From 901113a5cd5ba5b04aa1b17df4faffe4467e3398 Mon Sep 17 00:00:00 2001
From: Nadia Leonova <nnleon@Nadias-MacBook-Pro.local>
Date: Wed, 10 Jul 2024 17:51:28 +0100
Subject: [PATCH] updating tutorials

---
 README.md                                     |    2 +-
 .../tutorials/01-data-preparation-ghana.ipynb |  678 +----
 .../02-assess-damage-and-disruption.ipynb     | 2283 +----------------
 .../tutorials/03-test-multiple-failures.ipynb | 1545 +----------
 .../04-evaluate-adaptation-options.ipynb      | 1969 +-------------
 5 files changed, 405 insertions(+), 6072 deletions(-)

diff --git a/README.md b/README.md
index 49cdb89..d46f2aa 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ This should bring all dependencies with it. If any of these cause difficulties,
 try using a [conda](https://docs.conda.io/en/latest/miniconda.html) environment:
 
     conda env create -n snail_env \
-        python=3.8 geopandas shapely rasterio python-igraph
+        python=3.11 geopandas shapely rasterio python-igraph
     conda activate snail_env
     pip install nismod-snail
 
diff --git a/docs/source/tutorials/01-data-preparation-ghana.ipynb b/docs/source/tutorials/01-data-preparation-ghana.ipynb
index c259a29..167d30c 100644
--- a/docs/source/tutorials/01-data-preparation-ghana.ipynb
+++ b/docs/source/tutorials/01-data-preparation-ghana.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,21 +42,33 @@
    "source": [
     "### Activity 1: Extract infrastructure data\n",
     "\n",
-    "#### Step 1) On your desktop, create a folder called `ghana_tutorial`\n",
-    "\n",
-    "#### Step 2) Create a variable to store the folder location\n",
+    "#### Step 1) In the parent directory of this package, create a folder called `ghana_tutorial`\n",
     "\n",
-    "In the cell below, type in the path to your desktop, by changing NAME to match your username as shown on your computer"
+    "#### Step 2) Create a variable to store the folder location"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = (\n",
+    "    Path(os.getcwd()).resolve().parents[3]\n",
+    ")  # get parent directory of snail package\n",
+    "new_folder = os.path.join(dir, \"ghana_tutorial\")\n",
+    "if not os.path.exists(new_folder):\n",
+    "    os.makedirs(new_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# edit this if using a Mac (otherwise delete)\n",
-    "data_folder = Path(\"YOUR_PATH/ghana_tutorial\")\n",
+    "data_folder = Path(new_folder)  # Path(\"YOUR_PATH/ghana_tutorial\")\n",
     "\n",
     "# edit this if using Windows (otherwise delete)\n",
     "# data_folder = Path(\"C:YOUR_PATH/ghana_tutorial\")\n",
@@ -75,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +116,10 @@
     "# see https://pyproj4.github.io/\n",
     "from pyproj import Geod\n",
     "\n",
-    "from matplotlib import pyplot as plt"
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile"
    ]
   },
   {
@@ -123,6 +138,25 @@
     "Download the `ghana-latest-free.shp.zip` dataset from http://download.geofabrik.de/africa/ghana.html, extract the zip folder and save the extracted folder within your new folder `ghana_tutorial`"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_url = \"https://download.geofabrik.de/africa/ghana-latest-free.shp.zip\"\n",
+    "file_path = os.path.join(data_folder, \"ghana-osm.zip\")\n",
+    "# check if directory already exists\n",
+    "if not os.path.exists(Path(os.path.splitext(file_path)[0])):\n",
+    "    # check if data already exists in this directory\n",
+    "    # if len(os.listdir(Path(os.path.splitext(file_path)[0]))) == 0:\n",
+    "    urlretrieve(download_url, file_path)  # note: this can take a few minutes\n",
+    "    with zipfile.ZipFile(file_path, \"r\") as zip_ref:\n",
+    "        zip_ref.extractall(os.path.splitext(file_path)[0])\n",
+    "else:\n",
+    "    print(\"data already exists\")"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -133,12 +167,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "roads = gpd.read_file(\n",
-    "    data_folder / \"ghana-latest-free\" / \"gis_osm_roads_free_1.shp\"\n",
+    "    os.path.join(os.path.splitext(file_path)[0], \"gis_osm_roads_free_1.shp\")\n",
     ")"
    ]
   },
@@ -152,165 +186,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "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>osm_id</th>\n",
-       "      <th>code</th>\n",
-       "      <th>fclass</th>\n",
-       "      <th>name</th>\n",
-       "      <th>ref</th>\n",
-       "      <th>oneway</th>\n",
-       "      <th>maxspeed</th>\n",
-       "      <th>layer</th>\n",
-       "      <th>bridge</th>\n",
-       "      <th>tunnel</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790591</td>\n",
-       "      <td>5121</td>\n",
-       "      <td>unclassified</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>None</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>F</td>\n",
-       "      <td>LINESTRING (-0.17184 5.60847, -0.17182 5.60849...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4790592</td>\n",
-       "      <td>5122</td>\n",
-       "      <td>residential</td>\n",
-       "      <td>Nortei Ababio Road</td>\n",
-       "      <td>None</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>F</td>\n",
-       "      <td>LINESTRING (-0.18282 5.61197, -0.18336 5.61198...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>5115</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>None</td>\n",
-       "      <td>F</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>F</td>\n",
-       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4790596</td>\n",
-       "      <td>5121</td>\n",
-       "      <td>unclassified</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>None</td>\n",
-       "      <td>F</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>F</td>\n",
-       "      <td>LINESTRING (-0.17207 5.60853, -0.17207 5.60844...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4790597</td>\n",
-       "      <td>5122</td>\n",
-       "      <td>residential</td>\n",
-       "      <td>Volta Road</td>\n",
-       "      <td>None</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>F</td>\n",
-       "      <td>LINESTRING (-0.18282 5.61197, -0.18280 5.61262...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id  code        fclass                name   ref oneway  maxspeed  \\\n",
-       "0  4790591  5121  unclassified        Airport Road  None      B         0   \n",
-       "1  4790592  5122   residential  Nortei Ababio Road  None      B         0   \n",
-       "2  4790594  5115      tertiary        Airport Road  None      F         0   \n",
-       "3  4790596  5121  unclassified        Airport Road  None      F         0   \n",
-       "4  4790597  5122   residential          Volta Road  None      B         0   \n",
-       "\n",
-       "   layer bridge tunnel                                           geometry  \n",
-       "0      0      F      F  LINESTRING (-0.17184 5.60847, -0.17182 5.60849...  \n",
-       "1      0      F      F  LINESTRING (-0.18282 5.61197, -0.18336 5.61198...  \n",
-       "2      0      F      F  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...  \n",
-       "3      0      F      F  LINESTRING (-0.17207 5.60853, -0.17207 5.60844...  \n",
-       "4      0      F      F  LINESTRING (-0.18282 5.61197, -0.18280 5.61262...  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "roads.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['unclassified', 'residential', 'tertiary', 'tertiary_link',\n",
-       "       'secondary', 'trunk', 'service', 'primary', 'motorway_link',\n",
-       "       'trunk_link', 'primary_link', 'secondary_link', 'footway', 'path',\n",
-       "       'track', 'motorway', 'track_grade3', 'track_grade4', 'steps',\n",
-       "       'pedestrian', 'bridleway', 'cycleway', 'track_grade2',\n",
-       "       'track_grade5', 'track_grade1', 'living_street', 'busway'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "roads.fclass.unique()"
    ]
@@ -333,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -386,7 +273,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(road_network)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,158 +316,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>15992</th>\n",
-       "      <td>1290190247</td>\n",
-       "      <td>tertiary_link</td>\n",
-       "      <td>None</td>\n",
-       "      <td>LINESTRING (-0.12481 5.74521, -0.12490 5.74498)</td>\n",
-       "      <td>roade_15992</td>\n",
-       "      <td>roadn_12502</td>\n",
-       "      <td>roadn_12503</td>\n",
-       "      <td>27.700205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15993</th>\n",
-       "      <td>1290190248</td>\n",
-       "      <td>tertiary_link</td>\n",
-       "      <td>None</td>\n",
-       "      <td>LINESTRING (-0.13500 5.74826, -0.13501 5.74795)</td>\n",
-       "      <td>roade_15993</td>\n",
-       "      <td>roadn_12504</td>\n",
-       "      <td>roadn_12505</td>\n",
-       "      <td>33.975731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15994</th>\n",
-       "      <td>1290190251</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>LINESTRING (-0.12720 5.74611, -0.12490 5.74498)</td>\n",
-       "      <td>roade_15994</td>\n",
-       "      <td>roadn_10570</td>\n",
-       "      <td>roadn_12503</td>\n",
-       "      <td>283.569056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15995</th>\n",
-       "      <td>1290190251</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>LINESTRING (-0.12490 5.74498, -0.12394 5.74449...</td>\n",
-       "      <td>roade_15995</td>\n",
-       "      <td>roadn_12503</td>\n",
-       "      <td>roadn_12506</td>\n",
-       "      <td>888.391459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15996</th>\n",
-       "      <td>1290190252</td>\n",
-       "      <td>tertiary_link</td>\n",
-       "      <td>None</td>\n",
-       "      <td>LINESTRING (-0.14601 5.74863, -0.14594 5.74867...</td>\n",
-       "      <td>roade_15996</td>\n",
-       "      <td>roadn_12507</td>\n",
-       "      <td>roadn_12508</td>\n",
-       "      <td>39.078872</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           osm_id      road_type  name  \\\n",
-       "15992  1290190247  tertiary_link  None   \n",
-       "15993  1290190248  tertiary_link  None   \n",
-       "15994  1290190251       tertiary  None   \n",
-       "15995  1290190251       tertiary  None   \n",
-       "15996  1290190252  tertiary_link  None   \n",
-       "\n",
-       "                                                geometry           id  \\\n",
-       "15992    LINESTRING (-0.12481 5.74521, -0.12490 5.74498)  roade_15992   \n",
-       "15993    LINESTRING (-0.13500 5.74826, -0.13501 5.74795)  roade_15993   \n",
-       "15994    LINESTRING (-0.12720 5.74611, -0.12490 5.74498)  roade_15994   \n",
-       "15995  LINESTRING (-0.12490 5.74498, -0.12394 5.74449...  roade_15995   \n",
-       "15996  LINESTRING (-0.14601 5.74863, -0.14594 5.74867...  roade_15996   \n",
-       "\n",
-       "           from_id        to_id    length_m  \n",
-       "15992  roadn_12502  roadn_12503   27.700205  \n",
-       "15993  roadn_12504  roadn_12505   33.975731  \n",
-       "15994  roadn_10570  roadn_12503  283.569056  \n",
-       "15995  roadn_12503  roadn_12506  888.391459  \n",
-       "15996  roadn_12507  roadn_12508   39.078872  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "roads.tail(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Geographic 2D CRS: EPSG:4326>\n",
-       "Name: WGS 84\n",
-       "Axis Info [ellipsoidal]:\n",
-       "- Lat[north]: Geodetic latitude (degree)\n",
-       "- Lon[east]: Geodetic longitude (degree)\n",
-       "Area of Use:\n",
-       "- name: World.\n",
-       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
-       "Datum: World Geodetic System 1984 ensemble\n",
-       "- Ellipsoid: WGS 84\n",
-       "- Prime Meridian: Greenwich"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "roads.set_crs(4326, inplace=True)\n",
     "road_nodes.set_crs(4326, inplace=True)\n",
@@ -580,32 +336,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(170.1578223077743, 0.5, 'Latitude [deg]')"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "main_roads = roads[roads[\"road_type\"].isin([\"trunk\",\"secondary\",])]\n",
+    "main_roads = roads[\n",
+    "    roads[\"road_type\"].isin(\n",
+    "        [\n",
+    "            \"trunk\",\n",
+    "            \"secondary\",\n",
+    "        ]\n",
+    "    )\n",
+    "]\n",
     "\n",
     "f, ax = plt.subplots()\n",
     "\n",
@@ -631,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -665,9 +407,31 @@
     "Country-level extracts are available through the [Global Systemic Risk Assessment Tool (G-SRAT)](https://global.infrastructureresilience.org/downloads/). This section uses that service to download an extract for Ghana."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Alternative: download flood hazard data from Aqueduct"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download. There are some scripts and summary of the data you may find useful at [nismod/aqueduct](https://github.com/nismod/aqueduct).\n",
+    "\n",
+    "There are almost 700 files in the full Aqueduct dataset, of up to around 100MB each, so we don't recommend downloading all of them unless you intend to do further analysis.\n",
+    "\n",
+    "The next steps show how to clip a region out of the global dataset, in case you prefer to work from the original global Aqueduct files.\n",
+    "\n",
+    "To follow this step, we suggest downloading [inunriver_historical_000000000WATCH_1980_rp00100.tif](http://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp00100.tif) to work through the next steps. Save the downloaded file in a new folder titled `flood_layer` under your data_folder."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,35 +457,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "job_id = client.job_submit(country_iso, [\"wri_aqueduct.version_2\"])"
+    "# client.dataset_list()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "while not client.job_complete(job_id):\n",
-    "    print(\"Processing...\")\n",
-    "    time.sleep(1)"
+    "# client.dataset(\"wri_aqueduct.version_2\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -734,43 +488,11 @@
     ")"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Alternative: download flood hazard data from Aqueduct"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The full [Aqueduct dataset](https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps) is available to download. There are some scripts and summary of the data you may find useful at [nismod/aqueduct](https://github.com/nismod/aqueduct).\n",
-    "\n",
-    "There are almost 700 files in the full Aqueduct dataset, of up to around 100MB each, so we don't recommend downloading all of them unless you intend to do further analysis.\n",
-    "\n",
-    "The next steps show how to clip a region out of the global dataset, in case you prefer to work from the original global Aqueduct files.\n",
-    "\n",
-    "To follow this step, we suggest downloading [inunriver_historical_000000000WATCH_1980_rp00100.tif](http://wri-projects.s3.amazonaws.com/AqueductFloodTool/download/v2/inunriver_historical_000000000WATCH_1980_rp00100.tif) to work through the next steps. Save the downloaded file in a new folder titled `flood_layer` under your data_folder."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in /Users/ivannschlosser/Documents/oxford/ghana_tutorial/flood_layer\n",
-      "Looking in /Users/ivannschlosser/Documents/oxford/ghana_tutorial/flood_layer/gha\n",
-      "Looking in /Users/ivannschlosser/Documents/oxford/ghana_tutorial/flood_layer/gha/wri_aqueduct_version_2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "xmin = \"-3.262509\"\n",
     "ymin = \"4.737128\"\n",
@@ -841,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -850,7 +572,7 @@
     "    \"flood_layer\",\n",
     "    \"gha\",\n",
     "    \"wri_aqueduct_version_2\",\n",
-    "    \"inunriver_historical_000000000WATCH_1980_rp00100-gha.tif\",\n",
+    "    \"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha.tif\",\n",
     ")\n",
     "\n",
     "output_path = Path(\n",
@@ -870,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -899,10 +621,10 @@
     "    flood_intersections, grid\n",
     ")\n",
     "flood_data = snail.io.read_raster_band_data(flood_path)\n",
-    "flood_intersections[\n",
-    "    \"inunriver__epoch_historical__rcp_baseline__rp_100\"\n",
-    "] = snail.intersection.get_raster_values_for_splits(\n",
-    "    flood_intersections, flood_data\n",
+    "flood_intersections[\"inunriver__epoch_historical__rcp_baseline__rp_100\"] = (\n",
+    "    snail.intersection.get_raster_values_for_splits(\n",
+    "        flood_intersections, flood_data\n",
+    "    )\n",
     ")"
    ]
   },
@@ -916,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -928,105 +650,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>split</th>\n",
-       "      <th>index_i</th>\n",
-       "      <th>index_j</th>\n",
-       "      <th>inunriver__epoch_historical__rcp_baseline__rp_100</th>\n",
-       "      <th>flood_length_m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>15995</th>\n",
-       "      <td>1290190251</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15995</td>\n",
-       "      <td>roadn_12503</td>\n",
-       "      <td>roadn_12506</td>\n",
-       "      <td>888.391459</td>\n",
-       "      <td>LINESTRING (-0.12490 5.74498, -0.12394 5.74449...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>377</td>\n",
-       "      <td>650</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>888.391459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15996</th>\n",
-       "      <td>1290190252</td>\n",
-       "      <td>tertiary_link</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15996</td>\n",
-       "      <td>roadn_12507</td>\n",
-       "      <td>roadn_12508</td>\n",
-       "      <td>39.078872</td>\n",
-       "      <td>LINESTRING (-0.14601 5.74863, -0.14594 5.74867...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>374</td>\n",
-       "      <td>650</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>39.078872</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           osm_id      road_type  name           id      from_id        to_id  \\\n",
-       "15995  1290190251       tertiary  None  roade_15995  roadn_12503  roadn_12506   \n",
-       "15996  1290190252  tertiary_link  None  roade_15996  roadn_12507  roadn_12508   \n",
-       "\n",
-       "         length_m                                           geometry  split  \\\n",
-       "15995  888.391459  LINESTRING (-0.12490 5.74498, -0.12394 5.74449...      0   \n",
-       "15996   39.078872  LINESTRING (-0.14601 5.74863, -0.14594 5.74867...      0   \n",
-       "\n",
-       "       index_i  index_j  inunriver__epoch_historical__rcp_baseline__rp_100  \\\n",
-       "15995      377      650                                                0.0   \n",
-       "15996      374      650                                                0.0   \n",
-       "\n",
-       "       flood_length_m  \n",
-       "15995      888.391459  \n",
-       "15996       39.078872  "
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "flood_intersections.tail(2)"
    ]
@@ -1041,20 +667,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "765.1773274965389"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "exposed_1m = flood_intersections[\n",
     "    flood_intersections.inunriver__epoch_historical__rcp_baseline__rp_100 >= 1\n",
@@ -1065,20 +680,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "29441.60010046896"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "all_roads_in_dataset_length_km = roads.length_m.sum() * 1e-3\n",
     "all_roads_in_dataset_length_km"
@@ -1086,20 +690,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0259896651297954"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "proportion = exposed_length_km / all_roads_in_dataset_length_km\n",
     "proportion"
@@ -1107,27 +700,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.6% of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood'"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "f\"{proportion:.1%} of roads in this dataset are exposed to flood depths of >= 1m in a historical 1-in-100 year flood\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1144,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1161,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/docs/source/tutorials/02-assess-damage-and-disruption.ipynb b/docs/source/tutorials/02-assess-damage-and-disruption.ipynb
index c6e1401..b04b2a5 100644
--- a/docs/source/tutorials/02-assess-damage-and-disruption.ipynb
+++ b/docs/source/tutorials/02-assess-damage-and-disruption.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "brief-stephen",
    "metadata": {},
    "outputs": [],
@@ -77,12 +77,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "twelve-threat",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = Path(\"/Users/ivannschlosser/Documents/oxford/ghana_tutorial\")"
+    "dir = (\n",
+    "    Path(os.getcwd()).resolve().parents[3]\n",
+    ")  # get parent directory of snail package\n",
+    "data_folder = os.path.join(dir, \"ghana_tutorial\")\n",
+    "# data_folder = Path(\"YOUR_PATH/ghana_tutorial\")"
    ]
   },
   {
@@ -105,101 +109,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "driven-restoration",
    "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>path</th>\n",
-       "      <th>key</th>\n",
-       "      <th>grid_id</th>\n",
-       "      <th>bands</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>/Users/ivannschlosser/Documents/oxford/ghana_t...</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1,)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>/Users/ivannschlosser/Documents/oxford/ghana_t...</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1,)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>/Users/ivannschlosser/Documents/oxford/ghana_t...</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1,)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>/Users/ivannschlosser/Documents/oxford/ghana_t...</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1,)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>/Users/ivannschlosser/Documents/oxford/ghana_t...</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>(1,)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                path  \\\n",
-       "0  /Users/ivannschlosser/Documents/oxford/ghana_t...   \n",
-       "1  /Users/ivannschlosser/Documents/oxford/ghana_t...   \n",
-       "2  /Users/ivannschlosser/Documents/oxford/ghana_t...   \n",
-       "3  /Users/ivannschlosser/Documents/oxford/ghana_t...   \n",
-       "4  /Users/ivannschlosser/Documents/oxford/ghana_t...   \n",
-       "\n",
-       "                                                 key  grid_id bands  \n",
-       "0  wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
-       "1  wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
-       "2  wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
-       "3  wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  \n",
-       "4  wri_aqueduct-version_2-inuncoast_historical_wt...        0  (1,)  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "hazard_paths = sorted(\n",
-    "    glob(str(data_folder / \"flood_layer/gha/wri_aqueduct_version_2/wri*.tif\"))\n",
+    "    glob(str(data_folder + \"/flood_layer/gha/wri_aqueduct_version_2/wri*.tif\"))\n",
     ")\n",
     "hazard_files = pd.DataFrame({\"path\": hazard_paths})\n",
     "hazard_files[\"key\"] = [Path(path).stem for path in hazard_paths]\n",
@@ -210,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "81fe43d5",
    "metadata": {},
    "outputs": [],
@@ -230,92 +146,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "recreational-renaissance",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
-       "      <td>236.526837</td>\n",
-       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_1</td>\n",
-       "      <td>roadn_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>18.539418</td>\n",
-       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id road_type                   name       id  from_id        to_id  \\\n",
-       "0  4790594  tertiary           Airport Road  roade_0  roadn_0      roadn_1   \n",
-       "1  4790599  tertiary  South Liberation Link  roade_1  roadn_2  roadn_10807   \n",
-       "\n",
-       "     length_m                                           geometry  \n",
-       "0  236.526837  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...  \n",
-       "1   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "roads_file = data_folder / \"GHA_OSM_roads.gpkg\"\n",
+   "outputs": [],
+   "source": [
+    "roads_file = data_folder + \"/GHA_OSM_roads.gpkg\"\n",
     "roads = gpd.read_file(roads_file, layer=\"edges\")\n",
     "roads.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "dressed-madrid",
    "metadata": {},
    "outputs": [],
@@ -350,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ea2207fe",
    "metadata": {},
    "outputs": [],
@@ -359,7 +202,9 @@
     "output_file = os.path.join(\n",
     "    data_folder,\n",
     "    \"results\",\n",
-    "    str(roads_file.name).replace(\".gpkg\", \"_edges___exposure.geoparquet\"),\n",
+    "    str(Path(roads_file).name).replace(\n",
+    "        \".gpkg\", \"_edges___exposure.geoparquet\"\n",
+    "    ),\n",
     ")\n",
     "\n",
     "flood_intersections.to_parquet(output_file)"
@@ -367,41 +212,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "cc2f238c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['osm_id', 'road_type', 'name', 'id', 'from_id', 'to_id', 'length_m',\n",
-       "       'geometry', 'split', 'i_0',\n",
-       "       ...\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2050_rp01000-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00002-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha',\n",
-       "       'wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha'],\n",
-       "      dtype='object', length=390)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "flood_intersections.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "8d7fbaa1",
    "metadata": {},
    "outputs": [],
@@ -411,110 +232,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "id": "f345f2e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "blond-intervention",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>road_type</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>key</th>\n",
-       "      <th>depth_m</th>\n",
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-       "    <tr>\n",
-       "      <th>439</th>\n",
-       "      <td>roade_1429</td>\n",
-       "      <td>9</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>153.153316</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
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-       "      <th>440</th>\n",
-       "      <td>roade_1429</td>\n",
-       "      <td>10</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>28.151241</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>1.349324</td>\n",
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-       "    <tr>\n",
-       "      <th>444</th>\n",
-       "      <td>roade_1447</td>\n",
-       "      <td>9</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>412.834012</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.006246</td>\n",
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-       "    <tr>\n",
-       "      <th>450</th>\n",
-       "      <td>roade_1452</td>\n",
-       "      <td>3</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>929.965564</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.516396</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>452</th>\n",
-       "      <td>roade_1452</td>\n",
-       "      <td>5</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>921.598630</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.407549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             id  split road_type    length_m  \\\n",
-       "439  roade_1429      9  tertiary  153.153316   \n",
-       "440  roade_1429     10  tertiary   28.151241   \n",
-       "444  roade_1447      9  tertiary  412.834012   \n",
-       "450  roade_1452      3   primary  929.965564   \n",
-       "452  roade_1452      5   primary  921.598630   \n",
-       "\n",
-       "                                                   key   depth_m  \n",
-       "439  wri_aqueduct-version_2-inuncoast_historical_wt...  0.466355  \n",
-       "440  wri_aqueduct-version_2-inuncoast_historical_wt...  1.349324  \n",
-       "444  wri_aqueduct-version_2-inuncoast_historical_wt...  0.006246  \n",
-       "450  wri_aqueduct-version_2-inuncoast_historical_wt...  0.516396  \n",
-       "452  wri_aqueduct-version_2-inuncoast_historical_wt...  0.407549  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# find any max depth and filter > 0\n",
     "all_intersections = flood_intersections[\n",
@@ -537,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "320791b0",
    "metadata": {},
    "outputs": [],
@@ -557,147 +288,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "6430bb4a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>key</th>\n",
-       "      <th>depth_m</th>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>sub</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>439</th>\n",
-       "      <td>roade_1429</td>\n",
-       "      <td>9</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>153.153316</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.466355</td>\n",
-       "      <td>inuncoast</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>wtsub</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>440</th>\n",
-       "      <td>roade_1429</td>\n",
-       "      <td>10</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>28.151241</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>1.349324</td>\n",
-       "      <td>inuncoast</td>\n",
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-       "      <td>2030</td>\n",
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-       "    <tr>\n",
-       "      <th>444</th>\n",
-       "      <td>roade_1447</td>\n",
-       "      <td>9</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>412.834012</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.006246</td>\n",
-       "      <td>inuncoast</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>wtsub</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>450</th>\n",
-       "      <td>roade_1452</td>\n",
-       "      <td>3</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>929.965564</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.516396</td>\n",
-       "      <td>inuncoast</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>wtsub</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>452</th>\n",
-       "      <td>roade_1452</td>\n",
-       "      <td>5</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>921.598630</td>\n",
-       "      <td>wri_aqueduct-version_2-inuncoast_historical_wt...</td>\n",
-       "      <td>0.407549</td>\n",
-       "      <td>inuncoast</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>wtsub</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             id  split road_type    length_m  \\\n",
-       "439  roade_1429      9  tertiary  153.153316   \n",
-       "440  roade_1429     10  tertiary   28.151241   \n",
-       "444  roade_1447      9  tertiary  412.834012   \n",
-       "450  roade_1452      3   primary  929.965564   \n",
-       "452  roade_1452      5   primary  921.598630   \n",
-       "\n",
-       "                                                   key   depth_m     hazard  \\\n",
-       "439  wri_aqueduct-version_2-inuncoast_historical_wt...  0.466355  inuncoast   \n",
-       "440  wri_aqueduct-version_2-inuncoast_historical_wt...  1.349324  inuncoast   \n",
-       "444  wri_aqueduct-version_2-inuncoast_historical_wt...  0.006246  inuncoast   \n",
-       "450  wri_aqueduct-version_2-inuncoast_historical_wt...  0.516396  inuncoast   \n",
-       "452  wri_aqueduct-version_2-inuncoast_historical_wt...  0.407549  inuncoast   \n",
-       "\n",
-       "            rcp    sub epoch   rp  \n",
-       "439  historical  wtsub  2030  1.5  \n",
-       "440  historical  wtsub  2030  1.5  \n",
-       "444  historical  wtsub  2030  1.5  \n",
-       "450  historical  wtsub  2030  1.5  \n",
-       "452  historical  wtsub  2030  1.5  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "coast.rp = coast.rp.apply(lambda rp: float(rp.replace(\"_\", \".\").lstrip(\"0\")))\n",
     "coast.head(5)"
@@ -705,147 +299,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "849afbef",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>key</th>\n",
-       "      <th>depth_m</th>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>550753</th>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>2.243539</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>550755</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>364.644366</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>550756</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>1</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>158.050565</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>550757</th>\n",
-       "      <td>roade_127</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>54.297481</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>550758</th>\n",
-       "      <td>roade_128</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>715.652789</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id  split road_type    length_m  \\\n",
-       "550753   roade_56      0     trunk  256.660267   \n",
-       "550755  roade_126      0     trunk  364.644366   \n",
-       "550756  roade_126      1     trunk  158.050565   \n",
-       "550757  roade_127      0     trunk   54.297481   \n",
-       "550758  roade_128      0     trunk  715.652789   \n",
-       "\n",
-       "                                                      key   depth_m  \\\n",
-       "550753  wri_aqueduct-version_2-inunriver_historical_00...  2.243539   \n",
-       "550755  wri_aqueduct-version_2-inunriver_historical_00...  0.073757   \n",
-       "550756  wri_aqueduct-version_2-inunriver_historical_00...  0.073757   \n",
-       "550757  wri_aqueduct-version_2-inunriver_historical_00...  0.073757   \n",
-       "550758  wri_aqueduct-version_2-inunriver_historical_00...  0.073757   \n",
-       "\n",
-       "           hazard         rcp    gcm epoch     rp  \n",
-       "550753  inunriver  historical  WATCH  1980  00005  \n",
-       "550755  inunriver  historical  WATCH  1980  00005  \n",
-       "550756  inunriver  historical  WATCH  1980  00005  \n",
-       "550757  inunriver  historical  WATCH  1980  00005  \n",
-       "550758  inunriver  historical  WATCH  1980  00005  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# river.rp = river.rp.apply(lambda rp: float(rp.replace(\"_\", \".\").lstrip(\"0\")))\n",
     "river.gcm = river.gcm.str.replace(\"0\", \"\")\n",
@@ -863,129 +320,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "measured-worst",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>length_m</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"11\" valign=\"top\">inunriver</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">historical</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">WATCH</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
-       "      <th>00005</th>\n",
-       "      <td>278980.836489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00010</th>\n",
-       "      <td>461345.618367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00025</th>\n",
-       "      <td>521049.572286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00050</th>\n",
-       "      <td>557202.826411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00100</th>\n",
-       "      <td>589840.619653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">rcp8p5</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">NorESM1-M</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">2080</th>\n",
-       "      <th>00050</th>\n",
-       "      <td>306812.495606</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00100</th>\n",
-       "      <td>346457.694544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00250</th>\n",
-       "      <td>408674.849110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00500</th>\n",
-       "      <td>506771.887795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>01000</th>\n",
-       "      <td>574607.043246</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>208 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                 length_m\n",
-       "hazard    rcp        gcm       epoch rp                  \n",
-       "inunriver historical WATCH     1980  00005  278980.836489\n",
-       "                                     00010  461345.618367\n",
-       "                                     00025  521049.572286\n",
-       "                                     00050  557202.826411\n",
-       "                                     00100  589840.619653\n",
-       "...                                                   ...\n",
-       "          rcp8p5     NorESM1-M 2080  00050  306812.495606\n",
-       "                                     00100  346457.694544\n",
-       "                                     00250  408674.849110\n",
-       "                                     00500  506771.887795\n",
-       "                                     01000  574607.043246\n",
-       "\n",
-       "[208 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "summary = (\n",
     "    river[river.depth_m >= 2.0]\n",
@@ -1009,109 +347,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "a0f3962b",
    "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>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>probability</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>5</td>\n",
-       "      <td>278980.836489</td>\n",
-       "      <td>0.20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>10</td>\n",
-       "      <td>461345.618367</td>\n",
-       "      <td>0.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>25</td>\n",
-       "      <td>521049.572286</td>\n",
-       "      <td>0.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>50</td>\n",
-       "      <td>557202.826411</td>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>100</td>\n",
-       "      <td>589840.619653</td>\n",
-       "      <td>0.01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      hazard         rcp    gcm epoch   rp       length_m  probability\n",
-       "0  inunriver  historical  WATCH  1980    5  278980.836489         0.20\n",
-       "1  inunriver  historical  WATCH  1980   10  461345.618367         0.10\n",
-       "2  inunriver  historical  WATCH  1980   25  521049.572286         0.04\n",
-       "3  inunriver  historical  WATCH  1980   50  557202.826411         0.02\n",
-       "4  inunriver  historical  WATCH  1980  100  589840.619653         0.01"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot_data = summary.reset_index()\n",
     "plot_data = plot_data[plot_data.epoch.isin([\"1980\", \"2080\"])]\n",
@@ -1122,31 +361,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "favorite-product",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x2885e7690>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1676.5x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.relplot(\n",
     "    data=plot_data,\n",
@@ -1183,22 +401,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "above-neighbor",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<snail.damages.PiecewiseLinearDamageCurve at 0x2ad859850>,\n",
-       " <snail.damages.PiecewiseLinearDamageCurve at 0x2ad72a750>)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "paved = snail.damages.PiecewiseLinearDamageCurve(\n",
     "    pd.DataFrame(\n",
@@ -1221,42 +427,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "7e54e59d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Axes: xlabel='Hazard Intensity', ylabel='Damage Fraction'>,\n",
-       " <Axes: xlabel='Hazard Intensity', ylabel='Damage Fraction'>)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "paved.plot(), unpaved.plot()"
    ]
@@ -1276,67 +450,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "published-restriction",
    "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>kind</th>\n",
-       "      <th>cost_usd_per_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>paved_four_lane</td>\n",
-       "      <td>3800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>paved_two_lane</td>\n",
-       "      <td>932740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>unpaved</td>\n",
-       "      <td>22780</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              kind  cost_usd_per_km\n",
-       "0  paved_four_lane          3800000\n",
-       "1   paved_two_lane           932740\n",
-       "2          unpaved            22780"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "costs = pd.DataFrame(\n",
     "    {\n",
@@ -1358,30 +475,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "interracial-mason",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['motorway',\n",
-       " 'motorway_link',\n",
-       " 'primary',\n",
-       " 'primary_link',\n",
-       " 'secondary',\n",
-       " 'secondary_link',\n",
-       " 'tertiary',\n",
-       " 'tertiary_link',\n",
-       " 'trunk',\n",
-       " 'trunk_link']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sorted(river.road_type.unique())"
    ]
@@ -1397,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "acting-publicity",
    "metadata": {},
    "outputs": [],
@@ -1407,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "lesser-portable",
    "metadata": {},
    "outputs": [],
@@ -1426,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "after-hungary",
    "metadata": {},
    "outputs": [],
@@ -1445,112 +542,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "applied-operations",
    "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>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>key</th>\n",
-       "      <th>depth_m</th>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>paved</th>\n",
-       "      <th>kind</th>\n",
-       "      <th>cost_usd_per_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>2.243539</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "      <td>True</td>\n",
-       "      <td>paved_four_lane</td>\n",
-       "      <td>3800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>364.644366</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "      <td>True</td>\n",
-       "      <td>paved_four_lane</td>\n",
-       "      <td>3800000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          id  split road_type    length_m  \\\n",
-       "0   roade_56      0     trunk  256.660267   \n",
-       "1  roade_126      0     trunk  364.644366   \n",
-       "\n",
-       "                                                 key   depth_m     hazard  \\\n",
-       "0  wri_aqueduct-version_2-inunriver_historical_00...  2.243539  inunriver   \n",
-       "1  wri_aqueduct-version_2-inunriver_historical_00...  0.073757  inunriver   \n",
-       "\n",
-       "          rcp    gcm epoch     rp  paved             kind  cost_usd_per_km  \n",
-       "0  historical  WATCH  1980  00005   True  paved_four_lane          3800000  \n",
-       "1  historical  WATCH  1980  00005   True  paved_four_lane          3800000  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "river.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "official-anchor",
    "metadata": {},
    "outputs": [],
@@ -1575,115 +577,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "ranging-check",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>key</th>\n",
-       "      <th>depth_m</th>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>paved</th>\n",
-       "      <th>kind</th>\n",
-       "      <th>cost_usd_per_km</th>\n",
-       "      <th>proportion_damaged</th>\n",
-       "      <th>damage_usd</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>2.243539</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "      <td>True</td>\n",
-       "      <td>paved_four_lane</td>\n",
-       "      <td>3800000</td>\n",
-       "      <td>0.348708</td>\n",
-       "      <td>9.753090e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>0</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>364.644366</td>\n",
-       "      <td>wri_aqueduct-version_2-inunriver_historical_00...</td>\n",
-       "      <td>0.073757</td>\n",
-       "      <td>inunriver</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>00005</td>\n",
-       "      <td>True</td>\n",
-       "      <td>paved_four_lane</td>\n",
-       "      <td>3800000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.385649e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          id  split road_type    length_m  \\\n",
-       "0   roade_56      0     trunk  256.660267   \n",
-       "1  roade_126      0     trunk  364.644366   \n",
-       "\n",
-       "                                                 key   depth_m     hazard  \\\n",
-       "0  wri_aqueduct-version_2-inunriver_historical_00...  2.243539  inunriver   \n",
-       "1  wri_aqueduct-version_2-inunriver_historical_00...  0.073757  inunriver   \n",
-       "\n",
-       "          rcp    gcm epoch     rp  paved             kind  cost_usd_per_km  \\\n",
-       "0  historical  WATCH  1980  00005   True  paved_four_lane          3800000   \n",
-       "1  historical  WATCH  1980  00005   True  paved_four_lane          3800000   \n",
-       "\n",
-       "   proportion_damaged    damage_usd  \n",
-       "0            0.348708  9.753090e+05  \n",
-       "1            0.000000  1.385649e+06  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "river[\"damage_usd\"] = river.length_m * river.cost_usd_per_km * 1e-3\n",
     "river.head(2)"
@@ -1691,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "terminal-fundamentals",
    "metadata": {},
    "outputs": [],
@@ -1703,136 +600,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "equivalent-billy",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>damage_usd</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>road_type</th>\n",
-       "      <th>hazard</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">motorway</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">inunriver</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">historical</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">WATCH</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
-       "      <th>00005</th>\n",
-       "      <td>2.848229e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00010</th>\n",
-       "      <td>2.848229e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00025</th>\n",
-       "      <td>2.848229e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00050</th>\n",
-       "      <td>2.848229e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00100</th>\n",
-       "      <td>2.848229e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">trunk_link</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">inunriver</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">rcp8p5</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">NorESM1-M</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">2080</th>\n",
-       "      <th>00050</th>\n",
-       "      <td>1.473414e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00100</th>\n",
-       "      <td>1.473414e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00250</th>\n",
-       "      <td>1.473414e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>00500</th>\n",
-       "      <td>1.473414e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>01000</th>\n",
-       "      <td>1.473414e+07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2780 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                         damage_usd\n",
-       "road_type  hazard    rcp        gcm       epoch rp                 \n",
-       "motorway   inunriver historical WATCH     1980  00005  2.848229e+07\n",
-       "                                                00010  2.848229e+07\n",
-       "                                                00025  2.848229e+07\n",
-       "                                                00050  2.848229e+07\n",
-       "                                                00100  2.848229e+07\n",
-       "...                                                             ...\n",
-       "trunk_link inunriver rcp8p5     NorESM1-M 2080  00050  1.473414e+07\n",
-       "                                                00100  1.473414e+07\n",
-       "                                                00250  1.473414e+07\n",
-       "                                                00500  1.473414e+07\n",
-       "                                                01000  1.473414e+07\n",
-       "\n",
-       "[2780 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "summary = (\n",
     "    river.drop(\n",
@@ -1876,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "resident-seating",
    "metadata": {},
    "outputs": [],
@@ -1895,96 +666,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "comprehensive-separate",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rp5</th>\n",
-       "      <th>rp10</th>\n",
-       "      <th>rp25</th>\n",
-       "      <th>rp50</th>\n",
-       "      <th>rp100</th>\n",
-       "      <th>rp250</th>\n",
-       "      <th>rp500</th>\n",
-       "      <th>rp1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>roade_1002</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>roade_10029</th>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      rp5          rp10          rp25          rp50  \\\n",
-       "id                                                                    \n",
-       "roade_1002       0.000000      0.000000      0.000000      0.000000   \n",
-       "roade_10029  46079.916698  46079.916698  46079.916698  46079.916698   \n",
-       "\n",
-       "                    rp100         rp250         rp500        rp1000  \n",
-       "id                                                                   \n",
-       "roade_1002       0.000000      0.000000      0.000000   5788.380563  \n",
-       "roade_10029  46079.916698  46079.916698  46079.916698  46079.916698  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "historical = historical.groupby([\"id\", \"rp\"]).sum().reset_index()\n",
     "historical = historical.pivot(index=\"id\", columns=\"rp\").replace(\n",
@@ -2005,105 +690,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "legal-hello",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rp5</th>\n",
-       "      <th>rp10</th>\n",
-       "      <th>rp25</th>\n",
-       "      <th>rp50</th>\n",
-       "      <th>rp100</th>\n",
-       "      <th>rp250</th>\n",
-       "      <th>rp500</th>\n",
-       "      <th>rp1000</th>\n",
-       "      <th>ead_usd</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>roade_1002</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>roade_10029</th>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>46079.916698</td>\n",
-       "      <td>9169.903423</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      rp5          rp10          rp25          rp50  \\\n",
-       "id                                                                    \n",
-       "roade_1002       0.000000      0.000000      0.000000      0.000000   \n",
-       "roade_10029  46079.916698  46079.916698  46079.916698  46079.916698   \n",
-       "\n",
-       "                    rp100         rp250         rp500        rp1000  \\\n",
-       "id                                                                    \n",
-       "roade_1002       0.000000      0.000000      0.000000   5788.380563   \n",
-       "roade_10029  46079.916698  46079.916698  46079.916698  46079.916698   \n",
-       "\n",
-       "                 ead_usd  \n",
-       "id                        \n",
-       "roade_1002      0.000000  \n",
-       "roade_10029  9169.903423  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def calculate_ead(df):\n",
     "    rp_cols = sorted(\n",
@@ -2121,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "duplicate-wings",
    "metadata": {},
    "outputs": [],
@@ -2146,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "mathematical-istanbul",
    "metadata": {},
    "outputs": [],
@@ -2165,73 +755,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "corporate-david",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>damage_usd</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>00005</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5788.380563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>00010</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5788.380563</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           id     rp     rcp             gcm epoch   damage_usd\n",
-       "0  roade_1002  00005  rcp8p5  MIROC-ESM-CHEM  2080  5788.380563\n",
-       "1  roade_1002  00010  rcp8p5  MIROC-ESM-CHEM  2050  5788.380563"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "future = (\n",
     "    future.groupby([\"id\", \"rp\", \"rcp\", \"gcm\", \"epoch\"]).sum().reset_index()\n",
@@ -2250,116 +777,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "interpreted-compensation",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>rp2</th>\n",
-       "      <th>rp5</th>\n",
-       "      <th>rp10</th>\n",
-       "      <th>rp25</th>\n",
-       "      <th>rp50</th>\n",
-       "      <th>rp100</th>\n",
-       "      <th>rp250</th>\n",
-       "      <th>rp500</th>\n",
-       "      <th>rp1000</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>id</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">roade_1002</th>\n",
-       "      <th>historical</th>\n",
-       "      <th>WATCH</th>\n",
-       "      <th>1980</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rcp4p5</th>\n",
-       "      <th>MIROC-ESM-CHEM</th>\n",
-       "      <th>2030</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                            rp2  rp5  rp10  rp25  rp50  rp100  \\\n",
-       "id         rcp        gcm            epoch                                      \n",
-       "roade_1002 historical WATCH          1980   0.0  0.0   0.0   0.0   0.0    0.0   \n",
-       "           rcp4p5     MIROC-ESM-CHEM 2030   0.0  0.0   0.0   0.0   0.0    0.0   \n",
-       "\n",
-       "                                                  rp250        rp500  \\\n",
-       "id         rcp        gcm            epoch                             \n",
-       "roade_1002 historical WATCH          1980      0.000000     0.000000   \n",
-       "           rcp4p5     MIROC-ESM-CHEM 2030   5788.380563  5788.380563   \n",
-       "\n",
-       "                                                 rp1000  \n",
-       "id         rcp        gcm            epoch               \n",
-       "roade_1002 historical WATCH          1980   5788.380563  \n",
-       "           rcp4p5     MIROC-ESM-CHEM 2030   5788.380563  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "future = future.pivot(\n",
     "    index=[\"id\", \"rcp\", \"gcm\", \"epoch\"], columns=\"rp\"\n",
@@ -2379,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "heard-powell",
    "metadata": {},
    "outputs": [],
@@ -2389,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "challenging-cutting",
    "metadata": {},
    "outputs": [],
@@ -2408,240 +829,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "42794000",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['roade_1002', 'roade_10029', 'roade_1004', ..., 'roade_9962',\n",
-       "       'roade_9963', 'roade_9964'], dtype=object)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "future.reset_index().id.unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "boxed-jacob",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>rp2</th>\n",
-       "      <th>rp5</th>\n",
-       "      <th>rp10</th>\n",
-       "      <th>rp25</th>\n",
-       "      <th>rp50</th>\n",
-       "      <th>rp100</th>\n",
-       "      <th>rp250</th>\n",
-       "      <th>rp500</th>\n",
-       "      <th>rp1000</th>\n",
-       "      <th>ead_usd</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>historical</th>\n",
-       "      <th>WATCH</th>\n",
-       "      <th>1980</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"4\" valign=\"top\">rcp4p5</th>\n",
-       "      <th rowspan=\"3\" valign=\"top\">MIROC-ESM-CHEM</th>\n",
-       "      <th>2030</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>22.510369</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2050</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>32.800823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2080</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>444.418997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NorESM1-M</th>\n",
-       "      <th>2050</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">rcp8p5</th>\n",
-       "      <th rowspan=\"3\" valign=\"top\">MIROC-ESM-CHEM</th>\n",
-       "      <th>2030</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>13.023856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2050</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>187.157638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2080</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>5788.380563</td>\n",
-       "      <td>2245.248505</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                 rp2          rp5         rp10         rp25  \\\n",
-       "rcp        gcm            epoch                                               \n",
-       "historical WATCH          1980   0.0     0.000000     0.000000     0.000000   \n",
-       "rcp4p5     MIROC-ESM-CHEM 2030   0.0     0.000000     0.000000     0.000000   \n",
-       "                          2050   0.0     0.000000     0.000000     0.000000   \n",
-       "                          2080   0.0     0.000000     0.000000  5788.380563   \n",
-       "           NorESM1-M      2050   0.0     0.000000     0.000000     0.000000   \n",
-       "rcp8p5     MIROC-ESM-CHEM 2030   0.0     0.000000     0.000000     0.000000   \n",
-       "                          2050   0.0     0.000000  5788.380563  5788.380563   \n",
-       "                          2080   0.0  5788.380563  5788.380563  5788.380563   \n",
-       "\n",
-       "                                        rp50        rp100        rp250  \\\n",
-       "rcp        gcm            epoch                                          \n",
-       "historical WATCH          1980      0.000000     0.000000     0.000000   \n",
-       "rcp4p5     MIROC-ESM-CHEM 2030      0.000000     0.000000  5788.380563   \n",
-       "                          2050   5788.380563  5788.380563  5788.380563   \n",
-       "                          2080   5788.380563  5788.380563  5788.380563   \n",
-       "           NorESM1-M      2050      0.000000     0.000000     0.000000   \n",
-       "rcp8p5     MIROC-ESM-CHEM 2030      0.000000     0.000000     0.000000   \n",
-       "                          2050   5788.380563  5788.380563  5788.380563   \n",
-       "                          2080   5788.380563  5788.380563  5788.380563   \n",
-       "\n",
-       "                                       rp500       rp1000      ead_usd  \n",
-       "rcp        gcm            epoch                                         \n",
-       "historical WATCH          1980      0.000000  5788.380563     0.000000  \n",
-       "rcp4p5     MIROC-ESM-CHEM 2030   5788.380563  5788.380563    22.510369  \n",
-       "                          2050   5788.380563  5788.380563    32.800823  \n",
-       "                          2080   5788.380563  5788.380563   444.418997  \n",
-       "           NorESM1-M      2050      0.000000  5788.380563     0.000000  \n",
-       "rcp8p5     MIROC-ESM-CHEM 2030   5788.380563  5788.380563    13.023856  \n",
-       "                          2050   5788.380563  5788.380563   187.157638  \n",
-       "                          2080   5788.380563  5788.380563  2245.248505  "
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "future.loc[\"roade_1002\"]"
+   "outputs": [],
+   "source": [
+    "# future.loc[\"roade_1002\"]"
    ]
   },
   {
@@ -2655,299 +858,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "dominant-apparatus",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>ead_usd</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>5.678292e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>6.024297e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>6.015823e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.903214e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>6.183892e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>6.016411e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.436160e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>5.120018e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5.001816e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.133345e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>6.266596e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>6.179568e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>6.063889e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>5.510785e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5.646342e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.579299e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>5.967260e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5.920149e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.627618e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>6.032960e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>6.055638e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>HadGEM2-ES</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.999573e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>5.110443e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5.052677e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>IPSL-CM5A-LR</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>4.925050e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>6.330736e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>6.796243e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>6.838621e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>5.240312e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5.518583e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>5.663191e+08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           rcp             gcm  epoch       ead_usd\n",
-       "0   historical           WATCH   1980  5.678292e+08\n",
-       "1       rcp4p5      GFDL-ESM2M   2030  6.024297e+08\n",
-       "2       rcp4p5      GFDL-ESM2M   2050  6.015823e+08\n",
-       "3       rcp4p5      GFDL-ESM2M   2080  5.903214e+08\n",
-       "4       rcp4p5      HadGEM2-ES   2030  6.183892e+08\n",
-       "5       rcp4p5      HadGEM2-ES   2050  6.016411e+08\n",
-       "6       rcp4p5      HadGEM2-ES   2080  5.436160e+08\n",
-       "7       rcp4p5    IPSL-CM5A-LR   2030  5.120018e+08\n",
-       "8       rcp4p5    IPSL-CM5A-LR   2050  5.001816e+08\n",
-       "9       rcp4p5    IPSL-CM5A-LR   2080  5.133345e+08\n",
-       "10      rcp4p5  MIROC-ESM-CHEM   2030  6.266596e+08\n",
-       "11      rcp4p5  MIROC-ESM-CHEM   2050  6.179568e+08\n",
-       "12      rcp4p5  MIROC-ESM-CHEM   2080  6.063889e+08\n",
-       "13      rcp4p5       NorESM1-M   2030  5.510785e+08\n",
-       "14      rcp4p5       NorESM1-M   2050  5.646342e+08\n",
-       "15      rcp4p5       NorESM1-M   2080  5.579299e+08\n",
-       "16      rcp8p5      GFDL-ESM2M   2030  5.967260e+08\n",
-       "17      rcp8p5      GFDL-ESM2M   2050  5.920149e+08\n",
-       "18      rcp8p5      GFDL-ESM2M   2080  5.627618e+08\n",
-       "19      rcp8p5      HadGEM2-ES   2030  6.032960e+08\n",
-       "20      rcp8p5      HadGEM2-ES   2050  6.055638e+08\n",
-       "21      rcp8p5      HadGEM2-ES   2080  5.999573e+08\n",
-       "22      rcp8p5    IPSL-CM5A-LR   2030  5.110443e+08\n",
-       "23      rcp8p5    IPSL-CM5A-LR   2050  5.052677e+08\n",
-       "24      rcp8p5    IPSL-CM5A-LR   2080  4.925050e+08\n",
-       "25      rcp8p5  MIROC-ESM-CHEM   2030  6.330736e+08\n",
-       "26      rcp8p5  MIROC-ESM-CHEM   2050  6.796243e+08\n",
-       "27      rcp8p5  MIROC-ESM-CHEM   2080  6.838621e+08\n",
-       "28      rcp8p5       NorESM1-M   2030  5.240312e+08\n",
-       "29      rcp8p5       NorESM1-M   2050  5.518583e+08\n",
-       "30      rcp8p5       NorESM1-M   2080  5.663191e+08"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "summary = (\n",
     "    future.reset_index()[[\"rcp\", \"gcm\", \"epoch\", \"ead_usd\"]]\n",
@@ -2961,31 +875,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "acoustic-exposure",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x2adabbb10>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1676.5x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.lmplot(\n",
     "    data=summary,\n",
diff --git a/docs/source/tutorials/03-test-multiple-failures.ipynb b/docs/source/tutorials/03-test-multiple-failures.ipynb
index b8a19de..f25b78a 100644
--- a/docs/source/tutorials/03-test-multiple-failures.ipynb
+++ b/docs/source/tutorials/03-test-multiple-failures.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "rolled-wireless",
    "metadata": {},
    "outputs": [],
@@ -55,12 +55,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "international-saint",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = Path(\"YOUR_PATH/data\")"
+    "dir = (\n",
+    "    Path(os.getcwd()).resolve().parents[3]\n",
+    ")  # get parent directory of snail package\n",
+    "data_folder = os.path.join(dir, \"ghana_tutorial\")\n",
+    "# data_folder = Path(\"YOUR_PATH/ghana_tutorial\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53a0c038",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO add a note / auto download the geoboundaries dataset\n",
+    "# https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-ghana/resource/e324acb9-fd6b-4f5b-96ee-e2bb11917942"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad32f28",
+   "metadata": {},
+   "source": [
+    "### 0.5  Preliminary step\n",
+    "\n",
+    "Go to the geoboundatries at: https://www.geoboundaries.org/countryDownloads.html\n",
+    "\n",
+    "Search for *Ghana* in the **Name** field and download the data set from **Source** Open Street Map. It will show up as a folder in your downloads directory. \n",
+    "\n",
+    "Put this folder in the data directory of this tutorial. The one you specify in the `data_folder` variable. # TODO add these steps to do this automatically - https://www.geoboundaries.org/api.html#api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc9bf1c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://www.geoboundaries.org/api/current/gbOpen/GHA/ADM1/\n",
+    "\n",
+    "import requests\n",
+    "import json\n",
+    "from urllib.request import urlretrieve\n",
+    "import zipfile\n",
+    "\n",
+    "url = \"https://www.geoboundaries.org/api/current/gbOpen/GHA/ADM1/\"\n",
+    "response = requests.post(url)\n",
+    "test = json.loads(response.content)\n",
+    "download_url = test[\"staticDownloadLink\"]\n",
+    "file_path = os.path.join(data_folder, \"geoBoundaries-GHA-ADM1-all.zip\")\n",
+    "if not os.path.exists(Path(os.path.splitext(file_path)[0])):\n",
+    "    # check if data already exists in this directory\n",
+    "    # if len(os.listdir(Path(os.path.splitext(file_path)[0]))) == 0:\n",
+    "    urlretrieve(download_url, file_path)  # note: this can take a few minutes\n",
+    "    with zipfile.ZipFile(file_path, \"r\") as zip_ref:\n",
+    "        zip_ref.extractall(os.path.splitext(file_path)[0])\n",
+    "else:\n",
+    "    print(\"data already exists\")"
    ]
   },
   {
@@ -73,18 +131,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "middle-mercy",
    "metadata": {},
    "outputs": [],
    "source": [
-    "roads = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\")\n",
-    "road_nodes = gpd.read_file(data_folder / \"GHA_OSM_roads.gpkg\", layer=\"nodes\")"
+    "roads = gpd.read_file(data_folder + \"/GHA_OSM_roads.gpkg\", layer=\"edges\")\n",
+    "road_nodes = gpd.read_file(data_folder + \"/GHA_OSM_roads.gpkg\", layer=\"nodes\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "structured-hurricane",
    "metadata": {},
    "outputs": [],
@@ -100,99 +158,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "20abd8e0",
    "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>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Western North Region</td>\n",
-       "      <td>GH-WN</td>\n",
-       "      <td>POLYGON ((-2.57218 5.74991, -2.57080 5.71438, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Ahafo Region</td>\n",
-       "      <td>GH-AF</td>\n",
-       "      <td>POLYGON ((-2.42467 6.38815, -2.44938 6.39009, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Bono East Region</td>\n",
-       "      <td>GH-BE</td>\n",
-       "      <td>POLYGON ((-0.14542 7.90310, -0.14467 7.89674, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Savannah Region</td>\n",
-       "      <td>GH-SV</td>\n",
-       "      <td>POLYGON ((-0.38471 8.15282, -0.41897 8.15287, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>North East Region</td>\n",
-       "      <td>GH-NE</td>\n",
-       "      <td>POLYGON ((-1.23184 10.01754, -1.27442 10.01619...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              shapeName shapeISO  \\\n",
-       "0  Western North Region    GH-WN   \n",
-       "1          Ahafo Region    GH-AF   \n",
-       "2      Bono East Region    GH-BE   \n",
-       "3       Savannah Region    GH-SV   \n",
-       "4     North East Region    GH-NE   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((-2.57218 5.74991, -2.57080 5.71438, ...  \n",
-       "1  POLYGON ((-2.42467 6.38815, -2.44938 6.39009, ...  \n",
-       "2  POLYGON ((-0.14542 7.90310, -0.14467 7.89674, ...  \n",
-       "3  POLYGON ((-0.38471 8.15282, -0.41897 8.15287, ...  \n",
-       "4  POLYGON ((-1.23184 10.01754, -1.27442 10.01619...  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "regions.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "worse-million",
    "metadata": {},
    "outputs": [],
@@ -210,141 +186,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "alone-diameter",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
-       "      <td>236.526837</td>\n",
-       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_1</td>\n",
-       "      <td>roadn_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>18.539418</td>\n",
-       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>roadn_3</td>\n",
-       "      <td>124.758045</td>\n",
-       "      <td>LINESTRING (-0.17872 5.59977, -0.17786 5.59960...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4790600</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_3</td>\n",
-       "      <td>roadn_4</td>\n",
-       "      <td>roadn_6318</td>\n",
-       "      <td>38.030821</td>\n",
-       "      <td>LINESTRING (-0.17330 5.60560, -0.17327 5.60556...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4790600</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_4</td>\n",
-       "      <td>roadn_6318</td>\n",
-       "      <td>roadn_6317</td>\n",
-       "      <td>19.532483</td>\n",
-       "      <td>LINESTRING (-0.17300 5.60559, -0.17299 5.60561...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id road_type                   name       id      from_id  \\\n",
-       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
-       "1  4790599  tertiary  South Liberation Link  roade_1      roadn_2   \n",
-       "2  4790599  tertiary  South Liberation Link  roade_2  roadn_10807   \n",
-       "3  4790600  tertiary           Airport Road  roade_3      roadn_4   \n",
-       "4  4790600  tertiary           Airport Road  roade_4   roadn_6318   \n",
-       "\n",
-       "         to_id    length_m                                           geometry  \\\n",
-       "0      roadn_1  236.526837  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...   \n",
-       "1  roadn_10807   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)   \n",
-       "2      roadn_3  124.758045  LINESTRING (-0.17872 5.59977, -0.17786 5.59960...   \n",
-       "3   roadn_6318   38.030821  LINESTRING (-0.17330 5.60560, -0.17327 5.60556...   \n",
-       "4   roadn_6317   19.532483  LINESTRING (-0.17300 5.60559, -0.17299 5.60561...   \n",
-       "\n",
-       "              shapeName shapeISO  \n",
-       "0  Greater Accra Region    GH-AA  \n",
-       "1  Greater Accra Region    GH-AA  \n",
-       "2  Greater Accra Region    GH-AA  \n",
-       "3  Greater Accra Region    GH-AA  \n",
-       "4  Greater Accra Region    GH-AA  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "accra_roads = roads[roads.shapeName == \"Greater Accra Region\"]\n",
     "accra_roads.head(5)"
@@ -358,13 +203,13 @@
    "outputs": [],
    "source": [
     "exposure = gpd.read_parquet(\n",
-    "    data_folder / \"results\" / \"GHA_OSM_roads_edges___exposure.geoparquet\"\n",
+    "    data_folder + \"/results\" + \"/GHA_OSM_roads_edges___exposure.geoparquet\"\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "raised-lafayette",
    "metadata": {},
    "outputs": [],
@@ -374,273 +219,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "16b6fc9b",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>split</th>\n",
-       "      <th>i_0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
-       "      <td>48.717294</td>\n",
-       "      <td>LINESTRING (-0.17544 5.60550, -0.17500 5.60552)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>370</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
-       "      <td>187.809543</td>\n",
-       "      <td>LINESTRING (-0.17500 5.60552, -0.17418 5.60555...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>371</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_1</td>\n",
-       "      <td>roadn_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>18.539418</td>\n",
-       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>370</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>roadn_3</td>\n",
-       "      <td>124.758045</td>\n",
-       "      <td>LINESTRING (-0.17872 5.59977, -0.17786 5.59960...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>370</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4790600</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_3</td>\n",
-       "      <td>roadn_4</td>\n",
-       "      <td>roadn_6318</td>\n",
-       "      <td>38.030821</td>\n",
-       "      <td>LINESTRING (-0.17330 5.60560, -0.17327 5.60556...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>371</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 392 columns</p>\n",
-       "</div>"
-      ],
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-       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
-       "1  4790599  tertiary  South Liberation Link  roade_1      roadn_2   \n",
-       "2  4790599  tertiary  South Liberation Link  roade_2  roadn_10807   \n",
-       "3  4790600  tertiary           Airport Road  roade_3      roadn_4   \n",
-       "\n",
-       "         to_id    length_m                                           geometry  \\\n",
-       "0      roadn_1   48.717294    LINESTRING (-0.17544 5.60550, -0.17500 5.60552)   \n",
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-       "2      roadn_3  124.758045  LINESTRING (-0.17872 5.59977, -0.17786 5.59960...   \n",
-       "3   roadn_6318   38.030821  LINESTRING (-0.17330 5.60560, -0.17327 5.60556...   \n",
-       "\n",
-       "   split  i_0  ...  \\\n",
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-       "0      1  371  ...   \n",
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-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha  \\\n",
-       "0                                                0.0                         \n",
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-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha  \\\n",
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-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha  \\\n",
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-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "              shapeName  shapeISO  \n",
-       "0  Greater Accra Region     GH-AA  \n",
-       "0  Greater Accra Region     GH-AA  \n",
-       "1  Greater Accra Region     GH-AA  \n",
-       "2  Greater Accra Region     GH-AA  \n",
-       "3  Greater Accra Region     GH-AA  \n",
-       "\n",
-       "[5 rows x 392 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "exposure.head(5)"
    ]
@@ -655,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "presidential-writing",
    "metadata": {},
    "outputs": [],
@@ -665,333 +247,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "3ca9f390",
    "metadata": {},
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-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
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-       "      <th>2</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>roadn_3</td>\n",
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-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_3</td>\n",
-       "      <td>roadn_4</td>\n",
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-       "      <td>GH-AA</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 392 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id road_type                   name       id      from_id  \\\n",
-       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
-       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
-       "1  4790599  tertiary  South Liberation Link  roade_1      roadn_2   \n",
-       "2  4790599  tertiary  South Liberation Link  roade_2  roadn_10807   \n",
-       "3  4790600  tertiary           Airport Road  roade_3      roadn_4   \n",
-       "\n",
-       "         to_id    length_m                                           geometry  \\\n",
-       "0      roadn_1   48.717294    LINESTRING (-0.17544 5.60550, -0.17500 5.60552)   \n",
-       "0      roadn_1  187.809543  LINESTRING (-0.17500 5.60552, -0.17418 5.60555...   \n",
-       "1  roadn_10807   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)   \n",
-       "2      roadn_3  124.758045  LINESTRING (-0.17872 5.59977, -0.17786 5.59960...   \n",
-       "3   roadn_6318   38.030821  LINESTRING (-0.17330 5.60560, -0.17327 5.60556...   \n",
-       "\n",
-       "   split  i_0  ...  \\\n",
-       "0      0  370  ...   \n",
-       "0      1  371  ...   \n",
-       "1      0  370  ...   \n",
-       "2      0  370  ...   \n",
-       "3      0  371  ...   \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00005-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00010-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00025-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00050-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00100-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00250-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp00500-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "   wri_aqueduct-version_2-inunriver_rcp8p5_MIROC-ESM-CHEM_2080_rp01000-gha  \\\n",
-       "0                                                0.0                         \n",
-       "0                                                0.0                         \n",
-       "1                                                0.0                         \n",
-       "2                                                0.0                         \n",
-       "3                                                0.0                         \n",
-       "\n",
-       "              shapeName  shapeISO  \n",
-       "0  Greater Accra Region     GH-AA  \n",
-       "0  Greater Accra Region     GH-AA  \n",
-       "1  Greater Accra Region     GH-AA  \n",
-       "2  Greater Accra Region     GH-AA  \n",
-       "3  Greater Accra Region     GH-AA  \n",
-       "\n",
-       "[5 rows x 392 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "accra_exposure.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "5e78168b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00002-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00005-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00010-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00025-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00050-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00250-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00500-gha',\n",
-       " 'wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp01000-gha']"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "[col for col in accra_exposure.columns if \"1980\" in col]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "proof-prague",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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C2ed17V46Oib3NuTfBhPvWSFw7g1NrwW6iFD5X6lC7FIpSRPbB6ruV80SUwp1JyCpB3pl/7cR54w4HI4BR3Jv64NQF3eUqxGeF8WUn6tpDH8OJG9dbhtjwlB6JvgzIX1vMd21imCLaZrAsH5e95ti23AQAmN7tYTY4qTfntYvBTeAwDiMP6uLA7SvwOqF1oLqeyC0kW7WcB6Sn7Ls3qslzhlxOBwDjz8XiGPCfZtQ+23DeHFM6RkASO41TQMsu014OyACRJDClyvWwF5irQ8mDqYM6arzpIdI4WvwRkNwPMaEerWGif0QMGCXYAtdOBZLExgJfi3S1T4mDt4QpJ0ooOdFMWXnQ3RfVZFN/qtHtn9bcc6Iw+EYUEQyOhQusAYS6kGx4OpCdD+I7g25N5DU8hcm4yUg8h3wv8TkXlzx9vUCQwP4X4M0YALV/bt24XPtLvJ6X4tiguMhsjcUPoH0I93fMbKzaojkXuvcIfHng13cYUrSGK/YafPNSqvDsqJxzojD4RhYch9C/nWQOkxw3GBbs9JhjMGEt9cf8p+1v014OwAk8+IKsqqP2Hr9bkp6Hb3oCCl8oQ/6qtkRPwi8YZC6RVutu4EX3lQLsP0vkKbLOzGyudunk/qo/Me6yUDosKyCOGfE4XD0GJE8kp/avW1zb+qD8NZuLk1HBIpOWke1CJFd9Hv+Xaxfv0JM6hMtzkh5/6/drAonfZtLY8LbaTpFkkj60e7vV3oWYCDzFDb3fgdbNRcjt++M2Ow7kP8AAAk4Bx2cM+JwOHqI2CRS+zOk5ghs/ouud8h/BMF1Vb3S0S7ijYXgRBDRmSnLYIJjIPIDMFWYbk74HUxEGrQVeSDu+mM/gtBmkLoL8Rf1ehljDCSO16LU5K2I2O7tF1of4sepA9l4YftFxd4wVYhtJzIikoOG3wHFib51J7s2X5wz4nA4eoqJAAUIrgMN5yM21eGmIjnIvQmFzzHhTVaYiasaJjgcCtPBzipOfG2H4FiQRUjm8RVrXC8wdgn4M0C6l/7o0drFlBVkkOT1fVsruqd2MtmvoQOtl3b3Sxyr7db5j6C930dhuirELtNNAyDpe4tze3x10gMj1CFpvArJT1u1Oqb6EeeMOByOHmFMEMouh8ICyL+F1J/f8cb5D4GsFut1pc2wGmOMB80j7JtbYpfdJnaAPsg+hxTrDVZabIN+9yr6fWljDKbkV/pDfho293Xv1/ISEDsYAEnd3v39AkMxiZ/pfg2XYZdyyEUKgF/csK0zIv430PhXrSkpPx9T/SCEtwPykLwGWbI/0nhpr89nVcY5Iw6Ho8d4wWGYysvAGwH+50jm+Xa3k/zHqkQZ2cPVi3SCSAG8kRDaAulAPt0E14LoD1V5NHV3t9MKg4Eg+nsfqHqI8A4Q2RcwkPxrn5Yy8cMhsA5ICsl/3v0dEz/VcwyOg6UcGZGMPh/aGlmmZkQar4LgRjrwMLoXxoTxys7BlF8GoYlghkP+fSTzbJ/OaVXEOSMOh6NXmPA2ENkJCtOQ7DPtb5SdrEqUQTettDOk8FVxnH2sc1XRxGmq4Jm+H5I3rjgDe4o/VxVO6VuRaUcYYyB+uB4j91Lf1gquAcEJkP8ASS0vPNexDVGIHwr5N6DpH0hx1o0hp3bl327T2ivJmyHzgL5W+ts2k4JN7Id41Q9A/ADIv4fU/Rpb81Ns8mYk8wLiL+nTOa4KOGfE4XD0nmJ9gwlusNxLIjnIF7sNnNhZp5jCJ1D4CCTd6Th7LzQKkzgMAGn6G5Lt24V4wBBN05iB6KYpYih21Xij+r5W4jgITIDcx0h+Wvf3i+4D0T2BAtJ4qdZ7tEwSDrf8LsU2IU1X6z4lZ+CFJ7a/XskpxULvHORegcxzSN2JyKLtsUsOxzbdqv9X30KcM+JwOAB1HiQ/FfHndquIztosEIPQJKSlqHCp9fLTVOrcVGmxq6NDpDBLOzC6MbTNxA8t1jkIkry9azXQwUAEvKHIAA5FFH+xOiLB3knCL40JbwbB9cDOQpK36FTf7uxnDKb090AEbArJvaUzhryRqtbaTO51kDyENkXix3SyXghTeSOm+mFM2f9pN1JwbX1RMpC8Cak/W1NB3zJ65Iycf/75+uYv9TVixIhO97nmmmvYYIMNiMVirLfeetxxxx19MtjhcAwQ/jxkyQHIol2RRZOwDZcgyduQzAvYwqzlahRM4SPIPQf+FxBYe7nlTO4NnakS2b7Tu/3VHck8DclrtTsjtHm39jFl50F0H8i9jNSfUXQMVyL8WToU0SsdwGPMADtPHd7+oLQ4JyjzKGSe7PZuJjAUEseC/zkkr8ZIFux8kCSgg/3EZsEbCvkPMdJ5O7IxHia0ISZ+OF7Z7/GGPIEZ+grEDgWvBDKPI7UnIc16K98Sgj3dYaONNuLZZ1uLawKBjiceXnvttZxzzjnceOONbL311rz11luccMIJVFZWsu+++/bOYofDMUAU9O7c1hanxD6CyGJ9KbQ1UvgMCW2sGg/BTaDwnr4W3gzPW97ZaB6OZ7p5gV0dkfTjSP0ZgA/RfTGxvbq1nw7QOxvxZ6vUfvIaKD19YI3tCcU0zYBKndtv9LvXP3LzXnA41huqDnQPL/QmfpjWhOTeRIpiZhBG8p8jjX+G3DsQGAPhHXuVVjKBYZjEQUhoTaT2BJWjrz0VortD7AD9e1jF6bEzEgwGu4yGNPOvf/2LE088kUMOOQSACRMm8MYbb3DJJZd06oxks1my2VZPv6GhoadmOhyOHmKCa2OGvYJIFsl9oOPSC1/qhzOiF5jca+B/A/4/W3dsp17E2jzk3tUfwm4eTXvY1OOQvBLwIfYjTNmFPRpnbwLDIXE8UncKJG/DxvbDCy4foRoUmhVYB6C1txkTXB8ByL6IlPxCW877gLUFKBTrRULt13R0aEtgJBI7HApTIfOaPilNyJIfAxkgrL/jxLF96ioz4a2h8iak4RLITUZykyHzFBL5HibyHS3GXUXpcex0+vTpjBo1ivHjx3PooYfy1VdfdbhtNpslGm3bZx2LxXjrrbfI55dXGWzm4osvpry8vOVrzJjeD0RyOBw9w5gIXmRbvNJf4VX+HW/Io5iq24t57D9CZHfVDAmMBQKQewspzGy7SGEamJi2YAbXHYzTWKmR1P3QcLrKpcePwZRd1CNHpBkT/T7EfqLzUhr/NgCW9hJTWlQgHcDISOwgCO8MhRmaWukjxv8CiEB42x7XOIlNQWgD1YjJPafRD0lpaia0CVRcBbEDgb5HMEx4K0z1HZjSc8EMBbsIMv9FFn8X23ABYpN9PsZgYKQHcm9PPvkkqVSKddddlwULFnDBBRfw6aefMnXqVKqrlw+VnXvuudx666089thjbLHFFrz77rvsvffeLFy4kHnz5jFy5Mh2jtJ+ZGTMmDHU19dTVuYmHDocKwM2PxOaroDsk+ANx1T9CxNcEwBJ3oQ0XQPRffDK/zyodq5sSGEGsvgHgED8cEzpH/pUUyOFL5DF+wAWU3WvFmMOIiI5ZMHGAJhh72C8gfvMlqYbkabLILAGZshTfUpXSOZ5pO4kCG6EN+Sh5V+XDBS+RvzZmMKXiP81FL5WldnCJ7qRKSt20wSBfPGrSGAdnWQcGNUSLTSB0Yg3ChMcCYHR+n/UgwiPtWlIPwTZ/2nUEsCMgMhOmPiBmPCWvXsz+pGGhgbKy8u7vH73KK615557tjyeOHEi22+/PWuttRa33347p5++fL7yD3/4A9988w3bbbcdIsLw4cM55phjuPTSSzutNYlEIkQinUw7dDgcg44XWhMpOw+pnQ6FL5CaI5DKf+GFxiPZN0CSmOCEwTZz5cNfAIheQEvP67MYnAmujcR+BOn7Vb2z6q7BFZhrTtFgwJQM7LHih0PqVpV0Tz8K8R/3fi2vSr8X5mMzL4A/X9Mu/ix1Mpq1dMyQ1loqaO2AMuVFNdUQmIBGDv0vtf3dnwdSB+Q07VkYCvm3Nc0UGKeODQABJLQZmEooOQmvixEKnheDxE8g8RMk+zJSf57WfWUnI5lHYOhzmED3yioGmz4l2RKJBBMnTmT69Ontvh6Lxbjlllu4/vrrWbBgASNHjuSGG26gtLSUIUOG9OXQDodjJcAEqqHyX0jtURqarz0BW3k9kFc10fB2ON3VtoiJqkKnV9lvToMpORXJvQtSQLIvY6Lf6Zd1e4PYBj0/ExnwLirjxZH4cZB9HvFndutvzfoNGDsLCrMQW6sOR2EW2JriFo2QvBmwkFchM0LFuidTqhGM4E46vDCwJhJYExMcg+miPkYkX6y3mqcD/sLbahFucxuxPw/IQ+FTCKwJNT/Gxg7EJH7RrVoQE5kEQx5HGv5PO3kk0NLRsyrQJ2ckm83yySefMGlS59M4Q6EQa6yhb+Y999zDPvvs0271vcPhWPUwgWqk8naoOR68Uqg5AmQJmFKMqxdZDmOCSP5tldLvrzUDI5DoD1Xhs+lSJLJjr2pQ+sUWqdfz60xJtj+xRbXXoB5PW2lrMP7X4M9CCnO0Ddj/Wp2OwGikMFX3DW4OhfeXWiwChLQGxxsN4e0wwXGINxYTGgdmeQeyu+6kMSG1MTim3X1EfMRfiLFzkcz/tO6q8DWy+AdI4mhM4qQuW6WNF0ckpW3GscN0hMAqQo+ckTPOOIN9992XsWPHsnDhQi644AIaGho4+uijATjnnHOYO3dui5bI559/zltvvcW2225LbW0tf/vb3/j444+5/fbuDyRyOBwrP15gCLbqRqg5suiIxCG8/aBdEFdumusa+ldJ0yR+gqRuAZkNmf9CbP9+Xb/bNKdpBlB91VoL+emQeQbSd+mTfj128f6aVgmuvVSLLUAU7WoBzNrgDdE0SmBDiH5HJfgDYxFTAYGxy90sr4jonjEBrR1hJCa8FTb6Q2i6DMhB8kYkdT9SejpEf4TnhTpeKLgmZNEW/VWIHjkjc+bM4bDDDmPx4sUMHTqU7bbbjjfeeINx43QY0vz585k1q1UN0Pd9Lr/8cj777DNCoRC77rorr732GmuuuWa/noTD4Rh8vMBQpOpfyKKdtZOgb4HXby1iIiqu1c+FncargMQJSNM/VEgtuk+f2117g0ijdpME+ibTLpLTlEZhHsbORAqzgaxqdhQWA811G0VHQ+pbW3Ntbas6a2AsBNfGBEZCYAzijcELtB9hWJlSil54I6TyVsi+oLVAXjk0XQfJ25HS30Bkt3bTfCayG5K8EewCbMP5eGXnr3Dbe0OPumkGi+5W4zocjsFFxEcWbAWkoeIWvOgOg23SSof4C5FFOwEGM/zTfi02tTYFS34E/leYyjswkeVl+gcaSd6KNF4M0b3xKq7oeDsRsIvBn4PYBZjCjKKI22yt3yh8CVitP8mrgB6BNYu6N814ENwKQuMhuIFqrwTGairERJc/6CqKSAFJPwiZx1T/ByD6Y0ziCExow2W2FZ2Dk7oDpAEzfMqgiqINSDeNw+FwdEphKpAEk8BEnNhZuzQXFZpYv3e9eF4cG94M0l8h2WcHxxlpETwrx9pGjD9bUyf+HMRfCIWvtPslMFxntoBGLPzZrYsEJgBWtWq8IRD5rtagBMZhAqMQbxjUnAfBGKb8/zChb/fsI2OCmPjBSHQvJHmdpqcyDyOZB5DYjzAlv1ZHjOJE45KfI8l/ADr3yIRWEjG8TnDOiMPh6Dck9Zg+iHxnUFIEqwS2OJukv2aqLIOJfE/vorMvYO25A9osYP06jJ2H+PMx/lxNDxW+0BdT90HmRcTOa90hsA74xe5LrwrwiimrMRDaqtihMgYJjMMERoM3pH2HzSYRpoCfQLwhK1V6ZSAxXgmm9Axs7FBo+ptGStIPIIWvkcQxeNE9dDsTRLw1ITAUiA2qzd3FfVo4HI5+QQqztZgwtBnEjxpsc1ZaxDYUUwkD1GkU2VFbUfNTMf4n4G3U66Ws34Sxc4uplBooTC+mUeqh8Bl4IxB/OgQ3QAqfoN0oHlp9kdfWVW9IMaqxBgTXx3jl2tESGIMJjNQuk2XoyrmQ/OfqxATXxgsM3GTglRUvuAZU/A3JHYXUXwKSg7ozkKHPgFeFNPwZ7BwIDAFsl+utDDhnxOFw9AuSvlvHppsSvJVA+XFlxdjFSLHjY0DWN1HEqwKSSPphTKhjZ0QkjRTmYuwc8OcihW+0BdbXnwmMam2DDW0GbTpUguAv1kF1wbWKrbwCxFWVlxxU3duhcFdfohnG/wyxC4DVu3XchDdDqu+E2sOBDJK8UQt10/fo65GdMKFVY5yKc0YcDkefkcIMSN6qP3RStOigteWyWfFzIIgdqEWg2VewfgPkP1c9DlkM+amAhdybYJdAcFOk8OFSOy/dBjtBW3SLUQ1CW6oAV2A04o3GBEZjvPjyp7jwNbCL2o169AdS+FwfOB0bPC+AlJyC1B4LqbuRgL4npvQ8TOKIQbau+zhnxOFw9BlJPwmhLSEwGq+HE09XN0SKHSKBNQfsGCayE1J/JZgMLJrUKkDmlUL+XQhtpY4IADkIrqcOR2CNYpHoiBaHwwu03wHRaWQjtAnYBkTyA1PPISl9DzuJ+qxWhHeA0I7a2mwXQ3gSxA8abKt6hHNGHA5H3ylMgfzbmNjeg23Jyk/hw+J7tVe/Ly0ikH8PSd0BNIJNA2lV5AxuqZGN6O4qYR74nTofpqzDrp5eOxKFr8CfgWmOsPQjIhYyz2rbavB3/b7+qogxBkkcC3UnAwLll2LMqjXfzTkjDoej7zTrGEi+8+0ckP9Mv/djisHaDCb7JJJ+DHIvF58NQ2RfkFrIPQ/hDfHKzgBWgLhX84VQ+ldlVpd8G6RB58QEv90tvT3BRCYhwfFaWJx+EEpOGGyTeoRzRhwOR9/xRoA3GpHkatNm2RusX6epEmnQGoy+rldYoB1Mqf8gXlxVR4lAbF9M/EhMaAMk/SiSe2eptMwKwBsKXmO/p2kk9zY0/gUIQPT7A1aTsiqi0ZFToOlayL2O2MMw3gBPTe5H3LQ6h2MlR3Lvq6rlANxl9hfGq9ICycLXXW+8GmMKn0LhczAVXQ496wzJf4StOwMW/wBSd2v0I7AuRHbFDJuMV34RJrSBbhvaCmiAzCvYwqzOF+4vJAt2LkbS/bdkfjrSeIUK60X3xJT8qt/W/rZgot8FLwq5VyD178E2p0e4yIjDsRIjhTlI7c907kbyNiR+NMR+iBeoHmzT2uIN0e92cefbre40d4GE1uvxriJ5yPwPyb4EmYdaX4j8CBPdFSLfbVdozguOwoYn6QVqyQFI6VkQO7jf1V/bYPp3GKDk3kZqTwIEwrtgyi5ot4tndceYAMQPQurf1aGJiSMwxomeORyOvhIYhSk9A2m6Cux8yDwCTVdgAxM03G9C+sFvouCtoRoD8QNX+AeQtPPIsTxS+FQf9CBFI7YWST0AuVf1i1Ktl4jsikkcjelG95IpOwepPxvyU5CGP0D2I6TkVLzQwKjAttaM9L2A1WZegvrfgTRCaAtMxSXOEemM6L7QdLUK1aUexiQOa/Oy+PMwfRxiOBA4Z8ThWIkxxoP4IToOPv0Qki1qQ/ifgL+UHgRAcEOkMA2S10HZn/VueUVRmK7to6GNV9wxV0X8xRBYq1uFl5L/TLti8p/ojBZpUnn0+GFI7FC8wNBuH9YE14aqeyF1O5J8GPLToeEXSNVdA9N1EVwfCtMRf26fakZs+jGo/yMEqiG4B6bir9+qAXgDgTEhrR1J3QTJa5H4gS2D8kRyyOK9kOAGmPJLMMGxg2xtK84ZcThWAYyJQPxQTPxQbP5rjJ2F2CSGrObnJYNIFlL1YOcidb9Equ7GC2864LaJTUHmvypytcwEUccyeHHIfYmxC9t9WcTqyPjU3ZB7G8hAcBONfsWOgeheGC/Sqwu8MQFI/BQJfQdqDgNbj9SfB+V/6feUjfEqVWW28GWP9pP040jmqaKya1gH6QXXh+B6mPKLXMFqNzGxvZCmy8Eu1M6a+KHFVzwwJZjIdyAwYlBtXBbnjDgcPUREBjbf3gVeaBwwbrkLkgEkcTRSd5rODqk9GVt9H15wYEOy0nS1Dn8LrAGR3Qb0WKs8nl4AxJ/f5vcnthHJPAHJm1SOHYrCaEMgdiQmvGW//c154bWRyquQ2uMg8xAS2giT6OdZQsEJ+r0wo92XxaaQ3AcY/1PEX1SsOQpC04W6QWAC5L7Sx+EdMKVnaZTQ0S2MiUDiZ0jjBUjyFkzRGTEmCEOe7FPx9EDhnBGHo5uITSHJawAwpWd2ax/bcBEmsAbE9sd47StZ9ifGhJGyS6D2UAiuCTVHIFU3Y4LjB+R4kv8Mss9DYB1M6W9awsGODghOUCejWFtjM+9A9nHIPAwSA+q0HiR+KMSOwAuOHBAzTGQHKD1bIzDJm7HBdfAi2/fb+hIYpyqvksPmp2OkDgozkcIXUPgEcu9DaBMk/zYExoEUtBsrOBG8cogfgbELVA02skO/2bU6IdEDIPsySBZbWIQX1LTeyuiIgHNGHI7uk3sJkjdCYE2k5Iwu71TF1kDqNgQwsf0637YwC0lehyn7U7sdET3BC5RgK26A2iPBzkGWHIxUXIEX2alP6y6LiI8kbwD/K4jsgYm6qEhXGK9cL8B2Cbb2RCjMBzIgSQiOhPiZmOj3V0yBZvxoFcjKT4eGPyFDHtNUTj9gAqORwpd6Xkv2bi1rDqwFfjF1YyIQ3RMCG+jMHH8uBDfWotxVSB9jZcULlGL9meB/jfGnQ7D7NUaDgXNGHI7uEt4JCIE/Uy/AwbU63VxyH0BoC/CGYryKjreTgg658mdrYWPi2D6b6gVHIVX3IHW/0A/52tOwpb/CxA/vvxRT6t9aKxLaBlP2+/5Z81uMtSnIfaI/+F/plzcGKMNU3qrpiBWY/jPGIKXnwaJdiiqtL0Nkl35a20MCQ6FQC8S0ALUYLTGBkRCaCMG1BzXduVoQXFvTfoXpsJJHmFwSzuHoLiYG4e2AANJ4ZdfbFz4Hf4GGnTtb1gQxiZ+BiSPZyYj4/WNuYCim6k6IHgChDSF9H1J7CmKb+ry2+AuQ7POAp3fyK1kx3MqELczHNl4Oi3aF9H+ABBCH+E8wVTfhDXsIE9lxUC7MnheD2A/BVCGZl/p38cDa2mFVei7e0Ofxqm7FK/0FJv4jTGgd54isCALrgjcS8dsvmF6ZcM6Iw9FtfL179IZA9mls5nkdTNYR2Rc1Dx7aosuVJfojIAq51yD/cb9ZbExY55FE9wF/EeT+h9SdjNiGPq0rDReqiFZkN4j/pJ+s/fYgIkjuXWzdr6D2WEher9EHbyiQhMBEvLLzB6yWp0fEDlF5+vS/+/eiFRyt0T7/8/5b09EzmlvIs88g/aiGOxA4Z8Th6CbGhDHVj2q7XGAc1P0CaTin3X9ykVyLU2HCm3e5tueFILyV/pB7pV/tBvASB2Eqr4XgBlpIWPfLXsvLS/ZFyD4FBDAlv+y3OoNvAyI5JP0IUncaUnMYZJ7UdEx0X0zF1RD9vm4YXmNwDV0KL7QWhDYBfEg/0G/rGlMslFzJL4LfakLrqliiPxOp/ZWq+K6kOGfE4egBJrQ+ZvgUiB2oT6QfRJYciW38R5u7SslPA3JgKiGwZvfWjkzSfbOv9rPVxfXDm0LZxapkmXsDabxcdS16gLUppPGf+kP8aKcrUsT6S7CN/0Rqf4bUn1nU1whD7MeY6kfwKi7HRPcAqdEdvJVLzt/EDwZA0k/0+G+iY5rXcZeZwcIEJwDFm4Xci0jDhf2WBu5v3F+Jw9FDjAnjlZyEqbxJtTWIQvpeZNEu2PrfIbYecu+AGaYDvbqZG5fIzhDcSCW7/W8GxHYvvKHeoYe2hdStSOOfOk81LYNJ3aVzcsLfw5ScMiA2rkpI/jNs/bmwaGfIPgn+EvD0987QtgPrALApCIyFwOjBM7o9ontCeActzs693i9LCkE9V7NytpKuDhgThMh3wZRrNDfzApJ7b7DNahfnjDgcvcREdsQMeQZKTy5eXApaJLrkUMi8ALKwRzMgvMCI4kyPLGSeHli74wcDBlL3I7m3urWfSA5J3Q7+DEz0uxgvMWA2rsyI+EjmWWzdGciSfSF9P5BToa7EcTDkObzSk9sfZihNKufPyiVpbkxMdWnIIul7+2dRSYM/C3BpmkHFRPUGglKQbzCFzwbbonZxzojD0QeMCeBFdsCrvgdTdTd447XrpvC2btCNepE260X3BFBJ7AHExPaF8LZaK1B7IjZ1b9cRksxjWi/jDYPYPgNq38qI2CZs6n5k8R5I3cmQeVQdkOiemKp/YyquxIvvj+d1MuvFn63aG520eg8WJnYwEAe/VjVy+kxzmsbVFA0mJlicEO1PA+JINwYrDgbOGXE4+gkT3gqGPASEAW3nlcbrsKn7uv3hLpHdIbQ5mBi2MLDteKb0t2ASOvek8Qqk4dwOC9xEBMk8C6HtIHb0aqW0KoWvsQ0XIIsmQeo/4C/UsHfiBKi8Fa/iqu7LtQdGqgKrN2TgDe8hJrQhhHcECki6HyJzplTP1VT1fS1Hr5HgBmAmABUQ3hwT2mSwTWoX54w4HP2I58UhvBlQr0/YBdDwO2ThjtiGi7uMPnjBYi1B7hUoSs8PFCa0Iabyeig9HaROi3Hrz2m3vVOyr0D2WSh8BPGDBtSulQERQbKvY2tORGpPgNQdGtGQFJT9CTN0Ml7pmT2Sa9cheK9C/m1MF9ozg4UJT4T8O/1TN2KXQP5tLZh2DBomNB7kK6AGghuttPouToHV4ehvcm/o98QpKniWfhgKH2vBaHA9iH4XTFmHHwqm5DSk9ijVfYjtjQlvM2CmGuNh4oci3nAdV595FMm9DhVXYsJbt26YukfvmkOb4QUqBsyewcbaFGT+p+PXC0V9jNCWEBiLSRwD4T6Ik2WeADJgyla6iakthLfU7/l+KHK08wAV33MMIt4oCG6rn0HeSvp3h4uMOFZzrE3173qFGei/VQSTOBwvcRTekAcxJaeiBaN3IAu3RpYciOSntruGiWynQlREkabbEJvsVxvbPWZ0V0zZH1QkyS5CGv+OTd6KiGDzn2qEJ/cm5lsaFVGV1Mtg4c7Q9A+wtVr7E/8JlF2AV3UTJrJTrx0RkbzWAZk4JnHcypvmCm4EJMCrxhbm9W0tmwEzBAms2y+mOXqHMQZMAbwq8Fbe+EOPnJHzzz8fY0ybrxEjOve07rrrLjbddFPi8TgjR47k2GOPZcmSJX0y2uHoK9YmsXVnqkR3f5KdrLMgwpMw3lK58sSJxcFk0/TnwsdI/R+wDZe0K89uSs+G4HqQexZp+L8etd/2FhOcgKm6F+LHQf5dSD8Gyesg/UhxqNmBPeoOWhWQ3IfYutOg/kwdgkg9hNaDklMwQ1/GKztfRcH6SvoByP4PvBFI/Ki+rzdAGC8OwfFQ+ART+KjX64hkIDdZB+A1F1CuAN5/fgqvPfo2qUbXwdOG2AFgZ0PylhXyWdIbehwZ2WijjZg/f37L15QpUzrc9pVXXuGoo47iuOOOY+rUqdx33328/fbbHH/88X0y2uHoC7bud7BwC8g8Aqb/goMiogWOkobYXm1eMyaEV3YuZvg0zNCXIbKfdqakbkYW7YStOxubfgwpRmqMV4IpOwsIgD8PqT8XkUK/2doRxktgSs+C0nOgMAVpugJSt2juvyiMtaojUkAyT2FrTkBqDoLM45D/AMI7YyquwVT8Ay9+KMYr65/j2Sak6UoATPwneCt7S3RofUA1VHpNYQZgwVSssDRNQ00j//rz/dx09p2cuOlvmPrapyvkuKsCJrqXFqtLHsm9O9jmtEuPYzbBYLDLaEgzb7zxBmuuuSannnoqAOPHj+fEE0/k0ksv7XS/bDZLNptt+bmhoW9zNByrLyIFKExvEZ6y+WmQmQyEgByU/qH/jpX/rDgePYyJ7NzuNsYEITAcU/lXJPMC0vgX8GdofULmISS0nQ4ui/0IE94aKb8c6k+H/DuIVwqlZw+4/LoxBpM4EmtrikW0UryodL9Yc2XE2npM+n4k9zZkn9dOD0IQ3RuTOGbA1GQl9S+wNRAYD/HDBuQY/YkJrosAFL7o/SLNRate5QopmBQR/vWn+5kyeRpewGPCJuM4b78/cufUiUQitZA4Hi+04iI0KxvGK0FiP4HUjRqli2w12CYtR49vC6dPn86oUaMYP348hx56KF999VWH2+6www7MmTOHJ554AhFhwYIF3H///ey9996dHuPiiy+mvLy85WvMmDE9NdPhUFK3I0v2xzZeUfz5cWAhkANvM7xlIhh9Ivs4mGqIHYrxuladNNFdMUOewlT9B6I/AG8s2Lk676b+XCT9BMarhpLTILAR5D8rikitIOLHok5bGMxQJP3gKjH9c1mkMBPb8CeoORFpuhryn2q7aWRHzNAX8CouHThHRNLq/AbWgdKzMCY0IMfpT8Sr1mm7fXB6RYK6hrdi2nrnfvY+D//9CSZO2oCLn/odG++0PhfeW0aERzQCWvcrbN2ZSF8crFWd2P4qG5B5EOnHYZz9hZEeJJCefPJJUqkU6667LgsWLOCCCy7g008/ZerUqVRXtz9r4f777+fYY48lk8lQKBTYb7/9uP/++wmFOv6nbC8yMmbMGOrr6ykr65/QqePbj0gaWbgTSCOm7M9I9CBY9AOQmUACwlviVd3U9Tq2ATLPQmQnTGBYB8cSZPF3wZ+DKb8S0wsnx9osJv0vJP1w8a60KBoV2gzy07QLI7QBVPwT48+EwqeINwoT3lwjLv2MJG9DGi8CMwq8Sh34hoWSkyF2KF6gst+P2V+ICOTe0qLR9N2AFPU9hmIiOyDRfbQNe6DtSN2LNPweQttiqm5bJYYKSvq/SP1vILw9XtXtPdvXX4z4c0EKUHsYBNbAG/p8720RQfKfYQpTwYSQwCiMiUBgPCIWk38HyX8AyZu58S978tW0Mi7533kA2PST2o5uIkWVXMWUXfitLcTuClt3hor1hb/Trc++/qChoYHy8vIur989ckaWJZlMstZaa3HWWWdx+umnL/f6tGnT2H333TnttNP4/ve/z/z58znzzDPZeuutufnmm7t9nO6ezMqISH6VuBv6NmHTz0DmQbBJyL+hKYZhbyD5GVCzZ3GrEFTehBfZvsN1RApI6t/Q9DfVmAhtAuXX4QWXF6yS/BRkyYFgYpihr2shYG/tL8yD9F2QfQUkq45AvpjnNeU6eK/wIZplLaiTEv2+Fs1GtsV4vXMSRES7ZvxZSP4TaLoVvBKIHYIJb4Q0XqK1FaYCJADlZ2Oi+61UugUiWSTzjBajFj7RQYWShMgOKtYW2WGF2SsiyJIfQuFTTOnZmMRxK+S4fUXSjyH1p0NoW2qyf2P+l4tJN2WIxMOUVZUwZv3RBEPLO7+Lv36JquivdQaKP5NCLsnX08dQvvY9JMrjpJoyfPjCVLbY8QMqKmfy0pO7EIqNYO3NxzN0TDVNdUnqFjZQu6COuoX1BLxGdtjlJhBf21JBFW/9YjTeG611V6hQnx/8LtnQZZSUlyx/Ts3/n4ExmIorMSupCulAI4VZyOLvA76qBje3cg8g3b1+9+l2KpFIMHHiRKZPn97u6xdffDE77rgjZ555JgCbbLIJiUSCSZMmccEFFzBy5Kqdg+4KyU9B6n4F5Ze01WxwDBiSeRoafq8XaGPBDMWUn4cxHpJe6k6g9PzOHZHMC6q7UfgKmlVJTTnUHo0MeWi51kzJvKB33sG1++SIAHjBUVB6pn4Btu58KCxWCXF/Adg5gIHIdyD3PkitRlLynyP1ZyKJozCJk7s9O0YKs3XmTP5zdd4A/WgI6LC/8GaY8ESk8t+wZH91WPCg/k9I9mUo++Ogz6kRfxGSuhvS96gkv60HojoALnEUXnDCircp9xYQgtDE1inPqwCS+0DTWPm3ueK4v/DW05oaXH+btfn0rS+IxiMMHTuE0WuPoHxIGUPWqCKXzvH4DY/z7ynDiSYSIEmeuHtDHr6pnLlfntyy9vA1hzLiugIVm7zIM3eV8dYT2t4+ZoPRzP5kbst2w8YOYeGsRTwyM0k0Hofw9sX/w7DWo9hFYOdCZHe9AYjsSjC6F6EOCtJNaCIMfRnwVmvdExMci8QOhcLnSPJ6TPiGwTaphT45I9lslk8++YRJkya1+3oqlSIYbHuIQEDDlCtre1F/Ig1/Bn8OkrrLOSMrAPEXIfV/0KFQ0X0w0Z2R0CSMVwyNpx9p3bjx99jorniBdqIcqXuQBg31Et4RYmdBYD2oORyoVVGzyHdatxeBzMOaokkc2b/nZBsh+whIElN6JuI3QuM5gMGUnA7BtSD/PpJ7R1NJZCF5I5J9Gam8Ca+dtJLYWshORrKvAfmiGJeAKQGCKlkeGKcqsIWPMKF1ADCkEb/YoZA4E5KXq0ia5KHsXExgeL+ee3eQ/MdI6h5I/5eWgWyBNSBxPCa2d6+jRP1C6i4oTIHYwZiVcBZNe0jmf5D+l76Hfg2jJgQYOWE4ifI4Q8cMYfZn80jWp1g8ZwmzP211Htbdai1SjZbnHjuCvU86EFOYysJF77Bg1guEoyFyGXXoN9h2XerqqsmHt2fcRkEWz23i66lzaKpVLZ1EeZzK4eWstdl4Rq8zks8/24BNd9sVs8z/qdh6leUPTuh26msw/j5XRkzJz5DaX0Ihi226FeKH43mDr3vTozTNGWecwb777svYsWNZuHAhF1xwAZMnT2bKlCmMGzeOc845h7lz53LHHXcAcNttt3HCCSfw97//vSVN8+tf/xrP83jzzTe7beSqmKYR24As1IplM/SVDmsNViVEZKUKyS+LbbhUlTODG2Kq71suPWYXfV8HlVEADLAODLkGLziudZv0f6H+t0Be0xMlv2r5ILR1v4HMfzElp2FKft6yj2Se1cFpJoEZ+mqfIyNLI9kXkdqfQWAcZsj/9LlFkzQ8HVgLb+iTrduKQPZ5pOEPEBgFfh0mcRRi4io/7n+DZJ+D3EdAUdsktLW27YZ3xCR+ioS2xfPC2JrzIfcoeKV4wybr+v48ZNEuQAgz/GPt8Gm8QlM3gVGYqjsxK0BZVMRHsi9B8ia1HbQwD4OJHwXR7w16alT8+cii3QAfU/0optguu7JR800tyYY0qYY0Q0dlqAj+UiMO0YMxJT/T3+tS9Ui+7zP/q4Usmr2YRbOXsHhuDYvnLMG3lp3234atf9B2MGRTXZJYaZRkXYraBXWM3WCN5T5Dctk8DUsaKKsuIxz59qa03332I8ZPHEO6MUOsJErViP5zlH3fJ9OUIV4W79ZntG28HHJvawt2YAiF8GkEEru2BAv6kwFJ08yZM4fDDjuMxYsXM3ToULbbbjveeOMNxo3TD/P58+cza1Zrtf8xxxxDY2MjV199Nb/5zW+oqKhgt91245JLLunlaa06SGayqhlK/bfDEcl/iiRvhtIzwKtEmq7BRL+HCW08sMe1SUg/pK2uHVzkJT8dabpKIyLBzTCxfdu9GHlDdfiXXXIC5F8DPofan2GDEzDRPXWYXXYy4EN0L0zZn9r+Ywc3BO89xF9E87MiGST9kNZRxA/uV0cEQPxvtCU0uGGrLSW/1MiNiSNN/8SUaBjcGAPR7yLBdaH2Z2BnIY0XgKlApG6p89gUyEBkZwhtjQld3uJEtJxtaDTkhgGt3RDiL9KuEKweK7w1lP8FqT0GJIk0/h3KLxwwh1X/Fh7QlJI3tuiIBDUVEz8KL7zpgBy3N0j6cQiMBm/ECnFERPKQfoRLT5rBprtuxQ+O7VjML5/Ls2R+LRcc/Dc+f+dLRowfzvyvFrD3MVWcelEthLbDlP0W4y1fexEIBFhjnZGssU73UuwlFZq+K6supay6/Q6zcCTEkFHtN0B8m/j3xQ/x4Qta+7LxTuvz9dTZJCoSjN1Aa3AqhpYRjoVBIJPKYq2ltLKEmVNnk25MUzWigq+mzMIWfPyCxQt4NCxpJJ/Ns/YW4/n0zS/Yes/NOeu2X1AxtHX2ke/7GGP4/J0veeHfr5BN5YiVDqUkVs2wkXP47o+m88A199GU+oYTLjlisN6enjkj99xzT6ev33bbbcs9d8opp3DKKaf0yKiVEfHna6g8uHb3dkj/Cwqf9quOxaCSfQEyjyDZyfrhn/43krwBqu7ChLcYuONmnkYa/wSZRzHV9y73suQ/RmqO1CJFgMguSOyHdHo5LDkJavVuH38GeEOQ+jNaX0/8GlNy/PIX1eAGeteYfQ6RP+jryRsh+wwE1of4SX061XbJTVEbo0t158QOAVsHqf8g2Rd0hH1wfMvLXnAMUnU7krypWN8R1IiQiWIiOyORPfCCXbTLF94GvsIkWrsOjP814k+HUOusHBMci1TeCksOhsz9EJoAif4VNbSF+ZC+E3IfQv6t4oHLoeR0TGz/FRKN6QkiBUjdptGrkrMG9Fi2MAtT+BRp/CtSmMk6G/+AK054m0DAY8n8WhbMXMg+J+1BrCTKjWffycevfErdwnrW3XICn7+rhaDfzFzIRjuuxwsPfs2x/7c3ZVU/atcRcfQe3/eJJyIAROMRrG9prE3SWJskmogw8+PZLdtWDC+nboEO2tx4p/X5+BVNjU7YdBzzvvimZbtILEw2nQMgENSIxttPvs+vd/oDpVUlTNhkLN/MXMRHk6dRyKlg4lqbrcmXH8wEYM2Nx9BUux6JirE8dkuKzXZvwFqL5w3OlJiVV6h+JUJsEqk9Efw5UPEPTGTHLravhfxHgMVE2xe/WtUQW5Twj+6JKf0V4s+A3BtI8jYVdPLKBmagmxTTCe2MXJfsK0jTtap4GtoKkzgawjt0rfERHNv6uOpBnVDqz9XziB+GKTkJ004hnIlsjhACOx/8uYgJIk036mslJ2ECA/ABXpgK3lBYKgJljIHEidrSmH1e228rrm9jswkMw5Sd2+6SXcUtRArgL4LQ1khou9bt/WKNwDKS8F5wHFJyEtJ4MZJ+GBHBKzmhZ+fZDjY3BVK3QmGmdlME1tV0VeJYiB2AMbE+H2MgkNzrgAdeFSa6S/+uLWkk957eHGRfAn8m4lXp366pZNG8ONYKlx5zNWPWH8XsT+fx2PXPUDG8nEwyS6YpA4C1lkR5nKtevYBMKsfam63ZckFz9D+BQIA/PnI21teIRrI+xZL5tTTWNNFUm2TR7MXULWogl8kTL43p/1DAo7Qyzp7HfZd4WYxEWYxQNEwgGCAQ9AgEPKIlUcLRMKFIkJkfz+aGM+8gVhLjo5em8emb0wmGgy2OCECqIUXFsDI8z6OsupT1tl6bq8/5iEWzl1BeXTqoaXjnjHSLXFFKtwmpOxUp+T0mvifGRDvY/F3AQnAdTGD0CrW0r1hrIfcqXnSZouScTvE0ke105kr5pUj9uZB9Csk+pdsMeaL7kaNuIt5w8CaAzbW1MzcF6n6jnSTRAzBlv++W0BiA8apoLZSyeCUnIInjgUKntQbGxJDgmlCYDv4MrVsgo1Ndo3t2uF9vsTar7anIcqJcxhgoPUO7WbKTkcZLofTMftGxkPw0dYJMaZsUg/hz9EFgjeV3ih+j3Q5N/4SmK7HhLfB60TYo4mvkKf0UZB/TuSaFzyCyJ0T3gchurQXJKyuZJ8F+A4lf9MtAPBHLKw+9ybP/eoHzrn0IE6hubW8lCMFNQGrxyi9mx4MKPPTP80mUx1l/m3WYMHEcL93/BnUL6tnrhN0ZNnYItd/UsfluGzN+k3GMWmvliip9mzHGtDh8JRWJlhRWf7HZruX8851LWTRnCR88/zEzP57Fp+98wedvfUkmpbpdJZUlzP9KhQvX2mw8T9/6AmusN4qha1Rz+O8PdM7Iyo7xKqHqdqThT5B9HdK3Ik1/0pbG2H7LbS9EtAeeDpyVlRRb+BqWHADShB36Kt7SLXCShMA4xBuDAQ2Nl/8FSV4HuTcB0fbSfnZGKHwGJCG4ZqspIlD/GzBBiByMKf+DCiF1E2MCiImDZCA/E8KbFP8Ju1E8F9oS/Dok86Km4oKb6hwXLCJev/4zG/9rxJTqubcz+tsE14byy5D0PZC6BbF1SNlFfQ6zmvxbiKmC6PfbOje2VlU1l/pdtOxjDKbkZ1hpgtRDUHsSNro3JrI9RHbu2HEv0lIPkn0Fci/qk4G1tLC2/JIBU0jtb8RfDOmHtb4oukff18t/gtT/jjceXpvXHpnBovPXY9jIuaryG9lxuUjgxjvCvfNv1DvmYjHo/K8WULeogfW3WXulLkB39A9D16jme0e1RuSbi44bljTi533yuQKNNU1Mf+9LFs5axPeP3ZV1tphAomzgRQA7wzkj3cSYMJT9HySvQ3Ifqfpl0z/bdUZMeFPEzgM7F5v7cKUqrOsUb7SmPABS/4JSFbITW6N1CwDFYlyxTUhhujpqpWfqkLF+/qCT/DRI3gDkMKGlxpDbGvBnAmDKzuyRI9JKALDgTwOW/x12hAmujcg9kP5P8Zk8NF2h805MHAltAOHttI0wuLFGx3r7vtgakAawtR2uYWJ7gQmopoU/C+pPRcr/1HZicA+R3EcgNZhl60r8r8Gf3enaJvFTjRgVpkH+QyR9N5gEEt5Ri4Ij32lTj2D9hTorI3mznivohTwyCWKH4wVXrTt3Sd6ACtGF8YrzkHq1jhSQpmsgeT1Q4LBTLNVjD4HynTBDl+9IWZplC0VHThjOyAmurXV1pbnomGWKjnc+aHuOv3jwClaXxTkjPcCYEJScghRmQc10sIux6afxYt9vu51XhkR/CP48aLwMqu8cJIt7hucFsYFRWuyYbx0fLo3/KD4y2n1iwjrYzZQhVguqTOk5kDi2X+2R+j8DOYjsCrEft77Qxvnwe7e4VwZ+o6ZcekLsx9pS2lxDU5im34Pra8Fy7nWQAtLcchoYg0R/hInsAKHNeuaYNKdI/NmIrenQCTDR7yPlV2q0iDyy+F2k8pbeXwybO2+Wjcb4xeK5TgpGjVcB1fdD7jUkN0WjKXaeynJnX0dMACm/GMxIyNwH6XshtK06IoFxmMQxSGR/vMBKPtm2HWxhHqTuBsCU9L5o3/qLoeHPkH0OKEBkD0ZvcR4/3XrV78pzODrCOSO9wATGIOFtIP8x1J+JtTV4iWWmcUZ/CMmrVcp7VcIbDX4diIbVbfZdVbUkBqS1dqb5QmuqIbwbSCOSfV0LC/sJyb5evECthyn7Y9uCUpOA8C5aw5P/VC/0PSWwvsrFS6ZHuxkvjoS2hOx7wAJNI0R2QcI7YWiC/Cc6mwOjglemXItMk1dp/UP5pS0ThLs+VgUS+R7YWiQ/HRPZtsNtvdgPkMBIpOEcPWbNT7ElJ+IljunR+QGaign5iIm0FK9am1T7MYgZ1mkRrDFBiHwHE/kOUvJzKExVgbX0/SpvX/dzCExU0SryWl9RegEmfiDGBLossF1pSV4PoU3BK4XwTr1aQsTXKc2S06Ls+I8xsX362VCHY+XDOSO9wBgD5RcjjX9V9crkjUhgKCa6e+tG/hytdwhvN3iG9gZ/PtAIppiuyTwC3hhN31Tcql0z+fd1YFtwI/C/RBbvpVESKfTLwDadCXMP+NMhfsTyrZupW4p1BSFMb2W+vREaASh82TPb/G8g+zRQgMq78CKqrNuqzzFxKQ2SLJJ9Qwsa/S+h8BnScBHED8DEftT1sWyjOrx2PoaunSYT3lQl2xt+C8Ex0HgRYkox8R5KkRcLWA0/bV3bzkfy74BJ4AW6LzxojKfvSWgilhA0/QWtzUlA8zmZEDSer+snju22s7YyIYVZkL4PKOjMj16k5kRySNOVqvBrYlB1X9v0pMPxLcY5I73EmACUnoVIFtJ36pTL0FOYQDEvl31a21KD6w2uoT3A5qcXdSmA3Ey9kKbvAW8YVN2KV5QFJ9RapCreSHReRIMqhVb8tU/1CgCSuheyT0JoyzbhbpEskroLkv8CwJT9rvcaE+F19Voo9T207S6gAKGtWxyRjjAmoq3d0Z0Re7aqlabvQerf1LqKktPxlm4zXvZYjZdoG7FXBaHuabl4gXKovBbb+FfIf6Btv9Fdu/07kcLXramnpZ0Cv/h3ERi5/E7dwKYmF/VYxkF4B0icqL+77GQk87h2DWUeRjIPI7EDILIPJrLTKlNwKal7wcQhtEmHw8dELJL/DPLvaP2NpHSQHzmN0PlfqyNCcbKsc0QcqxHOGekDxhgoOwcpTNVQevpxTElR8KkwQ+sIlpph0hNEfJ2lEjtgwD+QRSySvB0yj9Fyt0oj0nAhUA7R3VsdkWUwXkKjRMmbwC5SZday3gu9ieQg93KxXbbtbBFpulEjNXYJxI+F2GGdrNQFgU0BDwhhCw14wa7v9kWykP9EZd8TR/focMar1ALowDCk6WrIPAV+Iza0JiZx0nLDu2xhBuQ/AwKYin90u2255Xglp6tAna1HGi6A8su7/DsS8ZGmf4AZAqENMEvpiYitg8Da6kz0EMl/DOnbwIYgsiem9LRWW6K7YqK7IrkjkdQtkP9UFXcLc5CmS5H4zzCx7/dLi+xAYfMzIP8eiEDJaS3PixS00D0/RZV9c++ANxKMaM0V6N9580RmgOAGmOheLjXjWO1wzkgfMSaEJH4KdadoTrzZGTERHR0uqR6vKTaF1J0KuZcw4U36v1126WMV5iD1v0Vbc6foBT73Hvif6RdDIfGrTtcwsX2R4ARYchgUPsPmP8SUX9ZGFbTb5D/Rwr3AeEz80FY7bQpSN2vNQcmvMYlj+uakhdYDLJCBwkcQ7EaOP/Mk5F4CbxwS3rXHtQ3GBKDklxDZVSfMZt+A/MtI+n4k/lNIHIvnqVNk8tOQwofamdOLIYvGeEj5ZSoLn3kMCW6A6UqILHUrZB5VkbVlHErjz0L8L6CHnWHif4PUnqRqpNEfY0pPbff3ZsKbYsJXYQtzIHk7ZP+nUaH0XUjTJZA4RmcF9dApWyEkr9FoR3QfTGg9JPkvJP+JRvckidZb5QAffA9iexbbnUsQk4DwloCHCW0CkV37RSvG4VjVcM5IP2ACa6qI1tIh/8AaKkzkz+v2OiKCNF4I6UeLHQ1RLbIcAEQE0vchjRfrB6Ypg7KLMLEDVUF28X4gCyFQjhfougjXC22ElP5GxbfyHyH152CqOx8f0C7N3S2BkW3qT6QwTVVYxcckTuiTIyL5qRhACAM5yH8I0c6dERFBkjoA0sR/hPF6P9DLhDbClF+IZF9Fmq7QzqXMw5C8Cyk9CYkdjmlWO/V630HhhdZHSk5WldzUnUhwLUx0t3a3tfkvIa2D+EzJrzDBthEQaU7TeN1vERWbanVEgutgys7psqbIC64B5b9D7C819ZG6E+xC/btqugMpOQGvnycj9wUpzChGFIHghsjivTXdEpxY/L8q1SF+ke9gwpsVZwy1vgerRhLK4Rh4nDPSHzTLUhcdBxEfTQGgeeRuIuknNUxNFkwZpvImTB80SsQ2ga3BLFOXIP581TBIPw0ktTaj/JKW7UygChvZCTIPquy52Hbl0ZfFJI6GyHeRxbtD/j1s4Zse60RI/hN9EFwmX57/WD/kw7v22hER8ZHkjdB0gwp3mah2LeQ/7XrfzFtFATaQ1KtIfqa2uAZGaAeSV40Jr7NcOsH6dZB7CyLfxVtGObRZtErSDxVbQucijVdC9h0dkQ4tui69JnYI5Keo49l0ORIYjRdqp44peQMUPtDtYwct/7qdrzZ3s0bHWh+aLtf6E68KU3F9j6IaxivHlJyAJI6G9KNI6lbtEmq8CEsOL3Fct9caSPzGO5n2ZozaJaOZtPetWt9jhkDsQEz4wqLOjIt0OBxd4ZyRfkBMrCgYltOIQ+417fYI7wiR7bu3Rv4T7YIIjIDYQZjEca3FsL2xKfcBUvdLCE7AVN3R+nzmSaT+D9o2G94NE9ka4scs/4FZcipk1FmRzKOY2P7dOq4JroFEvqs1M5nHWtNW3aUwrdhaulmrzSK6VmCNXqtair9Ei4xzr2ntQ2ERUBTZ8md1ui8AycuAvD62b0E2QKvGSRzwEXJIYH0o+xNeZFO1e8kPVLyMIDa8I5ScgRder3heeaTxGh2qFizW5JiY2kgWzBgIbdWr821G65rORyQF2Zeh5khs5S144dZZN5L/FDIP6R18/KftO3u2TjVTAqO6dzffdCXkPtEJsKW/xgTbkZDvlv1hiP8YYgcW5wDloPESrG3UCM4gFrjmc3mOnDiLJfPWxhjhP59tRvmITTDxI7SWyuFwdBvnjPQDxitBrIbVpelSWt5WE+tWF4PYeqTuF0AGAmMwpef0/W4qMFQvgrmFqg4aXFeHyqVu0ddDm0DpGZhQ+/UoXnAU1ivTaE/j36GbzggAsQOh7pfQdBUSPxjjda8VVAv+poGkWzoJRApIw/9BYb6qgkZ6rt8gubeQppv0Im9ikDhOi2QzT+gGdnan+9vcx1AozgLxxmubswSBRqAJTFg7IxDwP4HaI7GBsRA9EJojHBQgNxlq3sR65RDZTQsXC5+rqJvfBPGfQnh3aDhTpwPLfPBGtW9UDzAmBGX/h9Qep1GSxguxkoLwtuow517WDQMj8EId1PkUZoLUYrqRNpL0Y5C6Xn8ov6JfpjobYzClP0OSQST/EST/iRCFkhNXiEMiIrz37EfMmDKLSCzMnOnzmTHla5bMq2PcRmuw7lZrISU/wSvpWyeZw7G64pyRfsCYGBLdFzL/hczL4Bcr5Qvzu9xXxCL1v1ddksAamIq/9c+ws+TNqCx1FdJ0g7ZO2iUQWAei38OU/KLToXAAlPxKozV2Lrbpf3gl3YtKmMiuSGCMSsjn3obod7tnc/4zvdCbBASKF8X0g6rfEFwHYr9WmfVuImKR1G3QeClgIfJ9TMmpmNA6WG9kqzMiSWztaVD2e7xA9fILJf8BpCGwKd7Q+9o/jt8A6Tsg9W91TPzPIXlxcYsqiH5Hp6xKDdgMZCbr9FuvGhP9ASZ2QMt6NrcnpG4CClB3LLbqLrze6qkUMV4FVN6KNPwRMs+ghbuf6FDHYiuvie7b7r4irVM/xS7CsH672wHY/BSoP0d/SJyAF9u7T3Yvi0n8FAgi6QdUVDBQBvGf9OsxlmXB14u46bd38uJ/XmPtLcbzxXsz2GjH9UGETXfZiANP24ft9+1bBMvhWN1xzkg/Ycr/isQP104A/4tirUgOm3sPr5M7Q0neAnZx0RH5O8Yr77Mt1l+iXSnexmC/gtwrQIWmksovxAtv1q11vPiPsI3/ApkOqcuhu86IMUh0d8h9gBQ+x9A9Z4TkPzUt4Q3VYXaSRdL3QWhriOyOl2inlqEDxNZoOir/LpgK7V4o+z8wIWzNyeoksS7wBSCQ+xxqDkeqbmtTFyH5aSqDHlwfSs7s4Hw9TLACSk9FSk5Bmm6G1NXFaMkwCK6JV3GpFsFmn4Lk/dptFdkKEztk+ZB+aF0Ibq7zdwJrQc3PsZXX44XW7Pb5t2unV4apuBzxl0DudST3JuQ+AiyEJiGh7dpNwRgThNIzkfS9UHsiUno2xI9aLiIh/kKo+SWEJurE35LT+2Rvh+eROAqRPGRfQHLvYgbQGZn62mecu9eFjJ84llA4yCaTNqR8SBlrbbImW3xvIhMnbUA4uvK2HTscqwrOGeknjDEajg5vgfjzinUZWaj/EzLkgXajHVKYCU2XAXGdABzaeLltemVL7iUk/y54YyFxMiSvBZZo+mOpIWXdouQQaDwP7AysX9utzhoAExiL5G8srvHz7h0r+yKQh6oHAYqdOR+qcxI/uNsmS+49pO5XWn9jk1B2IV58Py1gbbwIcs9CaDstYk1/CViQueCnkJojoerfmMAQXSt5vUYQonvjRbfp+ryNwZQej038VOsmCtOh7LzW16J7QnTPLtYoRwrvg7euvh9GoPYnSMU1mPDm3X4fOlw/UA2xfTCxfbDpR6H+DJAFeIFO/jaie+jEaj7QDqzCV1D6m5YUnEgOqTsTApU6j6bqugEt3DTRnbXltxDvdoF1d/ELPi/d/wZP3vwcHzz/MRtPWp9CrsB171/G2A16V/vicDg6p//+gx0tmMAoTMVf9SLmT1Oxo3aQpn8AAuFN8OLdnxzbtQHNHTw5CAyH0t8B0WL6YEHP1or9GAgDAWg4v/v7hbcHDOTfVrXRThDxsenntEAVMIGhSPZFnRwMmLILul0QaAtzkdqfq5KsbYDqB9QR8YvPp+7VDUNbqyJocxFqcL1iO/bXSOOFiG1Qie/M02pD4sTunzvgeR5e2el4VdfiBXtYiNySiqqB8mu068cuRmqOwCb/0+muPcX4xVRisHMJdo2q/BVT+nsIbwPpfyOL90aSt2H9Rq3ryb8OhXlQ8c8eScb3isCaQKT4N/11vyyZy+Swyf8w973juPjwq3j/uSmICNWjqrj02fOcI+JwDCAuMjJAGK8SiewM+U+RzItI8j4wOQgMw3hDETFQ+AKIYkrP6NdjS3i34oV1XmsNQ2BNIKdS3D3A84JYPN03+1q39zPBsUj8WA2lp+7HtKNEK7YGST0Aqbu0fkKaILgJeEOR9OWqVhnZAxPdtfsGZ54DyiA4DsrOx5iQFqA2Xgn5NwCj6arcK8XJu6VACuwSTNWtSMOfIPc+Un8WmAn6PgbGYUId10n0N2LKdO6PrcELDkGq7lZhuuz/IPMY1p+BKT29X1RJpXn6cOZlxJ+LCYzucFtjDCSOgvBmSN1pOk049S9ougu8EOBhKv+G6agIth8xJoiENgVbhxSm905gr0hTXZLn736Zuy58kOtfXMIaY6ew108PoWr0RL539M6MHN/9OiWHw9E7nDMyQFibB5tW4bP0lxDcDgqvA6hAmjcKpBFMSa8ktjvD5N9C/DlATCWq/U+1i6Tsj73rPPA2BPsBUOhqy7bEf6LdO/7XSGFOm/ZOyTytF/zghjpi3sR00nHip6oemntNxbJMHBG/y5C/SF6H66X/AzIbCl9Dw/+piikAIfDWBztFO1UCoyD+MwiOh4ZztKMmMBZKz4DaX+qwODtZZePjXSiX9jPGn6MjBoqdNMYrgYp/IKnbIfUYpG5BCp9B5fV9d0hMs/ZHPbL4h0jZX/Biu3e+S2gTGPIEpB9C0o+D/yawvjp/veh26jXB8fr7zn/Uo5ZvHXb4Gk/ckeGZu6bx2VtfMGaD0dTMr+WRW9fiqN/ty6+uPxjjdV8jyOFw9I3V3hmR3LsqShWaiFACoXXw+kMjIPMk+HNRyXHAi2JKf4/YBeAv0nqS/Fsafl/8Y6Tqerw+3N0tjWQe1wex/VT50dZCdPfeD7Cr/DMs2RtIY9Mf4MU269ZuXnAsNrAWmCgi+dZptplnkLpfAz545Zjyv0B0L4yJtu4cGKPOSOpaJPcyVN3Z4cVBcu8iDX/QSJMpB6w6eSauF1tp1AtXcF0w60H+A4juh5c4REXJdBWkMB0vtAFScZnWP+BBcAgmsm3P3zOKLa7hLdrMeOkWwTH63S5EbArjxbXeJHEMElxToxK5V5H6s6H80q67ojojukuxW8gHKqD+19jC0ZjEyZ2mxoyJIOFtIPUQkNCZKrFDem9HLzChDZE0WqzdTWz6Bag/C6hn/vT9mPqqpnisbzn7jlPY+eDtMeE+vJ8Oh6NXrNbOiEgWqT9XW1ABgpsBIWziCLxY50WGnWELc7Xt0J8D4UkQ2hgTPxITGNKmW0Hy07SOITAM6s5EEkdAdL8+FeNZm4XMqwCY6N6YyHa9XqsZL7QOlhHAN5C9H7rpjKhBAjIVktci5Rch+Q+g7lzA13Mtv2S5qIdIru3wsMLHWpAb2hQKM5D8hxDaCCMFHULWdD1QrxNQQxtC7lUIb42puE47ewozVfAr+r3lFES9QAWWooBZ9g3tZAltrtLzdjYmvE3vxsEnb0OSNwClSHw/TOxQLRztDt4o/bLzdADbUtEGE9lFoyS1P4PM44ipKhaS9u4u3gQ31kgdFN+7WZC8Eck8i0R/iInuigktX08ith5puBgK70NkF0zFn1e8AFlwQ/2e/xQR6fL4c6bPZETpeXjBMSBjmHTgBozdZB823ml9Rq3Vy+nPDoejX1jNC1hDmJKT1GEIFiWy7Tyo/zWS+V+vVhQpQP1vwF+og7Mqr8crPa2lO2NpTGhDqL6/WKA4H6k/C1nyY2zuvV6fkSl8CrJEa0N6MWCtQ8KbA16x6LN7SPpB8IJAWMfD1/4CGv8BgWqI/QRT/pf2u4wkCOGddF5O86TYxn8gC7dCag6C1B1QcxxSe2SxHXhDiOyPGfo0pvyv2spbmI9kngTABNfEJI7sWMrcK9ZJ5D/S79nnVBo9vC3EftTt821zDqn/gG3UyEzTVciinbF1Z2Jz07rc1xijRaJmCFJYfraRieyEqbha36P0nUjtMYike2WnJg2DgAfxAzDlfwMzEsSD9D3Ikv2xjX8vjjgo7iEF7VbKvaLy/GUXD8pUXQmuq9EuqUUykzvf1p/H4ulXcsre67JgwRZQfT/r7XAcexy9i3NEHI6VgNXaGTHGw8QOwKu6GW/IfzHVdxcnaIre2Yp0ucay6OCz98AYTMkvux4MFhiKqfwnJn6Uin3ZGqg5Alv7CyT9kBZf9oicfvmz6ddfb/yHaAtsLbbQuWIpoNOAG/6k4l+hbYEQ5F7QjgtTgik9s8P3xuSe0QtdYE2dk+LPhqW3DYwBr0SF0UIbQ/wgvMpLMV5FMfoQ1jqZxkvbXEQ7xET0e2F6cSDetYBAaNOet0IDtuEKVCIeKDlG1W7JQeYRyP5Paxa6gyzGSG37Jkd3w5Scommp/AdI4996bCeA5KagfycWQptjYvtghr0IZWerXggC6UeQ2pOQ3Dsq8NZ4SVHNNqJS792N+PQznheDwGj9/SX/hq07A7E1y20n+WnIkkPYZIsnOOWSCNGhp+B5q/VHn8Ox0uH+I5fCmCCm5GQgpCPB0z1ro7TpxyGp2hqm7MLlBtR1fNwwpuREzJBnIFa86Gef0ZqA+jOx2Te7b0RgNOAVOx3u6pH9ndoY2RUoXrTrLux0W2uzSP0Z2sUT2gpTdSMs3RpryhBp6nB/Sd6kDyI74pUcixnyhBa2Vt2HqX4Ar+pWvGGv4A19Gq/qDrzYPm0XaJaft/OQ9BPtH0NySOZpbO2vNYoF4M9B0k8XB+cFIdJL9dDsc2AshLbBi+2jdlfdB2YNjeSkH+l6DU8v8GLbd0YATHhzTMXl+kPqbmy+ey2uYlOaZrENUPgQyIE3Ai9QoesagxfdBa/yGhV7s7PBn4nU/ARZsCWk/gsYTbG1k8JZoRSmgyT1e+ZRZMnRWJsBQGwjtvEfyJIDtdU7uDYb7PoHKof1XVjQ4XD0L6t1zUh7mOAEVZjMPI2kn8bED+3WfpKfCvXnaxQgtCkmtlfPjx0Ygik9DYnuoy2T+elgPGj4AzZ2EHgJjE0i/lzA18Jbyehsk9g+mMAaiMSh5Dd6wWu6CgmuoXUGfURVVY+GzN1QeB2b/wov1IFEefohIKBzdsr/ot0xdoGqifqLgTQs+TFSeRNmmQmyInndN7R1y/RYE5yA6aYcuvhzNe1lRqleR/JWJPaDliJPyX+uUuLpJ0AWFAfRBQGj0Zfk7UBCnYmaA7HxYzDhLcEEILAmJrhm10YERuh5xg9vee8Ib6o1QdnnEMl1PWzOq9L3oKXbpX1MZBIS2UMjao1/Ripv7LR2whZmwOKDgeLMHG+41qcUbV2O6N6QCaiQGUEgWfwag4R3797QvIEksJa2gEf3hNQ9+jts+icSWlsjOBIGjLaIl1/YLwrHDoej/3HOSHtE9y+KXaWwfg1eoPMuFJFMsTukvjiI7dd9OrwJrYMp/5PqcCzeT7tKsi/qsfJv60aBtVoLb9N3Q+5lxJ+tzpDx9E5Q6pHkf/rFGQEgcQpkbkFnppyMDHliuWJbkaze/dsFUPoX1RuRnM6BkSSUXw1NfwO7AKk5BCquwkR2bl2gMKOY5kr0vAuFYlSl8LFeyP0vwdYgqXsQk4D0XTooDrRI1Retu4juC43/B1IL/vtayGxQO7JPI6mbgAh4pUj1o3jt1P+0oTAd7HyMzFjGuFRxcF43LuG2BvJvF1MlnWNKT0cW/1A7wtKPYuI/7HjjxivbHl+S6owE121/+8ge0HR5Oy/MhsV7YKv/0/X7MaBYfZ8IQGR3SP0TCp8gzV1sgfFQeiNedMdBtNHhcHSFc0bawQuUYf3ZYL/B+DOhC2eE5K2qAukNw1RchvH65201XpXqOWSeQQqzQRr0bj8wRmfZBEYBvhZq5t4EjAp5eSUg9Wi6aQo2+x5epO+TU71QBFvya2j6K/hfIQu3RypvwAtv2rKNFm7W63sRL0aH7GK96IEKmEW2KRZAvo7UnoZU3YgX3lK3LRRrZIIb9LirSPJTIHW/HqfkZCh8qnfHTVcAXnFWTFAjSfGDMeGdMCaALczXAbxqrKbMortic1O0WNaUa9uwXYyxi6Cri69XrQXJ2dcwiePUNhHwZxWHKHZHE6N4MfW/wTb+Vd9DQqQyG/HVZ+szcdKGLUWjGs07HRov1rqXyDaYQAeqryakQwdLfo6JbIss2AL8z9sVDbOFr9VJa7N/hTpw2cc0fbNoT2xwR7whV3bjnPofU3EZ0nAZZB4qitoBlAFJTMkvIXHcoBTXOhyOnuGckY4ITQS/HCnM7HQEutgaJPc2qqR6Zr+HgY1XBvEDO72XblY3FcmpsqudrXUPTVdrcV/TXxD/EEz8wL7bkzgByb0Kudc1klDzM2z1XXihtbVYNPuSioclTsAUC0MlPwWCE1URlmBxguxNqipamKFh9YrLMV4FUiimc7oREVgWST2geiLe8GI30ZZQmKMXqsBo7eCJfm/5ziYpR/8VCsBwxNZD6l4MBgnvhAlvqzNqJIMUZnddJxH/CTScC7R2CknqX3qu4e27l8IzZWCqIf++6tUUxdtSi7fihTvfZeMNv4CqW/W9BEz8CCTzGPiLdaZP+V/baZm2kP8aSGqBtQkhgZFAFDHBVh0YySOpeyF151JS62FAoPphvOAobNN60HQNUA+Fl7G1J2FKf99G2G5FYLwqKP8j4k9v7YYKbAQlp2JifZ/j43A4Vgw9uvU8//zzVXxpqa8RIzpuizvmmGOW294Yw0YbbdRnwwec4FpQ+EwLWTtBGv+mnR+hzTTcP4gYE8bzQnjBCXixvbRwNLxjsdviQsQ2dr1IJ1ibQerPVEeEZmGoWqg5qfhYVFDM/6xYSFvUDGm6BQpTIHF0Sz2DMSFM+QVa35F7GUnerkvk3wL/yzY1IiKCbbgS+82m2MUHYWt/jV28N3bRbthFeyDpxxB/kRYcFz6F0t8V/9aimLJzMFX/wVQ/ipc4rN0WawJRWv8V6iB1F9Lwe6Thd5C8Hmk4Vy/KtlFVR/Ofdfo+mej3AQO5F9VZ9RdD46XqUET3wwTX7sabXatibXYRhHfQCbiJn7NkydZ87+BFUJiK1B6v72/x/aT8byB5yDwOyVuWX9P/CvwPITgREygKq0kK/E8x/iL9MT9FCz4b/6hRlJbfcwHKbsYLaurMKzkRhkwGbxsgDdnnkcV7YZtubLFpRWFMGFNxHSROxFTehjf0VjzniDgcqxQ97qbZaKONmD9/fsvXlClTOtz2qquuarPt7Nmzqaqq4qCDuj8KfrAwzTn09P1I8g5s9s3lPmQldR/kp4I3AlN6Sr9ODu0PTGA0pvR0CK4N0oQ0/bPXa4n4UHcy+PPRAtNNgLFAEGQWtuEv2qobKebmc6r7IA0XgiyG4HqYWNvfuzExTOJI/SF5Izb7vjoT0FLDYNP/RWpO1GLd4FpQ+FIdg8J0FZWzC5D0vUjyPxDcHKjCBMqXOkYYE1qvc0EsvxZtiQaCO0BofYjspumc0ESI7KznTApyz0HujQ6XEvGRZkcguo+qwObf1fWD62C60C0REWzqXkj/W/eJHYJXdhqm5CS80tPYYOfT2GDn32m6JP8RZJ5t2dcLjsOUnqbrNF2JXWZAozT+HQhDYGRrp1fRKZHCF8XOk4P0d2AqwK8Divolpb/Fi7cV0POClXjD7sQMeVQ1WchA9kVk8X5I9vVOz7O/MYEheKW/wUR6NnvJ4XCsHPQ4TRMMBjuNhixNeXk55eWtF4aHH36Y2tpajj322E73y2azZLPZlp8bGhp6ambfWaqgTxovgNBWWgQZ3gLx1tCoSeo/QD1EfgCYFunulQljPCg5A6k7CdIPYU05JrY3pllyfClEcuDPQkylhvgLMzTC4Q1BkrcWIyIRqLwdL7KNanIs2hvsF5D6N1ZimPA2WsNSmI7NPA/pB4A8VFzfvh5F5Pt60c++APXnac2LSUBoI2zTLTq518S1MNjEoeJSIKSRABNGkrfpADmLqoF6Y5D6c3TGSrFVtUv8z4sPPEz1te0U5QqyoDWaJ8lHMImj211Kmq6D1COAQGAExkQRvyhcFly3U6dIbC3SeJV2+pgKCE9scS6WxoTWQaJ7QPpepPBl2xRe7MeQfVWLnuvPx5b+Fi9aVHANb62F0KkbsInD8QJDdRBg/l1ougF1yALaQWMTkLtH9wvthpc4pkO7TXBtqLwDST8CTVdqS3XtcUjsICj5pR7H4XA4OqHHzsj06dMZNWoUkUiEbbfdlosuuogJE7rXdnnzzTez++67M25c54PhLr74Yv74xz/21LR+xYTWheoHkMzzUJgGhbkgc5DcZM3ni4/WGAQg+xSSfQoIItH9MaG1IPLd7rWBrgBMdDck/lPIva2zXpJXQ8nJSHA91cTw5+ssl+xz6DnFkOY7Yoy2ycpc1ZxI/BQvso2+Ygwy5CFYcjD4n0D6JsTuD2YIEIaG87RFNfYTvOgu7dtmPCi/HKk/B7LPAyF1/NKPQtMl+jMBqHoYExq3fPQpMBKpb1RHyQwDOxOy9VB3HFJ5S/dqePwlQAyIdhzdKv0DNF4ONICd3u4mkn4MkldBcFOI/xoT07SdBNbQok9/boey5ZJ7T2fO2PkQ3g4ie2Lih3bsvAQ3ADNUB/othTEGKb8Y6s/W+UcN5yHe5Zjw5niJI7GZZyD/IcafqWMLMs8Anh43uC6UX6h/Dw3nF1ccgVd9XRdvoB7XxPdHorsVhf+ma/py8VNIySkQP6zLYYcOh2P1xUgPZEaffPJJUqkU6667LgsWLOCCCy7g008/ZerUqVRXd67COH/+fMaMGcPdd9/NwQcf3Om27UVGxowZQ319PWVlZd01t1+xfg4azoLsk0VnJK36BgTBi+odvf0GAhM0Nw+a6y9KipvA4I4hFxEk9wokbyh23qAXn0IxKhDcBAofafRBfCCLipxlgQTgq9pm4qfLrW1tGpb8SFtpTRmIaMeJPwO80SrT3kVHg819CjXHACkgo5EB4kAtSFqLIxNHtX9u+alI7YkaDWAYUAMUNDVUeWv7dSJLH7vpP9B0ARDFG/F2+8ewTUjNT1Um3ozAG/6SpmRqjtD30BuufxN2LiR+jrdURENsElk0CaRJpdOXKSS2hRmw5CjVPQmsiam4AhPqvK5Kch8itSdAaANM5U3LDcsTySENf4T0feANxVQ/ggkMwS76vhbEBjZRlVpSaFipAjN8MpKfDjUHoTLxISi/HC/2g05tade+/BSNEjWPDwhupDofoQ17vJbD4Vh1aWhooLy8vMvrd4+KHPbcc08OPPBAJk6cyO67787jj+t02Ntvv73LfW+77TYqKirYf//9u9w2EolQVlbW5mswUe2MyyH7BCAQ2QGGvoQ37Bm8YU/iDXkIb9hLUP2MdlKEd0BrKfJI0xU6l6T2V9jMi7rWIGCMwYtMwlTeofNbvNFaLxA/ClN+CZSejhn6EmbY+zDkFQhuhToiAEmIH9GuIwJFWe7Sc/UHaQAaWzRQTNlvO3VErJ9HJIMJVOnMGjLaReKVQeJwKDlbl238O9Zf3P65hTbCVD8MscMhOBwSp4I3FCSHNF6+1GTeDghWFc+143SgysIX34/AMP2eeQQKs1R4TtL6FT8KU3LqMvsmIHFS8Tz+gc201pyICNT/Uc87egCm+sEuHREACU4EPG2Pzr66vL0mjCk9R9t4bQpp/LseS0ag4wLeQcXLmj8CGpFCA9QcAc2j88K7Fotxe44JTcRU/B1Teh4Qh8I0xF+gHT0Oh8OxDH1q7U0kEkycOJHp09sPWzcjItxyyy0ceeSRhMOrVs+/+N9oCsHW60yVxM8hcXy74XMvNA5CR0HiKM3/ZydDylfxLFurQmEmjIR31CF6kZ0wXucKm/2NMQZi+2Fi+7V9vvhdRNTxkgZ1VkLbgj+zRZ68Q0Lro6mOpQe2VbRczGyhAbLPAqKOSuEjyH0OwXWQQjFS440Fbw2I7oVXdkbRngKSexlsFlJ3Q2nbC32L/YEhmPJWTQwb212jDYXZUHcqUn5hu3UyepAwUEqX/w5igRIodvoIseKcnE2g9EyMsRAYt1w6wqYeheT1qNZJPdSdiJT+GmIHQ+5lTZEEhmFKftHtWTie52FjB2gRa+OVSGTH5aIjxitByv+uOiu5D5BFO2v0rjniFT0E/DzkHwB8WPI9IFM8wHqYysv7NInXmAAS20d1eEwUsq+rzozD4XAsQ5+ckWw2yyeffMKkSZM63W7y5Ml88cUXHHfccX053ApHsq8h9aerGmZwIlRcg4ls3619jVeJie0Psf2Rwgwk/aymb+xCDV37s5H6M5DwthDZHRPZBgJrrfgx7MuSeQKyL6usdun52hWTeQfCW3W+X83xQBStOckXn6zDLvkNyNfqfIDWdWA0JQG0anF44I2AwluQugUb2wcvtD7GBJHIJGj6JyTfQkp+0a3aAy+4NrbyWi2ozL2K1ByGhHeC5iiNV62RDBOAQhPNqme28Qbw4hh8xMQBo4+lAP4XgA+2Hpt9RZ2rwnsQ3q5DaXxb++tiRK2ZCFCDNF4MmechX3TEEr/o9iyjZkzJSciiPVTvJfVvaCeN5YXWwvoxsJ+2PhkYD+WXqi01S7ejZ1ofVt/aohPTW0RySN0pqnvjjYKSE/q0nsPh+PbSI2fkjDPOYN9992Xs2LEsXLiQCy64gIaGBo4+WjsLzjnnHObOncsdd9zRZr+bb76Zbbfdlo033rj/LB9ArLWQvlMvzLZGnYSKK3p8sWjGBMdjSk9ASo7XcHXmWe0AIa9FfpJFGs+HyB5I+RV4XqirJfsV62chfbvakv8YpAkSp+BFJ2FzxRSA5DvcX0Q7R7DzIH4q+OWQOU9fzP+3dUNTBqH1ILghJjgaQpshDMUEImBiiE3Cou2BmIqpDX0Sz0tgYocgDX9B0wtft0QmusILb4otPQvq/081PjIPtr4Y2BD85uLPdVqfT/4VzFBEFql4mwhiZ4OpAooTgHMvaJ2IndtyXjb9XwisiRduFWuztT9vrc/xhkPF1ZjQREj/p9j+2xyFGNmi1NoTjFcOpb9CGs5XpdnguLbS+oBtvBkKL9GajgmDPwrqTyvWNrVfMmbswq6VZjvBWguN5+n5mwSm8nqM66pxOBwd0CNnZM6cORx22GEsXryYoUOHst122/HGG2+0dMfMnz+fWbNmtdmnvr6eBx54gKuuuqr/rB4ApDALKczDyHxIPQZktVPBlOrk2V46IktjjIHQRloTUPorpPAFZCcj+U8hH1SF0tojsGWX4IXW7PPx2kNsnR6nMEOntvqzIfMCBIa2ymkHJmBKflY0ulTrDky0/fUkg9RfqAJdkoT4QVD31/YPXvkkXrjtBak5DmTrfwu5D8AbA/YrFeNa+B2sWRMi47TGxQshha+6PTRPz2U9kBotNg7tgAmpaJekX1QHIbA+hDaGVHOqsRLIa6eKbVCNltA6kP8KpA6IqN6JVwW2RFu8s//TjitvCBI7EBM/BFv7K70QSxOEd4GK61rH1scPg9ihiF1UlJdfo9vpmeWIHaoaLNlXkKY7keAmeIHK4nt6ic7jIQOMRofjNQHPg29QB8VvXav835C9A/KfIZn/9a3YNHUrFL4BPEzFlcsNRHQ4HI6l6VE3zWDR3Wrc3iDph5Dsi5B5EkI7QP614itlOvY+eRkApvoxbfcdIGxmMjT8TiMQgdGY8j+3KWQUEST/EQZfpwL3ok3SZl+G2p/TIvAV2kRrDpq7g0p/p6H56O4tLbHS9E+k6Urt8qj8Zxv1UPEXIzUHq/gYBkp/C8mpYB9t34DwD/GqLmvzlPhzkZojwf+maEdIC1lbohZFAutrl3Fs/w4LaTs87/qLVYMkvANe6a/1uZqfaatz2R+1tTd5Meqbjwa+hsDW2q5ccjZeySHY2lMh+xTEf4FX9iu1XQTS9yDZl4GozmshAeFJkHuqeM47YypvGND0m0gGqT1FO7qIQeV1GvnIvYTKuAdoW8sDYCBxujrBuUv0qYrrVOOl9kggjhn2qhbf9tSe7Mva6YOFsr/gxTsXenM4HN9eBqSb5tuI5N5WRwTAq9BOmOj+mOrb8UpPUFEuQJquHlA7vOjOmOr71EEofIwsOQC7+BBs/nMkeTuy+LvQ8Cek5lCk5mDENvVoffHnQeZlwNPBb6Et9Cv6w2IKJg+mDBM/sK02R+xg8IaBPxOpPRFrs0jyTmzjZUjDeeqIeMMwlTdD6rllHJFREDmRlrqQwnvL29V0A9gmMJVQeQdm2FNQ8nPaTJaNHQHhzSGwDhLtvC28PUxkK5WpzyxduxGC0KYQqITkzcXnCsBsfei/DTRB8mrskoNAghDYoo3iqWprHIZX+U9MxeUQOQTItToiJqFibwNcB2RMFFN6arEw+G1YtI2m3EDtaeOINP/Li8rCVy4lQNh0Iya8DRCF0IbqoHYTbR1/C1v3G6T2eMBC7EBM7IA+nZvD4Vg9WO0H5ZnI91RlNLTxcvl2AFNyCpL9H2RfxBZm4fVDuqZDWwIjofxipOH3qkhq50HtLxE7UzcIDVdxsvwUpP5iJPEjTGiLbl3spOka1ZwwwzDDXm6zj62v0rv+pquxhSmY+OEtaSkTGALVDyFL9tf0TsO5qrBKAfAgPAlTdg7S9BD4b7UeMHY6Xrm2s9q6Bsg83H55gq3V4kaTgODaGM+D6K5IelLxgmox0e9jItv24h0tnntgWwjtCPkvsIXZRYn2T1TIzVwILGzd2MRonjAMAb0g5z8EPgSqoOQX7R7DGIPEfgLZ/yx14CTUHIUNb6FCeOGti5OW+x8TmghVdyJLDqW1Jbv5xRJNFwGts2ayUHgLqbkGdfwE/E+RzNOABdvQpT4LgPUbMdnHNTqUfQZCW+pa0b0xZX8c/IJsh8OxSuCckeiuQMfthia0LhI7QkP6yeug/KKBtScwBEp/p9Nr/c8Ao+2iieMg9kPIf65TWQtvQ819qqIaPxwJbooJrd/uh7/1l0D6IZ2GW3r68tvYWh3IRhBStyLZZ2HIMy1qpCYwFCk5AxqugMx/i7UWI9WRix+CNFwNmduWOomdWhwRAGJ7QuY+kNlYfwne0rLwsUOh7kzgK4wpAGGMCWvnS+5VIIFgOp1a3BVeqAwbGAL5dzGZJ5DwJAiuAd6mxU6XKBCBoc+pxH/yBgiM1NRRZFvILgA+AOoxiSM6PlD6muKDEDAOvCYofKIS+6lvAJDwDvrehbdGQtvg9dOUW5t5Ger/QBtHxGuul4mDjerjwGgoORMa/wzJO4uaKSVATp2n/DTttvE/QwpzOpzCK/lpSOo/kH0JsQtQkbl1IbS5OiEDmNJ0OBzfPlb7NE13MLF9VK0y/UDxznHg0BqKw8CfCgSh+kG8oc8UpcFjmPCmmNKzILqH3sUXPtN0Sd3xSN3PtEB1KWzuQ6j/rToQJoiJ7N72eIWZkHsRKED8aMBoUatd2GY7E9sPwmvreHa7AEJbQeIYpPEfbR0RwFT+qu3P4W1QlU80QtLmtU2ARUABkQLWZrGFWsjPRIsrk0ABawuqeNrLEidtyc4gqX8BOS3INQFIPQjkIbhWUfMlB9QX1UnrdLvKZm0Tv/M7/ezL+j2yK96IJ6D8clT8rqjMS1Sd2vQDSP1vof5s7MJdsHVnI/43PT4na1PYhkuwC7aGuuP0OM3EfgmlJ6quiV0E4e0Bq+qr6Qc16hYYWWx1bkQjXWjKsugsSvq+NscTm0JS92PrztBIWfrf2lEU2RVTejZU3olXdpZzRBwOR49Z7SMj3cGEN0fiP4HU3UjdaUj5X/Fie/X7cUSySP2f1BEIro2puK7dLh4T3hQT3hRJHA/pB5H0kzrsLTsZqf05EjsAogdiSELqvmJ78hhM+fltLqbiz0OW/FBTEYEJUHIqpG5BdULaKmUaE0DKr4emv0J6KqRuQrIzwH9uqY3WxZQcgglvusy+HuIVW38zL8BSbazGK0HCe2m77MIdgFQxrZBqXaD2GAhtieTfVbuDG0NhKupLe+oweEOKTtJEoFSF2gJDNR0TXAfynwMxkBzUHKcXYamjpZuk8B6ycFtVU12azBvQgfLr0tj0ZFpqM5oVWOtuAxrBrIU39EmsbcTk30Nyb2kHjKSK78lDiF+HlJ3doV5Jm2Nl31URtfy72sXU+op+C26MV34qtv5cIAeh7TRqkY0ARt8TM0TfawmhHwO+vma/hvDhkHtNJ1aX/BLxayB5SzG61aQ1REQg+l1M7BAIb6f1M11a7nA4HO3jnJFuYkr/oEWjhS+g4WJs+jFM2VntDsMTyWldR/5jKMzAxPbFhLfs+iDJG6DwFXhDVEq7i/oU41VA4qeYxE81bF73S7AWkv+B7Ft60fa/0ghKxTWYQFu1V8l/BsGNwUQxZf+nw/FCmwIGMRXLXVy8QAjKz0EimyHJhyG/lCPCOEzJAZjEke0b660PtpF2C0fC6+jde+H95jODLi9tgl5AfZ2FI0m9uOcWA18BjVp0KnWQb543EykW434OgW2hWfm1heZ21xK0BTYKLIR8c71FJ0P3mm4ESsEbiRdaF9vwF5BXgEKLY+V5pRDZuaU2SWxSBdka/6H2LvkBNjAeIjurEF548xZVVWuzkLwW0k8W9U2KHVHEVLslMEFTaIRaU4mxn0D6TR0imHtDXzNlkDgEgttAw9lgC+ggREEjVKLHCO2og/0WnwT+6xpZCYwAyWFiByOxH+EVa0qstUy+9zUaljSx38m9k493OByrN84Z6SbGBKD8UiR5PWRfgtyzyOLnkMDaEFgTQuPAJiH3flGps6CpjPw7SPpu1Z8oPRPjVbW7vkgBST8Edg6UXtimhbZbBNfTi0/T1VoXkH0Ucs+BJDFlf17OEQEwgWok/zZ4w9XxsUkk/46+1oEYFoCJ7onU/6Xtk9EtIN5Jy618jc6sWX42iUn8DAnvA7kpEFlPi1klDHXXgH8XhHaCysvVJhEEW3RVLGBVHVWsFpFmX4fGi4EwmOLEX8aAF4DYj7WI1AOxBmpPBBYDZZihTyGIRpJS92o6yp8DMh91TpJAfbtTd60tgD8FSEP0R4ikwS7Wnwl0KNJmvARE90DEqNJt/kPtiEnNgPTDqmBKCaoT0uzIBYAYmIjW2yRO0ujSot31/QiOxwutD4AX3hgbP0CjO9SAnQHiQX6K1hDZmUAVyFAwc4tRkkbw3wd/WPGY3wAFLfotOQcT2RFjvDau4iVH/YPn736FYCjAO//7gD89fHbHfwcOh8PRDs4Z6QHGeJiSnyPRPZDGv0B2sqYG/OmtN6qhzcEvqChWcLymD7JPQfoRrQsovwATGN1mXWvT0HSpXvxMJSa+T7dtsjajhbXJG2jJ+9s5+l2S2sa7zByaFprHzzfPnTGx1s6L/Dt6h97eMfPzixfpZmLgz0Fyb0J427apIFujom7NMuz2Xew3m0HlZLyIRhqMCWHC4yA8rs1xpGx3pPYusPPxvMqW55eNmSz9s9Sfi06iRX83ke9B/GhMuG3XkQFsS7FnAFlykP7OCguAhVBYhKarDLCgdX3rYwLL/Nuk/oU6HjFInIwxMaTkTJCICskZg/VzeIH25zJ5se9B7HtI6W+1TiN5H9gvUecjtczWRut/So7Di/9YT7PpOpVcByi9oO3WpSchsaMxAUEyr2o0JPss6sgFgBowtthFtGipPZeqGSq/AS+2S7u2P3LNU0x7YzqBUIBC3uejl6aRakoTL4m1u73D4XC0h3NGeoEJroWpvBHxFyL5zzB2jl5wyUJkL0xwvKpqFi9+knsPSd4E+Q+RujOh4gpMYDjQXGB6XssV1fx/e+cdXlWR/vHPnNtzk9wUeu8dG0hTRAVFUOxiYZG1rWXtouLq2ntldxXbz1VZ7G3XjqiIihRBRAEpCkgNIaTcJDe3zvz+mJNGEkiAFGQ+z5Mn95w7M2fO5MJ57zvv+32Tr0SI2v1HrpSC4C0Q+1V7RuIrEclXo3zjoegxSGxF+CfVOJ6Kr9IZNt4T9bWFhfKdAaG39TaMe1D1ffOuqHCQCezQWyGFj6EczXTgZnQBqvhJ/S3cPUxnlZSmkBKCvMORqf/CSqrZra9w6fuyWtZqPWR0qc6AQWjdEmc7ROptiNIqu5XarqVcfyNdq75Ke5vC0VNnMlkd9LGMUPZwjm3RMvEVCc8EnODoUKZ+ajlbIgN3Q94lkCiE+DKtU1LdfaoEqvhVHViaWI42Enb2TDUH31hdWE/FIK4VY3Xpgk/0PTt7YXkqV/wVwolw2V4x75GoUEdw9kC4B6I8Y2HHqdoIE0mgfKBKFZQdeitL5NhZXUdXO/dQMITb52LMxSP5YsbXDD99MD5/9Wq9BoPBUBPGGNkLhKNF2YNuVxEOwn0YOG5H5V+tt23yLoCMGaDiOjjT2UvHFaQ9jfCO3O11VewnVMkHEPnC9qZkQOBhhOdwnXEDtUtBDs8CmY1wH1p+znMKlPwPLH+1dyVlAuRy+ygJ2KEl3L3Haw9BPIrKOdFubNduwQGOfpDIptI37uBVyNAhkP4alqOqoqwQpQZTtMp71d/PpyDth6l7CFb61JrbFj+FVif1QPqD2ptUcD3gh9SbEYlNKIT2bFmZkGuLrTkiVcfyjNbxLolVyPAKYDuUvKnXJ75KexxiC8FT2RiRsfVQ9JDe9hNu/XkALUUvfCAdwHrAjdVqrhYWK35OG5oyT7eNztVbO6IlpFZWt90ZYaUgmpXXChKATDpLFxMUbUDlAC3RnqAE0ALi3+l0b9+4ajVSIiVRNizfRNGOIkoKwyyds6LarSyDwWDYFSa1t4EQjlaIwGP6W378V1TuxaicM/RWiiyG9JewamOIxH9H5U6CyFxbhl2rb1reo2rtUQFQia12+q4DnBVqkET+p4M+LS+iuno0eX8pf22r0+LsgZV6MyJjOogU7WFQBToIMuUO8J0GQgHZ4Dyk8njxH2F7X2Txu+yMAl0t2dl19/ejJJR8qD0ioLU1dkX8ZyAErj5YnkPK691YfizPkYikc7RHJ7YECu4q7xdbVWUoK+UCXfsGIP9cCP5TC4BFPgMVAdxaxM6epwy9idw+EnacqA1KYnaQa2tIfgjRYg5Wi5mIjHuBOFiZOvOpaKr2ntiro5TSQa2EwTcay133lFrhOx0t7rZFX6vClhSJj4BMUBIVfLja/inpuqZONKwLKU649YzyGjwGg8FQS4xnpAERzvao9Je0NyS+Ci3BngnJlyCouSpuRVToRW3AOFogUqfoIn67e/BWN074S/3CdVBZf6USUPK2nqtneJU+Mp4NMVtLA5ddC8WhdUnQHiCVNBFCr+hYldhc/SMyylNQ5ZYKI5Zu20gonIIsfByafYjlTNPvKomK/wyEkTIK8RUIRJXUYQAVXw4qm/KP9G6+mQt760IG7WNP6cLo0yWf6Gq/lhdkBf2OyGrtEKq4LrJYpxcjgBJwtQXHYQjfWajIj3ZKrAOZe6NeP5Vr90wGLPCOhJSbsKp4HuytGpmjY1rkdnQhPxeEF6Ks1yC+HnDXuV5PZVoAW5FSkpvtJDdbcOuEbiQHEgw4qoQrH85g51RvgI+fn8Wn//6SjFZpdB/YlbNvPIX+w3vvxTwMBsOBijFGGhgR+dhWrPShvxHvgNDrqILJqKQJOuNmVx6O6FL9cPedi/ActecTiczV6Zre8uBWFVsFuMBqhXINrfo4zxlX/trRH2JrKIvPKL2/5L+ifKdA8YuQaK+3HmKL0XEQbv3wT7rYFlbLhXgOqHV272zIGYT0XYsVuALl6g/OATreIvtYcKSi5GZU6sNYvsqxJiK6EIVPx8DIjZTLnteA6zCIr7SzXkDJqDagHC2Q8VyIzNfZOK7B2hiJLdD3GpmNjAwC3AgkkICSmSBagKs1+E5E+E5CCK/2XERX2IZnGFikxyhNMfZfgkgaX6Z0uzNKOUB0s0XTOmkPUQxQK4CtELpbr6n7WF1KoJYoJSH6HSr8OcSW67GA5+5uzYfTm9Hz0GKKg04K8wQfrk/jz3f3JaXtrWX9iwqKeefxD5lxz9u07d6KkROGc9EDE3A461680WAwGMAYIw2KksWo4un6IOkSEDEtfmVlAApCM3TF08AjWO5Dqumfa4t9CVtRdA/nEfkGop8DFiLwUPkbkU/0Fo17NJZVOfNDBp8D8spPJCoUvRPlwZZCuHQAb+Bu3S/8FRQlwBEA5YDoZzoGwzsScs8FLGi2BHJOAuwYk5KpSNeh4OlibyVFtKGQ2AzCqXVeZC6W/9zye0psBELgSIVEPkTXVIldkJG1EJ0J7oHgGQIlL4PKRybCCLkRpQqA9ghHMir8mu4Um6+zcvCiDYpVUPgMqCxUwo5PcfbWBmLyNVieQ5CJHFThnfZ6CrufjWcc+K/Acu9e3ExYDpT6FUiG1LuxXF11sGvujRD7EO05EZByyW7HqoiSBXYQskdn0jgGQGI1lpUAFMvmpdhOKwtQ/POGVG551WLu+/N559EPAEE8GsPpdnDsucOZeMdZJkbEYDDsFcYYaSCUzEUF79WxFI52iJTLEKJ8+VXkW1TBLTpTIvcCZPKVCP/5ZaJXACpuf+MXyQir5lLMu5+MrXIqklD5V+taLVZbrcwJtnz7ToR2qlrsGgCeo6DoCS2rXhOxZRBfBI4xeotGNIPQR1rTwtkHrAwspx+Z8X+Qexnwu+4XvAC8k7QHwdFSa3X4X9AaILEFUHgHMrEdkv+KZTlA2Uqqyl7TxGLU9tEoR3uEbywqnq09NLEldpxGG3TIVAKCj6DUxrK1UdtOp3wLyQJHR0j5GwSf0gGyMkcLgAm3FgtzdIDkayH+G3LHBJ1BVNEAKcMP7iNqZYhUwkrGcum4GSEckPEYatscoBAc7bBc/Ws9lJQJyBlpzy8Myo1oNoNY1kROufAHsja4mftxZoVkHsHc/y3kvnOnsnLhr8hEgkQsgS/Fx62vXsuRpw+p270YDAZDNRhjpAFQ8U2o4N16a0IkI1KmVDJEAITnSGj2MaroH7o+SNGTqPBHOg3Y2VHHcxTcqDNvKmhu7BGe40DYdVNiy7XBIFK1V8TZX4umVaF0K8EJSRcgUiajIjO1sFs17ZVSqNArEHoBXIcgPEegSj4H9RuoPCi+X8uUuwfaHXKBggojSAi/CK7BCP95CO8YfTr1alTRc9oTEv8JgjehAg9DPB9EJx2jQhoQsbdrnKjgLdobI8NAMzvN+Kfye4p8DOzQ95ZYT3l8RDp4x+h0aysZKXxaUE1mI/zXIbyjkYkCKPoX5J6pY3kQ6CwdG9EW1Ga0EemBkv8g1Q6s5It3+2dSCr2+VmXlVxV6DZ2WnAqp9+x2nEqE39TBtok8dMzKZlTeFVgsxp8KZ16aTfaWdEafvYkXH2xDcYGDeDTBll+z8Pk9nHT58Rx56mCat8vc3ZUMBoOh1hhjpAFQxf+ni9G5R0DgcYS7+iA/YaUgUm9DugZB8FaIL0MF70a5B0PoVSChgxjT3t+r+QhhQfOZ2tMSX6oLvEXmAxKsZij3iGrCP+Nlv4VvrFY7LfVGVPMxUoVPaA+Gszc4e6O8Z+o4FUcrvaXhHKiDSJPsLYbQi0C+3duizCBwHA6eEyrPP/kvKKsLBK8HwqhEDsRXaLE2q5tWW43n2eM5dXwIDki9HMt7JDLrcLTBUFpzJg740d4CC2gF6EBbK3VK2XUtzxBkZBiEXtA1ihzdbA0O24NSNncfunquA9x9wXcvuAfb4nT/hFAuMvYLInDPLoOPBVIr4lo7GXtF/9JzdmRi7ZQuvDuEezAqeAcgwD0IoushOhtLgNsNfQ6P8tDry3j9qZYUF5T/Xdct+50rpl7AKVeMqdP1DAaDoTaYHLx6RskQhD8CQPgnYtVgiFTE8h2v9SC8J2mRq6JH7SqrLRH+C7Gc7fd6XkIILFcHLN84SH0QfPYDPzobgjfoDJFKVNiKcdr3IIWdDBOv1FJFl0DoGS1v7hmLFfi7TveUm7RUvn8iIvAwVuB2LMvehqokmV4hcyP8dLXxCFbSKETaI9rzEFuot79IQNKpiMy3IcPWEZFaCM7KfAXLe6S+9xYzIe1J8NgPVgFYKeA6BDJegfT/s69SoOsMobc3ZP7DttoqQNyu7Fu6n1H6OwEUgus4RPOvsNKfxPIegWU5Ecl/Bf9f9bwiH6DyLkYW/Z9W4K2OssDWRNkpGf5cBz0DpP69+n67QKcw99LzjX5Mxawjt53J7U9VHHmiG3+g/G8eyEzl5MsrG4UGg8GwrzCekXpGReeCoz2oTP3tuJYIRysIPKiDDWPL9RjJN4L78H0+R8uyUCm3gKOdlrmPrYH8KaiUyxCuUkVPD3prwCrP/hAxvb2SCCOLZug4EEtCyUfgOgocHbGS/1ThSilaXCvyNTjS9TdzQKmYFkRzdAf82vBSpeJoCWTBg1iBKVTB0RasNEhsB5GmCwz6L9QVZN39kEnjIPwpqvglcB9alqUkrAyENwMZ/QatsREDqw04WmC5D9VbTI6uoDyovNdQ/KDr/JRp/petnNaNkVvBeZC+p8iv6CJ9+WUqu6UIIRAp1yCdPaHoEZD5eksuNAOV9nCVWB2lnPa2XIVtmqJpgFPH2niO3M1ftioysghdSLBCTAxeve7EKA1S7nHQZtp3T2PlIm2snHXzySZI1WAw1BvGM1LPiMRGnZoqMhGlWha1RuoYBpULru5YnkH19kAQQiD850P6dHB2guhM1I6zkMGHkYkcyrZQRDWF/uLLIPSGzlQpvEMfxxaD//zK7axWOhsm/AUqOBVZ8pFOf1UxiP+ua/wkcmxDJLm8X8m/kbHCSkMpWYjKu1jLpzu6aW+Bs33l9fFfp7Nwol+hCqciZQmy4O/I8Oc6vTX2G9rrENLVjWmBDL2DKrgZEr+DXAHR+yD6CVUMEdFcpx1bAfBfDmlTsdKfA9/xQIEtqlY9lu8EyJyJ8F+k40jkFlTuBcjcK5DRZRX+JkqnH8fXAiDD3+u1JQ7Jk2scf5cU3oNeWwWkQOBpdBHAbMqzpdKIhhPkZ2u1WXeSi7SMqoUWDQaDYV9hPCP1TVlwZw2u+F2gip7TQa9WS0TK3/btvGrA8gxEubqgChxAHEIzIPp9hUnlIHNuRmQ+iCCKSqzVRob7cLCStTS53Ay+CViuyoXvSDoFSiTEN2tvQsGjqPD/IBG2H7JJtshYOtr4SQVsUbIdx0OreeVjiWRthMgfIeV6RHwVOCvXjLEcKajUm1F5f9ExKSUfaiMi/C1K3FdBrh4gBpG3ISyp1d/K2QF85yN8x1bKeMKRZr+oKhJWaW6WE5LOAO9onWUVngnR+ZD7BdI9DHynlsvDl/4zLbybUo0SK+nU3c+xOhJFOhVbAYSg4Cb7jVIjLgWIsv4XL+GQ3qbpOaArR59bVQTPYDAY9hXGM1LPKEoFzKpL9awZKUsg/CU4D4WUvyGs5N132kcIKwOR9g/wjtGxHImsyg3i76G29UKVfKaPXb2x0m7HSr0ekXyZPpfYisy7BBX5tqyb5RmKlfYwJN+kBc/YrANNE+vQQaRKC42l3KFfE6Q8VmUHMqtCRVpVYKuPRiG2ApF8CcJbteie8BwNnhMBoWvEyBU6u8XRfKeW0s6G2ZUh4gbvKYjM97AyX8NKGl3ZEAFQUcBXXgl5NwgrGRG4H50BVKTvO7oACu7RRRAB5HZk0euQ2ApEwDtOa9bE12vPUl3wHgnKy7KFHv52XieKgroqsFJaXn7m6x4SiRK69Q/Tpb82qC57/M+4XOZ7i8FgqD/M/zD1jJBbUHj0t/g69ctBJVaBszvC2/CBg0IIhG8c+MZpjZSizyH0LlAqdqYgNke/jGwt7+gdC+HPdU2W6FxtjAQe1mOV4rQNAauj3uIgDyJLdS2XxEa9RVLemPIAzunI8PkQ/Vl7REoDOYvfQ3qPx3JV1e9QstgObnWB1RVSp0Dx85DYsZsV8KANJLsqMQmwBmOl7boYHfEsoARUHXRg5HZgsw6gTb0fInMgOgfiv9oxKSEout1u7ILQNFRoGroAYTdInYLwHFGrS1mBu/jm05e5/88fYCF457mO9Brg5pVH42RvchDM8/D+S5k8/kl7Trr8ZCbe3ZEeA3ZfG8hgMBj2BmOM1DdWCyBSVr+l1qggOpV3R6MHDgorA5E6HuXtiip+HiJf7tRiJTJ7JFaLLxBWCirtaR3rEMqA8AdazM1zNMKy4w7iv2tp88Ra8ByG5UhDWm0h8iM4/Oi029Y6qFQU6AycUvJHUa6GWkqhVn2tBhVbDjIGxLUUff7lQIiaP/pOsLrrKsS+U7Bc7ZDbj9MGkrMWfwcV0/MX/t23Le0SXWJrpCyHomfA0RqkH7zXQdIIyHsEWGfPuSWwSXcUzSCxCpV/JWS8gnD1qfEaFfn4pY3EI4Kuh3Tgw+m5vPp4EaXrcehxfRl28uH4Wo9h+Jm1vgWDwWDYK4wxUs+omB3IWIeHE4AKz9IvHHufxrsvkOE5kH8FuPqBSIKMtyD3aVAf2g02IiMLsTyDdDyEux/K9Qgq+oOOzYgtA1vCXrj7oeKrgQhCbtRxFiqkg1tVCJJOA88grWxa9ITOtCmNHQHKDREn2ntRhMo5D2W1tBNECkHm6S2TxHrsgi6Uyc0DONqAbAaqgqw9LqxWyyvfd3QdJBJAkr0FsxtURM9L1WFbLbbQDtqNQeR99NZUAuJfQOxkWyi2hV6f1AdB/q7X1D0Sih+B6Hx++fIKtmb9iWPOTEIknYkQ7movtejzH1k0cykAv/24gaRUvY3YuX8H/v7WDbTvsXOxPoPBYKh/jDFSj6iS/+mCcY6uiKTzat8vPBtC08HRBZH0p913qGdUfB0U3Ka3EazmkDkVy9kaWj6OzE2D6AzdMLJKGxE2QlgozzEQ+RxV8ADKcyTCfzrC2Q2VNB5iK1CRuQhXf531orbq6rc54yD1AV0jJr6aqim1QFksThwIQ2KxvZtjP8jBrswbpTw40wOeseCfiOXuB4DMGkFpoThEZaVTGXoLgrfZY8a1YNvusLyABSKy+7alKA8V9Ncr4ITYl+hYEqGl7kOPQmw1EILiZ5nz+V/o0SuP919qxe8rPmDLah8jzutFh76HUVxQzIaVW8hal01BTpDsDduZ979Fla6QkpHM39+6gYHHVa2EbDAYDA2FMUbqAaVKUOGvIXgvEALvaIRvbA1tJYT/iwp/qWXKZZH+1q6KwD0G4TupYSdfZX4xVO5EIKZ1MdL/jWVVSFEuNUQAks+pOoCjla56yw4oWYmKLkR5jwQV1um/7sNRkblQNBUoArkD8EPwFhS5aMOiugq8NQWaesCRoSXPHV3B2QasduAahOVqUU37CvEuFcaU8c0QvAttyNhZLeFPkNuWQ/JfwXcMllVNdWVHK/s+1lV9rya8IyH8KZAPqY9rA8jyQWKDHgs32qiKQfw3IAQijWgswAOT5pKUmkFxQT6+FB+vPJLgg39Po+fh3Zj/4WIA0lulkZeVD8BBI/oST8Tp3L8DE+8YT/dD6lgnx2AwGOoBY4zsQ5TMRwXv0TEVqhicfcHqh0i+uuZO4fdRBRUEvZy97ZLzFsK955V59ynOvrp6rfuwSoaITFQwCLyXlaupVkA4O9nf+V0gukJiC5T8T2/H2MlcKu8vduNkUN2BZWjti1KPRmw3E3TZbXzgOg0r845dtpaFU3WGUOynym+oYlR0KcJ9sJbjJwp4wH+LrqVDAtQWKLwWCkGKZtB8FpblR8l8HZPiOVKrzxJFxn4vS2+WsbVQOE2nSSdPxkquENDraA3kgirRabfO9npbCx9QAv7LwNEW4exINLgAl/oIPMPZUXAmXQ5+ht9+XA+A5fSQSIQoKQoT3KF1WTJap9O5X3uSjuyF2+PiiFMHM/yM2ovvGQwGQ0NgjJF9SWKjzoxQIXC0A1dvSJ5crlhaDUoG7RL0ybq6rPNgRHyFlif3VE1VbWiEcEHSGajgMkhsRalohXiEcplyouur7a9EV6ATODMh42VtqBXcoA0PLCh+Bx2QGtUF9MqEt6Dq1oX9cAYgRQe4qpBdZC+m34v9D5n1BWS8iOWumgUiQzN1LR5lVQ6MBbBGgKsHMrEDYivtS56KlXIe0n8q5N8B0c/txh597cInUN6RqPzLtDGhBwJcUPwm0j8Gip6E6BL7/pxQ9CzKfxIq+j1EvoLoUn0vVgYUvwTxrfYYcSAJ3MMQ7r6o0OvMeetbDj9Wkda+E217dOWJr+9m2rUv8dXrc+l2SBe2b9hOj8O7Me6y4+l6cEf8gbrFKhkMBkNjIFQdhAruvPNO7rrrrkrnWrZsSVZWVg09IBKJcPfddzNjxgyysrJo164dt956KxdeeGGtJxkMBgkEAhQUFJCaWoeUyUZCJbaA1WqXRkhZ2+LpqMJ7wTsGK+0fDTC7uiNlHLaP0FV90/8Py1PusZFZvSkzStI+xfJWdvvL4g+geAa4uyFSb0BYGcjoAsi9Ch2UuitxMDcQgKSzIflKLMtCZh2L3lrpA/yMNlDs9F+rA8i1QFQbO94zEKl/K8tGUiqMyr3A9jr4gdydrpeJ1WoesvBxXdQOBzRfhOWo/ECXiXzIvRISC3Ubzykg19uxHEWUG01ucPQB8nQ2jusYiM3Wgzja65/47+Xia/5roNj+DIg+oLL0fYkAODMg9iN5BSN44b423DS98r/D0n/GjZ15ZTAYDBWp7fO7zqJnffv2ZevWrWU/P/9cs+w1wPjx4/niiy944YUXWLVqFa+99hq9evWq62X3K4SjTa0MEd3Y9jLUJlOjkbAsJ/hO1uXsY0srv5nyVPnr/NOr9BWiCORvEP0F7NowlnswpNwM1dQG1tiBpO4hkP4kVurVutAe6No3KHBlAK3RwaVesNoh0h+GlNt1vIgqgpKXUdtGIAs+1n2jC3ScCnFwVBM/kvak/l1aJFAEqhgiAJYjDTKm2vNPQGIZxH4A73GIFj9C0lXof1pRXdXXPQQyP8HKfFZXbgbbi5YH7mEg9FaOsJL1GrsOBbUC7SUK6jTv2CpAkN52MDe8eFs16yyMIWIwGPZb6rxN43Q6adWqFlkFwKeffsqcOXNYu3YtGRm6pkmnTp122y8SiRCJlGcjBIPBXbTez7Ef0OUu/iaKKE1trfyRsfzHIouGgpoHhJDbbsdqeXfZ+8ozDpwzIP4LKvQGwv9nZHQFFD5E+TaPAxwDtdfF6UFFv0cVPgaxjVD4ELIoFTzD0VsjALKsIjCRN4AQSCdYASz/eKR3LORdAvHlQBaUXIuMzQJPR50R5BoKajCU/Nm+fjKkPYLlHaAPnX0ACykTCKWqrxrsaIZ0Ha7TcuM5YLWF+DqUsiBpEoT+AUTAPQIrcE95x6RzddxMYg34r8DyjUaGXoeS/2m9kfT/g8g8KLhZe0R8J4JnGBT8DeQmNqzMo9PA6gJ6DQaDYf+lzp6RNWvW0KZNGzp37sw555zD2rVra2z7/vvvM3DgQB5++GHatm1Ljx49mDx5MiUlu37wPvDAAwQCgbKf9u2bhtZGfaDd6ynVZ3Y2EZSKQckbOrvDWY0aZ/MX0VsqgHqv0luWIxn814JrABQ+gowuh9zz0bVnnOA8W29XyJ9BaKNTuA9HpD8LSSeC5YfYVxB6AYrutXU80P3jW9D1aySwFRW8HxleqK+Z/gxYFdJV4x9B8bPaiBFuKLm2wiwDVCzMtyN/EBcf3YsT23dky68Vs212ImUKkATk6krD8R+h4EadfuscBAiIfY+MbyjrIjxHa4l9BELa4mWesVosLfKxjqmJfqnXRKRA0mlYniHM/mQCLz42kmtG7toTaTAYDPsjdTJGBg8ezPTp05k5cybPP/88WVlZDBs2jB07qpfWXrt2Ld9++y3Lli3jvffeY+rUqbz99tv89a9/3eV1brnlFgoKCsp+Nm6so3rp/kToDZ1N4RnW2DOpmfgakNmgCquVHbcsC9zj7aMIsmhOpfeFdxRYSeA62DZEbE+XaAFOn52G2w2CD5b3sQJYKVdDyt/A9xf0RzUCQgDpulHiO3TGS7KOx4jMhuIXkIUvaoE2hwfoVGEmCUBB8ftUtv6yEbaU/O+/bOKCHlPYkZWKlKJMFKw6hKuP9hjpI/0rOhPCs+3Cf0pL1uddWd5HWAjPcEChQm+glMJypNoGClAwBUreB0KQfCGWszPzPljEQxfO5PXHcrjogQk1zsdgMBj2V+pkjIwZM4YzzjiD/v37M2rUKD766CMAXn755WrbSykRQvDKK68waNAgxo4dy+OPP85LL720S++Ix+MhNTW10s8fERVdCvHFkFiH8J3S2NOpmTIV2LCOxaiOtFspeyAXXYUsLk+bFUKAfwrE1gI65RTnsaDcEP4EnN0h/hNEZqOiPyBlPjKRhyy4Ewqugsh8kIWAXwuENf8IvV0URacAhyH9AfBO0GqtxU9D7EdwpIL3WKC77lvGOqCgwrFF6ZbRXWc8SqQkSklhmIsfPI/0luk1r4sKgcqxD+xAXNEJSAUrzT7vhcRapAwjo0uRJR/pNGAcxEp+R0aX8tWbXxGP5qHr4SSACIlEOqHYsXz3wfc8MOEfKKU46dLjOPmKxs+wMhgMhn3NXqX2+v1++vfvz5o1a6p9v3Xr1rRt25ZAoFzZsnfv3iil2LRpE927d9+by+/3qMgscB0Grv4IR8vGnk6NCCsF5TkeEtmo2CpEhWyaUizLgRQdQa0HwlB4JrKoJzR7E8vh09V4K2avuLtBfAGQgPB3lH4UVe454Oyvs0yQ6OyUSleC0IfgOQ0iH6KzVpIRIhWVfAnkvKvToslApNyAKnhUB7vGNqJTiJW+ZqmQGekomkHsZ4SjFfk5+WDBgFH9OfOGk3e5LkopcBxky9gntEZI6q0I1yGogskQ9ergXSRkD0QbT1rALBpvw6x3Unnu9kcIh6I88n4zDhrcB5Im8OviOfzj+hirF18AwGEjDwKh+Os/a5+BZjAYDPsTdY4ZqUgkEuGXX36hdevW1b5/xBFHsGXLFoqKyh8oq1evxrIs2rVrtzeX/mMQWwqxHxDO3o09k90jt0P8R0RNnhGA5p8CA8uP1SrYPgC541bYcW6Fhk4IPYf2pHi12igCUCDa2BlGRWhPgV1cD4fuRwhC0yDyFuWaI0HtpYh8rfs5u0LmeyDSdYxJPB+IAGFI+xc6zqSUPObN3MJo/3Oc0fwCCnOKQcKiz37ikv43UFPmuwx/r7dUZK6uqpv4SXt3rDa6HpHcBvIXtNETp6IhgtWWvO1+XrxHEC6OgoJvPumj1W2TTmZr1pmsXqxjVZq3y6TroZ24870bcbqMLJDBYPhjUqf/3SZPnsy4cePo0KED2dnZ3HvvvQSDQSZNmgToWI/Nmzczffp0AM477zzuueceLrjgAu666y5ycnK48cYbufDCC/H5at6LPxBQSkJshT5w7QepzsKrf6viGptYlgWtXkWW/AYFZ6NjQ+IQe6tCqwz01ooucEfyjZB0EeRdpkXBUq4DZz8omQmJFTqDR3ntrBk7/Vnl73RlicoeCGQCzcHVD8vhQ0a+0XNQq9EekWa6+N5O+iIyLkhKjhPcUdnQ2rhyM5+++CVjLhxZ6byS+VoPRIXB3QuSroA8O60592xwNNPqro6OOnPG3sJRjj7aUFFbaNlaMfkfaUy9qRfXPHMlR5xSXtNnxFlD6T14GomEpFWnFiZl12Aw/OGpkzGyadMmzj33XHJycmjevDlDhgxh/vz5dOyodRK2bt3Khg3lmQPJycnMmjWLq666ioEDB5KZmcn48eO599579+1d7JfEdSVfZw+U8NeouNFQ7FY0y9kNZBAlC3c7V8vXFXyLkAX/gZJ7KQ8WFeiYETul1zoWK/kSACTFaLGyABQ9APHV4D0NfGcBTshbobdunF11jZzEdrRRU7EgXRzwQLxQ30/4+wrZN+i+RU9SXu3XAiS9Bkboc0Q71i7dQWFuDH+Gl5S0TLat2847T3xYxRjZsPQ/WOFf8WYcRma3B7AcAaSjtxY2k1t0QLJ3DLiPhtgaEoX/x9cfpPPTgiiJaCtad1ace9UWBhxTwsvLr8GXMaDKGrbo0Hw3q2wwGAx/HOqkwNpY7G8KrLVF5k6E6AJEyq0I/6QGv75SEkIvoqILQYZBbkVkTEdUU51WFtwNJTPA2Q+R+RpCeKoZcac+xbOgsGLmVBIQqtrQ9ReQswFLi35FZoMjE+IFWlY/6UQI/QeIQSJfV8YtVS0toyWQBmwAR3cdFBtZCWxGpxEDrgsh9m+7vTZENBlALkpBTpbFlWMO5dgJo3n3iY/wB5JITvfz9zeup+fh3QB4+e/P8u6/vuT4P5/AJQ/9CbfHhcweBjIH/Nci/H9GWEllMzs17RyKg9oAS2/hpKTYYviJW5j1ZjNeWT/NGB4Gg+EPS70psBr2IZZ+6Cu5vXGuH1uMKnpRP/xVESTWowpuqxInIeNZEP4MsLR42baDkdsGILePQeaMRe44C1kwBVnyATKhs1Rk4X+h8Cp7BKH7VmeIAMSe0yJgiTCEXwGrBXjHgqu9Tt8tekF7TBK/ATsqezvK2Ab8DrQBKxki7wCrKDNE8EHs04p3VeG13rYRAlLSJK3a5VGQHWToKQMoLgixbf12rhxyC3P/uwCAcVeeTZtuHfnvPz/mxVtfs29Rx7YI94BKhghAz0F9yl4X7JCEi6PMerMZ7Xu1JbNtRvVrYjAYDAcQJiKukVBKaXlyQLiHN/z1o9+jcieASAPfeHAPh4LJEP0BVfQcONuBzEIltmrdDLUD/QD3ABFQhSCTQG3TA8aWQuxXKPkQKdpCZEbFq1G+VePUY3gvgvC3wA8V2v0OgEg+H+E7CRldDWSAEpBwUl5Qr8R+3Rb4rUL/sJ6jbzzEllAe4FrKFvt3in0vpZWBy1N7XW4oCftJJCR3vzeFWf+Zw5NXvUAoWMLUy5/j/WkzKcwtYvDYw/j1h3W4PPY/ISsDldhIqCCP5J2U5pu3zyx77fI5SW8W4IL7zmPouAE4HI5d/JUMBoPhwMAYI42FzAaZpbMvXAc3eMyIEhk6DiRuV65VEf0Qj8yE4qdRlbwYLnTxN5/OdAncjXC2RckihCoGFUTFVkHsN1sgbXY1V/QAAXCkaA9H+J/2+YOAnyq1VAW3oJQPbTDkAoV20GrYbhHS4zl7Qvw3KrMJgrdS1RCpeNwRRK4djKvseysCUpn1VirbNiZxzpTDADhu4gi6HdaZyUffSf62ID9s0wqo2zftwOVxkkhoD8snbx7Fy3fBqInZ/OURfZXsDdt5aNKT/DRHByr7kj1c9MAETrjwWDy+3W9zGQwGw4GCMUYai/gq/Vt4sSxvg1/ecnVFuofr7YXoYm1ExJborSMRAGdvcLQER1sIz9WZLe4hkPYwlqWl0ysaUMI7GiklbN9ZSdYDVl9Q63TBt0Q2OA+BxHqwMiFhoVNeS3U/JBCB4OUVxpBUNibSgVyIf1zNncWrOTcEmF/heJntqCndvyzNoilm1JlBwokjOPL0wWWtO/ftwMl/Hc2Mu98GwO110bx9M0ac1ZM//f1MNv+6lX9dM4dEPEFSwMfUy59l/geL2bElr8xzkt4ywCNf3EHHPn/c0gYGg8Gwp5iYkcYiYUvcOzs13hz8F6H1O6T2koD21jhag9UW4R6K8F+GSJ2i34t+DpE5NY0GwadAVU6bpfk8rBavawOEsA4uTZmCaDEPAvcBS9EpuwJEK60zUoUOkPoUOh7kCHZOza1MJljH2vcFiL7grSl1OkiZ1gmAaIbTlcmpV56ON6my52LSnWfTtpuO8YlF41w97RIunzqJeDzOw39+kkQ8gXAIXr79TWa/+i07tuQBoBRceP+5vLHleWOIGAwGQw2YbJpGQka+gaKnQfixMp5v7OkAoKKLUaE3Ify51j6JfQ9WM0TaP1Hhb/Wx3IbV/PNq+8usPmjPhA9894F/AJZTC+LJ6HIovA/iGyH5CvCdDDkn6u0qAPcELZIW2wJsqDCqC/BrI8U/GYouBWI134Q4BNRmwA4KTn8f8s4APOAaBEl/02JlLKrQyQ0ktC6IozNWxtPVDr1jax5/7nkV4eIIbq8Lr9+LlJKi3HLtFeEQZLZMo1WXloy9eCTHnDccp9PEhRgMhgOT2j6/zTZNIyGc3VGxRYCFUmGEaPitmipzcg8AV39U0niILdOqq4n1qMLHwT1YGyNUX75eFr5G2RaJ5yyswEmV3rfcfZGu/uhCda9CdJ7W5MAHoiVYeZB8F+T82e7hBdeZEHsDyNfZPkUV5dCHQot/AyHIPhWwPU3qx8oTy7sIbbzEIe0eLEdzpOd5yB4LbEUHs9r1chKbIPmmSt2VUiyetZQlXy5j48rNhIt0Jk+0JEa0RBtFqZnJJKf7OWhEH8679Qxad2q60v4Gg8HQFDHGSGNhtQSRquMo4hvB1TTq9Ajh1kaJewDKdxaq8CEoeQdipZ6EGFKWYFlaQVcGX4DQU1SqIRP4W/Vjp0xBFT0Pxc9AZC3649dC17MJr4fEBiDPbp0EogAcvUC1BrmTNyZ1PJblAFKQZVky1WF7SLynYTlsPY/Yel1Ez9EbvCdD8HYoFV0rvJtQvDdvPDKHr9+aR0pGEllrtyOloiiv/B69qV4GHHsQQ046jGMnDMftce9qWQ0Gg8GwC4wx0kgIIVCuviCLUaq4LBhUSgnRb7XnIPlaLGvfZV2o2CqUSIPYMoT3KISo3stRNkcrCVLvBFdvVPABEBnaQ7H9SKRKR6ufZlOetgsk36xl4asbTwiUcqCDUROU1ZoBoB0kopSps1II0a/1se9oKJlNuTaIA4I3IoPT0EZEgqqkomNCSm/ei1ISISytlOpoByIDK2ksyjcGVfQsG39+iadvc7Hkm6uROw3pdDlwOB0k4pKRE4Zz08tX1nifBoPBYKgbxhhpTGQQ4ssRFWqtiNgCVP7FgBeRct2eD53YDiqKcLRGCAuVyEEVPqq3XxxtUEUPoXxnI5LOQJSVu7f7xrdD9CuIrQEZBxHXOiTxlToFWIUp29oAwAlWV8h4Acu5k8jGzjj7UW48xLXSqmsUuA4BZwsomQuRuaC2o42PAgh/TeU4kQQ6zqP6atGaCoYImRB9H5UTQXnHQ/wrkAXgHYxM7GDOmwt4e+p6Vn9fIcBUgD+QRHG+Npb8aUkcdFQfLnrwT7TtWlWh1mAwGAx7jjFGGhM7RRZZ7v5XxXbwZNLpCFE7179SinU/b+Db9xaw6LMf+dPV3zFwhN66UDhQjt7g7gPROXrbQxaA3AAlH6DC76My3gCcUPwyhP+r1VDLvBDJaEPAg37AlxY49IFzICRfj+XtW/t79nQof+0cjEg6FZF0RoX3j9BLUvIxBP8Jjk5gHQzRpfp9qxc4m0P0V3TMR21I0gZU4n0o/tKuCtxSj1n0CNHgGGTCwuP3kJTiY9CYg7n08QtICfhZt2wDxQUh+g7raQrWGQwGQz1hjJHGRKTr30qLeSmlQGoPgPCestvusmg+RF9GhudxxYBuJBL6YflJZkaZMQJeSCyDkmX60GoHaf/UsuuF9+lzeZdCfL2umlsmLOYHFFgZ2htipQNp4OgM/klYniP37J5VBRl23wngO72GhkIbHY4uWGm3ILd/r+9DbgbHKcA34BoKVmegGUT+DRShsMjeCG6vJC0TCgoE61ZsIr25g5JCiIQiHDK8GKyUMlXZQaMsSmJHc+iog+jYq12lWXTu16GauRkMBoNhX2KMkcYkvgwIoBxtEICKr9LxDKKFrjBbAzL6C+TejtboSMKyQnTuC+t+cdCqU3MOPX4wNHtCC5qFXoXQdFAJoBBiX8D2Q8BZoVJsbB46S8aB1t3wQ/NZWI7MqhffWyoGY3hPrdnboCJ6Sym2EpnzAyS22XMLgeWB5Pug5GVQUS2oRlsS8VUs/trD/Zd3Ilzs4Ngzcsjf7iaQEaVDzxhuj+KnBQdzyDG5tqBbO0i9hfRWvTn1yn1/qwaDwWCoHcYYaSSUzNOppIBw9de/5TaUygHRBWFVzceW0aUQ/LuO3SjTq4uA9xz+Oe9yXL7WVS+UciWkXIksmQNF0yCxBIhC/Ee7gQPwgmck+K6Gosu1Gmvxk5B6xz6+a8CqEOxacB3Sfw3C1Rph7VQwLqHQxkeRVqt1DbTrzSSgZBpWi++QlEDRg+gtJUk0Aq880YqSQr299cU7LbEsC4/PzY8LvIy9+Fgu+8cIRGZrs+ViMBgMTQhjjDQW0R/1b0cXhKUrvpLYoX+72lZqKiPzofCfELcfxkCZGLv3Uay0E3crpWv5RoBvBDK+UYutxVbrmBXXSYiUM8oezlJdBgU3lBXx2+ckKsi6R7+G2K8oZxeU+0iE/yyE5dfvOVKBAFidEIEbUCJdS8kXXA2yAFn4MvjPIRr8GKdawpJv/RxyZDFIbey4vU4OH3sY1z5/KWnpfwyhPIPBYPijYoyRxsJ1ECLwGKWBojKRB8VPgdUerBZIGYLit3VsR2Kd3SkFhATf2VAyE5xtECmH1+mylrM9pN1fc4P4enAdWi4Pv68RUXQQbAKsfuXy8cX3o2LfoqxOkPgZlAPYCo72KGcfLEc60AcZDOgYm+L7IPQNm3/dyH+fbcvs99IZeXaQpFQv6a0CnHjZ8Uy6fXz93IPBYDAY9ilGKKGREI5MhG8cwncKSsVgx6mQKAaZD5EvIPswKL63vIaNSIPkGxEtfgD3QHC2Aas5wrGbVNo6z6uN3g5JZO3TccsI/4bWGYmC/AGcfW1vSTN9r7EvIbZRv49PV9aNLUUphYytB99fKNM1UV/Tqft2LFeMaNRi9rvpKKsHx//5GGOIGAwGw36E8Yw0BVQRyNI0VYcdbOoDolqXI+kcrKTyrBMVek1Ls/sv3vdzcTTTv+X2fT82oIXSQG8zpeuAXWcLiOaAlCiZixAZgNRzif8MwQdQzhk6Eye2EOgOLEcB8Rj8PD+Aw+nAnxageacunH/7WfU0d4PBYDDUB8Yz0iRw2j8ewA+uoyFwN7RYitXszUqGiIz9pjNNRArCd84+n4myWoGzF1iZ1EsNRXdzoCtwsK4a7BoE8QIgjQ1rFJeO7MuGtUA8j0jRFrZuzOSXxREgAcIBnmMIqWEsmpNO7jY4rWd/Nq72oRQIJTjl8tG4vUaa3WAwGPYnjGekCSCsFESrFbVrHP5Qewc8xyGc9aCB4egAstBWW83TEvD7EKEiKH7TB1Fbjl4UgqM9P8xLZfvmBI9e152HPh7F9s3L+fedS3B5PZziOY7FX4ZZuXA9BbnZ/PZDB6D8/jv0bsuJl4yix8CaU6INBoPB0DQxxsj+RmItAMJ9aP2MH18PIhlcA0D4dtu8rihnb3AdC7G1enz3ABABcLSgxxA3ocJ/sfbnTTzx1+VsXrOVX5eUACVkbVzMup83EIvEadezNW6fi9TMFLod2oUTrxjFkNEDdnttg8FgMDRNjDGyHyFlCcSW6wNXPRkjse8gsRocRyLqwRixXJ1R6Y+hZB7C0baS3keHvoVMuO0Mvn5rHj9/vaJsm6j3kB607NCMfkf2pkOvtnQf0IXO/Tvgcu+60J/BYDAY9g+MMbIfIeLLUIkNWgDMddg+H18pCSXvAT7wjt3n45ciLH+5nkgFUjNSOP/O8bTu2hK320VqsxRSM1No36sN3iRvvc3HYDAYDI2LMUb2J0oL6qlw/SiIRr6A+GoQyQjvqH0/fi2wLIvRk45plGsbDAaDoXEw2TT7FXH9S+z77YlwcZhE8El9kDQBYQX2+TUMBoPBYKgO4xnZT5DF/4PCe/VBLBul1D7zjiileOLSZ4kWN+emf7nw+i/aJ+MaDAaDwVAbjGdkP0ApCYUPAEH7zGYoflaf3wdE82ZQEtzIvI+KWP3bXQgrbZ+MazAYDAZDbTCekf0AVfQ4YNdwoR+wDFX0JMSWo1JuxnK226NxpYxBwXW4op9x+zPw6/qp9BrWd19N22AwGAyGWmE8I00cVfJfiP4AWIjAI4iWbyFSbtX6HLGfIXc8MvxN3cdVimdvmMHG1QUAWP7T6Tl0zL6dvMFgMBgMtaBOxsidd96JEKLST6tWrWps/9VXX1VpL4Rg5cqVez3xAwEV+Q5VcAvEFkHytQjfKQjhQPgnIVLvAWcPrZYa/Bsy/1pUdGGtxl06Zzm3jXuAL175hutPLGJ77tmI1HvqJ0PHYDAYDIbdUOdtmr59+/L555+XHTscjt32WbVqFampqWXHzZs3r+tlD0zcA7USqrM9wv+Xyu85WttZNRGQORD+DBWeBYH7EL5TKzXN2byD//7rE5Z+tZwuB3fi4+f136//8N4MOP5EWvQ+3RgiBoPBYGg06myMOJ3OXXpDqqNFixakpaXVun0kEiESiZQdB4PBXbT+4yKEGzKeB7xlxkI0EuPDZz5jx6a1/OmKr/H4HJD6AERnQ/gTti6fynP3/0L7Xh1p16M1WeuyWf7dKpZ88TP+ND/rl28EYOzFIznnltNo3bllI96hwWAwGAx7YIysWbOGNm3a4PF4GDx4MPfffz9dunTZZZ9DDz2UcDhMnz59uO222zjmmF2LWj3wwAPcdddddZ3aH5KdJdm/en0uT1/3EoHmqRTnn8Q1T56A8IxA+U4mJ7stlxy1hBYds5j73x8A6D6gC9vWZ3Pw0X0ZOWE4W9duY/CJA+g7rGdj3I7BYDAYDFWokzEyePBgpk+fTo8ePdi2bRv33nsvw4YNY/ny5WRmZlZp37p1a5577jkGDBhAJBLhP//5DyNHjuSrr77iqKOOqvE6t9xyC9dff33ZcTAYpH379nWZ6h+WUg9JqCDE+ffchPCk2+ctnr7NQTQcp6QwzMg/DWfxZz+RkpHMRfefx2GjDjJbMQaDwWBoktTJGBkzpjzbon///gwdOpSuXbvy8ssvVzIeSunZsyc9e5Z/Ax86dCgbN27k0Ucf3aUx4vF48Hg8dZnaAUNqsxQAOvZtT0YrbYgUB0O8ct87fPP2fCyHxb0fTKHboZ0bc5oGg8FgMNSavdIZ8fv99O/fnzVr1tS6z5AhQ5gxY8beXPaAJq1FKn2P6IXlECTiCZZ+tZw3HvkfqxetJbNNOmdeP84YIgaDwWDYr9grYyQSifDLL78wfPjwWvdZsmQJrVu33pvLHtA0a5vJ8rkr8SV7mdTjKrat3w7AsFMP56wbTqbfEb0aeYYGg8FgMNSNOhkjkydPZty4cXTo0IHs7GzuvfdegsEgkyZNAnSsx+bNm5k+fToAU6dOpVOnTvTt25doNMqMGTN45513eOedd/b9nRwgZLZO57rnLuPb9xbw/SdLsCzBuMtHc+H955GU4tv9AAaDwWAwNDHqZIxs2rSJc889l5ycHJo3b86QIUOYP38+HTt2BGDr1q1s2LChrH00GmXy5Mls3rwZn89H3759+eijjxg7duy+vYsDjLEXj2TE+KHMe38RXQ/uSOf+HRt7SgaDwWAw7DFCKaUaexK7IxgMEggEKCgoqCSeZjAYDAaDoelS2+e3qU1jMBgMBoOhUTHGiMFgMBgMhkbFGCMGg8FgMBgaFWOMGAwGg8FgaFSMMWIwGAwGg6FRMcaIwWAwGAyGRsUYIwaDwWAwGBqVvZKDbyhKpVCCwWAjz8RgMBgMBkNtKX1u707SbL8wRgoLCwFo3759I8/EYDAYDAZDXSksLCQQCNT4/n6hwCqlZMuWLaSkpCCEaOzpNBjBYJD27duzceNGozy7B5j123PM2u0dZv32HLN2e0dTWz+lFIWFhbRp0wbLqjkyZL/wjFiWRbt27Rp7Go1Gampqk/hQ7a+Y9dtzzNrtHWb99hyzdntHU1q/XXlESjEBrAaDwWAwGBoVY4wYDAaDwWBoVIwx0oTxeDzccccdeDyexp7KfolZvz3HrN3eYdZvzzFrt3fsr+u3XwSwGgwGg8Fg+ONiPCMGg8FgMBgaFWOMGAwGg8FgaFSMMWIwGAwGg6FRMcaIwWAwGAyGRsUYIwaDwWAwGBoVY4w0MfLy8pg4cSKBQIBAIMDEiRPJz8+vdf9LL70UIQRTp06ttzk2Veq6drFYjJtvvpn+/fvj9/tp06YN559/Plu2bGm4STci06ZNo3Pnzni9XgYMGMA333yzy/Zz5sxhwIABeL1eunTpwjPPPNNAM2161GXt3n33XY477jiaN29OamoqQ4cOZebMmQ0426ZHXT97pcydOxen08khhxxSvxNswtR17SKRCLfeeisdO3bE4/HQtWtX/v3vfzfQbOuAMjQpTjjhBNWvXz/13Xffqe+++07169dPnXTSSbXq+95776mDDz5YtWnTRj3xxBP1O9EmSF3XLj8/X40aNUq98cYbauXKlWrevHlq8ODBasCAAQ0468bh9ddfVy6XSz3//PNqxYoV6pprrlF+v1/9/vvv1bZfu3atSkpKUtdcc41asWKFev7555XL5VJvv/12A8+88anr2l1zzTXqoYceUgsXLlSrV69Wt9xyi3K5XOqHH35o4Jk3Deq6fqXk5+erLl26qOOPP14dfPDBDTPZJsaerN3JJ5+sBg8erGbNmqXWrVunFixYoObOnduAs64dxhhpQqxYsUIBav78+WXn5s2bpwC1cuXKXfbdtGmTatu2rVq2bJnq2LHjAWeM7M3aVWThwoUK2O1/jPs7gwYNUpdddlmlc7169VJTpkyptv1NN92kevXqVencpZdeqoYMGVJvc2yq1HXtqqNPnz7qrrvu2tdT2y/Y0/U7++yz1W233abuuOOOA9YYqevaffLJJyoQCKgdO3Y0xPT2CrNN04SYN28egUCAwYMHl50bMmQIgUCA7777rsZ+UkomTpzIjTfeSN++fRtiqk2OPV27nSkoKEAIQVpaWj3MsmkQjUZZvHgxxx9/fKXzxx9/fI1rNW/evCrtR48ezaJFi4jFYvU216bGnqzdzkgpKSwsJCMjoz6m2KTZ0/V78cUX+e2337jjjjvqe4pNlj1Zu/fff5+BAwfy8MMP07ZtW3r06MHkyZMpKSlpiCnXif2iau+BQlZWFi1atKhyvkWLFmRlZdXY76GHHsLpdHL11VfX5/SaNHu6dhUJh8NMmTKF8847r8lUu6wPcnJySCQStGzZstL5li1b1rhWWVlZ1baPx+Pk5OTQunXreptvU2JP1m5nHnvsMYqLixk/fnx9TLFJsyfrt2bNGqZMmcI333yD03ngPrL2ZO3Wrl3Lt99+i9fr5b333iMnJ4crrriC3NzcJhc3YjwjDcCdd96JEGKXP4sWLQJACFGlv1Kq2vMAixcv5h//+AcvvfRSjW32Z+pz7SoSi8U455xzkFIybdq0fX4fTZGd12V3a1Vd++rOHwjUde1Kee2117jzzjt54403qjWeDxRqu36JRILzzjuPu+66ix49ejTU9Jo0dfnsSSkRQvDKK68waNAgxo4dy+OPP85LL73U5LwjB66Z2YBceeWVnHPOObts06lTJ3766Se2bdtW5b3t27dXsYZL+eabb8jOzqZDhw5l5xKJBDfccANTp05l/fr1ezX3xqY+166UWCzG+PHjWbduHV9++eUf2isC0KxZMxwOR5VvU9nZ2TWuVatWrapt73Q6yczMrLe5NjX2ZO1KeeONN7jooot46623GDVqVH1Os8lS1/UrLCxk0aJFLFmyhCuvvBLQD1ilFE6nk88++4xjjz22Qebe2OzJZ69169a0bduWQCBQdq53794opdi0aRPdu3ev1znXBWOMNADNmjWjWbNmu203dOhQCgoKWLhwIYMGDQJgwYIFFBQUMGzYsGr7TJw4scp/bKNHj2bixIlccMEFez/5RqY+1w7KDZE1a9Ywe/bsA+LB6na7GTBgALNmzeK0004rOz9r1ixOOeWUavsMHTqUDz74oNK5zz77jIEDB+Jyuep1vk2JPVk70B6RCy+8kNdee40TTzyxIabaJKnr+qWmpvLzzz9XOjdt2jS+/PJL3n77bTp37lzvc24q7Mln74gjjuCtt96iqKiI5ORkAFavXo1lWbRr165B5l1rGi101lAtJ5xwgjrooIPUvHnz1Lx581T//v2rpKf27NlTvfvuuzWOcSBm0yhV97WLxWLq5JNPVu3atVM//vij2rp1a9lPJBJpjFtoMEpTBF944QW1YsUKde211yq/36/Wr1+vlFJqypQpauLEiWXtS1N7r7vuOrVixQr1wgsvHPCpvbVdu1dffVU5nU711FNPVfqM5efnN9YtNCp1Xb+dOZCzaeq6doWFhapdu3bqzDPPVMuXL1dz5sxR3bt3VxdffHFj3UKNGGOkibFjxw41YcIElZKSolJSUtSECRNUXl5epTaAevHFF2sc40A1Ruq6duvWrVNAtT+zZ89u8Pk3NE899ZTq2LGjcrvd6rDDDlNz5swpe2/SpElqxIgRldp/9dVX6tBDD1Vut1t16tRJPf300w0846ZDXdZuxIgR1X7GJk2a1PATbyLU9bNXkQPZGFGq7mv3yy+/qFGjRimfz6fatWunrr/+ehUKhRp41rtHKGVHoRkMBoPBYDA0AiabxmAwGAwGQ6NijBGDwWAwGAyNijFGDAaDwWAwNCrGGDEYDAaDwdCoGGPEYDAYDAZDo2KMEYPBYDAYDI2KMUYMBoPBYDA0KsYYMRgMBoPB0KgYY8RgMBgMBkOjYowRg8FgMBgMjYoxRgwGg8FgMDQq/w+wEANEgIhKSQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "accra_exposure.plot(\n",
     "    column=\"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha\"\n",
@@ -1000,167 +279,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "2326d851",
    "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>id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>364.644366</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>158.050565</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>roade_127</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>54.297481</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>roade_128</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>715.652789</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>roade_128</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>360.198545</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15580</th>\n",
-       "      <td>roade_15580</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>362.299965</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15581</th>\n",
-       "      <td>roade_15581</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>12.047531</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15581</th>\n",
-       "      <td>roade_15581</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>5.683219</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15836</th>\n",
-       "      <td>roade_15836</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>University Farm Road</td>\n",
-       "      <td>186.172274</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15962</th>\n",
-       "      <td>roade_15962</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>70.280210</td>\n",
-       "      <td>0.617383</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>810 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                id road_type                  name    length_m  \\\n",
-       "126      roade_126     trunk          Winneba Road  364.644366   \n",
-       "126      roade_126     trunk          Winneba Road  158.050565   \n",
-       "127      roade_127     trunk          Winneba Road   54.297481   \n",
-       "128      roade_128     trunk          Winneba Road  715.652789   \n",
-       "128      roade_128     trunk          Winneba Road  360.198545   \n",
-       "...            ...       ...                   ...         ...   \n",
-       "15580  roade_15580  tertiary                  None  362.299965   \n",
-       "15581  roade_15581  tertiary                  None   12.047531   \n",
-       "15581  roade_15581  tertiary                  None    5.683219   \n",
-       "15836  roade_15836  tertiary  University Farm Road  186.172274   \n",
-       "15962  roade_15962  tertiary                  None   70.280210   \n",
-       "\n",
-       "       wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha  \n",
-       "126                                             0.617383                            \n",
-       "126                                             0.617383                            \n",
-       "127                                             0.617383                            \n",
-       "128                                             0.617383                            \n",
-       "128                                             0.617383                            \n",
-       "...                                                  ...                            \n",
-       "15580                                           0.617383                            \n",
-       "15581                                           0.617383                            \n",
-       "15581                                           0.617383                            \n",
-       "15836                                           0.617383                            \n",
-       "15962                                           0.617383                            \n",
-       "\n",
-       "[810 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "flood_col = \"wri_aqueduct-version_2-inunriver_historical_000000000WATCH_1980_rp00100-gha\"\n",
     "accra_exposure_100yr = accra_exposure[accra_exposure[flood_col] > 0.5].copy()\n",
@@ -1185,21 +307,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "proprietary-drinking",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Total direct exposure, in Accra under a historical 100-year flood, is estimated to be 198km (of 1942km total roads).'"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "(\n",
     "    \"Total direct exposure, \"\n",
@@ -1220,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "superb-american",
    "metadata": {},
    "outputs": [],
@@ -1242,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "pleasant-saturn",
    "metadata": {},
    "outputs": [],
@@ -1262,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "sustained-irrigation",
    "metadata": {},
    "outputs": [],
@@ -1278,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "smoking-stockholm",
    "metadata": {},
    "outputs": [],
@@ -1288,52 +399,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "guided-region",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Best route: 5.33km'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "f\"Best route: {round(route.length_m.sum() / 1e3, 2)}km\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "talented-contemporary",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Direct route'}>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = route.plot()\n",
     "ax.set_title(\"Direct route\")\n",
@@ -1351,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "horizontal-prerequisite",
    "metadata": {},
    "outputs": [],
@@ -1374,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "nasty-camera",
    "metadata": {},
    "outputs": [],
@@ -1402,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "193fff97",
    "metadata": {},
    "outputs": [],
@@ -1423,38 +502,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "chicken-session",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best route: 5.33km\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "single_failures = [(\"roadn_8900\", \"roadn_9227\")]\n",
     "single_fail_route = calc_route(\n",
@@ -1476,38 +527,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "decimal-cloud",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best route: 5.3km\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "both_lanes_failures = [\n",
     "    (\"roadn_8900\", \"roadn_9227\"),\n",
@@ -1532,38 +555,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "republican-payment",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best route: 5.3km\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "multi_failures = [\n",
     "    (road.from_id, road.to_id) for road in accra_exposure.itertuples()\n",
@@ -1615,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "cognitive-absolute",
    "metadata": {},
    "outputs": [],
@@ -1636,63 +631,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "breeding-pointer",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_choose_k(3, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "naughty-builder",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19900"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_choose_k(200, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "figured-examination",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1313400"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_choose_k(200, 3)"
    ]
@@ -1707,22 +669,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "compatible-surrey",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "With 16010 roads\n",
-      "there are 1 total possible combinations of 0 roads failing\n",
-      "there are 16,010 total possible combinations of 1 roads failing\n",
-      "there are 128,152,045 total possible combinations of 2 roads failing\n",
-      "there are 683,819,312,120 total possible combinations of 3 roads failing\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n = len(roads)\n",
     "print(f\"With {n} roads\")\n",
@@ -1742,93 +692,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "prerequisite-proof",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>4790611</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Patrice Lumumba Road</td>\n",
-       "      <td>roade_15</td>\n",
-       "      <td>roadn_5092</td>\n",
-       "      <td>roadn_14</td>\n",
-       "      <td>297.723049</td>\n",
-       "      <td>LINESTRING (-0.18609 5.60608, -0.18579 5.60652...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>4790615</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_16</td>\n",
-       "      <td>roadn_15</td>\n",
-       "      <td>roadn_16</td>\n",
-       "      <td>39.706989</td>\n",
-       "      <td>LINESTRING (-0.17723 5.60553, -0.17739 5.60552...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     osm_id road_type                  name        id     from_id     to_id  \\\n",
-       "15  4790611  tertiary  Patrice Lumumba Road  roade_15  roadn_5092  roadn_14   \n",
-       "16  4790615  tertiary          Airport Road  roade_16    roadn_15  roadn_16   \n",
-       "\n",
-       "      length_m                                           geometry  \\\n",
-       "15  297.723049  LINESTRING (-0.18609 5.60608, -0.18579 5.60652...   \n",
-       "16   39.706989  LINESTRING (-0.17723 5.60553, -0.17739 5.60552...   \n",
-       "\n",
-       "               shapeName shapeISO  \n",
-       "15  Greater Accra Region    GH-AA  \n",
-       "16  Greater Accra Region    GH-AA  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "k = 500\n",
     "ids = np.random.choice(roads.id, size=k, replace=False)\n",
@@ -1838,38 +705,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "underlying-popularity",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best route: 5.3km\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "random_failures = [\n",
     "    (road.from_id, road.to_id) for road in failed_roads.itertuples()\n",
@@ -1877,7 +716,6 @@
     "random_fail_route = calc_route(\n",
     "    roads, random_failures, \"roadn_6700\", \"roadn_1011\"\n",
     ")\n",
-    "\n",
     "print(f\"Best route: {round(random_fail_route.length_m.sum() / 1e3, 1)}km\")\n",
     "plot_route(random_fail_route)"
    ]
@@ -1892,25 +730,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "compound-lender",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4cd06222b9c3458990c96e473303e76b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "k = 500\n",
     "n_samples = 100\n",
@@ -1930,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "finished-passenger",
    "metadata": {},
    "outputs": [],
@@ -1948,88 +771,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "engaging-conditions",
    "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>length_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>5.635000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.789882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>5.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>5.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>5.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>5.725000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>10.300000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        length_km\n",
-       "count  100.000000\n",
-       "mean     5.635000\n",
-       "std      0.789882\n",
-       "min      5.300000\n",
-       "25%      5.300000\n",
-       "50%      5.300000\n",
-       "75%      5.725000\n",
-       "max     10.300000"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sampled_failures.describe()"
    ]
@@ -2044,31 +789,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "sharing-button",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='index', ylabel='length_km'>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sampled_failures.reset_index().plot.scatter(x=\"index\", y=\"length_km\")"
    ]
@@ -2085,31 +809,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "swiss-singapore",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='length_km', ylabel='Density'>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sns.kdeplot(sampled_failures.length_km, cumulative=True)"
    ]
diff --git a/docs/source/tutorials/04-evaluate-adaptation-options.ipynb b/docs/source/tutorials/04-evaluate-adaptation-options.ipynb
index 79487c2..2f16c88 100644
--- a/docs/source/tutorials/04-evaluate-adaptation-options.ipynb
+++ b/docs/source/tutorials/04-evaluate-adaptation-options.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "rocky-continent",
    "metadata": {},
    "outputs": [],
@@ -54,26 +54,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "exciting-portal",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_folder = Path(\"/Users/ivannschlosser/Documents/oxford/ghana_tutorial\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ba3dc65",
-   "metadata": {},
-   "source": [
-    "### 0.5  Preliminary step\n",
-    "\n",
-    "Go to the geoboundatries at: https://www.geoboundaries.org/countryDownloads.html \n",
-    "\n",
-    "Search for *Ghana* in the **Name** field and download the data set from **Source** Open Street Map. It will show up as a folder in your downloads directory. \n",
-    "\n",
-    "Put this folder in the data directory of this tutorial. The one you specify in the `data_folder` variable. "
+    "dir = (\n",
+    "    Path(os.getcwd()).resolve().parents[3]\n",
+    ")  # get parent directory of snail package\n",
+    "data_folder = os.path.join(dir, \"ghana_tutorial\")\n",
+    "# data_folder = Path(\"YOUR_PATH/ghana_tutorial\")"
    ]
   },
   {
@@ -96,54 +86,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "essential-translation",
    "metadata": {},
    "outputs": [],
    "source": [
     "regions = gpd.read_file(\n",
-    "    data_folder\n",
-    "    / \"geoBoundaries-GHA-ADM1-all\"\n",
-    "    / \"geoBoundaries-GHA-ADM1.shp\"\n",
+    "    data_folder + \"/geoBoundaries-GHA-ADM1-all\" + \"/geoBoundaries-GHA-ADM1.shp\"\n",
     ")[[\"shapeName\", \"shapeISO\", \"geometry\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "184e66bf",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(168.1215903043459, 0.5, 'Latitude [deg]')"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "f, ax = plt.subplots(1,1)\n",
+    "f, ax = plt.subplots(1, 1)\n",
     "\n",
     "regions.plot(\n",
-    "    ax=ax,\n",
-    "    alpha=1,\n",
-    "    linewidth=.5,\n",
-    "    column=\"shapeName\",\n",
-    "    edgecolor=\"black\"\n",
+    "    ax=ax, alpha=1, linewidth=0.5, column=\"shapeName\", edgecolor=\"black\"\n",
     ")\n",
     "\n",
     "ax.set_title(\"Admin boundaries of Ghana\")\n",
@@ -162,144 +125,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "wired-solomon",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>road_id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4790594</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_0</td>\n",
-       "      <td>roadn_0</td>\n",
-       "      <td>roadn_1</td>\n",
-       "      <td>236.526837</td>\n",
-       "      <td>LINESTRING (-0.17544 5.60550, -0.17418 5.60555...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_1</td>\n",
-       "      <td>roadn_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>18.539418</td>\n",
-       "      <td>LINESTRING (-0.17889 5.59979, -0.17872 5.59977)</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4790599</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>South Liberation Link</td>\n",
-       "      <td>roade_2</td>\n",
-       "      <td>roadn_10807</td>\n",
-       "      <td>roadn_3</td>\n",
-       "      <td>124.758045</td>\n",
-       "      <td>LINESTRING (-0.17872 5.59977, -0.17786 5.59960...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4790600</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_3</td>\n",
-       "      <td>roadn_4</td>\n",
-       "      <td>roadn_6318</td>\n",
-       "      <td>38.030821</td>\n",
-       "      <td>LINESTRING (-0.17330 5.60560, -0.17327 5.60556...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4790600</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>Airport Road</td>\n",
-       "      <td>roade_4</td>\n",
-       "      <td>roadn_6318</td>\n",
-       "      <td>roadn_6317</td>\n",
-       "      <td>19.532483</td>\n",
-       "      <td>LINESTRING (-0.17300 5.60559, -0.17299 5.60561...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id road_type                   name  road_id      from_id  \\\n",
-       "0  4790594  tertiary           Airport Road  roade_0      roadn_0   \n",
-       "1  4790599  tertiary  South Liberation Link  roade_1      roadn_2   \n",
-       "2  4790599  tertiary  South Liberation Link  roade_2  roadn_10807   \n",
-       "3  4790600  tertiary           Airport Road  roade_3      roadn_4   \n",
-       "4  4790600  tertiary           Airport Road  roade_4   roadn_6318   \n",
-       "\n",
-       "         to_id    length_m                                           geometry  \\\n",
-       "0      roadn_1  236.526837  LINESTRING (-0.17544 5.60550, -0.17418 5.60555...   \n",
-       "1  roadn_10807   18.539418    LINESTRING (-0.17889 5.59979, -0.17872 5.59977)   \n",
-       "2      roadn_3  124.758045  LINESTRING (-0.17872 5.59977, -0.17786 5.59960...   \n",
-       "3   roadn_6318   38.030821  LINESTRING (-0.17330 5.60560, -0.17327 5.60556...   \n",
-       "4   roadn_6317   19.532483  LINESTRING (-0.17300 5.60559, -0.17299 5.60561...   \n",
-       "\n",
-       "              shapeName shapeISO  \n",
-       "0  Greater Accra Region    GH-AA  \n",
-       "1  Greater Accra Region    GH-AA  \n",
-       "2  Greater Accra Region    GH-AA  \n",
-       "3  Greater Accra Region    GH-AA  \n",
-       "4  Greater Accra Region    GH-AA  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "roads = gpd.read_file(\n",
-    "    data_folder / \"GHA_OSM_roads.gpkg\", layer=\"edges\"\n",
+    "    data_folder + \"/GHA_OSM_roads.gpkg\", layer=\"edges\"\n",
     ").rename(columns={\"id\": \"road_id\"})\n",
     "roads = gpd.sjoin(roads, regions).drop(columns=\"index_right\")\n",
     "roads.head()"
@@ -316,99 +148,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "economic-technical",
    "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>road_id</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>ead_usd</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>22.510369</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>32.800823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>444.418997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      road_id         rcp             gcm  epoch     ead_usd\n",
-       "0  roade_1002  historical           WATCH   1980    0.000000\n",
-       "1  roade_1002      rcp4p5  MIROC-ESM-CHEM   2030   22.510369\n",
-       "2  roade_1002      rcp4p5  MIROC-ESM-CHEM   2050   32.800823\n",
-       "3  roade_1002      rcp4p5  MIROC-ESM-CHEM   2080  444.418997\n",
-       "4  roade_1002      rcp4p5       NorESM1-M   2050    0.000000"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "risk = pd.read_csv(data_folder / \"results\" / \"inunriver_damages_ead.csv\")[\n",
+    "risk = pd.read_csv(data_folder + \"/results\" + \"/inunriver_damages_ead.csv\")[\n",
     "    [\"id\", \"rcp\", \"gcm\", \"epoch\", \"ead_usd\"]\n",
     "].rename(columns={\"id\": \"road_id\"})\n",
     "risk.head()"
@@ -416,253 +161,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "composed-objective",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>road_id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>4845650</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>roadn_56</td>\n",
-       "      <td>roadn_57</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>LINESTRING (-1.16109 9.14004, -1.15927 9.14149)</td>\n",
-       "      <td>Savannah Region</td>\n",
-       "      <td>GH-SV</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>11154880</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>La Road</td>\n",
-       "      <td>roade_104</td>\n",
-       "      <td>roadn_111</td>\n",
-       "      <td>roadn_112</td>\n",
-       "      <td>443.190787</td>\n",
-       "      <td>LINESTRING (-0.17564 5.55326, -0.17568 5.55324...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>roadn_135</td>\n",
-       "      <td>roadn_9280</td>\n",
-       "      <td>522.694931</td>\n",
-       "      <td>LINESTRING (-0.31338 5.55362, -0.31494 5.55356...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_127</td>\n",
-       "      <td>roadn_9280</td>\n",
-       "      <td>roadn_9279</td>\n",
-       "      <td>54.297481</td>\n",
-       "      <td>LINESTRING (-0.31809 5.55347, -0.31858 5.55345)</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>128</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_128</td>\n",
-       "      <td>roadn_9279</td>\n",
-       "      <td>roadn_9643</td>\n",
-       "      <td>1075.851334</td>\n",
-       "      <td>LINESTRING (-0.31858 5.55345, -0.31866 5.55345...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15898</th>\n",
-       "      <td>1248150398</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15898</td>\n",
-       "      <td>roadn_12446</td>\n",
-       "      <td>roadn_3458</td>\n",
-       "      <td>51.436448</td>\n",
-       "      <td>LINESTRING (0.72365 5.77996, 0.72349 5.77952)</td>\n",
-       "      <td>Volta Region</td>\n",
-       "      <td>GH-TV</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15900</th>\n",
-       "      <td>1254900094</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15900</td>\n",
-       "      <td>roadn_12447</td>\n",
-       "      <td>roadn_2337</td>\n",
-       "      <td>881.490089</td>\n",
-       "      <td>LINESTRING (-2.16174 5.94774, -2.16216 5.94801...</td>\n",
-       "      <td>Western Region</td>\n",
-       "      <td>GH-WP</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15903</th>\n",
-       "      <td>1254900095</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15903</td>\n",
-       "      <td>roadn_824</td>\n",
-       "      <td>roadn_12447</td>\n",
-       "      <td>19.014423</td>\n",
-       "      <td>LINESTRING (-2.16160 5.94765, -2.16174 5.94774)</td>\n",
-       "      <td>Western Region</td>\n",
-       "      <td>GH-WP</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15938</th>\n",
-       "      <td>1271781725</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15938</td>\n",
-       "      <td>roadn_12471</td>\n",
-       "      <td>roadn_12472</td>\n",
-       "      <td>3205.733575</td>\n",
-       "      <td>LINESTRING (0.53592 7.93849, 0.53464 7.93894, ...</td>\n",
-       "      <td>Oti Region</td>\n",
-       "      <td>GH-OT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15962</th>\n",
-       "      <td>1286921686</td>\n",
-       "      <td>tertiary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15962</td>\n",
-       "      <td>roadn_12492</td>\n",
-       "      <td>roadn_12493</td>\n",
-       "      <td>70.280210</td>\n",
-       "      <td>LINESTRING (-0.23084 5.55077, -0.23096 5.55106...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2398 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           osm_id  road_type          name      road_id      from_id  \\\n",
-       "56        4845650      trunk          None     roade_56     roadn_56   \n",
-       "104      11154880    primary       La Road    roade_104    roadn_111   \n",
-       "126      11180537      trunk  Winneba Road    roade_126    roadn_135   \n",
-       "127      11180537      trunk  Winneba Road    roade_127   roadn_9280   \n",
-       "128      11180537      trunk  Winneba Road    roade_128   roadn_9279   \n",
-       "...           ...        ...           ...          ...          ...   \n",
-       "15898  1248150398   tertiary          None  roade_15898  roadn_12446   \n",
-       "15900  1254900094  secondary          None  roade_15900  roadn_12447   \n",
-       "15903  1254900095  secondary          None  roade_15903    roadn_824   \n",
-       "15938  1271781725   tertiary          None  roade_15938  roadn_12471   \n",
-       "15962  1286921686   tertiary          None  roade_15962  roadn_12492   \n",
-       "\n",
-       "             to_id     length_m  \\\n",
-       "56        roadn_57   256.660267   \n",
-       "104      roadn_112   443.190787   \n",
-       "126     roadn_9280   522.694931   \n",
-       "127     roadn_9279    54.297481   \n",
-       "128     roadn_9643  1075.851334   \n",
-       "...            ...          ...   \n",
-       "15898   roadn_3458    51.436448   \n",
-       "15900   roadn_2337   881.490089   \n",
-       "15903  roadn_12447    19.014423   \n",
-       "15938  roadn_12472  3205.733575   \n",
-       "15962  roadn_12493    70.280210   \n",
-       "\n",
-       "                                                geometry  \\\n",
-       "56       LINESTRING (-1.16109 9.14004, -1.15927 9.14149)   \n",
-       "104    LINESTRING (-0.17564 5.55326, -0.17568 5.55324...   \n",
-       "126    LINESTRING (-0.31338 5.55362, -0.31494 5.55356...   \n",
-       "127      LINESTRING (-0.31809 5.55347, -0.31858 5.55345)   \n",
-       "128    LINESTRING (-0.31858 5.55345, -0.31866 5.55345...   \n",
-       "...                                                  ...   \n",
-       "15898      LINESTRING (0.72365 5.77996, 0.72349 5.77952)   \n",
-       "15900  LINESTRING (-2.16174 5.94774, -2.16216 5.94801...   \n",
-       "15903    LINESTRING (-2.16160 5.94765, -2.16174 5.94774)   \n",
-       "15938  LINESTRING (0.53592 7.93849, 0.53464 7.93894, ...   \n",
-       "15962  LINESTRING (-0.23084 5.55077, -0.23096 5.55106...   \n",
-       "\n",
-       "                  shapeName shapeISO  \n",
-       "56          Savannah Region    GH-SV  \n",
-       "104    Greater Accra Region    GH-AA  \n",
-       "126    Greater Accra Region    GH-AA  \n",
-       "127    Greater Accra Region    GH-AA  \n",
-       "128    Greater Accra Region    GH-AA  \n",
-       "...                     ...      ...  \n",
-       "15898          Volta Region    GH-TV  \n",
-       "15900        Western Region    GH-WP  \n",
-       "15903        Western Region    GH-WP  \n",
-       "15938            Oti Region    GH-OT  \n",
-       "15962  Greater Accra Region    GH-AA  \n",
-       "\n",
-       "[2398 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "exposed_roads = roads[roads.road_id.isin(risk.road_id.unique())]\n",
     "exposed_roads.head()"
@@ -670,175 +172,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "sophisticated-pickup",
    "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>road_id</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>flood_length_m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>254.099235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>254.099235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>254.099235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>254.099235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>roade_1002</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>254.099235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65549</th>\n",
-       "      <td>roade_9964</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>71.129136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65550</th>\n",
-       "      <td>roade_9964</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>MIROC-ESM-CHEM</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>71.129136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65551</th>\n",
-       "      <td>roade_9964</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>71.129136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65552</th>\n",
-       "      <td>roade_9964</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>71.129136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65553</th>\n",
-       "      <td>roade_9964</td>\n",
-       "      <td>rcp8p5</td>\n",
-       "      <td>NorESM1-M</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>71.129136</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>65554 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          road_id         rcp             gcm  epoch    rp  flood_length_m\n",
-       "0      roade_1002  historical           WATCH   1980  1000      254.099235\n",
-       "1      roade_1002      rcp4p5  MIROC-ESM-CHEM   2030  1000      254.099235\n",
-       "2      roade_1002      rcp4p5  MIROC-ESM-CHEM   2050  1000      254.099235\n",
-       "3      roade_1002      rcp4p5  MIROC-ESM-CHEM   2080  1000      254.099235\n",
-       "4      roade_1002      rcp4p5       NorESM1-M   2050  1000      254.099235\n",
-       "...           ...         ...             ...    ...   ...             ...\n",
-       "65549  roade_9964      rcp8p5  MIROC-ESM-CHEM   2050  1000       71.129136\n",
-       "65550  roade_9964      rcp8p5  MIROC-ESM-CHEM   2080  1000       71.129136\n",
-       "65551  roade_9964      rcp8p5       NorESM1-M   2030  1000       71.129136\n",
-       "65552  roade_9964      rcp8p5       NorESM1-M   2050  1000       71.129136\n",
-       "65553  roade_9964      rcp8p5       NorESM1-M   2080  1000       71.129136\n",
-       "\n",
-       "[65554 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "exposure = pd.read_csv(data_folder / \"results\" / \"inunriver_damages_rp.csv\")[\n",
+    "exposure = pd.read_csv(data_folder + \"/results\" + \"/inunriver_damages_rp.csv\")[\n",
     "    [\"id\", \"length_m\", \"rcp\", \"gcm\", \"epoch\", \"rp\"]\n",
     "].rename(columns={\"id\": \"road_id\", \"length_m\": \"flood_length_m\"})\n",
     "\n",
     "# sum over any segments exposed within the same return period\n",
     "exposure = exposure.groupby([\"road_id\", \"rcp\", \"gcm\", \"epoch\", \"rp\"]).sum()\n",
     "\n",
-    "# # pick max length exposed over all return periods\n",
+    "# pick max length exposed over all return periods\n",
     "exposure = (\n",
     "    exposure.reset_index()\n",
     "    .groupby([\"road_id\", \"rcp\", \"gcm\", \"epoch\"])\n",
@@ -851,113 +197,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "italian-color",
    "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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>road_id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>rcp</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>ead_usd</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>flood_length_m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4845650</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>roadn_56</td>\n",
-       "      <td>roadn_57</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>LINESTRING (-1.16109 9.14004, -1.15927 9.14149)</td>\n",
-       "      <td>Savannah Region</td>\n",
-       "      <td>GH-SV</td>\n",
-       "      <td>historical</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>378311.530518</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>256.660267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4845650</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>roadn_56</td>\n",
-       "      <td>roadn_57</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>LINESTRING (-1.16109 9.14004, -1.15927 9.14149)</td>\n",
-       "      <td>Savannah Region</td>\n",
-       "      <td>GH-SV</td>\n",
-       "      <td>rcp4p5</td>\n",
-       "      <td>GFDL-ESM2M</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>486679.198956</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>256.660267</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    osm_id road_type  name   road_id   from_id     to_id    length_m  \\\n",
-       "0  4845650     trunk  None  roade_56  roadn_56  roadn_57  256.660267   \n",
-       "1  4845650     trunk  None  roade_56  roadn_56  roadn_57  256.660267   \n",
-       "\n",
-       "                                          geometry        shapeName shapeISO  \\\n",
-       "0  LINESTRING (-1.16109 9.14004, -1.15927 9.14149)  Savannah Region    GH-SV   \n",
-       "1  LINESTRING (-1.16109 9.14004, -1.15927 9.14149)  Savannah Region    GH-SV   \n",
-       "\n",
-       "          rcp         gcm  epoch        ead_usd    rp  flood_length_m  \n",
-       "0  historical       WATCH   1980  378311.530518  1000      256.660267  \n",
-       "1      rcp4p5  GFDL-ESM2M   2030  486679.198956  1000      256.660267  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "roads_with_risk = exposed_roads.merge(risk, on=\"road_id\").merge(\n",
+    "roads_with_risk = exposed_roads.merge(risk, on=\"road_id\", how=\"inner\").merge(\n",
     "    exposure, on=[\"road_id\", \"rcp\", \"gcm\", \"epoch\"]\n",
     ")\n",
     "roads_with_risk.head()"
@@ -980,87 +225,17 @@
    "source": [
     "Introduce costs of road upgrade options.\n",
     "\n",
-    "These costs are taken purely as an example, and further research is required to make reasonable estimates. They are intended represent upgrade to a bituminous or concrete road design, with a single-lane design for currently-unpaved roads. The routine maintenance costs are estimated for rehabilitation and routine maintenance that should take place every year. The periodic maintenance costs are estimated for resurfacing and surface treatment that may take place approximately every five years.\n",
+    "These costs are taken purely as an example, and further research is required to make reasonable estimates. They are intended to represent upgrade to a bituminous or concrete road design, with a single-lane design for currently-unpaved roads. The routine maintenance costs are estimated for rehabilitation and routine maintenance that should take place every year. The periodic maintenance costs are estimated for resurfacing and surface treatment that may take place approximately every five years.\n",
     "\n",
     "As before with cost estimates, the analysis is likely to be highly sensitive to these assumptions, which should be replaced by better estimates if available."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "furnished-closer",
    "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>kind</th>\n",
-       "      <th>initial_cost_usd_per_km</th>\n",
-       "      <th>routine_usd_per_km</th>\n",
-       "      <th>periodic_usd_per_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>1000000</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>500000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>50000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>single_lane</td>\n",
-       "      <td>125000</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>25000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          kind  initial_cost_usd_per_km  routine_usd_per_km  \\\n",
-       "0    four_lane                  1000000               20000   \n",
-       "1     two_lane                   500000               10000   \n",
-       "2  single_lane                   125000                5000   \n",
-       "\n",
-       "   periodic_usd_per_km  \n",
-       "0               100000  \n",
-       "1                50000  \n",
-       "2                25000  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "options = pd.DataFrame(\n",
     "    {\n",
@@ -1086,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "rapid-award",
    "metadata": {},
    "outputs": [],
@@ -1110,191 +285,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "indoor-digit",
    "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>year</th>\n",
-       "      <th>year_from_start</th>\n",
-       "      <th>discount_rate_norm</th>\n",
-       "      <th>kind</th>\n",
-       "      <th>initial_cost_usd_per_km</th>\n",
-       "      <th>routine_usd_per_km</th>\n",
-       "      <th>periodic_usd_per_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2020</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>1000000</td>\n",
-       "      <td>20000.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2020</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>500000</td>\n",
-       "      <td>10000.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2020</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>single_lane</td>\n",
-       "      <td>125000</td>\n",
-       "      <td>5000.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.970874</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19417.475728</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2021</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.970874</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9708.737864</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>178</th>\n",
-       "      <td>2079</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0.174825</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1748.250827</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>2079</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0.174825</td>\n",
-       "      <td>single_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>874.125414</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>180</th>\n",
-       "      <td>2080</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0.169733</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3394.661800</td>\n",
-       "      <td>16973.309002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>2080</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0.169733</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1697.330900</td>\n",
-       "      <td>8486.654501</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>2080</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0.169733</td>\n",
-       "      <td>single_lane</td>\n",
-       "      <td>0</td>\n",
-       "      <td>848.665450</td>\n",
-       "      <td>4243.327250</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>183 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     year  year_from_start  discount_rate_norm         kind  \\\n",
-       "0    2020                0            1.000000    four_lane   \n",
-       "1    2020                0            1.000000     two_lane   \n",
-       "2    2020                0            1.000000  single_lane   \n",
-       "3    2021                1            0.970874    four_lane   \n",
-       "4    2021                1            0.970874     two_lane   \n",
-       "..    ...              ...                 ...          ...   \n",
-       "178  2079               59            0.174825     two_lane   \n",
-       "179  2079               59            0.174825  single_lane   \n",
-       "180  2080               60            0.169733    four_lane   \n",
-       "181  2080               60            0.169733     two_lane   \n",
-       "182  2080               60            0.169733  single_lane   \n",
-       "\n",
-       "     initial_cost_usd_per_km  routine_usd_per_km  periodic_usd_per_km  \n",
-       "0                    1000000        20000.000000             0.000000  \n",
-       "1                     500000        10000.000000             0.000000  \n",
-       "2                     125000         5000.000000             0.000000  \n",
-       "3                          0        19417.475728             0.000000  \n",
-       "4                          0         9708.737864             0.000000  \n",
-       "..                       ...                 ...                  ...  \n",
-       "178                        0         1748.250827             0.000000  \n",
-       "179                        0          874.125414             0.000000  \n",
-       "180                        0         3394.661800         16973.309002  \n",
-       "181                        0         1697.330900          8486.654501  \n",
-       "182                        0          848.665450          4243.327250  \n",
-       "\n",
-       "[183 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# set up a costs dataframe\n",
     "costs = pd.DataFrame()\n",
@@ -1342,84 +336,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "intermediate-mouth",
    "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>kind</th>\n",
-       "      <th>initial_cost_usd_per_km</th>\n",
-       "      <th>routine_usd_per_km</th>\n",
-       "      <th>periodic_usd_per_km</th>\n",
-       "      <th>total_cost_usd_per_km</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>1000000</td>\n",
-       "      <td>573511.273322</td>\n",
-       "      <td>521281.893260</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>single_lane</td>\n",
-       "      <td>125000</td>\n",
-       "      <td>143377.818331</td>\n",
-       "      <td>130320.473315</td>\n",
-       "      <td>3.986983e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>500000</td>\n",
-       "      <td>286755.636661</td>\n",
-       "      <td>260640.946630</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          kind  initial_cost_usd_per_km  routine_usd_per_km  \\\n",
-       "0    four_lane                  1000000       573511.273322   \n",
-       "1  single_lane                   125000       143377.818331   \n",
-       "2     two_lane                   500000       286755.636661   \n",
-       "\n",
-       "   periodic_usd_per_km  total_cost_usd_per_km  \n",
-       "0        521281.893260           2.094793e+06  \n",
-       "1        130320.473315           3.986983e+05  \n",
-       "2        260640.946630           1.047397e+06  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "npv_costs = (\n",
     "    costs[\n",
@@ -1462,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "tracked-wagner",
    "metadata": {},
    "outputs": [],
@@ -1490,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "initial-independence",
    "metadata": {},
    "outputs": [],
@@ -1511,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "floppy-crowd",
    "metadata": {},
    "outputs": [],
@@ -1534,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "judicial-rehabilitation",
    "metadata": {},
    "outputs": [],
@@ -1546,21 +466,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "preliminary-plenty",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28.675563666119398"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "discount_rate_norm"
    ]
@@ -1576,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "accepted-charger",
    "metadata": {},
    "outputs": [],
@@ -1598,180 +507,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "affecting-piano",
    "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>length_m</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>ead_usd</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>flood_length_m</th>\n",
-       "      <th>total_cost_usd_per_km</th>\n",
-       "      <th>total_adaptation_cost_usd</th>\n",
-       "      <th>total_adaptation_benefit_usd</th>\n",
-       "      <th>bcr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2289.000000</td>\n",
-       "      <td>2289.0</td>\n",
-       "      <td>2.289000e+03</td>\n",
-       "      <td>2289.0</td>\n",
-       "      <td>2289.000000</td>\n",
-       "      <td>2.289000e+03</td>\n",
-       "      <td>2.289000e+03</td>\n",
-       "      <td>2.289000e+03</td>\n",
-       "      <td>2289.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3355.295411</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>2.568409e+05</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>1023.591527</td>\n",
-       "      <td>1.044275e+06</td>\n",
-       "      <td>8.504768e+05</td>\n",
-       "      <td>7.365056e+06</td>\n",
-       "      <td>7.688411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>7112.243878</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.547989e+05</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1959.500758</td>\n",
-       "      <td>6.363853e+05</td>\n",
-       "      <td>1.710622e+06</td>\n",
-       "      <td>2.164428e+07</td>\n",
-       "      <td>7.459590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.290015</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>0.303766</td>\n",
-       "      <td>3.986983e+05</td>\n",
-       "      <td>1.211108e+02</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>47.029436</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>3.174166e+03</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>41.962807</td>\n",
-       "      <td>3.986983e+05</td>\n",
-       "      <td>4.183918e+04</td>\n",
-       "      <td>9.102099e+04</td>\n",
-       "      <td>0.635519</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>358.632162</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>1.926271e+04</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>222.864055</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>1.930832e+05</td>\n",
-       "      <td>5.523691e+05</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3203.865501</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>1.451787e+05</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>969.521180</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>8.226995e+05</td>\n",
-       "      <td>4.163081e+06</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>73318.588787</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>1.306064e+07</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>17981.326559</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>1.856157e+07</td>\n",
-       "      <td>3.745212e+08</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           length_m   epoch       ead_usd      rp  flood_length_m  \\\n",
-       "count   2289.000000  2289.0  2.289000e+03  2289.0     2289.000000   \n",
-       "mean    3355.295411  1980.0  2.568409e+05  1000.0     1023.591527   \n",
-       "std     7112.243878     0.0  7.547989e+05     0.0     1959.500758   \n",
-       "min        1.290015  1980.0  0.000000e+00  1000.0        0.303766   \n",
-       "25%       47.029436  1980.0  3.174166e+03  1000.0       41.962807   \n",
-       "50%      358.632162  1980.0  1.926271e+04  1000.0      222.864055   \n",
-       "75%     3203.865501  1980.0  1.451787e+05  1000.0      969.521180   \n",
-       "max    73318.588787  1980.0  1.306064e+07  1000.0    17981.326559   \n",
-       "\n",
-       "       total_cost_usd_per_km  total_adaptation_cost_usd  \\\n",
-       "count           2.289000e+03               2.289000e+03   \n",
-       "mean            1.044275e+06               8.504768e+05   \n",
-       "std             6.363853e+05               1.710622e+06   \n",
-       "min             3.986983e+05               1.211108e+02   \n",
-       "25%             3.986983e+05               4.183918e+04   \n",
-       "50%             1.047397e+06               1.930832e+05   \n",
-       "75%             1.047397e+06               8.226995e+05   \n",
-       "max             2.094793e+06               1.856157e+07   \n",
-       "\n",
-       "       total_adaptation_benefit_usd          bcr  \n",
-       "count                  2.289000e+03  2289.000000  \n",
-       "mean                   7.365056e+06     7.688411  \n",
-       "std                    2.164428e+07     7.459590  \n",
-       "min                    0.000000e+00     0.000000  \n",
-       "25%                    9.102099e+04     0.635519  \n",
-       "50%                    5.523691e+05     9.905326  \n",
-       "75%                    4.163081e+06     9.905326  \n",
-       "max                    3.745212e+08    20.177240  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "historical = roads_with_costs[roads_with_costs.rcp == \"historical\"]\n",
     "historical.describe()"
@@ -1788,411 +527,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "treated-average",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>osm_id</th>\n",
-       "      <th>road_type</th>\n",
-       "      <th>name</th>\n",
-       "      <th>road_id</th>\n",
-       "      <th>from_id</th>\n",
-       "      <th>to_id</th>\n",
-       "      <th>length_m</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>shapeName</th>\n",
-       "      <th>shapeISO</th>\n",
-       "      <th>...</th>\n",
-       "      <th>gcm</th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>ead_usd</th>\n",
-       "      <th>rp</th>\n",
-       "      <th>flood_length_m</th>\n",
-       "      <th>kind</th>\n",
-       "      <th>total_cost_usd_per_km</th>\n",
-       "      <th>total_adaptation_cost_usd</th>\n",
-       "      <th>total_adaptation_benefit_usd</th>\n",
-       "      <th>bcr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>4845650</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_56</td>\n",
-       "      <td>roadn_56</td>\n",
-       "      <td>roadn_57</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>LINESTRING (-1.16109 9.14004, -1.15927 9.14149)</td>\n",
-       "      <td>Savannah Region</td>\n",
-       "      <td>GH-SV</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>3.783115e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>256.660267</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>5.376502e+05</td>\n",
-       "      <td>1.084830e+07</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_126</td>\n",
-       "      <td>roadn_135</td>\n",
-       "      <td>roadn_9280</td>\n",
-       "      <td>522.694931</td>\n",
-       "      <td>LINESTRING (-0.31338 5.55362, -0.31494 5.55356...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>7.704407e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>522.694931</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>1.094938e+06</td>\n",
-       "      <td>2.209282e+07</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_127</td>\n",
-       "      <td>roadn_9280</td>\n",
-       "      <td>roadn_9279</td>\n",
-       "      <td>54.297481</td>\n",
-       "      <td>LINESTRING (-0.31809 5.55347, -0.31858 5.55345)</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>8.003328e+04</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>54.297481</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>1.137420e+05</td>\n",
-       "      <td>2.294999e+06</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_128</td>\n",
-       "      <td>roadn_9279</td>\n",
-       "      <td>roadn_9643</td>\n",
-       "      <td>1075.851334</td>\n",
-       "      <td>LINESTRING (-0.31858 5.55345, -0.31866 5.55345...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>1.585781e+06</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1075.851334</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>2.253686e+06</td>\n",
-       "      <td>4.547316e+07</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>155</th>\n",
-       "      <td>11180537</td>\n",
-       "      <td>trunk</td>\n",
-       "      <td>Winneba Road</td>\n",
-       "      <td>roade_129</td>\n",
-       "      <td>roadn_9643</td>\n",
-       "      <td>roadn_402</td>\n",
-       "      <td>185.212407</td>\n",
-       "      <td>LINESTRING (-0.32808 5.55182, -0.32844 5.55168...</td>\n",
-       "      <td>Greater Accra Region</td>\n",
-       "      <td>GH-AA</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>2.729990e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>185.212407</td>\n",
-       "      <td>four_lane</td>\n",
-       "      <td>2.094793e+06</td>\n",
-       "      <td>3.879817e+05</td>\n",
-       "      <td>7.828399e+06</td>\n",
-       "      <td>20.177240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66091</th>\n",
-       "      <td>1216742901</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15720</td>\n",
-       "      <td>roadn_12346</td>\n",
-       "      <td>roadn_7316</td>\n",
-       "      <td>1378.103677</td>\n",
-       "      <td>LINESTRING (0.47833 6.04765, 0.47831 6.04739, ...</td>\n",
-       "      <td>Volta Region</td>\n",
-       "      <td>GH-TV</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>4.985972e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1378.103677</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>1.443421e+06</td>\n",
-       "      <td>1.429756e+07</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66122</th>\n",
-       "      <td>1216742902</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15721</td>\n",
-       "      <td>roadn_5230</td>\n",
-       "      <td>roadn_12346</td>\n",
-       "      <td>7.550321</td>\n",
-       "      <td>LINESTRING (0.47833 6.04772, 0.47833 6.04765)</td>\n",
-       "      <td>Volta Region</td>\n",
-       "      <td>GH-TV</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>2.731702e+03</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>7.550321</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>7.908181e+03</td>\n",
-       "      <td>7.833311e+04</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66341</th>\n",
-       "      <td>1244508503</td>\n",
-       "      <td>primary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15890</td>\n",
-       "      <td>roadn_3364</td>\n",
-       "      <td>roadn_7697</td>\n",
-       "      <td>7856.443658</td>\n",
-       "      <td>LINESTRING (-0.41152 6.64467, -0.41136 6.64472...</td>\n",
-       "      <td>Eastern Region</td>\n",
-       "      <td>GH-EP</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>5.893767e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>2598.165656</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>2.721310e+06</td>\n",
-       "      <td>1.690071e+07</td>\n",
-       "      <td>6.210505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66424</th>\n",
-       "      <td>1254900094</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15900</td>\n",
-       "      <td>roadn_12447</td>\n",
-       "      <td>roadn_2337</td>\n",
-       "      <td>881.490089</td>\n",
-       "      <td>LINESTRING (-2.16174 5.94774, -2.16216 5.94801...</td>\n",
-       "      <td>Western Region</td>\n",
-       "      <td>GH-WP</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>2.410725e+05</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>666.315202</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>6.978963e+05</td>\n",
-       "      <td>6.912890e+06</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66455</th>\n",
-       "      <td>1254900095</td>\n",
-       "      <td>secondary</td>\n",
-       "      <td>None</td>\n",
-       "      <td>roade_15903</td>\n",
-       "      <td>roadn_824</td>\n",
-       "      <td>roadn_12447</td>\n",
-       "      <td>19.014423</td>\n",
-       "      <td>LINESTRING (-2.16160 5.94765, -2.16174 5.94774)</td>\n",
-       "      <td>Western Region</td>\n",
-       "      <td>GH-WP</td>\n",
-       "      <td>...</td>\n",
-       "      <td>WATCH</td>\n",
-       "      <td>1980</td>\n",
-       "      <td>6.879408e+03</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>19.014423</td>\n",
-       "      <td>two_lane</td>\n",
-       "      <td>1.047397e+06</td>\n",
-       "      <td>1.991564e+04</td>\n",
-       "      <td>1.972709e+05</td>\n",
-       "      <td>9.905326</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1308 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           osm_id  road_type          name      road_id      from_id  \\\n",
-       "0         4845650      trunk          None     roade_56     roadn_56   \n",
-       "62       11180537      trunk  Winneba Road    roade_126    roadn_135   \n",
-       "93       11180537      trunk  Winneba Road    roade_127   roadn_9280   \n",
-       "124      11180537      trunk  Winneba Road    roade_128   roadn_9279   \n",
-       "155      11180537      trunk  Winneba Road    roade_129   roadn_9643   \n",
-       "...           ...        ...           ...          ...          ...   \n",
-       "66091  1216742901  secondary          None  roade_15720  roadn_12346   \n",
-       "66122  1216742902  secondary          None  roade_15721   roadn_5230   \n",
-       "66341  1244508503    primary          None  roade_15890   roadn_3364   \n",
-       "66424  1254900094  secondary          None  roade_15900  roadn_12447   \n",
-       "66455  1254900095  secondary          None  roade_15903    roadn_824   \n",
-       "\n",
-       "             to_id     length_m  \\\n",
-       "0         roadn_57   256.660267   \n",
-       "62      roadn_9280   522.694931   \n",
-       "93      roadn_9279    54.297481   \n",
-       "124     roadn_9643  1075.851334   \n",
-       "155      roadn_402   185.212407   \n",
-       "...            ...          ...   \n",
-       "66091   roadn_7316  1378.103677   \n",
-       "66122  roadn_12346     7.550321   \n",
-       "66341   roadn_7697  7856.443658   \n",
-       "66424   roadn_2337   881.490089   \n",
-       "66455  roadn_12447    19.014423   \n",
-       "\n",
-       "                                                geometry  \\\n",
-       "0        LINESTRING (-1.16109 9.14004, -1.15927 9.14149)   \n",
-       "62     LINESTRING (-0.31338 5.55362, -0.31494 5.55356...   \n",
-       "93       LINESTRING (-0.31809 5.55347, -0.31858 5.55345)   \n",
-       "124    LINESTRING (-0.31858 5.55345, -0.31866 5.55345...   \n",
-       "155    LINESTRING (-0.32808 5.55182, -0.32844 5.55168...   \n",
-       "...                                                  ...   \n",
-       "66091  LINESTRING (0.47833 6.04765, 0.47831 6.04739, ...   \n",
-       "66122      LINESTRING (0.47833 6.04772, 0.47833 6.04765)   \n",
-       "66341  LINESTRING (-0.41152 6.64467, -0.41136 6.64472...   \n",
-       "66424  LINESTRING (-2.16174 5.94774, -2.16216 5.94801...   \n",
-       "66455    LINESTRING (-2.16160 5.94765, -2.16174 5.94774)   \n",
-       "\n",
-       "                  shapeName shapeISO  ...    gcm epoch       ead_usd    rp  \\\n",
-       "0           Savannah Region    GH-SV  ...  WATCH  1980  3.783115e+05  1000   \n",
-       "62     Greater Accra Region    GH-AA  ...  WATCH  1980  7.704407e+05  1000   \n",
-       "93     Greater Accra Region    GH-AA  ...  WATCH  1980  8.003328e+04  1000   \n",
-       "124    Greater Accra Region    GH-AA  ...  WATCH  1980  1.585781e+06  1000   \n",
-       "155    Greater Accra Region    GH-AA  ...  WATCH  1980  2.729990e+05  1000   \n",
-       "...                     ...      ...  ...    ...   ...           ...   ...   \n",
-       "66091          Volta Region    GH-TV  ...  WATCH  1980  4.985972e+05  1000   \n",
-       "66122          Volta Region    GH-TV  ...  WATCH  1980  2.731702e+03  1000   \n",
-       "66341        Eastern Region    GH-EP  ...  WATCH  1980  5.893767e+05  1000   \n",
-       "66424        Western Region    GH-WP  ...  WATCH  1980  2.410725e+05  1000   \n",
-       "66455        Western Region    GH-WP  ...  WATCH  1980  6.879408e+03  1000   \n",
-       "\n",
-       "       flood_length_m       kind total_cost_usd_per_km  \\\n",
-       "0          256.660267  four_lane          2.094793e+06   \n",
-       "62         522.694931  four_lane          2.094793e+06   \n",
-       "93          54.297481  four_lane          2.094793e+06   \n",
-       "124       1075.851334  four_lane          2.094793e+06   \n",
-       "155        185.212407  four_lane          2.094793e+06   \n",
-       "...               ...        ...                   ...   \n",
-       "66091     1378.103677   two_lane          1.047397e+06   \n",
-       "66122        7.550321   two_lane          1.047397e+06   \n",
-       "66341     2598.165656   two_lane          1.047397e+06   \n",
-       "66424      666.315202   two_lane          1.047397e+06   \n",
-       "66455       19.014423   two_lane          1.047397e+06   \n",
-       "\n",
-       "       total_adaptation_cost_usd  total_adaptation_benefit_usd        bcr  \n",
-       "0                   5.376502e+05                  1.084830e+07  20.177240  \n",
-       "62                  1.094938e+06                  2.209282e+07  20.177240  \n",
-       "93                  1.137420e+05                  2.294999e+06  20.177240  \n",
-       "124                 2.253686e+06                  4.547316e+07  20.177240  \n",
-       "155                 3.879817e+05                  7.828399e+06  20.177240  \n",
-       "...                          ...                           ...        ...  \n",
-       "66091               1.443421e+06                  1.429756e+07   9.905326  \n",
-       "66122               7.908181e+03                  7.833311e+04   9.905326  \n",
-       "66341               2.721310e+06                  1.690071e+07   6.210505  \n",
-       "66424               6.978963e+05                  6.912890e+06   9.905326  \n",
-       "66455               1.991564e+04                  1.972709e+05   9.905326  \n",
-       "\n",
-       "[1308 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "candidates = historical[historical.bcr > 1]\n",
     "candidates.head(5)"
@@ -2211,206 +549,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "negative-liquid",
    "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>flood_length_m</th>\n",
-       "      <th>total_adaptation_benefit_usd</th>\n",
-       "      <th>total_adaptation_cost_usd</th>\n",
-       "      <th>bcr</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>shapeName</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Ahafo Region</th>\n",
-       "      <td>6067.892869</td>\n",
-       "      <td>5.228802e+07</td>\n",
-       "      <td>6.355490e+06</td>\n",
-       "      <td>9.100591</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ashanti Region</th>\n",
-       "      <td>25487.955929</td>\n",
-       "      <td>3.500343e+08</td>\n",
-       "      <td>3.152232e+07</td>\n",
-       "      <td>11.793901</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bono East Region</th>\n",
-       "      <td>26748.202679</td>\n",
-       "      <td>4.259816e+08</td>\n",
-       "      <td>3.704834e+07</td>\n",
-       "      <td>9.621116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bono Region</th>\n",
-       "      <td>7775.671910</td>\n",
-       "      <td>5.940093e+07</td>\n",
-       "      <td>8.144212e+06</td>\n",
-       "      <td>9.515985</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Central Region</th>\n",
-       "      <td>321277.001117</td>\n",
-       "      <td>5.925941e+09</td>\n",
-       "      <td>4.228584e+08</td>\n",
-       "      <td>15.181110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Eastern Region</th>\n",
-       "      <td>110959.074018</td>\n",
-       "      <td>1.338146e+09</td>\n",
-       "      <td>1.336653e+08</td>\n",
-       "      <td>10.916124</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Greater Accra Region</th>\n",
-       "      <td>109933.092232</td>\n",
-       "      <td>2.336059e+09</td>\n",
-       "      <td>1.644579e+08</td>\n",
-       "      <td>13.154460</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>North East Region</th>\n",
-       "      <td>24347.114853</td>\n",
-       "      <td>1.978790e+08</td>\n",
-       "      <td>2.743855e+07</td>\n",
-       "      <td>10.198693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Northern Region</th>\n",
-       "      <td>29609.527502</td>\n",
-       "      <td>2.888407e+08</td>\n",
-       "      <td>3.390779e+07</td>\n",
-       "      <td>12.310590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Oti Region</th>\n",
-       "      <td>23701.628849</td>\n",
-       "      <td>4.562131e+08</td>\n",
-       "      <td>3.301914e+07</td>\n",
-       "      <td>15.859723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Savannah Region</th>\n",
-       "      <td>53626.098428</td>\n",
-       "      <td>4.944966e+08</td>\n",
-       "      <td>7.166459e+07</td>\n",
-       "      <td>11.522111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Upper East Region</th>\n",
-       "      <td>12323.760210</td>\n",
-       "      <td>2.651146e+08</td>\n",
-       "      <td>1.764207e+07</td>\n",
-       "      <td>15.230693</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Upper West Region</th>\n",
-       "      <td>10514.617843</td>\n",
-       "      <td>1.967836e+08</td>\n",
-       "      <td>1.389307e+07</td>\n",
-       "      <td>12.706757</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Volta Region</th>\n",
-       "      <td>226094.705347</td>\n",
-       "      <td>2.852084e+09</td>\n",
-       "      <td>2.649487e+08</td>\n",
-       "      <td>13.419999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Western North Region</th>\n",
-       "      <td>24806.709407</td>\n",
-       "      <td>4.111308e+08</td>\n",
-       "      <td>3.247583e+07</td>\n",
-       "      <td>12.078313</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Western Region</th>\n",
-       "      <td>57944.643779</td>\n",
-       "      <td>9.362986e+08</td>\n",
-       "      <td>7.454809e+07</td>\n",
-       "      <td>11.330769</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      flood_length_m  total_adaptation_benefit_usd  \\\n",
-       "shapeName                                                            \n",
-       "Ahafo Region             6067.892869                  5.228802e+07   \n",
-       "Ashanti Region          25487.955929                  3.500343e+08   \n",
-       "Bono East Region        26748.202679                  4.259816e+08   \n",
-       "Bono Region              7775.671910                  5.940093e+07   \n",
-       "Central Region         321277.001117                  5.925941e+09   \n",
-       "Eastern Region         110959.074018                  1.338146e+09   \n",
-       "Greater Accra Region   109933.092232                  2.336059e+09   \n",
-       "North East Region       24347.114853                  1.978790e+08   \n",
-       "Northern Region         29609.527502                  2.888407e+08   \n",
-       "Oti Region              23701.628849                  4.562131e+08   \n",
-       "Savannah Region         53626.098428                  4.944966e+08   \n",
-       "Upper East Region       12323.760210                  2.651146e+08   \n",
-       "Upper West Region       10514.617843                  1.967836e+08   \n",
-       "Volta Region           226094.705347                  2.852084e+09   \n",
-       "Western North Region    24806.709407                  4.111308e+08   \n",
-       "Western Region          57944.643779                  9.362986e+08   \n",
-       "\n",
-       "                      total_adaptation_cost_usd        bcr  \n",
-       "shapeName                                                   \n",
-       "Ahafo Region                       6.355490e+06   9.100591  \n",
-       "Ashanti Region                     3.152232e+07  11.793901  \n",
-       "Bono East Region                   3.704834e+07   9.621116  \n",
-       "Bono Region                        8.144212e+06   9.515985  \n",
-       "Central Region                     4.228584e+08  15.181110  \n",
-       "Eastern Region                     1.336653e+08  10.916124  \n",
-       "Greater Accra Region               1.644579e+08  13.154460  \n",
-       "North East Region                  2.743855e+07  10.198693  \n",
-       "Northern Region                    3.390779e+07  12.310590  \n",
-       "Oti Region                         3.301914e+07  15.859723  \n",
-       "Savannah Region                    7.166459e+07  11.522111  \n",
-       "Upper East Region                  1.764207e+07  15.230693  \n",
-       "Upper West Region                  1.389307e+07  12.706757  \n",
-       "Volta Region                       2.649487e+08  13.419999  \n",
-       "Western North Region               3.247583e+07  12.078313  \n",
-       "Western Region                     7.454809e+07  11.330769  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "candidates.groupby(\"shapeName\").agg(\n",
     "    {\n",
@@ -2430,6 +572,19 @@
    "source": [
     "Given the aggregation, filtering and plotting you've seen throughout these tutorials, what other statistics would be interesting to explore from these results?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "357dfd1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# optional: delete .zip files downloaded in the process\n",
+    "# for item in os.listdir(data_folder):\n",
+    "#     if item.endswith(\".zip\"):\n",
+    "#         os.remove(os.path.join(data_folder, item))"
+   ]
   }
  ],
  "metadata": {