diff --git a/docs/source/notebooks/Delineation_workflow.ipynb b/docs/source/notebooks/Delineation_workflow.ipynb
index 594e8c80..95b530b7 100644
--- a/docs/source/notebooks/Delineation_workflow.ipynb
+++ b/docs/source/notebooks/Delineation_workflow.ipynb
@@ -22,19 +22,12 @@
     "import matplotlib.pyplot as plt\n",
     "import geopandas as gpd\n",
     "import json\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "import os\n",
+    "\n",
     "# Set environment variable RAVEN_WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
-    "# url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
-    "url = os.environ.get(\"RAVEN_WPS_URL\", \"http://localhost:9099\")\n",
+    "url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
     "\n",
+    "# Connect to the PAVICS-Hydro Raven WPS server\n",
     "wps = WPSClient(url)"
    ]
   },
@@ -49,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,26 +51,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"geometry\": {\"coordinates\": [[[[-71.3592, 50.4196], [-71.3621, 50.4226], [-71.3625, 50.425], [-71.3629, 50.4358], [-71.3705, 50.4434], [-71.3712, 50.4566], [-71.3746, 50.4601], [-71.3754, 50.4642], [-71.3712, 50.4684], [-71.3705, 50.4733], [-71.367, 50.4767], [-71.3663, 50.4983], [-71.3629, 50.5017], [-71.3621, 50.5108], [-71.3587, 50.5142], [-71.3583, 50.5208], [-71.3561, 50.5203], [-71.348, 50.5131], [-71.3436, 50.5119], [-71.3382, 50.5078], [-71.3353, 50.5089], [-71.3299, 50.5131], [-71.3257, 50.5114], [-71.3243, 50.5009], [-71.3186, 50.4994], [-71.3146, 50.4963], [-71.3105, 50.4994], [-71.3061, 50.5006], [-71.2927, 50.5131], [-71.2883, 50.5115], [-71.2869, 50.5061], [-71.2839, 50.5022], [-71.2833, 50.4958], [-71.2833, 50.4917], [-71.2875, 50.4917], [-71.298, 50.4911], [-71.302, 50.4881], [-71.3105, 50.4869], [-71.3199, 50.4786], [-71.3214, 50.4728], [-71.3306, 50.4632], [-71.3366, 50.4609], [-71.3491, 50.4533], [-71.3488, 50.4529], [-71.3472, 50.4513], [-71.3461, 50.4439], [-71.3479, 50.4291], [-71.3535, 50.4237], [-71.3592, 50.4196]]]], \"type\": \"MultiPolygon\"}, \"id\": \"96929\", \"properties\": {\"COAST\": 0, \"DIST_MAIN\": 490.9, \"DIST_SINK\": 490.9, \"ENDO\": 0, \"HYBAS_ID\": 7120270182, \"LAKE\": 0, \"MAIN_BAS\": 7120034330, \"NEXT_DOWN\": 7120270181, \"NEXT_SINK\": 7120034330, \"ORDER\": 1, \"PFAF_ID\": 724089370000, \"SIDE\": \"R\", \"SORT\": 96929, \"SUB_AREA\": 29.0, \"UP_AREA\": 9419.6, \"gml_id\": \"USGS_HydroBASINS_lake_na_lev12.96929\"}, \"type\": \"Feature\"}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get GeoJSON polygon of the delineated catchment.\n",
     "# We can either get links to the files stored on the server, or get the data directly. \n",
     "[feature_url, upstream_basins_url] = r_select.get(asobj=False)\n",
-    "[feature, upstream_basins] = r_select.get(asobj=True)\n",
-    "feature"
+    "[feature, upstream_basins] = r_select.get(asobj=True)"
    ]
   },
   {
@@ -89,22 +70,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4a74bb048>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6bbe091940>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -130,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +151,7 @@
        " 'perimeter': 33017.42666655582}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,25 +182,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "mimetype application/geo+json not in supported mimetypes ['application/vnd.geo+json', 'application/gml+xml', 'application/json', 'application/x-zipped-shp'].",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-1084d56c28fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Use the geoserver to extract the land cover over the appropriate bounding box (automatic)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnalcms_zonal_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeature_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselect_all_touching\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mband\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimple_categories\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m</home/tjs/miniconda3/envs/raven/lib/python3.6/site-packages/birdy/client/base.py-11>\u001b[0m in \u001b[0;36mnalcms_zonal_stats\u001b[0;34m(self, shape, raster, simple_categories, band, select_all_touching)\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/birdy/client/base.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, pid, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;34m\"\"\"Execute the process.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m         \u001b[0mwps_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         wps_outputs = [\n\u001b[1;32m    285\u001b[0m             \u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ComplexData\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataType\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/birdy/client/base.py\u001b[0m in \u001b[0;36m_build_inputs\u001b[0;34m(self, pid, **kwargs)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                     \u001b[0;31m# Guess the mimetype of the input value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m                     \u001b[0mmimetype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mguess_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msupported_mimetypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/birdy/utils.py\u001b[0m in \u001b[0;36mguess_type\u001b[0;34m(url, supported)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmime\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msupported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"mimetype {mime} not in supported mimetypes {supported}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: mimetype application/geo+json not in supported mimetypes ['application/vnd.geo+json', 'application/gml+xml', 'application/json', 'application/x-zipped-shp']."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Use the geoserver to extract the land cover over the appropriate bounding box (automatic)\n",
     "resp = wps.nalcms_zonal_stats(shape=feature_url, select_all_touching=True, band=1, simple_categories=True)"
@@ -227,9 +192,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '96929', 'gml_id': 'USGS_HydroBASINS_lake_na_lev12.96929', 'HYBAS_ID': 7120270182, 'NEXT_DOWN': 7120270181, 'NEXT_SINK': 7120034330, 'MAIN_BAS': 7120034330, 'DIST_SINK': 490.9, 'DIST_MAIN': 490.9, 'SUB_AREA': 29.0, 'UP_AREA': 9419.6, 'PFAF_ID': 724089370000, 'SIDE': 'R', 'LAKE': 0, 'ENDO': 0, 'COAST': 0, 'ORDER': 1, 'SORT': 96929, '1': 13532, '2': 335, '5': 4367, '6': 847, '8': 10239, '10': 60, '12': 380, '14': 21, '16': 39, '18': 3248, 'count': 33068, 'nodata': 8.0, 'nan': 0, 'Ocean': 0, 'Forest': 19081, 'Shrubs': 10239, 'Grass': 479, 'Wetland': 21, 'Crops': 0, 'Urban': 0, 'Water': 3248, 'SnowIce': 0} \n",
+      "\n",
+      "\n",
+      "[{'Ocean': 0, 'Forest': 19081, 'Shrubs': 10239, 'Grass': 479, 'Wetland': 21, 'Crops': 0, 'Urban': 0, 'Water': 3248, 'SnowIce': 0}]\n"
+     ]
+    }
+   ],
    "source": [
     "# Note that geojson needs to be installed for this to work. \n",
     "# $ pip install -r requirements_extra.txt\n",
@@ -240,9 +216,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{\"features\": [{\"geometry\": {\"coordinates\": [[[[1981874.096748, 967399.091191], [1981560.497165, 967640.799616], [1981434.936988, 967882.780767], [1980961.263774, 969007.129417], [1980150.453239, 969613.136897], [1979557.788254, 970982.006618], [1979190.993435, 971263.662311], [1978968.917959, 971674.108713], [1979068.737029, 972221.498664], [1978911.276644, 972753.897266], [1978998.518432, 973199.540058], [1978148.536013, 975486.000658], [1978229.072674, 975929.06883], [1977903.742496, 976905.067086], [1977984.173254, 977348.115496], [1977736.398881, 978051.400822], [1977900.425286, 978054.503503], [1978726.767107, 977503.147592], [1979063.162155, 977488.410214], [1979585.086481, 977194.438111], [1979728.314495, 977383.335657], [1979905.626466, 977961.050647], [1980249.760066, 977888.82216], [1980777.020525, 976821.614008], [1981210.625424, 976808.412368], [1981599.965266, 976584.172313], [1981738.25575, 977013.547429], [1981974.993178, 977251.010851], [1982328.051065, 978903.0378], [1982681.072971, 978846.538719], [1982996.790791, 978315.034465], [1983354.336434, 977981.589513], [1983659.560071, 977324.816337], [1983830.052137, 976894.322922], [1983556.471154, 976787.837664], [1982897.423789, 976458.687678], [1982761.508473, 976042.300741], [1982257.567097, 975700.92043], [1981989.759672, 974591.183905], [1982132.826199, 973944.08598], [1981931.599976, 972702.854607], [1981635.845096, 972309.345787], [1981135.976984, 971194.624495], [1981172.131364, 971160.205611], [1981342.839416, 971032.658661], [1981721.483719, 970283.259585], [1982217.756236, 968683.141738], [1982076.182758, 967974.105719], [1981874.096748, 967399.091191]]]], \"type\": \"MultiPolygon\"}, \"id\": \"0\", \"properties\": {\"1\": 13532, \"10\": 60, \"12\": 380, \"14\": 21, \"16\": 39, \"18\": 3248, \"2\": 335, \"5\": 4367, \"6\": 847, \"8\": 10239, \"COAST\": 0, \"Crops\": 0, \"DIST_MAIN\": 490.9, \"DIST_SINK\": 490.9, \"ENDO\": 0, \"Forest\": 19081, \"Grass\": 479, \"HYBAS_ID\": 7120270182, \"LAKE\": 0, \"MAIN_BAS\": 7120034330, \"NEXT_DOWN\": 7120270181, \"NEXT_SINK\": 7120034330, \"ORDER\": 1, \"Ocean\": 0, \"PFAF_ID\": 724089370000, \"SIDE\": \"R\", \"SORT\": 96929, \"SUB_AREA\": 29.0, \"Shrubs\": 10239, \"SnowIce\": 0, \"UP_AREA\": 9419.6, \"Urban\": 0, \"Water\": 3248, \"Wetland\": 21, \"count\": 33068, \"gml_id\": \"USGS_HydroBASINS_lake_na_lev12.96929\", \"id\": \"96929\", \"nan\": 0, \"nodata\": 8.0}, \"type\": \"Feature\"}], \"type\": \"FeatureCollection\"}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "features"
    ]
@@ -256,9 +243,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Ocean': 0.0,\n",
+       " 'Forest': 0.5770231039071005,\n",
+       " 'Shrubs': 0.3096346921495101,\n",
+       " 'Grass': 0.014485303011975323,\n",
+       " 'Wetland': 0.0006350550381033022,\n",
+       " 'Crops': 0.0,\n",
+       " 'Urban': 0.0,\n",
+       " 'Water': 0.09822184589331075,\n",
+       " 'SnowIce': 0.0}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# total = sum(lu.values())\n",
     "lu = statistics[0]\n",
@@ -277,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,9 +321,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'area': 28.76484946504176, 'longitude': -71.3409808346398, 'latitude': 50.478880424555875, 'gravelius': 1.7366297521025682, 'perimeter': 33017.42666655582, 'Ocean': 0.0, 'Forest': 0.5770231039071005, 'Shrubs': 0.3096346921495101, 'Grass': 0.014485303011975323, 'Wetland': 0.0006350550381033022, 'Crops': 0.0, 'Urban': 0.0, 'Water': 0.09822184589331075, 'SnowIce': 0.0, 'elevation': 490.04395604395603, 'slope': 3.9660612485567572, 'aspect': 116.79663053081183}\n"
+     ]
+    }
+   ],
    "source": [
     "all_properties={**shapeProperties, **landUse, **terrain_data}\n",
     "print(all_properties)"
@@ -334,9 +348,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# NBVAL_SKIP\n",
     "import cartopy.crs as ccrs\n",
@@ -374,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Perform_Regionalization.ipynb b/docs/source/notebooks/Perform_Regionalization.ipynb
index d36e0062..ccf84869 100644
--- a/docs/source/notebooks/Perform_Regionalization.ipynb
+++ b/docs/source/notebooks/Perform_Regionalization.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Calling HMETS on the Raven server\n",
+    "# Perform regionalization when no parameter set is available\n",
     "\n",
-    "Here we use birdy's WPS client to launch the HMETS hydrological model on the server and analyze the output. "
+    "Here we call the Regionalization WPS service to provide estimated streamflow (best estimate and ensemble) at an ungauged site using three pre-calibrated hydrological models and a large hydrometeorological database with catchment attributes (Extended CANOPEX). Multiple regionalization strategies are allowed. "
    ]
   },
   {
@@ -142,18 +142,14 @@
     "    latitude=54.4848,\n",
     "    longitude=-123.3659,\n",
     "    method='PS', # One of the methods described above\n",
-    "    model_name='HMETS', # One of the two models are allowed: HMETS and GR4JCN\n",
+    "    model_name='HMETS', # One of the three models are allowed: HMETS, GR4JCN and MOHYSE\n",
     "    min_nse=0.7, # Minimumcalibration NSE required to be considered a donor (for selecting good donor catchments)\n",
     "    ndonors=5, # Number of donors we want to use. Usually between 4 and 8 is a robust number.\n",
     "    properties=json.dumps({'latitude':54.4848, 'longitude':-123.3659, 'forest':0.4}),\n",
     "    )\n",
     "\n",
     "# Let's call the model with the timeseries, model parameters and other configuration parameters\n",
-    "resp = wps.regionalisation(ts=ts, **config)\n",
-    "\n",
-    "# # Wait for results to come in\n",
-    "# from time import sleep\n",
-    "# sleep(5)"
+    "resp = wps.regionalisation(ts=ts, **config)"
    ]
   },
   {
@@ -182,15 +178,361 @@
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
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+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
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+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'q_sim'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 732</li><li><span>nbasins</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d73f3678-204f-464b-ba1e-ccadd0172f6b' class='xr-array-in' type='checkbox' ><label for='section-d73f3678-204f-464b-ba1e-ccadd0172f6b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 277.9 539.4 507.8 479.0 452.5 ... 24.36 23.86 23.37 22.92 22.48</span></div><pre class='xr-array-data'>array([[  0.      ],\n",
+       "       [277.916569],\n",
+       "       [539.369893],\n",
+       "       ...,\n",
+       "       [ 23.374975],\n",
+       "       [ 22.916863],\n",
+       "       [ 22.479806]])</pre></div></li><li class='xr-section-item'><input id='section-27cb9bf5-de63-482e-8d60-9cabd881d2d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27cb9bf5-de63-482e-8d60-9cabd881d2d5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c0c919f-e07c-4951-825b-371291a82e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c0c919f-e07c-4951-825b-371291a82e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f2038ab-581e-4898-8e5f-3d109e8960ba' class='xr-var-data-in' type='checkbox'><label for='data-9f2038ab-581e-4898-8e5f-3d109e8960ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;Saumon&#x27;], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2002-01-01</div><input id='attrs-d4ad2f1d-25f7-4290-8012-ea39b6e9e4a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4ad2f1d-25f7-4290-8012-ea39b6e9e4a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e134bff-83ed-4639-80ed-4b113f8d03d0' class='xr-var-data-in' type='checkbox'><label for='data-3e134bff-83ed-4639-80ed-4b113f8d03d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2001-12-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2001-12-31T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-ccd9f157-ec36-46e1-876e-d005bd116785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd9f157-ec36-46e1-876e-d005bd116785' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.DataArray 'q_sim' (time: 732, nbasins: 1)>\n",
        "array([[  0.      ],\n",
        "       [277.916569],\n",
        "       [539.369893],\n",
        "       ...,\n",
-       "       [ 23.369867],\n",
-       "       [ 22.912411],\n",
-       "       [ 22.475852]])\n",
+       "       [ 23.374975],\n",
+       "       [ 22.916863],\n",
+       "       [ 22.479806]])\n",
        "Coordinates:\n",
        "    basin_name  (nbasins) object ...\n",
        "  * time        (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2002-01-01\n",
@@ -217,7 +559,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8cdccf2e80>]"
+       "[<matplotlib.lines.Line2D at 0x7f66bfc71c18>]"
       ]
      },
      "execution_count": 6,
@@ -226,7 +568,7 @@
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\n",
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BiOwBXgdeA940xjQkTqzkwhAcBMnPUgtEURTFkwvLGDMN+CSwEjgXeE9EViRSsKQixALJ9aeS6hNtZ6IoyqjGkwUiIuOBY4HjgTnAKuCNBMqVVIRWoosIhdnp1DarAlEUZfTi1YW1DXgX+IUx5uoEypOUuOeBOBRnp1PbogpEUZTRi9csrMOB+4DLReRtEblPRD6XQLmSitB5IADFOensa+kYYA9FUZSRj9eJhO+JyEZgI5Yb60rgBODPCZQtaQjNwgIoyvazsq5+WORRFEVJBrzGQJYAfuAtrNjHCcaYrYkULJkIjYGAurAURVG8xkDOMsZUJ1SSJCZ0HghAUXY6Te3ddHb3kp6qTY0VRRl9eD3zdYrIrSKyxL7dIiL5CZUsiQh0bQ+JgQDsUytEUZRRilcF8hegCasX1iVAI/DXRAmVrLhjIMXZlgKp1UC6oiijFK8KZKox5ifGmE327afAlHgKIiIZIrJYRN4TkVUi8lN7eZGIvCAi6+37Qtc+14nIBhFZJyJnxFMeN44F4hO3C8sPqAWiKMroxasCaROR45wnInIs0BZnWTqAk40xc4C5wJkicjRwLfCSMWY68JL9HBGZDVwGHAycCdwlIilxlgno30wRrBgIqAJRFGX04jWI/mXgXjvuIcA+4NOhG4nIRR5eq90Y83ToQmNFqpvtp2n2zQDnAx+1l98LLAS+by9/yBjTAWwWkQ3AUcDbHt+TZwbKwgK0Gl1RlFGL1zqQFcAcEcmznzcOsOn/AY8RHC4I5QSgnwIBsC2IpcA04E5jzDsiUm6M2WUfd5eIlNmbjwMWuXavspeFvuYXgS8CTJgwIYJYAxMuCys/M40Un6gFoijKqCWiAhGRbw2wHABjTOhY22eMMZ+N8pr3D7TOGNMDzBWRAuBRETkk0kuFe4kwr3kPcA/A/Pnz+633QjgLxOcTCrO0FkRRlNFLNAskdzAvZoy5Mk7b1IvIQqzYxh4RqbCtjwpgr71ZFVDp2m08sHMw8nrFDKB2irPTqW3WLCxFUUYn0RRIljHm+yLyCfdAqVgQkdOMMS9EWF8KdNnKIxNr5vqvgceBq4Bf2feP2bs8DjwoIrcCY4HpwOL9kXFgnCB6sNFTmuunWhWIoiijlGhZWGeLSBpwXRyOFa1vVgXwioi8j9X59wVjzJNYiuM0EVkPnGY/xxizCngYWA08C1xju8DiTrheWABleX72NLQn4pCKoihJTzQL5FmgBsgWEXfgXLASp/LcG4vI4wO8jgDFkQ5kjHkfq+tv6PJa4JQB9rkJuCnS68aDcDEQgDF5Gext6qC31+DzRcobUBRFGXlEVCDGmO8C3xWRx4wx53t4PadTb3PIcsFKsT0gCTcPBGBMfgbdvYbalk5Kc/3DIJmiKMrw4bUOZG3oAhH5tTHm+yGLFwGtxphXw2y/Lgb5koJw80AAyvMyANjT2K4KRFGUUYfXSvTTwiw7K3SBMeYsY8wr4V7AGHPCYARLJgaKgTgKZLfGQRRFGYVEVCAi8mURWQnMFJH3XbfNwPteDiAi58ZD0OEkoEDCxEAAdjeqAlEUZfQRzYX1IPAM8EvsHlQ2TcaYfR6PcSPwZAyyJQ1mgDTekpx0fAJ7VYEoijIKiRZEbwAaRCQ01pEjIjnGmG0ejnHApycN5MJKTfFRmutXC0RRlFGJ1yD6U1jZrAJkAJOBdVidcKPxpdhESx76XFj9dWF5Xga7G7WYUFGU0YenILox5lBjzGH2/XSslNw3wm0rIj+372+0901QdfjQEXBhhVlXnpeR1MWE7V09PPvBbjq6E1JjqSjKKCamYd7GmGXAkQOsfldE7gSWxCxVkjFQEB2sQHoyu7Bufm4dV9+/lGc/2D3coiiKMsLw5MIK6crrA+YB1WG2+wlQBHwS6BaRucaYG+Mh6HAyUCU6wNiCTBraumju6CbH79UjOHRU1Vlzv3RuiaIo8carBZLruvmxYiL9KtPtUbcAH7GfH/DKA8LPA3GoLMoEYPu+1iGVySspKZbMTe3dwyyJoigjDa8DpZz55LnWUxPaqsTNX4wx60TkL/EQMBkIdHMPY4FMKMoCLAUyqyKv/wbDTFd3LwBN7V3DLImiKCMNTxaIiBwiIsuBD4BVIrI0wrCnT9j3H4+HgMnAQGm8AJWFlgLZlqQWSFuXFTxXC0RRlHjj1YV1D/AtY8xEY8xE4Nv2snCMuCD6QPNAAAqy0sjxpwZiDclGW6elQPY2tTPp2qf40+ubhlkiRVFGCl4VSLa7x5UxZiGQHbqRHUQ/GSuIfoqI/DgeQg43kSwQEaGyKCtpYyCOBeJYSL98pl9fTEVRlJjwqkA2iciPRGSSffshsDl0oxEbRLfvw2VhAVQWZg6bC8sYw52vbGBrbUvY9Y4C2WXXqvT0xjQWXlEUpR9eFchngVLgP/atBPjMANv+FTgMeARARH4oIv8RkXn7Keuw4VggvgE0SGVRFlV1bYFsraFk275WfvPcOr72j+Vh1zsurNZOLSRUFCW+eM3CqgO+7nHbFSJynzHmMBE5DjgDuBm4G1gQs6TDSK8ZuBIdrEystq4eapqHfrDU9n1W7KWjqzfsescCURRFiTcxVaJ7wDlrnQPcbYx5DEhP0LESTsCwGECDOKm8A7mREslm+5i5GeGvBcJZHsNhKSkjm6b2LqZe/zRPr9w13KIoQ0iiFMgOEfkjcAnwtIj4E3ishNPXCyu8BplSauUTbKyOVB6TGGqarEaO4Way9/QaOrt7SU8N/ug7usNbK4oSKx/uaaan1/C/z2qSxmgiUSf1S4DngDONMfVY7U2+m6BjJZ4IvbAAxhdmkZ7qY8PeoVcgLR1WfUddS/9WJY77qjwv2K2mNSFKvHGs725N0hhVRIyBiMgduAqxQzHGhI2LGGNasYLtzvNdwAFr20bxYJHiE6aUZA+PAum0lEFtOAViu6/KcjMCsRKA5o5uneGuxJXNNZYCUe/o6CKaBbIEWIo1A2QesN6+zaUvzjHiiTQPxGFaWQ4bhsGF1dxhfQ11rZ109wS7phwFMrYgM3gftUCUOLOp2lIgDW1dtHX2cM2Dy9hRn5zFtUr8iKhAjDH3GmPuBaYDJxlj7jDG3AGcgqVERgV9I20H3mZaWQ5VdW20D3HWU6vtwjIG9rUGWyGOC2tKSXDNZ1OH9sUaLJOufYpfaRHmgGyyLZDmjm6eWrmLp97fxc+fXD3MUimJxmsMZCxWJ16HHHvZqCBSJbrDtLIcjBn6QHpzR581EdqyPaBASoMVSF2LKpDB0GknHfzh1Y3DLEly0ttr2FzTHMgEdOJxnZqsMeLxqkB+BSwXkb+JyN+AZcAvEiZVkhGtEh1gepmlX4c6DtLS2TeHJFSBtNrxkbLcjKDl1U3JOQCrq2dwJ5zunl7+/vaWICWaCOpadZZKJHY1ttPe1cuCycVAnzXSOcjvUznw8DrS9q9YRYCP2reP2K6tUYGJVggCTCrJItUnrNvdNDRC2bR09ATqUGpbgmezO+60zPQUbvnEHP7zlWNI9Ql7m5Jvhvv9i7Yy/QfPsHpno+d9nlq5ix89topbnl8XV1kaWrv4YEdD4HlNc/J9XsnEZjv+ceKMEgBWbK8H1AIZDXht5y7AqcAcpyhQRI6KpyAiUikir4jIGhFZJSLfsJcXicgLIrLevi907XOdiGwQkXUickY85XHjxQLxp6YwvTyXVYM4AcaDlo5uJhZbCqSmnwViKZCs9BQ+fsR45k0opCTHT3USKhDnhP32plrP+7yzeR9A3D/zT/9tMefe8Uagb9i+MBluSh+Ogj1qcjEpPmHNLuv7UAtk5OPVhXUXVoPET9rPm4A74yxLN/BtY8ws4GjgGhGZDVwLvGSMmQ68ZD/HXncZcDBwJnCXiKTEWSYLDzEQgEPG5vHBjoYhrfRu6+qhPC+DVJ/0u1J2srAy0/o+ljH5GUmZHZOZbsk4mAyx3XaDyFr7fXd098SlWeTybdYVtPN5qgKJjBNry8tMpbKwL+NP641GPl4VyAJjzDVAOwR6Y8W1NYkxZpcxZpn9uAlYA4zDGp3ruMvuBS6wH58PPGSM6TDGbAY2AHG1igKy2RpkoGaKDgePzaO2pZM9jUN3hd/e1UNWegrFOemBE6mDExvISu9TIFNLc4alXiUajs516lq84FhSNc2dNLR1cdqtr3HNA8v2WxYnprTTVrR6IoyMc6GSkZrClNKcwHKdgjny8apAuuyrewMgIqVAwuxTEZkEHA68A5TbhYhOQWKZvdk4YLtrtyp7Wdzp7XXkirzdIePyAYL854mkq6eXrh5DVnoKY/IzeXhJFZ+/dwn/Xb4D6Dvx5WakBfaZXp7D3qYOGtqG/s9d19LJ+j3hY0SOu2MwJ+u9djJAQ1sXz63azbZ9rTy7avd+K0hHgTgt8FtdSk37iPWnvbsv1uZOGW9sU8U70vGqQG7HCp6XichNwBvALxMhkIjkYLWC/6YxJpJzO9zpvN+/W0S+KCJLRGRJdXV1TDL1hdAja5BZFXmIwAc7h0aBOK6DjLQUfnD2LABeXLOHb/5zBTvr22hq78Kf6gvqhTXNvkIcDivkrN+9zmm3vRZ2nTO7vcVjRlVvr6GmuZPCLEs5vrxmb2Ddqx/G9j07OOmojgXS0tFX26N+/f602xaIP9XHtLI+C6Stq2fQmXXKgYXXLKwHgO9hKY1dwAXGmIfjLYyIpGEpjweMMU4rlD0iUmGvrwCcM0UVUOnafTywM4zs9xhj5htj5peWlsYkl3PVGc0CyfanMqUkm5VVQ6NAnD9uZnoKR00uCigRgPV7m2lq7w6yPsCyQAA2JliBGGN4euWuoMLK3Y3WFX1oxTz0pfB6VSB1rZ309Bpmj80DYNHmWuaMzycvI5Vt+9kVOcVuTBnOAmntGDUNGDzT3t1LRpoPEWFWRV7QOu16MLLxmoX1d2PMWmPMncaY3xtj1ojI3+MpiJ3p9WdgjTHmVteqx4Gr7MdXAY+5ll8mIn4RmYxVLb84njI5DMZpMW9CIcu21dE7BE3lHAvECZJ/5thJfPbYyQDsaWinqaObvJA2707jx/V7E5tuvHBdNV95YBm/e2k9EOz6qQ/jPuvqsdY3eVQgTiryrDHWCau+tYsx+RmMyc8InPhjxXGj7WqwLJBml9IYTIxmtNDW2RP4DR40xqqHcqxejR+NbLy6sA52P7HjIUfEWZZjgU8BJ4vICvt2NlYR42kish44zX6OMWYV8DCwGngWuMYYk5DLQxOlG6+bIycVUdfaxaaaxLuI3Gm6AKkpPr5/1kGAdbVvWSDBCmSoGj8u3mKl2L5n1wS4W8iH6xw8WAvECaA7FgjAmLwMxuRnBiydWHGSD55euZsL7nyTva7Xa1ELpB9tXT1k2AokIy2Fxdefwq2XzAGgUQPpI5po3XivA64HMkWkkb64QydwTzwFMca8wcCZsqcMsM9NwE3xlCM8keeBuJk/ySpTeXdLHdPKcqNsvX+4YyAO/tQUirLTbQXS1c+FBTC9PJcV2+tiPm57mOOG4mSELdpUy7baVrL8fdvWtnQyPWR7R4F4rSp3FIjbZVKen0F7V2+gDiEWjDE0d3STmZZCW1dPoCjOQS2Q/rR39QSlipflZVCUbSVpqgIZ2URrpvhLY0wu8BtjTJ4xJte+FRtjrhsiGYedwVggk0uyKc5O5137CjyRtIep8wAoz8uwXFhhLBCwAulVdW2B9MvBctyvX+ajv1kYcZu2rl6y0lNI8QkPLt4WZFmEt0CsD9mrBbK1tgWfWJ+3w7iCTMrzM6hp7og5eNvWZdWSfP2U6Tx2zbFcfMT4oPUaA+lPu8sCccizL1zUhTWy8RpEv05ECkXkKBE5wbklWrhkwUslOoFthPmTCodEgbS6guhuxuT5XRZIfwUyvTz2xo89dvZTNDdRW2cPE4uzmVySzeaa5iDLIrRrMPRlN3m1QFbuaGB6WW7QiWtKSQ4V+RkYQ8ztWpygb15mKnMqC/jIlOKg9WqB9Ke9ywqiuxlOBfLuln38/MnVmnI9BHgNon8eeA1ryuBP7fsbEidWctHXjdeDBgGOnlLM9n1tbN/XmkCp+lxYWaEKJD+DPYEYSH8XlpNqGUscZJvrPUWq+u7o7iEzzUd5Xga7GzuCZrPva0V3qMYAACAASURBVB44BtLe1Rs2SyuUD/c0M6vCchHmZ1rvcXJpNmPyrcaRuxtiq7ZvtE94Ti2I45L83plWbMmrhTSaaOvq6XcR41y4NA5DvdGPH1vFn97YzJKtsbtpFW94DaJ/AzgS2GqMOQmryG//ku0PILzMA3FzwgwrXXh/6xGiES4GApYLq6a5k9bOnrAWyKTibFJ9ElMmlrutR6Qmg22d1kmlIj+D3Q1tUS0Qt8spUqC6rbOHC+96kx31bVTaTSQf+fIx/OLCQ8nxp1IRUCAxWiAdTvGl9blNLM5mw01nccVREy3Zta1JP9o6e8hIDa9AhsMCGWcPUFu+rb8CqaprVcskjnhVIO3GmHYAEfEbY9YCByVOrOTCyzwQN1NKshlXkMlrCVYg7V3hYyBj8vrat4ezQNJTfUwqyWb9nsFbIO4rykg9tdrswOqY/Ez2NnUE7RfuJNzd0/enjjTwatm2ukCvKmfS4rSyHC5fMAHoe++7YrRAmsNU76em+MjLTMWf6kvKTsbDTXt3DxkhFkhqio+s9JRhCaI7F3o76oJ/A0u37uO4X7/C9Y9+MOQyjVS8KpAqESkA/gu8ICKPEaZob6QymBiItZ1w4kGlvLWxNqGVuH1pvMFWhuPGAcJaIADTy3JYH4MLy31C2FU/cBzESe10YhLOyNPSXH9YBdLZ04tdvxfRAnG3y6/Iz+i3Pj8zjYw0X6DR4mBxFI/jwnIQESs5YT9ThEci7Z09/S5iAAqz0odllopzsRJ6gbO11nK/vr2xZshlGql4DaJfaIypN8bcAPwIq+Dvgsh7jRz6KtG92iBw4oxSmju6ExpMb3O1kHDjViChhYQO08ty2FrbMugRvO4eWjsjWCDOScWR5fevbAAs6yzcSaWrp5fCLCv1szmCBeI+KcybWNhvvYhQkZ/JrhhP9Pe/s40cf2q/OfIA5Xn+mBXTSMapRA/FavA5DArEtiI31wR3JHAuXLp61IUVLyIqEHsWR9ANWInVCysn0r4jicG6sACOn15CRpqPZz/YnRCZwEmf9OHzBUtWkdd38ivN9Yfdd3p5Lr2m/58sGg2t1sk9PdVHVd3ASQJOYNWxEpyA+/jCrPBB9G5Due1+inTScZTPP794dCDTJ5QxdhrzYNnT2M572+v58kenBgLzbiqLsgLT9pQ+2gawQEpy/HEfxlXX0sl3//VexE6/jgWyqaYlaDvnt9PRranY8SKaBbIUWGLfh96WJFa05KEviO5dhWSlp3LSQWU888HuhLU1aesK/8fNz+o7+R00Jq/feujriTVYN1ZjexeZaSnMHV/AiqoGmtq7wloxjmyVhVlku/zjxTnpAwbRx9mzJPZEiDPUt3Zx8Ng8FoSk17qJtZ2JY924q9vdHDI2n+qmjqDK9NGOMSaoEt1NcXb8LZA/vraJfy2t4p/vbh9wm8b2Lg4qz8UY+PfSKn7z3Fp+89zagAVS29LpKdNPiU60QsLJxpgp9n3obcpQCTncxGKBAJx5yBiqmzpYGiYbJB60dvb0i384nDijlIw0Xz9fvsPkkmxSfBK2vXpzR/eAmSq1zZ0UZaczf1Ihq3Y0cNk9i/jhf4ODkr29xq4NSCHbn8rKG87gonnjuO6smRRmpdPe1RvUoBCs8adj8zPwCRFP0HWtnQFX10A4acyDVdyBCvvU8BX2M+0+T7HEjkYqTouasAokx09tS0dcL6Ac6yFSCnl7Vw+nzCpjamk2r6+v4c5XNnLnKxupbrIUiDGaTRcvIrYycRioaNAYE7439whjMJXobk6ZVY4/1cd/l+/gyElFcZerzXZhhePPV80n0v/Wn5rCxOKsfplYDW1dzPnp83zz1Ol889QZ/fbb29RBaa6fGeW5dPcaVu1sDOpzBX0nFac2wOcTbr1kLgD/fHcbYCmirKLUoH0y01MpzY0cZ6hv7Qobn3BTkZ9Bd6/h/R0NzK0siLhtkNxdzskw/GfqpA1Hct2NNgbKBAQYW5BBV4+hpqWDstz+CQ+x4CijtgFid932jJyMNGu41VZXZ+b3qvra0uxt6qAsLz4yjWa8ZmF913X7EfAEo6mQ0L73WkjokONP5ZxDK3h8xc6Y24ZEor2zfwGXQ2pK8ByQcFiZWMEWiBMY/9tbW8Lus7epnbJcf1ALkS01LXS6lEhol2A3zsl/u+sk3Ntr6OyxArHjCjKD1oWyr6VvBshAnDijlPQUH3cv3BBxu1Ci9fiqyM8gxSdU1SXfSODhYPXOxkAxarjPzKnHcKfTdvX09rv6tyxWb/+PGtslNlA2XLtz8ZKWwriCzKAYX3VTRyAm5wwjU/YPr1lYH3PdTgMOAfYkVrTkwes8kHBcemQlTR3dPL1yV5ylsoq0sgdwYXlhRnkuW2pbg9xJztX/QHMcrCs3P1Ndg4O6e03QlV4kBXJQueUGcqfjOm1M/KkpTC7JGTCw39zRTUNbdAtkYnE2nzyqkoXrqgeVRu1M1htIgaSm+BiTl5HwDgMHCmff/joX/+FtADLT+59KnJiWW+H+7MnVzPvZC0G/uZ8+sYqZP3rWk6vLafc/kJUaGK+b5mN8YWa/jCun3Xy11vPEBa8WSChVWEpkVLA/HtyjJhcxuSQ7YtAvVhrausJmC3nl6CnF9PQa3ljflxfvBJ+7w/yZX1y9h/pWK0AZGltxxwUCf+Iw1lFprp+SnPRAm/funt5AarA/1ceU0mz2NHaEbRniuI4m2K6kSMypLKCjuzdIsUWjPYoLC6CyKFMtEOinmMPF4iYWWVbqjU+u5sn3d7K3sZ2Hl1j/g7c21Aa2u/ftrYAV3I6GY6nsbgyvAJz1ftsCCcW5gNk7wP7K4PDaC+sOEbndvv0eeB14L7GiJRExxkCsfYRLj6xk8ZZ9QVfd8aCxff8UyJGTivCn+li8ua9Wxe0aCM1UueOVDUwpzebSI62q759fcAhXLJhAqk/43YvrA1eQkfziIsJx00p4fX0N6/c08c1/rmDBL14CwJ/mC8zUDmeFOIVglYXRFcgM+0SxdhCfebQgOlhpyJFcbKOF0CJSp327m8z0FI6YWEh1UwdffXA5X39oecC6W+dK3nAmQHqpsemwv6MBXViu355jAbkpz8sgPzNNOwrECa8WiDuV923g+8aYKxMmVZIRSxqvm0vnV5KZlsIfX9sYT7H22wJJT/VxyLj8oJkX7kLBvU0dQZXnNU0dHF5ZGIitXHn0RG668FC+f+ZM1u1p4k27wjeSCwvgI1OLqW3p5LTbXuPJ9/tce/7UFCaX9lcgxhi+9o/l3PjEajLTUgJuiEhML88hPdXHim31Ubd16LNAIimQTPY0doz6WoLQKu9wCgTgrivmceHh4wBYtGkf9XYdkVtZOIWwTheAd7fsGzDN1vmO9rV0hv0O3N9hOAukNNdPWa5fYyBxwmsM5F7X7QFjzJuJFiyZiDWN16EwO51PHjWBx1fsjFsGz+9eXE9rZ89+KRCAuZUFrNzREHBJuBXIok21HHbD84HMqYHaw1++YAIiBE7WbYE28+F/XvMm9K8gB+tEMqk4G5HgVvMNbV088d5OdtS3cey04ogn+L7XSmFuZcGgOgEE3B8Rkg8c6ye0z9JoIzQNu3gABVKel8Ftl87l5k/MCSzLTEsJ6lWWHlAg7azf08Qn/vA233hoRdjXa3cpjXBuqL44li+sUhtXmElZnl8tkDjh1YV1rogsF5F9ItIoIk32hMJRwWB7YYXj88dbs8r/9Prm/ZanpaOb2178EAguGoyFuXaswHGv1bd2kmvHN554z2p3dt/bWwOT+sLVlWT7U5lQlBVwFw3UJdhhWlkOt14yh6+fPI1zDqsILPen+shIS2FScTZrd/W5ONzVzNecNM3ze1swuYgPdjZ6bsHe3t1Demr/yn4348MEhkcjoWm0A3UFcDhxRimzKvK44WOzOXpKETtdLjDn097V0E61/V0/NUDSSXtXT6BhZjg3VptryJqIsPgHp/DMN44PrB9XkElZbobGQOKEVxfWb4GrgGLXZMLw5bojkMHOAwnH2IJMPj5vPA++s22/s3i2uALDU0v3r6OMUyex3HZjNbR1cci4fDLSfLyyzuom3NzRTVtXD70GcgborTVzTC5rdlvXFJFiIGC5Ai+aN55vnX4Qd14+L7DcUTizKvpeCwgUgD3w+QUcPoD1Eo75k4ro6TVB+f+R6OjqJSNK6rNTCzLa4yDutPTJJdkRlS5YrqNnvnE8nz52MhUFmUEWiNM8c3dDG41tkZV9e1cvE4ut7yDcULPQVOyy3AxmVeRxpD3XpTTHcmFVN3VoW/c44FWBbAc+MKP0E++133aU/0hUvnnadHw++M1z6/brdZz4wH2fPYpjp5Xs12uNL8ykODudFdvque/tLSzbVk9RTnrQrPE9je2BP/ZAle0zx+SxpaaFts4elwsruqsJ+hSN4zqaNSaPrbWtbK5pYenWuoAFUpITvq/XQDgDp9zWTCTCjWYNpTwvg4w0Hxv3ju6eWM6J+o3vn8RTXz9uUPuOzc+grtVqgdPR3RNI495R3xbU9j9cWm97Vw+Tiq042e6Gdm54fBWPrdjBLc+vo72rx2X9Bp/a/vaZo3j52yfi8wmluX46Xdl/Sux4LSL4HvC0iLwKBGw/Y8ytCZEqyQj8jPdTgVTkZ/L546bw+1c28LnjJjNnEFXSbpwA5Jzxse3vRkSYW1nAK+v28siyKsDyZ08pyQ7M3Wjv6uVDO2tmoPbwsyqs5ozr9zZRZwdKo7k1HCryM9hU0xLwhTvK66SbFwLwjVOmA1CSE7mFSSilOX6KstM9Z7+1dvaQPYCCdEjxCTPH5LF6V8OgZBlpOCfqgqz0AdvpDMSYfMsNuKuhPdAtOjs9hXe31AV9/nWtnRS7LhqMMXR091Ken0FJjp+lW+t45oPd/O0ta/3E4uxA8D1Upmx/KlNsa92pQN/b1EFBlLY4SmS8WiA3Aa1ABpDruo0OnELC/dUgwJdOnEJprp/rH10Zc0O3muZO0lKEvMzYiwjdzK0sCKoOvviI8VxgZ844QXonUyuSBQJW2uzO+jbyM9OinowdnJbvjivj8AnBivGJ93eS4pOoPbBCEREOKs9lbZh+X+FoHaCrbCizx+axemfjqHaBtHXa2U5RXH7hGGt/37vq+yZVXn/OLMYVZLJwXd8Qtj0hcYq+vls+5lYW8ExIp+v2rp7A6w3kagUosztU62yX/cfrt19kjLnIGPMTY8xPnVtCJUsi4hFEd8jNSOPG8w5m1c5G/i/GgHpNcwfF2f6Y04pDOWpyX5+ud39wKoeNL2BqaQ4PfH4BD3/pI0DfeNBwEw7BKu7LTEth9c5Gdta3Ra0Wd/ObT8zhgrljA/PHi3P8fP3kaVw6vxKwhlEVZadH9bOH46Axuazf0+Spyrm1s5tsvwcFUpFHY3t3xImMI522rh7SU3ykpgxegVTYv4373t7K9x95H7CsxaNDOizvCUm1ddfpfPXk/skUu1yjkyN1aHAKUbeFiUX+/uX1nPf7N0b1xcFg8HoJ+6KInG6MeT6h0iQp+5vGG8pZh1ZwxsHl3PL8Otq6evjssZMGZUrXNndQkhs/0/vwCYWMzc9g9tj8oPkhx04rwRhDYVYa79jFhgP1ofL5hEPtmpKGti6mlmaH3S4c4woy+e1lhwct+9bp1sTkxVv2sbmmZdDxD4eZY3Jp7exhe10rE4sjy9Ta2UOeh7Rox8W2emcj4z0UNY5E2iM08oyGk0X17Ko+CyInI5XZY/N4ZFnfdtUhFoi7xmNuZQGfPmYSy7bV8X6V5U7cUddGSY6frPSUQHHiQMfPTEvpF8cyxnDz81Z2Y11rF0XZ6Xywo4GMtBSmlY2a8UeDwusv4BrgWRFpG5VpvDFMJIzGzy44hLEFmdz+0nq+9o/lg7riqbYtkHiRnurj1e+dxJ+umt9vnYgwf1JRYHxupLThwycWsGJ7PZtrWjhpZllcZHP+uIONfzg4RYdeKtJbO7uDZpcMxMwxuYjAGo/B+ZFIW4RGntHITE/p9znn+tOYXGIp43Tbqgl1MTmTKh331A3nHcy/rz6GCw8fR0lOOjvq22jp7I7qOvX5hCml2UG1RtA3yRBgc00zq3Y2cO4db3DxH96K4V2ODrwWEuYaY3zGmMxRmcZr38dPfVjphS9/+0R+eM4sXl9fwxPve2+2uKOuLWybhv0hLYIr4pipfa6FSIWLH583HhHrpH/R4ePjItd0W4EMVOkcjRnlufgEVu2IHvRu9XhSzPanMrk4mw92jt5AelvXwLNovO7vpjgnPeBaErG+750NwS7CBjsT0D2mOT3Vx22XzuWEGaVU1bXR1N4dqGOKxNTSHDbVBCsQd2rx5ppWbrazJetbu6iPcbb7n17fxAcefnsHKtFG2s607+eFuw2NiMNPrPNAopGa4uMzx1rZWNf/ZyUb9ka/om3u6KautctTP6h4cczUvlRhf4Q+UTPKc3n1O1ZaZ6xXp6FccPg4MtNS+MLxsc0vy/ancvDYfBZtslxwkSy91s4ez92N51YWsGxr3aj1lbd2Rk95jsT3zpwJWE0vDx6bx9iCzIA78Lw5Y5lams0/Fm/n2F+9HGiI6YynDReHG1+QyZ7GdupaOz0lb0wpzaaqri2ojfxOV0xrw95m3t1SF0jo+MfiwTdD7ek1/PypNZx7xxtBy6/++1IeXV416NdLRqJZIN+y728Jc7s5gXIlFbHOA/FCik+4+4p5ZKSlcNVf3uX+RVsH7CC7r6WTO1+xZlyMj7MFEokZ5d79vxOKsyIqmcEfO5c1PzuTQ8blx/waH5lazPLtddz2woecffsbA86eaOnoJsuj4jtychG1LZ2jdkZ6e1cPmTHGQAC+dMIUVt94Bv/9yjE8/lWrjiQjLYVF153CTRceGiiQ3VHfxstr9wLW+AIItkAcjphURK+BNzfUekqEmF5mjbx1D1TbUmMF1XP9qSzZso/mjm4uOnwcJ8wo5a6FG8ImYvzx1Y28tCb8ZAt3nYlzodHR3cOzq3bz//4Z3Iv2hsdX8daGGg40oo20/aJ9f1KY28nxFERE/iIie0XkA9eyIhF5QUTW2/eFrnXXicgGEVknImfEU5ZQTLyj6CGMLcjkj586gurmDn743w+48K63ws4r+PUza7l7odWQMR41IF4REe68fB4/PGfWkB0znnxkajFdPYbfvbSeNbsaeWzFjn7b9PRaNQZe3TLOhMklg+i1NZJo64o9BgLWbyorPRURCQp4j8nPID3Vx2VHTWCKnYixdKuVAdgYwQI5flpJILnBi3V+2HjrgsTdpWDNrkZKctJZMKWIJfYxS3MzOPfQCprau8Nmbf3ymbV87t4lYY/hTo13CnGdZpLQp2BaOrr521tbuPxP7wTt/++lVWxJ8guUaC6sI0VkjOv5/4jIY3Zb93jPaP0bcGbIsmuBl4wx04GX7OeIyGzgMuBge5+7RCR+l70DEG8XlpsjJhby8Jc+wg/OnkVzRzdf/8fyoCtlYwxv2Fcox00rYULx0Gb/nHNYBZ+P0Y003ISOE/7F02t57cPqoGVOBfRAhZKhTC3Npjg7ncWbEzPvPtlp81gzEytzKwt4+dsf5eSZZYGphwELJEz9k88nnD93LAAFHvrDjS/MpDArjfddCmTljgZmVeQFpROX5vqZ6XQ02B2cN+Ru5xJu4qg7buKkfLuVyg2Pr6Kn1wTFXpZu3cc3H1rOBzsa+M6/3uPq+5dGfS/DSTQb9I9AJwTmov8KuA9oAO6JpyD2fPXQy7nzgXvtx/cCF7iWP2SM6TDGbAY2AEfFU55g2az7BOoPwPrTfOGEKfzqokNZtLmWL9y3JPDDXLu7iR31bfzqokO5//MLEizJyCLHn8r8iZbx+uDnF5CXmcrtL60P2sYZZlTsMdvLyk4rHFS335GEl7Yv8WBaWQ6balqsE219G+mpvgEV1+ULJnDh4eP43HHRL3REhMPGFwRSgKubOli7u4mjpxQH1UWV5fqZXmYlYoRm3blbwofry1Xnsjac+EqdS4E8unwHS7bsC2os+Yun1/LfFTsDcZNwr5tMRFMgKcYY5x9yKXCPMeYRY8yPAO9tUWOn3BizC8C+d3JDx2H153KospclBGceiC+RJoiLi+aN5zcXz+GNDTVc/qdFVDd18J9lVaT6hFNnlw+JDCONv39uAUt/eCrHTCvhvDljWbK1jknXPsX1j64E+q4MB5PtdczUErbtax1wBO9Ipq0rsRaIw7TSHDq7e9m+r5UX1+zlhOklA6bT52WkcdulcwOdDaIxZ3w+H+5porWzm5U7LEvkqMlFgWFkYFkgmel2h+gQC8RdKb9xb3BGF1itWBwcK2OfveyGj8229qtuCQreL91aF3DdAbR29Hgqgh0uoioQEXHsxVOAl13r4tNHIzbC/YLCfsoi8kURWSIiS6qrq8NtEpXeBGVhReLiI8Zz9xVHsGZXI2f89jX+7/XNnDqrPOaCutFOZnpKoK/SxUdUBvzu/12+g55ew74W62QwGAVysl3r4gR5RxP7GwPxylQ7jfu9qnp21Lcxb6L3bszROGx8Ab0GVu1sZN1uSwHMKM8lIy2FMw8ew1dPmhawsmZW5ParJXK7oz5/35J+Vm2Tq67kR4+t4uK73wpMcjz70Aoy0ny8tGYPf34juCPFlQsm8uTXjuMLx0+ms6c3qWeXRFMg/wBeFZHHgDasUbaIyDQsN1ai2SMiFfYxKwDnn1oFVLq2Gw/sDPcCxph7jDHzjTHzS0tLYxIiHu3cY+HMQ8bwry8dQ4+twT573OQhPf5IZXJJNs998wR+dsEhtHb2sHJHAzXN1slgMAq6siiLGeU5gSyc96vqBzWD/UAm0TEQB6eQ9NHlVuKDM2c9HjjNTJdsqWP5tjrGFWQG6pz+8Kkj+M4ZBwW2nWl3iHbPlgmdM/OPxduCnjvrnYr9JVvr+MublrIozE5nSkkOL63dy/oQ62VqWQ6HjMvnuOnW+Spc8D5ZiJaFdRPwbawA93Gudu4+4GuJFQ2Ax7HmkGDfP+ZafpmI+EVkMjAdWJwoIfpG2ibqCANz6Ph8XvzWifz3mmODfLPK/jGtLIezDrHyQ97cUBO4mhxsw8aTZ5azePM+lm2r47zfv8mX/p7cQc940GtnrA1FDCQ/M41zDq0INFl0ig3jQWmunxnlOdz+0npeXLOH0w8e2D080+5o8KGrMaczldEZiraroT3Indnc0U1Gmo/pZX0usV0N7eRmpJKW4gtKxT9+el+t1SFjrWyyifZ7Da2YTyaiJnIbYxYZYx41xrS4ln1ojFkWab/BIiL/wJq3fpCIVInI57CC9qeJyHrgNPs5xphVwMPAauBZ4BpjTMKGVA93rVhprj8w+EmJHyU5fmaOyeWtjZYCyc1IDbSU98pps8vp7jX8+pm1gJXsMNLnTDhjY4fChQXwiwsPDTx2WtPEi+OmldJmJwR87eTpA27npAi73VgtdoLLzRfPCQxGO+nmhYFZ7U3t3eT4rbjMH648gkvmW90ZnIsUx7q651NH8OerjuTqE6fyqaMnBlytE4uzKMxK47UPqwNeiGRjOOMYQRhjPjnAqlMG2P4mrDbzQ8ZwWCBKYjluWgn3vb2VVJ9vwLnekZg3oYDKosxAs0mAtzfWcuYhYyLsdWDT2hl54mS8yc9K46fnHUx6qm/QCj4ap84u4y9vbuazx06OGP8aV5BJjj+VVa72NS0d3fjEclGdfegYTp5Zxstr9/J+VQNHTiqipaObHL/ViHFaWY7d26sq8Pl95aRpnHHwmIAr7dqzZgYdU0Q457AK7l+0jV89s4YfnDM7ru89HsT32xihmDjOA1GSi/PmjqWzp5dXP6yOqd+WiHCh3fdrUnEWWekpvHkAVhQPBqdmJl7zaLxw1TGT+ORRE+L+usdMLWHZj04LineEw+cTjpxUyOvrawLng5YOq/WNiCAi3PKJOQAs2lgLWC4s91ySI+wEAGdSZo4/NepQuRvPO4RzDqvgocXbA5ZNMqEKxAOJ6oWlDD+HjssPtGopirHD8WePncSsijyuPWsmCyYX8fr66hHdI8tx0UVqrHkg4fXC4dTZ5WytbQ0UNrZ0dJPlaptSmJ3OzDG5AWu0ub07aADbIePyeeTLx/Crjx/mWTafT7hg7jiaOrpZtrU++g5DjCoQDySiG6+SHIgIv7l4DifMKOWcw2JzOxVkpfPMN47nzEMqOP3gMWypbWXZtpFbod4Y6Ek1MhSIV06ZaQXZX7Cz7lo6u/s13zx6SjFLtu6js7uXxvYucvzBn9EREwsZN4hha9ZrFpHiE15fH1sZQiJRBeKBPgtEVchIZE5lAfd99qiAK2p/OG/OWHL8qdz71tY4SJacjDQLxCtj8jM4bHw+L662FUiIBQLWyb69q5eVO+qpbuqgLG//67ZyM9I4vLIg0MoomVAF4oFAGu8wy6EkP9n+VK5YMIEn3t8Z1GdpJNEXAxldCgTg1FnlLN9uKYd9LZ393J5HTbb6aL2+vobalk7Kc71VxUfjuOklrNzRENQKJRlQBeIBjYEog+Gak6dRnO3nJ4+vSuo2FLEyWi0QgNMPLscYePaDXdQ0d/ablFmUnc5B5bk88Z5V11weBwsE4IQZpRgDr36YXG4sVSAeCMRAVIMoHsjLSOP7Zx7E8m313P7y+hEXUN/T2E6uPxV/nFNqDwQOKs/loPJc/rN8B9XNHZSG6VywYEoRG6utsrlyj325ojF3fAHleX6e+cD75NKhYPT9AmLBGLU+lEHx8XnjOX/uWH774noeeGdb9B2SnLbOHr737/f46ROr2FLbysSSrFF5QSUiXDRvHMu31dPZ3Ru29c0ps/oq2g+PUwGwzyeccfAYXv2wul8LleFEFYgHeo3GP5TB4fMJv710LsdPL+GXT6+hqi55+xl54Uv3L+XhJVX89c0tvPZhNROL49eT6kDjyqMnBkbdOoOp3Bw3rYSPzRnLRfPGUTDIoHxlBgAAEhhJREFU1jiROH/uONq7enlsRdi2f8OCKhAPGMyovNpS9g8R4ZcXWW04rn1k5QHlyqpt7mD1Tqt9+abqZl77sJpPHzMpsD5eV9YHItn+VB79yrFs+dU5LHANn3JI8Ql3fPJwbr1kblyPO29CAbMq8rjv7S1J81tSBeIBoxaIEiPjC7O49uxZvLGhhrtf3Tjc4niiq6eXk25eyNm3v86qnQ3c+ORqRODqE6fyrdNmMKeygAsPT9j4HWUARIRPHT2RtbubeHdLctQZqQLxgEEzsJTYueKoCZx7WAX/++w6HllaNdzihOWVtXt5ftXuwGOnWPCc299g4bpqPnPMZMbkZ/D1U6bz2DXHBhr+KUPLBYePpTg7vd/skeEiaZopJjOWBaIaRIkNn0+45ZI51LV28r1H3qen13DJkZXRdxwCXlm3l7JcP5/527sALP/RaTy8ZDtluX5uuWQOb2+s5bDxBSO6OeSBRFZ6Kl/+6FR+/tQa3tpYwzFTS6LvlEDUAvGAQX1Yyv7hT03hnk/N55ipxXzvkfe57YUPh71F98qqBj7z13c55/Y3AsvufnUjb9ndhI+fXsr3zpypyiPJuPLoiVTkZ3DjE6vp6ukdVllUgXhB9YcSB7L9qfz5qiO5aN44fvfSeq780zt8uKcpoEjqWjp5aPG2oKFFieSdzbVBzw+fUMA9r22itbMn0DlWST4y0lK44byDWbu7iXte2zSssqgC8YDGQJR4kZ7q45ZPzOF/Lz6MFdvrOf221zj7d6/zzqZaPv23d7n2Pyv52B1v8MzKxBeMrd7ZSFmuny8cP5kTZpTyf/8zP7DuxBmxjX9WhoYzDh7D2YeO4bYXPmTp1n3Rd0gQGgPxgDFGYyBK3BARLplfyUemFPPqh9XcvXAjl96zCIDvnnEQL63Zw5cfWMYFc8dy/dmzKMuLTzVzKLsa2qksygoaVPTKdz5KQ1tXXOsXlMTwy4sOY9XON/jy/ct45MvHUBnHcb9eUQvEA8aoBaLEn8qiLK48eiIvfOsEvnP6DL5z+gyuPnEqD37haL5+8jSeWrmL4//3FW54fBU769vifvy9Te2U5QZnU00uydbxyQcI+Zlp/PFTR9De1cMVf3qH3Q3tQy6DKhAPGDQGoiSOrPRUvnrydL568nRSfEJGWgrfOv0gXvh/J3LenLHcv2grx/36ZT7918U89f4uWjvj08qiuqmjnwJRDixmjsnjvs8tYF9LJxf/4S3W7Goc0uOrAvGAZYGoClGGlkkl2fzmE3N45Tsf5ZqTprFudxPXPLiMw298gc/8dTH3vb2F97bX09Y5+FGn7V09NLZ3J8w9pgwdcysLeODzC+js7uXjd7/Fv5dWDVmlusZAPGC1MhluKZTRSmVRFt8+/SC+eeoMFm2q5cU1e3hpzV5eWbcKsFpnzKrIZd6EQg4bX8BB5blML88hIy1lwNesqrNcYhVx6harDC9zKgt44mvH8dUHl/Gdf73Hk+/v5IfnzGZaWU5Cj6sKxAPaykRJBlJ8wrHTSjh2Wgk/Pnc22/e1sXpXAyt3NLBsaz3/XlrFfW9bkxBFYFJxNtPLcphcks34oiwqCzOZUJTFuMJMttRY7cYnlYzepogjjfK8DB764ke4960t3PrCh5x+26tcfMR4vnD8FKaX5ybkmKpAPGCMNlNUkgsRYUJxFhOKszjzkAoAunt62bqvlQ93N7F2dxMf7rFuC9dV0+kqOBPpG5I2eRR31R2JpPiEzx43mfPnjuWuhRv5+6KtPLykimOmFvODc2Zx8Nj+3YP3B1UgHtA6EOVAIDXFx9TSHKaW5nDWoRWB5b29hr1NHWyva2VbbSvb61rZVd9ORUEGBVmjb6rgaKA4x8+Pzp3NNSdN46F3t/HAom1kRnBpxooqEA+oC0s5kPH5hDH5GYzJz+DISUXDLY4yhBRlp/OVj07j6hOm4vPF/yymWVge0HkgiqIcyCRCeYAqEE+oBaIoitIfVSAe0BiIoihKfw5oBSIiZ4rIOhHZICLXJuo4VsaKahBFURQ3B6wCEZEU4E7gLGA28EkRmR15r1jRQkJFUZRQDlgFAhwFbDDGbDLGdAIPAecn4kAaA1EURenPgaxAxgHbXc+r7GVBiMgXRWSJiCyprq6O6UDajVdRFKU/B7ICCXdK79dBzBhzjzFmvjFmfmlpbENyfnLebJ7/fyfGtK+iKMpI5UAuJKwCKl3PxwM7E3GgrPQD+WNSFEVJDAeyBfIuMF1EJotIOnAZ8Pgwy6QoijJqOGAvrY0x3SLyVeA5IAX4izFm1TCLpSiKMmo4YBUIgDHmaeDp4ZZDURRlNHIgu7AURVGUYUQViKIoihITqkAURVGUmFAFoiiKosSEGNOv9m7EIiLVwNYYdy8BauIoTrxQuQZHssoFySlbMsoEKlcs7I9sE40x/SqxR5UC2R9EZIkxZv5wyxGKyjU4klUuSE7ZklEmULliIRGyqQtLURRFiQlVIIqiKEpMqALxzj3DLcAAqFyDI1nlguSULRllApUrFuIum8ZAFEVRlJhQC0RRFEWJCVUgiqIoSkyoAlEURVFiQhWIC5HkHFwrIrOGW4ZQROTbInK6/TipPjcRyXc9ThrZkkkWh2T8bYH+vmJhOGRRBQKIyPkici8wZ7hlCUVE7gCeFpFJwywKACJyuog8B3wf+B8AkySZGCJysoisAO4WkeshOWRL1t9Xsv22QH9fsTCcv68Deh7I/iAiYowxInIS8DOgC/iIiGw1xtQNt1yuRUVAHXCqiPzdGNMxHDIBacCPgROBXwLpwJEikgZ0D/cfSURygOuxvsvFwL0ikmWM+eEwyZN0v69k/G05cqG/r8HKkxS/r1FpgYT8kTYDZwDfBRYAhyWDXCKSYi9eBNwNXAFMHy6ZjDGdwGPGmOPtQV51wGXGmK4k+HP7gJz/3975x2pVlwH881xAQCEIAgmwXBADBYFsGCWSK1rYEq2VohM0aUuchltMVznoCgX2w+mSGrWSDRXFVSuy2ppgCs2ZUYplmkiyRpQYeIuNDe7TH8/3cE+3F7r3vefHw73PZzt733Pec977ud/3ed/nfH+c7wH2AjtVdS+wBLhCRCbX4OMuvjzGVt4r4qtbPm7iq88lkHQb3B+IyC0iMkZV96jqPlV9DNgPzBWRcTV6LRORsap6LN3r/cPAD4GtwJUi8jER+Z9JzUp2ukVE3qqqT6ftA1T1cWC3iMyvwqWB21IR+TiAqrYDCozCvuio6m6s3FrT/pW0D3uML4+x1ckr4qvrXq7iq08lEBG5HFgM3INl6i+KyIzcLvcDk7BMnj+u1ODo5DUd+LyInJ/Oyn6jqq8BLwE3A6uB0oO1QVl9QUSyNtajIjICm9n4WNkunbyGisi3seaODSLSH0BV9wN/AJbldr8NuEBEzq3iLNZjfHmMrQZeEV9dc3MXX30qgWAF+y1V3QqsxKp/N2cvquqzwNPAVLEOs1vT9rKDo5HXDem1j4jIE1in4o+wZoc3SvY5kdNnwcpDVV8HBgMXw/FqfumoahvwuKqOAbYA9+ZebgVmiMglIjIwnTluwdrXq8BjfHmMrRN5RXydHHfx1SsTSOeMm1vfDVwFoKp/AX4KnCEil+Z2fxBr33wImz+/sAzeTa/hIjIbuBvYoaozVHURMAYobOhlD8tqIzBLRAalL1OhnMTtx+lxGbBQRN6ZPP8F3AlciZ1ptwJzgH1Fu53Aq9b46oZTJbHVhFel8XUSt1rjy+vvVyN6ZQKh0xlBLgM/AhwWkQVpfR+wDThHjCHYl+o54DxVXd7p+Cq9HgMuAu5X1Vtzh12uqjsL8umu0zZSWaVtg4FNlNfM0NBNVf8tIi2q+jdgHfDd3D6bgC9jTTGjgPmp+aFQpKMj2k18dcOpqtjqrtc2qo2vhm4O4uu/fpe9xFdDVLXXLMBsYHMqxHOAfml7//QowHXAz+mYSHI5sDLbDxjtyGtFet4PaPHklHlV/DkeL4d8eQCvpmPGABdk/iV5tTbYnpVP5fHVA6fSYqsIrwriq6FbzfE1C6t13QlMy7n0y/lV/vt1sqXX1EBEZDTwTeBR4ADWnvopAFU9mnYbDPwCy9zrRWQsMBMbQ42qHlXVvzvyOpr2O6YFVuGLcMq8inLqotsxVW1PZ1rDcoetBbYDvwIGpX0LPesSkcXABqzj8pNpW9bBmv2tSuOrh06lxFZRXplbkV5dcasjvkSkRURWYDWdn2GJ4EbShYG5cqj89+v/UmW2KnMB5gEPpudnYGOjtwCT07ZVqfBnYhdQrcKqf+so6UzHq5dHp2643YGdgc1J6/OBF4CvAQNK9hoPfAh4Nbc9OztcWdPn6MrJs1cX3VbUFF9LgHel5yOxRPLu3Ou1ldlJvev6wwUU+ELgS8Claf0t2HDEiWl9RAqGtcDpwAPAhE7vcXpf8PLoVJQb1sR1VoleC9J6Cx3Ne08Cd+T2HV3x5+jGybNXEW4lx1drLu4HJ7eBaf1h4KN1lFm3/o+6BZooeAE+A+zE2gP/hGXvQcDtwD25QLkQ+A4wInd84e29Xr08OhXoVtZZfSOv64ChuX3OBQ4BZzY4vqrPsVYnz14FuVUVXy+mxyG5fQYAO4BJVZZZM8sp1weiVoqzgTWq+n2srfD9wAewdvOJIjJPrV33AHAmcASsrVFLGg7o0cujU4FupYzMOYHXB4E52eggVX0e6+Rfk3zm546v6nOs1cmzV0FuVcXX0uR1UW702RRgv6q+KHZh46zkJ2WWWTOcEglERBaJyFyxq1MB/giME5H+qvpLYBfwHuAfWFXvLhGZiP0YZRO1FR6wHr08Onl364LXs1gtaHx2jKouARaLyD+B6VLwhW4enTx7eXbrhtfb0+sjseG612I1kWkpeRTWcV8UbmfjTdl4DPZD0g68jF00cwM2qdk0YCLWwbUJuAsYqaobReQsbJqBycCnVfVgb/by6OTdrZteD2VewF4ReQfWqfoEcKOq7uqtTp69PLs16fVmYA/Wcb8Qq3FfrXaFuU/qbkNrtNAxImISsDE974+NONiAnYl+D7gGGJZevw9YnXuP0/qCl0cn72498GpNz4cBs3q7k2cvz2498FqVnr8PuKKMMit6cVUDSeOxW4F+IvIo8CbSVaiqelRsJsp92MiIB4DLsOroV7AsvyN7L7XJ4nqtl0cn724FeD2V9j2E3ROiVzp59vLsVoDXr9O+24tyKhs3fSAiMhd4BqvG/ZmOm6RcnHUiqbV9twJr1doO1wMXishT6bhtfcHLo5N3N49eHp08e3l28+pVOnVXgXLVvjnANbn1ddisodcCz6RtLVi74mbg7LRtODCuL3l5dPLu5tHLo5NnL89uXr3KXtzUQLDs/bB0TG62HXibqt6HVQlvUsvg47FbXO4BUNWDqvrXPubl0cm7m0cvj06evTy7efUqFTcJRFUPq+oR7Rh/PQ8bzgl2oc0UEdmCTVf8277s5dHJu5tHL49Onr08u3n1KhtXnehwfHplxS4cy+blb8NuaD8VeKWOjO3Ry6OTdzePXh6dPHt5dvPqVRZuaiA52rFhbq8B56WsfTvQrqpP1lj4Hr08Onl38+jl0cmzl2c3r17lUHSnShELdjVyOzbZ2fV1+3j28ujk3c2jl0cnz16e3bx6lbFkNyVxhYiMxy6y+YaqHqnbJ8Ojl0enDK9uHr08OoFfL/Dr5tWrDFwmkCAIgsA/HvtAgiAIglOASCBBEARBU0QCCYIgCJoiEkgQBEHQFJFAgqAkRGS4iCxNz8eKyCN1OwVBkcQorCAoCRE5G9iiqlNrVgmCUnA3lUkQ9CLWABNE5HfAS8AUVZ0qdqvSy4B+2PQWXwdOw64dOAJcoqqvi8gE4F5gFHAYuyvjC9X/G0HQmGjCCoLyuA14WVVnAMs7vTYVuAqYBawGDqvqTOymQovSPuuBm1T1fOBz2BThQeCGqIEEQT1sVdU2oE1EDgE/Sdufw+ZQGgK8F9gsItkxA6vXDIITEwkkCOohP8VFe269HftetgAHU+0lCFwSTVhBUB5twNBmDlTVN4BXROQTAGJML1IuCHpKJJAgKAlVPQBsF5FdwFebeIurgetF5PfA88CCIv2CoKfEMN4gCIKgKaIGEgRBEDRFJJAgCIKgKSKBBEEQBE0RCSQIgiBoikggQRAEQVNEAgmCIAiaIhJIEARB0BSRQIIgCIKm+A+MirVNQSGUqAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -256,22 +598,22 @@
       "Max:  <xarray.DataArray 'q_sim' ()>\n",
       "array(539.36989292)\n",
       "Mean:  <xarray.DataArray 'q_sim' ()>\n",
-      "array(120.04476061)\n",
+      "array(119.92177053)\n",
       "Monthly means:  <xarray.DataArray 'q_sim' (month: 12, nbasins: 1)>\n",
-      "array([[163.59117203],\n",
-      "       [ 86.6049509 ],\n",
-      "       [104.52956231],\n",
-      "       [167.92660932],\n",
-      "       [115.90261645],\n",
-      "       [125.09984299],\n",
-      "       [147.89506049],\n",
-      "       [134.87120303],\n",
-      "       [127.74529704],\n",
-      "       [110.20375339],\n",
-      "       [109.35779817],\n",
-      "       [ 45.02144745]])\n",
+      "array([[160.93082366],\n",
+      "       [ 82.52531876],\n",
+      "       [ 73.72276794],\n",
+      "       [159.31442212],\n",
+      "       [153.70776744],\n",
+      "       [130.69632646],\n",
+      "       [149.09884217],\n",
+      "       [135.01868718],\n",
+      "       [127.69917325],\n",
+      "       [109.88942476],\n",
+      "       [109.48369515],\n",
+      "       [ 44.82918717]])\n",
       "Coordinates:\n",
-      "    basin_name  (nbasins) object ...\n",
+      "    basin_name  (nbasins) object 'Saumon'\n",
       "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: nbasins\n"
      ]
@@ -298,11 +640,11 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8cdcc3d4a8>,\n",
-       " <matplotlib.lines.Line2D at 0x7f8cdc5367f0>,\n",
-       " <matplotlib.lines.Line2D at 0x7f8cdc536978>,\n",
-       " <matplotlib.lines.Line2D at 0x7f8cdc536ac8>,\n",
-       " <matplotlib.lines.Line2D at 0x7f8cdc536c18>]"
+       "[<matplotlib.lines.Line2D at 0x7f66bc4bc208>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483080>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc4831d0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483320>,\n",
+       " <matplotlib.lines.Line2D at 0x7f66bc483470>]"
       ]
      },
      "execution_count": 8,
@@ -311,7 +653,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -326,6 +668,27 @@
     "# Plot the simulations from the 5 donor parameter sets\n",
     "ensemble.q_sim.isel(nbasins=0).plot.line(hue='realization')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "http://localhost:9099/outputs/a3317734-8bf3-11ea-99bc-b052162515fb/qsim.nc\n",
+      "http://localhost:9099/outputs/a3317734-8bf3-11ea-99bc-b052162515fb/ensemble.nc\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can also obtain the data in netcdf format directly by changing asobj to False:\n",
+    "[hydrograph, ensemble] = resp.get(asobj=False)\n",
+    "print(hydrograph)\n",
+    "print(ensemble)"
+   ]
   }
  ],
  "metadata": {
@@ -344,7 +707,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Raven_run_parallel_basins.ipynb b/docs/source/notebooks/Raven_run_parallel_basins.ipynb
index 62d1900f..fdca0d8f 100644
--- a/docs/source/notebooks/Raven_run_parallel_basins.ipynb
+++ b/docs/source/notebooks/Raven_run_parallel_basins.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -65,23 +65,14 @@
     "# And get the response\n",
     "# With `asobj` set to False, only the reference to the output is returned in the response. \n",
     "# Setting `asobj` to True will retrieve the actual files and copy the locally.\n",
-    "\n",
     "[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_8_a1aex0/input2d.nc,-0.0371048,36.562,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_8_a1aex0/input2d.nc,-0.0888097,37.4623,\\n']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the diagnostics for both catchments\n",
     "print(diagnostics)"
@@ -96,32 +87,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8bad530828>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pandas.plotting import register_matplotlib_converters\n",
     "register_matplotlib_converters()\n",
@@ -140,17 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RV configuration http://localhost:9099/outputs/1f3d521a-623e-11ea-aa73-b052162515fb/rv.zip\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=False)\n",
     "print (\"RV configuration\", rv)"
@@ -173,7 +133,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Region_selection.ipynb b/docs/source/notebooks/Region_selection.ipynb
index 81fde406..9e960784 100644
--- a/docs/source/notebooks/Region_selection.ipynb
+++ b/docs/source/notebooks/Region_selection.ipynb
@@ -105,12 +105,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae337c54fadd42699aae3c132c9a70de",
+       "model_id": "0203199d66544648991c6f814ab87938",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Map(center=[48.63, -74.71], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_…"
+       "Map(basemap={'url': 'https://{s}.tile.opentopomap.org/{z}/{x}/{y}.png', 'max_zoom': 17, 'attribution': 'Map da…"
       ]
      },
      "metadata": {},
@@ -160,7 +160,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8b8a69483df42079009b154c35b87bf",
+       "model_id": "b468b5835bea4d8e88bd000bfe694a15",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,7 +240,61 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "3.6.7"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "3.6.10"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "version",
+       "op": "patch"
+      }
+     ],
+     "key": "language_info",
+     "op": "patch"
+    }
+   ]
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb b/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
index bfe08257..3d0255f4 100644
--- a/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
+++ b/docs/source/notebooks/Run_Raven_with_Parallel_parameters.ipynb
@@ -75,6 +75,368 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide data repr</title>\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<title>Show/Hide attributes</title>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
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+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8034efe9-1f21-43bd-844a-2830fbe6d5fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8034efe9-1f21-43bd-844a-2830fbe6d5fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>nbasins</span>: 1</li><li><span>params</span>: 2</li><li><span class='xr-has-index'>time</span>: 732</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1475e935-8893-4d0e-9344-ea411312c965' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1475e935-8893-4d0e-9344-ea411312c965' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36939e17-65d8-4302-8d9a-6bf387f242b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36939e17-65d8-4302-8d9a-6bf387f242b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab59eac4-4f2e-4adb-b0fd-fcb5627af87c' class='xr-var-data-in' type='checkbox'><label for='data-ab59eac4-4f2e-4adb-b0fd-fcb5627af87c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;Salmon&#x27;], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2002-01-01</div><input id='attrs-d689f92b-477d-4ae1-aee4-75ed40d223c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d689f92b-477d-4ae1-aee4-75ed40d223c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c709b9-a9ac-4eba-986f-83514be178cc' class='xr-var-data-in' type='checkbox'><label for='data-03c709b9-a9ac-4eba-986f-83514be178cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-03T00:00:00.000000000&#x27;, ..., &#x27;2001-12-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2001-12-31T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-db728803-a08c-45dc-81b6-ea6e0bec8a19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db728803-a08c-45dc-81b6-ea6e0bec8a19' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q_obs</span></div><div class='xr-var-dims'>(time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7909977-70cb-4fa8-ae02-9bc91de2d01e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7909977-70cb-4fa8-ae02-9bc91de2d01e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-665627c5-c2e7-4a7b-9094-e72f505d3e43' class='xr-var-data-in' type='checkbox'><label for='data-665627c5-c2e7-4a7b-9094-e72f505d3e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed outflows</dd></dl></div><pre class='xr-var-data'>array([[ nan],\n",
+       "       [11.1],\n",
+       "       [10.9],\n",
+       "       ...,\n",
+       "       [11.7],\n",
+       "       [12.1],\n",
+       "       [12.3]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>q_in</span></div><div class='xr-var-dims'>(time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-289dcc0d-2667-448f-83eb-60aa81077993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-289dcc0d-2667-448f-83eb-60aa81077993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af9bb6eb-5b4e-49da-8750-a7772968338c' class='xr-var-data-in' type='checkbox'><label for='data-af9bb6eb-5b4e-49da-8750-a7772968338c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Observed inflows</dd></dl></div><pre class='xr-var-data'>array([[nan],\n",
+       "       [nan],\n",
+       "       [nan],\n",
+       "       ...,\n",
+       "       [nan],\n",
+       "       [nan],\n",
+       "       [nan]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a637336-18e7-45e2-a6f7-203d118735bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a637336-18e7-45e2-a6f7-203d118735bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d30fbb6-553e-4e36-824a-abce4f6e4e65' class='xr-var-data-in' type='checkbox'><label for='data-8d30fbb6-553e-4e36-824a-abce4f6e4e65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm d**-1</dd><dt><span>long_name :</span></dt><dd>Precipitation</dd></dl></div><pre class='xr-var-data'>array([     nan, 2.478706, 0.628235, ..., 0.      , 0.003882, 0.      ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>q_sim</span></div><div class='xr-var-dims'>(params, time, nbasins)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c715038-0b43-4448-8ca3-55fadc83cc83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c715038-0b43-4448-8ca3-55fadc83cc83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01f02488-0730-40cd-8fcb-245189fbcb10' class='xr-var-data-in' type='checkbox'><label for='data-01f02488-0730-40cd-8fcb-245189fbcb10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div><pre class='xr-var-data'>array([[[ 0.      ],\n",
+       "        [ 0.165788],\n",
+       "        ...,\n",
+       "        [13.330653],\n",
+       "        [13.25446 ]],\n",
+       "\n",
+       "       [[ 0.      ],\n",
+       "        [ 0.123831],\n",
+       "        ...,\n",
+       "        [11.191918],\n",
+       "        [11.114937]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-7618a864-8e2a-426f-984d-7dfab75ace45' class='xr-section-summary-in' type='checkbox'  ><label for='section-7618a864-8e2a-426f-984d-7dfab75ace45' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>history :</span></dt><dd>Created on 2020-05-01 17:35:52 by Raven</dd><dt><span>description :</span></dt><dd>Standard Output</dd><dt><span>title :</span></dt><dd>Simulated river discharge</dd><dt><span>references :</span></dt><dd>Craig J.R. and the Raven Development Team Raven user&#x27;s and developer&#x27;s manual (Version 2.8) URL: http://raven.uwaterloo.ca/ (2018).</dd><dt><span>comment :</span></dt><dd>Raven Hydrological Framework version 2.9 rev#254</dd><dt><span>model_id :</span></dt><dd>gr4jcn</dd><dt><span>time_frequency :</span></dt><dd>day</dd><dt><span>time_coverage_start :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2002-01-01 00:00:00</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
        "<xarray.Dataset>\n",
        "Dimensions:     (nbasins: 1, params: 2, time: 732)\n",
@@ -90,11 +452,11 @@
        "Attributes:\n",
        "    Conventions:          CF-1.6\n",
        "    featureType:          timeSeries\n",
-       "    history:              Created on 2020-03-09 15:55:07 by Raven\n",
+       "    history:              Created on 2020-05-01 17:35:52 by Raven\n",
        "    description:          Standard Output\n",
        "    title:                Simulated river discharge\n",
        "    references:           Craig J.R. and the Raven Development Team Raven use...\n",
-       "    comment:              Raven Hydrological Framework version 2.9 rev#177\n",
+       "    comment:              Raven Hydrological Framework version 2.9 rev#254\n",
        "    model_id:             gr4jcn\n",
        "    time_frequency:       day\n",
        "    time_coverage_start:  2000-01-01 00:00:00\n",
@@ -119,8 +481,8 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc96e734be0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fc941575710>]"
+       "[<matplotlib.lines.Line2D at 0x7f1c8632c2b0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f1c84e988d0>]"
       ]
      },
      "execution_count": 4,
@@ -129,7 +491,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Zsu/iGIbRKqiVHmsWRV1SU1Pp169fvMWIKU3FKD4BhgEnAflmTRhGEmIjs5OeSGIUP1PVcuD6WAtjGEYCUsv1lPh+eCP6RKIo/uUGsx+PtTCGYSQgYUHaoFkUSUlTtZ5GUhPMXuCuG4aRTIQHsC1GkZQ0ZVFInblhGMlGrRiFZT0lIxbMNgyjccJjFDaOIimxYLZhGI0Tnh4bMIsiGWlyHIWqVohIP+AXIpIXfo6qnhM70QzDSAhqjcw2RZGMRDrg7i3gKWAqYLanYSQRwUCg2vVgrqfkJFJFUaGqj8RUEsMwEpKghikKC2YnJZEqin+IyJ+AD4DK0EZV/SomUhmGkTAEAv6aB4WlxyYlkSqKIcBPgDHUuJ7UXTcMoxUTDIRnPZlFkYxEqijOBfqralUshTEMI/EIBKwoYLITSXoswDdAh1gKYhhGYlIrJdZiFElJpBZFN2CZiMyjdowiqumxIrIWKAYCgF9V80UkB3gFyAPWAheq6u5o3tcwjIYJhCsHy3pKSiJVFH+KqRS1Ga2qO8LWbwWmq+pEEbnVXf99M8pjGEmNBq1xUbITkaJwS3nEiwnAKHd5MvAxpigMo9kIj1GYRZGcNFU99p2mLhDJMfuAAh+IyAIRudbd1k1VCwDcedcG5LhWROaLyPyW0pLQMFoCtawIy3pKSpqyKE4Ukbcb2S/AoCjKM0JVN4tIV+BDEVkW6YmqOgmYBJCfn2/dVQwjSgQDNT0oLOspOWlKUUyI4BpRS5lV1c3ufJuIvAkcC2wVkR6qWiAiPYBt0bqfYRhNEwxXDhajSEoaVRTNGZsQkWzAo6rF7vJpwN3A28BlwER3PqW5ZDIMA6gqqVm2GEVSEmnWU3PQDXhTRMCR60VVneam5L4qIlcB64EL4iijYSQdPWbcXLNiMYqkJGEUhaquBo6oZ/tO4JTml8gwjLpY9djkJNKR2dWISEcRGRoLYQzDSGzEgtlJSUSKQkQ+FpF27ijpb4BnROTB2IpmGEaiYRZFchKpRdFeVYuA84BnVPUY4NTYiWUYRkJiWU9JSaQxihQ3NfVC4PYYymMYRoJRmdmNjaUeBMVrDS6TkkgtiruB/wHfq+o8EekPrIydWIZhJBLzgocSxIOYRZGURGpRTFXV/4ZW3Ayl82MjkmEYiURRWTl+vAQRi1EkKZEqiiUishX4FJgFfKaqhbETyzCMRCGFIH68BPAgNo4iKYnI9aSqA4BLgMXAWcA3IrIwloIZhpEYeAkQwIviAbUyaslIRBaFiOQCI4CTcAbFLQVmx1AuwzAShFQC+PEQQGxkdpISqetpPTAP+Iuq/iyG8hiGkWCELIogHsRiFElJpFlPRwHPAT8SkS9E5Dm39pJhGK2cFIL4XEVh4yiSk0g73H0jIquAVTjup0uBkcBTMZTNMIx4EwziESWgTtaT01vMSDYijVHMB9KBz3FiEyNVdV0sBTMMIwEIOk2LnBiFZT0lK5HGKM5QVesvahjJRtAHQFZGOuoT60eRpEQao6gSkQdDPalF5AERaR9TyQzDiD+uRXFYr04E1NJjk5VIFcXTQDFOracLgSLgmVgJZRhGYqBuv2zxOjEKcz0lJ5G6ng5S1fCSHX+2AXeG0foJ+H2kAOJJJSheS49NUiK1KMpF5MTQioiMAMpjI5JhGImC31/lLHi9+EghJVgRX4GMuBCpRfFzYLIblxBgF3B53YNE5LwIrlWhqu/Vt0NEvMB8YJOqnuU2SnoFyAPWAheq6u4IZTYM4wDx+5xgtseTygp6M6r8XfBVQGpGnCUzmpNIx1EsBI4QkXbuelEDhz4JTMFRJg0xEqhXUQC/BL4D2rnrtwLTVXWiiNzqrv8+EpkNwzhwAn5HUZCSymI9GK/6Yfm7cLgVj04mGlUUIvLrBrYDoKp126G+r6pXNnHN5xvYngucCdwLhO47ARjlLk8GPsYUhWE0G35XUXi8Kcz2HM2WzAF0n/ILOHgc7FkHG+ZAfqP/8kYroKkYRdsmplqo6qVN3bCRYx4Gfge1Wmh1U9UC97wCoGtD1xWRa0Ppu9u325APw4gG4YoiICnM6HEt+Eqh4Bt46WJ451dQuiPOUhqxpinXU5aq/l5ELghvXLQ/iMhYVf2wgX1nAdtUdYGIjNqf66vqJGASQH5+viV7G0YUCLjBbI8nFa9H2JA1yNmx+SvwpjnLa2fD4B/ESUKjOWjKohgvIqnAbVG4V2N1oUYA54jIWuBlYIzrotrq9urGnW+LghyGYURIwOeOo0hJwStCkbcDtO8DmxZAVifnoOKCOEpoNAdNWRTTgB1AtoiEB7AFUFVtF36wiLzdwHUE6NTQTVT1Nlxl5FoUv1XVS0XkPuAyYKI7n9KEvIZhRJGAWy3W601BRAgq0OtoR1G07eEcVLYzfgIazUKjikJVbwFuEZEpqjohguuFKsuW1NkuwLH7Id9E4FW3pPl64IL9uIZhGPtJIOAoCo/Hg9cDwaBCr2Pg27ecNFmAsl1xlNBoDiIdR7Gs7gYR+Zuq1s1A+hIoU9VP6jl+eSQ3UtWPcbKbUNWdwCkRymgYRpQJuhaFx+vFI0JQ1bEoAEpdT7BZFK2eSEdmj61n2xl1N6jqGao6s74LqOrIfRHMMIz4Eww6SYgiHjwiBFShx5HUGipliqLV06iiEJGfi8hiYKCILAqb1gCLIrmBm9FkGEZLxC0CKB4PXo84rqf0NjWBbIAdK62qbCunKYviReBsnCDy2WHTMZGMmXC5e//FMwwjnmjQUQAiXjwCwZA+yHC7DBx8GpRsgaJN8RHQaBYaVRSqWqiqa3FGQ2vY1EZE+kR4j8bKeRiGkcBosMai8Hhc1xPAoHOc+dCLnPnGeXGQzmguIg1mv4ujIATIAPoBy4HBEZx73f6JZhhGvKmJUQgeETSkKMbcCUMuhE4DICUDNs6HwefGUVIjlkQUzFbVIao61J0fjJPqOru+Y0XkHnd+t3vu3GgJaxhGM6Mh15MHrwiBkO/J44VugyAlzQlum0XRqok066kWqvoVMKyB3fNE5HGccuGGYbRgVEPjKLyO6ymsEttzX6xlzAMfQ24+bF4Iod4VRqsjItdTnSqyHuBoYK/KeyLyJyAHuATwi8iRqmrBbMNooWi1BeHBI9S4noA/TlkKQFWPY0gLPAZbl9SMsTBaFZFaFOEVY9NxYhZ7jdRW1T+7i8e766YkDKMFo3XSYwP1pMFuaz/EWdhgXubWSqSNi/4MICJtnVWtW6IjnKdVdbmIPB0NAQ3DiCNuj2yPeGpqPblkp3kprQqwMZBDbsc8WDMLjvtZfOQ0YkpEFoWIHC4iXwNLgKUiskBEDm/g8FA9JmuBZRgtHA1lPXm8eMWt9eTSNiMVgK1FFdDvZFj7KQT8cZHTiC2Rup4mAb9W1b6q2hf4jbutPiyY3RzsWmP/lEbMCSkKQiU8whRFaoozRKqw3Af9R0FlEfytL5RaSY/WRqSKIju8hpNbuC+77kFuMHsMTjD7FBH5YzSENOpQtBkeORKm3xVvSYxWjoZcT+6Au2BYjMLjtkQurvDDIadD3klQVQIr3q/3Wt9v3sH2wtLYC21EnUgVxWoRuVNE8tzpDmBN3YMsmN1MlO925t9Pj68cRutHQ64nZxxFuKLwB5zlogofpGXBZVOhfW9Y9m69lxow6SBWPnR67GU2ok6kiuJKoAvwhjt1Bq5o4NhngKHA6wAicoeIvCEiljcXLUItKAOWt27ElvAYhcdDrWB2SaXj+iwqd12gIjDwTFg1A6rqtxxOiKyWqJFgRDoye7eq3qSqR7vTzaq6u4FjFwJ3quosETkRGAdMBv4VPbGTHbd8likKI9ZUZz1JrRiFqlYriuIKX83xA88EfwV8cCcUbgq7TKD5ZDaizn6NzI6A0F/FmcC/VHUKkBajeyUfQfcNLuBr/Dhj39j0Ffy1D5TsNZY0aQkfR9EuI5WFG/Yw5oGP2bCrvFppFFeEJVX0HQEd82D+UzD1purNFaXhnZSbpsIXqDW4z4gvsVIUm0TkCeBC4D0RSY/hvZKPoKMggqYoosvnj0JlIazZq0Fj0hJ6WHs8Xm4YPYDu7TJYvb2Uu99ZWn3M1qKKmhM8XrjkZWjbE1bNhEpnyFVZUW0HxN1TvyXv1vpjGaWVfgbeOY2HPloZ5U9j7C+xenhfCPwPOF1V9+CU9bilsRNEJENE5orINyKyVERCg/xyRORDEVnpzjvGSOYWw4adxQCUV1Q0caSxT2goFdQq44eojlGIl0E92/HlH07hovzefPSd0wZ15CFdWLalmC9Xh6XEdj0MznvCaXq0ykm4qCzdU7M/GODpz5xcmPKqvV1SpVWOhfL07L3yZYw40ejIbBF5FKe8eL2o6k0NbC/DCXqH1guAgiZkqQTGqGqJiKQCs0XkfeA8YLqqThSRW4FbcfpjJC0Bv2NJeINmUUSX0J+6KYpqqrOear6TX556MK/M3wDA9aMO4tvNRTw6YyXH9Q/retfnBKe50fJpMGgClaWFNZcs381QWcUwz3IKCk+mf5c2tW5Z5XfuGYqBGPGnKYtiPrAApwfF0cBKdzqSmjhEVFCHUGmQVHdSnJpSk93tk4EfRPO+LZEU96tPwf6Roor5xPfGtSg8Xm/1pp4dMhmW5xj2uR0zuXZkPz77ficL1oW5l7wpMGAsrPwAggGqwiyKysLt/F/aRO5MfZ7t2/Z+fwwpCiNxaKrD3WRVnQwcDIxW1UdV9VHgFBxlEVVExCsiC4FtwIeqOgfo5lokIcukawPnXisi80Vk/vbtrTsYqW4wOyW6utoIWRTmeqpGw8ZRhPPclcN5+vJ8cjtm8ePhfcnJTuP8f33ODx7/jILCcuegQ8+Ash2w6Sv85TUWRdmerVRIBgCBdV/udc+qgCmKRCPSGEVPnMqxIdq426KKqgZU9UggFzi2kXpS9Z07SVXzVTW/S5cu0RYtofD7LC02JoQsCksSqKE6PdZba3NmmpcxA7sBkJ2ewnUj+wOwcMMe/jlzFWVVfoL9TwHxwoppBMMURWXRNvZIOwCknl7bZlEkHpG2Qp0IfC0ioTIeJwN3xUQiQFX3iMjHwOnAVhHpoaoFItIDx9pIaoJW4ym2+C1JIETIosDTuJV1xYh+dMhK5fWvNvF/X67j/75cx6mHdeU/fY53FEVOzYjsqqIdBN13VCnbtde1fFWVvJV2B4/4z8PJsDfiTaQD7p4BhgNvutPxrksqaohIFxHp4C5nAqcCy4C3gcvcwy4DpkTzvi2RoN/eeGNC6KHor4yvHIlEda0nb6OHpaV4uGhYH+774VC6tE0H4KPvtrG1x8mwdQnZe5YBUK5ppGxbTIY6ythbubeiCJbu5kjPap5Ou7/RexaW+aj0m/u1OYi0zLjgPLiPCA2eE5FjoyxLD2CmiCwC5uHEKN7BsWbGishKYKy7ntQETFHEhpDryVceXzkSiQgVRYi+nbJ59xcn8vaNI0jzenhml9PUaEDBOxRpFrOCQ8nZNIO2bt5KasXeBR58vqYVdSCoHHH3B/zqlYWRfhLjAIjU9fRPIIhTGfZuoBinllNDfbP3GVVdBBxVz/adOMFzw8VcT7HCVRRmUdQQVj02Urq2y6BruwwuPa4vkz5fw825Q8nYvohiMvkweAzjyueT6Xqy0qv2VhSBqhpF7QsESfXufe9vNjpZVP9bunVfPo2xn0T66w9X1RuACnBqP2ElOeKGWrA1NoS+V4tRVBPej2JfuWH0QWSlpfBe4DgA2lHG3JTa75a9Kr+HOnWgAlU133+Fr37X0sbdjjLpm5O1z3IZ+06kv75PRLy4r1wi0gXHwjDigLmeYkTIkjBFUcN+WBQhOrVJ55qT+vPA5sEAfOUdwtknDKne/0pwDO2ChVSt/aLWeQFfuKKo/zETUiA28qV5iPTXfwQniN1VRO4FZgN/jZlURqOEXE9BtXz/qBIa6W6up2pCtZ689bh/IuGqk/pRkd2LQRVPc0/WrVx1Yn/+q2MACB57HZWawupPX611TjBMUVT66nezVrqKIrzjnhE7Is16egH4HY5yKAB+oKqvNn6WESs0pCis1ER0qXY9WTC7mn0MZtelTXoKN51yMGVkgCeFnOw0Vg27mwmVdzPqpJNZknYEbdf+j0DYILtgWDC7oqZcPAEAACAASURBVLJ+667cVRRBG03fLEQUzBaR/1PVn+Ckq9bdZjQzoaqxaooiuphFsTcNjMzeFy45tg+F5T6G98sB4OZxgzhpYA96tM9kx+Fn0+vru/jki9mcfOJI55ZhFkVVeQnQaa9rhlxSQbMomoVIf/3B4StuvOKY6ItjRELIojBFEWVC2WQWo6jhAC0KcMZY3HTKwQx3iwZmpHoZMaAzAINGXQTAus9erXZzaVh6sr+i/k55oRhFpY3ibhYaVRQicpuIFANDRaRIRIrd9W3YwLe4oaF+FKYoootZFHsTBUXRGN72PdnZYShHls5m9vc7AKgKy3oqKy2u97xyX4CLvDPpX7Ws3v1GdGmqKOBfVbUtcJ+qtlPVtu7USVVvayYZjTpUxyj2I2XRaBgNtZY1i6KGA8h6ipT2x5zPUM8a/vvBLAD8lTUWxeffbaj3nGBFCX9LfZLnPXfhtyKCMSfSYPZtItJRRI4VkZGhKdbCGfVTE8w2RRFNSsqcB1RRSUkTRyYRrjtIYvhSkjL0hwD03fwec1bvxF9VY9HNXbGRnSV7W3g9ihYBUEkqe8otXTzWRFrC42pgFk7Xuj+787tiJ5bRKEGLUcSC6thPlVkU1WjQScOOZen19rkEep/Aealf8Oj0lQTCYhTtdA9P1dPpLs0d0V1ENjtLrJpyrIn0NeGXOOU61qnqaJxSG6276UMCE3qgmZqILl51vldv0GIU1WiwWWJh3iMuoB+b2LV6ARu3uWU9UjK4KmcJkz9fy5odtYPa4itzDiFQr8VhRJdIFUWFqlPuUUTSVXUZcGjsxDIaxQ26ig2OjyoeUxR700yKgkE/QD0p/Dh7Hm2lDB+pMPRChpV/hjdQxriHZvHV+pq6UOJ3FIWXADtKzaKINZEqio1uCfC3gA9FZAqwOXZiGY2iTmqgxwoYHBiPHQsz/1K9GrIoPAF78FSjQZrFds3KQQ46hR+mzyGHYoo9bWHoxXj8Zbw/rpjMNC/XP/8V/3V7dWtlyKII8vyX6ygs9zHx/WVs3F3GtqIKVm+3OFM0iTSYfa6q7lHVu4A7gaew3tXxo9r1ZBbFAbFjOXzyt+rVUGtZCZhFUU1zWRQAQy4gvXQz52V9TZsOXaHP8dC+N73Wv81fzxvClqIKbnltEcu2FIHPcUW1TfEzd80urn1uPv/+ZBUXPfElt72xmB//Z071uAzjwGlqHEVO3QlYjFPrqU2zSGjshZhFEX2CQTyu4vWY66kaVW2+7LqB4yGrE6lVhaS17QweDwy9EFbNYHxfZeEfx9I+M5W/vrcMccuspAQqOGdwDnPWOA2QNu0p54vVOykorGD51vrHYBj7TlN/AQuA+e687jQ/tqIZDeJmPXk0zKIo3ARV9Y9iNeqhbk8PN+7jVw8pgQrYuCAOQiUeokE0lhlP4aRlw4hfOss7VzrzIy4BBJ4cQwffdm4cPYBPVmwnLViTmXbL8Mxalymrcl6kPllu+TbRoqkBd/1Utb87rzv1by4hjdp43IdaLYvioUHwzPg4SdQCqVv4z62fNSU4grL0LjD5bNj6bRwESzS0eSsADLvGmR/i9tjufDD86BUoLoC5k/jJ8X0RgSypsfp6y3Y++vXJfPArZ2iXCOR1yuKV+RuoshIfUSHSooD1Dq5T1VnRFceIiJDrSdQZEBV64yuwtpARU1VWe91VvkuDeVQOu4MfLfwpvHIpXDMDMjvEQcAEQYNocw7sTMuCWzdAapiVcPBYOOxs+OxhMir28NyVd5D5uuL3dCWlbBsse4cBZ48F4NPfjWZXaRU7Syu58tn5TP58LdeMtHfaAyXSv4BbwqY7galEecCdiPQWkZki8p2ILBWRX7rbc0TkQxFZ6c47RvO+LREJ7whmAbv9w1dHUbiuKB9edns7wwWTYc86ePM6CCbxW2lzBrNDZLQDb2rtbafdCzkHwYJnOanNZvJ7ppOSkwfH3QALnoUVHwDQOyeLI3p3YMzAbowZ2JWHP1rBtiIbQHmgRJr1dHbYNBY4HIh2s1o/8BtVPQw4DrhBRAYBtwLTVfVgYLq7ntSIhvnXNYkfYgeCr47rKRSjwOu4K/oeD+P+Ciumwaz74iBgYiDNbVE0RMe+jnWX1gY+fwxKtkN2Zxj7Z2jbEz5/BFZ+WOt3/eNZg/AFlInvW+HAA2V//wI24iiLqKGqBar6lbtcDHwH9AImAJPdwyZjabnVWU+AKYr9pa6iCNQoiurS1cde4wRTP/4rrPhfMwuYIMTDomiIzA5w1KWw9E3YthTa9nAsj+HXwtpP4YUfwru/hcKNULCIvM7ZXH1SP974ehML1u2Kt/QtmkhrPT0qIo+402PAp8A3sRJKRPJwyoTMAbqpagE4ygTo2sA514rIfBGZv31768528ATDLYqAuZ/2h7quJ/c79WlKTQBUBM56CHoMhdevgZ2rmlnI+COqiVVT7Nhra8rBt+3hzI+5HDKdpkgsfB4eGgxPnAQb5nLD6AF0b5fBH6csTci2qZX+ADOXbYu3GE0SqUURniL7BfB7Vb00FgKJSBvgdeBmVS2K9DxVnaSq+aqa36VLl1iIljCEu56CgQCExyyMyKhrUZQ5b5wlZFLpD/s+UzPhoufB43WC28VbYPPC5FHOieJ6CtHpIOg93Flu19OZZ3aEmxfBr7+rfexHfyY7zcsfzjyMpZuLeHHOuuaVNQLuevtbrnh2Ht9ujvhRFxcijVFMDpteUNXPYiGMiKTiKIkXVPUNd/NWEenh7u+B0zQpqfGEuZ6CwUB1FpSxD4RbFHOfhKdOBWCF5u6dUtmhD/zwadi+DB44FCadDO/+JkmC3Ankegpx0fNw2j1OJlSI9LaO4rjqIzjvSTjj77BuNqz5hLOH9mDEgE78fdpythRGP7D99frd5N/zEbubqDm1vbiSv01bhi+sf8aH324BYMPusoZOSwgidT2dJSJfi8iusE53UVWBIiI4pUG+U9UHw3a9DVzmLl+GddarFaMIBoNmUewH/rC+zLz32+rFDdql/vaaB412Hj5tukH3ITD/KXgvCZSFBmNbYnx/aNMVTviFkx1Vl97DnNHcx1wO7XJhxj0IcO8PhlAVCPKnt5dEvbTHP6avZEdJJfPX7W70uL9NW8a/Pl7FjDBXU2hw4PqdrUBRAA/jPKQ7hXW6q+dXOiBGAD8BxojIQncaD0wExorISmCsu57UePZSFP5Gjjbqo6py7zfLG7gNxdPwIK1jr4HfroDrPoUTfwXzn4Z3f92qlYUmmuspUlLS4eRbYOM8WPkBeZ2z+dXYQ/jf0q3c9PJC3v5mM+VV0XnBClkI3rCvqbwqwJSFm2oppZC6DfXPKK30VyuKtTsTu6pCRAPugA3AEo1hlS1VnU3DZSpPidV9WyK1FYUfDXoSzTmQ2Kjiq6hTXfTqGXw8aTsQqB2jqA8ROOVPgMDsBwGFMx9yahO1MiQYICix6Zcdc478Mcx+CGbcAwefxrUn9aeo3MekWauZ+s1mTjioE/931XC8nsb/e1ZvL2FnaRXD8nLq3e8LOI/Fksqav5v7/recpz9bQ5c26ZwwoDMA7TKdsSGh/hnhPTbW72odFsXvgPdE5DYR+XVoiqVgRsN4woLZGgwSqFu3yGicKTfSfubttbd1Prj6H74qkh7MInDKH+Gk3zgDvt65uVVaFh71E6CFKgpvKoy6DbYsgu+m4vEIvzt9IDN/O4obRw/g81U7eWT6yiYvc9sbi7ng31/w/bb6S5eHenYXltXEKLYWOxbr9rCmShU+R5FsduMkq9xS6Id2a8vq7aUJXe02UkVxL1AGZABtwyYjDoRbFIFAAL/fegbvEwuf32uTpretVhCVvggf+CIw5k446bfw1WR455etT1kEAwRaqkUBMOQC6HyI03fEjeX1zsnit+MO5fyjc/nH9JWMmDiDq56d55Qvr4eFG/YAsHZH/e6hUNrtnrKa/8P0FOfRWhTWzzvU23t3aRVbCiv4ZkMhHgly+SEVbNpTzlWT58ck2B4NInU95ajqaTGVxIgYb5ii0GAQbW0PpzgQnmMfkUURQgTG3OHMZ93npN2e8xikZsRAyubHq/6W63oCJ6159B/gv5fDkjdg6AXVu/7fDwbTISuVDbvK+HjFdpY+XcRbN4yge/vav116iodKf7D6QV+XUPJD+P4U1521OezBX+gqkk17yjnur9MB+EPbaVwy73kyTn6V2z7fwWkPfcI/Lj6K0QPrHS4WNyK1KD4SEVMUCYKHsBiFBvE35VM3apNdZ5xNSka12wnY94qjIjD6dscVtfi/TuXZktaRxS3agmMUIQ6bAN2GwMx7wF/jCspKS+HOswYx6af5vHX9CIorfFz57DyKK2orBI/70N9TVn/6a3GF390fZj24y9uLnfvNW7uL2d/vAGDxpsLq48Z5nXL253bbyrRfjiS3YxZXTp7HPz5aSTCBBghGqihuAKaJSHms0mONyKkVzA4ECJrrad9Ir5Owl5pZy4qoNz22KUSceMWFz8GWxfDkGChYdICCxh+P+glKpI6HBMXjgdPuht1r4YvH6z1kUM92PP7jo1m+tZifP/9VvS8LhQ1YFKVVfnd/jSLZ7SqVUOD6gn9/QRvKuMQ7Hdz2AMf1z6FLuhtf3LSAvM7ZvP7zEzj3yF489NEKrn5ufvX58SbSAXdtVdWjqpkxTI81IsRLeDDbTyBgFsW+sKO8zkMgJbPWIKgD6mEwaAJc+b7jD//PqU6gO4GDlE3hWBQtXFEAHDQGBp4Fs+6HgvqrD406tCsTzxvC7O938PvXF6GqqColFSFFsLeiUFVKK33kUFTLotjpDr7b5c6z07zclPImf019irGeBeT37cjLVw8nq9TpAc7G+aBKZpqXBy48grsnDGb2yh2Me/hTZi6Pv3XaVCvUge786Pqm5hHRqItXA/jUcQdoUAnaOIp9YntpHcWafyV+1/XkEZpOj22KnkfBdbOg7wkw9ZfwxEh48SLH0mhheDSAtnTXU4gz/uaM4H5iJDyaD8vf3+uQC/J789vTDuHNrzcxcdoyKnxB/K4LqKCeQHOlP8gPmc5XGT+jXcnq6u2hUdo7SqrwBYJUBYIclevk//whH574yTFQvBn8FZDTH7YugTeugcpiRISfHp/H278YQafsNK54Zh53vLV4L5dYc9KURRFKgX2gnun+GMplNIKXAFVuHkIgGLD02H1EwjoDDqx4hnELjq0OLmanpTTqenpxznrG3P9xgxky1bTpApe+7gS6/RWw/guYNBpm3AtfvwCVLaOfs1f9rUdRtM+Fn38GR/8USrY6hR53r93rsBtGD+DS4/rwxCerufudpQBkpHr48Nut/Oz/FtTqb1Fa6ecUz1cAdCx3akkFgkpFeQnneD5nS1E5v371G3wBpUPbNgD0S91NpzbpNR0Uz34ERt8BS16HSaOqi08O7N6OKTeO4KoT+/HCnPWc+uAnTFtSEJc02qZaoV7rzkfXM41pHhGNungJ4nMVhQYDBH2mKPaFLKnxJVeQzvKw/Pjs9BSKK/ys2l5Sr6vhja82snpHKW98tanpf1iPF0beAjfOg5sWwsAzYdbfYcr18OgxzoMhwd1SntbiegqR3RnOedRRGCLw3ytgw1yoqFH8IsLd5xzOeUf34qW5jmvoPz8dxo+G92HWyu2Me3gWL8xZ57qdAtWDXcsrKrjmufk8PvN7bva+xiNpj3Ftr/VM/WYzAN1T3UF1oUSHzV8DAj2PdEaRXzbVKU751GmwyQlyZ6R6ufOsQbx5/QhystP52fNfcfXk+cxasb1Zq+E2+hcgIsOADaq6xV3/KXA+sA64S1WtyHsc8BKgyv3nbXElPOY/4/RBzjsxbiJkSsMm/ICubdhSVMEpD3wCQPvMVPI6Z5PXKYse7TOr6/lMmrWaV+dv4KjeHTi6T0eO6tORI3q3p21Gav0XzsqBC56FHSuhdDv87w/w2pXwzcsw/n6nMU8C4iGAelqRogjRoQ+c+SC8cTU8NRbEC+Pvg2FXAU6m030/PAIU5q3eznEpyzlxwvFcdnwef3p7Cbe/uYQlmwo57+jcagt1WNcgTxUU8eG3W3kg1cls+t3wDAafdBS7y3y0Xfuic++iTfD2Tc7Ym175jjsMnP+Jqz6E58+DZ8+C85+CgeMBOLJ3B6beOIJnPlvLI9NXMn3ZNg7t1pZbxw9k1CFdkBjX42rqL+AJ4FSo7ps9EfgFcCQwCfhhTKUz6sWrAfzi9BTWYABtSa6nd2525ncVNn5cDEln7zTHw3u1Y8mmIv549iCmfrOZ/l2y2VFcxbpdpazdUcaCdbvZtGcz6SkeHr3kKHaVVvHV+t18tX4PM5c7/U9E4LDu7Ti2Xw7H9c9hWF6O42IIIQJdDnGmq6fD3ElOeYl/Hgcn/RqOv7F2r+gEoFXFKOoy9ALomOe0vP3yX/DeLU684KDRAHg9woMXHUnwjZ/hmfwSXPk/Du1zHC9efRwPfLicx2eu4qW5G5ic6rgqrzgimytGj2HxxkL8U1+HrSCFGzjrFLcc+hLXkihYWNPfftxfasvUeYCjLF68EF6+xLFIR90GHi8pXg/XjOzPT47vy/+WbuHBD1dwxTPzOOGgTtx2xmEMyW0fs6+qKUXhDbMaLgImqerrwOsisjBmUhmNkkIAn6SCOoqiVoxCNfGqfSYY6eydcvjCVcexvaSSAV3b8JvTDq33vApfAF8gWG01XHxsH8DJhlm4YQ8L1u1m/tpdvDxvPc9+vhaAg7u2ISvNy/fbSjiyTweO6ZvDtqIKOrVJ47j+F5B/7XgyZ9zhKIwFzzmtPQefmzC/YQqtWFGAU2229zA4+DR4ehz89zLnQd2l5m/As3aWs1C40Vn3CLeMG0huxyxue2Mxnbxub5NS54VhSG576KxOs+g9blaTKmxfXnPf9n3gl9/UXx+sbTe4cprTrW/WfY6L6txJkN0JcNxRE47sxRmH9+DFOet4ZMb3nP3YbM44vDu/PPVgBnaPfkJqk4pCRFJU1Y9TmO/afTjXiBFeAvipcT1puOspGACv/TQNEvCRjo+Fwf487ne66noE2mel0j6rAbeRS0aql4zUvR+a7TNTOfmQLpx8iDOQr8ofZPGmPcxZs4u5a3axraiSs4b25JuNe3hk+ko6ZKVSXOHn8ZmrSPEIR/S+ngsGnc5ZBY/R5rUrYM4TzuC9vBHR//z7iEcDBD2Nfy+tgox2cMnLTkrzCz90LL427uhoj/ub1xlEecmxfTi+fyd6PR+APXX2h5aLnPgEH0+EykKnmKSIo5gaKyKZmgkTHoPcfHj/d47VedZDcNhZ1YekpXi4fEQ/zjsml/98uoZnZq9h7ppdfH7bGNJToqvcm3qivAR8IiI7gHKcFqiIyAAgfr6DJEaDQVIkiL/aogg6Xe6qDwhgOrwR3KDlm4GT+DCYD0Cb9Oh+X2kpHo7pm8MxfXO4flTtfeVVATJSPZRWBViwbjdfrt7Jl6t3cvvCHP4QvJ1LUj7mN5veIOfZ8ezuPoLMcX8ko99xUZVvX/C2dosinI594UcvwzNnOunMl78LaVk19btK9x7PkNc5G3xuMkTpjpodJU5DIopdRbHyA2d+1E+cjLhIEIH8Kxxl8dbP4ZUfO7Wrzvi7E/NyaZeRyq/HHsKVI/JYsbUk6koCmniiqOq9IjId6AF8EFZm3IMTqzCamUDAsSUC4rzlqQYIBupYFIlKImT4VDgF3go1u3pTzw7NFxfITHP+idukp9SyQkoq/a7iOJifrzqToQWv87OCKWRMHkcJ2WxL7cmKfpfS8diLOTKvS0weBvXhJQCtMZjdEL2OgR8+BS//2Gl9e/GLUO42JCr4BioKIaNOLKDCfWcurcei2LXaScPd/JUTb4hUSYTTfQhcMxM+fdDJmls102nONOxqpzWsS4esNI7tV38p9AOlyb8AVf2ynm0rYiKN0SR+v5MYG/CkQACCgWCtYHYw4EvcNjOB+Jca0fI9CFBEFgC/HnsI5x7VK75CUVdxDKSkciRff38L/jlPIjtX0798Maev+BMFy//BI8HTWdH7fI4Y0JfjD+rM0Nz2pHpj86t7NVDjekkWBp4J5zwCb/8CXroYfG7V2FUz4MFBcOYDMPQi543fVwEBN+a1azUsmAzdD4fKIsi/0ikS+c1Lzv7B5+6/TN5UGPV7Jwtqxr0w7ymnhe/w6xwFlNnhwD5zEyTRq0LrwO9zMnZCFgXBgNM3O7TfHyAtHoJFQqDxnsLNQWXJLjKosShuOuXg+ArUAG3SUzhpcB4MvtfZEAxSunQaaZ/+g1u2vUjZ5jd5ef3J/PrD09ie2otheTkcf1Anju/ficN7ta+3GU9hmY+fPD2HS4f35cJhvSOSw0sATYYYRV2O/qnzYvOuO+Z4wj+hbXcn1vDmdU5plgsm1yQdDDwLVkyDqTfVXKPXMXDUpY67qaIQug0+cLm6D3HcY8VbYcb/c2pXffMSnHyr0/41JTb//aYoWhgBv2M9BD3OH0RQA7ViFIEELhCo/sq4d+IrK9pJBlBENn07ZcVZmn3A4yF7yHiyh4yHgm/I+vwxrlj6BlcGp7GqTT4vbhvFgyuGUEUqbdNTGNYvh+H9cjiufycO6daWHSWVfLV+N4s2FvK7jYs4c2gPsiOIzTiupySzKEIMuwrE48QXDj/fKR3ffxR8/TxMu9UJfJ/hdmYeNMEZl1G+C76fDsvfc0q4QGySEtp2c4Ldx14D0/4A798Cnz8KJ/8OjvxR1H+zhFEUIvI0cBawTVUPd7flAK8AecBa4EJVbbyDeSsn4HctCldR1B1Hkch1n3xVFTXWTpzSeMuLdgJwx/nHcXL+Ec1+/6jQ4wg4/0lk7N2w8HkOWvAcd5bcz+0dO7Gq1zm84xnD1M2lzFjWcDG5I+/+gEE925PqEXrnZDEsL4fDe7UjK81Lv85t8HqEYFBJIQjJaFGEyL/CmUJ4vHDMZc6b/YsXwcs/crZ3Geg8vNt2g66HwQk3No98PY6Ay99x3GIz7oHP/gFHXBL12ySMogCeBR4DngvbdiswXVUnisit7vrv4yBbwhB0LYqQoiDgr5UeG2ju3hSrP3FGWrfr2eSh/qrKGkXhr4xLc5/KEuc9o22Hzs1+76jTrofjnz7x17B6Jp4Fz3Lwsuf4lT7Dr3ocQXH++XyeOZrlJZnkZKfxXUERl3TfRGV2Lh9uSmHhht2owqcrt/Pm15tqLpuRwogBnRneL4cf4U9ei6Ixeh0NV3/kDNLLyoEeQ+MniwgMOMWpkFuyNSbp8QmjKFR1lojk1dk8ARjlLk8GPibJFYXfDQgHPM6I32DAXytGEbI4mo2Xf+S8cZ12T5OH+qvCqm/6K+KiKAKlu6nUFNq1bUVV8j1eGHCqM5Vsg8WvwaKXafvxHxknXsZ1G+xke7XtAd/MAW86xxxxEYw+BzxeNPc41hZ7WLG1mJIKP3PW7OTTlTt4f8kWfpoeJC0tvWkZkpGOfeHHr8ZbihpEnDhKDEgYRdEA3VS1AEBVC0Skwf6AInIt7oDAPn36NJN4zU/ALQCoXldRBH21XE/NWkk2GICqErSiKKLYg88Xpij2JbA98y9w0CnQZ/g+i1iXYPkeisiiQ3bChvwPjDZd4fjrnWnbMlj0ipOa2bEvrJkFucc6ZSsWvw5fOca7eNPo1+9k+h00GrI6cf64Uej5Q1m1rRjPv5TBubFJuTRaDomuKCJGVSfh1J8iPz8/ARL2Y0PQTcXTFOdtPOj3x09R+JxqmKs3beWgJg4FCPhqSmeovyKywLa/Ej75mzPdVeh0jfvgdhhzJ/Q+dp9F9lQWUqTZ5Ga0mj/9huk6EE79U/37qkphwxwnVrRqBix7F77/0NknHqTfSAYMmgCAx0b6Jz2J/hewVUR6uNZEDyD+rZ7iTChGEVIUGvCjYeMTgs2oKLSqFAGqyiPrrRAIcz35wuMVjVGytWa5bBe8fjXsWO6Uh75xnjNydh9IqSqikOxmG7CWsKRlOz5tcPzbp93jjCwu2QrfTnF6f7/zK2d/qLqpkbQk7Ngsl7eBy9zly4ApcZQlIai2GFJCMQpfbUXhbz5FUVXulC5I8ZdFdHy4ReGvLI/sJuH1c76d4iiJwedC0UaY95+IZQ2R6iui1JPd9IHJhogzarj74TDmdrjpa6fe0Xn/cfLzjaQmYRSFiLwEfAEcKiIbReQqnLLmY0VkJTDWXU9qQuMkxLUoCPhr+fsDzZgeW1HmKIrUwL4rCl/V3m0l6yNYVFCzssat4jn0Yuh8CKzfq2hAk2T6dlPqjV055laDiFNjaOgF1S8lRvKSMK4nVW0o+feUZhUkwQmGrAe3b0HdrKdaBQJjTMiiSA1EZh2EK4pAhIqidMcGqh0fG+YCsM7Xjj49jkCWv++UYu4/ymn60lQZA1Xa+XZSmN4KUmMNoxlJGEVhRIa6FoUnzQ1mB3y1LIrmbGLkc2MTacEwRaEKb98Ig34AB4+tdbxW1Vge/qrIlEvltlV4NZ312pWBRU5t//OfX8vtJ07g3D57YOELMO9JZwRtz6McpZGWDYjTxaz7EMjs6PjZfeWk4qM0zRSFYewLpihaGKEYRcj1pEF/naKAB6goirfAv0+En7zl+KsbwVfhWBQZGvbQ377MKXGw/H343epax2uYcojIoijbRecl/6GSVNZqdwaygTJNZydtuX1uKqf+4SXapihsnAerP4Y1n8Dsh91S63XwpEAnp65Tefp+VPA0jCTGFEULI+R68qa5rVAD/lpVWQORVmgNlfyuW0Zj5YdOp66Z9zrpr2PvdsoE1IO/wrEQ0jXsob9xnjPvmLe37L4aiyLgj0BRuNdard1ZHOzH6d55bNLOnHtUb974ehPLtxSTn5fj1NLJGwHc7vSb0KCjGAo3wMb5zuC+ok2wfg7bJYcdbQ9r+t6GYVSTMMFsIzJCFkNIURCsXcJDI41RfPYP+HMHqKodiN5Q7J6//D3nLf2blxu8hL/SjeGK6AAAFbtJREFUsSjS8IM7IlzLnX4Puyv2HsoS7noKVjUx4K5kG8z5NwDXBX/Pp8EhAHwUPLq65v6KrSV7n5fRzolVpLdxau4c/ROncNqpd8GV7zNG/02gY//G720YRi3MomhhhFJhU6otCh+yH+Mo9OOJzoC3sp21xiJs2FNJeAHq8pR2NNTWJ1hRWrPiK4WUNLZs20YPYPOuIjrWvWfYyOygr4kYxSNHQZWjCNb7O1KS3Z3jeZmCigDv9GpPRqqHNTvqURSN4AsEKa7wk9NaR2UbRowwi6KFEYpHpKS7D/c6RQE1wvRY8TsP6qLd22ttTwvWdgktW7OhwWsEq2oUhbrWRdEepx1kjqeelNlw11NYBtReBPzVSgJg5CFd6ZSdRkFpEBBystPo0T6TgsL63VfaQCe93WWOFdOxid7YhmHUxhRFCyNYbVHUBLMJhgez9y09trxoV631VH9prfWMQMNv7eGupNDobI/bFrJNsJ7R2mFWRPqe753AefhDfcUH8OBg+Pat6k3fBXtz7w8Or2UFdMxKo0f7jHoVxa7SKsY+NIvb3ljMlsIKvli1k5P+PoMbXvyKqd84YzI6mkVhGPuEuZ5aGCGLIi3TtSiCfqRWjGLfsp4qinfWWpeq2orBW1XY8MlhFkJFaTHpgKeqCIC2WgyVJU6sIHRtfzk7tB1p+Mld/iwsf9ZJXe06CLoc6lQ9rSyC168C4M3ACCb6LmFmmzS6t3cUY7uMFDLTvHRvn8G0JVu44pm5BBQCwSCBoLKtqJLVO0r5flsJL81dX33vDbvKeXeRoyh6d2xBDYsMIwEwRdHCCLpppekZWfjVgwYDSDAsRrGPI7N9pbX7QIVcSCG8VQ3XcZIwRVFZ6iiIVF/Y8btW1cqYEl85xZrJhKr/x5Nj0xjeZhts+9apcrrkdbSymEXdzmVIuzIe/y6TB/0/RPGQlZZCj/ZOpCSkME4f3J0VW4vZWVqFR4QUj+DxCN3aZXDFif3okJlKcYWfrDQvx/bLIRBUyn0BMlO99M4xRWEY+4IpihZGKAiclplFAK/jdqqlKCJwPYUFv/2lu2Hbd7Duc6f1Yx2LIsO3p8HLhCsKX4WjIDpUbGBxMI8hnrXw7FlOffzsLpDdmW5Fi9hMOkW0YUunI+HIXjUXU+XJGUv4y4frefryfB5YPL/WvXp2cBREqtfxlp42uDunDY5N7X3DMGpjiqKFEcocSkvPwo8HCfqQcOUQgevJX15U/cMHy3ejz56JlO2Eof+/vTOPkqq8EvjvVvXC0g3arAICyr7JKogGiQtGyYmoExU1btE44xaXCcfEGHVw15lJNBPNMBqDe9SY0VEjzhzURDCySQBZFJCWzYaWXum1qu788b3uLpruku6u5VVxf+fU6apX71X/+utbdevbLyDoNR01kFfv9WGU7zpoF7tAqIo6DZIjYbqtfhJ2vkO30D5eC3+Xd7JOZd5xQTcnY/9eKFpPbqicNZHjAagLRQ6UEmH9Xuf+waamDvZ8b1/nEX3cQh6TBzUfS2UYRqKxRJFueDWKTp27UkUWRMKIhqjXINkSPqSmp6qKEhr3d6sudUkCCBVtIKeuqQZRq9nkR0rdGktPzYLT7oIZtzY+HwhVs0X7kav1DCjZCFVfsiN7MO/VTmBXXX9+MvtMJGpC3/2vr+OZjwrdnxE+eGTSJm9exOJNbsXYqccUcP+5bnb4tGN7sOz20+iRZwvUGUaysUSRZmiohogK2TmdCEsAIiECkXpqySab8CF1ZldVlDYmCqkppUI7ky/VfP3FGnLrm2oUG3QgE2QL/O+d7sD7D7iJcJEQaJjhlcv5UMfwg/qf8+TcKZw+ug83PbGUbRUlEIpQXh2ie9RQ1Jr6MDlZAepCEeqa7e0dCkfYsscliu37qsnJCvD0FcfTNbcpRHt3S/7WqYZhWKJIP0I11JJNZxEiBBFv1FOdZAM1BELVrgYQY/e36sqmDuxAbdOopvLdW+gWakoUGyNHMyGwBb78iK2RvgzIqSFn9QsQCEAgi+KsvrwfngzA/jqXoIrKm4as7qmoaZYoIhzROZviyloeXrSJ1z7ZiSpEVAmFlbpwhJ55ORRX1nHpCYMOSBKGYaQOeyemGYFQDbWSQ2do7MwWDVNJVwqoYNK6+2DdfdR+/zlyx36vxdeoL3O7xu3TPI6o2UG+uOasSEkhXSNNieLd/PN4r2wi50w5hhuWHcmJg/rw7FVN+1bf8tTHFJXXkF9aw9NLtnHG6L7sKq1m6uAClm3bx+Y9lXTJzaIuFKE2FKa4spYju+Rw66zh/HndV4hAQAQBRIQRffO5ddZwakJhRvbthmEY/sASRZoh4VrqvU1EwxJENExAQ1RmFbAydCST2eDO++MP2b9oANK1J9l9RpDdb5ybr9B7NPVfbQTg08BwZtSvanztwXsXk6vV/Cp0Hi+GTmXkwBEsLulJWXEBYfaxs/TAZTdq6sP0zMvl5tOHc93zqxh15zsATDvWJYprn19Fc6YeU8DcqQOZO3VgQsrHMIz4Y4nCJ3xeVMHQ3nkUV9bRMy/ngE7gaALhGurEJYqQZHN82SLKtCs7coew9ZRHWbz8LXZnD2LC3jcoKC2nV1kZw3b/iYK/P9v4GmOAndqTPQWTYN8q6jXII6ELmNV1C9tru/BuYAZFFHDN8F588Nle/rbVjXzaunc/Vy9cwdEFnTn6yC5s3lPJKSN7M3vcUVw+fRALPypk1FHd+MEJgxjSK4/ymnpyswLkZAXIzQqSEwwwpr/VFAwj3bBE0Qofbipi/a5Srjx5WOPY/USx5vMv2PfM5bx41BWM3f0atZN+xEXnzuE/n1pAGLjuqmsazw2E66gTN/Kn/OS7WbNiIUft30Btr/GcP3MSzJyEqrK77Hx2l9Wwu6ya5Xsr2bmzkMjutXSr2Ew/iikumEzvKRcy8a3p5EqIscOHcv4G1yQ17zsjePnEwQQEyqvr2V5SxZlj+vLqyh0Ufl3F0i3FVNW5zujZY48C4O6zx3DrrBGNfRLnTIyaI2EYRlojrS2g5idE5EzgUSAIPKmqMffOnjJliq5YsSLWKa2ydPNeFq/bzqRVP+MEWcdj/R4hb9AkgsFA4+zfrIAQbPjpHQ+KdywY9Vyg5Wv2lFfx6drVjBk3kR55nVj28sPcWPPbRof1Oph1Z/6RCxa5juJ7h7xAt8J3kYHTOWnHk+RHShl2R/v+vpr6MDtKqumVn0v3ztmEI0pdKEJuVoANX5Xz6c5yThvVO+YwVFVl3/46SqrqGNIrr9Xaj2EY6YOIrFTVKS0+5/dEISJB4DNgFrADWA5cpKrrW7umvYmiqKSczb+azUmy9qDnIiqECRAhQNi7RRBCBA88pk33m8478GcPyhkcKKIw0psS8pgQaNoJriK3D/m1RVRpLl3kwBVWazWb/eSyp9NgRv5sSZv/PsMwjNaIlSjSoelpKrBZVbcCiMhLwByg1UTRLlTZ+PKdzJS1VIy+mPwR34b+k2D96xCuRyIhgpEwEgkTiIQJht0WpDmRMHrALYRGwm75bw1DJOIeR8KohiASBulL/ch/pNtnfyE/HKK06zi6zrie7Kws8nuNoGb1K9Qse4bqXiOBAFL5FUeedhOVi39NsHQbPcedG9c/3TAMIxbpUKP4PnCmql7tPb4UmKaqNzQ77xrgGoCBAwdOLiwsbNPvKS7aQdYT0yjsPpXxN7928BahhmEYGUy61yha+sQ+KLup6gJgAbimp7b+kp59BrDr0ncZM2iEJQnDMIwo0iFR7IADduccAOxKxC/qN2RMIl7WMAwjrUmHHe6WA8NE5BgRyQHmAm+k2MkwDOOwwfc1ClUNicgNwCLc8NjfqeqnKdYyDMM4bPB9ogBQ1beBt1PtYRiGcTiSDk1PhmEYRgqxRGEYhmHExBKFYRiGERNLFIZhGEZMfD8zuz2IyF6gbVOzm+gJFMdRJ16YV9swr7ZhXm0jE70GqWqvlp7IyETREURkRWvT2FOJebUN82ob5tU2Djcva3oyDMMwYmKJwjAMw4iJJYqDWZBqgVYwr7ZhXm3DvNrGYeVlfRSGYRhGTKxGYRiGYcTEEoVhGIYRE0sUhmEYRkwOu0Qh4s/t60RkVKodWkJE/llEzvDu+6bsRKR71H3feIH/fMDiq61YfB3IYZMoRGSOiCwExqfapTki8mvgbREZnGKVRkTkDBFZBNwGXAagPhj5ICKnishq4AkRuR384QX+jTGLr0PH4qtl0mI/ivYiIqKqKiKnAPcA9cB0ESlU1ZJUe0UdKgBKgNNF5FlVrU2VF5AN3AnMBB4AcoDjRSQbCKXyTSMiecDtuP/lMmChiHRR1TtS6OS7GLP4arefxVcrZGyNotmb5QvgO8A8YBpwnB+8RCToHf4b8ARwCTAslV6qWge8rqozvA2jSoC5qlqf4jdxAMgDtgOfqOp24GrgQhEZmSIn38WYxVe7/Sy+YpCRicLbOvU1EblFRPqq6jZV3a2qi4EiYKaI9E+h180i0k9Vw94+4GcCfwLeA+aKyHki0uLiXAn2ukVEjlLV5d7xbFX9ANgqImclyyfK6zoR+QcAVY0ACvTCvaFR1a24cpvvnZ+0tls/xpjFV5u9LL4OkYxLFCJyLnA58Bgu694hIhOiTnkeGI7LytHXJTQImnmNB24XkcneN6wVqloMfA78GLgPSEpQtlBePxeRhjbQkIgU4FbiDSfDx3PKF5Hf4pooFopIFoCqFgHrgZujTv8pME1ExiTrG6kfY8ziq01OFl9tJOMSBa7wnlDV94C7cVW2Hzc8qaprgOXAWHEdV7d5xxMdBC15Xes9910R+SuuY++/cU0F5Qn2ieV1E7gyUdV9QGfgFGisoicUVa0APlDVvsCbwG+inp4PTBCR2SKS630TfBPX9p0s/BhjFl+HiMVX20nbRNE8e0Y93gpcDKCqhcBbQFcROTvq9Bdx7Y9/wK3fHrds3EavI0RkOvAosFRVJ6jqZUBfIK7DGTtYXs8BU0Wkk/fGSYbXG97Pm4GLRGSY51gJPAzMxX1rng/MAHbH0+sbXFMaY21wSlp8tdErafEVwyvl8eXXz7CWSNtEQbMMH5VNXwWqRGSO93g38D4wWhx5uDfOWuA4VZ3X7Ppkei0GTgaeV9Xboi47V1U/iZNPe7zexysv71hn4CUS0zzQopeq7heRgKp+BTwOPBl1zkvA/bjmk17AWV6zQdyRpg5h38RYG5ySGV/tKivvWCLjq0Uvn8TXAZ+/fomvFlHVtLoB04FXvIIaDQS941neTwGuBN6hadHDecDdDecBvX3kdZd3PwgE/ObV4JZEr8ZyiC4P4Evvmr7AtAb3BMbY/BaON5RP0mOsA07JiK92eyU4vlr08kF8TcXVoh4GxkX5BKMck/4ZFuuWVjUKEekN/AfwNvA1rq3zhwCqGvJO6wwswmXhBSLSD5iIG3+MqoZUdY+PvELeeWGNf7NOh70a3JLoFVbViPetqXvUZQ8BS4C/AJ28c+P+DUpELgcW4joQL/CONXR2Nvy+pMZYB50SGV8d9mpwS6ZXquJLRAIicheu9vJn3Af+9XgT6KLKIemfYd9IMrNSHDLxLOBF735X3LjiN4GR3rF7vQKeiJtkdC+uyvY4CfjWYl4J87oH921qhvf4LGAj8K9AdhJibABwBvBl1PGGb3t3J7vM/OiU5l53pTC+rgYmefd74BLGlKjnU1Jm3+idql98iIV6EfAvwNne4564IX5DvccF3j/9IaAL8AIwpNlrdDGv9PbCNU0dneAYm+M9DtDULPchcE/Uub2TUWZ+dMpkryTE1/yo2O/s+eV6j18GvpfsMmvz35FqgVYKV4B/Aj7BtdVtwmXiTsAvgMeiAuJbwH8BBVHXx70t1rxS4pXIb50tuV0J5EedMwYoA/q0cH0i2vt955ThXsmMr8+8n3lR52QDS4HhySqz9t582UehrqSmAw+q6tO4drxvA6fh2rWHisgsdW2uXwN9gFpw7YCaoCF25pV0r4RNwmrF7XRgRsNoHFX9FNfh/qDndFbU9XEvMz86ZbhXMuPrOs/t5KjRXqOAIlX9TNwkwKmeoySqzNqLbxKFiFwmIjPFzdQE2AD0F5EsVf0/YB1wArAXVz37pYgMxX3oNCw2FvegNK/M8DpEtzW4ms2AhmtU9WrgchEpAcZLnCeE+dHJvBLuNsh7vgduGOwVuJrFOC9JxLUTPR6kdPVYL7P2xX1gRIAtuIkl1+IW5xoHDMV1NL0E/BLooarPicjRuOn1I4EfqWqpeZlXB93+0OAGbBeRY3EdnH8FrlfVdZnqZF5JdTsS2IbrRL8IV4u+RN2Ma3+SqjYvmkYgDAee8+5n4Xr3F+K+Wf4OuBTo7j3/e+C+qNfIMS/zSoDbfO9+d2BqpjuZV9Ld7vXunwRcmAi3eN+SXqPwxjPPB4Ii8jbQDW9GpqqGxK2auBs3EuEF4BxcNfIBXMZe2vBa6hY8My/zirfbx965Zbh9CTLSybxS5vaRd+6SeHolkqT2UYjITGAlruq1maaNOE5p6MhR1zY9H3hIXbveAuBbIvKxd9375mVe6eTmRyfzyjy3hJLM6gtuca1Lox4/jlvh8gpgpXcsgGvzewUY7B07AuhvXuaVjm5+dDKvzHNL5C3Zo55WAi9L0yJdS4CBqvp7XDXuRnXZeABuW8RtAKpaqqo7zcu80tTNj07mlXluCSOpiUJVq1S1VpvGL8/CDZMENxlllIi8iVtCd5V5mVcmuPnRybwyzy2RpGR4rJeNFTfBqmFd+ArcxuZjgS9SkX3NKzO8/OrmRyfzyjy3RJCqCXcR3NCxYuA4LwP/Aoio6ocpLGDzygwvv7r50cm8Ms8t/sS70+NQb7jZuRHcol1XpcrDvDLTy69ufnQyr8xzi/etYVOMpCMiA3ATUf5dVWtTItEC5tU2/OoF/nTzoxOYV3vws1u8SVmiMAzDMNID3ywKaBiGYfgTSxSGYRhGTCxRGIZhGDGxRGEYhmHExBKFYXQQETlCRK7z7vcTkVdT7WQY8cRGPRlGBxGRwcCbqjo2xSqGkRBSusOdYWQIDwJDRGQ18DkwSlXHitvi8hwgiFvW4d+AHNzY+1pgtqruE5EhwG+AXkAVbqe/jcn/MwyjZazpyTA6zk+BLao6AZjX7LmxwMXAVOA+oEpVJ+I2r7nMO2cBcKOqTgZ+glu62jB8g9UoDCOxvKeqFUCFiJQB/+MdX4tbIygPOBF4RUQarslNvqZhtI4lCsNILNFLO0SiHkdw778AUOrVRgzDl1jTk2F0nAogvz0Xqmo58IWInA8gjvHxlDOMjmKJwjA6iKp+DSwRkXXAI+14iUuAq0Tk78CnwJx4+hlGR7HhsYZhGEZMrEZhGIZhxMQShWEYhhETSxSGYRhGTCxRGIZhGDGxRGEYhmHExBKFYRiGERNLFIZhGEZMLFEYhmEYMfl/Uvaegio/PTgAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -157,7 +519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_fdil183e/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0371048,36.562,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_fdil183e/Salmon-River-Near-Prince-George_meteo_daily.nc,0.0198906,35.5431,\\n']\n"
+      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.117301,37.9493,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0559845,36.8933,\\n']\n"
      ]
     }
    ],
diff --git a/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb b/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb
new file mode 100644
index 00000000..45586469
--- /dev/null
+++ b/docs/source/notebooks/Running_models_with_multiple_timeseries_files.ipynb
@@ -0,0 +1,628 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running a hydrological model with multiple timeseries files\n",
+    "\n",
+    "In this notebook, we show how to provide the hydrological model with multiple timeseries files. For example, one file could contain meteorological data and the other contain streamflow data, or all variables could be separate (i.e. precip, temperature, streamflow, etc.). The following instructions should make it easier to understand how to do this. for this example, we actually start from a netcdf file containing all information, and from there divide it into multiple time series netcdf files. We then use the split files to drive the model. In most user cases, different files will be provided directly by the user so no need to pre-split your files!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Cookie-cutter template necessary to provide the tools, packages and paths for the project. All notebooks\n",
+    "# need this template (or a slightly adjusted one depending on the required packages)\n",
+    "from birdy import WPSClient\n",
+    "\n",
+    "from example_data import TESTDATA\n",
+    "import datetime as dt\n",
+    "from pathlib import Path\n",
+    "from urllib.request import urlretrieve\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import os\n",
+    "import json\n",
+    "import netCDF4 as nc\n",
+    "from zipfile import ZipFile\n",
+    "import glob\n",
+    "import tempfile\n",
+    "\n",
+    "# Set environment variable RAVEN_WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
+    "url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
+    "wps = WPSClient(url)\n",
+    "\n",
+    "# DATA MAIN SOURCE - DAP link to CANOPEX dataset\n",
+    "CANOPEX_DAP = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc'\n",
+    "CANOPEX_URL = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/birdhouse/ets/Watersheds_5797_cfcompliant.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</symbol>\n",
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+       "<title>Show/Hide attributes</title>\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
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+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
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+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       ".xr-section-summary-in:checked + label > span {\n",
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+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
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+       "  content: ')';\n",
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+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
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+       "\n",
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+       "  font-weight: bold;\n",
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+       "\n",
+       ".xr-var-list,\n",
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+       "  display: contents;\n",
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+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
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+       "\n",
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
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+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d1269d0a-6c43-4300-93d8-c1b158cf0133' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1269d0a-6c43-4300-93d8-c1b158cf0133' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 22280</li><li><span class='xr-has-index'>watershed</span>: 5797</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b68460ef-d13e-4371-ba35-a888b224d60c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b68460ef-d13e-4371-ba35-a888b224d60c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-01 ... 2010-12-31</div><input id='attrs-4602fb6b-7d16-4ea1-ae64-dc798d13ff1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4602fb6b-7d16-4ea1-ae64-dc798d13ff1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-937f71cd-dfec-42a8-88e2-1d84cf322b29' class='xr-var-data-in' type='checkbox'><label for='data-937f71cd-dfec-42a8-88e2-1d84cf322b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>_ChunkSizes :</span></dt><dd>22280</dd></dl></div><pre class='xr-var-data'>array([&#x27;1950-01-01T00:00:00.000000000&#x27;, &#x27;1950-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1950-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>watershed</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;St. John River at Ninemile Bridge, Maine&#x27; ... b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;</div><input id='attrs-7328e3bc-9cb8-42b6-bf6c-e98699305415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7328e3bc-9cb8-42b6-bf6c-e98699305415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1c502af-bdc1-4a94-8af8-5166a6ae2059' class='xr-var-data-in' type='checkbox'><label for='data-d1c502af-bdc1-4a94-8af8-5166a6ae2059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name of watershed</dd><dt><span>encoding :</span></dt><dd>utf-8</dd><dt><span>standard_name :</span></dt><dd>watershed_name</dd></dl></div><pre class='xr-var-data'>array([b&#x27;St. John River at Ninemile Bridge, Maine&#x27;,\n",
+       "       b&#x27;St. John River at Dickey, Maine&#x27;, b&#x27;Fish River near Fort Kent, Maine&#x27;,\n",
+       "       ..., b&#x27;MIDDLE THAMES RIVER AT THAMESFORD&#x27;,\n",
+       "       b&#x27;BIG OTTER CREEK AT TILLSONBURG&#x27;, b&#x27;KETTLE CREEK AT ST. THOMAS&#x27;],\n",
+       "      dtype=&#x27;|S64&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-19bfc366-3227-4bf0-8451-2648fb83222c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19bfc366-3227-4bf0-8451-2648fb83222c' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>drainage_area</span></div><div class='xr-var-dims'>(watershed)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c4acf3a-6bfe-4f56-9c40-5f69d9ba42de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4acf3a-6bfe-4f56-9c40-5f69d9ba42de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f30cc0-7736-467a-b117-2b3c641bb482' class='xr-var-data-in' type='checkbox'><label for='data-71f30cc0-7736-467a-b117-2b3c641bb482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>drainage_area_at_river_stretch_outlet</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>long_name :</span></dt><dd>drainage_area</dd><dt><span>units :</span></dt><dd>km2</dd><dt><span>_ChunkSizes :</span></dt><dd>5797</dd></dl></div><pre class='xr-var-data'>array([3471.689421, 6938.20108 , 2260.093113, ...,  306.      ,  354.1     ,\n",
+       "        330.88    ])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6143d64b-63d4-476a-a565-c4b747cbe794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6143d64b-63d4-476a-a565-c4b747cbe794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af055a53-c32e-4ae6-9d5c-0c0ae1ae384b' class='xr-var-data-in' type='checkbox'><label for='data-af055a53-c32e-4ae6-9d5c-0c0ae1ae384b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Precipitation</dd><dt><span>standard_name :</span></dt><dd>precipitation_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71697964-f834-4665-a163-ffce8631386a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71697964-f834-4665-a163-ffce8631386a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66eed30a-28d4-469d-a469-b117c8131e76' class='xr-var-data-in' type='checkbox'><label for='data-66eed30a-28d4-469d-a469-b117c8131e76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmin</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f7a6cc1-466f-4882-9d0e-857a7821f922' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f7a6cc1-466f-4882-9d0e-857a7821f922' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e7abeb-0533-4509-a6fb-a8309fad5162' class='xr-var-data-in' type='checkbox'><label for='data-10e7abeb-0533-4509-a6fb-a8309fad5162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Daily Minimum Near-Surface Air Temperature\t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>(watershed, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d76e0222-8665-425e-a2bd-b57f5348d3d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d76e0222-8665-425e-a2bd-b57f5348d3d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78fb298c-3c6f-4728-8ca5-8be4d2043c3f' class='xr-var-data-in' type='checkbox'><label for='data-78fb298c-3c6f-4728-8ca5-8be4d2043c3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>discharge</dd><dt><span>standard_name :</span></dt><dd>water_volume_transport_in_river_channel</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>_ChunkSizes :</span></dt><dd>[ 363 1393]</dd></dl></div><pre class='xr-var-data'>[129157160 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-31680162-37f6-4e7c-aa12-92af57bac815' class='xr-section-summary-in' type='checkbox'  ><label for='section-31680162-37f6-4e7c-aa12-92af57bac815' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Hydrometeorological data for lumped hydrological modelling of 5797 catchments in North America</dd><dt><span>institute_id :</span></dt><dd>ETS</dd><dt><span>contact :</span></dt><dd>Richard Arsenault: richard.arsenault@etsmtl.ca</dd><dt><span>date_created :</span></dt><dd>2020-08-01</dd><dt><span>source :</span></dt><dd>Hydrometric data from USGS National Water Information Service and ECCC Water Survey Canada. Meteorological data from ECCC stations, NRCan 10km gridded interpolated dataset and Livneh 2014 database. Catchment areas from ECCC HYDAT, HydroSheds and USGS.</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>cdm_data_type :</span></dt><dd>station</dd><dt><span>license :</span></dt><dd>ODC-BY</dd><dt><span>keywords :</span></dt><dd>hydrology, North America, streamflow, hydrometeorological, PAVICS, PAVICS-Hydro, modelling</dd><dt><span>activity :</span></dt><dd>PAVICS_Hydro</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>summary :</span></dt><dd>Hydrometeorological database for the PAVICS-Hydro platform, including precipitation, temperature, discharge and catchment area to drive the RAVEN hydrological modelling framework. Provided by the HC3 Laboratory at École de technologie supérieure, Montréal, Canada.</dd><dt><span>institution :</span></dt><dd>ETS (École de technologie supérieure)</dd><dt><span>DODS.strlen :</span></dt><dd>72</dd><dt><span>DODS.dimName :</span></dt><dd>string72</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (time: 22280, watershed: 5797)\n",
+       "Coordinates:\n",
+       "  * time           (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2010-12-31\n",
+       "  * watershed      (watershed) |S64 b'St. John River at Ninemile Bridge, Maine' ... b'KETTLE CREEK AT ST. THOMAS'\n",
+       "Data variables:\n",
+       "    drainage_area  (watershed) float64 ...\n",
+       "    pr             (watershed, time) float64 ...\n",
+       "    tasmax         (watershed, time) float64 ...\n",
+       "    tasmin         (watershed, time) float64 ...\n",
+       "    discharge      (watershed, time) float64 ...\n",
+       "Attributes:\n",
+       "    title:          Hydrometeorological data for lumped hydrological modellin...\n",
+       "    institute_id:   ETS\n",
+       "    contact:        Richard Arsenault: richard.arsenault@etsmtl.ca\n",
+       "    date_created:   2020-08-01\n",
+       "    source:         Hydrometric data from USGS National Water Information Ser...\n",
+       "    featureType:    timeSeries\n",
+       "    cdm_data_type:  station\n",
+       "    license:        ODC-BY\n",
+       "    keywords:       hydrology, North America, streamflow, hydrometeorological...\n",
+       "    activity:       PAVICS_Hydro\n",
+       "    Conventions:    CF-1.6, ACDD-1.3\n",
+       "    summary:        Hydrometeorological database for the PAVICS-Hydro platfor...\n",
+       "    institution:    ETS (École de technologie supérieure)\n",
+       "    DODS.strlen:    72\n",
+       "    DODS.dimName:   string72"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Open Canopex dataset using DAP link\n",
+    "ds = xr.open_dataset(CANOPEX_DAP)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup some parameters to run the models. See the \"canopex.ipynb\" notebook for more detailed information\n",
+    "# on these parameters. The data we use comes from the extended CANOPEX database.\n",
+    "start = dt.datetime(1998, 1, 1)\n",
+    "stop = dt.datetime(2010, 12, 31)\n",
+    "watershedID = 5600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basin name:  b'WHITEMOUTH RIVER NEAR WHITEMOUTH'\n",
+      "Latitude:  49.51119663557124  °N\n",
+      "Area:  3650.476384548832  km^2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# With this info, we can gather some properties from the CANOPEX database:\n",
+    "tmp=pd.read_csv(TESTDATA['canopex_attributes'])\n",
+    "basin_area=tmp['area'][watershedID]\n",
+    "basin_latitude = tmp['latitude'][watershedID]\n",
+    "basin_longitude = tmp['longitude'][watershedID]\n",
+    "basin_elevation = tmp['elevation'][watershedID]\n",
+    "basin_name=ds.watershed[watershedID].data\n",
+    "\n",
+    "print(\"Basin name: \", basin_name)\n",
+    "print(\"Latitude: \", basin_latitude, \" °N\")\n",
+    "print(\"Area: \", basin_area, \" km^2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SECTION TO SEPARATE DISCHARGE AND MET DATA TO RECOMBINE LATER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the 2 new files, i.e. the meteorological data and the streamflow data\n",
+    "filepathMet = os.getcwd()+\"/CANOPEX_Met.nc\"\n",
+    "filepathQobs=os.getcwd()+\"/CANOPEX_Qobs.nc\"\n",
+    "\n",
+    "# Do the extraction for the selected catchment\n",
+    "newBasin=ds.isel(watershed=watershedID)\n",
+    "\n",
+    "# Generate the streamflow time-series netcdf\n",
+    "Qobsfile = newBasin['discharge']\n",
+    "Qobsfile.to_netcdf(filepathQobs)\n",
+    "\n",
+    "# Generate the meteorological time-series netcdf\n",
+    "newBasin=newBasin[['drainage_area','pr','tasmax','tasmin']]\n",
+    "newBasin.to_netcdf(filepathMet)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here is where we run the model with multiple input time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_fufyza3l/CANOPEX_Qobs.nc,-0.12679,30.5267,\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The model parameters. We are forcing values here just so the model runs, the models are probably very bad choices!\n",
+    "\n",
+    "params = '9.5019, 0.2774, 6.3942, 0.6884, 1.2875, 5.4134, 2.3641, 0.0973, 0.0464, 0.1998, 0.0222, -1.0919, ' \\\n",
+    "         '2.6851, 0.3740, 1.0000, 0.4739, 0.0114, 0.0243, 0.0069, 310.7211, 916.1947'\n",
+    "\n",
+    "# Model configuration parameters. Please see \"canopex.ipynb\" for more details. \n",
+    "# This remains unchanged with multiple timeseries!\n",
+    "config = dict(\n",
+    "    start_date=start,\n",
+    "    end_date=stop,\n",
+    "    area=basin_area,\n",
+    "    elevation=basin_elevation,\n",
+    "    latitude=basin_latitude,\n",
+    "    longitude=basin_longitude,\n",
+    "    run_name='test_hmets_NRCAN',\n",
+    "    rain_snow_fraction='RAINSNOW_DINGMAN', \n",
+    "    nc_spec=json.dumps({'tasmax': {'linear_transform': (1.0, -273.15)},'tasmin': {'linear_transform': (1.0, -273.15)},'pr': {'linear_transform': (86400.0, 0.0)}},),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Here is where we must tell the model that there are multiple input files. The idea is to combine them into a list of strings,\n",
+    "# with each string representing a path to a netcdf file. So we could do something like this:\n",
+    "ts_combined=[str(filepathMet),str(filepathQobs)]\n",
+    "resp = wps.raven_hmets(ts=ts_combined, params=params, **config)\n",
+    "\n",
+    "# And get the response\n",
+    "# With `asobj` set to False, only the reference to the output is returned in the response. \n",
+    "# Setting `asobj` to True will retrieve the actual files and copy the locally. \n",
+    "[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=True)\n",
+    "print(diagnostics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You can even invert the order of the netcdf files. Raven will detect which files contain which variables, so the order is not important!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_6r8q6yk0/CANOPEX_Qobs.nc,-0.12679,30.5267,\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test with reversed timeseries files:\n",
+    "ts_combined=[str(filepathQobs),str(filepathMet)]\n",
+    "resp = wps.raven_hmets(ts=ts_combined, params=params, **config)\n",
+    "\n",
+    "# And get the response\n",
+    "# With `asobj` set to False, only the reference to the output is returned in the response. \n",
+    "# Setting `asobj` to True will retrieve the actual files and copy the locally. \n",
+    "[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=True)\n",
+    "print(diagnostics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### As you can see, the NSE values and RMSE values are identical. You can pass as many NetCDF files as you have variables in any order and it will still work."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/source/notebooks/canopex.ipynb b/docs/source/notebooks/canopex.ipynb
index 6911caa8..10a1e4ed 100644
--- a/docs/source/notebooks/canopex.ipynb
+++ b/docs/source/notebooks/canopex.ipynb
@@ -4,17 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Running HMETS using NRCAN forcing data\n",
+    "# Running HMETS on the 5797 basins of the extended CANOPEX dataset\n",
     "\n",
-    "Here we use birdy's WPS client to launch the HMETS hydrological model on the server and analyze the output. We also prepare NRCAN daily data for Canadian catchments."
+    "Here we use birdy's WPS client to launch the HMETS hydrological model on the server and analyze the output. We also prepare and gather data directly from the CANOPEX dataset made available freely for all users."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
+    "#Cookie-cutter template necessary to provide the tools, packages and paths for the project. All notebooks\n",
+    "# need this template (or a slightly adjusted one depending on the required packages)\n",
     "from birdy import WPSClient\n",
     "\n",
     "from example_data import TESTDATA\n",
@@ -23,6 +25,7 @@
     "from urllib.request import urlretrieve\n",
     "import xarray as xr\n",
     "import numpy as np\n",
+    "import pandas as pd\n",
     "from matplotlib import pyplot as plt\n",
     "import os\n",
     "import json\n",
@@ -33,24 +36,56 @@
     "\n",
     "# Set environment variable RAVEN_WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
     "url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
-    "wps = WPSClient(url)"
+    "wps = WPSClient(url)\n",
+    "\n",
+    "# DATA MAIN SOURCE - DAP link to CANOPEX dataset\n",
+    "CANOPEX_DAP = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc'\n",
+    "CANOPEX_URL = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/birdhouse/ets/Watersheds_5797_cfcompliant.nc'\n",
+    "ds = xr.open_dataset(CANOPEX_DAP)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# SETUP THE RUN PARAMETERS, for now only the start and end years of the simulation, the rest is hard-coded to \n",
-    "# the Salmon-river example\n",
-    "\n",
-    "start = dt.datetime(2007, 1, 1)\n",
-    "stop = dt.datetime(2008, 1, 1)\n",
+    "# SETUP THE RUN PARAMETERS, for now only the start and end years of the simulation and choosing the watershed\n",
+    "start = dt.datetime(1998, 1, 1)\n",
+    "stop = dt.datetime(2010, 12, 31)\n",
     "\n",
+    "# For now, we have no mechanism to link a location (lon, lat) to a given watershed. Let's pick one at random\n",
+    "# from the dataset: \n",
+    "watershedID = 5600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basin name:  b'WHITEMOUTH RIVER NEAR WHITEMOUTH'\n",
+      "Latitude:  49.51119663557124  °N\n",
+      "Area:  3650.476384548832  km^2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# With this info, we can gather some properties from the CANOPEX database:\n",
+    "tmp=pd.read_csv(TESTDATA['canopex_attributes'])\n",
+    "basin_area=tmp['area'][watershedID]\n",
+    "basin_latitude = tmp['latitude'][watershedID]\n",
+    "basin_longitude = tmp['longitude'][watershedID]\n",
+    "basin_elevation = tmp['elevation'][watershedID]\n",
+    "basin_name=ds.watershed[watershedID].data\n",
     "\n",
-    "# The shapefile of the catchment to model using ERA5 data. All files (.shp, .shx, etc.) must be zipped into one file.\n",
-    "vec = TESTDATA[\"watershed_vector\"]"
+    "print(\"Basin name: \", basin_name)\n",
+    "print(\"Latitude: \", basin_latitude, \" °N\")\n",
+    "print(\"Area: \", basin_area, \" km^2\")"
    ]
   },
   {
@@ -59,92 +94,150 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# DATA MAIN SOURCE - DAP link\n",
-    "CANOPEX_DAP = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ets/Watersheds_5797_cfcompliant.nc'\n",
-    "CANOPEX_URL = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/fileServer/birdhouse/ets/Watersheds_5797_cfcompliant.nc'\n",
-    "ds = xr.open_dataset(CANOPEX_DAP)\n"
+    "# Ideally we would be able to pass the DAP link directly to Raven, but there are still some issues \n",
+    "# to fix to be able to do that. For now, we'll download the series at the point of interest. \n",
+    "path = Path(tempfile.mkdtemp()) /  \"ts.nc\"\n",
+    "ts = ds.isel(watershed=watershedID).sel(time=slice(start, stop))\n",
+    "ts.to_netcdf(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now, we might have the model and data, but we don't have model parameters! We need to calibrate. This next snippet shows how to do so."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The model parameters bounds can either be set independently or we can use the defaults. We will use the defaults,\n",
+    "# but if you wanted to use different values you could pass them as a comma delimited string, as so:\n",
+    "lowerBounds = '0.3, 0.01, 0.5, 0.15, 0.0, 0.0, -2.0, 0.01, 0.0, 0.01, 0.005, -5.0, ' \\\n",
+    "                   '0.0, 0.0, 0.0, 0.0, 0.00001, 0.0, 0.00001, 0.0, 0.0'\n",
+    "upperBounds = '20.0, 5.0, 13.0, 1.5, 20.0, 20.0, 3.0, 0.2, 0.1, 0.3, 0.1, 2.0, 5.0, ' \\\n",
+    "                    '1.0, 3.0, 1.0, 0.02, 0.1, 0.01, 0.5, 2.0'\n",
+    "\n",
+    "# ... and add them to the config dictionary below by uncommenting the two lines: \n",
+    "\n",
+    "# We'll definitely want to adjust the optimization algorithm, random seed and number of model evaluations\n",
+    "algorithm='DDS'\n",
+    "max_iterations=50 # This is to keep computing time fast for the demo, you should definitely increase this to a few hundreds or thousands.\n",
+    "random_seed=1, # This is to allow reproduction. If it is left uncommented in the configuration below, the results will always be the same. Therefore it should always be commented except for debugging cases\n",
+    "\n",
+    "# We will also calibrate on only a subset of the years for now to keep the computations faster in this notebook.\n",
+    "start_calib=dt.datetime(1998,1,1)\n",
+    "end_calib=dt.datetime(1999,12,31)\n",
+    "\n",
+    "# Model configuration parameters\n",
+    "config = dict(\n",
+    "    start_date=start_calib,\n",
+    "    end_date=end_calib,\n",
+    "    area=basin_area,\n",
+    "    elevation=basin_elevation,\n",
+    "    # upperBounds=upperBounds,\n",
+    "    # lowerBounds=lowerBounds,\n",
+    "    random_seed=random_seed,\n",
+    "    algorithm=algorithm,\n",
+    "    max_iterations=max_iterations,\n",
+    "    latitude=basin_latitude,\n",
+    "    longitude=basin_longitude,\n",
+    "    suppress_output=True, # This will make raven much faster as it will not write the model outputs to file after each evaluation.\n",
+    "    run_name='test_hmets_NRCAN',\n",
+    "    rain_snow_fraction='RAINSNOW_DINGMAN', \n",
+    "    nc_spec=json.dumps({'tasmax': {'linear_transform': (1.0, -273.15)},'tasmin': {'linear_transform': (1.0, -273.15)},'pr': {'linear_transform': (86400.0, 0.0)}},), # This allows transforming and scaling units for Raven\n",
+    ")\n",
+    "\n",
+    "# Let's call the model with the timeseries (meteorological and observed streamflow for calibration!),\n",
+    "# model parameters and other configuration parameters\n",
+    "resp = wps.ostrich_hmets(ts=str(path), **config)\n",
+    "\n",
+    "# And get the response\n",
+    "# With `asobj` set to False, only the reference to the output is returned in the response. \n",
+    "# Setting `asobj` to True will retrieve the actual files and copy the locally. \n",
+    "[calibration, hydrograph, storage, solution, diagnostics, calibparams, rv] = resp.get(asobj=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "b'WHITEMOUTH RIVER NEAR WHITEMOUTH'\n",
-      "Area:  3750.0\n"
+      "6.273179, 0.181421, 9.809273, 1.318625, 0.7437743, 19.7939, 2.116731, 0.06310419, 0.0610826, 0.2518587, 0.09584428, -2.260224, 4.879866, 0.8887099, 0.9076767, 0.8126115, 0.01505687, 0.01624556, 8.24966e-05, 78.20397, 760.9848999999999\n"
      ]
     }
    ],
    "source": [
-    "# Specify the watershed outlet (here in this example this is the Salmon River)\n",
-    "lon, lat = -123.3659, 54.4848\n",
-    "\n",
-    "# For now, we have no mechanism to link a location (lon, lat) to a given watershed. Let's pick one at random: \n",
-    "wid = 5600\n",
-    "print(ds.watershed[wid].data)\n",
-    "\n",
-    "# With a watershed contour, we'll be able to get the elevation. \n",
-    "\n",
-    "area = ds.drainage_area.isel(watershed=wid).data\n",
-    "print(\"Area: \", area)\n",
-    "elevation = 350  # Random value"
+    "# We can see the calibrated parameters:\n",
+    "print(calibparams)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_4kf3e_v5/ts.nc,0.402713,19.5566,\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# Ideally we would be able to pass the DAP link directly to Raven, but there are still some issues \n",
-    "# to fix to be able to do that. For now, we'll download the series at the point of interest. \n",
-    "path = Path(tempfile.mkdtemp()) /  \"ts.nc\"\n",
-    "ts = ds.isel(watershed=wid).sel(time=slice(start, stop))\n",
-    "ts.to_netcdf(path)\n",
-    "\n",
-    "# Add precision on time format for Raven\n",
-    "D = nc.Dataset(path, mode=\"a\")\n",
-    "D.variables[\"time\"].units += \" 00:00:00\"\n",
-    "D.close()"
+    "# And also the NSE value:\n",
+    "print(diagnostics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### At this stage, we have calibrated the model on the observations for the desired dates. Now, let's run the model on a longer time period and look at the hydrograph"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# THIS FAILS: NEED STATION IDX IN NETCDF FILE?! \n",
-    "import json\n",
     "# The model parameters. Can either be a string of comma separated values, a list, an array or a named tuple. \n",
-    "params = '9.5019, 0.2774, 6.3942, 0.6884, 1.2875, 5.4134, 2.3641, 0.0973, 0.0464, 0.1998, 0.0222, -1.0919, ' \\\n",
-    "            '     2.6851, 0.3740, 1.0000, 0.4739, 0.0114, 0.0243, 0.0069, 310.7211, 916.1947'\n",
+    "# Here we will use the calibrated parameters, but we could overwrite them with our own parameters as so:\n",
+    "\n",
+    "# calibparams = '9.5019, 0.2774, 6.3942, 0.6884, 1.2875, 5.4134, 2.3641, 0.0973, 0.0464, 0.1998, 0.0222, -1.0919, ' \\\n",
+    "#               '     2.6851, 0.3740, 1.0000, 0.4739, 0.0114, 0.0243, 0.0069, 310.7211, 916.1947'\n",
     "\n",
     "# Model configuration parameters\n",
     "config = dict(\n",
     "    start_date=start,\n",
     "    end_date=stop,\n",
-    "    area=area,\n",
-    "    elevation=elevation,\n",
-    "    latitude=lat,\n",
-    "    longitude=lon,\n",
+    "    area=basin_area,\n",
+    "    elevation=basin_elevation,\n",
+    "    latitude=basin_latitude,\n",
+    "    longitude=basin_longitude,\n",
     "    run_name='test_hmets_NRCAN',\n",
     "    rain_snow_fraction='RAINSNOW_DINGMAN', \n",
     "    nc_spec=json.dumps({'tasmax': {'linear_transform': (1.0, -273.15)},'tasmin': {'linear_transform': (1.0, -273.15)},'pr': {'linear_transform': (86400.0, 0.0)}},),\n",
-    "    nc_index=wid,  #This is where we set the watershed index.\n",
     ")\n",
     "\n",
     "# Let's call the model with the timeseries, model parameters and other configuration parameters\n",
-    "resp = wps.raven_hmets(ts=str(path), params=params, **config)\n"
+    "resp = wps.raven_hmets(ts=str(path), params=calibparams, nc_index=watershedID, **config)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +264,28 @@
      "output_type": "stream",
      "text": [
       "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
-      "HYDROGRAPH,/tmp/pywps_process_au0lzdgh/input.nc,-25.4393,124.503,\n",
+      "HYDROGRAPH,/tmp/pywps_process_q2xb9nna/ts.nc,0.415524,21.9858,\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can analyze and plot the data directly here to see what it looks like, or we could download the data directly by\n",
+    "# changing the asobj=True to asobj=False in the cell above this one. \n",
+    "print(diagnostics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\n",
+      "HYDROGRAPH,/tmp/pywps_process_q2xb9nna/ts.nc,0.415524,21.9858,\n",
       "\n"
      ]
     }
@@ -189,22 +303,368 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
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+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'q_sim'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4748</li><li><span>nbasins</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b8a7f5ce-41b9-4296-a488-49bd2352a3d8' class='xr-array-in' type='checkbox' ><label for='section-b8a7f5ce-41b9-4296-a488-49bd2352a3d8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 14.08 27.75 26.93 26.14 25.37 ... 13.5 13.22 12.93 12.66 11.95</span></div><pre class='xr-array-data'>array([[ 0.      ],\n",
+       "       [14.083714],\n",
+       "       [27.751569],\n",
+       "       ...,\n",
+       "       [12.934327],\n",
+       "       [12.657255],\n",
+       "       [11.947663]])</pre></div></li><li class='xr-section-item'><input id='section-18ce73f3-b6c9-41fd-b0aa-bdafdf4dddfe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18ce73f3-b6c9-41fd-b0aa-bdafdf4dddfe' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1998-01-01 ... 2010-12-31</div><input id='attrs-b7fbee86-fb44-454f-aaf5-755a9206a9a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7fbee86-fb44-454f-aaf5-755a9206a9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fa29662-c0c1-460b-a30a-facfa8cfc9d2' class='xr-var-data-in' type='checkbox'><label for='data-1fa29662-c0c1-460b-a30a-facfa8cfc9d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;1998-01-03T00:00:00.000000000&#x27;, ..., &#x27;2010-12-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2010-12-30T00:00:00.000000000&#x27;, &#x27;2010-12-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>basin_name</span></div><div class='xr-var-dims'>(nbasins)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce3fcf66-555a-471d-8854-672ed2117514' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce3fcf66-555a-471d-8854-672ed2117514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f9ce334-1867-4167-8728-0a5addf2d07e' class='xr-var-data-in' type='checkbox'><label for='data-8f9ce334-1867-4167-8728-0a5addf2d07e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Name/ID of sub-basins with simulated outflows</dd><dt><span>cf_role :</span></dt><dd>timeseries_id</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><pre class='xr-var-data'>array([&#x27;watershed&#x27;], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-51d1b8b4-178b-4d45-925d-f5a9d896ad4e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51d1b8b4-178b-4d45-925d-f5a9d896ad4e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m**3 s**-1</dd><dt><span>long_name :</span></dt><dd>Simulated outflows</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "<xarray.DataArray 'q_sim' (time: 366, nbasins: 1)>\n",
-       "array([[  0.      ],\n",
-       "       [150.782482],\n",
-       "       [298.437317],\n",
+       "<xarray.DataArray 'q_sim' (time: 4748, nbasins: 1)>\n",
+       "array([[ 0.      ],\n",
+       "       [14.083714],\n",
+       "       [27.751569],\n",
        "       ...,\n",
-       "       [ 42.477336],\n",
-       "       [ 41.920524],\n",
-       "       [ 41.336371]])\n",
+       "       [12.934327],\n",
+       "       [12.657255],\n",
+       "       [11.947663]])\n",
        "Coordinates:\n",
-       "  * time        (time) datetime64[ns] 2007-01-01 2007-01-02 ... 2008-01-01\n",
+       "  * time        (time) datetime64[ns] 1998-01-01 1998-01-02 ... 2010-12-31\n",
        "    basin_name  (nbasins) object ...\n",
        "Dimensions without coordinates: nbasins\n",
        "Attributes:\n",
@@ -212,7 +672,7 @@
        "    long_name:  Simulated outflows"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,22 +683,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f33e998d7b8>]"
+       "[<matplotlib.lines.Line2D at 0x7ff979be5e10>]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -250,15 +710,15 @@
     }
    ],
    "source": [
+    "# Plot the simulated hydrograph\n",
     "from pandas.plotting import register_matplotlib_converters\n",
     "register_matplotlib_converters()\n",
-    "\n",
     "hydrograph.q_sim.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -266,34 +726,69 @@
      "output_type": "stream",
      "text": [
       "Max:  <xarray.DataArray 'q_sim' ()>\n",
-      "array(298.43731743)\n",
+      "array(179.3093905)\n",
       "Mean:  <xarray.DataArray 'q_sim' ()>\n",
-      "array(135.05876966)\n",
+      "array(27.15435609)\n",
       "Monthly means:  <xarray.DataArray 'q_sim' (month: 12, nbasins: 1)>\n",
-      "array([[215.87049663],\n",
-      "       [147.92838764],\n",
-      "       [108.6157387 ],\n",
-      "       [135.89996268],\n",
-      "       [135.00015769],\n",
-      "       [189.15784161],\n",
-      "       [190.23502812],\n",
-      "       [129.84620532],\n",
-      "       [ 98.26109929],\n",
-      "       [117.64861918],\n",
-      "       [ 96.67709398],\n",
-      "       [ 53.55034852]])\n",
+      "array([[ 5.71283249],\n",
+      "       [ 3.78964147],\n",
+      "       [ 5.75843344],\n",
+      "       [36.12224458],\n",
+      "       [71.88336396],\n",
+      "       [54.28036951],\n",
+      "       [35.5306146 ],\n",
+      "       [17.93740983],\n",
+      "       [22.41492954],\n",
+      "       [27.44353433],\n",
+      "       [29.01578923],\n",
+      "       [14.94742054]])\n",
       "Coordinates:\n",
-      "    basin_name  (nbasins) object ...\n",
+      "    basin_name  (nbasins) object 'watershed'\n",
       "  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: nbasins\n"
      ]
     }
    ],
    "source": [
+    "# You can also get statistics from the data directly here.\n",
     "print(\"Max: \", hydrograph.q_sim.max())\n",
     "print(\"Mean: \", hydrograph.q_sim.mean())\n",
     "print(\"Monthly means: \", hydrograph.q_sim.groupby(hydrograph.time.dt.month).mean(dim='time'))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For an example of how to download the data directly to analyze locally on your own computer/server, see here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_Hydrographs.nc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_WatershedStorage.nc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_solution.rvc\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/test_hmets_NRCAN-0_Diagnostics.csv\n",
+      "http://localhost:9099/outputs/8bdc2c8e-8bf6-11ea-aad1-b052162515fb/rv.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rerun the analysis of the WPS response, this type by using asobj=False. \n",
+    "[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=False)\n",
+    "print(hydrograph)\n",
+    "print(storage)\n",
+    "print(solution)\n",
+    "print(diagnostics)\n",
+    "print(rv)"
+   ]
   }
  ],
  "metadata": {
@@ -312,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/example_data.py b/docs/source/notebooks/example_data.py
index 9aa03ed3..b1d2b902 100644
--- a/docs/source/notebooks/example_data.py
+++ b/docs/source/notebooks/example_data.py
@@ -99,3 +99,5 @@
     TD / "hydro_simulations" / "raven-gr4j-cemaneige-sim_hmets-0_Hydrographs.nc"
 )
 TESTDATA["input2d"] = TD / "input2d" / "input2d.nc"
+
+TESTDATA["canopex_attributes"] = TD / "regionalisation_data" / "gauged_catchment_properties.csv"
diff --git a/docs/source/notebooks/getting_variables_other_than_flow.ipynb b/docs/source/notebooks/getting_variables_other_than_flow.ipynb
index 33320345..5e40bff2 100644
--- a/docs/source/notebooks/getting_variables_other_than_flow.ipynb
+++ b/docs/source/notebooks/getting_variables_other_than_flow.ipynb
@@ -76,7 +76,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f96c6d89b38>]"
+       "[<matplotlib.lines.Line2D at 0x7fb4a5f26b00>]"
       ]
      },
      "execution_count": 3,
@@ -85,7 +85,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -118,7 +118,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f96c572d438>]"
+       "[<matplotlib.lines.Line2D at 0x7fb4a5e9e278>]"
       ]
      },
      "execution_count": 4,
@@ -127,7 +127,7 @@
     },
     {
      "data": {
-      "image/png": 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RkJtZ5/rOH9aecl+Q//7YsHILlYEg6fsRPqqJ8w/qwNYKP+9OXYUvEMQXULLTvRTmZu3yd/pg5hrenboqXFLcpIYZ6ecMcRLZ7yVBf6qQUehT1IRebfKqHXdWkiacY5pTEBGviEwDSoDPVHUC8GtgjKruvvp3EbASQFX9wGageSz1GcnL1wucSU/NG2XQvnkOh3Ur4LVJK2qVmAutI5y5H83wqqN3myYc2KEpL09Y0aBmOFfG0FMAGNgun95t8nhp/PJwX6pQB9u3rx7ONSM6h8e+PGHFHp5CdfRr6xQAvJwECec/vjcbcKqu9kYo4fzqpJVJteZ1TI2CqgZUdQDQFhgiIocBZwJ/39djisgVIjJZRCavX7++rqQaScZDn8wHnHwCwPlDncTc53OjT8xV+J2LTmaM7nwvGNaBpRu27VMSPFmp3M9Ec02ICBcM68C8tVvCs3qz3bUu2jXL4eZjenDWoHZ0aJ7DzFWbwzcHNU0+FBHOHdqeeWu3MGVFacz0V8VfPp7H7e/OBKBky84QZ2YUpb0XHVTM+i0VfDRr93vkxBGX6iNVLQXGAiOALsAiEVkG5IjIInfYKqAdgIikAU2APfoJqOrTqjpIVQcVFBTsvtuo5/zrmyV8t2jnRbZNfjbgJObaNMniP+OXRX2sUNvsWF3kjuvbiuaNMnjxh4aTcPYFNKpFdvaHkwe0ITcrjV+9PAVgj7Uu/nJGPz647lAaZ6YxZvpqPOLkDWrilAFF5Gam8cL3y2Ihu0rWlZXz5FeLeWn8Ch79bAFTXYP0wqVDonr/4d0KKG6eE1fNNRHL6qMCEcl3n2cDRwE/qmorVS1W1WJgu5tYBhgDXOQ+PwP4UhPtBxpxRVX566cL+OO7s/AI/HpEl/A6CGleD+cf1IHvFm3ca0uESMJGIUbhkMw0L2cNbscXc9clXbJwXwgElUBQyfDWfbgtkpyMNM4a1C78OruKVfEaZ6Zx2gFO+WluVnpUrTcaZaZx5qB2fDhzDevK4tPifEPEinP/98VCnhm3hHSvMLRjdGXQHo9w0fBipqwoZcZP8fVwqiOWnkJrYKyIzAAm4eQU3t/L+GeB5q7ncCNwWwy1GUnIlgo/O3wBlmzYRlD3DBmcM7g9Weke/v19dAu4hHMKMQyHnDu0PQAvN4DyVJ+br0lPi319x2+P6R5+Xt2nXXhQB2d/LeRceFAHAqq87JanhtZ0qCt8u+W0NkdUGjXJTmfy8p/p1TqvVos6nXFgWxplePl3kngLsaw+mqGqA1W1n6r2UdW7qhjTOOJ5uaqeqapdVHWIqiZvxygjJpSU7brO7+79ipo2yuDUgUW8PWVVuDppb1S4SdNYFrG1bZrDqJ4teXXiimrbNNQXKgOx9awiyUr38u9LBgPQuZp5JF0Kc7n9hJ7868JBUR+3uEUjRnYv5JUJyyn3BTjyr19z6INjwzcI67dUhHNNtaWs3EfvP33C796esXObaxQK8zI52e311KeoSa2Om5uVzhkHtuX96Wt28TwShc1oNpKGEtflH97ZKTpr2mjPOPIlB3ekwh+Mqjy10h+MqZcQ4tJDOvLzdl+9X66zMsY5mN05onshi+87nm4tc6sdc/mhnRhUyxnpFx9czIatlRz24Fi2lDvzIT6YuRpVZfC9n3PeMxP2SW9JWQWVgSCvTlzJNwvXs6Xct0t11DUjunBgh6ZccnDHWh/7wuHFzrGTYALeXmum3NBPTaxX1SPrSI+RwixzZ7L+/viejJm+miEd96xI7tYyl0O6tOA/PyznisM67XX2bYU/EJcL3NCOzejdJo/nvl3K2YPbxdQziSXh8FEcPIUQ+9KmuyYO6dKCroWNWVgSbpjAs98u5YXvnRDf5OU/s2bzDlo3ya7VcbdFLPJzwbMTSfMIo3q2BByjkJuVzltXD98nzZ0LGnNYtwL+M345Vx3ROa5/g92p6ZO9wEl7eZwMWAmQUSfMWbOZ3Mw0erXO4/fH96RxZtX3LJccXMzasnI+nrV2rzXp8fIURIRLD+7IwpKtXPLvSQTq6byFkKeQyAtSXSAivHz5UC47pCP92zbh7lP6MGtVGdNW7kzk/nsfOu9u223lN39Q+dht4lfduVobLjm4mJItFbw/I7EdX2v661+pqsv38lgG/CoOOo0UYFHJVrq1ysVTw93jiO6FFDfP4dpXp3Lx85OqNQwV/tjW3EdyYv/W5GR4+Wr++nrbEylUrbV7iWh9pDAviz+e2Iv3fn0IZxyws6X3747rwQn9WvPKhBVVrsHwxqSV1SamQ605/n7OQG4+pjv/PP/A8L668A6P6FZAt5aNeerrJQmdgLfX/xhV/bamA0QzxjCiYcPWSlrm1dySwuMRbjiqG+DMfP7v5KoXha+Mo1HITPPy1c1H0Kt1Hk99vXiPKpX6wI5Kd4ZxRv32FHYnO8PLHSf14vi+rbjy8M5ceVgntlT491gJrqzcxy1vzeDQB8dWeZzt7vfTu00e14zowlG9WjKyRyGP/KJ/negUEa44rDPz1m4JT9pLBFH99UXkRBGZKiKbRKRMRLaISFnN7zSM6NmwtYIWjaPrUzR6QBGz7jyGQR2acstbM6psYz12fgmeOMb3C3OzuPmY7qzeXM6YJFr0JVpCbSdqU05ZX7jk4I48cZ5zZ9+vbT5DOzbjue+W7mK815TunNtQfNsHvDdt18KBkKcQChV5PU5r8NMiPJH95eT+bWiVl8XT4xJXfBntLcFjOBPLmqtqnqrmqmr1nZ4Mo5b4AkFKt/to3ij65nWNM9O459Q+ZHg93P7erF1c7oXrtlDhDzIvyoludcUR3Qvo3jKXp8YtTngPntoS9hQaoFHYnSsP78SazeX8b/pO471m864TEK9/bRp/+Xhe+HUop9CoDvIH1ZGR5uGSg4v5fvHGhE1mi9YorARm2QxjI1aEOpo2b1y7tZR7tMrjjyf1YvrKUiYs3RTeHmqh3Covq+5ERoGIcOXhnViwbmtSLqCyN8IN6qqYYdzQOKJbId1aNuafXy8ONzQMtWZ/8Ix+XHW405jvya8Ws8itYtpW4Uck9kbz3KHtyc1K46mvE+MtRGsUbgE+FJHficiNoUcshRmpRWhBnRa1NAoAZx7YlqY56Zz99Hie+MpppbWtwrnAPX7ewLoTGSUn9W9D48w0Lnth8i53oslOKnkKHo9w9RGdWbBuK1+4xnuTu170yf3bcOux3fnjib0A+OfXi8P7m2Sn11gIsb/kZqVz/rAOfDhrDUvdNUHiSbRG4V5gO5AF5EY8DKNO2LjNmcnZPMqcQiRZ6V5+4fbSefDj+Wze7mN7pePq52TEztWvjnSvhwdO7wvAPR/MSaq2yHtj91bWDZ2T+rWhbdNs/jF2EarK1nI/6V4hM82ZBX/ZIR25eHgx705dxarSHZSUVVAYg7U5quKSg4tJ93r49StTwudyvIjWKLRR1dNU9Q5VvTP0iKkyI6XY6HoKzRvV3lMAuPHobtzs9tN54YdlYU+hUQKMAsCJ/drw70sGs66sgrenVF0dlWyE2nRkpUD4CJwmi1cd3pnpK0v5YfFGtlb4aZyZtkt56S8P6wTAM+OWULKlgsLc+IQjC3OzuHBYB2avLuNf30TX66uuiNYofCgiR8dUiZHShFz0ffEUwCkJvWZEF47sUchz3y1lvdtDJiczcRe4w7sV0L9dPo9/tahelKimUvgoxBkHtqUgN5PHv1rE1gr/HknkovxsRg8o4rVJK5i/dkvcPAWA20/sxaiehTz77dJw5VM8iNYoXA18LCI7rCTViIZyX2CPao7q8Ad2VgnlZe3fnf2vRnShdLuPZ7917q4S5SmAk3S+bmQXVm7awXv1oER1hy9Amkfq/Yzm2pCV7uWXh3bku0Ub+XbhhipnJl99RCfKfUF2+AK0b54TV33XjuzK5h0+XvxhWdw+M6q/vluC6lHVbCtJNaLh3g/mctD9X0Y1CSfUtExk/2eGHtihKQd1as76LY6nkJWe2AvcyB6F9G6Tx+NjFyV9+4tyX7BBzlGoiXOHdqBJdjolWyrIreKmpEthLkd0d7r5HNWrZVy19W+Xz+HdCvjXN0vjlluI+j9GRPqJyMkiclroEUthRv0mtCzhw5/Mr7FeP9Rp8q9n1s3M0FuP6xF+nujmdCLCtSO7snTDtoT3tKkJfzAY81XXkpHGmWlcPLwYgNWlVS/O88yFg/j0hsPo3aZ2bbHrguuO7MKmbZW8EqcOqtHOaH4OeA44nZ3N8E6MoS6jnhOyAzNXba5xXeXQnIK8KJZcjIYB7fL5zaiuHB3nu7rqOLpXS3q0yuXvXy4K18QnI/6g4vWkTugokksOLgaqb2yX7vXstcV3LDmwQzOGd27OU+OWxGXNjmgDrsNUtVdMlRgNCn9Q6dEqlx2+AI99voBRPQurvGsvK/dx9UvOWr1NalicvTb8ZlS3OjvW/uLxCL8e2YVfvzKVD2et4cR+bRItqUoCASUtxjX4yUp+TgbvX3sITfex+i3WXDuyK+c8M57XJ63kIteriRXR3hb8ICJmFIyo8QWc+PS1I7sye3UZn81ZV+W4bxZsCK9vXFeeQjJyXJ/WdC1szGOfL0za3ILjKaSmUQBnxbSi/NqtsRAvhnVqxpDiZjz51eKYewvRGoUXcQzDfBGZISIzo1yAx0hR/AEl3SucMqANxc1zeOzzhVXmFkK5B6h6pbWGgtcj3HhUNxaVbN2j0VqyEAgGSUvBnEJ9QES4flRX1paV81oUqw7uD9EahWeBC4Bj2ZlPOClWooz6T8C960zzerh2ZFfmrCnjk9l7egs//ex4Cc9eNChuE4MSxTG9W9G7TR6Pfb4wKectpLqnkOwM79ycYZ2a8Y+xi8NzSmJBtEZhvaqOUdWlkYvsxEyVUe/xBYPhevfRA9rQsUUjHvt8wR6J1p9+3k7XwsYc2TM5ksKxxOMRfnt0d1Zs2s4bk1fW/IY4Ewimbk6hPiDinD8btlbwwg/LYvY50RqFqSLyioicYyWpRjT4I5KWaV4P1x3ZhXlrt/Dx7LVs2lbJgLs+5Z2pP/HTzzto2zQ547ix4IjuBRzQPp+/f7EoLpUktcEXSN3qo/rCoOJmHNG9gH9+vbjKlePqgmjPgGygAjgaK0k1osAXCJIWMTP25P5FdClszF8/nc/8tVso3e7jhtenM3t1WYMPG0UiIvz2mO6sLSvn5TjVnUdLIBg0T6EecNNR3Snd7uO5b5fF5PhRlaSq6iUx+XSjXjNm+mrKdvg4f1iHPfYFgrrLRCivGzq56qUfedLtcxSiLktR6wPDO7fg4C7NeWLsIs4e3C6mi7bUBssp1A/6tm3CDaO6cUjXFjE5/l49BRG5oqYDRDPGaJj8+7ul3DFmdngRkkiqmgh1TO+W9G+Xzzi39cXk20fx3MWDuGZEl7joTSZuOro7G7dVxr0D5t7Y3ZAbycv1o7pyYIemMTl2TeGj2yJzCFU8Tgeuj4kyI+nZuK2SQFB5MGLJwhC+QJD03e46RYRbj+0eft28UQYje7SkSXZqeQoAB7RvynF9WvHUuMW7lOUmEvMUDKg5fPQ1NZeeflZHWox6xoYtFeRmpvHpnHVMXLqJIR2bhff5A1plzfvwzi2471RnAZpE9yVKNLcc24PP5qzj0c8Wcv9pfRMth0BQyUjBhnjGruzVKOxPLkFEsoBxQKb7OW+q6h0i8jIwCPABE4ErVdUnzhXi/4DjcVZ5u1hVp+zr5xt1iz/gtA4OLUKyozLAtsoA147swhuTV3Lfh3N551fDwxd6f3DXRHMk5w5tH0/pSUvHFo04f1gHXvxhGZceXEzXBPXWCWGeggG16JK6D1QAI1W1PzAAOFZEhgEvAz2AvjhVTZe7448DurqPK4AnY6jNqCUXPz+Jvn/+lF+9PIVAUNngLmLTrmkONx3VnWkrS/lg5prweH9Q9wgfGXty3ZFdaZSRxv0f7RmCizdWfWRADI2COoQykOnuQ1X1Q3ef4ngKbd0xo4EX3V3jgXwRaR0rfUbt+HbRBgA+mrWWhz+dHzYKLXIzOP3AtvRolcuDH8+nwu/U3vut5j0qmjXK4JqRXfhyXgnfu99xonD+ZmYUUp2Y/teKiFdEpgElwGeqOiFiXzpO64yP3U1FQOQ0z5/cbdvXV+EAACAASURBVEYEz4xbwimPfxduIhcvBrTLZ1inZpzYrzXPfrOUqStKAWjeKBOvR7jtuB6s2LSdl8Y7tfe+QGr25t8XLh5eTFF+Nhc+N5GPZ61NmI5AsOo8kJFa1FSS+rcoHvdU935VDajqABxvYIiI9InY/QQwTlW/qY1gEblCRCaLyOT162te1auh8dWCEqatLOW+D+bW2TGve3Uqj49dtNcxFf4guVnp/PHEXng9wl3vzwGghbtm7eHdCjikSwv+9sVCSrdX4rcLTNRkpXu5+Zju+IPKVS/9yJzViVnpNpXXUzB2UtMZMBr4sYbH6TV9iKqWAmNxGuohIncABcCNEcNWAe0iXrd1t+1+rKdVdZCqDiooKKjpoxscgnOh/WDmGiYs2bjfx1NVPp2zlkc/W1DlfIMQFf4AmWkeWuZl8XDECmnN3f7zIsIfTujJlnIfj362wO2jYxeYaDm5fxt+dURnAP48ZnaNq9XFAr/lFAxqNgqPquoLe3sAT1X1RhEpEJF893k2cBQwT0QuB44BzlHVyFaRY4ALxWEYsFlV1+xx4BTHFwjSr20T2jTJ4sqXfmThui37dbzS7T7KfUH8QeXu9+dUezGq8AXJTHPKFU/o15r+bZ1lCSPX9O3ZOo9zh7bnP+OdXol2gYkej0e45dge3H9aXyYu28T/ZsT/1A9YTsGgBqOgqo/VdIC9jGkNjHXXXZiEk1N4H/gn0BJnfYZpIvInd/yHwBJgEfAM8KvofoXUIhBUGmem8fdzD8DnD/L7d2bu113lms3OxKnBxU35esF6Ov7uQ/7x5cI9xlX4g2Sm7zxd3rp6ONP+dNQe4246qju57mI51ZWkGtXzi0Ht6FOUx30fzI3bQu0h/Daj2aDmnEKaiFwpIh+7i+vMEJGPROQqN1FcLao6Q1UHqmo/Ve2jqne529NUtbOqDnAfoe2qqte4+/qq6uS6+zUbDk6s3sOBHZryp5N6MWnZz7y7H4u2rC1zEtY3H7NzsfuHP13AkvW7hpJC4aMQaV4P+Tl7Ll3YtFEGN4zqCmAXmH3A6xH+fFJv1paV88TYxTW/oQ4J2DwFg5rDR//BmWPwZ5xJZccDdwL9gZdiqsyoksi475kHtmNAu3zu/WAeZfvYRnd1qeMptG+Ww+tXDKNTQSNE4I7d4toV/p3ho5o4f1gHrhnROSXWSIgFg4qbcerAIp4et4TlG7fF7XP9lgcyqNkoHKiqV6vqeFX9yX2MV9WrgYHxEGjsSuQ6BR6PcPfoPmzcVsGjny3Yh2MFWbN5B16PUJCbydBOzfnypiP404m9+GbhhnB5pKpS6Q/u4insjTSvh5uP6UHHFo1qrclwuO24HqR5hbvfr7sqs5owT8GAmo3CJhE5U0TC40TEIyJnAT/HVppRFbuXevZt24Rzh7Tnhe+XMXdN7UoZhz/wJY+PXUzL3MxdLgYXDOtAz9Z53PX+HLZV+KnwO/UAkTkFI7a0zMvi2pFd+XzuOj6fs+cyprHAqo8MqNkonA2cAawTkQUisgBYC5zm7jPiTFWlnjcf050m2en86b1ZUSedA0GlZIszK7kgb9dFbtK8Hu4e3Zs1m8v5+5eLdhqFKMNHRt1w2SEd6VrYmDvGzI5L0tk8BQNqrj5apqpnqWoBcBBwkKoWutuSpxF8CuEL7Hk3l5+TwW3H9WDSsp95a8qeSedFJVv3MBaRF5n7Tu2z+1sYVNyMMw5sy7++WcLs1ZsBog4fGXVDRpqH+07ry6rSHTz2+Z4VYXWJquIL2BrNRs3VRweEnqvqRlXdY7ZU5Bgj9lTXiuDMA9txQPt87v1gDhvdvkQA89aWMeqRr3n+u2UAbN7hJKS3VTg9iu47tS+92zSp8rNuO64HORlebnlzBuBcpIz4Mri4GecMacez3y6N6UznoHvPYDOajZrOgOdFpKmINKvuATwbD6GGQ3WLq3s8wgOn92NrhZ+73RYUACs2bgfg4U/n8+7UVQy861PGzi9hm+spNMqsPiTUonEmtx3Xk59+dspW2zbNrstfxYiSW4/tQdOcdH7/zkwCwdjMdPYHnRChtSYxajIKTXBaWUym+jYX+1YLaewTgWD1jea6tczl6iO68O601Xw1vwSA9a7X4AsEue3tGQQV/vD2TErKnO2NMva+ztLZg9uRk+EYjgPax2b5P2Pv5OdkcPsJvZi2spRXJiyPyWeEjI3lFIyacgrFQBfgAlXtWM1jSFyUGkDN7Y2vGdGZzgWN+MM7s9hW4aekrAIR+O3R3Sn3BcnwelhTVs49HzjeRE2Lxns8wre3juTdaw7epaWFEV9GD2jDIV1a8ODH81lXVvfLd/pdo2A5BaPGAKLbn+gfcdBiRIHTiqD6P1tmmpcHTu/HqtIdPPLZAkq2VNAsJ4PLD+3EgHb5DCpuykUHFTPbjU/vLXwUolmjDAa0y6+z38GoPSLCPaf0wRcM8of9bG1SFYGAGQXDoaY1mkN8ISKnA29rIto3GmH8wWCNLv7g4macN7Q9z3+3lMaZaXRs0QivR3j1l8MIqqLAp7PXsnpzeY2egpE8FLdoxG+P7s49H8xlzPTVjB5Qd8uNhDwFr/WrSnmiPQOuBP4LVIpImYhsEZHENH1PcaJd5vLW43pQkJtJWbmftk1zAMjO8NIoM43GmWk8fGZ/hhQ3o00TSx7XJy45uCMD2uXz5zGzw6vf1QUBCx8ZLlEZBVXNVVWPqqarap77Oi/W4oxdCQYV1ejKBvOy0rn3lL4AtGqStcf+4V1a8MZVB5GdYXmC+oTXIzx0Rj+2VQS4Y8zsOjtuqPrIEs1GVEbBXePgfBH5o/u6nYhYgjkOTFiykQuencDm7T58tSwbHNWrJS9dNpTrRnaNpUQjznRtmcv1o7rywYw1vDt13zvkRmKeghEi2vDREzgzms91X28FHo+JImMXpq4s5ZuFG/jz/2bv0z/uIV1b0CRnr13OjXrIlYd1YkhxM/7wzkxWbtq+38fzBawk1XCI1igMVdVrgHIAVf0Z2LOZvlHn+AOOd/DO1FX8b/pqwBavMZxz4NGzB+AR4aY3pu/3pLadNxx2bqU60Z4BPhHxAgrOUptAcO9vMeqC0B1c36Im3Pk/Z26BufgGQFF+Nn8+uTcTl23iX98s2a9jWU7BCBGtUfgb8A5QKCL3At8C98dMlREmVIL66Fn9d04wslYEhstpBxRxbO9W/PXTBbVunR6J5RSMENFWH70M3IJjCNYAp6jqG7EUZjiEFtXpUpjLrcc6S2ZmWPjIcBER7jutL3nZ6Rz3f99w+ENjmfFTKde8PIVHPp0f9XHshsMIEdXMJRH5j6peAMyrYpsRQ3yBnTOYLxleTPNGGYzoUZhgVUYy0axRBg+e0ZdL/z2Z5Ru3c9V/fqRkSwX+oDKwfdOozhfLKRghoj0Deke+cPMLB9a9HGN3/MFg+O7N4xFOGVhEk2yrJjJ2ZWSPlvz++B5cMKwD61yDkOH1cPOb01m/peZJbn6rPjJcalpP4XcisgXoFzGTeQtQArwXF4UpjrPwid29GTVzxWGdufuUPuF5KQ+d2Y8t5X5++9/plGwp32u/pICFjwyXvYaPVPV+4H4RuV9VfxcnTUYE/kD1rbINoyquO7ILx/ZpRfdWuWwp93P7u7MYcu8XXHJwMXec1LvK91j1kREi2kTz79zFdoaIyGGhR6zFGU4C0O7ejNogInRvlQvAeUPbc0iXFgA8/90yvpi7rsr3+K1LquESbZuLy4FxwCfAne7PP8dOlhHCFwiSbuEjYx8REV68dAgTfn8kPVvncdVLPzJ9Zeke4/y2yI7hEu3V5npgMLBcVUcAA4E9zyyjzvEHzFMw9g+PR2iZl8WrvxxKYW4W5z4znqfHLabcFwiPseojI0S0Z0C5qpYDiEimqs4Duu/tDSKSJSITRWS6iMwWkTvd7R1FZIKILBKR10UkI3Rc9/Uid3/xvv9aDQd/MGj/qEadkJ+TwT/OHci2ygD3fTiP37w2LZx8tpyCESLaq81PIpIPvAt8JiLvATUtFlsBjFTV/sAA4FgRGQb8BXhUVbsAPwOXueMvA352tz/qjkt5nHkK9o9q1A0D2zflP5cN4YS+rfl49lqecdtj2IxmI0RUk9dU9VT36Z9FZCzQBPi4hvcoTjdVgHT3ocBIdnZbfQEnN/EkMJqdeYo3gX+IiKT6Sm/OPAXzFIy649CuBRzSpQX6ivKXj+fTr22+zWg2wtQ0T+ExEfmFiITX/VPVr1V1jKpW1nRwEfGKyDSceQ2fAYuBUlX1u0N+AkLHLgJWup/hBzYDzWv7CzU0fG6bC8OoS0SEv5zejw7Nc/j1K1NZtmEb4KzxbaQ2Nd2CLgJOAb4TkWUi8oqI/FpEBopIjbevqhpQ1QFAW2AI0GN/BYvIFSIyWUQmr1+/fn8Pl/Q48xTMUzDqntysdP55/oFsq/DzxFeLyc1Mo0Vj64if6uz1aqOq/1DVc1W1GBgOvA10wlmvOerqI1UtBcbiLNSTLyKhsFVbILR01CqgHYC7vwmwsYpjPa2qg1R1UEFBQbQS6i02T8GIJd1a5vLA6c6yrV1bNkbEzrVUp8acgjhnSV8co3Aw0AvHg/hPDe8rAHyqWioi2cBROMnjscAZwGvARexslzHGff2Du//LVM8ngLW5MGLP6AFFbNxaSZv8PdfyNlKPvRoFEfkMyAOmAeOB+1R1bpTHbg284DbP8wBvqOr7IjIHeE1E7gGmAs+6458F/iMii4BNwNm1/m0aINbmwogHlx7SMdESjCShJk9hCdAP6IoTytkgIutVdUNNB1bVGTiT3HbfvgQnv7D79nLgzGhEpxJO+Mg8BcMw4kNNDfGuBBCRPGAYTgjpGjc0NEtVL4q9xNTGmbxmnoJhGPEhqnkKOBPRtgM73OdtAStTiAOBgNosU8Mw4kZN8xQeFZEJOEtw3gnkAv8Euqtq3zjoS3kCqnitIsQwjDhRk6ewFHgJmKaqgRrGGjEgEASvJZoNw4gTNeUU/hYvIUbVBIJB8xQMw4gbVtaS5ASCllMwDCN+mFFIcoJq7YwNw4gfNU1ea7a3/aq6qW7lGLvjDwbNKBiGETdqSjT/iNPuuqqrkuL0QTJiSDAIHsspGIYRJ2pKNNvc9wQTUGudbRhG/KgpfNRDVeeJyAFV7VfVKbGRZQCoKoGg4jGjYBhGnKgpfHQjcAXw1yr2hVZRM2KEuxiWlaQahhE3agofXeH+HBEfOUYkAVsi0TCMOBNV7yMRSQeuBg5zN30FPKWqvhjpMthpFCzRbBhGvIi2Id6TQDrwhPv6Anfb5bEQZTgE3DWGrHO2YRjxIlqjMFhV+0e8/lJEpsdCkLGTkKfgtZXXDMOIE9FebQIi0jn0QkQ6AdYgL8aEjYJFjwzDiBPRego3A2NFZAnORLYOwCUxU2UAkZ6CWQXDMOJDVEZBVb8Qka5Ad3fTfFWtiJ0sAyCoFj4yDCO+1LTIzmARaQXgGoEBwN3AQzX1RTL2H3/QEs2GYcSXmi43TwGVACJyGPAA8CKwGXg6ttKMoJWkGoYRZ2oKH3kjOqGeBTytqm8Bb4nItNhKM2zymmEY8aYmT8ErIiHDcSTwZcS+aJPUxj7iN0/BMIw4U9OF/VXgaxHZAOwAvgEQkS44ISQjhuxMNJtRMAwjPtTU++heEfkCaA18qupepRwP49pYi0t1wuEjMwqGYcSJGkNAqjq+im0LYiPHiMR6HxmGEW9iVuwoIu1EZKyIzBGR2SJyvbt9gIiMF5FpIjJZRIa420VE/iYii0RkRnVrOKQSNnnNMIx4E8tksR+4SVWniEgu8KOIfAY8CNypqh+JyPHu6yOA44Cu7mMoTsO9oTHUl/QELKdgGEaciZmnoKprQiuzqeoWYC5QhLM4T547rAmw2n0+GnhRHcYD+SLSOlb66gPmKRiGEW/iUlYqIsXAQGAC8BvgExF5GMcoDXeHFQErI972k7ttTTw0JiM7G+KZUTAMIz7EvIGCiDQG3gJ+o6plOIv13KCq7YAbgGdrebwr3FzE5PXr19e94CQiaJ6CYRhxJqZGwV2x7S3gZVV92918ERB6/l9giPt8FdAu4u1t3W27oKpPq+ogVR1UUFAQG+FJgt+MgmEYcSaW1UeC4wXMVdVHInatBg53n48EFrrPxwAXulVIw4DNqpqyoSOwnIJhGPEnljmFg3GW7ZwZ0Sfp98Avgf9z22eUA1e4+z4EjgcWAdux9RqoDAQByEizNqmGYcSHmBkFVf0WZ0GeqjiwivEKXBMrPfURX8goWO9swzDihF1tkpiQUUg3o2AYRpywq00S4/M7OYV0Cx8ZhhEn7GqTxFSGPQVLNBuGER/MKCQxlX7LKRiGEV/sapPEWE7BMIx4Y1ebJMZnJamGYcQZu9okMZUBW2THMIz4YkYhifEFgmR4PYg1xDMMI06YUUhifP6gVR4ZhhFXzCgkMZWBoM1RMAwjrtgVJ4nxBYJWeWQYRlyxK04SU+lXm6NgGEZcsStOEuMLBK0c1TCMuGJXnCTGCR9ZotkwjPhhRiGJ8QWCpHnsT2QYRvywK04S4wuoeQqGYcQVMwpJjD8YJM0SzYZhxBG74iQx/oDa+syGYcQVMwpJjD9o4SPDMOKLGYUkxh9UvJZoNgwjjtgVJ4kJBIOkW/jIMIw4kpJGocIf4L1pq1DVREvZK5ZTMAwj3qSkUXh7yiquf20aFz0/ibWbyyn3BRItqUqcnEJK/okMw0gQaYkWkAjOGtQOXyDI/R/OY9j9XwBwZI9C7jm1D62bZCdY3U78gaB5CoZhxJWUvA31eIQLDyrmo+sP5bBuBQwpbsZ3izdw9CPjeG3iiqQJK/mDSppVHxmGEUdS0lMIUdyiES9eOgSA5Ru3cetbM7jt7Zm8P2MN95/Wl3bNchKqzx9QW4rTMIy4kpKeQlV0aN6IVy4fxr2n9mHaylKOeWwcL3y/jGAwcV6D4ynYn8gwjPgRsyuOiLQTkbEiMkdEZovI9RH7rhWRee72ByO2/05EFonIfBE5JlbaqsPjEc4b2oFPbjiMQcXNuGPMbM5+ejxLN2yLtxTAbXNhnoJhGHEklrehfuAmVe0FDAOuEZFeIjICGA30V9XewMMAItILOBvoDRwLPCEi3hjqq5ai/GxeuGQwD53Rj3lryzj2sXH848uFVPqDcdURCKh1STUMI67E7IqjqmtUdYr7fAswFygCrgYeUNUKd1+J+5bRwGuqWqGqS4FFwJBY6asJEeHMQe347MbDGdWzJQ9/uoDj//YNE5duipsGXzBoiWbDMOJKXG5DRaQYGAhMALoBh4rIBBH5WkQGu8OKgJURb/vJ3bb7sa4QkckiMnn9+vWxFQ60zMvi8fMO4PmLB1PuC/CLp37gljen8/O2yph/diBoiWbDMOJLzI2CiDQG3gJ+o6plOBVPzXBCSjcDb4hI1Fc+VX1aVQep6qCCgoKYaK6KET0K+eyGw7nq8M68PWUVRz7yNW/9+FPMyldVFZ9VHxmGEWdiahREJB3HILysqm+7m38C3laHiUAQaAGsAtpFvL2tuy1pyM7wcttxPXj/ukMobp7DTf+dzrnPTGDx+q11/lmhoierPjIMI57EsvpIgGeBuar6SMSud4ER7phuQAawARgDnC0imSLSEegKTIyVvv2hR6s83rxqOPee2ofZqzdz7GPj+MvH89hW4a+zz/AFnKS2zWg2DCOexPI29GDgAmCkiExzH8cDzwGdRGQW8Bpwkes1zAbeAOYAHwPXqGpyNiViZ/nqFzcdwegBRTz51WKO/OvXjJm+uk5CSgHXVbD1FAzDiCcxm9Gsqt8C1V3Rzq/mPfcC98ZKUywoyM3k4TP7c86Q9twxZhbXvTqVl8cv567RfejeKrfa9wWDij+oZKR5qPAH2LC1kqL8nX2X/AHHKNh6CoZhxJOUbnNRlxzYoSnvXXMIr01awUOfzOf4v33DhQd14Lyh7Slu3miX3ICqcvo/v2fqilIO6tScgtxMxkxfzaAOTVlbVk67pjlcfmhHwDwFwzDiiyRL87d9YdCgQTp58uREy9iDn7dV8tfP5vPyhBWoQo9WufzpxF4M79KC37w2lW8XbWTD1goKczOpDAQp3e6jZV4mG7dW4t+trcZ9p/bl3KHtE/SbGIbREBGRH1V1UFX7zFOIAU0bZXDPKX05Z0h7Ppm1lrenruLcf02gKD+bVaU7ABjVs5BHzxpAIKj8+/tljOheSMu8LDLTPKSneXh87CJe/H4Zxc0T25TPMIzUwjyFOFDuC/Dst0v551eL2VLh5/MbD6dLYeNEyzIMI0UxTyHBZKV7uWZEF644rBOrft5BcYtGiZZkGIZRJVbaEkfSvR4zCIZhJDVmFAzDMIwwZhQMwzCMMGYUDMMwjDBmFAzDMIwwZhQMwzCMMGYUDMMwjDBmFAzDMIww9XpGs4isB5bv49tb4KzjkGyYrtqTrNqSUVcyagLTtS/sj7YOqlrl0pX12ijsDyIyubpp3onEdNWeZNWWjLqSUROYrn0hVtosfGQYhmGEMaNgGIZhhEllo/B0ogVUg+mqPcmqLRl1JaMmMF37Qky0pWxOwTAMw9iTVPYUDMMwjN0wo2AkPSJiC1XXAvu+aod9X7vS4I1CMv7BRSTH/ZlU2kRkhIi0TLSO+oaIJNv/UWMAEfEmWkgkInKyiHROtI4qCH9PyfY/CfE/v5LtZK4TRORYEbk49DKRWkKIiEdE2ovIt8CfADRJEjoiMkpEJgH/JYnOCREZLSJvA/eISKdE64nEvcDdmGgdIcShUES+Av4FoKqBxKpycM+vH4BngdaJ1hNCRE4Qkc+BR0TkMEiq/8mEnV9JcwGoC0QkXUQeAp4D7hWRHqoaTIY7JlUNApWAAgNF5BBI7J2JiDQRkfeAPwC3AEuBQYnW5X5+b+Ae4HmgEfBbERnt7kvYeSsiaSJyK/A34GERGZAM55h7MSt3H/1E5DhI3HflGqnGIvI/4Hb3MR7okEhdEfqKgXuBvwNzgStE5HJ3X0qfXw3KKKiqD5gBDAceBe52tyfFHRPQA+cEfAH4FST8zsQL/FdVR6jqWGAMMDQJdAEMBj5T1f/h/POOAy4VkTzXwCYEVfUD83H+ljcCT7nbE3qOuReytsA04DZ2eqMJ+a7UYSvwkqoeoapfAJ8AoxOpK4LOwLeq+h7Ojce/gGtFpKl7EU7ITVEynF/13iiIyLUicouIHO1uel1VlwH/AdqJyAnuuLQE6ToyYvNiIADMBHwicrqIdEiArltF5EhV3aSqL7nbBceL8buv43puiMhxItIzYtMs4FgRyVTV9cDXwErginjqcrVdJyIPiMgv3E0fqGq5qj4GFIrIue649ARoOh3CF9nVQDfgO2CNiFwlIl3jpWk3XWe6ul53t3uAn4GVIpIZT03u558hIkMjNv0EnO6eX+Wq+hXwPa4xjbO25Dq/VLVePoB0nPDCOOASYAVwLJAdMeYSnLuBhOty950G3OI+vw/YCDztvpYEfV9ZEWOOAxbG+ftqA/wArAIeCX0POF7Mv4Bb3deZwEnumMZx0ibADTgX2TNwvLyLgcKIMacCq+L4fVWnqRlO6O8Od9xvgW3A/9zXaQnSVRAxZjgwL87nVyHODcVq4F3AE7HvReCxCP39gTeBlql6fqlqvfYU/DghhmtV9XmcmOWZuDFxl/8CG0XkfAARGZggXWeJyBBgEdDbjeOfDvyI4yqi7hkQZ11nuttCTAKmi8iwGGuJpAJ4HbgM55/klIh9LwPHiEhXVa3Aycm0UCcsEXPcv8kI4HZVfRPnH7gfcEzEmHeABSLyW3CSqgnQNAA4ClgLHCoiH+IY/u+AJe5bYxp+qEZXf5wbj9CY74GfROTkWGrZTVcJ8J6rYw1wZcTuO4ETRaS3q78c2AKk7PkF9TR8JCLifqHTcU48VPVFYD1wsIg0d7dtxcktvCgiJUD7BOkqAQ4BugItgSlAT+CPwEgRKUyQrtD31cIdmolzYS6LpZ5IVHUjTlLte5wL2JEi0lydGOo3OHd5T4tIG+AwIBCPUGBE+GwycKir9WNgIY5h7x4x/GrgQRFZCxQlQNN8nL/rQJywyCRV7Q2cDRwhIkWxvOnYi64FON9VD3dcHjAP8MVKSzW6/g7MAT4FThCR1q7GxTgVUU+4hR/n43gWMc93JOP5FaJeGYXQFxlxgm8G2ohIW/f1OziJ0nR3/EE4J8QbwEB1kkqJ0PUuMAyYAJymqneqEwOeB5zl3s0kQlfo+0pzx60CWuEajlixe75CVYOqWoZzZ+vDuZihqn5VvRMYC/wfzl3Ureok42KqS3cmQhcBuSLS1339NdAEyHXfMwB4BngLOEBVX0iApnGunhLgKlW9wx2/CTjY/bvWKbX8rhq748pwkuExmwtTlS5V9bnnzPc4/3PXR4y5H8cwXAZ0By5T1R0x1CeR2kjw+VUVSW0UROQQEXlSREKVOkF3eyjh8hlOFcFgEUlT1Qk4sdVQcncDcKmqnl2X/xj7oOsHnIvt4aq6XUS87t375roMhezH9zUy4jBHq+qrdaWpBl2e3ao8ZgNTgR4ikhfyoFT1LuAiVT2prg2oiBwQSuRF/KNGTvyaiBN6O9r9zubg3K2FwpQbgV+p6pmqujpBmmbjlHoOVNXy0Pnlvr8uz6/9/a4AzlbVf9eVphp0yW7n1wacCrtuItJWnHkdTV2v+UpV/YWqrq1jbUNE5DERuVxEPKEbtESeXzWRtEZBRA4AnsQJtRwvIo+KSCj04XN/TsQpQT0CuNB963qchA2qulBVJyWJrpIIXYG6duf38/uaFzqOqm6Lo66gqqo480s8bt7gDZx/klnAWHFnWKvq9jrWJSJyN/AFcKOIHOxu97gGO+B+7iIcF78zTqknOHmQ5e7+lao6M0k0LXP31+n5VVe63DHlcdSl7vmVKU6VUUBVx+HcfMzCuStv4eqqrCtdroZ0EXkEp6R0Hk5o6q/utaSuiQAABftJREFUPm8izq9oSVqjAAzBiY0+A/wS2I4TDwy5Vfe4X/rbOGGQs0VkBs5Fbprpqje67sIJ8eW746/HceVfwbnzXRcLUe5Fcy5wFvCEqy1sqELaRORZnIKAvwFDRORHYBNOzX2yafq0rjU1AF134FSxtXZfX4WTbH4K6KeqC2OhDSf8sxo4QVX/CVyKk9RuFjII8T6/okbjWOq0twfwC5zJGsPd10Nw7hpbua9H4iSNz3DHvgx0iXh/AU5liumq37pOBXrE6Rzz4CTWewKvAee429Nwqnl219YYyG/omhqgrs4R7x8VqTMG2m4ChrivW7s/M92f7+KE+Tzx/M5q/XskXIBTj/4nHGt5I87ErpOA3sCDOInY0B/5DhwrH1lb7zVdDUJXzOroq9F2WsT+bOA895+2qbstM2K/JxU0NXBdMTnv96LtlN3GtMEJ3ea7r9Nj/Z3t6yPh4SN1XKnuwE2q+ghO7fC1OBeP1cAAceqI/TglbmepG5d049Axqb82XXHXFZOKomq03YHT0qCnu38HTmXKKpz5I+BUyYTKeeu8RDEZNTVwXTGbp1GNtutl1xn6RwATVLVURBrhVBiFzv1Et/zYhUQ1y7pQRA4XkVAceR3Q1M2+v4lTpnUU8CXOhJK73XFtgIni1qnX9ZdpuhqGrii0vY2TbDxL3CoQVV2KE364U0TKcMJeqHsr11A1ma6YawtV/uUBU0XkUpwKu0GutqQyCED8wkc4cb/WODXnX+CsL/oyThzt9zjuV8i16oGT/Gzpvn4O+ABnxm2dxgNNV8PQtQ/auuNM5gvFfXviVIl8ipOAbLCaTFdCtH2JU1X0Qiy01envGZcPceN5OM26XgptwylVfA6n8uRjnBmrOe7+N4Ab3OfpRPRQMV2mq460vQ5c7z5vDZzY0DWZrrhr+437/AZ2yzMk6yOm7QJct+5uwCtOP5Y83B4sqhoQkV/j9CP5K04J4tnuH/Z1nJmt37tjfTilk6bLdNWlNj9Oj39UdQ3wfkPVZLoSpu0Hd+yjdakrlsQspyAih+Nk45vixJbvxrlAjBCnORzqJGjuBB5SZ1bhp8CFIjIVJ0FZ55M2TFfD0JWs2pJRk+lqeNpiSqxcEJwmTxdEvH4Cp7HTxcCP7jYPTvuHN4F27rZWQCfTZbrqo7Zk1GS6Gp62WD5iWX30I/CG7Ozx8R3QXp2+J14RuVadzHtbwKeqKwFUda2qLqnyiKbLdCW/tmTUZLoanraYETOjoKrbVbVCd9YHH8XOePIlQE8ReR94FacvTlwwXQ1DV7JqS0ZNpqvhaYsl8ehL7wUUp13uGHfzFpwSrj7AUo1Ba1/TlRq6klVbMmoyXQ1PWyyIx+S1IE4p4gagn2tZ/wgEVfXbBH6Zpqth6EpWbcmoyXQ1PG11T10nKap64CwwEwS+xVnEIuHJFNPVcHQlq7Zk1GS6Gp62un6EFkmPKeKs9HUB8Ig6PfOTAtNVO5JVFySntmTUBKZrX0hmbXVNXIyCYRiGUT9IeJdUwzAMI3kwo2AYhmGEMaNgGIZhhDGjYBiGYYQxo2AYhmGEMaNgGLVARPJF5Ffu8zYi8maiNRlGXWIlqYZRC0SkGHhfVfskWIphxISY9z4yjAbGA0BnEZkGLAR6qmofEbkY+P/27l8XgiiK4/j3KChQegLRSRDReAQNkagkGlHqSLyAilbjFZQeQOUBBIlGPAHBJpto9ihm3GyEYnYnmy2+n27+3MmZYvLLvZk5swVMAwvAOTBJ9cHTF7CRmW8RMQ9cAHNAFzjIzKfR34b0N5ePpGZOgOfMXAaOfx1bBLaBNeAU6GbmCtXft/bqcy6Bw8xcBY6oevRLY8OZgtSem8zsAJ2I+ACu6/33VI3UZoB14CoifsZMjb5M6X+GgtSe/p44vb7tHtWzNgG817MMaSy5fCQ10wFmBxmYmZ/AS0TsAERlqc3ipGEZClIDmfkK3EbEA3A2wCV2gf2IuAMegc0265OG5SupkqTCmYIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBXf3MKo1YXDMa8AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -187,11 +187,11 @@
       "Attributes:\n",
       "    Conventions:          CF-1.6\n",
       "    featureType:          timeSeries\n",
-      "    history:              Created on 2020-03-09 15:41:34 by Raven\n",
+      "    history:              Created on 2020-04-30 15:26:42 by Raven\n",
       "    description:          Standard Output\n",
       "    title:                Simulated river discharge\n",
       "    references:           Craig J.R. and the Raven Development Team Raven use...\n",
-      "    comment:              Raven Hydrological Framework version 2.9 rev#177\n",
+      "    comment:              Raven Hydrological Framework version 2.9 rev#254\n",
       "    model_id:             gr4jcn\n",
       "    time_frequency:       day\n",
       "    time_coverage_start:  2000-01-01 00:00:00\n",
@@ -219,7 +219,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "http://localhost:9099/outputs/fbfa6bc6-623d-11ea-9d79-b052162515fb/raven-gr4j-cemaneige-sim-0_WatershedStorage.nc\n"
+      "http://localhost:9099/outputs/85ac85d6-8b18-11ea-bece-b8ca3a8f5177/raven-gr4j-cemaneige-sim-0_WatershedStorage.nc\n"
      ]
     }
    ],
@@ -245,7 +245,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/gridded_data_subset.ipynb b/docs/source/notebooks/gridded_data_subset.ipynb
index e4f82b9f..fa92dbbf 100644
--- a/docs/source/notebooks/gridded_data_subset.ipynb
+++ b/docs/source/notebooks/gridded_data_subset.ipynb
@@ -15,32 +15,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import netCDF4 as nc\n",
-    "import numpy as np\n",
-    "import salem\n",
-    "import xarray as xr\n",
     "from birdy import WPSClient\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pyproj import CRS\n",
     "\n",
+    "from example_data import TESTDATA\n",
     "import datetime as dt\n",
+    "from urllib.request import urlretrieve\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
     "import os\n",
     "import json\n",
+    "import netCDF4 as nc\n",
+    "import salem\n",
+    "from zipfile import ZipFile\n",
     "import glob\n",
     "import tempfile\n",
     "from pathlib import Path\n",
-    "from urllib.request import urlretrieve\n",
-    "from zipfile import ZipFile\n",
     "\n",
-    "from example_data import TESTDATA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "# Set environment variable RAVEN_WPS_URL to \"http://localhost:9099\" to run on the default local server\n",
     "url = os.environ.get(\"RAVEN_WPS_URL\", \"https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps\")\n",
     "wps = WPSClient(url)\n",
@@ -51,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +73,7 @@
     "ZipFile(vec,'r').extractall(tmp)\n",
     "shp = list(tmp.glob(\"*.shp\"))[0]\n",
     "shdf=salem.read_shapefile(shp)\n",
-    "# shdf.crs=CRS(4326) # This is needed in certain cases!\n",
+    "shdf.crs=salem.wgs84 # This is needed in certain cases!\n",
     "\n",
     "lon_min=shdf['min_x'][0]\n",
     "lon_max=shdf['max_x'][0]\n",
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -128,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,31 +150,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "crs not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-1dc0aee27199>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;31m# Special treatment for ERA5 in North America: ECMWF stores ERA5 longitude in 0:360 format rather than -180:180. We need to reassign the longitudes here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m360\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0msub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msalem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'longitude'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkeep_attrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;31m# Define the path to the netcdf file and write to disk (the basin averaged data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/sio.py\u001b[0m in \u001b[0;36mroi\u001b[0;34m(self, ds, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'grid'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregion_of_interest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m         coords = {self.y_dim: self._obj[self.y_dim].values,\n\u001b[1;32m    507\u001b[0m                   self.x_dim: self._obj[self.x_dim].values}\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mregion_of_interest\u001b[0;34m(self, shape, geometry, grid, corners, crs, roi, all_touched)\u001b[0m\n\u001b[1;32m   1024\u001b[0m             \u001b[0;31m# corner grid is needed for rasterio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m             shape = transform_geopandas(shape, to_crs=self.corner_grid,\n\u001b[0;32m-> 1026\u001b[0;31m                                         inplace=inplace)\n\u001b[0m\u001b[1;32m   1027\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mrasterio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrasterize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mtransform_geopandas\u001b[0;34m(gdf, to_crs, inplace)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m     \u001b[0;31m# Do the job and set the new attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1303\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_crs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   3846\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3847\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3848\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3850\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(geom)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m     \u001b[0;31m# Do the job and set the new attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1303\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_crs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(func, geom)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Multi'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'GeometryCollection'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Type %r not recognized'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Multi'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'GeometryCollection'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Type %r not recognized'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/shapely/ops.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(func, geom)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Polygon'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 shell = type(geom.exterior)(\n\u001b[0;32m--> 234\u001b[0;31m                     zip(*func(*izip(*geom.exterior.coords))))\n\u001b[0m\u001b[1;32m    235\u001b[0m                 holes = list(type(ring)(zip(*func(*izip(*ring.coords))))\n\u001b[1;32m    236\u001b[0m                              for ring in geom.interiors)\n",
-      "\u001b[0;32m~/miniconda3/envs/raven/lib/python3.6/site-packages/salem/gis.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, x, y, z, crs, nearest, maskout)\u001b[0m\n\u001b[1;32m    671\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_crs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mij_to_crs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'crs not understood'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;31m# Then to local grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: crs not understood"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if dataset=='ERA5':\n",
     "    tsfile=tmp / 'ERA5_ts.nc'\n",
@@ -212,9 +181,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<salem.graphics.Map at 0x7f0b7730f550>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Map of precip snapshot\n",
     "ds.pr.isel(time=2).salem.roi(shape=shdf).salem.quick_map()"
@@ -222,16 +214,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'pr' (time: 1441)>\n",
+       "array([1.9725623e-05, 2.7161006e-05, 3.7725804e-05, ..., 1.8322079e-06,\n",
+       "       1.3108788e-06, 1.3241714e-06], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2000-12-31 2000-12-31T01:00:00 ... 2001-03-01\n",
+       "Attributes:\n",
+       "    units:       m\n",
+       "    long_name:   Total precipitation\n",
+       "    pyproj_srs:  +proj=longlat +datum=WGS84 +no_defs"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sub.pr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,9 +309,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ":TimeStamp 2001-02-28 00:00:00.00\n",
+      ":HRUStateVariableTable\n",
+      "  :Attributes,SURFACE_WATER,ATMOSPHERE,ATMOS_PRECIP,PONDED_WATER,SOIL[0],SOIL[1],SNOW,SNOW_LIQ,CUM_SNOWMELT,CONVOLUTION[0],CONVOLUTION[1],AET,CONV_STOR[0],CONV_STOR[1],CONV_STOR[2],CONV_STOR[3],CONV_STOR[4],CONV_STOR[5],CONV_STOR[6],CONV_STOR[7],CONV_STOR[8],CONV_STOR[9],CONV_STOR[10],CONV_STOR[11],CONV_STOR[12],CONV_STOR[13],CONV_STOR[14],CONV_STOR[15],CONV_STOR[16],CONV_STOR[17],CONV_STOR[18],CONV_STOR[19],CONV_STOR[20],CONV_STOR[21],CONV_STOR[22],CONV_STOR[23],CONV_STOR[24],CONV_STOR[25],CONV_STOR[26],CONV_STOR[27],CONV_STOR[28],CONV_STOR[29],CONV_STOR[30],CONV_STOR[31],CONV_STOR[32],CONV_STOR[33],CONV_STOR[34],CONV_STOR[35],CONV_STOR[36],CONV_STOR[37],CONV_STOR[38],CONV_STOR[39],CONV_STOR[40],CONV_STOR[41],CONV_STOR[42],CONV_STOR[43],CONV_STOR[44],CONV_STOR[45],CONV_STOR[46],CONV_STOR[47],CONV_STOR[48],CONV_STOR[49],CONV_STOR[50],CONV_STOR[51],CONV_STOR[52],CONV_STOR[53],CONV_STOR[54],CONV_STOR[55],CONV_STOR[56],CONV_STOR[57],CONV_STOR[58],CONV_STOR[59],CONV_STOR[60],CONV_STOR[61],CONV_STOR[62],CONV_STOR[63],CONV_STOR[64],CONV_STOR[65],CONV_STOR[66],CONV_STOR[67],CONV_STOR[68],CONV_STOR[69],CONV_STOR[70],CONV_STOR[71],CONV_STOR[72],CONV_STOR[73],CONV_STOR[74],CONV_STOR[75],CONV_STOR[76],CONV_STOR[77],CONV_STOR[78],CONV_STOR[79],CONV_STOR[80],CONV_STOR[81],CONV_STOR[82],CONV_STOR[83],CONV_STOR[84],CONV_STOR[85],CONV_STOR[86],CONV_STOR[87],CONV_STOR[88],CONV_STOR[89],CONV_STOR[90],CONV_STOR[91],CONV_STOR[92],CONV_STOR[93],CONV_STOR[94],CONV_STOR[95],CONV_STOR[96],CONV_STOR[97],CONV_STOR[98],CONV_STOR[99]\n",
+      "  :Units,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm,mm\n",
+      "  1,0.00000,0.00000,-3.91667,0.00000,19.18146,339.42372,3.91667,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000\n",
+      ":EndHRUStateVariableTable\n",
+      ":BasinStateVariables\n",
+      "  :BasinIndex 1,watershed\n",
+      "    :ChannelStorage, 0.00000\n",
+      "    :RivuletStorage, 63759773.48786\n",
+      "    :Qout,1,1475.92068,1492.85062\n",
+      "    :Qlat,3,1475.92068,1492.85062,1510.14332,1475.92068\n",
+      "    :Qin ,20,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000\n",
+      ":EndBasinStateVariables\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(diagnostics)"
    ]
@@ -314,18 +347,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'q_sim' (time: 59, nbasins: 1)>\n",
+       "array([[   0.      ],\n",
+       "       [1804.451682],\n",
+       "       [3571.474031],\n",
+       "       [3497.758444],\n",
+       "       [3426.289786],\n",
+       "       [3356.990978],\n",
+       "       [3289.787652],\n",
+       "       [3224.608061],\n",
+       "       [3161.382979],\n",
+       "       [3100.045617],\n",
+       "       [3040.531534],\n",
+       "       [2982.778556],\n",
+       "       [2926.726694],\n",
+       "       [2872.318067],\n",
+       "       [2819.496829],\n",
+       "       [2768.209095],\n",
+       "       [2718.402874],\n",
+       "       [2670.027999],\n",
+       "       [2623.036064],\n",
+       "       [2577.380364],\n",
+       "       [2533.01583 ],\n",
+       "       [2489.898976],\n",
+       "       [2447.98784 ],\n",
+       "       [2407.24193 ],\n",
+       "       [2367.622175],\n",
+       "       [2329.090872],\n",
+       "       [2291.611637],\n",
+       "       [2255.149362],\n",
+       "       [2219.670167],\n",
+       "       [2185.141357],\n",
+       "       [2151.53138 ],\n",
+       "       [2118.809788],\n",
+       "       [2086.947198],\n",
+       "       [2055.915251],\n",
+       "       [2025.68658 ],\n",
+       "       [1996.234772],\n",
+       "       [1967.534337],\n",
+       "       [1939.560674],\n",
+       "       [1912.290037],\n",
+       "       [1885.69951 ],\n",
+       "       [1859.766975],\n",
+       "       [1834.471081],\n",
+       "       [1809.791224],\n",
+       "       [1785.707513],\n",
+       "       [1762.20075 ],\n",
+       "       [1739.252403],\n",
+       "       [1716.844583],\n",
+       "       [1694.960024],\n",
+       "       [1673.582056],\n",
+       "       [1652.694588],\n",
+       "       [1632.282085],\n",
+       "       [1612.329551],\n",
+       "       [1592.822507],\n",
+       "       [1573.746977],\n",
+       "       [1555.089464],\n",
+       "       [1536.83694 ],\n",
+       "       [1518.976824],\n",
+       "       [1501.49697 ],\n",
+       "       [1484.38565 ]])\n",
+       "Coordinates:\n",
+       "  * time        (time) datetime64[ns] 2001-01-01 2001-01-02 ... 2001-02-28\n",
+       "    basin_name  (nbasins) object ...\n",
+       "Dimensions without coordinates: nbasins\n",
+       "Attributes:\n",
+       "    units:      m**3 s**-1\n",
+       "    long_name:  Simulated outflows"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "hydrograph.q_sim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f0b5aaf97b8>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pandas.plotting import register_matplotlib_converters\n",
     "register_matplotlib_converters()\n",
@@ -335,9 +468,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(3571.47403105)\n",
+      "Mean:  <xarray.DataArray 'q_sim' ()>\n",
+      "array(2231.11144315)\n",
+      "Monthly means:  <xarray.DataArray 'q_sim' (month: 2, nbasins: 1)>\n",
+      "array([[2648.69867205],\n",
+      "       [1768.78272545]])\n",
+      "Coordinates:\n",
+      "    basin_name  (nbasins) object ...\n",
+      "  * month       (month) int64 1 2\n",
+      "Dimensions without coordinates: nbasins\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Max: \", hydrograph.q_sim.max())\n",
     "print(\"Mean: \", hydrograph.q_sim.mean())\n",
@@ -361,7 +512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/multi_model_simulation.ipynb b/docs/source/notebooks/multi_model_simulation.ipynb
index ddd3ae6e..72966d6d 100644
--- a/docs/source/notebooks/multi_model_simulation.ipynb
+++ b/docs/source/notebooks/multi_model_simulation.ipynb
@@ -76,7 +76,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_vtlvojxr/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0371048,36.562,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_vtlvojxr/Salmon-River-Near-Prince-George_meteo_daily.nc,-7.03141,101.745,\\n']\n",
+      "['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_1m9rxaij/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.117301,37.9493,\\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\\nHYDROGRAPH,/tmp/pywps_process_1m9rxaij/Salmon-River-Near-Prince-George_meteo_daily.nc,-3.0132,71.9223,\\n']\n",
       "[<xarray.Dataset>\n",
       "Dimensions:     (nbasins: 1, time: 732)\n",
       "Coordinates:\n",
@@ -91,11 +91,11 @@
       "Attributes:\n",
       "    Conventions:          CF-1.6\n",
       "    featureType:          timeSeries\n",
-      "    history:              Created on 2020-03-09 15:41:59 by Raven\n",
+      "    history:              Created on 2020-04-30 15:25:50 by Raven\n",
       "    description:          Standard Output\n",
       "    title:                Simulated river discharge\n",
       "    references:           Craig J.R. and the Raven Development Team Raven use...\n",
-      "    comment:              Raven Hydrological Framework version 2.9 rev#177\n",
+      "    comment:              Raven Hydrological Framework version 2.9 rev#254\n",
       "    model_id:             gr4jcn\n",
       "    time_frequency:       day\n",
       "    time_coverage_start:  2000-01-01 00:00:00\n",
@@ -113,11 +113,11 @@
       "Attributes:\n",
       "    Conventions:          CF-1.6\n",
       "    featureType:          timeSeries\n",
-      "    history:              Created on 2020-03-09 15:41:59 by Raven\n",
+      "    history:              Created on 2020-04-30 15:25:50 by Raven\n",
       "    description:          Standard Output\n",
       "    title:                Simulated river discharge\n",
       "    references:           Craig J.R. and the Raven Development Team Raven use...\n",
-      "    comment:              Raven Hydrological Framework version 2.9 rev#177\n",
+      "    comment:              Raven Hydrological Framework version 2.9 rev#254\n",
       "    model_id:             hmets\n",
       "    time_frequency:       day\n",
       "    time_coverage_start:  2000-01-01 00:00:00\n",
@@ -154,9 +154,9 @@
        "       [ 0.165788],\n",
        "       [ 0.559366],\n",
        "       ...,\n",
-       "       [28.077935],\n",
-       "       [27.835868],\n",
-       "       [27.597955]])\n",
+       "       [13.407794],\n",
+       "       [13.330653],\n",
+       "       [13.25446 ]])\n",
        "Coordinates:\n",
        "  * time        (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2002-01-01\n",
        "    basin_name  (nbasins) object ...\n",
@@ -183,7 +183,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f6066e6ad68>]"
+       "[<matplotlib.lines.Line2D at 0x7faee45f9940>]"
       ]
      },
      "execution_count": 5,
@@ -192,7 +192,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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FwG2+SwOJG0D+B40xM40xM3Nycjor3MHHssfYdfG6zo2tKIrSzQgWA/HxMvBv4FWgKVzCiEgCVnn8xxjzIoAxptDv/EPAa85uPjDY7/Y8YFdYBDM+vRRAgaRl26KKxWvD8mhFUZRoxa0CqTXG3BVOQUREsEpqrTHmdr/jA4wxBc7uOcCXzvZc4GkRuR0bRB8NLA6LcD4FEsgCAeh3CBSpAlEUJbZwq0DuFJGbgHeAOt9BX9A7RBwBfAdYJSLLnWM3AheIyFSse2or8GPn2atF5DlgDTaD68rwZmAB0orHr98E+PwJaGqCOG0zryhKbOBWgUzCvtyPp9mFZZz9kGCMWUDguMYbbdxzK3BrqGRolbZcWGAtkIYqKNve3GhKURSlh+NWgZwDjDDG1IdTmOjFZ4G0crrfeLsuWqsKRFGUmMGtv2UF0CecgkQ1wSyQnLF2XbSmS8RRFEWJBtxaILnAOhFZwoExkK+HRapowzheu9aC6Mm9oPcQDaQrihJTuFUgN4VViqgniAUCmomlKErM4UqBOCVNYpdgabxgFcjmD8DbAJ6ErpFLURQlggSrxvtaW+fdXtP9cWGB9J8E3noo+apLJFIURYk0wSyQI4OULBFgfAjliU7cWCADpth1wQrInRB+mRRFUSJMMAVylosxYiC114UFkjkSEtOtApl6YZdIpSiKEknaVCAxH/vw4cYCiYuzbqyCFV0jk6IoSoTRuhuucGGBgHVjFay0zaUURVF6OKpA3ODGAgGrQBqqYM+m8MukKIoSYdqtQESkr4hMDocw0UuQYoo+/APpiqIoPRxXCkREPhSRXiKSiS1r8qhTRj02CFbKxEf2WIhPhoLlbV+nKIrSA3BrgfR2ugN+A3jUGDMDODF8YkUpwVxYnnibwqsWiKIoMYBbBRIvIgOA82juCBg7uLVAoDmQbg7qrqsoitKjcKtA/gC8DWw0xiwRkRHAhvCJFWUEK6boz4ApUFcGezeHVyZFUZQI47aY4qvGmOd9O8aYzcA3wyNSNNIOa2LQTLvOXwpZI8MjjqIoShTg1gL5UkQ+EZG/ishpItI7rFJFG27TeMEWVUzMgPzwtGdXFEWJFlwpEGPMKOACYBVwBrDCr295DNCOGEicBwZNhx2qQBRF6dm4TePNA44AjgKmAauBZ8MoV3TRHgsEYPAsKFwN9VXhk0lRFCXCuHVhbQeuAd40xsw2xpxujPlLKAURkcEi8oGIrBWR1SJytXM8U0TeFZENzrqv3z03iMhGEVkvIqeEUp4DaYcFApA3C4wXdn4eNokURVEijVsFMg14ArhQRBaKyBMi8sMQy9IIXGuMOQQ4HLhSRMYD1wPzjDGjgXnOPs6584EJwKnAfSLiCbFMlvZaIHm+QLq6sRRF6bm4jYGsAB4HHgXeB44BfhdKQYwxBcaYz53tCmAtMAhbUv5x57LHgbOd7bOAZ4wxdcaYLcBGYFYoZfKTzlm7VCCpmZA1GnYsCY84iqIoUYDbGMhSYCFwDrAOONoYMyxcQonIMKzVswjINcYUgFUyQD/nskHADr/b8p1jLce6TESWisjS4uLijgnUXgsEbBwkf3HkJxQ21MKLl+m8FEVRQo5bF9YcY8wkY8yPjTFPGmO2hUsgEUkHXgCuccqntHppgGMHva2NMQ8aY2YaY2bm5OR0UCqXxRT9GTwLqvdE/sWdvxhWPgtzfxZZORRF6XG4fSPWi8jtvl/yInJbOOaCiEgCVnn8xxjzonO40CmjgrMuco7nA4P9bs8DdoVaJqB9pUx8DJlt19s+Dbk47aJmn11XFkZWDkVRehxuFcgjQAW2FtZ5QDk2HhIyRESAfwNrjTH+lX7nApc425cAr/gdP19EkkRkODAaCFPUugMurOwxkJYDWz8Oj0huKdtp1z5FoiiKEiLcljIZaYzxL11ySxgmEh4BfAdY5Tf2jcBfgeecrK/twLkAxpjVIvIcsAabwXWlMSY8rQA7YoGIwLAjYesCe397lE8oqXCMsuo90FADCSmRkUNRlB6HWwVSIyJHGmMWAIjIEUBNKAVxxm7tLXtCK/fcCtwaSjkC0p5iiv4MOxJWvwT7tkDmiNDL5YYG589kmqBwDeTNiIwciqL0ONwqkCuAx524hwB7ge+1vEhEvuFirFpjzBuuJYwq2qtAjrLrrQsip0Aa65q3C5arAlEUJWS4UiDGmOXAFBHp5ey3lh31EDZG0dab9migeymQjqTxgl8cZAFM/27o5XKDtx76DIW6clsh+NBQz/9UFCVWaVOBiMgvWjkOQItgN9hSJz8IMuZT7REwOuhADASa4yBb5kcuDuKth/gkGHIyrH8DvA3gSeh6ORRF6XEEy8LKCLIcgDHm4mAPdHNN1NFRCwRg5PFQUQBFa0Mrk1sa68GTBBPOgdpS2PxhZORQFKXHEcyFlWqM+bWInOvfUKojiMhJxph3OzNG5OjEbPKRTvx/43uQOz404rQHb521OEYcB3HxsH0hjD6p6+VQFKXHEcwCOc2Z3HdDCJ717xCMERk6Y4H0HgT9xlsFEgka66wLKyEZskZFzhJSFKXHEcwCeQsoAdJExD9wLoAxxvTyv1hE5rYyjgBZHZYy4nQwBuJj1Amw6AGoq4Sk9JBJ5Qpvg1UgADnjoGBF1z5fUZQeS5sKxBhzHXCdiLxijDnLxXhHARcDlS2OC2GrlNsFdMYCARh1Inx6t83GGntq6ORyg7cOkh093288rHkF6qshMbVr5VAUpcfhtpTJupYHRORvAa77DKg2xnzUYvkQWN8JOSNMB4op+jNkNiSkRsaN1VgPnkS73e8QwEBJN/5TKIoSNbh9IwaKus5pecAYM8cY80GgAYwxR7dHsKiiI6VM/IlPguFHw8Z3u768u7euhQJB4yCKooSENhWIiFwhIquAcSKy0m/ZAqx08wAROSMUgkaWTrqwAEafDPu2QvFBxlx48c0DAeg73Kb0Fq3pWhkURemRBLNAngbOxM4uP9NvmdGO+Rx/6Lh4UUJnLRCAcafb+9e0lmcQJhrrmycOeuIhZwwUdbESUxSlR9KmAjHGlBljtgK/xv4M9y3pIjLE5TMiVIY2hHS0mKI/Gf1h8GGw9tXQyOQWb521OnzkHKIuLEVRQoLbGMjrwGvOeh6wGXjT5b0/7oBcUUYILBCAQ86EwlVd26Ww0c+FBTYOUp4PtWVdJ4OiKD0SVwrEaWc72VmPxqbkLgh0rYj8yVn/wbk3TE2eupDOpvH6OORMu177WufGaQ9evywssKm8oG4sRVE6TYfyUo0xnwOHtnJ6iYjcCyztsFRRR4gskL5DYcAUWNtFcZCmJmhqaKFAfJlYGkhXFKVzuCrn3qIqbxwwHSgOcN1NQCZwAdAoIlONMT0giO6sQ1FN95Az4f0/QVk+9M7r/Hht4a2363g/BdJnCCSmqwJRFKXTuLVA/CvwJmFjIQfNTDfG3OJsznb2u7/yAEJmgQBMdDoDr/pf58cKhtdpJuUfRBexVogG0hVF6SRuG0rdAiAiGXbXtCxV4s8jxpj1IvJIKASMCkIVAwHbmXDQTKtAjrym8+O1hbfBrv2D6GDjIGtfjWyvdkVRuj2uLBARmSgiXwBfAqtFZJmITGzl8nOd9TdDIWB0EEILBGDyeTYbK9xWgK+dbcsGUv3GQ81eqCwK7/MVRenRuHVhPQj8whgz1BgzFLjWORaIDgXRReQRESkSkS/9jt0sIjtFZLmznOZ37gYR2Sgi60XklPY8q93st0BCNN6Ec0A8sKpTLVaCE8iFBc19SYpWh/f5iqL0aNwqkDT/GldOccS0lhc5QfTjsUH0E0Tk9+2Q5TEgUKnafxpjpjrLG85zxgPnAxOce+4TEU87ntVOOllMsSXp/WDEsVaBhLM2VmOAIDr4pfJqHERRlI7j9o24WUR+JyLDnOW3wJaWF3UmiG6MmQ/sdXn5WcAzxpg6Y8wWYCPhLBcfilImLZl0LpRuhx1hnCbjy8LytFAgadmQ1g8KNRNLUZSO41aB/ADIAV50lmzg+61c+ygwGXgBQER+KyIvisj0Dsp4lVPA8RER6escGwTs8Lsm3zkWJkIYRPdxyBkQnxxeN9Z+BZJ08Lnc8ZrKqyhKp3A7E32fMeZnxpjpznKNMWZfK9cuB35njJkvIkcCpwCPA/d3QL77gZHAVKAAuM05HuhNHtAXJCKXichSEVlaXHzQ1BV3+GphhdICScqAsXNg9YvN2VKhxhdEb+nCAuvGKl5nJxsqiqJ0gBA59Q/C66xPB+43xrwCBHiLtY0xptAY4zXGNAEP0eymygcG+12aB+xqZYwHjTEzjTEzc3Jy2iuCbxC7DnXK66RzoXoPbP4wtOP6aC2IDlaBNFRD6dbwPFtRlB5PuBTIThF5ADgPeENEkjryLBEZ4Ld7DjaNGGAucL6IJInIcGA0EMZgQhhiIACjToLkPrDyudCO68Nn2bSMgUBzIF3jIIqidJBwKZDzgLeBU40xpdjyJte1dYOI/BdYCIwVkXwR+SHwdxFZJSIrgeOAnwMYY1YDzwFrgLeAK40x3laG7jzhskDiE2H8WbDudaivCu3YEMSFNc6uNRNLUZQO0uZMdBG5m1ZiCwDGmJ+1crwaG2z37RdgYxitYoy5IMDhf7dx/a3ArW2NGTrCZIEATDkfPn8cvnwRpn8ntGO3FURPTIPMkbDri9A+U4lNKovhnd/CUb+AnLGRlkbpIoJZIEuBZUAytoDiBmeZSnOco+cTLgsEYMhs605a/EDo54S0ZYEADDkcdnzW9X3alZ7H0kdg5TOw7LFIS6J0IcE6Ej5ujHkcG2M4zhhztzHmbuAErBKJEcJogYjArB/B7lWhnxPS2jwQH0MOt0H8PRtD+1wl9qivsOvdqyIrh9KluI2BDMRW4vWR7hyLDcJpgQBMOg+SelsrJJQ01tp1fHLg80Nm2/X2haF9rhJ7NDjftR2LoLY8srIoXYZbBfJX4AsReUxEHgM+B/4cNqmijjBaIABJ6TDtIlgzFyoKQzduMAWSNQpSs2D7Z6F7phKbNNbYtbceNrwTWVmULsPtRMJHgcOAl5xltuPaig1C2VCqNQ691HYP/DyEH+v+GEgrCkQEBh+uFojSeRpqoe8wSM+FdV3YslmJKG7LuQtwIjDFNylQRMJXeyrqCHExxUBkjYSRx8PSR0M3M72x1sY/4tqQe8jhsHdzaC0fJfZorIWENPsd3jJfKxzECG7fiPdhCyT6Um0rgHvDIlE0Eo5iioE49EdQscvOCwkFDbWtWx8+9sdBPg3NM5XYpLEWEpJh+NE2MeOrtyItkdIFuFUghxljrgRqwdbGogOlSbovIe4H0hpjToE+Q+HTu0OTWtvoQoEMmAIJqbBN3VhKJ2iohfgUq0AAnrlA5xjFAG4VSIPTb8MAiEgOEDs2ajiKKQYizgNH/Ax2LoUtH3V+vMa64AokPhHyZqoFonSOxhrbOrl3HpzkdHHY+klkZVLCjlsFchc2eN5PRG4FFgB/CZtU0Ua403j9mXox9BoE8/7QeSuksfbgfuiBGHoE7P4Sako79zwldmmohYQUu33E1ZCSCXs2RFYmJey4zcL6D/ArrNIoAM42xoSpAmA00kUxELB+5GNvgJ3LYO3czo3lxgIBJw5iwtvcSunZNNYc+F3LGgUlOkG1p+M2C+tJY8w6Y8y9xph7jDFrReTJcAsXNXSlBQIw5QLIGWetEG9jx8dprLEKKRh5h9psrblXQeFquGcWfKW5/Eo7aKg98LuWPVotkBjArQtrgv+OEw+ZEXpxopUutEAAPPFw4s22xMgXndDTbi2QxFQ4/AqoLIT7vwYl60M7H0Xp+TQ6QXQfWaPs90lnpfdo2lQgInKDiFQAk0WkXEQqnP0i4JUukTAa6GoLBGDMqTD4MPjo781lItqL2xgI2MDnpHOb90u3d+yZSmzS8ruWPdqu1Qrp0QQrpvgXY0wG8H/GmF7GmAxnyTLG3NBFMkYBXWyBgFVWx//OzgtZ2mpV+7ZpqGkObLphxHHN24Wr7f2KEgxjDv6uZTkKROMgPZo2+4H4MMbcICJ9sVV5k/2Ozw+XYFFFJCwQgOFHwYhj4ePbYfoltmZWe6irgMSM4Nf5mHweVBVBXSV8/A9bWXVwDBUcUDqGt/WIRmwAACAASURBVB4wB7pLM4fbyg1qgfRo3AbRLwXmY7sM3uKsbw6fWNFGBCwQH8f/HqpLYNH9gc831sP9R8KyADGLuor2KR1PAhz5c1uXC2wmmGJ59juw8vlISxGd+CxVfwskPslOii1RBdKTcRtEvxo4FNhmjDkOmAYUh02qaCNSFghA3gwYezp8cjfU7Dv4fEUBFK6CV3924MQtY6C+EhLbabUA9BoAGQMgf2nH5e5JlOXblOoXL420JNFJa1Wfs0drr5kejlsFUmuMqQUQkSRjzDoghvpWdkExxbY4/jdQVw6f3HXwuaqS5u03f9Ws7BrroKkRktrhwvJn6Ndg68farRAOVKRadPJgWlMgWaNhzyYtrNiDcftGzBeRPsDLwLsi8gqwK3xiRRldVUyxNXInwMRvwqJ/QWXRgeeqnP3pl0DhlzD3p/DyT6Bytz3eUQUy/Bibhlm8vuNy9xTKdjRvr3w2cnJEK74swZZzjrJH2blI5fldL5PSJbidiX6OMabUGHMz8Dvg38DZoRRERB4RkSIR+dLvWKaIvCsiG5x1X79zN4jIRhFZLyKnhFKWNoTskscE5LgbrVXx8W0HHvcplEMvhbh4O29k+X9gkdPdsCMuLLDBe4D7DrO/ImOZit12jsPI421yQSBXYizjayYV3yLjL2uUXWscpMcSbB5IZssFWIWthdXBN1OrPAac2uLY9cA8Y8xoYJ6zj4iMB87HTnA8FbjPmdwYHrqqmGJbZI2EqRfYfiHlfsafzwLJHgP9Jzcf/+w+u25v5paPvkPhiGvs9vo3OjZGT6GyEDJy4YTfQ20ZrH450hJFF61aII6Xu2ht18qjdBnBLJBlwFJn3XIJaYTVSQne2+LwWYAvvehxmq2es4BnjDF1xpgtwEYgfPmmkQyi+3P0dWC8sOCfzceqSiCpl/3Pe8Y/7UvuB35lSHoN6vjzTroF0nLUjVWx23baGzAVMkfCGlUgB9CaBZKRa7sUasfLHkuwiYTDjTEjnHXLZUQXyJdrjClwZCkA+jnHBwF+jmnynWNhIkoCyX2HwdSLYNljNjMIrAsrLdtuD5wKR10LQw6Dfk71mQFTO/fM7LGxrUC+escmE2SPsT8gJpxjO+75Jy/EOj4LJFDVg5EnwMb3oGqP3V/0INw+waaYK90et/NAjg60hFu4tkQKcCzgW15ELhORpSKytLi4g5nH0WKBABz9SyvPWzfY1rdVxZDW7+DrfvgO/HJj2+1s3ZAz1tbGisVsrMoieNop7zLsSLuecLZ1aa6JnUo+QfFlYQWqejDjEnv+qzftd+jN62xQ/csXulZGJSy4fbtc57f8DniVrplIWCgiAwCctS8FKR8Y7HddHq1khRljHjTGzDTGzMzJyemgGBHOwvKnzxCb1rt2Lrx+rXUPpAf4dyWlBz7eXnLGWb9/ZQymr757k01MmPN/MPFb9ljuRMg5BFY8E1nZoonW0njBxuUyBsIrV8Ib1zUf19YBPQK3WVhn+i0nAROBrnijzAUucbYvobmA41zgfBFJEpHh2BIr4ftGRpMFAna2+PTv2oq5TY025TZc5Iyx6+J14XtGNFK9F1Y9Z7PbDrvMVkgG+x2YeiHkL9bsIh9tKRAROPNOu73kIatMBh+uVQ56CB31b+RjlUjIEJH/AguBsSKSLyI/BP4KnCQiG4CTnH2MMauB54A1wFvAlcYYbyjlOZAoskB8HHO9/TV8xj9h1o/C95yccXZd/FX4nhFtNHlhwe1WOU+96ODzk88D8cDyp7tetmiksc6uW+s9M+ZkuGYVTPsOfPdlmw5dvF5LvfcAXBVTFJG7aX6LxgFTgRWhFMQYc0Erp05o5fpbgVtDKUOrRJsFAtB7EFzRBT2n03MhuXdsWSAL74FP77YB8wGTDz6f0R9GnWjdWMf/1vayj2V8tbDa6j3TZwicdY/dHjQdMFCwwhYMVbotbi0Q/1TehcCvjTEXh02qqCMKLZCuQsRmYpV8Zd06tWWw8F673VPxuVfm/L31a6ZeaEvtb/6wS0SKanwWiMdl75lcx3mh80O6PW7Lucd2e7potEC6kpwx8NXb8Pfhzce2fgIX9FAXzr5t1s2SHiC7zcfYOZCSCUsehlEBjeTYobHWKg+3GX8Z/SG5jy29o3Rr3KbxniEiX4jIXr/OhDHkwIxwMcVIkzPOpgv7U7A8MrIEo3yXjWF0htJtds5NW8Qn2djT+jegKIbce4ForHXXOtmHiHVj7VgUPpmULsHtG/EObBZUll9nwl5hlCu6iHQxxUiTHaDwckUB1Fd3vSxtsfI5uP2Qg+uFtYfGelvrKmNA8Gtn/djOvv7kzo4/ryfQntbJPkYeb+Nq2z8Lj0xKl+BWgewAvjQmFmeT0VwLK5ZdWD5GngDH/dZ+JtGUillf3TzPoDPZUdXOjOnUrODXpmXBzO/DymdiK0utJY11rWdgtcaM79mU3nl/CItIStfgVoH8CnjDqYD7C98STsGiixi3QHoPaa5zdOadcPgV1ue97vXIyuXP+jegthTGnw37tnTcrVTtlCjxlYcJxpG/gIRUeO8mq7hqyzr23O5MQ037XFhg2wxMu9hOhN27Jbp+jCiucatAbgWqsf3QM/yW2CDWg+hxcba7nCfJFmdMSoeRx8G619oucVJZFP4SKKU7oGynVSCp2XDyH+3xzR90bDxfjatUlwokPQdmX2Wf//IV8OJlHXtud6axrv0uLIBBM6wle9dUeOj45nRgpdvgKgsLyDTGnBxWSboFMapAwNaCSurVnGkz6kT46i0o3wm98w6+fsnDttTKqJPg/KchPrHt8atKbFpne+YFNNTCHU5KaFJvGH+mnW/Qewhs+8RaSu3F11/FrQUCMPtKWzBw51L7mRSthX6HtP/Z3ZXG2oMr8bphYItCn3s2Qv9JoZFJ6RLcWiDviUjsKpBYt0AATvkzXPJq876v98juVYGv3/apXW98F5Y/FXz8F38Ej5/Rvvkl2z9t3q4rg7Gn2e3RJ8LGeR1zJ+1eaS2tvsODX+sjuRf8aB5ct9kJqgdoPdyT6UgQHWw6rz+xXPW5m+JWgVwJvCUiNTGdxhvLFojIgXn+/SfatObP7gvc87qyyNY8yhwJG94LPn6lkybcniJ7+UsBgQnfsLPlRxxnj093KsB+fLv7scD+UNj8EQyYEtxiCkRalq0+u+q55nL7sUB703j9OeH3zRMLNSOr2+G2mGKGMSbOGJMS02m8sWyBtCQxDXrl2d4YCwK8qCuL7ES8AZOhsBUrxZ/s0Xa9e6V7GfKX2nLz33wYfvo5JKba4wOn2jTRNa+0LwZT+KWVdcq33d/TktlX2mcuvK/jY3Q3OpKF5eOoa21JnlEnWWs1RhM9uyvBWtqOc9bTAy1dI2I0oF/qgJz3mF0Hanlb5SiQ3IlQut26k/ZuhpKNrQzmfMZu3RjGQP4SGDTT1qJqGbMYe5rNxmqPW8QX/8jtRJ3QPkNg0rds06+eXO7Fn45kYbVk/Fmwb6utj6V0G4JZIL5U3dsCLP8Io1zRhVoggRk0Aw673AaN/d1Yvsl46bnNQdFdy+GuaXD/7MC/Musq7brE5Qu/qgRq9lpXWiDGnmYr5n7ejio8NfvsOqWv+3sCccTV0FBlEwligY5mYfkzwmlJkL+k8/IoXUawlraXOevjAizHd42I0YDGQFql/2RoqIaiNc3HfGVP0nKag+2+2dreemsZtKS+yq5LNgaOqbRk31a7bi3Y3WuAtQQ+f9L9jHmfxZCS6e761sidAKNPhkX/ir7Z+uGgMzEQH70H2889WkvkKAEJ5sI6VET6++1/V0ReEZG7RKST/8u6EWqBtM6IY+16wzvNx3zdC9P72Rf5tIth07zm87sCvCTqHQuksQbKdhx8viWl2+y679DWr5n2HaivcD/hMVQWCMAR19hZ7cv/0/mxop1QKBARGDgt8HdDiVqCubAeAOrB9kXHNnR6AigDHgyvaNFEjBdTbIveg2DokbD00WbLwWeBpOfa9fRLDrwnkJ+7vsoG5cGWjg/G7lW23WxbRQ+HHmHnhLh9idfstXNdPG6nR7XB0K9B3iz49C7wNnZ+vGjFmNAoELDJD0VrmzPylKgn2BvRY4zxRQK/DTxojHnBGPM7YFR4RYsiYr2YYjCmfwfKtjf7r8ud9vRpTk/2AX4TxvpPDuymqK9snljmJvC9ZT7kHQoJbUxgi4uDqRfYnh1lO4OPWb03NNYH2F/UR15jEwjWvByaMaOQ6tpaME2UNYbgx1W/8WC88I9RsC5AYoYSdQRVICLi+zl2AvC+37kQ/EzrJsR6McVgjD3NzgR/8zpY+5qdG5KW0zxDPT7R1ow6/XarJHYtPziQXl8FfYbaIobBAuk1pVYJuekFP+V8wNiCh8Go2QepIfTMjpljKxkvuKPHpqe+u8K6Ej/cVNn5wbL8fpNufLfz43V3tsyPemssmAL5L/CRiLwC1AAfA4jIKKwbK0ZQC6RNknvB1++07odnL7IuqFmXHdjq9cSb4NAfWmukttT+MvfR1GQVSFK60/2wtVRfrDvopcutUh9+dHDZMkfAkK/B8v8Gf4nXhNACAWsBHfEzO7dk47zg13dD6mtt8oMkdDILCyBrZPP2vm2dH687s/tLePxMeO67Uf3jI1gW1q3AtcBjwJF+5dzjgJ+GV7QoQoPowZlwDlw6D86+H65dD8f8KvB1PneWvxuroRowdnJi9ui2YyBLHoKv3rTbeTPdyTb1AtizwZm53gbVezufgdWSSefZsuWf3BHacaOE+jpbADEuFDGQpAz4/pt2TsiuL6L6xRlW9m6Bx04HxJbr+ez+SEvUKkEdl8aYz4wxLxljqvyOfWWM+Ty8okUTaoG4YsBk2yu8ZY0jf3In2PkZ/oF0XwpvYhpkj7El1VubhOcrd3Hcb9zPPRh/tl3/+0R49ju2zEhD7cHX1ewLrQUC1n03+yew9WPI73kly+trnTTltmJR7WHo16xrsmbvgVZqrFCzDx47w1rpZ91jJ8q+fYOdmBqFdIu0IhHZKiKrRGS5iCx1jmWKyLsissFZh/h/vh9qgYSOhGRbqXbHYmsRGNOcwpuY0VzSpGRD4PuL1sDY01u3cAKR3Atm/tBur50L/5wAt42Bf06ERU4yYZPXzpYPZQzEx4zv2Vpdn/wz9GNHmOoqWxKvVkLgwvLhq2TsJhuvp7D6ZTu36alvQXm+rRE27WL47is2y/Hd30dlZYNuoUAcjjPGTDXG+PwW1wPzjDGjgXnOfphQCySkDJhqf5E/fALc0qe5lpbPhQUHvzxq9tlMqpKvYNC09j/z9Nvglxvg4hfgkDOtsijbYX/d1ZQ6c09M4NL0nSUpAw79kU0wKFwT/PpuRFO1DYXua0oN3aDZTgfM1n5E9DRqSuH5S+DOKbYlwOyr7DwisHHB0/8BdRXw0d8iK2cAupMCaclZgK9OxePA2WF70n79oQokJIw+6cD9L5xy70npNhPLk3igAqkqgb8Ng3+OB8RaIO1FxE5sHHUifPspuOBZOPcxaGqEz59oflllj2lzmA4z+0prCb11fY/y7ZvaUgBKm0LkwgKbiZfcx8atYoFdLaIBs686MAGl3yHWil3ycNSVvO8uCsQA74jIMhHxtXzLNcYUADjrfoFuFJHLRGSpiCwtLu5oSpxaICHlkK/blN6L/gfn/7f5eGK6/Y+TNerAX5+rX2rePvdRyB3feRnGnmoD/8OOgkUPQOFqezxrdOfHDkRqpu0lv+Uj28mxhyB11oW1tzGECkTESaaIEQXii43Nvgq+8bCt3tCSY2+0/z9evdpdqZ8uorsokCOMMdOBOcCVzqx4VxhjHjTGzDTGzMzJyenY0zUGElri4mxK7+iTYNxptk0uNMcfskfbjoLv3Qzr37Il3lOz4KZS+9IPJV/7mfU5f3CrzcBKywrt+P7M/IGdLPfWDVDb/dvpNDUZGqqsBbLHG0IFAlaR72kjnbunUFcBC++xlu8pt8LkcwNfl55jm7ptXwjLHulaGdugWygQY8wuZ10EvATMAgpFZACAsy4KowTOWhVIWLhyEXz/LTtnA+y8jbpyWPBPO69k4/v2xRsOBT76JDs73lsf+gyslnji4cw7bRvgd34b3md1AY9+uhVPfTlNRihp6EADrrbIHg0VBfYF29Mo22mzEMt22u94banNXgzG1Atthtq7N0Opi3pxXUDUKxARSRORDN82cDLwJTAX8BVZugR4JWxCqAUSXpIyYOjs5v3J59lA95l32hhFeb7tyR4ORGwsJHusnS8SbgbPgq/91JaZd9OpMYqpW/ECV8e/SAMequpDHNdpLRvP2wg7u3E6dJMX7v8aPHA03DUVPr7NNj878ufB7xWx/ydME/zvB7ZtQoSJegUC5AILRGQFsBh43RjzFraw40kisgE4ydkPE6pAupTUTBvonvE922+k3wSY/t3wPS9rJFy1GI6+LnzP8OfYGyFnHMy9KipTM93yk+I/ApAkjVTVh7hgZFYr2Xiv/xweOt6W+eiOlOVbi6PfBGv1gk3scEvmcDjrbshfDO/+LjwytoOor2dljNkMTAlwfA+2PldXCIG6ryLEnOhLXew0Ccl2xv6/T4bHv25TizNyIy1V+/CrMPzs0FuozveGdvysUTYTa8t8p56ZQ4HT8njrAnelbKKNvZvses7frHL85E6YelH7xpj4TdixBBbdb5u6TT4v9HK6pDtYIJHHNKn1oYSWQdPhwmftC+WRU2z5iu5ERQEAj2Rew+b+p1BVF2ILxBMPo06wfWb8s458v9p9FQkC8fkTcPuEqIkTHMAeR4FkjbSJJNeshJQ+7R/n5D/adgWvXAnbF4VWxnagCsQVaoEoYWDUCXDJq9al8cgptv5Td6EsH4C61AGkJcZT19hEo7f5Re9tCkFMZPTJtreMf920qhK7zl/aep+VdW/YuNn8v3dehlDR5LUFEovWQkIqZARI1W0PngTr5u2dB89c2Nyhs4tRBeIGY9QCUcJD3kz4wdu2OdZDx8Obv7Y9xqOdWjsDvTGxD6mJdtJbdYN1Y+Xvq2bkjW8wd8Wuzj1jxHF27Yt3GGO7PPYZanvOF64KfJ+vNM6KZ6Fid+dk6Czz/mgzrR47A/51BCz9t802DMX7JDUTLnzOJpo8/e39f5OuRBWIK9QCUcJIzli4fIGdJ7LoX/ZlE+kXXzB8L+mkdNKSbCi1us4qkAUbrJVw89zVroZavauM+z4MMOcjI9cG033uqtoy23Bq3Bl2vzXXTdkOGDgdMPDx7Qeeqy3rukoA9VXw8T/sfKbtnzYnBvSfFLpnZI+Gbz9p58w8/70u736pCsQN3cgC+WRjCe+vK4y0GEp7Sc209brOfQwKv4T7ZsOq/0Vv2RNHgUhSBumOAjnib+/z0/9+wV3zbOptaXU9b325O6g765EFW/n7W+vZvqeaqrpGrnt+BTv2OlV+B0xurhJQab/X75cPgN6DYUeAOEhTk51fMfxom/q97VOrgHZ+bgt4/nWItfK6gpZxrR/NgwufhzNCXFRz+NFwxh2w6X3b1K0LvzNRn4UVHXQfC+Sih+2vsq1/7UC9KCXyTDjHTpp8+Sfwwg/hyxdtxk6fwZGW7ACaaiuIwyqQY8bmcOmRwymurOP9dUVU1DbytZFZbNtTzeVPLWNQnxQG9U1hWFYqJx6SS4InjpnD+pKRnADAF9v3AfDRhmIyUxN5flk+6wsrmHvVkbb8/5cvNBe/BO7/op7DJ88kdesCG1vwrxtVWQhNDTY2EOex3SAfOcWeO+43dr34ATjhd3b+UTjZu9muz3/a1rNK7g1jTg7Ps6Z/x1ohn9xhJ8Se8PvwPKcFqkDc0I0sEB819V5SEj3BL1Sij5yx8MN3YOG9tsTKXVNh0rlwxNXNpc4jTGNtBYlAfEo6vZIT+O0Ztj5ZTb2Xj74qYtbwLHolx/PumkKeWrSNPZX1vLFqN88ttcH3PqkJfHvmYE6d2J/NJbYfzIuf5zO4r63q+1VhBeW1DfTKnWgfWLjaljwHCshiTZ9jmbn+JZvOO8KvtbET3L9pfgU3nXM0cR/f1nxujd9c4x2LbRJDZ/ngL9aNNOlbB58r32nXgw8Pb4kcHyfebBMyPr4N4lPgmPDPa1IF4oruY4H42FJSxfiBvSIthtJR4jy2He6Ec6wi+fxxWPFf22f98Cus2yKCP2q8NRXUmESSEw8sYZKS6OHUic0ZRnMmDWDOJLtf2+Dly51l1DR4+c9n23l4wRYemG9/pZ82qT9vry7ki+2lZCTHU1HbyBF/eZ/LpiXb1qeb3ocvngRgt+nLEhnCTLCFMHMnQFq2fUbJVpKBz/aksCV9Kn5Ncq1rcPzZtifM9s86r0DqKuAjZ/5y9hgb2/D/m1QWQVxC+Evk+BCB0/9pZ6h/8CfbzOyIq8P6SFUgbugmFkilXy5+Zajz8mMYYwwX/3sR35qRxznTwtAvpC36DIY5f7UNtBY/aF+YX70JOYfArB/B5G/bMvhdTFNdJdUkk5zgPoyanOBh5jBbMPOo0TkUldfyv8/zKa9p5NqTx1BUUcfLX+zk9EkDqKht5KGPN3PH4l0cHz+MCfP/D4B/NJxLI/Hc/WkhFyQPoM/61/HOBc8FTwOwd9cmBgI7TTZLtpUz8sfzbQ+Wly+3Qow8zqa8bl8YWMiqEusuyxoZ+Lw/+Uuatx84CqZ9xyr33An2WGURpOXY4qFdRVyc7WTorbNNqGrLrOsuLjzeCA2iuyb6FUhxRXP6Z3WoS0vEMAVltXyycQ8/f3ZF8IvDRWomHHs9/GINnHWf/XX5+i/g9vHw/q22T0QXBk+baiuoMikkJ3T8xdSvVzI/OXYU188ZR4InjkF9UrjyuFEMy05jUl5v7rpgGh9ddxzzJ/15/z33eM/mhStmc9L4XK6s/AEAnvWvs+lP07n7lfnkb91AuUmlJi6NW99Yy08/bOLSFaPZ47EWyhVL+7M9fYozj6ThQIG8jXDHZJvA4KuWvPkje20gyu1kSpJ72/UXT9o6V74JjJWFtgdNVxPngXMesArt49tsim9NaVgepRaIG7qJBbK3qrm4Wm1DiEtLxDAr8+1/vqj4CiSkwLSLbGXWHYth4d12wtz8v9vJacOPhhHH2qqtvQeFTQxTW0YFnVMgbsjrm8oV555O5aHvs3h7Bbenj2LG0ExmDM1k16njWPlBHJNX/JGRjZvYvOQtxslmihP68cplR/DIJ1v4YF0R+6obOIk/MUx2s2p7E3GmN/cm1vDq228z++iTyE532vEWLLfzSwDWv2HrlL19g92f8A341iMHfgmqnUmNP19t56e8/Rvb6+Xjf9iihxUFza0KuhpPAnz9bhg4zWadPXSc7b3Tb1xIH6MKxBUGJPqNtdLqZgVSXW8VyAMfbeKpRdv4+FfHR0qsbsdLX+Tz0he7eOIHswDYsbcGgPi4aNAgDiIw5DC77NsGmz+0y8Z5sPJZe02fITaAO+QwGDLbur1aulMaaiA+ud3aMa62lFKTTkqYFYiP9GEzOH7YgccG9klh4Ck/gO1Pwb4t/G3oUhqLt9MwYAa9B/Xm9vOmUtfoparOS1VdI2sLyjl0WCZvLcyEBXex7JO3uWZBHEeOyubsqQOY89V9JMfF246YC++1fWjA1uVa/SJ87Spbe8pHVQl4kmyjp6QMOP8/8Pq1sOwxOOwKKNnAurSZxBdVMqpf227G11buYvKgPgzJskkE+fuq+eXzKzh3xmC+OaODblMRWy4ld4IteRKGX0CqQNzQTYop+lsgPgXylzfXAVDf2ERifPQrwWjA56raW1VPZloiheW1ADR4DZV1jfvnPUQNfYfCjEvs0tQERWvs7O3tC20HxFXP2et6D4GjfgGDD7M+/l1fwJPfgJwxMOVCyBphZ3+78JdLbSll9GNApD+L1Ey4ejk8ciqJ2xeSCDBy1v7TSfEekuI9ZKYlMjjTvpwvOPFwWJnHL/qVkZI9grnLd/Hy/57knMQXeLLpVCQuhYt32y6Yb0x/iIRBUzjxjaOQlc8foECq9u2mhl4s+XL3/kQBjv4VrHoeHj0VvHU8uC6ZpbuXMP9XdlZ9fWMTp9wxn2/NyOPK40YBsLuslquetmVsFv/mBHbuq+EPr63hi+2lLNu2jz1VdYzql87InHTy+qbiae8PmSGHw5WLwxIHibL/CVGKaeoO+oPS6mafbk39gS6soopa8pwUSSUwtQ1e7pzX3H9i/e4K8vqmsM03qQ3Yvqe61ey2naU1ZKUlht2t0yZxcdB/ol1m/8T++PEFjRc9AK9dY68Tj53VDVD8lZ2ABrYC7pDZcNyNdhJfK3jqSik1w5mYFuJGUh3l6/fAKz+xVsE4F3OgBs+iV/4Sfn3xOK7LXEDcG3+jMS6JnTNuwFOyFrZZBfKbT73sYyP3JUzh8EVP8/fKc5k5IpfDhmfStDuf8oY0fvfKaqYM7kNur2Q8Gblwyl+sLMDCpgkU7K3en1b/4foitpRU8X9vr+frUwaybU81F/+7eUb9rFvn7d++fs44nl2ygz+/sW7/saT4OIZnpzEsK41h2WmMyLbrYdmp5KQnIa1ZGWEKoqsCcUU3sUCq64kTaDLNFoiPwvK6qFYg3iZDbYN3f1mMSLBgQwn3f7hp//4FDzXPdE5J8FDT4OXqZ77YL2OcQJwIcSJU1jWypqCcXsnxjMixrp2URGdJ8OzfT/ZtJ8QduJ/oIdETh4iQmughLSmetCQP6UnxpCR4Wn8xBEPE9pDIHA6Tz4ei1TbgXrzezlOYdRn0GmgnoZXvsm6wr96ydbmGH23v6zvMxl76DLUNsZJ6kdhQThlp9I0WBZI9ys6dccuQw61b6qlvEbfxXQDi5/yZ6w+dCk2T4A82a+v9332TbXurKV6ym8yVv6B8zXv8cpmdmzIvcQv5Jo+Syjq+9tf3SfAIA/ukMDRzFOcPvpbFu+oowM7/OOT3bzGoTwo1frHJo/7+wf7toSqXCQAAFH5JREFUX5w0hiVb99LoNcwY2pepg/tw4vhcLj9mJKXV9WwqrmRjkV02F1fxVVEF89YV0uBtTpxIS/TQv3cyA3qn0L93Mv17JTv79tjw7LSQzw1TBeKGbhJE31dVT3Z6EqU1DVQ32CwsESu+zw0Trfzp9TU8+slWNtw6hwRPZFxt6wsPbp/aLyMJA/zh6xN4deUuSqsb8MQJIoIxhiZjaGqCjOR45kzsjwhU1DZS2+ClqKKBmnovtQ1N1DR4qan3HvACcUucQFpSPOlJ8Y5iiSc9yUNaYjzpyc3H05PiSXOUz4HXNiujtJwJJASoxVQa14ey9AaGTvoWVO2B9/9oXVw7lx5cpC+pN3GmkQrJoFdyN32FTDkfNr5nrbO8WbaEjC/pIM4Dl74Padn0TUu0SnLAxbDhj9yb+zGbjjmfLzdtY+SCAjKnnsUL02azbncF+ftq2LG3mq17qri2aBZ1jV7+8o1JVNU1sqWkipoGL3sq6zltUn+GZKaxuaSS8ppGzp42kAG9W+8p3yc1cX/igD+N3iZ2ldayuaSSrSVVbNtbze6yWgrKalmwoYSiilr8q8g8+cNZHDU6J6QfYzf963c13cMC2VddT9/UROq9TftdWPFxQoPXsK868u0v2+I/n20HYNueKkb1C3OJiVb4dFPJ/u1vzxxMr5R4fnP6+P3H9vu5O4ExhrrGpv3KxKdYahu81DU20WQMNfVequobqXSCv1V1jVTU2rX/8T2V1fZ4vT3n/2u0LRI9caQlWUWTFB+HJ07Yua+GqnoveX1TrNWTeC6pieeTMsBDZlwV6Z5GhjZsZmD9NrIad1Ozr4Avmg7tuGUUaZJ7w0XPt34+b8aB+/FJcOItyKs/Y9TWyYxyDvcdPD7gy72pyVATxKKePbJzs9PjPXEMyUq1gfexB59v9DZRXFm3X6lMGNi7U88LKEPIR+yJdBsLpIG+aQlU1jVSWXvgPBD/+EhX8MTCrbyzupCnLj3M1fW9UuIpqaxn3e4K1wqkpLKOgtJaJuV1/j9GaXU9Czft4fJjRnLs2BwOHxGe0hMiQnKCdV2Fen6yL+Oo0k+pVNY17s9CqnSUUaVzrqrOS31jE41NTUwc2JvMtESKKuqoabAKrabeS2l1A5vroa4xjtqG4dQ2DKG2sQlvk+G4saH9NRv1zLgEBkyBzR/YFN/METDlgoCXxsVJRN2xYBXMgN4pDOidwrRwPSNM4/YwuocFsre6njG56VTVedlbXU9tg3f/r9Lyms4rkKKKWpI8HnqnJgS99p3VhSzYWEJxRR05GTbPvrbBS11DU8D7fYHnbXuqDzoXCGMMlz2xlM+3l/LncyaRGB/HHe99RW6vZG46czyT89rX5W3xlr00GTh+XD9mDc8MfkMU4p9xFG4avE14usGPqpAzcKpdFEAViDu6iQVSWl1Pn9REquu97K2qp6Syzu+cOwVSUlnHZU8s5ZoTx3D0mAN/Yc66dR4ZSfHMGNaXsbkZ3HBa4MJ+xhjWFNiZvCvzSznhkFy8TYYj//Y+CZ44Pvn18cT5pSIaY9jnpCAXlNUEHNPbZPDECcUVdTy5cCv/WbSdPc49f3p9DcbYOkzb9lRz+ZPL+MNZEzHO2D7HTkllHTv31TA4M5VGbxNlNQ2UVjeQmhTPZ5v2kJ4Uz5TBoTfzeyKRilMp0UW3ViAicipwJ+ABHjbG/DU8T4p+C6S+sYm9ThC9tsHLhsLKA0qblLm0QB5ZsIXPt5dyzwcbmT0yi2cWb+fUiQP268+KukY+XF/Mh+uLufbksYiAR+QAhVBYXrd/TsqK/DJOOCSXdbvLKam0x9btrjggFbawvI4qJ2azqaiKusYDA8079lZz/oOLOGFcP3aX1/LRV8UATM7rzU1nTuCb938KwD0XTiMl0cOFDy3i0icCl5/wZan5SE6Io7bBtmL97uyhJMVrBWNFcUu3VSAi4gHuBU4C8oElIjLXGLMm5A9rYYGUVNaRmughNTH8H19BWQ11DU2kJHqoa2hiSJb99VxQVkte35T9Qcyte6poMjAyJ42a+kaKK+v2txRNSfDw/roiLnr4M3IzkhmalUZyQhxJ8XH7/fG+YOpLX9gS1Ct2lHLji6t4flk+//poM+fNPLgfxf+9vY43v9xNZloil8weRlJCHImeOL7YYUt/JHrieOqzbeytqmPNrvL9913/4koGZ6Za68A0W0fpSfEs3LyHsb9966BneeKEZ5faGkNZaYkc9//tnX+QXWV5xz/f3Ww2u25+EMwSIQE0gUkwIVBiMC0xogLGWkFba5ABtFCmoFgcZVAqBUMQ0LZOaQEHO2rGqChWW5umZcYKVoMooVCBlqqBBKqQkh+EDYFNdu/TP95zd082uyF79557nt19PjN37jnnnnP3s+997n3O+573fc+8Tlafs4BJLc1c+uY5vLyvl7fM60QS//ax5X13x6t+bFIqhyOntbHjxb1MzP73jtYJVCrGyz29DRtVHQRjBZnXO569ApKWAteZ2VnZ+icBzOzGoY5ZvHixbdw4xMRoB2Hr2kto2/x9zmr+Irte2tc3xqKtpZlp7S00DWjeyv9oASirvfSv9/0P+62Te7362pPbXtzvjm5HTWujp1Jh6wvdHDGllYqlQYPV2XfXXX4ajz/bxcfvSqOpW5rFJ1fM59Z7fsWMya0819Xd1/QzFOe/8Ri+ev+WA7Z3Tm5lSlsLK98wm7X3b2Hz9j10Tm7NegftX2s4Ykorq89ZyJ0/e4r7Nm3HMC467bUArH/kWZSNoRDpeVp7C9e88wR+smk7e3srB/ztt87v5H93vET7xGaWzjl89Pb+CYJRiKQHzWzxAdtHcQL5A+DtZnZxtn4+cKqZfXjAfpcAlwAcffTRp2zZcuAP4yux8ZbzmL19A58+/u95zdQ2Oie30mvGjt172fXSvqytPe3b1+K+/xPVcu5fP/jr1YVZh7Ux67A2dr20j/aJE3j46efprRiLZk/lkV+/QHtLM5NamvjNrpeZ1tbCZ96zkJbmJna+uJfd3T1Mamnuu4hdpae3QndPpa/raPV5b0+FjkkTmDOjg6e276Grex/zZk5h03O72dbVzezp7X3TQXT3pB4609pb2NdrbN/d3fce3T0V5nZ2MLWtpe9/ix/8IBi9jMUE8l7grAEJZImZXT7UMbXWQHbs3En7BGPS5NHZOycIgmAkDJVARu01ENJ1j3zD/CzgN0X8oemHNeiOYkEQBKOI0dwX7wHgOEmvlTQRWAl8r2SnIAiCccOorYGYWY+kDwN3k7rxfsnMHitZKwiCYNwwahMIgJmtB9aX7REEQTAeGc1NWEEQBEGJRAIJgiAIaiISSBAEQVATkUCCIAiCmhi1AwlrQdJzwPCHoideDWx7xb0aT3gND69e4NPNoxOEVy2MxO0YMzvgBjDjKoGMBEkbBxuJWTbhNTy8eoFPN49OEF61UIRbNGEFQRAENREJJAiCIKiJSCCHzh1lCwxBeA0Pr17g082jE4RXLdTdLa6BBEEQBDURNZAgCIKgJiKBBEEQBDURCSQIgiCoiUggOeT0vquS5pftMBBJH5N0ZrbsqtwkTc0tu3Hz5FLFY2xBxFctlOESCQSQdLakNcCisl0GIulvgPWSji1ZBQBJZ0q6G7gKuADAnPTEkPQWSQ8Dt0u6Gny4eY0vb7EFEV+1UGZ8jer7gYwESTIzk3Q6cD2wD1gqaYuZ7SzbK7dpOrATeJukr5pZdxlOQAvw58By4EZgIvAGSS1AT9lfJEkdwNWkz/JnwBpJ7Wb2qZJ83MWXx9iqehHxNVwfF/E1LmsgA75ITwJnAVcCpwInevCS1Jxtvh+4HTgPOK4sJzPbC/yjmS3LbuS1E1hpZvscfLmbgA7gaeAhM3sauBh4n6R5Jfi4iy+PsZX3ivgalo+b+Bp3CSS7De53JH1U0kwz22xmz5jZD4CtwHJJR5XodYWkI82sN7vX+9uB7wL3ACslvUfSAZOaFez0UUmvMbMHsu0tZvZD4AlJKxrhMojbZZJ+H8DMKoABM0hfdMzsCVK5rcr2b0j7sMf48hhbA7wivg7dy1V8jasEIundwIXALaRM/SlJJ+V2+RpwPCmT548rNDgGeC0CrpZ0SnZWttHMtgG/BD4C3AAUHqyDlNWfSaq2sfZImk6a2bi3aJcBXpMlfYHU3LFG0gQAM9sK/BdwRW73TwCnSnp9I85iPcaXx9gaxCvi69Dc3MXXuEogpIK93czuAa4jVf8+Un3RzH4OPAAsULpgdlW2vejgGMzr0uy135X0I9JFxX8gNTu8ULDPUE5/Cqk8zGwH0AacDn3V/MIxsy7gh2Y2E1gH3Jp7eRVwkqR3SGrNzhzXkdrXG4HH+PIYW0N5RXwdHHfxNSYTyMCMm1t/Ang/gJltAf4ZeJWkd+V2/wapffObpPnz65bBh+k1TdJS4K+B+8zsJDO7AJgJ1K3r5QjLai2wRNKk7MtUVw7i9r3s+QrgXEnHZZ67gc8CK0ln2quAZcAz9XYbwqvU+BqGU0NiqwavhsbXQdxKjS+vv1+DMSYTCAPOCHIZ+NvAHklnZ+vPAPcCJyjRQfpSPQKcaGZXDji+kV4/AN4EfM3Mrsod9m4ze6hOPsN1upesrLJtbcCdFNfMMKibmb0oqcnMngVuA/4ut8+dwGdITTEzgBVZ80NdUf+FaDfxNQynRsXWcL3upbHxNaibg/ja73fZS3wNipmNmQewFLgrK8QTgOZs+4TsWcAHgX+lfyLJK4HrqvsBnY68rs2Wm4EmT05VrwZ/jn3lkC8P4KnsmJnAqVX/grxWDbK9Wj4Nj68ROBUWW/XwakB8DepWcnwtIdW6PgsszLk05/wa/vt1sMeYqYFI6gT+FlgPbCe1p/4RgJn1ZLu1AXeTMvcdko4ETib1ocbMeszs/xx59WT79Vodq/D1cKp61cvpEN16zaySnWlNzR12M7AB+HdgUrZvXc+6JF0IrCFduPzDbFv1Amv1bzU0vkboVEhs1cur6lZPr0NxKyO+JDVJupZU0/kXUiL4ENnAwFw5NPz36xVpZLYq8gGcAXwjW34VqW/0OmBetm11VvgnkwZQrSZV/26joDMdr14enYbhdj3pDGxZtr4CeBz4C6ClYK9ZwJnAU7nt1bPD60r6HF05efY6RLdrS4qvi4HfypYPJyWSxbnXSyuzg3qX9YfrUODnAp8G3pWtv5rUHXFutj49C4abgXbg68CcAe/RPh68PDrVy43UxDW7QK+zs/Um+pv3fgxcn9u3s8Gfoxsnz171cCs4vlbl4r4tc2vN1r8F/F4ZZTas/6NsgRoKXsCfAA+R2gP/h5S9JwHXALfkAuU04IvA9NzxdW/v9erl0amObkWd1Q/m9UFgcm6f1wO7gCMGOb5Rn2OpTp696uTWqPj6RfbckdunBbgPOL6RZVbLY9RdA7FUikuBm8zsy6S2wjcDbyW1m8+VdIaldt3twBFAN6S2RiuoO6BHL49OdXQrpGfOEF5vA5ZVeweZ2WOki/w3ZT4rcsc36nMs1cmzV53cGhVfl2Veb8r1PpsPbDWzXygNbFyS+anIMquFUZFAJF0gabnS6FSA/waOkjTBzL4PPAq8EXiOVNX7vKS5pB+j6kRtdQ9Yj14enby7HYLXz0m1oFnVY8zsYuBCSTuBRarzQDePTp69PLsNw+uY7PXDSd11P0CqiSzMkkfdLtzXC7ez8WbZeCbph6QCbCINmrmUNKnZQmAu6QLXncDngcPNbK2k2aRpBuYBf2xmz49lL49O3t2G6fXNqhfwtKTXkS6q/gj4kJk9OladPHt5dqvR6zBgM+nC/bmkGvd5lkaY+6TsNrTBHvT3iDgeWJstTyD1OFhDOhP9EnA+MDV7/SvADbn3mDgevDw6eXcbgdeqbHkqsGSsO3n28uw2Aq/V2fLvAO8roszq/XBVA8n6Y68CmiWtB6aQjUI1sx6lmSifIfWM+DpwDqk6eiMpy99XfS9Lk8WNWS+PTt7d6uD102zfXaR7QoxJJ89ent3q4PWTbN8N9XIqGjfXQCQtBx4kVeN+Rf9NUk6vXkSy1Pa9CrjZUtvhHcBpkn6aHXfvePDy6OTdzaOXRyfPXp7dvHoVTtlVoFy1bxlwfm79NtKsoR8AHsy2NZHaFe8Cjs22TQOOGk9eHp28u3n08ujk2cuzm1evoh9uaiCk7P0t9U9utgE42sy+QqoSXm4pg88i3eJyM4CZPW9mvx5nXh6dvLt59PLo5NnLs5tXr0Jxk0DMbI+ZdVt//+szSN05IQ20mS9pHWm64v8Yz14enby7efTy6OTZy7ObV6+icXURHfqmVzbSwLHqvPxdpBvaLwCeLCNje/Ty6OTdzaOXRyfPXp7dvHoVhZsaSI4KqZvbNuDELGtfA1TM7MclFr5HL49O3t08enl08uzl2c2rVzHU+6JKPR6k0cgV0mRnF5Xt49nLo5N3N49eHp08e3l28+pVxKN6UxJXSJpFGmTzV2bWXbZPFY9eHp2qeHXz6OXRCfx6gV83r15F4DKBBEEQBP7xeA0kCIIgGAVEAgmCIAhqIhJIEARBUBORQIIgCIKaiAQSBAUhaZqky7LlIyV9u2ynIKgn0QsrCApC0rHAOjNbULJKEBSCu6lMgmAMcRMwR9LDwC+B+Wa2QOlWpecAzaTpLf4SmEgaO9ANvMPMdkiaA9wKzAD2kO7K+Hjj/40gGJxowgqC4vgEsMnMTgKuHPDaAuD9wBLgBmCPmZ1MuqnQBdk+dwCXm9kpwMdJU4QHgRuiBhIE5XCPmXUBXZJ2Af+UbX+ENIdSB/DbwF2Sqse0Nl4zCIYmEkgQlEN+iotKbr1C+l42Ac9ntZcgcEk0YQVBcXQBk2s50MxeAJ6U9F4AJRbVUy4IRkokkCAoCDPbDmyQ9CjwuRre4jzgIkn/CTwGnF1PvyAYKdGNNwiCIKiJqIEEQRAENREJJAiCIKiJSCBBEARBTUQCCYIgCGoiEkgQBEFQE5FAgiAIgpqIBBIEQRDURCSQIAiCoCb+H65JJcuSfjHiAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -267,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,
diff --git a/raven/processes/wps_hydrobasins_shape_selection.py b/raven/processes/wps_hydrobasins_shape_selection.py
index 34d765b7..fbd5adf5 100644
--- a/raven/processes/wps_hydrobasins_shape_selection.py
+++ b/raven/processes/wps_hydrobasins_shape_selection.py
@@ -85,7 +85,7 @@ def _handler(self, request, response):
         domain = gis.select_hybas_domain(bbox)
         hybas_gml = gis.get_hydrobasins_location_wfs(bbox, lakes=lakes, level=level, domain=domain)
 
-        if isinstance(shape_url, str):
+        if isinstance(hybas_gml, str):
             write_flags = "w"
         else:
             write_flags = "wb"
diff --git a/raven/processes/wps_raven_gr4j_cemaneige.py b/raven/processes/wps_raven_gr4j_cemaneige.py
index b71cb886..0873b9bf 100644
--- a/raven/processes/wps_raven_gr4j_cemaneige.py
+++ b/raven/processes/wps_raven_gr4j_cemaneige.py
@@ -50,5 +50,5 @@ class RavenGR4JCemaNeigeProcess(RavenProcess):
     tuple_inputs = {'params': GR4JCN.params}
 
     inputs = [wio.ts, wio.nc_spec, params, wio.start_date, wio.end_date, wio.nc_index, wio.duration, wio.run_name,
-              wio.name, wio.area, wio.latitude, wio.longitude, wio.elevation, wio.evaporation,
+              wio.name, wio.area, wio.latitude, wio.longitude, wio.elevation,   wio.evaporation,
               wio.rain_snow_fraction]
diff --git a/setup.cfg b/setup.cfg
index 817ec44d..43b81636 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,7 +3,7 @@ current_version = 0.10.0
 commit = False
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+).(?P<patch>\d+)(\-(?P<release>[a-z]+))?
-serialize = 
+serialize =
 	{major}.{minor}.{patch}-{release}
 	{major}.{minor}.{patch}
 
@@ -12,7 +12,7 @@ description-file = README.rst
 
 [bumpversion:part:release]
 optional_value = final
-values = 
+values =
 	beta
 	final
 
@@ -29,19 +29,19 @@ search = Version={current_version}
 replace = {new_version}
 
 [tool:pytest]
-addopts = 
+addopts =
 	--strict
 	--tb=native
 python_files = test_*.py
 norecursedirs = src .git bin
-markers = 
+markers =
 	online: mark test to need internet connection
 	slow: mark test to be slow
 
 [flake8]
 ignore = F401,E402,E401, W503
 max-line-length = 120
-exclude = 
+exclude =
 	.git,
 	__pycache__,
 	docs/source/conf.py,
diff --git a/tests/conftest.py b/tests/conftest.py
index 2a4fab79..36f09af1 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -46,7 +46,7 @@ def era5_hr():
     if not path.exists():
         # Fetch the data and save to disk if the file has not been created yet.
         path.parent.mkdir(exist_ok=True)
-        thredds = URL("https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ecmwf/era5")
+        thredds = URL("https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/datasets/reanalyses/era5.ncml")
         tas = str(thredds / "tas_era5_reanalysis_hourly_2018.nc")
         pr = str(thredds / "pr_era5_reanalysis_hourly_2018.nc")
 
diff --git a/tests/test_ERA5.py b/tests/test_ERA5.py
index 4eeadcd2..76876230 100644
--- a/tests/test_ERA5.py
+++ b/tests/test_ERA5.py
@@ -10,14 +10,12 @@
 from raven.processes import RavenHMETSProcess
 from raven.models import HMETS
 import json
-
 import matplotlib.pyplot as plt
 
 params = (9.5019, 0.2774, 6.3942, 0.6884, 1.2875, 5.4134, 2.3641, 0.0973, 0.0464, 0.1998, 0.0222, -1.0919,
           2.6851, 0.3740, 1.0000, 0.4739, 0.0114, 0.0243, 0.0069, 310.7211, 916.1947)
 
 
-@pytest.mark.skip(reason="Hanging test due to ncML. Should be removed in a future PR")
 class TestRavenERA5:
     def test_simple(self, era5_hr):
         model = HMETS()
@@ -37,7 +35,6 @@ def test_simple(self, era5_hr):
               )
 
 
-@pytest.mark.skip(reason="Hanging test due to ncML. Should be removed in a future PR")
 class TestRavenERA5Process:
 
     def test_simple(self, era5_hr):
@@ -69,14 +66,13 @@ def test_simple(self, era5_hr):
                     latitude=54.4848,
                     longitude=-123.3659,
                     rain_snow_fraction="RAINSNOW_DINGMAN",
-                    pr=json.dumps({'pr': {'linear_transform': (24000.0,0.0), 'time_shift': -.25}}),
+                    pr=json.dumps({'pr': {'linear_transform': (24000.0, 0.0), 'time_shift': -.25}}),
                     tas=json.dumps({'tas': {'linear_transform': (1.0, -273.15), 'time_shift': -.25}}),
                     )
 
         resp = client.get(
             service='WPS', request='Execute', version='1.0.0', identifier='raven-hmets',
             datainputs=datainputs)
-                
         assert_response_success(resp)
 #        out = get_output(resp.xml)
 #        tmp,_= urlretrieve(out['hydrograph'])