAAAAAADgrCLKS9UvkMrRF5HfiqmUO8/oexBJKQAAAACAP6hcT7p1lt1RoCS6PGL2J/XT2Nz+pRDUaL4HAAAAAPB9u1ZJnwyWZj1mdyQoCauTcyqlIJJSAAAAAAB/cGyPtHa6tOlHuyNBSTgcOTNUSoHmewAAAAAAf7D/T3O6a4WtYaAE5jwtzXvGnKf5HkSlFAAAAADAH2Rn2B0BSurQX7nzNN+DSEoBAAAAAICyYFVHNb1Cuvg+e2OBTyApBQAAAAAASp9VHZXSSqpQw95Y4BNISgEAAAAAgDKQUynlIBUBE2cCAAAAAAAofVal1IKXpQ1p9sYCn0BSCgAAAADg+xr3szsClJSVlDq6S1o73d5Y4BPC7A4AAAAAAICziignJTWVQsPtjgTFFRmXO8/oexBJKQAAAACAP4hNlO74xe4oUBL9XpaqNJR+eEiu/qUQ1Gi+BwAAAADwfX8tlL4YLi16ze5IUCIOc2KQlAJJKQAAAACAP9j/p7RiirRpjt2RoCSskfdovgfRfA8AAAAA4A9OHjCnG3+QnE4phBoLvzNrjDT/xZwFKqVApRQAAAAAwB84s90WSGj4pb3rcueplIKolAIAAAAA+AW3RJThlBRqWyQoJqsfqRbXSReOtDUU+AYqpQAAAAAAvs/Im5SC37GOW62LpMRG9sYCn0BSCgAAAADgX0hK+amcxKKDVARMnAkAAAAAAP9CUso/WcdtyVvShjR7Y4FPICkFAAAAAPB959+SO09Syj9ZTTD/XiYtm2RvLPAJdHQOAAAAAPB9EeWkirVo+uXPwqJy50ksQiSlAAAAAAD+ICxSunul3VGgJAZOk36bKn05zLPjegQtUswAAAAAAN/352zpu39Lqz+1OxKUhMNhTqmUgkhKAQAAAAD8wc7fpF//J22ZZ3ckKAlX80sqpUBSCgAAAADgD6zmXsvfk04csDcWFM8PD0vTbzfnqZSCSEoBAAAAAPyBexIjO9O+OFB8O5bmzpOUgujoHAAAAADgD4xst3kSGv4pp9qt1U3mD4IelVIAAAAAAN/nnogiKeWfrOPWoIdU/Tx7Y4FPICkFAAAAAPB9JKX8n9UvmINUBEycCQAAAAAA30dSyv9Zx23FVGlDmr2xwCeQlAIAAAAA+L72I3LnSUr5qZxKqXVfS/NftDcU+AQ6OgcAAAAA+L6YBCmhjpR5UnI47I4GJUViESIpBQAAAADwFyN+szsClMRtc6X130rTBspVNYWgRlIKAAAAAOD71n8nbV8s1b5YqtfF7mhQbDlVblRKQfQpBQAAAADwB5vnSL+Ml7YttDsSlIQ18p5BpRRISgEAAAAA/IFVWfPTc9KBLfbGguL5/kHpk5vMeSqlIJJSAAAAAAB/4J7EyDxhXxwovs1zpKxTOQtUSoE+pQAAAAAA/sCZnTtPlY1/sprsXTBUatTb3ljgE0hKAQAAAAB8n3siiqSUf7KO2zmXS7UutDcW+ASa7wEAAAAAfJ97x9h0ku2frKSUg1QETJwJAAAAAADfR6VUAMhJJv7+hbRxpq2RwDeQlAIAAAAA+L5L/pM7T6WUf7KSiYtfl2b858zbIiiQlAIAAAAA+L74alJSUykyXnLYHQyKhWo35EFH5wAAAAAA/3DHL3ZHgJK4c6m09WdpyhVUu0ESSSkAAAAAgD9Y+4X0zx9SvS5StfPsjgbFERYphceY81RKQSSlAAAAAAD+YO3n0u9fStEVSUr5M9fIe1RKgT6lAAAAAAD+wKqs+e4+adcqe2NB8Xx3v/TFHeY8lVIQlVIAAAAAAH/gdEtinDxoXxwovt+/lI7tNufpUwoiKQUAAAAA8AeM3Ob/rOPW7k4ptY29scAnkJQCAAAAAPg+j0QUVTZ+yTqG514vJTWxNxb4BPqUAgAAAAD4PiPbbZ5KKf+Uk0x0kIqAiTMBAAAAAOD7PJrvUSnll6xjuHGGtOlHe2OBTyApBQAAAADwfT2fzZ2nUso/WcnEmY9I0++wNxb4BJJSAAAAAADfV6WB2Tl2SBiVUv7K/biRWITo6BwAAAAA4C+GzLA7ApTE3SukPWukd/uKzuohkZQCAAAAAPiD1Z9KR3dJDXtJleraHQ2KIyZBiqlszlMpBZGUAgAAAAD4g1/flLYvkirUJCnlz6yR92iCCdGnFAAAAADAH1iVNR8PkrYtsjcWFM93/5a+HWXOUykFUSkFAAAAAPAH7kmMIzvtiwPFt/w9KetUzgKVUiApBQAAAADwB+5JKaps/JN13C66V0qobW8s8AkkpQAAAAAAvs/IdpunysYvWUmp1kOluBR7Y4FPoE8pAAAAAIDvo1LK/1nJRAepCJg4EwAAAAAAvs+9OoqklH+yjtvW+dKWn+2NBT6BpBQAAAAAwPf1e1kKjTTnSUr5qZzE4mdDpKlX2hsKfAJJKQAAAACA76t2nlSnkzlPUsr/5O0HjH7BIDo6BwAAAAD4i2unmlNHqL1xoHhGrpGO/C2904PEIiSRlAIAAAAA+INVH0vpR6VGfaTySXZHg6JyOKQKqVJITkKRpBREUgoAAAAA4A/mPCkd3ColNSUp5c9cI+/RfA829yn12muvqXnz5oqLi1NcXJzatWun77//3vW4YRgaM2aMUlJSFB0drU6dOmnt2rUe+0hPT9ddd92lypUrq1y5curXr5927Njhsc3Bgwc1aNAgxcfHKz4+XoMGDdKhQ4fK4iUCAAAAALzBqqyZfpu06Ud7Y0HRZWdKaQ9JMx4xl6mUgmxOSlWvXl3PPPOMli5dqqVLl+qSSy7RZZdd5ko8jR07Vi+88IImTpyoJUuWKDk5Wd26ddPRo0dd+xg5cqSmT5+uadOmaf78+Tp27Jj69Omj7Oxs1zYDBw7UihUrlJaWprS0NK1YsUKDBg0q89cLAAAAACgmq2Psg1ul/ZtsDQXF4MySFr0irf44dx2dnQc9W5vv9e3b12P5ySef1GuvvaZFixapSZMmGj9+vB5++GENGDBAkvTuu+8qKSlJH3zwgW6//XYdPnxYb7/9tt5//3117dpVkjRlyhSlpqZq1qxZ6tGjh9atW6e0tDQtWrRIbdq0kSS9+eabateunTZs2KCGDRuW7YsGAAAAABSde2UNVTb+x/2YdXxQioy1Lxb4DFsrpdxlZ2dr2rRpOn78uNq1a6ctW7Zo9+7d6t69u2ubyMhIdezYUQsWLJAkLVu2TJmZmR7bpKSkqGnTpq5tFi5cqPj4eFdCSpLatm2r+Ph41zYAAAAAAB9HUsq/uVdFXXi31P4us/NzBDXbOzpfvXq12rVrp1OnTik2NlbTp09XkyZNXAmjpCTPDuySkpL0119/SZJ2796tiIgIVaxYMd82u3fvdm2TmJiY73kTExNd2xQkPT1d6enpruUjR45IkjIzM5WZmVmMV+obrNj9+TUgMHAuwpdxfsLXcY7CX3CuwpvCnNmyUhjZWZlylvC84vwsY5kZCrdms7Ml3vez8udztLAx256UatiwoVasWKFDhw7ps88+00033aR58+a5HnfkyZwahpFvXV55tylo+7Pt5+mnn9Zjjz2Wb/2MGTMUExNzxuf3BzNnzrQ7BEAS5yJ8G+cnfB3nKPwF5yq8oWf6SUXmzK/7/Xdt2v+dV/bL+Vk2wrKOq3fO/KLPX5PhCNXBmDpuo/HhdPzxHD1x4kShtrM9KRUREaF69epJks4//3wtWbJEL730kh544AFJZqVT1apVXdvv3bvXVT2VnJysjIwMHTx40KNaau/evWrfvr1rmz179uR73n379uWrwnI3evRo3Xvvva7lI0eOKDU1Vd27d1dcXFwJXrG9MjMzNXPmTHXr1k3h4eFn/wWglHAuwpdxfsLXcY7CX3Cuwpscjd6RMf1WOU4dVuPGDdWwba8S7Y/zs4ydPCStNmc7bHxSkpT5wA4pLMq+mHycP5+jVmuzs7E9KZWXYRhKT09X7dq1lZycrJkzZ6ply5aSpIyMDM2bN0/PPvusJOm8885TeHi4Zs6cqauvvlqStGvXLq1Zs0Zjx46VJLVr106HDx/Wr7/+qtatW0uSFi9erMOHD7sSVwWJjIxUZGRkvvXh4eF+dzIUJFBeB/wf5yJ8GecnfB3nKPwF5yq8omF3qWFvaeUHCnU4FOqlc4rzs4xkhuZbFR4WJvHen5U/nqOFjdfWpNRDDz2kSy+9VKmpqTp69KimTZumuXPnKi0tTQ6HQyNHjtRTTz2l+vXrq379+nrqqacUExOjgQMHSpLi4+M1ZMgQjRo1SpUqVVJCQoLuu+8+NWvWzDUaX+PGjdWzZ08NHTpUb7zxhiTptttuU58+fRh5DwAAAAD8Sa/npJ5PU13jj6LipeFLpIxj0pudzXV0WB/0bE1K7dmzR4MGDdKuXbsUHx+v5s2bKy0tTd26dZMk3X///Tp58qSGDRumgwcPqk2bNpoxY4bKly/v2seLL76osLAwXX311Tp58qS6dOmiyZMnKzQ0Nws7depUjRgxwjVKX79+/TRx4sSyfbEAAAAAgOJb9YlkZEuNekvhJKX8TkioVKWBlOHW1xBJqaBna1Lq7bffPuPjDodDY8aM0ZgxY067TVRUlCZMmKAJEyacdpuEhARNmTKluGECAAAAAOz29Qgp84R090opsvzZt4dvcu/Y3DDsiwM+wef6lAIAAAAAIB+rqubrkVLr26RGJevoHGUs/aj0y0uSMzt3HZVSQY+kFAAAAADA91kJjM1zpNQ2JKX8Tfox6afn8qykUirYkZQCAAAAAPg+j6oakhl+xzp+jlCp4/2SHFJo/hHvEVxISgEAAAAAfJ97UopmX34oJ5EYEiZ1etDeUOAzQs6+CQAAAAAANjIMklL+zlUpRRoCuaiUAgAAAAD4tryjtJGU8j+upJRD2rvOXK7cQAoNtzcubzMM8zWiUEhRAgAAAAB8m8MhXTlJKl/VXCYp5X+sxKIjRHq1nfRae+nEfntj8rbvH5RePEc6ccDuSPwGSSkAAAAAgG9zOKSmA6RmV5rLJKX8j+uYOXKb8OWtgPN3i1+TjvwtLX3H7kj8Bs33AAAAAAD+ocN9UtthUkSs3ZGgqOKqSbfNNRNSb15i9nseaMnF8HJS5nEpuZndkfgNklIAAAAAAN+WnSWt/8asmGrYWwrlq6zfCY+SUlqa867OzgOsUqpiLWnvWik0wu5I/AZ/yQAAAAAA35Z1UvrkJnP+4d0kpfydq/legFVKhQTo6ypF/CUDAAAAAHybMzt3fsZ/pFodpHP62xYOiuH4fmn5ZLOJm3JGpwu05M3u1eb06G574/AjJKUAAAAAAL7NPXmx5C1zSlLKvxzfK83+rxRTOXA7Orcc32t3BH6DpBQAAAAAwLflTV4EajIjkFmJRYdDajdMys6QouLtjam0VKxtdwR+g6QUAAAAAMC35W3mFWjNvoKBKykVIl3yH3tjKS2RcVL6EUbfK4KQs28CAAAAAICNSEr5P1d1m8PWMEqV1feZg1RLYVEpBQAAAADwbUZ2nmWSUn7HvVLq0DbJmSXFVZfCIuyNy5syj5vT7Ex74/AjpO8AAAAAAL4tKl667BWpUj1zmT6l/I97UuqNi6WXW0oHNtsbU2nZ+IPdEfgNklIAAAAAAN8WUU5qeYPUclDOCpJS/ifnmDkcbs3bAvU4BnATRS+j+R4AAAAAwD+0ulFq1EeKirM7EhRV5QbSzd9LIeHSh9ea6wKtGWa5KtLxfYE7qmApICkFAAAAAPBtGcelrb9IoeFS3c52R4PiiCwv1WxvzluVUoHWDLNqC+nPWXR0XgQkpQAAAAAAvu3obumDq6TIeGn0NrujQUk5cpq3BVqllCPUnAba6ypFJKUAAAAAAL7NmTP6XvphafZ/pcQmUrMr7Y0JRXNkl/T7l1J0xcDsUyorI7eD86xT9sbiR4pVU/bmm29q48aN3o4FAAAAAID83CtPfh4nrf/GvlhQPAe3SGkPSD8959Z8L4AqirJO5s47s+yLw88UKyk1btw4NWrUSCkpKbruuuv0xhtvaP369d6ODQAAAACA/MmLQOuLKBhYx9DhkFpcJ7W+TYqpbG9M3mRV80lSlUb2xeFnipWUWr9+vf7++2+NGzdO8fHxevHFF3XOOecoOTlZ1157rbdjBAAAAAAEs3xJqQCqsAkWViLRESJ1eUTq9ZxUIdXemLzJPSlVp5NtYfibYvcplZycrOuuu079+vXT/PnzNW3aNE2ZMkWffvqpN+MDAAAAAAQ7klL+z3XMHLaGUWqMnKSUIyS3I3ecVbGSUt9//73mzZunuXPnauXKlTrnnHN08cUX67PPPlOHDh28HSMAAAAAIJgZ2XmWab7nf9wqpU4ckLIzzU7PwyLsDctbrEopwyllpUthkfbG4yeKlZTq3bu3qlSpolGjRumHH35QfHy8t+MCAAAAAMAUV1269DlpzWfS9kVUSvkj9z6l3rzE7Pj8lh+kGm3tjctb3BOnv/5Pan+XfbH4kWL1KfXCCy/owgsv1HPPPaeGDRvqmmuu0WuvvaZ169Z5Oz4AAAAAQLCLrSK1uU1qNchcJinlf9yTUq7R9wKo4s19xD1n9um3g4diJaVGjhypzz//XPv27dPMmTPVoUMHzZo1Sy1atFDVqlW9HSMAAAAAAFLDXtLtP0u9xtodCYqqakvp+s+kXs/n9rkUSMnF8lWlSvXN+UB6XaWs2B2dS9Jvv/2muXPnas6cOfr555/ldDpVvXp1b8UGAAAAAIB08pC0Z40UHiNVa2V3NCiOcpWk+l3NeatSSgFUKRUeLdVsJ+3fmL8PNJxWsSql+vXrp4SEBF1wwQWaOnWqGjRooPfff18HDhzQkiVLvB0jAAAAACCY7VkrTe4tTb/d7kjgFQFYKSUFZrPEUlasSqkGDRrotttu08UXX6y4uDhvxwQAAAAAQC4refHPH9JPz0sVakjNr7Y3JhTNoe3S5jlSucTATN4c2yctm2zOB1qyrRQVKyn1/PPPezsOAAAAAAAK5t4c6sfHpdoXk5TyN7tXS1/dJVW/wC0pFUDJmyM7cufp6LzQitV8T5LmzZunvn37ql69eqpfv7769eunn3/+2ZuxAQAAAACQP3kRSBU2wcJ1DB1So97SuTdI5ZNtDcmrnG7naGJj++LwM8VKSk2ZMkVdu3ZVTEyMRowYoTvvvFPR0dHq0qWLPvjgA2/HCAAAAAAIZvmSUgFUYRM0chKJjhDpkoel/q8EVvLGmWVOK9aWmg6wNxY/Uqzme08++aTGjh2re+65x7Xu7rvv1gsvvKDHH39cAwcO9FqAAAAAAIAgl7cyiqSU/7GOmcNhbxylxWpiGhJqbxx+pliVUps3b1bfvn3zre/Xr5+2bNlS4qAAAAAAAHChUsr/GW6VUhknpFNHpOxMe2PyJlc/Ug76lCqCYiWlUlNTNXv27HzrZ8+erdTU1BIHBQAAAACAS+X6UtcxUqM+5jJJKf/j3qfU5F7SM6nSn/nzCn7Lar63f6M04z/2xuJHitV8b9SoURoxYoRWrFih9u3by+FwaP78+Zo8ebJeeuklb8cIAAAAAAhmCXWki+6RNnwvrf+GpJRfsiqlHLmj7ymAOqx3HyGS87PQipWUuuOOO5ScnKxx48bp448/liQ1btxYH330kS677DKvBggAAAAAgCQptY10yw9SRKzdkaCoUttIV78nxVSSZj5qrguk5E3VllL1C6QdS2i+VwTFSkpJ0uWXX67LL7/cm7EAAAAAAJDfiQPSwS1SZLxUo63d0aA44qubP1JupVTeDuz9WblKUt0uZlIqkJJtpaxYfUoBAAAAAFBmNs+V3rxE+mak3ZHAG6wR+AIteWONvGdQKVVYha6UqlixohyFHLrxwIEDxQ4IAAAAAAAPVvLiyN/SwlelmASpxbX2xoSiObhV2rFUKp8cmH1KHdgsLX/fnA+0ZFspKnRSavz48aUYBgAAAAAAp2F9yT+wRfphtJR4Dkkpf7P1F+nLYVK9bpICsFLq7+XS4W3mvDOAXlcpK3RSauXKlXr88cdVrlw5/fTTT2rfvr3CwordJRUAAAAAAIVjJS9Cw6XsjMBKZgQL65g5HFLti6XySVJcNXtj8ib3czK5qX1x+JlC9yk1YcIEHTt2TJLUuXNnmugBAAAAAMqGNZpZSE5hBEkpP5TTVM8RInUeLV01WUptbWtEXmWdo3W7SG3vsDcWP1LoUqdatWrp5ZdfVvfu3WUYhhYuXKiKFSsWuO3FF1/stQABAAAAAEHOSkKRlPJfrmNWuL6q/Y4zy5yG0KKsKAr9bj333HP617/+paeffloOh0OXX355gds5HA5lZ9PTPAAAAADAS1xNv0I8l+E/DLdKKcPIOYYOKaTQDbh8mzXinjUCHwql0Ee/f//+2r17t44cOSLDMLRhwwYdPHgw3w/N+gAAAAAAXlW1udTxAanZleYySSn/496n1PuXS/9NkFZ/bG9M3mQ139vwnfTpEHtj8SNFriuLjY3VnDlzVLt2bTo6BwAAAACUvpSW5s/2JdKSt0hK+SWrUsrhVvFm2BeOt7mfk9np9sXhZ4qVVerYsaMkae/evdq7d6+ceYY7bN68eckjAwCUntn/lRa9ZnbC2OX/7I4GAACgcCrXl274XAqPsTsSFFXtjtLlb5gj7v0y3lwXSMnF+t2ket2kP2cGVrKtlBUrKbV8+XLdeOONWrdunYw8bzZ9SgGAHzh5SMo8IaUftTsSAACAsztxQDq+T4qMk+p1sTsaFEfl+uaPJC14OWdlACVvKtaSGvc1k1JOciKFVawexQYPHqwGDRpowYIF2rx5s7Zs2eL62bx5s7djBAB429K3zemv/7M3DgAAgMJY9ZH0Smtpxn/sjgRekTMCXyBVSkl0xF8MxaqU2rJliz7//HPVq1fP2/EAAAAAAODJ+pJ/6pC0bLIUFiW1uNbOiFBUB7ZI+9ZLcSmB2afUnrXSms/MeYNKqcIqVqVUly5dtHLlSm/HAgAAAABAflZS6uge6eu7pZn0iel3NnwnfXittGCi2dm5FFgVReu/lTbPMedpvldoxaqUeuutt3TTTTdpzZo1atq0qcLDwz0e79evn1eCAwAAAADA9SU/JNScBlIyI1hYx8zhkFJamcvxqfbG5E3uiajkZvbF4WeKlZRasGCB5s+fr++//z7fY3R0DgD+xGF3AAAAAGdnJTRCwz2X4T+spnqOEKnjv+2NpTRYTfZa3yZ1f9zeWPxIsZrvjRgxQoMGDdKuXbvkdDo9fkhIAYAfqdLI7ggAAADOzlVlQ6WU33IdswC9KWpVSlnnKAqlWEmp/fv365577lFSUpK34wEAlIXaF5vTi++zNw4AAIDCsKpsQsI8l+FH3CqlApEzy5yGkJQqimKdDQMGDNCcOXO8HQsAoKzU7ig1vVKqUNPuSAAAAM6u+vlSuzulup3NZZJS/sdV7Sbp89ulxxOlX9+0NSSvsl7fwonSe/1tDcWfFKtPqQYNGmj06NGaP3++mjVrlq+j8xEjRnglOABAKaFCCgAA+JO6nc2fA5ulHx+n+Z4/cu9TypkpZafnVhcFAveOztOP2heHnyn26HuxsbGaN2+e5s2b5/GYw+EgKQUAvu63KeZQyg16Sv1ftTsaAACAwolNkq6ZmtvhOfxHva5SdEWpcgNp2WRzXSAlF1tcIx3fJ635NLfTc5xVsZJSW7Zs8XYcAICylH5UOrFfOnXY7kgAAADO7uRB8/olsrzUuI/d0aA4Us41fyRp+XvmNJCaYaa0lFpcl5OUCqBkWykL0B7GAABnlPagOd0878zbAQAA+IL546XxzaR5z9kdCbzBkTMCX6Alb6zX5Qyw11WKilUpJUk7duzQV199pW3btikjI8PjsRdeeKHEgQEAykCgjn4CAAACi5W8yDolrfpYkkNqfpWtIaGIDmyWDm2T4qq7XYMGUKXUzt+kP9LM+UBLtpWiYiWlZs+erX79+ql27drasGGDmjZtqq1bt8owDLVq1crbMQIASot1NwcAAMCXWV/y049Knw+VHKEkpfzNig+ln8ZKFwyVOQSfAit5s/gNaeWH5jx9ShVasW6Rjx49WqNGjdKaNWsUFRWlzz77TNu3b1fHjh111VX8YwAAv0GlFAAA8AdW8iIkzHPZXxiGtPUX6fg/dkdiH+uYOUKkKg2kWh2k+FR7Y/Im99H3EpvYF4efKda3kXXr1ummm26SJIWFhenkyZOKjY3Vf//7Xz377LNeDRAAUIpISgEAAH9gJTRCrcY+hn91kr1xpjS5l/TSuXZHYqOc4+UIkS66Rxr8jdTsSntD8iarOqrnM9JVk+yNxY8U69tIuXLllJ6eLklKSUnRpk2bXI/9808QZ34BwN8kNrY7AgAAgLPLWykl+VdSaudv5jTjqL1x2MlVKRWg3Uc4s8ypI9TeOPxMsfqUatu2rX755Rc1adJEvXv31qhRo7R69Wp9/vnnatu2rbdjBAB4W0SslHFM6vey3ZEAAACcXUFJKX/qJLtme3NauaG9cdjJvfleILKa74WQlCqKYiWlXnjhBR07dkySNGbMGB07dkwfffSR6tWrpxdffNGrAQIASkGDHlLGCSmivN2RAAAAnF2NdmZSo9r5kv5nrjOckvwkARAWaU6z0+2Nw07ulW0/PCytmCpdOFK6aKRdEXmXlXT79l7ztQ390d54/ESxklJ16tRxzcfExOjVV1/1WkAAgDJw5Tt2RwAAAFB4za40f04dyV3nT52dh0aY06wMe+Owk3ulVMYx6eRBKfOkvTF5k9V8T5KO7rYvDj9TrKSUJB06dEiffvqpNm3apH//+99KSEjQ8uXLlZSUpGrVqnkzRgCAt22dL31xh1lCfsOndkcDAABQOOHR0oA3JTnyNOXzceu/MadHd9obh50aXiqVT5ZSWkprp5vr/CmxeDZtbpcq1JCWvOU5Eh/OqFh/xatWrVLXrl0VHx+vrVu3aujQoUpISND06dP1119/6b333vN2nAAAbzEMKStdOrRNioyzOxoAAICzO3VEys6UImKk5lfbHQ2Ko9ZF5o8k/f5lzko/6hfsbOp1lcpXNZNSBkmpwipWD2P33nuvBg8erI0bNyoqKsq1/tJLL9VPP/3kteAAAKUg84Q0ZYA5nx7EI8AAAAD/8f0D0nN1pF//Z3ckxRNT2e4IfEzOCHyBVCkl5XbiHmivqxQVq1JqyZIleuONN/Ktr1atmnbvpu0kAPi07Mzc+UAd/QQAAAQW9y/5676RZEgNekqh4baFVCThOcUcDXvZG4edDm6Vju2T4lLckjcBVCm1Y5n091JznuZ7hVaspFRUVJSOHDmSb/2GDRtUpUqVEgcFAChF7p0wOhz2xQEAAFBYVlLKMKSPrjfnH/hLiq5gW0hFYiUpQvxktMDSsGCitORN6eL7c69BA6mi6IfR0vbF5nwgJdtKWbFukV922WX673//q8xM8267w+HQtm3b9OCDD+qKK67waoAAAC9zr5QKpAsBAAAQuKw+etw7N/en6xjr+mvd10E8Al9OosYRIsWnStXOk+Kr2xuSN7nf+K3S0L44/EyxklLPP/+89u3bp8TERJ08eVIdO3ZUvXr1VL58eT355JPejhEA4E1Ot6SU048u5gAAQPCyElAeSSk/qkZx7/g6WPv0tI6hwyG1GyYN/VFqPdTemLzJqoYb+LF060x7Y/EjxWq+FxcXp/nz5+vHH3/U8uXL5XQ61apVK3Xt2tXb8QEAvM29Uiqhtn1xAAAAFJYrKRWaf50/iEvJnc8O0kopV1IqQPs0NWiiWRxFTkplZWUpKipKK1as0CWXXKJLLrmkNOICAJQWq7Q4uqJ001f2xgIAAFAY7gkNR4i57E9JqXMul74YZo6CnJ1udzT2sCrbArVPU6sFgoOkVFEUOUUZFhammjVrKjub3uQBwC+FRUn1ukq1OtgdCQAAQOHU6iA1v1aqVM9t5DY/SkpJUmiEOQ3WPqVcx8shzR8vvXCONOdpOyPyLuvG7/v9pQnn2xqKPylW873//Oc/Gj16tKZMmaKEhARvxwQAKE0Va0o3fGZ3FAAAAIXX5vbceX9NSoVFmtNgrZRy7+j81GHpyA7p1CFbI/Iq937D9m+0Lw4/U6yk1Msvv6w///xTKSkpqlmzpsqVK+fx+PLly70SHACglBz8S/rgaik8Rrptjt3RAAAAFF7vcWZTsKh4uyMpvF9elo7tMeeDtU+pRn2kCjWlmu2ljTkdgftTZ/Vn0+E+Mxn18zhz2emUQgK0/ywvKlZS6rLLLpMjUNuBAkAwcGZJ+9ZLEbF2RwIAAHB2mackGWYTuFY32h1N0WUcy50P1uZ7DS81fyTpz9nm1N+q3c7k3OukEwdyk1KGU8XoMSnoFCspNWbMGC+HAQAoM1vnS5N7m/NO+gcEAAB+YNp10qYfpcvfkFpca3c0RWf1NxRVQapU19ZQfIJV5BJISSnJf0eHtFGx0nZ16tTR/v37860/dOiQ6tSpU+j9PP3007rgggtUvnx5JSYmqn///tqwYYPHNoZhaMyYMUpJSVF0dLQ6deqktWvXemyTnp6uu+66S5UrV1a5cuXUr18/7dixw2ObgwcPatCgQYqPj1d8fLwGDRqkQ4cOFf5FA0CgyDqVO2+QlAIAAH7AffS9LT+ZlTYZJ+yNqSisG4HnXi/FJtobi10ObZf2rJWO78/tF0wB1Hxvx1Lzx8J1dqEUKym1devWAkffS09Pz5cMOpN58+Zp+PDhWrRokWbOnKmsrCx1795dx48fd20zduxYvfDCC5o4caKWLFmi5ORkdevWTUePHnVtM3LkSE2fPl3Tpk3T/PnzdezYMfXp08cjxoEDB2rFihVKS0tTWlqaVqxYoUGDBhXn5QOAf8vOyp2nUgoAAPgD96TUtBukKQOko7vsjakorARFMPcxNPsx6bX20qppkgKwUurDa83z0hJIr60UFan53ldffeWa/+GHHxQfn9uxXHZ2tmbPnq3atWsXen9paWkey5MmTVJiYqKWLVumiy++WIZhaPz48Xr44Yc1YIB5cN99910lJSXpgw8+0O23367Dhw/r7bff1vvvv6+uXbtKkqZMmaLU1FTNmjVLPXr00Lp165SWlqZFixapTZs2kqQ333xT7dq104YNG9SwYcOivA0A4N+cmbnz3MEBAAD+wHAbuc0fm345c2LdOEtq8y8pvrq98djB/RjGVpESm0jlq9obkzc53W78VqwdWJ24l6IiJaX69+8vSXI4HLrppps8HgsPD1etWrU0bty4Ygdz+PBhSVJCQoIkacuWLdq9e7e6d+/u2iYyMlIdO3bUggULdPvtt2vZsmXKzMz02CYlJUVNmzbVggUL1KNHDy1cuFDx8fGuhJQktW3bVvHx8VqwYAFJKQDBJTvTc5mRQQAAgK+zqrv9NimVk7DYt07a8rPZKXawcR0vh3T+LeZPILESj3cukyrXszcWP1KkpJQz502uXbu2lixZosqVK3stEMMwdO+99+qiiy5S06ZNJUm7d++WJCUlJXlsm5SUpL/++su1TUREhCpWrJhvG+v3d+/ercTE/O12ExMTXdvklZ6ervT0dNfykSNHJEmZmZnKzMws8Hf8gRW7P78GBAbORfs4Mk+5/vkb8TWUlZkuhRRr3IuAxfkJX8c5Cn/BuQpvCXVmK0RSltNQqCNEDkmZmRlSCc6tsjw/Q2KTZHWBnZVxQkYQ/k1YxzDbacgZgK8/zMgyz0unUaLz0p0//w8tbMxF+hayePFiHThwQFu2bHGte++99/Too4/q+PHj6t+/vyZMmKDIyMiiRSvpzjvv1KpVqzR//vx8jzmsTHgOwzDyrcsr7zYFbX+m/Tz99NN67LHH8q2fMWOGYmJizvjc/mDmzJl2hwBI4ly0Q+r+5WolaU/55lpU5z4pbYbdIfkszk/4Os5R+AvOVZRUh4P7lSBp2fLf1CIjU1GSfv5pno5Gby7xvsvm/Kyn8yu0VrVDv+r3Vb9pyy7vFXj4i/N37VQ1SWt+/11b931ndzhe1yczQ6GS5sz7SScjfvfqvv3xf+iJE4UbiKBISalHH31UnTt31qWXXipJWr16tYYMGaLBgwercePGeu6555SSkqIxY8YUKdi77rpLX331lX766SdVr57btjY5OVmSWelUtWpuW9O9e/e6qqeSk5OVkZGhgwcPelRL7d27V+3bt3dts2fPnnzPu2/fvnxVWJbRo0fr3nvvdS0fOXJEqamp6t69u+Li4or0+nxJZmamZs6cqW7duik8PNzucBDEOBft49gYJqexWpWrnadeXXrZHY5P4vyEr+Mchb/gXIW3hET/Juf+jTqvfW+FfvyhlHVEHS66UEpqWux9lvX5Gfrl19KhX3VOw3pq3Db4rsFCP/1YOiQ1bdpU54QeVOiiiXI2uFTOS/7P7tC8ImSlJEPqtvUZKTZJWQM/lWIqlWif/vw/1GptdjZFSkqtXLlSTzzxhGt52rRpatOmjd58801JUmpqqh599NFCJ6UMw9Bdd92l6dOna+7cufk6Sa9du7aSk5M1c+ZMtWzZUpKUkZGhefPm6dlnn5UknXfeeQoPD9fMmTN19dVXS5J27dqlNWvWaOzYsZKkdu3a6fDhw/r111/VunVrSWbV1+HDh12Jq7wiIyMLrPgKDw/3u5OhIIHyOuD/OBdt0KS3+SO5yshRMM5P+DrOUfgLzlWUWPcxknKGjw8xr2DCQ0MkL5xXZXZ+hpvfL0OVrdBg/HvIaaQUGhYupR+W9m9U6PHzAue9yOk3zHF8r3R8r8IdhlfOT8k//4cWNt4iJaUOHjzoUVk0b9489ezZ07V8wQUXaPv27YXe3/Dhw/XBBx/oyy+/VPny5V39O8XHxys6OloOh0MjR47UU089pfr166t+/fp66qmnFBMTo4EDB7q2HTJkiEaNGqVKlSopISFB9913n5o1a+Yaja9x48bq2bOnhg4dqjfeeEOSdNttt6lPnz50cg4gOGVnSW93M0ffu+lrKSr+7L8DAADgCzo9KGWelMqn2B1J4aU9JP02xZzPyrA3Frs07itVqiclN5e2LcxZGUAj1HV5xOzsfE5OIQ+jXBdKkZJSSUlJ2rJli1JTU5WRkaHly5d79Lt09OjRImXvXnvtNUlSp06dPNZPmjRJgwcPliTdf//9OnnypIYNG6aDBw+qTZs2mjFjhsqXL+/a/sUXX1RYWJiuvvpqnTx5Ul26dNHkyZMVGppbAzB16lSNGDHCNUpfv379NHHixKK8fAAIHI4Qaedycz7vaHwAAAC+xjByR907b7CtoRTL8b2589npp98ukLW4Nnd+2yJz6k8jKJ5Nh1Hm9OfnpaxTgfXaSlGRklI9e/bUgw8+qGeffVZffPGFYmJi1KFDB9fjq1atUt26dQu9P8M4e1bU4XBozJgxZ2wSGBUVpQkTJmjChAmn3SYhIUFTpkwpdGwAELAWvS79PC532cldHAAA4ONev0jas0Ya9IVUt7Pd0RSddb1VuYHUqI+9sfgCR4g5LUROwO84copjuMYulCIlpZ544gkNGDBAHTt2VGxsrN59911FRES4Hn/nnXdclUgAAB+VfsTzbh2lxQAAwNdZVSeOEGnnCrP5XtI5UpSfDEJlXW+1vk2qfr69sdjlyC4p66RUropbUipAqomc2dLuVbkJKSlwXlspK1JSqkqVKvr55591+PBhxcbGejSPk6RPPvlEsbGxXg0QAOBleZvrcRcHAAD4Ovek1Kc3Swc2S7fMkGq0sTeuwrKut6xkTDD66k7pz1nSZa/mNsUMlMRNxnHpf53M+bAocxoor62UFesvIj4+Pl9CSjKbyLlXTgEAfJAzT1KKSikAQEGWvCVNvdqsSAHs5p6U8scqGysptW2htGetvbHYxWqq5wiRIuOkCjXNqqlAkDPyniQpLkWKTZZruEGcUZEqpQAAAYBKKQBAYXyb02nvms+lltfbGwvgUWnkh1U21k3A1Z+YCYyrJtsaji1ciUWH2en5udfZG483uZ+Ldy3PrQTDWQVx7SAABCn3Ozkxle2LAwDg2yrWNqeV6tkbByAVXCklP+okOy4ldz4rw744bOVWKRVoXDd5HSSkiigAzwYAwBlZlVIdH5Tu3yRVKvyoqQCAIGJVdoTQuAI+wEpKhYT6Z/O9vi9J/V8z57PT7Y3FLq7jFYBJG+umb0j+bo5wZiSlACDYxKVIyc2l8sl2RwIA8GXWnX++ZMEX1LpIqtdVior3z6SUJIXm9L+cHaSVUq4+pRzSum+kNy6W0kbbG5O3uCfxP75Reru7tO8Pe2PyE9z2AIBgc/F95g8AAGdy5G9zumWelHKuraEA6v9q7ry/J6WCtfmee0fnJ/ZLu1ZK5avaG5O3uPo8C5X+/k06vE1KP2pvTH6CpBQABKtPbpaO7jbLyas0sDsaAICvyjxldwSAp9a3Ssf/ye33zB98dqvZybkUvM33GveVkppICXWk3avNdYYf9Qt2JlHxUof7pNBwaeWH5jp/S5rahKQUAASrv5dKh7ZJ6UfsjgQAAKDwzhtsdwRFd2BL7nywVkq1/Vfu/J415jRQEjcxCVKXR8z5VR+ZU4MRrguDpBQABJuv75Y2zzUTUpLbaCEAABQgtordEQDSi83MJl9Df5QSG9kdTdFZCYqaF0ktb7A3Fl/gjyMoFpYjpx++QEm4lTKSUgAQbI7ulg5uzV3mLg4AoCD1ukp/zpLCou2OBJAyjkmZx81OsvdvMpcr1JSiK9gdWeFYo7NddI9Uv6u9sdjlxAFzFOjoCv7bL9jpZJ4yb/iGReS+Nm78Fgqj7wFAsMnO9FzmAxMAUJCQnPvX1pdpwE5W8sIRIk2/3Ry57a9f7I2pKJw58YcE8VfwqVdJ4xpIf86W5DDXBUpSat866ZULpHcuzR2xNFBeWykL4r8IAAhSzjxJKSqlAAAF+SPNnGaesDcOQPIcuc0fq2ys661dq6TtS+yNxTZuxzA8SipXxewgPBC4ko5hUkQ5KaK8WdWHs6L5HgAEm+w8d7yplAIA5OV0+7Lv4D42fICrUsrhlpTyo/6IrOutWY9K4eWkh3faG48d3I9hk8vMn0BhVZSGhEi3zrI3Fj/DJwwABBv3SqnwcvbFAQDwXe5N9ppdZV8cgMWqNPLXSqnYJLmarGWn2xqKbdyr3QKN6/wMtTcOP0SlFAAEG+uLxsCPpQY97I0F3vfLS9KxvdIFt0oJte2OBoC/cm/aHcJXBvgAV5VNqH8mpW7+Vjr+j/RcXfNazOkMvv6lXMcrAJu1WZVw/L8sMt4xAAg28alS+lEpsrzdkaA0zPw/cxqbJF04wt5YAPgv90opvmTBF9RoK2VlSGFRuX31+FNSSpJCI3LnszOkkCj7YrGFVSnlkLbOl358Uko6R+r9vL1heYOVyA8JldIekvb+LnV60DxvcUZ8wgBAsLnmfbsjQFnwtwt1AL7FPSm1/D2pzW32xQJI0o1fui1YSSk/6lNKksIic+ez083OvoOJe/O9E/ulbQsC53rF6ZaU+nuptH2xdP4t9sbkJ0hKAUCwmvWYtHuVdNG9Uq0L7Y4G3saoigBKwn0QjJMH7YsDKEizq6TqF0iJjeyOpPDeH+D5t5SVYV8sdmnUW6p6rlS+qpRxLGelnyUWTyc+VWo7XCqfJG3IGbk0UBJupYykFAAEq7+XSlt+klpcZ3ckKA1OLoQAlIB7UoovVvA1rQbZHUHR7VphVgdZsoMwKdX5odz5/X+a00D5/1KlgdTzKXP+jxnmlBuEhUJSCgCCzaTe5p26U4fNZScfmAEpUC7yANgjsrwUnSCdPODZlA+wg9MpPV/PbPY1/FcpJsHuiIrOut5qfo1UuYEUEeQjILs6qw+QSil3IQH82koBSSkACDb71ksn/pHKp5jL3MUJHO7VURxXACURESOdO1BaOJGkFOxnOD2rjI7sMpt/xSZKUfH2xVUUVlKq4wNSpbr2xmKX9KNmoiY8xn87qz+djOPmTd+w6NyEGzd+CyXIxqAEAMiZaU6tzjb5wAwc7l8cA+UiD4B9XF+sSErBZu6faSGh0jf3SBPPl37/8vS/42vcR2cLVv/rJD2TKu34Nff/S6D0KfVHmvTiOdInN0mOnGPMtVihkJQCgGBjJaGspBQVNYHD4ZDqdJLia0iN+9kdDQB/duqI9McP5jw3L2A392sVR4hb0y8/+tJv/R0d/EvatVJKP3bm7QOR++h7IWFSeDkpLEBGILSq1UNCzR9HqAIm4VbKaL4HAMEmm0qpgBUanmfIbAAopsM7pH82mPPRFe2NBXBPPjlC/LPpl5VYm3a9lHFUGvytVOsie2Mqa67j5ZDqdZEe3mlrOF5lVZQ6QqXrP7E3Fj9DUgoAgo2r+V7OnSl/uqADAJQN6wt0bLLU6QF7YwHyJaX8sFIquqJ5YzAs0kxKBePoe9bxcgRggy2aZxYbSSkACCZOZ+4FwTVTpXKVc+82wv9lZ5p9GhzYYjbjq9rc7ogA+Cvrrn8IXxfgA9yruh2h/jly27//NKf/6ywd2yNlBWFSymrOFojXntY5yv/MIgvAFCUA4LScWVKFGubIe2ERgXlREMyO7pY+ukGa+Yi09B27owHgz5zc9YcPcYRIKS2lqi38t/mexeo+ITvd3jjsYLglpXatkt4fIH19t70xeYur+V6ItGCC2Uzzjxn2xuQnSOMBQDAJi5BGrrY7CpQWq2mmFJzNAgB4j/UF69Bf0uz/Sl3+z954ENyi4qTb5uYu+2OllCU0wpwGY6WUe59SJw9Km2ZLVRrZGpLXGG4dnf+9TFr/jVT7Yntj8hMkpQAgWC15W9o8V2p2ldSEkdoCQrbbsO1ZQXgHFoD3ON3+nxzcalsYQIHqdZNik6TkpnZHUjiZJ6UpV3j2pRSMN48a9pJO7Df718o4bq7zx8RiQao0lM4bbFbzbf3FXOePlXw2ICkFAMFq52/Suq/odyiQeFRKkZQCUALuffgwSit8zbnXSbrO7igKLztD+isnUVGvW866IPyc7v187vyRv81poCRual+cWxn110Jzyv/OQiEpBQDB5Pg/5p268Gjzjo5kdn6OwOBe2RCMzQIAeE/l+uYd/10rPf+3AHY4tld6s4sUGi6NWG53NEXnnpw4p7+U3ExKDvKbgq6qsQCplHJn9cUXKAm3UkZSCgCCSeYJadcKKSxKSmxirjNKeBfHMKQ1n5n7S2pS4hBRAh7N907ZFwcA/xeXIl1wq/TVXdzth/2yM6TD23L7Yzp5UMo4IUWWN/ub8nXuyYkWA6WQIB1vLDvL7OTcESLJjzurL0jGCfPaKzzarc8z/ncWRpD+NQBAkMrOad4VEp57F6ekXzb+nCV9NkR6rV3J9oOSo6NzAN7ksD4nqJSCzazEhXVO/vAf6cUm0pK37IupKFzXWo7gTUhJ0rgG0n8TpH3r/buz+oIsfEUaW1v6/gH/Hh3SBkH8FwEAQcj6YhEalnthV9K7OGFR5jSE4lvblU+WmlwmxdeQLgyQIZYB2OPYXmnrfHOeu/2wmysplfP11d++9Ft/QyGh0okD0v5N0rF99sZkBysB5QjJrZayjqW/cz/GDprvFQXfIAAgmJRGpVREjDmNTS7ZflByFWtJV79ndxQAAsHW+dLKD8z5SD9oHoXAli8p5WdVNta1liNU+nmctHCiefOo23/tjausuZI0Din1AmnMITuj8S73Y9zrWanXc9ywLSTeJQAIJlbzrtBwtwu6Et7FCQnP2TfNOwAgYFifDbU7Sle/a28sgHuFjfvUXypRDKcUHiOFReb2ixWUA5LkOY6BxFUpFSaFRdgbi58hKQUAwcTqCDskTLrkP1Kn0WaCqiT2rjOnx3aXbD8oufRj0sGtZl8NsUlS7Q52RwTAXzndPi8Au7mqUHKaevlbUqpiTenhXeb83GfMaXa6ffHYxZVcDJAme+5c/zND7Y3DD/EpAwDBxOGQYipLMQnm3TpvOLDZO/tByW2aLX18ozkfmyTd94e98QDwX3zBgi8JDZOqNDJH25PcKm38pPmeu2CulHJPSh3YIs18RIquKPWbYG9c3uBKnIZIqz6WNs6UGvaUml5hb1x+gKQUAAST6udL92/y8k798IIwULk3ocwKwjuwALzH+n+ycYb0xXCp/yv2xoPgllBHGr44d9nfOjp3ZyWlgnGUXPc+pdKPSOu+Dpw+Sa3XFhIq7Vwhrf5YiqtKUqoQSEoBQLDa8L30+5dSzQulVoOKv59Q2s37jGy3pFQwXuwC8B73QTB2rbQvDqAgqW3Mmy9Vz7U7ksI5sEX67t9SucpStfPMdcHYfK9+NynrlBRRTso4lrMyQG5uVj1Xana1lNxM2vmbuc4fk6Y2ICkFAMFq7+/Syg/N/kJKkpSKijenTS7zTlwoPqsje4lKKQAl456UYiAL+JpmV5o//uLUYenPmVL5FKlGO3NdMDbfcx804dhecxooiZsW15g/Um4i319Gh7RZAHZ7DwA4ra2/SJN6Sd8/YA5ZK3l+8SgOqyKHiin7ZbslpYzskh9bAMGrRlupUR9znqQU7LZ3nTSxtfRuP7sjKR7XyGyhUmITqfXtZn9DwczVWb1NiZtdK81WA6XBem1chxUKlVIAEEyO75X++sWcj69uTo2SJqVyEiEh4eaFRSCOqOIv8n5xzEqXImLsiQWAf6vaXGo/Qlr/DUkp2C/jhPTPBinzhLmcedJsBhYa6R+fc86caiBHiJR6gfkT7OzuF+x/nc1r4Ht+l+KrlXx/WRmSDPN62LrxGyhVYKWMSikACCbZbkN8e6tS6tQhc7ryA/MiEfbJ+8UxGPurAOA9ITn3r0t68wIoKcMtqSNJc5+Wnq0lzXnStpCKhNEsTU9WlR5PlI7ssncExdKozvr6bumJRGnBy25VYPzvLAySUgAQTKw+h0LDcy+MSvqB2fr2/PuHPZLOkS64VarcUOr5rHkHGQCK49B26e9l5jxNUGC3vEkp15d+P6lEsa61HKFmRc3R3eZPsMk6Zd4wc4RIsrFSKvNk7jGJivPOPt2baIZQKVUUNN8DgGDi3tTOW+3dYxNz5/niYq/aF5s/AFBSK6aa1SiSFBZlbyyAvyelnG4Ji20LpPcuk6o0loYvsjeusuY6jg6pUj3p4d1yJafKUvqRnDhCpIhY7+zTdYzDzKbPbW7n5mAhUSkFAMHEVSkV5r27OCGhcl1QZFMpBQABwWpu1Pp2acRye2MBTpuU8pPRzVzxh+YmKoKtib37sXKESCEhUni0FG5D0vtUTlLKcErbF3tnn9b/TEeo2c9ZdEX/6O/MB1ApBQDBxNWnVLjU/FqpcT8prIR3cdZ/J1d/AHSGa6/0o1LmKenAZrOMPLmZFFne7qgA+COnWx+EgN1czd/8tFKqbmfp0UNmvLtWmOuyMuyMqOx5JBBtHhTHqpSSckeRLilX8z3qfoqKTxkACCaOECks2myKERHjnTs4G3/InadPKXv9PE6a/2Lu8tAfpWrn2RcPAP/lSkrxBQs+IDRSiq8hxVXNWWHzyG3F4XDkqZQKsqSUe4fmDod0/B/ph4fN6v3LXinbUE4dzp33VpW/NcJiSJi06Ufp9y+laudLrQZ5Z/8BjKQUAASTNreZP96U7VYdRZ9S9spbqRZsd2EBeI/1/3zBBGnfH9L1H9sbD4JbjTbSPatzl/2tUspdWLA233M7Vg6HlH5cWjXNvFla1kkp90opb1X5uzff2/O7tGyy2aE6SamzIikFAMHq7+XSb1PMjibbDSv+ftzv9JW0KSBKJjtvUuqUPXEA8H/uX9Q2/WhfHEBBkppIza6SqrWyO5LC2bFUWvCy2bn5udeZ64LxxlHti81mfO4D7tiRWGzYSyqfIh3d6b2kVOoF5ujWFWpIe9aa67hZWygkpQAgWB3YLC19W6rVwTtJqUufk+Kreyc2FE/e5pNB1zQAgNe4f1Gjv0D4msZ9zR9/cXi72Zyr5j/SeYPNdcFWKRUaLt30de7yqUPm1I6kVFikVLGmmZTyVvO9i/+dO79vvTn1x0o+G5CUAoBgsvx9ad1X5oVcZJy5rqQfmNaXldDwku0HJZev+V6QXfAC8J6Gvcy+bxa/Jskw+0uhfynY5a+F0g+jpcRzpP5l3NTLG5xuHbVHxkotbzD/voL578qqlJJNIyhagziURtLdVQVGpVRhkJQCgGCyb720cYZUpaGU2sZcV9IPY6sah6SU/fI236NSCkBx1e8mVb8gJykl87MiJMLemBC8Th2Sdv5m9tcjmUkeZ7bZN5E/XH9YNwBDQs1Rccu6DyWfZGNn9as/lbb+bM7HVPL+/v25zzMbBGlaFgCClGs0pXDPC7uSsBIfXw6Xti0u2b5QMnmb71EpBaAkQtzuX3PHH3ZyrzSSpIWvSE9Ukb68076YisKKPySIa0LSj0rP1JCeqWl2AO5K3NhQKWX1k9flUaluZ+/s852e0mMJ0vpvc1+bk6RUYQTxXwUABCH3pnYhOUmpkn7R6P2CNCGno9Fg6x/B19RoZyYbHQ4poY5UtbndEQHwVwe3mkO2W+hXCnZyrzSS7G/6VVTuI7NJ0qkj5k296Iq5rynQGU7p1OGcBYd5rWI+UPaxWHFExXlvn9mZOdfUDrdrbJJShUFSCgCCidWZozcrpSrVlZKaSnvW8KXFbuffbP4AQEnNGiOtnW7Oh4Tx/x32sr7cW8kof2seZd0AtJIVz9Uzb+SNXG2O1hYM3I+VI0SKqSz9e7OZnDIMtyRVGUg/Yk4j4723T/djfM4Aqe4lUniM9/YfwEhKAUAwcVVKheV2rOmNCzqrHD1vn0YAAP9kfV70fkG6YIi9sQD+npTK2/wwLNJMSmUFUd+P7s30HCHmdWi5UujPqTBO5SSlPr/V7K+s9dCS79O9Gi4y1vxBoZCUAoBg4l4pVaOddPcqKSyqZPtcOknatcKcz9unEcpWxnFzevKQdOIfqVwVKS7F1pAA+ClnnsoOwE6upFRONY2/JaVa3SS1uFauzr1DcwYNCKYBSTySUmVYFVUQq1JKyr12KilnniamKDSSUgAQTFx9MoRJ4dFSxZol3+eiV3Pnad5hr2kDpc1zzT4qTh6UOtwndXnE7qgA+CPXwBh8XYAPCI0wR0mLzOkDyGHjyG3FERpm/riWraRUMPXFmadSKuOE9MND5vpe4zzfn9KWfjR33ls3VN2b7+1aKa2cZvbv6Y0qrADHpwwABJMr35YGvOndfbrf5cumUspWVmVDRKyZlAqqi10AXmUlpb4cLq3/Tuo7XopNtDUkBLEm/cwfiysp5ScdnecVlpOUCqrme+59SjnMZNCySebypWNVpqmJU26VUiXtW9W1H7fme/v/NG/a1upAUqoQQs6+CQAgoITktOM/tF2a8R/pp+dKtj8rERVeTopJKHl8KD7rWESUM6fBdLELwLvcK183fOtZWQDYrUItqVEfqVoruyMpnD9mSNPvkJa/Zy6HRprTYLp5FBImVb9Aqna+uexwS0WUdcXbiOVS7YvNeW/dUK3RVqrT2axW99ZgQkGCSikACFbH9koLJkjxNaSL/138/Vgf5rfOlJLO8U5sKB6rBN0a7SWYLnYBeFfeL1N8uYIvqd/V/PEXe9ZIKz8wEzGtbsytlAqmPqViEqRbZ7mtcOtXqqyTUvHVpeTm0pafvNd877JXcuf3/2lO/aV5qc2olAKAYDLnKenjm6Rti91G3yvhFw3rgsrqH+FMnE7p2/ukFR+W7DlRsHyVUiSlABRT86uli+7NXabPQNhpQ5o0qbc0+792R1I8roEDcq696veQml8rlQviJrEelVI2NMMszZGjrc7OS3qNHSSolAKAYLLlZ2nbAumcy83OF6WS3/12jehXiI+UbQukJW+aP+deV7LnRX7Wl8aInGGISUoBKK7zBpvTFR9Ix3bz5Qr2OrpL+mu+FBVvdyTFY/39WM26GITEcwS+sqwoOviX9Ov/pD9+kFJaSfHVvP8c/jY6pM2olAKAYGKVKIeG5yaRSnr326qUevlcaek7Z942OqfPqZjKJXtOFMyVlMqplAqmZgEASoe3PiuAkjDyVBqt+FD6byVp6lX2xVQUTreR2YLVkZ3SuMbSyy3NZfdKKZVhpdTBrdLCieaxuG2O1G547mP/bJReaSOt+rjo+x3fXHo6Vdq7nj6liohKKQAIJq6qpnDvlRYP/lb69GbpyN9SxvEzb2s18fNW+/1gsXmedOqQVPMiqVyl029Xr6uU2Fiq302qUENKbFJmIQIIMAf/MhNR1v9rvlzBTlbzLvdEhjPLf85LV1LNSvI6zX4fHaG5/UsFuuxM6ehOKSw6Z4VNlVLpOSPvRcblf+ybe6R966XPh5pNmIu6X2vfVEoVCUkpAAgm1p3ukFC3uzgl/MCs0Uaq00laMfXsI5iEWu33SUoVyXs5w2BfM0Vq3Pf02/V8umziARD4PrxW2vt77jKVUrCT9eXe+rLvb1/6nXma731yo7Tua6nX81LrofbFVabyJBZDw6WRq83lyDJslnkqJ3EUVUBS6mw3V8/Eup4OCTVH4hu+RAqPPvPvQBLN9wAguGS7N9/zUkfnktudv7Psa/Ncc5p5ouTPGYysCyl/43Ta04kpgOKzklA3fCY98o+U2sbeeBDcXEmpnKSOvyWl8jY/tCrHg+kmXb7EosOs6o6vnvu+lAWrmunPWdKLTaVvR+U+1uZf5rRi7aLv13BrohkZK1VpIFVILVmsQYJKKQAIJq5KqXAprro0bJE5X1yZp8x+pJa/m7P/IlxcGYZnJ5comHsy52wJxOws82Io65TZd4MkVapberEVxjM1pFOHpRG/5XauD8D3uQ+cEFqCzwnAGwpKaLiv93WdRkvt75bCo8zl0Ehzmh1EA5K4mmDafO3nfoPv8Hbp+L7cZauLhMjYou83bzUcCo2kFAAEE/eOzsMizP6HSiL9qPTDaLf9F6F5R3Zm8PSjUBLud1HPdvH9XB0zAdTreem7+8w+pYYtLN34zuTUETMeqWQl8QDKnusmBl8X4AMcoVJ4TO51g6tSyk+qcCPK5Q5CIuW+jqwgGpCkoKTUDw+b6zuPliLLl00c7n1KpR/xrPIPjzGnmSeLvl/3/5mHtknL35OiK3p2pI4C8SkDAMFk+BIzMRXmpTbueUd3O1sZusfdIz+5kLSb+3t8tuaR2W6VDZKUZfMdWPfn95cvDgBM1v+bn54zvyy2v0uq2sLemBC82v7L/LG4Ojz308+WoKyUsm6suSWlFr4iyZAuvLvsklLWzbKYBDMp5X7tum2ROT20rej7dW++d3C7+b8zoS5JqUKgTykACCbhUeaHfmiYWbky5ynpxyeKvz/35npVW0jlq555e+vuWP3uUlhk8Z83mDiLUCllbWvdjc2bNCxr7hfbdJIMFOzoHmn7Emn/Jrsj8WT9zW6eJ63+RDr8t73xAO5iE6U6naWUlnZHUjirPjH7Lto4y1y2KqXs/pwuS6HhUuI5nlX6djTD7DpGumOBdN7N5rL7ddaJ/ea0zb/y/dpZ1Whn9r0XGuFWyecno0PajEopAAhWmaekec+a850eKl4nk9bdpagK0u0/FX77UJrtFZr7HbyzJXZcfcDkJKWyTpVOTIXlfrEdTJ25AkWxapo08/+kFgOly1+zO5pcVqVUWISUdZLEMnxLzfbSjV/YHUXhbf3JbM5VPlmq3zW3UiqYmu8l1JaGLfBc5wjJSUiVYcVbucrmz9515rL79UlJrlNv/i53/kBOywB/6fPMZlRKAUAw+eou6Yth0vF/PJNQxb2TYyUdCtsJrvVhn3Xq7E3RYHJP7NTpdPrtnM7cix9XUsrmi1335w+mu8FAUYT6aMXEeYOl1rdL5RLNZe74w06rP5WmXiX9+qbdkRRP3k6wExtLjfpISU3si8kX2DmKonXt6p5wd13XlvDmqVUB5iQpVRhUSgFAMFn5kdmkqtODZnWTxZldvNGVivrhHZdiTv+cJR3cav/IcP6gfIp0/xbzvS6ffPrt3MvPXc33bO6rwv35fe0LN+Arfv/SnK772t448uryiDl9t5+0fyM3EmCv/ZukjTOk+Op2R1I81t+PNXBAsyvNn6BnQ/O9+ePNa5IKNaQqjcypxepvau5TZj96ETHFe44QKqWKgkopoLiO7rG/E2GgqKzERUh47gemVIJKqZy7S0f+lsY3k9JGn3n7xn3MkUgkmnMVVkiI2RnnmRJSkuedPlelVLq9HYy7V0pZxx2Apx1LzandSeTTsb5E03wPdrK+3FuVNX/Olp5OlSb1ti+monDvBDtY7dsgTThPeufS3HV2jKK46DVpzpNmtdrwxdKA/+WPRyraCHzpx6SxdaXnG5jdY9CnVJFQKQUUx4HN0sstzREVRiy3OxrfceqI+WU4mD9wfZl7867QcM9hvov7ZaNyfemGz6Ulb0kbvpOO7zv771hVVU6SUoV2YLO0f7NZaXbaUn+H1OBS81hGJ5jNbsIizGPusOlvMixCSm5uxl2tlT0xAD7PR0cPO/y3+XnuaoZCUgo2sr7cuzf3Sj8iZRyzL6aiyNt8z7XeWbw+Pf1R1ilp/5/mQDsWOzo6Tz9iTiPj8j/mnkTKKkJSypkpnfjHnA8JzT3OVEoVCkkpoDjW53Rkd8DHRsqx05Fd0guNpNS20pAf7I4GBXH/QhES5nlhVNxmGdEVpHpdzLL6Dd8VrvrJV/tP8VUH/zKT4JJ0wa1S73EFbxcRIw2clrvca2zpx3Y2VVtI//rZ7igA32ZnNeOZjG9qfqGqdp65TFIKdnJVSuVcu9iRzCgJ6+/HunG74gPpy+HmaMQDP7IvrrKUt9pNMgfJMQwprlrZxJCdKWWeMOej4gt+3FKUSin3vqMcoWan7rfNY2CfQiIpBRRHclO7I/A9/2wwpyTqfJd7ZVJo3uZ7Jbyos/Z1ti8tsx+XDm8352m+VzjH9uTO2z2aHoBS4INJKffK2iveMpvfhpezNyYEt7wJDTuafZVEvvhDzXXB1BWI61g5ctdVrl+2MaQfzZ0/sEV6p6cUV1W6Madvv17PS+u/Meet5FVhuCqsHGblW0i0lHKuNyIOCiSlgOIon9NZc0EZ9mBlfdBYo/TA97gngULCzbuMt/5ofngW91w+uFXaPNf8kc6elHK/60RSqnDcK8qKcvF6dI+ZxIpLKV4n9t7yyWBpw/dmhVfLG+yLA/BVCXXMJi012tkdSS73JizRFekTDvZzJXVyEhp2jtpWHH1fkno8aTavl8zm7VJwVY1b3xUcNjZXtDoyD4uWZJg31d2b6cVVNf8nH9hcxEqpPJVwKBKSUkBxWF/w+FKdy6rgCI+yNw6cnnvCyDqHq59Xsn3u/E36+u6Cn6Mg7hdfwXQhVhLu/2fOVCl1YIs08QLzy+O/N0ovn2ve5Ruxwiwjt8O6r6W10835YLobDBRFhRpmUuq8wXZHkitvc2/AbqetlPKTpFS5yuaPJTTSnAbTZ2PexKIk/TxOyjghtb3D8/0pLVZ/UlFxuf/bsvNcu4bnjLhXlEqpvH2GnTwkLX3bXL5oZHGjDRp8ygDFcXS3OS3KP6tAdzync7+/l5l3Qtw/cOAbohOk+7eYSQ5vHZ+8idmzJWrdmxCWxcVHIPBISp3h4jU703x/rWRfWKT5P8rO5N/JQ7nzJPGBglmjVPpS3yPuSalVH0u7VkqN+0n1u9oXE4Jb9yekbo/nr7bxl6RUXq5KqSBKSllNld2vQRdMlE4ekJpfXTbXhafcOjm3btC6X5sueUvas0YKi8pNHBaGa3TFnPTKqcPS7P+aFVkkpc6KpBRQHDt+zZ0PplEzzsR9JA1nlr3NhVCwkBApJsFz3cJXzbLl84eYnZYXlSvR4JAq1ZPiqxdu+65jzE6wcXYezffOUCllfYm0/vZ84S6s+8U2lXFAwa54U9q9Jv//Zzu5D36xbZG0+mOpYi2SUrCXw5Gb0IiMk1LbmJWGdjIMacs8KamZVK7S6bdb+o7ZJKzZ1VLV5m6f0UH02Rgabv4fiU/NXVfWHdZXayX96xfz+UIKaPmy/H1zevX7Uq0LC7/fkDDzujYs2lx2JU2LOZBQkCEpBRRHZHlz2uBSElIFyTpFUspfzHlKyjgqNelfzKRUzsVUw17SdR8UfntfqgjwdYXtU8q602fdpfOF/iqyaK4JnNWxvdIHV5n9VY5aZ3c0JvekVFhk/nWA3ao2l4bMsDsKafUn0udDz/73u+ZzaevPUtVzzditv6tgqpRKaSndvTLPSispVUYd1keUyx2w6sBmc+peGZr3Bl9hxVc3RxK0WH1L+WslXxnj2zRQHFZGPawIZZ0Bz+3DJDM4RghbuvWAaj34rd76ebPdoRTO0d1m/08z/y93XUgJy9+tv4XCfnhbiYmQMM/hc3F67hdL7YaffjurT4SQvJVSNv49elRK0XwPKJDry6kPJW5Dw80+rlrekPv/nTv+sNOyd6WPb8rtp9BXrPvKnB7deebtnHmad0UnSHUvkWoWoRonENnZDNM6Fu7XWa6bpyW8uW69LpL5hWJrUuqnn35S3759lZKSIofDoS+++MLjccMwNGbMGKWkpCg6OlqdOnXS2rVrPbZJT0/XXXfdpcqVK6tcuXLq16+fduzY4bHNwYMHNWjQIMXHxys+Pl6DBg3SoUOHSvnVIaBZ1QokpXLV6Zw7HyTD1l/5+kJJ0hPfrtNrczfZHE0hnNgvLZssrXCraLI6ZCzuh6ZVnVPYyqfKDczp9/dLK6YU7zmDTaM+0sjV0r3rpSaXnX471909q1IqZ9CBM1VXvd1deqWNtG+Dd2LNy71SyklSCijQ/BfN6Yl/7I3DXVScOVrYZa8U/MUNKGu7Vki/fyHt+8PuSIrH1edQznVX5XrSoOlS/1fti8kXlHXzvc3zpHnPmaNGh0VJ8TU8mxNaSanJvaUlbxf/eazraxllVwXmx2xNSh0/flwtWrTQxIkTC3x87NixeuGFFzRx4kQtWbJEycnJ6tatm44ePeraZuTIkZo+fbqmTZum+fPn69ixY+rTp4+ys3O/YA0cOFArVqxQWlqa0tLStGLFCg0aNKjUXx8CmHX3f9VH0vH99sbiKxIbSZHx5nwwjSSS49m09b5fMWVVqoS43f1xlRcXMyllfXjvXiW90lb64Nozb3/Jf6TGfT3jwZlFxJh9ZsRVPfN2xWm+t32xtG+99PuXJY+zIO6VUu4XfQByrfrI7gjOjKQUfEHe0fd2rZKebyj9r5NtIUmSKtU3p2e7OZd3dLZg9Pcy6fWLzIo3i3U8VUaJmz9nSXOekDbOlGITpXtWS3ctzX3cfSS+4/sKv989v0vjm0nv9DSXQ9yOM0mps7K1T6lLL71Ul156aYGPGYah8ePH6+GHH9aAAQMkSe+++66SkpL0wQcf6Pbbb9fhw4f19ttv6/3331fXrmbHi1OmTFFqaqpmzZqlHj16aN26dUpLS9OiRYvUpk0bSdKbb76pdu3aacOGDWrYsGHZvFgEFvcv0xlHz9yxYTAJj5LSDwdFpZTTmf8D5olv1+mJb83+BFITojXvvs4KCfGhUQjzVtJIJa+UatzP7OD84FZpxn8Kl9wqqGNJnFnGCTPxZzilmu0L3iayvFT74tzkT+O+Zt8VZ+t8XnK7KPSy6Irm+dHiOun8m0vnOQB4nzPbHD0qJCz3yxVJKdjJldTJua5yZknHdtvfh2m5Kua0cb8zb5e3UioYpR+Vdq/Oc81ZxpVS6TnFLZFxBT/ufiOvKKOsZ52UDm1zGx3S7frfyBa9Jp2Zz747W7Zs0e7du9W9e3fXusjISHXs2FELFiyQJC1btkyZmZke26SkpKhp06aubRYuXKj4+HhXQkqS2rZtq/j4eNc2QJFl0U9KPnvWSsf2mPNBkJRauDm3Qu6XBy/J9/j2AyfV9unZZRnS2RVYKVXCO+CV6prJj+qtC7+fUB/ogNufbPlJ+uQm6Z0e0rSBp9+uagvppq9zmwJcdI/U5wUpuVnB27v36VVaSan2d0l3LZMuvq909g8EGl/pf+TAZmlsbfPOv+tzgn4AYSPry76V1LGzLyJ3lepJza6SarQ983bW9ZEV/7F90lPVpSeSgqeSxpWwcbvmuP5j6ba5ud07lLb0I+bUGrQqL4+k1MnC79eZJ+kYUV66+XvplhnBXR1XSD47+t7u3bslSUlJSR7rk5KS9Ndff7m2iYiIUMWKFfNtY/3+7t27lZiYmG//iYmJrm0Kkp6ervT03MTDkSPmCZyZmanMTP9NQlix+/Nr8AkN+yr8l/GSpMz0ExLvp0IW/0+hkpzn3qDsyo3P+p74+7n4waK/XPOJ5cK0/rFu+mLFTq3YcVjTlpj92u09mq5X5/yhoRfVtitMD46MkwqTZISEKSvnfQ9zhMghKSszQ0YJjoXDaX6gGNmZrn0XJPTDaxSy2UzWZWeektNHj78vnZ8h235V6EZzhCEjK/2M72+RZByXlZ7MNuSzxwIF86VzFCXjXueRmX4itz84O2WcUrgkIyRUWa2HSy1vliJii3W9w7kKbwh1ZilEUrbTMD+vsp3mOerMLtHnYonPz2oXyJFTPX+m66gwZ7Z5veXM2c4phWeYVTuZ6Sftr/gqA46sTPNa0VDuMavUKHeDMvgfEXrqqEIkZYVGyTh1QqHvXipHdpaybvzaTFQN+kqhPz2rkA3fypl+XNmFjMmRmW6+NkdI7mtLucCcZmebP8Xkz/9DCxuzzyalLA6HZ9MXwzDyrcsr7zYFbX+2/Tz99NN67LHH8q2fMWOGYmJizha2z5s5c6bdIfi9buEJisk8oAU//ahDMVvtDsd2Lf/apBqS1u3N0J8/zCr07/nrufjtmtx/n999950kKVpSuzCpbVtp5CLz8bE/bFTo7nVK9oF/G1WOrFZ7SUeOn9TcnJgrJg2WIzFbR5ZvVdbKPUXeZ4Xjm1UufbdCDKdaSTp14phm5Oy7IB13bVKFnPmN69dqw+HTb+sLfOH8bLB7rRpbC5kn9d2333qWhZ9GqDNdodnpyg6JVHZo/kEZIjKPyGpAv279H9p0sHSORa1/flSjXZ9pV/z5WlmDJnze5gvnKErGGr7gr4SLtSrtBzlD7P9yGndimzpLSs/I0g9zvNOygHMVJdFq+3alKvfzynWOnjqpH85w3VFYxT0/Y0/9rS7rRisjtJy+b/7aabeLqXyLQhMydGLNfmWv+06hznT1yXnsh+++VnaoDySjS1mVI6vUXtLho0c1zwvHrDja7dmpREkr167Xjp0/6LJdKyRJs374ThlhZvVUnWMV1EzSzr82aVkh46x0dJ0uknTs+En9WEqvzR//h544UbgmkD6blEpOTpZkVjpVrZrbuevevXtd1VPJycnKyMjQwYMHPaql9u7dq/bt27u22bMn/xetffv25avCcjd69Gjde++9ruUjR44oNTVV3bt3V1zcadqg+oHMzEzNnDlT3bp1U3i4/Rc9/ixs66PSwQO6sG1rGVbTpSAWOv1z6YDU6JwWatC611m39/dz8e6FZuVKk6rl1atXu3yPX3Bxui4cO0+S9PTKMG18vHu+bcqaY2OYtEkqXyFBvXqd/RgVRkja/Qr94x05G5j7i4oIO+O+w/5+Rsqphq5fp5bqdvZOHN7mS+dnyNyV0i5z/v/ZO+vwqNEuiv/G6kaBFnd3d11YfGGVdXd3d99v3V1Y1o0V3N3d3Yu1pbSl3pHvjzc2Mxlrp8VynqeQySSZjCTvfc8991wTLkYMG6KbUTVtm4Rl8v246vXGcdl4LP/chnnzBByDX8bZ/U7vA+cchE1isWXzJjTvFf7vwjzrWSwHxwFQLzWR2mH63Rk4vX6jBsoBpx3WisVaN3xDregq/revLBxZD9shMjq23OOF8Vs1EA5Y/v0PTkDLVq1o3n0EpG8Rv9HIiHL9Rsv9+8zcCVvBZia083A6YP2tAAwdNABikkN/7TMMpl0RsBsSEpOUz8q8ZhwUZuNse3nghi5hgOWHz+AktO/cjXYtR+Fab8bkcjJ4YH+IF/yDaW0mHPqJWtWTSA3yOzXtjYNdEJeQKN6b04F59TfgcuLsdKPPju2m7VMw75iGY/ibPlWyZ/I9VK42C4TTlpRq2LAhNWrUYObMmXTs2BGAkpIS5s+fz//+9z8AOnfujM1mY+bMmYwdOxaAI0eOsGnTJt58800AevbsSU5ODitWrKBbN0EcLF++nJycHIW40kNkZCSRkd4/HpvNdsb9GPRwtryPU4ZDa+DEXgCsOMH4LBW/IsvcV7A06AW1OwW125n4W3Rpav+v7F5f9/xrJ9t457L2PPzHegD2nyimSUpcpZ2jLpoOggc2YTZbMIfrM5eMO82R4r2ZHKX+v0/ZUyEiDkv1ZlhO8+/+9Ph9uvt02UwOsOlI75wlUJSDyV4ovl9bNAAWl13/c9aY8PvcprzIOai+nNMevt+dAQWnx2/UQJlRovqX2KJiT594Qro/mCxWbAeXwI5pUKsjtBtb5kMav1UD5cKFn8LoD7GYbVisNrAJf0qTyxmW31WZf5/zXxXnUZof4v424a3kcmIznSNzCYvwkjKbzWo8sPxTyNqDpVE/qFqv4s9Bmq9YI2LEZ262gaMYm1k6v4XvivtdfC3M8SnBxy2STZbJLP2OHCaY8RQAlk7XgM3HHGDRW3B0I+bWo6G5fgM4GWfiPTTY8z2lpFReXh67du1SHu/du5d169aRnJxMvXr1eOCBB3jttddo2rQpTZs25bXXXiMmJoarrhJGr4mJidx88808/PDDVK1aleTkZB555BHatm2rdONr2bIlw4YN49Zbb+WLL74A4LbbbmPUqFFG5z0DZcfiD9Rlw6xZQDY3txdC+tagSakzEav3n1CWx3So5XO7SzrXUUipZ/7ZyK+3eSuqKhW2KEiq675u45+QnwktRno/Fwxk8/TIOEioDVFJAbaXrpdr/4a6hsIwKHg2U7AX6xt0OmUje2lol7Ny2sYMWiQ3hOFvwvapkFRBgaD2/mg0hTBgwBvWaLhvLRzfAzmHoEoDsAZoLV8ZUEyZrUI1texTaHdFuUgpAwbKBWskoBEMWKNEI49AcUdlwukEs4/GIUs/EZ3fOl0HCVLsaIkUcbPDxzh9tsESAXGpEKPtWl7J3fcu/BQKs6FaU+mcBCmFs1TES3NfEeufOgwRscEf1xYtzNqr1BePtWbu/ppEREhkla9Y7RzBKSWlVq1axcCBA5XHcrnc9ddfz7hx43jssccoLCzkrrvu4sSJE3Tv3p0ZM2YQH68G4++99x5Wq5WxY8dSWFjIoEGDGDduHBaL6nL/008/cd999yld+kaPHs3HH39cSe/SwFkJeaI19HVoNOCUnsppA+3N9CzvvvfLClX9kRAVXAZg2Z6sijqd8mHR+3Bsoxicy0RKSddC1Sbw0HtBbC8RE+eAoWfY4EVK+bi+5Emk/NnKPlL+gt3ut4u/ioJbp1KDwDdgwAtmMyQ3gi8HQFEO3LNKnSydSmhJKbmblOs06QxowACIxModi071Wbh7PDpKwOzDG2rZZ0I93PR8lZSyRghSyn6OjI+NB8IjO9zXKZ9fJXUgrO4hSpHvb06He5wSqrdf/V5wz0r1sfZ34Y9wk1TtZ/vcKRBOKSk1YMAAtzIYT5hMJl544QVeeOEFn9tERUXx0Ucf8dFHH/ncJjk5mR9//LE8p2rAgDvkiVZ0FfVmdq5DezM9y9n+v9akBb3tm5e047G/NgCQU1BKYswpJGP2L4WtE6Fme2h/uVhnLmdLZVmdYwkys+8IcXsDapAUmQD9H1Ozal7beSqlpM/4VAa72gDPafe9nQED5zrke+LpQt7GVIW2YyE+Vb2nGNewgVOJZZ8L1V6Hq6Bh31N9NvpwFAtVuh6cEqlr1ky/6/WEkvxzO1FnKmccWl7I5JOj1D0JWN7vxGRSyjP9vrcDy8T/+Znle70zHD70hQYMGPALOWg8HST2pwt636cu2wtP3XlUIpoG4RF1aec6yvKb07dV5OkExpH1sOwTUSsvw6TJEJUFoSqfUluJ/z/rBX/rmG8b8MZ5z8Bdy+HeNdDrXohO0t9Oq2yAwEqp3XPgs97w3336z4cDhlLKgAH/yMuAmc9DfoZ4fLpcJykt4JKvYMgrGiWBQUoZOIXYtxDW/wzHdwXetjKhFVj4SwLJ149Jk8y+6je4YZJQfZ2zkMv3KkkpteIrUUopk0BxqRAnDM6VRCvAF/3g+wvK91oK4eYnxi6VutMd31m+1zrDYZBSBgyUBXLQOPFB2Lc4+P12zoKJ98P6XyvmvE4lWo2BrreI5bNYKaVVd17RLbAPj1ljJv3T8gMVck5BQ1azabt7lLcsQ74W7MXw1SD4oj84/ExcrvsXhv3P/XwM+EdcipggxlX3v50nQRjIUyo/E45tgjXfw7/3hOdcvc5JE6CfDiVJBgycbshPh8Xvq49PR+81RSlllO8ZOIWQf3/yRD/nEHzYET49xX6d8jVbuzNE6DQhkSHHWedyhcXehfDNUJj8iLquspVSc1+D6U+ppNRdS+CR7SJpqo1Zjm6AY5uDP+7OmfBJD/d4SiYgg3lvp0opdprAIKUMGCgL5ElecQ5k7vC/rRZH1sHqcbDvNKiBrwjIZMdZTDZsPJSjLF/csXZQ+9zev5GybHecwkFHT+FXXqVU7/vhoi+EBP3QKvEbdwaYVMmkyemiCDhTkLED0lYJ3xk9xKVC7S7CnwYgtY0wJvZlKC9n5wBK8sJ7rjJiqooM5M0zYcwnFfMaBgycyfAkjU+X+6LTIVQfTqdBShk4PSBP2rXqk6w9kLX31J0TqGrk7nf4N8ZWSLVzmJQqOA4Hl7mTPaZKNjr3V+3imRQoDaHyozAbMrZCtiYBLf9Wg7l3nuOk1Cn1lDJg4IxFWTtK7ZdUVWt/gDFnmdn+3oVweK1YPouVUv+uO6wsV4kNrnzz/kFN+WL+HgC+X7qfm/ucIpl2RSilGvYT/5dqiMhAJR4WTf2+gcBY/6sIvOdLCrNr/xFmoZ5of7nqFQbQfJj48wVtsFVR1+z1/1XMcQ0YOFvgeR88XUipbZPh92uhbnfocpNYZ5TvGTiV8CSlTrUXkYy63cEWA4l1/G8nn6dWKfXDxXB4DVz6LTQ+r+LO8bSBVG2g7Ux34aciHklpWTmnIMc7lkjv5zzvv6UFoqxQa1ruC3pKuCt/Fv/HpejvoyWrIhMCv8ZZDIOUMmCgLOj3KPx5o1gOpY1r9sHA25yp+OVKKDkJV/4Gdbqe6rOpMMzfkRHyPjER6q325UlbTiEpJf1WrZqBWBo8xy/ezbWtXJiCGXj1oDXu9EU2OUrhgw6QKxnFB1JUGRDY+AfsmqU+DheBVBmklAEDBvzDM4Y4Xch6l8aUuflw0RUwlPboBgyEG16T/kpW2PjCwKcgbbWIgYtyIcoHuaD4PmpIi+KTUHgCSgr09znboBCLmlizVsfKe32nU4095Vj437shc6foqJ7SAm6aLtTjP14inrcXqR3y/B5bxzMsENGojb0GPh3cezhLYZTvGTBQFrS5GDpeI5ZDympWkonfqYCswqnZDmKrntpzqUDsSi9bmdOgFj6yJJUJneyQfeCzXF/yOB/tqcHz/4VQOy9j91zYNgWKstV1vmTKjlKVkJIfGwgMz88p2PJYp1MQTyX5+s9rSamKVGcc2wLvtRUt7w0YMOAOrTFy9zshse6pOxctnBoCICpReMLJbewNGDgVOF2VUgB/3gDjx/i39LhxKtwyG+JrquuUrpvnSGJI9mUtawK0vNDGOvJnf3QjHFwOhVmCeK/XAxoOULcLtoRPr7tiwPPRfO9WHeXWOQSDlDJgoKxQOludYRPrjO2w9kc4uCJ8x3Q6NJkHH61wzzK0qBEf0vZPjVRlyRvTfHgCVTSU8j114HtieSTzne3JoArjl+4P/ZjTn4JfrxSG2YrviC+lVIn/xwb04UVK+Qhe578J77aChe+Ix1v+hldrwE9j9bfXekpVlFLq22Hw9WDIOSBMaQ0YMOAOeVJSpysMf0PtUHqq4dnN04CBUw1fpNSpTvi6XOqcwN9YWqsD1OniTj7Ivkb+uvadTVCaBWlIqU0TYNnncGJfxb++Nqknfw9mHUsJi1VdHywppVe+t/43WPUdFGT5OB/5ezed8/fac/vdGzBQVuxdAGkSqRPKZK6y2p36w+65MO1xaHWhbwPkUKG9yS94WwTVspLsLEX/ZgE6oXmgcfU4ZfmVyVv47fZT0C1m0PPQ616ITlZW/bk6zW2TEruTCGsI+QqZWLJEiAHcafftO6Id8Ov3hpTTZPJ1usOTvPOllCrIgtxDUCyp+RTi3Mc9yqbpElRRWdq0lervwSAhDRjwhvYeejpBS0pl7IANv0F8Deh266k9LwPnLsb+IOKISCmeOl2UUh92UAmVUMdSZZw+V8ZHHU+pJR8JX63khlClQcW+vJ5SSvY5dZYKk/KtkyA+FZLq+o9pPeHZHRJE4rYgU8y3YpK994mIheYjYPsUmPEMDH019Pd0lsAgpQwYKAt+vlxVGZxpA4lMpm35J3zH1BJzyz6BRgPPSlJK2zmvX4iklBbL9/rImFQ04qqLPwnfL9lHT/Nm6prSWe1sxm5Xbb5dvJc7+jcO/pjaCVVsNUGY+AoQZQWV2Qo3TinjmzgHIX9uEfHCs8IXEe6pbLAGyNye97QwQp/6BFRrFr7zVc7H6R7MGSbJBgx4o/F5cMcikTHPPQyR8eLvVENbinJiLyx8W3i/GKSUgVOF6CT3x2YLJDcWpWDBmlFXBLQqJ1/VEy4XLHpPnHO321SPIlkpdabNJcoKk1nEMtqkWGV234tKhBsmi89bfl1F5W8X1STTn4SaHeC+taEd2xYDCXUgthrjFu/lm8V7mYtZkC2+3ltkHLS+WJBSRzeW8U2dHTBIKQMGygJ5knf7QqjaJPj9Lv4Svh5UMecULPIzw39MT2lrsJ43ZxjWa8ruOtevEvL+z4xsySuTtwJQbHcQaT21bYGf/28zn9tmMMyykqdLb2K3ozZvTN0WIiklBWAWGzy4KcC2p6ki4HSH/BlHxkmklI/ry6n5LkAlpfwFu7U7wy0zw3OenvAycD5Hgm4DBkJBVCLUaAu/Xwdb/oURb58exI/WlFkuRzGIZQOnE2KS4b41p/os3Mc6f0mj2S+K5Y7XqqRUMGV/ZxPaXir+3CCTUpVQTWKNhAZ93NfJpJTDrolTbaEfu+PV4g944YnJAGRE2qlpgmM5+aTW8LGfTbI9OUvnTsHC8JQyYCBUOB1q3XBiHYiI8b+9FnGp4n+9NqSVhWBro0OB5430LL2xLtB03ouyhU4oXduzvrL8Q1n8m8qL1eNgzqtwbAvL9xwHwCEFA21rxfnZ0Q9kwsQcxADukCY0ZRnsz2XIQVLri2DAk6L9tO52HkopJdg9RdejZ5DtKDk9SpgNGDgdoRgenyY+lUn1oMUoQVwrSgIfTSwMGKgMLP4AJj8MRwMkwCobbkopH8kX7bWj9RxKbgS1Ogml+bmKU12GqS3fC3Py1CnF2LeMW4nTqRP/FGYLmwNw9/k8B2EopQwYCBVu9cghTq4TasNTR05thwUtKeV0gjkM3HR0FRjyKuxbCDumnbUZnwU7MwJv5AdaZdQrk7dyS99G5T2l0LD+VziwFGq04Yof9gLglHITI1qn8MRBsdmMzUcZ0tpXSscDilIqiAHcZILqLSF7P7zZSJC0dy0N9V2ce7j8JyjNFyV2/sp6vJRSAQxUJ94P6dtEGV/DfuE7Xxl6wbnTbpCSBgxocWA57J0Pu+eIx6dLF66m54s/gH2LxP+GUsrAqcSW/+DQKmgyGGq0OdVno0I71vmKf11aUkoz/R74pPg7l6GUXVZC0urkMaFIjUlWFVsRcRCZIMgxbfJ00oNweC2c/zI07Bv0S5zIV38PNqsVHGDByfK9WfRs7NGdPH2LIFsBSs/OhH6wMJRSBgyECu2AM+MZWDM++H2XfABzX4WsveE/r2Bh15BS4VJQxCRDr3ugz0PicUWosU4DrD2QXe5jdGugY3RYWVC670UpghWzRQRHCZHqcPDYXxuCP6ZW6vzXLfDtcEF06KFqY7h7Gdw8AwqOiz8DgZHSQqgVAvnMeKrWAhmdH90EB5fB9xfA+23Dc65aaO+V1ZpBahtjUmvAgCf2LxZxgXw/dJTiOt0UhVrPFQMGThU8u++V5MNnveHTnqduQu9yqWNsr/uE75oetEop06m1bjil2DkTxl8Ic19X11WmUurEXpj6KMx5RV132Xfw5EHocJUa05ptkLlTkFL56cEde+2P8NV55M58U1lVPUGUaZpx8u86nQ7E2nnYWVplEiwMUsqAgVChzYisHidusMFi/a+w9GM4eSTspxU0SiuAlJIRyFj5LEHtpOgy7/vMqJbK8q70vHCcTvCQFTMapV5clLTsdNCihiA9sgtCKB8Z+Q6MfBdiqsKh1XBgCRRl+9/Hco4Ze4YLBVmQvhWyD+o/n1BLKNFipExcdBVoeYHo7KIH7b3g5LHwniuIzHBMNaEQvWcl3LlY9dEwYMCAgEe53oczN9PwySl8u+gUJq88YZTvGTgd4ElKARzbJNQmrlP029Rev30fFt2n9eDyUb53riEnDfbMhaPa5GclGp3L8xNrlP7z2vI9OV4JlvDMOQSHVlOQsUdZZZIISDNO/tElpTRx8DlevmeQUgYMhAovn5QQJvDyJPCfu8QE81Tgwk+9z6e8KMiCgyshR5osn+Vsf3k677Wrk6QsvzZlaxjOJgTI34vG0yw2Wlp2OXjjknbK+iM5Qf42Ol4NXW8WJtzBZtPl8q3TxTvldMfiD4W8e9G78GkPmP8//e2GviqUaO0uE48TasLlP8KYj/W31wZAjuLw+z1VaQCP7YaHtoT3uAYMnE3wUDJGICavL03aou9BUllY8Ba8mCxKWBSjc4OUMnAKIRM7crmXlpw6VX5ELqcoJ2zYzzfRAb6VUss+g/fawOyXKu4cTytI9zTtdzfkZbj6L6jXq+JfXiadrD4sJ5ya8j35+wyWLJL2PV6g+a5HvMVD5sfY5apNUanOb1R7/7ee20k7w1PKgIFQEZUAQ1+H9T+L9p2hqD1kQivnABSfFGVvlY1GA4SxotMePin+gWXw65WQ0hpuneve6vUsgbacon+z8BhSztkWpCQ4XFAyRCoplRAdCScAp4MOdZOU9VM3HuWmPg1DO745ANl0cAX8e7eo3wdDKRUs5r0ugqJut4nH4VIiepLSjlLfgZoBAwYqBh7Xsw11XL775zV8dk3nyj4jAW1Tl2rN4bb5/ifdBgxUNOQ4TCZ1TgdSyhYF1/wlFMzHNgmvzKS63tsppJTJ3cu1OE8kdPPL51l6xkDve6rTpfJeX77fahtOLfsMdkyHDldD8+FijhRbDZZKSfxgE/jS/TIzX0NKNRlEYscaZC/e5/98GvaH6/8L/n2chTCUUgYMhIqoROh5F/R5UDwOiZTS1g6fwhK3+9bCAxuhSv3A2wYD+X1FJ0HtTsID5yyDttSuZ+PykVJPDlc/n6LSSsw8S9+TS0NKFba/DsaOh5aj3TadvDGIElNHKeycBXvmSab5AdqGF+VC5g61fNVRanRjCwbyPUYm80JRIrpcQh6u9zl7BloVqXD8ciB82Ml36aEBA+cqpOvbVb0lv9gHss7ZWHlq6qaj5BefIh8np6abZ0QM1OpwVo7tBs4geJbvaUipYzmnWKG/+AP4epBvn9noKnDDZG/iwXqadd2saLh0lFKVCYd3cpaM7aKkMGuP6KreZBDUbK8p3/NM4Nnhm6Hw953u6yXiMbPA/Z59YYfaynJukcf3rJMsPldhkFIGDJQVZfHF0RJRp6LDTn6mMOLbNSu8xw1Uo30WYP4ONYuVGF2+7mE39lYVSF8t2ONnyzBD+q1mFau3/pqtekGrMVC9mdumq/efCHy84pPw0yUwfgzg0rTV9TGJkq8VRUnnMspBAsHlUj9P2ejcF6H91y3wURdBFIIgCl+qCq9U1zeV95Skh1u5dngdfDcC/rtXBHtZu8/aJggGDJQZ0nW3NLofT9pvZaKzF5Pu7aM8PfyDhafmvLSklAEDpwP8kFJD3ptX+eejhTIn8DE+WyOgQR/vTreWc8OLVYFCSpnUdTtnwerv4fjuin99u8YzSoYSu3oQRnKs6hkrHVotmsSs/9k94SfdMx0SvWIxm2DnLNplTScFEVNP23TU/Vh6JBnAjhnwQQf45+5g39kZD4OUMmAgVBTliHI1+eYZ7ETO5Tr1SqmMbaJ8auoT4T2u4lVkg0Xvw7w3zrrWpgt2Zga/cWmRX8+wCKt6631n5o7ynFZouGk63DqHHYUJyqrqce4DYXtNCV9AKJk9k1BJBSrfkwf8yDio2QHqdDO6OQWC9rNUSCkf11b2QTi+Uw2gzGZ1QulJBjkdQtkYoenoF+57Un6m6Cx2eJ0aAHoGfQYMnOuQJknzd+cqq9rUTqR9nUQADmQVsGDHKSjtUUgpCxSegIXviPHdgIFw4vsL4It+cPJo4G2v/QfuX6+We2lIKTOnqHwvcye8WhOWfSIeh6B4Sj9ZxPgVh6X9zhU7Ax2l1NKPYeJ9kLaq4l9ejwTS+qGmrRZNrNJWicqY6GR3AssT2thKIk2dEr3SqmYCzH4R09+30dJ8AMC7A1+drjD4Rdg6EcaNEnETiHLOE3shL4jr4iyBQUoZMBAqjmyAb4fCrOfFY3uQA4mjBOVmDKfGDFwmSo7vhC/6w77F4TmuVik163nhgVNSyZ3lKhghTQq+GQwfdhDBig+MbFuz/CcVKlJbQe3ObM9Uf7OmY5tg459wZD0Ao0I5L4dHxskWDbZYP9tLwVpUItw+H26ZKfwYDPiGNlCNlMhEX+SRTPhYNEo++fP1JKXMFnh0FzyVJoKiWh3dM5fhgDb4MzouGjCgj/6PsWf0X0x2dieeAj68SJTv/X5HT2WT675dweJdISRGwgGnNMk3W0UybvZLvpssGDBQFuQfh70LRPxRHETMmFBTNNCQyqqcLjjqqsIRl/BnzSk8BUkPe5G7ksbX+FyYDcu/cCvv6//mPDYeK/S/39kIk8WdlJKXK8MXrMlguOoP0SlRhkxKOUph638w8X7YNAEGPA6P74XznnY/htzhGKBYTSZgjYLoKhS4BOHVqmaCYmthkkjTxbs8VOs120OfB4Q9w76F4l4L6hxKtm04B2CQUgYMhAp5olW1Cdy3Dq79O7j9zDaxvTw5OxWkVKFGvXNkXfiMFe3SoBoRe2rfXyUgISpAKYPLBUc3iYHlnzt9lqc9PbKlsjxve+Uanu/U+GOx/lf462bY+AcAw9vWUJ46nB2g1EohQaTv/Lp/4OnD0Gq0/vaeJJaBwNAqiyIDeErJpJ9ZS0pJ8nO7n+/ylllw2zxIqFXm09SF1lDU6LhowIA+khty7Qwz/c0b2Bh1C6P3vQJApNXC1Pv7Kptd/fVyfly2nyM5hW6NNyoM2vK9YDurGjAQCgo1NgHBdjjTYN6OdHoUf0LP4o85QYK3CqUy4JmY9lW+l5cOUx+DGc8qqwpLHRS7xNiYmXOyos7w9EL32+H5LLj0W3WdkhCrhPtaYh1oNsTdXF1rPeHQSe55oloTiBRKVoo139vg5+HxfXzkuBiAVrUSFMKtSdUAnfVk/yo5vpOPG2mQUgYMGPAFrdojuSHEpwa3n9kstq/ZXjwOVmEVTniWlIWLONIa9cktTc+y8j0Z/ZpV97+BVhGXthKWfKi7Wa0kdYB6+u9NYTo7PygpgPlvwqL32XVUk9lRzMlFFqdOFbVz4lTP2ndPKIN3kJ4jeqRJWXBkA+xfAnnnQLcaLYlTrRn0uhc6XKW/rUyAyt8paFoan4LrUdt6WSGlDKWUAQOeOJRdSAmabL2EljUT3PylnvlnEz1fn0PDJ6dw6/gKLnWp1hQaDRSdqAxSykBFQJssCSYeXfC2IHWkhhlfzHf35PxlxSlopOE5pvmK7V064zOQTRw7nLWZe+QcTtZVplJKD1rrCc9kqy9EScp1rVIKcDpVYk2QUuL77tGwiv5xTuwXHlWF2eKxHKspSql43d3ORhjuhQYMhAq9dqKh4Oo/xE0qwk+ZU0Wh0IOUCpfpcIM+MOApQbhtnQTFnLVKqYCklDbbZ7b67S5Xp0o0aScKORRIkRQOFJ+Eua8CJrabflPXy62VXd6Krikbj3Bzn4Ze6xWEqnyyxUBSPYhLgU+6CzXZzTPEulDwzRARzLYdC5d8Fdq+ZxqikoQXmKNUTBKHvOL29Nzt6XSpX4X4KJuP8j2ZJPbIQmftEQaa8TXgsu8q5tzdlFJG+Z4BA7rY+Cc3W+aSYMoXjz2ukTa1E/n37t6M+cS93H7mlmMVe17dbhV/oPqcuJxiTAt3qa+BcxPaZEkw8ejqcZBzEFpfCEl1Wb7XPabdeiRXd7cKhacyytcYJyeNTO6k1AJne4aUiGS1Y8UBrugWYjx0VkC6n1SGAvTQakjfCimtRLdwEIlV+XtR4lob7JkPC96C1DYw/A3349wwWST9tKV8QNoJ9Xfcoka8Qrh1bZAIK8T6Xel5NEmRFFArvhSeWjLkWE0uZzWUUgYMGPAJ+YZVfBJmPgezXw5uv7x0mPm86DARleCVLakUFHh0VAsrKfU4NB+mKjPOovr4WZrgf0DzQKRUkZiEm61w9wro+5DPTV+7qK2yXOHElEwSWqPILtRku+XfoU6GKmAHPodHRmnhu/DjJbB9qv727S6DBzbC6A/h5BHxV5bfiZxd3XeKulJVJqwRUK8HNOzr9dTAt+dx43crafvCDLFCt3zPQxIuo/AEHFgi1Hw/XgLvtYGDK8J77lqlVGIdqNKw/Co5AwbONqz8hmdtP9LYJBsee5e4tq+bxL43RrLvjZE8O6qVsr5SyvjAPV4xOqYaCBe0yZJg4lHP7nvAL7ZXmBDxHNXJDu+5BQutMqr/E9D6Iv3ttI0DAIfT+9p9YsLGcJ/d6Yetk+DXq2H5l+q6ylRKbflXNHza9Je6ru8joqRw1Lvu5XuFJ0ScKXmuKtjwB3zaE6Y+6l4pMP1pYn+9iD5m8T3GR9mU7zsxUr2H/qctM/WMzeyeSimDlDJgwIAvyJNoexEs/kCw3MHg5FFY/D4s/7zCTi0gPJVS/nxmygrZWLkijn2KcN+va5XllPgAxtwJNeHZdHgmA6o29rupVnX11rRt5TrHgFBKLFVVU1KMTc0OaSYaHYLtwJdQG4b9D/o9Kh4f3QC7ZkH2gcD7KsoZw2MoaDjs4rM9vptvF+1lb2a+8pTT6RLET1I9lYgCqNcTmg33yuYpEwBbNJw8JrLPWm+EcMEaLf6u+g3uX6dLrhkwcC6juFhci/kumUD2T9Rf1qWOsrzmQIDEQbhg1ky8jBI+A+GCW0fq0Eip/cfF+NfBvItO5l1EIGKJvOJK/n3KSqk63WDgk0LFpQeXu1Jql8bb84trOyvLPy3fXxFnefrg+E7YNkn42sqoTE8pRcGtUfhrlZ/aZKvsyempNC/Jg9J87/j18FqqZiwjATU2Uwk3Ncb+Z91h7/ORIcdm0cnC1D8uJfB7OktgkFIGDIQKOfsvl98Fq/SQB9+TR+Dfe8TkvbLR92G4+GtoPEg8DpfPzIl9cGyLyCrIbVbPovK9ghIxmHSqlxT8TmazIHpyDsHx3QE3dxukKgJKJzSVVGuWEq9RSqkDZtCdAeNToccd0Pl68djs7YniE0Y5V3DIz4Sln8LanwRx9H5bnJ/34aVJW9w2m7jhMNwwSSjRarZTnxj6Klz1K9Tt5n5cLSllraDvotut8MxRuPiL8B7XgIGzCHn5YgKTj3RvDnAdJkSpasM/VqVV2Hnxz93wel1Y8ZVBShmoGLipo4IoCVVK4Mx8vXCvWCXtZzIJQmPS+gqOpTwRlQT1ekGNtv63U7pZiqn3WolQbm46wNC5o/nFJkrzn/mnEjxGTyVkdaeWCOr9AIz9ARoNqPjX13rg6kFrS6HYH3gQpvL8ZvsU2D1HXS91zstFY8/S9yG4+CvR5VjCgSwNyaW931siVBuGYa/B/et9e4iehTBIKQMGQkXtTjDwGWh/pXjsKAmuDlpL0qz9AY6eAplunS6ihKpWR8HCh6sL2qwX4LOesOF3GP0R3DwLancJuNuZgPk7VDPtty5rH9rOeenwXiv4uKvP34jWs6lCSzF0BuKmqXFqFkejlAqpA58WcmmWr0nLym/gi/6w+MPQCKxzGTkHYfqTMPc1hVB0lngTvm/P2K67+7RNR1m+57j3E3Lmzxaj+uOdRSW3BgycKSgqEvfYPIWUCv6e+MfqCiSlSvOFia/LKe4RN06Fm2e6JTYMGCgX5Ml+4/N8K4y00CilflgmFEVOaSprllQ2v66sZLPzhn3hpqlw/kvCqyhzl/52itG5iH1klaMVJ2Rup5lV2ERUVkXuKYNSoqchpep1F12bqzSo+NfX80LdPUeUFC58B3rfD5f/CE0GaZRSHnGw9nHaanVZJqVcasMgGp8H7cZCUj1GtdNJ+Mpzw+FvwrMZ0OaSMr6xMx8GKWXAQKio1RH6PypuMgC4gsscek74TkX3PRmDnoXH94r3EQ5oCY9aHaFuV4hOCs+xTzHu+0Ut3WtcPYja7iPr4efLYfrTECENTC6Hz4nGfec1VZYDdrsrDzSeUjKapsRB8+Fw4efQ6TplvbYD35SNR3wfM/+46IJ3TFLtSKqrH5fsYp+mtExBTpqQbOceNpRSwULb4VAiFK0mJxYcvHNZe6rECCLwYJY3efjW9G3c8eNqLv9yGTn5Hp9zZSiltJj+NHzeB7ZOrLjXMGDgDIS9RIyfaa7q0OZSaHp+wH1qS91b9XxpwgatB47ZDPV7CcVlsN1WDRgIhLaXwuP74JJvgtteIaVUfx6zWSalxHPrDmaH8QRDwP7F8GkP+Otm/eerN4er/4QLPgBgzYFsAIqlnmMJEaqf0vG8szlBJCulThEFYfeuGiAnTZQUHlwhhActLxBdR301itGSUsU56rKilIpBDxd2qK0ewiF93/Jc0CD7DVLKgIEyQyv9DGYy50v+WVlwuWDtj7BtcvgJMR3C42xBTqEgBdrWTgxuh9wjsGOaIGtsmoHJc1CTkBijlmK8NHGL7jZhgTQQuzTZoWap8ZDaGjpc6V3eJcEvKXVgCXw3HCY9IB5LXd8yc/MZ8PY87+21XU3k83AaSim/0PobaK6vSEq5pHMdHhnaXFnn/GoQfNEPcg+zYEcGn8zdzfPW79keeR1zvnrM/biVoZRa+Q38dBms/1WU+B7dCPkZAXczYOBcQoRJkD9Z8S3g0m/g/BcD7nNp5zoBtyk3nO7KDgMGwg6LDaKrQExycNtLpFSRXSVjbVbx+xzQtKruLpWGQD6Z0VUE4dywH6B6SpVKpJTVpe73+fzAlg9nLPTK9w4sh41/+laZhROKlYVGKSXf4zwFBj7L97SklOTF6XQIZSmQ64pVEoakrRLJuBP73XxkF+2SOpr6mj99N1LEcxk7gn1nZzwMUsqAgVCRe1hMrgqz1XXBkFJeSqlKzoQU5YiOE79eFf4OF9rMw85ZsORjOLwuvK9xCrB0t1r29M5YndK9ohxY/oUwipbhNtm3qYOdD1IKoG6yGPiO5lYgUVm7M9w8kxOD31VWNU2ND7ibnM3ThYcM2o7IXlqkjGVukUdwJg/4lghhAp/apmxEpuyJpjX0Pluh/Yw1RLhs6jq2S11lnevIBjiynvScAq77VnTSc2Ei0mTncKZHCZ/LJX6jthg1OAs3UZ6+FXbOEJ5qhrG9AQO6kK/lzo1qBNhShZaUOpjle2wpFzxJqeVfirG9IhoiGDh3sfJr+G6ESGIEwi2z4K5lTDqgEgo2q4g7RmpsB4pKK7FD5Iqv4K0mMOt58dgRWmwfGy2SlybNnOAryS/rrIRLRym1/DOhMNs9u+JfX07KWzTCAtl6wlEq5jAb/xR+sLYYKfaKcK+r1PrxyvdDiZACOEkMrWoliAcL3oLfroE984iwqu/5P9lHtv2V0P9xUUXw8xWw7mex/thGqevf2V7PqcIgpQwYCBXLPhVlKMs+VdcFozzy1fazsiB33rPFwL5FMG4UzHg2PMeWswjWKFj/C8x4WiiFznDcr+m610yPwJn0IEx9DH7S1IDL36vchdBXTboGz41qrSxXmGw7OgnqdmOrSe0IWC0uAk7sh21T4NBqt807BmPq7pBJJjGgL98vBmWZlHp9ylaP7TVKqct/gDsXQ70eIb8VLvla+Jtc8XPo+55pkEkcsxXMFkpcIgDv3UCUktosmmFcUp2N/GSZsqpjY+FhEEUpq/Zpum92vRmePgIXfwmJ9aBac4hMCPO5azKSRrmmAQMAFNsd5EsdwkrsTm4reYhrS56gY7u24p7qZ6yQUTdZVeH+WVG+Ukr5nkRKzXxWjO2FldTxz8DZj83/wOSHRelb1p7A21drCiktWX1YtQcwRSVCVBKd66tqq6mb/Ci8w42iHKEAlsdqX/OBnDRY8wNsn+a2unmdamLBWUr/U632qgzoeUopHerCnDDXQ9+H4dJvoUEfdZ1Fo5Sa97ogyI6sE818ns0QJaZaZVeV+uqyTEqVFEB0FfJcUZRipVVNKZ6SS0093ts/6w6JhfaXw8CnRJn0jqmQvkUQYMVSd8aIIGxDzhIYpJQBA6FCqf+NhDuXwD2rvNut66HlKLhjMXS8RjpOJSulCqRAMjoZCjJh38Lwma1rPaVk5ctZ0H0v/aR4X01TfAwKW/4V/2s/R0UpJal4ZFKqRMdjScLglmrL1w9m7yzTuQaLHcfULLfJZBJKll+vhEXvu213Vbd6ynJ2gY8gy0Mpdc3BC2hQ9BNv2q8A4JcVHoajSimajXIhJln4m6S0LN9xzgRoPmOn00Ux4rMe00YNwBtVi8WEE4uUUZMVay9f2IaOjWoBEE0xV3+93Pv4JpPo8nLPChEchRPajKQc9BmklIFzGKUOJ82fmUbr56eTfrKIlfuyWO1qzkJnOzpXLYWXq8LbzUI6ZoWTUvKkyleJiwEDZUXaSnU5CDJWxpYjGrXe/evgif2YU9RS9p+WHQjDyQUJOa6JlBKXvpRSxzbDf/fA/DfcVreuV01Zfvi8BspyhSkgTzX6PwbPpMPw/2lWSoRPZbi81+suzMST1SZDbkopp8YywRd63Qtjx4tlmZRKrA2P76NNsVD8KUopmcySjO5rJYo5ktdb1Saw7UWqMX6kQUoZMGDAF+QBxxIp/HiqNQ3O+DO6CtRoAymtxONTpZSKqaISJuE6B0UdFK2WGJ3hnbz+07QV/vTqTvobReqopxQDaWmA8VWTroFJk4EZv3R/SOcZNI5sEKUXO2e6rzfrZ3Eu6aSWh7zmqXiSIRMMZiu7M/JwYQZMbiSerAgQ28uqn3KQUqWFwn/g6FneNlmGJkBatvc4PzvO4xv7cPq2UgOqR4Y2x4ZaruDAwqAWKVzbo75CEkeaSii2O3FWpDGyJxwastoo3zNggB+Xqff3239Yzaytaum3zSaNnUEStxazGDcOhdIhNRSktIS63SFWmjTLY4WzEkujDJzd0NoaBENKzX8T5r1B2hH/TWFW7a9ENZ88zimklI/rV75uTBa3cbhNvVRIrAvJjWhXS42dKjpBecpgtkgxgSYOrEyllB7kc3GWBp88lb9vTTmzKBsV9+VWNSUfWiXGFt/5mI6q2TkAGdvFn0zMlRapKikwlFIGDBjwA/mGZfXDovtDx2vhoW1wwfthO6WgUCCRUtHJYPXRUaKs6HIj9LoPEmprCK8KCpQrCdquez69l4a/Kf5v2F9dp+1qBtDucuh+hxrY+8BlGo+QCiEODiyFGU/T7IhH9zOT/kTDbFaJst9X+cjEazyibvl+lbJ66v19leW3pm9Xt4+IhZhqIvMz6UH4sJOQ74eCnDT4dgh83huWfBTavmci6vWCa/6CwS/wx6o0Xrdfzcv2a4lOVlsLD2tdA4uGlCrFwjc3dBUPpN9hFCJQ/nSeZCS69BNhQr7574o7d7tGSWeU7xkwwIuaZhZrD2Qzb8sRrrNM5wrLHHWjIK+Ryyra7Hz4/+DmGdB4oHisKKUMUspAmKD15gkmZlzwFsx7nQi7vvL8Ys8Jf2VAHudk8sBX+Z6sfDFb2J2hkg5t66fAg5vgvrVuqpgKU0CejlASs5WQNNsxXcSd+ZnqOoVwt2uSrRIp9efNMH6MiD21qNNNVMpcq8ZQsnk9QKPqsWLBI8bWduA7nlcs4rBPusFxKTYrLYASieiyxajndg7AIKUMGAgVdo1SaumnMOcVYX4eCLvnwvy34PAaSKipr7KpSChKqWSNeidMSqle98KQlyGp7lmhlCosUYPuK7rW9b1hhDToaMk9RTUmKaUGPimC+6qN8YfHhrVQlj+eWwEdSKTzOu75tShZHO+JxsPnq2UkusahmtKyvZn5DDGv5PvYj7CuVg1Lxy3Zp24/6l14bDd0uUmYw2ftDt2fRPtZz3i2cuTepxLxqdBkMNTtyt9rD+luYjab3JRSG14aqT4pXevREin19owduFwuoZzbOQOyD8Kyz+GT7rDg7fCeu1YpFZUIcannhjm9AQNBIv1ENi/ZvucN29fuaoEgiJ/LuqiklFdTiYqAUb5nINzQElGB4lGXS/ntOaTpa4OqMYI0+P4CyNzFjb1VBXF6RTaO0UIe5+JSRSzc6x797TSNA9YcUOOe2Ej3SosxHWpVxFmePtj0F/x1K2z4XV1XmUqp6U/DH9dL6iQJjQbC08fgtvnuHY8B9i6APfPcm1v9eCl82FF0FY6T7Dd2TKf6hLHcbfkH0Ph9ery35jXUud+UjUfUODo6SfxvLzon/aTAIKUMGAgdSvmeTXSMWPBWkKTUbJj7CuyaVbHn5wtapVRFqpnOAk+p5/9TS8NeGN3a94Yx1aBGW6jaRF036Dl4NhMGvxDSa1aPVzuBvDuzAlrASiRhnt2j1NSHUgrg9v4qkfbRHB0peb2eMOg5VsYIZVQj0xH6O5bC4bXcP6ipspkuoaXIpUOc4LhJ/F2nH/l5ZAMcWBZ4uzIigXy6VS3yKnX4644eZJmr4oyphk2r4kyoDQ36UrNZZ2VVl1dmKeTenhwnv8xfCxnbGD9jGRd+sjh8Jyv/piwRwsjzkR3Q79HwHd+AgTMImw/neK2TO+8B7t4hQailOtWroixP3lAJxs4mjZrAgIFwQDuOBVLu24uViX0hIl5qUSMBDi4XxEFxLm3rJCqba0tlKxTytRpXHYa8IsY6PSgebWbW7M/2ebgHB6vJwLUHKrEMsbJwZANs/N2jQ7dQSlWKvYA2WSbDbBHNicwW7/I9PQuO/AzIT3dPimbtIfX4MpqbPbxU/SR+/1l3WJ0rRSVJr1MgfudJ9USi/xyCQUoZMBAqtEbnoZSkyJPnE/tg6hNextIVjraXwiXfiPajMnEUgrGkX2TsIPfobho9MZEP5ks35HCpsE4BtOVqUTY/0tm1Pwhiqttt7ustNnXAK86Dk0eDaqP90hiVADtwPMwml9LAV4wYaKvESAOunwFT2772k7m7vY9Zpwv0fZgnt4iBs1Qy2MZRyt0DVaLuf9O2ee9b1nIuz8A1XCWo4YDLBV/0hW+HQl5GeI55bDOs/l50zATGR7zO7/k3wZ75bps1bVCP5Of2YH5sN5g1Q3vjgXDDJFpe+y5NJK+v4/klbDkgvGw+XXyYtFwR6EdQyrqD2czccoyw4Lp/4LkT0Pri8BzPgIEzGC9pSvdu7SsUHRGIiarTZFHL6iGo+6LWi/CPVQf9bFlGfH8BvN1cufcY5XsGwg5tDGq2+t4O3JrF5CNi2JY1E/Blkv3t4n1hOMEgkFAHarSD+AAKJ1kFZLaw9qAH2TT+QvisD2TtoUG1WGX1kxPC1IzotIL0PWnuXxtSx/BgyZ0MnVIJSmql2sWHBYu2SzRoDMh1/M8WvSvmc6WFogsjkOtSO6MC0OFqGPW+UGN5YPX+E+qcUlFKFUOtDvDARrh1jtc+ZzMMUsqAgVDR+iLo/QCktgltYi3fxPKPC4XV5gkVdoq6qN5cEFN1uwrm3xIp/soLhx0+6UrC551IIJ8/CjvzXeP3oO9D5T/2KYC2O93HV3X0v/HBFbBnrt/Oevx3D7zTHNb9HPC1r+2htpm94bsVAbcPCdJALHdvU3yyZGmxj4nGdT3Vc3L4yGLJdfROk1reoSW0vpODwymPwncjYe/CcpBShf4fn0poTbxPBqGeDAa758LE+zi5WJREyt+fzw4/fjDzwX7Kcu7JXAAKXZEKURlhEhPkW8ev8t65rDCb3UkyAwbOUSzfm6UsPy6Va9tMqi+fm7FuiA0B1hzILu/peSM/E/KOqgqPiz6Ha/+Bak387mbAQNCQVSKX/wjXBoiJS6SSJms0Tmn62rJmvKa7mSB95O5mecWVpOgb8DjcsRA6XAUn9kPmTv14SmN0vuNYnvtz6Vvh2EalbCtJShpuOxo4mXnGQSbnNKTUDbMt/O3sy05XHTakZVfs69t1lFIn9sOE22DywzDqPbjoC6EyB32llFxlcmCpmM8V5Sik1Ek8SKlG/YXvbo02yqruDdXuyco10OpCeCYDbpxazjd45sKIFA0YCBUdroTzX4TandSJtS9jQy3kG2FUovvjU4GqjeHZdHhoc/mPpSnTKyKCNFd1XtycynbHmVkXf+N3aoviUe0CvAc5SNKWXSx6T3gc7F0oHutlWXzAZDLRWDJH3JOZL7x/wgWFlBLEUbNU6ZxrdYSR70DPu3V3e3So2mZ5/NJ97k9mH4TD66iOyPr1apoq1ksd4766rouy6ZJdmUK2vX+R8JGSO1aG2o3tdCalrBFqtjRc3ggSabc3W/xf7JImrmW4f5hMJra8NBSAKMT+bRvU4OnR7QFoVlXNHJ7ID7Mh+aYJ8O0wmPt6eI9rwMAZAG0J8/MXtMJqMRNhMRMple+ZrZFiktZiFLQaoyYLAqBL/SqBNyorZDJKVrA06C2Ul1GJvvcxYCAUXPUbPLAJGp8XeFsp3nJGqEqiljUTvDx7buqj+kqFNYYKBJcLPmgHH3fR98ps0Bsu+x76PqysUhTrHkm6/13STtnmaE4FVx04HbBrtrtnUkVC/k4097gsTbxx+RcVZ38AuHmhKijOhQ2/wdaJ0Go0tL9CVS7p2Z14VoIUn9QopWIJhAslQ34zTrVKISJWxJAasu5cg0FKGTBQHoRUvifXDSe6P64sbJ0k/gqyAm8bCjST4xLUTO/Q9xdUbkAQJsjtteVSJ7/IlYynvxwATomE2LcINv0JOVI5hUxKlQRXZjbuxm7K8vdak/DyQi7fk0iNZrJSqmpj6HoLNB+uu1t8lPqdajtHAaKD25f9ucE6HYDuTWRSSgyy57dKVTa96uvl7sGAdO38vWov247mBv8+TufyPdBtE1xW2B1OTuSJ97czUyYVZVLK4/6RfQC+Hgw/jXVff2wz/K+hMOUEYiKsrHpmMKnR4tq8Y3AbTFLGsFWKmjm8/cfV5T5/Jj0Ev18PmbsgL11kFTMrwC/NgIHTHF8t2KMsXyMpYiff10cp31NiiSt+grHjRUOSIHBB+wpM/miMmQ0YqBBEVxG+ORGBJ/KyIr3ErJZ41akS7UVKjdU0p1my+3j4zjUQzGa1Y5te0iipHrS+UJBTEhRfONkHUtpviCZ2enmSR9wVbmz8A368GP64oWJfR4YyLxDkS1GpgxamAww2r6aR6TCFpQ7sjgo0PFeUUlHqOvl700uS6imlPJOhxbkqKUUMTbXzh4wdsHsOZKljwPA2NQAPT0EtSbbuF/iiP8x/M6i3dLbAIKUMGAgVWXvFzaW0sJykVCUrpaY9Ab9d7XZjDAuk7EGJy4ITM5+Nqs6VltkMNy/n0s+Xhve1KhhztqleOp9d3cn/xp6Em0yOyIOVPJApA1pw5EndZFX6+4InCVQe9LoP1zV/M8nZEwiSdJNwW79GyvLWIxoCSVJElUrqq4QY6b1qBnZt+Z9dUhRmFDh4e/EJ9jtT2JIFw95f6NaNxi9qtoeBT6uPTyelFISVlGry9FR+WCwM5k+WigDOYXYPXhUU50HaSjjkQSaZraLzpoaMrhYXSc04aaJpi1HKeM2OEhKjRXC2Ym8YyOudM2DLP1Cco1HGhVmBZcDAGYB3NM0r5K5MTVPjmXp3d7FSW0oSAka0raks70oPc6mPp1Jq60RY9a13a3QDBsqDnEMimfLbNf63S20Nty9kRmt1om4ymbxIqQRNIu2bRXvDfrpe+OMGeL8dbJ+mXsdBltd3kpWOFvf9tH5xkzdWcBODyux8p30d6XV/Xn6Aay0z+TriHUaZhUrqrRnbfe1dPjjsqjJJe8+1aEipzf/AtilqBYxNIj61sYusmpLNyYty3Tyl+jatrm67/DP44SK3boNJMSKOc2Ficc3roec9Ql331y3w950iqX1k3Tl3rzVIKQMGQsXPlwvVQdpKNbsRTAnSqVZKKd33pEHwl6vg+9HlV05Jk+MiyetmeMoJXrd9w+3Wiazef4LSisx4hBk3jVO9dBTPJV/wKiMrcP9fVkgp5XvBkyfakrlj4WprXL0Z6Sm9OOASGThFKVWQJUyz03z7CD05vIWyPPyDheoT0iBd4pImLTod9Z4b1UpZ3p+eDcB9f2zmY8dF9C95n68cowC4+NMlrD+YHfh91GwP/R+DG6bA9ZMgpUXgfSoLB1fAIelzLCcpJV83EYgASib+alZNEht43j+cHh1jZCjSc4/t710Fz2VB7c7inpRQB2Kq8ucdPZVNfl5+oFzvQTUU1TSFMDp3GTiH0a9ZdfcVVRvDVb/DBR+o61wu76SHD2i7tk5cH+bJq+KBI00VFrwFkx4U/jcGDIQDM56FqY/BzumwK4Cpc0Qs1GzHkoI67us1ymtPzNmWHqYT9YPcI5C9XxBK8virZ+mRuQs2/YXzgOoX2rFuklhQlFLqfrLvHPjoYCyjpEC/XDBYyHOC8hwjFHh4Sr03cwdOSTVlNonnvpgf5uS5DJMJLv4KRn8MEZrErEy8lxbAH9fDr1eqxNNl34tYqfMN4rHTKWwvUttCvFA8UXwSl8uJw2Uil1j6Nq2meU25mZD3XKiYCO7PGA1DXxUx3MY/YOt/avwYGWAecpbBIKUMGAgVSglSJAz7H9w2D5qeH3i/ke/CTdOhmfB0qVSllL0YSiUzbrksYPcc2DtfyE7LgeIiQcIUY+OBwU2V7EOUJEt97t8w+FZVArSlhv09Jw56KMnTfyzXmsvSYD3pbwDcNaCxshxO02mtiXu1OGkyc2g1jB8tJhs+YDKZuF6jeNopH0ciY+1YRKle64vgqcNw9R/KtlaLmbrJ4jMwy8oqlxikR7evxW+39VC2HfPJYrYHa+zZoDc07Ht6+ZsUaVq+l5OU+lIq97FK5T0yKdW0VlWxgSfJ5JBVDR6klFVDSjk9giKzRZQctBgh/OUu+cqNjH3q73J2/tG2Xi6rsb0BA2cRejWu6r4iOknEBE0GiccfdIAXk+Dw2pCPPXF9mJoryPBUSind9wxi2UCYsPIb2DZJLNsLgyJj3dTaAHctgWcz3MritNYBFQ6HNvkiK550xrldM+HPm8hf8JGyqp1MSumMj7f0Vb2xPpm7y/frf9gB/tdAqHXKgmhpTlBZnlKDX4DH9ireWieL7bgkUmqo5nvbeSzMyk8QMU+7sdDpWpUIBJVM1Hahlr8Ts8Xd58lshltmwZ2LIFEiSItPsm3YrzQu/pEFzrZ00xqZ+2gmJMfgmXlynKSpqpDnExHBVzScDTBIKQMGQoU8aFgjoHozwZgH4/9QrSnU66HexOxFQWdDyw1lsmyCSGkSr5Al5VPi/LlMDJbFRHDngMYKGROJ+Jx+WVFOtUUl4SeNKuSty9r52VJCaSFEJqiPS3wopWSvBJkUDAImk0nJgG9IywmwdZDYOgnz6nHUNR1zXx+kdPuF0a2V5fPfWwBAcbH47ZRi5fZ+jcTAHhHrpdb57TahvrGZxKDcuVEqu14dzodXdqR7o6r8qiGmhr6/AKePLn+AMFc/tll0sTzdIBNR8TWh/ZXlOtRb04V83SaRUncNasm+N0YS06QvdLpOZOm0UCaQFvf18nUOQasznxnZUllevb8cSko566vtLBaqsb0BA2cRlAYTgVCG62RPZvBjTFBIaSHuM/IYZpBSBoJAXrGdm8et5F1NyaouXC5382iX03/SIm0VLHiblKMLAp7DTb1VQienoILHHLtmTmD1k3yRSImsApWciIuUrqmYahCX6jZ+y2W+AB/N8UFKOeyQJ8V0xzaV7fyXfiz+z6mkWD0iRsyZImIpKBH3Elkp1bKGen+86uvllXM+oO+b55ng04PGrmHxrkzAhAszsZGa48nfqcudlHrlwjZEUEoD0xGOH96rxmoup1rBEmmQUgYMGPAHbUlKWRBTDe5ZDfevD985BYJWCiq3Z9frKFEGfLYqh6/tw/nT0Y9Iq0UhpWrFqbeXqRVdEx8GPPOPOqCnxEf52VJClfrw5EFIkhREkgmnl6dU9RbQ4Rpo2C+k8/n5lu7K8h+rDoa0ry6WfkLvba/QxrTPfb0y0fAjD0cQZVd2Uw1ENx3K4UCGIMxKsdKlgW9itlZSNB9c0YHIqBhc1mieHNUO66bf4fO+MPtlejSqykdXdlS2H/aBn6Bz0XvwWS/49SpY8ZUgqE4XyL+BGm3LHkyUFuFa9zPVEJ9terMrYewPQoUGovvn6I+g+TD3/QKV74H623SUws9XwB836hrw39JX9RC75LNy+MIpGeQIQyll4JxFzoksLrfM5RrLTFqZ9rs/eWI/rP1JKJehTNdJhalCrvtXqAGqSsrdIMcKA+cmCkscXPjJYto8P53Z29L5cPZOpXGMLhwl3skwf4ry/YthzssMNQUek3o0UuORh/9YF3D7ckFPKaVXCSGREpkFOqTulT/DIzugxUi31Zd1VksVM07qHLNEoyYq69hakKkuB9NJPIz4Yam4H8pKKVwu2kvqsYyTxf4TlGVBSb5o+LRrtvt6L1LKpJJJ26fBr1fDko/wwuAX4O6V0OEqFu3K9H4efCZ+h7ZOpbHpMPMiH8b27XnusVq+dCxDKWXAgAG/UJRSkbBrFix8Fw4Eweiv/BqWfipMh6s1EZ04Kqv1p1yip61PlsvLymkUneaqziv2a8no/JDbcSNd6gB6509ryvUaFQ2t75WWeAkK8mda6oOUatgXLvxEdLgLAdoyqkf/3BDaOelB7r6HB2nhI4ujh1cvVNU5oz5axD7JI0ouLePYZmHSOOcVr33HdKhN9Sc3YnrmKNRsJzJBRzcILwZEFym5zG/HsTymbTqqfxLy53twGUx5BPYtDnjelYZwSK7nvorpnzv5NkKYud58yQWiRXFKS//7ycoKz+ye2aJOdGUCuiQfdkyFzRNEMHZsM3x1niCqJNw3qKmyHJTXl9f52NUgzBop7gsR8e4dbwwYOAeQuW4S/7N9xSu270jJ8Gh3fmgV/HuXiCPAv9LCB7Qd+MI+idNCKUMxlFIGvNHyuWms8xgrtF0nvaAXe/qLR6WkT75LED+NqkkKvmlPibHr8DplU5PJhMUs4utZWyvYV0o7J2h3OXS/U/Ua0kIicw/lBK/cemlMG2X5vHfmeW+gJZHKahlg0lABRdllO0YoWP8bTLwfdkznvVkeajqXk6+u7aw8/HSen7LFsiD3sGj49OeN7uujkkRJ4V3S/dkSoc7Pcg6KEtO0leJx5i54pyV8ORCqNBAVMyYzt+59kE9t7yuWCwrk43iQ+SaTSem+l1tqll5T+i7yM8T/hqeUAQMG/MKuyf5v+Q9mvwh7A8uJmf8mTH9SldpWJvRM88pgwO0Jbc33Q+c3EwtWNVN033lNlOfTTgTXfe5U4NO5u5XlZ0a28rOlDqo2gdQ26sT/0V3w+D4xWJUTt/dXFStZ+eXMYEmBUzE2UjTmuIoJYxDZb7PZxLgbuyqPJzt68LF9DCfiJWP2vGOw/mfYPjXw+SjlXNL7Kshi3gVFWCRj7zt+XK3/nj27GAbZ1bBSIJNSmyfAup/Ldow14wFoZxZdg5JjPQxcHaWiHNfTP8IklebqBTF1u0P93sgtmJUyPpNZfA/2YuEtdlT1kHpwsEpKjfmkDMSfdlJtiRC+e0+lwfX/hX4sAwbOYNj3qUSUucTjutWWuGr/D6F8b3DLFGU56C6mZYGhlDLgA1pPziibOrUct2Sf752045CcyPGn3JdIqQJEYqNFTWms279YJFnkibyEiff0UZZ/WOahUAwntNdw/0dh+BuqulALKfGXXxo8cRwdYVFMs08W2b3j6PhUaCappgvKaGmgVUtXhtn5gSWwehwc2UBRqdQxMUaOSV2kJKiJq7dnBCgBDRW+Kl3MZlFSKM9ftIpz+bdZLMV3JSfh5GH3uVzhCXqbNzHYvBqrzSMxqBide3/vYzuKe3exy4bDheorZTILA/rTyTO1EmCQUgYMhAKXy928V2n/GgRhIA/A1iiY+zpMf7ryul1Uaw4Xfw0Dn1bX2aLcz6sMmLLxKDEUUZPjVLVIwYRVPe6DMlEF9Pnf3DK/TkVDm61xqwX3hz3z4IeLoXpzuHMxNJACIFuUGExkBZLTKUjBMnQ5fHSI2oWv3IbnslLKZeOKbvXU9SEopQAGNE9Rgr1/nH142345A8+TzPvNIfgGeXoMLfkIy4RbmDNCnbR1enkm2QUe15ZMospBRTmVfmGFHLSAaJ9epmOo779v02qwdyFs/BOypIzz8i/gjXpCJaZFowHw5AG4ebr3MW+YBDdOgcTa4rHW98xk0m1jbTKZhE+YhJDVUrZoeCYdnkxTPWkMGDgHEZ+xWn3gqWbQxhNQpvK9mAh1zPovnGbnH3UWnYZPSpMvw1PKgA+s1YwPa549n+5ao2dfkMcha7SIG802/w2ApGsn3yVizJY1JE9PWYniUR7Vqpbq+fmsxp4h7EhuKJKTgcY5qdGIU5p6u3VoW/gufDNEqIg88M31aiJQN45udSH0ewxqBOGFqgc5mTbmE0jWIdPCDel70nJztXpeIZpBtRDdmF8ao3qYHjgexsSj5/3W63kdGwSNbxTg3szo8DqY+xqs+AKAXGLp08SjUVKzYTD0ddFQxgOXtBe/gRJs/LR8v1phcem3IrkdTBOtswgGKWXAQChwuaDnPdD1VsnQWQ4gg+ikJw+21ihY+okwFywDUVEmxKdCu8tEGZAMa5Rg8Mvh8TJ10xGGm1ewNOpe+OtmsTImGS7/Ca76HRMwvI0qY161r5LebxkxoHkQXfdknNgPu2fDkQDeYIdWw+t14MsBIZ+P1WKmWpz4ja3ef4Jie9kz1KXFgrwpwcbYLpqWyiYNeRYk2tZJZOFjA5XHl3SSjhdo0vLjJfDTZeJ376kIyDsGpfnUN6Xz9Ai1VK3DSzPJKdSQXHIgG1PV/fHpgFjN76esUnpNYP3aRW1h2afi2pLVmIoSsRwNCjxLTH34YDwxXG1JPeaTxTR4YjL/rjsU3GvIZFdkfOWVKRswcLqhOI/UAk223/O+oKgsbO7/l3FcDmsHvqw9EhkuzR77PQpX/ByyP6KBsx//rVN/dzERVu7RqOR9Go3Lk3tbtPBTei7Tf5m6h1KqZU2ZlPLdrOVlDbnh1bUvXLhpGty7WqijinJEiZg2QSVDSvw5pKn3eS1UhSNZe+Dgcl2z8QirmTEd1BLdXekex+5wJZz3NNTuVLbzl8+1eguwBJmULQ+k7+nQCTWG6d5/BHS9Gep0AeDaHmq35xu+WxG+19ZWunhi0kMw52UY8ioMfU1d70VKyUm9aGEuP/9/sGocALmuGPo00ZCNAPV7Qs+71MS1BlFSZUAxVtGp/N7V8PSxwHYNZykMUsqAgVBgNsPQV2Hk2+5dxgIpQ1wud6WUVX8SWKm47l94Pks1UC4Dth09SaRJeu+yQsoaCS1HQdPBYDLx6dXqQHnp5+UwTa4gaEsQHx/Wws+WHtDzDyrIggm3waQH1XVKl8OyKXqm3N9XWb51/Go/W/pHSZEYSIuxUadKjPpEYh04/2Xo94iPPfVRNzmGfY+1ZN8jzYlwSr9t+Xpw6lwPTqfwYNs5Q1wPnoqA6Cri/8IT3NqvEY8NU1Vi7V+cQVGpRMjJn6NMSpWHnAk3et0DV/8plovLFgD/L+IeDruSea30Suomx2gyd9LnFY57hycpJfvYeBzTZDLx/uUd3Nbd/+s6/+2pfeH4bqEs/OPGwNsaMHC24NBqLGgmy0onXAlak2SAWp2gyWB3gjsIyCVTJ8LVaczlUif5crKhXndhxJwUou+igbMenmSodmLus4SvahPR8Oem6d5dY/Uge0p5lu/5IaWu7dlAWR798aLAr1Fe/HkzvNsStvzr/VyLUeQO+5B/HL0BD1JKUSvrX7/vje2gLA9+d75aLrl9KnzSAyaHFr+5Qc9ztiIhnfqBE2pMbDa7J65MJhONqwvl2Z7MfLfy0HLB7kcptWa8ULi3vgg6XKWul5vWyKSUXUOmyp+ZZDifSwx9moZw71a8XqUYLDpJVFuco4k8g5QyYKA8kAPJQFlN7WRPNv2FyptQH9ssOk5kbFfXBRMEBIEoZJNHfQNjk8nEW5eqsuIyTWgrEP+btk1ZVjJvwUDutLbpT1HmsPRTYRK54TfY8Lu6XTlJqZT4KGIjxHe1YEeGmyl7KHD6MjqPT4Xe90Hn60M/6O/XwcedhUcAaEoWdRRdWqLKYlMnOnIQJgeWUknrXQOa8OBgtfyzxbPThImvopSSygMCKaXyM2HVd2VXLoUKz6xaiPgstxe9ij/mOyRVo3xvkUsjfd07ds+B70fD7Je8D/rLVfBWE0EKgnv5nvaYjmIv34MLO9Zmz2sjuF9jfP7W9O2Bg8TcI/DXLTDlUfU1d8+GfZUwMTBg4HTBQY8mKF7le5p28gDnvwjX/AWN+of0MqM1ZudhgfYeHqZYwcDZi+OS/2OEVYzjJs2k2mfMZ40Q3pvVmug/7wkpEVggGZ3XTtL474AuKQUwSCJ/Sh0ub5VRuKFTCq+gZjumW89jjUvENfWrasr9/HXtQ5A2D2nsMPq9JZXxnTwKGVuFkjqjjP5Lciw1/03vrnQVAel7OqBRSpG1R1hiZKq/lXE3dlOWwzZvkO+3ekopX0nVSGleoCilNFYlHkReritWIdMU5ByCA8tEYs7H+ZS4RDw8Y/NREYN9dR6MGwWF2cG8q7MGBillwEAocNgh+wDkSd085JtYINWCdgJZXqVUXjocCrGb3YbfRMeJ1d+H/noBEImHUgpg018i6yDdxC/romZW35q+3b0c6xSjzJ1ZtJOL47vg5BFvBQpoDOXzdY0Og8Hshwcoy3eVsZPh3SX3cXvJgxRFpQTeOFg4pDI9eYD35ynlaXwdEQdxqSIzBLDkQ/F/muqddf/gplzdXfW/avTUFOh0PfS6T5U3ByL7fr0aJj2gkiMVDTlI8TQiDxFXdJXet6fHga97R+5h2DvfzaxcQVG2MIH1DKqU8j1NgKbz3ZnNJh48vxmT7lXl58//t9n/GyjMgo1/wOa/3V9DT0VnwMDZigPC5HyBQ+pc6rN8z4fHSZAY3b62slyeMm8F2hJsOYFwYLnwvEnfpr+PgXMeWnK0aYpQmJQEk0ib+zr8ciXs96OmH/kuU7p8w1KnKMlTiK8ApNTHV6lq/cHvzg98LqHAXgIfdYFPe4kyOGVOoJ+onrvdR7wZRNfN+wY1VYi4g1mFvDZlq3o/ydwO/95dprfAfWuh220iwbp/SdmOERJEHLw/SxO7rRkP48fAqm+UVXWTVUX/2zN2qGr58sCfUkqOX7dPg4Mr1fVyTGcvFDG8Nn6KdE9k5xLjRsgCovnPt0Nh8fver1m1MXS/g5lO0XFw1c8vcPjLy4Ttx76FYLGFTyV2BsAgpQwYCAW5h+D9tvC+pPzx1yknbZVqduzZ7ao8SqkP2sNXA93a3waEXve9NePFhF2r6gkB+48LpVCkSdMOV8bEB+C/e1XyDpj1kOpD0f7FGRzOPvUG1dr22Xf0D9HgscQj41ZaoDFA1JBSEdLA6nKW2SekRmIUNosY6GZuOVYmtdQCZ3umO7tyYbdm7k+UFooBOK0MpYGeKh4l06TjKeXwUEo1HSx8JK78xd3PysNw/dWL2tKpXpLyuME/dZmYeid0v4M953/LvQf70/uNORSU+PCxOih1vlr/awhvrIz4+XL4QlI4lEEpdSJtByPMy7jMMo+ba+0VAZTTs3zPx71D/szNOp4Qnmq9ZkPh2Uy4YbJ6zOgqgiT08xttU1vtBDN+6X4c/trPe3a5CbbU2YCBswk97+Jz+wV8Yr+Qt6o8DyPecn++9YXC1Lbj1eV6mR6NVGPp2WVNtGihvYfLvoOrvoG/b1MVlwYMACV2dfzWklJaXyldQuHIBpjxLKz5QYzT26dAzkHfL1S9GfOKmpKJZ0cyfaNzGdERFl6+sI3y+Im/Nvh+jVBhL4LjOyF9sxh7/VVPHNuCc9tUGpl0fN/k8T1AonrR46qX55cL9pB2THOtl7X7HrjZJ1Q4pO/JJX1vsREWDbHoHlPMekhVjA58e175X7tGWxj1nvAG9oTspzXtccWCw+VyURxVlZMPHyDrwTSO5haTURJBcXILsmw12ar5uBwuE7muGCauP8xfq9P4dcUBxi/dx8r92QBsOpTNW9O38erkLTz/7yaenLCRhxdbuTf7CmbFjQGgn3kDtY7MVI7X4Lm5NHxySvnf9xmCSnA0M2DgLIKn1L71RcJcMK6G97ZfDxL/371CSJRvmCIkvdpuV2VRSsmlN3vnQ60Owe0jqzaiNKx++lbYNgmqNdXfJwCmbjoKaJRSWnWQNUrUqWsmzk1S4nlwcDOl013/dxbyWBmbhYQLkzYeUZbvHBAiKSWbQ8ZWFyqUknx3A0QZNjXbQ2mB764fATD3kQFK55WmT09l3xsjg973YJZa4nZ5Vw8/kJw0+GYwRCaK7m2hwNPvKLkxPLJL3yxTvnZMZu9yEK3/kk5WaMJdvWn2zFQl+L33l7XcC0CU9FdIq+emM/2BfjSv4cMXodnQIN9UOXBiv0oiOYrF9R3C971p4b98GiEpxqZ8Cc02esvNfd075O9Cj5SSiSytqsxiU4kiW5To9BIE5jzcn/PeEdnmB35bx0dXdtTfUHOvzC4oYf+RAtpr1xswcC6gyWDekK7V5g3qQ5027s+ntHQ3tZ3yKKz9CQY8Icqqg4TVouaYv1m0lxFta5brtHWVUkb3PQM6WLgzQ1nu1biqsjyybU3u/3UdAH+sOujm7wSIGHTJh6JzrJzIC1COv+2oTrLn+okirjb51llc26O+0oHv15UHuXtgEzclTpnhqQBXki86sf3qcXxu+YKPXWP4xHSV+3NBdt00mUxsfGEIbV+YAcCMNTu5SR7yC8vRSKgySalhb8Cg5/j1daGK69YwGadLqGSKS+3knCyi1OGixO7E4XQRYTVTYndyJKeI92ftoE2tREocTkodTortTkrsYrlEs1zscFJqd1HicEjrxPGK7U5KHW3EtvOXuO33e4ET2Qlt09F8Lnp6CqUOvcRbHPAcHIaUNdtYEQV2l5mmxeOx4qT0l7VuW99uOU5XG2w9lM0n+3VK+DQoRI0XhXfaueUtZZBSBgyEgI3702kLajYkqW5g08+cg1C9OTTora7TeriEAu2Eve1lwe+np5RSJqpl87WaKhE6qqeUZvLt49j3D25KzaQoHvtTZKre3GClZcfjDGypQ+pVAl6bvFVZToy2+dlSBzL5EFdDQ0rplO/J/klOu3heHvxDRJ0qMQxsXp2520UAeMv3q/j6+i5B7TthxS4us8yj2GWjYfJw9yeVDFUZpNFOj9IyixXifJg8ehJYWhRlq8s+yvF2vDKct6ZvY/q8+RQRyWFXVaW1soyh7y/gf5e05fKuaskfNTvAkXXQ5aaAb6fckH3GRr4rAm1zaL+p/Xu30Ve7oihX87lJw3V8TWh9sfd9R54oWnReUykhLb86sVF11dh/4vrD/O+Stm4t6RVIE3GHOYIOL82kGjmsigIcJTR4YhL73hhV7nMxAJGlOdhelULpke9A11tO7QkZ8IlmqUEYCTtKRal3Oa7V1fvDNLGs1kzcV+QkguIZaJBSBlRoTc615Kh2+eO5u7xJKbscL8UEF48u+ZjOx3aylz6cREMoBdkxbv1zQ2j/kiBz+r45N6TEnk/IySGzTTRCUpJGOuSS0n3P4m5yDqJxUkS8fnzkgfgoG8+NasVLk7YQj4bEK8wWXnA+POBcLhclbuSNC2f6dpJn3kdU+joAck6ks2lXptjGg+hRHnuutzspkUifEoeTUrufbR3qcp70Hc7dnsFHu/dwvxV+XbGP55f49rV6f9bOgJ9PWVEcaVY4oCKXVZeQsppNRFjN2CxmIqxmEsxVoBisJidRlFBIFD0aJRNhtRBhEdu2yk6EDGhSPZobGjYg0mpWjhFHPtGUQGQ8lsg4mqyqBsfEa+UTTePqsXx5XXBx/tkAg5QyYCAIpJ8soturs2ln2s1/kVDoshIdaKc6XSFtpX5wOeZjkRFJrBPaiWhLxqKSgt9Pj5SyBZeZ8oX1aaKL0BpnU27slCq6Bsnw07Z+bJe61EiI4rpvRZvXG79fzVuXtnPznaosHM0V51cjQd+k3S8uGweXfAvrfhSliqUF3gbSMtpeBphCJik88d2N3WjwhCi5mrX1GD8vP8BVGs8lX5i2ajtTbV/idJnA9Kr7k/7MyQNBUfEE8b6cpYKckz+DjO0w8X7RRa//Y+p2F37q8xCPDm7Mo0u7A/Bu85+5onY6tapWYQY9uO0HUX74+F8b+XD2LhY8NhCL2aQSXqFcL2WF1IGF+r2FV0CIiCk8Ctp4sjgXBr8oMqDVJTVF9eZw2XfeO/st35NL/qR70bYpsHmCaO3e6bqQz3PJE+fR6405ALR6brp+cC/9NnYdl4w8NW/MgpOvF+7hlr6NQn5tA+5IztcE6QdXGKTU6YRDq3HlZ1KT42SQSM+CObDSKXzx5Il02iqhVk1tIwyfg1RM6OGaHvX4cVmIaldfiE6Ce1a6rzOVY6wwcHYiL53kTd+RQB9yifV6ulpcBJl5JRzL1UnAKnYHUWo8avdBxrpcMOt5nrfYmVLaxZ2UChKJMTaeGtGC16YIT7S7f17DJxq/qTJBTizLMa+UsC4tKaSgoJRijVKnSn4RSYDDZSY6wsKCHRkqkRM1muIho8S2i/d6qX60BJCs+gGIM2k/Lxdj359CpjPejRSSSSI9kqWraRt/RK5THh9IS+Pqr5d7bVfxMGn+FYb5kRaVvJFjdRndGiRjs5qI0GwTYTUTKS9bzNisZuV5+f8qJUdIKkrDHleTkqQmRFhNRFgsRFjNVJsQCydFCWSbutVYOvY85Tgxc5/HknMA0+Dn3atLXC44NJtBn6yhmAhu7N2A5y9o7f7Wli6D6dCxTiIdR3s8N+8NmPe6SJqOeg8OVVdIqZrVqzH7ngFh+nzPDBiklAEDAfDpvF28OU10rYuQStWO5Dlp6HJhytojulXEpQhvCC3kls75GcKEeOtEsa7NxWWasAKqtNYS6a7GCQSFlNKU74WpA+ChOiNgdG/3lTb/x+7XrDr/3NmDCz8Tfj+P/rmBp/7eyDU96nNtj/puaoyKQm6R6m3z1MiWfrb0A7NZ/UxL8jWtYj1Iros+L9vxdbDz1eE0fXoqAE/9vZE1B07w9mXt/e6Tm5cPUaLzXrSnCWN5SjI81U/FJ2HWC+JYF3zgvm1yI3juuOofZS+CA0shvpbaYaR6C/9dp2QlEvBQ91gY/wBUbcKQe1ez6PGBSnnjoexCGj81hcn39aG1fOzSfO/jhRvy+UWW7fdb25TpvqIoF5oPC25npXwvCKXUsc3ChDwiViWlfr1adCq86HNIbuj3pWolRXNRx9r8vfYQIDrj3D3Qo4OSlEHOc4iJbPNaybiyTJS4rNiw88rkrdzQq4FbNt1A6HAjpQrKUb5hIPxY9hmmjX8w0nI14xxDabzgAbG+9UVqx6sVX8GGX+H8l6HafZryn9BJqVv6NFJIqV3peTRJCfM4apTvGfDEv3fznHUGnU1beTn6Ca+nn7+gNfdK5Uwr9mbRraHqfeZmd6BXYq6FvVj53RUQRaPqsRTbBeFjXvw+5vRN5La+npM1uqlkjEalU2J3UOJwUTVWVfRP3nAEu2MVLWsmUFRiZ/s+M6smbcXughK7KyjVT62S/fwCnCgx0eOZqZzvstDHNID5C8xMnTfD7S28bj3ElVZwYObP1Wn8uTqtTB+5FrmuWI64kqlpEvf+4xlH2OMKbky1mk1UtbqThdUsBTRPjZeIHqH00ap+IiwqAaQle2wa8sfmQShFKMogE5FWMym7J5B3YB3Pbm/IKlcLFj9xHlVXrIUlcHX3Olw7aoS3WTgoCVmAR4c1p2uDZK9tAmLRP7DseWh/JfTxiMtvnQEbf4eZzxEVGUnNRM0ca88c0eWw262waYKIn7rdCt1vx1W7M5dZ3qOeKZ3qSQ8DHsSTv2oEu4aY1f4PohnQOQaDlDJgwA9KHU6FkAJoUT0ScqEUK8/+u4lXmu6GqY9CvV7upFRehuieAJB/HDJ3wtTHhNqhzcVlPyGZlHIUw+px3L29PZM1vkg392nIk8NbeE/0ioWqyY2U8jQ/LiN0vSuCILxa10rg+U52XlwjbkOlDhffLd7Hd4v3EW2zsPnFoZjNFVdP/dWCPcryqPL4b0RXgaR6EjF5MTQ5Pwxn5xs2i5mlT55Hz9eFUkUObj67uhPt6iZRKzHKbUAvdTiJNMkdEnX8jeTsd1nK97rdCiUFakmioxRWfi2WR76rLyM3S79NrSKgSPp9BlIzyb9Vk0X9LUvr6lSJYc9rIxjx4ULFd2Lkh4tYesn71Jx8A/x8BTwbBgNgX7CXqBPJTROEQqvD1UET0DmFpQop5bDGYrHnu3ttaeF0CvVUTFXhpSHDbNUvZUisCzXaQaxUMqCn6Du0WnSQ9PWaHnjv8g4KKfXW9O1c2LG22qIblAxyiUtMssffPhBTZDa7D+dS9OFCAK7+ejm/3d4zqNczoA83Uqoy/EAMBI98cT0fdyVgxyp8c+yFgryXSSlZaSHfD/01TwmABtVUpcrXC/fwxiVhNm2USamyjBUGzkjYHbLaxuWm+pGJmarRTanJDNqZ9hITYWHKxiNeXj8yxn6xlNv7N1LW9z+0n/OB+XvzKHKVMBSYuGoP47a5e/2UOJxEl2YzUzpOAZHsycin+TPTAPje9h/9LRt4emMNJji949loipgY8Qyrnc143H6b23PTNx9j+mZJmoIZjvgxWtdBnCkfIqHEZaHY7mQSPZhED7dtZNWPRfosnJhpkhKnqnss5oCqH0/iRyZ6Hp9wG9hhXsSDNDAf462RdTme3IntR3PJKSzl4k51iImwuBFGEdIxzWYTbCyAvxDJwZOHqRlRxPQH++m80zBi+ULY9w8tzTewytFCxA0RIk6wgHtMo8G2l4fR4lnxnV/2+VJ2vjocW6hJLYeO3YiMhJoQLd2XPcso5URj8UnR8Or4TiVu3XEsj57mLbQ376Gwis69UYmxdYz4le6r0utpxQZltPo4k2GQUgYM+MFz/6ptz1c8PYiUIwvgZyjByo/LDvBMEwtR4J3VPLwG8uTC4AzvNqTbp8HhtaJ8poGHysgf5Mk7sGnZNCanudelf7NoL98s2ktSjI3pD/QjVS5LG/KKOA+tAkKRS4eulNJ2zhveJEooXSLi1AlxkCqs5EhY/dRArvxmJTuOqaWJhaUOGj01hdXPDKZqXPlaZfvCR3N2KctlIr8m3Ca+1yEvwwMb1fXWqt7bOp3is9CaS5cDNROj2fziUFo/P11Zd+dPa3xu31IipWyROuo6s2bAdLl8BgS6GPScx7E0Q4rWi0QPsqLHUaoSeif2iq6QzUdCrM7nqCVTImLd1yG+x2kP9GPapiPc8aP4PEb+VcSaKMTkz2EP2n8iZGhLazf+AUc3QJ1uQZNS/63Zz5WIbKe5Zhs4uFxc79unAS5o0EeU3zpK4bVa4p7z6B71c+r7kPjTQ697xJ8MXe8zuftP8AqNFU8Nottrwv+h9xtz3Mr4DqYOZlTRlwD0b1ad6EjxubeqlUC1uEgy84pZvjeLozlF1EgsQ/msAbAXkViwT31cHqNbA+GHTEohEeiR8RIppSF+5etNbp4ixwjlbAjw68qD5SOlsg/CT5dBVALOG6ezPi2bmnml1ABKS0sp/yhmQIae148gYxyqYseDpAne68epHKPE7tAc27fXj5ZQ8tdgFaCtqSYTI0UZ2Z7MPO7yE4cAfDFfTQbWtWaAFbYft1OEiaFWOJ6Tw+rj3uR6HVMuREKRy4YD97jCKRV9RVlNJFps7sSNxcx5jvU0yTtME/NhJjd6mgiLiQU7M926BgKcV9NJi2ZNiI6wKuRNINVPQnYyJbPqkRhTlUVXDHQjjGwWM1azSUkSLnrzSygABya3rnIA7F8C898Qiethr/v/0DXIOFnMuzN38IvjPGKcRfw5+QgHXauU579auFcoxqt6diyUIFdRpLaCq36rHCLEo/seAI0HipgutbWPnSDKZuH7m7pxvWT90fTpqex5bURo8btnV2BPePqkypCtT4pPaioiRPx0dMF39DeL33V0vI56q04XGPiMsF7wOh8PpZQck3W9FUa+HejdnHUwSCkDBvzglxWqP0NKfBTk16Kg7XVMWyNuJLf/vJHvI/AKIHNPpMthKDmZh0ls4HHj2T5ZTL4t1tBIqYb9YMTbMOUR0o8d9blZdkEp3V+bzfA2Nfj06k6YWo3x3iiQXNoP5M57ALVn3gl75sFFX0L7y8XKfo8Ib5M6gQ36EqJtzHhQHaAv+GgRGw8J8q3zK7P8d1QLA9zk5KFg+1QxuTjv2cDbfjtE+Itd8Qu0GFG21/NAbKSVfW+M5N0Z2/lQQ7DpQe6QaI3QmfybNAGey+n+OFRoSSlHqXs2KmM7zH4JEmrDiDfdy1Tq9RB/H3QQ/lw3NfNBSmnIFD9Kv2FtavL5NZ2448c15Gh9LgpP+DZiLy8cpcIXxlGqBnZywBcEZi1fz7UmJ8UuK5FVmwhSqjhXtGAvyoF7VonAyGITBHBhFuQd1f+cAkH22XJrfCBPhoNvvpCSEMXTI1ry6hTRMOC/9YeVluB931mE6FKDlyH//EcHKIRqj9dnh8dw9hyE6ch6LC5NKZVRvnd6oUBVSgHiestPd78vKL58sidN2cv3QIxnK/aG4XdgLxLlKlGJNHpKtCRvbWpIY9Pd7Jpfgz8G2omNPLOmEP5UP1qiRkvoBOrw5b3eJbZ38/LRIZQCeP2cjjCZUFQ2MumyPbcuJS4LVUx5nF+rmJyIWl6qH5vFrKhqAe4a0BibxUzPvbFwGLo3q8W2pnfwl/V5km2RfG61uHn92Cwm4nN3wp+QJ7m5vnVpO4a2qUGExUzk79/DzvW8dlEbXus4xPvENxyDCWJx/NWtFNXLreNXMXPLMWWzOUfMvHhNHepWDSHerN8T2oukZB2QGhUUgMsMFvfjZJ4sAIso3/NCwXHYMzdkj9f7BjXl3Zk7+MJxgc9tRn64iJfHtPY2mgc1mRadDDUrqx22S/pXQybV7Sb+/CEvg/41SulUL4k1B7IBaPTUlNDmCIoHmI6h/LLPYNtkYWnQ3CNO15JScsxpjWL/8XxiN/2A8pVG6ZB/tTuJv2DOp9d90P0ONel6juHMGlEMGKhETNWUxf16myTHrdGGmEs+YmXmUtibRYl0CR0+nsOvM7az4VAO87ZncKNlOc9LseWNWztTZ9syPoxA9RpSlEQhdt8D0X0LSDKJwWTxE+cpZTNZ+SXc8cNqVuwTQenUTUdp+KTkrVPL42bZagy0GBVSy3oZ2s/GSwUGovNYGTHx3j58Nm83/5smzCiHvr+AWQ/1o0lK+IipQxql16NDdbIXgeByqYO5dvDY8i/smQ+Nz4OWmu5i5TSV94eHhjTnoSHiPWTll7DpUA4bD+WwIS2bYruTsV3qcn5MAvyAe726jIgYGPCUWlYXLEqLIPsAxFZTS1G02SVP35G8dNg2CapJn7eszHFqylSiqwi1lK8yJDdSSio9sxcJJZrH+Q9rU5NXBiZyYuFXyrq/F2/goqGDQnmXwSM+Fe5cLJZ/vVr8H2QpHMCqdBe3mx8kwZTPW62HCH+t+r1hgZQt03628TUkUuqY38yiT+RJZYyxGqWlPCkO8Z50a79GCil13y9rGd2+FpM2qN2YLu5YW5XY/3UrFOcSe8EHjO1Sh99XCU+N3Rl5NA63j1z6Nlj7g/DqCfW3fYbAlCYyxs463TCnrRDkpZ/uSwYqES6XUCejIaWipP/dSCmP8TOxLtTrKTz4yoA7+jdSSKm8YjtxZSWOJDNzlybRsNnVkM0uoba++fuV/Hqbd+mtL9VPflExh/JFgxQnZg8lTzlVP56EUhlVP6cL5A5fER7lWyrRE7zXjy/DZz3VT4TF4kYo+VP9AGAv4e+PHiYiR/xWvhpkhtb65dj7juezViIS7hvUlCibBfJehfwHaB+dTPuEABYKB8XrFrjEdTKibU2VFFU8e3TKo8BdsZ+frpBSX13XhZ3HTnL+ewuUp/u+taB8SZJV3wlLj1YXwtjv3Z763TGAVc7m7I3WGbPLOP7y5UD2NbHC5T9QHF2dHUfzSIy2UTc5WsxFxolmBc/+u5kZW47xw83d3fcvluLYMvpglglSF3EnpuCF+U4nvC18K/968hADPlzJ/uMinh76/gIu7FCL9y7voOtF5QZlvqITC2/+WyQDu98OzT26VEuk1LHMDCwnsqkGzN59kpsnzON7rYesHinl93w8khLyGHGOwiClDBjwAW05VI9G7mqEX2/tQYeXZlBSLC6h4uIiN7VKkkkYHv9gH8waVzOamEWWqBgbkeC3O10grMmETkAi+Qxtnerm45IcG8Hvd/Qkp7CUji/NwOmCKIp5/+P3ubRPG4aOuEQ9UDlKyVZpW057yk/DgDsHNKZFjXhlQB387gLmPNw/bAboH81WfVjKZJZYWqgGQM5S+KK/GOwaD4RV34iBxY2Uko2mw09KaZEcG0G/ZtXp18xDDbRri/hfj4C0RcOAx0N/sfQt8NVAoXx6SDq+Z/meFooiwMM7xWkXvlTWSFVh5JOU0pTvaUvP7IW6maVrmpTC0n+Uxz/NW8eDc4uY/+gA6ldVty8scbDpcA4b0nLYmJbNhkM57MnIx4SFpl3yaFU7REl7pM7kMwDyiWa6sysXdqgFTTtCU8mbTP7ctAbmcani8z+pZnlZPQ52zBAmyu0ucz/4lv9g5rNisnvR5yIwB1E2KUPO1JWBKJdVaSBKiF+etIX+5vUMNa/kyuZjgQ5iw53TBXFSlMsbF7dTSKlB78wPr1rqyHr44SKRfY6uIpSbZyNcToqt8VibDIG0FYBLfL4xZVR/GggfirKVe2CWtnwP3O8LyqREur7bjRV/AeBL9VNLEw88+Ns6rulR302xE6zqp2reTh4HjheoE/0WNeIVv75le7Lo9fps7E5XCKofK2w4FZ29vOGp+tESNWX1+om0ehs+i23cVT9+t5W9fs4EnDzCRTnj1ceH1ojxRwfjbuxG+xeF8ffN36/kp1t6iPFHOwb5g5QEzBeGGe4qvUCkVNtL4T+pfD0vw43wbZoaz743RvLx7B28PVPEhff+spaPruwY3Hl5QrqOdx3JIjKrgLrJqm/jEmcbltCGK1rqdJqWx99QFJJOh7AKAbAXEZm9h7ZxkcLjFBjYIsXNf3ThzkwaPDGZ7a8MI9IqJS5MZkGkRCXCym9EUrDzjWVvxhQMNOV73eVKhdwjogtpTLL+a2sU3Kacg8x/dCD/rD3EA7+tA+CfdYf5Z91hfri5G32b+lHD+yvf01pKSCh1OOnyyizuL83kJiv8tXQbHU2ZVLPA3xsF+V+qLSfVI6UKssR7i4zzTjYo8yfN+Sz7DLZPEWbsHa7y/V7OQhiklAEDOjiYpZIH9w3StP8sKQB7EWZbNBteGMqxrfHwG0SY3CfgfepY4ShcO7ADccntWfOnGIzn7T7JUNAopUKT6B+e9w3WOR+BWSilPr+ms+52idE29rw+kplbjvHaDxP5KuJdcpfHMGZPKn/c0YsIa3iUA61qJqg3eW224Mh6OL5LqD3KouRADKhfXdeFW8eL+vjz3pnP8xe04sbe/juDBYNfV4ZmZukFrX9QZAIcWSeWZYmu1cO7KUym8gFhLxbeBA36unsnpbaBy8aFt5tHgWidS4yGsDWZRPmfy6FDSsm1+tJ5WWwQES8e/307bP1P3dYXKZVQC3reI15T+xmX6pNSSpmahCqmPHBB/7fm0b1hMsPb1OCFiVt8vkUXJkZ8tIQ2tRP4567ewXeKkyafaUePEV9QSmKMf/I3r1j9rK7oVk9zAi71c9Qab8bXkHbUlPAe3SjKgmu08X4BRwmc2KcEq0qZV6wmeJODtBDK92QMa6NmuV+eJD7P1qa9XGWdA/trQYcr3d+DsxSz2cTlXery2ypxLe7JyAsP6Zx9AMZdIJo71OooWi2fpXD2up9pJ5owosf5WLreBNFJhkoqjAjG60dL5BRrlqNy9jASyHVFUyI5MO3INtMMmLhiK0t2bqDE7uK+jBPUBz6Yd4CVS5aHVfUzc8sxt/KkUNDadJTHI6HEpRIkxcd2MNScRpqrGptdDTmcEzippqh+LGac9hLiY6OJtFo0BNBpovoxEDpyD7s/PrzW56aJ0TaibRYKSx0s3nWcmVuOcX6rVHWDgytEJ8qqTXD0e4xfVhzgx2X7+eyazjSsFsvxxFbcXfIMdpeZp0a0cD+4/D36IqVs0dDnIUEG+/BMur1fQ4WUmrj+MLf0aUj7ukn+3r3Annkw8zmo3RlGvcf+XAf1gbTMHG54c67uLgNb6BBxljIkhbTk9rqfYf7/oOM1MOYTZXXNxGh2vTqcJlLHZoDmz0xj/fNDSIy2iYSknJT8coD4Duv3qWBSSlVKdWsoxY8bfoNZz0P7q+Ciz7z3sUVDlYaCNJO8dS/sWJuBLVIUshPg2m9WcF3P+rw0RicOAo3RuU75nhSbzpryB+aTdYlKachVXwkS/aQUb8ZRSJRJHKMIcYyeDauA7PSiF4dunwr/3iWaIF3zp/tzjfqLOUR16Td9eC1Mk7pYNuir/x7OYhiklAEDOrjo08XK8oODNaTU2h8lQ3K6GgAAjFFJREFUae4YGDue1Cpi8lk7zsK+FzWZ/r/+gaPAkfVcVH0pJdVtkA35TivHcotILYNSavzSfdhmTeRK614Aks35mAIYU5/fKpWON7SGX+Ak0axPy6HZM1PpUDeJny+rTcyi1wRBNvrDoM9jyW61bf3TI1vCFB2l1OrvhWKo/xNlJqXk89eqMF6cuIUXJ24JmwH6BZL/TciQg4GIOHdfHsnY1k3FA2DzNuWuEPxxg8iwXPABdL5BXR+f6jODicslVDdOB6S0Ct4IXCpNcSM2QKilHA7v7lGKgaQUDEQlwFNSS+QfLxX/22LEZ1SYrf+a1ZrC0FfVx5d8I353vurvPY5zRZtYZm4Qy8v3ZrE8SO+VTYdyafL0VP9lpDtnwtTHmVPYiM0nY7nXCrPW7eKFVTNoXD2Wiff2ISZC/7N9d8YO+po3EE8B3au2F+d9fLf7RhYPpRS4K6Vk8sqs8xqe/nEPbBTElDarFxknSMIy4qdbunP116oKomGSDfJxz0ha3LPBr1/cViGlzguXWmrHdEFIpbSC6/6FaU/CvoVw7T8VG2ifKphM4nONOnM9KIL1+vHVor2sXj/epWHh8/pJII/Z5jvcElZPHu1PVVNHNu+syyHE7/64eTTVTTksOpDIETJ9Hc4vPFU/x/PVZFej6rHER9lCVv3UyXfAKtEpTMYXndJotvk9frUP4Ampi9nk+/oEpfopLS1lypQpjBjRD5vNsEk/K3BSkFKHXFXZ0egGBg72f/9e8+z5tHxOdE+7dfwq1o8+RmJpJrQcLQiujb+z3NmCy6ephMLAt+ex45XhPPTffpY5WwHwex8PtUkgUgpg8PMB385b3ew8ukKMn2M+WexmjeET+ZkiCRuVyHeL97Jm1l4+ioAIvLtntjTtJ8mUR98a7b2PoySFQui6Kcehlkg1UaXjK2i1mNn3xkju/nkNkzcI6432L87wNgkPpFQPE1wuYU3vwkQPWSklq93wc8+NrS5IKTn2RJCd+94Yyeytx7j5e5HAHr90P9XjIrlXKyiQ0fYykaSt38vrqd1ZxTQGBhdM4bvJDl60X688t7TmtZw34jUur5NC5J/XwdESvr5okPAE/klTpqk3H/On5Ot6C3TVPD6xX10OZxL5DIFBShkw4IHjecVk5omgrmuDKu7ZNIeH9LNKA7jqD+HLo4V8U981E3bN5OKbZnPj50mku5J47p35bBoamqfUkxM28MuKg3xqy1fWmVxOKDkZsIa5mk28l0KTeo7rDmZzwXubmR0pddsIgZSSMwcAvZtU0/eUCrL7XjAY1qYmcx8ZwMC35ynrOr8yixkP9qNZauiT6C2HVZ8fN8IxFCh+UnFCmWCNEu9VHiy9SCnpcUkFklKlhYKQAlj5tTsp5Q9OB3wmDdCP7Q2+9Ecm4GKrua+/Z6UgRuJS3dd7lu9pISuaqjSE9M3BB0VtL/X/vPY4na5nULuebL20B33fnEtmnnrtPTm8Bbf0bYRFE6CVlpby36QpfLAzngNZgswZ/O4Cfr+9p745fn4GZO3G4oglzyWI2HiT2G93Rj6tnpvOiLY1+PjKTl7lGd8u3ssPtkn0tWyCvS0Fyfb7dZDaVt3I01MK3JVSDj+klKLUk65Hs8Xb8P3qP7z3CwG9m7j/Di7tUB0W435fUEycReBtNpu4tHMd/lwtyMmV+7LKVk6rhdz1tH4vcW/M3CnUU2mroGpjdmfkMeid+QBsfGEI8VFn6ATZFRphUh7Vj5tZcxm9frTHOJu8fiJ1lDpC9ZNCkbUJEzaq12i7XkOJsJhp7kbetCLCaqardIy6x2bTZt2L5FbvzM4Bn5ZJ9fP5/N28MVV4Mu7JEDHDO5e155LOdYJ/swezYBXYXaryrlnNJNgMg1tUhU1i3Zr9J/QNlA2c9Sg8fpBoYJWzOU3PfwBq+ffDiY6wMPX+vgz/YCEA26d9Tjfzdu6cnkcREXwXAVF4Vw90fGkG+SXCt8ps0umWfNEXMOZT3xYSy7+Ak0clQqKVz/OLsMDnV3fgjp/WAaKj7LgbuzKguZ8SQyn+PZhr58WJWxgqlYC1qRHFT0O78/hfG0g7UUiE1cwrMX/TuWQFHKgFVa9xP45SvheKUkqKZSPjhVE5+G128clVnWhYdTsfzxVWI21emM6Wl4apG1QSKbWj60vctWkwGa4kXqonvWYgYtFeop6fhpSSMahlKtteHkaLZwXp+c7MHVSNi+Sq7vXcN2wySPx5wOl0sed4MY2l212phh4Zf1M3d0uMK39237l2Z2FN0NZH2bXS4dqh/7wWNs1csjJ9vk4TGKSUAQMekAdMgPE3eZgCeko/I+OhmU63j+53QJPBME3IYm1RcbiaDmHz9gwotjNx83EuACat3Yu12VGGtanh83zWHDjBLytEVjUJiQwZ+ppg2IMxKZcGriZ1arLi8kGM/mgxR3OLKJakp86SQr1eILooKlVvqqPaSSU7SicKDRFTDs8sPTSsFsu+N0bywn+bGbdkHwBD3vNDEPjBOzO2K8tlLheSySV50LDFiPda4EMpJZOWFamU2jZZXR75nvtzx3eLbF5SfajjUfKpLffxl2n0hC+lVJX6+tv7I01kRVOVBv5JqcJsQQhGJgRnCCmTXT3uUtosRwOrnhlMXrGd4lKHX8Wd1QyzH+zLnB3Huf2H1QCM/WIp9w9qyj3nNVENvAFKxOQvjyj+cfSm5/kXc37LFrT4ZZ/iwzJl41EabZzCg4Obcb9EiH4o+ZvVNkm/ncS6qupJLpEEdzIvtbVQa9btoflspM8sUoeolX+P9ootH13/3BBu/3GVMBxdukis1J63opRSs8FvXtJOIaUu+3wpUTYzy58cHLDk0SdkUkomRet0EX5Lh1ZR3PpShZACaPvCjLB3/iuv6kdL9vhT/YxI/5KeeTM5xDAuP1SN8/Mn0rJkE5Mtg1lCO6X060zv8BWho8JRW7SbyuX1o11fUV4/DZ4Q9+W7BjTmsWEtAmwNbI6HZZlUN+dTvXG1wNvr4OY+DRVSSsbDf6ynV5Oq1EwMoPyQYbZAQh3Ss4UCr23tROXeXS1aHTOe/XczV3arF3xps4GzBocO7KEJcNRVhdE1g0sQtqyZwAsXtOKFiVsUAqqQCCUejaaYG3s34KHzm3H7D6tZsvs4+SUO2pt20da8l7EjhnkfNFCnsg2/w6FVkNxQJN3ifcfbg1qk8OYl7XjsLyGpvuG7lYxoW4OPruzklrRSIJFIW9PF/3LzowSrk95NqrHo8fPUbX/4Cnaj3+HYEgmYpL8gISulohJUGwVtzKCDR4Y2Z+72dDYfzqWgxMFPz1xMm5gsJiffSKfcYoYB389ZS7OU4/RsXIbOvkFgcXoku121AUFUAoF9wVaPE8QPCF8wHUTZLKx/fohSzvfU3xvpVD+JFjUCx4qP/bWBQRpvqJv7NaNnqz60rpUQ+L4vx1y+zt3feyvMFs9HxIp7rtYGxVBKGTBwbuN4XjHpJ8Xg0qV+FfWGKcPuR+2hRdPB4m/l13B8JxRk8uW1vWj2jKjrfml3U742vcRxEkj7cbW3jFaCw+ni4k+XKI971DRDOlC9efBd8+SBKzKelPgolj01iMkbjvD8z6Le3ewoovVzU2mSEk/n+sncN6gJSTH67+/ZfzYpy29eKrWPbX2RqPHW1uork+DwkFIyXhjdmj5NqnGL5DM19oulPD6sBXcOCK4sZ+exk8zell7+E6nfE57LUt9fRKzohuarfC+1DbS8QJQUVRTW/yL+7/841O3q/tyu2aLstPVFwltKC5McCLmUjktBQc9Tyh/0lFK/XCXUfscl4/m2l0Kbi32XWa0ZLwy7210BF38Bu+cKcqxhf1Gi6AmZ7NLxkYiLtAbdmWpo6xpMuKuXci1+MHsnH0hk0qR7+9CmdqJyneW7oolIrs2AASIYnfZAPdJPFtHjtdmKIuS9WTt4b9YO9rw2gndn7sCEk9om6fNMqqt+to4SGP2R+F9L5jXsJ/60OCHKeqnSwPsNaD3NDq+Dxe9DjXbQ96Gg3n+wSIyxqR259BSUipGomg03m018d0NXpalBUamT9i/N8NvYwJ/qJzXzEPHA3qI4Du/KJMncjNZA9o4ldFgwzetYDZ6YzF0DGpdZ9eNZRlZZqp9Rtl0kWzJJLzWz5kA219vWM8CyhIUF9TjkCKwADU31I5s1h+b140koyYbP54TXz9GNkHuEOqZ00lwpomV51h5hBh1bXXiJAOycBbigXg8xufEocS0LbFK5jt3h5M/VaTwxQbSs7/n6nOBJ2Nqd4aHNjJVItfNbpar3IJfDray+ydNTw07uGjj9kX1UlBoddSVjyj0s/JUi40TCxA9u6N2QLUdyidogfuPFRBAbGwel0CzZyvMXCKXxT7d0p+GTQv19nmUd91snQLYF0CGm/EFOFv53LzQfAVf+4nfzsV3r0qpWAqM+EomVKRuPMmXjFEa2q8m7Y9urJuGgzAlk37g3L+8Cf6NfhicrZfR8/6o1hedP+LXj8IImtlcU7oWBLQkm39dXIcs7mnfRqmQ/bx8YSpzZyjArUHiCK79aBsDFnWrz5iXtwko6r9C1TZCVUj4GUK06SkcpJSMx2sbypwbR/bXZAAx7f6Fyb8ortvPl7/9yeYeq1G7cTvnMSqX7ZH+b+r1YbJG0raOpQknfBks+FPHukJfdX7T1hVCnq34MCiop5dQhpcaNgmMb4ZoJQsHlppQKX8fxMwUGKWXAgAZalZRX61TwLt+zF8PGP0UA2el679bjsdXFhHvLf0Sc2M/draL4ZEsUGSSR4UpSNrtl/Cq+vcGDSAD6v6UaJX57QxfMU7LFAx9mjbrQDlwSRrarSRVrT/hdehslRaxPc7I+LYdvF4vJ7fU96/Oih1ngH5KiAVD9cUa96/2asoy6AsrVBrdKdSMI/jdtG/+bto3J9/WhdS0xiLhcLvZk5rN093GW7RF/ckmmjGCJLJ8wW9QMnfy/tjucFkF2VCozTh6F3XOk17rc+3mZPNPrOALivTjtwcmLZfhSSi14S0jIe94NiZpykQ5Xi89Amy3au0CQUjLqdBWkjC/IqjyZZJn5rJj8XTNBPyCQlVJRiYI4tZd4l60FiU71qjDvkQEM0JSRAoz6aBF7Xx/BkfQMaiG6A316lbsaLSU+ij2vj2TrkVy3e0yjp0TAXY1cIk2lInhJqK2qyhwl0Om6wCfncgkjcxAlkJ6wakipzJ2i9XF+pjsptexzkYlsf5XSva88qp9OR7JoAiw/mM+K2TspdTi5Mt9BTWD84p1sWpvs5vXTt2k1Fu5UPXXOe2c+jarFqmVkGuLHn+rnGks92poG8Mu8UtbNXU5tTCyOgtgTW4mkRMnIa/HpvN3eBwoD/Kl+dqWrzRKqxUXStnaCYvgsmzX7U/30XHIC8qBxzVQ+6due9psaw84l3NQpiQu69XY/xpnc4etMxZrxsOJLrrCM4W375aLUfM90mPQgNB+pklITbhUTybuWQ0qLsJBSMqwWM1d0q8fh7EKlO/B7M3fw4PnNgto/t0idWJ/fKhUOSpM2p51hbWq6XbN3/bSaT6/Wb7xi4CzFycNgFqQUexcIM+d6PQOSUgBvXtoe0iyQDb/cdZ5IXnyOWyLTZDKx4NGB9HtrLjFoEoCeWP29UMO2vkhUKHhCW9KWF1xSsk3tRDfVDcDkDUcUT6aXx7SmS4Nk0rek0R8oxsotfRpSvaYZWl+sNhXRQk76mXQInrIQ8S4XxNcUqmA5OVh4QpAfJhPsWySafeiUgW1/ZRhdX5lFrFPEVPmuKLJdYo5QxaSOTRPWHGLCmkNc3LE2b1/W3m3ccDpdLNtznF5NQlN0pu75k4eth5ni0MyxApXvaYmoxufpbyMfPyFKUeMBfDpvFzf0akCb56fzZ8Qb1N69g0Wd3qPPaNEERfaiern0Ws6vZybq0BJvb9XCLFj3E1RtAnvni+/wyt9E3JlQS/z5gqKU0omvPbvvWQ2llAEDBghCJQWqUkrbPv3fu8Ryh6vAHCkGhJ0zBHEkk0crvgDg0UHPMXrIbdSvGkOUzULLZ6dRWOpgzrZ00k8WMW9bhiIb1qJVzQTOa5EKE7LFio1/wvIvxYS1QW//b0yuO/cod+rVXJ38R+lM1r5fup9dGXmidS+wap86sH93ozeB5gbFiPmI/+3KiE71qjD/0QH0f2uesm7kh4uC3r9Ho2QeHdI8fCeUUEuUbw15RQQBnj5LFY1NE8RgXquTCLqObYZWo9Xn9VQrWpgsgN27Y54/tBglAi9PI/vV30POQWhzqTspZTaL60MLi0eJVgB/NC/ST0u26GHoa9DrPkhbCW/Ug3q94Kap+tsGgQZSGWlRqYP3Zu7giwV7AHj230203rSPKxGkVNvEQlj0nXh/Pe9W9m9ZM4F9b4zkss+XsHKfWqIol+454mqQkefAXmijDuAqPsn6A1mUOPD2+im1YyrMIt8cB8W5XOayEIGZd1cUUuza4qb6iSk6yj222uQ74lkyeyXXAAuOmHnjg4WK6uf+wjlc7JzDJ7ur8uHvceVW/bxnS6eJBWZsP8E3W3YA8DlPYseCc4sZSPN/AGBPZn7AbcBd9TPdMoq5kuqnucVMhCWB4yeqUJUTtDHtZbWrOSPa1qDU4XLrTHZ7v0ZlUv24dRALUvXz6uQtbqRUZl4xc7dnBK82cTphtijprpGSyoBWqdiyasNOqB1ZSO1gukYZqFhIqtnjLjHuNqoeC5nSGCyPyeBtCWDxVhOWFw8Naa6QUh/M3sn1vRqQHBtA7Q3M365OBFvUiIdDMiklJlc/3NxdUVxM2XiUf9Ye4sKOtcN23gZObzxYeid1TJmciG0MtaUE5pH1IqkSTMMU2ePQGuXdjENCvaoxfH9TNwonjIci9Cfq+xeL7m3VW3iTUvZi9+stSFIKVBPtA8cL6KdJEIMoWwW423KM/jYodVl5ZpSkhL/sO/0DyoSLnoVBWdBsCDwslenKcxOXUyTjVn0Dc14R3WdHvee1a6TVwoYXhsKbDiiACQ8OEyrqf35gdLNoqvZ0b1wyYe0hJqw9xJaXhioJaTmpVjspmsVP+CeKtDjfPp8+1s3sdGruFYGMzmVSauQ70GJEwNe4oXdDhZR6c9p23pwmbDtkA/pvlh3mgQ0z3ZLVhVHViapWHw4t8a6GkZP6RTmis7jbOQeA4imlQ7h5Ch20VRaGp5QBA+cutAoIXZUUaEqQpBuI9sZlLxaT/uIc+EVSq3T0NDOMEjL+E/thy7+sGZVAy7+FcqPbq7N9ntuke/sIObBssH1kA+xfJMq0ApFSTYdAVJKQB2thsYrB0Wln/ZN9ILE2TqeLP1enKcTY4l3HeeafjbSvk8Sjf6pk2UDZ+LEoR5xXTFX3TI9MRuQe8n9u5UD9qoIgGLd4rzL4BMJbl7bjsi4SGedyweSHxUAx8t3QMlVbJ8GmP6HRAGEofu3fgfdxuQTp40nEhANdbxFlW047fCdJ2588pA5qdk3wpwezBRyEVr7X+Xr99WY1mx4Q8vVTrZnwUjJbYOtEQfC1v8J7e5mUkj26bAFIqcQ6kFgHV0keJsBZcJyTBaVBqX4KiktYlWGiYM0hHC6Tl7Gz1vrhx2UHeNOaD1YocEXxzE9zeOXo85wwV+G6Ve29CSWHe3Aik1JrcuK47PXZRFLC9igw4eLrz9/lmKsKK13ufjTrIm8lyZTPwOJ32OuqybN8QQL55C44gB5+4C0Anij4BaywKz+aLTlqsJ5tNYmIwF5Msd07eArV62e+6THWmouw2+K4MjIhJK+fO35crbzu5V3qkplXzKr9J6hfNYZO9aowsEUKHeslBa36WfpaC3qWLKWjeRc/vXA/UZJMf+72dG78TpQNZuWX8NZlOl2Rwgyn08VXC/fqPvfujO08FAxZnpsGjmJcZhsFERIBLidA/BjdGqhESJOo4y5BtEdaLcILD9xbuds9JiX+lFIZO0TCI9hmFBoseeI8er0h1LQXfLQo8CRy/xLaTn2I1601eNJ+qyBX5cm05t6+45Xhii3BA7+tY0yHWmdH+aWBgDjoSuWgK5Ub2jYQCpKIOBGjZm4PruuyVvnsg5QC6N+sOjSNh43ok1KKEkWH0PC8H+ani+1C+I3WqxrDvjdGUlBi55l/NjFhjRrXlmAl05XABd1bBj6Q0iFXJ+FdWgR/3Szi6bHj3b2FgoE1AnrcLWI+s0UQUgCrvtUlpRQUa5r2NB0CdyyC2BR6x1fTJeRaPTedfW+M5E7NGH0ou5AZm48ypLVvry4tzBLxVK+qpqKgdhcY+LSwJtGDL2W+H8x5uD/naTwkAapFuaAEirF5VU/Me2QATJPKR3yRUrJFBwT/HVVtCn0e0q8CUIQOHqSUySJsP84xGKSUAQPAgeMFnCwSA0bfptX0VVIgVDBtx0INqSuWlmCQa8hlw2FbrCjF0UIeeLP2wMxniU5tw+VdPlPaost4cngLZfJ2Ucfa0qTLBk8fFT45c14RpFQwXTJqthd/erDFiCySRFqYzSbGdq3LJZ3r0FjKgvy47AA/ok50X7tI0xFsw+8w5RFodSGM1bRFTWkJo973bXodRtzQuyE39G7IwawC0k4UknaigMRoG90bVvVvlnzyqPD8AmFM72sw1EP6FlECFZUYXJe7Lf/CHzcIpc6NkwNuHjKsESJ75HKJ77S0QARfMinlCEYpRWhG5zpwOl1gtmEGcvILKMwpUrx+InZNI273JLJTe3Gk0SWU2J30cJqJBua0fIlj8a2xLNnK2HnX4MLEB+kdKHGgkkEOJxceSKM78PemLCYeWMk9R4vpBHw8YwNT5tbw6fXT2LmXKRFwPOMIXV+a4f9NuMECuzYHtWUOsRxwVieDRJbuL4ZIiHQUsPFQTsB9a5tEwHXIVQ2r2YTFGk0JViKw83HER2SZkrgy4Qc3r5+i9CSw5zO6kZn98bV0lDz6qp8eG36Hg9C3Yyu+b9tNUf3UW70ANsDVnWswZsBAt+NUttfP9leG0fwZ4f+kvTduSMthQ1qO0uxg3XPnKwQTIAK83ENCqSkRl3aHk9n5jYi3HMEak+i2/UBNV6U/Vqfx3AWtKrwb34O/r1OWZz7Yj6ap8Yra5MM5u7hzQBPf448MOVOb3FCdkIXgKWKgEiD5wh1H4wsiT2xk5YbTCU4pbrAGIKWO74ZPugry/sFNhIpaSdE0qhbLnsx8DmUH0fCgIIsGRVvJNGsSFXV7wAUfiHOQEGE18/ddvbhIKqd/4q+N/E/2mzRw1kLb9GZYmxqCCElpKVTJmTuDI6XsGlIqLlV0//WVOJMaieiW7/kzkpb9pCIT1Fi3ODewKlsHMRFW3h3bgXfHdgCg2O5gT0ZfIqt8SKx23HA6xPXr6S2qlO/p3N9NZtg2SSzbi0InpQCGvSbtr7l3DHjS9/b2EjU2jIyH6CSIdfcIlQk5uQM4QJdXZnoROrf9sJqdrw53b/6igzGfLOYJabl9XQ25XqezdxMeLWRSyl4i1Hi+5jQaNKoeR/u6Saw/mA3AJZ3qUOuoGbKgxKXSH29e2o6xXerCjumiyqB+H6jbzf1gEfJ9XEN8Wj2+X19IaQGDn9d/zrN8LzYF7lvn/ds5R2CQUgYMgFsmQM/bSUHHq8WfDLNFDDAuhxpEykRRdBVoPlyoNWa9KAZHecCV/7cX8cYlbZWJ13U96/PSGD/suC1a/CkTkOwQ3qUO7l8vboYeHkgWs8mtvaqMWQ/1o0mKJsjOFkaXXvXUMcnQ5cbynVuIqJscQ93kGCBI4+34GsLs+egG4YcQCiklZ7o9s3YzngFM0OdB92y2JVIETGHovudyuXA4XV5du2TVT4Po6kSW7mfT9h0crxpPqd1Jk2NZNAA2Z5Swdtl+L9VP9+pjwVHC/DmHyDHluXn9ePoFlTqc2EtLSSlNI8MZT4YjlhKHS/H6mRZRRAsz3PnDCpY41fd7n2UaD9n+Ycr2kzwzRyjp5kY4aGiGT2ZtZbXLSQSljJUUQt/OXk8u7p9vT9sJsMCGYyXMOZzORTboZIHME9lsceTiiXstEygkknkuEcAkkQ+4MJlMAVU/NjPkZmdRMzWFKJvFzetHa/gs+xG9ar+GV7mG1y9uSz9HLkyHGFMx467riNUW4UYoebZ5j8xvSUn6cEbHp3BhA8kofMGTIkDaPIHk+FimP+hhbP5dA9h/iAd7JEDbjsH/gLaLzGjTho1oqm1zvFuoOJIiXCRVidHbs/xY8jEcWi2IXNlTRweRVgvf3dhVUTH5QoeXZrq3uj++Ez7rJZSbj8mllZv5xTGSrx0jWf6IdyvoFU8PUlSqvd+YI0oaKggldif/rjusPG6aKu6lCx8bSN83xRg04O25LH9KxxdFi+PiN+dK1vjiyS3BK7idt4Eg4aGUAjSklDR+yIQUqAmuyHgxLnmOqTslMj3noJjc6qktAuCdse0V8mjToRzRoMEXJFWHAzOxMklavZn480BHua07gkR+cUxrd7LYwFmHNevXcb/lL/a4atK1gVROlVRfkFLZ+mpdN7hccOscoRCKqSZ+z/4UgHKVgG6nPT9+RHLTkITakIsgpPLSy0RKeSLSaqFlTY/Obll74cMOIin99GH357rfIXy4UnS6cLoluIMs3V30PmyfIvxstfOSNGncjK0O/R7zvb/8mUJA/6LXL26nkFJaQurnW7pzlVTm1+uNOcx/dAA2KT7yRO835nAouxBzhPieBreq6fc13SArlCbcIv5/NjOoqoMJd/bi2m+W069Zde7o3xjeFef+5z0DRDMHLXbPhYytQtXk+ZxnKZ3ZFlyJaiB4NgCyWEWy6RyFQUoZOOexMU1VMlzZrW5Apt8L1khBNuiRUrU6ir8Nv8O+hRozO9Uo3WQyhd65Ri7VCGYCkrZKSKJTWnllQfwFAVE2C2uePZ9OL8/kvBYpfHN9F2+lxAmJlNIzdTzdYTIJQ06ZlOp2q9vTTon4KXUr63JR4nCQfCKLZCCtwMr+XZmkbvqSavunkHRCdDn6N/ICciJSlJKt1ONZXApkZJ3g4383UeKQS7lcIXf4KnE43VTqF5kX0tJ8gBmOzqxyteCPiEi6muGTiUuYKmXmXrcepoEVpmzJ4pON3ln2j5Am6/tOAie9nvdEHVM6P0c+QJHLRovicWhr2RyI68eGw83rJxEXOCEmOormMfFEWM1YsyPBAV3rxlAlNpUIq4niXVFEuoq4rkMi+bF1VcWOxUzbrRGQCUM7NKRlg3a021gTDsDVnaozUKP6ibBYiDC7aP7lVQDcceuD8BXYTA52PdsbS0xSQNVPaWkpU6ZMYcSITthsvoOfe85rQqvnRKviPk2qcWW3eiKbJ3UvHtAwOnBjgrhGkNrIfV2/R+DgCtg8QT/4kn3b8o7BzOeFeq/HXdB4oP5rfDkAinLVjHNcivvzsqeNnDktL+a8ItSjnW9Qg6wDS0U2uGFfv6QUCBXT5heHYjJpmipIeHnSFr5ZJErgHv5jPVXjIhjQPEWoHwHi1DKCX1aoE6TUBO/sc0p8lGLYnFtkZ2NajnvXnTDiGo1Hh7Z8qm5yDF3qV2HV/hMcyy0OTBhEV4H6vXHV6gQ5UFzqYPHBUgZA+RMVBsoPp1NVSrk0SZwoj/I9u+Zak8v3qjeHO9RmCAq0ZU3FJ4WqIURoyaNH/9zA1Pv7+t5YIqXsLgvnt/bRVUoDbXngJZ8tYfJ9fo5t4IzH9vXLeND2F+udjbCYXxUr5TgwGFLKZApK7aJAW2amdyzQJ6Xq9RTKE0cJ/HKlSkp52lmEC/7Kb6UGIrowmcS+jhL3+4I/ZO6Eg8uhmWTZUJgt3tvGP8Tjhv29my9pUVoAkYkiqW6xCoJw6cdiXnH+S17Et1bBDPDLrT3o2bgq7eokEnN4Ke0LdtPquSLkeHDaA31pUSMBl8tF06enYpdMKk2S2sgtBivIgtzDgvzx7CDsdEKzoWJ83yfdGwuOi8RyAFjMJn6+tYe6Qqka0FGiySSTNlkgwxop7tHy/qEomUoKhL+u2epdPRLIWuMcg0FKGTjnccHHqkH2ayMbim5eKa31b+alhUJma4lQB0KLDUrRlO9li/+1QaPd40aoUUoFjUOrYcVXonQwFFJqxrNwYAlc9r1oXRoCkmMj/BNmslIqSadM78h60Ua1Vkfd7CqIZJnd4aTUZXcnXtzavPvu8KVuq1mns96X6qdxcQLvAzlb5zDklRkUO9VyMX8dvt6z7eUiC4xblcHXy5fzvHUdN1o3Ks8/P3UP2agmsR1MGVwaCUUFeXy/dL+/jzxkDLGsZrhlBcct1dlrbcdJVzI4oVVCIfujE7BZzWxyXMCXrk6cjGrC0JhUn6ofWTHky+tHXl/lxAaYAua46sy6u7+b6ifpp7fgyH6+u64j5hbD1ROduQgWw8VdGnLxUEn183kSHD3AE+mPC+P0K36Cd6tC7iEe6ZsCtT1KABJGw5EG9OjQmx716kJ6NTgATatY3FU/4OYlUa1GAyG1thdiLc6G2BC6VwZATISVn2/pzkdzdvH9TZLk2xohrnF7kTSBLOPryfcUT38DUAOyk0cF2ZO2UjRb8IXMXe6dDj29GbQdRcOBNeMFYdZqDCCRUoqJs07Qp4PYSP0Q5dlRrbiyW10Gv7sAgBu+WynuU7KJrUS4/bRcvdb+vquXMOB12r1KI8bd2E0pV77g40Wse+58kmICG0GHgpcmbmGF1CyiSoyN2knuQe2vt/WgydPCm2fUR4v833fbXgptL8VZWkr+v1No89JsrNiJ4wtmPzQ6WK2ogYpC4QllgnxCr3zPXiSVzmgmrnrXuBbyRL/fo2UipGRUibFxoqCUrUe8laVauJx2TIgkw/mtpHtNXoZI4kQmCD9LDWolRdOwWix7M/PZfDiX9NwiUnRIYANnBw4d2A0m0XlPoZbkCXcwpJQepj4ufErPf9m7S+6It4T6sHYn7/38eUpZI9WkSJtLxPHD2Yhmwdui83GXm8R9WU44O0sFmeKPFPKEJVLcE4JVSsllwPJ9ZcqjsPF39fkq9cW51WjvnZAGUcXx5AH1czNbYM7LYrnvw15J60irhVkP9WPwuwt4akQLejYWx/znrt6YXxLj1V5XDWY4xb1h2PsL+fOOnlz6+VLNMcx0rV8FDuJuFL71P5h4PzQfAVf+4n6eZjNcIlltvNVUWFPkpQdFSnlBLm3U60RtluKTQ2vEPM7zPhsZDwVlIKUOLocfLhQeUXcuVtc7ncIOxlHsQwF47sEgpQyc0/hd41fyfas1mF6XJnUPbdVv8fnbNbBrFoz5VJXLWjwUBlqlVGmRIITSVoh1CilVhglgxg5Y/4uY6HW63v21/EEauEqssRQXlbqpfmJWfoglayfpLW8kJ6mVtyGzh2LHbb3dyUPpe4gB3lpRyIG1a91UP3dnvETP4kV8EX0rf9gu0Ff92C24ls0K/jMII66zTKeZKQ2skEgeVfN3ssXVwOf2NotJIWqSXSXggui4RJpHxRNbkgiaRHbP5nUxRUQp5WF1SkphByRH2Lm3TxMvw+eQOnx5eARZvv4fHIEnrxrGk83PhykzYMUy7u0az72D5Gx1ADP87IMiEEqoHZyXwXYxsEckpLiXc4JCPJhdHkbnCsGiUf3csRCWfQ7THlfXR1cRvkB6v+2ut7g/bn8F1OmibwhZlC3+t8WKY8ckS8fNQiFJwoReTarRa8nN8FU2XPS58NaIjFdJqUBY9pkIsJoNVz//nDQ4uEwsmwMopbIk4+wqft6XLVqQUrcvFPe2SI+yA2skYCq3rxgg1Fh5Umc7rRQ9jO3um6TE8+Yl7ZSmDP+tP8xo6TVdcSnc9N0K5mq6h3Xc+AqMGyc6Mna/3e1YFrOJT6/uxF0/rQFEWeCOV4YTYQ1RNesDj/25nt9XpRFNEYVEsehxb5Npq8XMqxe14em/hZLxo9k7uXeQ/2x+UamDp1aJMM6OlWzi6fzaXP64oyddG4Ruhm0gTLBFwUVf8NzvS7BjVUt8IhPgws/EvcFkEmXzo94TZGmgyeuJIK7xIPDu5R2Ustj9x/OpX1V/MnQ4K4/aCFKqXzNpEn9wOfx2NdTpBrfM9Npn8n19FNVot9dmh64AN3DGIMmRCVbINGnIjqZD4Lr/ILmR7x1lFGQJE+6oRFWlvv5XMW73edCblKrTxfex/HlKaXHe04HPK1RkbBfd/5pLCTgtuewoAbMmnjqyQcRB1ZupRJIW1ggooQyklHR/iZG+i/p9oGE/WP0dLHwHrvgZWvi5FrUJdtl7qyBLt5KiSUq813VtdqjJ9Tf7RzEytQP3/7oOwI2QqlMlWox9X7/r/rrgn1jUIi5FkFL5Gf6384X+j4n4RI+kkxs57F8Me+dLCTUN7loqSuf/vMn79+kP8nvzbCRkNsPFXwR/nHMABill4JxFUamDxzQd5frbl6hPZu3RJ6WU9s0aln30x+By4EqojcPhxJl3nAig0JrAiYwMav1wEQAH+79Plr0uBbuPY8rNowfgKC1k4tpDQal+uh3bzBhg1TEX8xZm8AiQdvgQ93+2xG0/bYevEruT6aZj1DXB2HFbWOdyH+x+j5hAN/N2ntpUm6nOwIbMWsRTwDNRYsL93RYowL1+vr81gZ5WMJ08zC57nt4hcGthhqbDl4fJsq8OX4KkCaz6UY6jUf10W/gxycfU7/zb/oXkdernpvrR7ufW4Wvch7APHh7VmYfb9oMFK2CO+vRnN/RyH3Cz4mEHxJpLeTiY7lqhQCmhlLKUclmWTAoEg2+HiY5et87Vz0R6QjYP1cs4XvKVCLziPfwCfKl+iqTfnezxEJUkrc8OfB51uvgOVj0Vi60vEgRRZMWUZnF0k/hc5MA4Ml4ETsUnRaB1dKNoWW31eP+F2TBNsv58SnMNTX1cNT7VK9+Ts4THd6nfhz8vApnsshfrf29db4Vut4XWhdIXTuwT/0cluavEQlRKBcLYrnUVUuq+X9YyeoBQSn2xJp+5djVo/fOOnrBjmVBJZe3RPdaItjV5ZEgz3p6xA4Bmz0zl2h71aZoax8DmKZJfXWjYfDiHkR8KJa4JJ6si7yQmLglT0RyIrOO1/dXd6yuk1Dszd3Brv0be3jySia7dHEnbl9SOrfWSYziQJTzcLvt8Kf+7pC2Xdz0Dy6rPBkTE4mp3OeN/ERPPa3pI34PZ4q5mtNiEwkKL/OPw9SDxW31go3o9yr/b5EahKzA00Jr7P/33Jn68pTsLd2ZQPT6SFjVUonrLoRPUBuxYVPN/ne57WsREWLn3vCZ8NEcY8T85YSOvX9xWd1sDZzZqmoTqMzFVo5JPqKUfN+shL12ocqKTVVLKFiPGfV/ddH1h8AvC0FtPbbLhD1Ha3mwY1PPRVbs88NeR21HsnuT78ybhe3jDZGjQx/tYoaqV5YSXTHDJJFLVRjDgcTi2UXQzzgmhC3Z0kiClQvEmtEWLRPma70ky5TGmQ23qVInmks9UQqpTvSQm3CUlRy/8TPhZuZWy+SnBtJdIFSpWNXbRdsELBb3u8f2cNs7SU67GpYi/h7eG9ppyGWQ4En5nOQxSykBYkVds584fVzO8TU2u6l4xAbE/rx/Vp0cleHypfl6ZrN5YejepSv7hLchD2k/T/t/eeYdHUbVt/J4t6b1XQgk19F4FpAsIVhREsPf66qeor6K+9t6wIzYUBQsCilTpnUAoSWihpfeebJnvj2dnZ3Z3tgRiSOD5XVeuJLuzs1POnPOc+zzlH2zw93EII3upuBApAJ5Zlok1y9ZYXhdQb9Sg3rQZogh0E/zRR3Mzju6Ox+adqTjqLUAriLhmpR75OArgKMJQjj0+gNZswCOL9kCEewMzSncW0AGHS7X4vDACizAPZfBHfaXrwSPAmwb4CsjuppLXj0lDg2CsP9DW299O0BHgpdMqPHZsRaKEumPAAaBGF4KHJvR08PrpcCIN2L8CU9ua0W34QAevHw3M2Lh+HcaPHQN/Xy/y+mnCCl9YYxEAUq4GqgsRk9AWsPf6cYa1EkyA7W+AwsTsz0HvL3+ugeWIXVJbJos3Uj6H5NEkBsQoKiCd2kYGTEx3IFAlP4jGw5VGCVflee3zAUhIxpu91490/JIYJYlIakZRZb68mucu0a/9fse95Hr788WaiNXSFq7+gu5zRAdaEV7+KCUdtV+tLbN4a/qG2RrVykSsagZSRAeqeqnRUmivb5jr5K1SMQOjE4P/HCe5qkieW/YimaZxRSkA+HRmH9z1LZWnXrp5D67UAgViCADAV6/F7v+OppxURZak4JYk4Wrcf3l7ZOZVYuk+6hu+3SaF/1EFxvEpMXjjuu5OK/SdKanGz7vO4K8DucjIs/WQi0Ux/IU6oCoP+Oc1YPyrqpOodY+NwMg31wMAxr6zARv+zy5HWOERiPMG4og5AcBrAIAZ/RPx0tXdkbPkSexK3YvXjdPwxBIgr7wODzrxthJFEW3mUMjihsdH2pbnlsjaTF6MV35AYdiMx2w5VmT9e2rPeBdb2qHRyF5RZpOc52Tye5Q77uvJQI9pwJSPPNtf2Rkac4ITHMadTUcLrZUfAeDLWX0xqjOND2m5VegrBqBSYTdYRSnRbsVfwX/GdrSKUj/sOIXZg1ujY4yH4yrTIjCZRUSDRKlWbdq52doJysp7EtaFE7u0FqJIY6iXP4159t7crsJZ05cBh36jRZyEfuQpbTIAQQ1Isu0K60K1lKhaKUrZjXPSc6NxMvW2Ltp46inlRJSSUhdIVTIlG8Oe4+spWXpCP9ku8Q2l8MuGFsyQKpJbxtc+SWH46+FhGP/uRswcmIQXpyq82SOSHT9v7ZtUPKXSfgZ+v5c8lySb81w9pVyhvC8eJFH3GKsXmF2/aTaTcKn1blz7qwXDohSjiiiKKK8xos5kgpdWY+OxczS/EqeKq1FYWQ+DyYzuCcHw99LhjZUZVkN845FCFFTUwUevcZnrh16TKonJoV/OvH7c5fo5Vw4fPQF/n1Lr/yVnMrDS2NNhO6NXLaABzlaYkWNWzweVJrZFmqktef3oNCgVghCOMnQMrIWvlx/0Wg18NX54RnwZgtYbQ30joNNp3Xr9jDyuB7KBHh3aYG6nPqq5fhwqfGk0CJlXC5iB3x8dD11IvK3Xzw/fABn78ey4Nni274iGXbRiX8D/HvhqtLhruIph4t0F2A9EmQsR1c7RVdZgMCDICwjx00Ovb+KuyGwiYx0Axr5IBntDuG0VYKiSwzG9FBM6tVhzL3+g7UgSBszGxhvwJC8pv3C5OoiUXF/J6udd5xWTShTbuxc7Q1ql8nO8r05RC9/753Vg2zz6WzIsB9wNdL0aiFPx2Pp0OFWvufMfIK4n3cOc/WSMtRpou601jDbE82M8V0xG2ZCWRClleePlj9Lv3V85ilKlFoMxJNH2dcklX+/nEG4GgDzarv8aOLQUOLDEfcUWqa0uvAEY9iglUv+3cBZqdC7he1WFLnOAjEuR80pEgrzuCsQQzJ/dF5d3UgiwUkiJE08pifdv7AVBgE2VPIm/Dubir4O52PTESCRYKhRml9bgnu/3WMtOqzFjQCu8mOINLLS8sOcboPOVQPsxDtu2ifBH94Rg7D9ThlPF1Xjgh7144coUhPrTtcvLOoBoiDBYzLceYWbMndwZABCbvRqTtUfwnXEMTiMab6/KxMmiarx1vWNC4aGvyRVnL3tjnXqo1QJLVa2FNwCPZTg9P8aOgkzsXb0KrQVvZImxtrnRsjZT0tukITQunN1Nk0rJ49M+/EcSpdpdTjmdfr2zYTl7tnwAbP8EGPIwMOZ5AMA703rgkUX7HDa97etdWP3oZTCLwPuF/fA+KDfMVdIGGs/Gif1zx6L7XKoWOO7dDeiZGII7h7Z2G5nTYtnzLfWvrhJZX0TsPVVi9ZTq1N7O8/vAL0D+YaDnja7D+CRvKGWCZ53FdrKvUmyql8fQjhM8SzEgIVXf8wsH9n4L/PEg0H4cMOMn15/zFMmrSfJy0mho8cVscPR4kp4bwcmC2n07yD7ytLKmvSglVWBNX0bXN8gihpc78ZQqOQkcX2cb+dGQfLVKpHutWPTpFBPkeQivqxBMSYDSKSqPV+U37PgAstNy99P5RnZ2FIKUtqlayoTdCyh3Z7frgLYjPP9ewYmnVNkp4L0eZOM9neP5/i5iWJS6RNlyrAizFuz+V7/jndWZ/+r+PcFLq0G3hGCr109ZjcFh8jB7cGsk1+wDFB6ZV8TXIqRXV4cwsnZ/64FSYM6kbni01VB46TQIyt4I77pCmJOGkegj5fqRvH7mxQP5Zfi2exow7kbF6riTKlnOWCIC2UD35CR07++hF1p9tdXVPiAoFLAPBXG2MuUJYW2BCa86fz/YMiBK4k9zojybrotG7xhm5glaHaBVeKXo/dT/lvAOAG7+reHf4w5XieaVuKvwYXUvbqAopSYWpC0mo7TTFbZldSe9Td5KSgPorKIPkrx82rio2iQZq9I1PrYOWHq/upFpDd+zGFmiSG7potj4QpWytLJ96WBDrZz0fNYfjp+VVjGD7UQpqVJXjxspgaozPM01I10zQxVdN3tR6uweYNPb1JbO16vM6illNylpyEqwyUAGW/lZ4D+Z6h5+FhbeMQDTP9+Olea+qPJPxLt3ToMmzm77MItwXnqSjFMX5Zzfu6EX3rtBFnZXpOVY800BJOhsmzMKX246js83nnC6n0fHdMADlyfTOLBtk+2bx9apilIAsOSewWhvSXr+x75s/LFPFsju0q7GHD1wQozFfSPaokOdYpz1CwOKgPnT2iHlR8u+9pzBkj1nkDZ3rNXD65nf0nC21NZjbum+bFzZw0noTWWu03NkVDiwBPfnvopQ7Sg8bbzN9r2/nwGy9wA3/kir/t9OJS/Xhy2FMuxFKSjGk4ZUN5PIIy8/RMriwdSe8Tai1F2XtcWnG0islYoHSLx2jSL8zipKqYfvSQT56PHutJ54eFEqACD1dCnu/SEVgA6DRtQjOqQRvRAuNKWnaQwCgA5jXXurXiQ8sXgfllpEKa9Qu3Fr+yeUeyy6i2eilF5lQc9gZ49KnumA7HWu5PAfwImNVHlWyu0kYRWlFJ7I5yJoOMMavqdo050mkgBh7+EsiVLOvGIaIrYBdL30/rKtoPT0MdbKi63ObHB7725AXmisaIBIsv5VqhQMkD1iNrkW1vZ+R+klUq5StBEX4XuSKBUQCbQeRte69WWeH59EbRnwuWXe9Wyx4/vdb6D+GVD3Tj+xETiwmI5/yENUodATnOWUsiZdb9yiKi0ZFqUuMYqr6vHDMQ22bf13BSmJq3vHO3j9zFtPSnr3hGBc2yfBmrNHEn7Kauqx6lAepvVrhchAb4XnEOX6qag1YkVaDt5dfcTt99ebzHh3Wk/EBvtgwnsbcSTfNrfRwjsGYHC7CGDnNhtRqq02H20Hqkz011CH2SEuApDKhv/xJk2ub1wExLUBTm4lV9SoLjRYSBP33QuAYY+de5UFZQJ1AFj5NHXWY19ynnTPmmRZUB/Mna1MNQbSJLsilyaYjekOe75IRn1wgjx4VhaQa3dkR+efc4ZPMBlXQfGOlUP+TaTzUMbmm4yUWL8yj7wxNFpF9Ucng58zTylnoYbJo2l1Tik6SRz8lVbqguNt39f7OnqRKduEFGbnCoOdy7/eRftNuQqI7Slvs+4lYMMblDtp4pvuv6shSEazRicbGMf/AXJSSRg21pL4GdnJ8bPSPQyxE5olTykpmakaZjNQnkPPsTtPKWWuD7X+orqIjHtl2Oe5Ip2T/TGNfIoSjepUvAnt0erlCUtemroodfwfoPg4Bve9BeseG4Fg3zEI83fSxgNjZXGw7JRnyXgtXNEtFlmvTsSS3Wfwn59pMj/wlTU22wR46/DeDT2t4U8OFFg8jcKTKQ/YsbXq2wHQazX4+5HLMPadDQ7vtRFostA5pReuGJWMFSsUopRlbPA3lSH9xZno9F+5fHc3i+eKMx78YS8md4+1DZ++Zyvw8SAyqhsz7PhixzKJKkQQ+rexSxYsTSBry2WBXrlYoJxYSt6lZ3ZTm5HyBZadcT/xA+ieSaJU3kHKLRMcD0EQsOqRy3AopxxX9oiDIAjo1SoEd3+3x+bjH03vjYndFYs2bnJKKZnaKx6CAGvCY4kBr67HjqdHISrwPCrzFR6lftGT/If/NtmKa5abpp4rqDlweBmQvpy85aR21EBEUUTKcytRXW/EZOElRAsl+MHewzckiUQpd8KpdSxXtANprLYPMZfEE52P+mLCya3Ajk/JY92pKBVB3kuAXKW1MVDLM3v91+rbim48pRrKg3tt/08aQmNA66H02ypKOfGUqrNcV+VCWnQKeV7npql/Ro2DvwEFh4HEAZSX0l3ftOMzqs4d010eh10lOrcugkYCHcbRz7kgFaMStOrHFxBJdljpKXWhSJmc3kUaAAc0Ts5NOh5l27nEYVHqEuJwTjlu/GwbSmvoAWkX6Y9jBVWq247oGImbBiShss6IV/48jLxyengmdI3BkxM6WSu2iKKIU8XVlJsg3B9DkiOw6lAe7vhmFwDgjmFt5cozAP46IK+47j9Thv1nytA1PgjX9E5Aj8QQXD1PTjy9+jANHJ1jg9yWL1bSJykU/duE4WOL+DXs9XUO2/RvHYYFt/ajXCMAUGgx7NuNAo6tcR7iodaJ2Fff++NB2p+UzNBP4U2iND73fEODQs/pnnlu2ItS+3+iFZ/BDzgXpbz8gCvepImY2uqMs5UpTyg6RkKCX5j6ZMUvgq6NqZ48k0LdePM0JZKHkXRM+38CfrmDKpaoebMoEUVgyW0kLo57mQaq9mM8d79tzMndgHsoJ5ZkbNEXAF9dQb8fO0ptw9puG+AplZ8OLJhIZZSveN12++7XOQ9VkPZlcj9xsRn4JVGmPAc4u4uubztFlTKzST4PSaywev+o5EnyC7OtHmN1S1dZITtflCuO0r09vBTY+YW8TbvLaZJprLFdTXfnKZX5N03C1PIwfNCbViZvW6Ue7qjk2i8pRHDTO3LlPiWNWBkP0xfRM29fYaghgvwP0ykpLEBJ5JNH275vNgHfXEl/J/RFGymnhTM0GvImKzhM/XsDRCmJa/okoM5oxlO/2hrse/7rQgyTkMaYfndQYvuCw9TWneQ26RBNVY7MZhELd5zC1uNFSAjxxcgj5UAJ0KFLLzhk5pLCN6qL4aPXIuvViXjq1zQs3K4+QTz8wnjsPV2C6Z9vBwD85+d9ePv6nvIGEe3lUJTSU82rD1fw445TOJRTjq7xweiVGIL20Rc2h5G5sgAaAMViEGbaL25Jz0RdOYUiA7ZitSDI46b0LKb9DGz/GBh0v3w/yrMdQ37tqciV+7utH9IksMc0AED76ECb6zS+ayweGtUe762hZ27f1aUI3nMHUDEWGPIgbdQAUQoApvSMxxRLPq1P1h/Bq3/RM9D/pTVYcEs/jOh4DuKIyQh8aFnseDjNUcw/H7Z/Buz5GugzW06+7Q5lFdOcfeqiVNmZhqcIaCyMdeT9seMzILw9kHcACHCs/ukOk1lEu6dWWP4TcFyMw8r/3QZo7exK6X5IqQWcHpfF5lQuVEn2if14bs3h6WT8kMZcey8bUbQN35PyFVXmN54dpvWiRRZntpUSq6eUk6n30TXAjs8plPdcwuv9w8mrWFrsk8L3KrLVvYPr7cL/AKD7NLJVolI8+06TESi2CDRXf+7ZGCHZhsrrH9WJQozDVVKBSJ5tajlMG4LRAxHImmZC5R4pxTtP7reEfxSlpVAWfVEej5ZFKQkWpS4hkqMCEBHgBV+hHu9M74+ByZ4ZBFN7OU/SKQgCksL9bcoKj+kiT3huXbATW+eMsv5/93eOHloHzpbjwNlDTr/DlSB1eacoPH9limplpI7RgVb3cYn2UQFY/uAwx1LfUrWoDuPIeAhrq672J48GKvJsJ7v2FaXsxSNlh69cFVr1LG2bPNozUWr2MgpJkiarvqHUWbuK/fYOdG1cSQbBuQzOi26iqiY3LXGcMAI0CZwyj85N8hZbOI08Bu76B9A2vJpVoyGtHEkGlJSD6dQ28mzxcnFshmpaSQKAca94/p0f9qPJ8B1rgVjHHC/nhEbjOKnV6skAqy4kb6mASPeDsaAiJP08i/ax41NgwmuetxEpFt9sN2XeOo8m5r1ucsyfMuYF+bXT24CfZwOtBtuKUkpD1cFTyoNqPYoJe6NjNgHBreRnE3AUZLJTgZfjgIF3A2P/J7/uLqdUXRlVKVJbffWPIFGqItdlOJqVShcJ6qW24WnlH1dotO4nzK6oLQcyVsj/5x1w3CbXUjnVK5C8Ug21ZHwHRDufvLQfQ2El51GBUSrg8dSvabhreFvMmdDZsw9KolSrAZQPLXsv5fNQVmNTQaMRcNPAJNwkiRtvWEIx1Ix3aVxSjAkvX9UN0/u3wqQP5PDB+BBf/HLvYPh6aclT2MIve87ilz1n5c8NaEWCSV4aedo0M1GqrMaAHs+re4C9O62nS9vl36S0MBthAIrEIJucZwDktldXIYcv21cQtYpSlmdRCtENb0f2SckJEgndPWP5B+3+d25nAcAjYzrgkTEd6J8NbwJZG229HUNa0Zh3DuHPtw1pjaMZh7H4BI01s7/aaX0vIsALA9qEw2QWsfpwHoxmEaM6ReHL2f0cdyQ99wDl5+rZSKKUKNJzk3eA8sb0vwM19SY89ONe/H0oDxO7xeKjGSrCf7uRwMinyRM3Z7/j+zs+B1Y+BUz7nsL7mpLqYgoPzSHvznfzuuHdz2sALEdUoDdC/PQI8tFj5qAkq3joDFmQAlKivLD8UfXQY49DTK05pRSi1FWf0H1QjqNAw0SpsrPUr/a4kZ4xSUD1C5e3MxvoXivt+HNl9jL11yWvGKXdZE107sSLqDIfyPyT7LZzzfmo9IYPiAZGPEV9hlpYnBRJ4aWwVYITGiaglp6kvkrv57iwpoahVl5sUqYciO3h3C5WFtYxm0iQry2Vk6t7ippXm5K8g2T/BycCATGO7ysF6IaEWoYkkg1tjyci2SUGi1KXEHqtBl/e3Bu7N61Dn6RQ9x84DzpGByIjrwI5ZbIHzrO/yxOLr27ph2BfPa79eAvMdh6NE7vH4u3re2DB5iy88me69fVu8cEY3zUG47vGoF2kXd4WFab2ikdRVT1eXEaG2JJ7Bjs/7xt/pI7Oy9+1wTXpHcfXJJXbVC8bNoAsSnWaSKtvgK26bi3/6qGXkn3407kmJFTS91ZS8IMbaLyLorwS5iqfkdKbpiQLyLSEk2T8BXS5umHf2Zhc9hjQ7zZZSAxPpoGo7DRwcgvQXkVkk5BcngHbfAhn95B3TFQXoPv1jp8zG+mnoeWOz4XAGIsolQugq9zGnK3IBMXR5LjtcPr/yGqgwPLsPbjXzrASSVj0j1T3kpNEWvvV9My/gBP/kIu5VZRSqcYmhfFJ1fMklNdNeo5cVZRLW0x5EdqPpZBMKVfCv+EpFd0FeMTO3d1elOp2DbDmBceJy5QPSaxM6G/7elQXOj9DtfOcA5LHU2WeZ8cprTiqhW80pqeUM46tpUS48X2Avrc43y4nFTZVeHJVRKmszfQ7aTAZ+dl7gS9G0XP8iMr2ABU1aASmD2glV5ctyACOrKJQovi+6iGyJiPQeTJQkEneCm1H0vEecy9K2VBbJt/DcBXPOWvlSts23jU+2GXC2V3PjEbf/622ee3Y0lexbNkxTAivhLbV4OaV9+LkVmTuXY8/d2XiHX0eHjHcC2tOEgsPL0rFw4tSkRjmi/gQXwxrH4l7hreTi3z8i9SU0vNYhCDHBTClp5QkSsXbiVIRHai/k0JaJM/t0DY06ZdEKQxxfSB59qJUA0qZq3l1BMYAg+71fB92DIsR0b9XV/zfL7bPZ2FlPZan2Xoar0nPxw2fbcWPdw6yeb36yAb4ASgUg2BsNREqU0cg7xB5hum8HWy24wWVeHJJGnZkFWNkx0h8OasftYnlj1KFNwDlx7aju6IqIQAsT8tB5NKDmHsleZDkldciq7AKA9qGyyHPuXZ9+8a3gTWUXL4mcx18/yVRqtZgwrO/H8BPu2xzBz2o/QWP6vehWAzAo4Z7sN4s58nLr6hDfgVNinedLMFDP6Y6FXLv+naX9e+hbQLwnTAX+GstLSbZp2XwVJTqMA645U/bcdJZQQurJ7ITD0hl6Nf315L4Wl1MtjdAHsySiOATQrZFZX7jiFJqzBtMgvDsFUBrxTM65GES2Jx5/Ej2V04qLUC7mosUHQN+v4+8oa79Un0bjQYY8YTzfVjD987Ds1QZll5dROOal5/zMNbsvWRj+Ed67q2szGFafhZ4txvZss/kNWxB3Z0dnHeQFgHjeqqnC7BxMGiERXUO33OARalLjLgQX6Q2QeXJT2f2wQhLWeufd53GxO6x+Gar7M470uK2ffwV2VAuqKhDRICXNafFXcPbqVd1awC3DW2D24a6ybUCUMfWUGFGQjLWjXW2KzOSaCSp/4LG1sA7X88EqyhV6nybilxLLooY9fAfd3lonFFdRAmTAc9WRwCauEko89tcCATB1iARBFrx3PMNhW+6EqWUoVpSSGRVkZxAseMV6qKUNdSskfJ3iSKw5HbylLrs/2xXFwOigDzIuROsKzJOVnemfSu3R5NRTvY46H5Hw6G+Epg3gP5+Kttx9dIavmfnKaVWfU+jIko5E1ul9qb3k6+7K0+pPV8DJzZQ24/s6Fgu2Q1C+h/oeXI+UDsE0Duv/uYUaVUtoT8lxQyOJ1Eqd79t6EB0Cv3YE9mB8i+tnutcEJDu64rH3IebbP0IOGLxKvFXEaUay1PqzC6aELYa5Fg1sCCDKiAZql2LUmcsk6CE/pQfrTCTVliVq5NZFs8fyeivsISGq4Um/pssvo08iQBqm92vBya+bbsartXZTo7bXU5J5Z2V6naGaKacHdmp9Lwb7J4xqzegi4UKYx3lfEkaan2OIgK8ceKVK7D1eBEWbM7C34fycJkmDcO1+zGn4DaUR96E99r2hA7A3KUHsWBLFgA3Cz3/IpXbv0GHQwvRwTKcVnSZgZtvnAEA2HKs0BqOCACni2twurgG244X442VGRiaHIHPbu4jh+7/C/gZSgCBPKUckPrpggwSzQUtTYKU3KlIOWA2yZ7cYW2pfDvgWULtPItnVLvLSRBukChlsWOchRqdI1f1isP1/ZOQV16LN1dm4OfdzguhbDtejPsX7sGH08lDKTOvAqfW/IbRWuAT42R88fpGZP5vgq3wV3aW+ozU7+kaXfEm9mdX4LGf9yEzzzan6LqMArR9agUeG9sB16SuguRzHFSVhSBUoRy2Y9uCLVloF+mP77efQnpuBQJQjeu7h+HZST2oGldcb0AUkVteh+8+fgmP1b4PAHjfOBVvbxoCbJKFrqk943BV7wQMS444Z6H0YHYZJr6/yen7Q7XUL71pnIb15l64Y1gbnMkrxPrMAtTA0RaQhFyJ1uF+0Gk1OKrIxfpd/G/Arj0kjA66z9GrRvKmLD3lOkQuIMrz3FZuPaU08naSN+Cer4GB9wIPpipyq1q+t7bUIu6r5HhsTEx2Y6kUBuuMoDhatCg6QuNb50nOt60qBE5tBUJbn/vxmY0ABMfiLCc2Amk/UXt2NU4DQKFFlIrsSLlElz0MJI9xLkqd3ka/Ww20bRv1VbS4pvNxnB+0u5zEqMBY2a4yWeZb9l51rpASizvLreouPFkpSjUkfM9kkIU1ZWQDJzp3gEUp5l+hdYQ8eDy+eD8eXyyvIG2dox7PHhnYTNTi+moacLVelFND+XrpKYp9VqL09pAm0jofRdJGReUzZScsdWqeeErVVdLE0zeUQn80Ws88pY6tBX67h8Lrblri/ns8RcrJFBjr2o21+ARNgPwiZFFq1HNUYc1+QtVQzCYS3CI6NE5ugHaXW0Qp5wmIAahXLFF+v7OE7lJ7qG8kUaq6iCqBAMDIZ2zfkybm0kR93MskBDjLPaZcqdn+MeW78Q1VdyGXBledr7qRaA3fs0uablYRpaQk3v+8SgKMq3at8wV6zZQNUMC10Gf1WAyx/Lbs1xNRymyCbsktSAJgzFgB9L3Z/WfskYwnL38yLo11ZPTUlFCOEU/C29SEPCXKqkTukNoCoN4OlB6f50NOKiW7N9Q6ilKeVt+TPEg6TyIDvaaEPPekybvZBJyy5B/c/zOw4wt5VdydKGU2kfHbGMK4KALDHgUW30L31lBNBS0iOrr2KEkcADy0v+HhcL6hwBVvOH+/+zRK8O9MsBBFYNFM4MhKYPxrFEpqQRAEDG4XYQ3nK//fA4ARyDAnYk9ajoMnCwBc8/EWzJvRG1d0O4cKpudB0YHVCNAAOWIYYoVi3Oy1DsAMoLYcg49/gKzkLfi43Yd4beVRh89uOlqILs+uxH/GdMADo9o77vx8MRkRKtAYkdJexRNAmtgcswhPUV1c51orz6bnRaMnAWDUfz0/lm7X0op/0lAa18pOUWiss4mcsQ7Y8gHQYbw8MVMmZTbUWELBBApDPQ+ig3zwxnU98MZ1jiE7ZrOItpZwsWX7c7BsP4k5GpiR6k0evNvMFDbb4Zk/kfXqRBzNr8SXm45jUul3GHLqU9pRbRkmPv0xDoquF9+++nsn7vchgbhM9EOwUI1umuPw6zQa793QEwDQ5dmVAID//i57n43W7MGzmfNQ8OPliLzzdxwrqMSoOXTcS7z+BDTAx8bJeNt4PcZpdmCw5iA+NF6FAoTgt9Rs/JYqV9V8aFR7nC6uxp8HclFjMGFUpyjcNqwNBrUNty0+AODA2TKbcFwlXloNOsQEQFNfiV7l1P7bDZiErCsvB36/Hzi9EJj2AdDrGutn/kzLwT2K6qISWUW24+rR68uApfMBCMDVX6iHeQUl0PvGGgq7akhS9fQVtCjY5jKgyxT59fg+wA0L3YtS5YqE3hW5JOLbL7x2vYZsgPPNTyTx0yzq9694U+7PJdHDfmHOE9oOpzHvxD+uRSlJaPN2I8qUZ9PYGRDtuPh1/ddULEXplQyQeL3nG8p56FaUsoTiRXSQvXelHFNqnLKIUokDbV8/sopSRrQaDNz6p+17U+fZ/u8VQHZ4VUHDRCm3uVUtkkhBunoOLqXNb1+0xxXFx4GP+tOi0ROKar3+EUCnSeeU3/JihUUp5l9jzoRONuF3ADAkORyxwQ14mJuC9OVA6kLycOk1gzwK1v0P6DnDtjPM/BNYfCttp6ywplNM5uxD9wB5Emw/cW6IZ0J1EbDvB+pMx79i+x2uRCnrwOXCPffIairjm9DPtauvEk9C9wCK7V/2CIWrSINR+0ZyYV/7IiVtnvweJSb1FJOBJmYhrcj9XBLV2gwn46Yg3VqlSBUpH5UzV1616iHKbRorfK/EhTBoDeuyeEr1nunZPotPyF5Sw5+gCcmS20kkuHc7GVvKSihqWMP37D2lVFaFhj1GSXy9AmXPEklEMtbStZIG/8BoCnWzOc8oYOJb6m79NWWW/VmeE8lTylBFz5wrl+mTcsEFseu1zreTOPALTebaj6EKc4AiTMfyDOq8Lfl5DpC3VEgihXMdWUnGYjs7sd5slj0bnHkrdJpI4oy75xCQ21/KVUC0Si4GyZA+X0+pYovRpeaFqeYZp8ZZywQpvi/lBjEbbQ3CvAMUyuYVSBOVvDQKnQVcT4Iqcsn1XxSBpz3Mw+UKQQC6Xk0/okjJhP/8P+qbOl0hr2KX55DxLE2qdF7/Tn4m+xVve3Z8Ru0NIE8ShShlQ3UxgoyUIDhDJPHUF7WogxfMsHW1vtcymQ3y0cEsApV1zpNgB3rrcPeIdrh3RDuHibanfLF0HW7X5MMgarGj56uYsu9OEkEH3kNhVLsXALWluGd0Ae5RhCyeLq7GFe9tRIXl+N5alYm3VmVi9aOXITnKsf84kleBMe9sgJ+XFluevBwhfp6tZlfX1+Op+nsRLpThykEqz1m7UcCUMEt+FIN6rhclUj6pkFbuq+3Z034M/QA0TlTkkIdWokquJoDyH619kX5LFa6U31l2Fpg/jvJizXETnnUeaDQCjrw0Ae2ftp2cdhGyECTUwKgPwOQYI6af+QKLTZeh9ZPyNlfqNwKKQx6oOYSDJuqLIgK88c60HhjWnsaunVnFuO6TreinIW+Pw+ZEGMM7olvJanw/XgdcJodVbpszyqbiZutwP/Qoo8n3slN6bFqwE2vSabyNRAn6aGiyHjT8fizt3Akdf3sZ3oUHcMKvBxaUO+amkhLMS6xJz7fuzx1ju0Tj7Wk9EeCt6M9ObQcWaIDgRNx2pWVs8Q2lvEZndpCta2GCpbrooexy/LLnDJbuy7aG9knsujkAul9upX9GPOncm1znRUV+gmLlkHk1sjZRWHZ8H7k9ntlBxUG0XraiVGCMvOighiRKhbYBni0BXkkg0eLMDkePnRFPOn7+fDi+njyvlOOm1slYmnuAjjWivfOFpjbD6Roc/8f190oLeu5EqR2fk1eus6rDagWQpKrJZ3e7TwgvJZKP6CDnOSw5qV5xWxTlUPxWtmG58mKjE/tZiX+ELEqp5VZ0RlA8RRU4C4uU+rrqIrIv/O3ab/sx1C4PL7O1R9xhDS+1W6hN6Avc8L3n+7kEYFGK+de4a3g7zB7SGqeLa3CyiFb0nZbLvpCc2kZup0HxNFBLk6niE7bbHfiFftuXde9zC3kixfaUV2qUolR4MhAY52gINsRTSk3s8kSUqvVg4KouBI6uojwknopS9tXrnCGF9h23rAoHxtEEvOgYUFPh/HPuqK8GdlL+B2z9COg9y3NvqbLTJDDqfGyTD/qFkbvy2V20quxMyJHygylFBKXA4WyS0djhe6VZ9FtNkGhoriHr56JolVLQAH1vI6Pi2DpqI2d3A0mD6G/AccCWGPwA5ckJtPOekJKoKw0VydNHaSR4B5EYJppI0HUVVusTBPS7Xf09KSeVlKPKOxjofCU9N6Z616LUod8BACfDhiHOmfGopOw0lQaP6OD43lk5HwdiupOgkrPfIihtJREweYyjKGWqAw7+Iv+txoC7aHLbZpj7Y5SES72fuvt6UALwVM755zeQQo1CVUQprQcryOXZlLBc0FDoszInh4SUT6rVQCC2O4UESH2vK08p/ygAAmCuB8rPnF/ogz2CQIb/4T8oQfSyR4CZv9J7fzxIoZNTPwF63mj7uYo8CiVxl7S1PIc8Q1sNdD6hcUXeQeBvhZdNTSmNHfYVgaRtASAkCQcfvhb1H4+ALi8Vk+tewoM3XYNxKTE4XlCJy9+SJ07lte4rslXUGfHGygy8sTIDbSP8kRjmh7OlNTCbRcwYmIQb+iXC39u5WVpaXY+M7SsAPbBPbIcpV00D8ueRd97nlwOT36fnKvV74NBSmwlpYpgf0p4fh7OlNRjyquwNO/rtDda/k6MCMDQ5At9vPwmDiSZH1fUm9HxhFW4Z0hrPTXZdkaqosg59/rcOAH3v0x1VMh5Fd6EfV/w4g0JWp8yT80nZr6bbh7O6I6oziVL5h9RFKVGk0FqAchFK45xSEJcmbR5W3zsf9FoNjrw0AS+vOIw1h/NxqrgaJWIgCvs8jAhfDe4qOwTkrkWeGIo9Jup3fVCH3hoqKLDQeDmm69ZitO8RXDlrCHokhjh8R7/WYch6dSLEPzcA24HOA8bRdV65WhbGLcQE+2DdYyPwxOL9eOmqrmgfHYjqj18E8oB95nZYk56PYFQiXCjHE50KgBMA4vtgxhiLR0jyZUDhAcztXoy5k0hgKas24IVlh7Bkj/MQRlfcNbwtnhzfSV3gbTUAePKkXEgDIA9NADi903F7AF3igtAlrguemWTXPnMPAAuuIFu1wwSa2LtCrc+25/AftBg67D9ye5SSnjfURhp4Ly1e+ISQyNLpClrs+msOkDyKPAVdpWQ4H6zJsxVjqjI/4/bPgKOrKYn7Z8Pp2Xn0sHNP3dZDAQgUFueiOqtHC86A7M2m9CJzR0xXWkCqKabx3FWaj+mLaJ6h1VsiRCx5MEtOOqYMEQTgoVSyf2K7O74HONrPJgMAwXYByT+SjktKgO4pYW2Ay5/2bFu1MdbLH7j+m4Z9JyCLUmY3CxAMi1LMv4u3TovkqAAkRzVAVW5qpER9kZYJpTSZKlGIUrXlcvhZV7sE3UmDAFhUf70PhUUo3Yx9gqh0sf0qp9VT6hxFqf63A71vVp9USFhXU1wMXJLhnp3q2rVfidVTyk3lmyCFoNB+HA0Ku74Elv8H2najgaBzCIsCgEO/UUJCgIz3U1sp4bEnSAk4Q1o5CllDHiTBS62aIEADpLEWgEC5XSTsE4Gr0ZBKcZ4gnYeaMNh2OHkQSVXJTm4igy9psGvxzssfmPWH7Wuth9L1ztpIbV1ZCUWNkFbq7UIy3jSKwb7W0q4l4Qig4wuIkidQkihltFSj0vm693Axm+S2LwleGg3lznKH2WT1uskO6Y84gJJz1lWQYKeGWs6LdiOpHbUZLr8W2x3Yt1BOiCvlFFIL5VO6mBuc9BFavbXEu1vciaIajeuqk/aUnqLnwX6l0pWnlCfhe5X5FP6m9XLu+dN5Mh1rQIxtOCegnqBUQqOh4ypIJ2H8fESpygJg91ckgkhhERoNeW7+PJs8DSWkMcb+Ph9ZRV6b4cnAXRvUV60lDiwmAbPjRODGherb1JZTDrLaUuCaL+Vn3VBDua9MdRSaNeYF12HPkihlOS8vH38AIpZPCwMs1eTaRgZgz3/HYOhra1Fdb7sCPKpTFFLig3GmpBrHC6pwvKDSQbQ6XliF44Vy+OmLyw5Zi5JIhPjp0TMxBPEhvvh+O/V37+jp2DoOtHhO9L0F+OMh+ls0k/Cc+j1Nese/6nBN40N8kfXqRLyy4jA+3XDc5r2j+ZU2+XOUfLU5C19tzgIAJIX7ISncH2F+1J7TcyuQnuu4yHLOSdVLT9K4VldGntmBcfKzWVVEOf1qSoCn85z3hYVHSOCN6UYLLkMeAvrfJXtB2HN2Dz0XWi/LRNAyjik9W93lXGlk9FoNnpucoi4G7vgcSPsJl/sfx5awUPx3Uhd0r9sLfGuEGBSPa655GvhqLQZpM4B41xN3QfKMTRpM4nxYO9UxrE2EP36622LrmQzwK6K2uF9si9Ga3fjC6y2YYnpC2/1OoDyZQnMkkoYA2+bJgjqAYD893rq+B966Xr3qWFmNAScUzwgA+BQdQtK2Z+ETHAHBqwsgPuP8Ofbyt003kWgpppF/yL2tJ3nIGGqAhdeT50jiQODa+a77KU9Rq75ntZHsxrvsvUDhURJWY7o67ssvzHYxpeME8nbJTydvepNBFqXMZnqvrrxhXjbOkLyhlMmzpWMpOgasf5n+Xv+qnNJAsJsL2J9LYn9LqH+xC1HKA9sekEUptfyFP99Ci3/jXrFd/NN5U7+RvYcWI93lnlW2o7B25LlcfEw9j61WDySo9EHKZPVKMlYAP91MC3c3WdJVSLZnQ0Updyi/+1wWfpxhPTc7UcqdF9olyCUlSs2bNw9vvPEGcnJykJKSgnfffRfDhnmwwsy0PNJX0MPecYL7ba2J+iyDt9QBV+TI4UMZK8igj+gARKsMihIhrdRDItQMx1HP0sCiFkpjj73XB+BajJLwJO48OIGEuJIT5DXmSYWY5FFkrCa5WRFT5hy4dj5NMi3uu8KpLdB0udHJB92wewH99o8k0cSdC7MSV6GHSpdxNbR64ObfLeW4nQlyTkSp6C7kZeRq0twQXJ2HMnn2to+Bv54kV/rHXcT6O6PNMBKlTmygvE9S+J5fA5N/W3NKKSY5+3+i31KiaIkRc2glUSkOZv5JxkmrQcCtf8mvn9hIQkuby2SjtrZMft+ThMBKTm8HKvMg+gSjIDAFQsYKYPEsasv3bFbfn7WKjV3OAfs8bokDgC5TZXFLWsVWKxagNFbsS8afC1Luh4O/AtctcL3t8fUk2DgTbQoyKbF/fSXQ7Trg8mdoW1GUxXy1PAmeeErF9QTu3yEnAQXo+uYfpusgCCTuSCG79p6i7nJKhbWlyXfxcQCjXG/risw/qQx8+nLgLkWoRXg7EpiUgpAkIEd0tN1HfB+afOSlAel/uO5/Mixt3pkwCtC+dlkqMU16V54snNlJ5+sfBVz5ofPcchL5tqIUoruQsC29biHMT49Dt/jRuBioWgfNgdPF1Zj+xTacLnYvzpdWG7A+QznxEDFYQ8cQ2NniWdjtOvJsDGtLbcJUT2GdFdk0oXISqjbnis6Yc0VnZOZV4L01R7B8v23OrPEpMfhoRm8UVdah/8trbN47WVSNk0WO4q4GZryv/wCZMZPxwF1OcorVllMofur3wJgX5YpbSqz53Qwk0CvHZN9Q6t/MRjpHZ+NQ6kIK2+kzm4TStiPUt5OQvKS6TKX7HdfL8TOSKGWqB/58gkS/CzWpakUeSD1wFD/fOYAW/VbTcyi0GQ7vxF5kF9SV0SJAXC9KVeAXRpUyJWrLyHsVoHw2QbHAg475lRzIP0wLVN5BeOq6SQioPgMsfwvagsNU7KDndNu8itKiWWEGCdrunkEAwb4kylox1gNLH6Wcj7kAsveRHekpAVFkL5SeJA9ee+9ciS0f0DjX+2Yax654A9jwJjD9R88WLrJTqV8MTQJ63aS+jSRK6VVEKftqummLqXjG4AfVRSkAOLAEWPYoVZS+4nXKHfXrPbQIpAwjPLkZ+HoSteV7t9nmjW0oZpMckqW0a6TnVxKkAEsbs9iG7ooH3PKXe+FPsu3dLSJbRSkVT6mMP+laj3nB8b34PhZRag/lpvOU8LY0nhU11NZ04iklCU9K0VESpSobKErVlNCil0+wk/FKKUqpeJPXVQDrXqZFyCvf9/x7JYcE+/C9rR8Bq/5LXn72ebMuUS4ZUWrRokV4+OGHMW/ePAwZMgSffvopJkyYgEOHDqFVKzfeHkzLYvcCeeW0763kueSsTPeqZy3hJoIsSvmGUqhPXRm9F9WZBjwASLna0QgryKRVzdAk9yEYSqRVK09Q85TyBE9dfFsPpclk1kZHUSo7lSbNynCtzpPpxx0+QRbDsJzchyM70kTHPwpCVT7Cqo6434c9Fbnk/qvRAXdvbrjIo/SUOldcfdZZ+N5lj9NPY+FJCGV5NrD2Jfp75NPnNoFobZkEn95BK5jWnFJOwvdObSPDL6a7nM8EAG5bTUa8MufPWSfGf59Zjq9Jwo99Kd6F0yhP1IOpsqAsPS9eAY4rXjWl9NwPfVhdODn4GwBA7DABokYHsc1wEkFKTwFLHwCu+9rxOtZbnjNnJasl4ntTclEJq6eUk/YkicVSBc/zwdPy1zs+p6IKrQbTsWb8SZP7vreSYGSoIU8gKeF/2s8kCgz7D4XRGqppZVBNaJMMPft8Y2ook8W+3oYmwo8cdEyu6xtKHoH5h+jvcDeTDOme24dnN5R0SyUtpTeEhLJ97PkGgEjHZl/y3C+MEqL/8xp5QSX0Uw/rqCkhb1AA6Dje+THpfWlCZKojAVyasLS5DLh7kyXxsGIybDLQ/bIXWo115NEYZQnjkcSpPFtRCmteIOHDLxyYsdh2su+ExDA/bPw/x8mw2Szi70N5WHUoD6mnS3CswDGJ/5TWJkTlVQBaH6rMCJA3iBQmCdDkpcM48iw7/Lvz/EkWOkQH4qPpvfHRdPX3o4J8kPXqRKTnluPrLVn4YYfziomPRe7ApIrtQEU6YLwZ0KkI2GVnKO8YAGx804koZWn7BRl0bZW2gsbybBUfo4UJZ32HVIXM1SKakk6TaMLWe6Zz4dM6mRYp9KrzZOdVthqb0lNAbhotSviFWRLEB1Lfm3+IbC8pD0+by2gimDSExsnaMmDTu8Dq5wAIwNBHKPefVk82UsrVNFY680pRI9sydsX1xOiUWMAcDay22DoFGSSeKBcj/cKAqBQSdk9uBlKmNvwabHqHBCm/CPUiJBL7FgFbP6D+2L4qa2J/uiand6qLUgd+ob5o6CNyXqBOEylsz1MPqdw0YMPrlD/NmSglCU/KEFTJO9jem9zqiezEc/bERhqbAbt8rpZ8R8p+t80w8hbN/IsW62YsPndhVenxq5xjxPYg8bHIUmjh8meAIQ8DL1qOw911VL5vNqtvL4pkC7mz7aVohZpiigKQREWTUb4Haou68X2AnZ/L7VyNfT+S+N31anmRKMzifaaW7Pz76yitw4g5js+as5xSkvCktBuTR9N9bjWA5mYb3qTvty+sYs/hP6idtB8HzPjJ8X3lopFa/j5RJG9HgBZDPEmbADj3lDLV0WvsLWXlkhGl3n77bdx22224/XbKP/Luu+9i5cqV+Pjjj/HKK69c4KO7gCg7qYsFvR8Z1GYDsGs+rWhd/blt6ERNKU2spFxHo56VBy5BAMJaU5WZ4uO08i5VZLMP3QOA/YvIuOx/F03caktpMumpWFKeTRN5tX1bj1dFlKrIJSMFsM2NpERy8XW3mtJ6GK2USmXWARoMVjxGXjI+IbQykDSEjOSGdKLSMZSdIVFKEMgY2v8jIisOeL4ficAY4D/pJJKci9dRkUUIcybmFB+nRIbZe2kFSWo32XspjMLZdwbFk/DmSWJsd+QdohW+2J60cq3maVduWdlXm5SIIiXrXnAF/Z/Qj3KfnQsR7ekZqMyj5yBpMD1bzrzkTmwgD5LYHiToSiKC2srwlR8A314FjJ7r/PtrSsi7bpVlRdhelND7kihVUwLAIkoFxQG3r1UPVVv+KBkyRUeBWcscDT4vf8A3FOZOVwJHjPT/tQsowe+h30m0GXCn7WfUPKXc8dEAeTVRTcAB5OdWyg13Pgx+kCZg7rwB248h4//UFuBNhcCz7wfysonpSjmQ/CMpT8aWD6kfXf8K5R/T6MngVFsIaDsceDRdPReO2QT88zqFm6RcJb+u1VN4W/4hageH/yAjL3m0HH6ROIDe7zlDDsN2hlWUUjGaS06SV2z6cuq3BtxDFe3s20hdpVw9zVUC3mNrZQHCP1K93xx4L40hJVnAN1OA2Sscn5Wja2iVNbKz65BDQaD3CzMoXO+GhbLxH9nB9trsXkChfr1vdlwpv/ozYMpHshEdJYlSivC6nV+QIAXQ5O/ryVQAxJUnlws0GgHju8ZgfNcYEhEOL6Pn1z+Sxhz/SGobdScpLMdVPqUuV5IodWgpeSM1gtHfKSYIr1zdHa9c3V19g7oK4P2H6e/hTzj30FROIuOdeEBKQvrq5+hZKDlJ5yR5C4S0ovYrLbCoIQmIkrAI0DXNO0hChb1I3X60+7w7viE0FtSWU9ikNAE9thaAQHbEifVkm3SY4HzhQqIgk0T3pCHu+86Dv9IYIE0oNVrynDy+juyn4ETKLQbIbXDat3Qtd3xuEaQAQKR2m76M2nhif+DaLx2/z2zJaXh0FXkbx/WiSpvS85e9l37HWYRYjYaEsZObyTNRzaOn9RB1Uaqugj6j8yU7NCDa0WbLTwc2vEF/T3jN0Xtl+2c0Dg56gO5HbhrZlvYk9KeFhDM7HN87u5uqNQOOiaobErIn2SSu2qfVU0ox/3CW4kAtPF6JJNgDtkKb9AzYJ1wf9zJdo6OrgcyVroV+V9gkN1d48ox4gipdFh2lIj+XPW4bkugqfE9J1iYSzq75kmxnJZc/TT/O0kVI+ATL4m35WdkzTFpIA9TFPinMt67SMczMbCYbau3/aGFNqooLULuO6uy4kFaRR3kVIQBjX3T8Pmn/9qHBaukiulxJgvjmd2kMA2icLT5O91YSlExGmgNufJNsBmmOp2abAPKY4qw6n/I6NSRHl3S/7StSS97gyrZziXNJiFL19fXYvXs3nnzSturC2LFjsWXLlgt0VM2A0lPAB31potB+rGykmE00SLYbKRvwFbk02bR2gJbf0v+J/eUJQnk2GYPVhTRRFLRkTEk/7S6XO7yqIlkYEkUKmSs9RZ/1DaPOR3Ihr8ynyYKxjlYotF40iOl9yXiO6U5u592vJ+Og5CTwyx00YL3blQbjsS+S27dGR52d3o8mVvYTtdA2FlHqBBncZiOtONoPDIBsKBakA38+Ttdp1LPkOeCO+irg++vJ3fXUNpp8aXU0sdPq6fon9icRDbBNCG2oodVKQUOCT9Jg2l/+Yeowb/kL6DWTzjeul+vjkBJTFh6hCYFPMPDTTHmwry2l0CmvQGovk993b3BKBCVQUuGjayjsD7CKUm0K1kC75FZg5JPyanxuGp2DoKF2WHKC7oNWT/el7y0kTCg9ugqPkBDSeTKsbsASXgHyttXF1iTWTleYS04Ca56ne565klZgqvLp+OsqaKKnFuL46CHH1+wpPk6eOKUnyePIK4CMT60XtV8pP5ChhlzoAWD180Df2SQMSmFPnSYC92yhhLTOvOckQQqgUJ5zzQMhCLQafmAJVe6a/J7rUsWtBlH7zdlHwstlj1HIgM6HwmmVxk3SIGDOGed5UXZ+Secv5Q+L6S5Xt5PwCab+4qsJJAi2HUHPidJLS8moZykU6uRmel4DY6mf0upJuBn9HDDyKYhGo8WIAuVAGPMCsHIOsPIpOt7YnrJniLQi66q0uz3Fx0nc8wpQ71cAoNoiRkvJw88H7wC5cqcrQlsDE14HfreEH8X2oGt8civ1T9Ep5HVTepq8UJJH02rp388Ak96hc5Guhz1Sf12QKQv9Enu/o9e8AmiSqlwZje5KotOS22QPrVHP0SQRoPZ5drfz5LFKpDFNmctv5dP0rEuCtcRvd5MoP30R/V+SBez9niY7pjrqj6I6O/+uGIVh7iznnW8IcPNSar+FmcAnQ4EHdsuT9My/yfgG5Iporpj8HvDjjbTC/XYnmnireSv4hlK7T1ssh5BLz2byKNt+RTrHylwSUsPbkRdEaBsab09tpXHvu2voGK/5Uh4Xf7uPJi5aPfVzfuHUHwTGUFtQTq4P/0GeeQd/dRSUu15jCQEPdOv9hOTRQHAr6gsM1fS9P80iYb3KEr4R24P6E68A8rCU7o+xniYyZgP1txod3QupQmhQPK3QA2SzHLR4aR1dQ/sOa0sJ752hFBuchSIlj6IJqW8I9RO5+2yvk7SgsuF1uk+dLH29oZbaStEx2QtTmVT976epDQuCbT6doHhrOJxLtHrg/p00NkviWvoK4MfpdE21enkSqfWiBZVJ71jbckzpbggHaymXYNrPchEIvR8w6D7yKAHI1sv8i+y6mhL6DklEUSbRThxA9uOZnTROz/yNxh0pP47kCbXxLfp/2GN0zZc+RM9a5l/qHusZfwE/2OXqy91PIZfjXyVRr9MkGtOUIeYRHWhcWfYwtT37XDxJQ6gCppS8/uxuYPP71PfYhKwJZMOOnkttQRTJvjEbSJTreo3tfrMsYxkAbJ0nizpqIZuth9LnpfdObqVrVpJFY7yxlr5DLaTLUyTbqvgYece0GU7ieMeJ8sT/6Gr6rRQArOF7lr45P53urVSF1plwmbNP/luaW5zeSXYn4ChKhbejxYDN75LoY6gGglvL79dXUdtwRmgbeewf/gQtpigFvPIcIPUH+luyV5ShW55U0RRFsjVy04DPRlA4aHAi2StaPY2PrYe6F9wFgZ6HgnTqEyLaU18tLUBrvdVFmvBk4P9OyPPC2jISRaVIBSntSUCM7YJnbA9ZkDr4K7VFrZecRzM6RV2wD0+m16//Vn4tbTHdf8Axh+mer2VBqu1I6ge2f0Jt4ealdE5lp6nPkzzaKiwLuco8Zkok+1qZ91SJjQdbA/LqefnR4o8kTmVtJgFd6v/Ot7jMRcQlIUoVFhbCZDIhOtrWwyE6Ohq5ubmqn6mrq0NdnayCl5fTSrXBYIDB4EHoQTNFOnaDwQDhyBroTHU08ZImXwqMUz+FGEgr+MLJ7dD94tzQMl7xDsQgGoiE7DTo/nrC6bYmXQDMUbTaKOQegG7Jbc63DW0DcyIZIUJeOnTLHna+7chnYQ6zrAKEJtPP7JXQ/vkoNKe2Amd2wKDxBgwGQOMNXPuNJadTV3pNgdB5KoTobjAnDAQiO0HwDgUgQlS59xqNF1UgPiHnFTF6h6hu64gOmjbDoc1LA3Z86vCuufsNMMX0AoY9AfS9kwYjab9+0dBFdIBQmEmrfunLrJ8T/SJgNJmA9hPoB3A4Rxv8oqELbQPToAch1tUCWj8II/8L7conYRr/JoTMFdBseQ9CfQXEs7thNIuu96fkhkXQHP4d5sEPyp9pPQI6v3Doq4uA9KUwDHvM+p4m7RdoN7/tdHeGzlcBfopn2VAN3afDIRiq5AmCAjG0DYxtRtI/+kDoYroDxjoYEwapn0OrocDt66H98zFoTm8Dtn0k78svHMbY3p6fuwJh77fQrnkOguQ5Zoc55WqYuli85aK6QdttGoRjayCUnaIwGQXG8A4Q/WMAX8tArXI80rBq7jwFpvCO53TM1mPvORParM0wxfVz364TBgK3r4N2+SPQnN1pNRzEyE4wn9gA87D/c3Q5dxLOJZhF6CyClLn9eJimfkrCj+IYhLGvQLvuRQh5aUDqd0DqdxAjO8HYeoT68QXEQ3P5s9CufIK8PRQY+twO6OnYpOpb1v6+z+3QZm2CJmM5sOwRmPrcCnMUTeaFEU9Dd+KfBjz3gHDTb4AoQgxtDej81e9hGa00m09vh6n3OXq6nQtdr4cQlgxREhBEkYTz6O50nD7hQEy4fMwp1wHJ4+m+msyAd6jL9qZJXwHtmuccXhf1fjCNfx2i3ec10V2hTfsJqK+EqPOF2HkyTD1ukrfpNIV+APftPKQNdDofGCd/ZN1Wm50KTdERiIIGYuJAiB2vAIx10Gx5F6YOE633VMg9DN2G1627MnW+EmajC+PUKwh4YD80e76GuecM58cWEAfM+AW6byZDqMyFoSKfxigAmow/oc2lnGvGdmNs2pdyPLcS1xe45W/ofroJQmEGxKUPwBjT2zF3SuuR0HkFQCg/C/xyu81bhtvWATGKCaDGG7rQNhBKTsBYeIzG+sAE4DaLiDiwDtrf7oImYznE0ztgFDXytc07AI3kwWKHGNIaRum+AdCtexVCPnnPihEdIYYnA1WFEGqKYPYJg9nTPkzwAu7brcjrZYA+Y7ntNrlyHjtz9xtgirMIXfXV0LuwX8ydp8AUa5mQiiL0i22fS+PlcyGKgvN7LXhb+2ZDjJOxpP+9QN+7bCevinamCWpFNkdJFswHlsDUjgR44dh66NbLwrMYlQKjLkC+F5GdoSnJIoFDganvHTD7hHuW+F8rFUywHHfSMOiiukCw5BsT/cKBgBgI+Qch5u6HUfACLHZzj9NfQ3eiVD4+QQsExkAoPwuT1sd6f4X8TOikcCxpWwgQk4bA1OUa63cLcX2hA2DsdgM59SUOph/lNdX4ADOXQnPwV5iHPEJt4vbu0Oz6HGJoO/X+OiDWeo9E7yCY+98F4ewuaI6vgyGmJ+2/9Qj6UVwLTWgbSHfMEBDveG9bDwfuT6VFNYMBQlkOdId+o+8JTiQBtLqIbITc/TAKeuvxaWJ7QnNsDYzjXrNpCwCAuH7QXP4cNNs+glCVT/vT+arbKmHtgSmfWo9bu/k9aDL/lK9zZGcYp3xC/bjJSSoCd/jHQhvbk557xfzC8NAh62KDtvMUaA7/DqNPmHwPEgcD9+6kPsVggCZ9ObRrZdvH6BWkboOHtIYWgKj1ItvUbAAiu8jPmW+E43UY9BB0+36AUHICWHwL0O9uAIOpL63Og37xrU5Pz9R7No39On+g/z3QVBbajgO+ERBu/gPC0TUwx/Si7y7Kko/HDM/ssesXQvv7PdBkbXCwVcxthsMUP8D9PgAIgx8CTEaIIcl0XQ/9Ae3OzwAAol8YjM6ORR8oP2uHlkEnLZQCEL0DYR70IMz97nSwySS0G9+BJnefzWum+H7q/XhQK+C+PSRMSf3V1o+gsYhZRt8I23vf+Spo93wHMeUqmPvdCeHQb9AuvQ/Cqa0wmEyAaAACE6C57AnAOxBiWDsIx9dCyE+Hudcs9ede6wvNwPsBjc7pWCPdQ6M+0GNbD1o/YIJlTmMwkP2z7UP5mij6PleojvctBE+PWRBFd75/LZ/s7GzEx8djy5YtGDRokPX1l156Cd9++y3S09MdPjN37lw8//zzDq8vXLgQfn4XSbibKCKw9ixiyvYiovIwBKuSL8Cg9cWJyDEoDKRVtoiKQ2ift8z241aPFAHHo8YiP4iEpuDqLCTnLUe9LhAGiwGjFQ3QmA3QiAacCR2MosDOlm1PIOXsIkieV3X6YFR7RaJOFwgvYyVyg3uh1J9W9AJrTqNzzhKYBR3Mgg6CaILWXA+tWA9AwMnwy5Adqr7a52MoQVR5Gk6FDWv0+F2f+iJ0O/M99CZyMa7XBWJ/4izU69zEeiuIL9mG6LJUaEQjNKIJguV3fmA3HIt2nqxdEE0Iqc5CRMUhhFYdhUHnjwqfeJT5tkJBUAPyWwGIKdsDs6Cz3kcANm674RWH0bpoHY5FjUepn0oungaiNdUhpPo4QquP42jUBGvcdauiDYgv2QpBFGHS6FHlHY1qrygIohH+9fk4ED8dZo3tyk5i0UYkFm9S+xrU6kOxp7WcfF5vrIRB6+++HYhmJBZvRkRlOiq9o1Hmm4QS/2QYdA3whlHgYyhBypmFEAUtqryjUKcLhtZcBy9TFQTRhFK/1g7tV2OuR2LxJkSXp0EEqN1DRHrsNaj0cZ3/IrZ0F8Ir03E49jqYLoR7sGhG66L1iC3dCcESBlTq1xqH4m7w+BkURCM6Zy9GvS7Qpo04fpeIsKojSCpaD9/6IlR7RSA1yYW3gmhGl+yfEFibjTpdIOp1AdCIJhyKmwazs1UyADpjFbrk/IyA2lzkBvfG8SjymNOYDUjOW46j0RNdfr6hhFQdQ1LRPzgcey3q9W5CcFsQcSXb0brQ1lPKoAtAesxVqPBNcNhea6pFh7w/UOUdjeyQfjBqnax0ekhi0UacDpfzQUSWp0FrrkdRQCeb51tvrLD0FdTugqpPoXXRWtTqQlDjFY7s0P4waRrv2QqsOYtOOYuRHnuN9TrElu5EZMVB1OjDcCR6ssfPjs5Ug045v6DaKxzHo9TDUxKKNyOxeLP1+ZTG4dRWt6La23YRL7Z0F5IK1yMtYQaq1Poe0Yzwykx4mSqQEyJ7MkWV7bP2cRrRCG9DGfzqC+BjKEOtPgSpSbIg1uPUfADA6bChKPZv33hjtaUvqtMFoU4XBC9jBUJqshBUcxoa0YiCwBQci6JxVjAb0efkJxY7QwuNaIbOXAudqQaAiKKATsiItYSXiiIGH33V+jUl/u1wOPY6t8cdXpEOjViPAuVY2wC8DOXoenYhqr0ikB3SH+V+tCAYU7obsWW7UOkdhwrfeBQGdIJRK9uroVVH0THnN2hExwlCXlBPl7aGK3zqi9C6cB2K/dujICgFoqBDSNVx6Mw1KAxMsW7X98SH8DJWwixokR/UDWdDB6FOF4TQ6mNk9+nJgyKgNhtdzi4CBEAUdCgM6IickH6o1dt5BYtmdD27EKfDhqDMz84r6XwQRaRk/wgRwNHoSVZbLrDmLCp8451+TGOuR9ezC5Eb1Av5we7zAApmIzrl/oLskH4o821tbTfehlJEVhzE2ZABEC15vEIrj0AjmlAU2Mnl/qLL9yG2bBcKA7rY9HHO6JS9GIG12ajyjkKVdzTOhg6waTPnjGhGUO0ZRFYcRHhlOnSmWuxs8wAMOhK7I8vT4GMoxemwIU7H9biSbUgs3gyTxhvVXpHIiJ2q2t/qjZVoU7gaZ0KHoNpb9qgJrToKb2M5coPVc91FVBxE+7xlEEQzskP6IyuSPPm9DaXok/Wx01PLDe7ltE91iiiiQ97vMGp8GvZZ0Yz4ku0Iqj0D3/pi+BhKAIgo82uNg/HnVigooXgzosrTIAoCskP6Iy/YTSQFgPjiLQitPo4afRhqvcJQEJjido6TcvYHBNWchiCaIQoCjBofHIqbhiofz4pidMpejIC6XNTpgnAw/gYHu18QTSRsWwiuPoHE4i04kDDDo/2fCwnFmxFadQxpCTc5t0fdEF+8BdHl+6Az10KEFofirvf4mrRUqqurMX36dJSVlSEoyLkte0mIUvX19fDz88PPP/+Mq66Sc1U89NBDSE1NxT///OPwGTVPqcTERBQWFrq8oM0dg8GAVatWYcyYMdDrG2/yxDANhdsi05zh9sk0d7iNMi0FbqtMc4bbJ9PcaclttLy8HBEREW5FqUsifM/Lywt9+vTBqlWrbESpVatWYcoU9aSv3t7e8PZWUeT1+hbXGNS4WM6DaflwW2SaM9w+meYOt1GmpcBtlWnOcPtkmjstsY16eryXhCgFAI8++ihmzpyJvn37YtCgQfjss89w6tQp3H333e4/zDAMwzAMwzAMwzAMwzQql4woNW3aNBQVFeGFF15ATk4OunbtihUrViApyUlZeIZhGIZhGIZhGIZhGOZf45IRpQDg3nvvxb333nuhD4NhGIZhGIZhGIZhGOaS59xSxzMMwzAMwzAMwzAMwzDMecCiFMMwDMMwDMMwDMMwDNPksCjFMAzDMAzDMAzDMAzDNDksSjEMwzAMwzAMwzAMwzBNDotSDMMwDMMwDMMwDMMwTJPDohTDMAzDMAzDMAzDMAzT5LAoxTAMwzAMwzAMwzAMwzQ5LEoxDMMwDMMwDMMwDMMwTQ6LUgzDMAzDMAzDMAzDMEyTw6IUwzAMwzAMwzAMwzAM0+SwKMUwDMMwDMMwDMMwDMM0OSxKMQzDMAzDMAzDMAzDME0Oi1IMwzAMwzAMwzAMwzBMk8OiFMMwDMMwDMMwDMMwDNPksCjFMAzDMAzDMAzDMAzDNDksSjEMwzAMwzAMwzAMwzBNju5CH0BLQRRFAEB5efkFPpLzw2AwoLq6GuXl5dDr9Rf6cJhLGG6LTHOG2yfT3OE2yrQUuK0yzRlun0xzpyW3UUk7kbQUZ7Ao5SEVFRUAgMTExAt8JAzDMAzDMAzDMAzDMM2fiooKBAcHO31fEN3JVgwAwGw2Izs7G4GBgRAE4UIfzjlTXl6OxMREnD59GkFBQRf6cJhLGG6LTHOG2yfT3OE2yrQUuK0yzRlun0xzpyW3UVEUUVFRgbi4OGg0zjNHsaeUh2g0GiQkJFzow2g0goKCWlyjZi5OuC0yzRlun0xzh9so01Lgtso0Z7h9Ms2dltpGXXlISXCic4ZhGIZhGIZhGIZhGKbJYVGKYRiGYRiGYRiGYRiGaXJYlLrE8Pb2xnPPPQdvb+8LfSjMJQ63RaY5w+2Tae5wG2VaCtxWmeYMt0+muXMptFFOdM4wDMMwDMMwDMMwDMM0OewpxTAMwzAMwzAMwzAMwzQ5LEoxDMMwDMMwDMMwDMMwTQ6LUgzDMAzDMAzDMAzDMEyTw6JUM2HDhg2YPHky4uLiIAgCfvvtN5v38/LyMHv2bMTFxcHPzw/jx4/HkSNHbLY5duwYrrrqKkRGRiIoKAjXX3898vLybLYpKSnBzJkzERwcjODgYMycOROlpaVujy8tLQ3Dhw+Hr68v4uPj8cILL0CZjiwnJwfTp09Hx44dodFo8PDDD5/rpWAuIC29HW7atAlDhgxBeHg4fH190alTJ7zzzjvnfD2Y5kdLb6Pr16+HIAgOP+np6ed8TZjmQ0tvn7Nnz1ZtnykpKed8TZjmSUtvqwDw0UcfoXPnzvD19UXHjh3xzTffnNO1YJonzbmN1tbWYvbs2ejWrRt0Oh2mTp3qsA3PjS5umqp9vvTSSxg8eDD8/PwQEhLi8fG1tLk7i1LNhKqqKvTo0QMffvihw3uiKGLq1Kk4fvw4fv/9d+zduxdJSUkYPXo0qqqqrJ8fO3YsBEHA2rVrsXnzZtTX12Py5Mkwm83WfU2fPh2pqan466+/8NdffyE1NRUzZ850eWzl5eUYM2YM4uLisHPnTnzwwQd488038fbbb1u3qaurQ2RkJJ5++mn06NGjka4K09S09Hbo7++P+++/Hxs2bMDhw4fxzDPP4JlnnsFnn33WSFeIudC09DYqkZGRgZycHOtP+/btz/PKMM2Blt4+33vvPZt2efr0aYSFheG6665rpCvENBdaelv9+OOPMWfOHMydOxcHDx7E888/j/vuuw9//PFHI10h5kLTnNuoyWSCr68vHnzwQYwePVp1G54bXdw0Vfusr6/Hddddh3vuucfjY2uRc3eRaXYAEH/99Vfr/xkZGSIA8cCBA9bXjEajGBYWJn7++eeiKIriypUrRY1GI5aVlVm3KS4uFgGIq1atEkVRFA8dOiQCELdt22bdZuvWrSIAMT093enxzJs3TwwODhZra2utr73yyitiXFycaDabHbYfPny4+NBDDzX4vJnmRUtvhxJXXXWVeNNNN3l+4kyLoSW20XXr1okAxJKSkvM6d6b50xLbpz2//vqrKAiCmJWV1bCTZ1oULbGtDho0SHzsscdsPvfQQw+JQ4YMOYcrwDR3mlsbVTJr1ixxypQpLrfhudHFzb/VPpV89dVXYnBwsEfH0xLn7uwp1QKoq6sDAPj4+Fhf02q18PLywqZNm6zbCIIAb29v6zY+Pj7QaDTWbbZu3Yrg4GAMGDDAus3AgQMRHByMLVu2OP3+rVu3Yvjw4Tb7HjduHLKzs5GVldUo58g0f1piO9y7dy+2bNmC4cOHN/yEmRZHS2qjvXr1QmxsLEaNGoV169ad+0kzLYaW1D4lvvzyS4wePRpJSUkNP2GmxdIS2mpdXZ3N8QGAr68vduzYAYPBcI5nzrQULnQbZRhXNFb7PFda4tydRakWQKdOnZCUlIQ5c+agpKQE9fX1ePXVV5Gbm4ucnBwA1IH6+/vjiSeeQHV1NaqqqvD444/DbDZbt8nNzUVUVJTD/qOiopCbm+v0+3NzcxEdHW3zmvS/q88xFxctqR0mJCTA29sbffv2xX333Yfbb7/9vM6daRm0hDYaGxuLzz77DEuWLMEvv/yCjh07YtSoUdiwYUOjXAOm+dIS2qeSnJwc/Pnnn9x/XoK0hLY6btw4fPHFF9i9ezdEUcSuXbswf/58GAwGFBYWNsp1YJovF7qNMowrGqt9nistce7OolQLQK/XY8mSJcjMzERYWBj8/Pywfv16TJgwAVqtFgAQGRmJn3/+GX/88QcCAgIQHByMsrIy9O7d27oNAAiC4LB/URStr6ekpCAgIAABAQGYMGGC08+JlkRpavtjLk5aUjvcuHEjdu3ahU8++QTvvvsufvjhh8a5CEyzpiW00Y4dO+KOO+5A7969MWjQIMybNw8TJ07Em2++2bgXg2l2tIT2qWTBggUICQlRTeDLXNy0hLb63//+FxMmTMDAgQOh1+sxZcoUzJ49GwBsvp+5OGkObZRhnNGY7dMdF8vcXXehD4DxjD59+iA1NRVlZWWor69HZGQkBgwYgL59+1q3GTt2LI4dO4bCwkLodDqEhIQgJiYGbdq0AQDExMQ4ZPQHgIKCAqt6umLFCqvbs6+vr/Vz9qpqfn4+ADiosMzFTUtph9J3devWDXl5eZg7dy5uvPHGxrgETDOnpbRRJQMHDsR33313HmfNtBRaSvsURRHz58/HzJkz4eXl1Uhnz7Qkmntb9fX1xfz58/Hpp58iLy/P6oUaGBiIiIiIRr4aTHPkQrZRhnFHY7RPT7hY5u7sKdXCCA4ORmRkJI4cOYJdu3ZhypQpDttEREQgJCQEa9euRX5+Pq688koAwKBBg1BWVoYdO3ZYt92+fTvKysowePBgAEBSUhKSk5ORnJyM+Ph46+c2bNiA+vp66+f+/vtvxMXFoXXr1v/i2TLNlZbUDkVRtMZ2M5cOLamN7t27F7GxsY1x2kwLobm3z3/++QdHjx7Fbbfd1tinzrQwmntb1ev1SEhIgFarxY8//ohJkyZBo+HpzaXEhWijDOMp59M+PeGimbtfgOTqjAoVFRXi3r17xb1794oAxLffflvcu3evePLkSVEURfGnn34S161bJx47dkz87bffxKSkJPHqq6+22cf8+fPFrVu3ikePHhW//fZbMSwsTHz00Udtthk/frzYvXt3cevWreLWrVvFbt26iZMmTXJ5bKWlpWJ0dLR44403imlpaeIvv/wiBgUFiW+++abNdtLx9+nTR5w+fbq4d+9e8eDBg41wdZimoqW3ww8//FBcunSpmJmZKWZmZorz588Xg4KCxKeffrqRrhBzoWnpbfSdd94Rf/31VzEzM1M8cOCA+OSTT4oAxCVLljTSFWIuJC29fUrcdNNN4oABA87zajDNmZbeVjMyMsRvv/1WzMzMFLdv3y5OmzZNDAsLE0+cONE4F4i54DTnNiqKonjw4EFx79694uTJk8URI0ZYj1UJz40uXpqqfZ48eVLcu3ev+Pzzz4sBAQHW76yoqHB6bC1x7s6iVDNBKhNu/zNr1ixRFEXxvffeExMSEkS9Xi+2atVKfOaZZ8S6ujqbfTzxxBNidHS0qNfrxfbt24tvvfWWQ9nHoqIiccaMGWJgYKAYGBgozpgxw6PS5Pv37xeHDRsment7izExMeLcuXMd9q12/ElJSedzWZgmpqW3w/fff19MSUkR/fz8xKCgILFXr17ivHnzRJPJdN7XhmketPQ2+tprr4nt2rUTfXx8xNDQUHHo0KHi8uXLz/u6MM2Dlt4+RZGMWV9fX/Gzzz47r2vBNG9aels9dOiQ2LNnT9HX11cMCgoSp0yZIqanp5/3dWGaD829jSYlJakenxKeG128NFX7nDVrlur3rFu3zuXxtbS5u2A5IIZhGIZhGIZhGIZhGIZpMjjommEYhmEYhmEYhmEYhmlyWJRiGIZhGIZhGIZhGIZhmhwWpRiGYRiGYRiGYRiGYZgmh0UphmEYhmEYhmEYhmEYpslhUYphGIZhGIZhGIZhGIZpcliUYhiGYRiGYRiGYRiGYZocFqUYhmEYhmEYhmEYhmGYJodFKYZhGIZhGIZhGIZhGKbJYVGKYRiGYRimGTN37lz07NnzQh8GwzAMwzBMoyOIoihe6INgGIZhGIa5FBEEweX7s2bNwocffoi6ujqEh4c30VExDMMwDMM0DSxKMQzDMAzDXCByc3Otfy9atAjPPvssMjIyrK/5+voiODj4QhwawzAMwzDMvw6H7zEMwzAMw1wgYmJirD/BwcEQBMHhNfvwvdmzZ2Pq1Kl4+eWXER0djZCQEDz//PMwGo14/PHHERYWhoSEBMyfP9/mu86ePYtp06YhNDQU4eHhmDJlCrKyspr2hBmGYRiGYRSwKMUwDMMwDNPCWLt2LbKzs7Fhwwa8/fbbmDt3LiZNmoTQ0FBs374dd999N+6++26cPn0aAFBdXY2RI0ciICAAGzZswKZNmxAQEIDx48ejvr7+Ap8NwzAMwzCXKixKMQzDMAzDtDDCwsLw/vvvo2PHjrj11lvRsWNHVFdX46mnnkL79u0xZ84ceHl5YfPmzQCAH3/8ERqNBl988QW6deuGzp0746uvvsKpU6ewfv36C3syDMMwDMNcsugu9AEwDMMwDMMwDSMlJQUajby2GB0dja5du1r/12q1CA8PR35+PgBg9+7dOHr0KAIDA232U1tbi2PHjjXNQTMMwzAMw9jBohTDMAzDMEwLQ6/X2/wvCILqa2azGQBgNpvRp08ffP/99w77ioyM/PcOlGEYhmEYxgUsSjEMwzAMw1zk9O7dG4sWLUJUVBSCgoIu9OEwDMMwDMMA4JxSDMMwDMMwFz0zZsxAREQEpkyZgo0bN+LEiRP4559/8NBDD+HMmTMX+vAYhmEYhrlEYVGKYRiGYRjmIsfPzw8bNmxAq1atcPXVV6Nz58649dZbUVNTw55TDMMwDMNcMARRFMULfRAMwzAMwzAMwzAMwzDMpQV7SjEMwzAMwzAMwzAMwzBNDotSDMMwDMMwDMMwDMMwTJPDohTDMAzDMAzDMAzDMAzT5LAoxTAMwzAMwzAMwzAMwzQ5LEoxDMMwDMMwDMMwDMMwTQ6LUgzDMAzDMAzDMAzDMEyTw6IUwzAMwzAMwzAMwzAM0+SwKMUwDMMwDMMwDMMwDMM0OSxKMQzDMAzDMAzDMAzDME0Oi1IMwzAMwzAMwzAMwzBMk8OiFMMwDMMwDMMwDMMwDNPksCjFMAzDMAzDMAzDMAzDNDn/D1pEzip17my6AAAAAElFTkSuQmCC",
       "text/plain": [
-       "(<Figure size 720x720 with 2 Axes>,\n",
-       " (<AxesSubplot:title={'center':'Hydrograph'}, ylabel='Discharge (m$^3$ s$^{-1}$)'>,\n",
-       "  <AxesSubplot:>))"
+       "<Figure size 1200x600 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "ewatercycle.analysis.hydrograph(\n",
-    "    discharge=discharge,\n",
-    "    reference='observation', \n",
-    "    filename ='figures/case1_HelloWorldHydrograph.png'\n",
-    ")"
+    "# Plot observed and simulated streamflow\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "observations.streamflow.plot(label='Observed')\n",
+    "observations.simulation.plot(label='Simulated', linestyle='--')\n",
+    "plt.legend()\n",
+    "plt.title('Observed vs Simulated Streamflow')\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Streamflow')\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6ae81fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ewatercycle.analysis.hydrograph(\n",
+    "#     discharge=observations,\n",
+    "#     reference='observation', \n",
+    "#     filename ='figures/case1_HelloWorldHydrograph.png'\n",
+    "# )"
    ]
   },
   {
@@ -732,7 +817,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

From b2ab7a88c060e51ca5f41571efe55d9710f98568 Mon Sep 17 00:00:00 2001
From: rolfhut <r.w.hut@tudelft.nl>
Date: Sat, 19 Apr 2025 11:34:12 +0200
Subject: [PATCH 2/2] added documentation in notebook

---
 Case1_Marrmot_Merrimack_ESMValTool.ipynb | 25 ++++++++++++++++++------
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/Case1_Marrmot_Merrimack_ESMValTool.ipynb b/Case1_Marrmot_Merrimack_ESMValTool.ipynb
index c055d60..5917537 100644
--- a/Case1_Marrmot_Merrimack_ESMValTool.ipynb
+++ b/Case1_Marrmot_Merrimack_ESMValTool.ipynb
@@ -19,9 +19,9 @@
     "\n",
     "The model used in this notebook is: [MARRMoT](https://github.com/wknoben/MARRMoT) M01 (traditional bucket) (Knoben et al 2019, [10.5194/gmd-12-2463-2019](https://doi.org/10.5194/gmd-12-2463-2019)). MARRMoT is a suite of hydrological models and the chosen M01 model is the most simple model within it: it represents the entire catchment as a traditional single bucket. \n",
     "\n",
-    "The catchment chosen as an example is the Merrimack. This model is too simple for the basin used as example and uncalibrated on top of that: this notebook is purely intended to demonstrate how to run a model and generate forcing data. It is not intended as an accurate simulations of discharge in this given basin, which is covered in the other, more complex, case study notebooks in this repository.\n",
+    "The catchment chosen as an example is the Merrimack. This model is too simple for the basin used as example and uncalibrated on top of that: this notebook is purely intended to demonstrate how to run a model and generate forcing data. It is not intended as an accurate simulations of discharge in this given basin, which is covered in the other, more complex, case study notebooks.\n",
     "\n",
-    "Furthermore, this notebook shows that a model written in a language other than Python (in this case: Matlab) can still be run from a Jupyter notebook within eWaterCycle."
+    "Furthermore, this notebook shows that a model written in a language other than Python (in this case: Matlab) can be run from a Jupyter notebook within eWaterCycle using container technology."
    ]
   },
   {
@@ -117,6 +117,7 @@
     "experiment_initial_soil_moisture_storage = 0.9 * experiment_maximum_soil_moisture_storage\n",
     "\n",
     "discharge_variable = \"flux_out_Q\"\n",
+    "\n",
     "# flux_out_Q unit conversion factor from mm/day to m3/s\n",
     "conversion_mmday2m3s = 1 / (1000 * 86400)"
    ]
@@ -429,6 +430,14 @@
     "print(cfg_dir)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "870a87c2-2fde-4d27-8100-976d53f11d1c",
+   "metadata": {},
+   "source": [
+    "During setup we have changed some of the parameters, see the difference below."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -579,7 +588,9 @@
     "### Import GRDC observations\n",
     "\n",
     "Read the GRDC data for the period of the experiment, for the station given above. \n",
-    "If you are not on jupyter.ewatercycle.org or Cartesius, download the GRDC https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html and set the `grdc_location` field in `~/.config/.ewatercycle/ewatercycle.yaml` to your download location."
+    "If you are not on jupyter.ewatercycle.org or Cartesius, download the GRDC https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html and set the `grdc_location` field in `~/.config/.ewatercycle/ewatercycle.yaml` to your download location.\n",
+    "\n",
+    "You can also specify the optional data_home argument if you have downloaded GRDC data yourself to another location. If unspecified this defaults to ```None``` and data will be assumed to be in the location specified in your config file. "
    ]
   },
   {
@@ -648,9 +659,10 @@
    ],
    "source": [
     "observations = ewatercycle.observation.grdc.get_grdc_data(\n",
-    "    station_id=grdc_station_id,\n",
-    "    start_time=experiment_start_date,\n",
-    "    end_time=experiment_end_date,\n",
+    "    station_id = grdc_station_id,\n",
+    "    start_time = experiment_start_date,\n",
+    "    end_time = experiment_end_date,\n",
+    "    data_home = None \n",
     ")\n",
     "print(observations)"
    ]
@@ -784,6 +796,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "## ToDo: make sure this works, but requires an update to the hydrograph function of eWaterCycle main package to accept Xarray\n",
     "# ewatercycle.analysis.hydrograph(\n",
     "#     discharge=observations,\n",
     "#     reference='observation', \n",