diff --git a/notebooks/epanet_res.ipynb b/notebooks/epanet_res.ipynb
index e407ae43..86cc2bd9 100644
--- a/notebooks/epanet_res.ipynb
+++ b/notebooks/epanet_res.ipynb
@@ -12,8 +12,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:38.199634Z",
+     "iopub.status.busy": "2024-09-04T12:38:38.199488Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.459426Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.458336Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D\n",
@@ -38,8 +45,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.462861Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.462208Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.616620Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.615992Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path = \"../tests/testdata/epanet.res\"\n",
@@ -57,9 +71,237 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.619849Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.619644Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.640655Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.640104Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Demand:10</th>\n",
+       "      <th>Head:10</th>\n",
+       "      <th>Pressure:10</th>\n",
+       "      <th>WaterQuality:10</th>\n",
+       "      <th>Demand:11</th>\n",
+       "      <th>Head:11</th>\n",
+       "      <th>Pressure:11</th>\n",
+       "      <th>WaterQuality:11</th>\n",
+       "      <th>Demand:12</th>\n",
+       "      <th>Head:12</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ReactorRate:31</th>\n",
+       "      <th>FrictionFactor:31</th>\n",
+       "      <th>Flow:9</th>\n",
+       "      <th>Velocity:9</th>\n",
+       "      <th>HeadlossPer1000Unit:9</th>\n",
+       "      <th>AvgWaterQuality:9</th>\n",
+       "      <th>StatusCode:9</th>\n",
+       "      <th>Setting:9</th>\n",
+       "      <th>ReactorRate:9</th>\n",
+       "      <th>FrictionFactor:9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>301.663696</td>\n",
+       "      <td>85.255707</td>\n",
+       "      <td>0.5</td>\n",
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+       "      <td>0.5</td>\n",
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+       "      <td>...</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>0.5</td>\n",
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+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 148 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Demand:10     Head:10  Pressure:10  WaterQuality:10  \\\n",
+       "2022-10-13 00:00:00        0.0  306.125000    89.716995              0.5   \n",
+       "2022-10-13 01:00:00        0.0  306.862823    90.454834              0.5   \n",
+       "2022-10-13 02:00:00        0.0  307.325562    90.917557              0.5   \n",
+       "2022-10-13 03:00:00        0.0  307.824982    91.416985              0.5   \n",
+       "2022-10-13 04:00:00        0.0  308.052460    91.644470              0.5   \n",
+       "\n",
+       "                     Demand:11     Head:11  Pressure:11  WaterQuality:11  \\\n",
+       "2022-10-13 00:00:00   9.463531  300.298187    83.890175              0.5   \n",
+       "2022-10-13 01:00:00   9.463531  301.137360    84.729355              0.5   \n",
+       "2022-10-13 02:00:00  11.356236  301.663696    85.255707              0.5   \n",
+       "2022-10-13 03:00:00  11.356236  302.231873    85.823860              0.5   \n",
+       "2022-10-13 04:00:00  13.248942  302.490662    86.082672              0.5   \n",
+       "\n",
+       "                     Demand:12     Head:12  ...  ReactorRate:31  \\\n",
+       "2022-10-13 00:00:00   9.463531  295.677277  ...             0.0   \n",
+       "2022-10-13 01:00:00   9.463531  296.611542  ...             0.0   \n",
+       "2022-10-13 02:00:00  11.356236  297.515137  ...             0.0   \n",
+       "2022-10-13 03:00:00  11.356236  298.146301  ...             0.0   \n",
+       "2022-10-13 04:00:00  13.248942  298.756622  ...             0.0   \n",
+       "\n",
+       "                     FrictionFactor:31      Flow:9  Velocity:9  \\\n",
+       "2022-10-13 00:00:00           0.048410  117.737534         0.0   \n",
+       "2022-10-13 01:00:00           0.048470  116.627487         0.0   \n",
+       "2022-10-13 02:00:00           0.048168  115.925919         0.0   \n",
+       "2022-10-13 03:00:00           0.048195  115.163910         0.0   \n",
+       "2022-10-13 04:00:00           0.047632  114.815155         0.0   \n",
+       "\n",
+       "                     HeadlossPer1000Unit:9  AvgWaterQuality:9  StatusCode:9  \\\n",
+       "2022-10-13 00:00:00             -62.285000                0.0           3.0   \n",
+       "2022-10-13 01:00:00             -63.022831                0.0           3.0   \n",
+       "2022-10-13 02:00:00             -63.485558                0.0           3.0   \n",
+       "2022-10-13 03:00:00             -63.984989                0.0           3.0   \n",
+       "2022-10-13 04:00:00             -64.212471                0.0           3.0   \n",
+       "\n",
+       "                     Setting:9  ReactorRate:9  FrictionFactor:9  \n",
+       "2022-10-13 00:00:00        1.0            0.0               0.0  \n",
+       "2022-10-13 01:00:00        1.0            0.0               0.0  \n",
+       "2022-10-13 02:00:00        1.0            0.0               0.0  \n",
+       "2022-10-13 03:00:00        1.0            0.0               0.0  \n",
+       "2022-10-13 04:00:00        1.0            0.0               0.0  \n",
+       "\n",
+       "[5 rows x 148 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -74,9 +316,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.643217Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.642989Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.650395Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.649815Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Demand',\n",
+       " 'Head',\n",
+       " 'Pressure',\n",
+       " 'WaterQuality',\n",
+       " 'Flow',\n",
+       " 'Velocity',\n",
+       " 'HeadlossPer1000Unit',\n",
+       " 'AvgWaterQuality',\n",
+       " 'StatusCode',\n",
+       " 'Setting',\n",
+       " 'ReactorRate',\n",
+       " 'FrictionFactor']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res.quantities"
    ]
@@ -113,23 +384,261 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.652781Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.652589Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.657542Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.656976Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start node ID: 10\n",
+      "End node ID: 11\n"
+     ]
+    }
+   ],
    "source": [
     "pipe_id = '10'\n",
     "pipe_data_item = res.reaches[pipe_id]\n",
-    "node_start = res.data.Nodes[pipe_data_item.StartNodeIndex]\n",
-    "node_end = res.data.Nodes[pipe_data_item.EndNodeIndex]\n",
+    "node_start = res.data.Nodes[pipe_data_item[0].StartNodeIndex]\n",
+    "node_end = res.data.Nodes[pipe_data_item[0].EndNodeIndex]\n",
     "print(f'Start node ID: {node_start.ID}')\n",
     "print(f'End node ID: {node_end.ID}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.659632Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.659314Z",
+     "iopub.status.idle": "2024-09-04T12:38:39.666721Z",
+     "shell.execute_reply": "2024-09-04T12:38:39.666110Z"
+    }
+   },
+   "outputs": [
+    {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Flow:10</th>\n",
+       "      <th>Pressure:10</th>\n",
+       "      <th>Pressure:11</th>\n",
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+       "  </thead>\n",
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+       "    <tr>\n",
+       "      <th>2022-10-13 06:00:00</th>\n",
+       "      <td>114.390770</td>\n",
+       "      <td>91.920341</td>\n",
+       "      <td>86.396553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 07:00:00</th>\n",
+       "      <td>114.311264</td>\n",
+       "      <td>91.971931</td>\n",
+       "      <td>86.455261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 08:00:00</th>\n",
+       "      <td>113.833015</td>\n",
+       "      <td>92.281364</td>\n",
+       "      <td>86.807350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 09:00:00</th>\n",
+       "      <td>113.436226</td>\n",
+       "      <td>92.537125</td>\n",
+       "      <td>87.098404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 10:00:00</th>\n",
+       "      <td>112.646294</td>\n",
+       "      <td>93.043625</td>\n",
+       "      <td>87.674835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 11:00:00</th>\n",
+       "      <td>111.941292</td>\n",
+       "      <td>93.492691</td>\n",
+       "      <td>88.185966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 12:00:00</th>\n",
+       "      <td>110.851845</td>\n",
+       "      <td>94.181076</td>\n",
+       "      <td>88.969604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 13:00:00</th>\n",
+       "      <td>-0.000053</td>\n",
+       "      <td>84.220718</td>\n",
+       "      <td>84.220718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 14:00:00</th>\n",
+       "      <td>-0.000052</td>\n",
+       "      <td>83.050537</td>\n",
+       "      <td>83.050537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 15:00:00</th>\n",
+       "      <td>-0.000051</td>\n",
+       "      <td>81.976448</td>\n",
+       "      <td>81.976448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 16:00:00</th>\n",
+       "      <td>-0.000050</td>\n",
+       "      <td>81.041527</td>\n",
+       "      <td>81.041527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 17:00:00</th>\n",
+       "      <td>-0.000049</td>\n",
+       "      <td>80.235962</td>\n",
+       "      <td>80.235962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 18:00:00</th>\n",
+       "      <td>-0.000048</td>\n",
+       "      <td>79.534828</td>\n",
+       "      <td>79.534828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 19:00:00</th>\n",
+       "      <td>-0.000048</td>\n",
+       "      <td>78.997780</td>\n",
+       "      <td>78.997780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 20:00:00</th>\n",
+       "      <td>-0.000047</td>\n",
+       "      <td>78.356293</td>\n",
+       "      <td>78.356293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 21:00:00</th>\n",
+       "      <td>-0.000046</td>\n",
+       "      <td>77.550705</td>\n",
+       "      <td>77.550705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 22:00:00</th>\n",
+       "      <td>-0.000046</td>\n",
+       "      <td>76.605965</td>\n",
+       "      <td>76.605965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 23:00:00</th>\n",
+       "      <td>120.466057</td>\n",
+       "      <td>87.873650</td>\n",
+       "      <td>81.794281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-14 00:00:00</th>\n",
+       "      <td>119.382088</td>\n",
+       "      <td>88.611023</td>\n",
+       "      <td>82.632576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Flow:10  Pressure:10  Pressure:11\n",
+       "2022-10-13 00:00:00  117.737534    89.716995    83.890175\n",
+       "2022-10-13 01:00:00  116.627487    90.454834    84.729355\n",
+       "2022-10-13 02:00:00  115.925919    90.917557    85.255707\n",
+       "2022-10-13 03:00:00  115.163910    91.416985    85.823860\n",
+       "2022-10-13 04:00:00  114.815155    91.644470    86.082672\n",
+       "2022-10-13 05:00:00  114.398529    91.915298    86.390823\n",
+       "2022-10-13 06:00:00  114.390770    91.920341    86.396553\n",
+       "2022-10-13 07:00:00  114.311264    91.971931    86.455261\n",
+       "2022-10-13 08:00:00  113.833015    92.281364    86.807350\n",
+       "2022-10-13 09:00:00  113.436226    92.537125    87.098404\n",
+       "2022-10-13 10:00:00  112.646294    93.043625    87.674835\n",
+       "2022-10-13 11:00:00  111.941292    93.492691    88.185966\n",
+       "2022-10-13 12:00:00  110.851845    94.181076    88.969604\n",
+       "2022-10-13 13:00:00   -0.000053    84.220718    84.220718\n",
+       "2022-10-13 14:00:00   -0.000052    83.050537    83.050537\n",
+       "2022-10-13 15:00:00   -0.000051    81.976448    81.976448\n",
+       "2022-10-13 16:00:00   -0.000050    81.041527    81.041527\n",
+       "2022-10-13 17:00:00   -0.000049    80.235962    80.235962\n",
+       "2022-10-13 18:00:00   -0.000048    79.534828    79.534828\n",
+       "2022-10-13 19:00:00   -0.000048    78.997780    78.997780\n",
+       "2022-10-13 20:00:00   -0.000047    78.356293    78.356293\n",
+       "2022-10-13 21:00:00   -0.000046    77.550705    77.550705\n",
+       "2022-10-13 22:00:00   -0.000046    76.605965    76.605965\n",
+       "2022-10-13 23:00:00  120.466057    87.873650    81.794281\n",
+       "2022-10-14 00:00:00  119.382088    88.611023    82.632576"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[['Flow:10', 'Pressure:10', 'Pressure:11']]"
    ]
@@ -144,9 +653,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:39.669023Z",
+     "iopub.status.busy": "2024-09-04T12:38:39.668655Z",
+     "iopub.status.idle": "2024-09-04T12:38:40.455937Z",
+     "shell.execute_reply": "2024-09-04T12:38:40.455003Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pipe_query = QueryDataReach('Flow', pipe_id)\n",
     "df_pipe = res.read(queries=[pipe_query])\n",
diff --git a/notebooks/epanet_resx.ipynb b/notebooks/epanet_resx.ipynb
index 3e4243a4..f26fca42 100644
--- a/notebooks/epanet_resx.ipynb
+++ b/notebooks/epanet_resx.ipynb
@@ -12,8 +12,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:42.007303Z",
+     "iopub.status.busy": "2024-09-04T12:38:42.006911Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.419160Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.418155Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D\n",
@@ -38,8 +45,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.421932Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.421575Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.569196Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.568216Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path = \"../tests/testdata/epanet.resx\"\n",
@@ -57,9 +71,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.572565Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.572232Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.589116Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.588470Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Volume:9</th>\n",
+       "      <th>Volume Percentage:9</th>\n",
+       "      <th>Volume:2</th>\n",
+       "      <th>Volume Percentage:2</th>\n",
+       "      <th>Pump efficiency:9</th>\n",
+       "      <th>Pump energy costs:9</th>\n",
+       "      <th>Pump energy:9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 00:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6806.106934</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95.844238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 01:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6980.124023</td>\n",
+       "      <td>82.045425</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>96.065292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 02:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7150.145020</td>\n",
+       "      <td>84.043869</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>96.188499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 03:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7267.672852</td>\n",
+       "      <td>85.425308</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>96.307945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-10-13 04:00:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7382.457520</td>\n",
+       "      <td>86.774513</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>96.357651</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Volume:9  Volume Percentage:9     Volume:2  \\\n",
+       "2022-10-13 00:00:00       0.0                  0.0  6806.106934   \n",
+       "2022-10-13 01:00:00       0.0                  0.0  6980.124023   \n",
+       "2022-10-13 02:00:00       0.0                  0.0  7150.145020   \n",
+       "2022-10-13 03:00:00       0.0                  0.0  7267.672852   \n",
+       "2022-10-13 04:00:00       0.0                  0.0  7382.457520   \n",
+       "\n",
+       "                     Volume Percentage:2  Pump efficiency:9  \\\n",
+       "2022-10-13 00:00:00            80.000000               75.0   \n",
+       "2022-10-13 01:00:00            82.045425               75.0   \n",
+       "2022-10-13 02:00:00            84.043869               75.0   \n",
+       "2022-10-13 03:00:00            85.425308               75.0   \n",
+       "2022-10-13 04:00:00            86.774513               75.0   \n",
+       "\n",
+       "                     Pump energy costs:9  Pump energy:9  \n",
+       "2022-10-13 00:00:00                  0.0      95.844238  \n",
+       "2022-10-13 01:00:00                  0.0      96.065292  \n",
+       "2022-10-13 02:00:00                  0.0      96.188499  \n",
+       "2022-10-13 03:00:00                  0.0      96.307945  \n",
+       "2022-10-13 04:00:00                  0.0      96.357651  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -74,9 +208,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.591447Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.591244Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.596447Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.595813Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Volume',\n",
+       " 'Volume Percentage',\n",
+       " 'Pump efficiency',\n",
+       " 'Pump energy costs',\n",
+       " 'Pump energy']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res.quantities"
    ]
@@ -106,9 +262,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.599043Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.598511Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.605197Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.604238Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start node ID: 9\n",
+      "End node ID: 2\n"
+     ]
+    }
+   ],
    "source": [
     "pipe_id = '9'\n",
     "pipe_data_item = res.reaches[pipe_id]\n",
@@ -122,9 +294,231 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.608324Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.607890Z",
+     "iopub.status.idle": "2024-09-04T12:38:43.617704Z",
+     "shell.execute_reply": "2024-09-04T12:38:43.616850Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>Pump energy:9</th>\n",
+       "      <th>Volume:2</th>\n",
+       "      <th>Volume:9</th>\n",
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+       "      <th>2022-10-13 12:00:00</th>\n",
+       "      <td>96.706551</td>\n",
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+       "      <th>2022-10-13 13:00:00</th>\n",
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+       "      <th>2022-10-13 15:00:00</th>\n",
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+       "      <th>2022-10-13 23:00:00</th>\n",
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+       "      <th>2022-10-14 00:00:00</th>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "                     Pump energy:9     Volume:2  Volume:9\n",
+       "2022-10-13 00:00:00      95.844238  6806.106934       0.0\n",
+       "2022-10-13 01:00:00      96.065292  6980.124023       0.0\n",
+       "2022-10-13 02:00:00      96.188499  7150.145020       0.0\n",
+       "2022-10-13 03:00:00      96.307945  7267.672852       0.0\n",
+       "2022-10-13 04:00:00      96.357651  7382.457520       0.0\n",
+       "2022-10-13 05:00:00      96.412949  7446.020020       0.0\n",
+       "2022-10-13 06:00:00      96.413940  7508.082031       0.0\n",
+       "2022-10-13 07:00:00      96.424004  7520.149414       0.0\n",
+       "2022-10-13 08:00:00      96.480957  7531.930176       0.0\n",
+       "2022-10-13 09:00:00      96.523834  7591.957031       0.0\n",
+       "2022-10-13 10:00:00      96.597382  7650.554688       0.0\n",
+       "2022-10-13 11:00:00      96.649826  7756.276367       0.0\n",
+       "2022-10-13 12:00:00      96.706551  7859.459961       0.0\n",
+       "2022-10-13 13:00:00       0.000000  7826.227051       0.0\n",
+       "2022-10-13 14:00:00       0.000000  7576.391113       0.0\n",
+       "2022-10-13 15:00:00       0.000000  7376.522461       0.0\n",
+       "2022-10-13 16:00:00       0.000000  7176.653320       0.0\n",
+       "2022-10-13 17:00:00       0.000000  7026.751953       0.0\n",
+       "2022-10-13 18:00:00       0.000000  6876.850098       0.0\n",
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+       "2022-10-13 22:00:00       0.000000  6377.177734       0.0\n",
+       "2022-10-13 23:00:00      95.163124  6311.523926       0.0\n",
+       "2022-10-14 00:00:00      95.457344  6545.330566       0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[['Pump energy:9', 'Volume:2', 'Volume:9']]"
    ]
@@ -139,9 +533,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:43.620545Z",
+     "iopub.status.busy": "2024-09-04T12:38:43.620060Z",
+     "iopub.status.idle": "2024-09-04T12:38:44.371787Z",
+     "shell.execute_reply": "2024-09-04T12:38:44.370884Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pipe_query = QueryDataReach('Pump energy', pipe_id)\n",
     "df_pipe = res.read(queries=[pipe_query])\n",
diff --git a/notebooks/mikeio1d_dataframe_examples.ipynb b/notebooks/mikeio1d_dataframe_examples.ipynb
index f0eaa2a1..8494e4ce 100644
--- a/notebooks/mikeio1d_dataframe_examples.ipynb
+++ b/notebooks/mikeio1d_dataframe_examples.ipynb
@@ -12,8 +12,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:15.344123Z",
+     "iopub.status.busy": "2024-09-04T12:38:15.343819Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.026372Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.025748Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D\n",
@@ -37,9 +44,183 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.029392Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.029083Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.051522Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.050629Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Discharge:basin_left1:1.002</th>\n",
+       "      <th>Discharge:basin_left1:17.889</th>\n",
+       "      <th>Discharge:basin_left1:57.3595</th>\n",
+       "      <th>Discharge:basin_left1:101.493</th>\n",
+       "      <th>Discharge:basin_left1:144.075</th>\n",
+       "      <th>Discharge:basin_left1:175.496</th>\n",
+       "      <th>Discharge:basin_left1:197.549</th>\n",
+       "      <th>Discharge:basin_left1:228.411</th>\n",
+       "      <th>Discharge:basin_left1:255.612</th>\n",
+       "      <th>Discharge:basin_left1:274.626</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Discharge:tributary:95</th>\n",
+       "      <th>Discharge:tributary:110</th>\n",
+       "      <th>Discharge:tributary:125</th>\n",
+       "      <th>Discharge:tributary:140</th>\n",
+       "      <th>Discharge:tributary:175</th>\n",
+       "      <th>Discharge:tributary:225</th>\n",
+       "      <th>Discharge:tributary:275</th>\n",
+       "      <th>Discharge:tributary:350</th>\n",
+       "      <th>Discharge:tributary:425</th>\n",
+       "      <th>Discharge:tributary:475</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.013857</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.050522</td>\n",
+       "      <td>0.053607</td>\n",
+       "      <td>0.090468</td>\n",
+       "      <td>0.107519</td>\n",
+       "      <td>0.144026</td>\n",
+       "      <td>0.177496</td>\n",
+       "      <td>0.191049</td>\n",
+       "      <td>0.197755</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Discharge:basin_left1:1.002  \\\n",
+       "2000-02-18 00:06:00                          0.0   \n",
+       "2000-02-18 00:16:00                         -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:17.889  \\\n",
+       "2000-02-18 00:06:00                           0.0   \n",
+       "2000-02-18 00:16:00                          -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:57.3595  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:101.493  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:144.075  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:175.496  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:197.549  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:228.411  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:255.612  \\\n",
+       "2000-02-18 00:06:00                            0.0   \n",
+       "2000-02-18 00:16:00                           -0.0   \n",
+       "\n",
+       "                     Discharge:basin_left1:274.626  ...  \\\n",
+       "2000-02-18 00:06:00                            0.0  ...   \n",
+       "2000-02-18 00:16:00                           -0.0  ...   \n",
+       "\n",
+       "                     Discharge:tributary:95  Discharge:tributary:110  \\\n",
+       "2000-02-18 00:06:00                0.000000                      0.0   \n",
+       "2000-02-18 00:16:00                0.013857                     -0.0   \n",
+       "\n",
+       "                     Discharge:tributary:125  Discharge:tributary:140  \\\n",
+       "2000-02-18 00:06:00                 0.000000                 0.000000   \n",
+       "2000-02-18 00:16:00                 0.050522                 0.053607   \n",
+       "\n",
+       "                     Discharge:tributary:175  Discharge:tributary:225  \\\n",
+       "2000-02-18 00:06:00                 0.000000                 0.000000   \n",
+       "2000-02-18 00:16:00                 0.090468                 0.107519   \n",
+       "\n",
+       "                     Discharge:tributary:275  Discharge:tributary:350  \\\n",
+       "2000-02-18 00:06:00                 0.000000                 0.000000   \n",
+       "2000-02-18 00:16:00                 0.144026                 0.177496   \n",
+       "\n",
+       "                     Discharge:tributary:425  Discharge:tributary:475  \n",
+       "2000-02-18 00:06:00                 0.000000                 0.000000  \n",
+       "2000-02-18 00:16:00                 0.191049                 0.197755  \n",
+       "\n",
+       "[2 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# The default column mode concatenates information about the column with a ':' delimiter.   \n",
     "df = res.reaches.Discharge.read()\n",
@@ -48,9 +229,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.054104Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.053689Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.077676Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.076919Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"21\" halign=\"left\">Discharge</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"21\" halign=\"left\">Reach</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">basin_left1</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>1.0020</th>\n",
+       "      <th>17.8890</th>\n",
+       "      <th>57.3595</th>\n",
+       "      <th>101.4935</th>\n",
+       "      <th>144.0745</th>\n",
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+       "      <th>228.4110</th>\n",
+       "      <th>255.6120</th>\n",
+       "      <th>274.6260</th>\n",
+       "      <th>...</th>\n",
+       "      <th>95.0000</th>\n",
+       "      <th>110.0000</th>\n",
+       "      <th>125.0000</th>\n",
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+       "      <th>275.0000</th>\n",
+       "      <th>350.0000</th>\n",
+       "      <th>425.0000</th>\n",
+       "      <th>475.0000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>246.6-413.6</th>\n",
+       "      <th>246.6-413.6</th>\n",
+       "      <th>...</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.013857</td>\n",
+       "      <td>-0.0</td>\n",
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+       "      <td>0.053607</td>\n",
+       "      <td>0.090468</td>\n",
+       "      <td>0.107519</td>\n",
+       "      <td>0.144026</td>\n",
+       "      <td>0.177496</td>\n",
+       "      <td>0.191049</td>\n",
+       "      <td>0.197755</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity              Discharge                                          \\\n",
+       "group                     Reach                                           \n",
+       "name                basin_left1                                           \n",
+       "chainage               1.0020    17.8890   57.3595   101.4935  144.0745   \n",
+       "tag                   0.0-246.6 0.0-246.6 0.0-246.6 0.0-246.6 0.0-246.6   \n",
+       "2000-02-18 00:06:00         0.0       0.0       0.0       0.0       0.0   \n",
+       "2000-02-18 00:16:00        -0.0      -0.0      -0.0      -0.0      -0.0   \n",
+       "\n",
+       "quantity                                                                   \\\n",
+       "group                                                                       \n",
+       "name                                                                        \n",
+       "chainage             175.4965  197.5490  228.4110    255.6120    274.6260   \n",
+       "tag                 0.0-246.6 0.0-246.6 0.0-246.6 246.6-413.6 246.6-413.6   \n",
+       "2000-02-18 00:06:00       0.0       0.0       0.0         0.0         0.0   \n",
+       "2000-02-18 00:16:00      -0.0      -0.0      -0.0        -0.0        -0.0   \n",
+       "\n",
+       "quantity             ...                                              \\\n",
+       "group                ...                                               \n",
+       "name                 ...  tributary                                    \n",
+       "chainage             ...   95.0000    110.0000   125.0000   140.0000   \n",
+       "tag                  ... 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00  ...   0.000000        0.0   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00  ...   0.013857       -0.0   0.050522   0.053607   \n",
+       "\n",
+       "quantity                                                                    \\\n",
+       "group                                                                        \n",
+       "name                                                                         \n",
+       "chainage              175.0000   225.0000   275.0000   350.0000   425.0000   \n",
+       "tag                 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00   0.090468   0.107519   0.144026   0.177496   0.191049   \n",
+       "\n",
+       "quantity                        \n",
+       "group                           \n",
+       "name                            \n",
+       "chainage              475.0000  \n",
+       "tag                 50.0-500.0  \n",
+       "2000-02-18 00:06:00   0.000000  \n",
+       "2000-02-18 00:16:00   0.197755  \n",
+       "\n",
+       "[2 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# A hierarchical column mode is also supported. Only relevant levels are included with 'compact'. For full hierarchy use 'all'.\n",
     "df = res.reaches.Discharge.read(column_mode='compact')\n",
@@ -59,9 +432,207 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.079971Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.079710Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.098120Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.097552Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=1.002, tag='0.0-246.6', duplicate=0, derived=False)</th>\n",
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+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=125.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=140.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=175.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=225.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=275.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=350.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=425.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "      <th>TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=475.0, tag='50.0-500.0', duplicate=0, derived=False)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.013857</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.050522</td>\n",
+       "      <td>0.053607</td>\n",
+       "      <td>0.090468</td>\n",
+       "      <td>0.107519</td>\n",
+       "      <td>0.144026</td>\n",
+       "      <td>0.177496</td>\n",
+       "      <td>0.191049</td>\n",
+       "      <td>0.197755</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=1.002, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                    \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                    \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=17.889, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                     \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                     \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=57.3595, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                      \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                      \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=101.4935, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                       \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                       \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=144.0745, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                       \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                       \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=175.4965, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                       \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                       \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=197.54899999999998, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                                 \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                                 \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=228.411, tag='0.0-246.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                      \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                      \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=255.612, tag='246.6-413.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                        \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                        \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='basin_left1', chainage=274.626, tag='246.6-413.6', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                        \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                        \n",
+       "\n",
+       "                     ...  \\\n",
+       "2000-02-18 00:06:00  ...   \n",
+       "2000-02-18 00:16:00  ...   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=95.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                  \n",
+       "2000-02-18 00:16:00                                           0.013857                                                                                  \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=110.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                                0.0                                                                                   \n",
+       "2000-02-18 00:16:00                                               -0.0                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=125.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.050522                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=140.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.053607                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=175.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.090468                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=225.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.107519                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=275.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.144026                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=350.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.177496                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=425.0, tag='50.0-500.0', duplicate=0, derived=False)  \\\n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                   \n",
+       "2000-02-18 00:16:00                                           0.191049                                                                                   \n",
+       "\n",
+       "                     TimeSeriesId(quantity='Discharge', group='Reach', name='tributary', chainage=475.0, tag='50.0-500.0', duplicate=0, derived=False)  \n",
+       "2000-02-18 00:06:00                                           0.000000                                                                                  \n",
+       "2000-02-18 00:16:00                                           0.197755                                                                                  \n",
+       "\n",
+       "[2 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# It's also possible to have a TimeSeriesId column index.\n",
     "# TimeSeriesId is an object that uniquely identifies each time series.\n",
@@ -86,9 +657,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.100744Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.100215Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.162389Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.161839Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mikeio1d.pandas_extension.mikeio1d_accessor.Mikeio1dAccessor at 0x7fd4d1b72de0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# To use the .m1d accessor, the DataFrame must have a MultiIndex column (e.g. column_mode='all' or 'compact').\n",
     "# The .m1d accessor exists on the DataFrame itself.\n",
@@ -105,9 +694,334 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.164489Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.164318Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.240635Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.239795Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">WaterLevel</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">FlowVelocity</th>\n",
+       "      <th>Variable:TwoTimeSensorGateLevel</th>\n",
+       "      <th>Water level:Sensor:s.h.river53745.34</th>\n",
+       "      <th>Gate level:Sensor:SensorGateLevel</th>\n",
+       "      <th>Discharge:Sensor:SensorGauge1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">Node</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Reach</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th>'basin_left1', 0</th>\n",
+       "      <th>'basin_left1', 483.74285236705026</th>\n",
+       "      <th>'basin_left2', 0</th>\n",
+       "      <th>'basin_left2', 441.71489580142429</th>\n",
+       "      <th>'basin_right', -10</th>\n",
+       "      <th>'basin_right', 720</th>\n",
+       "      <th>'river', 53100</th>\n",
+       "      <th>'river', 55124.2276598819</th>\n",
+       "      <th>'tributary', 500</th>\n",
+       "      <th>'basin_left1', 246.61</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">tributary</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>...</th>\n",
+       "      <th>175.00000</th>\n",
+       "      <th>225.00000</th>\n",
+       "      <th>275.00000</th>\n",
+       "      <th>350.00000</th>\n",
+       "      <th>425.00000</th>\n",
+       "      <th>475.00000</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>...</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duplicate</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>derived</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>...</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "      <th>False</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
+       "      <td>57.426998</td>\n",
+       "      <td>53.495998</td>\n",
+       "      <td>54.002998</td>\n",
+       "      <td>59.200001</td>\n",
+       "      <td>53.652</td>\n",
+       "      <td>58.342999</td>\n",
+       "      <td>51.835999</td>\n",
+       "      <td>53.194</td>\n",
+       "      <td>55.480000</td>\n",
+       "      <td>53.065304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>51.905998</td>\n",
+       "      <td>53.77</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>57.426998</td>\n",
+       "      <td>53.495998</td>\n",
+       "      <td>54.002998</td>\n",
+       "      <td>59.200001</td>\n",
+       "      <td>53.652</td>\n",
+       "      <td>58.342999</td>\n",
+       "      <td>51.836006</td>\n",
+       "      <td>53.194</td>\n",
+       "      <td>55.946224</td>\n",
+       "      <td>53.065304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.608204</td>\n",
+       "      <td>0.517946</td>\n",
+       "      <td>0.449898</td>\n",
+       "      <td>0.446345</td>\n",
+       "      <td>0.419857</td>\n",
+       "      <td>0.417569</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>51.905998</td>\n",
+       "      <td>53.77</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 498 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity                  WaterLevel                                    \\\n",
+       "group                           Node                                     \n",
+       "name                'basin_left1', 0 'basin_left1', 483.74285236705026   \n",
+       "chainage                         NaN                               NaN   \n",
+       "tag                                                                      \n",
+       "duplicate                          0                                 0   \n",
+       "derived                        False                             False   \n",
+       "2000-02-18 00:06:00        57.426998                         53.495998   \n",
+       "2000-02-18 00:16:00        57.426998                         53.495998   \n",
+       "\n",
+       "quantity                                                                \\\n",
+       "group                                                                    \n",
+       "name                'basin_left2', 0 'basin_left2', 441.71489580142429   \n",
+       "chainage                         NaN                               NaN   \n",
+       "tag                                                                      \n",
+       "duplicate                          0                                 0   \n",
+       "derived                        False                             False   \n",
+       "2000-02-18 00:06:00        54.002998                         59.200001   \n",
+       "2000-02-18 00:16:00        54.002998                         59.200001   \n",
+       "\n",
+       "quantity                                                                  \\\n",
+       "group                                                                      \n",
+       "name                'basin_right', -10 'basin_right', 720 'river', 53100   \n",
+       "chainage                           NaN                NaN            NaN   \n",
+       "tag                                                                        \n",
+       "duplicate                            0                  0              0   \n",
+       "derived                          False              False          False   \n",
+       "2000-02-18 00:06:00             53.652          58.342999      51.835999   \n",
+       "2000-02-18 00:16:00             53.652          58.342999      51.836006   \n",
+       "\n",
+       "quantity                                                        \\\n",
+       "group                                                            \n",
+       "name                'river', 55124.2276598819 'tributary', 500   \n",
+       "chainage                                  NaN              NaN   \n",
+       "tag                                                              \n",
+       "duplicate                                   0                0   \n",
+       "derived                                 False            False   \n",
+       "2000-02-18 00:06:00                    53.194        55.480000   \n",
+       "2000-02-18 00:16:00                    53.194        55.946224   \n",
+       "\n",
+       "quantity                                   ... FlowVelocity              \\\n",
+       "group                                      ...        Reach               \n",
+       "name                'basin_left1', 246.61  ...    tributary               \n",
+       "chainage                              NaN  ...  175.00000   225.00000     \n",
+       "tag                                        ...   50.0-500.0  50.0-500.0   \n",
+       "duplicate                               0  ...            0           0   \n",
+       "derived                             False  ...        False       False   \n",
+       "2000-02-18 00:06:00             53.065304  ...     0.000000    0.000000   \n",
+       "2000-02-18 00:16:00             53.065304  ...     0.608204    0.517946   \n",
+       "\n",
+       "quantity                                                             \\\n",
+       "group                                                                 \n",
+       "name                                                                  \n",
+       "chainage            275.00000   350.00000   425.00000   475.00000     \n",
+       "tag                  50.0-500.0  50.0-500.0  50.0-500.0  50.0-500.0   \n",
+       "duplicate                     0           0           0           0   \n",
+       "derived                   False       False       False       False   \n",
+       "2000-02-18 00:06:00    0.000000    0.000000    0.000000    0.000000   \n",
+       "2000-02-18 00:16:00    0.449898    0.446345    0.419857    0.417569   \n",
+       "\n",
+       "quantity            Variable:TwoTimeSensorGateLevel  \\\n",
+       "group                                        Global   \n",
+       "name                                                  \n",
+       "chainage                                        NaN   \n",
+       "tag                                                   \n",
+       "duplicate                                         0   \n",
+       "derived                                       False   \n",
+       "2000-02-18 00:06:00                      107.540001   \n",
+       "2000-02-18 00:16:00                      107.540001   \n",
+       "\n",
+       "quantity            Water level:Sensor:s.h.river53745.34  \\\n",
+       "group                                             Global   \n",
+       "name                                                       \n",
+       "chainage                                             NaN   \n",
+       "tag                                                        \n",
+       "duplicate                                              0   \n",
+       "derived                                            False   \n",
+       "2000-02-18 00:06:00                            51.905998   \n",
+       "2000-02-18 00:16:00                            51.905998   \n",
+       "\n",
+       "quantity            Gate level:Sensor:SensorGateLevel  \\\n",
+       "group                                          Global   \n",
+       "name                                                    \n",
+       "chainage                                          NaN   \n",
+       "tag                                                     \n",
+       "duplicate                                           0   \n",
+       "derived                                         False   \n",
+       "2000-02-18 00:06:00                             53.77   \n",
+       "2000-02-18 00:16:00                             53.77   \n",
+       "\n",
+       "quantity            Discharge:Sensor:SensorGauge1  \n",
+       "group                                      Global  \n",
+       "name                                               \n",
+       "chainage                                      NaN  \n",
+       "tag                                                \n",
+       "duplicate                                       0  \n",
+       "derived                                     False  \n",
+       "2000-02-18 00:06:00                           0.0  \n",
+       "2000-02-18 00:16:00                          -0.0  \n",
+       "\n",
+       "[2 rows x 498 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's make some queries on the DataFrame itself with help from the .m1d accessor.\n",
     "# Let's read the entire file into a DataFrame with column_mode='all'.\n",
@@ -117,9 +1031,266 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.242604Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.242427Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.259239Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.258431Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">WaterLevel</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">FlowVelocity</th>\n",
+       "      <th>Variable:TwoTimeSensorGateLevel</th>\n",
+       "      <th>Water level:Sensor:s.h.river53745.34</th>\n",
+       "      <th>Gate level:Sensor:SensorGateLevel</th>\n",
+       "      <th>Discharge:Sensor:SensorGauge1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">Node</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">Reach</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "      <th>Global</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th>'basin_left1', 0</th>\n",
+       "      <th>'basin_left1', 483.74285236705026</th>\n",
+       "      <th>'basin_left2', 0</th>\n",
+       "      <th>'basin_left2', 441.71489580142429</th>\n",
+       "      <th>'basin_right', -10</th>\n",
+       "      <th>'basin_right', 720</th>\n",
+       "      <th>'river', 53100</th>\n",
+       "      <th>'river', 55124.2276598819</th>\n",
+       "      <th>'tributary', 500</th>\n",
+       "      <th>'basin_left1', 246.61</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">tributary</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>...</th>\n",
+       "      <th>175.00000</th>\n",
+       "      <th>225.00000</th>\n",
+       "      <th>275.00000</th>\n",
+       "      <th>350.00000</th>\n",
+       "      <th>425.00000</th>\n",
+       "      <th>475.00000</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "      <th>NaN</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>...</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
+       "      <td>57.426998</td>\n",
+       "      <td>53.495998</td>\n",
+       "      <td>54.002998</td>\n",
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+       "      <td>53.194</td>\n",
+       "      <td>55.480000</td>\n",
+       "      <td>53.065304</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>53.77</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>57.426998</td>\n",
+       "      <td>53.495998</td>\n",
+       "      <td>54.002998</td>\n",
+       "      <td>59.200001</td>\n",
+       "      <td>53.652</td>\n",
+       "      <td>58.342999</td>\n",
+       "      <td>51.836006</td>\n",
+       "      <td>53.194</td>\n",
+       "      <td>55.946224</td>\n",
+       "      <td>53.065304</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.608204</td>\n",
+       "      <td>0.517946</td>\n",
+       "      <td>0.449898</td>\n",
+       "      <td>0.446345</td>\n",
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+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 498 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity                  WaterLevel                                    \\\n",
+       "group                           Node                                     \n",
+       "name                'basin_left1', 0 'basin_left1', 483.74285236705026   \n",
+       "chainage                         NaN                               NaN   \n",
+       "tag                                                                      \n",
+       "2000-02-18 00:06:00        57.426998                         53.495998   \n",
+       "2000-02-18 00:16:00        57.426998                         53.495998   \n",
+       "\n",
+       "quantity                                                                \\\n",
+       "group                                                                    \n",
+       "name                'basin_left2', 0 'basin_left2', 441.71489580142429   \n",
+       "chainage                         NaN                               NaN   \n",
+       "tag                                                                      \n",
+       "2000-02-18 00:06:00        54.002998                         59.200001   \n",
+       "2000-02-18 00:16:00        54.002998                         59.200001   \n",
+       "\n",
+       "quantity                                                                  \\\n",
+       "group                                                                      \n",
+       "name                'basin_right', -10 'basin_right', 720 'river', 53100   \n",
+       "chainage                           NaN                NaN            NaN   \n",
+       "tag                                                                        \n",
+       "2000-02-18 00:06:00             53.652          58.342999      51.835999   \n",
+       "2000-02-18 00:16:00             53.652          58.342999      51.836006   \n",
+       "\n",
+       "quantity                                                        \\\n",
+       "group                                                            \n",
+       "name                'river', 55124.2276598819 'tributary', 500   \n",
+       "chainage                                  NaN              NaN   \n",
+       "tag                                                              \n",
+       "2000-02-18 00:06:00                    53.194        55.480000   \n",
+       "2000-02-18 00:16:00                    53.194        55.946224   \n",
+       "\n",
+       "quantity                                   ... FlowVelocity              \\\n",
+       "group                                      ...        Reach               \n",
+       "name                'basin_left1', 246.61  ...    tributary               \n",
+       "chainage                              NaN  ...  175.00000   225.00000     \n",
+       "tag                                        ...   50.0-500.0  50.0-500.0   \n",
+       "2000-02-18 00:06:00             53.065304  ...     0.000000    0.000000   \n",
+       "2000-02-18 00:16:00             53.065304  ...     0.608204    0.517946   \n",
+       "\n",
+       "quantity                                                             \\\n",
+       "group                                                                 \n",
+       "name                                                                  \n",
+       "chainage            275.00000   350.00000   425.00000   475.00000     \n",
+       "tag                  50.0-500.0  50.0-500.0  50.0-500.0  50.0-500.0   \n",
+       "2000-02-18 00:06:00    0.000000    0.000000    0.000000    0.000000   \n",
+       "2000-02-18 00:16:00    0.449898    0.446345    0.419857    0.417569   \n",
+       "\n",
+       "quantity            Variable:TwoTimeSensorGateLevel  \\\n",
+       "group                                        Global   \n",
+       "name                                                  \n",
+       "chainage                                        NaN   \n",
+       "tag                                                   \n",
+       "2000-02-18 00:06:00                      107.540001   \n",
+       "2000-02-18 00:16:00                      107.540001   \n",
+       "\n",
+       "quantity            Water level:Sensor:s.h.river53745.34  \\\n",
+       "group                                             Global   \n",
+       "name                                                       \n",
+       "chainage                                             NaN   \n",
+       "tag                                                        \n",
+       "2000-02-18 00:06:00                            51.905998   \n",
+       "2000-02-18 00:16:00                            51.905998   \n",
+       "\n",
+       "quantity            Gate level:Sensor:SensorGateLevel  \\\n",
+       "group                                          Global   \n",
+       "name                                                    \n",
+       "chainage                                          NaN   \n",
+       "tag                                                     \n",
+       "2000-02-18 00:06:00                             53.77   \n",
+       "2000-02-18 00:16:00                             53.77   \n",
+       "\n",
+       "quantity            Discharge:Sensor:SensorGauge1  \n",
+       "group                                      Global  \n",
+       "name                                               \n",
+       "chainage                                      NaN  \n",
+       "tag                                                \n",
+       "2000-02-18 00:06:00                           0.0  \n",
+       "2000-02-18 00:16:00                          -0.0  \n",
+       "\n",
+       "[2 rows x 498 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# That's a lot of detail included and is a bit busy. We can use .m1d.compact() to remove redundant levels.\n",
     "df = df.m1d.compact()\n",
@@ -135,9 +1306,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.261711Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.261303Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.288424Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.287608Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       "    .dataframe thead tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"21\" halign=\"left\">Discharge</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"21\" halign=\"left\">Reach</th>\n",
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+       "      <th>name</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">basin_left1</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>1.0020</th>\n",
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+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>-0.0</td>\n",
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+       "      <td>0.197755</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity              Discharge                                          \\\n",
+       "group                     Reach                                           \n",
+       "name                basin_left1                                           \n",
+       "chainage               1.0020    17.8890   57.3595   101.4935  144.0745   \n",
+       "tag                   0.0-246.6 0.0-246.6 0.0-246.6 0.0-246.6 0.0-246.6   \n",
+       "2000-02-18 00:06:00         0.0       0.0       0.0       0.0       0.0   \n",
+       "2000-02-18 00:16:00        -0.0      -0.0      -0.0      -0.0      -0.0   \n",
+       "\n",
+       "quantity                                                                   \\\n",
+       "group                                                                       \n",
+       "name                                                                        \n",
+       "chainage             175.4965  197.5490  228.4110    255.6120    274.6260   \n",
+       "tag                 0.0-246.6 0.0-246.6 0.0-246.6 246.6-413.6 246.6-413.6   \n",
+       "2000-02-18 00:06:00       0.0       0.0       0.0         0.0         0.0   \n",
+       "2000-02-18 00:16:00      -0.0      -0.0      -0.0        -0.0        -0.0   \n",
+       "\n",
+       "quantity             ...                                              \\\n",
+       "group                ...                                               \n",
+       "name                 ...  tributary                                    \n",
+       "chainage             ...   95.0000    110.0000   125.0000   140.0000   \n",
+       "tag                  ... 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00  ...   0.000000        0.0   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00  ...   0.013857       -0.0   0.050522   0.053607   \n",
+       "\n",
+       "quantity                                                                    \\\n",
+       "group                                                                        \n",
+       "name                                                                         \n",
+       "chainage              175.0000   225.0000   275.0000   350.0000   425.0000   \n",
+       "tag                 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00   0.090468   0.107519   0.144026   0.177496   0.191049   \n",
+       "\n",
+       "quantity                        \n",
+       "group                           \n",
+       "name                            \n",
+       "chainage              475.0000  \n",
+       "tag                 50.0-500.0  \n",
+       "2000-02-18 00:06:00   0.000000  \n",
+       "2000-02-18 00:16:00   0.197755  \n",
+       "\n",
+       "[2 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's get Discharge for the reaches.\n",
     "df = df.m1d.query(\"group=='Reach' and quantity=='Discharge'\")\n",
@@ -146,9 +1509,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.291230Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.291031Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.310931Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.310136Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">Discharge</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">Reach</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>70.0</th>\n",
+       "      <th>95.0</th>\n",
+       "      <th>110.0</th>\n",
+       "      <th>125.0</th>\n",
+       "      <th>140.0</th>\n",
+       "      <th>175.0</th>\n",
+       "      <th>225.0</th>\n",
+       "      <th>275.0</th>\n",
+       "      <th>350.0</th>\n",
+       "      <th>425.0</th>\n",
+       "      <th>475.0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:06:00</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000-02-18 00:16:00</th>\n",
+       "      <td>0.020551</td>\n",
+       "      <td>0.013857</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.050522</td>\n",
+       "      <td>0.053607</td>\n",
+       "      <td>0.090468</td>\n",
+       "      <td>0.107519</td>\n",
+       "      <td>0.144026</td>\n",
+       "      <td>0.177496</td>\n",
+       "      <td>0.191049</td>\n",
+       "      <td>0.197755</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity             Discharge                                              \\\n",
+       "group                    Reach                                               \n",
+       "name                 tributary                                               \n",
+       "chainage                 70.0       95.0       110.0      125.0      140.0   \n",
+       "tag                 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00   0.000000   0.000000        0.0   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00   0.020551   0.013857       -0.0   0.050522   0.053607   \n",
+       "\n",
+       "quantity                                                                    \\\n",
+       "group                                                                        \n",
+       "name                                                                         \n",
+       "chainage                 175.0      225.0      275.0      350.0      425.0   \n",
+       "tag                 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "2000-02-18 00:06:00   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
+       "2000-02-18 00:16:00   0.090468   0.107519   0.144026   0.177496   0.191049   \n",
+       "\n",
+       "quantity                        \n",
+       "group                           \n",
+       "name                            \n",
+       "chainage                 475.0  \n",
+       "tag                 50.0-500.0  \n",
+       "2000-02-18 00:06:00   0.000000  \n",
+       "2000-02-18 00:16:00   0.197755  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's look at all the reaches with 'trib' in their name.\n",
     "df = df.m1d.query(\"name.str.contains('trib')\")\n",
@@ -157,9 +1651,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.313493Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.313066Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.340713Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.339828Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">Discharge</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">Reach</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th colspan=\"11\" halign=\"left\">tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>70.0</th>\n",
+       "      <th>95.0</th>\n",
+       "      <th>110.0</th>\n",
+       "      <th>125.0</th>\n",
+       "      <th>140.0</th>\n",
+       "      <th>175.0</th>\n",
+       "      <th>225.0</th>\n",
+       "      <th>275.0</th>\n",
+       "      <th>350.0</th>\n",
+       "      <th>425.0</th>\n",
+       "      <th>475.0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.027786</td>\n",
+       "      <td>1.698108</td>\n",
+       "      <td>1.452274</td>\n",
+       "      <td>1.198294</td>\n",
+       "      <td>0.945816</td>\n",
+       "      <td>0.696441</td>\n",
+       "      <td>0.665936</td>\n",
+       "      <td>0.59445</td>\n",
+       "      <td>0.589273</td>\n",
+       "      <td>0.495095</td>\n",
+       "      <td>0.296315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity  Discharge                                                         \\\n",
+       "group         Reach                                                          \n",
+       "name      tributary                                                          \n",
+       "chainage      70.0       95.0       110.0      125.0      140.0      175.0   \n",
+       "tag      50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0   \n",
+       "max        2.027786   1.698108   1.452274   1.198294   0.945816   0.696441   \n",
+       "\n",
+       "quantity                                                         \n",
+       "group                                                            \n",
+       "name                                                             \n",
+       "chainage      225.0      275.0      350.0      425.0      475.0  \n",
+       "tag      50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0 50.0-500.0  \n",
+       "max        0.665936    0.59445   0.589273   0.495095   0.296315  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's look at the max discharge for each reach.\n",
     "df.agg(['max'])"
@@ -167,9 +1768,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.342750Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.342563Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.360577Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.359808Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>tag</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th>name</th>\n",
+       "      <th>chainage</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">Discharge</th>\n",
+       "      <th rowspan=\"11\" valign=\"top\">tributary</th>\n",
+       "      <th>70.0</th>\n",
+       "      <td>2.027786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95.0</th>\n",
+       "      <td>1.698108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110.0</th>\n",
+       "      <td>1.452274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.0</th>\n",
+       "      <td>1.198294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140.0</th>\n",
+       "      <td>0.945816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175.0</th>\n",
+       "      <td>0.696441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225.0</th>\n",
+       "      <td>0.665936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275.0</th>\n",
+       "      <td>0.594450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350.0</th>\n",
+       "      <td>0.589273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425.0</th>\n",
+       "      <td>0.495095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475.0</th>\n",
+       "      <td>0.296315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    max\n",
+       "tag                          50.0-500.0\n",
+       "quantity  name      chainage           \n",
+       "Discharge tributary 70.0       2.027786\n",
+       "                    95.0       1.698108\n",
+       "                    110.0      1.452274\n",
+       "                    125.0      1.198294\n",
+       "                    140.0      0.945816\n",
+       "                    175.0      0.696441\n",
+       "                    225.0      0.665936\n",
+       "                    275.0      0.594450\n",
+       "                    350.0      0.589273\n",
+       "                    425.0      0.495095\n",
+       "                    475.0      0.296315"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Use some standard pandas methods to format the table a different way\n",
     "# Tip: Chaining methods in brackets is a great way to explore the data. Comment out lines from bottom up to see the effect.\n",
@@ -184,9 +1907,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.362953Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.362639Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.455965Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.454895Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>tag</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th>name</th>\n",
+       "      <th>chainage</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">Discharge</th>\n",
+       "      <th rowspan=\"11\" valign=\"top\">tributary</th>\n",
+       "      <th>70.0</th>\n",
+       "      <td>2.027786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95.0</th>\n",
+       "      <td>1.698108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110.0</th>\n",
+       "      <td>1.452274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125.0</th>\n",
+       "      <td>1.198294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140.0</th>\n",
+       "      <td>0.945816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175.0</th>\n",
+       "      <td>0.696441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225.0</th>\n",
+       "      <td>0.665936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275.0</th>\n",
+       "      <td>0.594450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350.0</th>\n",
+       "      <td>0.589273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425.0</th>\n",
+       "      <td>0.495095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475.0</th>\n",
+       "      <td>0.296315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    max\n",
+       "tag                          50.0-500.0\n",
+       "quantity  name      chainage           \n",
+       "Discharge tributary 70.0       2.027786\n",
+       "                    95.0       1.698108\n",
+       "                    110.0      1.452274\n",
+       "                    125.0      1.198294\n",
+       "                    140.0      0.945816\n",
+       "                    175.0      0.696441\n",
+       "                    225.0      0.665936\n",
+       "                    275.0      0.594450\n",
+       "                    350.0      0.589273\n",
+       "                    425.0      0.495095\n",
+       "                    475.0      0.296315"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's start from scratch and use bracket chaining to create the same table\n",
     "df = (\n",
@@ -211,9 +2056,322 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.458269Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.458061Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.578446Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.577735Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>quantity</th>\n",
+       "      <th colspan=\"18\" halign=\"left\">Discharge</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <th colspan=\"18\" halign=\"left\">Reach</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">basin_left1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">basin_left2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">basin_right</th>\n",
+       "      <th>link_basin_left</th>\n",
+       "      <th>link_basin_left1_2</th>\n",
+       "      <th>link_basin_left2_2</th>\n",
+       "      <th>link_basin_right</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">river</th>\n",
+       "      <th>tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>246.6-413.6</th>\n",
+       "      <th>413.6-483.7</th>\n",
+       "      <th>0.0-159.1</th>\n",
+       "      <th>159.1-441.7</th>\n",
+       "      <th>-10.0-636.4</th>\n",
+       "      <th>636.4-720.0</th>\n",
+       "      <th>0.0-64.0</th>\n",
+       "      <th>0.0-58.6</th>\n",
+       "      <th>0.0-69.0</th>\n",
+       "      <th>0.0-80.4</th>\n",
+       "      <th>53100.0-53950.0</th>\n",
+       "      <th>53950.0-54000.0</th>\n",
+       "      <th>54000.0-54050.0</th>\n",
+       "      <th>54050.0-54150.0</th>\n",
+       "      <th>54150.0-54350.0</th>\n",
+       "      <th>54350.0-55124.2</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.649067</td>\n",
+       "      <td>-0.360366</td>\n",
+       "      <td>0.025040</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.054045</td>\n",
+       "      <td>0.008020</td>\n",
+       "      <td>4.492208</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.737202</td>\n",
+       "      <td>72.182541</td>\n",
+       "      <td>70.029579</td>\n",
+       "      <td>74.721962</td>\n",
+       "      <td>74.971359</td>\n",
+       "      <td>75.507965</td>\n",
+       "      <td>76.910072</td>\n",
+       "      <td>0.548908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.888455</td>\n",
+       "      <td>0.820759</td>\n",
+       "      <td>0.059312</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.918259</td>\n",
+       "      <td>0.044539</td>\n",
+       "      <td>5.646258</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.961021</td>\n",
+       "      <td>29.658510</td>\n",
+       "      <td>27.060493</td>\n",
+       "      <td>29.563745</td>\n",
+       "      <td>29.512341</td>\n",
+       "      <td>28.967613</td>\n",
+       "      <td>27.771280</td>\n",
+       "      <td>0.489293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.000545</td>\n",
+       "      <td>-2.174818</td>\n",
+       "      <td>-0.009495</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-0.000000</td>\n",
+       "      <td>-2.671227</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.049854</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-1.117922</td>\n",
+       "      <td>-0.003883</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.036112</td>\n",
+       "      <td>75.885757</td>\n",
+       "      <td>76.290787</td>\n",
+       "      <td>76.206879</td>\n",
+       "      <td>75.494148</td>\n",
+       "      <td>75.799934</td>\n",
+       "      <td>76.689796</td>\n",
+       "      <td>0.200483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.183094</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.000986</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.485793</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.286976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.579083</td>\n",
+       "      <td>78.726158</td>\n",
+       "      <td>81.531731</td>\n",
+       "      <td>85.145889</td>\n",
+       "      <td>84.436142</td>\n",
+       "      <td>84.838776</td>\n",
+       "      <td>86.002777</td>\n",
+       "      <td>0.393951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.034901</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.796013</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.329815</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.944139</td>\n",
+       "      <td>86.603371</td>\n",
+       "      <td>84.478760</td>\n",
+       "      <td>93.835732</td>\n",
+       "      <td>94.904182</td>\n",
+       "      <td>93.394226</td>\n",
+       "      <td>93.584366</td>\n",
+       "      <td>0.627040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.773971</td>\n",
+       "      <td>1.041439</td>\n",
+       "      <td>0.202153</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>9.116076</td>\n",
+       "      <td>0.366389</td>\n",
+       "      <td>13.542682</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>11.380744</td>\n",
+       "      <td>102.722717</td>\n",
+       "      <td>90.085312</td>\n",
+       "      <td>99.735207</td>\n",
+       "      <td>100.169701</td>\n",
+       "      <td>100.301697</td>\n",
+       "      <td>100.134666</td>\n",
+       "      <td>2.027786</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity   Discharge                                                  \\\n",
+       "group          Reach                                                   \n",
+       "name     basin_left1                         basin_left2               \n",
+       "tag        0.0-246.6 246.6-413.6 413.6-483.7   0.0-159.1 159.1-441.7   \n",
+       "count      73.000000   73.000000   73.000000        73.0        73.0   \n",
+       "mean        2.649067   -0.360366    0.025040         0.0         0.0   \n",
+       "std         2.888455    0.820759    0.059312         0.0         0.0   \n",
+       "min        -0.000545   -2.174818   -0.009495        -0.0        -0.0   \n",
+       "25%         0.000000   -1.117922   -0.003883         0.0         0.0   \n",
+       "50%         1.183094    0.000000   -0.000986         0.0         0.0   \n",
+       "75%         5.034901    0.000000    0.000000         0.0         0.0   \n",
+       "max         7.773971    1.041439    0.202153        -0.0        -0.0   \n",
+       "\n",
+       "quantity                                                             \\\n",
+       "group                                                                 \n",
+       "name     basin_right             link_basin_left link_basin_left1_2   \n",
+       "tag      -10.0-636.4 636.4-720.0        0.0-64.0           0.0-58.6   \n",
+       "count      73.000000   73.000000       73.000000               73.0   \n",
+       "mean        4.054045    0.008020        4.492208                0.0   \n",
+       "std         2.918259    0.044539        5.646258                0.0   \n",
+       "min        -0.000000   -0.000000       -2.671227               -0.0   \n",
+       "25%         0.000000    0.000000        0.000000                0.0   \n",
+       "50%         4.485793    0.000000        1.286976                0.0   \n",
+       "75%         5.796013    0.000000        9.329815                0.0   \n",
+       "max         9.116076    0.366389       13.542682               -0.0   \n",
+       "\n",
+       "quantity                                                                      \\\n",
+       "group                                                                          \n",
+       "name     link_basin_left2_2 link_basin_right           river                   \n",
+       "tag                0.0-69.0         0.0-80.4 53100.0-53950.0 53950.0-54000.0   \n",
+       "count                  73.0        73.000000       73.000000       73.000000   \n",
+       "mean                    0.0         5.737202       72.182541       70.029579   \n",
+       "std                     0.0         3.961021       29.658510       27.060493   \n",
+       "min                    -0.0         0.000000        0.000000        0.000000   \n",
+       "25%                     0.0         0.036112       75.885757       76.290787   \n",
+       "50%                     0.0         6.579083       78.726158       81.531731   \n",
+       "75%                     0.0         8.944139       86.603371       84.478760   \n",
+       "max                    -0.0        11.380744      102.722717       90.085312   \n",
+       "\n",
+       "quantity                                                                  \\\n",
+       "group                                                                      \n",
+       "name                                                                       \n",
+       "tag      54000.0-54050.0 54050.0-54150.0 54150.0-54350.0 54350.0-55124.2   \n",
+       "count          73.000000       73.000000       73.000000       73.000000   \n",
+       "mean           74.721962       74.971359       75.507965       76.910072   \n",
+       "std            29.563745       29.512341       28.967613       27.771280   \n",
+       "min             0.000000        0.000000       -0.049854        0.000000   \n",
+       "25%            76.206879       75.494148       75.799934       76.689796   \n",
+       "50%            85.145889       84.436142       84.838776       86.002777   \n",
+       "75%            93.835732       94.904182       93.394226       93.584366   \n",
+       "max            99.735207      100.169701      100.301697      100.134666   \n",
+       "\n",
+       "quantity             \n",
+       "group                \n",
+       "name      tributary  \n",
+       "tag      50.0-500.0  \n",
+       "count     73.000000  \n",
+       "mean       0.548908  \n",
+       "std        0.489293  \n",
+       "min        0.000000  \n",
+       "25%        0.200483  \n",
+       "50%        0.393951  \n",
+       "75%        0.627040  \n",
+       "max        2.027786  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Now let's try something different. We aggregate the max discharge for each reach, then look at descriptive staistics along the time axis.\n",
     "# Here, 'count' is the number of time steps and 'mean' is the mean of the max discharges of all Q-points along a reach.\n",
@@ -235,9 +2393,359 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.581097Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.580771Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.723289Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.722599Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">basin_left1</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">basin_left2</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"8\" halign=\"left\">river</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">tributary</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chainage</th>\n",
+       "      <th>1.002000</th>\n",
+       "      <th>228.411000</th>\n",
+       "      <th>255.612000</th>\n",
+       "      <th>377.384500</th>\n",
+       "      <th>447.534000</th>\n",
+       "      <th>482.596926</th>\n",
+       "      <th>14.597000</th>\n",
+       "      <th>126.599500</th>\n",
+       "      <th>186.746500</th>\n",
+       "      <th>437.951448</th>\n",
+       "      <th>...</th>\n",
+       "      <th>53975.000000</th>\n",
+       "      <th>54025.000000</th>\n",
+       "      <th>54075.000000</th>\n",
+       "      <th>54125.000000</th>\n",
+       "      <th>54175.000000</th>\n",
+       "      <th>54324.998000</th>\n",
+       "      <th>54375.000000</th>\n",
+       "      <th>55112.113830</th>\n",
+       "      <th>70.000000</th>\n",
+       "      <th>475.000000</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tag</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>0.0-246.6</th>\n",
+       "      <th>246.6-413.6</th>\n",
+       "      <th>246.6-413.6</th>\n",
+       "      <th>413.6-483.7</th>\n",
+       "      <th>413.6-483.7</th>\n",
+       "      <th>0.0-159.1</th>\n",
+       "      <th>0.0-159.1</th>\n",
+       "      <th>159.1-441.7</th>\n",
+       "      <th>159.1-441.7</th>\n",
+       "      <th>...</th>\n",
+       "      <th>53950.0-54000.0</th>\n",
+       "      <th>54000.0-54050.0</th>\n",
+       "      <th>54050.0-54150.0</th>\n",
+       "      <th>54050.0-54150.0</th>\n",
+       "      <th>54150.0-54350.0</th>\n",
+       "      <th>54150.0-54350.0</th>\n",
+       "      <th>54350.0-55124.2</th>\n",
+       "      <th>54350.0-55124.2</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "      <th>50.0-500.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-0.000061</td>\n",
+       "      <td>2.306082</td>\n",
+       "      <td>-1.521666</td>\n",
+       "      <td>-0.416949</td>\n",
+       "      <td>-0.049141</td>\n",
+       "      <td>-0.001555</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>70.029579</td>\n",
+       "      <td>74.721962</td>\n",
+       "      <td>74.744926</td>\n",
+       "      <td>74.757668</td>\n",
+       "      <td>75.087944</td>\n",
+       "      <td>75.408974</td>\n",
+       "      <td>75.508713</td>\n",
+       "      <td>76.717072</td>\n",
+       "      <td>-0.211621</td>\n",
+       "      <td>0.188293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.000171</td>\n",
+       "      <td>3.264935</td>\n",
+       "      <td>1.941555</td>\n",
+       "      <td>0.753996</td>\n",
+       "      <td>0.129560</td>\n",
+       "      <td>0.004101</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.060493</td>\n",
+       "      <td>29.563745</td>\n",
+       "      <td>29.496099</td>\n",
+       "      <td>29.449482</td>\n",
+       "      <td>29.105959</td>\n",
+       "      <td>28.908295</td>\n",
+       "      <td>28.846025</td>\n",
+       "      <td>27.701380</td>\n",
+       "      <td>1.107742</td>\n",
+       "      <td>0.049437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.000545</td>\n",
+       "      <td>-2.167155</td>\n",
+       "      <td>-4.498637</td>\n",
+       "      <td>-2.174818</td>\n",
+       "      <td>-0.299972</td>\n",
+       "      <td>-0.009495</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.120941</td>\n",
+       "      <td>-0.049854</td>\n",
+       "      <td>-0.032419</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-2.884536</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-3.195148</td>\n",
+       "      <td>-1.117922</td>\n",
+       "      <td>-0.122781</td>\n",
+       "      <td>-0.003883</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>76.290787</td>\n",
+       "      <td>76.206879</td>\n",
+       "      <td>75.494148</td>\n",
+       "      <td>74.803177</td>\n",
+       "      <td>75.529945</td>\n",
+       "      <td>75.799934</td>\n",
+       "      <td>75.881004</td>\n",
+       "      <td>76.689796</td>\n",
+       "      <td>-0.840475</td>\n",
+       "      <td>0.164611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.138214</td>\n",
+       "      <td>-0.351519</td>\n",
+       "      <td>-0.255342</td>\n",
+       "      <td>-0.031151</td>\n",
+       "      <td>-0.000986</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>81.531731</td>\n",
+       "      <td>85.145889</td>\n",
+       "      <td>84.436142</td>\n",
+       "      <td>83.805542</td>\n",
+       "      <td>84.253838</td>\n",
+       "      <td>84.838776</td>\n",
+       "      <td>85.320358</td>\n",
+       "      <td>85.784035</td>\n",
+       "      <td>-0.223872</td>\n",
+       "      <td>0.186661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.034901</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>84.478760</td>\n",
+       "      <td>93.835732</td>\n",
+       "      <td>94.714996</td>\n",
+       "      <td>94.904182</td>\n",
+       "      <td>93.394226</td>\n",
+       "      <td>93.146133</td>\n",
+       "      <td>93.251762</td>\n",
+       "      <td>93.584366</td>\n",
+       "      <td>0.340223</td>\n",
+       "      <td>0.211712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.000301</td>\n",
+       "      <td>7.773971</td>\n",
+       "      <td>0.993395</td>\n",
+       "      <td>0.848020</td>\n",
+       "      <td>0.202153</td>\n",
+       "      <td>0.006400</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>90.085312</td>\n",
+       "      <td>99.735207</td>\n",
+       "      <td>99.707542</td>\n",
+       "      <td>100.169701</td>\n",
+       "      <td>100.264687</td>\n",
+       "      <td>100.144592</td>\n",
+       "      <td>100.042068</td>\n",
+       "      <td>99.999596</td>\n",
+       "      <td>2.027786</td>\n",
+       "      <td>0.296315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "name      basin_left1                                                      \\\n",
+       "chainage 1.002000     228.411000   255.612000   377.384500   447.534000     \n",
+       "tag         0.0-246.6    0.0-246.6  246.6-413.6  246.6-413.6  413.6-483.7   \n",
+       "count       73.000000    73.000000    73.000000    73.000000    73.000000   \n",
+       "mean        -0.000061     2.306082    -1.521666    -0.416949    -0.049141   \n",
+       "std          0.000171     3.264935     1.941555     0.753996     0.129560   \n",
+       "min         -0.000545    -2.167155    -4.498637    -2.174818    -0.299972   \n",
+       "25%          0.000000     0.000000    -3.195148    -1.117922    -0.122781   \n",
+       "50%          0.000000     1.138214    -0.351519    -0.255342    -0.031151   \n",
+       "75%          0.000000     5.034901     0.000000     0.000000     0.000000   \n",
+       "max          0.000301     7.773971     0.993395     0.848020     0.202153   \n",
+       "\n",
+       "name                   basin_left2                                         \\\n",
+       "chainage 482.596926   14.597000    126.599500   186.746500   437.951448     \n",
+       "tag       413.6-483.7    0.0-159.1    0.0-159.1  159.1-441.7  159.1-441.7   \n",
+       "count       73.000000         73.0         73.0         73.0         73.0   \n",
+       "mean        -0.001555          0.0          0.0          0.0          0.0   \n",
+       "std          0.004101          0.0          0.0          0.0          0.0   \n",
+       "min         -0.009495         -0.0         -0.0         -0.0         -0.0   \n",
+       "25%         -0.003883          0.0          0.0          0.0          0.0   \n",
+       "50%         -0.000986          0.0          0.0          0.0          0.0   \n",
+       "75%          0.000000          0.0          0.0          0.0          0.0   \n",
+       "max          0.006400         -0.0         -0.0         -0.0         -0.0   \n",
+       "\n",
+       "name      ...           river                                                  \\\n",
+       "chainage  ...    53975.000000    54025.000000    54075.000000    54125.000000   \n",
+       "tag       ... 53950.0-54000.0 54000.0-54050.0 54050.0-54150.0 54050.0-54150.0   \n",
+       "count     ...       73.000000       73.000000       73.000000       73.000000   \n",
+       "mean      ...       70.029579       74.721962       74.744926       74.757668   \n",
+       "std       ...       27.060493       29.563745       29.496099       29.449482   \n",
+       "min       ...        0.000000        0.000000        0.000000        0.000000   \n",
+       "25%       ...       76.290787       76.206879       75.494148       74.803177   \n",
+       "50%       ...       81.531731       85.145889       84.436142       83.805542   \n",
+       "75%       ...       84.478760       93.835732       94.714996       94.904182   \n",
+       "max       ...       90.085312       99.735207       99.707542      100.169701   \n",
+       "\n",
+       "name                                                                      \\\n",
+       "chainage    54175.000000    54324.998000    54375.000000    55112.113830   \n",
+       "tag      54150.0-54350.0 54150.0-54350.0 54350.0-55124.2 54350.0-55124.2   \n",
+       "count          73.000000       73.000000       73.000000       73.000000   \n",
+       "mean           75.087944       75.408974       75.508713       76.717072   \n",
+       "std            29.105959       28.908295       28.846025       27.701380   \n",
+       "min            -0.120941       -0.049854       -0.032419        0.000000   \n",
+       "25%            75.529945       75.799934       75.881004       76.689796   \n",
+       "50%            84.253838       84.838776       85.320358       85.784035   \n",
+       "75%            93.394226       93.146133       93.251762       93.584366   \n",
+       "max           100.264687      100.144592      100.042068       99.999596   \n",
+       "\n",
+       "name        tributary               \n",
+       "chainage 70.000000    475.000000    \n",
+       "tag        50.0-500.0   50.0-500.0  \n",
+       "count       73.000000    73.000000  \n",
+       "mean        -0.211621     0.188293  \n",
+       "std          1.107742     0.049437  \n",
+       "min         -2.884536     0.000000  \n",
+       "25%         -0.840475     0.164611  \n",
+       "50%         -0.223872     0.186661  \n",
+       "75%          0.340223     0.211712  \n",
+       "max          2.027786     0.296315  \n",
+       "\n",
+       "[8 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Similarly, let's look at the mean of the first and last Q points.\n",
     "(\n",
@@ -259,9 +2767,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:17.725610Z",
+     "iopub.status.busy": "2024-09-04T12:38:17.725391Z",
+     "iopub.status.idle": "2024-09-04T12:38:17.802481Z",
+     "shell.execute_reply": "2024-09-04T12:38:17.801559Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>quantity</th>\n",
+       "      <th>ControlStrategyId</th>\n",
+       "      <th>Discharge</th>\n",
+       "      <th>Discharge:Sensor:SensorGauge1</th>\n",
+       "      <th>DischargeInStructure</th>\n",
+       "      <th>FlowAreaInStructure</th>\n",
+       "      <th>FlowVelocity</th>\n",
+       "      <th>FlowVelocityInStructure</th>\n",
+       "      <th>Gate level:Sensor:SensorGateLevel</th>\n",
+       "      <th>GateLevel</th>\n",
+       "      <th>ManningResistanceNumber</th>\n",
+       "      <th>Variable:TwoTimeSensorGateLevel</th>\n",
+       "      <th>Water level:Sensor:s.h.river53745.34</th>\n",
+       "      <th>WaterLevel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.917808</td>\n",
+       "      <td>79.343079</td>\n",
+       "      <td>61.396931</td>\n",
+       "      <td>77.747810</td>\n",
+       "      <td>47.783684</td>\n",
+       "      <td>3.353872</td>\n",
+       "      <td>11.329100</td>\n",
+       "      <td>53.769997</td>\n",
+       "      <td>53.769997</td>\n",
+       "      <td>32.665512</td>\n",
+       "      <td>107.539993</td>\n",
+       "      <td>56.858250</td>\n",
+       "      <td>59.200008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.702247</td>\n",
+       "      <td>28.940966</td>\n",
+       "      <td>26.605530</td>\n",
+       "      <td>29.185286</td>\n",
+       "      <td>18.166550</td>\n",
+       "      <td>0.818552</td>\n",
+       "      <td>5.346817</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.485120</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.846656</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>51.905998</td>\n",
+       "      <td>59.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>80.783112</td>\n",
+       "      <td>69.281067</td>\n",
+       "      <td>76.470184</td>\n",
+       "      <td>53.639187</td>\n",
+       "      <td>3.652213</td>\n",
+       "      <td>11.170768</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>33.597073</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>57.567822</td>\n",
+       "      <td>59.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>86.655334</td>\n",
+       "      <td>74.483246</td>\n",
+       "      <td>86.155029</td>\n",
+       "      <td>56.015728</td>\n",
+       "      <td>3.719032</td>\n",
+       "      <td>11.416133</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>33.614704</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>57.708385</td>\n",
+       "      <td>59.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>99.554710</td>\n",
+       "      <td>75.963501</td>\n",
+       "      <td>99.477150</td>\n",
+       "      <td>57.551910</td>\n",
+       "      <td>3.736953</td>\n",
+       "      <td>11.514374</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>33.618801</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>57.743835</td>\n",
+       "      <td>59.200001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>102.722717</td>\n",
+       "      <td>77.521797</td>\n",
+       "      <td>100.247269</td>\n",
+       "      <td>59.036644</td>\n",
+       "      <td>3.754651</td>\n",
+       "      <td>19.363970</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>53.770000</td>\n",
+       "      <td>33.622650</td>\n",
+       "      <td>107.540001</td>\n",
+       "      <td>57.777584</td>\n",
+       "      <td>59.200001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "quantity  ControlStrategyId   Discharge  Discharge:Sensor:SensorGauge1  \\\n",
+       "count             73.000000   73.000000                      73.000000   \n",
+       "mean               5.917808   79.343079                      61.396931   \n",
+       "std                0.702247   28.940966                      26.605530   \n",
+       "min                0.000000    0.000000                       0.000000   \n",
+       "25%                6.000000   80.783112                      69.281067   \n",
+       "50%                6.000000   86.655334                      74.483246   \n",
+       "75%                6.000000   99.554710                      75.963501   \n",
+       "max                6.000000  102.722717                      77.521797   \n",
+       "\n",
+       "quantity  DischargeInStructure  FlowAreaInStructure  FlowVelocity  \\\n",
+       "count                73.000000            73.000000     73.000000   \n",
+       "mean                 77.747810            47.783684      3.353872   \n",
+       "std                  29.185286            18.166550      0.818552   \n",
+       "min                   0.000000             0.000000      0.000000   \n",
+       "25%                  76.470184            53.639187      3.652213   \n",
+       "50%                  86.155029            56.015728      3.719032   \n",
+       "75%                  99.477150            57.551910      3.736953   \n",
+       "max                 100.247269            59.036644      3.754651   \n",
+       "\n",
+       "quantity  FlowVelocityInStructure  Gate level:Sensor:SensorGateLevel  \\\n",
+       "count                   73.000000                          73.000000   \n",
+       "mean                    11.329100                          53.769997   \n",
+       "std                      5.346817                           0.000000   \n",
+       "min                      0.000000                          53.770000   \n",
+       "25%                     11.170768                          53.770000   \n",
+       "50%                     11.416133                          53.770000   \n",
+       "75%                     11.514374                          53.770000   \n",
+       "max                     19.363970                          53.770000   \n",
+       "\n",
+       "quantity  GateLevel  ManningResistanceNumber  Variable:TwoTimeSensorGateLevel  \\\n",
+       "count     73.000000                73.000000                        73.000000   \n",
+       "mean      53.769997                32.665512                       107.539993   \n",
+       "std        0.000000                 2.485120                         0.000000   \n",
+       "min       53.770000                25.000000                       107.540001   \n",
+       "25%       53.770000                33.597073                       107.540001   \n",
+       "50%       53.770000                33.614704                       107.540001   \n",
+       "75%       53.770000                33.618801                       107.540001   \n",
+       "max       53.770000                33.622650                       107.540001   \n",
+       "\n",
+       "quantity  Water level:Sensor:s.h.river53745.34  WaterLevel  \n",
+       "count                                73.000000   73.000000  \n",
+       "mean                                 56.858250   59.200008  \n",
+       "std                                   1.846656    0.000000  \n",
+       "min                                  51.905998   59.200001  \n",
+       "25%                                  57.567822   59.200001  \n",
+       "50%                                  57.708385   59.200001  \n",
+       "75%                                  57.743835   59.200001  \n",
+       "max                                  57.777584   59.200001  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# We can similarly use the .m1d accessor to group by dimensions other than chainage.\n",
     "# Below we describe how the global maximum of each quantity in the system varies with time.\n",
diff --git a/notebooks/res1d.ipynb b/notebooks/res1d.ipynb
index 232b0174..7c407f9b 100644
--- a/notebooks/res1d.ipynb
+++ b/notebooks/res1d.ipynb
@@ -30,8 +30,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:10.113411Z",
+     "iopub.status.busy": "2024-09-04T12:38:10.113197Z",
+     "iopub.status.idle": "2024-09-04T12:38:11.335576Z",
+     "shell.execute_reply": "2024-09-04T12:38:11.334802Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D"
@@ -55,9 +62,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:11.338355Z",
+     "iopub.status.busy": "2024-09-04T12:38:11.338091Z",
+     "iopub.status.idle": "2024-09-04T12:38:11.574357Z",
+     "shell.execute_reply": "2024-09-04T12:38:11.573700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start time: 1994-08-07 16:35:00\n",
+      "End time: 1994-08-07 18:35:00\n",
+      "# Timesteps: 110\n",
+      "# Catchments: 0\n",
+      "# Nodes: 119\n",
+      "# Reaches: 118\n",
+      "# Globals: 0\n",
+      "0 - WaterLevel <m>\n",
+      "1 - Discharge <m^3/s>\n"
+     ]
+    }
+   ],
    "source": [
     "file_path_network = '../tests/testdata/network.res1d'\n",
     "res1d_network = Res1D(file_path_network)\n",
@@ -74,9 +104,258 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:11.576334Z",
+     "iopub.status.busy": "2024-09-04T12:38:11.576170Z",
+     "iopub.status.idle": "2024-09-04T12:38:11.631692Z",
+     "shell.execute_reply": "2024-09-04T12:38:11.631217Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WaterLevel:1</th>\n",
+       "      <th>WaterLevel:2</th>\n",
+       "      <th>WaterLevel:3</th>\n",
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+       "      <th>WaterLevel:9</th>\n",
+       "      <th>WaterLevel:10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Discharge:99l1:22.2508</th>\n",
+       "      <th>WaterLevel:9l1:0</th>\n",
+       "      <th>WaterLevel:9l1:10</th>\n",
+       "      <th>Discharge:9l1:5</th>\n",
+       "      <th>WaterLevel:Weir:119w1:0</th>\n",
+       "      <th>WaterLevel:Weir:119w1:1</th>\n",
+       "      <th>Discharge:Weir:119w1:0.5</th>\n",
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+       "      <th>Discharge:Pump:115p1:41.214</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:35:00.000</th>\n",
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+       "      <th>1994-08-07 16:37:07.560</th>\n",
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+       "    <tr>\n",
+       "      <th>1994-08-07 16:38:55.828</th>\n",
+       "      <td>195.052994</td>\n",
+       "      <td>195.821503</td>\n",
+       "      <td>195.8815</td>\n",
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+       "      <td>193.615005</td>\n",
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+       "      <td>193.765228</td>\n",
+       "      <td>193.776077</td>\n",
+       "      <td>193.804993</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000002</td>\n",
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+       "      <td>193.550079</td>\n",
+       "      <td>188.479996</td>\n",
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+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>1994-08-07 16:39:55.828</th>\n",
+       "      <td>195.052994</td>\n",
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+       "      <td>193.550095</td>\n",
+       "      <td>188.479996</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>193.309860</td>\n",
+       "      <td>195.005005</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 495 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         WaterLevel:1  WaterLevel:2  WaterLevel:3  \\\n",
+       "1994-08-07 16:35:00.000    195.052994    195.821503      195.8815   \n",
+       "1994-08-07 16:36:01.870    195.052994    195.821701      195.8815   \n",
+       "1994-08-07 16:37:07.560    195.052994    195.821640      195.8815   \n",
+       "1994-08-07 16:38:55.828    195.052994    195.821503      195.8815   \n",
+       "1994-08-07 16:39:55.828    195.052994    195.821503      195.8815   \n",
+       "\n",
+       "                         WaterLevel:4  WaterLevel:5  WaterLevel:6  \\\n",
+       "1994-08-07 16:35:00.000    193.604996    193.615005    193.625000   \n",
+       "1994-08-07 16:36:01.870    193.604996    193.615005    193.625320   \n",
+       "1994-08-07 16:37:07.560    193.604996    193.615005    193.625671   \n",
+       "1994-08-07 16:38:55.828    193.604996    193.615005    193.626236   \n",
+       "1994-08-07 16:39:55.828    193.604996    193.615005    193.626556   \n",
+       "\n",
+       "                         WaterLevel:7  WaterLevel:8  WaterLevel:9  \\\n",
+       "1994-08-07 16:35:00.000    193.675003    193.764999    193.774994   \n",
+       "1994-08-07 16:36:01.870    193.675110    193.765060    193.775116   \n",
+       "1994-08-07 16:37:07.560    193.675369    193.765106    193.775513   \n",
+       "1994-08-07 16:38:55.828    193.675751    193.765228    193.776077   \n",
+       "1994-08-07 16:39:55.828    193.675949    193.765335    193.776352   \n",
+       "\n",
+       "                         WaterLevel:10  ...  Discharge:99l1:22.2508  \\\n",
+       "1994-08-07 16:35:00.000     193.804993  ...                0.000002   \n",
+       "1994-08-07 16:36:01.870     193.804993  ...                0.000002   \n",
+       "1994-08-07 16:37:07.560     193.804993  ...                0.000002   \n",
+       "1994-08-07 16:38:55.828     193.804993  ...                0.000002   \n",
+       "1994-08-07 16:39:55.828     193.804993  ...                0.000002   \n",
+       "\n",
+       "                         WaterLevel:9l1:0  WaterLevel:9l1:10  Discharge:9l1:5  \\\n",
+       "1994-08-07 16:35:00.000        193.774994         193.764999         0.000031   \n",
+       "1994-08-07 16:36:01.870        193.775070         193.765060         0.000031   \n",
+       "1994-08-07 16:37:07.560        193.775391         193.765106         0.000033   \n",
+       "1994-08-07 16:38:55.828        193.775894         193.765228         0.000037   \n",
+       "1994-08-07 16:39:55.828        193.776154         193.765335         0.000039   \n",
+       "\n",
+       "                         WaterLevel:Weir:119w1:0  WaterLevel:Weir:119w1:1  \\\n",
+       "1994-08-07 16:35:00.000               193.550003               188.479996   \n",
+       "1994-08-07 16:36:01.870               193.550003               188.479996   \n",
+       "1994-08-07 16:37:07.560               193.550034               188.479996   \n",
+       "1994-08-07 16:38:55.828               193.550079               188.479996   \n",
+       "1994-08-07 16:39:55.828               193.550095               188.479996   \n",
+       "\n",
+       "                         Discharge:Weir:119w1:0.5  WaterLevel:Pump:115p1:0  \\\n",
+       "1994-08-07 16:35:00.000                       0.0               193.304993   \n",
+       "1994-08-07 16:36:01.870                       0.0               193.306061   \n",
+       "1994-08-07 16:37:07.560                       0.0               193.307144   \n",
+       "1994-08-07 16:38:55.828                       0.0               193.308884   \n",
+       "1994-08-07 16:39:55.828                       0.0               193.309860   \n",
+       "\n",
+       "                         WaterLevel:Pump:115p1:82.4281  \\\n",
+       "1994-08-07 16:35:00.000                     195.005005   \n",
+       "1994-08-07 16:36:01.870                     195.005005   \n",
+       "1994-08-07 16:37:07.560                     195.005005   \n",
+       "1994-08-07 16:38:55.828                     195.005005   \n",
+       "1994-08-07 16:39:55.828                     195.005005   \n",
+       "\n",
+       "                         Discharge:Pump:115p1:41.214  \n",
+       "1994-08-07 16:35:00.000                          0.0  \n",
+       "1994-08-07 16:36:01.870                          0.0  \n",
+       "1994-08-07 16:37:07.560                          0.0  \n",
+       "1994-08-07 16:38:55.828                          0.0  \n",
+       "1994-08-07 16:39:55.828                          0.0  \n",
+       "\n",
+       "[5 rows x 495 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_network = res1d_network.read_all()\n",
     "df_network.head()"
@@ -92,18 +371,161 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:11.633711Z",
+     "iopub.status.busy": "2024-09-04T12:38:11.633278Z",
+     "iopub.status.idle": "2024-09-04T12:38:11.640038Z",
+     "shell.execute_reply": "2024-09-04T12:38:11.639575Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <td>194.688354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:35:00.000</th>\n",
+       "      <td>195.450409</td>\n",
+       "      <td>194.686172</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>110 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         WaterLevel:100l1:0  WaterLevel:100l1:47.6827\n",
+       "1994-08-07 16:35:00.000          195.441498                194.661499\n",
+       "1994-08-07 16:36:01.870          195.441498                194.661621\n",
+       "1994-08-07 16:37:07.560          195.441498                194.661728\n",
+       "1994-08-07 16:38:55.828          195.441498                194.661804\n",
+       "1994-08-07 16:39:55.828          195.441498                194.661972\n",
+       "...                                     ...                       ...\n",
+       "1994-08-07 18:30:07.967          195.455109                194.689072\n",
+       "1994-08-07 18:31:07.967          195.455063                194.688934\n",
+       "1994-08-07 18:32:07.967          195.455002                194.688812\n",
+       "1994-08-07 18:33:07.967          195.453049                194.688354\n",
+       "1994-08-07 18:35:00.000          195.450409                194.686172\n",
+       "\n",
+       "[110 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_network[['WaterLevel:100l1:0','WaterLevel:100l1:47.6827']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:11.642632Z",
+     "iopub.status.busy": "2024-09-04T12:38:11.642345Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.051613Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.050589Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_network[['WaterLevel:100l1:0','WaterLevel:100l1:47.6827']].plot()"
    ]
@@ -126,9 +548,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.054943Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.054657Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.105572Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.105105Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start time: 1994-08-07 16:35:00\n",
+      "End time: 1994-08-07 18:35:00\n",
+      "# Timesteps: 108\n",
+      "# Catchments: 31\n",
+      "# Nodes: 0\n",
+      "# Reaches: 0\n",
+      "# Globals: 0\n",
+      "0 - TotalRunOff <m^3/s>\n",
+      "1 - ActualRainfall <m/s>\n",
+      "2 - ZinkLoadRR <kg/s>\n",
+      "3 - ZinkMassAccumulatedRR <kg>\n",
+      "4 - ZinkRR <mg/l>\n"
+     ]
+    }
+   ],
    "source": [
     "file_path_catchments = '../tests/testdata/catchments.res1d'\n",
     "res1d_catchments = Res1D(file_path_catchments)\n",
@@ -144,9 +592,265 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.107479Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.107172Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.130470Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.129973Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TotalRunOff:100_16_16</th>\n",
+       "      <th>ActualRainfall:100_16_16</th>\n",
+       "      <th>ZinkLoadRR:100_16_16</th>\n",
+       "      <th>ZinkMassAccumulatedRR:100_16_16</th>\n",
+       "      <th>ZinkRR:100_16_16</th>\n",
+       "      <th>TotalRunOff:105_1_1</th>\n",
+       "      <th>ActualRainfall:105_1_1</th>\n",
+       "      <th>ZinkLoadRR:105_1_1</th>\n",
+       "      <th>ZinkMassAccumulatedRR:105_1_1</th>\n",
+       "      <th>ZinkRR:105_1_1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TotalRunOff:94_9_9</th>\n",
+       "      <th>ActualRainfall:94_9_9</th>\n",
+       "      <th>ZinkLoadRR:94_9_9</th>\n",
+       "      <th>ZinkMassAccumulatedRR:94_9_9</th>\n",
+       "      <th>ZinkRR:94_9_9</th>\n",
+       "      <th>TotalRunOff:9_3_3</th>\n",
+       "      <th>ActualRainfall:9_3_3</th>\n",
+       "      <th>ZinkLoadRR:9_3_3</th>\n",
+       "      <th>ZinkMassAccumulatedRR:9_3_3</th>\n",
+       "      <th>ZinkRR:9_3_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:35:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:36:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:37:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:38:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:39:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 155 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     TotalRunOff:100_16_16  ActualRainfall:100_16_16  \\\n",
+       "1994-08-07 16:35:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:36:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:37:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:38:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:39:00                    0.0              3.333333e-07   \n",
+       "\n",
+       "                     ZinkLoadRR:100_16_16  ZinkMassAccumulatedRR:100_16_16  \\\n",
+       "1994-08-07 16:35:00                   0.0                              0.0   \n",
+       "1994-08-07 16:36:00                   0.0                              0.0   \n",
+       "1994-08-07 16:37:00                   0.0                              0.0   \n",
+       "1994-08-07 16:38:00                   0.0                              0.0   \n",
+       "1994-08-07 16:39:00                   0.0                              0.0   \n",
+       "\n",
+       "                     ZinkRR:100_16_16  TotalRunOff:105_1_1  \\\n",
+       "1994-08-07 16:35:00             100.0                  0.0   \n",
+       "1994-08-07 16:36:00             100.0                  0.0   \n",
+       "1994-08-07 16:37:00             100.0                  0.0   \n",
+       "1994-08-07 16:38:00             100.0                  0.0   \n",
+       "1994-08-07 16:39:00             100.0                  0.0   \n",
+       "\n",
+       "                     ActualRainfall:105_1_1  ZinkLoadRR:105_1_1  \\\n",
+       "1994-08-07 16:35:00            3.333333e-07                 0.0   \n",
+       "1994-08-07 16:36:00            3.333333e-07                 0.0   \n",
+       "1994-08-07 16:37:00            3.333333e-07                 0.0   \n",
+       "1994-08-07 16:38:00            3.333333e-07                 0.0   \n",
+       "1994-08-07 16:39:00            3.333333e-07                 0.0   \n",
+       "\n",
+       "                     ZinkMassAccumulatedRR:105_1_1  ZinkRR:105_1_1  ...  \\\n",
+       "1994-08-07 16:35:00                            0.0           100.0  ...   \n",
+       "1994-08-07 16:36:00                            0.0           100.0  ...   \n",
+       "1994-08-07 16:37:00                            0.0           100.0  ...   \n",
+       "1994-08-07 16:38:00                            0.0           100.0  ...   \n",
+       "1994-08-07 16:39:00                            0.0           100.0  ...   \n",
+       "\n",
+       "                     TotalRunOff:94_9_9  ActualRainfall:94_9_9  \\\n",
+       "1994-08-07 16:35:00                 0.0           3.333333e-07   \n",
+       "1994-08-07 16:36:00                 0.0           3.333333e-07   \n",
+       "1994-08-07 16:37:00                 0.0           3.333333e-07   \n",
+       "1994-08-07 16:38:00                 0.0           3.333333e-07   \n",
+       "1994-08-07 16:39:00                 0.0           3.333333e-07   \n",
+       "\n",
+       "                     ZinkLoadRR:94_9_9  ZinkMassAccumulatedRR:94_9_9  \\\n",
+       "1994-08-07 16:35:00                0.0                           0.0   \n",
+       "1994-08-07 16:36:00                0.0                           0.0   \n",
+       "1994-08-07 16:37:00                0.0                           0.0   \n",
+       "1994-08-07 16:38:00                0.0                           0.0   \n",
+       "1994-08-07 16:39:00                0.0                           0.0   \n",
+       "\n",
+       "                     ZinkRR:94_9_9  TotalRunOff:9_3_3  ActualRainfall:9_3_3  \\\n",
+       "1994-08-07 16:35:00          100.0                0.0          3.333333e-07   \n",
+       "1994-08-07 16:36:00          100.0                0.0          3.333333e-07   \n",
+       "1994-08-07 16:37:00          100.0                0.0          3.333333e-07   \n",
+       "1994-08-07 16:38:00          100.0                0.0          3.333333e-07   \n",
+       "1994-08-07 16:39:00          100.0                0.0          3.333333e-07   \n",
+       "\n",
+       "                     ZinkLoadRR:9_3_3  ZinkMassAccumulatedRR:9_3_3  \\\n",
+       "1994-08-07 16:35:00               0.0                          0.0   \n",
+       "1994-08-07 16:36:00               0.0                          0.0   \n",
+       "1994-08-07 16:37:00               0.0                          0.0   \n",
+       "1994-08-07 16:38:00               0.0                          0.0   \n",
+       "1994-08-07 16:39:00               0.0                          0.0   \n",
+       "\n",
+       "                     ZinkRR:9_3_3  \n",
+       "1994-08-07 16:35:00         100.0  \n",
+       "1994-08-07 16:36:00         100.0  \n",
+       "1994-08-07 16:37:00         100.0  \n",
+       "1994-08-07 16:38:00         100.0  \n",
+       "1994-08-07 16:39:00         100.0  \n",
+       "\n",
+       "[5 rows x 155 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_catchments = res1d_catchments.read_all()\n",
     "df_catchments.head()"
@@ -170,18 +874,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.132646Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.132357Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.135706Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.135174Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['WaterLevel', 'Discharge']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_network.quantities"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.137665Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.137380Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.140697Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.140240Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['TotalRunOff',\n",
+       " 'ActualRainfall',\n",
+       " 'ZinkLoadRR',\n",
+       " 'ZinkMassAccumulatedRR',\n",
+       " 'ZinkRR']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_catchments.quantities"
    ]
@@ -196,27 +940,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.142404Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.142125Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.145484Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.145014Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;ResultNodes&gt;\n",
+       "    <style>\n",
+       "        ul {\n",
+       "            margin: 0px;\n",
+       "            padding: 0px;\n",
+       "            padding-left: 2em;\n",
+       "        }\n",
+       "    </style>\n",
+       "    <details><summary>Names (119)</summary><ul><li>1</li><li>2</li><li>3</li><li>4</li><li>5</li><li>6</li><li>7</li><li>8</li><li>9</li><li>10</li><li>11</li><li>13</li><li>14</li><li>15</li><li>17</li><li>20</li><li>21</li><li>22</li><li>23</li><li>24</li><li>25</li><li>26</li><li>27</li><li>28</li><li>29</li><li>30</li><li>31</li><li>32</li><li>33</li><li>34</li><li>35</li><li>36</li><li>37</li><li>38</li><li>39</li><li>40</li><li>41</li><li>42</li><li>43</li><li>44</li><li>45</li><li>47</li><li>48</li><li>49</li><li>50</li><li>51</li><li>52</li><li>53</li><li>54</li><li>56</li><li>57</li><li>59</li><li>60</li><li>61</li><li>62</li><li>63</li><li>64</li><li>65</li><li>66</li><li>67</li><li>68</li><li>69</li><li>70</li><li>71</li><li>72</li><li>73</li><li>74</li><li>75</li><li>76</li><li>77</li><li>78</li><li>79</li><li>80</li><li>81</li><li>82</li><li>83</li><li>84</li><li>85</li><li>86</li><li>87</li><li>88</li><li>89</li><li>90</li><li>91</li><li>92</li><li>93</li><li>94</li><li>95</li><li>96</li><li>97</li><li>98</li><li>99</li><li>100</li><li>101</li><li>102</li><li>103</li><li>104</li><li>105</li><li>106</li><li>107</li><li>108</li><li>109</li><li>110</li><li>111</li><li>112</li><li>113</li><li>114</li><li>12</li><li>16</li><li>46</li><li>55</li><li>58</li><li>116</li><li>117</li><li>118</li><li>115</li><li>119</li><li>120</li><li>Weir Outlet:119w1</li></ul></details><details><summary>Quantities (1)</summary><ul><li>WaterLevel</li></ul></details>"
+      ],
+      "text/plain": [
+       "<ResultNodes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_network.nodes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.148066Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.147898Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.151974Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.151145Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;ResultReaches&gt;\n",
+       "    <style>\n",
+       "        ul {\n",
+       "            margin: 0px;\n",
+       "            padding: 0px;\n",
+       "            padding-left: 2em;\n",
+       "        }\n",
+       "    </style>\n",
+       "    <details><summary>Names (118)</summary><ul><li>100l1</li><li>101l1</li><li>102l1</li><li>103l1</li><li>104l1</li><li>105l1</li><li>106l1</li><li>107l1</li><li>108l1</li><li>109l1</li><li>10l1</li><li>110l1</li><li>111l1</li><li>112l1</li><li>113l1</li><li>114l1</li><li>116l1</li><li>117l1</li><li>118l1</li><li>119l1</li><li>11l1</li><li>12l1</li><li>13l1</li><li>14l1</li><li>15l1</li><li>16l1</li><li>17l1</li><li>1l1</li><li>20l1</li><li>21l1</li><li>22l1</li><li>23l1</li><li>24l1</li><li>25l1</li><li>26l1</li><li>27l1</li><li>28l1</li><li>29l1</li><li>2l1</li><li>30l1</li><li>31l1</li><li>32l1</li><li>33l1</li><li>34l1</li><li>35l1</li><li>36l1</li><li>37l1</li><li>38l1</li><li>39l1</li><li>3l1</li><li>40l1</li><li>41l1</li><li>42l1</li><li>43l1</li><li>44l1</li><li>45l1</li><li>46l1</li><li>47l1</li><li>48l1</li><li>49l1</li><li>4l1</li><li>50l1</li><li>51l1</li><li>52l1</li><li>53l1</li><li>54l1</li><li>55l1</li><li>56l1</li><li>57l1</li><li>58l1</li><li>59l1</li><li>5l1</li><li>60l1</li><li>61l1</li><li>62l1</li><li>63l1</li><li>64l1</li><li>65l1</li><li>66l1</li><li>67l1</li><li>68l1</li><li>69l1</li><li>6l1</li><li>70l1</li><li>71l1</li><li>72l1</li><li>73l1</li><li>74l1</li><li>75l1</li><li>76l1</li><li>77l1</li><li>78l1</li><li>79l1</li><li>7l1</li><li>80l1</li><li>81l1</li><li>82l1</li><li>83l1</li><li>84l1</li><li>85l1</li><li>86l1</li><li>87l1</li><li>88l1</li><li>89l1</li><li>8l1</li><li>90l1</li><li>91l1</li><li>92l1</li><li>93l1</li><li>94l1</li><li>95l1</li><li>96l1</li><li>97l1</li><li>98l1</li><li>99l1</li><li>9l1</li><li>Weir:119w1</li><li>Pump:115p1</li></ul></details><details><summary>Quantities (2)</summary><ul><li>WaterLevel</li><li>Discharge</li></ul></details>"
+      ],
+      "text/plain": [
+       "<ResultReaches>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_network.reaches"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.154208Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.154013Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.157922Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.157387Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;ResultCatchments&gt;\n",
+       "    <style>\n",
+       "        ul {\n",
+       "            margin: 0px;\n",
+       "            padding: 0px;\n",
+       "            padding-left: 2em;\n",
+       "        }\n",
+       "    </style>\n",
+       "    <details><summary>Names (31)</summary><ul><li>100_16_16</li><li>105_1_1</li><li>10_22_22</li><li>113_21_21</li><li>118_30_30</li><li>119_32_32</li><li>14_20_20</li><li>20_2_2</li><li>22_8_8</li><li>25_26_26</li><li>28_6_6</li><li>29_4_4</li><li>32_19_19</li><li>33_24_24</li><li>34_23_23</li><li>40_29_29</li><li>43_11_11</li><li>48_13_13</li><li>51_14_14</li><li>57_17_17</li><li>5_31_31</li><li>64_12_12</li><li>67_18_18</li><li>6_25_25</li><li>76_7_7</li><li>79_10_10</li><li>82_27_27</li><li>84_15_15</li><li>90_28_28</li><li>94_9_9</li><li>9_3_3</li></ul></details><details><summary>Quantities (5)</summary><ul><li>TotalRunOff</li><li>ActualRainfall</li><li>ZinkLoadRR</li><li>ZinkMassAccumulatedRR</li><li>ZinkRR</li></ul></details>"
+      ],
+      "text/plain": [
+       "<ResultCatchments>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_catchments.catchments"
    ]
@@ -244,8 +1075,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.159867Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.159691Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.163457Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.162554Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d.res1d import QueryDataNode, QueryDataReach, QueryDataCatchment"
@@ -253,8 +1091,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.165415Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.165234Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.168472Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.167790Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Read a specific reach\n",
@@ -269,9 +1114,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.170300Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.170110Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.310962Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.310480Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+JSUlAABbW9sbG5DLoVQqceDAAQBARkYGDh8+DHd3dzzwwANo1aoVgoODpeX1UVpaih9++AHPPfccZDKZwbJ169ahZcuW6NKlCyIiIlBYWNgANSMiIiJz0KCBT0BAANRqNSIiIpCdnY3S0lIsWrQIqamp0Gg0AIDExEQAwIIFCzB16lRs374dPXr0wMCBA2vsC1STLVu2ICcnBxMnTjSYP2bMGPzwww+IjIxEREQEvv/+e4wbN67WfEpKSpCXl2cwERERkfky+lJXXaytrbFp0yZMnjwZrq6uUCgUCA0NRVhYGIQQAACdTgcAeP755zFp0iQAQFBQEHbt2oXVq1dj4cKFt9zON998g7CwMKhUKoP506ZNk/7ftWtXeHp6YuDAgUhISEDbtm2r5bNw4UK88847t11fIiIialoa/Hb2nj17IiYmBjk5OdBoNNi+fTsyMzPh5+cHAPD09AQAdOrUyWC9jh07IiUl5Zb5X7p0CTt37pT6C9Wld+/eAID4+Pgal0dERCA3N1eaLl++fMs8iYiIqOm6a+P4ODs7w83NDXFxcYiOjsbw4cMB6G9LV6lUiI2NNUh/8eJFeHt73zLfNWvWwN3dHY8++ugt08bExAC4EWzdTKlUwsnJyWAiIiIi82X0pa6CggKDFpSkpCTExMTA1dUVarUaGzduhJubG9RqNU6fPo2ZM2ciPDwcgwcPBgDIZDLMnTsX8+fPR2BgILp3745vv/0WFy5cwM8//yzlO3DgQIwYMQIzZsyQ5ul0OqxZswYTJkyAlZVh0RMSErB+/Xo88sgjaNGiBU6dOoXZs2ejX79+6Natm9EfDBEREZkfowOf6OhohISESO/nzJkDAJgwYQLWrl0LjUaDOXPmID09HZ6ennj22Wcxb948gzxmzZqF4uJizJ49G1lZWQgMDMSOHTsM+uEkJCTg2rVrBuvt3LkTKSkpeO6556qVy8bGBjt37sQnn3wCrVYLLy8vjBw5Em+99ZaxVSQiIiIzJROVvY4JeXl5cHZ2Rm5uLi97ERERNRHGnL/5rC4iIiKyGAx8iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIiIgsBgMfIiIishgMfIiIiMhiMPAhIiIii8HAh4iIiCwGAx8iIiKyGAx8iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIiIgsBgMfIiIishgMfIiIiMhiMPAhIiIii8HAh4iIiCwGAx8iIiKyGAx8iIiIyGIw8CEiIiKLYXTgs2/fPgwbNgwqlQoymQxbtmwxWJ6eno6JEydCpVLB3t4eQ4cORVxcXLV8oqKiMGDAADg4OMDJyQn9+vVDUVFRrdtdsGABZDKZwRQQEGCQpri4GNOnT0eLFi3QrFkzjBw5Eunp6cZWkYiIiMyU0YGPVqtFYGAgVqxYUW2ZEALh4eFITEzE1q1bceLECXh7eyM0NBRarVZKFxUVhaFDh2Lw4ME4cuQIjh49ihkzZkAur7s4nTt3hkajkaYDBw4YLJ89ezZ+++03bNy4EXv37sWVK1fwxBNPGFtFIiIiMlNWxq4QFhaGsLCwGpfFxcXh0KFDOHPmDDp37gwA+OKLL+Dh4YENGzZgypQpAPQByiuvvII33nhDWtff3//WhbWygoeHR43LcnNz8c0332D9+vUYMGAAAGDNmjXo2LEjDh06hD59+hhVTyIiIjI/DdrHp6SkBABga2t7YwNyOZRKpdQ6k5GRgcOHD8Pd3R0PPPAAWrVqheDg4GqtNzWJi4uDSqWCn58fxo4di5SUFGnZsWPHUFZWhtDQUGleQEAA1Go1oqKiai1vXl6ewURERETmq0EDn8pAIyIiAtnZ2SgtLcWiRYuQmpoKjUYDAEhMTASg77MzdepUbN++HT169MDAgQNr7AtUqXfv3li7di22b9+OL774AklJSXj44YeRn58PAEhLS4ONjQ1cXFwM1mvVqhXS0tJqzHPhwoVwdnaWJi8vrwb4FIiIiKixatDAx9raGps2bcLFixfh6uoKe3t7REZGIiwsTOq/o9PpAADPP/88Jk2ahKCgICxduhT+/v5YvXp1rXmHhYXhqaeeQrdu3TBkyBD88ccfyMnJwU8//XTb5Y2IiEBubq40Xb58+bbzIiIiosbP6D4+t9KzZ0/ExMQgNzcXpaWlcHNzQ+/evdGrVy8AgKenJwCgU6dOBut17NjR4NLVrbi4uKBDhw6Ij48HAHh4eKC0tBQ5OTkGrT7p6em19gtSKpVQKpXGVI+IiIiasLs2jo+zszPc3NwQFxeH6OhoDB8+HADg4+MDlUqF2NhYg/QXL16Et7d3vfMvKChAQkKCFEj17NkT1tbW2LVrl5QmNjYWKSkp6Nu3bwPUiIiIiJo6o1t8CgoKpFYWAEhKSkJMTAxcXV2hVquxceNGuLm5Qa1W4/Tp05g5cybCw8MxePBgAIBMJsPcuXMxf/58BAYGonv37vj2229x4cIF/Pzzz1K+AwcOxIgRIzBjxgwAwKuvvophw4bB29sbV65cwfz586FQKDB69GgA+kBr8uTJmDNnDlxdXeHk5ISXX34Zffv25R1dREREBOA2Ap/o6GiEhIRI7+fMmQMAmDBhAtauXQuNRoM5c+YgPT0dnp6eePbZZzFv3jyDPGbNmoXi4mLMnj0bWVlZCAwMxI4dO9C2bVspTUJCAq5duya9T01NxejRo5GZmQk3Nzc89NBDOHToENzc3KQ0S5cuhVwux8iRI1FSUoIhQ4bg888/N7aKREREZKZkQghh6kI0Fnl5eXB2dkZubi6cnJxMXRwiIiKqB2PO33xWFxEREVkMBj5ERERkMRj4EBERkcVg4ENEREQWg4EPERERWQwGPkRERGQxGPgQERGRxWDgQ0RERBaDgQ8RERFZDAY+REREZDEY+BAREZHFYOBDREREFoOBDxEREVkMBj5ERERkMRj4EBERkcVg4ENEREQWg4EPERERWQwGPkRERGQxGPgQERGRxWDgQ0RERBaDgQ8RERFZDAY+REREZDEY+BAREZHFYOBDREREFoOBDxEREVkMBj5ERERkMYwOfPbt24dhw4ZBpVJBJpNhy5YtBsvT09MxceJEqFQq2NvbY+jQoYiLi6uWT1RUFAYMGAAHBwc4OTmhX79+KCoqqnW7CxcuxH333QdHR0e4u7sjPDwcsbGxBmn69+8PmUxmML3wwgvGVpGIiIjMlNGBj1arRWBgIFasWFFtmRAC4eHhSExMxNatW3HixAl4e3sjNDQUWq1WShcVFYWhQ4di8ODBOHLkCI4ePYoZM2ZALq+9OHv37sX06dNx6NAh7NixA2VlZRg8eLBBvgAwdepUaDQaaVq8eLGxVSQiIiIzJRNCiNteWSbD5s2bER4eDgC4ePEi/P39cebMGXTu3BkAoNPp4OHhgQ8++ABTpkwBAPTp0weDBg3Ce++9d9sFv3r1Ktzd3bF3717069cPgL7Fp3v37vjkk09uK8+8vDw4OzsjNzcXTk5Ot102IiIiuneMOX83aB+fkpISAICtre2NDcjlUCqVOHDgAAAgIyMDhw8fhru7Ox544AG0atUKwcHB0vL6ys3NBQC4uroazF+3bh1atmyJLl26ICIiAoWFhXWWNy8vz2AiIiIi89WggU9AQADUajUiIiKQnZ2N0tJSLFq0CKmpqdBoNACAxMREAMCCBQswdepUbN++HT169MDAgQNr7AtUE51Oh1mzZuHBBx9Ely5dpPljxozBDz/8gMjISEREROD777/HuHHjas1n4cKFcHZ2liYvL687qD0RERE1dlYNmZm1tTU2bdqEyZMnw9XVFQqFAqGhoQgLC0PlFTWdTgcAeP755zFp0iQAQFBQEHbt2oXVq1dj4cKFt9zO9OnTcebMmWqtRNOmTZP+37VrV3h6emLgwIFISEhA27Ztq+UTERGBOXPmSO/z8vIY/BAREZmxBg18AKBnz56IiYlBbm4uSktL4ebmht69e6NXr14AAE9PTwBAp06dDNbr2LEjUlJSbpn/jBkzsG3bNuzbtw9t2rSpM23v3r0BAPHx8TUGPkqlEkqlsl71IiIioqbvro3j4+zsDDc3N8TFxSE6OhrDhw8HAPj4+EClUlW7Ff3ixYvw9vauNT8hBGbMmIHNmzdj9+7d8PX1vWUZYmJiANwItoiIiMiyGd3iU1BQgPj4eOl9UlISYmJi4OrqCrVajY0bN8LNzQ1qtRqnT5/GzJkzER4ejsGDBwPQ3wk2d+5czJ8/H4GBgejevTu+/fZbXLhwAT///LOU78CBAzFixAjMmDEDgP7y1vr167F161Y4OjoiLS0NgD7AsrOzQ0JCAtavX49HHnkELVq0wKlTpzB79mz069cP3bp1u6MPiYiIiMyEMFJkZKQAUG2aMGGCEEKITz/9VLRp00ZYW1sLtVot3nrrLVFSUlItn4ULF4o2bdoIe3t70bdvX7F//36D5d7e3mL+/PnS+5q2CUCsWbNGCCFESkqK6Nevn3B1dRVKpVK0a9dOzJ07V+Tm5ta7brm5uQKAUesQERGRaRlz/r6jcXzMDcfxISIAqKioQFlZmamLQURV2NjY1DrQsTHn7wbv3ExE1FQJIZCWloacnBxTF4WIbiKXy+Hr6wsbG5s7yoeBDxHRdZVBj7u7O+zt7SGTyUxdJCKCfiicK1euQKPRQK1W39HfJgMfIiLoL29VBj0tWrQwdXGI6CZubm64cuUKysvLYW1tfdv53LXb2YmImpLKPj329vYmLgkR1aTyEldFRcUd5cPAh4ioCl7eImqcGupvk4EPERERWQwGPkREZPb69++PWbNmmboY1Agw8CEiasJWrlwJR0dHlJeXS/MKCgpgbW2N/v37G6Tds2cPZDIZEhIS6syzMl1D3da/du1auLi4NEhed9Mrr7yCnj17QqlUonv37qYuDt0lDHyIiJqwkJAQFBQUIDo6Wpq3f/9+eHh44PDhwyguLpbmR0ZGQq1W1/jQ5rtBCGEQkDUFzz33HJ555hlTF4PuIgY+RERNmL+/Pzw9PbFnzx5p3p49ezB8+HD4+vri0KFDBvNDQkLw/fffo1evXnB0dISHhwfGjBmDjIwMAEBycjJCQkIAAM2bN4dMJsPEiRMB6MdSWbhwIXx9fWFnZ4fAwECDZyxWthT9+eefUsvJgQMHblmHnJwcTJkyBW5ubnBycsKAAQNw8uRJAPoHWMtkMly4cMFgnaVLlxoEcGfOnEFYWBiaNWuGVq1aYfz48bh27ZpRn+WyZcswffp0+Pn5GbUeNS0MfIiIaiCEQGFpuUkmY58kFBISgsjISOl9ZGQk+vfvj+DgYGl+UVERDh8+jJCQEJSVleG9997DyZMnsWXLFiQnJ0vBjZeXF3755RcAQGxsLDQaDT799FMAwMKFC/Hdd99h5cqVOHv2LGbPno1x48Zh7969BuV544038OGHH+L8+fP1ekj0U089hYyMDPz55584duwYevTogYEDByIrKwsdOnRAr169sG7dOoN11q1bhzFjxgDQB04DBgxAUFAQoqOjsX37dqSnp+Ppp5+udZsLFiyAj4/PLctG5ocDGBIR1aCorAKd3v7LJNs+9+4Q2NvU//AcEhKCWbNmoby8HEVFRThx4gSCg4NRVlaGlStXAgCioqJQUlKCkJAQqNVqaV0/Pz8sW7YM9913HwoKCtCsWTO4uroCANzd3aW+OSUlJfjggw+wc+dO9O3bV1r3wIEDWLVqFYKDg6U83333XQwaNKheZT9w4ACOHDmCjIwMKJVKAMDHH3+MLVu24Oeff8a0adMwduxYfPbZZ3jvvfcA6FuBjh07hh9++AEA8NlnnyEoKAgffPCBlO/q1avh5eWFixcvokOHDtW227Jly3t2yY8aF7b4EBE1cf3794dWq8XRo0exf/9+dOjQAW5ubggODpb6+ezZswd+fn5Qq9U4duwYhg0bBrVaDUdHRyloSUlJqXUb8fHxKCwsxKBBg9CsWTNp+u6776p1lu7Vq1e9y37y5EkUFBSgRYsWBvkmJSVJ+Y4aNQrJycnSZbt169ahR48eCAgIkPKIjIw0WL9yWW0duWfMmIFdu3bVu5xkPtjiQ0RUAztrBc69O8Rk2zZGu3bt0KZNG0RGRiI7O1sKZFQqFby8vHDw4EFERkZiwIAB0Gq1GDJkCIYMGYJ169bBzc0NKSkpGDJkCEpLS2vdRkFBAQDg999/R+vWrQ2WVbbUVHJwcKh32QsKCqr1UapU2drk4eGBAQMGYP369ejTpw/Wr1+PF1980SCPYcOGYdGiRdXy8PT0rHdZyDIw8CEiqoFMJjPqcpOphYSEYM+ePcjOzsbcuXOl+f369cOff/6JI0eO4MUXX8SFCxeQmZmJDz/8EF5eXgBgcEcYUPOjATp16gSlUomUlBSDy1p3qkePHkhLS4OVlVWdfW7Gjh2L1157DaNHj0ZiYiJGjRplkMcvv/wCHx8fWFk1nX1GpsFLXUREZiAkJAQHDhxATEyMQWASHByMVatWobS0VOrfY2Njg+XLlyMxMRG//vqr1Hemkre3N2QyGbZt24arV6+ioKAAjo6OePXVVzF79mx8++23SEhIwPHjx7F8+XJ8++23tyxfRUUFYmJiDKbz588jNDQUffv2RXh4OP7++28kJyfj4MGDePPNNw0CsieeeAL5+fl48cUXERISApVKJS2bPn06srKyMHr0aBw9ehQJCQn466+/MGnSpFqf6/TZZ59h4MCBBvPi4+MRExODtLQ0FBUVSeWsqyWMmiBBktzcXAFA5ObmmrooRHSPFRUViXPnzomioiJTF+W2JCUlCQAiICDAYH5ycrIAIPz9/aV569evFz4+PkKpVIq+ffuKX3/9VQAQJ06ckNK8++67wsPDQ8hkMjFhwgQhhBA6nU588sknwt/fX1hbWws3NzcxZMgQsXfvXiGEEJGRkQKAyM7ONijDmjVrBIBqU9u2bYUQQuTl5YmXX35ZqFQqYW1tLby8vMTYsWNFSkqKQT5PP/20ACBWr15drf4XL14UI0aMEC4uLsLOzk4EBASIWbNmCZ1OJ4QQIjg4WMycOVNKP3/+fOHt7W2QR3BwcI3lTEpKutXHT/dAXX+jxpy/ZUIYed+kGcvLy4OzszNyc3Ph5ORk6uIQ0T1UXFyMpKQk+Pr6wtbW1tTFIaKb1PU3asz5m5e6iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIiIgsBgMfIiIishhGBz779u3DsGHDoFKpIJPJsGXLFoPl6enpmDhxIlQqFezt7TF06FDExcVVyycqKgoDBgyAg4MDnJyc0K9fPxQVFdW57RUrVsDHxwe2trbo3bs3jhw5YrC8uLgY06dPl57yO3LkSKSnpxtbRSIiIjJTRgc+Wq0WgYGBWLFiRbVlQgiEh4cjMTERW7duxYkTJ+Dt7Y3Q0FBotVopXVRUFIYOHYrBgwfjyJEjOHr0KGbMmAG5vPbi/N///R/mzJmD+fPn4/jx4wgMDMSQIUOQkZEhpZk9ezZ+++03bNy4EXv37sWVK1fwxBNPGFtFIiIyM/3798esWbNMXQwAgI+PDz755BNTF8Ny3clzMwCIzZs3S+9jY2MFAHHmzBlpXkVFhXBzcxNfffWVNK93797irbfeMmpb999/v5g+fbpBviqVSixcuFAIIUROTo6wtrYWGzdulNKcP39eABBRUVH12gaf1UVkuZrqs7q++OIL0axZM1FWVibNy8/PF1ZWViI4ONggbeWztOLj4+vMs7Znbt2uNWvWCGdn5wbJ63bd/Kyumrz88suiR48ewsbGRgQGBtaY5uTJk+Khhx4SSqVStGnTRixatKjW/DZs2CAAiOHDhxvM9/b2FkuXLpXer1q1SgQHBwtHR8d6f+779+8XDzzwgHB1dRW2trbC399fLFmypNb0CxcuFABu+RnUp/y//PKLGDRokHB1da32jLdK06ZNE35+fsLW1la0bNlSPP744+L8+fMGaerzeVfVUM/qatA+PiUlJQBg8AwNuVwOpVKJAwcOAAAyMjJw+PBhuLu744EHHkCrVq0QHBwsLa9JaWkpjh07htDQUIN8Q0NDERUVBQA4duwYysrKDNIEBARArVZLaWoqb15ensFERNSUhISEoKCgwOBJ5vv374eHhwcOHz6M4uJiaX5kZCTUajXatm17T8omhEB5efk92VZDee655/DMM8/UuCwvLw+DBw+Gt7c3jh07ho8++ggLFizAl19+WS1tcnIyXn31VTz88MO33GZhYSGGDh2K//mf/6l3OR0cHDBjxgzs27cP58+fx1tvvYW33nqrxrIcPXoUq1atQrdu3eqdf13l12q1eOihh7Bo0aJa1+/ZsyfWrFmD8+fP46+//oIQAoMHD0ZFRYVBuro+77ulQQOfykAjIiIC2dnZKC0txaJFi5CamgqNRgMASExMBAAsWLAAU6dOxfbt29GjRw8MHDiwxr5AAHDt2jVUVFSgVatWBvNbtWqFtLQ0AEBaWhpsbGzg4uJSa5qbLVy4EM7OztLk5eV1J9UnIrrn/P394enpiT179kjz9uzZg+HDh8PX1xeHDh0ymB8SEoLvv/8evXr1gqOjIzw8PDBmzBip20BycjJCQkIAAM2bN4dMJsPEiRMBADqdDgsXLoSvry/s7OwQGBiIn3/+2SB/mUyGP//8Ez179jT40VuXnJwcTJkyBW5ubnBycsKAAQNw8uRJAMDFixchk8lw4cIFg3WWLl1qEMCdOXMGYWFhaNasGVq1aoXx48fj2rVrRn2Wy5Ytw/Tp0+Hn51fj8nXr1qG0tBSrV69G586dMWrUKLzyyitYsmSJQbqKigqMHTsW77zzTq15VTVr1iy88cYb6NOnT73LGhQUhNGjR6Nz587w8fHBuHHjMGTIEOzfv98gXUFBAcaOHYuvvvoKzZs3r1fetyr/+PHj8fbbbxs0NNxs2rRp6NevH3x8fNCjRw+8//77uHz5MpKTk6U0t/q875YGDXysra2xadMmXLx4Ea6urrC3t0dkZCTCwsKk/js6nQ4A8Pzzz2PSpEkICgrC0qVL4e/vj9WrVzdkcW4pIiICubm50nT58uV7un0iasSEAEq1ppmEMKqoISEhiIyMlN5HRkaif//+CA4OluYXFRXh8OHDCAkJQVlZGd577z2cPHkSW7ZsQXJyshTceHl54ZdffgEAxMbGQqPR4NNPPwWg/7H43XffYeXKlTh79ixmz56NcePGYe/evQbleeONN/Dhhx/i/Pnz9WpleOqpp5CRkYE///wTx44dk34MZ2VloUOHDujVqxfWrVtnsM66deswZswYAPrAacCAAQgKCkJ0dDS2b9+O9PR0PP3007Vuc8GCBfDx8bll2aqKiopCv379YGNjI80bMmQIYmNjkZ2dLc1799134e7ujsmTJxuVf1369+8v7aOanDhxAgcPHkRwcLDB/OnTp+PRRx+tM0i5WUOXX6vVYs2aNfD19W0UDQxWDZ1hz549ERMTg9zcXJSWlsLNzQ29e/dGr169AACenp4AgE6dOhms17FjR6SkpNSYZ8uWLaFQKKrdoZWeng4PDw8AgIeHB0pLS5GTk2PQ6lM1zc2USiWUSuVt1ZOIzFxZIfCByjTb/p8rgI1DvZOHhIRg1qxZKC8vR1FREU6cOIHg4GCUlZVh5cqVAPQn7ZKSEoSEhECtVkvr+vn5YdmyZbjvvvtQUFCAZs2awdXVFQDg7u4uHU9LSkrwwQcfYOfOnejbt6+07oEDB7Bq1SqDE+67776LQYMG1avsBw4cwJEjR5CRkSEdjz/++GNs2bIFP//8M6ZNm4axY8fis88+w3vvvQdA3wp07Ngx/PDDDwCAzz77DEFBQfjggw+kfFevXg0vLy9cvHgRHTp0qLbdli1bGn3JLy0tDb6+vgbzKq9EpKWloXnz5jhw4AC++eYbxMTEGJX3rajVaun8WVWbNm1w9epVlJeXY8GCBZgyZYq07Mcff8Tx48dx9OjRem+nIcv/+eef47XXXoNWq4W/vz927NhhEDSayl0bx8fZ2Rlubm6Ii4tDdHQ0hg8fDkDfm12lUiE2NtYg/cWLF+Ht7V1jXjY2NujZsyd27dolzdPpdNi1a5f0B9izZ09YW1sbpImNjUVKSoqUhojIHPXv3x9arRZHjx7F/v370aFDB7i5uSE4OFjq57Nnzx74+flBrVbj2LFjGDZsGNRqNRwdHaWgpbYfnwAQHx+PwsJCDBo0CM2aNZOm7777DgkJCQZpK3/o1sfJkydRUFAgDUNSOSUlJUn5jho1CsnJydJlu3Xr1qFHjx4ICAiQ8oiMjDRYv3LZzWWrNGPGDIPzRUPIz8/H+PHj8dVXX6Fly5YNmvd3332HhQsXVpu/f/9+REdHY+XKlfjkk0+wYcMGAMDly5cxc+ZMrFu3zqDf7b0s/9ixY3HixAns3bsXHTp0wNNPP23Q58xUjG7xKSgoQHx8vPQ+KSkJMTExcHV1hVqtxsaNG+Hm5ga1Wo3Tp09j5syZCA8Px+DBgwEAMpkMc+fOxfz58xEYGIju3bvj22+/xYULFwyuFQ8cOBAjRozAjBkzAABz5szBhAkT0KtXL9x///345JNPoNVqMWnSJAD6QGvy5MmYM2cOXF1d4eTkhJdffhl9+/Y16ropEREAwNpe3/Jiqm0boV27dmjTpg0iIyORnZ0tBTIqlQpeXl44ePAgIiMjMWDAAGi1WgwZMgRDhgzBunXr4ObmhpSUFAwZMgSlpaW1bqOgoAAA8Pvvv6N169YGy25uOXdwqH9rVUFBQbU+SpUqW5s8PDwwYMAArF+/Hn369MH69evx4osvGuQxbNiwGjvb1tRKcrs8PDxqvPJQuSwhIQHJyckYNmyYtLyye4eVlRViY2MbvGN5ZQtU165dkZ6ejgULFmD06NE4duwYMjIy0KNHDyltRUUF9u3bh88++wwlJSVQKBQGeTV0+Sv7z7Zv3x59+vRB8+bNsXnzZowePfpOqnzHjA58oqOjpY5vgD4gAYAJEyZg7dq10Gg0mDNnDtLT0+Hp6Ylnn30W8+bNM8hj1qxZKC4uxuzZs5GVlYXAwEDs2LHD4ANNSEgw6Jj2zDPP4OrVq3j77beRlpaG7t27Y/v27QYdnpcuXQq5XI6RI0eipKQEQ4YMweeff25sFYmIAJnMqMtNphYSEoI9e/YgOzsbc+fOleb369cPf/75J44cOYIXX3wRFy5cQGZmJj788EOpv0XVO8IASJcjqt6B06lTJyiVSqSkpFTrR3InevTogbS0NFhZWdXZ52bs2LF47bXXMHr0aCQmJmLUqFEGefzyyy/w8fGBlVWD9+CQ9O3bF2+++SbKyspgbW0NANixYwf8/f3RvHlz2NnZ4fTp0wbrvPXWW8jPz8enn3561/u36HQ66e7qgQMHVivLpEmTEBAQgNdff71a0APob1C6W+UXQkAIIZXPpG55w7sF4Tg+RJarqY7jU2n16tXCzs5OWFlZibS0NGn+t99+K40Pc+XKFZGRkSFsbGzE3LlzRUJCgti6davo0KGDwXgsqampQiaTibVr14qMjAyRn58vhBDizTffFC1atBBr164V8fHx4tixY2LZsmVi7dq1Qojax/9Zs2aNaNasmThx4oTBdO7cOaHT6cRDDz0kAgMDxV9//SWSkpLEP//8I/7nf/5HHD16VMojLy9P2NnZicDAQDFw4ECD/P/991/h5uYmnnzySXHkyBERHx8vtm/fLiZOnCjKy8uFENXH8Vm+fLkYMGCAQT5xcXHixIkT4vnnnxcdOnSQyllSUiKE0I8X16pVKzF+/Hhx5swZ8eOPPwp7e3uxatWqWvfLhAkTbjmOj0ajESdOnBBfffWVACD27dsnTpw4ITIzM6U048ePF2+88Yb0/rPPPhO//vqruHjxorh48aL4+uuvhaOjo3jzzTdrLUtNYxndnG99yp+ZmSlOnDghfv/9dwFA/Pjjj+LEiRNCo9EIIYRISEgQH3zwgYiOjhaXLl0S//zzjxg2bJhwdXUV6enpUj63+rxv1lDj+DDwqYKBD5HlauqBT1JSkgAgAgICDOYnJycLAMLf31+at379euHj4yOUSqXo27ev+PXXX6sNRPfuu+8KDw8PIZPJxIQJE4QQQuh0OvHJJ58If39/YW1tLdzc3MSQIUPE3r17hRB1Bz4Aqk1t27YVQuiDmpdfflmoVCphbW0tvLy8xNixY0VKSopBPk8//bQAIFavXl2t/hcvXhQjRowQLi4uws7OTgQEBIhZs2YJnU4nhKh+0p8/f77w9vY2yCM4OLjGciYlJUlpqg5g2Lp1a/Hhhx/Wuk+EqF/gM3/+/Bq3u2bNGoOyVe4HIYRYtmyZ6Ny5s7C3txdOTk4iKChIfP7556KioqLWstQU+Nycb33KX9v+nD9/vhBCH4iGhYUJd3d3YW1tLdq0aSPGjBkjLly4UG3bt/q8q2qowEcmhJH3TZqxvLw8ODs7Izc3F05OTqYuDhHdQ8XFxUhKSoKvr2+9O4MS0b1T19+oMedvPp2diIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIqAre70HUODXU3yYDHyIiQBqQrrCw0MQlIaKaVI4sXtPgi8a4e0NcEhE1IQqFAi4uLsjIyAAA2NvbQyaTmbhURAToR6W+evUq7O3t73h0bgY+RETXeXh4AIAU/BBR4yGXy6FWq+/4BwkDHyKi62QyGTw9PeHu7o6ysjJTF4eIqrCxsYFcfuc9dBj4EBHdRKFQ3HE/AiJqnNi5mYiIiCwGAx8iIiKyGAx8iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIiIgsBgMfIiIishgMfIiIiMhiMPAhIiIii8HAh4iIiCwGAx8iIiKyGEYHPvv27cOwYcOgUqkgk8mwZcsWg+Xp6emYOHEiVCoV7O3tMXToUMTFxRmk6d+/P2QymcH0wgsv1Lndm9NXTh999JGUxsfHp9ryDz/80NgqEhERkZkyOvDRarUIDAzEihUrqi0TQiA8PByJiYnYunUrTpw4AW9vb4SGhkKr1RqknTp1KjQajTQtXry4zu1WTavRaLB69WrIZDKMHDnSIN27775rkO7ll182topERERkpqyMXSEsLAxhYWE1LouLi8OhQ4dw5swZdO7cGQDwxRdfwMPDAxs2bMCUKVOktPb29vDw8Kj3dm9Ou3XrVoSEhMDPz89gvqOjo1H5EhERkeVo0D4+JSUlAABbW9sbG5DLoVQqceDAAYO069atQ8uWLdGlSxdERESgsLCw3ttJT0/H77//jsmTJ1db9uGHH6JFixYICgrCRx99hPLy8tusDREREZkbo1t86hIQEAC1Wo2IiAisWrUKDg4OWLp0KVJTU6HRaKR0Y8aMgbe3N1QqFU6dOoXXX38dsbGx2LRpU7228+2338LR0RFPPPGEwfxXXnkFPXr0gKurKw4ePIiIiAhoNBosWbKkxnxKSkqkYA0A8vLybqPWRERE1FTIhBDitleWybB582aEh4dL844dO4bJkyfj5MmTUCgUCA0NhVwuhxACf/75Z4357N69GwMHDkR8fDzatm17y+0GBARg0KBBWL58eZ3pVq9ejeeffx4FBQVQKpXVli9YsADvvPNOtfm5ublwcnK6ZTmIiIjI9PLy8uDs7Fyv83eD387es2dPxMTEICcnBxqNBtu3b0dmZma1vjhV9e7dGwAQHx9/y/z379+P2NhYg/5CdeVbXl6O5OTkGpdHREQgNzdXmi5fvnzLPImIiKjpatBLXVU5OzsD0Hd4jo6OxnvvvVdr2piYGACAp6fnLfP95ptv0LNnTwQGBt4ybUxMDORyOdzd3WtcrlQqa2wJIiIiIvNkdOBTUFBg0DKTlJSEmJgYuLq6Qq1WY+PGjXBzc4Narcbp06cxc+ZMhIeHY/DgwQCAhIQErF+/Ho888ghatGiBU6dOYfbs2ejXrx+6desm5RsQEICFCxdixIgR0ry8vDxs3LgR//3vf6uVKyoqCocPH0ZISAgcHR0RFRWF2bNnY9y4cWjevLmx1SQiIiIzZHTgEx0djZCQEOn9nDlzAAATJkzA2rVrodFoMGfOHKSnp8PT0xPPPvss5s2bJ6W3sbHBzp078cknn0Cr1cLLywsjR47EW2+9ZbCd2NhY5ObmGsz78ccfIYTA6NGjq5VLqVTixx9/xIIFC1BSUgJfX1/Mnj1bKh8RERHRHXVuNjfGdI4iIiKixsGknZuJiIiIGisGPkRERGQxGPgQERGRxWDgQ0RERBbjro3jQ0TUUPKKy7D7fAZKK3TSvDbN7fBA25YmLBURNUUMfIio0Xvn13P45Xhqtfkrx/XE0C4eJigRETVVvNRFRI1ablEZtp26AgB4qF1LhPi7oVsb/cjwb24+jWsFJXWtTkRkgC0+RNSo/XbyCkrKdfBv5YjvJ98PmUyGkvIKDP/sH1xIy0fEptP4cnxPyGQyUxeViJoAtvgQUaO28Zj+EtdTvdpIwY3SSoElT3eHtUKGHefS8cvxf01ZRCJqQhj4EFGjdTE9Hycv58BKLkN4UGuDZZ1UTpgV2gEA8M6vZ/FvTpEpikhETQwDHyJqtDZGXwYADOzojpbNlNWWP9/PD0FqF+SXlGPuxpPQ6fgEHiKqGwMfImqUyip02HxCfwnrqZ5eNaaxUsix5OnusLNW4GBCJlbuS7iXRSSiJoiBDxE1SpEXMnCtoBRujkr093erNZ1vSwe883hnAMB//76I4ynZ96qIRNQEMfAhokbpp2h9p+YnglrDSlH3oeqpXm0wLFCFCp3AKxtOILeo7F4UkYiaIAY+RNToZOQXIzI2A4A+qLkVmUyG/x3RBV6udkjNLsL/bD4NIdjfh4iqY+BDRI3Obyc1qNAJBKld0M7dsV7rONlaY/noHrCSy/D7KQ1+PHr5LpeSiJoiBj5E1Oj8eVoDABgeqDJqve5eLnh1iD8AYP6vZ3EqNaehi0ZETRwDHyJqVNJyixF9Sd9BeWgXT6PXn/awH0I7tkJpuQ4vfH8MmXykBRFVwcCHiBqV7Wf0rT29vJvDw9nW6PXlchmWPBMI35YOuJJbjFd+PIHyKk91JyLLxsCHiBqVP06nAQDCuhrf2lPJydYaq8b3hL2NAv/EZ+Kjv2MbqnhE1MQx8CGiRiMjrxhHL2UBAMK6eNxRXh1aOeKjJwMBAKv2JkpPeCciy8bAh4gaje1n0yAEEKR2gcrF7o7ze7SbJ6b18wMA/Oenkzh5OeeO8ySipo2BDxE1Gn9cv5vrkdvo1Fyb14cGYECAO0rKdZj6XTQ0uXyYKZElY+BDRI3C1fwSHEm6fpmr651d5qpKIZfh01Hd4d/KERn5JZj6XTQKS8sbLH8ialoY+BBRo/DX2TToBBDYxhltmts3aN6Ottb4ekIvtHCwwZl/8zDn//gkdyJLxcCHiBqFystcd3I3V128XO2xanxP2Cjk2H42DRGbTjP4IbJADHyIyOSuFZTgUGImgIbt33OzXj6uWPpMd8hlwP9FX8a8rWf4TC8iC2N04LNv3z4MGzYMKpUKMpkMW7ZsMVienp6OiRMnQqVSwd7eHkOHDkVcXJxBmv79+0MmkxlML7zwQp3bnThxYrV1hg4dapAmKysLY8eOhZOTE1xcXDB58mQUFBQYW0Uiusd+jbkCnQC6tXGGukXDXua62aPdPLHk6e6QyYB1h1Ow4NezDH6ILIjRgY9Wq0VgYCBWrFhRbZkQAuHh4UhMTMTWrVtx4sQJeHt7IzQ0FFqt1iDt1KlTodFopGnx4sW33PbQoUMN1tmwYYPB8rFjx+Ls2bPYsWMHtm3bhn379mHatGnGVpGI7rFfjqcCAEb2uPWT2BtCeFBrLB7ZDTIZ8G3UJby37TyDHyILYWXsCmFhYQgLC6txWVxcHA4dOoQzZ86gc+fOAIAvvvgCHh4e2LBhA6ZMmSKltbe3h4eHcXduKJXKWtc5f/48tm/fjqNHj6JXr14AgOXLl+ORRx7Bxx9/DJXKuIcdEtG9cSEtD2ev5MFaIcPjRj6U9E481csLFTqBNzadxup/kpBfXIaFT3SFlYI9AIjMWYP+hZeU6B8GaGt74/k6crkcSqUSBw4cMEi7bt06tGzZEl26dEFERAQKCwtvmf+ePXvg7u4Of39/vPjii8jMzJSWRUVFwcXFRQp6ACA0NBRyuRyHDx+utbx5eXkGExHdW5uO/wsAGBDgjuYONvd026PuV+OjJ7tBIZdh47FUvPDDcRSXVdzTMhDRvdWggU9AQADUajUiIiKQnZ2N0tJSLFq0CKmpqdBoNFK6MWPG4IcffkBkZCQiIiLw/fffY9y4cXXmPXToUHz33XfYtWsXFi1ahL179yIsLAwVFfqDVFpaGtzd3Q3WsbKygqurK9LS0mrMc+HChXB2dpYmLy+vO/wEiMgY5RU6bD6hD3yeuEeXuW72VC8vrBzXEzZWcuw8n45nvzmC3KIyk5SFiO4+oy911cXa2hqbNm3C5MmT4erqCoVCgdDQUISFhRlcP6/a76Zr167w9PTEwIEDkZCQgLZt29aY96hRowzW6datG9q2bYs9e/Zg4MCBt1XeiIgIzJkzR3qfl5fH4IfoHtoffw1X80vQ3N4aIf7ut17hLhnUqRW+f+5+TPk2GkeSszBixT/4bEwPdFI5maxMRHR3NPjF7J49eyImJgY5OTnQaDTYvn07MjMz4efnV+s6vXv3BgDEx8fXezt+fn5o2bKltI6HhwcyMjIM0pSXlyMrK6vWfkFKpRJOTk4GExHdO78c03dqHt69NWysTNu3prdfC/z4fB94Otsi8ZoW4Z//gx8OXWKnZyIzc9eONM7OznBzc0NcXByio6MxfPjwWtPGxMQAADw96z9+R2pqKjIzM6V1+vbti5ycHBw7dkxKs3v3buh0OimwIqLGI7eoDH+fSwdw7+7mupXOKmf8/srDGBDgjtJyHd7acgYzNpxAfjEvfRGZC6MDn4KCAsTExEjBSlJSEmJiYpCSkgIA2LhxI/bs2SPd0j5o0CCEh4dj8ODBAICEhAS89957OHbsGJKTk/Hrr7/i2WefRb9+/dCtWzdpOwEBAdi8ebO0zblz5+LQoUNITk7Grl27MHz4cLRr1w5DhgwBAHTs2BFDhw7F1KlTceTIEfzzzz+YMWMGRo0axTu6iBqhP05rUFquQ3v3ZujSuvG0tro62ODrZ3vhfx4JgJVcht9PafD4Z//gQhpvfiAyB0YHPtHR0QgKCkJQUBAAYM6cOQgKCsLbb78NANBoNBg/fjwCAgLwyiuvYPz48Qbj7djY2GDnzp0YPHgwAgIC8J///AcjR47Eb7/9ZrCd2NhY5ObmAgAUCgVOnTqFxx9/HB06dMDkyZPRs2dP7N+/H0qlUlpn3bp1CAgIwMCBA/HII4/goYcewpdffmn8p0JEd13lZa6RPdtAJpOZuDSG5HIZpvVri59e6AuVsy2SrmkRvuIfbD6RauqiEdEdkglewJbk5eXB2dkZubm57O9DdBfFpuVjyCf7oJDLcPCNAWjlZHvrlUwkS1uKmT+ewP64awCAsb3VeHtYJyitFCYuGRFVMub8zZG6iOieW3f4EgBgUMdWjTroAfSXvtZOuh+vDGwPQP+Yi6dWRuFy1q3HHiOixoeBDxHdU9qScmnQwnF9vE1cmvpRyGWYM6gD1ky6Dy721jiVmovHlh/ArvPppi4aERmJgQ8R3VNbY66goKQcvi0d8EDbFqYujlFC/N3x+ysPo7uXC3KLyjD522h8+OcFlFXoTF00IqonBj5EdM8IIfDDIf1lrrG91ZDLG1en5vpo7WKHn57vi4kP+AAAVu5NwNOreOmLqKlg4ENE98yJyzk4p8mD0kqOJ3s2jrF7boeNlRwLHu+ML8b2gKOtFU6k5OCRZfvxx2nNrVcmIpNi4ENE90xla89j3VRwsb+3DyS9G8K6euKPVx5GD7UL8ovL8dK643j951Mc8JCoEWPgQ0T3RLa2FNtO6VtExvVRm7g0DcfL1R7/93xfvNS/LWQy4P+iL2PoJ/txMP6aqYtGRDVg4ENE98TGY5dRWq5DZ5UTunu5mLo4DcpaIcdrQwOwYWofeLna4d+cIoz5+jDe3noGhaXlpi4eEVXBwIeI7rrSch3W/pMMABjfx7vRjdTcUPr4tcD2mf0wtre+Reu7qEsYtGQfdl/gbe9EjQUDHyK66349eQVXcovh5qhEeFBrUxfnrnJQWuF/R3TFd8/dj9Yu+taf59ZG46V1x5CeV2zq4hFZPAY+RHRX6XQCK/cmAAAmP+QLW2vLeNRDvw5u2DGnH57v5weFXIY/Tqch9L97se7wJfBJQUSmw8CHiO6qHefTEZ9RAEdbK+kSkKWwt7FCxCMd8duMhxDo5YL8knK8ufkMxn59mOP+EJkIAx8iumuEEPh8j761Z3wfbzjaWpu4RKbRSeWETS8+gHmPdYKttRwHEzIx5JN9+PZgMnQ6tv4Q3UsMfIjorolKzMTJyzlQWskx6UFfUxfHpBRyGSY/5IvtM/vhfl9XFJZWYP6vZzHum8O4klNk6uIRWQwGPkR013xxvbXn6V5ecHNUmrg0jYNPSwf8OLUP3h3eGXbWChxMyMTQT/Zha8y/pi4akUVg4ENEd8XJyznYH3cNCrkM0/r5mbo4jYpcLsOzfX3w+yv6vj95xeWY+WMMXtlwArmFHPWZ6G5i4ENEDa64rAJzfz4JAHg8UAUvV3sTl6hx8nNrhp9f6IuZA9tDIZfh15NX8Miy/YhOzjJ10YjMFgMfImpwH/55ARfTC9CymRJvPtrR1MVp1KwVcswe1AE/v9AX3i3s8W9OEZ758hCW74pDBTs+EzU4Bj5E1KAiL2Rg7cFkAMDHT3VDy2bs21MfQerm2PbyQwjvrkKFTuC/Oy5i3NeHocllx2eihsTAh4gazNX8EukS16QHfdDf393EJWpaHG2tsfSZ7vj4qUDY2ygQlZiJwUv34aejlznoIVEDYeBDRA2iQicw9+eTuFZQigAPR7w+NMDURWqSZDIZnuzZBr+9fH3Qw+JyvPbLKUxYcxT/8rZ3ojvGwIeI7lhRaQVe+OEY9sRehY2VHJ+OCrKYR1PcLW3dmuGXF/oiIiwANlZy7Lt4FUOW7sPX+xNRWq4zdfGImiwGPkR0RzILSjD6q0PYcS4dNlZyLBvVHf4ejqYullmwUsjxfHBb/DnzYfT0bo6CknK8//t5DP10HyJjM0xdPKImSSZ44ViSl5cHZ2dn5ObmwsnJydTFIWr0LmVqMWH1ESRnFsLZzhpfT+iF+3xcTV0ss1ShE9gYfRkf/x2LawWlAID+/m54dbA/urR2NnHpiEzLmPM3A58qGPgQ1V9ecRnCPtmPf3OK0NrFDt8+dz/auTczdbHMXl5xGT7bHY81/yShrEJ/+A7t2AqzQtszACKLxcDnNjHwIaq/iE2nseFICtSu9vj5xb5wd7Q1dZEsStI1LT7deRG/nryCyuF+Bga444X+bdHLuzlkMplpC0h0Dxlz/ja6j8++ffswbNgwqFQqyGQybNmyxWB5eno6Jk6cCJVKBXt7ewwdOhRxcXEGafr37w+ZTGYwvfDCC7Vus6ysDK+//jq6du0KBwcHqFQqPPvss7hy5YpBOh8fn2r5fvjhh8ZWkYhu4Z/4a9hwJAUAsPjJbgx6TMC3pQM+GRWEHXOCMSKoNeQyYNeFDDy1MgojPj+IP05rOAAiUQ2MDny0Wi0CAwOxYsWKasuEEAgPD0diYiK2bt2KEydOwNvbG6GhodBqtQZpp06dCo1GI02LFy+udZuFhYU4fvw45s2bh+PHj2PTpk2IjY3F448/Xi3tu+++a5Dvyy+/bGwViagO2pJyvP7LKQDAs3290cevhYlLZNnaujXD0me6Y+ecYIy+3ws2VnLEXM7BS+uOI+TjPfh6fyJyCktNXUyiRsPK2BXCwsIQFhZW47K4uDgcOnQIZ86cQefOnQEAX3zxBTw8PLBhwwZMmTJFSmtvbw8PD496bdPZ2Rk7duwwmPfZZ5/h/vvvR0pKCtRqtTTf0dGx3vkSkfEWb7+A1Gx9vx6O1dN4+Lk1w8InumHOIH98f+gSvo9KRkpWId7//TwW/xWLR7t6YkxvNS+DkcVr0NvZS0pKAAC2tjeaveVyOZRKJQ4cOGCQdt26dWjZsiW6dOmCiIgIFBYWGrWt3NxcyGQyuLi4GMz/8MMP0aJFCwQFBeGjjz5CeXl5neXNy8szmIiodocTM/Ft1CUAwKKR3eCgNPq3E91lbo5KzBnUAQffGIj/HdEFnTydUFquw+YT/+KplVEYuGQvPtl5EQlXC0xdVCKTaNCjVkBAANRqNSIiIrBq1So4ODhg6dKlSE1NhUajkdKNGTMG3t7eUKlUOHXqFF5//XXExsZi06ZN9dpOcXExXn/9dYwePdqgE9Mrr7yCHj16wNXVFQcPHkRERAQ0Gg2WLFlSYz4LFy7EO++8c2eVJrIgn0XGAwBG3eeFh9q3NHFpqC52NgqM7e2NMfercSo1F+sPp+DXk1eQeFWLT3bG4ZOdceiscsKwQBUe6+aJNs3tTV1konviju7qkslk2Lx5M8LDw6V5x44dw+TJk3Hy5EkoFAqEhoZCLpdDCIE///yzxnx2796NgQMHIj4+Hm3btq1zm2VlZRg5ciRSU1OxZ8+eOntvr169Gs8//zwKCgqgVFZ/UGJJSYnUSgXoe4V7eXnxri6iGpSUV6Dbgr9RUq7D37P7oUMrDlLY1OQXl2HHuXT8dvIK9sddQ3mVzs9Bahc81k2FR7t6wsOZndWpaTHmrq4Gb6fu2bMnYmJikJubi9LSUri5uaF3797o1atXrev07t0bAG4Z+JSVleHpp5/GpUuXsHv37ltWrnfv3igvL0dycjL8/f2rLVcqlTUGRERU3fFLOSgp18HNUYn2HK+nSXK0tcYTPdrgiR5tkK0txZ9n0vDryX9xOCkLJ1JycCIlB+//fg491c3R398NwR3c0VnlBLmcfYLIfNy1C/TOzvqBtOLi4hAdHY333nuv1rQxMTEAAE9Pz1rTVAY9cXFxiIyMRIsWt76TJCYmBnK5HO7ufEI00Z06mHANAPBA2xbsHGsGmjvYYExvNcb0ViMjrxh/nNZg2ykNoi9lS9PHf19ECwcbPNS+JR5u74YH27WAp7OdqYtOdEeMDnwKCgoQHx8vvU9KSkJMTAxcXV2hVquxceNGuLm5Qa1W4/Tp05g5cybCw8MxePBgAEBCQgLWr1+PRx55BC1atMCpU6cwe/Zs9OvXD926dZPyDQgIwMKFCzFixAiUlZXhySefxPHjx7Ft2zZUVFQgLS0NAODq6gobGxtERUXh8OHDCAkJgaOjI6KiojB79myMGzcOzZs3v9PPicji/ROvD3webMu+PebG3ckWEx/0xcQHfXElpwi7LmRg38WrOBh/DZnaUmyNuYKtMfpx0/zcHPBQu5Z4oG0L9PZtgeYONiYuPZFxjO7js2fPHoSEhFSbP2HCBKxduxbLli3DRx99hPT0dHh6euLZZ5/FvHnzYGOj/+O4fPkyxo0bhzNnzkCr1cLLywsjRozAW2+9ZXDpSiaTYc2aNZg4cSKSk5Ph6+tbY3kiIyPRv39/HD9+HC+99BIuXLiAkpIS+Pr6Yvz48ZgzZ069L2dx5GaimhWUlCPwnb9RoRPY/1oIvFzZEdYSlJbrcDwlG/suXsU/CZk4nZqDqmMiymRAgIcT+vq1QN+2LXCfT3O42DMQonuPj6y4TQx8iGq2+0I6nlsbDbWrPfa9Vv2HD1mG3MIyRCVm4p/4a4hKzER8RvVb4n1bOiCwjTO6e7kg0MsFHT2dYGutMEFpyZKYtHMzEZmff+IzAQAPtuMozZbM2d4aQ7t4YGgX/SCxGfnFOJSYhaiETBxOzETiNS2Srk9brl8as1bI0MnTCUHq5ujh3Rw91C5o7WLHfmJkMgx8iOiWKvv3PMD+PVSFu6MtHg9U4fFAFQAgW1uKk6k5OHk5FydTcxBzOQdZ2lKcTM3FydRcrD2YDABo2cwGXVo7o+v1qXNrZ6icbRkM0T3BwIeI6pRZUIILafkAgL5t2eJDtWvuYIP+/u7o76+/k1YIgdTsIhxPycaJlBwcT8nGuSt5uFZQij2xV7En9qq0rp21At4t7OHb0gG+LR3g09IBftdfWzjYMCiiBsPAh4jqFJWov8wV4OGIls047hXVn0wmg5erPbxc7TG8e2sAQHFZBS6k5eN0ag5O/5uLU6m5iM8oQNH1+ZVBdlV21gq0bm6H1i520msb6dUe7o5KjjVE9cbAh4jqVNm/h5e5qCHYWivQ3csF3b1cpHllFTqkZhch+ZoWide0SK7SV+hKbhGKyioQn1FQY2dqALBRyKFysYWXqz3aNLeH2lU/ebewh1dzezjbW9+j2lFTwMCHiOoUdX3gQnZsprvFWiGXLnHdfM9gcVkFNLnF+De7CP/mFCI1uwj/5hRdf18ETW4xSit0SM4sRHJmzQ+7drS1gldze3i52qG1iz1ULrZo09wOKhc7eDjboqUDW4wsCQMfIqrVvzlFSM4shEIuw/2+rqYuDlkgW2uFFBTVpLxCh7S8YqRmF+FyViEuX39NySrEpcxCXCsoQX5xOc5p8nBOk1djHtYKGVo52cLT2RYeznbwcFLCzVEJd0dbuDnq/+/WTAkXe2v2NTIDDHyIqFZ7YjMAAN3aOMPRlpcLqPGxUsjRprn+Elcfv+qtkkWlFUjNLsTl7EJczirClZwipF5vMdLkFiEjvwRlFfpO2KnZRQCya9+WXAYXexu4Olijub0NXB1uTM3tbdCiWfV5HMOo8WHgQ0Q1Kq/Q4ev9SQCAoZ09TFwaottjZ6NA+1aOaN/KscblZRU6XM0vgSa3GGm5xdDkFiE9rxhX80twtaBE/5pfguzCMpTrBK4VlOBaQUm9t+9go0BzBxu42FvD2e7G5GRrDafK/1eZ72JnDRd7azjaWkPBy293BQMfIqrRb6euIOmaFi721hjbx9vUxSG6K6wVcqhc9P196lJSXoEsbSmytWXILixFprYU2dpSZFVOhaXIKig1WFauE9CWVkBbWtmaVH8yGeBka43m9tZwtrdBc3t9K5OLvTVc7W3QvEqrkquDDZpfb4WyVsjv5OOwCAx8iKiaCp3A8t36hxFPfdgPzZQ8VJBlU1op4OlsV++n0wshkF9SjqwCfSCUW1SK3KIy5BWVI7eorNqUV+X/haUVEALSe9TSabsmjkoruDjogyMX+xstTS7XW5ac7KzhZGsFJ1t9q1IzWys0U1rB0dYKSiu5RfRh4tGMiKrZduoKEq/qW3smPOBj6uIQNTkymUx/OcvWGj61dMyuTWm5DrlFZcgpLEV2of41p1Df0lT5Pkurb13K0t5YphNAfkk58kvKcTnLuBYmAFDIZXCwUaCZ0grNbK3goNQHRQ42lf9XVJtfNXBytNUHVY621rCxarwtTwx8iMhAhU7g011xAIApD/mytYfoHrOxkkt3k9WXTieQW1QZHOkvyWUVlkotSTmF+tf84jLkFZfrX4vKoS0pR0FpOYTQ/+3nFZcjr7gcyL2zOtjbKAz6NEktT/Y26NDKEU/2bHNnG7gDPKIRkYHK1h5nO7b2EDUVcrkMzR30fX+MpdMJaEvLoS2pQEGJPhjSlpTr/19ajoLichSUVEjzCqour5x3PWAqKCkHABSWVqCwVD8G0836dXBj4EPm6XJWIdYfSUFhSTlsbRSws74+XW9KrbyrwdHWSrru3ExpZRHXmBurqn17Jj/ky1vYiSyAXC6D4/U+P3eqQieQX3yjv1JlS1PO9X5MOYWlRl/6a2gMfKjBpecV47Pd8fjxaArKKoRR69oo5GjRTD8eRgsHJVo0s0HLZkq4OthIt3k6Xb8VtJnSCvZKfRCltFJALkOdQZNOJ1CuEyjX6fSvFdf/XyFQcX1ZxfVlFToBnQ6oEAI6ISCEQIUO1/+v77goAAhxfR708wBAXP9HQJ8W19NVfhJV04nriaV0uJH++hKD9zW5OV9jlZTrUHT911nyNS3iMwrgZGuFiQ/63FZ+RGS5FNfHOnKxN77l6V5h4EMNJrOgBCv3JuC7qEsoKdcB0D/moIe6OYpKK1BUdn0q1Ten5hWXI7+oDHnXrzWXVuhQWqGDJre4xubR+rCSyyCXyyCDYQBSrhN1Bg9kaPJDfnBiaw8RmSEGPnTHhBDYcOQyPvjjvHR9t5d3c/xnsD/6tq3/852KSiuQVViKzIISZBaU4ur118yCkuu3gxre+qktKYe2tMIgj3KdAHT1j3DkMsBKLoeVQgaFXD9ZyWWQy/T/v/EKyGUyyKq8ynD9VaYPtPT/1+crLQOkmTJUXX799fq6gOG6kN1Ig5qW17KsNnUtt1bIYW+jgJ21FextFGjlpMSUh/3qzpCIqIli4EN3JDW7EG/8choH4vUPsuzS2gmvDvZHcAc3o/vq2Nko0NrGDq1vMZBYVTqdQFFZBUrKdfrLU0LfulOpsgRWCpkU4FjJr///eusQERFZDgY+dFt0OoEfj95o5VFayTF3iD8mPeh7T4dZl8tlcFBawaH+d30SEZEFY+BD9SaEQMzlHGw7pcHvpzRIy9P3w+nl3RyLn+wGP7dmJi4hERFR3Rj43APnruRhxZ54UxfDeAL6Dsfl+iklqxD/5twYDdTR1gozB7a/5608REREt4uBzz1wraAEv5/SmLoYDcLeRoHQjq0wLFCFfh1aQmmlMHWRiIiI6o2Bzz3Q1r0Z3nm8s6mLcVusFXLYWOknJ1sr9PZtATsbBjtERNQ0MfC5B1q72HHofyIiokag8T4+lYiIiKiBGR347Nu3D8OGDYNKpYJMJsOWLVsMlqenp2PixIlQqVSwt7fH0KFDERcXZ5Cmf//++oHbqkwvvPBCndsVQuDtt9+Gp6cn7OzsEBoaWi3frKwsjB07Fk5OTnBxccHkyZNRUFBgbBWJiIjITBkd+Gi1WgQGBmLFihXVlgkhEB4ejsTERGzduhUnTpyAt7c3QkNDodVqDdJOnToVGo1GmhYvXlzndhcvXoxly5Zh5cqVOHz4MBwcHDBkyBAUF994tMHYsWNx9uxZ7NixA9u2bcO+ffswbdo0Y6tIRERE5krcAQBi8+bN0vvY2FgBQJw5c0aaV1FRIdzc3MRXX30lzQsODhYzZ86s93Z0Op3w8PAQH330kTQvJydHKJVKsWHDBiGEEOfOnRMAxNGjR6U0f/75p5DJZOLff/+t13Zyc3MFAJGbm1vvshEREZFpGXP+btA+PiUlJQAAW1tbaZ5cLodSqcSBAwcM0q5btw4tW7ZEly5dEBERgcLCwlrzTUpKQlpaGkJDQ6V5zs7O6N27N6KiogAAUVFRcHFxQa9evaQ0oaGhkMvlOHz4cK3lzcvLM5iIiIjIfDVo4BMQEAC1Wo2IiAhkZ2ejtLQUixYtQmpqKjSaG+PYjBkzBj/88AMiIyMRERGB77//HuPGjas137S0NABAq1atDOa3atVKWpaWlgZ3d3eD5VZWVnB1dZXS3GzhwoVwdnaWJi8vr9uqNxERETUNDXo7u7W1NTZt2oTJkyfD1dUVCoUCoaGhCAsLgxA3HhxZtd9N165d4enpiYEDByIhIQFt27ZtyCLVKSIiAnPmzJHe5+XlMfghIiIyYw1+O3vPnj0RExODnJwcaDQabN++HZmZmfDz86t1nd69ewMA4uNrfqyDh4cHAP0dY1Wlp6dLyzw8PJCRkWGwvLy8HFlZWVKamymVSjg5ORlMREREZL7u2jg+zs7OcHNzQ1xcHKKjozF8+PBa08bExAAAPD09a1zu6+sLDw8P7Nq1S5qXl5eHw4cPo2/fvgCAvn37IicnB8eOHZPS7N69GzqdTgqsiIiIyLIZfamroKDAoGUmKSkJMTExcHV1hVqtxsaNG+Hm5ga1Wo3Tp09j5syZCA8Px+DBgwEACQkJWL9+PR555BG0aNECp06dwuzZs9GvXz9069ZNyjcgIAALFy7EiBEjIJPJMGvWLLz//vto3749fH19MW/ePKhUKoSHhwMAOnbsiKFDh2Lq1KlYuXIlysrKMGPGDIwaNQoqleoOPyYiIiIyC8beMhYZGSkAVJsmTJgghBDi008/FW3atBHW1tZCrVaLt956S5SUlEjrp6SkiH79+glXV1ehVCpFu3btxNy5c6vdggZArFmzRnqv0+nEvHnzRKtWrYRSqRQDBw4UsbGxButkZmaK0aNHi2bNmgknJycxadIkkZ+fX++68XZ2IiKipseY87dMiCq9ji1cbm4uXFxccPnyZfb3ISIiaiIqb07KycmBs7NznWn5kNIq8vPzAYB3dhERETVB+fn5twx82OJThU6nw5UrV+Do6AiZTCZFkObaAmTu9QPMv46sX9Nn7nU09/oB5l/HplA/IQTy8/OhUqkgl9d93xZbfKqQy+Vo06ZNtfnmfqu7udcPMP86sn5Nn7nX0dzrB5h/HRt7/W7V0lPprt3OTkRERNTYMPAhIiIii8HApw5KpRLz58+HUqk0dVHuCnOvH2D+dWT9mj5zr6O51w8w/zqaW/3YuZmIiIgsBlt8iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHzILOh0OlMXge6QOe7DkpISHD9+HIB+ZFkiMj0GPjDPA1JxcTH27dsHwDzr9/nnn+Oll17C559/jmvXrt1yiPKmiPuwaVu0aBFcXV2xceNGCCEgk8lMXaS7yhy/o0VFRfjjjz8AmGf9bmYJdQQsNPBZvnw5xowZgwULFiAxMdHsDkiLFy+Gk5MT1qxZY3YHXI1Gg+DgYHz88cfIycnBwoUL0b9/f+zevdvURWtQ3IdN1+7du6FWq7Fy5UqsWbMGCxcuNKv9V8ncj6MfffQRHBwcsGzZMuh0OrOrH2D++7BWwoJkZmaKoUOHCh8fHzF9+nTRoUMH0a5dO/HNN9+YumgNYvfu3cLLy0v4+PiIn376ydTFuSt++ukn0alTJ3Ht2jUhhBClpaXiwQcfFI8++qg4fPiwiUt357gPm/Y+/Pvvv4WTk5MIDQ2V5uXm5hqk0el097pYDcrcj6N79uwRbdq0Meu/QXPfh7diUQ8pPXbsGBITE7F79274+voCAMaNG4cvv/wSrVu3xpAhQ0xcwtsXHR2NiRMnwt3dHdHR0QCAnJwc2NnZwcrKCgqFokm3HFT+4jp9+jScnZ1ha2sLALC2tsb777+Pt99+G6tXr8b9999v4pLePu7Dpr8Pu3XrhkcffRQAkJKSgrVr12Lnzp2wsbGBn58f/vvf/8LR0dHEpbwz5nwcTUxMxCuvvAIbGxskJCQAALKysiCXy2FnZ2c2Ixeb8z6sD4u41FXZafLy5cvQ6XQGT5d97bXX4Onpif/+97+mKl6DaNeuHUaPHg0rKyvExsbivffeQ2hoKEJCQhAeHt4kmzH37duHs2fPoqioCHK5HDKZDOXl5dBqtRBCSPu1f//+CAkJwZkzZ7Bz504Tl9p44vp1dXPch5GRkdi/fz+uXr0KmUxmdvtw7dq1+M9//oOKigoAQKtWrfDEE0/g7Nmz6NKlC3bt2oXRo0cjKCgIv/76K5577jkkJSWZuNS3x5yPo5V/gyqVCs8//zwKCgpw8uRJvP/++3jooYcQGhqK/v374+DBgyYu6Z0x531oDLMNfLZv346tW7ciKSkJ5eXlAACtVgtra2tcu3ZNStetWzc8/vjjuHbtGtatW2eq4hrt66+/xjPPPCPVzcXFBcOGDYNCoUDXrl0RGRmJGTNm4IknnkBcXByef/55HDp0yMSlrp+TJ0+ia9euGD16NMLCwhAWFiZ1MHzuuedw9uxZ7NmzB3K5XKr/qFGjkJWVhQsXLpiy6EbRaDQAAJlMBiGEWe3DgwcPIjAwEFOnTsWkSZMQFhaG33//HYB+H547d67J78PExERMnz4d27Ztw/r166X54eHhGDp0KGbPno1NmzbhxRdfxEcffYTNmzdj586d+Oeff0xYauP8/fff2Lt3LzIzM6XO5+Z0HK1s1an8G7S1tUVYWBgCAwMRFBSEffv2Yd68eXj55ZdhbW2N//znP9iyZYtpC20kcz8X3hbTXWW7Ow4ePCi6dOki2rZtKzp16iQ6duwoPv/8cyGE/lq7UqkUn332mcE6ly5dEgMHDhRz5swRZWVlpii2UfLz84Wnp6dwdnYWH3/8sTS/vLxcLF++XMybN09oNBpp/unTp0WnTp3Eu+++K8rLy01R5HorKioSTz/9tJg4caJITU0VUVFRYujQoaJ79+5i7969Qgghnn76adG5c2dRUVFhsO6AAQPEs88+K4Ro3P0ooqOjRc+ePcXIkSNFcnKyEEJI+6Wp78Py8nKxbNky4enpKd566y2RlpYmzpw5I3r16iXmzp0rKioqRHl5uRgzZkyT3odCCLF3717h4eEhxowZIx577DGRnp4uLUtOThb//vuv9L6yLvfff7+YMmXKPS+rsc6cOSO6du0qWrduLXx8fERQUJBYuXKlEMI8jqPHjx8XvXr1Er179xbHjx8XQtz4G9TpdGLLli3izTfflP4+hRAiKSlJDBo0SEyaNEkUFRWZpNzGsIRz4e0ymxYfIQRWr16Np556CuHh4YiOjsa2bdvQtWtX7N+/Hzk5OXBycsLLL7+MRYsWITk5WVpXrVbDzc0NsbGxsLKyavS39MXHx0OhUGDSpEn47rvvkJKSAgBQKBR48sknMX36dHh4eADQfy5dunSBr68vjh8/DoVCYcqi35JGo8GWLVvw1FNPoXXr1ujTpw8WLVqEtm3b4tVXXwUAvPPOO7h8+TLefvttlJWVAYD06unpCQCN9pLQtm3b8Nxzz0Eul+PYsWPS7eoKhQIVFRXSPnzppZea5D4sLi6Gra0tli1bhgULFqBly5bo3Lkz2rdvD2dnZ8jlcigUCkRERCA1NbVJ7sNKKSkpmDZtGsLDw5Geno6vv/5aWubt7Q2VSgUAUr+sK1euIDc3F2q12lRFrrfly5ejQ4cOOHnyJLZt24a+ffti/vz52Lp1K5ycnPDiiy822ePozp07MXnyZNjb26OkpAS//fYbdDodFAqF1A8tODgYM2fOhLe3NwD9PvTx8YGXlxcSEhKk/mmNkSWdC2+X2QQ+5eXlcHV1xYcffoi3334bTk5O8PX1hUqlQps2beDi4gIAmD9/PsrKyvDOO+8gLS1NWl8IAXd39ybReTQvLw+PPvooHn30Udjb2+ODDz6Qlnl4eKBVq1YG6XNycnDt2jXpj7gxKykpQceOHVFYWCjN69atGyZNmoR///0Xq1atQkBAAJYuXYrly5djypQp+Ouvv/Df//4X58+flzqWNkaVB9fQ0FBs374dXbt2xQ8//IAzZ84AgHQpwcPDQwp6KjWVfejg4IBhw4bh8ccfh0KhgEKhwHfffYe///4bycnJWLVqFS5fvowuXbpgyZIlTW4fAjf6SVy9ehVXrlzBU089hcDAQERGRuLKlSuIi4szSC+TyVBaWoqvv/4ajo6OePrpp01R7HrLzMzEjz/+iPDwcLRo0QKdO3fGW2+9hcceewwvvfQSAOCDDz5AaWlpkzyOurq64sEHH8SGDRvw0EMPYdeuXdJQCpVldnFxgZubm7SOEAIlJSVIS0uDj49Pow4ILOlceNvueRvTXZSdnS1KSkqk999//71wcXERjz/+uPjggw/EsWPHhBBC/Prrr8LX11cMGDBArFu3Tvzv//6vaNmypdi2bZupil4vlc3l3333nXjkkUeEEEK8//77olOnTuLcuXPi7NmzIisrS0pfUVEhiouLxQcffCC6du0qoqOjTVLuuuh0OoNLN5mZmaJTp05i/vz5QqvVSvOvXr0qXnjhBREcHCwKCwuFEEJ88803YsiQISIwMFB06NBB/P777/e8/MbKzs4W2dnZQgghTp06JVq3bi0WL14sNZ3ffHmnKe7DqiZPniysra3F66+/LmbOnCl69uwpQkJCREFBgRBCiK+//rrR78Pa6vfyyy+L//3f/xVC6C+d9OzZUzg7OwuVSiWuXr0qhBDi999/F9OnTxedOnUSfn5+Ys+ePfe07LcjKytL9OjRQ/z3v/81mH/mzBnh5uYm5s+fL4QQ4rfffmuSx9GysjJpiIH4+HjRu3dvMX36dJGTkyOEqP43WFpaKrRarVi0aJHo0KGDiIyMvNdFNpq5nwvvVJMPfGo74M6ZM0c4OjqKt99+W7zzzjti0KBBwsfHR/rC//333+KZZ54RDz74oOjcuXOj3dE11e/jjz8Ws2fPFkII8e+//4p+/foJe3t74eHhIRITE4UQQvz555/ihRdeEB07dhR+fn5i165d97TctcnPzxfffPONuHz5ssH8kpISkZeXJ4QQ4o033hBt2rSRrr1XWrFihbjvvvukOla6+X1jUttJs/L6+csvvyx69Ogh9u3bVy1NU9yHlUFpZf3i4+MN+ipt3bpVdOnSRfz2228G6zamfVif+gkhxKxZs8RPP/0ktFqtGDNmjFAoFEKlUonVq1dLaeLj48Vjjz0mli1bds/Kfzuqfkfz8/PFI488IqZOnWrQb6mkpETMnz9ftGnTRgpcd+zY0WSPo5XzPvzwQ3HfffeJdevWVUuze/duMXPmTOHv7y98fX3Fjh077npZb5e5nwsbUpMKfAoKCqRItari4mIpWi8tLRVCCHHlyhWDP9qTJ0+KDh06iC+//NJg3appTK0+9RNCiAULFoglS5aIoqIi6YDbokULg19o6enpYvz48WLFihX3pOz18e677wqFQiFkMpl0UqmoqBBHjhwRXbt2FWvXrpXStmrVSrzwwgsGJ5+ff/5Z2NjYiLS0NGndxqY+J82Kigqp7NnZ2aJdu3Zi9uzZIjMzU1ouhBAajabJ7cPvvvuuxvUqA6Ht27cLa2trERUVJa3bmBhTv969e4vg4GDh6Ogo/WJ+/PHHxZgxYww6vza2zujl5eU1dhwvLi6WvoNLliwR/v7+YtOmTQZpfv/9dxEYGCgOHTpkML8xHUfrql/lcbSiokJKk5eXJ0JDQ8WoUaNEUlKSlIcQQmRkZIi5c+dWO2+YWn3q2JTPhXdbkwl83n//fdGyZUsxatQo6deGEEKcPXtW9OrVS2p+rVT5paj8AkdFRQlHR0fpl2ZjO+AaU7/hw4eL7t27C0dHR9G/f3+xZcsWMWXKFNG3b1+DdRtLHX/55Rfh4eEhOnToIJYsWSI6duwovv32W2l5aWmp6NGjh1izZo10wvi///s/4evrK2bOnClSU1NFYWGhePHFF8Xo0aOlP+jGxtigoDIYWL58uWjbtq347bffRGJionj77belA3BT3Ie1neiLiorEq6++Kp555hmDy5iNgTH1q7yEMG/ePOHv7y82bNggfSc/+ugj0aFDB/HHH3+YpB63snjxYjF8+HAxZcoUcejQIWlfnT59WvTq1UvMmzdPStunTx/x9NNPi5MnT0rzdu3aJaysrMSFCxeEEI3vzrtb1e/m80Tl8h9//FEEBQWJZcuWiaSkJPHSSy+J2NhYIUTTr2NTOxfeC40+8CktLRUTJ04U7du3F1u2bBH//vuvwYmvrKxMPPHEE2L58uW1nhBLS0vF22+/LR577DFpmPzGwpj6VQYFX3zxhWjbtq348ccfpbQ///yz8PHxEd9//71J6lETnU4nxo8fL2Qymfjkk0+EEEJotVrRpk0b8dVXXwkhhHQSqWm/rFixQnTs2FG0bdtWtG/fXrRu3bpR9pG43aCg6gG1T58+ws/PT1hbW4sHHnhAXLlypVEccO90HxYUFIiDBw+Kn3/+WQQGBoqOHTuK/fv337sK3MKd1C83N1fqr1W5r65du1btEm1jcPr0aREUFCS6dOki3nvvPdG9e3fRuXNncebMGSGE/jgzcuRIsXz5cqll8q+//hL333+/ePTRR0VcXJzIyckRb7zxhggLCzNogW4MjKlfbeeJp59+Wnh6egobGxvRtWtXcenSpUbxN1ipIerYmM+F91KjD3xOnjwpgoKCpJ2r0WhERkaGQctG1Q69lbRarfjnn3/EDz/8IAIDA0X79u0bZae0263fzR3x8vLyxPnz5+9BiY1z4sQJqfyVvywefvhhaayWmn5tVB1PIzU1Vfzxxx+NKqCrdKdBgRD6VpDNmzcLNzc30a5dO/Hzzz/fm8Ib4Xb2YaXU1FQxYcIE0alTJ4PWhMbkTurXmE6MdXnrrbdEWFiY9Lel1WqFTCYzCEIrg7iqtm3bJnr16iXUarXw8/MTnp6e4u+//75Xxa63262fEPq/0Y0bNwpPT0/h5+cnfvnll3tRZKPdbh2byrnwXmr0gc/mzZtFr169RF5ennjttdeEt7e36NGjh+jRo4c0oF1NNBqNmDt3rujRo0e1pr/G5Hbr11gPuLX90qgsb0lJiXjxxRfFoEGDRH5+frV0O3fuFKtXrzboRNqY3clJUwgh4uLihJ2dnXj11VfvbkGN0FD7sDJ4P3v2bI3pTMXSvqM5OTkGA0gKIcSFCxfEY489Jl1SvVnV721OTo44cuRIowzKhbi9+lWVn58v7OzsxH/+85+7XNLbdyd1bCrnwnupUQU+NR2QFi9eLB5++GHx5ZdfiuDgYPHXX3+JXbt2iQEDBoiHH35YbN++XUp74MAB8cEHH0hRb1JSkkHLiak1VP1qu+3SlPbu3Su6dOlSZyfAyvK+/vrromvXrgbzKk2ePFl4eHg0ytu2hbg7J83G0t+loffhkSNH7l5hb4Mlfkcryz527FjRvn17sWTJEvHxxx+LZs2aCV9fX+Hh4SEmT54sDh48KK1z83Gmsbkb9WtsIzE3VB0b67nQ1BpF4FPTAakyqo2LixMymUy0aNHC4O6W8+fPi9DQUDF79mypk+iCBQuEh4dHoxsLpKHr15g6TpaVlYkvv/xStGvXTrRu3Vr4+fkZ3L5cVWWd//77b2FnZyfi4+OFEPo/7MplGRkZjfJ2SnM+aZr7PjT3+lWq6TtaeVkkOztbzJ8/X4wbN060aNFCfPfdd0Kj0YjffvtN9O/fX0yaNEkUFxcLIYSYP39+ozvOCGH+9ROi4evY2M6FjYVJA59bHZAqx0CZNm2akMlk1Zpax48fL4YPHy69z87OblTXLs29fkLom2Dfe+898f7774v4+Hjh4eEh5s6dW+c6f//9t+jQoYP466+/DOY3phasSpZw0jT3fWju9bvVd7TqZat58+aJ8ePHG9Rjzpw54oEHHpBuZW9sxxlzr58QllHHxsSkj6zQarVIT0/HxIkTsXfvXhQWFmLJkiXScplMBoVCgalTp8LZ2Rl//PEHsrKypOVyuRwtWrSQ3ru4uKB///73sgp1Mvf6AYCzszOefPJJzJo1C23btsX777+P5cuX4/Tp07Wu06dPH2g0GuTm5gK48QiAxjg8+q32YVWVj5wAID3TB9DXSy6XQwgBNze3RvdIBnPfh+Zev1t9R6t+LyMjI9GtWzfIZDKpThUVFXB0dISzszOAxnecMff6AZZRx0bF1JHX+fPnDYavt7W1FadOnaqWbtWqVcLJyUk89dRT4o8//hDvvPOOaNOmTaMeSVMI869fVZUtG/fdd58YMWJEjeO5VFRUiIKCAhEQENBo7/K5WX33YaW8vDzh6OgofvrpJyFE0xonw1z3YSVzrd+tvqOV38GIiAjRsmVL8csvv4jz58+L999/X3h6eor169ebpNz1Ze71E8Iy6thYmDzwqVSfA9KaNWvEkCFDxH333Se6devWaIbwr4+mXL/MzEzx9ddf13qJ52Z79uwRMpms1ks65eXlja7ja3005ZOmue9Dc69ffd3qO5qXlycGDBgg1Gq1aN++vejatWujOc7Uh7nXTwjLqKOp3fXApyEOSDfv+JsfB2BK5l6/iIgIIZPJhEwmq3UcjJqMGjVKdO/eXWi1WnHhwgWxYcOGRntXgbmfNM19H5p7/YRomO9oZYtBfn6+SE5Olh4b0hiYe/2EsIw6NhV3NfAx9wOSOdfvhx9+EK6urqJLly5iyZIlokOHDkaN43H58mXh6OgogoODhUwmExMmTGh0dRSC+7AujX0fmnv9Kpnzd1QI86+fEJZRx6bkrgQ+5n5AMuf6VVRUiPDwcCGTycQXX3whhBDi6tWrwsPDQ2zYsEEIces7W4qLi8X3338vrK2tRadOncTmzZvvdrGNxn3YdPehudevkjl/R4Uw//oJYRl1bIoaNPAx9wOSudev0tGjR6XB9yqbVoOCgsSMGTMM5tWkoqJC/PDDD8La2lq88847d7+wRuI+bPr7UAjzrp+5f0fNvX5CWEYdm7IGb/Ex5wOSEOZZv4MHD4orV65Um1/5h1lUVCSeffZZ8fjjj9drhNNLly41mtGIa8J92PT2obnX72bm+B2tytzrJ4Rl1LGpsrrT2+GjoqLg4+MDT09PAECvXr0qb5OHXC5HcXExunbtipSUFBQXF8PW1rbWvORyOR5++GHk5OTA3t7+TovWIMy5frt27cLUqVOh0+lQUVGBsLAwvPrqq+jQoQOEENKYJba2tnBwcEBsbCxsbW2h0+kMxpW4mVqtvldVqBfuw6a7D829fpXM+TsKmH/9AMuoo7m47QEMd+3aBT8/P4wePRr3338/pk2bhosXLwJAjQek9PR06YBUF7Va3Sh2tLnX7/Lly3jrrbcwbtw47Nq1C0uXLsWuXbsQERGBS5cuSYNjVdYnNDQU586dg0ajqfOE0phwHzbtfWju9QPM/ztq7vUDLKOOZud2molSUlJEnz59xLx580R8fLzYuHGj8PPzE0888YRITk4WQuib6iqb8n755Rfh6OhYY1N1Y2Tu9ROi+mMVhNDXo1+/fuL555+vln7Lli2iQ4cOdT4xvjHhPmz6+9Dc62fu31Fzr58QllFHc3RbP4suXLiAkydPYsKECWjbti2efPJJfPTRR7h27RoWLlwIQN9UV/mrS6FQwNPTE3FxcQ0Xsd1F5l4/AMjKykLHjh1RUVEhzRs+fDiGDBmC/fv3Y+/evQCA8vJyAPoh/CubaAH9L5nGjPuw6e9Dc6+fuX9Hzb1+gGXU0RzdVuBj7gckc68fAHTu3Bnnzp3DhQsXpHkKhQKPPPIIvLy8sHXrVgCAlZUVdDodbGxs4OHhgZMnTwJonM8sqor7sOnvQ3Ovn7l/R829foBl1NEc3VbgY+4HJHOvHwB06dIFISEhWLJkCQoKCqT53bt3h7u7OxITE6X+E3K5HPb29ti4cSPmzp1rwlLXH/dh09+H5l4/c/+Omnv9AMuoo1m63WtkYWFhIjg4WLpdr9L48ePF8OHDDa5rFhcXi6NHj97upkzC3OsnhBAxMTHCyspKfPHFF6KkpESa/+abb4p27dqZsGQNg/uw6e9Dc6+fuX9Hzb1+QlhGHc3NbQc+5n5AMvf6VXrzzTeFSqUSX3/9tSgoKBB5eXliyJAhYsGCBaYu2h3jPmz6+1AI866fuX9Hzb1+QlhGHc3NHQ1gaM4HJCHMv36VXnrpJeHp6Sl69+4tvL29RadOncTZs2dNXawGwX1oHsy5fub+HTX3+glhGXU0JzIh7qx31fTp07F582ao1WqkpaXBwcEBGzduRKdOnRrqapxJmXv9AKC4uBjnz5/H8ePHoVQqMW7cOFMXqUFxHzZ95l4/c/+Omnv9AMuoo7m448DH3A9I5l4/S8B9SI2duX9Hzb1+gGXU0VzcceBDRERE1FQ0jXHdiYiIiBoAAx8iIiKyGAx8iIiIyGIw8CEiIiKLwcCHiIiILAYDHyIiIrIYDHyIiIjIYjDwISIiIovBwIeIiIgsBgMfIiIishgMfIiIiMhiMPAhIiIii/H/x/zS+jKlkqoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_network_query = res1d_network.read(queries_network)\n",
     "df_network_query.plot()"
@@ -279,8 +1152,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.313337Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.313160Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.316036Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.315573Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Read a specific catchment\n",
@@ -295,9 +1175,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.318358Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.317712Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.445931Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.445013Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_catchments_query = res1d_catchments.read(queries_catchments)\n",
     "df_catchments_query.plot()"
@@ -349,9 +1257,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.448722Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.448524Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.567763Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.567125Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Assign aliases for reaches and nodes\n",
     "reaches = res1d_network.reaches\n",
@@ -369,9 +1305,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.570136Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.569603Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.686672Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.685646Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Assign alias for catchments\n",
     "catchments = res1d_catchments.catchments\n",
@@ -403,9 +1367,198 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.688945Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.688660Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.827893Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.827286Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "    .dataframe tbody tr th {\n",
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+       "    <tr>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>110 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         WaterLevel:1  WaterLevel:2  WaterLevel:3  \\\n",
+       "1994-08-07 16:35:00.000    195.052994    195.821503      195.8815   \n",
+       "1994-08-07 16:36:01.870    195.052994    195.821701      195.8815   \n",
+       "1994-08-07 16:37:07.560    195.052994    195.821640      195.8815   \n",
+       "1994-08-07 16:38:55.828    195.052994    195.821503      195.8815   \n",
+       "1994-08-07 16:39:55.828    195.052994    195.821503      195.8815   \n",
+       "...                               ...           ...           ...   \n",
+       "1994-08-07 18:30:07.967    195.119919    195.822769      195.8815   \n",
+       "1994-08-07 18:31:07.967    195.118607    195.822769      195.8815   \n",
+       "1994-08-07 18:32:07.967    195.117310    195.822769      195.8815   \n",
+       "1994-08-07 18:33:07.967    195.115753    195.822769      195.8815   \n",
+       "1994-08-07 18:35:00.000    195.112534    195.822769      195.8815   \n",
+       "\n",
+       "                         WaterLevel:99l1:0  WaterLevel:99l1:44.5016  \\\n",
+       "1994-08-07 16:35:00.000         194.661499               194.581497   \n",
+       "1994-08-07 16:36:01.870         194.661575               194.581497   \n",
+       "1994-08-07 16:37:07.560         194.661667               194.581497   \n",
+       "1994-08-07 16:38:55.828         194.661865               194.581497   \n",
+       "1994-08-07 16:39:55.828         194.661911               194.581497   \n",
+       "...                                    ...                      ...   \n",
+       "1994-08-07 18:30:07.967         194.685715               194.607559   \n",
+       "1994-08-07 18:31:07.967         194.685608               194.607407   \n",
+       "1994-08-07 18:32:07.967         194.685516               194.607254   \n",
+       "1994-08-07 18:33:07.967         194.685074               194.607101   \n",
+       "1994-08-07 18:35:00.000         194.682861               194.606323   \n",
+       "\n",
+       "                         Discharge:99l1:22.2508  \n",
+       "1994-08-07 16:35:00.000                0.000002  \n",
+       "1994-08-07 16:36:01.870                0.000002  \n",
+       "1994-08-07 16:37:07.560                0.000002  \n",
+       "1994-08-07 16:38:55.828                0.000002  \n",
+       "1994-08-07 16:39:55.828                0.000002  \n",
+       "...                                         ...  \n",
+       "1994-08-07 18:30:07.967                0.000605  \n",
+       "1994-08-07 18:31:07.967                0.000600  \n",
+       "1994-08-07 18:32:07.967                0.000595  \n",
+       "1994-08-07 18:33:07.967                0.000589  \n",
+       "1994-08-07 18:35:00.000                0.000531  \n",
+       "\n",
+       "[110 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "file_path_network = '../tests/testdata/network.res1d'\n",
     "res1d_network_filtered = Res1D(file_path_network, nodes=['1', '2', '3'], reaches=['99l1'])\n",
@@ -423,9 +1576,186 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.829442Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.829296Z",
+     "iopub.status.idle": "2024-09-04T12:38:12.894134Z",
+     "shell.execute_reply": "2024-09-04T12:38:12.893663Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TotalRunOff:100_16_16</th>\n",
+       "      <th>ActualRainfall:100_16_16</th>\n",
+       "      <th>ZinkLoadRR:100_16_16</th>\n",
+       "      <th>ZinkMassAccumulatedRR:100_16_16</th>\n",
+       "      <th>ZinkRR:100_16_16</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:35:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:36:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:37:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:38:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 16:39:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.333333e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:18:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:19:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:20:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:24:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994-08-07 18:35:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     TotalRunOff:100_16_16  ActualRainfall:100_16_16  \\\n",
+       "1994-08-07 16:35:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:36:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:37:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:38:00                    0.0              3.333333e-07   \n",
+       "1994-08-07 16:39:00                    0.0              3.333333e-07   \n",
+       "...                                    ...                       ...   \n",
+       "1994-08-07 18:18:00                    0.0              0.000000e+00   \n",
+       "1994-08-07 18:19:00                    0.0              0.000000e+00   \n",
+       "1994-08-07 18:20:00                    0.0              0.000000e+00   \n",
+       "1994-08-07 18:24:00                    0.0              0.000000e+00   \n",
+       "1994-08-07 18:35:00                    0.0              0.000000e+00   \n",
+       "\n",
+       "                     ZinkLoadRR:100_16_16  ZinkMassAccumulatedRR:100_16_16  \\\n",
+       "1994-08-07 16:35:00                   0.0                         0.000000   \n",
+       "1994-08-07 16:36:00                   0.0                         0.000000   \n",
+       "1994-08-07 16:37:00                   0.0                         0.000000   \n",
+       "1994-08-07 16:38:00                   0.0                         0.000000   \n",
+       "1994-08-07 16:39:00                   0.0                         0.000000   \n",
+       "...                                   ...                              ...   \n",
+       "1994-08-07 18:18:00                   0.0                        10.661672   \n",
+       "1994-08-07 18:19:00                   0.0                        10.661672   \n",
+       "1994-08-07 18:20:00                   0.0                        10.661672   \n",
+       "1994-08-07 18:24:00                   0.0                        10.661672   \n",
+       "1994-08-07 18:35:00                   0.0                        10.661672   \n",
+       "\n",
+       "                     ZinkRR:100_16_16  \n",
+       "1994-08-07 16:35:00             100.0  \n",
+       "1994-08-07 16:36:00             100.0  \n",
+       "1994-08-07 16:37:00             100.0  \n",
+       "1994-08-07 16:38:00             100.0  \n",
+       "1994-08-07 16:39:00             100.0  \n",
+       "...                               ...  \n",
+       "1994-08-07 18:18:00             100.0  \n",
+       "1994-08-07 18:19:00             100.0  \n",
+       "1994-08-07 18:20:00             100.0  \n",
+       "1994-08-07 18:24:00             100.0  \n",
+       "1994-08-07 18:35:00             100.0  \n",
+       "\n",
+       "[108 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "file_path_catchments = '../tests/testdata/catchments.res1d'\n",
     "res1d_catchments_filtered = Res1D(file_path_catchments, catchments=['100_16_16'])\n",
@@ -451,8 +1781,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:12.895830Z",
+     "iopub.status.busy": "2024-09-04T12:38:12.895658Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.041300Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.040669Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path_network = '../tests/testdata/network.res1d'\n",
@@ -469,9 +1806,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.043991Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.043677Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.058030Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.057125Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current maximum water level:  200.06491\n"
+     ]
+    }
+   ],
    "source": [
     "res1d_network_mod.nodes.WaterLevel.add()\n",
     "df_network_mod = res1d_network_mod.read(column_mode='all')\n",
@@ -488,8 +1840,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.059841Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.059656Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.182294Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.181585Z"
+    }
+   },
    "outputs": [],
    "source": [
     "df_network_mod = df_network_mod.multiply(2.0)\n",
@@ -507,9 +1866,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.184944Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.184260Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.192878Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.192125Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current maximum water level:  400.12982\n"
+     ]
+    }
+   ],
    "source": [
     "res1d_network_mod.nodes.WaterLevel.add()\n",
     "df_network_mod = res1d_network_mod.read()\n",
@@ -526,9 +1900,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.195026Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.194844Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.343311Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.342633Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New file path: ../tests/testdata/NetworkFactorTwo.res1d\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current maximum water level: 400.12982\n"
+     ]
+    }
+   ],
    "source": [
     "print('New file path:', file_path_new)\n",
     "res1d_network_new = Res1D(file_path_new)\n",
@@ -555,8 +1951,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.345237Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.345071Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.492626Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.491775Z"
+    }
+   },
    "outputs": [],
    "source": [
     "res1d_network.nodes.WaterLevel.add()\n",
@@ -582,8 +1985,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.494946Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.494765Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.498773Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.498326Z"
+    }
+   },
    "outputs": [],
    "source": [
     "values_start = res1d_network.get_reach_start_values(\"9l1\", \"WaterLevel\")\n",
@@ -600,8 +2010,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.500453Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.500251Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.503870Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.502993Z"
+    }
+   },
    "outputs": [],
    "source": [
     "values_sum = res1d_network.get_reach_sum_values(\"9l1\", \"Discharge\")"
@@ -617,9 +2034,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:13.506289Z",
+     "iopub.status.busy": "2024-09-04T12:38:13.506127Z",
+     "iopub.status.idle": "2024-09-04T12:38:13.659229Z",
+     "shell.execute_reply": "2024-09-04T12:38:13.658738Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='9l1: Discharge'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "pd.Series(values_sum, index = res1d_network.time_index).plot(ylabel=\"9l1: \" + \"Discharge\")"
diff --git a/notebooks/res1d_export_to_shapefile.ipynb b/notebooks/res1d_export_to_shapefile.ipynb
index a883cadf..32f6e54a 100644
--- a/notebooks/res1d_export_to_shapefile.ipynb
+++ b/notebooks/res1d_export_to_shapefile.ipynb
@@ -10,8 +10,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:29.380089Z",
+     "iopub.status.busy": "2024-09-04T12:38:29.379807Z",
+     "iopub.status.idle": "2024-09-04T12:38:30.682847Z",
+     "shell.execute_reply": "2024-09-04T12:38:30.682130Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D"
@@ -19,9 +26,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:30.686451Z",
+     "iopub.status.busy": "2024-09-04T12:38:30.685880Z",
+     "iopub.status.idle": "2024-09-04T12:38:31.034467Z",
+     "shell.execute_reply": "2024-09-04T12:38:31.033628Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mikeio1d.Res1D>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res = Res1D(\"../tests/testdata/network.res1d\")\n",
     "res"
@@ -36,9 +61,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:31.037535Z",
+     "iopub.status.busy": "2024-09-04T12:38:31.036860Z",
+     "iopub.status.idle": "2024-09-04T12:38:31.796099Z",
+     "shell.execute_reply": "2024-09-04T12:38:31.795047Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ryan/mikeio1d/mikeio1d/various.py:53: UserWarning: Could not parse projection string. Returning None.\n",
+      "  warnings.warn(\"Could not parse projection string. Returning None.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>tag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>1</td>\n",
+       "      <td>POINT (-687934.6 -1056500.699)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>2</td>\n",
+       "      <td>POINT (-687914.8 -1056556.399)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>3</td>\n",
+       "      <td>POINT (-687907.899 -1056507)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>4</td>\n",
+       "      <td>POINT (-687918.199 -1056576.199)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>5</td>\n",
+       "      <td>POINT (-687835.5 -1056565.2)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  group name                          geometry  tag\n",
+       "0  Node    1    POINT (-687934.6 -1056500.699)  NaN\n",
+       "1  Node    2    POINT (-687914.8 -1056556.399)  NaN\n",
+       "2  Node    3      POINT (-687907.899 -1056507)  NaN\n",
+       "3  Node    4  POINT (-687918.199 -1056576.199)  NaN\n",
+       "4  Node    5      POINT (-687835.5 -1056565.2)  NaN"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Let's read the network as a geopandas dataframe.\n",
     "df = res.network.to_geopandas()\n",
@@ -47,9 +168,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:31.799230Z",
+     "iopub.status.busy": "2024-09-04T12:38:31.798896Z",
+     "iopub.status.idle": "2024-09-04T12:38:32.346798Z",
+     "shell.execute_reply": "2024-09-04T12:38:32.345822Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Now let's plot the network.\n",
     "df.plot()"
@@ -67,9 +216,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:32.349209Z",
+     "iopub.status.busy": "2024-09-04T12:38:32.348886Z",
+     "iopub.status.idle": "2024-09-04T12:38:32.741595Z",
+     "shell.execute_reply": "2024-09-04T12:38:32.741125Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ryan/mikeio1d/mikeio1d/various.py:53: UserWarning: Could not parse projection string. Returning None.\n",
+      "  warnings.warn(\"Could not parse projection string. Returning None.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>tag</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>max_WaterLevel</th>\n",
+       "      <th>max_Discharge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Reach</td>\n",
+       "      <td>100l1</td>\n",
+       "      <td>0.0-47.7</td>\n",
+       "      <td>LINESTRING (-687887.601 -1056368.901, -687887....</td>\n",
+       "      <td>196.808563</td>\n",
+       "      <td>0.099751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Reach</td>\n",
+       "      <td>101l1</td>\n",
+       "      <td>0.0-66.4</td>\n",
+       "      <td>LINESTRING (-687859.5 -1056308.7, -687859.5 -1...</td>\n",
+       "      <td>196.880066</td>\n",
+       "      <td>0.019202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Reach</td>\n",
+       "      <td>102l1</td>\n",
+       "      <td>0.0-10.9</td>\n",
+       "      <td>LINESTRING (-687931.201 -1056476.501, -687931....</td>\n",
+       "      <td>196.077560</td>\n",
+       "      <td>0.326383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Reach</td>\n",
+       "      <td>103l1</td>\n",
+       "      <td>0.0-26.1</td>\n",
+       "      <td>LINESTRING (-687847.1 -1056498.799, -687847.1 ...</td>\n",
+       "      <td>196.522049</td>\n",
+       "      <td>0.001056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Reach</td>\n",
+       "      <td>104l1</td>\n",
+       "      <td>0.0-34.4</td>\n",
+       "      <td>LINESTRING (-687631.201 -1056393.199, -687631....</td>\n",
+       "      <td>197.072220</td>\n",
+       "      <td>0.000025</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   group   name       tag                                           geometry  \\\n",
+       "0  Reach  100l1  0.0-47.7  LINESTRING (-687887.601 -1056368.901, -687887....   \n",
+       "1  Reach  101l1  0.0-66.4  LINESTRING (-687859.5 -1056308.7, -687859.5 -1...   \n",
+       "2  Reach  102l1  0.0-10.9  LINESTRING (-687931.201 -1056476.501, -687931....   \n",
+       "3  Reach  103l1  0.0-26.1  LINESTRING (-687847.1 -1056498.799, -687847.1 ...   \n",
+       "4  Reach  104l1  0.0-34.4  LINESTRING (-687631.201 -1056393.199, -687631....   \n",
+       "\n",
+       "   max_WaterLevel  max_Discharge  \n",
+       "0      196.808563       0.099751  \n",
+       "1      196.880066       0.019202  \n",
+       "2      196.077560       0.326383  \n",
+       "3      196.522049       0.001056  \n",
+       "4      197.072220       0.000025  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Here we read the reaches, and choose to get the maximum value of all available quantities.\n",
     "df_reaches = res.reaches.to_geopandas(agg='max')\n",
@@ -78,9 +342,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:32.744105Z",
+     "iopub.status.busy": "2024-09-04T12:38:32.743385Z",
+     "iopub.status.idle": "2024-09-04T12:38:32.986389Z",
+     "shell.execute_reply": "2024-09-04T12:38:32.985238Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ryan/mikeio1d/mikeio1d/various.py:53: UserWarning: Could not parse projection string. Returning None.\n",
+      "  warnings.warn(\"Could not parse projection string. Returning None.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>max_WaterLevel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>1</td>\n",
+       "      <td>POINT (-687934.6 -1056500.699)</td>\n",
+       "      <td>195.669006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>2</td>\n",
+       "      <td>POINT (-687914.8 -1056556.399)</td>\n",
+       "      <td>195.822968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>3</td>\n",
+       "      <td>POINT (-687907.899 -1056507)</td>\n",
+       "      <td>195.881500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>4</td>\n",
+       "      <td>POINT (-687918.199 -1056576.199)</td>\n",
+       "      <td>194.661331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Node</td>\n",
+       "      <td>5</td>\n",
+       "      <td>POINT (-687835.5 -1056565.2)</td>\n",
+       "      <td>194.793060</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  group name                          geometry  max_WaterLevel\n",
+       "0  Node    1    POINT (-687934.6 -1056500.699)      195.669006\n",
+       "1  Node    2    POINT (-687914.8 -1056556.399)      195.822968\n",
+       "2  Node    3      POINT (-687907.899 -1056507)      195.881500\n",
+       "3  Node    4  POINT (-687918.199 -1056576.199)      194.661331\n",
+       "4  Node    5      POINT (-687835.5 -1056565.2)      194.793060"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Similarly, we can do the same for nodes.\n",
     "df_nodes = res.nodes.to_geopandas(agg='max')\n",
@@ -89,9 +449,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:32.989036Z",
+     "iopub.status.busy": "2024-09-04T12:38:32.988535Z",
+     "iopub.status.idle": "2024-09-04T12:38:33.171440Z",
+     "shell.execute_reply": "2024-09-04T12:38:33.170196Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Now let's plot the max discharge for each reach.\n",
     "df_reaches.plot(column='max_Discharge', cmap=\"RdYlGn_r\", legend=True)"
@@ -99,9 +487,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:33.174274Z",
+     "iopub.status.busy": "2024-09-04T12:38:33.173806Z",
+     "iopub.status.idle": "2024-09-04T12:38:33.372099Z",
+     "shell.execute_reply": "2024-09-04T12:38:33.370716Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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2QkLGzR5SU1Nxd3dvQckEAkFHQSjyZub48eNcvXoVExMT5s2bh0qlavRcJiYmBAQEEBAQQHl5OdHR0URERHDt2jWSkpIJO5xMjZvwVCr2SxFJXIpIokdvr0bLIGgYk0b2YOXaExQWl9XoYpcBF8sCVqxYQf/+/Rk7diyWlqJUr0DQEHRGcK039frWRPj0mpHExEQOHDgAwJQpU3B2djba3Gq1mj59+vDggw+yZMkShgWPprS47i+iUqng1PEYo8kguD2WFmr+9eZcrC3NkKgs/AOVQW4KhcSLj47hrsE9ADh37hxfffUVp06dQq9vzzG0AkHLUqXIm3q0V4RF3kyUlpayYcMGZFkmKCiIfv36NdtaVlZW9OoZBByuc5wsyxQWFDWbHIKa6e7nyvqvF/P7kYucOHeNigodPbt7MGN8b9ycbYCBDBw4kF27dpGens7u3bs5e/YskydPxs/Pr7XFFwgEbRxJvgPymZq7uf2fkWWZdevWER0djYODA48//jhqtbpZ16yo0HHv5E8pKiyrc1z3fjIDhnrTt29fevbsKYrOtCH0ej3nzp3jwIEDlJaWAhAYGMjEiROxs7NrXeEEgkbQ3PfeqvmPRXpgZd00B3NRoZ67eqW0mJ4wJsIibwZOnz5NdHQ0SqWSuXPnNrsSB1CplEyfM4B1/z1R416sJIGpmQIndz0JCQkkJCSwa9cuevbsSb9+/fDy8qo1CK6oqIyLF5PR6fR06+aGk5N1jeMETUOhUDBw4EB69uzJoUOHOHPmDJcuXSImJoZhw4Zx1113NSnGQiDoqNzpe+RCkRuZ1NRU9u7dC8CECRNaNBL5L4+MJOJcAhcvJFTWdZcBhYTSRInSRMH7n/8F3672XLhwgbCwMHJycggPDyc8PBwHBwf69u1Lnz59DE+jFRU6vltxkO3bwtBotEBlatzIkf4898IkbG0tWuyz3UmYm5szZcoU+vfvz+7du4mPj+fIkSOEh4czceJEevToITIPBAKBAeFaNyLl5eWsWLGCnJwc/P39uffee1v0hivLMpt+PMYv/zlAaXG54X0PP2f+tmwugUFe1cYmJiYSFhZGVFQUFRUVQKWi7tKlC3379mX1L1GcOnn1lhxohULC09OBr/6zEEvL5vc23MnIssylS5fYs2cP+fmVTXV8fHyYMmUKrq6urSydQFA3LeVaPxDpZRTX+theie3StS4UuZGQZZnNmzcTERGBjY0NTz75JObm5s2yVm1898+dbPrx2C3vK5QSnXyc+PzXp7C0NrvlvEajISoqivDwcBISEgDIzZGIuFC7G1eSJB5/Ygzz7x1ivA8gqJWKigqOHz/O8ePH0Wq1SJLEwIEDGTNmTIt/zwSC+tJSinx/hDeWTVTkxYV6xgUltEtFLlzrRiI8PJyIiAgkSWLu3Lk13lzTEnMI2RtJWakG766uDBnbAxOVcZqhxMek1ajEoTKHPDk+iy3/Pc5fnh53y3lTU1P69etHv379yM7OJjw8nB9/CEWSdMhyzR4FWZb5bUeYUOQthEqlYvTo0fTt25e9e/dy8eJFzpw5Q2RkJGPGjGHAgAEoFCKbVHBnIvbIBU0mMzOTnTt3AjBmzBi8vKoXXCkvq+CL19ZzcHsYklSZP6zT6rFztOKlT++n/13dmyzDnk1nUSgV6HU15x/r9TI7152uUZHfjKOjI+PGjWPr5mRSUpLrHJuVJVLZKip0nAuLp6CgFDc3W3r19GzW7RQ7OzvmzZtHXFwcu3fvJiMjg507d3L27FmmTJmCj49Ps60tEAjaJkKRN5C4i8lsWnGQE7vPoynX4uPvjrWvFrVrBV26duGuu+665ZpP/vYrx/dEglxpyVb1pM7PKebtxT/wybqn8W9itbW0pNxalXgVOZmF6HT6etX2dnSyRqGQ6mj4ISMptBw6dIi+ffvekelR23eEsfKHwxTelPLn7m7HkucnM6C/b7Ou3blzZ5544glCQ0M5ePAg6enp/Pjjj/Ts2ZMJEyZga2vbrOsLBG0JnaxAJzfNI9We+5ELX1wDOLUvkmemfsyBzaGUFJWjrdBxNSqJ8B1pxB0pY8aMmbdYY9cupXBsd0RlFPmfkGUZvSyz+su9TZbN2tYcxW0UtJm5ab0bdEyY2KvOrl0g4eJSweHDh/niiy/4+eefiYiIQKvVNkDq9sumLaF8/sXv1ZQ4QFpaPq++tpbz5xOaXQaFQsHgwYN55plnGDhwIJIkERUVxVdffcXhw4cNAYwCQUdHj4QeRROP9utaF4q8nhTll7DsqVXodfrqlu8NXZcRU8LRredvue7Ib+frVJ56nZ4zh6IpKaq7kMvtGD2tT50WuUKpYNzM+leXGzKkC717e6FQ3PrlVioVODtb8/gTU+ncuTMA165dY9OmTXz66afs3LmT1NTUhn+IdkJpqYaV39dcRU+WZfR6+ObbAy0mj4WFBdOmTePxxx/H29sbrbbSU/Kf//yHS5cuiR72AkEHRyjyerJ/wxk0ZRW1t6OUYMvKQ7fcNAsLSmrrY2JAlmWKb1OR7Xb0HdqF3oM716h4FQoJtZmK2YtudfvXhlKp4IMP5zN27K05yz17efLvrxYwdOgAFixYwHPPPceoUaOwtbWlrKyMM2fOsGLFCr799ltOnz5tqFJWE1qtjtDQOA4cuEhUVFK7UDrHT8RQVla7tSvLMldi0khIzG5BqcDNzY1FixYxZ84crK2tycvLY926dfz8889kZGS0qCwCQUsiaq0L6sWV89eRFArk2qxeGZKvZVJeqsHM4o/candvx9u4qEGhhJ/+9z1BvSt7i7u7uzc4YEqSJN7+egFvPbWSqNDKILWqPW7XTva89vn9eHg7NmhOc3NTlr4+g8WPjyE8/Do6nZ6AAA98fZ2qjbOzs2P06NGMHDmSuLg4wsLCiI6OJi0tjV27drFnzx4CAgLo168ffn5+hs+2a+d5vvvuEPl5JYa5PD0deH7JZPr1a7tBWzk5RbeJH6gkN6cYb6+G/cybiiRJ9OrVi+7du3Ps2DFOnDhBXFwcy5cvZ/DgwYwePRozs1tTEAWC9oxx9sjbvhFRG0KR1xMTlUm9ntf+vE89btYAfvx4Fzpq/pJICnD3N6O4pIiTJ09y8uRJHBwcCAoKolevXjg5OdV4XU1YWKrpNdYSlYspztZd8Pb0oUugB32G+jUpNcnZ2ZoJE3rddpxCoaBLly506dKF0tJSQwW59PR0oqKiiIqKwtbWlr59+5KepmLFt0dumSM5OZdXXvqVTz59gN59vBstc3Pi6Gh9WyVeOc6qBaSpGVNTU8aOHUu/fv3Ys2cP0dHRnDp1ioiICMP7Il1NIOgYiIIw9eTojjA+eHJVrecVSoleQ7ry0bpnbjm3edVRVry/rdLFLt98jQJHVxs+Xfd/5ORnEBERweXLl6sFjLm7uxMUFETPnj1rlF1TXsGRnRc4uusC+bmF5Ban4drVhNffe7HNRC6npqYSFhZGREQEZWVl6HQQckKFXlfzo5EkSXTr5so33z7cwpLWj7KyCubM/zelpTW71yVJwt/fjf98ubCFJaudq1evsnv3brKysoBKN/yUKVPw9m6bD0uCjkFLFYTZeL47ltZNq8lRXKhjTp8r7bIgjFDk9URboWPxyPfITMmrNajsvZ+fZOCYHjWeO7gtjP/9ew8p8ZU3UqWJglHT+vLoq9NwcP5DpvLyci5fvkxkZCSxsbHV9ox9fX3p1asXPXr0wNzcnMzUPF596FtSrmcjSdKNsZU11u0crbCxt6B7b2+mPzisyeltxqCiooLo6Gg2bTzOkcO5tx2/6qfH8W7gdkBLsX1HGJ9/8TuVT2Z/PJBU1Qn4/JMH6NXLs9XkqwmdTseZM2c4dOgQ5eWVJXyDgoIYP3684e8iP6eIvRvOkBCThpm5KcMm96ZPcFdR213QKFpKka8/H4BFExV5SaGOeX2ihSJvqxjry5R8LYNX7/2KrNQ8w3sKhYRelnny3TnMfGRUndfLskzStUxKS8px93LE2q7upiPFxcVcvHiRyMhIQ+nUyjUVdO3alX2r4klLyP/jwaKGX6VSqUCn07NgyWTu/79xlRb875Hs3xFGfk4xHt6OTJk7iP4teLPesP403y4/cFv39Cef3k+/2+RjFxaWkptbgq2teYs2cZFlmaWvfUXY+SIqKv74uXl7O/LCs5Po00a3BaDye7V//37CwsKAyqpxI0aMoDhFxddvbkSn06OQJJBAp9Xj39ebd79fjK1D620VCNonQpG3DEKRN5CyUg1Htp3jl2+2UF5awZDRfbn3ycl4dHY2krQ1k5eXR2RkJJGRkaSnp5OfpuPSvpsi3evxa/zbp/ez7sejXI/NQFJIyHrZUA1uxMRevPrRfJQmxikZWxcHDlzk/fe23nbcuAkWDB1aGSvg4uJS7VxCQjbff3+IE8djDA8Egwf78cijo+jWza1Z5L6Z6Oho1q5di1KpYtz4OZSX63Fzs8O/u1u7sV5TUlLYtWsXSUlJ5KVUEL2npMZxCqWC7r29+Gzjs+3mswnaBi2lyNeE9zCKIr+v70WhyNsqzfFl+uKLL8jLy+PRRx/F07NlXagZGRn8572NnP497g/9fZtfo0IhYeZgRWlZRY1bA5IE9z8+hgV/Hd8MElenrKyCubO/qHWPGcDaWqbfgD/Ou7i40LNnT3r16kVenpZnn/kZjaYC3U3lmBQKCRMTBZ988gA9m9GtLcsyK1asIC0tjbvuuotx4+oue9uWkWWZiIgIlj21mrxUDbXEZALw8dq/0muwX8sJJ2j3tJQiXx3eyyiK/IG+ke1SkYuo9XaIi4sLXp7ehCqvo9PWXZa1Cp0MxTe1Nv0zsgxbV4dw32OjMFXX3vXMGJiZqXj8ibF88a/fbzlXuccMb7w5D7VZKVFRUcTExJCRkUFGRgYHDx4kKsqCsjLtLc8uer2MVqvnww93MGf2QA4duEhxcTk+vs5Mn9GPPn29jWJRVqXWmZqaEhwc3OT5WhNJkuji2428FE2d45QmCkL2RAhFLmiT6GQJXS0NnhoyR3tFKPJGUuXIaC1XY/feXvVW4gAopEptXYe8xYVlXLucRkALBMbNmNkfExMlK1dWzyP38LDj+Rcm03+AL1AZjFVaWkp0dDRRUVFERFwjO6v2MrB6nZ6UxBy++mKP4b2E69kcOnCRaXf34/kXJzfpdybLMocPV1Z1GzJkCBYWLbcv31yUl9evlGtdRXAEAkHrIRR5OyV4fE/sna3Jzy6qV06zJIFcDwVWn7mMxdRpfZg4qRfhYdfJz6/sHtajZ6dbFK25ubmhzeqBAxGEntlR84SyDDU82+hubCX8tj0Mvy7OzLxnYKNlvnTpEunp6ajV6nZvjVdha2+Jjb0FBbk175FD5QOSb/fmjz0QCBqDDgW6JhYqra3WR3tAVIRoJK1tkZuolLz9zUJMzVS3LQELINejtY/aTIVvN1cjSFd/TEyUDBzkx7jxPenZ6/YtQO3t6967kqjjxyHBul9PNvphRZZlDh06BMDQoUNr7DnfHlGaKJn24HCkGsr7AiCBSq1i7KwBLSuYQFBP9LLCKEd7pf1KLsC/jzdPfTAeN38T1JYmqNQqJKn6w0VVpbm5i0fSNdCj1g5pCoXEpNkDsbBU13i+rRAU5ImtbS0KVK4zVgtkSE8vIDOjoFFrR0VFkZmZiVqtZujQoY2ao60y8+G7sHG+9aFQoVQgSRIvfnw/ljYd48FFIOhoCNd6I2lti7yKxLSr+A5U8/hrEwkODiYtMYcdv5zgxJ5IKjRaugV5MmPBXfQN7kpqYg5/W7iCnOwiQ1vVqkIyAX28eOT5ia36WeqDiYmSBQtH8OW/99x+cC0kJyfj4mrToN+dXq837I0HBwd3uHrlBw/vp/t4M3KumpN1RUt+TjFI0P+u7tz79Hh6DRJBboK2y53uWm82Rf7+++/z22+/ER4ejqmpKXl5ebeMSUhI4KmnnuLgwYNYWVmxcOFCli1bhonJH2IdOnSIJUuWEBUVhZeXF2+88QaLFi1qLrHbFTk5OSQlJRkaZQC4eTmw+NXpLH51+i3j3b0c+GbTs+xcf5q928IozCvBzdOeqfMGM3Z6X0xN28dz3cyZ/SkpKefHVUfR62WUysoGJpWF7er6Y5RRmcK27es5esyOXr160bt3b5ydb60BoNfLhJ25xr5dF8jOLMTEVEepLgtXDzVDhgxpts/WGkRERHD+/HlMTBW8/NEiPD09KS4sQ22mavYMBoHAGOhpetR5A0KH2xzNdufWaDTMmzeP4OBgvv/++1vO63Q6pk2bhpubGydOnCA1NZUFCxagUqn44IMPAIiLi2PatGk8+eST/PLLL+zfv5/Fixfj7u7OpEmTmkv0etEWLPILFy4A4Ofnh7W1db2usbGz4L7HRnPfY6ObUbLmRZIkHnhgGFOn9uXAgSgyMwuxt7dk1MgAXnj2Z7IyC2vcB5ckiQGDnFGrs8jLy+PYsWMcO3YMV1dXQ5MaW1tbNOVa3n11HWdCYlEoJfQ6uTJYUJao6GGNQtE+HnjqQ25uLjt2VAYPjhw50lB73boFq+QJBIKm0Wx3pHfffReAH3/8scbze/bs4eLFi+zbtw9XV1f69u3Le++9xyuvvMI777yDqakpy5cvp3Pnznz66acABAYGcuzYMT7//PNWV+StjSzLBkXeu3fvVpamdbCzs2D27EHV3vvHsvm8+Nz/KC4uNyjzqpajw+/qzpvvzEYv6wz17GNiYkhPTyc9PZ19+/bh4+NDTIRM6KkkAPQ3ggSrDP2YSzms/Gof/7dkcst90GZCp9OxceNGNBoN3t7ejBw5srVFEggahR4F+ia61pt6fWvSaqZFSEgIQUFBuLr+ESU9adIknnrqKaKioujXrx8hISGMH1+90tikSZN4/vnn65y7vLzc0BQCKqv/GJvWtsiTkpLIzc1FpVIREBDQKjK0Rfy6uPD9T4+zfes59u+LorREg7ePI3fP7M+IUQEolQqUKOjVqxe9evWitLSUixcvEhERwfXr14m9cp3QE5XWd03Issxvm8+y4LHRWFm3733yQ4cOkZycjJmZGbNnzxZtTQXtFuP0I2+/3/9WU+RpaWnVlDhgeJ2WllbnmIKCAkpLS2tN/1m2bJnBI9BRqbLGAwMDMTU1bWVp2hYOjlYsfGQkCx+5vYVpbm7OgAEDGDBgAPn5+az9ZT+n5cg6r6mo0BERdp3gkf7GErnFiYuL49ixYwDcfffdbablrUAgaDgNegR59dVXkSSpziM6Orq5ZK03S5cuJT8/33AkJiYafY3WtMh1Oh1RUVHAnetWbw5sbW3p2qVbvcZqG1JVr41RUlLC5s2bAejXrx89etTcelcgaC/okYxytFcapMhffPFFLl26VOfh51e/NBU3NzfS09OrvVf12s3Nrc4xNjY2dRbjUKvV2NjYVDs6EjExMZSWlmJlZUXnzp1bW5wORfdAj3qN6xbg3sySNA+yLLNt2zYKCwtxcnJi8uT2v9cvEFS51pt6NIQjR45w99134+HhgSRJbNmypdr59PR0Fi1ahIeHBxYWFkyePJmYmJhqY65evco999yDs7MzNjY2zJ8//xadVx8aJLmzszMBAQF1HvV18wYHBxMREUFGRobhvb1792JjY2OwEIKDg9m/f3+16/bu3dsmSmO2pkVe5VYPCgoS+5pGxtPbkX6DOqOsrXCOUmLwsG64edi1rGBGIjQ0lMuXL6NUKpkzZ47YlhF0CKryyJt6NITi4mL69OnD119/fcs5WZaZNWsW165dY+vWrYSFheHj48P48eMpLi42XD9x4kQkSeLAgQMcP34cjUbD3XffjV7fMI9fs+2RJyQkkJOTQ0JCAjqdjvDwcAC6du2KlZUVEydOpEePHjz00EP885//JC0tjTfeeIOnn34atbqyutiTTz7JV199xcsvv8wjjzzCgQMHWLduHb/99ltzid1gWlKRy7JMcXEJV65cAYRbvbl48Y0ZPPPwd+TmFHNzqTOFQsLF1ZYXXrs1R789kJGRwZ49lYV0xo8fb/B8CQSChjNlyhSmTJlS47mYmBhOnjxJZGQkPXv2BOCbb77Bzc2NX3/9lcWLF3P8+HHi4+MJCwszeI1/+ukn7O3tOXDgwC2B3nXRbIr8rbfe4qeffjK87tevHwAHDx5k9OjRKJVKduzYwVNPPUVwcDCWlpYsXLiQv//974ZrOnfuzG+//cYLL7zAF198gaenJytXrmwTqWct2cY98sw11q84ROiRaPQ6GXM7Bd0HO9RYyETQdJxdbBg51YZzJ4vJyzCltFSHvYMlU2f2Z8bcQVi3w1KlFRUVbNy4Ea1WS9euXTtcURvBnY1eltA3tSDMjev/nOWkVqsNxmV9qcqaurkCpEKhQK1Wc+zYMRYvXkx5eTmSJFWb28zMDIVCwbFjx9qGIv/xxx9rzSGvwsfHh507d9Y5ZvTo0YSFhRlRMuPQUq71PRvO8PnSdSgUCkNOc2menvN7svh4ya+8/PkDwr1uZOLi4sjKTsUv0ITnv30GS0vL1hapyezZs4eMjAwsLS2ZNWtWq5cWFgiMid4IJVqr8si9vKq3cX777bd55513GjRXQEAA3t7eLF26lG+//RZLS0s+//xzkpKSSE1NBSobL1laWvLKK6/wwQcfIMsyr776KjqdzjCmvggN0IbJTM3ji9fXg1zZRvLPHP7tPPs2nW0FyTo2R44cAaB///4dQolHR0cTGhoKwD333NMhPpNA0FwkJiZWy3paunRpg+dQqVRs2rSJK1eu4ODggIWFBQcPHmTKlCkGw8vZ2Zn169ezfft2rKyssLW1JS8vj/79+zfYOOs4tSZbmJawyHevPVVnOy9JIbH1p2NMnDuo9kGCBpGQkMD169dRKBQMHz68tcVpMgUFBWzbtg2oDB7t0qVLK0skEBgfY7QhrbreWJlOAwYMIDw8nPz8fDQaDc7OzgwZMoSBAwcaxkycOJGrV6+SlZWFiYkJdnZ2uLm51Tv7qwqhyNswVy8m19k7W9bLxF9ORZZl4So1EkePHgWgb9++7T5tUa/Xs3nzZkpLS3F3d2fcuHGtLZJA0CzokNA1MQ+8qdfXRlWxpZiYGEJDQ3nvvfduGePk5ATAgQMHyMjIYMaMGQ1aQyjyRtISFrnKVIWkkAwtR2tCqVIKJW4kUlNTiY2NRZIk7rrrrnpfJ8sy2RmFaLU6nFxsMFEpm1HK+lMVFatSqZgzZw5KZduQSyDoCBQVFREbG2t4HRcXR3h4OA4ODnh7e7N+/XqcnZ3x9vYmIiKC5557jlmzZjFx4h/toletWkVgYCDOzs6EhITw3HPP8cILL+Dv37CqkUKRt2EGjw3k2O4LtZ5XKBUMHSuqchmLKms8KCgIe3v7el2z/7fzrP3+CAnXMgGwtjXn7nsHc9+jI1u1BWhSUhIHDx4EYOrUqTg6OraaLAJBc2NM13p9CQ0NZcyYMYbXS5YsAWDhwoX8+OOPpKamsmTJEtLT03F3d2fBggW8+eab1ea4fPkyS5cuJScnB19fX15//XVeeOGFBssuFHkjaQmLfOTUPvz3s93kZBbeGuwmVcowZ/GoZlv/TiIjI4NLly4B1Nsa/9/yg/xv+UFu/goU5pfy68ojRJy9zvvfLGiVHu9lZWVs3LgRWZbp1asXffr0aXEZBIKWREfTXeO6Bo4fPXp0nWnIzz77LM8++2ydc3z44Yd8+OGHDVz5VkTUehtGbaZi2X8fx9Hlxl6tVHlICgkTEyWvfP4A/n28W1XGjkJVA5EqN9ftSLiWyf+WV1q8f/5blvUykefi2d0KGQWyLLNz507y8vKws7Nj2rRpYutFIOjgCIu8kbRUHrmnnwsr973Cey//i7iobDp5eNI/OJCJ8wZh72TdrGvfKeTk5BAZGUlpgZ74MxXM/+Rdyssq8O7izPS/DGP8rP4oTarvL+/aGIpSqUBXQ1ogVCYbbFtzihn3tWzhlQsXLhAREYEkScyePbtaQQqBoKPSGq71toRQ5O0ApYmE2rmMbiPMePbZB+q9fyuoH8eOHSM/TcvlQxqQIw3K+eqlVP712gZC9kXxxpcPGYLYdDodMdGJtSpxAGRITcxpdtnLSjWUFJZhbWdBYVGBocDS6NGjbylsIRB0VEQ/ckGjaMmmKVlZWeh0OtRqNXZ2ds2+XkdHo9ESsjeKxKsZoNATee0cl4+Uo9dLyDc1K6jKFjh98BKr/rWdgKGOXL9+nYSEBFLTyiu3OurI81ebN1+wW9ylFH754ndC9kSg18uYmqnw7GmFXVcd3QI7NyjqXiBo78hGaEMqt+M2pkKRN5KWrLVeVa7Pzc1N7Hc2kTOHo/n4pbUU5pWgNFGg1+lv2uOu+Xcqy7D9fyEkF5sbfv7undVkJ5fVuo5SqWD05CAjS19J5JlrvPbgN+i0ekOdAU1ZBdfO5WJ6ScGjj44VZXsFgjsIocibSEso1rS0NADRraqJXAq/zrtP/WTIANBp698qsKIU/Hy6E9CzKz4+PjjYO/L0fd+QdD3LUAO/ispgRAX3PDjMqPJDZZGXfz73P7QVulvrC8igLZNZ/dl+3lj+sNHXFgjaKne6a739St7KtKRFLhS5cVj91X5k+dYo8/oya9ZMBg8ejKurKypTE5Z9u4guAe5ApQWuNLlR4tHWnPe/WYCXr5OxRDcQdvQKmSm5tRYJ0utlTuyJICejoMbzAkFHpKr7WVOP9oqwyJtIc1vksiwLRW4ESorKCT16uc497dqQJInOAe7Y2FdvNuLobM2///cEUWHXOX0sBm2Fjm49PBg+rkez5Y9fj0lDoZBuW7o3OS4TB5f2XWJWIBDUD6HI2zh5eXmUl5ejVCpF//EmUFJU1iglDpUPU/MfH13jOUmS6NXfl179fRstW0MwMzdFXw+XgpmFaQtIIxC0DXRGaGPa1Otbk/YreStys1u9uS3yKmvc2dlZ1MpuArYOlpiZ10+5Vf1OlcrKP4+HnpvAqGltozra4HE9bvudU1sp0ZkUt5BEAkHrc6e71oUib+MIt7pxUJmaMHHuQBTK2r/yCqXEk2/OYNDoAIIG+zH1/qF8s+MFHnh6fAtKWjdObnZMnDeYujJlPHqbsnbdWtatW0dBgdgrFwg6OsK13ghawyIXirzp3P/0OE4duERGSm61gDdJqgyAe+K1Gcx4aBgzH2rbfcj7TnYn9IyK7GsVKJSKSvn1MjLwwLMTcA5UcOrUKS5dusTVq1cZO3YsgwYNEilpgg6LHgX6JtqlTb2+NRGKvI1Tpcjd3d1bWZL2j52DFa98OYdlf1tFVrwO+Ub2mVcXFx58ZgIjpvRuXQHrQVpaGkePHaHbKEsefHYkqZeLKcgrxtXTgfFzBuHkZgdAnz592LFjB8nJyezevZsLFy4wffp08T0SdEh0soSuia7xpl7fmghF3giKC0rJT9EAUFpUjoWFhdHXKMgtJuz4JeIjsrByNsHV1dXoa9yJRMdE0iVYzbQFPRjYdzjmlqa4ezu2i0I7Wq2WTZs2odfrCQgIYNLM0bXK7ebmxiOPPMLZs2fZv38/KSkpfPfddwwZMoQxY8ZgaiqC4QSCjoJQ5A2gvFTDqmXb2PW/42jKtQA8svfvTHpgGI++PhN1PYOp6qKspJzlb29i/4bTaCsqG+tJEnxRspan35+HpY15k9e4UyksLCQyMhKAEaOH4+Hh0coSNYwDBw6QmZmJpaUl06dPv+3Dh0KhYNCgQQQEBPD7778TFRXFyZMnuXjxIlOmTCEgIKCFJBcImhdjBKuJYLc7AJ1Wx9sLv2XbqiMGJQ6gKdfy209HefOhbwyKtylrvLlgOXvXnqw2lyzD4W1neWX+l5SXapq0xp1MaGgoer0eLy+vdqfE4+PjCQkJAWDGjBlYWlre5oo/sLa2Zu7cuTzwwAPY2dlRUFDA2rVrWbt2Lfn5+c0lskDQYsg3up815ZBFZbeOz7Hfwjl//EqNFbX0epmIkFiO/RbWpDWO7zpP5MmrNRb70OtkrkYmcWBTaJPWuFPRarWcPVvZH3zIkJZtLdpUysrK2LJlCwD9+/ene/fujZqnW7du/N///R/Dhw9HoVAQHR3Nf/7zH06ePIleX/9ytQJBW0OHZJSjvSJc6/Vk9+oTdVbUkiSJX7/eidKx9JbyrfV9ve4/xw0R1DWuoZDYvfoEU/5i/BreHZ3IyEiKi4uxsbFpdy7l3bt3k5+fj729PZMmTWrSXCqVivHjxxMUFMSOHTtISkri999/NwTDeXh4kJyay6bfwjhyMoaKCi3+Xd2YPa0/g/v5totYAoHgTkMo8nqSnphTd1lMWSYtIZsDBw40eo20xNw664DLepmk+HSioqLw9PTExsZG3FjrgSzLnDp1CoBBgwa1q8I6Fy9e5Pz580iSxD333GO0IDVXV1dDMNy+fftITU1l5cqVOHv0YMPuBHS6PzqrnT4XR0joNebdPYC/PjpGfOcEbQ693PQ97jpu720eocjria2TFWkJ2XU2S7FztKJfv36G1zff8P5886vpddLR0yTn59Td1MNEy4YNG4DKvU9PT086deqEl5cX7u7uqFTN1wO7vZKQkEBaWhomJib079+/tcWpN4WFhezYsQOA4cOH4+XlZdT5JUli4MCBhmC4sPBI1v12DZ2++ndTd+MOt377WXp0d2fcyECjyiEQNJWqfe6mztFeEYq8nkyYN4Tos/G1D5Bg3hOTmT5jRKPXMCt15uPnfq5zzKCJ3XFwV5KWlkZhYSGXLl3i0qVLQGWUsqurK56enobD3t7+jregqqzx3r17N0uqYHMgyzLbt2+ntLQUNzc3Ro8e3WxrWVlZMWfOHDIKrNBduFDrOIUksW5bqFDkAkEbQyjyejJ2ziC2rDxEclymoZ91FQqlAjdvR8bNHdykNe6a3pct3x/ialRyjWt06uzM8+8+gpmFmoqKClJSUkhKSjIcRUVFpKamkpqaypkzZwCwsLCoptg7depUb/dsemI2O34+TujBS+h1OnoN6crdC+/CN6D9RHzn5eURHR0NtK8gt7NnzxITE4NSqeSee+5pke2AtMzyOmM09LLMpZg0dDq9oQ69QNAW0COhb2KwWlOvb02EIq8nZhZqPtrwLP/860+EH7tS7VzQ0K68/NVCzC3VTVrDVK3ig1+f5t+vrOHYb+f/cONLMHB0IC989gBmFpVrqFQqfHx88PHxASotuPz8/GqKPTU1lZKSEq5cucKVK5UyS5KEi4tLNeXu6HhrQZTT+6N477Hv0etlw0NF8rVMdv5ynGeW3cvUdhJwd+bMGWRZpnPnzri4uLS2OPUiJyeHPXv2ADBu3LgWk1uhkJCQkOtoEydJzV+WWCBoKKKym6De2DvbsGztM4SFRPHdv37G1NSUF954Ch9/45W9tLK14LXlj5CZkkvEyViQIXBgZ9x9nOq8TpIk7OzssLOzo1evXkBlylVqaipJSUkkJyeTmJhIQUEB6enppKenG9KxzMzMqil2tcKSfzz+Azqtrpp1pruh0L9cupYuPTrh38/HaJ+7OdBoNJw7dw5oP9a4Xq9n8+bNVFRU4Ovry9ChQ1ts7f69vdl/NLqOETJujqZkZ2eJlroCQRtCKPJG4Opjj2sPc6ytrY2qxG/G2cOesbMHNWkOExMTvLy8qgVJFRQUGJR6cnIyKSkplJWVERsbS2xsLACJZ0uoqNDW2r9bqVCw5YfDvPLlgibJ19xcuHCBsrIy7O3tG5173dIcO3aMpKQk1Go1s2bNalHrd/zIQL797xGKistrydCQcLEtZPny5QwaNIjRo0djZmbWYvIJBLUhgt0EDUanq6y6ZmLS/n58NjY22NjYEBhYGbCk0+lIT0+v5pKPTM6vVYlXXqPn3JG6LLfW5+aUs8GDB7cLd3BqaiqHDx8GYOrUqdja2rbo+uZmpnz89lxefHs9xSUaw9aOUimh08k8OGcgNqbpXL58mVOnThEREcHYsWPp16+f6KwmaFX0GKFEq9gjv7OoUuTtKR+5NpRKJR4eHnh4eDB4cGWwXuyeT4jJSKzzupKSEj777DM8PT3x8PAw/L+tNOO4du0aWVlZmJqaVksJbKtUVFQYGqL06NGDoKCgVpEjsJs7q5cvZseeCI6diqFcoyWgmxuzpvTDv0tl456rV6+ye/dusrKy2LFjB2fPnmXy5Ml4e3u3iswCwZ2OUOSNQKutrLXeHi3y+tDvLn+uRiah19VSxU4BNu6qW9LfJEnC2dnZoNg7deqEi4tLq1hrVdZ43759UaubFoTYEuzfv5+srCysrKyYNm1aq3oQ7GwseHDuEB6cW3NcQZcuXXjyySc5c+YMhw4dIjU1lVWrVhEUFMT48eOxsbFpYYkFdzqyEaLWZWGR31l0JIu8JqY+OJyN3x4ASa7RxS7r4eUPF2PXyYzk5GTDkZ+fT0ZGBhkZGYSHhwOVDzseHh506tTJcNja2jarosrOziYmJgZoH0Fu165dMzx4zJgxo13kuiuVSoYOHUpQUBD79+8nLCyMiIgIoqOjGTFiBMHBwdUedCt0Oq4kZ1Kh1ePn5oCNhdhbFxiPO737mVDkjaCjW+Sung688tVCPnr6J2QwpJ8plQp0Oj2Pv30PfYb5AxjS36CyEllVbntKSgrJycmUl5eTkJBAQkKCYZylpWU1xd6pUyejBk1VKcXu3bvj4OBgtHmbg7KyMrZu3QrAwIED6datWytL1DAsLS2ZMWMGAwcOZPfu3SQmJnLgwAHCwsKYOHEi3bt355dDYazae4acolIAVEoFUwcFsmTWSGwthUIXNB0R7CZoMB3dIgcYMa0vvv7ubP/xKGcOXkSn1dM7uAt3LxxZa9qZtbU1/v7++PtXKnlZlsnOzjakvyUnJ5Oenk5xcXG13HYAR0fHaordzc2t3j/fzJRcdvz3GMd3X0BTVoFeXYJLgClDHmr71vjOnTspKCjAwcGBCRMmtLY4jcbDw4OHH36YiIgI9u3bR25uLmvXriVe4UJYRlm1sRU6PTtOXyQiPpX/LrkPK/O2v/UhELRlhCJvBFUWeUdW5ABeXV35v3/MbfT1kiTh5OSEk5MTffv2Bf7Ibb/ZJZ+bm0t2djbZ2dlcuFEiVKlU4ubmVk25Ozg43OKSjzx1lTcXflupwPV/FNDJjtNwwCuCzq93brMR61FRUURERBi9IUprIUkSvXv3JiAggKNHj7LryCnCsspqHKvTy8Sn5/Lr4XAem9z2H7gEbRvhWm8m4uPjee+99zhw4ABpaWl4eHjw4IMP8vrrr1e7YV24cIGnn36aM2fO4OzszDPPPMPLL79cba7169fz5ptvEh8fT7du3fjoo4+YOnVqc4l+W9pz+llrU1Nue0lJSTXFnpycTGlpqeHfVZiZmVVX7HZOvPPId5SXVVTvE3/jnxtXHKRbby9GzWh7jVJubogyYsQIPD09W1ki42Fqasq4ceM4lqpBcSqa2jqd62WZ9ccvCEUuaDKiRGszER0djV6v59tvv6Vr165ERkby2GOPUVxczCeffAJUFieZOHEi48ePZ/ny5URERPDII49gZ2fH448/DsCJEye4//77WbZsGdOnT2f16tXMmjWLc+fOGSqYtTR3gmu9JbGwsKBbt26G/WFZlsnNza2m2FNTUykrK+Pq1atcvXoVgLRL5RQXVlp8sgSyUkLS/hF7KikkNn13qM0pclmW2bp1K2VlZbi7uzNy5MjWFqlZyC7W1KrEq8jIK0KW5TbrNREI2gPNpsgnT57M5MmTDa/9/Py4fPky33zzjUGR//LLL2g0Gn744QdMTU3p2bMn4eHhfPbZZwZF/sUXXzB58mReeuklAN577z327t3LV199xfLly5tL/Drp6MFurY0kSTg4OODg4GDIp64qXHOzco9Ji6fEy5SsodYUdzEDhYQqT4t9aBEOoUWgl7lyPgFthQ4TVdt56AoNDeXq1auYmJgwe/bsDvtAaGthhlIhGdqg1oSZiYLy8nJRIU7QJO5013qLhunl5+dXiyIOCQlh5MiR1VztkyZN4vLly+Tm5hrGjB8/vto8kyZNIiQkpNZ1ysvLKSgoqHYYE2GRtzxVhWsGDRrErFmzePrppzEd1oXrDzpT7FepxAEqbJVkjLUl4V4nqoJQ25Kxl52dbWiIMn78eJyc6q6h356ZMjCgTiUuIeNhUsbnn3/Ovn37KCoqakHpBB2JKkXe1KO90mKKPDY2li+//JInnnjC8F5aWhqurq7VxlW9TktLq3NM1fmaWLZsGba2tobj5v1YY3CnBLu1ZXJKSzjsXVz5QnnTH6AkgUKixEdNziAr7DzMuBJz5Y9Ocq1IVUMUrVaLn5+foZJeR2WovzcDunRCUcOTlEKSMDM1YainFRqNhuPHj/PFF1/w22+/kZeXV+ucFTodhaW11YIXCO5MGqzIX331VSRJqvOo6v9cRXJyMpMnT2bevHk89thjRhO+NpYuXUp+fr7hSEysu9xoQxEWeeuz/nIkeuTazW0JcgZa4RyoZN26daxatapaLntrcPToUZKTkzEzM2PmzJkdfl9YoZD495OzGN27C1D1jFX5mT2dbPnxhft56dn/47777sPT0xOtVktoaCj//ve/2bx5M5mZmYa5rqVns3T1bga/9hXD3vwPd731DZ/tOEJucWmrfDZB2+JOt8gbvMn74osvsmjRojrH+Pn5Gf6dkpLCmDFjGDZsGCtWrKg2zs3NjfT09GrvVb12c3Orc0zV+ZpQq9XNWpZTRK23PpeyM1FIErraLG1JQmtrwpS/jCbs9BkSExNZtWoV/v7+jBs3rsXacFYFciUnJ1driHKnlDG1NDPls8V3k5CZx/GLcWi0Onp4uTKwm6fhQcbf35/u3btz/fp1jh49yrVr17hw4QIXLlwgICAAxy6BLN14mAqdFlvnfLy7p2HjUMwV7Wme3b6b9yc/i7dd7fcDQcfnTt8jb7AmcnZ2rvdNMDk5mTFjxjBgwABWrVp1S83t4OBgXn/9dSoqKlCpVADs3bsXf39/7O3tDWP279/P888/b7hu7969BAcHN1R0oyFc662PmdIECYk627QB3bt2ZfjgIRw+fJiwsDAuX77MlStX6NevH6NHj8ba2trosqUmZLHpu0Mc2BxKSVE5Tm62OHZVYuOrp3ffXq3WEKU18Xa2w3tU7c1rJEnC19cXX19fUlJSOHbs2I06/tHsCk2iXJYIGBiHb2Aaej0oFCDLYG0fw1dX/8bzPf6Bh7lvy30ggaAN0WwmZXJyMqNHj8bHx4dPPvmkmpusypp+4IEHePfdd3n00Ud55ZVXiIyM5IsvvuDzzz83jH3uuecYNWoUn376KdOmTWPNmjWEhobeYt23JMIib33G+3ZlTXREreclWcZDo+N/P/0XJycn+vfvz8MPP8yJEyeIjo7m3LlznIu4QHHPzpzUFZBSXIi1qZp7/HryaOAgOlk1zmKOjUzklfu+pqy0wlDaNistn6w0sLpkwrPPjG3UvHcSHh4ezJ8/n6ysLH7csZeSyDQ6dUnHN7AyLkbxpyBGvaKclVff57Ue/8FEoWolqQWtiUzT88Dbc9RFswW77d27l9jYWPbv34+npyfu7u6GowpbW1v27NlDXFwcAwYM4MUXX+Stt94ypJ4BDBs2jNWrV7NixQr69OnDhg0b2LJlS6vlkIPYI28LjPHuTDd7x9q/wJLEbHcfVCoVWVlZ7Nmzh59++gmlUsmkSZNw9vRgq7OCdXkJJBTmo5X15JaX8lP0WaZs/4FLuRkNlkmv1/P+//1UTYnfTEmujtX/2tvgee9UnJyccPLthkKCzj1SqG0XRaGAIl0ekfmnW1ZAQZtB7JE3E4sWLbrtXjpA7969OXr0aJ1j5s2bx7x584wkWdMRrvXWR6lQsHLCDGb9+gO5SgklElV/h0pJ4qPRk5jdvSfl5eVERERw7tw5UlNTiYqKIioqinMeluSqb30M0MkyxVoNTx3azMFZjzcoIC38eAxpCdm1ntfrZfZtDOXRpTOwtDFv8Ge+E1GrTFCqtFjb1R3UJqHgWnEUfe2HA1CuK+Fq0WlKdfnYqFzxsxqIUhIetI6K2CMXNBjhWm8bXDt/gRlZJRQ42aHq4U+5TkeAoxPzAnrhZG4JVAY+Dhw4kIEDB5Kamsq5c+c4GxlBpEpba/9hnSwTX5jHibTrDHf3rbc8sZFJKJSKGq3xKio0WhKvZhBQS+MZQXVGBfrxyW/1cHrKlX+XsixzOnsDxzN/QSuXw404CgulLRPc/4q/zV3NLbJA0OIITdQIhGu99SkqKiIkJAQF8NiYCfTo0eO217i7uzNt2jRcBwTxw55f6xwryTI/7dtF14mzb6ljAJXR6BUaHSpTpcFqV6mU9cpXV5mKP7v64uFgw/hevSjMj8TSprTWbENZ0nPpcByr/T8k2frIzWcAKNHlszXpfeZ4vUsX646dv38nIixyQYMRJVpbn8OHD1NRUUGnTp0IDAxs0LXmpvVLTczNymb58uX06dOHMWPGYGtrS05GPhu+Pcjv605RUliGiUqJu7cj/Ub4M2Rcz+rNW2rAwcUGX3+RKtUQ3p03kWc3hiLZ1hzcKMkSUoUJ+mRTkvoeqyPkSeJQ+vf4WQ3q8Dn8dxp3uiJvv53UWxFhkbcuOTk5nDt3Dqgsc9rQm7K/nTNOZhZ1jpEliSAzWwDOnz/Pl19+yfrVW3h6yids/fEoJTeatWgrdCRezWDbj0d5/aHlmNsqqSt4dv5T41CaiO9NQwg9dRL3aD26azfKO990w5VQoFaa8389/s6wmf5IJnW1aZHJ1iSQVR7frPIK7gyOHDnC3XffjYeHB5IksWXLlmrn09PTWbRoER4eHlhYWDB58mRiYmKqjUlLS+Ohhx7Czc0NS0tL+vfvz8aNGxssi1DkjUBY5C3P5awsNkRFsS06mm1796LX6+natSu+vr4NnstEoeDJXrW3zpRk6FSiQ87IwcrKCmdnZ3Q6HRv+fZy8nKI698BL83WY21R+LxTKyj8v5Y3/z148mhmLRjRY3jsVWZbZt28f+/fvRyFJTLWdzyOdXyPQtj92Kidc1J6Mc53DSwH/wtuyKw6uVkj1uKWV6PJbQHpBSyLLklGOhlBcXEyfPn34+uuva5BHZtasWVy7do2tW7cSFhaGj48P48ePp7i42DBuwYIFXL58mW3bthEREcHs2bOZP38+YWFhDZJFaKJGICzyliMhL48Xf9/N2ZQUw3uSLNPVTM2i0aMbPe8jgYO4XpjHz5fDUEoKdLIehSShl2V6O7mztFMQIQcPUVBQQFFREZYqewrS8uo1t4nClHd/eJijv4VTmF+Cm5cjk+4dQucAj0bLe6chyzI7d+4kNDQUqPS8DB9eGZEeYFNzYRkblQvybRunVo4TdCxaox/5lClTmDJlSo3nYmJiOHnyJJGRkfTs2ROAb775Bjc3N3799VcWL14MVLbp/uabbwx9F9544w0+//xzzp49S79+tRdQ+jNCkTcCkX7WMmQWFzNv7RpySqunHsmSRKypKX8/c5qVHrMatd+pkCTeGzKR+V17sybmPNcL87BXmzOjcyBjOnXBRKGgf4+eHD9+nOPHj5MQk377SQGtlURhbglWtua8+OkDDZZLUJmPv3XrVi5cuADA9OnTGTBgwG2v87MahJnCijJ9zV3UJBR4mAdgbyoeqAS18+dumY0p+V1eXg5QrT2vQqFArVZz7NgxgyIfNmwYa9euZdq0adjZ2bFu3TrKysoY3UAjRSjyRiDSz1qG78+dJae0tMZ66jJwMC6O08lJDPFsfHe7IEc3ghxrDj4zNTVlzJgx9OvXj5X/WkMMl287X9YcS/RqSC0u4PZx9II/o9Vq2bhxI9HR0SgUCmbNmlXvkrYmClMmuD/N9uSP4E/leyUUKCQlY92eqPV6QfvFmMFuf+6W+fbbb/POO+80aK6AgAC8vb1ZunQp3377LZaWlnz++eckJSWRmppqGLdu3TruvfdeHB0dMTExwcLCgs2bN9O1a9cGrSc0USMQrvWWYUNUVO1NUags/LLx4sUmKfL6YGdnx1MvLuLIr29SUa6tdZxeCaVdTdCbSvw9/yADNd2xNRWFX+qLRqNhzZo1xMXFoVQqmT9/Pt27d2/QHIG2ozGRTDmU8T25mj+2Y9zMujPe/SnczRs2n6B90Jg97prmAEhMTKzW1KgxDbhUKhWbNm3i0UcfxcHBAaVSyfjx45kyZUq1FNU333yTvLw89u3bh5OTE1u2bGH+/PkcPXq0QT0ZhCJvBCLYrWXILa27mpdOlskqLmkRWSytzZj18Eg2fHugxlKhsgQFw9XozSuDrdLLi1hz7RxPBAxvEfnaO6WlpaxevZqkpCRMTU2577776Ny5c6Pm6mYzjK7WwaSXXeWHn5ejLTRh+iOvYG9ub2SpBR0RGxsbo3QnHDBgAOHh4eTn56PRaHB2dmbIkCEMHDgQgKtXr/LVV19V20fv06cPR48e5euvv2b58uX1XktErTcCYZG3DE6WlnWeV0oS7tZWLSQNLPzbVMbeU/lHKEs3jht/QUV9Tcme9kdKmx6Z9XENizy9UykqKuKnn34iKSkJMzMzFixY0GglXoUkSbiZd8W2ojNykTm5ublGklbQFmnLtdZtbW1xdnYmJiaG0NBQZs6cCUBJSaUR8ueuoEqlEr3+9kGbNyNMygYQGpXA2t/PcfKKGZIE2p+P8OD0Ifj7iijY5uC+Xr34+vRp9LW413WyzNwbT7ItgdJEycRFQWRURHMmXYO2TEZro6BogJpyr1v/lLLLi2uY5c5Foy8ntvASGrkcdzMvXM08yM/P5+effyY7OxtLS0seeuihGivpNRZ7e3uysrKEIu/gGNO1Xl+KioqIjY01vI6LiyM8PBwHBwe8vb1Zv349zs7OeHt7ExERwXPPPcesWbOYOHEiULmP3rVrV5544gk++eQTHB0d2bJlC3v37mXHjh0NkkUo8nryw+aTrNh4AqVCQqevfII6cDqW/adiefOJSUy5S4Q2GZtF/fqz+dIlkgsKbmkxKAHT/f3p6+Ze06XNQmpqKtu3b8fSyQS38e6Ea3Pq3MN3MTN+r/P2iF7WsydtMwcydlCuLzO8763ugu6IJaXZGmxtbVmwYAEODg5GXdvevtKdLhR5x0Y2gkXdUEUeGhrKmDFjDK+XLFkCwMKFC/nxxx9JTU1lyZIlpKen4+7uzoIFC3jzzTcN41UqFTt37uTVV1/l7rvvpqioiK5du/LTTz8xderUBskiFHk9CI1KYMXGEwDobirBWfXv91b8Tu/uHnRysWsN8Tos9ubmfDI0mL/9toMkExNDA2pzExMW9O3Hi8OHt1ipzeLiYtauXYtWq6Vbt26MGjSQx0+srf0CWaZniSkajQZTU9MWkbGtsjnpvxzJ+v2W9xNKryL1U+BzoR8Pz38YW1tbo68tFLmguRg9enSdvRWeffZZnn322Trn6NatW6Mquf0Zocjrwdrfw25Y4rX/0jbtv8Az949sQanuDC6FhjKutIyu/frh0rMHpkolAz06YdmCylGn07Fhwwby8/NxcHBg9uzZmKrVjHLrytG0q+j/5C9QAFYasLiWzIqUFcyZMwd395bzHLQlMspSa1TiAChAVulxHmfZLEochCK/U5Ch1n71DZmjvSKC3erB+SvJdSpxvV4m7FJiC0p0Z5CQkEB8fDwKhYLpo0YxoUtXRvl2blElDrB3717i4+MN0dRmZmYoJImvgufxQJcBmCr+CHpUIDG+UwArB8/HwdKa7OxsVq5cSUhISL06o3U0zuQcQVHXbUaCsIIQdHLtaX1NQSjyO4Oqym5NPdorwiKvB8rbum9lUlNTWLVqFT4+Pvj6+uLl5YVKpWoR+ToqR45UtqPs27dvs1lsN1NWquHwtjAO/xZOcWEZ3l1d6TbQibDoUwDcc889ODs7G8arlSa81W8Kz/UczbmsRLSyniB7D9wsKlNXuj/pzfbt24mOjmbPnj1cu3aNmTNnYmXVcpH2rU1+xe0VqFauoFRXgpVJ01N+/oydnR0AZWVllJaWYm4u8voFHQ+hyOvB0N6+7AmJrsMql3Cy0JKQkEBCQgJHjx5FoVDQqVMnfH198fHxwcvL647fK20IycnJXL16FUmSuOuuu5p9vYzkXF554BvSEnOQJAlZlomNTGLfRhnXABUL/jaBgICAGq+1NTVnjMethUYsLCyYP38+Z8+e5ffffyc2Npbly5cza9asBlduaq9Yq+xuO8ZEMsFMUXc3usZiamqKpaUlxcXF5OXlCUXeQWmNqPW2hFDk9eDeyf35/UR0jeckCdQqE9554V7yc9K5fv068fHxFBQUkJiYSGJiokGxe3h4GBS7t7e3UOx1cPToUQB69+5tcI82F7Is885jP5CRkmd4DaDXVf4/PbqCkpSGV3eCynzmgQMH4u3tzcaNG8nIyOCXX34hODiYcePGdfhaBIMcRrAvfWut5xUoGGB/FyaK5rsV2dvbU1xcTG5u7h0bq9DR0csS0h3cj1wo8noQ0NmVN5+YxHsrfkeWZUNQhUKSMFUp+fRvs+ji2wl8O9G/f39kWSYvL4/4+HiDYs/PzycpKYmkpCSOHTuGJEm3KPbGlALsiKSlpXH5cmVd85awxiNOXSUuOrX2ARJsWHGYqQ8E31K8ob64uLiwePFi9u7dy5kzZwgJCSE+Pp45c+bg6OjYSMnbPjZ6e2zSXClwTb+lT7sCBWqlGRPdZjWrDPb29iQlJYl9ckGHRSjyejLlrh4EdfPgk+82cykuEzs7Wybd1YeZY4JwtKtegUySJOzt7bG3tze0ovuzYs/LyyM5OZnk5GSOHz9uUOxVe+yNUeyyLJOUkUeFVo+Hsw1mpu1zj/7YsWMA9OzZEycnp2Zf79yxGJQmCnTaWqopyZCelENmSh6uno3Pc1apVEydOhU/Pz+2bdtGamoq3377LVOnTqVPnz4tlkrXUmg0Gn799VeUSXbY9JEo8c1GK1cYzncy9+UvPk/ipDZeAZhbZNAVovaKwNvlBKk24YRnnaKLzRysTX2abU1ByyPLRohab8exqEKRNwBPVzuGdjXHXlfKtGljDTVz64OdnR19+/alb9++QKVir1Lqf1bsJ06cQJIk3N3dqyn2m1vi3Ywsy+w4GsWq7adIysgHwFyt4p4xvXn8nmAszNqPCz8rK4uoqCgARowY0SJr6nX1K4dYq6JvIAEBAXh4eLB582bi4+PZunUrV69eZdq0abX+jtsbOp2O9evXk5SUhLmZOQ8P+z9sHK24XBiJRl9Z2c3TwrdZZcgtj+ZIyv+hsc/HksqSurH5a4jJX00/p5fpaju/WdcXtBxij1zQIAoLCwGwtm5a1S47Ozvs7Ozo06cPAPn5+dUUe25uLikpKaSkpBASElJZO9rNzaDYfXx8DDf97zaHsHLryWrzl5ZXsGbPOcIuJ/Ht0vmYqduHdV61N+7v72/UUp11EdDP57ZK2tbBEpdOxturt7Gx4aGHHuL48eMcPHiQyMhIkpKSmDNnDp6enkZbpzWQZZlt27YRGxuLiYkJDzzwAC4ulWWM+9gNbhEZtPoSjqY8TYW+EKQ/vPoylX0SwrI+wsa0My7mg1pEHoGgORGKvIEYS5H/GVtbW3r37k3v3r2Byub2VUr9+vXr5OTkkJqaSmpqKidPViptNzc3LO3dWflbbI1z6vUy0fHprNsXzoJpbf+GlZOTQ0REBAAjR7ZccZ0hYwNxcrMlOz2/RveaJEnMWHgXJirjBqYpFApGjBhB586d2bhxI3l5efzwww+MGTOG4cOHN3o/vqUp0RajR4+lsjKtbs+ePVy4cAFJkpg3b94t/Z1bgoSi3ZTr82o9L6Hkct7PQpF3EIRFLqg3er2eoqIiwPiK/M/Y2NhUU+yFhYXVFHt2djZpaWlcjMpFQoVcSzEDWYaNB863C0V+7NgxZFmma9eueHh4tNi6ShMl85cE8+3ru9FpMZR4khQSsl5mwMjuzHtiTJ1zNAVPT0+eeOIJfvvtNyIjIzlw4ADXrl3jnnvuMUo7xeZAlmVCc0PYk76D5NIEAJxMXfAp6k7iyTQkJGbOnNngfuLGIq3kJJV2eM0bnzI60ktOIctyh4tNuBMRUeuCelNcXGz4w7e8TYtNY2NtbU1QUJCh2XxhYSHXr18n5pfjyPl1d9lKzSogJSUVNzfXRlt5xWUa8opKsbMyx7IZ9tzz8/M5f/480LLWOEB2djYRV07Te6Yl5mWdiDmbQUlxGZ6dXZj+0DDGzOiH0qR508TMzMyYPXs2Xbp0YefOncTHx7N8+XJmzJhRa/56a7I9dQO707Yi3fQAmaXJIEuVgbqfNXOdHjRsG7UGlS70uqOXZIwT8yBofUSwm6DeVLnVraysWt3taW1tTa9evejim8jVtLqK1YBSkvnuuxWYmprSqVMnvLy88PLywtPT87bBVddSsvlmewgHz8ei18soFRLj+nfjyenB+LoZr1PV8ePH0ev1hqp4LYVOp2Pz5s1UVFTg39OPBQsWtJqFJkkSffv2xcvLi40bN5KamsratWsZOHAgEydObDOVAq8XX2N3WmVuuPxnZSlBuWch5p1bV1ZHdS9Sig9TuzJX4KDuIaxxQYdAKPIG0Fz7401h/BB/dp24VOt5hQT+7uao1RWUl5cTFxdHXFyc4byzszOenp4G5e7o6Gi4uV1KSGfxJ+vRaLXobzwo6PQy+8/FcCwyjh/+di/dPZ1rXLchFBYWcu7cOaDlrfEjR46QnJyMmZkZs2bNahM3dkdHRx599FH2799PSEgIoaGhJCQkMGfOHEPQWGtyJHMfChToa7FoJRQcztxLf/uWCWyrCV/rmUTlrkAva2oZoaeb7QMtKpOg+ai0yJu6R24kYVoBocgbQFtU5MN6+xLo68qVhIxbrHKFJKFSKXn32fvwcrUjMzOTxMREkpKSSExMJCcnh8zMTDIzMwkLCwPA3NwcT09PPD09+XxfDOUVWvR/+obr9DJlGi3/+N8+/vvq/U3+DCEhIeh0Ory8vPD19W3yfPWlquoewLRp01qknnt9USqVTJw4ET8/P7Zs2UJGRgbfffcdkyZNYsCAAa36wJFcllCrEodKl3VKaes2ETIzcWCo6zJC0l6+IVNltDqyBJJMF5u5eFlNbEUJBcZEBLsJ6s3NrvW2glKh4IuXZrP0qx2cvZSIUiEhSRJanR57G3M+/Ovd+LhXusBdXV1xdXU15L8XFxdXU+wpKSmUlpYSExPDmYtXuZ5V++fU62Ui49OISc6iW6eai7YkZeax5mA4B8Jj0VToCPRx4b7RfRnW09egiIqLiwkNDQUqrfHmUlCa8gqO7Iog/GQssl6me+9OxKaeQZZlevfuTa9evZpl3abStWtXnnzySbZu3UpsbCy//fYbV69e5e6778bConnqk98OteL2ue6mitavXdDJcjQTPH/hSv6vJOTvp0JXhlTixl3+z+NhMbpNeF8EAmMgFHkDaIsWOYCdlTnfvDqP6Ph0jp+PQ1OhI8DXhRF9/TCpI0jL0tKSgIAAQzCVTqcjLS2NxMREdp+5DFk5t137TMRlfJxtbqkbfzo6gWe/2oJOrzd4Ck5evM7xyHgmDvbHxdeGIk0FmqwM9BUV+Lq706VLlyb8FGon9mIKbz6+irzsIhRKBSBzYHs4ShPoP8WeKVOmNMu6xsLKyooHHniAkydPsm/fPqKjo0lOTmb27Nkt6sGooq/dIGIKo28puVqFAgX97Ye2rFC1YKvuxiCXt3AsepCfV/+Ms7Mznfo3XwaCoHWQaXo/8XbsWReKvCG0VUVeRYCvKwG+jS+iolQq6dSpE506daLMzJltUdtue82xw4eIPb0fV1dXg0ve3smFJd9so0Knq7bvVKXQ95y+jOYq6C0VaPU6FApr/s+rc7NYSAW5xbz2yEqKi8qA6lXcdFoI31NA3rMluHm27YpqkiQRHByMr68vGzduJDs7m59++okRI0YwevToFgu+1Ov1FJ4rR2GjRG+q48+txiUkTBQqRjtPaBF56kvVtkl+fr5IOeuA3Omu9fZRcaKN0NYVuTEZEuiN+W1qtauVEp0dzZFlmbS0NEJDQ9myZQtvfrGKknJNrcEjMqAsAK1eD0joJYmvzkay/8pVo3+OPZvOUlRQZuhk9md0Wj3bV5+s8VxbxN3dnccff9xQ6vfo0aOsWrWKvLy8Zl9bq9WyceNGwk6FYR/iiaVU+XegQImCSs+PhdKSZ7q+gqO66UGQxqQqH1+j0VBWVtbK0ggExkVY5A2gSpG31SIdxsRcrWLRpIF8sz2k1jFPzBjOokmDDC1bk5KSSE5O5mxEdp1zS4CygkqNfuMhWCFJfHnkJOO6G9e9fnxflKEtaU3odTLHfo/gsZenGnXd5sTU1JSZM2fSpUsXduzYQVJSEsuXL2f69OnNttdfWlrKmjVrSEhIQKlUMmfS/QT2DORC3jmiCyOR0eNn2Y3+9kPbxP74n1GpVFhYWFBSUkJBQYHoS97RuMN960KR1xOtVktJSQlwZ1jkAI9OGUJRmYb/7TuLhIRCIRnS0BZOGsjCiZVBczY2NvTs2ZOePXsCELNyJylnrxii3WVkKqxBa1H5WlUkoSyGmzdZ9bLMxbQMUvIL8LA13oNSeWlt6Ud/oCnXGm29lqRXr154enqyceNGkpKS2LhxI1evXmXKlClG7XWfn5/PL7/8QmZmJmq1mnvvvZfOnTsD0M9+EP3s237VQKh0r5eUlJCfn99idfwFLYQRXOu0Y9e6UOT1pKo0q0KhuGOe5hUKiRfmjOS+MX3ZdTqarPxinG0tmTokEFf72h9mhgR683toZT9xrZlMkbeMrMLwxFvuJCOVg7KcW8oqlmgqMCbdenYiITYDXS0dzhRKiS6B7kZdsyWxs7Pj4Ycf5tChQxw9epTw8HASExOZM2cO7u5N/1zp6en88ssvFBYWYm1tzV/+8pd2qwRtbW1JTU1tkW0IQcsiKrsJ6sXN++OtGSiTX1DKvkMXSUvPx9bGgnGjAnB3s2vWNd0dbHhkcv2Le0wa5M+/Nx8jt7yUIl8ZuSoS46Yfm2wKWpWMSSGGMp+mSiVuNsb1dky7fyh7Np2t9bxeJ3P3A8FGXbOlUSgUjB07Fj8/PzZv3kx2djYrV65k/PjxDB06tNHf1/j4eNasWUN5eTnOzs785S9/aVO59g2lSvaCgoJWlkQgMC5CkdeTthDotn5LKMt/OIROp0epVKDXy3z30xFmTO3Lc0+Nx0TZNmIXzU1VfPXMPdz/3zXICn3NaUo33tObglIDSkliVlAgVmrj7q927eFBz+GORB3PrtZDQ5IkZFlm2n1DGDy67dUybwy+vr48+eSTbNu2jejoaPbs2cO1a9eYOXNmg2sfREZGsmXLFnQ6Hd7e3tx3333t3hN1c+S6oGMhotabkRkzZuDt7Y2ZmRnu7u489NBDpKSkVBtz4cIFRowYgZmZGV5eXvzzn/+8ZZ7169cTEBCAmZkZQUFB7Ny5sznFrpHWVuS790Xy1YoDaLV6ZBm0Wr1hv3r7znCWf3+wVeSqjR4+rlh5mteaa1yFrJJRShLutta8MGa40eU4cOAA1l7F9BhtTtcef7iaO/u7sWTZXJ5+a2aHSkUyNzdn/vz5TJs2DRMTE2JjY1m+fDmxsTW3uq2JkJAQNm7ciE6nIzAwkIceeqjdK3EQirxDI0vGOdopzWqRjxkzhtdeew13d3eSk5P529/+xty5czlx4gRQ6eKaOHEi48ePZ/ny5URERPDII49gZ2fH448/DsCJEye4//77WbZsGdOnT2f16tXMmjWLc+fOtWg1rtZU5Hq9zPc/H6v1vAxs2h7Gg/cGY2fbOtW+aqJUe5v9bqnSMr5vQG+eGTEUB0vjyn7lyhWOHz8OwCN/nUPPnj3RaLTIehm1WdtoQNIcSJLEwIED8fb2ZuPGjWRkZPDLL78QHBzMuHHjUCqVlOvKOZd3ljxNLjYqW/rbD8BMYcaePXsM/e4HDx7MpEmTWr1BkLEQilzQUWlWRf7CCy8Y/u3j48Orr77KrFmzqKioQKVS8csvv6DRaPjhhx8wNTWlZ8+ehIeH89lnnxkU+RdffMHkyZN56aWXAHjvvffYu3cvX331FcuXL29O8avRUn3IayLmajoZmXXv6+l0eo6fimXaxN4tJNXt6WLvyLm0lFtqtVehlCSmBfrz9qSxRl87Ly+PzZs3AzBo0CBDRL2p6Z2zm+Ti4sLixYvZu3cvZ86cISQkhPj4eDpN8GBn7nbK9eWG5if/u/4TfjndKD5ZgoTE+PHjGTZsWIfyVlQp8sLCQvR6fYd5QBGIYLcW+ybn5OTwyy+/MGzYMEM7xpCQEEaOHFktVWbSpElcvnyZ3Nxcw5jx48dXm2vSpEmEhNSe31xeXk5BQUG1o6m0pkVeUo8UKoVCoqTk9uNaisT8fBzMzWtV4gA6WeahoL5GX1un07FhwwbKysrw8PBg4sQ7tzmGSqVi6tSphj3uWMUVNmdvoFxfDmBoflIhV3DZ/iLFnQu55557GD58eIdS4gAWFmZYd8rGrmscMVnbqNAXtbZIAmMhG+lopzS7In/llVewtLTE0dGRhIQEtm7dajiXlpZ2SypL1eu0tLQ6x1Sdr4lly5Zha2trOIzR37o1FXknD/vbjtHrZbw6Ga8/eFP46sxJRv13Jfuu1b0vu7B3XwZ6dDL6+nv37jW0Jp07dy4mJneOFV4b/v7+PPbEYxT1LKjzhlXSs4iAXh0j+O9m0ktOsjNhBl5jjuM2+AIXCt9je/xELuaurLNgkEDQHmiwIn/11VeRJKnOIzo62jD+pZdeIiwsjD179qBUKlmwYEGz/+EsXbqU/Px8w5GY2PSWilVWfWsochcna4YM7IxCUbOFJEkSTo5WDOrv27KC1cDGS1F8evI4MtTa6NLLxpZ/jB7P2yON41IvK9VQXla5H3/x4kVOnToFwKxZs7C3v/1D0J1ChiIdjUpTZwBiuVzOxYKolhOqBcgqDeNo6rOU6jIAqHI06ORyonK+4WLut60oncAYVEWtN/VorzTYVHnxxRdZtGhRnWP8/PwM/3ZycsLJyYnu3bsTGBiIl5cXJ0+eJDg4GDc3N9LT06tdW/Xazc3N8P+axlSdrwm1Wo1arW7Ix6oTjUZDeXmlK7K1otafe2o8j/51FWWlFcg33YkVisqKa6+9OA1lK6efybLMv8+E3JzlVSMrp8+iu2PNrU8bsta+rWFs+uk4cVcqvTO+3V0wdynA1l1m+PDh+Pv7N2mNjkaxtrhe44q0HcvlfCHnK+Q6fKeXcn+gq+29qJXioa9dcwc7VhqsyJ2dnXF2blxDBL2+0karUorBwcG8/vrrhuA3qHSL+vv7Gyyp4OBg9u/fz/PPP2+YZ+/evQQHt1wRj6pAN5VKZdQHhPpQUaFDr9dja61iUK9yLl+TycxVGVLPBvT14ZEH76JHgEeLylUTcXm5JNwmIlghSeyPu9okRS7LMv/5YAfbV5+sto8bH5MBVyBgkB1j3zR+AF17x0ldv5+5k2nTHrLaEiUVqWSXhdc5RkZPUtE+utjOaxmhBAIj02ybh6dOneLMmTPcdddd2Nvbc/XqVd588026dOliUMIPPPAA7777Lo8++iivvPIKkZGRfPHFF3z++eeGeZ577jlGjRrFp59+yrRp01izZg2hoaGsWLGiuUS/hdao6nby9FV+XXuKCxGV2wL2dqY4OegYN8KFB/+ykJzcYmxszLG1aTv5vWXa29csV0gS5Tpdk9Y5dyLW0LGs2jbNjX9Gn8kj+nwSPfv7NGmdjoaXuTee5l4klybdsFCrIyHhYOpId+uO48ko1+fddoyEkjJdbvMLI2g2REGYZsLCwoJNmzYxbtw4/P39efTRR+nduzeHDx82WLW2trbs2bOHuLg4BgwYwIsvvshbb71lSD0DGDZsGKtXr2bFihX06dOHDRs2sGXLlg6dQ75+4xmWvrGByKgkw3u5eeXEXDMhPsEGc3NTvDwd2pQSB/CxtUOtrPvZUKvXE+DYtBaX2389iaKObQSlUsH2X9tPa9KWQpIkHvJZiFJSGsriGpArFfkCn0UopI6TlmWudOZ2VYlktFiYtM/68YIb3OFR681mkQcFBXHgwIHbjuvduzdHjx6tc8y8efOYN6/13F4tqcgTErP5z7eVP7cq93kllTejk6evs//gRSaM69nssjQUS1NT5vXoya+RF9DVENCokCQczM0Z19mvhqvrT+ylVPS1NEGBypz62EsptZ6/k+li1ZWX/V9jfdIaYoquGN43zVMz3GQEPW1b7gG5JTAzccLNIpj0klPI1OwJUkpqPK3GtbBkAuMicdsykvWao30i8nLqQUsq8h07z6NUSOj0NT8eKiSJzVvOtklFDvC3oXcRkpjAtdwc5Ju2IZSShIlCwVeT70alVDZpjfpUZTMzb3s9sdsKflZ+vBLwGpnlmeRp8shOyuL33/aQqE6ifFh5i8eBNDe9HZ5jf+ki9HI5cg25FL0dn0elaFgteoGgLdFxfGjNSEsq8pjY9FqVOFT27b4Wn9nscjQWWzMzlrh707usAvMbH0MBTO/mz9Z7H2RIJ88mrzFiYs9aU/GqCOz/R131kpJyDh+8xG/bwwgPu/4nT8edi7PamW7W3RgSMBRHR0fKy8s5d+5ca4tldGzVXRnb6Qcc1NW9DeZKFwa5vEtX2/mtJJnAaAjXuuB2tKQiN1OrkKS6ywWqVE2zaJsTjUZD5Llz9CvVEIySotJSZs2YwYB+/Yy2xrR7h7D5v8cpL6shuE4CpQmkFlxg1y5zMtIsWLf6JOXlf4x1c7fjb69Mo28byLtvC0iSRHBwMDt27ODUqVMMGTKkw5UvtVN3Z6znKgo18RRVJGOqtMZB3RNJart/S4IGYAxFLBR5x6WgoJSk63mUlbSMIh8+rCsnT1+t9bxSqWDkXW03qvjcuXOUlpZiaWlJcXExNhYW9AkKMuoaaguJXmPUnN+vRavBkD+v0+mxtbNgyoJuXE+9xLpfQ0mMv1Uhpafl88qLvzJiai+yC0tRm5owfHAXxo8MxNzsznTJ9+7dmwMHDpCfn09YVBie3TphaWKFlUnHcjlbm/piberb2mIIBEZFKPJaSE/N47v/HODooUvodTKg5B/Ju1j8f+MZMqxrs607bkwPfvz5ODk5RbdY5ZJUecydPbDZ1m8KOp3OUAPf1NSU4uJiBg4caNQSqXq9nk2bNmFqXcHUxV74uvTnYlgCkiTRZ7AfIyf3wlStIvTMBZb+bXuNc8iyjFYnc2BXBFrrSg/IiTNX+WH1cf71j3vx8XQ0mrztBZVKRcCQAPbk7uabsi8hsvL9IJve3OM5B28LkconaMMYow2pSD/rWKSn5vHXxatuUuKVXI/L5o2/rWH/7xHNtra5uSkvvTAKU9PKoJyqym2SBGq1ivfemU1n36albzUXERERFBQUYGZmRm5uLgqFgkGDBhl1jaNHj3L9+nVMTU259755TJ03mL99MJcX35/D+Jn9MFVXBsIlXi+nrihUCVBoZdDLhgem3PwSlry1Hk3F7fPhOxoppSnssdpJqXtxtR9bVEEkH1x6j5jCmNYTTiC4DVXdz5p6tFeERV4DK785QEFBSTUlDn8UH/nin7sYNtIf82aIjJZlmfPhJxjSX4OVTRf0OKDT6gkMcGfC+F5YWbbNiGJZlg195m1tbSkrK6NXr15YWRnPNXv9+nUOHz4MwNSpU3F0rN1yzs0tRqmU0Grr/uuU9DLyjcA5vV4mM7uQwyeuMGFUD6PJ3R743/WfKjui/enRXo8eWZb5If47Puj1UYfriCYQdASEIv8ThQWlHD0YfYsSv5nSUg1HDlxi0rQ+Rl///PnzpKamYmZmylNPzDKqImxOrly5QmZmJqampmRmVkbVDxkyxGjzl5aWsmnTJmRZpnfv3vTpU/fP3sXFBl0dueZwIz7mT9HvCoXEqXNxd5QiTy9L50rR5VrPy8hklmdwpegy/tYdrzOaoANwhwe7Cdf6n8jMKLitAlCaKEhNNn5Jx/Lycvbv3w/AqFGj2o0Sl2WZY8eOAeDi4oJer8fb2xsPD+PUf5dlmW3btlFQUICDgwNTp0697TWjxgZiYlJ7RLIMyCYS/DmNTQattu7ff0cjvaz2lsA3k1bPcQJBi1O1R97Uo50iFPmfsLIyu+0YWS9jWY9xDeXo0aMUFRXh4OBgVGu2uUlISCApKQmFQkFWVhZgXGs8NDSU6OhoFAoFc+fOrVfBEmtrcx59YkyN56oevHUWtyp6GZnA7rV31uuImCnr9102Uxj/Oy8QCJqOUOR/wsXNlu6B7nXuBcqyzMgxxnUx5ubmcvJkZX3wiRMnomxi9bOW5Pjx4wB4enpSVlaGra0tAQHG+fmkp6fz+++/AzBhwgTc3d1vc8UfzJ0/hCUvT8XBsbpnQ1ZKaK1NkG+p1y4jIePhdGdZ5F2sumJtYlPnGBNJRZCtcdMIBQJjIcnGOdorQpHXwMOPj+ZGF4lbkCSYcnc/XN3tjLrm3r170el0+Pn50b17d6PO3Zykp6cTE1MZ0VzV7nXw4MGNKihSVqJh14Yz/HPpej5+bT07N5xiza/r0Ol0dOvWrVFW/tTp/fh1/TN8/uVD/OPD+az86XFG390b2USB4uYSsgoJhSTRq7OGA/t2s2vXLnRN7NLWXlBKSmZ4zKpzzETXSViYWLaMQAJBQ2mFym5Hjhzh7rvvxsPDA0mS2LJlS7Xz6enpLFq0CA8PDywsLJg8ebLhXgkQHx+PJEk1HuvXr2+QLCLYrQYGDunC63+fzWcf7qCkWIOkqHSnS5LE1Jn9+euSSUZdLy4ujkuXLiFJEpMmTWpXkcFVkere3t4kJCSgUqno379/g+eJCrvO23/9L0UFZSiUlZ9///ZwTEyh/0QbZs6c2eifi9JEQVAfb8PrN56fxtD+ndm4I4yr8RmoVEpGDOnGvBkDSE26zMGDBzl9+jSZmZnMnTsXCwuLRq3bnhjtPIZSXQlbkjcjo0chKZBlGRmZcS4TmNVpdmuLKBDUTivkkRcXF9OnTx8eeeQRZs+u/vchyzKzZs1CpVKxdetWbGxs+Oyzzxg/fjwXL17E0tISLy8vUlNTq123YsUKPv74Y6ZMmdIgWYQir4VR43ow9K5uHD0YzeGDp0jPSGHilL7MnX/7QKuGoNfrDa7jgQMH4uLiYtT5m5O8vDwiIqrn1Pft2xczs4btpWam5fP6Ez9SXl4BUC1jQKuBiAOlaF6UsTSSQahQSEwc3ZOJo29tPNPNzxVnZ2c2b95MXFwcK1eu5L777mtXv5fGIEkSU92nc5fTSE7lhJCjycHGxIZBDkNIyqpg58Ur2JqZMcTXC9N2tO0jEDQXU6ZMqVXhxsTEcPLkSSIjI+nZs/I+88033+Dm5savv/7K4sWLUSqVuLlVj8fZvHkz8+fPb3Cgs1DkdaBWqxg/OQg7p3J++y2Z8ooCo69x7tw50tPTMTMzY/To0UafvzkJCQlBlmW8vLxISEgAGhfk9tu6U2jKK5BraWZSVqZl96Yz3P94zcFrxiYwMBAHBwfWrFlDbm4u33//PbNnz8bfv+2WxjUWNiobJrhWepxOX0/iLz/sIC7njwwNO3Mzlowezn39e7eWiALBrRgx/aygoPp9Xq1WN7gjYHl5OUA1o0ahUKBWqzl27BiLFy++5ZqzZ88SHh7O119/3UDBxR55vaiyxjIyMow6b1lZGQcPHgRg9OjR7cqFW1JSYuiUVSV3t27d6izSUhtH90bV2ZFM1ssc2xvVOEEbiaurK4899hg+Pj5oNBrWrFnDsWPHDEWBOjrnklJYtHoj13Orp1nmlZbx1q79/HS643VJE7RjjLhH7uXlha2treFYtmxZg8UJCAjA29ubpUuXkpubi0aj4aOPPiIpKekWd3oV33//PYGBgQwbNqzB6wlFXg+cnStLohYUFBietIzB4cOHKSkpwcnJiYED22b99No4deoUWq0WV1dX4uLiABg6dGij5iovq7j9mPLbjzE2FhYWPPTQQ4bfzf79+9m0aRMVFS0vS0vz0f4j6GWZ2p6vPjt0nKJyTcsKJRC0AImJieTn5xuOpUuXNngOlUrFpk2buHLlCg4ODlhYWHDw4EGmTJlSYyBwaWkpq1ev5tFHH22UzEKR1wNzc3ND57OqqmVNJSsri9OnTwMwadKkdpVuptFoOHPmDFBpuWo0GpydnencuXOj5uveo5Ohg1lNKJUKugYap7hMQ1EqlUybNo2pU6eiUCiIjIxk1apVt7jfOhIJuXmEJaWir8P7UFqhZc9lUX9d0EYwokVuY2NT7WioW72KAQMGEB4eTl5eHqmpqezevZvs7Gz8/PxuGbthwwZKSkpYsGBBo9YSiryeVFnlxnKv79mzB71eT/fu3enatfm6qTUHVa1K7e3tq+2NNzaqfPp9Q+qspqfT6bn7vsZZ+8Zi0KBBPPTQQ5ibm5Oamsp3331HUlJSq8rUXGQVl9x2jFKSyCwqbgFpBIJ60IYru9na2uLs7ExMTAyhoaHMnDnzljHff/89M2bMMOiZhiIUeT0xpiKPjY0lJiYGhULBxIkTmzxfS3Jzq1I/Pz/y8vIwNzend+/GBz/1HeJH4KAbe+s3/S1JN8qn3rt4FD37tX4bTV9fXx577DFcXFwoKirixx9/JDw8vLXFMjrOVrdPD9DJMi7tpIRwTcgVMegLPkSf+yz6/LeRNaF3TPyDwDgUFRURHh5uuAfExcURHh5uMG7Wr1/PoUOHuHbtGlu3bmXChAnMmjXrlnt+bGwsR44cqTEArr6IqPV6UhXw1lTXuk6nM6SbDR48uFHBYa1JVatSKysrw89iwIABqFSqRs959uxZbL0L6K5UUZ5tx/WYynm79+jEnEV3MXJS26koZm9vzyOPPMKWLVuIjo5m69atZGRkMH78+EYVwWmLeNnZ0s/TnfPJabW6181VJkzwb1+eJKjM75ULl0HJj4AS0AMK5NJfwXQU2P8bSTJvXSEFDcYYldkaen1oaChjxvyRSbNkyRIAFi5cyI8//khqaipLliwhPT0dd3d3FixYwJtvvnnLPD/88AOenp5NMuqEIq8nxopcDw0NJSsrCwsLC0aNGmUM0Zqdcp2WxPx8FJJkKMfao0cPTp8+3eCe42dPXmXz6hAizl1HkiCwtwdaVRJW9hIPPDqJoUOHotFU9gM3NW2bX0+1Ws38+fM5dOgQR44cISQkhMzMTObMmdPgHPq2yqvjRvKXn9cDNQe8vTjmLqzUxm/j2+yU/HBDiQPoqv9fcxQ5/y0ku49bQTBBk2iF7mejR4+u04vz7LPP8uyzz952ng8++IAPPvigYYv/ibZ5p2yDVLnWi4qKKC0txdy84U/tJSUlHDp0CICxY8e2+Zt+mbaCL0+f5OeI8xRqKqP1LXV6+lma07O4cn+0R48e2NjUXae7il9/OMKPX+9HoZQMRV/CTsUjyzBwtLMhB72tKvCbkSSJMWPG4OLiwpYtW4iNjTUUj3Fycmpt8ZpMP08PfvrLHN7YuY+47D9S0NSynjldvFkwqF8rStc4ZFmDXLSijhF6KNuGrHsRSXlnNc4RtG/a/h2zjaBWq7G1tSU/P5/MzEy8vb1vf9GfOHjwIGVlZbi6utKvX9u+EWp0OhZt28SZlORq7tVihcQxtYKcuKsMpv4pZ1HnE/jx68oWrTdXbquaOvRQJteupNHFv/5NUdoCPXv2NBSPyc7OZuXKlcydO7fdBTDWxCBvT3Y/sZDzKWkk5eWTn5nB5cMHsUiMo6KioknbKa1CRRTIt2s/LEP5IbC4ryUkEgiMQsfY1GshmhLwlpGRwdmzZwGYPHlym99PXX8xklPJSbfukd6ITL9oZsJFG0fc69lzfNva07ekmMmATq1Ea24CZiZsW3fKGKK3OO7u7jz22GN4eXlRXl7O6tWrOXHiRDW3m06np1yjbXcBVZIk0beTO9N7BnD/yLtwtLOjtLSUCxcutLZoDUeuT967VM9xgraEhBG6n7X2h2gCbVubtDEaq8hlWeb3339HlmUCAwPx9fVtBumMy88R4XV/sWUI1Zbz9o599Zov6nxCtRQzrbkJ5S4WVNibobVVU2anZsfJK5w4Hds0wVsJKysrFixYQN++fZFlmb1797J161aiopN488OtjJ/7GRPmfs7cR5bzvw2nWqXATVNRKBQMHjwYqCwI1N4eSjDpyu1veTKoAltCGoExacPpZy2BUOQNoL6R66WlGn7bEc4rL/3KM0//xDtvreH8+WsolUomTJjQEqI2mev5eXXHfkggK2H9uUgiU9JvO5+JyR9fNa25CVpbNSiq/+Fogdf+vonTZ+MaJ3QrY2JiwowZM5g8eTKSJLH/cAT/98qvHDsZg+7GdkJmdhHf/XyU599YR1k7VOb9+/fH1NSUzMxMrl692triNAhJ6QjqyVRGq9eEEpR+oGpfVRYFAqHIG0B9ItdTUnJ5ZNEKPvtkJ2dD47gYlczxY3GEhaooL/PEzs6uhaRtGlaq20Qk34gSVSokNoZF3na+ISP8USglZEBrXffcX6880P6svRtIksSQIUOYM/deIuPUyLKM7k9h37IscykmlV82tL+tBLVabYjvOHWq/ckv2bwBSg9uvfUpQTJHsvusXbURFtygFfqRtyWEIm8AVdHIJSUlFBffWtVKp9Oz9OW1ZGcVAn8EclX9/8SxFPa1cPOPxjLTPxBlXTc0CZQaCZ1eJiW/8LbzzZg/GAnQq5W3WOI3I8sQn5DN1TjjlMJtLeKSytHpJWrbedPrZTbvDENbR0W7tkpVdkFsbKzRSha3FJLSCclxA1g+jowtABUVSjCfh+S4BUnVo5UlFDQKocgF9cXU1NRgUdd0Aws9c42kpByDG/XPSBKsWX2iXVibi/r0x1ylqvnLLYOkBYWm0iK3s6hHGp2yDP9BCjCR/niyqYPcvPZd/jPmWgYmddSPBygoLGuXn9Pe3p6AgAAAQk6eJLu4pF01UJEU9iislyC5nOL97+7jwx/updTkFSSThmeiCARtAaHIG0hd7vXQM3F1Nv+QZYiPzyI///a1rFsbTxsbVk6chsrgVsCg1CUdqAolJCot8hlBdQcHFRcXs27dOuxcZO4a726IfK8LJ0frJn6C1sXUVFmvB3xTVfvMAO0zcCDnFWpevxBD8L++pf8nX/PQ/9ZzIi6htUWrNwqFAjMza0CiqKiotcURNIEmR6wboTJcayIUeQOpK3Jdr6+fm7Q2i72tURB7lZl5JVgUKVCWgbIMVPkSqgIJSZZQSBKDfT0J9qvdktHr9WzYsIGCggIcHR15ccn9WFvVbsErJIluXVzo7NO+i6oMH9y1zkYwCoVED393bG3aXznQEk0Fbxw+RZRCTflNWwdnEpJ5ePVGtkRcbEXpGoalZWVd+ZKStv9wLagD4VoXNIS6ItcDe3Sq8+YN4ORsjb397ZtStDalpaWEhoZihczfB/XBsVTGpFSBUiehQEICJgZ245v7Z6Kow8Let28f8fHxmJqacu+992JtZckzT4yrcawkSUgKiWcer/l8e6JPT08Cu7ujrCUeQK+XeWhe63Z0ayzfnwwlMi3jlvueXpaRgdd/20tuSWlriNYgdHoN5t5p2PS8xtXSrRRWpLa2SAJBo2iffr1W5GZFLstytQjXkSMD+PqrvRQVlqGvoUC1JMGcOYNQ1BHs1VY4ffo0Go0GFxcXtNmZTKEYuy7dcewWgEqp5K6uPnja2dY5R1RUlKFT2syZMw3ejElje2KiVPDvb/eSl19mGO/j5cDzT02gTy+v5vtgLYQkSXz4xj387Z0NxFzLQKlUoNfpkan8zjz72DiGD2671d9kWSY6J5Os0hLcLK3oZl/pIdHLMr+cPV9nr3KtTs/miIs8MmRAS4nbYBKLTnAk7R/I3Quw0UskSNdIiNtAV5spDHd5CaWiHdaRv5MxhkXdji1yocgbiJOTE5IkUVpaSlFREdbWf+zlmqpN+Mf783j5b79SXl5hiOlSKCT0eplhw7szZ+7gVpK8/mg0GkNqUVBQEPv370cC/jJpfL375WZmZrJ161YAhg0bRo8e1aOBx40KRFeexK49J/Hy7sK0KWPp3tW1Q6X+2NtZ8t1nCzgdFsfhE1eIv55ESWE64+7qxpzp/VtbvFo5nBjHP04eJCYv2/BeLydX3g4eS1cbR3JuY20rFBKxmTnNLWajSSs9z76UV5Fv3LklxR938NiC3ehlLaPd324t8QSNoDW6n7UlhGu9gZiYmGBvbw/U7F7v2cuTFd8/ipc3qExlzMxU+Pu78+prM3j73dkoTdr+j/zcuXOUlpZib29PWloaUNkcpb5KvKysjLVr11JRUUHnzp0ZN65mV3lWViZ21npGBHfDv5tbh1LiVSgUEkMH+PHKM5N55a/j8fPQkpGe0GYzF/Zdj2XR7g3E3qTEAS5mZXD/jrVcyE677RwSEuZtOIgvLOuHG/+qOSXjWuFe8jTXW1IkgaBJtH2t0ga5fWGYUnz9KhgzzoTtO1/kq28WMWFirzoj2tsKOp3O4A7v3bs3UVGVee8jRoyo1/WyLLN161ays7OxsbFhzpw5tdaVT0+vrAjn6upqBMnbPl5eXpiamlJcXGx4QGpL6PR63ji2F7hVxemR0csyH5w6xDBfrzprDGj1eiYGtM1tgzJdPqmlZ5GpPZZFQkFc4f4WlErQZO7wEq1t97G5DePs7Ex0dHStijwurrLEqI+PT5tvjvJnLly4QEFBAVZWVuTm5gLQvXt33Nxubeuo0Wg5euQyJ07EoCnX4tfFBWcXDdHR0SiVSubPn2+ICv4zpaWlFBQUAH88GHV0lEolvr6+XLlyhatXr+Lu3rY6vYWkJpBWUnsalh6Zy7lZPBI8gJD4xBrHKCWJXu6uDPb2bC4xm4RGd/s0MwlFvcYJ2hB3+B55i2iZ8vJy+vbtiyRJhIeHVzt34cIFRowYgZmZGV5eXvzzn/+85fr169cTEBCAmZkZQUFB7Ny5syXErpXb1VyPj48HoHPnzi0lklHQ6/UcP34cgD59+hAREQHUbI2npOTy8KIVfPD+No4cjubEiRhW/3KCzz89TWqKgilTptCpU6da16p6CLK1tW3zfdmNSZcuXQDaZJ3ylKLbV+gDsLMy59NZU1GbKJEApQTSja2CXu6ufHvvrDa7TWJh4ohSqjuQTY8Oa1Xt311B20PkkbcAL7/8Mh41tLssKChg4sSJ+Pj4cPbsWT7++GPeeecdVqxYYRhz4sQJ7r//fh599FHCwsKYNWsWs2bNIjLy9vW9m4s/R67fjE6n4/r1yv219qbIo6Ojyc7OxszMjJKSEmRZxs/PD0/P6taVVqvjlZfWkJlRaVFXRehX/l8i5ooJEo51rnWnudWrqFLkCQkJaDTNVw0to6SIb8JP8frRPXxy5igxuVm1ji0tLeXs2bOcO36iXnM7mpkzvac/x597nNcnjmainw/+soZZ5hLrFt2Hg0XbzY03UZjRxXoiUq2NU0CBCV1s2kdzI4EAWsC1vmvXLvbs2cPGjRvZtWtXtXO//PILGo2GH374AVNTU3r27El4eDifffYZjz/+OABffPEFkydP5qWXXgLgvffeY+/evXz11VcsX768ucWvEUdHRxQKBeXl5RQUFGBr+0caVlJSElqtFgsLi3oHh7UFZFnm2LFjQOXeeFXv9JEjR94y9vjxGFJS8mqdS6GQWLMmhP4DfGsdU6XI7xS3ehUODg7Y2dmRl5dHfHw83bt3N/oaX4ed5LPQY8hUFtiRZZmvwk4yq2sP/jlqMqZKJVqtlitXrhAREUFMTAw6nQ4VYOpmjqaO9Eh3S2v6u1ZaqzZmZiwY1I97e/fgww8joLCc4uJirKysjP6ZjEl/p8UklZyiVJuDjO6mMxIgM9TlOdRKm9YST9AY7nDXerMq8vT0dB577DG2bNmChYXFLedDQkIYOXIkpqZ/uLomTZrERx99RG5uLvb29oSEhLBkyZJq102aNIktW7bUum55eTnl5eWG11V7scZCqVTi4OBAVlYWmZmZ1RR51f54586d26x7sSauXbtGamoqKpUKnU6HTqfD29sbHx+fW8aeOhmLUqmotfiNXi9z7mw8Wq0OE5OaLZ8q1/qdZpFLkkSXLl04e/YssbGxRlfka6Iv8PGZo4bXN+d7b429iL6sjPFaFRcvXqz2N+Lq6kpQUBDeZgreP3es1vlfGzL6lgJAarUaZ2dnMjMzSUpKMtRhb6tYmDhxt9cKQrO+4VrhfoMyt1V5099pMZ2tx7SyhIIGYwzXeDtW5M3mWpdlmUWLFvHkk08ycGDN/X3T0tJuuZFXva6K6q1tTF1Rv8uWLcPW1tZweHkZv8BIbZHrVfvjvr6+Rl/T2MiyTFG5htKKCoM1HhQUxPnz54GarXEAbYXutulTskytil6W5TvWtQ7QtWtlRPe1a9eMOq9Or+dfZ4/Xel4Gtide5fj5MMrLy7GxsWHYsGE8+eSTPPnkkwwfPpzHBgTz92HjsLzRxrZKZduqzfhs9FTu7lKzkq7afklOTjbmR2o2LFXOjHJ/i/v9tpH++1BSdwxjguM3QokL2iUNtshfffVVPvroozrHXLp0iT179lBYWMjSpUsbLVxjWbp0aTUrvqCgwOjKvMptfnPAW0VFBUlJSUDb3h/XyzKrwy/wY+g54nPzAHDSaQkyUTFQoUCr1eLh4YGfn1+N13ft7saBA7XX05YkcPewR61W1Xg+Ly+PiooKlEoljo5176V3RHx9fZEkiezsbPLy8urdo760ooLtV6O5mJ2JWqlknE8XBrl1Mnh+orIzSCuuO9paBqRunVk4dCQ+Pj41eo0W9OzPPP8g9l+/aqjsNsbbD7Wy9tuFp6cnYWFhhu9/e8HMxBZlqQua0tJqHgpBO0O41hvGiy++yKJFi+oc4+fnx4EDBwgJCUGtVlc7N3DgQP7yl7/w008/4ebmZrDMqqh6XZXuVNuYmtKhqlCr1besa2xqssgTExPR6XTY2Njg4ODQrOs3Fr0s8+KOXWy/dLlap+wshZKDShMKI6IIpNIar21rYNKkIH5YeZiKCl2N5wHmzKnZCwN//I6dnZ3bXXqeMajK0EhISCA2NrZWj9XNHEqI45n92ynUaDBRKECGb8+foa+LOysnz8LJ3JLCsrLbzqNQKOjao8dtPUbmJiqm12J918TNFrler29Xv1czMzNKS0spLW379eEFtSAUecNwdnauVxDXv//9b/7xj38YXqekpDBp0iTWrl3LkCFDAAgODub111+noqIClarSetu7dy/+/v6G6mnBwcHs37+f559/3jDX3r17CQ4ObqjoRqWmmuvtYX/8t0uX2X7pMvCn7+0NeUMVKno5WdW5d2tra8Err07j/X9sqyxzeaOQgnSj1XjwsG7cPaP2EqR3slu9ii5dunD9egIXIi7j7euPk4NVrTX4o7IyWPz7ZnQ3uutpb+qyF5GZxv1bfuVZazfOXIwCW6nONrF6WaabvfG9IE5OTpiamqLRaMjMzGxXv1tzc3Nyc3Mpq8eDkEDQFmm2YDdv7+qtLasiWbt06WJ4en/ggQd49913efTRR3nllVeIjIzkiy++4PPPPzdc99xzzzFq1Cg+/fRTpk2bxpo1awgNDa2WotYaODg4oFQqqaioIC8vD3t7e4Mib8v74/89F45CkmpteiHJMplOLrd9EDFR5dC3XwXpaWoyMyW0Wh3ePk7cc89Apk7rU2cVuyovxp0WsV7F/7d3n2FRXev/8L97T6P3joCgdBAQg2LBRqyJGj2mmMQUU0w80ah/k5iYaNrx5JdjykkzVfMkxpyYGDVqIsZeQAVBegdBeh/aMG09L8bZMsJQB2ZG1ue65krYe82etR2Ye1a7FyEExeUE8RkmOHm1HLt/3wlHewvcv3gC/nFvZJd/u50pl0AI6bbBoCAEeeJGHLxeCY8OBXwszFDMR7d5y1gwcDa3wFT3rhMYB4tlWbi7u6OoqAg3btwwqkCuzmNAW+TGa6TnWtdrZjdra2vExcVhzZo1iIyMhIODA9544w1u6Rmg2nDjp59+wpYtW/Dqq6/C19cXBw4cQEhIiB5rrvrgsre3R3V1Naqrq2Fqaory8nIAhj0+nltT2+POVYRhUCnX3mUOAM3NzTh//jwsrQgee3wBQkNDu+wE15OR3CInhOCjr07g9yPJQKfBjZq6Fnz23WmcTs7DJ288CP7NYE4IwV9FeVD08J4xhKDJxQGbp92NJx3tsfTgT6iXtGk8h8cw4DEsPpq1ELwh6vbuHMgjIw1357PbmZqq1r3TFjllrIYtkI8ePbrbmc7jxo3DuXPnunnGLcuXL8fy5cuHqmoD5uTkhOrqatTU1IC5uV7Xzs5OYzmaoRHx+WiVybSeZwCYCrqfpKZ28uRJSKVSjBo1ivtC1dcgLpPJUF+v2hlrJAbyaxk3bgZxAOj6b5aRXIYVO/fgx9UrIOTx0NjcDJmy5z3uwTBwdHfjks0cXroSHyVdwP68THQo5GAZBrO9xmDd+MkIdhi6f3N1T5vRTXijLXLKyNFc64PQeeZ6a2srAMPuVgeAef6++N+1NK0tPAJgrp/2DS8qKiq4NLtz587tcwCXyOT4MzkHZ9NykdsmgpspDwx/5O35fPCvFPBYBopu9qsHVP/+xYmV2PTHr4gUd6C4uBjmDiZoZbWPfbMMg7E2tyZXOptbYHvMXGydPBuNHe2wFIq45WRDSR3Ia2trIZFIjCb1rrqetEVuxEb4ZDfjmVpqgDrPXO880c2QPR4ZAR7LdtMWBFgGcLIwx6Kg7mcrE0Jw7NgxAEBISEiX1K3aZJRWYs7b3+CN/8XhZFYxSmUCJIh5mPPON0gpKh/orRilouu1WoM4oGqjC5qBIxUlyC0uAiEEE3mmPX5hUhKC+wNCuxw34fPhYm45LEEcAMzNzblJqsaynhygXet3ApprnRowBwdHNNQCyZdrcC2xGtIOw2+R+9jb4auli1RdMYSAxa0NL1wsLfHjA/+AubD7D/7s7Gxcv34dfD4fsbGxfXq9uuY2PL1zP5raVB+SSgKQm18jWiRSPPvVflQ29m2zjjuBmVnvQVXJB+QsA+9JUVi7di0+X/Ucxju7dcmopv55S/RMjLI0jOEcY+xep13rdwgyyIcRo4F8gNKvlWDd03uQkcRDQRZBfiaDK2d4+OazntdXGwJ7STuWtYsxlVHCjwXGKGRYG+SHE08/AR/7W120coUSV5KKcOzvdFxKLEBcXBwA1QTEvs4D+DUhDa0d0m4n2CkJgUQmxy8XU3VzY0ZgxuSeU7ISAG039xcKDgmBra0tTPh87LlnOdZETISN6FZ3dbC9E3bOWYxV4wxnYpl6xzvaIqeo4UPHyAegIK8KL63bA7lcPQlJ1TIiBDh6KAXt7TJs3rZEb/XrTUJCAgQA5oxyQ15eHszNzfHc3DkQ8G7lRf/7VCY+++okGhrbuGNCAUFogDmmTJnS59f6Oy2vx1nySkJwPDUPaxf0/ZrGhhCC0tJSpKamIi8jE3wBIJMzXbryCAMoeUCzFyDi8RBkf2t5nglfgI13TcXa8dGoaW+FiMeHvWnX/Qv0rXOLvD8rGfSJtsjvACN8jJwG8gH4cdc5KBRKkG7GOgkhOBmXjodWTsFoH8Pb/ay8vBwlJSVgWRa1taqtLaOjo7mEPIAqiL/zf4e7PFcqA5LSlLh0pRjTpvRtsw+JVN57GVnvZYxRbW0tUlNTkZaWhsbGRu749HBznMgE0ErUuXTAEEAhAmomADBlcb9/KKy6yU4o4PHgZmG4O3O5uLiAx+Ohvb0dDQ0NBpvhsDPaIjd+dB051S/t7VJcPJvD7cHdHR6Pxcm4dDy52vA2YEhISACgajmVlJTAxMREI0WoXK7Ap1+d1PJsVdT59KuTmBLtqzUTWWdBo5xQWteodYIXj2UQ6H7nJIZpbm5Geno60tLSUFFRwR0XCoUIDAxEaGgovL298WSzGA9+9SPabrSDAOiwBSTOAGEZjHdyxeZJ3W9YY+h4PB5cXV1x48YN3LhxwygCOW2RU8aOBvJ+amvt6DGIq4nFhvehIBaLkZGRAUAVcABg0qRJGnnpk6+VoLFTd3p3qqrFSM8sw7iQ3metPzglDEeTc7SeVygJHpwS1pfqD6vKlmZk1lVDwPIw3sWtx5nfHR0dyM7ORmpqKoqKirh8CSzLYuzYsQgNDYW/v79Gr4eHtQ2Orn0KP2el4vvky5C0t2OUQIR/Tp2JJb6BPW5QYuhGjRqFotIbuJpbAFfvMXC0NNd3lXqkDuRyuRxyuRx8vvH+249YtGud6g9LK1OIRHx0dGjvDiaEwNnFMGYRd3b58mUolUpu72ihUIioqCiNMvUNrX26Vl/LRXi746nZd+GbE1c0UsMyUP3dPDItAtF+nj1eYzjVtLViy7njOF5cwNXVjC/AY6ER2HjXVNWGJQAUCgUKCgqQlpaG7OxsyOW3fh9GjRqFcePGITg4GGZm2sexrUUmeDY8CgvsXfHdd9/B1JRguV+wUW04crvKpmYcKGnAuQ5LkKRivJ/0FSJHu+Ofd0cjykf32wnrQuf17hKJhEsnTRkP2rVO9YtQyMfdC8bhz0PJUCi0J/W4e/644a1YL6RSKZKSkgCACzpRUVHc+KCavX3fPsQcHfr+YbduwVT4uzli9+kkZJSq0rN62Vvi2blTsHB8gMFMiGrqkGD5gb0obW7SmKDXJpdhZ/Jl3BCL8VJAGNLS0pCRkYG2tls9F/b29ggNDUVoaGi/u5Pd3NwgFArR3t6OyspKuLm56eyehlNFYzMe/OwnNLS2c0sMASD5ejlWffMbPnr4HswO1p5sSF8YhoGJiQkkEgna29tpIKeMDg3kA/Dw49Nw8WwuGhtboewmmD/21HQ4OFrqoWbaXbt2jWttNDQ0QCAQYNKkSV3KRYzzhL2dBerqu9/XmmEAVxcbBAX0L9jMC/fHvHB/7P7/fkBhYSGWLpiJ8PDAAd3LUPk+PRkltwVxNQLgj4JsKK4kw1mmWq1gbm6OkJAQhIaGws3NbcBfSHg8Hry9vZGTk4PCwkKjDeQ7/jyLhrb2LlkDlUQV1rf8Fodp/qMhNMCua56pCK1yCRpam/u0uyNlYEZ417rx9uHpkYOjJT75+glET/ED02nCl529Oda/vAAPPz5Vj7XrihDCTXJTj9NGRkbC3Lzr2CWPx2Ltc7PVz9Q4p45TL6yePeCgZWFmCh6jGlc2NHszU3tcKscQggIzIcaNG4eHH34YGzZswLx58+Du7j7oXgV1RsDCwsJBXUdfGlvbEZee12PqWXF7B/7OKBjeivXieks9Nlw6gK9dWvC7Hw/3XtmLF+J/Q564Rt9Vo/pjsMlgjDwpjOF9NTYSTi7W2Pbv5airbcYnH3+LltYmPP/PxfDx8dF31brIzc1FfX09hEIhGhoawOPxMHnyZK3lp0/1x4p/BOC3Q5nokN4KUC7O1lj3XCwmRY0ZcF2EQiGkAJrbep5Qpw/Vbd33QqgRhoGDjzfuW3Sfzl9bveFJSUkJZDKZxsQ4Y1DeKO4x9SwA8FkWxbUNw1Sj3uWJa/DAyd1ok0uhvPlrrgTB8bJsnKnIx08zVyLE1lW/laSoPqCBfJDsHSwxeowNrl9v0hgz1TdCCKqbWyFXKnExPh6Aar2sVCpFREQELC21d/0rFAq0igsQHdGBceExsLN3g6ODBYICBt593C6TYVfCVXyXdR1inhV+vZCCmMpGrJ4ahUgP9wFdUxcUCgXy8/ORnp4OoUKJ9h6W1PEYBs5DNH5qb28PS0tLNDc3o6SkhAvsxsJc1IfUs4TAog/lhstriUfQJpd2GQpQEIIOpRwvX/kDh+9+2mDmcFDa0clu1KCpu6hbWnpu0Q0HQgj2p2TimwtXUFSnav2IoIQ/RAhoaoKAZXvNzJaZmQmxWAwLC3MsumfaoJfjtMtkeOzHX5FaXsV1XRMA5wuv41zhdXy0dAHmBfYtwYwudM60lpmZya0fHmshQLqZkEvScjsFIVjqFzwkdWIYBmPGjEFKSgoKCwuNLpB72ttgrJM9CqrrtPZQEhDEhhjGZLd8cS2S67Tng1cSgtymaqQ2lCPMTn9fNKk+GuFj5DSQ64A6kKu3MtWnD06cx9cXEjV2N+sAi1QIUQMWL4T6wcbGRuvzCSGIv9mCj4qK0sma2q8uXtEI4mqKm5OgNh08hineXrA06ZrJrCcKpRIJN26gqqUFDmZmiPbw0Egze7vq6mqkpaUhLS0NTU1N3HELCwsEBwdjmZ8vnjgXh3pJW5dWGsswmOLuiSnuQ7dUztvbmwvkxoZhGKydMxlrf/xDy3lgaWQI3GwMIytdYXNtn8oViGtpIDcGNJBTg6VerqLvFnlGRRW+vpAIoLvfSQYV4KPFvucxv5KSElRUVIDP52tkfBsohVKJn5K0TyIjAKRyOQ6mZ+GRCeF9vm5cfj62njqJqk7/5nampngtZjruCwrijonFYi54V1VVccdvz7SmXrv9m8NDePHEUVyturW9KsswWOobhLenxQ5pN6t6fkVlZSVaW1u7nYxoyGYHj8VbS+/Gu4dOQipXgGFUXwwJGCyOCMKWxbP0XUWOGb9vXfx9LUdR+kQDuQ4YSov8l6Q08FhG66QjBgwOZuVj5bSJWq+hbo2HhYX1mMykrxra2tHQ1nOWOx7LIre6by0kADhRUIDn/jjU5Xh9ezs2HvsLHVIp/JRKpKWlobi4mDvPsix8fX0RGhoKPz+/bieUeVrZYP99K5BZW420mioIeCymunvByXzo1xZbWFjAycmJ298+JCRkyF9T15bdFYI5ob7481oOEtIyUX69CDFjPfD88rn6rpqGuxw8YSUwgVimPb+6CY+Pqc6GN3mV6mqkj5HT5Wc6oG6R6zuQ51XX9ThzmAAoum3WcFNLO376MwmvfnIYL32wH39fKYBCiW7XmA+EiaBv3xVN+tiFTwjB22dOq/5fS5k3T57AgT/+4IK4p6cnFi5ciI0bN+LBBx9EcHBwr7PCgxyc8EBgKJb6BQ9LEFdTt8qNsXtdzdJEhPsnjsOLcyYjkC+FpK5a31XqQsTj47nAnueKrPKbBAtB/4Z7KD3Rw/Kzs2fP4t577+VySBw4cEDjfFVVFR5//HG4ubnBzMwM8+bNQ15eXpfrxMfHY9asWTA3N4eVlRViYmL6nfeftsh1wFAmu/Vl5rCAf2sM+UJKITZ/chhSmRzqpKmEmCC/kYdHWgkcHAZfJwuRCHd5uiOptFxr97pcqUSsf98mQV2rrERJp/Ht7nQwDNrs7bEkPBwhISE9zgkwND4+PkhISEBhYaHRbAOqjYuLCxiGQXNzM5qbm3tcKaEPq/wmoUnaji+zL6p++5UEDMuAAFg59i6sDZ6u7ypSBqy1tRVhYWF48sknsXTpUo1zhBAsWbIEAoEABw8ehJWVFT744APExsYiMzOTixnx8fGYN28eNm/ejE8++QR8Ph/Xrl3rd5pmGsh1oHPX+nB/+BJCkFZWhf1X01FQVddLYcBaqGphFJbV4aWPDqm2Y1WfBAAw6JARvPDer/jtP0/CwmzwLZLnpkzEqr37uz3HYxiEuDrjLs++TSiqaetbr8ddM6Zjakhon+toKLy8vMCyLJqamlBfXw97e3t9V2nAhEIhHBwcUFNTg/Lycvj7++u7ShoYhsHG0FlYMSYSH535A+klhRjj5IpNc5bBw9xW39XTK7lcgfiUIlTWNcPG0hRTxvvAzMRw5wvoo2t9/vz5mD9/frfn8vLykJCQgPT0dAQHq1a6fPHFF3BxccHevXvx1FNPAQDWr1+PtWvX4pVXXuGeO5C/E9q1rgPqrnWFQjGsGcsUSiVeP/A3HvhyL35NSkd5Q7P2LqKbx8pqm6BQKvG/Y1dvTkTqSkkImlracfR8pk7qOXWMF/51zxzwWRYsw4DHMNzmI0EuTvjygcW9fvmRy+VIT0/H1bPn+vSaLkaaL1soFMLDQ7W5iDF3r6up082Wl5f3UlJ/XM2scY+FF6IqCWJ5TiM+iJ+8lIt7n/8SL+04iA+/P4k3PjmChat3Yu+RRG5nP4Ojw651sVis8RjIZ7r6OZ035GFZFiKRCOfPnwegWkVz6dIlODk5YfLkyXB2dsb06dO58/1BA7kOCAQCCIWqb6vD2b3+1dkr+O1qOgDVdqAMACg6FbgtqDNygBBArlDiTGJ+z+PpBDiTlK+zui4LD8a5dU9j48wpuDckAPdHhGDXiqXY9+RDsDPvflIdIQQVFRU4evQoduzYgd9++w3tJSWwUii6La9mb2qGKZ5eOqv7cFOPkxcVFem5JoOnDuSd92Y3ZAYbqIbJuaQCvPbRH2hsVo3Rqv85JB0y/PfHM/j5aJIeazc8PDw8YG1tzT22b9/e72sEBATA09MTmzdvRkNDA6RSKd577z3cuHGD+1tQf1Hftm0bnn76afz1118YP348Zs+e3e1Yek9o17qOWFhYoL6+Hq2trXDQxeByL6RyOXZf6PpHxQAgipv/oz5GAChVh1ysLSAS8CGT9xwMAaBDqn2r1oGwNzfD05Pv6rVcW1sbUlNTkZKSorFkzMrKCmFhYYhydMTaE39r7VHYOnMm1+I3Rj4+Pjh16hSKioqgVCqNelvTzi1yQx7zN9R6DSdCCD7dc4bbYrg7X+27iMWzxxleN7sO15GXlpbCyupWvgORqP/DiwKBAPv378eqVatgZ2cHHo+H2NhYzJ8/n/uyqFSqNl969tln8cQTTwAAIiIicOLECXz33Xf9+gJBA7mOmJubc4F8OGSUV0Ms6b7LhwE0uovUH1EMw+DhSeEAAL/RTkjJKYNSS6ucxzII9HEZdD2v1zfiSGYOGiUSeNhY497gANiYmnQpp1QqkZ+fj5SUFOTk5HC/5DweD4GBgQgPD9dY721haYltp06iuLGRu4abpSVejZmOBX7DlyVuKFhZ2aG81gRl1QRXntwJby8nLJoXhikTx4LtIYWsIXJ2dgbLsmhtbYVYLIa1tbW+q9QtdSAfyS3y3OJqlFT0nAtf0iHDhauFuHtywDDVqm8YaLRdBnwNQNVg6BzIByoyMhIpKSloamqCVCqFo6MjJk6cyOXncHVV5fQI6pT3AgACAwNRUlLSr9eigVxHhjspjKy77uXOYz23/VazDIMITzc8Eh0BALj/7ghczeohRaWSYOmsge+pLlUo8Maff2N/aiZ4DAOGYaBQKvHvE2fwauwMPBwZBgCora1FcnIyUlNTNf7tXF1dERERgZCQkC57pgNAzOjROPH4E0iprEBFsyqz2wR3d7BG3rIqr2zEC6/sRW0dC4Cgua0VdQ3FuHy1CDHRvtj68iLwecbTQhcIBHByckJlZSXKy8sNMpB3yOW4VF2PdFaItvomTGttg72W4Z47WVNz70ueGAZoFBvOnhLGQP07n5eXh8TERLz99tsAgNGjR8PNzQ05OTka5XNzc7VOotOGBnIdGe6kML5ODuCzLOQ3W65QAIyyaznCAHaWpnh4UjienDYBopvrumdMGIvFM0Jx8HTazQxcqvIsy0CpJFj38Ax4uw98xvQ7cafwe6pqspyCEO4FZAol3jx2Eg0V5TCtKseNG7e+TJiZmWHcuHEIDw+Hs7Nzr6/BMAwiXN0QcYdsUEUIwavv/I6GBvXvkOpLibrX5FxCHn769RJWPhCtpxoOjKurKxfIAwMNaw/6wxk52PrXCTR3dIBhRbhWWY8/PvkaT0SNx8aZU43+i2F/ONr2nkmQEMDZwTDS7GrQQ4rWlpYW5OffmkdUVFSElJQU2NnZwdPTE/v27YOjoyM8PT2RlpaGdevWYcmSJZgzZw4A1efXpk2bsHXrVoSFhSE8PBzff/89srOz8euvv/arLjSQ68hwryW3NTfFglB/HEnNhkJBwHYTxAHV+PgTkyOxaobm2DTDMNj8ZCzC/d3x819XkXO9GgwDRAZ64JGFEzApdPSA61YpbsYvKena/y4Iwa5rGbhX3gqWYeDr64vw8HD4+fmB10Ou9DvdtfQbKLquPcMdIcCvh5Lw0NIoCATG8+/k5uaG5ORkg5u5fiqvEBsPHuV+T8nNoC1XKvF1QiLAAJtmTtNfBYcJIQS5ubmIi4uDtYkCTRIW2jqqbSxNER3uPbwV7AN9LD9LTEzEzJkzuZ83bNgAAHjsscewe/duVFRUYMOGDaiqqoKrqytWrlyJ119/XeMaL774IiQSCdavX4/6+nqEhYXh+PHj/d40iQZyHdFHdrfNC2YgvawSxRUN3fWmc3aevISHJofDTKiZzYxhGCyYGoQFU4OgUCrBgNHJGOzfuQU9jzUyDJrBQ1D0ZMyPnmRwiUL05VpGaY8pdgGgSdyOG+UN8PYa+gmVumKIE94IIdhxuudlPrsuXcWqqEitqyruBNXV1Th27Bg3g3r8aHOcy2OhJNCYP6OeALfxidkaSaUMhh5a5DNmzOjxc27t2rVYu3Ztr9d55ZVXNNaRD4TxDLYZOH3kW7cxM8Gr82cA6HmiR5tUhvM5xT1ei8eyOptIVdXQ0KeJJ37BITSIjwDOzs7g8XiQSCRo7DQ5UZ+K6huQW6N9y1VAlachLkd3SzANSVtbG44cOYKdO3eisLAQPB4PU6ZMwdZXXsDObQ8hwEdzaGuUqy3+b+NixEYbVlIfSoW2yHVEXzugSfuwjAwAmtq1bw6hCzU1NcjIyEBGRgay6hqh5PfcimEZBh42hjfxSZ/Cgj16bI0DgLWVKUa5GVfCEh6PB2dnZ5SXl6O8vBy2tvqvf424udcyLMOgUTK0fzfDTaFQ4PLlyzhz5gyXtCQwMBB33303974Ej3XFt28/jOKyOlTdzOzmN9rJIHpSejRyFxzQQK4r+toBbZRd34KhRx/L9UdtbS0XvGtqarjjnjwezFkWbUplt39bPIZBrP/YETkzuCdhIaPg7eWAktLuN79hGGDZveONanxczc3NDeXl5SgrK+NSVg63jo4O5OTkIDMzE6n5BQBjqvpH1UJByB3zZZMQgry8PMTFxaGuTpXK2cXFBXPnzsXo0aO7fc5od3uMHsSE1+E00nc/o4FcR9QtcplMBqlUymV6G2p/JGV1TpPeBQPA2doCUT4eOnm92tpaZGZmIiMjA9XVt3a1YlkWY8eORXBwMPz8/DC9rAKrfzkIAmhslsICsDUzxSuzY3RSnzsJwzD415b78MIre1FX34Jb/2yqGRDTJvni4eW62ZVuuOkrw1vn4J2fnw/FzWWbIgBepixK5QTdzRNloNrFLdavf5OODNHt4+Dm5uaYNWsWwsPDjTrZEHULDeQ6IhQKIRAIIJPJ0NLSAjs7uyF/zZLaRuw6naj6QbXsWDOY3/z5rWVzBjX+XVdXh4yMDGRmZmpkWmNZFmPGjEFwcDD8/f018gpPH+ONHx9Zjk/OJeBisSq5AUsIvKHAzhXL4G5tgEtYDICbiw12f/oE/vw7HXGnM9DcIoG8owmu9h1YuTzEqNaQdzacE94kEglyc3O7BG8AcHBwQFBQEIKCgvAwj48Hvv8ZbVKZaonkTeqavTlvNkR93F7XELW1teHUqVNISkoCIQQ8Hg+TJk3CtGnTBpStzKDpYbKbITHe31IDZG5ujsbGRrS2tg5LID+UlAmWZUCUBESJbqcuChgeJo7xQENbOw4mZyK/ph5mAj5ig3xx12h3rR+o9fX1XPCurKzkjrMsCx8fHy54d5esRS3Swx27VyxDfVs7mtrb8ftPe9Bc34yGshvwcjSeWdfDzdLCBPcvmYD7l6gyQB09ehRXrlxBcvJV+Pr2bbtXQ+Po6Ag+nw+pVIq6ujqdpzGWSCRcy7ugoEBr8HZyujXW6wxg3+MPYfvfZ3C2oJj7HPdzcsDGGVMxY6zhLbPqC4VCgStXruDMmTOQ3BzjDwwMRGxs7LB8LukD7VqndMbCwgKNjY3DNuGtsvHWhB0GAJSaXyoZADKlAr9cuYZ//3UWcqUSLKOK9j8kpCDCwxWfP7IENmaqlnR9fT3XbX578Pb29kZQUBACAwN7DN7dsTMzhZ2ZKSaEheHUqVNITU1FeHj4wG56BJowYQKuXLmC7Oxsg9zXuy9YloWrqytKS0tRXl6uk0Del+AdHBwMR0dHrV9Yfezt8PUD96G6pQXlTc2wNjHBaDsbw5/Y1Y3uxsGdnZ0xb948rePg1J2BBnIdGu4Jb7bmXQPq7R8/PJbBO4dPg9w8oSC3RgRTb1Ti2e9/wwuhXsjMzNQYv2QYBj4+PggKCkJAQADMzAY/MW3cuHGIO3UKx4tvoOjocdhYmGOOvy8CnR0Hfe07mZOTEzw8PFBaWork5GTExBjn/ILOgXzcuIGl/9VF8O6Ok4UFnIx061tANQ4eFxeHgoICACNwHJx2rVO6MtzZ3aaNccOuM9q3FeSxDKwsTFAvk3SbuEBBCFLLq/FzVREcGQUYhtFoeesieHcWX1GN34VWkCkJEq9lAAA+O38JM8Z644PFC2AhMrAdlQxIZGQkSktLcfXqVUydOtWoPpwrqptQUlaPdrkZlKT/e5P3NXg7OTnpuuoGr62tDadPn0ZiYiI3Dj5x4kTExMTceePgPaBd60No9OjRuH79usax7du3a2SxSU1NxZo1a3DlyhU4OjrihRdewEsvvaTxnH379uH1119HcXExfH198d5772HBggVDWfUBGc7sbrm5uTh5cD+cGBbVSn63y2gEPB5qJe09pv1hQNBsaY/Hp9+FwMBA7suIrl2+fgPrDxyFkgBgGI3JRWcLirH+wFF8/cCSIXntO0FQUBD++usvNDU1oaCgAL6+vvquUq9uVDRgx1d/48q167cWVvDMwcuqx5ErX2HGJD8smxcBN+euS7x6C97BwcHcmPdINBLHwSnthrxF/tZbb+Hpp5/mfu48vicWizFnzhzExsZi586dSEtLw5NPPgkbGxs888wzAICLFy/ioYcewvbt23HPPffgp59+wpIlS3D16lWEhIQMdfX7RZdd61KZHCev5uNkcj7aJFKMcXfAfdNC4OlkgzNnzuDs2bMQy1g0iS3AigClALfyKDKqDVQsFHy0CnveU5zH8uDj58dtrTdUPj2foPWckhCcKShCRmUVgl163yxlJBIIBAgPD0dCQgKSkpIMPpBXVjfh2Vf2oKW1QxXEGQAsA0II5EoGlbXN+OXoVeyPu4b/bL4PkSGeGsE7Pz+f28oWUE2W6zxhbaTSNg4+d+5ceHsb5+Q8naBd60PL0tISLi7d72u9Z88eSKVSfPfddxAKhQgODkZKSgo++OADLpB//PHHmDdvHjZt2gQAePvtt3H8+HF8+umn2Llz51BXv1901bVeWd+M53b8ipLqRrAMAyUhuJxVgh+PJSLYQoimMjHaJSJAwIdIpADMWSj5DJQ3301GCTAKoAkS8B0J5D2MFcqVSpRXN+FSTgmi/DyGZJKPWCJBwvXSHsvwWBZ/ZuXRQN6DyMhIJCQkIDc3F2KxWCd7Jg+V7365iJbWDlVim5tBHIBGz5FSSSCTybFp+36snOOA0pIiGrx7UFNTg2PHjnHj4GZmZpg1axYiIiKMaqhlSNBAPrT+/e9/4+2334anpydWrFiB9evXg39zbWZ8fDxiYmI0kqfMnTsX7733HhoaGmBra4v4+HhuV5nOZQ4cOKD1NTs6Orj0g4Cq5T8cdNG1rlQSrP3v7yirbVL9fLMLWqFQwrRGjpIbMqj6yhkQmQKCNoAvVqDVWQCG3B6ECWyULOp53Se9UP/iXk4vQULqdYxxscd/n1mMUfa6zWbVJpX1WoYB0CqV6vR17zQODg7w8vLC9evXcfXqVcyYMUPfVeqWpEOG42ezbmWnY6Dauq2bL4mEAB1SOc4lXYenvZIL3uoJa5T2cfBp06Zp5G4YyegY+RBau3Ytxo8fDzs7O1y8eBGbN29GRUUFPvjgAwBAZWVll+4g9T7UlZWVsLW1RWVlZZe9qZ2dnTWWR91u+/btePPNN3V8N73TRYv8UlYJCsrquhwXNinA69BM4cZ9LCoAsxoZWl0Ft31YMghwcEe9qRTZFTUaGdbUQZzXfmuXo+Lqeqz6ZB/2v7IS5ia6m3hmZ24Gc6EArT0EdIVSCW87/efgNnSRkZFcII+JiTHIllijuB0yueqro6pbvbdeHgZCC1c8//y9NHh30t04eEBAAO6++246Dk5p6Hcgf+WVV/Dee+/1WCYrKwsBAQEaLelx48ZBKBTi2Wefxfbt24d0RuXmzZs1XlssFsPDQzcpSnuibpFLpVLIZDIIBIJenqGSVVCJvxNy0NrWgfzaBvBYFopOXYxQEghblFp3FGMA8GQAr4NAYXKrFI9lEOjhhGcXRePb84n4+XIqGtraAQIwcoAnBdhOe64olARVDc04kpiF+6eG9fPutRPyeFgeFoIfElM0JrndXmZxSKDOXvNOpV5N0NzcjLy8PPj7G85uVC0tLcjIyEDKtXR0TTOoHcsycHFxoUG8k7y8PBw7doyOg/cV7Vrvn40bN+Lxxx/vsYyPj0+3xydOnAi5XI7i4mL4+/vDxcVFI+UnAO5n9bi6tjLaxt0BQCQS6WXphUgkAo/Hg0KhQGtrK2xsbHos3y6RYcvHf+BiShF4PBYMAAmfQMlnND4DWTnptduHQB3Ibx1TKgmWxYyDuUiItbMnY83MSbj33V24USvu8SP2z6QcnQZyAFgzdRJOFxSjtKFRI5izjGoC1JvzZ8PalHYT9obP5yMsLAzx8fFISkrSeyBvbW1FVlYWMjIyUFxczB13tBahVswHCG4tfdTSMlcqCSJDhv6LtjGoqalBXFwc8vNV26fScfC+YQgB08Pe4H29hrHqdyB3dHQc8DfnlJQUsCzLTVyJjo7Ga6+9ptF6PX78OPz9/bkt9aKjo3HixAm8+OKL3HWOHz+O6OjoAdVhKDEMA3Nzc4jF4j4F8jc/P4qE1GIAqjFw1TVw810Z+KQz9QS5F/8RAy/nW93VPJaFpEPe45UJgOb2jh5KDIy1qQl+eexBfHIuHr9ey0C7TNXNPs7NBf+cOgkxY0br/DXvVJGRkYiPj0deXh4aGxt7/T3Ttfb2di54FxUVaeQocHd3R3BwMJZYuWD92wchlytUY+W87n/r2Ju5DmKnBAxX9Q3S7ePgLMty68HpODjVmyEbI4+Pj8elS5cwc+ZMWFpaIj4+HuvXr8cjjzzCBekVK1bgzTffxKpVq/Dyyy8jPT0dH3/8MT788EPuOuvWrcP06dOxY8cOLFy4ED///DMSExPx1VdfDVXVB8XCwgJisbjXcfLisjqcuZLf5TgrBxQiaEwOUgoYEFY1G10bBoD8Zrd6qI8LHp97F6aHdd25abSzLRpa2jXHyzvhsQx8XIZm/M3G1ASvz5mJTTOnobqlBWYCARwshmbd+p3M3t4e3t7eKCoqwtWrVzFr1qwhf0310rCMjAwUFBRozC53dXVFcHAwgoODNb5U/PfN+/H2x0dRVtkIoiS3Zq4DUPdjmpkIsePVZTAR9W0Y6k6jUCiQmJiI06dP03HwwaBd60NDJBLh559/xrZt29DR0QFvb2+sX79eY+za2toacXFxWLNmDSIjI+Hg4IA33niDW3oGAJMnT8ZPP/2ELVu24NVXX4Wvry8OHDhgcGvI1Xqa8EYIgbhZlWXtzJV8sCzDTTRTYwDwJUQVlNXBnGEgteBBKFZ025rm8Rj4jXXBe9vvh4DHg2kPH4r3TwlDUn6Z1vMKJcE/pgwsfWZfmQj48LS1GdLXuNNFRkaiqKgIycnJmD59Ong83e9RLpVKueB9+y5iTk5OCA4ORkhIiNaAE+Lvhp8/W4XkjFJcv1GPRnEbSiobUFBSi+amRliLWrFiyQQEjLkzlxxW1zXj0IlUpOdVgMdjMTFsNObHBMHSXNXCVq8Hr62tBaD6N503bx4dBx8AOmt9iIwfPx4JCdqTgKiNGzcO586d67HM8uXLsXz5cl1VbUh1lxSGEIKjx1Lx86+XUXqjHgBg6mim9RsgqwAEbQRyIQMTcwGkcjncAxxhIwbysyuhnkjE3Iz1zk7WeHPLEliZ9d4Fd3e4L/66Ogan0wq6ffn7JoXgrrGj+nnX1HALCAiAubk5WlpakJubi8BA3UwUlMlk3Bagubm5kMtvJRRSZ1Trz9IwhmEwPsQT40M8NY4nJibiyJEjKC7MBTBDJ3U3JMcvZOOtz/4EIYT7sp6QUoRvfrmIrc/PRnHuVY1x8JkzZ2L8+PF0HJwaEJprXcfUM9fVLXJCCD7+/DgO/JGsMdenpbEdSgvtrSiGAOZKFkf/8wzMTYXctf7zwfe4lFgOhmcBVxd73D0rGLGzgmDax+ViPJbF+08sxPcnkvBtXDzaZKouUmcbCzw2MxIPxUQY5c5PIw2Px0N4eDguXLiApKSkQQVyuVyO/Px8ZGRkICcnBzLZrWWCtra2XMu78xaggxUYGIijR4+ioqIC9fX1d0w3sqRDhn1/JWPnT+e6fFEmBGht78DmD44gxrcVIgEdB9cZ2rVO6dLtLfLkayU48EcyANUfshorI4A661U3H448lsH8mGAuiKueT6CUVyE0QIZVq5Zi1KiBtZwFPB6emhOFjvxEFFfWYvGiRZg8PgwsSwO4MYmMjMSFCxdQUFDAJVDqK4VCgYKCAi54d06gZG1tzbW8XV1dh+SLnbm5Oby9vVFYWIj09HSj3dGts9+OpeCLn86iTaI9XwIhgJwAMqE71j93H+zt7Yexhncu2rVO6dTt2d0OHE4Gj8dys9LVGAD8ViXkFl270liWgaujNVbfP0XjeHl5OSQSCUxMTODm5jaoehJC0NjQAAseQaC3Bw3iRsjW1hYurqNxIbEMq9b9AB5fCD8fZyxZGIEJ4V5dArBCoUBRUREyMjKQnZ3NTa4CVKmU1cHb3d19WHplgoODUVhYiIyMDKMP5Af+TsWO7070sTQDCaxpEKd0hgZyHTM1NUVLE3C9oAH1dS0oKKzuEsTVeHICplkBnrUQHVCCEMDMVIhFM0Px+JKJsLbU3G9cnWPZ29t70GNpLS0tkMvlYBhm2JcvUbqRkV2O/XF1kHTwAXQA6EBdXQvOX8rHsnvH44WnZ4EQguvXryM9PR1ZWVlob2/nnm9ubo6goCCEhITAw2No8uz3JDAwEEeOHEF1dTVqamqMNiGMVCbHzr09z/O5nTrzHaUjtGud0pU/DyXj+69Oob6OBdCCiyc/gomlSNWFrqXFyyoALwtLfPHfx9AukcLKwgR8fvdj54WFhQC0J9zpj/p61aQ7GxubIZnxTA0tiUSGV976DVKZEp1zDqjzm//2x1VIWqvAKso1Jl6amZkhMDAQwcHB8PLy0uvkKlNTU4wZMwZ5eXnIyMgw2NzxvbmSVgJxi6T3gjexLINgX9chrNHIQ7vWKZ3Y+/157Np5SuMYURJIxBIIAMgsBN0Gc5ZlMHN6IExNBDA10b5srKOjAzdu3AAAjBnTdX14f6kDeX/GVSnDceJcNsTNPQUPgjPxpYgKVg3FqIO3LnpzdCk4OJgL5NOnTzfKiZZNze29F+qMAEtih3aJ54hDW+TUYNXVNGP3V6e7Paee4MbrUEBhqvnPzbIMzMyEuGd+7+lQi4uLoVQqYWtrq5Pgqw7kd8ps4ZEmNeMGeCxza4exLhi0tDNYtux+BAb6GWyvi7+/P3g8Hmpra1FdXd1lgyRj4OLQt+1k2Zv7sb/y7By4O9sMbaWoEYUGch04/meqaqdGLedVm5oooTAh4N3sNlcolLCxNsN7by+HvZ1Fr6+hHh/XRWscABoaGgDQQG5s1BPWbpSWas3O19nYsWMNNogDgImJCcaOHcslnjG2QN7R0YGK4lSYCJSQyBhoS63MMMCMKF88sDASoX6Dm6hKdc+Yu8YHiwZyHaiqaLzZJdjDbxIBNr4wFwXFNSAAxoWMQswUfwgEffuQ1eX4OEC71o2JTCZDfn4+srOzuaViCikPhGjfGIhhGIz1cYRJD8M1hiI4OJgL5DNnzjSo7vUOqRxVtWKIhHw42Vtq1C0/Px+HDx9GU1MTglx4SC5VTU7t/CnAMAxYlsHHr/0D44PpxjBDhhDN9b0DvYaRooFcB6ysTXstw7IMYmcH417T/u/z3djYiLq6OjAMo5P0jYQQLpDTJTCGSSKRIC8vD1lZWcjPz9dI0mJubo55YX648b8itLbJuqT5BVTv8YP3RQ1nlQfM398ffD4f9fX1qKyshKur/ieCtbR14Nt9F3HoZBrab64LH+PpgCeWTkJ0uCeOHTuGa9euAVBNGF25chFqm1n864vDqKq/NXfB39sJa1fOQHggzZZIDR0ayHVgxt0h2Pv9Ba3nWR6D6Kn+MB1AEAdutcZHjRqlkwxQ7e3tXAIQuvTMcLS2tiInJwfZ2dkoLCzUyG1ubW2NwMBABAYGYtSoUWBZFoEhldiw5Re0tUm5bnYej4FCQfDQsijMjjGOHcWEQiF8fX2RlZWF9PR0vQfy1nYpntv6M4pu1Gl8SSosrcWWjw5jnCfgaqHK3Dhx4kTMmjULQqEQ3gDui7ZEalY9wiMnYuqkSIweRb8oDwc6a50aNO8xTpg1JwSnjmdobOmootqS8JFV0wZ8ffX4uK671a2srLjtYyn9EIvFyM7ORlZWFq5fv67x++Pg4MAFbxcXly5dzv5jXfDjzlU4fCwVZy7mQtIhg+/NhDBhwcbVAgwODua2Ro2NjdVr9/rew4ldgjhwq+c1tYRgzER7PLh8MTw8bnWXK5VKlJWVwdJEidip4+DiQoP4sKGz1ild2LhlEUzMhPjrUDKUSsLtbCY0AaJiLDDaZ2DJLpRKJYqKigDobqIbnbGuO4QQpKbfwMWEfMhkCozxccSsGYE95r6vr69HVlYWsrKyUFamuROdq6srAgMDERAQ0KcEKbY25nj0gWg8+kD0oO9Fn/z8/CAQCNDU1ISysrIBpx8eLEII9seldDtcocYwDOw8IjSCOADU1NRAKpVCKBTCyclpqKtKURwayHVEIODhxZcX4tEnYxB/PhcSiQyu7la4kHAY7e2NiI+Px9SpU/t93YqKCrS3t0MkEsHd3V0ndaUT3XSjobEVr279DVk5qm0qAdVqhM++PIk3Ni/CpCjVFy9CCKqrq7ngXV1drXEdT09PBAQEIDAwcMQOdQgEAvj7+yM9PR0ZGRl6C+RtEhkaxT2vC2cYBjeqmrocV+d5cHd3N6i1+iMBo1Q9BnsNY0UDuY7ZO1rinvsiuZ/NraQ4ePAgzp49i5CQkH5/UKvHx0ePHq2zDwe69GzwFAolXnptHwqKqrmf1dolUrz25n5se3UuJG1VyM7O5r48AQDLshg9ejTX8lbn5x/pgoODuUA+Z84cvXSviwQ8rjdNG4ZhNDYzUlMHcn19CRnRaNc6NZTCwsKQnJyMkpISHDt2DA888EC/nq/L9eOEEKQUlSOhoAotUh6srG0Gfc2R6kpSEfIKqro9R4hqSOSTzw8j2E8145nH42Hs2LEIDAyEn58fTE17X+kw0owdOxYikQjNzc0oLS2Fp6dn70/Sobq6Opw6dQqO5lJUN/NBtKwJVyiUmDXJH4Dqbyq5qgKnrhchobQI5kI+luuo54yi+ooG8iHGMAwWLlyIL7/8EtnZ2cjNzYWfn1+fniuVSlFaWgpg8IE8Pvs63v3fCdyoVXcJmiPjx7NYfx+DxZOCB3XtkejU2aweW26EADX1LIKDgxEYGAhfX18IhQNbtTBS8Pl8+Pv7IzU1Fenp6cMWyFtaWnDmzBlcvXoVSqUSPg4salpUk0Bvn7vKsgxC/dwQETQKtW1tePbPg0iqLAePYUCghNLKBKnnT2CnpTmi3GjLfLjQWevUkHNycsKkSZNw8eJF/Pnnn/D29u7TbHF1WlYbG5tBjWdfyS3Fms9/B7mt76ixrQNb98RBoVRi6eTQAV//Ttbe3o6amhrU1tZq/Dc5uQ1KJQttmbwAgBAGixYtgVBI/8z6Kjg4GKmpqcjKysK8efOGdKxZIpHg4sWLSEhI4Nbpjx07FrNnz0ZxpQRb/3sYza0d4PNYEEKgUBJEBnvgnfX3QkEIVv7xK3LqagEACkJU6dsANHZIsPKP33D4/kcw1pbOXB8WNCEMNRymT5+O9PR0NDY24ty5c5g1a1avz+ncrT7Q8UJCCN7ffxoEROvv6Qe/n8WCCYEwGaEBhxCClpaWbgN2553DOjMz5aOnIA4ATo6WNIj305gxY2BiYoKWlhZcv35dJwmQbieXy3HlyhWcO3eO29bV3d0dsbGxGD16NADAxQU4tHM1Tl/KQ35JDURCPqZNGAN/b1UK2bjCfGTW1nR7fSUhkCkU+Do5Ee/Nmqvz+lNd0RY5NSyEQiHmzZuHX375BRcuXMC4cePg4ODQ43N0kZa1oKIOuWW1PZZpkUhxLqMQd0f0rct/qCiVBDKZHEIhf0gmOhFC0NjY2G3AVifI6Y6VlRUcHR3h4OAABwcHODo6gsAET67+Xmu+c4ZhcN+iyG7PUdrxeDwEBAQgJSUFGRkZOg3kSqUSqampOH36NJqaVENMDg4OmDVrFgICArr8zomEfMydFoi5COxyrSP5OeAxjKol3g0FIfgjP4cGcmpY0EA+jAICAuDr64u8vDwcPXoUjz76qNaA1dTUhNra2kGnZa0Rd9+i7IxhgNo+lBsqZWX1+HlvAv4+ngGpVA5LSxMsvCcc9z8wEdbWZv2+nkKhQH19PWpqarhgrX7I5fJun8MwDGxtbbsEbAcHB4hE3ec0X/PsLHyy8wRYhtEI6CzLINDfDUsXje933SkgJCQEKSkpyMrKwoIFCwbdvU4IQW5uLk6ePMkt/bO0tMSMGTMQHh4+oOu3yKRag7hau0wGQohB5Y6/Y9FZ69RwYRgG8+fPR1FREYqKipCeno7Q0O7HptWtcTc3t0HNcHa0Mu+1DCGAQx/KDYW8vEpseHEPOjpkUChUf0nNzRL88r9LOH0qC598thJ2WnaHk0qlXIDu3Lqur6/vJsOeCo/Hg729PRek1f+1t7cHn9+/P4dlSybA2dkaP+y9iJzcSgCAlZUpltwTgRX3T4JIRLPmDYS3tzfMzMzQ1taGoqKiQU30LCkpwYkTJ1BSUgJAtdva1KlTERUVNaishj42tjjdQ4ucAeBhZU2D+DChXevUsLK1tcW0adNw6tQpxMXFwdfXt9v86epAPtjZ6mNc7eHn7oC88lqtY+QWJkLEhOgm/Wt/EELwzlsHIZF03fhDqSSoqRHj0/8ex6aX53XbHa7uHu2OUCjUCNTq/9ra2up0AtXUaF9MjfZFY1MbpFI57OwswOfRZCCDwbIsAgMDkZSUhIyMjAH9DVRXV+PEiRPIzc0FoJoRHxUVhalTp+pk6d+DQePwdUpSj2VWhoYP+nUoqi9oINeDyZMn49q1a6ivr8epU6cwf/58jfOEEJ0FcoZh8P/um47Vn+0HmO4nvK1fEgORYPh/FdJSS3HjRr3W8woFwZkz2eiQpULbyi0zM7Mu3eGOjo6wtLQc1taQzQCGACjtgoODkZSUhKysLCxcuLDPe6o3NTXh9OnTSElJAaD6/Q8PD8eMGTNgZWWls/qNsbXDi3dF46Mr8bh9A2OWYRDu5IJHQsJ19npUL+isdWq48fl8LFy4ED/88AOuXLmC8PBwjR2fKisr0dbWBqFQqJO0rFH+nvjs+fvw1k9xqGho4Y7bWZhi3eJpw7aOXCKRoKGhgXucPJHXp+e1tTJwcLDs0rp2dHSEmRkNoHciLy8vmJubo7W1FYWFhfD19e2xfFtbG86dO4crV65wu8YFBgZi1qxZvU4qHagXoybD09oGnyYmoLBRlS3RSijCipBxWDchGib9HKqhBo52rVN64ePjg5CQEKSnp+PIkSNYtWoV14JULzvz9vbuc0ukJ60tElSlV2NcswDm5a2wHWWBx55ahIlBXhDo4PpqSqUSYrFYI1h3fqiX+qhVlLMAeOhtGddjjz2K8AjdL0OiDBfLsggKCsLly1fw95kkiDtE8HSzg+Nt8yWkUikSEhJw8eJFbuXB6NGjMXv27GFJlbrUPwj3+QWivKUZUoUCbpaWEPHoxyo1vOhvnB7NmTMHubm5KCsrw9WrVxEZqVqupMttSxMvFeDN135Fh0SV8IKAoKmyBZ9d/wOjPljR713Zbm9Vd340NTVBqex55wFzc3PY2trC1tYWYWGmyM9L6bFHy9raFCGhw5uqk9I/QggqxKY4k2uGjswy/BC3DwwDTBnvg/VPzoaTnTmSk5Nx5swZtLSoepmcnZ0RGxs7qLwLA8EwDNwtdddtTw2AHmatnz17Fu+//z6SkpJQUVGB33//HUuWLOHOV1VV4eWXX0ZcXBwaGxsRExODTz75RKN3acaMGThz5ozGdZ999lns3LmzX3WhgVyPLC0tMXPmTBw7dgxxccfRUMWirLQB8fElsHEa/Ph4yfVavPHyL5DLFZ2CpeoDrr6uBS+t+xG7fn4e5ua3llcplUo0NTVpBOjGxkY0NDSgvr4eEomkx9fk8XiwsbGBnZ0dl5Gu8+P2NKU52XIcP54BoiXV6YMrosHn667XgDIO3/9+Cd/uvwrg1sRBQoD45CKkvvQ9ZgYTSFpV3dk2NjaYOXMmQkND6SzxEUofXeutra0ICwvDk08+iaVLl2qcI4RgyZIlEAgEOHjwIKysrPDBBx8gNjYWmZmZMDe/tUro6aefxltvvcX9PJDhQhrI9SwqKgpxRy/j3JEGnPr9D7A8BkqFEshk8KXgNDa+vggik4Etk/n9l8tQKruf4KZUEjTUt+Krzw9gbIAZF6wbGxu1Lt1S69yqVgdt9c/9nWT24vp5aBZLEB+fD97NVJgMw0ChUOIfy6OwfHlUf2+bMnI19S345n8Xuz2nUBI0t3YgKU+Ou8aaISYmBhMmTNDJEBRF9cf8+fO7TFRWy8vLQ0JCAtLT0xEcrJqD9MUXX8DFxQV79+7FU089xZU1MzODi4vLoOpCA7me5WVXIP54E27Oz4FSQaBuNZ89kQmZTIGt/3d/r9chhEAikaClpYV7nDqeprG9ZjfPwsWzuRC3aZbh8XhdWtKdA7cuN/8QiQR4+91/ICurHH8fT0dTYzucXawxb/44eHrSPNUj0Z9nM9BlKngnBAwqxUKsXv0sLC31k/+AMjBKonoM9hoAxGKxxmGRSKQ1KZQ26vkanZcWsywLkUiE8+fPawTyPXv24Mcff4SLiwvuvfdevP766/1uldNArmc/fnP25udV11asUklw4XQ2ki7nwNHFVCNIt7a2dvlZPVtXTSLpeVMPgIGJiSliYsZrBOvhXrrFMAyCgtwRFES3f6SAqppmVba8HgYt5QoCiUwJy2GsF2XAdDhG7uHhoXF469at2LZtW78uFRAQAE9PT2zevBlffvklzM3N8eGHH+LGjRuoqKjgyq1YsQJeXl5wc3NDamoqXn75ZeTk5GD//v39ej0ayPWovU2Kyxfye+zKZhiCTz/8GaMD+nZNExMTWFhYwMLCAvkutagqb9c6mYzlMZgYHYyZM2cOoPYUNTSsLU17Hd5hGQbmZv1rJVF3LgY6GCO/+d/S0lKNnAP9bY0DgEAgwP79+7Fq1SrY2dmBx+MhNjYW8+fP1/jdfuaZZ7j/Dw0NhaurK2bPno2CgoJ+zZGigVyP2to6ev3AIgCUCha2ttZcgDY3N9f4f0tLS+7/O6cZHe2RiXde1/7NTqkguGcJ3diDMix3Tw3A7v0JWs/zWAbT7hoLMxO6vzule1ZWVjpJHhQZGYmUlBQ0NTVBKpXC0dEREydOxIQJE7Q+Z+LEiQCA/Px8GsiNhZW1GUxMBZC0y7SWYRgWC++ZhftXTun39WNmBiJ2Xij+/isNDHMrcRHLMlAqCZ5+fna/l59R1FDzHmWPeTFBOHYus0tvEssw4PFYPLFskn4qRxkmA87sZm1tDUA1AS4xMRFvv/221rLqjISdE4T1BQ3keiQQ8DBvUQQO/Xrl5iS3rliGwd0LwwZ0fYZhsOm1RQgN88T+Xy7jepFq/+Tg0FG4/+HJmDSl52xZFKUvm1fPgZmJAAf/ToVCqVrJQAiBs4Ml3nhhPnxHO+m7ipQB0cfys5aWFuTn53M/FxUVISUlBXZ2dvD09MS+ffvg6OgIT09PpKWlYd26dViyZAnmzJkDQJUv5KeffsKCBQtgb2+P1NRUrF+/HjExMRg3bly/6kIDuZ6teHIa4s/moqa6qdtg/vTaWNjad7/7V1+wLIMFiyKwYFEEOjpkYFkWAgFdqkMZNgGfh//3VCye+Ec0Ll4tRJtEBp9R9ogM8QTL0rXilP4lJiZqzC/asGEDAOCxxx7D7t27UVFRgQ0bNqCqqgqurq5YuXIlXn/9da68UCjE33//jY8++gitra3w8PDAsmXLsGXLln7XhSG9DdLeAcRiMaytrdHU1KTTjRN0paGuBd98+jdOHUuHXK5aCmZiTvDUmjlY9I9oPdeOoihqYIb6s1d9/akzt4HP77qLZH/I5RKcP7XNYONET2iL3ADY2ltg09YleG7DPFSUNSDu+J+oqSuFnYui9ydTFEWNcAwhYAbZJh3s8/VpSDdOPnLkCCZOnAhTU1PY2tpq5KEFgJKSEixcuBBmZmZwcnLCpk2bIJfLNcqcPn0a48ePh0gkwtixY7F79+6hrLJeWViawDfAFROjx4FhgOzsbH1XiaIoijJwQxbIf/vtNzz66KN44okncO3aNVy4cAErVqzgzisUCixcuBBSqRQXL17E999/j927d+ONN97gyhQVFWHhwoWYOXMmUlJS8OKLL+Kpp57CsWPHhqraBiEgQLVovKysrEuWIYqiKOo2Sh09jNSQdK3L5XKsW7cO77//PlatWsUdDwoK4v4/Li4OmZmZ+Pvvv+Hs7Izw8HC8/fbbePnll7Ft2zYIhULs3LkT3t7e2LFjBwDV/sLnz5/Hhx9+iLlz5w5F1Q2ChYUFPDw8UFpaiuzsbERF0XzjFEVR2tCu9SFw9epVlJWVgWVZREREwNXVFfPnz0d6ejpXJj4+HqGhoXB2duaOzZ07F2KxGBkZGVyZ2NhYjWvPnTsX8fHxPb5+R0cHxGKxxsPYqFvltHudoiiK6smQBPLCwkIAwLZt27BlyxYcPnwYtra2mDFjBurr6wEAlZWVGkEcAPdzZWVlj2XEYjHa29u1vv727dthbW3NPW7PnWsM1IG8uLi4x3ulKIoa8YiOHkaqX4H8lVdeAcMwPT6ys7OhVKoGG1577TUsW7YMkZGR2LVrFxiGwb59+4bkRjrbvHkzmpqauEdpaemQv6au2dnZwcnJCYQQ5Obm6rs6FEVRhkud2W2wDyPVrzHyjRs34vHHH++xjI+PD7e7S+cxcZFIBB8fH5SUlAAAXFxccPnyZY3nVlVVcefU/1Uf61zGysoKpqamWuswkG3nDFFAQACqq6uRnZ2NsLCBZXejKIq60+kjs5sh6Vcgd3R0hKNj77m5IyMjIRKJkJOTg6lTpwIAZDIZiouL4eXlBQCIjo7Gu+++i+rqajg5qdItHj9+HFZWVtwXgOjoaBw9elTj2sePH0d09MhIkhIQEICzZ88iPz8fMpkMAoFA31WiKIqiDMyQjJFbWVlh9erV2Lp1K+Li4pCTk4PnnnsOALB8+XIAwJw5cxAUFIRHH30U165dw7Fjx7BlyxasWbOGa02vXr0ahYWFeOmll5CdnY3PP/8cv/zyC9avXz8U1TY4Li4usLa2hlwu18jpS1EURXUywrvWh2wd+fvvv48HH3wQjz76KO666y5cv34dJ0+ehK2tLQCAx+Ph8OHD4PF4iI6OxiOPPIKVK1firbfe4q7h7e2NI0eO4Pjx4wgLC8OOHTvwzTff3NFLzzpjGIbOXqcoiuoFo9TNw1gNWYpWgUCA//znP/jPf/6jtYyXl1eXrvPbzZgxA8nJybquntEIDAzEpUuXkJubC4VCAR6PbnhCURRF3TKkKVqpwfPw8ICZmRkkEgmuX7+u7+pQFEUZHtq1ThkylmXh7+8PAMjKytJzbSiKogwQXUdOGTr1OHlOTg5GwK6zFEVRVD/QQG4EfHx8IBQK0dzcjLKyMn1Xh6IoyqCoc60P9mGsaCA3Anw+H76+vgDo7HWKoqgu6Bg5ZQzoMjSKoiiqOzSQGwlfX1/weDzU1dWhpqZG39WhKIoyHASD34vceBvkNJAbC5FIBG9vbwC0VU5RFNUZHSOnjAbtXqcoiuoGgQ7GyPV9EwNHA7kRUa8nLy8vR1NTk55rQ1EURRkCGsiNiIWFBTw9PQHQVjlFURSHzlqnjAntXqcoirrNYCe6qR9GigZyI6MO5NevX0dbW5uea0NRFEXp25DtfkYNDVtbWzg7O6MouwyfvbYHdjZ2uGt2CEKn+IFhGH1Xj6IoatjpYta5Mc9ap4HcyNRWNOL8l4WoLawFoErXuu+DI7BysMTrP6xB6GQ//VaQoihquOlijNuIAzntWjci4oZWrI5562YQv+1cbTNeWvgecpOL9FAziqIoSl9oIDciv3wah5bqRq3niZLgvy/+MHwVoiiKMgR01jplLP78/myvZfJTilFb3jAMtaEoijIQNJBTxqKtWdKncvWVjUNbEYqiKMpg0MluRsTUwgRtko5ey9k6WQ9DbSiKogyEEsBgF+3QdeTUcJj36LRey/iEesJxlN0w1IaiKMow0E1TKKNx/wt3w8zeSut5hmXwwoePDmONKIqiDAAdI6eMhY29JT4/8zrcA9y7nBOY8vHu/g0IvGuMHmpGURRF6QsdIzcyLh72+PbS2yjMuIG//xeP9lYJrjdkwcpTCIcxlvquHkVR1PBTEoAZZItaabwtchrIjZRP8Cg889ZyAMChQ4eQnJyM+Ph4bnc0iqKoEYNmdqOMXXR0NADVjmj19fV6rg1FURQ1nGggvwM4Ojpi7NixAICEhAQ914aiKGq46WKiG22RU3qmbpWnpKSgvb1dz7WhKIoaRnTWOnUn8Pb2hrOzM2QyGZKSkvRdHYqiKGqY0EB+h2AYBpMmTQIAXL58GQqFQs81oiiKGiZKopuHkaKB/A4SEhICCwsLNDc3IyMjQ9/VoSiKGh5EqZuHkaKB/A7C5/MRFRUFAIiPjwcx4jEfiqIoqm9oIL/DREZGgs/no7KyEsXFxfquDkVR1NCjk92oO4mZmRnCw8MB0KVoFEWNEHSMnLrTqCe95ebmora2Vs+1oSiKGmJ6aJGfPXsW9957L9zc3MAwDA4cOKBxvqqqCo8//jjc3NxgZmaGefPmIS8vT0v1CebPn9/tdfqCBvI7kL29Pfz9/QHQVjlFUdRQaG1tRVhYGD777LMu5wghWLJkCQoLC3Hw4EEkJyfDy8sLsbGxaG1t7VL+o48+AsMMfEN1mmv9DjVp0iTk5OTg2rVrmDVrFszMzPRdJYqiqKFBoINc6/0rPn/+fMyfP7/bc3l5eUhISEB6ejqCg4MBAF988QVcXFywd+9ePPXUU1zZlJQU7NixA4mJiXB1dR1Q1WmL/A7l5eUFV1dXyOVyJCYm6rs6FEVRQ0eHXetisVjj0dHR0e/qqJ9jYmLCHWNZFiKRCOfPn+eOtbW1YcWKFfjss8/g4uIy4NsfskB++vRpMAzT7ePKlStcudTUVEybNg0mJibw8PDA//3f/3W51r59+xAQEAATExOEhobi6NGjQ1XtOwbDMFza1suXL0Mul+u5RhRFUYbPw8MD1tbW3GP79u39vkZAQAA8PT2xefNmNDQ0QCqV4r333sONGzdQUVHBlVu/fj0mT56MxYsXD6rOQxbIJ0+ejIqKCo3HU089BW9vb0yYMAGA6pvPnDlz4OXlhaSkJLz//vvYtm0bvvrqK+46Fy9exEMPPYRVq1YhOTkZS5YswZIlS5Cenj5UVb9jBAUFwcrKCq2trUhLS9N3dSiKooaGUqmbB4DS0lI0NTVxj82bN/e7OgKBAPv370dubi7s7OxgZmaGU6dOYf78+WBZVdg9dOgQTp48iY8++mjQtz9kgVwoFMLFxYV72Nvb4+DBg3jiiSe4Qf09e/ZAKpXiu+++Q3BwMB588EGsXbsWH3zwAXedjz/+GPPmzcOmTZsQGBiIt99+G+PHj8enn346VFW/Y/B4PC5BTEJCAk0QQ1HUnUmHXetWVlYaD5FINKAqRUZGIiUlBY2NjaioqMBff/2Furo6+Pj4AABOnjyJgoIC2NjYgM/ng89XTVlbtmwZZsyY0a/XGrYx8kOHDqGurg5PPPEEdyw+Ph4xMTEQCoXcsblz5yInJwcNDQ1cmdjYWI1rzZ07F/Hx8Vpfq6Ojo8s4x0gVGRkJgUCAirJK/P71YcR9fxrp57NoUKcoihoG1tbWcHR0RF5eHhITE7lu9FdeeQWpqalISUnhHgDw4YcfYteuXf16jWGbtf7tt99i7ty5GDVqFHessrIS3t7eGuWcnZ25c7a2tqisrOSOdS5TWVmp9bW2b9+ON998U4e1N14ikQjKYgEu/3AN8R2Z3HF3X1ds+Ho1xsUE6bF2FEVROqCLzGz9fH5LSwvy8/O5n4uKipCSkgI7Ozt4enpi3759cHR0hKenJ9LS0rBu3TosWbIEc+bMAQCut/p2np6eXeJib/rdIn/llVe0TmJTP7KzszWec+PGDRw7dgyrVq3q78sNyObNmzXGOEpLS4fldQ3R3u2/48w3l6Ho0NwQoLygEi/f/RYyE3L1VDOKoigd0UNmt8TERERERCAiIgIAsGHDBkREROCNN94AAFRUVODRRx9FQEAA1q5di0cffRR79+7V+a0DA2iRb9y4EY8//niPZdRjAGq7du2Cvb09Fi1apHHcxcUFVVVVGsfUP6u/qWgr09NUfZFINOBxjTuJuL4ZP7y1r9tzREmgZAi+3bwHO07R3guKoqj+mDFjRo9DlGvXrsXatWv7dc2BDnn2O5A7OjrC0dGxz+UJIdi1axdWrlwJgUCgcS46OhqvvfYaZDIZd+748ePw9/eHra0tV+bEiRN48cUXuecdP36cW1pFaXfu1wQoZNr3JVcqlEg9k4nq0lo4eTgMY80oiqJ0hxAlyCC3IR3s8/VpyCe7nTx5EkVFRRqZbNRWrFgBoVCIVatWISMjA//73//w8ccfY8OGDVyZdevW4a+//sKOHTuQnZ2Nbdu2ITExEf/85z+HuupGr76yESyv97e4oappGGpDURQ1RIgOutWNeALwkAfyb7/9FpMnT0ZAQECXc9bW1oiLi0NRUREiIyOxceNGvPHGG3jmmWe4MpMnT8ZPP/2Er776CmFhYfj1119x4MABhISEDHXVjZ69mx0UCu0tcq6cq83QV4aiKGqojPBtTBkyAtYhicViWFtbo6mpCVZWVvquzrBpaWzF/W5PQyaRdXue5bEInxmM9+LeGOaaURQ1Egz1Z6/6+rOtHwWfEfb+hB7IiRQnmn4wyjhBc63fwSxszLHq3RXdnmNZBjwBD0/9+5FhrhVFUZSO6TCzmzGigfwOt2z9PXhx5zOwdtT8huk9zgsfnH4TvuN9tDyToijKSIzwrnW6jekIsPCZuzH3iZlIO5eFlsY2uPo4YWx4/xIOUBRFUYaJBvIRgi/gI2JWqL6rQVEUpXNEqQRhRu7yMxrIKYqiKONGCIDhTdFqSOgYOUVRFEUZMdoipyiKooybkgDMyG2R00BOURRFGTdCAAxyjNuIAzntWqcoiqIoI0Zb5BRFUZRRI0oCMsiudWNOckoDOUVRFGXciBKD71qny88oiqIoSi9GeoucjpFTFEVRlBEbES1y9TctsVis55pQFEWNHOrP3KFu7cpJx6C7xuXofpdIYzAiAnlzczMAwMPDQ881oSiKGnmam5thbW2t8+sKhUK4uLjgfOVRnVzPxcUFQuHgtkPVhxGxH7lSqUR5eTksLS3BMMyQvY5YLIaHhwdKS0uNbj9bNXoPhoHeg2Gg9zA4hBA0NzfDzc0NLDs0I7kSiQRSqVQn1xIKhTAxMdHJtYbTiGiRsyyLUaNGDdvrWVlZGe0fvRq9B8NA78Ew0HsYuKFoiXdmYmJilMFXl+hkN4qiKIoyYjSQUxRFUZQRo4Fch0QiEbZu3QqRSKTvqgwYvQfDQO/BMNB7oIzBiJjsRlEURVF3KtoipyiKoigjRgM5RVEURRkxGsgpiqIoyojRQE5RFEVRRowGcgBHjhzBxIkTYWpqCltbWyxZskTj/JUrVzB79mzY2NjA1tYWc+fOxbVr1zTKHDt2DJMmTYKlpSUcHR2xbNkyFBcXa5Q5ffo0xo8fD5FIhLFjx2L37t1d6vLZZ59h9OjRMDExwcSJE3H58mWN8xKJBGvWrIG9vT0sLCywbNkyVFVVDfoetm3bBoZhujzMzc25MjKZDG+99RbGjBkDExMThIWF4a+//jKqewCAjz76CP7+/jA1NYWHhwfWr18PiURiNPcwY8aMbsssXLiQK0MIwRtvvAFXV1eYmpoiNjYWeXl5GnWpr6/Hww8/DCsrK9jY2GDVqlVoaWkZtvehsbERa9asgaurK0QiEfz8/HD0qGaqTUN+H3bv3t3l/O2JSYzhfVD7+eefwTBMl9cZzD1Qw4SMcL/++iuxtbUlX3zxBcnJySEZGRnkf//7H3e+ubmZ2NnZkccff5xkZ2eT9PR0smzZMuLs7EykUikhhJDCwkIiEonI5s2bSX5+PklKSiIxMTEkIiKCu05hYSExMzMjGzZsIJmZmeSTTz4hPB6P/PXXX1yZn3/+mQiFQvLdd9+RjIwM8vTTTxMbGxtSVVXFlVm9ejXx8PAgJ06cIImJiWTSpEnE399/0PfQ3NxMKioqNB5BQUHkscce467z0ksvETc3N3LkyBFSUFBAPv/8c2JiYkKuXr1qNPewZ88eIhKJyJ49e0hRURE5duwYcXV1JevXrzeae6irq9M4n56eTng8Htm1axdX5t///jextrYmBw4cINeuXSOLFi0i3t7epL29nSszb948EhYWRhISEsi5c+fI2LFjydSpU4flHjo6OsiECRPIggULyPnz50lRURE5ffo0SUlJMZr3YdeuXcTKykqjTGVlJenM0N8HtaKiIuLu7k6mTZtGFi9erJN7eOihh7q8DjU0RnQgl8lkxN3dnXzzzTday1y5coUAICUlJdyx1NRUAoDk5eURQgjZt28f4fP5RKFQcGUOHTpEGIbh/qheeuklEhwcrHHtBx54gMydO5f7OSoqiqxZs4b7WaFQEDc3N7J9+3ZCCCGNjY1EIBCQffv2cWXS0tIIALJ58+ZB3cPtUlJSCABy9uxZ7pirqyv59NNPNcotXbqUPPzww0ZzD2vWrCGzZs3SKLdhwwYyZcoUo7mH23344YfE0tKStLS0EEIIUSqVxMXFhbz//vtcmcbGRiISicjevXsJIYRkZmYSAOTKlStcmcOHDxMA5D//+c+Q38MXX3xBfHx8uL+P7hj6+7Br1y5ibW2t9XWM4X0ghBC5XE4mT55MvvnmG/LYY49pBPKB3sOff/5JGIYhZWVlWu+B0p0R3bV+9epVlJWVgWVZREREwNXVFfPnz0d6ejpXxt/fH/b29vj2228hlUrR3t6Ob7/9FoGBgRg9ejQAIDIyEizLYteuXVAoFGhqasIPP/yA2NhYCAQCAEB8fDxiY2M1Xn/u3LmIj48HAEilUiQlJWmUYVkWsbGxXJmkpCTIZDKNMm1tbQCAoqKiQd3D7b755hv4+flh2rRp3LGOjo4uXYempqY4f/680dzD5MmTkZSUxHXRFhYW4ujRo1iwYIHR3MPtvv32Wzz44INcl2lRUREqKys16mdtbY2JEydy9xAfHw8bGxtMmDCBK2Nrazts93Do0CFER0djzZo1cHZ2RkhICP71r39BoVAAMJ73oaWlBV5eXvDw8MDixYuRkZHBnTOG9wEA3nrrLTg5OWHVqlVdnjPQe4iNjQXLsrh06VK3daF0TN/fJPRp7969BADx9PQkv/76K0lMTCQPPfQQsbe3J3V1dVy5tLQ0MmbMGMKyLGFZlvj7+5Pi4mKNa50+fZo4OTkRHo9HAJDo6GjS0NDAnff19SX/+te/NJ5z5MgRAoC0tbWRsrIyAoBcvHhRo8ymTZtIVFQUIUTVLSwUCru9Bysrq0Hfg1p7ezuxtbUl7733nsbxhx56iAQFBZHc3FyiUChIXFwcMTU15epkDPdACCEff/wxEQgEhM/nEwBk9erV3DljuQe1S5cuEQDk0qVL3LELFy4QAKS8vFyj7PLly8n9999PCCHk3XffJX5+ft3eg52d3ZDfg7+/PxGJROTJJ58kiYmJ5OeffyZ2dnZk27ZthBDjeB8uXrxIvv/+e5KcnExOnz5N7rnnHmJlZUVKS0sJIcbxPpw7d464u7uTmpoaQgjp0iIf6D0QQoijoyP5/PPPu60PpVt3ZCB/+eWXCYAeH1lZWWTPnj0EAPnyyy+550okEuLg4EB27txJCCGkra2NREVFkZUrV5LLly+T+Ph4smzZMhIcHEza2toIIYRUVFQQX19fsmnTJnL16lVy5swZMn36dDJ79myiVCoJIf0P5P29hzlz5gzqHjr76aefCJ/P7zLeV11dTRYvXkxYliU8Ho/4+fmR559/npiYmBBCun74GuI9nDp1ijg7O5Ovv/6apKamkv379xMPDw/y1ltvGc09dPbMM8+Q0NBQjWO3f/j29x5WrFgx5Pfg6+tLPDw8iFwu547t2LGDuLi4GOX7QAghUqmUjBkzhmzZssUo3gexWExGjx5Njh49yh2jgdw43ZGBvLq6mmRlZfX46OjoICdPniQAyLlz5zSeHxUVRV599VVCCCHffPMNcXJy0hj/7ujoIGZmZtwY0ZYtW8iECRM0rlFaWkoAkPj4eEIIIdOmTSPr1q3TKPPdd98RKysr7po8Ho/8/vvvGvewePFiMnPmTJKVlUV27drFtb5uv4d//vOfg7qHzmbNmkWWLFmi9d+3vb2d3LhxgyiVSvLSSy+RoKAgo7mHqVOnkv/3//6fxrEffviBmJqaEoVCYRT3oNbS0kKsrKzIRx99pHG8oKCAACDJycka9zBhwgTyyCOPkKysLPLOO+8QS0tLjb+H48ePEwDk3XffHfJ7iImJIbNnz9Y4dvToUQKAdHR0GNX70Nk//vEP8uCDDxJCDP99SE5OJgAIj8fjHgzDEIZhCI/HI/n5+V3uQS0mJoasXbuWEELIt99+S2xsbDTOy2QywuPxyP79+/v070YNzh05Ru7o6IiAgIAeH0KhEJGRkRCJRMjJyeGeK5PJUFxcDC8vLwCqMTeWZcEwDFdG/bNSqdQo0xmPxwMArkx0dDROnDihUeb48eOIjo4GAK4+6jKOjo7w8/NDYmIi5syZg4CAANx3330QCAQoLS3l7kG9v7ClpeWg7kGtqKgIp06d6na8TM3ExATu7u6Qy+X47bffsHjxYqO5h57eK0KIUdyD2r59+9DR0YFHHnlE47i3tzdcXFw07sHNzQ1paWlYuHAhAgICsHTpUjQ3N6O1tZW7h+bmZgDQ2FxjqO5hypQpyM/P13hObm4uXF1dIRQKjep9UFMoFEhLS4Orq6tRvA8BAQFIS0tDSkoK91i0aBFmzpyJlJQUeHh4dLkHABCLxbh06RL32RUdHY3GxkYkJSVxZU6ePAmlUomJEyf2+u9G6YC+v0no27p164i7uzs5duwYyc7OJqtWrSJOTk6kvr6eEEJIVlYWEYlE5LnnniOZmZkkPT2dPPLII8Ta2prrbjpx4gRhGIa8+eabJDc3lyQlJZG5c+cSLy8vrptLvfxs06ZNJCsri3z22WfdLj8TiURk9+7dJDMzkzzzzDPExsZGozts9erVxNPTk5w8eZIkJiaS6Oho4uLiMuh7UNuyZQtxc3PT6PJUS0hIIL/99hspKCggZ8+eJbNmzSLe3t4acwEM/R62bt1KLC0tyd69e0lhYSGJi4sjY8aM4boJjeEe1KZOnUoeeOCBbs/9+9//JjY2NuTgwYMkNTWVLF68uNslQxEREeTSpUvk/PnzxNfXl/j5+Q3LPZSUlBBLS0vyz3/+k+Tk5JDDhw8TJycn8s477xjN+/Dmm2+SY8eOkYKCApKUlEQefPBBYmJiQjIyMozmfbjd7V3rg7kHuvxs+Iz4QC6VSsnGjRuJk5MTsbS0JLGxsSQ9PV2jTFxcHJkyZQqxtrYmtra2ZNasWVyXudrevXtJREQEMTc3J46OjmTRokUkKytLo8ypU6dIeHg4EQqFxMfHR2Pdr9onn3xCPD09iVAoJFFRUSQhIUHjfHt7O3n++eeJra0tMTMzI/fddx8pKSnRyT0oFAoyatQorvvudqdPnyaBgYFEJBIRe3t78uijj3a7vMSQ70Emk5Ft27aRMWPGEBMTE+Lh4UGef/55jS8jhn4PhBCSnZ1NAJC4uLhuzyuVSvL6668TZ2dnIhKJyOzZs0lOTo5Gmbq6OvLQQw8RCwsLYmVlRZ544glSX18/bPdw8eJFMnHiRCISiYiPjw959913uwQbQ34fXnzxRa5uzs7OZMGCBRo5FQgxjvehs+4C+UDvobm5uU+vSQ0e3caUoiiKoozYHTlGTlEURVEjBQ3kFEVRFGXEaCCnKIqiKCNGAzlFURRFGTEayCmKoijKiNFATlEURVFGjAZyiqIoijJiNJBTFEVRlBGjgZyiKIqijBgN5BRFURRlxGggpyiKoigjRgM5RVEURRmx/x/AzbzlyFNcGAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Similarly, we can do the same for nodes. Note we plot the reaches in the background for context.\n",
     "ax = df_reaches.plot(color=\"gray\", zorder=-1)\n",
@@ -110,9 +526,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:33.374875Z",
+     "iopub.status.busy": "2024-09-04T12:38:33.374648Z",
+     "iopub.status.idle": "2024-09-04T12:38:33.529133Z",
+     "shell.execute_reply": "2024-09-04T12:38:33.528259Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>group</th>\n",
+       "      <th>name</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>max_TotalRunOff</th>\n",
+       "      <th>max_ActualRainfall</th>\n",
+       "      <th>max_ZinkLoadRR</th>\n",
+       "      <th>max_ZinkMassAccumulatedRR</th>\n",
+       "      <th>max_ZinkRR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Catchment</td>\n",
+       "      <td>100_16_16</td>\n",
+       "      <td>POLYGON ((-687895.163 -1056385.779, -687849.02...</td>\n",
+       "      <td>0.215565</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.021556</td>\n",
+       "      <td>10.661672</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Catchment</td>\n",
+       "      <td>105_1_1</td>\n",
+       "      <td>POLYGON ((-687467.053 -1056337.76, -687566.378...</td>\n",
+       "      <td>0.378048</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.037805</td>\n",
+       "      <td>18.574373</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Catchment</td>\n",
+       "      <td>10_22_22</td>\n",
+       "      <td>POLYGON ((-687436.273 -1056620.891, -687515.47...</td>\n",
+       "      <td>0.414988</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.041499</td>\n",
+       "      <td>22.898754</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Catchment</td>\n",
+       "      <td>113_21_21</td>\n",
+       "      <td>POLYGON ((-687293.487 -1056430.077, -687392.14...</td>\n",
+       "      <td>0.395227</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.039523</td>\n",
+       "      <td>21.803301</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Catchment</td>\n",
+       "      <td>118_30_30</td>\n",
+       "      <td>POLYGON ((-687912.055 -1056002.872, -688031.24...</td>\n",
+       "      <td>0.322125</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>0.032212</td>\n",
+       "      <td>17.857996</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       group       name                                           geometry  \\\n",
+       "0  Catchment  100_16_16  POLYGON ((-687895.163 -1056385.779, -687849.02...   \n",
+       "1  Catchment    105_1_1  POLYGON ((-687467.053 -1056337.76, -687566.378...   \n",
+       "2  Catchment   10_22_22  POLYGON ((-687436.273 -1056620.891, -687515.47...   \n",
+       "3  Catchment  113_21_21  POLYGON ((-687293.487 -1056430.077, -687392.14...   \n",
+       "4  Catchment  118_30_30  POLYGON ((-687912.055 -1056002.872, -688031.24...   \n",
+       "\n",
+       "   max_TotalRunOff  max_ActualRainfall  max_ZinkLoadRR  \\\n",
+       "0         0.215565            0.000011        0.021556   \n",
+       "1         0.378048            0.000011        0.037805   \n",
+       "2         0.414988            0.000011        0.041499   \n",
+       "3         0.395227            0.000011        0.039523   \n",
+       "4         0.322125            0.000011        0.032212   \n",
+       "\n",
+       "   max_ZinkMassAccumulatedRR  max_ZinkRR  \n",
+       "0                  10.661672       100.0  \n",
+       "1                  18.574373       100.0  \n",
+       "2                  22.898754       100.0  \n",
+       "3                  21.803301       100.0  \n",
+       "4                  17.857996       100.0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Finally, we look at a similar example but for catchments.\n",
     "res = Res1D(\"../tests/testdata/catchments.res1d\")\n",
@@ -122,9 +664,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:33.531603Z",
+     "iopub.status.busy": "2024-09-04T12:38:33.531367Z",
+     "iopub.status.idle": "2024-09-04T12:38:33.759169Z",
+     "shell.execute_reply": "2024-09-04T12:38:33.758421Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Let's plot the catchment runoff.\n",
     "df_catchments.plot(column='max_TotalRunOff', cmap='Blues', legend=True, alpha=0.75)"
@@ -148,9 +718,382 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:33.761760Z",
+     "iopub.status.busy": "2024-09-04T12:38:33.761548Z",
+     "iopub.status.idle": "2024-09-04T12:38:34.608684Z",
+     "shell.execute_reply": "2024-09-04T12:38:34.607777Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
+       "&lt;html&gt;\n",
+       "&lt;head&gt;\n",
+       "    \n",
+       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
+       "    \n",
+       "        &lt;script&gt;\n",
+       "            L_NO_TOUCH = false;\n",
+       "            L_DISABLE_3D = false;\n",
+       "        &lt;/script&gt;\n",
+       "    \n",
+       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
+       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://code.jquery.com/jquery-3.7.1.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
+       "    \n",
+       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
+       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
+       "            &lt;style&gt;\n",
+       "                #map_3dc9bd54ebbcd3e2ab73ca8ba78bc718 {\n",
+       "                    position: relative;\n",
+       "                    width: 100.0%;\n",
+       "                    height: 100.0%;\n",
+       "                    left: 0.0%;\n",
+       "                    top: 0.0%;\n",
+       "                }\n",
+       "                .leaflet-container { font-size: 1rem; }\n",
+       "            &lt;/style&gt;\n",
+       "        \n",
+       "    \n",
+       "                    &lt;style&gt;\n",
+       "                        .foliumtooltip {\n",
+       "                            \n",
+       "                        }\n",
+       "                       .foliumtooltip table{\n",
+       "                            margin: auto;\n",
+       "                        }\n",
+       "                        .foliumtooltip tr{\n",
+       "                            text-align: left;\n",
+       "                        }\n",
+       "                        .foliumtooltip th{\n",
+       "                            padding: 2px; padding-right: 8px;\n",
+       "                        }\n",
+       "                    &lt;/style&gt;\n",
+       "            \n",
+       "    \n",
+       "                    &lt;style&gt;\n",
+       "                        .foliumpopup {\n",
+       "                            margin: auto;\n",
+       "                        }\n",
+       "                       .foliumpopup table{\n",
+       "                            margin: auto;\n",
+       "                        }\n",
+       "                        .foliumpopup tr{\n",
+       "                            text-align: left;\n",
+       "                        }\n",
+       "                        .foliumpopup th{\n",
+       "                            padding: 2px; padding-right: 8px;\n",
+       "                        }\n",
+       "                    &lt;/style&gt;\n",
+       "            \n",
+       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js&quot;&gt;&lt;/script&gt;\n",
+       "&lt;/head&gt;\n",
+       "&lt;body&gt;\n",
+       "    \n",
+       "    \n",
+       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_3dc9bd54ebbcd3e2ab73ca8ba78bc718&quot; &gt;&lt;/div&gt;\n",
+       "        \n",
+       "&lt;/body&gt;\n",
+       "&lt;script&gt;\n",
+       "    \n",
+       "    \n",
+       "            var map_3dc9bd54ebbcd3e2ab73ca8ba78bc718 = L.map(\n",
+       "                &quot;map_3dc9bd54ebbcd3e2ab73ca8ba78bc718&quot;,\n",
+       "                {\n",
+       "                    center: [-1056366.4501953125, -687695.2000427246],\n",
+       "                    crs: L.CRS.Simple,\n",
+       "                    zoom: 10,\n",
+       "                    zoomControl: true,\n",
+       "                    preferCanvas: false,\n",
+       "                }\n",
+       "            );\n",
+       "            L.control.scale().addTo(map_3dc9bd54ebbcd3e2ab73ca8ba78bc718);\n",
+       "\n",
+       "            \n",
+       "\n",
+       "        \n",
+       "    \n",
+       "            map_3dc9bd54ebbcd3e2ab73ca8ba78bc718.fitBounds(\n",
+       "                [[-1056709.4993896484, -688020.8009033203], [-1056023.4010009766, -687369.5991821289]],\n",
+       "                {}\n",
+       "            );\n",
+       "        \n",
+       "    \n",
+       "        function geo_json_c7855acc1e6a93dda374993510dd3b1f_styler(feature) {\n",
+       "            switch(feature.id) {\n",
+       "                case &quot;0&quot;: case &quot;114&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#481769&quot;, &quot;fillColor&quot;: &quot;#481769&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;1&quot;: case &quot;34&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#450559&quot;, &quot;fillColor&quot;: &quot;#450559&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;2&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#414487&quot;, &quot;fillColor&quot;: &quot;#414487&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;3&quot;: case &quot;4&quot;: case &quot;6&quot;: case &quot;7&quot;: case &quot;8&quot;: case &quot;9&quot;: case &quot;11&quot;: case &quot;26&quot;: case &quot;38&quot;: case &quot;44&quot;: case &quot;49&quot;: case &quot;64&quot;: case &quot;65&quot;: case &quot;70&quot;: case &quot;72&quot;: case &quot;73&quot;: case &quot;75&quot;: case &quot;77&quot;: case &quot;81&quot;: case &quot;85&quot;: case &quot;86&quot;: case &quot;87&quot;: case &quot;90&quot;: case &quot;94&quot;: case &quot;95&quot;: case &quot;111&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#440154&quot;, &quot;fillColor&quot;: &quot;#440154&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;5&quot;: case &quot;29&quot;: case &quot;45&quot;: case &quot;46&quot;: case &quot;47&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482173&quot;, &quot;fillColor&quot;: &quot;#482173&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;10&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#424086&quot;, &quot;fillColor&quot;: &quot;#424086&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;12&quot;: case &quot;52&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#471365&quot;, &quot;fillColor&quot;: &quot;#471365&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;13&quot;: case &quot;36&quot;: case &quot;55&quot;: case &quot;61&quot;: case &quot;63&quot;: case &quot;110&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#440256&quot;, &quot;fillColor&quot;: &quot;#440256&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;14&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#472a7a&quot;, &quot;fillColor&quot;: &quot;#472a7a&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;15&quot;: case &quot;109&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#481467&quot;, &quot;fillColor&quot;: &quot;#481467&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;16&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#fde725&quot;, &quot;fillColor&quot;: &quot;#fde725&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;17&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#481c6e&quot;, &quot;fillColor&quot;: &quot;#481c6e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;18&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#3d4d8a&quot;, &quot;fillColor&quot;: &quot;#3d4d8a&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;19&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482071&quot;, &quot;fillColor&quot;: &quot;#482071&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;20&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482979&quot;, &quot;fillColor&quot;: &quot;#482979&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;21&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#21918c&quot;, &quot;fillColor&quot;: &quot;#21918c&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;22&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#2a788e&quot;, &quot;fillColor&quot;: &quot;#2a788e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;23&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#29798e&quot;, &quot;fillColor&quot;: &quot;#29798e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;24&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#33638d&quot;, &quot;fillColor&quot;: &quot;#33638d&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;25&quot;: case &quot;97&quot;: case &quot;105&quot;: case &quot;117&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#443983&quot;, &quot;fillColor&quot;: &quot;#443983&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;27&quot;: case &quot;30&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#443b84&quot;, &quot;fillColor&quot;: &quot;#443b84&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;28&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482878&quot;, &quot;fillColor&quot;: &quot;#482878&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;31&quot;: case &quot;32&quot;: case &quot;33&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482677&quot;, &quot;fillColor&quot;: &quot;#482677&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;35&quot;: case &quot;59&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#450457&quot;, &quot;fillColor&quot;: &quot;#450457&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;37&quot;: case &quot;91&quot;: case &quot;92&quot;: case &quot;106&quot;: case &quot;108&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#471164&quot;, &quot;fillColor&quot;: &quot;#471164&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;39&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#470d60&quot;, &quot;fillColor&quot;: &quot;#470d60&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;40&quot;: case &quot;74&quot;: case &quot;76&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#460a5d&quot;, &quot;fillColor&quot;: &quot;#460a5d&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;41&quot;: case &quot;53&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#460b5e&quot;, &quot;fillColor&quot;: &quot;#460b5e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;42&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#404688&quot;, &quot;fillColor&quot;: &quot;#404688&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;43&quot;: case &quot;48&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#472d7b&quot;, &quot;fillColor&quot;: &quot;#472d7b&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;50&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482475&quot;, &quot;fillColor&quot;: &quot;#482475&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;51&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#481a6c&quot;, &quot;fillColor&quot;: &quot;#481a6c&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;54&quot;: case &quot;78&quot;: case &quot;79&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#46085c&quot;, &quot;fillColor&quot;: &quot;#46085c&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;56&quot;: case &quot;57&quot;: case &quot;58&quot;: case &quot;67&quot;: case &quot;68&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#470e61&quot;, &quot;fillColor&quot;: &quot;#470e61&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;60&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#287c8e&quot;, &quot;fillColor&quot;: &quot;#287c8e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;62&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#482576&quot;, &quot;fillColor&quot;: &quot;#482576&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;66&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#433e85&quot;, &quot;fillColor&quot;: &quot;#433e85&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;69&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#3a538b&quot;, &quot;fillColor&quot;: &quot;#3a538b&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;71&quot;: case &quot;93&quot;: case &quot;104&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#26828e&quot;, &quot;fillColor&quot;: &quot;#26828e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;80&quot;: case &quot;83&quot;: case &quot;84&quot;: case &quot;88&quot;: case &quot;89&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#46075a&quot;, &quot;fillColor&quot;: &quot;#46075a&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;82&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#228c8d&quot;, &quot;fillColor&quot;: &quot;#228c8d&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;96&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#32658e&quot;, &quot;fillColor&quot;: &quot;#32658e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;98&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#433d84&quot;, &quot;fillColor&quot;: &quot;#433d84&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;102&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#463480&quot;, &quot;fillColor&quot;: &quot;#463480&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;103&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#453581&quot;, &quot;fillColor&quot;: &quot;#453581&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;107&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#471063&quot;, &quot;fillColor&quot;: &quot;#471063&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;112&quot;: case &quot;113&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#481668&quot;, &quot;fillColor&quot;: &quot;#481668&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;115&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#23888e&quot;, &quot;fillColor&quot;: &quot;#23888e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                case &quot;116&quot;: \n",
+       "                    return {&quot;color&quot;: &quot;#c5e021&quot;, &quot;fillColor&quot;: &quot;#c5e021&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "                default:\n",
+       "                    return {&quot;color&quot;: &quot;#46307e&quot;, &quot;fillColor&quot;: &quot;#46307e&quot;, &quot;fillOpacity&quot;: 0.5, &quot;weight&quot;: 2};\n",
+       "            }\n",
+       "        }\n",
+       "        function geo_json_c7855acc1e6a93dda374993510dd3b1f_highlighter(feature) {\n",
+       "            switch(feature.id) {\n",
+       "                default:\n",
+       "                    return {&quot;fillOpacity&quot;: 0.75};\n",
+       "            }\n",
+       "        }\n",
+       "        function geo_json_c7855acc1e6a93dda374993510dd3b1f_pointToLayer(feature, latlng) {\n",
+       "            var opts = {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#3388ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;#3388ff&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3};\n",
+       "            \n",
+       "            let style = geo_json_c7855acc1e6a93dda374993510dd3b1f_styler(feature)\n",
+       "            Object.assign(opts, style)\n",
+       "            \n",
+       "            return new L.CircleMarker(latlng, opts)\n",
+       "        }\n",
+       "\n",
+       "        function geo_json_c7855acc1e6a93dda374993510dd3b1f_onEachFeature(feature, layer) {\n",
+       "            layer.on({\n",
+       "                mouseout: function(e) {\n",
+       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
+       "                            geo_json_c7855acc1e6a93dda374993510dd3b1f.resetStyle(e.target);\n",
+       "                    }\n",
+       "                },\n",
+       "                mouseover: function(e) {\n",
+       "                    if(typeof e.target.setStyle === &quot;function&quot;){\n",
+       "                        const highlightStyle = geo_json_c7855acc1e6a93dda374993510dd3b1f_highlighter(e.target.feature)\n",
+       "                        e.target.setStyle(highlightStyle);\n",
+       "                    }\n",
+       "                },\n",
+       "            });\n",
+       "        };\n",
+       "        var geo_json_c7855acc1e6a93dda374993510dd3b1f = L.geoJson(null, {\n",
+       "                onEachFeature: geo_json_c7855acc1e6a93dda374993510dd3b1f_onEachFeature,\n",
+       "            \n",
+       "                style: geo_json_c7855acc1e6a93dda374993510dd3b1f_styler,\n",
+       "                pointToLayer: geo_json_c7855acc1e6a93dda374993510dd3b1f_pointToLayer,\n",
+       "        });\n",
+       "\n",
+       "        function geo_json_c7855acc1e6a93dda374993510dd3b1f_add (data) {\n",
+       "            geo_json_c7855acc1e6a93dda374993510dd3b1f\n",
+       "                .addData(data);\n",
+       "        }\n",
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&quot;Feature&quot;}, {&quot;bbox&quot;: [-687740.200378418, -1056305.000427246, -687647.4011230469, -1056237.80078125], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687647.4011230469, -1056305.000427246], [-687647.4011230469, -1056305.000427246], [-687670.6009368896, -1056288.200515747], [-687693.8007507324, -1056271.400604248], [-687717.0005645752, -1056254.600692749], [-687740.200378418, -1056237.80078125], [-687740.200378418, -1056237.80078125]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;105&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#443983&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.27117177844047546, &quot;max_WaterLevel&quot;: 198.34243774414062, &quot;name&quot;: &quot;90l1&quot;, &quot;tag&quot;: &quot;0.0-114.6&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687647.4011230469, -1056305.000427246, -687611.6010131836, -1056273.1002807617], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687611.6010131836, -1056273.1002807617], [-687611.6010131836, -1056273.1002807617], [-687629.5010681152, -1056289.050354004], [-687647.4011230469, -1056305.000427246], [-687647.4011230469, -1056305.000427246]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;106&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#471164&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.07110153138637543, &quot;max_WaterLevel&quot;: 198.5537567138672, &quot;name&quot;: &quot;91l1&quot;, &quot;tag&quot;: &quot;0.0-48.0&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687611.6010131836, -1056273.1002807617, -687588.799987793, -1056248.0988769531], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687588.799987793, -1056248.0988769531], [-687588.799987793, -1056248.0988769531], [-687600.2005004883, -1056260.5995788574], [-687611.6010131836, -1056273.1002807617], [-687611.6010131836, -1056273.1002807617]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;107&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#471063&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.06748456507921219, &quot;max_WaterLevel&quot;: 198.6054229736328, &quot;name&quot;: &quot;92l1&quot;, &quot;tag&quot;: &quot;0.0-33.8&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687588.799987793, -1056248.0988769531, -687552.9998779297, -1056214.3989868164], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687552.9998779297, -1056214.3989868164], [-687552.9998779297, -1056214.3989868164], [-687570.8999328613, -1056231.2489318848], [-687588.799987793, -1056248.0988769531], [-687588.799987793, -1056248.0988769531]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;108&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#471164&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.07178615033626556, &quot;max_WaterLevel&quot;: 198.67498779296875, &quot;name&quot;: &quot;93l1&quot;, &quot;tag&quot;: &quot;0.0-49.2&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687533.3001098633, -1056265.6007080078, -687527.7005004883, -1056223.4002075195], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687527.7005004883, -1056265.6007080078], [-687527.7005004883, -1056265.6007080078], [-687530.5003051758, -1056244.5004577637], [-687533.3001098633, -1056223.4002075195], [-687533.3001098633, -1056223.4002075195]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;109&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#481467&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.0842847004532814, &quot;max_WaterLevel&quot;: 199.12982177734375, &quot;name&quot;: &quot;94l1&quot;, &quot;tag&quot;: &quot;0.0-42.6&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687527.7005004883, -1056309.2006835938, -687522.5991821289, -1056265.6007080078], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687522.5991821289, -1056309.2006835938], [-687522.5991821289, -1056309.2006835938], [-687525.1498413086, -1056287.4006958008], [-687527.7005004883, -1056265.6007080078], [-687527.7005004883, -1056265.6007080078]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;110&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#440256&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.008788511157035828, &quot;max_WaterLevel&quot;: 199.23451232910156, &quot;name&quot;: &quot;95l1&quot;, &quot;tag&quot;: &quot;0.0-43.9&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687647.4011230469, -1056332.1997070312, -687635.1007080078, -1056305.000427246], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687635.1007080078, -1056332.1997070312], [-687635.1007080078, -1056332.1997070312], [-687641.2509155273, -1056318.6000671387], [-687647.4011230469, -1056305.000427246], [-687647.4011230469, -1056305.000427246]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;111&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#440154&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.0052521307952702045, &quot;max_WaterLevel&quot;: 198.50375366210938, &quot;name&quot;: &quot;96l1&quot;, &quot;tag&quot;: &quot;0.0-29.9&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687935.2008056641, -1056476.5010986328, -687931.2006835938, -1056465.8001098633], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687935.2008056641, -1056465.8001098633], [-687935.2008056641, -1056465.8001098633], [-687933.2007446289, -1056471.150604248], [-687931.2006835938, -1056476.5010986328], [-687931.2006835938, -1056476.5010986328]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;112&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#481668&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.08958256989717484, &quot;max_WaterLevel&quot;: 196.1864776611328, &quot;name&quot;: &quot;97l1&quot;, &quot;tag&quot;: &quot;0.0-11.4&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687935.2008056641, -1056465.8001098633, -687925.9993286133, -1056452.698791504], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687925.9993286133, -1056452.698791504], [-687925.9993286133, -1056452.698791504], [-687930.6000671387, -1056459.2494506836], [-687935.2008056641, -1056465.8001098633], [-687935.2008056641, -1056465.8001098633]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;113&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#481668&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.09246139973402023, &quot;max_WaterLevel&quot;: 196.314697265625, &quot;name&quot;: &quot;98l1&quot;, &quot;tag&quot;: &quot;0.0-16.0&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687925.9993286133, -1056452.698791504, -687907.999206543, -1056412.0], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687907.999206543, -1056412.0], [-687907.999206543, -1056412.0], [-687916.9992675781, -1056432.349395752], [-687925.9993286133, -1056452.698791504], [-687925.9993286133, -1056452.698791504]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;114&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#481769&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.09570194035768509, &quot;max_WaterLevel&quot;: 196.55276489257812, &quot;name&quot;: &quot;99l1&quot;, &quot;tag&quot;: &quot;0.0-44.5&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687593.8989868164, -1056502.2009887695, -687587.6008911133, -1056501.400024414], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687587.6008911133, -1056502.2009887695], [-687587.6008911133, -1056502.2009887695], [-687590.7499389648, -1056501.8005065918], [-687593.8989868164, -1056501.400024414], [-687593.8989868164, -1056501.400024414]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;115&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#23888e&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.761049211025238, &quot;max_WaterLevel&quot;: 195.1750946044922, &quot;name&quot;: &quot;9l1&quot;, &quot;tag&quot;: &quot;0.0-10.0&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-687942.8002929688, -1056652.400024414, -687941.8002929688, -1056652.400024414], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-687942.8002929688, -1056652.400024414], [-687942.8002929688, -1056652.400024414], [-687942.3002929688, -1056652.400024414], [-687941.8002929688, -1056652.400024414], [-687941.8002929688, -1056652.400024414]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;116&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#c5e021&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 1.491186261177063, &quot;max_WaterLevel&quot;: 194.03335571289062, &quot;name&quot;: &quot;Weir:119w1&quot;, &quot;tag&quot;: &quot;0.0-1.0&quot;}, &quot;type&quot;: &quot;Feature&quot;}, {&quot;bbox&quot;: [-688012.1000976562, -1056246.8997802734, -688009.8997802734, -1056164.5010986328], &quot;geometry&quot;: {&quot;coordinates&quot;: [[-688009.8997802734, -1056164.5010986328], [-688009.8997802734, -1056164.5010986328], [-688010.9999389648, -1056205.7004394531], [-688012.1000976562, -1056246.8997802734], [-688012.1000976562, -1056246.8997802734]], &quot;type&quot;: &quot;LineString&quot;}, &quot;id&quot;: &quot;117&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;#443983&quot;, &quot;group&quot;: &quot;Reach&quot;, &quot;max_Discharge&quot;: 0.2715153694152832, &quot;max_WaterLevel&quot;: 195.3955078125, &quot;name&quot;: &quot;Pump:115p1&quot;, &quot;tag&quot;: &quot;0.0-82.4&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    geo_json_c7855acc1e6a93dda374993510dd3b1f.bindTooltip(\n",
+       "    function(layer){\n",
+       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
+       "    \n",
+       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
+       "    let fields = [&quot;name&quot;, &quot;max_Discharge&quot;];\n",
+       "    let aliases = [&quot;name&quot;, &quot;max_Discharge&quot;];\n",
+       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
+       "        String(\n",
+       "        fields.map(\n",
+       "        (v,i)=&gt;\n",
+       "        `&lt;tr&gt;\n",
+       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
+       "            \n",
+       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
+       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
+       "    +&#x27;&lt;/table&gt;&#x27;;\n",
+       "    div.innerHTML=table;\n",
+       "    \n",
+       "    return div\n",
+       "    }\n",
+       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
+       "                     \n",
+       "    \n",
+       "    geo_json_c7855acc1e6a93dda374993510dd3b1f.bindPopup(\n",
+       "    function(layer){\n",
+       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
+       "    \n",
+       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
+       "    let fields = [&quot;group&quot;, &quot;name&quot;, &quot;tag&quot;, &quot;max_WaterLevel&quot;, &quot;max_Discharge&quot;];\n",
+       "    let aliases = [&quot;group&quot;, &quot;name&quot;, &quot;tag&quot;, &quot;max_WaterLevel&quot;, &quot;max_Discharge&quot;];\n",
+       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
+       "        String(\n",
+       "        fields.map(\n",
+       "        (v,i)=&gt;\n",
+       "        `&lt;tr&gt;\n",
+       "            &lt;th&gt;${aliases[i].toLocaleString()}&lt;/th&gt;\n",
+       "            \n",
+       "            &lt;td&gt;${handleObject(layer.feature.properties[v]).toLocaleString()}&lt;/td&gt;\n",
+       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
+       "    +&#x27;&lt;/table&gt;&#x27;;\n",
+       "    div.innerHTML=table;\n",
+       "    \n",
+       "    return div\n",
+       "    }\n",
+       "    ,{&quot;className&quot;: &quot;foliumpopup&quot;});\n",
+       "                     \n",
+       "    \n",
+       "            geo_json_c7855acc1e6a93dda374993510dd3b1f.addTo(map_3dc9bd54ebbcd3e2ab73ca8ba78bc718);\n",
+       "        \n",
+       "    \n",
+       "    var color_map_9e3cf2e93c7da794ba489b82768ae315 = {};\n",
+       "\n",
+       "    \n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.color = d3.scale.threshold()\n",
+       "              .domain([1.6153337583091343e-06, 0.0032728663547822905, 0.006544117375806272, 0.009815368396830254, 0.013086619417854235, 0.01635787043887822, 0.0196291214599022, 0.02290037248092618, 0.02617162350195016, 0.029442874522974144, 0.03271412554399813, 0.035985376565022105, 0.03925662758604609, 0.042527878607070066, 0.04579912962809405, 0.049070380649118034, 0.05234163167014201, 0.055612882691165995, 0.05888413371218998, 0.062155384733213956, 0.06542663575423795, 0.06869788677526192, 0.0719691377962859, 0.07524038881730989, 0.07851163983833387, 0.08178289085935785, 0.08505414188038182, 0.08832539290140581, 0.09159664392242979, 0.09486789494345377, 0.09813914596447776, 0.10141039698550174, 0.10468164800652571, 0.1079528990275497, 0.11122415004857368, 0.11449540106959766, 0.11776665209062165, 0.12103790311164563, 0.1243091541326696, 0.1275804051536936, 0.13085165617471758, 0.13412290719574155, 0.13739415821676554, 0.14066540923778953, 0.1439366602588135, 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&#x27;#dbe318ff&#x27;, &#x27;#dde318ff&#x27;, &#x27;#dee318ff&#x27;, &#x27;#dfe318ff&#x27;, &#x27;#e0e418ff&#x27;, &#x27;#e2e418ff&#x27;, &#x27;#e3e418ff&#x27;, &#x27;#e5e418ff&#x27;, &#x27;#e6e419ff&#x27;, &#x27;#e7e419ff&#x27;, &#x27;#e8e519ff&#x27;, &#x27;#eae519ff&#x27;, &#x27;#ebe51aff&#x27;, &#x27;#ece51aff&#x27;, &#x27;#ede51bff&#x27;, &#x27;#efe51bff&#x27;, &#x27;#f0e51cff&#x27;, &#x27;#f1e51dff&#x27;, &#x27;#f3e61dff&#x27;, &#x27;#f4e61eff&#x27;, &#x27;#f5e61eff&#x27;, &#x27;#f6e61fff&#x27;, &#x27;#f7e620ff&#x27;, &#x27;#f8e621ff&#x27;, &#x27;#fae722ff&#x27;, &#x27;#fbe723ff&#x27;, &#x27;#fce724ff&#x27;, &#x27;#fde725ff&#x27;]);\n",
+       "    \n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.x = d3.scale.linear()\n",
+       "              .domain([1.6153337583091343e-06, 1.632355874824725])\n",
+       "              .range([0, 450 - 50]);\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.legend = L.control({position: &#x27;topright&#x27;});\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.legend.onAdd = function (map) {var div = L.DomUtil.create(&#x27;div&#x27;, &#x27;legend&#x27;); return div};\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.legend.addTo(map_3dc9bd54ebbcd3e2ab73ca8ba78bc718);\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.xAxis = d3.svg.axis()\n",
+       "        .scale(color_map_9e3cf2e93c7da794ba489b82768ae315.x)\n",
+       "        .orient(&quot;top&quot;)\n",
+       "        .tickSize(1)\n",
+       "        .tickValues([1.6153337583091343e-06, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.16643773590930785, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.3328738564848574, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.499309977060407, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.6657460976359565, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.8321822182115061, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 0.9986183387870556, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 1.165054459362605, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 1.3314905799381547, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, 1.4979267005137042, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;]);\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)\n",
+       "        .attr(&quot;id&quot;, &#x27;legend&#x27;)\n",
+       "        .attr(&quot;width&quot;, 450)\n",
+       "        .attr(&quot;height&quot;, 40);\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.g = color_map_9e3cf2e93c7da794ba489b82768ae315.svg.append(&quot;g&quot;)\n",
+       "        .attr(&quot;class&quot;, &quot;key&quot;)\n",
+       "        .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.g.selectAll(&quot;rect&quot;)\n",
+       "        .data(color_map_9e3cf2e93c7da794ba489b82768ae315.color.range().map(function(d, i) {\n",
+       "          return {\n",
+       "            x0: i ? color_map_9e3cf2e93c7da794ba489b82768ae315.x(color_map_9e3cf2e93c7da794ba489b82768ae315.color.domain()[i - 1]) : color_map_9e3cf2e93c7da794ba489b82768ae315.x.range()[0],\n",
+       "            x1: i &lt; color_map_9e3cf2e93c7da794ba489b82768ae315.color.domain().length ? color_map_9e3cf2e93c7da794ba489b82768ae315.x(color_map_9e3cf2e93c7da794ba489b82768ae315.color.domain()[i]) : color_map_9e3cf2e93c7da794ba489b82768ae315.x.range()[1],\n",
+       "            z: d\n",
+       "          };\n",
+       "        }))\n",
+       "      .enter().append(&quot;rect&quot;)\n",
+       "        .attr(&quot;height&quot;, 40 - 30)\n",
+       "        .attr(&quot;x&quot;, function(d) { return d.x0; })\n",
+       "        .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })\n",
+       "        .style(&quot;fill&quot;, function(d) { return d.z; });\n",
+       "\n",
+       "    color_map_9e3cf2e93c7da794ba489b82768ae315.g.call(color_map_9e3cf2e93c7da794ba489b82768ae315.xAxis).append(&quot;text&quot;)\n",
+       "        .attr(&quot;class&quot;, &quot;caption&quot;)\n",
+       "        .attr(&quot;y&quot;, 21)\n",
+       "        .text(&quot;max_Discharge&quot;);\n",
+       "&lt;/script&gt;\n",
+       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x7f3ae33fcbf0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "map = df_reaches.explore(column=\"max_Discharge\", legend=True, tiles=\"cartodb positron\", tooltip=[\"name\", \"max_Discharge\"], popup=True)\n",
     "map"
@@ -165,8 +1108,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:34.611101Z",
+     "iopub.status.busy": "2024-09-04T12:38:34.610760Z",
+     "iopub.status.idle": "2024-09-04T12:38:34.634865Z",
+     "shell.execute_reply": "2024-09-04T12:38:34.634287Z"
+    }
+   },
    "outputs": [],
    "source": [
     "map.save(\"results.html\")"
@@ -181,8 +1131,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:34.637067Z",
+     "iopub.status.busy": "2024-09-04T12:38:34.636868Z",
+     "iopub.status.idle": "2024-09-04T12:38:34.640395Z",
+     "shell.execute_reply": "2024-09-04T12:38:34.639838Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Shapefile fields are limited to 10 characters.\n",
@@ -192,9 +1149,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:34.642554Z",
+     "iopub.status.busy": "2024-09-04T12:38:34.642268Z",
+     "iopub.status.idle": "2024-09-04T12:38:34.670090Z",
+     "shell.execute_reply": "2024-09-04T12:38:34.669383Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ryan/.pyenv/versions/mikeio1d-dev/lib/python3.12/site-packages/pyogrio/raw.py:698: UserWarning: 'crs' was not provided.  The output dataset will not have projection information defined and may not be usable in other systems.\n",
+      "  warnings.warn(\n",
+      "/home/ryan/.pyenv/versions/mikeio1d-dev/lib/python3.12/site-packages/pyogrio/raw.py:698: UserWarning: 'crs' was not provided.  The output dataset will not have projection information defined and may not be usable in other systems.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "# Shapefiles require all geometries to be of the same type\n",
     "df_reaches.to_file(\"reaches.shp\")\n",
@@ -211,8 +1186,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:34.672667Z",
+     "iopub.status.busy": "2024-09-04T12:38:34.672311Z",
+     "iopub.status.idle": "2024-09-04T12:38:34.677661Z",
+     "shell.execute_reply": "2024-09-04T12:38:34.677110Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
diff --git a/notebooks/res1d_lts.ipynb b/notebooks/res1d_lts.ipynb
index 2d83db42..3657a523 100644
--- a/notebooks/res1d_lts.ipynb
+++ b/notebooks/res1d_lts.ipynb
@@ -12,8 +12,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:21.129891Z",
+     "iopub.status.busy": "2024-09-04T12:38:21.129310Z",
+     "iopub.status.idle": "2024-09-04T12:38:22.637746Z",
+     "shell.execute_reply": "2024-09-04T12:38:22.636359Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D"
@@ -37,8 +44,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:22.640740Z",
+     "iopub.status.busy": "2024-09-04T12:38:22.640344Z",
+     "iopub.status.idle": "2024-09-04T12:38:24.519081Z",
+     "shell.execute_reply": "2024-09-04T12:38:24.518250Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path_events = \"../tests/testdata/lts_event_statistics.res1d\"\n",
@@ -57,9 +71,495 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:24.523647Z",
+     "iopub.status.busy": "2024-09-04T12:38:24.522719Z",
+     "iopub.status.idle": "2024-09-04T12:38:24.545817Z",
+     "shell.execute_reply": "2024-09-04T12:38:24.545279Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
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+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "      <th>DischargeIntegratedTime:A0.0327</th>\n",
+       "      <th>DischargeMaximum:A0.0327</th>\n",
+       "      <th>DischargeMaximumTime:A0.0327</th>\n",
+       "      <th>DischargeDuration:A0.0327</th>\n",
+       "      <th>DischargeDurationTime:A0.0327</th>\n",
+       "      <th>Component_1Maximum:A0.0327</th>\n",
+       "      <th>Component_1MaximumTime:A0.0327</th>\n",
+       "      <th>...</th>\n",
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+       "      <th>Component_1MaximumTime:Pump:B4.1510p2:80.0006</th>\n",
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+       "      <th>Component_2Maximum:Pump:B4.1510p2:80.0006</th>\n",
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+       "      <th>Component_2MaximumTime:Pump:B4.1510p2:80.0006</th>\n",
+       "      <th>Component_1TransportIntegrated:Pump:B4.1510p2:40.0003</th>\n",
+       "      <th>Component_1TransportIntegratedTime:Pump:B4.1510p2:40.0003</th>\n",
+       "      <th>Component_2TransportIntegrated:Pump:B4.1510p2:40.0003</th>\n",
+       "      <th>Component_2TransportIntegratedTime:Pump:B4.1510p2:40.0003</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>4684.849609</td>\n",
+       "      <td>1959-08-15 09:36:24</td>\n",
+       "      <td>1.256756</td>\n",
+       "      <td>1961-06-13 15:58:24</td>\n",
+       "      <td>9.108004</td>\n",
+       "      <td>1957-07-20 09:39:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1961-09-05 15:54:40</td>\n",
+       "      <td>1957-06-11 10:27:52</td>\n",
+       "      <td>5.000000e+01</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1961-09-05 15:54:40</td>\n",
+       "      <td>1957-06-11 10:27:52</td>\n",
+       "      <td>67.307213</td>\n",
+       "      <td>1961-09-05 14:31:28</td>\n",
+       "      <td>33.653606</td>\n",
+       "      <td>1961-09-05 14:31:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>4608.099609</td>\n",
+       "      <td>1961-09-05 12:17:20</td>\n",
+       "      <td>1.236605</td>\n",
+       "      <td>1961-09-05 15:02:24</td>\n",
+       "      <td>6.788862</td>\n",
+       "      <td>1957-06-11 04:12:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-07-20 17:42:00</td>\n",
+       "      <td>1957-07-20 16:33:04</td>\n",
+       "      <td>5.000000e+01</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-07-20 17:42:00</td>\n",
+       "      <td>1957-07-20 16:33:04</td>\n",
+       "      <td>45.068405</td>\n",
+       "      <td>1961-06-13 15:44:16</td>\n",
+       "      <td>22.534203</td>\n",
+       "      <td>1961-06-13 15:44:16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>3744.287842</td>\n",
+       "      <td>1961-06-13 15:38:24</td>\n",
+       "      <td>0.958780</td>\n",
+       "      <td>1959-08-15 13:38:08</td>\n",
+       "      <td>5.560763</td>\n",
+       "      <td>1959-08-15 09:36:24</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1958-05-26 00:36:16</td>\n",
+       "      <td>1958-05-26 00:44:24</td>\n",
+       "      <td>5.000000e+01</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1958-05-26 00:36:16</td>\n",
+       "      <td>1958-05-26 00:44:24</td>\n",
+       "      <td>43.389675</td>\n",
+       "      <td>1958-05-25 23:16:16</td>\n",
+       "      <td>21.694838</td>\n",
+       "      <td>1958-05-25 23:16:16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>3631.110596</td>\n",
+       "      <td>1958-05-25 21:11:20</td>\n",
+       "      <td>0.941482</td>\n",
+       "      <td>1958-05-25 23:35:16</td>\n",
+       "      <td>4.831801</td>\n",
+       "      <td>1961-09-05 12:17:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1961-06-13 18:35:28</td>\n",
+       "      <td>1961-09-05 15:54:56</td>\n",
+       "      <td>5.000000e+01</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1961-06-13 18:35:28</td>\n",
+       "      <td>1961-09-05 15:54:56</td>\n",
+       "      <td>35.251358</td>\n",
+       "      <td>1959-08-15 13:26:56</td>\n",
+       "      <td>17.625679</td>\n",
+       "      <td>1959-08-15 13:26:56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>3070.262939</td>\n",
+       "      <td>1957-07-20 09:39:20</td>\n",
+       "      <td>0.878175</td>\n",
+       "      <td>1957-07-27 16:30:04</td>\n",
+       "      <td>4.549961</td>\n",
+       "      <td>1958-05-25 21:11:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1959-08-15 16:23:04</td>\n",
+       "      <td>1959-08-15 16:04:08</td>\n",
+       "      <td>5.000000e+01</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1959-08-15 16:23:04</td>\n",
+       "      <td>1959-08-15 16:04:08</td>\n",
+       "      <td>23.928904</td>\n",
+       "      <td>1957-06-04 15:09:51</td>\n",
+       "      <td>11.964452</td>\n",
+       "      <td>1957-06-04 15:09:51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>2337.491455</td>\n",
+       "      <td>1957-06-11 04:12:20</td>\n",
+       "      <td>0.830876</td>\n",
+       "      <td>1962-07-21 15:19:28</td>\n",
+       "      <td>4.169593</td>\n",
+       "      <td>1958-05-24 19:07:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1961-06-13 18:10:56</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1961-06-13 18:10:56</td>\n",
+       "      <td>23.925802</td>\n",
+       "      <td>1962-07-21 14:31:28</td>\n",
+       "      <td>11.962901</td>\n",
+       "      <td>1962-07-21 14:31:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>2319.675293</td>\n",
+       "      <td>1957-06-04 14:28:20</td>\n",
+       "      <td>0.763936</td>\n",
+       "      <td>1959-08-15 10:02:00</td>\n",
+       "      <td>4.119199</td>\n",
+       "      <td>1958-11-10 13:35:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1958-11-10 16:18:44</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1958-11-10 16:18:44</td>\n",
+       "      <td>21.306870</td>\n",
+       "      <td>1957-07-27 16:17:58</td>\n",
+       "      <td>10.653435</td>\n",
+       "      <td>1957-07-27 16:17:58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>2229.712158</td>\n",
+       "      <td>1962-07-21 13:50:24</td>\n",
+       "      <td>0.742085</td>\n",
+       "      <td>1957-06-04 15:22:01</td>\n",
+       "      <td>3.769463</td>\n",
+       "      <td>1957-09-07 22:18:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1962-06-19 15:37:52</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1962-06-19 15:37:52</td>\n",
+       "      <td>18.159843</td>\n",
+       "      <td>1959-08-15 09:49:36</td>\n",
+       "      <td>9.079922</td>\n",
+       "      <td>1959-08-15 09:49:36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>1884.727661</td>\n",
+       "      <td>1962-06-19 12:18:24</td>\n",
+       "      <td>0.710324</td>\n",
+       "      <td>1957-06-11 09:53:49</td>\n",
+       "      <td>3.661735</td>\n",
+       "      <td>1959-07-02 03:15:20</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1957-06-04 16:38:19</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1957-06-04 16:38:19</td>\n",
+       "      <td>15.088912</td>\n",
+       "      <td>1957-07-20 15:23:58</td>\n",
+       "      <td>7.544456</td>\n",
+       "      <td>1957-07-20 15:23:58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>1610.750977</td>\n",
+       "      <td>1958-11-10 13:35:20</td>\n",
+       "      <td>0.669762</td>\n",
+       "      <td>1961-08-22 06:47:12</td>\n",
+       "      <td>3.626211</td>\n",
+       "      <td>1962-06-19 12:18:24</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>1957-01-01</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1962-07-21 15:56:16</td>\n",
+       "      <td>-1.000000e-35</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1957-01-01 00:00:00</td>\n",
+       "      <td>1962-07-21 15:56:16</td>\n",
+       "      <td>13.787300</td>\n",
+       "      <td>1959-07-28 06:34:48</td>\n",
+       "      <td>6.893650</td>\n",
+       "      <td>1959-07-28 06:34:48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 1562 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   WaterLevelMaximum:A0.0327 WaterLevelMaximumTime:A0.0327  \\\n",
+       "0              -1.000000e-35                    1957-01-01   \n",
+       "1              -1.000000e-35                    1957-01-01   \n",
+       "2              -1.000000e-35                    1957-01-01   \n",
+       "3              -1.000000e-35                    1957-01-01   \n",
+       "4              -1.000000e-35                    1957-01-01   \n",
+       "5              -1.000000e-35                    1957-01-01   \n",
+       "6              -1.000000e-35                    1957-01-01   \n",
+       "7              -1.000000e-35                    1957-01-01   \n",
+       "8              -1.000000e-35                    1957-01-01   \n",
+       "9              -1.000000e-35                    1957-01-01   \n",
+       "\n",
+       "   DischargeIntegrated:A0.0327 DischargeIntegratedTime:A0.0327  \\\n",
+       "0                  4684.849609             1959-08-15 09:36:24   \n",
+       "1                  4608.099609             1961-09-05 12:17:20   \n",
+       "2                  3744.287842             1961-06-13 15:38:24   \n",
+       "3                  3631.110596             1958-05-25 21:11:20   \n",
+       "4                  3070.262939             1957-07-20 09:39:20   \n",
+       "5                  2337.491455             1957-06-11 04:12:20   \n",
+       "6                  2319.675293             1957-06-04 14:28:20   \n",
+       "7                  2229.712158             1962-07-21 13:50:24   \n",
+       "8                  1884.727661             1962-06-19 12:18:24   \n",
+       "9                  1610.750977             1958-11-10 13:35:20   \n",
+       "\n",
+       "   DischargeMaximum:A0.0327 DischargeMaximumTime:A0.0327  \\\n",
+       "0                  1.256756          1961-06-13 15:58:24   \n",
+       "1                  1.236605          1961-09-05 15:02:24   \n",
+       "2                  0.958780          1959-08-15 13:38:08   \n",
+       "3                  0.941482          1958-05-25 23:35:16   \n",
+       "4                  0.878175          1957-07-27 16:30:04   \n",
+       "5                  0.830876          1962-07-21 15:19:28   \n",
+       "6                  0.763936          1959-08-15 10:02:00   \n",
+       "7                  0.742085          1957-06-04 15:22:01   \n",
+       "8                  0.710324          1957-06-11 09:53:49   \n",
+       "9                  0.669762          1961-08-22 06:47:12   \n",
+       "\n",
+       "   DischargeDuration:A0.0327 DischargeDurationTime:A0.0327  \\\n",
+       "0                   9.108004           1957-07-20 09:39:20   \n",
+       "1                   6.788862           1957-06-11 04:12:20   \n",
+       "2                   5.560763           1959-08-15 09:36:24   \n",
+       "3                   4.831801           1961-09-05 12:17:20   \n",
+       "4                   4.549961           1958-05-25 21:11:20   \n",
+       "5                   4.169593           1958-05-24 19:07:20   \n",
+       "6                   4.119199           1958-11-10 13:35:20   \n",
+       "7                   3.769463           1957-09-07 22:18:20   \n",
+       "8                   3.661735           1959-07-02 03:15:20   \n",
+       "9                   3.626211           1962-06-19 12:18:24   \n",
+       "\n",
+       "   Component_1Maximum:A0.0327 Component_1MaximumTime:A0.0327  ...  \\\n",
+       "0               -1.000000e-35                     1957-01-01  ...   \n",
+       "1               -1.000000e-35                     1957-01-01  ...   \n",
+       "2               -1.000000e-35                     1957-01-01  ...   \n",
+       "3               -1.000000e-35                     1957-01-01  ...   \n",
+       "4               -1.000000e-35                     1957-01-01  ...   \n",
+       "5               -1.000000e-35                     1957-01-01  ...   \n",
+       "6               -1.000000e-35                     1957-01-01  ...   \n",
+       "7               -1.000000e-35                     1957-01-01  ...   \n",
+       "8               -1.000000e-35                     1957-01-01  ...   \n",
+       "9               -1.000000e-35                     1957-01-01  ...   \n",
+       "\n",
+       "   Component_1MaximumTime:Pump:B4.1510p2:0  \\\n",
+       "0                      1961-09-05 15:54:40   \n",
+       "1                      1957-07-20 17:42:00   \n",
+       "2                      1958-05-26 00:36:16   \n",
+       "3                      1961-06-13 18:35:28   \n",
+       "4                      1959-08-15 16:23:04   \n",
+       "5                      1957-01-01 00:00:00   \n",
+       "6                      1957-01-01 00:00:00   \n",
+       "7                      1957-01-01 00:00:00   \n",
+       "8                      1957-01-01 00:00:00   \n",
+       "9                      1957-01-01 00:00:00   \n",
+       "\n",
+       "  Component_1MaximumTime:Pump:B4.1510p2:80.0006  \\\n",
+       "0                           1957-06-11 10:27:52   \n",
+       "1                           1957-07-20 16:33:04   \n",
+       "2                           1958-05-26 00:44:24   \n",
+       "3                           1961-09-05 15:54:56   \n",
+       "4                           1959-08-15 16:04:08   \n",
+       "5                           1961-06-13 18:10:56   \n",
+       "6                           1958-11-10 16:18:44   \n",
+       "7                           1962-06-19 15:37:52   \n",
+       "8                           1957-06-04 16:38:19   \n",
+       "9                           1962-07-21 15:56:16   \n",
+       "\n",
+       "   Component_2Maximum:Pump:B4.1510p2:0  \\\n",
+       "0                         5.000000e+01   \n",
+       "1                         5.000000e+01   \n",
+       "2                         5.000000e+01   \n",
+       "3                         5.000000e+01   \n",
+       "4                         5.000000e+01   \n",
+       "5                        -1.000000e-35   \n",
+       "6                        -1.000000e-35   \n",
+       "7                        -1.000000e-35   \n",
+       "8                        -1.000000e-35   \n",
+       "9                        -1.000000e-35   \n",
+       "\n",
+       "  Component_2Maximum:Pump:B4.1510p2:80.0006  \\\n",
+       "0                                      50.0   \n",
+       "1                                      50.0   \n",
+       "2                                      50.0   \n",
+       "3                                      50.0   \n",
+       "4                                      50.0   \n",
+       "5                                      50.0   \n",
+       "6                                      50.0   \n",
+       "7                                      50.0   \n",
+       "8                                      50.0   \n",
+       "9                                      50.0   \n",
+       "\n",
+       "   Component_2MaximumTime:Pump:B4.1510p2:0  \\\n",
+       "0                      1961-09-05 15:54:40   \n",
+       "1                      1957-07-20 17:42:00   \n",
+       "2                      1958-05-26 00:36:16   \n",
+       "3                      1961-06-13 18:35:28   \n",
+       "4                      1959-08-15 16:23:04   \n",
+       "5                      1957-01-01 00:00:00   \n",
+       "6                      1957-01-01 00:00:00   \n",
+       "7                      1957-01-01 00:00:00   \n",
+       "8                      1957-01-01 00:00:00   \n",
+       "9                      1957-01-01 00:00:00   \n",
+       "\n",
+       "  Component_2MaximumTime:Pump:B4.1510p2:80.0006  \\\n",
+       "0                           1957-06-11 10:27:52   \n",
+       "1                           1957-07-20 16:33:04   \n",
+       "2                           1958-05-26 00:44:24   \n",
+       "3                           1961-09-05 15:54:56   \n",
+       "4                           1959-08-15 16:04:08   \n",
+       "5                           1961-06-13 18:10:56   \n",
+       "6                           1958-11-10 16:18:44   \n",
+       "7                           1962-06-19 15:37:52   \n",
+       "8                           1957-06-04 16:38:19   \n",
+       "9                           1962-07-21 15:56:16   \n",
+       "\n",
+       "   Component_1TransportIntegrated:Pump:B4.1510p2:40.0003  \\\n",
+       "0                                          67.307213       \n",
+       "1                                          45.068405       \n",
+       "2                                          43.389675       \n",
+       "3                                          35.251358       \n",
+       "4                                          23.928904       \n",
+       "5                                          23.925802       \n",
+       "6                                          21.306870       \n",
+       "7                                          18.159843       \n",
+       "8                                          15.088912       \n",
+       "9                                          13.787300       \n",
+       "\n",
+       "  Component_1TransportIntegratedTime:Pump:B4.1510p2:40.0003  \\\n",
+       "0                                1961-09-05 14:31:28          \n",
+       "1                                1961-06-13 15:44:16          \n",
+       "2                                1958-05-25 23:16:16          \n",
+       "3                                1959-08-15 13:26:56          \n",
+       "4                                1957-06-04 15:09:51          \n",
+       "5                                1962-07-21 14:31:28          \n",
+       "6                                1957-07-27 16:17:58          \n",
+       "7                                1959-08-15 09:49:36          \n",
+       "8                                1957-07-20 15:23:58          \n",
+       "9                                1959-07-28 06:34:48          \n",
+       "\n",
+       "   Component_2TransportIntegrated:Pump:B4.1510p2:40.0003  \\\n",
+       "0                                          33.653606       \n",
+       "1                                          22.534203       \n",
+       "2                                          21.694838       \n",
+       "3                                          17.625679       \n",
+       "4                                          11.964452       \n",
+       "5                                          11.962901       \n",
+       "6                                          10.653435       \n",
+       "7                                           9.079922       \n",
+       "8                                           7.544456       \n",
+       "9                                           6.893650       \n",
+       "\n",
+       "  Component_2TransportIntegratedTime:Pump:B4.1510p2:40.0003  \n",
+       "0                                1961-09-05 14:31:28         \n",
+       "1                                1961-06-13 15:44:16         \n",
+       "2                                1958-05-25 23:16:16         \n",
+       "3                                1959-08-15 13:26:56         \n",
+       "4                                1957-06-04 15:09:51         \n",
+       "5                                1962-07-21 14:31:28         \n",
+       "6                                1957-07-27 16:17:58         \n",
+       "7                                1959-08-15 09:49:36         \n",
+       "8                                1957-07-20 15:23:58         \n",
+       "9                                1959-07-28 06:34:48         \n",
+       "\n",
+       "[10 rows x 1562 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_events"
    ]
@@ -74,9 +574,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:24.547925Z",
+     "iopub.status.busy": "2024-09-04T12:38:24.547737Z",
+     "iopub.status.idle": "2024-09-04T12:38:24.551956Z",
+     "shell.execute_reply": "2024-09-04T12:38:24.551261Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['WaterLevelMaximum',\n",
+       " 'WaterLevelMaximumTime',\n",
+       " 'DischargeIntegrated',\n",
+       " 'DischargeIntegratedTime',\n",
+       " 'DischargeMaximum',\n",
+       " 'DischargeMaximumTime',\n",
+       " 'DischargeDuration',\n",
+       " 'DischargeDurationTime',\n",
+       " 'Component_1Maximum',\n",
+       " 'Component_1MaximumTime',\n",
+       " 'Component_2Maximum',\n",
+       " 'Component_2MaximumTime',\n",
+       " 'Component_1TransportIntegrated',\n",
+       " 'Component_1TransportIntegratedTime',\n",
+       " 'Component_2TransportIntegrated',\n",
+       " 'Component_2TransportIntegratedTime',\n",
+       " 'SurchargeMaximum',\n",
+       " 'SurchargeMaximumTime',\n",
+       " 'SurchargeIntegrated',\n",
+       " 'SurchargeIntegratedTime',\n",
+       " 'SurchargeDuration',\n",
+       " 'SurchargeDurationTime',\n",
+       " 'FlowVelocityMaximum',\n",
+       " 'FlowVelocityMaximumTime']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res1d_events.quantities"
    ]
@@ -91,9 +632,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:24.554372Z",
+     "iopub.status.busy": "2024-09-04T12:38:24.554184Z",
+     "iopub.status.idle": "2024-09-04T12:38:24.560832Z",
+     "shell.execute_reply": "2024-09-04T12:38:24.560313Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>WaterLevelMaximum:B4.1200</th>\n",
+       "      <th>WaterLevelMaximumTime:B4.1200</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20.357243</td>\n",
+       "      <td>1961-06-13 15:55:44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.273409</td>\n",
+       "      <td>1961-09-05 14:48:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.067089</td>\n",
+       "      <td>1959-08-15 13:30:48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19.239136</td>\n",
+       "      <td>1957-07-27 16:22:48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19.165405</td>\n",
+       "      <td>1962-07-21 15:15:28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>19.103199</td>\n",
+       "      <td>1961-08-22 06:40:48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>18.928940</td>\n",
+       "      <td>1958-05-25 23:21:04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>18.692797</td>\n",
+       "      <td>1959-07-11 19:06:48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>18.399147</td>\n",
+       "      <td>1961-04-06 20:36:16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>18.359352</td>\n",
+       "      <td>1959-08-15 09:56:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   WaterLevelMaximum:B4.1200 WaterLevelMaximumTime:B4.1200\n",
+       "0                  20.357243           1961-06-13 15:55:44\n",
+       "1                  20.273409           1961-09-05 14:48:00\n",
+       "2                  20.067089           1959-08-15 13:30:48\n",
+       "3                  19.239136           1957-07-27 16:22:48\n",
+       "4                  19.165405           1962-07-21 15:15:28\n",
+       "5                  19.103199           1961-08-22 06:40:48\n",
+       "6                  18.928940           1958-05-25 23:21:04\n",
+       "7                  18.692797           1959-07-11 19:06:48\n",
+       "8                  18.399147           1961-04-06 20:36:16\n",
+       "9                  18.359352           1959-08-15 09:56:00"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_events[['WaterLevelMaximum:B4.1200', 'WaterLevelMaximumTime:B4.1200']]"
    ]
@@ -116,9 +763,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:24.563619Z",
+     "iopub.status.busy": "2024-09-04T12:38:24.563409Z",
+     "iopub.status.idle": "2024-09-04T12:38:24.574155Z",
+     "shell.execute_reply": "2024-09-04T12:38:24.573268Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>DischargeIntegrated:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeIntegratedTime:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeMaximum:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeMaximumTime:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeDuration:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeDurationTime:B4.1200l1:26.6666</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1081.991333</td>\n",
+       "      <td>1961-09-05 12:17:20</td>\n",
+       "      <td>0.464411</td>\n",
+       "      <td>1961-06-13 15:44:16</td>\n",
+       "      <td>9.108004</td>\n",
+       "      <td>1957-07-20 09:39:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1048.237183</td>\n",
+       "      <td>1959-08-15 09:36:24</td>\n",
+       "      <td>0.436320</td>\n",
+       "      <td>1959-08-15 13:27:52</td>\n",
+       "      <td>6.788862</td>\n",
+       "      <td>1957-06-11 04:12:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>908.647827</td>\n",
+       "      <td>1961-06-13 15:38:24</td>\n",
+       "      <td>0.377782</td>\n",
+       "      <td>1961-09-05 14:41:20</td>\n",
+       "      <td>5.560763</td>\n",
+       "      <td>1959-08-15 09:36:24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>815.226196</td>\n",
+       "      <td>1958-05-25 21:11:20</td>\n",
+       "      <td>0.352194</td>\n",
+       "      <td>1961-08-22 06:39:12</td>\n",
+       "      <td>4.831801</td>\n",
+       "      <td>1961-09-05 12:17:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>664.019165</td>\n",
+       "      <td>1957-07-20 09:39:20</td>\n",
+       "      <td>0.328294</td>\n",
+       "      <td>1959-07-11 19:05:04</td>\n",
+       "      <td>4.549961</td>\n",
+       "      <td>1958-05-25 21:11:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>508.447784</td>\n",
+       "      <td>1957-06-04 14:28:20</td>\n",
+       "      <td>0.325543</td>\n",
+       "      <td>1957-07-27 16:21:48</td>\n",
+       "      <td>4.169593</td>\n",
+       "      <td>1958-05-24 19:07:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>501.339294</td>\n",
+       "      <td>1957-06-11 04:12:20</td>\n",
+       "      <td>0.314239</td>\n",
+       "      <td>1958-05-25 23:18:24</td>\n",
+       "      <td>4.119199</td>\n",
+       "      <td>1958-11-10 13:35:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>490.517639</td>\n",
+       "      <td>1962-07-21 13:50:24</td>\n",
+       "      <td>0.312885</td>\n",
+       "      <td>1962-07-21 15:14:56</td>\n",
+       "      <td>3.769463</td>\n",
+       "      <td>1957-09-07 22:18:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>418.507965</td>\n",
+       "      <td>1962-06-19 12:18:24</td>\n",
+       "      <td>0.298825</td>\n",
+       "      <td>1961-04-06 20:34:24</td>\n",
+       "      <td>3.661735</td>\n",
+       "      <td>1959-07-02 03:15:20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>356.224335</td>\n",
+       "      <td>1957-07-27 16:09:20</td>\n",
+       "      <td>0.286779</td>\n",
+       "      <td>1961-09-25 00:51:28</td>\n",
+       "      <td>3.626211</td>\n",
+       "      <td>1962-06-19 12:18:24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   DischargeIntegrated:B4.1200l1:26.6666  \\\n",
+       "0                            1081.991333   \n",
+       "1                            1048.237183   \n",
+       "2                             908.647827   \n",
+       "3                             815.226196   \n",
+       "4                             664.019165   \n",
+       "5                             508.447784   \n",
+       "6                             501.339294   \n",
+       "7                             490.517639   \n",
+       "8                             418.507965   \n",
+       "9                             356.224335   \n",
+       "\n",
+       "  DischargeIntegratedTime:B4.1200l1:26.6666  \\\n",
+       "0                       1961-09-05 12:17:20   \n",
+       "1                       1959-08-15 09:36:24   \n",
+       "2                       1961-06-13 15:38:24   \n",
+       "3                       1958-05-25 21:11:20   \n",
+       "4                       1957-07-20 09:39:20   \n",
+       "5                       1957-06-04 14:28:20   \n",
+       "6                       1957-06-11 04:12:20   \n",
+       "7                       1962-07-21 13:50:24   \n",
+       "8                       1962-06-19 12:18:24   \n",
+       "9                       1957-07-27 16:09:20   \n",
+       "\n",
+       "   DischargeMaximum:B4.1200l1:26.6666 DischargeMaximumTime:B4.1200l1:26.6666  \\\n",
+       "0                            0.464411                    1961-06-13 15:44:16   \n",
+       "1                            0.436320                    1959-08-15 13:27:52   \n",
+       "2                            0.377782                    1961-09-05 14:41:20   \n",
+       "3                            0.352194                    1961-08-22 06:39:12   \n",
+       "4                            0.328294                    1959-07-11 19:05:04   \n",
+       "5                            0.325543                    1957-07-27 16:21:48   \n",
+       "6                            0.314239                    1958-05-25 23:18:24   \n",
+       "7                            0.312885                    1962-07-21 15:14:56   \n",
+       "8                            0.298825                    1961-04-06 20:34:24   \n",
+       "9                            0.286779                    1961-09-25 00:51:28   \n",
+       "\n",
+       "   DischargeDuration:B4.1200l1:26.6666 DischargeDurationTime:B4.1200l1:26.6666  \n",
+       "0                             9.108004                     1957-07-20 09:39:20  \n",
+       "1                             6.788862                     1957-06-11 04:12:20  \n",
+       "2                             5.560763                     1959-08-15 09:36:24  \n",
+       "3                             4.831801                     1961-09-05 12:17:20  \n",
+       "4                             4.549961                     1958-05-25 21:11:20  \n",
+       "5                             4.169593                     1958-05-24 19:07:20  \n",
+       "6                             4.119199                     1958-11-10 13:35:20  \n",
+       "7                             3.769463                     1957-09-07 22:18:20  \n",
+       "8                             3.661735                     1959-07-02 03:15:20  \n",
+       "9                             3.626211                     1962-06-19 12:18:24  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "quantities = [c for c in df_events.columns if 'Discharge' in c and 'B4.1200l1:26.666' in c]\n",
     "df_events[quantities]"
@@ -142,9 +975,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:24.576683Z",
+     "iopub.status.busy": "2024-09-04T12:38:24.576272Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.107965Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.107265Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_events[['WaterLevelMaximum:B4.1200', 'WaterLevelMaximum:B4.1200l1:0', 'WaterLevelMaximum:B4.1200l1:479.999', 'WaterLevelMaximum:B4.1485']].plot();"
    ]
@@ -167,8 +1018,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.110708Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.110347Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.393541Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.392852Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path_monthly = \"../tests/testdata/lts_monthly_statistics.res1d\"\n",
@@ -186,9 +1044,555 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.395995Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.395796Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.415672Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.414821Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>DischargeIntegratedMonthly:A0.0327</th>\n",
+       "      <th>DischargeIntegratedMonthlyCount:A0.0327</th>\n",
+       "      <th>DischargeIntegratedMonthlyDuration:A0.0327</th>\n",
+       "      <th>Component_1TransportIntegratedMonthly:A0.0327</th>\n",
+       "      <th>Component_1TransportIntegratedMonthlyCount:A0.0327</th>\n",
+       "      <th>Component_1TransportIntegratedMonthlyDuration:A0.0327</th>\n",
+       "      <th>Component_2TransportIntegratedMonthly:A0.0327</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyCount:A0.0327</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyDuration:A0.0327</th>\n",
+       "      <th>DischargeIntegratedMonthly:B4.1200</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyDuration:Pump:B4.1510p2:40.0003</th>\n",
+       "      <th>DischargeIntegratedMonthlyOutlets</th>\n",
+       "      <th>DischargeIntegratedMonthlyWeirs</th>\n",
+       "      <th>DischargeIntegratedMonthlyTotalOutflow</th>\n",
+       "      <th>Component_1TransportIntegratedMonthlyTotalEmission</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyTotalEmission</th>\n",
+       "      <th>Component_1TransportIntegratedMonthlyOutlets</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyOutlets</th>\n",
+       "      <th>Component_1TransportIntegratedMonthlyWeirs</th>\n",
+       "      <th>Component_2TransportIntegratedMonthlyWeirs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1957-01-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-02-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-03-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-04-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-05-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-06-01</th>\n",
+       "      <td>4657.166504</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.395469</td>\n",
+       "      <td>454.666199</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.395469</td>\n",
+       "      <td>227.333099</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.395469</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.265970</td>\n",
+       "      <td>4657.166504</td>\n",
+       "      <td>31.408979</td>\n",
+       "      <td>4688.575684</td>\n",
+       "      <td>458.651001</td>\n",
+       "      <td>229.325500</td>\n",
+       "      <td>455.510712</td>\n",
+       "      <td>227.755356</td>\n",
+       "      <td>3.140271</td>\n",
+       "      <td>1.570136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-07-01</th>\n",
+       "      <td>4643.333496</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.702984</td>\n",
+       "      <td>453.083099</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.702984</td>\n",
+       "      <td>226.541550</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.702984</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.124598</td>\n",
+       "      <td>4643.335449</td>\n",
+       "      <td>64.702248</td>\n",
+       "      <td>4708.037598</td>\n",
+       "      <td>460.440796</td>\n",
+       "      <td>230.220398</td>\n",
+       "      <td>454.080963</td>\n",
+       "      <td>227.040482</td>\n",
+       "      <td>6.359827</td>\n",
+       "      <td>3.179913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-08-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-09-01</th>\n",
+       "      <td>1432.653320</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.769463</td>\n",
+       "      <td>137.650818</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.769463</td>\n",
+       "      <td>68.825409</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.769463</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.261507</td>\n",
+       "      <td>1432.653320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1432.653320</td>\n",
+       "      <td>138.132156</td>\n",
+       "      <td>69.066078</td>\n",
+       "      <td>138.132156</td>\n",
+       "      <td>69.066078</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-10-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 759 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            DischargeIntegratedMonthly:A0.0327  \\\n",
+       "1957-01-01                            0.000000   \n",
+       "1957-02-01                            0.000000   \n",
+       "1957-03-01                            0.000000   \n",
+       "1957-04-01                            0.000000   \n",
+       "1957-05-01                            0.000000   \n",
+       "1957-06-01                         4657.166504   \n",
+       "1957-07-01                         4643.333496   \n",
+       "1957-08-01                            0.000000   \n",
+       "1957-09-01                         1432.653320   \n",
+       "1957-10-01                            0.000000   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyCount:A0.0327  \\\n",
+       "1957-01-01                                      0.0   \n",
+       "1957-02-01                                      0.0   \n",
+       "1957-03-01                                      0.0   \n",
+       "1957-04-01                                      0.0   \n",
+       "1957-05-01                                      0.0   \n",
+       "1957-06-01                                      2.0   \n",
+       "1957-07-01                                      2.0   \n",
+       "1957-08-01                                      0.0   \n",
+       "1957-09-01                                      1.0   \n",
+       "1957-10-01                                      0.0   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyDuration:A0.0327  \\\n",
+       "1957-01-01                                    0.000000   \n",
+       "1957-02-01                                    0.000000   \n",
+       "1957-03-01                                    0.000000   \n",
+       "1957-04-01                                    0.000000   \n",
+       "1957-05-01                                    0.000000   \n",
+       "1957-06-01                                    9.395469   \n",
+       "1957-07-01                                   10.702984   \n",
+       "1957-08-01                                    0.000000   \n",
+       "1957-09-01                                    3.769463   \n",
+       "1957-10-01                                    0.000000   \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthly:A0.0327  \\\n",
+       "1957-01-01                                       0.000000   \n",
+       "1957-02-01                                       0.000000   \n",
+       "1957-03-01                                       0.000000   \n",
+       "1957-04-01                                       0.000000   \n",
+       "1957-05-01                                       0.000000   \n",
+       "1957-06-01                                     454.666199   \n",
+       "1957-07-01                                     453.083099   \n",
+       "1957-08-01                                       0.000000   \n",
+       "1957-09-01                                     137.650818   \n",
+       "1957-10-01                                       0.000000   \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthlyCount:A0.0327  \\\n",
+       "1957-01-01                                                0.0    \n",
+       "1957-02-01                                                0.0    \n",
+       "1957-03-01                                                0.0    \n",
+       "1957-04-01                                                0.0    \n",
+       "1957-05-01                                                0.0    \n",
+       "1957-06-01                                                2.0    \n",
+       "1957-07-01                                                2.0    \n",
+       "1957-08-01                                                0.0    \n",
+       "1957-09-01                                                1.0    \n",
+       "1957-10-01                                                0.0    \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthlyDuration:A0.0327  \\\n",
+       "1957-01-01                                           0.000000       \n",
+       "1957-02-01                                           0.000000       \n",
+       "1957-03-01                                           0.000000       \n",
+       "1957-04-01                                           0.000000       \n",
+       "1957-05-01                                           0.000000       \n",
+       "1957-06-01                                           9.395469       \n",
+       "1957-07-01                                          10.702984       \n",
+       "1957-08-01                                           0.000000       \n",
+       "1957-09-01                                           3.769463       \n",
+       "1957-10-01                                           0.000000       \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthly:A0.0327  \\\n",
+       "1957-01-01                                       0.000000   \n",
+       "1957-02-01                                       0.000000   \n",
+       "1957-03-01                                       0.000000   \n",
+       "1957-04-01                                       0.000000   \n",
+       "1957-05-01                                       0.000000   \n",
+       "1957-06-01                                     227.333099   \n",
+       "1957-07-01                                     226.541550   \n",
+       "1957-08-01                                       0.000000   \n",
+       "1957-09-01                                      68.825409   \n",
+       "1957-10-01                                       0.000000   \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyCount:A0.0327  \\\n",
+       "1957-01-01                                                0.0    \n",
+       "1957-02-01                                                0.0    \n",
+       "1957-03-01                                                0.0    \n",
+       "1957-04-01                                                0.0    \n",
+       "1957-05-01                                                0.0    \n",
+       "1957-06-01                                                2.0    \n",
+       "1957-07-01                                                2.0    \n",
+       "1957-08-01                                                0.0    \n",
+       "1957-09-01                                                1.0    \n",
+       "1957-10-01                                                0.0    \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyDuration:A0.0327  \\\n",
+       "1957-01-01                                           0.000000       \n",
+       "1957-02-01                                           0.000000       \n",
+       "1957-03-01                                           0.000000       \n",
+       "1957-04-01                                           0.000000       \n",
+       "1957-05-01                                           0.000000       \n",
+       "1957-06-01                                           9.395469       \n",
+       "1957-07-01                                          10.702984       \n",
+       "1957-08-01                                           0.000000       \n",
+       "1957-09-01                                           3.769463       \n",
+       "1957-10-01                                           0.000000       \n",
+       "\n",
+       "            DischargeIntegratedMonthly:B4.1200  ...  \\\n",
+       "1957-01-01                                 0.0  ...   \n",
+       "1957-02-01                                 0.0  ...   \n",
+       "1957-03-01                                 0.0  ...   \n",
+       "1957-04-01                                 0.0  ...   \n",
+       "1957-05-01                                 0.0  ...   \n",
+       "1957-06-01                                 0.0  ...   \n",
+       "1957-07-01                                 0.0  ...   \n",
+       "1957-08-01                                 0.0  ...   \n",
+       "1957-09-01                                 0.0  ...   \n",
+       "1957-10-01                                 0.0  ...   \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyDuration:Pump:B4.1510p2:40.0003  \\\n",
+       "1957-01-01                                           0.000000                      \n",
+       "1957-02-01                                           0.000000                      \n",
+       "1957-03-01                                           0.000000                      \n",
+       "1957-04-01                                           0.000000                      \n",
+       "1957-05-01                                           0.000000                      \n",
+       "1957-06-01                                           1.265970                      \n",
+       "1957-07-01                                           1.124598                      \n",
+       "1957-08-01                                           0.000000                      \n",
+       "1957-09-01                                           0.261507                      \n",
+       "1957-10-01                                           0.000000                      \n",
+       "\n",
+       "            DischargeIntegratedMonthlyOutlets  \\\n",
+       "1957-01-01                           0.000000   \n",
+       "1957-02-01                           0.000000   \n",
+       "1957-03-01                           0.000000   \n",
+       "1957-04-01                           0.000000   \n",
+       "1957-05-01                           0.000000   \n",
+       "1957-06-01                        4657.166504   \n",
+       "1957-07-01                        4643.335449   \n",
+       "1957-08-01                           0.000000   \n",
+       "1957-09-01                        1432.653320   \n",
+       "1957-10-01                           0.000000   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyWeirs  \\\n",
+       "1957-01-01                         0.000000   \n",
+       "1957-02-01                         0.000000   \n",
+       "1957-03-01                         0.000000   \n",
+       "1957-04-01                         0.000000   \n",
+       "1957-05-01                         0.000000   \n",
+       "1957-06-01                        31.408979   \n",
+       "1957-07-01                        64.702248   \n",
+       "1957-08-01                         0.000000   \n",
+       "1957-09-01                         0.000000   \n",
+       "1957-10-01                         0.000000   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyTotalOutflow  \\\n",
+       "1957-01-01                                0.000000   \n",
+       "1957-02-01                                0.000000   \n",
+       "1957-03-01                                0.000000   \n",
+       "1957-04-01                                0.000000   \n",
+       "1957-05-01                                0.000000   \n",
+       "1957-06-01                             4688.575684   \n",
+       "1957-07-01                             4708.037598   \n",
+       "1957-08-01                                0.000000   \n",
+       "1957-09-01                             1432.653320   \n",
+       "1957-10-01                                0.000000   \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthlyTotalEmission  \\\n",
+       "1957-01-01                                           0.000000    \n",
+       "1957-02-01                                           0.000000    \n",
+       "1957-03-01                                           0.000000    \n",
+       "1957-04-01                                           0.000000    \n",
+       "1957-05-01                                           0.000000    \n",
+       "1957-06-01                                         458.651001    \n",
+       "1957-07-01                                         460.440796    \n",
+       "1957-08-01                                           0.000000    \n",
+       "1957-09-01                                         138.132156    \n",
+       "1957-10-01                                           0.000000    \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyTotalEmission  \\\n",
+       "1957-01-01                                           0.000000    \n",
+       "1957-02-01                                           0.000000    \n",
+       "1957-03-01                                           0.000000    \n",
+       "1957-04-01                                           0.000000    \n",
+       "1957-05-01                                           0.000000    \n",
+       "1957-06-01                                         229.325500    \n",
+       "1957-07-01                                         230.220398    \n",
+       "1957-08-01                                           0.000000    \n",
+       "1957-09-01                                          69.066078    \n",
+       "1957-10-01                                           0.000000    \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthlyOutlets  \\\n",
+       "1957-01-01                                      0.000000   \n",
+       "1957-02-01                                      0.000000   \n",
+       "1957-03-01                                      0.000000   \n",
+       "1957-04-01                                      0.000000   \n",
+       "1957-05-01                                      0.000000   \n",
+       "1957-06-01                                    455.510712   \n",
+       "1957-07-01                                    454.080963   \n",
+       "1957-08-01                                      0.000000   \n",
+       "1957-09-01                                    138.132156   \n",
+       "1957-10-01                                      0.000000   \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyOutlets  \\\n",
+       "1957-01-01                                      0.000000   \n",
+       "1957-02-01                                      0.000000   \n",
+       "1957-03-01                                      0.000000   \n",
+       "1957-04-01                                      0.000000   \n",
+       "1957-05-01                                      0.000000   \n",
+       "1957-06-01                                    227.755356   \n",
+       "1957-07-01                                    227.040482   \n",
+       "1957-08-01                                      0.000000   \n",
+       "1957-09-01                                     69.066078   \n",
+       "1957-10-01                                      0.000000   \n",
+       "\n",
+       "            Component_1TransportIntegratedMonthlyWeirs  \\\n",
+       "1957-01-01                                    0.000000   \n",
+       "1957-02-01                                    0.000000   \n",
+       "1957-03-01                                    0.000000   \n",
+       "1957-04-01                                    0.000000   \n",
+       "1957-05-01                                    0.000000   \n",
+       "1957-06-01                                    3.140271   \n",
+       "1957-07-01                                    6.359827   \n",
+       "1957-08-01                                    0.000000   \n",
+       "1957-09-01                                    0.000000   \n",
+       "1957-10-01                                    0.000000   \n",
+       "\n",
+       "            Component_2TransportIntegratedMonthlyWeirs  \n",
+       "1957-01-01                                    0.000000  \n",
+       "1957-02-01                                    0.000000  \n",
+       "1957-03-01                                    0.000000  \n",
+       "1957-04-01                                    0.000000  \n",
+       "1957-05-01                                    0.000000  \n",
+       "1957-06-01                                    1.570136  \n",
+       "1957-07-01                                    3.179913  \n",
+       "1957-08-01                                    0.000000  \n",
+       "1957-09-01                                    0.000000  \n",
+       "1957-10-01                                    0.000000  \n",
+       "\n",
+       "[10 rows x 759 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_monthly.head(10)"
    ]
@@ -208,9 +1612,150 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.419034Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.418324Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.428773Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.427955Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>DischargeIntegratedMonthly:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeIntegratedMonthlyCount:B4.1200l1:26.6666</th>\n",
+       "      <th>DischargeIntegratedMonthlyDuration:B4.1200l1:26.6666</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1957-01-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-02-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-03-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-04-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-05-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-06-01</th>\n",
+       "      <td>1009.787048</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.395469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-07-01</th>\n",
+       "      <td>1020.243530</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>10.702984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-08-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-09-01</th>\n",
+       "      <td>315.122833</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.769463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957-10-01</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            DischargeIntegratedMonthly:B4.1200l1:26.6666  \\\n",
+       "1957-01-01                                      0.000000   \n",
+       "1957-02-01                                      0.000000   \n",
+       "1957-03-01                                      0.000000   \n",
+       "1957-04-01                                      0.000000   \n",
+       "1957-05-01                                      0.000000   \n",
+       "1957-06-01                                   1009.787048   \n",
+       "1957-07-01                                   1020.243530   \n",
+       "1957-08-01                                      0.000000   \n",
+       "1957-09-01                                    315.122833   \n",
+       "1957-10-01                                      0.000000   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyCount:B4.1200l1:26.6666  \\\n",
+       "1957-01-01                                                0.0   \n",
+       "1957-02-01                                                0.0   \n",
+       "1957-03-01                                                0.0   \n",
+       "1957-04-01                                                0.0   \n",
+       "1957-05-01                                                0.0   \n",
+       "1957-06-01                                                2.0   \n",
+       "1957-07-01                                                2.0   \n",
+       "1957-08-01                                                0.0   \n",
+       "1957-09-01                                                1.0   \n",
+       "1957-10-01                                                0.0   \n",
+       "\n",
+       "            DischargeIntegratedMonthlyDuration:B4.1200l1:26.6666  \n",
+       "1957-01-01                                           0.000000     \n",
+       "1957-02-01                                           0.000000     \n",
+       "1957-03-01                                           0.000000     \n",
+       "1957-04-01                                           0.000000     \n",
+       "1957-05-01                                           0.000000     \n",
+       "1957-06-01                                           9.395469     \n",
+       "1957-07-01                                          10.702984     \n",
+       "1957-08-01                                           0.000000     \n",
+       "1957-09-01                                           3.769463     \n",
+       "1957-10-01                                           0.000000     "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "quantities = [c for c in df_monthly.columns if 'Discharge' in c and 'B4.1200l1:26.666' in c]\n",
     "df_monthly[quantities].head(10)"
@@ -226,9 +1771,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.431153Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.430935Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.598074Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.597110Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DischargeIntegratedMonthly:B4.1200l1:26.6666\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(quantities[0])\n",
     "df_monthly[quantities[0]].plot();"
@@ -236,9 +1806,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.600834Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.600472Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.773543Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.772810Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DischargeIntegratedMonthlyCount:B4.1200l1:26.6666\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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r8ZnPfAYnnXRSzfft3bsXs2bNcr02a9Ys7N27t+ZnBgYG8K1vfSvsqTWMf7vnObz2Tu1QWyoFLP3YDEzr6mjiWbUmH4zmcfWdO3BYRxt2fmv5ZJ9OopBtEjpcvZ9Y/UTM5WeDr+PfH3oVBwslrPn8xyb7dFqCG7e+gnue2Y1pU9rxD6fNn+zTaRlCGzVXXHEFdu7ciUcffTTK8wEArF271uXdyeVy6Ovri/x7dBk6WAAAXHRaH6Yf5jZcbnnkzyiUBIYPFWnUABg+VIQQwMhYEcVS2VWJQxpL0ap+8uj9xPATMZD9uYrnfuhAfpLPpHXYP3wIAPDh+LpEKoQyaq688krcd999eOSRR3DkkUf6vnf27NnYt2+f67V9+/Zh9uzZNT+TzWaRzbaei1Hucv/lzGOw4IjDXL/7xRO78OGBAt374zjHYaxIo6ZZCCFQ8MqpYUk3MZjhscrCTaPcZvhQEQC9r9VorTRCCFx55ZW499578dBDD2HBggWBn+nv78fWrVtdr23ZsgX9/f16Z9oCyAdKLhBO5GuHeIMBcE8+nIiaR6ksIMZTvtrTHuEn/i2IgeQOji/gvH8tcoekoceNihMtT80VV1yBjRs3YvPmzeju7rbyYnp7ezFlyhQAwKpVqzBv3jwMDAwAAK666iosXboUN9xwA8477zzccccd2LZtG2655ZaIL6WxCCGQL/kZNZVFQ74n6TgnH5YRNw+ZTwMA7W3eJd1CCKRSqQmfJaRVkQs45xIbaehxTNxoeWp+8pOfYGhoCMuWLcOcOXOsnzvvvNN6z65du7Bnzx7r30uWLMHGjRtxyy23YOHChfjlL3+JTZs2+SYXtyLORVruep1Y7n16agBUh5+4k2gWBYdR3ZaeGH4CaHgT88gdpFeiGttTw+fZiZanRgjvUmYnDz/88ITXLrjgAlxwwQU6X9VyuIwaL09NO3MWnDD8NDnIJGGgWnzPNsTHimXXvwlpdaz8Ec4lAIBDhZLloeGa44bZm4rIGyedcu+AJZShd+P0WNF71TykpyaVAjKO+9TZMoF/D2ISQgjbK8F7F4Bt5AFcc6qhUaOIfJg62tKe+Qi2uBlvMIDhp8miUJ7YIgEAUqkUK6CIkRwqlK0mrbx3K0gjD6ChVw2NGkXsyidvt70VfirwoQMYfposirKZpac3kYY3MQ/XAs57F4CdYwTQ0KuGRo0i8sbxyqdxvs6HroLbqOFD1ywKlvCeV94XVYWJeQzTqJkAw0+1oVGjiOWpaa9l1DCnxonTY8VFtHnInBpnDo2E4SdiIkMHHQs4PeEA6L3yg0aNIvmg8JNDB4S4y4ZZQtw8ZPVTdU4NQG8iMRPnAs65pELOYehxzXFDo0YRPzVhgCXd1bD6aXLwapEgsQQiOQkSg3Dlj3AuAVDtqeGa44RGjSLS7Vk7p4bhJyfMqZkcLE9N2k9LifcoMQfmj0yEeUa1oVGjiLxxOmoYNR1UFHZR3dCSNAeZU+PlqbFlB2hkEnOoDj+Vy8EisHEn58oz4vzqhEaNIoEl3UzCdMGS7snBThRm9ROJB84FHGBeDcDwkx80ahQJLulm+MmJO6eGD12zKPqVdDNRmBiIcwEHaJQD1To1HA8nNGoUkQ+SVzNLgAtGNQw/TQ7Fsor4Ho1MYg7OnBqA9y/APCM/aNQoolz9RK8EAIafJou85alhfzISD5xeCYD3L+D2XpXKwlISJzRqlMkHGTWyXJY3FwB32TAnoeZR9M2pYTI7MY8J4SfOJ8wz8oFGjSJ2Tk1A+IkLBgA2tJwsVMT38iX+PYg5TPTU8P5lnlFtaNQoEtwmgfkKThh+mhws8T3PnBpWPxHzmJhTk+z7t1gq40Devc4kfUyc0KhRRBorHR47YMChU8ObCwAVhSeLQrF2+In3KDER6ZXoZPgUgNvI42Z6IjRqFLGrn1jSrQLDT5NDsSzDT6x+IuYzVizh0PjcO6M7a72WZKSR19WRwdRsGwCuO05o1CgSKL7H3k8uGH6aHArUqSExwumVmH6YNGqSff/KJOHuzjYq2XvQNtknYArB4nu8uZzQqJkc7OonD08NFYWJYUijpjvbhinsXQbA7vvU09luKYhzM21DT40iqiXdSX/gJK6Sbmr3NA2r95NXQ0uGn4hhyMqnnint7DI/jgw/OceE644NPTWK2NVP/iXdSX/gJM6FkxoKzaNQDi7p5gRITEEu4N2dbTTKx5Hhp57ONmsTw3XHhp4aRZQVhYslCJHsLrJCCHf4ieGOpuEbfuJOlxiGvYC3M3w6jm3otdPQ84BGjSKqDS3Lwq5ASSqFkoDTrqNnoHkU/NokMCeBGIaVPzKljZ7GcXLjeUaVMWH4qRoaNYrI3UFHQKIwwBusetfAXUTz8M2pyXBXR8wi50iKpVeigpVn5ByThHuvnNCoUSSopNspypf0xNhqoy7pRl4zkW0SvIxvemqIaVjhJybFWrgShSklMgEaNYoEhZ/S6ZRl2CT9oau+/nyxnPg8o2bBNgkkTjgThanJUsEqc+9s45rjAY0aReRN01lDURhgdYlEeqqc62rSx6RZFJXE97irI2YgF3CGn2zc4Sd6r6qhUaNIPiD8BFBVWCJLuLs7263X+NA1h4JC9RP/FsQUbJ2aNmt+TXr1np0o7Ag/JTzlwQmNGkWCSrorv2PJLGC7hw/ryCA1vrYmfUyahax+8tSpYU4NMQx3+TKNcsDpqXFUhFELzIJGjQLFUhml8TJtX08Nw08AnKG6DF3GTabom1NT+VuUysLSsyGklXHp1HAuAWCXubsMvYTnGTmhUaOA00ipVdLt/F3SbzA56XS0pbm7ajJFP0+NwyCnyjMxAerUuCmXBYbHnDo1HJNqaNQooGrUcCdRQRp1WaenJuGGXrOQxoqX+J7z3uXfg5hA7hAVhZ2M5IuWsGllTLjmVEOjRgF5w7RnUsh4uPUl9EpUcOYf8aFrLnabhImPdiadssJSSb9HSetTLJUxMmaXL3PTaOfTdLSlx8P7XHOqoVGjgOV58MmnAVj9JHFq+vChay5Fq6Glt/HNhYGYgjRogEr+SAdDLY4S90ovaqY8TIRGjQIqlU/O3yf9BnOOF8WhmovV+8mjTQJgd5nn34O0OnIBn9KeGc/P41zi1KgBuEnxgkaNAnllo4YLBuDW9KGOQnMp+OTUADS8iTkMOTRqAEpmAHaOUfcUadRwzamGRo0CVjilPSD81EZxKKA6/DQ+Jqy2aQoyp6bDI6cG4M6OmINTowbgvQu4NWoArjle0KhRQDn8xJwaAM7qJ0dODT0DTaHg0yYB4G6XmIOtUVNZwDspHmmXuEtDj2MyARo1Cjh1V/xg/kgFZ0dzxsGbi5/4HsBJkJjDsKMbNcCGrICzRYI7JJf0jbQTGjUK2NVPQZ4axjeBqvBTOx+6ZuLXJgFwGt78e5DWxqlRA7jDT0KKtSSMmonCCTb0qqFRo4DT8+CHfYMle8Fw6dTQU9NU/BpaAvTUEHOQC3h3p9srURa2dEHSyFV7r/g8T4BGjQJOz4MfXMArUFF48vBrkwDQhU/MoXoBdyliJ3SOlWXu0tCj53UiNGoUsDwP7SzpVsHKQcqkHYJZfOiagZVTQ/E9YjjDVeEnd5uPZN6/uQmJwlxzqqFRo0BeNfzE6icAdvm2q/qJD13DEUIEi+/Rm0gMIVelU5NJp6ywalIlIqyKsCkTS7qTmmdUjbZR88gjj+CLX/wi5s6di1QqhU2bNvm+/+GHH0YqlZrws3fv3rDn3HR0FYWTXi7rTKzmmDQPZ55BbZ0aGpnEDKp1agCGTyd4ahxrUlINvWq0jZrR0VEsXLgQN910k9bnXn75ZezZs8f6mTlzpu5XTxrS1UlFYTVcJd30XjUNmU8D+ISfmFhIDKFapwagp9HOqXGXuQPJHZNq2oLf4ubcc8/Fueeeq/1FM2fOxLRp07Q/1wrImyVQp4ZJsQDY0HKyKJTtMWZODTGd4TF3ojCQ7PtXCDEhJNeeSSGVAoQYX3c6J/MMW4Om5dQsWrQIc+bMwec//3n84Q9/8H3v2NgYcrmc62cy0S7pTuAD58SZWM3qp+ZRcBiO7TVyamh4E1OwPTUOoybBibEHCyUrxCzHJJVKcd2pouFGzZw5c7B+/Xrcc889uOeee9DX14dly5bhmWeeqfmZgYEB9Pb2Wj99fX2NPk1f1Eu6k/vAObFzajJ84JqInPDSKSBdS1GY9ygxgHJZOFoCeISfEmiUy9BTJp1CV4e9weYz7UY7/KTL8ccfj+OPP97695IlS/Daa69h3bp1+M///E/Pz6xduxZr1qyx/p3L5SbVsHH2MvKD+QoVvBWFkz0mzcAW3qt9n9LIJCYwmi9C5r07w09JlohwihGmUvamhd5XNw03arw47bTT8Oijj9b8fTabRTabbeIZ+TNW0gw/JVRDQZJ35CCxH1bzCBLeA5hoScxAeiU6MmmXhzzJ1ZTVlU8SblTcTIpOzY4dOzBnzpzJ+OpQKPd+ohsQAKufJgvpqamVJAw4chK4qyMtjK0m7PZKJHmOrdaokXCj4kbbUzMyMoJXX33V+vdf/vIX7NixA9OnT8f8+fOxdu1avPXWW/jZz34GAPjhD3+IBQsW4BOf+AQOHTqEW2+9FQ899BB++9vfRncVDcYKpwQqCid3F+HEq/dT0sekGQQJ7wHc1REzkAt4N70SFrU9NRVDj3NsBW2jZtu2bTjrrLOsf8vcl4svvhgbNmzAnj17sGvXLuv3+Xwe//qv/4q33noLXV1d+OQnP4n/+Z//cR2j1bFKujP+4acOWswA3EZgkndWzUa2SOjw89RwAiQGYHejrvJKJDhvMVfV90mS5DHxQtuoWbZsma8c84YNG1z/vuaaa3DNNddon1groa0oXCqjXBY1K1DiTKlsS/W7qp8Y7mg4lqeGOTXEcLw0aoBkKwrbhh69V36w95MClqJwYPWT7clJqmS10wOQbUujkzk1TUMlp4beRGICdvipVv5I8uaT6q7lkiQbel7QqFFAuaGlq4tsMm8w52RDReHmYlU/MaeGGE6wVyJ580l113JJksfECxo1CqiGn9rSKciIU1IXDTlWmXQKbY5yTD5wjUe2SWhvY/UTMZtaXokkexqdOjVOkqzd4wWNGgWcsv9+VCSrk+2ZsDRqxvM65ANXKgsUExqSaxZFreon/i1I62J7JarDT8lNdJeJwjXDTwkcEy9o1ChgK+T6h58AJF6Xpbr8nV1km4etKOxX/ZTs+5OYgfTUsKTbJrAijN5XADRqlFANPznfk9QF/FCVUKGzs3kSd1fNxEoU9vPUsG0FMYCaQnMJXsCHayYKy6rb5Bl6XtCoCUAI4ZL9DyLJMV9gYkfzTDpleQ6SOibNwkoU9rlPnWKIftIMhEwmQUJzSZxLaurUsPrJBY2aAMaqSpSDSPoN5tXR3J6IuJNoJFJ8r91HH8n5d0mq7ABpfYZr5o8w/MTqJ39o1ATgNmoUcmoS/NAB3knVfOiagy2+F6xTA/DvQVqXWpU+SVXPHSuWrGueYOglPI+zGho1AcgbJZXyT8CUJH0Bt5t/2gYgVYWbgy2+V/ux7nD8jn8P0ooIIYLDTwm7d6XnKpUCurM1wk8JXXOqoVETgLNDt7NbbC2SfoN5hp/aGX5qBjKnpsPHqKnIDnBnR1qXQ4Wy5XWcoFOTSea9Kz1XUzvaJrTf6eCm0QWNmgBk3oFK6AlwZucn66GTeCVV2xMRH7pGIsX32gJ6jiXdm0haG1nlk04Bh3W4592khp9qadQATHmohkZNAGNVJcpBJH3B8Cp/Z8y3ORQVGloCVBUmrY1To6baO55U8b1aOUYA15xqaNQEUC0mF0RSHzpJdUl35f/tMmLSOFTE9wDu7EhrM1RDowZI7gJeqxoM4JpTDY2aAOTD45en4IQ6NX4l3ckck2Yh8xDagzw1NDJJC1MrSRhIrtfXHhMPQy+hIbla0KgJwMvz4EfSd8FWuM6rpJvhjoZStKqfgjw1NDJJ61KrGzWQ3OqnWho1ANecamjUBCATfnXDT0ldMDzDTwndXTWbYnncU+PTJgGgN5G0NswfmUitruUA15xqaNQEoNP3CUh2bxKA4afJJK/sqaGRSVoX/wVc9jkqo1xOTpuPWl3LAXrCq6FRE0Ce4SctvMYrqburZlO0EoVZ/UTMxWpm6RFqcTXITVCbD9t7xfBTEDRqAtD21CTcKzHmpVNj7ST40DUSq6GlsqcmmfcoaW3sbtReXgl7s5Sk+9fWqak9JkkaDz9o1ARgl3TremqSeYN56tQkfEyaRWHcHd8WkFPDnR1pZexu1BO9Eu2ZFKR0TZLu32GlijDOrwCNmkDC5tTkE/TAOfFKrOZOojkUiqo6Nfx7kNbFrvSZ6JVwtflIUPjUCsn55BmVysIKQScZGjUByAenQ9GoSXpLAD/xvaSOSbMollVzaqhTQ1oXv0RhIJlGua2y7B+SS1KeUS1o1ATgVc3jR9KTML0bWjLc0QwKqm0SGH4iLcywFX6auIADybx//XRqnBvupK47TmjUBEDxPT28c2qSt7OaDGxPjX/4iV19SSvjt4ADycshKZbKGM1X1hMv71UmnbKe+aSMiR80agLQ9tQkPNRiKwp7hJ+4iDaUQlE1UZhGJmldZKilt0b4yQrxJ2Q+GRkrWv9fy3tlpz0kczPthEZNAJbuChWFlZAxXYafmk9B0VOTdG8iaV3yxTIOjRsrNT01soFjQvJHZJLwlPZMzXw5K+0hoeuOExo1AWiHnxK+gMvrdunUZPjANYOiZkNL/j1IqyFLlwFgaq2cGku1PRlzbM5Ht0dCb7gNjZoArHCKbvgpoTeX13hxEW0OBdU2CQlPZieti9SomZptQybtfR8nbT7x61ouoffVhkZNAF6eBz+cvUmSiF9DS5YQNxbLqKH4HjEUP40aSdJC/H4aNRIrJJeQMfGDRk0AodskJHQX7N/QkotoI5Fdujva1HJqkmp4k9YlSKMGSJ5R7qdRI0laRZgfNGoCqKekW4jkdJEFACGEPV7tHuGnhBp6zULm1Ch7avj3IC1GkEYNkLzwaVCJO5A8Q88PGjUB2L2f9Dw1ZWHvnJNCoSQg7Tiv8BN3EY1FOacmYe57Yg56C3gy7t9hn2aWEj7TNjRqAtBOFHYYP0m7wZy7BIafmo80atSrn/j3IK2FSvipI2H3r0qiMAU1bWjUBGDrrqiFnzocC0pSSg4lziQ15zh0JGxnNVkol3TTc0ZaFCsp1jdROFmFB3JMvLqWS7hRsaFRE4CupyadTiW2qaW83o5MGmlHOaZzEkpanlEzkeJ7bTVKYSVJT2YnrcuwlRQbXOmTlPlVS6cmIWPiB42aAKTl26mYUwMk9warVSnm/HfSxqSZ6IvvcVdHWoucUv5Isu7fYSWdmmQZen7QqAnA9j6ohZ8AO9ySFPeopFZStTN0xzLixiCEsBLTmShMTEUpUbg9WfkjSjo1DClb0KgJwKtEOYik7SQkdqjObQC2Z1JIpdzvIdFSKNlhPdWcmqQZ3aT1UdOpSZZRrqRTk9A1xwsaNT4US2WUxne/qjk1QHKbi9UKP6VSKT50DaZYtu+1oIaWMuerWBYo0nNGWgglnZqEzSVqZe7Mk5PQqPHBaZSoVj9V3pss96jEr6VE0nZXzcbpqQkU33N4HRkOJK0EdWrclMsCI2M6eUbxH5MgaNT44CpR1vHUJGwnIclbobqJBmBSDb1mUSjpe2oA/j1Ia2EnCgdrsiQhfDqaL0JquCrp1CRszfGCRo0P0uptz6Rqdoz1IqleCSv85JHTwYeusdgtElJIpfzv1bZM2ir7Tto9SlqXktMrwYaWAGwjryOTRqfHZlFCT40NjRof7OaM6qEnwJmJnqwF3K+lBB+6xqLaIkGSVG8iaV1GxhdwIECnJkHzqxWO8wk9Acnrh+UHjRofdDt0S5KmeCnxEypM0u5qMpDl3O0B+TSSpCazk9ZFVvl0tqd9w/1JCmVbfZ98jDyAmxQn2kbNI488gi9+8YuYO3cuUqkUNm3aFPiZhx9+GJ/61KeQzWZx7LHHYsOGDSFOtfnIh0Ynn8b5/qQtGH4dzVlG3FhCe2oSsDAQMxhSSBIGkrVBkp6abp8cI8AeE86vIYya0dFRLFy4EDfddJPS+//yl7/gvPPOw1lnnYUdO3bg6quvxle+8hU8+OCD2ifbbOzwk66nJpmuQL/x4k6isag2s5RY3sQS/x6kNVDRYwGSNZfYzSxVxyRZa44X/iPlwbnnnotzzz1X+f3r16/HggULcMMNNwAATjjhBDz66KNYt24dli9frvv1TcXP8+BHkh46J1b4yTOnJpmGXrNQbZEgYVdf0moMK1Q+AXbLmiQs4Col7gAVhZ00PKdmcHAQZ599tuu15cuXY3BwsOZnxsbGkMvlXD+TgV/iqx+qVrMQAv/5+Bt46vX3w51gi+EbfopgJzEyVsT637+GXe8dCH2Mal58O4db//fPrpJoE5Hie+rhp+S48El4HnppHzbveEvrM2HnNeUFPEEbpGGFXliA83lO1kbai4YbNXv37sWsWbNcr82aNQu5XA4HDx70/MzAwAB6e3utn76+vkafpif5sInCikmYL+0dxv+9aSf+7Z7nwp1giyGF3DzDT+31P3Sbd7yF7/7mJdy49ZXQx6jmO/e/iP/nv/+IR195N7JjTgb5ol3SrYLc7R4scBIk3pTLAldu3I6r79yB90fzyp8LO6/lFNSEAYdOjeEbERVyCl3LAXpenbRk9dPatWsxNDRk/bz55puTch51h58CFoz9w2MAgHfG/2s68nq9Equl4Fs9noH9ufHxGoluvKxjGv43kJ4a1fCTnCSdZbSEOBnNF3EgX4IQwPuj6s9H2HnNLl9Wq/QpJaDNh9XMkjk1ymjn1Ogye/Zs7Nu3z/Xavn370NPTgylTpnh+JpvNIpvNNvrUAvErUfZD9QaTLeVHxooolwXSGgJ/rYhfCXwUnXWlK1aOWxTIY+YiPOZkoJtTI3fDpl83aRzDDoM3p2H8hp3X1MuX7U3mWLGMNsV73kSGx/QMPYafmuCp6e/vx9atW12vbdmyBf39/Y3+6rrx62Xkh6qMt7TChQBG8ubvmNVyasI/dHIBlju6KLCOabjHQrekWy4cUY4liRdOg1fnPgk7r9kdutXCT0D8PRO2pyYoUZg5chJto2ZkZAQ7duzAjh07AFRKtnfs2IFdu3YBqISOVq1aZb3/sssuw5///Gdcc801eOmll/DjH/8Yd911F7761a9GcwUNJLz4ntoNFnbSaFX8FYXr11GQYxSVAVIolXEgX3Id21RkQ0tV8T25cJhuzJHGIRdUQO8+CW8MqSUKZ9Ipq79Z3D0TqoaeU/BVCOH73rijbdRs27YNp5xyCk455RQAwJo1a3DKKafgm9/8JgBgz549loEDAAsWLMB///d/Y8uWLVi4cCFuuOEG3HrrrS1fzg00vqTb+cA7JxBT8VcUrj/mG7Wnxu1eN9uosXJq2jQ9NYZfN2kc7vlJ3zip/L++MRSUKAwkpwLKEt9TVBQGkpFA7Yd2Ts2yZct8LUEvteBly5Zh+/btul816cjEV+2SbkXNAOeiGmWeyGShoihcz85KjtdYsYyxYknb2Jx4vILj/802KgtWQ0u9ROE4GNOkMch8DkDv+Qg7r6nq1ACVRXxkLP7hlrB5RvXOjSYT3wyrCKg7/BSwi3C5aQ1fVIEgReH6Y765iI0Ql3vd8PBT0VIUVvTUTGGiMPHHHX7S8NSEnNds9Vw1owaId/hJCKEcfmrPpJAaf/Tj7r0KgkaND80NP5m/uFi6Pn5duut44KI2QuJkVFqJwqo5NUwUJgFEE37STzDuDVjAAfViDJM5VChbHtggQy+VSjlkM+Jr6KlAo8aHxicKxyenA7CvtyMz0QjsqHNnJYRwubKjMELiZFRaicKK96p08ZsediONI7zHRX9ecz7fQfkjQDIUseXYZdIpdHUEb6ypVVOBRo0PodskKOfUxCxRWMVTE/KBG82XUHakckWRgxSnnCYrUVhRE4Q6NSSI8Lkx+mFi5/OtFH6KIEev1Rl2JE6nUsHPtVXWzfATqYXtedDUqcno6dQA5i+qgCOxugE5NdWelCiMQFeOzrhQmKlYicKaOjUjhl83aRz16tTofE6+rz2Tslp4+BFFOLvVGVLUqJEkIc9IBRo1PthdpzVzahR3EW73bgyMGoXqp7Ax8OrxiWK8nBOu6QKItvienqKwEBWDjpBqItGpUXxOnUnCSl6JBIWfgpKEJdkE5BmpQKPGB79qHj9Uqp+cwm9AzMJPvjo14XYR1eMTTaJw9MecLGSbBFWvYmd7xvqbxMFLSKInjKcm7Lwm36eiUQMkwythadRkVT018Tf0VKBR40P91U+1b67qRoLx8NQEKwqHfeCqF95ISrobcMzJolCW1U/q/cOoVUP8CCNOGXZeG7a8EooLuGLeosnYuj2Khl4CxkQFGjU+hK9+GncDlso18xXitKACQLksrLwO395PIWPgjQk/xc9To9Pcj1o1xA/n83CoUFYKa4Sd13Q0aoBkKArrj0n8vVcq0KjxwU93xQ9nDk4tyeoJC6rhC4vzOr2MwM46qxUaE36qNpTMNSwLmuJ7ALVqSG2cwm8SlTBl2HnNatyo6JWwijFi3BLAHhM1o6YjAYaeCjRqfLBzasKFn4DaN5hTgwAwf2FxXqdXV3OpXVNv9ZM1XhHq1MThb2Dp1Gh5aqhVQ7xxCr/pPHNh5zXt/BG5SSrE1yuh0wsLoE6NhEaND34NGv1oS6cgUxtqeSbkrmdObyeAyoRhcndVeZ3plHdeR73xXlmhI8crSp2aKI85WRSt6iednBqGn4g38llIp4CZ3VkAagZK2HlNPt+6lT5xXsBV+z5JGH6qQKPGB0unRtOoSaVSDgVd//DTkYdPAQCUygIHDd51OJOqvUoy5QNXKgtrAdZBTqhyvKLUqbGOabDHojieu9Wu2CYBcIafzL1u0hhyjsTdXg2PXth5TT7f2jk1MTZqrDFRTZ5OwJioQKPGh7Al3ZXP+N9gctKY1dPpcNWau7gEqS87Q3hh4uByvOZN63L9OyzlssDI+O7QOqbB4ad8CE+N3BWb7KEijWHIUWKt49ELO6/ldKufEuCVsBOF9aqfqFNDamLL/uu3cQ966Jw7k54YhAEOBYTqOhTyjPyQE+M8y1NT31gNjxUhveLzpklXubnjb3fpDuGpMfi6SWNwVt7oJJSHnde0dWqsnJr4LuBWnhHDT1rQqKmBEMKufgrjqQnIIck5NAjk7sRkT0GQpk8mnbIqc8K4R4erQkWj+VKoMFb18bJtacwYzxkwOWG2aCUK61Q/jS86BnsISWNw5nNY85OSpybcvDZ8iOGnarR1alj9BIBGTU2CSpSDCLrBvHZCJi+qKgagPRHp7yTkZHnktCnWayN1yPs7yyV1Ju1WpTCeU9Omk1MTg+smjcHO52izjF+lnJqQ85ptDOl6JeK7gIfXqYnvmKhAo6YGzhtDt6S78pmg8JN0t7bHQgRNpaN5PQ+dnGSnT+3AlPFwYD0eBme5ZBwSZgvFEDk1MTCmSWOwn492LY9L2HnNaUSp0BHzUMtYsWSF9FWNmriPiSo0amogPSyplJ5LXxK0gNuy4G2WNkMcwk9+vYc6QqoKO4XAejrbIylFdsb+41DaXCzrd5SPgzFNGkPO0SHafj50En7V5zXn862ePxLv8JNzozGVOjVa0KipgbPySaVrbDXB1U/OmLX6pNGqqPTJCpvINla0hcCiChcNH5oYfjLZY1EI0Sahm4rCpAbOTZdOovBwiHnN9XxzAQdgj2N3ts2qIgtCFrQwp4Z4ouJ58EN6JWqV1zk1COJQhSKVPf3DT+F2V3Ks0ingsI5MJAmuznJJ56RtqgCi9NSECT+ZLvxIoifnkSisplOjP6+5n2/d6qd4hlp0NWoAVj9JaNTUwFITDlHODSjk1DhzOqaYn9Oh0vwzrI6C0zWdSqUi8dR4xf6LBgsgFoohxPfGr9t04UcSPXY5cZvWpivMvOZ8vtOqXonxDVJcNVl0WyQA7kbKSYZGTQ3qEd4D/F2BTuG3uOR0NDL8NFTV7C6KsIkz9j+lPWO8AGKhrN/Qckp7xmppYep1k8bgFMOz5qeA5y3svCa9QmEW8LiGn5w5TaqwpLsCjZoaqHge/PB76EbytvBbd6dezLpVUTECw4afqjUsdEpMVY6ZSqUcxzTzb1AMkVOTSqViYVCT6PHWqfF/3sLOa7otEgCnDlg8PYzOnCZV6u2vFxdo1NQgr+B58MPPKyEf4mxbGp3tmVgkqlrjpVLSrbmTyFU1dosy/CSPZbpmi60orJfUHgfhRxI9Xjo1I2NFlMq1c6/Czms5TZG5ynfEu/pJV6MGYE6NhEZNDcYUFmk//B46Zz4H4FB2NXRBBRTDTyF3V874PhBNI8bqviqma9XkS/riewC1aog3bgkFe2Ed8blPws5rYTw1QQ2DTUe3bQQQ/5CcKjRqalB/Tk1tr0S1a7Hb8AUVsK/Tr6O5rCTTrn6qanYXiU5N1U7I9DCMpVPTpuupMfu6SfTki2WX8FtHWxqd4/OZ330Sdl6zypdDeCXyxXIsK/d0G3wCzKmR0Kipgb1Ihws/2Qu4R/hpQjjF/IWlsTk13uGnevJfqvuqOMubTaQY0lMTB+FHEi3O52pqlSdzyOc+CTuv5cLkjzgb5MbQM1E956nA8FMFGjU1qDtR2Kd8uVqDQP63skMy84bUCj9pXmO1hHokOjVVLm9rAjZ0cS+U9HVqAOfCY6YxR6In5yH8ppIfE3ZeC5Uo7Jhn4mjU6LaNAOKfZ6QKjZoa1B1+8supqdIgmNrRBilabGpug5JOjdxJaOoo5Krc0/Um9VZk2avj/2YnChesROFwOTWmXjeJnuocNkAtPybsvKbbzBKoJMTLY8fRM6HbNgIIrwMWN2jU1EDF8+CHnyuw2rWYTqcwNWt2CEpLUVi3+snayVV7asKN1YF8yariiENeU7ksIItStI2aGAg/kmjxyudQ0YYKO68NH5poRAWRSqVCV1OaQDidmsp4FMvCqoZMIjRqamArCkevU+PlWjRdq0ZPfC+kTs0Ut1dlZKyIsk+Jae3jVSaMtnTK6vgt/xYm6tRI4T1AP/xkeoI0iR6vfA4VrZqw81qY8BMQ73BLKJ0ax9ybZFVhGjU1yJcapyjspUFgulZNXiX8FLaku0aicFkAo3n98XLuRGWzUpMThWWSMKDXJgEw35gm0eNtnAQb/WHntTA6NYC7AipuVM95KjgrT+PovVKFRk0NLE9NQ8T35A2rF7NuZRpZ/VQd48+2pa3qsjBGiGfOgMEidIVSeE+N6cY0iR6vfA6VMGXYeS2sp6YjptU+xVLZajehE5LLpFNW25M4eq9UoVFTg0a2SRge84tZm7m4WF3N/XRqQisKV8ard3y8XPL+IYwQrx2lyWGYgsNT06bYEFBiujFNosfLOFF5PsLOa2HKl4H4is1JgwbQSxQGWNYN0Kipibwp/BZpP/wUL72SwEzXqmlUQ8tqITBJPR6Gao0a57FN9FgUHc0sZThNFdONaRI91TlsgGpujP68li+WrQ7x+uGneObUyDloSntGe/2x0h5iNiY60KipQf2emsrN5alT45EEZi+qpho1GtVPGg+clxAYUF8FlJe722SdmrDCe4DZCdKkMXjlc6jlxujPa67nO6tp1ITUvWp1hkJo1EjiXBGmCo2aGtjVT41raKkbs25l7BykYJ0ancQ+OcFOdQiBAfVp1dgaNRNzasaKZeNct/mQwnuA+7pNFX4k0RJapybEvOZ8vnU6zAPxDT+F0aiRWHNsKbnPMo2aGtQrvtdZow28EMK7ZNLw3Aa9hpb6npqeqoS5bqsao47qJ8f4myyAKD01HZqLAmD2dZPGMOwhhmeFKWvMT2HntTAaNZK4h5+q5zwV2P+JRk1N5O63bkXhqpvrYKGEYpXwG2B+aa1SSbc1CanvIqw4fZXaaD3j5XVMl1CYYX+DsC0SgHgIP5Jo8TL6e63wrLfhG3ZeCyMyJ4lrSXd1uwkdwmwc4waNmhpEWdLt7CIrH+KMQ/gNcOY2mLdbFkIo5tTox3urpdcl9YWfahzTUK0auZiEyakBzE6SJtHjHX6yc2O8umKHndfCNLOU2Emx8Qq1hNGokbD6iUZNTaxwSmhF4coDVxb2ogO4wynOShWTe/AUHTL92UxtI9CvIqwWtTQs6mlqWfOYhmrV2H2f9D01gLnXTRqDVy8mt+ClR55gyHktrEYNYIdb4+aV8DIqVQkzx8YNGjU1qLuhpcMYct5gXn1VALNLa53Xp+Sp0dhFeMX3AXu8pDaGDrWPaaa3LGwzS4np+VwkOkplYemkOHM6sm1py2j2Mn69ysCB4Hlt2CNpXxW7+ileC3it+UkF5tTQqKlJvSXdzqRNZ8lhrRiyyTo1zuvzS1YNo6GQq5Eo3BMQ4w91TEO9ZVZJd0ijxmSDmkTLiMOgd1bfpFIp3+cj7LxWa5OnQlxDLV45TarEdUx0oFFTg3pzatLplLWzcTYXC8rnOJAvGddh1VITzqSR9lG0dSb2ecXlvfAqEwXqM0DkBDzhmIZq1TjF98JArRoikc9TZ3t6gvCbn1ZN2HmtnvBTXKuf6gk/xbXMXYdQRs1NN92Eo48+Gp2dnTj99NPx5JNP1nzvhg0bkEqlXD+dnZ2hT7hZ1Bt+qnx2oiuwVhKY8wY2Lfyh6tVy/l61i2ytZnf15IHUSk401VOTL8pE4ZBGjaHXTaJnyMfI8BO8rGWcBM1rYZtZAvH1StTnvYqnoaeD9op95513Ys2aNbjuuuvwzDPPYOHChVi+fDn2799f8zM9PT3Ys2eP9fPGG2/UddLNQEV3JQgvq9mrAy5QCR10dVS+y7TFRaXyCXCPpepDN1zDFRs2/+VQoWSVgE4sEzczp8b21ITMqTFc+JFEh18+h59WTS3jJGhes3Vq6ihfjln+SF06NSzp1jdqfvCDH+DSSy/FJZdcghNPPBHr169HV1cXbrvttpqfSaVSmD17tvUza9asuk66GeTrrH4CvHcSfvHSHkNzG/KKBmClN1Hl/1UnokCdmholprWQE0YqVRGecx3T0CogmVPDRGFSL7XyzQD/PLaw81p9OjXjrWgMC9cHwTyj+tCaBfP5PJ5++mmcffbZ9gHSaZx99tkYHBys+bmRkREcddRR6Ovrw/nnn48XXnjB93vGxsaQy+VcP82kWCpbZdh1hZ88EmNr5XMA5uY2qIafUqmU9kMXpFNTKAmr4aXO8aZm2ybk/5iqU1OP+B5AnRpiUyuHDfDv4xR2XqtLpyamfY68uqSrwuonTaPm3XffRalUmuBpmTVrFvbu3ev5meOPPx633XYbNm/ejJ///Ocol8tYsmQJdu/eXfN7BgYG0Nvba/309fXpnGbdOC3/SMJPjhts2OchNjW3QV6fSkdZXW2JWrH6wzoykDaJznj5JSaamihcqKOhJWDudZPo8dKokdiClz6eGs15rS6dmhh6JSrtJqIYExo1DaO/vx+rVq3CokWLsHTpUvzqV7/CjBkzcPPNN9f8zNq1azE0NGT9vPnmm40+TRdOI0S39bsTr4fOTy2yuw5BuclEJ6na8l4p7iRqxfhTqZStVaNh1KjkDJjmsai7+slQY5pET61eawDQ7dNGxKvvk/U5n3mtLp2aGC7go/mSJWTK8FM4tO6kI444AplMBvv27XO9vm/fPsyePVvpGO3t7TjllFPw6quv1nxPNptFNpvVObVIkQ9JWzrl6gyti3+isN9OyKzFRSepWuehK5UFhj2EwCQ9U9owdLCAIQ0j0DdnwNDFvVBnTg11aoikVg6b8zVfj4vG51zPNyt9ANjj2JFJh0p9iKOhp4vWqHV0dGDx4sXYunWr9Vq5XMbWrVvR39+vdIxSqYTnn38ec+bM0TvTJhJFOXfl8+OJbB6Kwl47E1NzOlSrnwC9h66WEJgkjBGiEvs3LQxTrDenxtBcLhI9vvOTTx+nMPOa+/mup9InPl4J5zg6202oousJjyPad9KaNWtw8cUX49RTT8Vpp52GH/7whxgdHcUll1wCAFi1ahXmzZuHgYEBAMD111+PM844A8ceeyw+/PBDfP/738cbb7yBr3zlK9FeSYTYfZ/C59MA3l4JPzetqYuqLVSoYtRMNPRq4ScEBgR3APY7pl/sf3RcKCysQm+zsdok1NnQ0rTrJtHjm3Pm87yFmdecz3eY3MU4Jgr7ecpUsAROY1YRpoO2UXPhhRfinXfewTe/+U3s3bsXixYtwgMPPGAlD+/atQtpx+T6wQcf4NJLL8XevXtx+OGHY/HixXjsscdw4oknRncVEaOzSPvhpRlQS6cG8NeBaGW0wk8aOgpBcuFhtGr8kvCmOnaLI2NFTOvqUD7uZGIlCof01FQLpB1+mBnXTaJHTafGqzRbf16zvRJhF/D4hZ/8cppUsKuf4uO90iXUyF155ZW48sorPX/38MMPu/69bt06rFu3LszXTBr5UrThJ2kkHSqUrAfQM/ZsaG5DXrGk2/keFZdx0K4lTA6S3zHbx4XCDuRLyB00x6ipV3yvzXHdNGqSjZpOjft5Czuv1VO6DLjbrsSFejRqAObUAOz95Em9fZ8k1Qu4n/AbYG5ug15OjXrM1y9OD4QzAv0mbdcxDfob2OJ74ZPaTbxuEj1+3hOnnpFT8DLsvFbvAt4Zx5yag+GrwQAqCgM0ajwZi0BNGJhoNcuH2kv4DTA5UdhuaBmEjo5CkIZFmM7mysc0KK8pbyUKh79fTbxuEj1yUe31yjkbNz7ypbLr+Q07r/nl4ajQkYlf+Kke3R7AqQMWH0NPFxo1HsgbQmWR9qN6AffTqAGceg5mLSw6idU64Se/+D4QTlfGPqb3Tsgvb6BVsTw1dcgP0FNDgoTfXIKXjjkq7LxWTzdqIJ5eiXpK3AFWPwE0ajyJzlPjTtry03Jwvm7awiKvT6f6SS9RuFaoSN8IDEo+NrEPUr05NYBj4THImCPREiT85hS8dD4fYee1qPJHSmVhyRqYju2pqS/PKE6Gni40ajyIOqdGhgdUc0RGxoool9WbNE42qr2fAL3Oun6aMkB9icKBxzTIW2ZXP9UTfjLvukm0yL99eyZV81m2Q7628Rt2XqunmSXgnp/jsojXXxHG8BONGg8iE9+rWsCDYshyUhACGMmbs2MOoygsK8z8CGp2F7VOjeuYBnksLJ2aSBKFzbluEi1OL2Yt4TevZy7svObXB08Fp3ZVbIyag/7h8SBk+ClOFWG60KjxQMfz4Ed1qMVPywEAOtsz1oNq0o65UdVPQY3ddHVqiqUyDuRLisc0Z/yLVkPLOowaJgonnqAcNsA7TBl2XqvXK5FJpyxDPi6eiXqaWQLu8JOzQi1J0KjxQMfz4Ee1KzAon8P5O5O0asLp1KiHn2pNsr2a4adhBVl2Owxjzvhbnpo6jHBTm3mS6FDJ5/Dy1ISd1+rVqQH0FMpNwK9LugrOOTipqsI0ajyILFG4KjvfdtP6TBoGatWEUxRWDz8FxeoPFcpax+vqyNTMPzGxCqhYltVPdeTUGHjdJFpUPCdysR0+pGeceM1r9SYKA/FLjK27IiyGeUa60KjxIOqGljLUElQlAJiZ2yCvz6s/UzW2jkL9bRKmVsn7Bx5PITHRxDBMoc6GloCZ102iRSWfw8v4HVYwTrzmtXp1agCHbEYMSpiFEEpeLz+ceXVxGJMw0KjxQGeR9qNaCClIzwEwU6tGxwjU0VGQk56XEBhQialPzarn1agkJpoYhilEIb5noDFNokUln8Nrfgo7rwVJNqgQp2qfsWLZqmQM671KpVKxGpMw0KjxILKcmqrwU1BCXeV35oUBwlQ/BT1wQggldU0drRq12L+BOjXjE2FHHZ4aExOkSbSo5HPY85N6orD7c5X3up7vusJP8VEVluORTlWEDsMSt5CcLjRqPIg6/JSvyqnxjVkb6CnQyUFSfeCcQmAqMX4VI0Slr4qJei2Fsqx+ok4NCY+Vz5H1Cz/Vzo3RmddcQn91hJ90cvRaHec41iqpVyHpqsI0ajyIrqS7uk2CgqfAwNyGMIrCQdUKTiGwTh9jSadaTCUx0Zp8DRJALBQjyKkx8LpJtCg9Hx7Gr0puTPW8Jo2ioOc7iGyMcmqG6tSokVSLviYNGjUeWCXKCr2M/KjeRSi5aQ2sQpEPj171k/8D55wo/XYtOmETndi/EMCoIQKIUbZJME34kURHUF80IJxODTBxXnMqe9fllZCbpBgs4PVq1EhsQ89871UYaNR4ELn4XqGMYqmM0XHhN383rdzRmLOw2G0ldMJP/g9cUDm3RC/8FHxMl1CYISFAq6FlHUaN87pNCn2S6LDDTyp6M5X3hp3XokgSBuLlqckdCg6PqxCnPKMw0KjxwA6nRCW+V8bIWLDwG+DQgRgzx1Ojl1Oj9sCpJhHqGIGquhhh2i9MJoVy/eEnwLzrJtGikijcW6VToyJo6TymnNeiSBIGYpZTo1AYoUIcu5frQKPGA3kz1F3S7YhtfnjAFn7z21GbpihcLgvL9duh4ClQ3Vmp6jWESRQOPqZZeU2FYv3ie4B5102iRSeMdLBQQr5Ytowa3XktCo0aQE/3qtWJQowQmCglkjRo1HgQdaIwALw7MgYg+CHuNqyk2BnLVslBUg0/qcT3Ab3+T6oN9EzTqilG7Kkx5bpJdAghlAwNt+BlQXnzUT2vqYaXg4hTqCUqQ4/VT2QCUZd0A7ZRo5wjYshu2fng6FQ/qYaf/OL7gF7IJKdQUl85plmGZSGCnBrAPIOaRMdYsWxtUPzmKKfgZe5QUVnWv3peizzUEoOk2HpbJEioU0MmYCW+1ln91J5JQSb2vzOSB6CRz3GoaESXVWkAplNqXaJV4705RU9NmEThoORE0wzLomxoWa+nxrDrJtHhFn4LeD4cFYe6eWrD4/Oa6vMdRJwW8HqbWUqoKEwmEFX4ySlZ/c6wDD8FLaiV35fKAgcN2H041YRVSjPleJTKwlqMvVDdyUWtU+M6piFhGOmpqadNAmDedZPocAq/pQM2J85O9rZMgtq8Vhyf16IrX45P+El10xVE3DqX60KjxoN8ROGnyjEqN5hl1AQsqFPaM8iMTyomJAvrdjR3huT8tCWGFXctqjo15bKwKtCCDSWzWgbI6qd2BU+ZHyZ2iCfRoOM5cYYpVauYquc1FXVvFeLkqVFpDKoCq5/IBMYiEt8DMMFTE/QQp1Ipo3I6dPOPnBVlfols+jo1/gbgSL4IYbVdUA0/tb5RWSoL67rqzakxrfKORIdqDhvgzmNT1VapnteiqvSJVUl3ZDo18RmTMNCoqUIIEVn4CbAfOtXqJ8Cs3Abd5p+ZdMrK/fDbSaiHnyoTwMhYESUfeX95vI62NDoDjFWTjMqCw9tVf/WTOddNokXHU9Pj0KrRSfi1P1eILFE4ViXdUY1JjAQJw0CjpgpnSKRenRrAfuhUw0+AWaW18sHRGSsVHQX18JP9+xGf8dIpl+yZYs74Fx2GXN2eGgM7xJNo0MlxcRq/qs+p89i5g0Wtz/kRp/JlOSa9dScKxyfPKAw0aqpw3giR5tRoeGpMKq0NU/5uTUR+nhpFTZmK50W2Nag9XirCYhKTxr/gGEOV6jM/dDR/SLywhCkVFtRuV/hJ3Rjq9gg/MdRSIV8sW4UhkfV+MnxMwkKjpgqnxa+ikBuEDD/JTHSVh9gkufowobogVWEhhKvhXRByvIZ8xktVo8Z5PBPGXyYJp1KwEjHDYtJ1k2jRMTIs5WkNnRqgKhdHw4jyIy5eCWdy/lQmT9cFjZoqnJ6HerrHSqoXeyU3rWPSaHV0c2oq7/XfSTiFwFTKG1XCJjrlkqrJx62A1cwyXf/9atJ1k2jRyo2xwuOa4afxee2dkbzW8+1HXBZw+cxNzbbVvTmJU0guDDRqqogySbhyHPdir7SodpqT22A1/1Qs6QaCdRTkdasIgQHOEmy/nBr1agvnpN3qAohFS6OmfgPcuZNu9esm0aJnnDh1ajQ2C+P31+4PDgBQf779iIui8LDGOAYh1y4/yYw4Q6OminyE5dxAOE9Nt0GltfLB0cup8d9dOUNPQUJg8n2VzwWHn3Ri/4WSwKEW3+3I8a83nwawr7tYbv3rJtGiY5yE0ampfE4aNQcBVLwSKs+3H9YGyfAFPKpwHMCcGho1VUTuqakyjvRi1iZ4aqIPP+kmEaqETXTCT10dDqGwFv8byGaWUVTqmXTdJFrs3Bi9HLbhMXVtFTmvvTVu1ES6gBtuhEeVOA048owMH5Ow0KipwgqnRBZ+qvLUxCxRNVyisH9yn65eg1Vi6uupUd9RuoTCWvxvYIWf0vXfryZdN4mWMDo1+4fHLOFHnXltb+6Q8meC6IhLTk1EGjVAfPKMwkKjpgp5I3RoeB78cO6gVYTfALN0UqS3RUunJmB3NawxwVbeFzxew4o9aqqP2epJs1J8L4qcGoBaNUkljE6NFLvUndfk5+ptZgnEJ9QSlW4PEJ8xCQuNmiqiTxS2j6NqhZukk9KI6icd7QtAbbx0ZdlN+RvIZpb1Cu9J7OtubWOORIvM6VARfqsOUek+p7WOEwYVzSsT0MlpCoK9n4iLMGJyfjgXe2UvgUGJwlZOjVb1k3qisAoq4TrdBnqmhABlp/P2qDw1hlw3iQ6n8JvK8+EUvAT057Va/w6DVelTLBtdsaeT0xQEc2qIC3uRjr76qVvRS2BUonAYReGgnBpFNWGJkk6NpvfHLqtvbcOyUI4upwYw57pJdLiE37L6BoruvFbr32FwzjsmeyZ0cpqCYPiJuAhTouxHNsyOZnySyBfLONTi+gv5MOGnAPeoTny/8j4VnRq9mLWcXIZb3LCM3FMzhYnCSWPYIfzWphjGdD5HuvOa/bnovBKA2WXdunOeH0E6YHGHRk0V0Vc/OcJPigvq1I42SHHYVk8WrqtNQq2cGk3NBkunpoYBUmm7oJunY0YI0E4UjiqnxpwkdRINYfI5nGGqMPNa9THC0p5JWcc0OdwSqU4Nc2qIkzCJr364E4XVHuJ0OmW5gVs9BGWFn0IoCteahHQ1G3ot74L3QnywULK6WWvn1LT4+NuJwhHn1LT4dZPo0M1hA9ybgzDzGhDNAp5KpWIRbolWp6YyHsWysDy5SYJGTRWWUaOxSPsRpvrJ+d5WDwNYJfAanoIgbQl9nRr/tgZy0s6kU+jqUDNWTQnDSPG9qKqfTLluEh26OWyV9zqNmrDGUP1GDWDPPSZ7JqLUqXHKa5gckgsLjZoqLN2ViBYJ5w2mszMxRasmTGJ10M4qrE5NWQCj+YnHdPZVUW362GNIGKZgie9F66lp9esm0REmn6MnRPip+r1RJMUC8WjgGKVOjXPtMnlMwkKjpoowJcp+hCnpBszRSQlX/RTgqdGcZLNtaSv84uVh0NWoAcwZ/+hzasy4bhIdYfI5ukOEnyqfcxhDEXlqTA8/lcrCajcRhU5NWyZtbXJM9l6FhUZNFZHn1DiMozAx61ZPVA2VKByws7ImWcXxqsj7184F0dWoAZydiFt7cZdtEqLyLJpy3SQ6wuRzOL0s4XNxojZqzFzARxxe0Sh0agDzDb16oFFTRfTie87wk/6k0eo75qgVhQslWwgsTIzfywjU9fw439vqei2Rt0kw5LpJdITJ53AZJyHmNd3P+RGke9XqyPmpsz0dSWNaID5Ky2EINYI33XQTjj76aHR2duL000/Hk08+6fv+u+++Gx//+MfR2dmJk08+Gffff3+ok20G+cjbJDjDT/qTRqvrpORDJFb76Sg4czlUhcAAp1aNV/hJz/MDGKRTE7X4niHXTaJDN4et8t76E4V1nm8/5Nxjqi5LmE1XEE6l5aShPRPeeeedWLNmDa677jo888wzWLhwIZYvX479+/d7vv+xxx7DRRddhNWrV2P79u1YuXIlVq5ciZ07d9Z98o3Arn5qgKJwiES81g8/RZtTI3eNh3VktPJE/LRqbAlyndh/5XiHCuWWduEWitGK75ly3SQ67PCTfs6Z7ufkvKb7fPtheqglTHg8CNPHpB6076of/OAHuPTSS3HJJZfgxBNPxPr169HV1YXbbrvN8/033ngjVqxYga997Ws44YQT8O1vfxuf+tSn8KMf/ajuk28EUTe0dPVICRNOafEdc6jwk484VJik3sr7axuBoRKFs2YIIMo2CZE1tDTkukl06OawVb83zLwWRZWPxPReR2HnPD9MH5N60DIN8/k8nn76aaxdu9Z6LZ1O4+yzz8bg4KDnZwYHB7FmzRrXa8uXL8emTZtqfs/Y2BjGxsasf+dyOQDAwG/+iM6uqTqnrM1LeyrfFZVR05GpL/z0zK4P8K3/74VIzqURyCQ3nfGSSa1vf3hwwrXtHToEQN8VK9//62ffxuvvjbp+N/jae9rHlEJhw4eK+O5vXop0FxUlz7zxAYDocmpMuW4SHa+9MwJAzzjpnRKuikm+N8pQi8xD+dX23dj59lBkx20Wr71Tma8aMSa3P/Y6tvxxX2THDcOhAyNN/T6tGevdd99FqVTCrFmzXK/PmjULL730kudn9u7d6/n+vXv31vyegYEBfOtb35rw+i8e34V0tkvnlENzeFdHJMfpndJeEX1rzygLvwHAzJ4sAODN9w/i9j+8Hsm5NIpMOqX1QH5kamVshw4Wal6bvH5VZvZ0AgCefuMDPD2+0FczS/OYs3o6MXxoBL98erfW5yaDaVOiuV8Bs66bRMes8WdIhSOmZpFOAYd1tIWa13Sfbz8+cljl3v/Dq+/hD6++F9lxm43u/OTH4eNjsuXFyTVoAKA8dqCp39eS27C1a9e6vDu5XA59fX249K8WoPOwxnpqAGB2TyeWHPORSI7V29WOH3/pU+jWEH4DgL86bgauP/8T2Jc7FMl5NJJPHjkNvV3qRs2xM7vx/16wEH9519uCz6RS+OtF87TO4ZIlRyPblsaBvHfIZNqUDvz1orlax/zB/7kQv31hHwQmqhS3Eodl23DRp+dHdjxTrptEx4IjpuJjs7qV3z+tqwM/+afF4+FKvXntO39zEk5fEM38CgD/1+eOw9xpU4zOH+nIZHDBqUdGdrxv/h8n4tfPvo1SefLDT4dGR/DNHzbv+1LCS1e+Bvl8Hl1dXfjlL3+JlStXWq9ffPHF+PDDD7F58+YJn5k/fz7WrFmDq6++2nrtuuuuw6ZNm/Dss88qfW8ul0Nvby+GhobQ09OjerqEEEIImUSavX5rJY50dHRg8eLF2Lp1q/VauVzG1q1b0d/f7/mZ/v5+1/sBYMuWLTXfTwghhBASBu3w05o1a3DxxRfj1FNPxWmnnYYf/vCHGB0dxSWXXAIAWLVqFebNm4eBgQEAwFVXXYWlS5fihhtuwHnnnYc77rgD27Ztwy233BLtlRBCCCEk0WgbNRdeeCHeeecdfPOb38TevXuxaNEiPPDAA1Yy8K5du5B2CIEtWbIEGzduxLXXXotvfOMbOO6447Bp0yacdNJJ0V0FIYQQQhKPVk7NZMGcGkIIIcQ8WjqnhhBCCCGkVaFRQwghhJBYQKOGEEIIIbGARg0hhBBCYgGNGkIIIYTEAho1hBBCCIkFNGoIIYQQEgto1BBCCCEkFtCoIYQQQkgs0G6TMBlI0eNcLjfJZ0IIIYQQVeS63azmBUYYNe+99x4AoK+vb5LPhBBCCCG6vPfee+jt7W349xhh1EyfPh1ApVlmVIPy6U9/Gk899VQkxzLlmLlcDn19fXjzzTcj7cHR6tfdiOOZMpYm/G0aMZYmXHfUxzTlnmzEMU24J4HWv+5GHHNoaAjz58+31vFGY4RRI7t+9/b2RnaDZTKZyJtrmXLMnp6eSI9pwnU34hyB1h9LE/42kijH0pTr5vPdmseTtPpYmvC3kch1vNEkNlH4iiuuSOwxo8aE6zZhHAEzrtuEsTTlujmWrXm8RmHCdZsylrVIiWZl79RBs1uXxxWOY3RwLKODYxkNHMfo4FhGR7PH0ghPTTabxXXXXYdsNjvZp2I0HMfo4FhGB8cyGjiO0cGxjI5mj6URnhpCCCGEkCCM8NQQQgghhARBo4YQQgghsYBGDSGEEEJiAY0aQgghhMSCphk1jzzyCL74xS9i7ty5SKVS2LRpk+v3+/btw5e//GXMnTsXXV1dWLFiBV555RXXe5YtW4ZUKuX6ueyyy6zfb9iwYcLv5c/+/fubcZkNpxnjCABPPfUUPve5z2HatGk4/PDDsXz5cjz77LONvrym0qyx3Lp1K5YsWYLu7m7Mnj0b//Zv/4Zisdjoy2sqUYwlAAwODuKzn/0sDjvsMPT09ODMM8/EwYMHrd+///77+NKXvoSenh5MmzYNq1evxsjISKMvr2k0axy/853vYMmSJejq6sK0adMafFWTQzPG8vXXX8fq1auxYMECTJkyBccccwyuu+465PP5Zlxi02jWffnXf/3XmD9/Pjo7OzFnzhz88z//M95++22tc22aUTM6OoqFCxfipptumvA7IQRWrlyJP//5z9i8eTO2b9+Oo446CmeffTZGR0dd77300kuxZ88e6+d73/ue9bsLL7zQ9bs9e/Zg+fLlWLp0KWbOnNnwa2wGzRjHkZERrFixAvPnz8cTTzyBRx99FN3d3Vi+fDkKhULDr7FZNGMsn332WXzhC1/AihUrsH37dtx555349a9/ja9//esNv75mEsVYDg4OYsWKFTjnnHPw5JNP4qmnnsKVV17pUiL90pe+hBdeeAFbtmzBfffdh0ceeQT/8i//0pRrbAbNGsd8Po8LLrgAl19+eVOuazJoxli+9NJLKJfLuPnmm/HCCy9g3bp1WL9+Pb7xjW807TqbQbPuy7POOgt33XUXXn75Zdxzzz147bXX8Pd///d6JysmAQDi3nvvtf798ssvCwBi586d1mulUknMmDFD/Md//If12tKlS8VVV12l/D379+8X7e3t4mc/+1kUp91yNGocn3rqKQFA7Nq1y3rtueeeEwDEK6+8Euk1tAqNGsu1a9eKU0891fXar3/9a9HZ2SlyuVxk599KhB3L008/XVx77bU1j/viiy8KAOKpp56yXvvNb34jUqmUeOutt6K9iBagUePo5Pbbbxe9vb1RnXLL0oyxlHzve98TCxYsqPucW5VmjuXmzZtFKpUS+Xxe+TMtkVMzNjYGAOjs7LReS6fTyGazePTRR13v/cUvfoEjjjgCJ510EtauXYsDBw7UPO7PfvYzdHV16Vt6hhLVOB5//PH4yEc+gp/+9KfI5/M4ePAgfvrTn+KEE07A0Ucf3ZRrmWyiGsuxsTHXMQBgypQpOHToEJ5++ukGXkHroDKW+/fvxxNPPIGZM2diyZIlmDVrFpYuXeoa68HBQUybNg2nnnqq9drZZ5+NdDqNJ554oklXM3lENY6ksWM5NDTUtOaNrUCjxvL999/HL37xCyxZsgTt7e3qJ6RlNkUEqiy9fD4v5s+fLy644ALx/vvvi7GxMfHd735XABDnnHOO9b6bb75ZPPDAA+K5554TP//5z8W8efPE3/zN39T8nhNOOEFcfvnljbyUSaWR4/j888+LY445RqTTaZFOp8Xxxx8vXn/99WZdWtNp1Fg++OCDIp1Oi40bN4pisSh2794t/uqv/koAEBs3bmzmJTaNMGM5ODgoAIjp06eL2267TTzzzDPi6quvFh0dHeJPf/qTEEKI73znO+JjH/vYhO+bMWOG+PGPf9yUa2smjRpHJ0n11DRiLIUQ4pVXXhE9PT3illtuacZlTQqNHstrrrlGdHV1CQDijDPOEO+++67e+dV9hSGoHhQhhNi2bZtYuHChACAymYxYvny5OPfcc8WKFStqHmfr1q0CgHj11Vcn/O6xxx4TAMS2bduiPv2WoVHjeODAAXHaaaeJVatWiSeffFIMDg6Kv/u7vxOf+MQnxIEDBxp5SZNGI+/JG264QfT09IhMJiO6urrEwMCAACDuuOOORl3OpBJmLP/whz8IAGLt2rWuz5188sni61//uhCCRo0Q0Yyjk6QaNUJEP5a7d+8WxxxzjFi9enXDrqMVaPRYvvPOO+Lll18Wv/3tb8VnPvMZ8YUvfEGUy2Xl82tT9+k0lsWLF2PHjh0YGhpCPp/HjBkzcPrpp7tczdWcfvrpAIBXX30VxxxzjOt3t956KxYtWoTFixc39LxbjSjGcePGjXj99dcxODhoJXFt3LgRhx9+ODZv3ox/+Id/aMq1TDZR3ZNr1qzBV7/6VezZsweHH344Xn/9daxduxYf/ehHm3IdrUDQWM6ZMwcAcOKJJ7o+d8IJJ2DXrl0AgNmzZ0+oYiwWi3j//fcxe/bsJlzF5BPFOJIKUY7l22+/jbPOOgtLlizBLbfc0pwLaCGiHMsjjjgCRxxxBD72sY/hhBNOQF9fHx5//HH09/crnUtL5NQ46e3txYwZM/DKK69g27ZtOP/882u+d8eOHQDsAZOMjIzgrrvuwurVqxt5qi1NPeN44MABpNNppFIp6z3y3+VyuaHn3YpEcU+mUinMnTsXU6ZMwX/913+hr68Pn/rUpxp52i1JrbE8+uijMXfuXLz88suu9//pT3/CUUcdBQDo7+/Hhx9+6MpFeuihh1Auly1jMinUM47ETb1j+dZbb2HZsmVYvHgxbr/9dlc1T9KI+r6U643M21EinANKn+HhYbF9+3axfft2AUD84Ac/ENu3bxdvvPGGEEKIu+66S/zud78Tr732mti0aZM46qijxN/+7d9an3/11VfF9ddfL7Zt2yb+8pe/iM2bN4uPfvSj4swzz5zwXbfeeqvo7OwUH3zwQbMur2k0Yxz/+Mc/imw2Ky6//HLx4osvip07d4p/+qd/Er29veLtt99u+jU3imbdk9/73vfEc889J3bu3Cmuv/560d7ePsF9azr1jqUQQqxbt0709PSIu+++W7zyyivi2muvFZ2dna5Q3ooVK8Qpp5winnjiCfHoo4+K4447Tlx00UVNvdZG0qxxfOONN8T27dvFt771LTF16lTrO4eHh5t6vY2kGWO5e/duceyxx4rPfe5zYvfu3WLPnj3WT5xoxlg+/vjj4t///d/F9u3bxeuvvy62bt0qlixZIo455hhx6NAh5XNtmlHzu9/9TgCY8HPxxRcLIYS48cYbxZFHHina29vF/PnzxbXXXivGxsasz+/atUuceeaZYvr06SKbzYpjjz1WfO1rXxNDQ0MTvqu/v1/84z/+Y7Murak0axxlPLO3t1ccfvjh4rOf/awYHBxs5qU2nGaN5VlnnSV6e3tFZ2enOP3008X999/fzMtsCvWOpWRgYEAceeSRoqurS/T394v//d//df3+vffeExdddJGYOnWq6OnpEZdcckmsFuJmjePFF1/s+T2/+93vmnCVzaEZY3n77bd7fkcT/QVNoRlj+dxzz4mzzjrLmk+PPvpocdlll4ndu3drnWtKCCHU/TqEEEIIIa1JcoN/hBBCCIkVNGoIIYQQEgto1BBCCCEkFtCoIYQQQkgsoFFDCCGEkFhAo4YQQgghsYBGDSGEEEJiAY0aQgghhMQCGjWEEEIIiQU0agghhBASC2jUEEIIISQW0KghhBBCSCz4/wGFKB+4RpPjgwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(quantities[1])\n",
     "df_monthly[quantities[1]].plot();"
@@ -246,9 +1841,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:25.776521Z",
+     "iopub.status.busy": "2024-09-04T12:38:25.776285Z",
+     "iopub.status.idle": "2024-09-04T12:38:25.922698Z",
+     "shell.execute_reply": "2024-09-04T12:38:25.918959Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DischargeIntegratedMonthlyDuration:B4.1200l1:26.6666\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(quantities[2])\n",
     "df_monthly[quantities[2]].plot();"
diff --git a/notebooks/swmm.ipynb b/notebooks/swmm.ipynb
index 7b81263e..aebfc26d 100644
--- a/notebooks/swmm.ipynb
+++ b/notebooks/swmm.ipynb
@@ -12,8 +12,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:06.827162Z",
+     "iopub.status.busy": "2024-09-04T12:38:06.825012Z",
+     "iopub.status.idle": "2024-09-04T12:38:07.956411Z",
+     "shell.execute_reply": "2024-09-04T12:38:07.955605Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from mikeio1d import Res1D\n",
@@ -38,8 +45,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:07.959012Z",
+     "iopub.status.busy": "2024-09-04T12:38:07.958537Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.107600Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.107104Z"
+    }
+   },
    "outputs": [],
    "source": [
     "file_path = \"../tests/testdata/swmm.out\"\n",
@@ -57,9 +71,258 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.109636Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.109218Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.124007Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.123256Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SWMM_NODE_DEPTH:9</th>\n",
+       "      <th>SWMM_NODE_HEAD:9</th>\n",
+       "      <th>SWMM_NODE_VOLUME:9</th>\n",
+       "      <th>SWMM_NODE_LATFLOW:9</th>\n",
+       "      <th>SWMM_NODE_INFLOW:9</th>\n",
+       "      <th>SWMM_NODE_OVERFLOW:9</th>\n",
+       "      <th>SWMM_NODE_QUAL:9</th>\n",
+       "      <th>SWMM_NODE_QUAL:9</th>\n",
+       "      <th>SWMM_NODE_DEPTH:10</th>\n",
+       "      <th>SWMM_NODE_HEAD:10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SWMM_SYS_DWFLOW</th>\n",
+       "      <th>SWMM_SYS_GWFLOW</th>\n",
+       "      <th>SWMM_SYS_INFLOW</th>\n",
+       "      <th>SWMM_SYS_EXFLOW</th>\n",
+       "      <th>SWMM_SYS_INFLOW</th>\n",
+       "      <th>SWMM_SYS_FLOODING</th>\n",
+       "      <th>SWMM_SYS_OUTFLOW</th>\n",
+       "      <th>SWMM_SYS_STORAGE</th>\n",
+       "      <th>SWMM_SYS_EVAP</th>\n",
+       "      <th>SWMM_SYS_PET</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 01:00:00.001</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>995.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 02:00:00.001</th>\n",
+       "      <td>0.288774</td>\n",
+       "      <td>1000.288757</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.239633</td>\n",
+       "      <td>1.239633</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.720075</td>\n",
+       "      <td>3.144015</td>\n",
+       "      <td>0.520309</td>\n",
+       "      <td>995.520325</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.216826</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.128909</td>\n",
+       "      <td>1656.397217</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 03:00:00.001</th>\n",
+       "      <td>0.418453</td>\n",
+       "      <td>1000.418457</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.595429</td>\n",
+       "      <td>2.595429</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.389509</td>\n",
+       "      <td>3.077902</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.030855</td>\n",
+       "      <td>0.521967</td>\n",
+       "      <td>12.470544</td>\n",
+       "      <td>2796.561035</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 04:00:00.001</th>\n",
+       "      <td>0.567633</td>\n",
+       "      <td>1000.567627</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.645479</td>\n",
+       "      <td>4.645479</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.810841</td>\n",
+       "      <td>2.962168</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.252975</td>\n",
+       "      <td>4.511855</td>\n",
+       "      <td>19.521162</td>\n",
+       "      <td>3780.683594</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 05:00:00.001</th>\n",
+       "      <td>0.425047</td>\n",
+       "      <td>1000.425049</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.675368</td>\n",
+       "      <td>2.675368</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.338782</td>\n",
+       "      <td>2.867756</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.172027</td>\n",
+       "      <td>0.555154</td>\n",
+       "      <td>13.623661</td>\n",
+       "      <td>2913.430420</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 298 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         SWMM_NODE_DEPTH:9  SWMM_NODE_HEAD:9  \\\n",
+       "1998-01-01 01:00:00.001           0.000000       1000.000000   \n",
+       "1998-01-01 02:00:00.001           0.288774       1000.288757   \n",
+       "1998-01-01 03:00:00.001           0.418453       1000.418457   \n",
+       "1998-01-01 04:00:00.001           0.567633       1000.567627   \n",
+       "1998-01-01 05:00:00.001           0.425047       1000.425049   \n",
+       "\n",
+       "                         SWMM_NODE_VOLUME:9  SWMM_NODE_LATFLOW:9  \\\n",
+       "1998-01-01 01:00:00.001                 0.0             0.000000   \n",
+       "1998-01-01 02:00:00.001                 0.0             1.239633   \n",
+       "1998-01-01 03:00:00.001                 0.0             2.595429   \n",
+       "1998-01-01 04:00:00.001                 0.0             4.645479   \n",
+       "1998-01-01 05:00:00.001                 0.0             2.675368   \n",
+       "\n",
+       "                         SWMM_NODE_INFLOW:9  SWMM_NODE_OVERFLOW:9  \\\n",
+       "1998-01-01 01:00:00.001            0.000000                   0.0   \n",
+       "1998-01-01 02:00:00.001            1.239633                   0.0   \n",
+       "1998-01-01 03:00:00.001            2.595429                   0.0   \n",
+       "1998-01-01 04:00:00.001            4.645479                   0.0   \n",
+       "1998-01-01 05:00:00.001            2.675368                   0.0   \n",
+       "\n",
+       "                         SWMM_NODE_QUAL:9  SWMM_NODE_QUAL:9  \\\n",
+       "1998-01-01 01:00:00.001          0.000000          0.000000   \n",
+       "1998-01-01 02:00:00.001         15.720075          3.144015   \n",
+       "1998-01-01 03:00:00.001         15.389509          3.077902   \n",
+       "1998-01-01 04:00:00.001         14.810841          2.962168   \n",
+       "1998-01-01 05:00:00.001         14.338782          2.867756   \n",
+       "\n",
+       "                         SWMM_NODE_DEPTH:10  SWMM_NODE_HEAD:10  ...  \\\n",
+       "1998-01-01 01:00:00.001            0.000000         995.000000  ...   \n",
+       "1998-01-01 02:00:00.001            0.520309         995.520325  ...   \n",
+       "1998-01-01 03:00:00.001            3.000000         998.000000  ...   \n",
+       "1998-01-01 04:00:00.001            3.000000         998.000000  ...   \n",
+       "1998-01-01 05:00:00.001            3.000000         998.000000  ...   \n",
+       "\n",
+       "                         SWMM_SYS_DWFLOW  SWMM_SYS_GWFLOW  SWMM_SYS_INFLOW  \\\n",
+       "1998-01-01 01:00:00.001              0.0              0.0              0.0   \n",
+       "1998-01-01 02:00:00.001              0.0              0.0              0.0   \n",
+       "1998-01-01 03:00:00.001              0.0              0.0              0.0   \n",
+       "1998-01-01 04:00:00.001              0.0              0.0              0.0   \n",
+       "1998-01-01 05:00:00.001              0.0              0.0              0.0   \n",
+       "\n",
+       "                         SWMM_SYS_EXFLOW  SWMM_SYS_INFLOW  SWMM_SYS_FLOODING  \\\n",
+       "1998-01-01 01:00:00.001              0.0         0.000000           0.000000   \n",
+       "1998-01-01 02:00:00.001              0.0         6.216826           0.000000   \n",
+       "1998-01-01 03:00:00.001              0.0        13.030855           0.521967   \n",
+       "1998-01-01 04:00:00.001              0.0        24.252975           4.511855   \n",
+       "1998-01-01 05:00:00.001              0.0        14.172027           0.555154   \n",
+       "\n",
+       "                         SWMM_SYS_OUTFLOW  SWMM_SYS_STORAGE  SWMM_SYS_EVAP  \\\n",
+       "1998-01-01 01:00:00.001          0.000000          0.000000            0.0   \n",
+       "1998-01-01 02:00:00.001          6.128909       1656.397217            0.0   \n",
+       "1998-01-01 03:00:00.001         12.470544       2796.561035            0.0   \n",
+       "1998-01-01 04:00:00.001         19.521162       3780.683594            0.0   \n",
+       "1998-01-01 05:00:00.001         13.623661       2913.430420            0.0   \n",
+       "\n",
+       "                         SWMM_SYS_PET  \n",
+       "1998-01-01 01:00:00.001           0.0  \n",
+       "1998-01-01 02:00:00.001           0.0  \n",
+       "1998-01-01 03:00:00.001           0.0  \n",
+       "1998-01-01 04:00:00.001           0.0  \n",
+       "1998-01-01 05:00:00.001           0.0  \n",
+       "\n",
+       "[5 rows x 298 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()"
    ]
@@ -74,9 +337,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.148716Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.148449Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.152540Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.151921Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['SWMM_NODE_DEPTH',\n",
+       " 'SWMM_NODE_HEAD',\n",
+       " 'SWMM_NODE_VOLUME',\n",
+       " 'SWMM_NODE_LATFLOW',\n",
+       " 'SWMM_NODE_INFLOW',\n",
+       " 'SWMM_NODE_OVERFLOW',\n",
+       " 'SWMM_NODE_QUAL',\n",
+       " 'SWMM_LINK_FLOW',\n",
+       " 'SWMM_LINK_DEPTH',\n",
+       " 'SWMM_LINK_VELOCITY',\n",
+       " 'SWMM_LINK_Froude_Number',\n",
+       " 'SWMM_LINK_CAPACITY',\n",
+       " 'SWMM_LINK_QUAL',\n",
+       " 'SWMM_SUBCATCH_RAINFALL',\n",
+       " 'SWMM_SUBCATCH_SNOWDEPTH',\n",
+       " 'SWMM_SUBCATCH_EVAP',\n",
+       " 'SWMM_SUBCATCH_INFIL',\n",
+       " 'SWMM_SUBCATCH_RUNOFF',\n",
+       " 'SWMM_SUBCATCH_GW_FLOW',\n",
+       " 'SWMM_SUBCATCH_GW_ELEV',\n",
+       " 'SWMM_SUBCATCH_SOIL_MOIST',\n",
+       " 'SWMM_SUBCATCH_WASHOFF',\n",
+       " 'SWMM_SYS_TEMPERATURE',\n",
+       " 'SWMM_SYS_RAINFALL',\n",
+       " 'SWMM_SYS_SNOWDEPTH',\n",
+       " 'SWMM_SYS_INFIL',\n",
+       " 'SWMM_SYS_RUNOFF',\n",
+       " 'SWMM_SYS_DWFLOW',\n",
+       " 'SWMM_SYS_GWFLOW',\n",
+       " 'SWMM_SYS_INFLOW',\n",
+       " 'SWMM_SYS_EXFLOW',\n",
+       " 'SWMM_SYS_FLOODING',\n",
+       " 'SWMM_SYS_OUTFLOW',\n",
+       " 'SWMM_SYS_STORAGE',\n",
+       " 'SWMM_SYS_EVAP',\n",
+       " 'SWMM_SYS_PET']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res.quantities"
    ]
@@ -91,23 +407,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.154401Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.154190Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.158256Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.157796Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start node ID: 17\n",
+      "End node ID: 18\n"
+     ]
+    }
+   ],
    "source": [
     "pipe_id = '10'\n",
     "pipe_data_item = res.reaches[pipe_id]\n",
-    "node_start = res.data.Nodes[pipe_data_item.StartNodeIndex]\n",
-    "node_end = res.data.Nodes[pipe_data_item.EndNodeIndex]\n",
+    "node_start = res.data.Nodes[pipe_data_item[0].StartNodeIndex]\n",
+    "node_end = res.data.Nodes[pipe_data_item[0].EndNodeIndex]\n",
     "print(f'Start node ID: {node_start.ID}')\n",
     "print(f'End node ID: {node_end.ID}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.159910Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.159695Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.165113Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.164686Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SWMM_LINK_FLOW:10</th>\n",
+       "      <th>SWMM_NODE_DEPTH:17</th>\n",
+       "      <th>SWMM_NODE_DEPTH:18</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 01:00:00.001</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 02:00:00.001</th>\n",
+       "      <td>5.876935</td>\n",
+       "      <td>0.607021</td>\n",
+       "      <td>0.572612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 03:00:00.001</th>\n",
+       "      <td>11.923845</td>\n",
+       "      <td>0.886635</td>\n",
+       "      <td>0.833084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 04:00:00.001</th>\n",
+       "      <td>18.203714</td>\n",
+       "      <td>1.139519</td>\n",
+       "      <td>1.063005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 05:00:00.001</th>\n",
+       "      <td>12.643229</td>\n",
+       "      <td>0.916358</td>\n",
+       "      <td>0.860591</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         SWMM_LINK_FLOW:10  SWMM_NODE_DEPTH:17  \\\n",
+       "1998-01-01 01:00:00.001           0.000000            0.000000   \n",
+       "1998-01-01 02:00:00.001           5.876935            0.607021   \n",
+       "1998-01-01 03:00:00.001          11.923845            0.886635   \n",
+       "1998-01-01 04:00:00.001          18.203714            1.139519   \n",
+       "1998-01-01 05:00:00.001          12.643229            0.916358   \n",
+       "\n",
+       "                         SWMM_NODE_DEPTH:18  \n",
+       "1998-01-01 01:00:00.001            0.000000  \n",
+       "1998-01-01 02:00:00.001            0.572612  \n",
+       "1998-01-01 03:00:00.001            0.833084  \n",
+       "1998-01-01 04:00:00.001            1.063005  \n",
+       "1998-01-01 05:00:00.001            0.860591  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[['SWMM_LINK_FLOW:10', 'SWMM_NODE_DEPTH:17', 'SWMM_NODE_DEPTH:18']].head()"
    ]
@@ -122,9 +543,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.166899Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.166526Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.662531Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.662078Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pipe_query = QueryDataReach('SWMM_LINK_FLOW', pipe_id)\n",
     "df_pipe = res.read(queries=[pipe_query])\n",
@@ -146,9 +595,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.664619Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.664217Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.667802Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.667186Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "&lt;ResultCatchments&gt;\n",
+       "    <style>\n",
+       "        ul {\n",
+       "            margin: 0px;\n",
+       "            padding: 0px;\n",
+       "            padding-left: 2em;\n",
+       "        }\n",
+       "    </style>\n",
+       "    <details><summary>Names (8)</summary><ul><li>1</li><li>2</li><li>3</li><li>4</li><li>5</li><li>6</li><li>7</li><li>8</li></ul></details><details><summary>Quantities (9)</summary><ul><li>SWMM_SUBCATCH_RAINFALL</li><li>SWMM_SUBCATCH_SNOWDEPTH</li><li>SWMM_SUBCATCH_EVAP</li><li>SWMM_SUBCATCH_INFIL</li><li>SWMM_SUBCATCH_RUNOFF</li><li>SWMM_SUBCATCH_GW_FLOW</li><li>SWMM_SUBCATCH_GW_ELEV</li><li>SWMM_SUBCATCH_SOIL_MOIST</li><li>SWMM_SUBCATCH_WASHOFF</li></ul></details>"
+      ],
+      "text/plain": [
+       "<ResultCatchments>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "res.catchments"
    ]
@@ -163,13 +641,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.669716Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.669427Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.673289Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.672849Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['SWMM_SUBCATCH_RAINFALL', 'SWMM_SUBCATCH_SNOWDEPTH', 'SWMM_SUBCATCH_EVAP', 'SWMM_SUBCATCH_INFIL', 'SWMM_SUBCATCH_RUNOFF', 'SWMM_SUBCATCH_GW_FLOW', 'SWMM_SUBCATCH_GW_ELEV', 'SWMM_SUBCATCH_SOIL_MOIST', 'SWMM_SUBCATCH_WASHOFF', 'SWMM_SUBCATCH_WASHOFF']\n"
+     ]
+    }
+   ],
    "source": [
     "catchment_id = '5'\n",
     "swmm_catchment = res.catchments[catchment_id]\n",
-    "catchment_quantities = [data_item.Quantity.Id for data_item in swmm_catchment.DataItems]\n",
+    "catchment_quantities = [data_item.Quantity.Id for data_item in swmm_catchment._catchment.DataItems]\n",
     "print(catchment_quantities)"
    ]
   },
@@ -183,8 +676,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.675288Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.674906Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.678571Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.678121Z"
+    }
+   },
    "outputs": [],
    "source": [
     "catchment_queries = [QueryDataCatchment('SWMM_SUBCATCH_RUNOFF', catchment_id) for catchment_id in res.catchments]\n",
@@ -193,9 +693,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.680232Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.680035Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.686899Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.686409Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:1</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:2</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:3</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:4</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:5</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:6</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:7</th>\n",
+       "      <th>SWMM_SUBCATCH_RUNOFF:8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 01:00:00.001</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 02:00:00.001</th>\n",
+       "      <td>1.243824</td>\n",
+       "      <td>1.243824</td>\n",
+       "      <td>0.629613</td>\n",
+       "      <td>0.629613</td>\n",
+       "      <td>1.814816</td>\n",
+       "      <td>0.302342</td>\n",
+       "      <td>0.100822</td>\n",
+       "      <td>0.251970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 03:00:00.001</th>\n",
+       "      <td>2.597976</td>\n",
+       "      <td>2.563968</td>\n",
+       "      <td>1.302650</td>\n",
+       "      <td>1.302650</td>\n",
+       "      <td>3.821663</td>\n",
+       "      <td>0.649161</td>\n",
+       "      <td>0.244563</td>\n",
+       "      <td>0.548222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 04:00:00.001</th>\n",
+       "      <td>4.656198</td>\n",
+       "      <td>4.524055</td>\n",
+       "      <td>2.449128</td>\n",
+       "      <td>2.449128</td>\n",
+       "      <td>6.562110</td>\n",
+       "      <td>1.495427</td>\n",
+       "      <td>0.789465</td>\n",
+       "      <td>1.327465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998-01-01 05:00:00.001</th>\n",
+       "      <td>2.675617</td>\n",
+       "      <td>2.511513</td>\n",
+       "      <td>1.369764</td>\n",
+       "      <td>1.369764</td>\n",
+       "      <td>3.585666</td>\n",
+       "      <td>1.082219</td>\n",
+       "      <td>0.596153</td>\n",
+       "      <td>0.981333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         SWMM_SUBCATCH_RUNOFF:1  SWMM_SUBCATCH_RUNOFF:2  \\\n",
+       "1998-01-01 01:00:00.001                0.000000                0.000000   \n",
+       "1998-01-01 02:00:00.001                1.243824                1.243824   \n",
+       "1998-01-01 03:00:00.001                2.597976                2.563968   \n",
+       "1998-01-01 04:00:00.001                4.656198                4.524055   \n",
+       "1998-01-01 05:00:00.001                2.675617                2.511513   \n",
+       "\n",
+       "                         SWMM_SUBCATCH_RUNOFF:3  SWMM_SUBCATCH_RUNOFF:4  \\\n",
+       "1998-01-01 01:00:00.001                0.000000                0.000000   \n",
+       "1998-01-01 02:00:00.001                0.629613                0.629613   \n",
+       "1998-01-01 03:00:00.001                1.302650                1.302650   \n",
+       "1998-01-01 04:00:00.001                2.449128                2.449128   \n",
+       "1998-01-01 05:00:00.001                1.369764                1.369764   \n",
+       "\n",
+       "                         SWMM_SUBCATCH_RUNOFF:5  SWMM_SUBCATCH_RUNOFF:6  \\\n",
+       "1998-01-01 01:00:00.001                0.000000                0.000000   \n",
+       "1998-01-01 02:00:00.001                1.814816                0.302342   \n",
+       "1998-01-01 03:00:00.001                3.821663                0.649161   \n",
+       "1998-01-01 04:00:00.001                6.562110                1.495427   \n",
+       "1998-01-01 05:00:00.001                3.585666                1.082219   \n",
+       "\n",
+       "                         SWMM_SUBCATCH_RUNOFF:7  SWMM_SUBCATCH_RUNOFF:8  \n",
+       "1998-01-01 01:00:00.001                0.000000                0.000000  \n",
+       "1998-01-01 02:00:00.001                0.100822                0.251970  \n",
+       "1998-01-01 03:00:00.001                0.244563                0.548222  \n",
+       "1998-01-01 04:00:00.001                0.789465                1.327465  \n",
+       "1998-01-01 05:00:00.001                0.596153                0.981333  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_catchments.head()"
    ]
@@ -210,9 +843,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-09-04T12:38:08.688683Z",
+     "iopub.status.busy": "2024-09-04T12:38:08.688421Z",
+     "iopub.status.idle": "2024-09-04T12:38:08.787750Z",
+     "shell.execute_reply": "2024-09-04T12:38:08.787178Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SUlIwcuTIVo/16NEDmZmZJzyuqmidcaNrOaWYu6WIiMiebNVzQy2nE0cr3HDmhoiI7M5WMzdt2bBhg9lDiCnj6oW06PQRcbcUERHZHWdubKaoqnnmJgpn3AAtMzd1/iBqfYGofA4iIqJoYrixmaNV0TvjBgB6uF3oEd/UmM3ZGyIisiOGG5v5prwWANA3s0fUPoenuZ+HfTdERGRHDDc24gsEcaSyHgDQLyspap8nO5n3SxERkX0x3NhI4bF6hARIincaASQaslO5Y4qIiOyL4cZGDoYtSWmaFrXP0zJzw3BDRET2w3BjI9+U1wEA+kdxSQoAPJy5ISIiG2O4sZFvyqLfTAxw5oaIiOyN4cZG9J1S/TKjPXPD3VJERGRfDDc2crB5WapfjGZuSrlbioiIbIjhxib8gRC+rWgON1nRDTd6z015rR+BYCiqn4uIiCjSGG5s4tuKOoQESIxzGvc/RUtGUjycDg0iTQGHiIjIThhubEJfkuqbmRTVbeAA4HBoxtKUfpcVERGRXTDc2MSBMr2ZOLpLUjr97qqjDDdERGQzDDc2oR/gF+1+G11uuh5u6mPy+YiIiCKF4cYmvjF2SkV3G7guJy0RAJeliIjIfhhubCIWt4GHy2leljrCcENERDbDcGMDjcEQvq1oWh7qH6NlKX3m5mgll6WIiMheGG5s4HBFPYIhQUKcI+rbwHVsKCYiIrtiuLGBA/qSVEYPOBzR3Qau0xuKi70NCIYkJp+TiIgoEhhubOCgvg08yreBh/OkJMDp0BAICcpreMcUERHZB8ONDXwTozulwjkdmrEExqZiIiKyE4YbG4j1TimdvmOqiGfdEBGRjTDc2IBxG3gMl6WAlh1TRyo5c0NERPbBcGNxgWAIhcdivywFtMzc8JRiIiKyE4YbiztcWY9ASOB2OdA7NSGmn5vbwYmIyI4Ybizum7DbwGO1DVyXm958kB/DDRER2QjDjcUdNKmZGAhvKGa4ISIi+2C4sbgDzWfcxOrahXDG5Zk8yI+IiGyE4cbiDoYtS8VadoobToeGYEhQxoP8iIjIJhhuLE4/4ybWO6WApoP8eukH+fECTSIisgmGGwtrtQ3chGUpAMhhUzEREdkMw42FHa1qQGNQEO9yICfG28B13A5ORER2w3BjYfqSVJ+M2G8D1+Xq4YbLUkREZBO2CzfLli3D6NGjkZqaitTUVEyaNAl///vfzR5WVHxTZl6/jU7fMXXUy5kbIiKyB9uFm/z8fDzxxBPYvHkzNm3ahAsuuACXX345duzYYfbQIq7lNvDY75TS5XDmhoiIbMZl9gA6a+bMma3+/POf/xzLli3DJ598ghEjRpg0qugwDvAzqZkYYEMxERHZj+1mbsIFg0GsWLECtbW1mDRpktnDiTjjAD9Tl6WaZm5Kqn0IBEOmjYOIqCve/6oYt/9xM6rqGs0eCsWA7WZuAGDbtm2YNGkSGhoakJycjNWrV2P48OFtPtfn88HnazmAzuv1xmqYXRIMCQqPNS0FmXGAny4r2Q2XQ0MgJCit8Rk9OEREdvLbD/bh828qMGlgFq47p6/Zw6Eos+XMzRlnnIEtW7bg008/xW233Ybrr78eO3fubPO5ixcvRlpamvFWUFAQ49GeniOV9fAHQ4h3OowLLM3gdGjolcrt4ERkb8Xepl9ydx6pMnkkFAu2DDfx8fEYNGgQzjrrLCxevBhjxozB008/3eZzFy5ciKqqKuOtsLAwxqM9Pfq1CwUZiXCatA1c19JUzHBDRPYjIiitbgo3O47YY/aeusaWy1LHC4VCrZaewrndbrjd7hiPqOvMvHbheDnpicDBChyt4o4pIrKfWn8Q9Y1BAMCuo9VoDIYQ57Tl7/bUQbYLNwsXLsSMGTPQp08fVFdX47XXXsOGDRuwdu1as4cWUfoZN32tEG54SjER2VhJ2Dld/mAIe0tqMCwn1cQRUbTZLtyUlJTgBz/4AY4ePYq0tDSMHj0aa9euxUUXXWT20CJKP+Omf5Z5zcS6lnDDmRsish99SUq344iX4UZxtgs3L730ktlDiAnjjBvO3BARdUlpzfHhpgr/dVa+SaOhWOCiowWFQoKDx/SZGyuEm+aD/NhQTEQ2pM/cuJo3Z7CpWH0MNxZ01NsAfyCEOKdmzJqYKSddP8ivgQf5EZHtlDSHm/H9egIAdh7xIhQSM4dEUcZwY0EHm5uJC3omwWWBjv6sHm7EOTWEpOVFgojILvSZm3MGZCLe5UCNL4BDzbPjpCbzf3LSCQ7o28AtsCQFAA4e5EdENqaHm9y0RAztnQKAS1OqY7ixIP0APzOvXTged0wRkV3p4SY71Y0RuU27pHbwpGKlMdxYkH7GjRUO8NOxqZiI7EpfTs9OdmNEbhoAYDtnbpTGcGNB31hsWQrgdnAisqdgSHCstinceFJaZm52HqmCCJuKVWW7c25UFwqJsSzVj8tSRERdUl7rQ0gAhwZkJruRkhAHhwaU1fhRUu0z+glJLZy5sZji6gb4AiG4HBryTLwN/Hg5zWPhzA0R2Yneb5PRww2nQ0NivBMDs5MBsO9GZQw3FnNA3waeYY1t4DrO3BCRHRn9NiktFygbTcWH2XejKuv89CQA1twpBbQ0FJdU+9DIg/yIyCZK2wg3I/P0pmLO3KiK4cZijGZiC+2UAoDMHvGIc2oQHuRHRDaihxtPWLgZbmwH58yNqhhuLKZlG7i1Zm4cDg299aWpSi5NEZE9tDVzMyKnaebm24p6VNU1mjIuii6GG4sxlqUstA1cl5PKpmIispfSsDNudGlJccjv2fR6tuMol6ZUxHBjISJiLEv1t9iyFNBygSabionILtqauQGAkc2H+bGpWE0MNxZS7PWhoTEEp0NDXk/rbAPXGacUc+aGiGyitObEnhsAvIZBcQw3FqLP2uT3TESchbaB64zt4LyCgYhsosTb9Hp1/MzNiDw2FavMej9Bu7GDFt0ppeNZN0RkJ7W+AGr9QQBthJvmZal9pTWob34OqYPhxkIOlFnv2oVwXJYiIjspa16SSohzINnd+rYhT4obWcnxCAmwq4izN6phuLEQfeamr1VnbpobiktrfPAHeJAfEVlbyxk3CdA0rdX7NE3jDeEKY7ixkG+at4H3t+A2cADISIpHvNPRfJAfZ2+IyNraunohXPgN4aQWhhuLEJGwmRtrLku1OsiPS1NEZHFtnXETTp+5YVOxehhuLKK02oc6fxAODcjvac1wA4Q3FTPcEJG1GctSqe3P3OwqquadeYphuLEIfUkqv2cS4l3W/bLk8AoGIrKJU83c9MlIQrLbBX8ghL0lNbEcGkWZdX+KdjP6nVJWXZLS5aRzxxQR2YPeG3iynhuHQ+MlmopiuLEIq94GfjyedUNEdqGfTnyycAPwpGJVMdxYhH5hZj+L7pTS8awbIrKL8K3gJ8OmYjUx3FjEgTJ95sbiy1JsKCYiGwiFBGU1fgAdm7n56ogXoZDEZGwUfQw3FhC+Ddz6MzdN4aaMB/kRkYUdq/Mj2BxWMpPjT/q8QZ5kxLscqPYFcOhYXayGR1HGcGMBpTU+1BrbwK13G3i4jB7xiHc1HeRX7OXsDRFZk74kldEjvt2LiOOcDgztnQKAS1MqYbixAL3fJjc9EW6X0+TRtE/TNC5NEZHltfTbnHxJSsemYvUw3FiAvg3cqtcuHI87pojI6k519UK44WwqVg7DjQV8Y/FrF47HHVNEZHWnOsAvXPjMjQibilXAcGMB+unEVj/jRsdTionI6oxwc5KrF8IN650KhwaU1fiNGR+yN4YbCzhokwP8dDylmIiszjjArwMzN4nxTgzMTgbAvhtV2C7cLF68GBMmTEBKSgo8Hg+uuOIK7N692+xhnTYRwTdl+gF+NlmWSmVDMRFZW4m3/asXjmcsTR1m340KbBduPvjgA9xxxx345JNPsG7dOjQ2NuI//uM/UFtba/bQTkt5rR81vgA0i98GHi4nnQ3FRGRtHbl6IRxPKlaLy+wBdNaaNWta/fmVV16Bx+PB5s2bcd5555k0qtOnL0nlpiUiIc7a28B1ekNxWY0fvkDQ8tvXiaj76cjVC+H0mZvtXJZSgu3CzfGqqpr+IWZkZLT5fp/PB5+vpUHM67VWKj9gsyUpAOiZFAe3ywFfIITiKh/62GSXFxF1Dw2NQVQ3BAB0fubm24p6VNU1Ii0pLmrjo+iz3bJUuFAohLvvvhtTpkzByJEj23zO4sWLkZaWZrwVFBTEeJTtO2hsA7dHMzFw/EF+XJoiImvRZ23iXQ6kJnTsd/i0pDjjhPgdRzl7Y3e2Djd33HEHtm/fjhUrVpz0OQsXLkRVVZXxVlhYGMMRnpq+Dby/jcINwLNuiMi6SsLOuNE0rcMfpy9N7WTfje3ZdllqwYIF+Otf/4qNGzciPz//pM9zu91wuzs2LWkG/XRiuxzgp2tpKma4ISJrMfptOnDGTbgRuWlYu6MY2w9z5sbubBduRAR33nknVq9ejQ0bNqB///5mD+m0iYhxOrHVbwM/HpeliMiqOnPGTbiWk4o5c2N3tgs3d9xxB1577TX85S9/QUpKCoqKigAAaWlpSEy09o3ax6uoa0R1Q9M28D4ZNpu5aV6WOlLJmRsispbSTp5xoxuZ19RUvK+0BvX+IBLjuRPUrmzXc7Ns2TJUVVXh/PPPR05OjvH2pz/9yeyhddqB5iWpnNQE22wD1+kzN0VeztwQkbXoMzcd3Qau86S4kZUcj5AAu4o4e2Nntpu5UelSs4M2XZICwhqKOXNDRBZT2okbwcNpmobhuWnYuKcUO454MbZPz2gMj2LAdjM3KtF3StlpG7hOn7kpr/WjoTFo8miIiFqcbrgBWt8QTvbFcGOi/aU1AID+NjrAT5eeFIeEuKZ/PsVezt4QkXWUdCHcjOQ1DEpguDHRvtKmZSn9Nlo70TQNuTzrhogsJhQSlBk9N6c/c7OrqBqNwVBEx0axw3BjklBIjJmbQR77hRsA6M3t4ERkMVX1jWgMNvVmZibHd/rj+2QkIdntgj8Qwr7m12iyH4YbkxyurIcvEEK802Gb28CPx+3gRGQ1+pJUelLcaV3q63BoGJ7TfInmYS5N2RXDjUn2Gf02PeB0dPx4cCsxtoNzWYqILKK0+vQO8As3nE3FtsdwY5K9JU3hZqDHfjuldC1XMHBZioisobSm6Zetzl69EE4/zI9NxfbFcGMSOzcT61quYODMDRFZQyRmbvSm4q+OeBEKqXO2WnfCcGMSfVnK3uGGu6WIyFpKvKe/DVw3yJOMeJcD1b4ADh2ri9TQKIYYbkxi951SAIyt4Md4kB8RWYRxaWYXwk2c04EzeqUA4NKUXTHcmKCyzo+yGj+ApoZiu0pNdCGx+U4sNhUTkRXoy1KdvVfqeDyp2N4YbkygL0nlpiWgh9t213sZNE0zmoqPsKmYiCygK1cvhBvBpmJbY7gxwb6S5mZiGy9J6bgdnIispCtXL4QLn7lR6cLm7oLhxgQqNBPr2FRMRFbhCwRRVd8I4PSuXgg3rHcqHBpQVuM3AhPZB8ONCYxwo8DMTS6vYCAii9B7GeOcGtIS47r0dyXGO41fQLcfZt+N3TDcmKDljBv7NhPreuszN7yCgYhMFn7GjaZ1/eT3Uc19N9sYbmyH4SbGfIGgcW7CIBWWpYyGYoYbIjJXibfpdair/Ta6UfnN4eZbhhu7YbiJsYPldQiGBCluV8S+Ac3U0lDMZSkiMlfLGTdd2wauG90cbrZy5sZ2GG5ibF/znVIDPMkRmTY1m95QXFHXiHo/D/IjIvNEahu4bnhOGhxa099b7OXstJ0w3MRYy04p+/fbAEBqggtJ8c0H+fGbn4hMFOlwkxjvxGBP00nFW7k0ZSsMNzGmNxPb+dqFcJqmtVygWcmlKSIyT6TOuAnX0ndTGbG/k6KP4SbGVDrjRpebzrNuiMh8LVcvRDDccMeULTHcxJCIGD03KoWb3qk864aIzBfpZSkgbObmME8qthOGmxgq8jag1h+Ey6Ghb2aS2cOJmJzmmRtuBycis4hIq3NuImV4TiqcDg1lNX7OTtsIw00M6XdK9clMQpxTnf/0vF+KiMzmrQ/AHwwBiOzMTUKcE0N6NTUVc2nKPtT5CWsDer+NCof3hdPDzRE2FBORSUprmn65Sk1wISHOGdG/e1Re0yWaPMzPPhhuYkilO6XC6WfdcCs4EZklGjuldKPy0wHwMD87YbiJob0KNhMDLVcwVPIgPyIySTSaiXWj81q2g7Op2B4YbmJItQP8dKkJcUh2uwAAR7hjiohM0LINPDJXL4Q7o3cKXA4NFXWNOMzld1tguImR6oZGFHubvvkGKDZzAwC92VRMRCaK5sxNQpwTZ/Rubipm340tMNzEyP7mk4mzU9xIS4wzeTSRx6ZiIjJTNMMNwEs07YbhJkZU3Sml43ZwIjJTSRTOuAk3srnvZjvDjS0w3MRIy04ptfptdPqOKR7kR0RmMHpuUqM0c5OXDqDpAk02FVsfw02MqLpTStcyc8NlKSKKvdKa6C5LDemdjHinA1X1jSg8xtc5q7NduNm4cSNmzpyJ3NxcaJqGP//5z2YPqUP028CVDTe8PJOITNIYDOFYrR9A9Jal3C4nhubwpGK7sF24qa2txZgxY/Dcc8+ZPZQOawyGcLC8OdwodoCfLpcNxURkkrLmWRuXQ0PPpPiofR6972br4cqofQ6KDJfZA+isGTNmYMaMGWYPo1MKj9WhMShIjHMiJzXyZzBYgT5z420IoNjbgF6K1klE1qP322Qlu+FwaFH7PKPz0vAauB3cDmw3c9NZPp8PXq+31VusGUtSnh5R/cYzU7LbhTML0gEA739VYu5giKhbifY2cN2o5u3g2w6zqdjqlA83ixcvRlpamvFWUFAQ8zGo3kysu2h4LwDAe18VmzwSIupOYhVuhvRKQbzLgeqGAA6W10X1c1HXKB9uFi5ciKqqKuOtsLAw5mNouXahe4Sbf+4tQ60vYPJoiKi7KDGuXohuuIlzOjAsp+mGcB7mZ23Khxu3243U1NRWb7HWXcLNYE8y+mQkwR8I4cOvy8weDhF1E7GauQFaLtHkYX7Wpny4MZuIYF+J2gf46TRN49IUEcVcLMON3nez9dvKqH8uOn22Czc1NTXYsmULtmzZAgA4cOAAtmzZgkOHDpk7sJMoq/HD2xCAQwP6ZaodbgBg+rCmcPOPXSUIhthwR0TRV1LddL5WtM64CTfKmLnxIsTXOMuyXbjZtGkTxo4di7FjxwIAfvzjH2Ps2LF46KGHTB5Z2/Rm4oKMJCTEOU0eTfSN79cTaYlxOFbrxxeHKsweDhF1A/rpxNG6eiHcYE8y3C4HanwBfNN8fhlZj+3Czfnnnw8ROeHtlVdeMXtobeou/Ta6OKcD087IBgC8t5NLU0QUXSLSsiyVHP3ztVxOB0bkNvVu8qRi67JduLGblnCj/pKU7qLhvQEA6xhuiCjKanwBNDSGAABZKdE7nTicvjS1lYf5WRbDTZSpfqdUW84bkoU4p4b9ZbVGuCMiigZ9G3iy24Wk+Ngcuj8qPx0AZ26sjOEmylp2SnWfcJOSEIdzBmQC4NIUEUVXaYzOuAk3unnH1I7DVdw4YVEMN1FU5w/gcPNFkoO60cwNAPxH85ZwLk0RUTQZ90rFMNwMzE5GYpwTtf4gDpRxdtqKGG6iaH/zklRGj3j07BGbtWCruLB5S/jmQxUob97JQEQUabE840bndGhGUzH7bqyJ4SaKumMzsS43PREjclMh0nTmDRFRNMTq6oXjhV+iSdbDcBNF3bGZOJx+oB9PKyaiaDFj5gZo6bvZxpkbS2K4iaLudsbN8fSrGDbuKUNDY9Dk0RCRivQD/GJxOnE4fTv4jiNeNhVbEMNNFOk7pQZ1o51S4UbkpiInLQH1jUF8tI8XaRJR5Jk1c9M/Kxk94p2obwzyyAsLYriJkmBIsL+sey9LaZpmLE2t28m+GyKKvNLme6U8KdE/nTic06FhBA/zsyyGmyg5XFEPfyCEeJcDeT0TzR6OacJvCeclc0QUSYFgCOW1fgCxn7kBWpamtvGGcMthuIkSfZpyQFYPOB2ayaMxz8QBGUh2u1Ba7cNW7iogogg6VuuHCODQmo7ciLXR3DFlWQw3UdLdm4l1bpcT3xnCizSJKPL0beCZyW5TfokMbyoOBEMx//x0cgw3UdKdz7g53vThHgA8rZiIIsuMqxfC9cvsgWS3C75ACF+XsKnYShhuomRvN7xT6mSmneGB06Fhd3E1DpXXmT0cIlKEWTuldA6HhpF5TScVc2nKWhhuoqS7H+AXLj0pHhP69QTAA/2IKHLMOuMm3Gj9hnDumLIUhpsoOFbrx7HmDv4BXJYCgLAt4Qw3RBQZJd7mbeCp5oUbve+GGyasheEmCvY399vkpSciKd5l8misQd8S/tk3x1BV12jyaIhIBVaYudHDzVdHvWhkU7FlMNxEgbENnLM2hr6ZPTCkVzKCIcGGPTzQj4i6rqXnJrYH+IXrm5mElAQX/IEQ9hRXmzYOao3hJgr2dvNrF05GX5p6l0tTRBQBZjcUA00nsfMSTethuIkCNhO3bXrz0tQHu0vhD3D6loi6psTkreC6kXk8zM9qGG6igAf4te3M/HRkJbtR4wvg0wPlZg+HiGys1hdAnT8IwNyZGwAYnZcOgOHGShhuIqyhMYjCY01nuQz0sOcmnMOhYfqwpgP9eFoxEXWFviSVFO9ED7e5Gzf0ZaldR6s5K20RDDcRdrC8DiEBUhJcpnbwW1X4lnARXqRJRKfH2Cll8qwNAOT3TERaYhz8QTYVWwXDTYSFL0lpWve9MPNkzh2chYQ4B45UNWDnUa/ZwyEimyrxWqPfBmjdVLyVTcWWwHATYdwp1b6EOCemDtYv0uSWcCI6PaXVTQf4WWHmBmg572bb4UpzB0IAGG4ijs3Ep3aRvjT1VZHJIyEiu7LCAX7hjO3gbCq2BIabCONt4Kc2bagHmgZsP+zF0ap6s4dDRDZk3Aieat4BfuH07eC7i6rR0Bg0eTTEcBNBoZBgX0nzGTdcljqp7BQ3xvXRL9Lk0hQRdZ5+xo1VZm7y0hOR0SMejUHB7iI2FZuN4SaCirwNqG8MwuXQ0CcjyezhWBov0iSirrDC6cThNE3jJZoWwnATQXozcb+sHohz8j9tey4a3nTezcf7ylDdwIs0iahzrBZugJam4u3cMWU6/gSOIPbbdNzA7GT0z+qBxqDgw6/LzB4OEdlIMCQor/UDsMZWcN2ofM7cWAXDTQRxp1THaVrLacWrNn+L8uadD0REp3Ks1o9gSKBpQEaPeLOHY9B3TO0u8mLhm1uxZvtRzkybxNwzqxVjNBMz3HTI9GG9sPzDA3h/VwnOevw9DO2dgimDsjB5YCbO7p+BlIQ4s4dIRBakL0ll9oiHy0ItAL1TEzAiNxU7jnjx+meFeP2zQrgcGsb17Ynzz8jGd4ZkY3hOKg94jQFbhpvnnnsOTz75JIqKijBmzBg8++yzOPvss80eVsvMDXdKdcjZ/TPwo+lD8PftR7GrqNp4e+mfB+B0aBiTn4bJA7MweVAmxvXpiYQ4p9lDJiIL0M+4ybLITimdpmlYddtkfLy/HB/sLsUHe0pxoKwWnx04hs8OHMMv1+xGdoob3xnSFHSmDs5CepJ1Zp5UoonNLvj505/+hB/84Af47W9/i4kTJ2Lp0qVYuXIldu/eDY/Hc8qP93q9SEtLQ1VVFVJTUyM2Lm9DI0Y/8i4AYOsj/4FUzjp0SlmNDx/vK8dH+8rw0b5yHCyva/V+t8uB8f16YvLALEwZlIUhvZKRFG/LbE5EXbRyUyH+5/+24rwh2fjfG83/xbY9B8trsXFPKTbsLsVH+8pRH3YGjkMDzixIx3eGeDCubzpy0xORm5aIxHj+IteWzvz8tl24mThxIiZMmIDf/OY3AIBQKISCggLceeed+MlPfnLKj49WuPnyUAWufP4j9Ep149OfTo/Y39tdfVtRh4/2NoWdf+0rN6ahwyW7XfCkuJGd4oYnNQGeFHfTW6obnhT9zwlITXRxGphIIc9v2ItfrtmN/xyXj6f+e4zZw+kwXyCIzw9U4IM9JfhgTyn2FNe0+bz0pDjkpCUiLz0BOWmJyElPQG5aInLTE5GTloDeaQndckduZ35+2+pXX7/fj82bN2PhwoXGYw6HA9OnT8fHH3/c5sf4fD74fC0/GL3e6FzWuK+U/TaRlN8zCf89IQn/PaEAIoJ9pTX4V3PY+WT/MVTVN6LGF0CNL4D9ZbXt/l1ulwNZyW64Xd3vxYBIRfpOKSttA+8It8uJcwdn4dzBWbj/EuBwZT027inFxj2l2FtSgyOV9aj1B1FZ14jKukZ8dZLLhTWt6fDCZLd1f4RffXYfzD9vgGmf37r/ZdpQVlaGYDCIXr16tXq8V69e2LVrV5sfs3jxYvzsZz+L+ti4Uyp6NE3DIE8KBnlScP3kfgCAGl8AJd4GlFT7mt70/3/cY96GAHyBEA5X8poHItWMzIvc7LsZ8tITcfXZfXD12X0AACICb0MAR6vqcbSyAUf0/62sb/r/VQ04WtUAfyBkvM5Z1bE6v6mf31bh5nQsXLgQP/7xj40/e71eFBQURPzz3HreQFww1IO0RPbaxEKy24Xk7GQMOEWYbGgMorTah9IaH4IhW63AElE70hLjMFixzRuapiEtMQ5piXEY2rvt4CbSdMbP0coGNASse4dVb5Pv/LJVuMnKyoLT6URxcesj+4uLi9G7d+82P8btdsPtjv7UZVpSHCb0y4j656HOSYhzoiAjCQW8DoOIFKBpGrKS3ZbbKWY1tmpCiI+Px1lnnYX333/feCwUCuH999/HpEmTTBwZERERWYWtZm4A4Mc//jGuv/56jB8/HmeffTaWLl2K2tpazJ071+yhERERkQXYLtx8//vfR2lpKR566CEUFRXhzDPPxJo1a05oMiYiIqLuyXbn3HRVtM65ISIioujpzM9vW/XcEBEREZ0Kww0REREpheGGiIiIlMJwQ0REREphuCEiIiKlMNwQERGRUhhuiIiISCm2O8Svq/Rjfbzetq+SJyIiIuvRf2535Hi+bhduqqurASAqN4MTERFRdFVXVyMtLa3d53S7E4pDoRCOHDmClJQUaJp20ud5vV4UFBSgsLDQ9icZq1QLoFY9KtUCqFWPSrUAatWjUi2AWvVEsxYRQXV1NXJzc+FwtN9V0+1mbhwOB/Lz8zv8/NTUVNv/Y9OpVAugVj0q1QKoVY9KtQBq1aNSLYBa9USrllPN2OjYUExERERKYbghIiIipTDcnITb7cbDDz8Mt9tt9lC6TKVaALXqUakWQK16VKoFUKselWoB1KrHKrV0u4ZiIiIiUhtnboiIiEgpDDdERESkFIYbIiIiUgrDDRERESmF4YaIiCiKuG8n9rpluAmFQggGg2YPIyJ8Ph+2bNkCALavqbGxEd9++63xZ7u/IIRCIYRCIbOHERENDQ145pln8I9//MPsoUSESq8BgP2/V8L5/X7s2bMHAJT4/gkEAjh27JjZw4gIO/0763bhZsmSJbjgggtwzTXXYPXq1aiqqgJgz2+iAwcOIDk5GVdccQUqKyvhdDptWQfQ9HUZM2YMrrzySlxxxRXYvn07NE2zbT3PPPMMLrvsMsyZMwdvvPGG8e/Mjp5//nl4PB689dZbqKqqQkNDg9lD6hKVXgMAYOnSpbjssstw++2349NPP4Xf7zd7SKdt27ZtSE9Px2233Ybq6mo4HA5b/UA93pIlSzB+/HjMnDkTd955J/bv3w/AXiFB99xzz+Hqq6/GggUL8OGHH1r+31m3CTeNjY244YYb8PTTT+OKK65ATU0N7r//ftx5550AcMpLuKxo165d6N+/P/r27YsnnngCANq9DNSKqqurcdVVV2HZsmV49NFHceONN6KqqgoLFy4EYL+vy7Zt2zB58mQ899xzmDZtGiorK/H444/j2WefNXtop2Xt2rVYvnw5li1bhvfeew9XXnklEhISbPnirNprQG1tLf7zP/8TTz/9NMaOHYtNmzbhuuuuw2OPPWb20E7bZ599htTUVMTHx+OVV14xezinraysDDNnzsSyZctw77334qKLLsKmTZuwdOlSAPZ6nf7yyy8xYcIEPPvssxg8eDA+/fRT3HLLLVi5cqXZQ2ufdBO7d++WoUOHytq1a43H/vCHP0h6ero8//zzIiISDAbNGl6nhEIhERF57bXXZObMmfLII4/IsGHD5N///reI2KcOEZF//etfMnToUNm6davx2D333CM33HCDiDTVqtdrdZWVlXL33XfLtddeK2VlZcbj8+bNk2uuuUbq6upMHF3n6P+Gbr31VrnuuutEROTQoUOybNkyef/992X//v0iIrb52oio9RogIvLZZ5/J4MGD5YsvvjAee+ihh2TgwIHy+uuvmziyztP/HS1btkzmzp0r119/vUyfPl327t0rIvb6uoiIvPPOOzJixAg5ePCg8disWbPkf/7nf0TEPt83R48elblz58r8+fOlsrLSeHzq1Knywx/+UESsW4u9flU5DfpUc2NjI/bv348BAwYY75s1axZuv/12/OQnP0FdXZ3lf3OT5t+W9dT/2WefYfr06bjuuuuQnZ2NX/ziFwDs8Ruo/nWpqanBoUOHEBcXZ7xv+/btyM/Px44dO6BpmuV/ywn/umRkZOD2229HZmYmGhsbAQCDBw/Gpk2bkJiYaOYwO0SvxeFwIBAI4KOPPsLMmTOxevVqjB49Gq+//jpuuOEGXHjhhdi1a5flvzaAWq8BQEs9lZWVKC8vR3Z2tvG+m266Ceeffz4efPBBs4bXKce/pq1btw4XX3wxbrrpJlRXV+P3v/89AHu8pgEtX5tvv/0WNTU1rWY4a2pq0K9fPxw+fNjy3zf6uOPj45GcnIxbb70VaWlp8Pl8AIDx48fj888/B2DdWSh7/IvppFWrVuGFF17A1q1bjf6AyspKjBgxAuvXrzeel5ycjHnz5iErKwuPPPIIAOutu7dVi77WmZSUhOrqavTv3x833HADtmzZghtvvBHz5s1DWVmZmcNuU1u1ZGRkYMKECbj44otxzz33ICMjAwcOHMD69etxySWXYP78+SaP+uQ+/fRTADB6g1JTU3Hfffdh0qRJAACXywUAKC4uNh6zqvBagKbvA5fLheHDh+OFF17AypUr8dJLL2HNmjXYuHEj+vbti5tvvhmVlZUmjvrk1q5di7fffhv79+9HIBAAAFRVVdnyNQAA3n77baxatQqHDh0yGqGrqqqQl5eHr776ynheQUEB5syZA4fDgaeeesqs4bZLr+XgwYNGLfoPzaysLASDQUyePBkXXHAB/vWvf+GOO+7APffcY+aQ29VWPb1790avXr0we/ZsPPzww8jIyMC+ffuwbNkyTJ8+HQ888AAA6/1b27ZtG4CW17SMjAw8+eSTGDduHAAY90UVFhZi6tSppo2zQ8ycNoq0ffv2yfjx4yU/P1/Gjh0r+fn5xpS6z+eT888/X+bOnSuHDx82Pqa+vl4efPBBGTdunFRVVZk19BO0Vcv111/f6jmTJ0+Wt956S0REXn31VUlPTxeHwyHPPPOMiFhnurC9r4uISGFhobz55pty9tlny8MPPyyNjY1SWVkp69evF03TZPPmzSJinXq2bt0qkydPFk3T5I033hARkUAgYLz/+HHOmDFDnn766TbfZ7b2agmFQvLEE09IXl6eDBo0SIqLi43x79u3TzRNk48//ti0sbfls88+k1GjRsmgQYNk+PDhMmjQIFm0aJGIiDQ0NMh3vvMdufHGG23xGiAismvXLjnzzDMlPz9fhg4dKoMGDZKf/vSnIiJSV1cnffr0kZ/85CdSW1trfExZWZnccMMNMmvWLEsthbZVywMPPNDqOSNHjpR//vOfItK0RJWamipxcXGWe00Taf9rEwgEZNOmTbJ8+XIZNmyYPPHEE9LQ0CDffvutvPzyy6JpWqslK7Nt2bJFJk6cKLm5ufLee++JyMlf00KhkEyePNl4vbAqpWZu/u///g9utxtfffUV3n33XTz99NNYtWoVHnzwQcTHx+Omm27CP/7xD6xbt874mISEBPTs2dNy23bbqmXlypX42c9+hvr6egDAoEGDUFNTg8svvxzz5s3DxIkTMXToUGPGwCr1nOzr8sgjj8Dn8yE/Px99+vTBwYMHcdNNN8HlciEtLQ0jR45EXl6esf3YCtOfmzZtwoIFC5CZmYmZM2fi+eefRyAQgNPpPGGKHWhqLPzoo49w1llnGe8rKSkxZezHa6+WQCAATdNw4YUXolevXqitrYXH44GmaQgEAsjKykL//v2NYwisYO3atZg3bx5mzJiBzz//HG+//TZmz56NVatWYfv27XC73bj++uuxfv16W7wGAMDLL78Mj8eDHTt2YO3atbj11luxdOlSvPjii0hMTMRdd92FZcuWYdOmTcbHZGZmIiMjAyUlJZZaCj2+lltuuQVLlizByy+/DADwer0444wzICKYOXMm7r77bowcORKjR49GXl4eAGvtMmqrnqVLl+Kll16C0+nEWWedhYEDB6K2thY33XQT3G438vLycNZZZyE3Nxf//Oc/zS4BAPDhhx9i/vz5SEtLw5AhQ/Dqq68iGAye9DVt79692LZtG0aOHGk8VlFREfNxn5K52SpyAoGAjBs3Tu67775Wj7/wwgsSHx8vGzduFBGRyy+/XC688EJZt26d8ZzHH39czj33XGloaIjpmE+mvVrcbrds2LBBRET69u0rmqbJpZdeKjt37pTy8nK58847JT8/X44ePWrG0E/QXi0JCQmyfv16ERFZt26dTJo0ST7//HPjOevWrZNhw4a1ajY2W0VFhSxYsEB27NghK1eulDFjxsgvfvELEWm76XHFihUycOBAEWn6jfrGG2+UkSNHtpo5MEt7tYT/1vbcc89JQkJCq9+yP/zwQxk9erTR8Gm2QCAgy5cvlwULFkhZWZnxm+Z7770nAwcOlO3btxvPveyyy2T69OmWfg0QaZqZyc/Pl1//+tfGYz6fTx544AFJS0szfvMfNWqUXHHFFcaGAhGRu+66S2bOnCmNjY2xHnab2qulZ8+ecuDAARERSUlJEU3TZMaMGbJ7924pLCyUWbNmyXnnnSfl5eXmDL4Np6pn3759IiLy0ksvyUUXXdRqlmb16tUyfPhwo2azHTp0SG6++WbZu3evPPnkkzJx4kT53//9XxE5ccZGROSZZ56RM888U0REysvLZe7cuXLJJZdIaWlp7AffDiXCjf5D5bvf/a7Mnj271WMiIuPHj5dLL71URET+/e9/y6xZsyQjI0N++tOfysKFC6Vnz57y3HPPxX7gbehMLRs3bpQ///nPrV7A3nnnHbnvvvvk2LFjpk/hdqSWyy+/XESapkWnTp0qEydOlOXLl8ujjz4qvXv3lgULFkhdXZ3ptYi0fHPrSwAVFRVy9913y6hRo+TQoUMi0joUiIj87Gc/k3nz5smSJUskJSVFzjnnHNmzZ09sB96GjtTi8/mM9z355JPidDrlggsukFtuuUU8Ho8sWLBA6uvrLfG1ERHZsWOHVFRUtHrs0KFD0qtXL9m9e7fx2NatWy39GiDS8n0yefJkufvuu0Wk5WtWVVUlgwcPlvnz54uIyAcffCCTJk2SoUOHytNPPy2PPPKIZGRkyB//+EdzBn+cjtRy6623iojIG2+8IW+99Var76Nly5bJvffeK16vN8Yjb1tn6tmwYYMMGjRIZs2aJatWrZJFixZJ79695d577xWfz2eZ753q6moRETly5Ij813/9l1x66aVSXFwsIif+0nbHHXfIAw88IEuXLpWUlBSZMGGCZX7JCadEuBFp+gL86le/kjFjxsi2bdtEpOXFed26deJwOIykXF5eLg8//LBcffXVrfpWrKIztej0bxKrfLPoOlKLvq14zZo1cuWVVxoh5+233zZt3Keif8OvX79epkyZIrfddtsJz/H7/TJ27FjRNE369u0rb775ZqyH2SEdqUVE5M0335RHHnlErrrqKvnrX/8ayyF2SvjxAb///e9l+PDh4vf7W71IV1RUWPo1QKSpR+j222+X733ve8b3iF7D7373O0lISJBjx46JiMjOnTvltttukxkzZsjYsWMt971zqloSExNbbTUWaXktO/4XBivozNdm+fLlMm3aNBk/fryMGzfOcl8bnf7f+dVXX5VzzjlHFi9efMJzKioqJC8vTzRNk/z8fFm9enWMR9lxtgs3bU39698E69evl6lTp8pdd93V6v2VlZVyxhlnyG9+85tWj5v9TdOVWqz0W6ZI12p59tlnWz1eVFQUvYF2UEfP1fD5fLJo0SI544wzjEZIfSaturpa5s6dKy+++GLUxtkRXa3FioH5VPQx33DDDXLLLbec9Hlmvga0V4c+/hUrVsi4ceNkyZIlrd6/bds2GTp06AmBWf8NPNYiUcuf//znqI6xMyJRz6pVq1o9btYyVEe///W66urqZP78+XL++ecbLQH690lpaalceOGF8sILL0RnsBFki4bi3/3ud3jhhRcAtH3egd7sdP755+O8887Dhx9+iFWrVhnvLysrQ0VFBQoKCgC0NKU5nc5oD/0EkaolPz8/NgNuR6Rq6dOnD4CWBuhevXpFe+htOlU9xxMRxMfH49JLL8WQIUPw1FNP4dChQ7juuuvwt7/9DcnJyfjd735nynb2SNaydu3aaA/3lDpbj2779u2YMmUKgKZzRhYuXIi9e/ca7zfjNQBoOpZ/zpw5WLBgAT799FPjTCQ5roHz+9//Ps444wy888472LBhg/Hxfr8fhYWF6N27d6uPS05OjmEVTSJVi8fjifnY2xKpenJycgC0vK7169cvdkU0O1Ut4fTt34mJibjqqqsgInjllVdw8OBBzJkzB1u3bkVWVhbWrFmDm2++OdaldJ5psaoDtmzZIhdccIFomibTpk1rd0uwnk737NkjN954o2RmZso777wj+/fvl6eeekpGjRplaq8Da7FmLSKdq+dknnnmGUlISBCXyyUDBgwwrSaVahHpWj379++X/v37y/79++Wll16S9PR0GTFihHzzzTfRHvZJffzxxzJ69GgZNWqUPPjggzJq1CgZM2aMrFy58oTn6t87n376qVxyySUyfPhw2bx5sxQVFcnjjz8ukydPliNHjsS6BINKtYioVU9najmZe+65R9LT08Xtdku/fv1k7969lpvFbY9lw00wGJRFixbJ7Nmz5cUXX5Szzz5b7r//fmPKv73/yKWlpfL9739fBgwYIP369ZOcnBz5y1/+Equhn4C1NLFaLSJdq0ekadnmr3/9q+Tk5Ei/fv1s+7URsVYtIl2v55lnnhGXyyV9+/aVnj17yquvvhqLYZ9UYWGhzJkzRxYsWCA1NTXG4xMmTDDORzlZTVu2bJELL7xQCgoKjO+dd955JybjbotKtYioVU9XahFp6hVcvXq15OTkSP/+/S3dV9MeS4Yb/T/8jh075MMPPxQRkf/3//6fnHvuufL3v/+91XPaU1xcbGybNgtrOZEVahGJTD319fXy3e9+VxYuXBjdwZ6CSrWIRKaeRx99VJKSkuSxxx6L7mA76PDhw7JgwQKjsV7fdn7jjTfKxRdf3ObHhNfo8/nk66+/Nj0IiKhVi4ha9ZxOLeFqa2tl6NChcu+990Z1nNGmiVjjVKQVK1aguroakydPxqBBg+B2u42DhADgwIEDuPbaazFq1Cj8/Oc/R2ZmJkTkpAe7tfe+aGMt1qwFiGw9oVAIDocDjY2Nre7GihWVagEi/29t+/bt6Nu3L1JSUmJZhiG8ngEDBiAxMRH19fUnHKz3ve99D1OmTMH9999vyjg7QqVaALXqiWQt+vebz+czrlqwLZNClWHnzp0ycuRI6devn4waNUr69OljnBGg0zu1f/3rX8v48ePllVdeMWOop8RarFmLiFr1qFSLSPer5/jf+MeNG2fMRlmNSrWIqFWPSrVEg+nh5qGHHpILLrhA6uvrpaioSF5//XWJi4uTJ5980lhb11/Y6urqZMaMGTJ79mzj0KAdO3aIiDXOd2Et1qxFRK16VKpFpHvWozekfvnll5KammqclSIiljqJV6VaRNSqR6VaosHUcFNfXy9Dhw41LrbT/epXv5KUlJRWl/LpL26rVq2SCRMmyI9+9CO59NJLJTMz0xLnorAWa9YiolY9KtUi0r3rEWm69mHKlCkiInL06FG55pprZNasWSccaGcGlWoRUaselWqJFtPCjZ4ozz33XJk3b56ItD5Qa9iwYXLttdeecABRWVmZ9OnTRzRNk+9973umbuvUsRZr1iKiVj0q1SLCekRE5s6dK4sWLZInnnhCkpKSZOrUqVJYWBjbgbdBpVpE1KpHpVqiydSZm8bGRnn00Udl3LhxxhSzfjT/qlWrJC4urtVlXBs2bBBN02TEiBHGDgqrYC3WrEVErXpUqkWke9dz9OhRSU9PF03TpH///rJmzRrTxt0WlWoRUaselWqJFtNOKG5sbITL5cJZZ52FhIQEvPjiiwCA+Ph4AMDUqVORl5eHd955x/iY8ePH45lnnsH27dtx7rnnmjLutrAWa9YCqFWPSrUA3beev/3tb8bHnHnmmXjllVewf/9+XHzxxaaMuy0q1QKoVY9KtURVtFKTfrHe8U1+bd3lct9998nYsWNbdXJv3bpVsrKy5L333jvpx8UKa7FmLSJq1aNSLSKs51T1tPV3xYpKtYioVY9KtZgp4uFm/fr1MnjwYNE0zbj9tK2Lu1asWCF5eXmyYcMG2bNnj1xzzTWSk5Mj7777rhw4cEAee+wxGTdunBw8eDDSQ+ww1mLNWkTUqkelWkRYj5XrUakWEbXqUakWK4houNm0aZNcdtllctNNN8ns2bNlyJAhrd4fCoWkvLxcvvvd74rH45ElS5aI3+8XkaZ1wcsuu0wGDhwoffr0kfz8fHn33XcjObxOYS3WrEVErXpUqkWE9Vi5HpVqEVGrHpVqsYqIhpvCwkJ56qmnZNeuXbJz507p2bOn/PKXvxSRlgRaX18vzz33nBw9evSEjw8EAlJUVCQffPBBJId1WlhLCyvVIqJWPSrVIsJ6jmelelSqRUStelSqxSq6FG4++uijE24+1dNkKBSSxx9/XJKTk43DgvSDhU6mrSm4WGEtJ2dmLSJq1aNSLSKsx8r1qFSLiFr1qFSLVZ1WuHnvvfekf//+0rdvX8nPz5f58+fL7t27RaTpC6M3Lx0+fFiGDRsmN9xwg/E+q2Et1qxFRK16VKpFhPVYuR6VahFRqx6VarG6ToebQ4cOyTnnnCMPPvig7N27V1auXCkDBgyQWbNmGYdphafM1157TTRNky+++EJEmtJp+DXsZmIt1qxFRK16VKpFhPVYuR6VahFRqx6VarGDToebd999VxITE42Dg0SaDg0677zz5JZbbjEe05Om1+uVGTNmyHe+8x3ZunWrzJgxQ37729+avq1ThLVYtRYRtepRqRYR1mPlelSqRUStelSqxQ46HW5WrFgh48aNM6bSRJqamX7+85/L8OHDZcOGDcZjujfffFM0TRNN02TatGly7NixCAy961iLNWsRUaselWoRYT0i1q1HpVpE1KpHpVrsoNPhZtu2bZKQkCB/+ctfWj3+5ZdfysUXXyw/+tGPjMf8fr/84Q9/ELfbLRMmTJDPP/+86yOOINZizVpE1KpHpVpEWI+V61GpFhG16lGpFjvo9PULI0eOxLRp07BkyRLU1NQYj5955pnweDzYv38/QqEQAKC2thbbt2/H0qVL8dlnn2H8+PGRO1o5AliLNWsB1KpHpVoA1mPlelSqBVCrHpVqsYXTSURbtmwRl8sly5YtMy7rEhG5//77ZdCgQRFLXrHAWqxLpXpUqkWE9ViZSrWIqFWPSrVYnet0AtGYMWNw33334bHHHkNcXByuuuoqhEIhbNq0Cddee22k81dUsRbrUqkelWoBWI+VqVQLoFY9KtVieV1JRrfffrvk5OTIxIkTpW/fvjJ8+HDZsWNHpIJXTLEW61KpHpVqEWE9VqZSLSJq1aNSLValiYicbjBqaGjAV199hS+++AJut9vWyZO1WJdK9ahUC8B6rEylWgC16lGpFqvqUrghIiIisppO75YiIiIisjKGGyIiIlIKww0REREpheGGiIiIlMJwQ0REREphuCEiIiKlMNwQERGRUhhuiIiISCkMN0RERKQUhhsiIiJSCsMNERERKYXhhoiIiJTy/wGATjZ9vLYWiwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_catchments['SWMM_SUBCATCH_RUNOFF:5'].plot();"
    ]