diff --git a/Optimization of control rules/Evaluation_results.ipynb b/Optimization of control rules/Evaluation_results.ipynb
index a4af0d98cd9814d7c4299773f0207f11e3ca86c8..e09f5ffb0d1249c05bddd0f68c5441619ca48663 100644
--- a/Optimization of control rules/Evaluation_results.ipynb
+++ b/Optimization of control rules/Evaluation_results.ipynb
@@ -57,161 +57,10 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "0f4282cd",
"metadata": {},
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gid</th>\n",
- " <th>Start_Time</th>\n",
- " <th>End_Time</th>\n",
- " <th>File</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>359</td>\n",
- " <td>2022-04-24 21:00:00</td>\n",
- " <td>2022-04-25 06:00:00</td>\n",
- " <td>359BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>366</td>\n",
- " <td>2022-05-23 13:55:00</td>\n",
- " <td>2022-05-24 10:00:00</td>\n",
- " <td>366BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>371</td>\n",
- " <td>2022-06-05 09:30:00</td>\n",
- " <td>2022-06-06 02:00:00</td>\n",
- " <td>371BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>378</td>\n",
- " <td>2022-06-23 23:05:00</td>\n",
- " <td>2022-06-24 22:30:00</td>\n",
- " <td>378BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>385</td>\n",
- " <td>2022-07-20 21:35:00</td>\n",
- " <td>2022-07-21 17:30:00</td>\n",
- " <td>385BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>393</td>\n",
- " <td>2022-09-06 21:15:00</td>\n",
- " <td>2022-09-07 06:00:00</td>\n",
- " <td>393BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>400</td>\n",
- " <td>2022-09-18 04:30:00</td>\n",
- " <td>2022-09-19 01:00:00</td>\n",
- " <td>400BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>428</td>\n",
- " <td>2022-11-18 17:40:00</td>\n",
- " <td>2022-11-19 12:30:00</td>\n",
- " <td>428BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>481</td>\n",
- " <td>2023-02-05 00:30:00</td>\n",
- " <td>2023-02-05 18:00:00</td>\n",
- " <td>481BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>502</td>\n",
- " <td>2023-03-25 01:45:00</td>\n",
- " <td>2023-03-26 18:30:00</td>\n",
- " <td>502BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>532</td>\n",
- " <td>2023-05-21 15:55:00</td>\n",
- " <td>2023-05-22 00:00:00</td>\n",
- " <td>532BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>568</td>\n",
- " <td>2023-08-25 04:15:00</td>\n",
- " <td>2023-08-25 23:00:00</td>\n",
- " <td>568BaseDefault_Network_HD.res1d</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gid Start_Time End_Time \\\n",
- "0 359 2022-04-24 21:00:00 2022-04-25 06:00:00 \n",
- "1 366 2022-05-23 13:55:00 2022-05-24 10:00:00 \n",
- "2 371 2022-06-05 09:30:00 2022-06-06 02:00:00 \n",
- "3 378 2022-06-23 23:05:00 2022-06-24 22:30:00 \n",
- "4 385 2022-07-20 21:35:00 2022-07-21 17:30:00 \n",
- "5 393 2022-09-06 21:15:00 2022-09-07 06:00:00 \n",
- "6 400 2022-09-18 04:30:00 2022-09-19 01:00:00 \n",
- "7 428 2022-11-18 17:40:00 2022-11-19 12:30:00 \n",
- "8 481 2023-02-05 00:30:00 2023-02-05 18:00:00 \n",
- "9 502 2023-03-25 01:45:00 2023-03-26 18:30:00 \n",
- "10 532 2023-05-21 15:55:00 2023-05-22 00:00:00 \n",
- "11 568 2023-08-25 04:15:00 2023-08-25 23:00:00 \n",
- "\n",
- " File \n",
- "0 359BaseDefault_Network_HD.res1d \n",
- "1 366BaseDefault_Network_HD.res1d \n",
- "2 371BaseDefault_Network_HD.res1d \n",
- "3 378BaseDefault_Network_HD.res1d \n",
- "4 385BaseDefault_Network_HD.res1d \n",
- "5 393BaseDefault_Network_HD.res1d \n",
- "6 400BaseDefault_Network_HD.res1d \n",
- "7 428BaseDefault_Network_HD.res1d \n",
- "8 481BaseDefault_Network_HD.res1d \n",
- "9 502BaseDefault_Network_HD.res1d \n",
- "10 532BaseDefault_Network_HD.res1d \n",
- "11 568BaseDefault_Network_HD.res1d "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"events = pd.read_excel(events_file)\n",
"events"
@@ -219,488 +68,10 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "73dbdd78",
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Type</th>\n",
- " <th>Node</th>\n",
- " <th>Bottom level</th>\n",
- " <th>Inflow</th>\n",
- " <th>Outflow</th>\n",
- " <th>Weir</th>\n",
- " <th>weir crest level</th>\n",
- " <th>Weir 2</th>\n",
- " <th>weir crest level 2</th>\n",
- " <th>max retention storage</th>\n",
- " <th>comment</th>\n",
- " <th>max level if no weir</th>\n",
- " <th>AFS concentration</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>ID Bauwerk</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></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>RKB 0337</th>\n",
- " <td>Retention tank</td>\n",
- " <td>S0000763</td>\n",
- " <td>45.83</td>\n",
- " <td>H0062724</td>\n",
- " <td>H0052270</td>\n",
- " <td>W_RKB0337</td>\n",
- " <td>48.830</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>7018.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RUEBKrade</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0003529</td>\n",
- " <td>47.02</td>\n",
- " <td>H0029240</td>\n",
- " <td>H0006389</td>\n",
- " <td>W1_RUEBKrade</td>\n",
- " <td>50.250</td>\n",
- " <td>W2_RUEBKrade</td>\n",
- " <td>50.25</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RUB 0901</th>\n",
- " <td>Retention tank + sewer</td>\n",
- " <td>S0051890</td>\n",
- " <td>43.79</td>\n",
- " <td>H0069212</td>\n",
- " <td>H0064707</td>\n",
- " <td>W_SKU0901_Ü</td>\n",
- " <td>48.060</td>\n",
- " <td>W_SKU0901_1</td>\n",
- " <td>47.94</td>\n",
- " <td>1662.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>SKU 0216</th>\n",
- " <td>Retention tank + sewer</td>\n",
- " <td>S0033803</td>\n",
- " <td>45.03</td>\n",
- " <td>H0070324</td>\n",
- " <td>H0070328</td>\n",
- " <td>W_SKU0216</td>\n",
- " <td>46.285</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>319.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>SKU 0222</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0115403_</td>\n",
- " <td>43.61</td>\n",
- " <td>H0136665_</td>\n",
- " <td>H0136358</td>\n",
- " <td>W_SKU0222</td>\n",
- " <td>45.270</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1271.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>SKU 0228</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0017545</td>\n",
- " <td>42.79</td>\n",
- " <td>H0046422,H0000205</td>\n",
- " <td>H0070149</td>\n",
- " <td>W_SKU0228_BÜ</td>\n",
- " <td>44.160</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1330.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>SKU 0225</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0024291</td>\n",
- " <td>44.13</td>\n",
- " <td>H0010621</td>\n",
- " <td>H0059805</td>\n",
- " <td>W_SKU0225</td>\n",
- " <td>45.220</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>190.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RRK 0301</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0150758</td>\n",
- " <td>42.26</td>\n",
- " <td>H0010820</td>\n",
- " <td>H0185969</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>15300.0</td>\n",
- " <td>max level is the hub 0301 at</td>\n",
- " <td>45.54</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RRB 0301 I</th>\n",
- " <td>Retention tank</td>\n",
- " <td>RRB0301_I</td>\n",
- " <td>40.72</td>\n",
- " <td>W_RRB0301</td>\n",
- " <td>Zulauf_MWP0301_I,RRB0301_I_Entl</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>8850.0</td>\n",
- " <td>Staulevel nach Beschreibung</td>\n",
- " <td>45.54</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RRB 0301 II</th>\n",
- " <td>Retention tank</td>\n",
- " <td>RRB0301_II</td>\n",
- " <td>40.75</td>\n",
- " <td>W_RRB0301_BÜ</td>\n",
- " <td>RRB0301_II_Entl,Zulauf_MWP0301_II</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>8850.0</td>\n",
- " <td>Staulevel nach Beschreibung</td>\n",
- " <td>45.54</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RUE 0139/MWP 0112</th>\n",
- " <td>Retention tank</td>\n",
- " <td>S0145156</td>\n",
- " <td>40.69</td>\n",
- " <td>H0176026,H0175401</td>\n",
- " <td>H0061164</td>\n",
- " <td>W_RUE0139</td>\n",
- " <td>41.225</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>817.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>MWE 0145</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0029838</td>\n",
- " <td>42.13</td>\n",
- " <td>H0001696,H0015435</td>\n",
- " <td>H0050959</td>\n",
- " <td>W_MWE0145</td>\n",
- " <td>43.340</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>939.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>MWP_0142</th>\n",
- " <td>Retention tank + sewer</td>\n",
- " <td>S0025581</td>\n",
- " <td>39.62</td>\n",
- " <td>H0048354,H0070185</td>\n",
- " <td>MWP_0142</td>\n",
- " <td>W_SKU0147</td>\n",
- " <td>42.245</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>334.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>W_SKU0148</th>\n",
- " <td>Retention tank + sewer</td>\n",
- " <td>S0027792</td>\n",
- " <td>41.70</td>\n",
- " <td>H0055349</td>\n",
- " <td>Link_SKU0148_DN800Stz,H0056886</td>\n",
- " <td>W_SKU0148</td>\n",
- " <td>43.040</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1016.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>W_SKU0133_1</th>\n",
- " <td>Retention sewer</td>\n",
- " <td>S0023122</td>\n",
- " <td>41.45</td>\n",
- " <td>H0005604</td>\n",
- " <td>H0055351</td>\n",
- " <td>W_SKU0133_1</td>\n",
- " <td>43.000</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>5885.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>MWP 0932</th>\n",
- " <td>Retention tank</td>\n",
- " <td>S0058295</td>\n",
- " <td>40.57</td>\n",
- " <td>H0048460</td>\n",
- " <td>MWP_0932_I,MWP_0932_II</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>54.0</td>\n",
- " <td>no max level from a weir</td>\n",
- " <td>45.04</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>MWP 0314</th>\n",
- " <td>Retention tank</td>\n",
- " <td>S0064461</td>\n",
- " <td>39.68</td>\n",
- " <td>H0015334,H0069570</td>\n",
- " <td>MWP_0314_I,MWP_0314_II,MWP_0314_III</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>1484.0</td>\n",
- " <td>no max level from a weir</td>\n",
- " <td>41.69</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>RWP 0302</th>\n",
- " <td>Retention tank</td>\n",
- " <td>S0152138</td>\n",
- " <td>41.55</td>\n",
- " <td>H0186613</td>\n",
- " <td>MWP_0302_I,MWP_0302_II</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>30.0</td>\n",
- " <td>no max level from a weir</td>\n",
- " <td>43.56</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Type Node Bottom level \\\n",
- "ID Bauwerk \n",
- "RKB 0337 Retention tank S0000763 45.83 \n",
- "RUEBKrade Retention sewer S0003529 47.02 \n",
- "RUB 0901 Retention tank + sewer S0051890 43.79 \n",
- "SKU 0216 Retention tank + sewer S0033803 45.03 \n",
- "SKU 0222 Retention sewer S0115403_ 43.61 \n",
- "SKU 0228 Retention sewer S0017545 42.79 \n",
- "SKU 0225 Retention sewer S0024291 44.13 \n",
- "RRK 0301 Retention sewer S0150758 42.26 \n",
- "RRB 0301 I Retention tank RRB0301_I 40.72 \n",
- "RRB 0301 II Retention tank RRB0301_II 40.75 \n",
- "RUE 0139/MWP 0112 Retention tank S0145156 40.69 \n",
- "MWE 0145 Retention sewer S0029838 42.13 \n",
- "MWP_0142 Retention tank + sewer S0025581 39.62 \n",
- "W_SKU0148 Retention tank + sewer S0027792 41.70 \n",
- "W_SKU0133_1 Retention sewer S0023122 41.45 \n",
- "MWP 0932 Retention tank S0058295 40.57 \n",
- "MWP 0314 Retention tank S0064461 39.68 \n",
- "RWP 0302 Retention tank S0152138 41.55 \n",
- "\n",
- " Inflow Outflow \\\n",
- "ID Bauwerk \n",
- "RKB 0337 H0062724 H0052270 \n",
- "RUEBKrade H0029240 H0006389 \n",
- "RUB 0901 H0069212 H0064707 \n",
- "SKU 0216 H0070324 H0070328 \n",
- "SKU 0222 H0136665_ H0136358 \n",
- "SKU 0228 H0046422,H0000205 H0070149 \n",
- "SKU 0225 H0010621 H0059805 \n",
- "RRK 0301 H0010820 H0185969 \n",
- "RRB 0301 I W_RRB0301 Zulauf_MWP0301_I,RRB0301_I_Entl \n",
- "RRB 0301 II W_RRB0301_BÜ RRB0301_II_Entl,Zulauf_MWP0301_II \n",
- "RUE 0139/MWP 0112 H0176026,H0175401 H0061164 \n",
- "MWE 0145 H0001696,H0015435 H0050959 \n",
- "MWP_0142 H0048354,H0070185 MWP_0142 \n",
- "W_SKU0148 H0055349 Link_SKU0148_DN800Stz,H0056886 \n",
- "W_SKU0133_1 H0005604 H0055351 \n",
- "MWP 0932 H0048460 MWP_0932_I,MWP_0932_II \n",
- "MWP 0314 H0015334,H0069570 MWP_0314_I,MWP_0314_II,MWP_0314_III \n",
- "RWP 0302 H0186613 MWP_0302_I,MWP_0302_II \n",
- "\n",
- " Weir weir crest level Weir 2 \\\n",
- "ID Bauwerk \n",
- "RKB 0337 W_RKB0337 48.830 NaN \n",
- "RUEBKrade W1_RUEBKrade 50.250 W2_RUEBKrade \n",
- "RUB 0901 W_SKU0901_Ü 48.060 W_SKU0901_1 \n",
- "SKU 0216 W_SKU0216 46.285 NaN \n",
- "SKU 0222 W_SKU0222 45.270 NaN \n",
- "SKU 0228 W_SKU0228_BÜ 44.160 NaN \n",
- "SKU 0225 W_SKU0225 45.220 NaN \n",
- "RRK 0301 NaN NaN NaN \n",
- "RRB 0301 I NaN NaN NaN \n",
- "RRB 0301 II NaN NaN NaN \n",
- "RUE 0139/MWP 0112 W_RUE0139 41.225 NaN \n",
- "MWE 0145 W_MWE0145 43.340 NaN \n",
- "MWP_0142 W_SKU0147 42.245 NaN \n",
- "W_SKU0148 W_SKU0148 43.040 NaN \n",
- "W_SKU0133_1 W_SKU0133_1 43.000 NaN \n",
- "MWP 0932 NaN NaN NaN \n",
- "MWP 0314 NaN NaN NaN \n",
- "RWP 0302 NaN NaN NaN \n",
- "\n",
- " weir crest level 2 max retention storage \\\n",
- "ID Bauwerk \n",
- "RKB 0337 NaN 7018.0 \n",
- "RUEBKrade 50.25 NaN \n",
- "RUB 0901 47.94 1662.0 \n",
- "SKU 0216 NaN 319.0 \n",
- "SKU 0222 NaN 1271.0 \n",
- "SKU 0228 NaN 1330.0 \n",
- "SKU 0225 NaN 190.0 \n",
- "RRK 0301 NaN 15300.0 \n",
- "RRB 0301 I NaN 8850.0 \n",
- "RRB 0301 II NaN 8850.0 \n",
- "RUE 0139/MWP 0112 NaN 817.0 \n",
- "MWE 0145 NaN 939.0 \n",
- "MWP_0142 NaN 334.0 \n",
- "W_SKU0148 NaN 1016.0 \n",
- "W_SKU0133_1 NaN 5885.0 \n",
- "MWP 0932 NaN 54.0 \n",
- "MWP 0314 NaN 1484.0 \n",
- "RWP 0302 NaN 30.0 \n",
- "\n",
- " comment max level if no weir \\\n",
- "ID Bauwerk \n",
- "RKB 0337 NaN NaN \n",
- "RUEBKrade NaN NaN \n",
- "RUB 0901 NaN NaN \n",
- "SKU 0216 NaN NaN \n",
- "SKU 0222 NaN NaN \n",
- "SKU 0228 NaN NaN \n",
- "SKU 0225 NaN NaN \n",
- "RRK 0301 max level is the hub 0301 at 45.54 \n",
- "RRB 0301 I Staulevel nach Beschreibung 45.54 \n",
- "RRB 0301 II Staulevel nach Beschreibung 45.54 \n",
- "RUE 0139/MWP 0112 NaN NaN \n",
- "MWE 0145 NaN NaN \n",
- "MWP_0142 NaN NaN \n",
- "W_SKU0148 NaN NaN \n",
- "W_SKU0133_1 NaN NaN \n",
- "MWP 0932 no max level from a weir 45.04 \n",
- "MWP 0314 no max level from a weir 41.69 \n",
- "RWP 0302 no max level from a weir 43.56 \n",
- "\n",
- " AFS concentration \n",
- "ID Bauwerk \n",
- "RKB 0337 4.0 \n",
- "RUEBKrade 4.0 \n",
- "RUB 0901 4.0 \n",
- "SKU 0216 4.0 \n",
- "SKU 0222 4.0 \n",
- "SKU 0228 4.0 \n",
- "SKU 0225 4.0 \n",
- "RRK 0301 NaN \n",
- "RRB 0301 I NaN \n",
- "RRB 0301 II NaN \n",
- "RUE 0139/MWP 0112 4.0 \n",
- "MWE 0145 4.0 \n",
- "MWP_0142 4.0 \n",
- "W_SKU0148 4.0 \n",
- "W_SKU0133_1 4.0 \n",
- "MWP 0932 NaN \n",
- "MWP 0314 NaN \n",
- "RWP 0302 NaN "
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"storages = pd.read_excel(storages_file, sheet_name=\"simplified\", index_col=0, dtype={\n",
" \"weir crest level\": float,\n",
@@ -795,30 +166,10 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"id": "304394db",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2025-03-21 13:39:59,981 - INFO - Verarbeite Datei: 359BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:40:10,134 - INFO - Verarbeite Datei: 366BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:40:21,532 - INFO - Verarbeite Datei: 371BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:40:32,308 - INFO - Verarbeite Datei: 378BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:40:44,051 - INFO - Verarbeite Datei: 385BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:40:58,589 - INFO - Verarbeite Datei: 393BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:41:08,790 - INFO - Verarbeite Datei: 400BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:41:22,692 - INFO - Verarbeite Datei: 428BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:41:34,692 - INFO - Verarbeite Datei: 481BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:41:45,987 - INFO - Verarbeite Datei: 502BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:42:03,538 - INFO - Verarbeite Datei: 532BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:42:13,544 - INFO - Verarbeite Datei: 568BaseDefault_Network_HD.res1d\n",
- "2025-03-21 13:42:26,644 - INFO - Ergebnisse gespeichert in R:\\M_883_Entfrachten\\Durchführung\\Daten\\2_Algorithmen und Steuerungsziele\\Read Overflow\\Overflow_Results.xlsx\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"all_results = []\n",
"\n",
@@ -865,21 +216,10 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": null,
"id": "326beb0e",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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1awYjIyOV9z948OCt269cuYIWLVrAxsYGTk5OGDRoEJ4+faqx+eZjokRERERvZdiwYUhLS5NeXl5er/SZPHkyKlSoUOD7Z82apfL+smXLvnV7t27dUL16dcTHx+Py5cu4ePEivv/+e3knWAAmSkRERCSL8PBw7Nu3D0FBQbJv+/bt2+jRowcMDQ3h4OCA9u3b4/Lly7LHeRkTJSIiInor69evh62tLWrVqoW5c+ciLy9PasvJycHAgQOxePFiGBoaFvj+H374Aba2tmjQoAHWr1//Tu3jxo3D+vXr8ezZM8TFxWHbtm1o166dvBMsAC/mJiIiojcaNWoU5syZA1tbW5w9exaff/45lEolxo4dCwCYM2cOGjRogKZNmyIkJOSV98+YMQPu7u4wNTXF4cOH8fnnn8PCwgKfffbZW7W3bt0affv2hYWFBXJzcxEQEIB+/fppfN48okRERERv1LBhQzg4OEBPTw+NGzfG119/jU2bNgEAbt68iWXLlmHOnDlq3+/l5QUrKysYGBjAz88PgwcPlt7/pvbExET4+vpi4MCBePr0KZ48eQIzMzP06NFDs5MGjygRERHRe3ixmnVoaCji4+NRrVo1AEB2djZSU1Nhb2+P3bt3w9PT87Xvf9P2b926hWfPnmHUqFFQKBQwNDTE4MGD0bp1a5lm85pxaDwCERERlXibN29GSkoKhBA4d+4cZs6ciU6dOgEAPv/8c9y8eRMRERGIiIjAypUrYWFhgYiICDRo0ABJSUnYs2cPnj59itzcXBw6dAjLli2T3v+m9ho1asDc3BxLlixBTk4OUlNTsWLFCjRo0EDj81YIIYTGo5RiKSkpsLKyQnJyMgtOEhFRidW0aVNcunQJOTk5KFeuHPr3749x48YVeGQoJCQEAQEBSEpKAgAkJCSgbdu2uHr1KgDAzc0NY8aMka4xelM7AJw4cQJBQUGIjIyEnp4ePvroI8yfPx+VKlV6r/m87b/PTJQ0jIkSERHpukE762l0+7+0uyj7Nt/232eeeiMiIiJSg4kSERERkRpMlIiIiIjUYKJEREREpAYTJSIiIiI1mCgRERERqcFEiYiIiEgNJkpEREREajBRIiIiIlKDiRIRERGRGkyUiIiIiNRgokRERESkBhMlIiIiIjWYKBERERGpUewSpSlTpkChUKi8atSoIbVnZGRg+PDhsLOzg7m5OTp16oT4+HiVbcTGxsLf3x+mpqYoU6YMxo8fj5ycHJU+ISEhaNiwIYyMjFClShWsXbv2lbEsXrwYbm5uMDY2hqenJ86cOaORORMREVHxVOwSJQCoVasWHj58KL1CQ0OltrFjx2Lnzp3YsmULjh49igcPHqBjx45Se25uLvz9/ZGVlYWTJ09i3bp1WLt2LSZPniz1iYmJgb+/P3x8fBAREYExY8ZgwIAB2L9/v9Rn06ZNCAwMRHBwMM6fP4969erBz88Pjx49KpoPgYiIiLROIYQQ2h7Ei6ZMmYLt27cjIiLilbbk5GQ4ODhgw4YN6Ny5MwAgOjoaNWvWRFhYGBo3boy9e/eibdu2ePDgARwdHQEAy5YtQ1BQEBISEmBoaIigoCDs3r0bkZGR0ra7du2KpKQk7Nu3DwDg6emJDz/8EIsWLQIA5OXlwcXFBSNHjsTXX3/91vNJSUmBlZUVkpOTYWlp+b4fCxERUbE1aGc9jW7/l3YXZd/m2/77XCyPKN24cQNly5ZFpUqV0L17d8TGxgIAwsPDkZ2dDV9fX6lvjRo14OrqirCwMABAWFgY6tSpIyVJAODn54eUlBRERUVJfV7cRn6f/G1kZWUhPDxcpY9SqYSvr6/UR53MzEykpKSovIiIiKhkKnaJkqenJ9auXYt9+/Zh6dKliImJwSeffILU1FTExcXB0NAQ1tbWKu9xdHREXFwcACAuLk4lScpvz297XZ+UlBQ8e/YMjx8/Rm5uboF98rehzowZM2BlZSW9XFxc3vkzICIiouJBX9sDeFnr1q2l/69bty48PT1RoUIFbN68GSYmJloc2duZOHEiAgMDpeWUlBQmS0RERCVUsTui9DJra2tUq1YNN2/ehJOTE7KyspCUlKTSJz4+Hk5OTgAAJyenV+6Cy19+Ux9LS0uYmJjA3t4eenp6BfbJ34Y6RkZGsLS0VHkRERFRyVTsE6W0tDTcunULzs7O8PDwgIGBAQ4dOiS1X7t2DbGxsfDy8gIAeHl54fLlyyp3px08eBCWlpZwd3eX+ry4jfw++dswNDSEh4eHSp+8vDwcOnRI6kNERES6r9glSuPGjcPRo0dx584dnDx5Ep999hn09PTw5ZdfwsrKCv3790dgYCCOHDmC8PBw9O3bF15eXmjcuDEAoFWrVnB3d0fPnj1x8eJF7N+/H5MmTcLw4cNhZGQEABgyZAhu376NCRMmIDo6GkuWLMHmzZsxduxYaRyBgYFYsWIF1q1bh6tXr2Lo0KFIT09H3759tfK5EBERUdErdtco/fvvv/jyyy/x33//wcHBAR9//DFOnToFBwcHAMC8efOgVCrRqVMnZGZmws/PD0uWLJHer6enh127dmHo0KHw8vKCmZkZevfuje+++07qU7FiRezevRtjx47FggULUL58eaxcuRJ+fn5Sny+++AIJCQmYPHky4uLiUL9+fezbt++VC7yJiIhIdxW7Okq6hnWUiIhI17GOEhEREVEpxESJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEaTJSIiIiI1GCiRERERKQGEyUiIiIiNZgoEREREanBRImIiIhIDSZKRERERGowUSIiIiJSg4kSERERkRpMlIiIiIjUYKJEREREpAYTJSIiIiI1mCgRERERqcFEiYiIiEgNJkpEREREajBRIiIiIlKDiRIRERGRGkyUiIiIiNRgokRERESkBhMlIiIiIjWYKBERERGpwUSJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEaTJSIiIiI1GCiRERERKRGsU+UZs6cCYVCgTFjxkjrMjIyMHz4cNjZ2cHc3BydOnVCfHy8yvtiY2Ph7+8PU1NTlClTBuPHj0dOTo5Kn5CQEDRs2BBGRkaoUqUK1q5d+0r8xYsXw83NDcbGxvD09MSZM2c0MU0iIiIqhop1onT27FksX74cdevWVVk/duxY7Ny5E1u2bMHRo0fx4MEDdOzYUWrPzc2Fv78/srKycPLkSaxbtw5r167F5MmTpT4xMTHw9/eHj48PIiIiMGbMGAwYMAD79++X+mzatAmBgYEIDg7G+fPnUa9ePfj5+eHRo0eanzwRERFpnUIIIbQ9iIKkpaWhYcOGWLJkCX744QfUr18f8+fPR3JyMhwcHLBhwwZ07twZABAdHY2aNWsiLCwMjRs3xt69e9G2bVs8ePAAjo6OAIBly5YhKCgICQkJMDQ0RFBQEHbv3o3IyEgpZteuXZGUlIR9+/YBADw9PfHhhx9i0aJFAIC8vDy4uLhg5MiR+Prrr99qHikpKbCyskJycjIsLS3l/IiIiIiKhUE762l0+7+0uyj7Nt/23+die0Rp+PDh8Pf3h6+vr8r68PBwZGdnq6yvUaMGXF1dERYWBgAICwtDnTp1pCQJAPz8/JCSkoKoqCipz8vb9vPzk7aRlZWF8PBwlT5KpRK+vr5Sn4JkZmYiJSVF5UVEREQlk762B1CQP/74A+fPn8fZs2dfaYuLi4OhoSGsra1V1js6OiIuLk7q82KSlN+e3/a6PikpKXj27BkSExORm5tbYJ/o6Gi1Y58xYwamTp36dhMlIiKiYq3YHVG6d+8eRo8ejd9//x3GxsbaHs47mzhxIpKTk6XXvXv3tD0kIiIiek/FLlEKDw/Ho0eP0LBhQ+jr60NfXx9Hjx7FwoULoa+vD0dHR2RlZSEpKUnlffHx8XBycgIAODk5vXIXXP7ym/pYWlrCxMQE9vb20NPTK7BP/jYKYmRkBEtLS5UXERERlUzvfOqtYcOGb9XP2NgYJ0+efOcBtWjRApcvX1ZZ17dvX9SoUQNBQUFwcXGBgYEBDh06hE6dOgEArl27htjYWHh5eQEAvLy8MG3aNDx69AhlypQBABw8eBCWlpZwd3eX+uzZs0clzsGDB6VtGBoawsPDA4cOHUJAQACA5xdzHzp0CCNGjHjneREREVHJ886J0tWrV7Fs2bLX9hFCYPTo0e81IAsLC9SuXVtlnZmZGezs7KT1/fv3R2BgIGxtbWFpaYmRI0fCy8sLjRs3BgC0atUK7u7u6NmzJ2bPno24uDhMmjQJw4cPh5GREQBgyJAhWLRoESZMmIB+/frh8OHD2Lx5M3bv3i3FDQwMRO/evfHBBx+gUaNGmD9/PtLT09G3b9/3mhsRERGVLO+cKNWtWxe9e/d+Y78VK1a814Dexrx586BUKtGpUydkZmbCz88PS5Yskdr19PSwa9cuDB06FF5eXjAzM0Pv3r3x3XffSX0qVqyI3bt3Y+zYsViwYAHKly+PlStXws/PT+rzxRdfICEhAZMnT0ZcXBzq16+Pffv2vXKBNxEREemmYltHSVewjhIREek61lEiIiIiKoXeOlG6cuUKevbsiblz5+LcuXNo3LgxWrRogYiICA0Oj4iIiEh73voapSFDhuD7779HUlISOnXqhF27dsHS0hJ9+/bF4cOHNTlGIiIiIq1460RJqVTC29sbADB79mzUqVMHAKBQKDQzMiIiIiIte+tTb9nZ2dL/r1y5Uvr/3NxceUdEREREVEy8daK0bds25N8gV7NmTQDPHxw7c+ZMzYyMiIiISMve+tRbfoXrFxkaGqJx48bIycnB06dPVdp4KzwRERGVdIUqD3Dq1CnUq1cPxsbGsLGxgY2NDaytrWFjYyPX+IiIiIi05p0rc7+od+/e+PLLL/HHH3/A1NRUrjERERERFQuFSpQePXqE4OBg3vlGREREOqlQp966deuGHTt2yDUWIiIiomKlUEeUfvjhBzRu3BizZ89+5UGxf/31V6EGRkRERKRthUqUevToAUNDQ3z88ce8RomIiIh0TqESpaNHj+Lhw4ewsLCQazxERERExUahrlFyd3dHamqqXGMhIiIiKlYKdUSpY8eOaNu2LYYOHfrKNUrt27cv1MCIiIiItK1QidLy5csBANOnT1dZr1AomCgRERFRiVeoRCkmJkaucRAREREVO4W6RomIiIhIl71zouTr6/tW/fz8/N55MERERETFyTufejt58iR27twJIcRr+50+ffq9B0VERERUHLxzolSmTBmMGjXqjf2cnJzea0BERERExcU7J0p37tzRwDCIiIiIih9ezE1ERESkBhMlIiIiIjWYKBERERGpIUuiJITAw4cP5dgUERERUbFRqEQpNTUVvXr1grGxMapUqQIA2L59OyZPnizL4IiIiIi0qVCJ0siRI5Gbm4vIyEgYGhoCABo3boxNmzbJMjgiIiIibSrUs9727duHmJgYmJiYQKFQAHhePyk+Pl6WwRERERFpU6GOKBkZGSEnJ0dl3X///QdbW9tCDYqIiIioOChUotSuXTsMGzYMycnJAIDMzExMmDABn3322Xtvc+nSpahbty4sLS1haWkJLy8v7N27V2rPyMjA8OHDYWdnB3Nzc3Tq1OmVI1ixsbHw9/eHqakpypQpg/Hjx7+S0IWEhKBhw4YwMjJClSpVsHbt2lfGsnjxYri5ucHY2Bienp44c+bMe8+LiIiISp5CJUqzZs1CZmYm7OzskJSUBHNzc6SkpOC77757722WL18eM2fORHh4OM6dO4fmzZujQ4cOiIqKAgCMHTsWO3fuxJYtW3D06FE8ePAAHTt2lN6fm5sLf39/ZGVl4eTJk1i3bh3Wrl2rcoF5TEwM/P394ePjg4iICIwZMwYDBgzA/v37pT6bNm1CYGAggoODcf78edSrVw9+fn549OjRe8+NiIiIShaFeNPTbd9CQkIC7t69CxcXFzg6OsoxLhW2traYM2cOOnfuDAcHB2zYsAGdO3cGAERHR6NmzZoICwtD48aNsXfvXrRt2xYPHjyQxrJs2TIEBQUhISEBhoaGCAoKwu7duxEZGSnF6Nq1K5KSkrBv3z4AgKenJz788EMsWrQIAJCXlwcXFxeMHDkSX3/99VuPPSUlBVZWVkhOToalpaVcHwkREVGxMWhnPY1u/5d2F2Xf5tv++yxLHSUHBwd88MEHsidJubm5+OOPP5Ceng4vLy+Eh4cjOzsbvr6+Up8aNWrA1dUVYWFhAICwsDDUqVNHZSx+fn5ISUmRjkqFhYWpbCO/T/42srKyEB4ertJHqVTC19dX6kNERES6r1B3vVWsWFG62+1FRkZGcHV1xeeff45+/foV2Od1Ll++DC8vL2RkZMDc3Bzbtm2Du7s7IiIiYGhoCGtra5X+jo6OiIuLAwDExcW9krDlL7+pT0pKCp49e4bExETk5uYW2Cc6Ovq1Y8/MzERmZqa0nJKS8vYTJyIiomKlUEeUBg0aBKVSiWHDhmHmzJkYOnQo9PX10a1bN/j4+GDy5Mn4/vvv33m71atXR0REBE6fPo2hQ4eid+/euHLlSmGGWmRmzJgBKysr6eXi4qLtIREREdF7KtQRpb/++gv79u2TqnIDQEBAALp164azZ8+iZcuW6NKlyztX6jY0NJS26eHhgbNnz2LBggX44osvkJWVhaSkJJWjSvHx8XBycgLwvI7Ty3en5d8V92Kfl++Ui4+Ph6WlJUxMTKCnpwc9Pb0C++RvQ52JEyciMDBQWk5JSWGyREREVEIV6ojS9evX4erqqrLOxcUFN27cAPA8yZHjLrG8vDxkZmbCw8MDBgYGOHTokNR27do1xMbGwsvLCwDg5eWFy5cvq8Q9ePAgLC0t4e7uLvV5cRv5ffK3YWhoCA8PD5U+eXl5OHTokNRHHSMjI6m0Qf6LiIiISqZCJUqenp4YOXIkEhMTAQBPnjzBmDFj8OGHHwJ4nsSUKVPmnbY5ceJEHDt2DHfu3MHly5cxceJEhISEoHv37rCyskL//v0RGBiII0eOIDw8HH379oWXlxcaN24MAGjVqhXc3d3Rs2dPXLx4Efv378ekSZMwfPhwGBkZAQCGDBmC27dvY8KECYiOjsaSJUuwefNmjB07VhpHYGAgVqxYgXXr1uHq1asYOnQo0tPT0bdv38J8ZERERFSCFOrU25o1a9CtWzfY29vD2NgYGRkZ+Oijj7BhwwYAwNOnT7Fs2bJ32uajR4/Qq1cvPHz4EFZWVqhbty7279+Pli1bAgDmzZsHpVKJTp06ITMzE35+fliyZIn0fj09PezatQtDhw6Fl5cXzMzM0Lt3b5XaThUrVsTu3bsxduxYLFiwAOXLl8fKlSvh5+cn9fniiy+QkJCAyZMnIy4uDvXr18e+ffs0Uv6AiIiIiqf3rqOUl5eHq1evolq1aoiPj8eDBw9QtmxZlC9fXu4xlmiso0RERLpOl+sovfcRJaVSiUaNGiEtLQ3ly5dngkREREQ6p1DXKNWtWxe3bt2SayxERERExUqhrlHq0KGD9GBcFxcXKJX/l3e1b9++0IMjIiIi0qZCJUrLly8HAPz0008q6xUKBRMlIiIiKvEKlSjFxMTINQ4iIiKiYkeWh+ISERER6aJCHVECgLCwMISEhODx48d4sdLAy6fjiIiIiEqaQh1RWrx4MVq0aIEzZ85g8eLFiImJwfLlyxEXFyfX+IiIiIi0plCJ0vz587F3715s27YNJiYm2LZtG7Zs2SI9KoSIiIioJCtUohQfHw9vb+/nG1IqIYRA69atsWPHDlkGR0RERKRNhUqUnJyc8ODBAwCAm5sbQkJCEBUVpVJPiYiIiKikKlRGM3ToUJw+fRoAEBgYiFatWqFBgwYYPny4LIMjIiIi0qZC3fU2duxY6f+7d++Opk2bIi0tDTVr1iz0wIiIiIi0rdDlAV7k4uIi5+aIiIiItKpQidL9+/fx7bffIjw8HKmpqSptt2/fLtTAiIiIiLStUIlSjx49YGpqiqCgIJiZmck1JiIiIqJioVCJUnh4OB4/fgxDQ0O5xkNERERUbBTqrrdatWqxCjcRERHprHc+ovRiMcmOHTuiffv2GD58OBwdHVX6tW/fvvCjIyIiItKid06URo8e/cq66dOnqywrFAomSkRERFTivXOiFBMTo4lxEBERERU7hbpG6f79+3jy5InKusTEROmxJkREREQlWaESpY4dO+LevXsq6+7evYtOnToValBERERExUGhEqVr166hXr16Kuvq1auHq1evFmpQRERERMVBoRIla2trxMfHq6yLj4+Hubl5oQZFREREVBwUKlFq27Yt+vbti/v37wN4fs3SwIEDeccbERER6YRCJUozZsyAsbExXFxcYGpqChcXF+jr62PWrFlyjY+IiIhIawr1CBMLCwv89ddfePToEe7evYsKFSqgTJkyco2NiIiISKsKdURp9erViIyMRJkyZWBoaIiWLVvCw8MDly9flmt8RERERFpTqETphx9+kB5dEhQUBD8/P3To0AGjRo2SZXBERERE2lSoU2+PHz+Gg4MDMjIyEBYWhr///hv6+vqYP3++TMMjIiIi0p5CJUo2Nja4ceMGIiMj4eHhASMjI2RkZCAvL0+u8RERERFpTaESpdGjR6N+/foAgHXr1gEAQkNDUbNmzffe5owZM/DXX38hOjoaJiYmaNKkCWbNmoXq1atLfTIyMvDVV1/hjz/+QGZmJvz8/LBkyRLpNCAAxMbGYujQoThy5AjMzc3Ru3dvzJgxA/r6/zflkJAQBAYGIioqCi4uLpg0aRL69OmjMp7Fixdjzpw5iIuLQ7169fDzzz+jUaNG7z0/IiIqOorWv2h0+2LvII1un7SvUNcoBQYGIiIiApcvX0bnzp0BAK6urlixYsV7b/Po0aMYPnw4Tp06hYMHDyI7OxutWrVCenq61Gfs2LHYuXMntmzZgqNHj+LBgwfo2LGj1J6bmwt/f39kZWXh5MmTWLduHdauXYvJkydLfWJiYuDv7w8fHx9ERERgzJgxGDBgAPbv3y/12bRpEwIDAxEcHIzz58+jXr168PPzw6NHj957fkRERFRyKIQQQtuDeJ2EhASUKVMGR48eRdOmTZGcnAwHBwds2LBBSs6io6NRs2ZNhIWFoXHjxti7dy/atm2LBw8eSEeZli1bhqCgICQkJMDQ0BBBQUHYvXs3IiMjpVhdu3ZFUlIS9u3bBwDw9PTEhx9+iEWLFgEA8vLy4OLigpEjR+Lrr79+q/GnpKTAysoKycnJsLS0lPOjISKiN+ARpaIxaGe9N3cqhF/aXZR9m2/77/M7n3rbs2cP2rRpAwDYsWOH2n5yVedOTk4GANja2gIAwsPDkZ2dDV9fX6lPjRo14OrqKiVKYWFhqFOnjsqpOD8/PwwdOhRRUVFo0KABwsLCVLaR32fMmDEAgKysLISHh2PixIlSu1KphK+vL8LCwtSONzMzE5mZmdJySkrK+0+eiIiItOqdE6UJEyZIidLo0aML7KNQKGRJlPLy8jBmzBh89NFHqF27NgAgLi4OhoaGsLa2Vunr6OiIuLg4qc+LSVJ+e37b6/qkpKTg2bNnSExMRG5uboF9oqOj1Y55xowZmDp16rtPloiIiIqdd06UXjxVFRMTI+tgXjZ8+HBERkYiNDRUo3HkNHHiRAQGBkrLKSkpcHFx0eKIiIiI6H29911vCQkJmDt3Lo4ePYonT57A1tYWPj4+CAwMhL29faEHNmLECOzatQvHjh1D+fLlpfVOTk7IyspCUlKSylGl+Ph4ODk5SX3OnDmjsr34+HipLf+/+ete7GNpaQkTExPo6elBT0+vwD752yiIkZERjIyM3n3CREREVOy8111vjx8/xgcffIDdu3fD19cXY8eOha+vL3bt2oUPPvgAjx8/fu8BCSEwYsQIbNu2DYcPH0bFihVV2j08PGBgYIBDhw5J665du4bY2Fh4eXkBALy8vHD58mWVu9MOHjwIS0tLuLu7S31e3EZ+n/xtGBoawsPDQ6VPXl4eDh06JPUhIiIi3fZeR5RmzpyJJk2a4LfffoOenp60fsqUKejduzdmzZqFOXPmvNeAhg8fjg0bNuDvv/+GhYWFdE2RlZUVTExMYGVlhf79+yMwMBC2trawtLTEyJEj4eXlhcaNGwMAWrVqBXd3d/Ts2ROzZ89GXFwcJk2ahOHDh0tHe4YMGYJFixZhwoQJ6NevHw4fPozNmzdj9+7d0lgCAwPRu3dvfPDBB2jUqBHmz5+P9PR09O3b973mRkRERCXLeyVKBw4cwObNm1WSJADQ09PDpEmT0Llz5/dOlJYuXQoAaNasmcr6NWvWSMUg582bB6VSiU6dOqkUnHxxHLt27cLQoUPh5eUFMzMz9O7dG999953Up2LFiti9ezfGjh2LBQsWoHz58li5ciX8/PykPl988QUSEhIwefJkxMXFoX79+ti3b98rF3gTERGRbnqvOko2NjZITEx87/bShHWUiIi0h3WUioYu11F6r2uU3vQsNz7rjYiIiHTBe516y8zMxM8//wx1B6OysrIKNSgiIiKi4uC9EqXGjRvjr7/+em07ERERUUn3XolSSEiIzMMgIiIiKn7e6xolIiIiotKAiRIRERGRGkyUiIiIiNRgokRERESkBhMlIiIiIjWYKBERERGpwUSJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEa7/UIEyLSPEXrXzS6fbF3kEa3T0SkC3hEiYiIiEgNJkpEREREajBRIiIiIlKDiRIRERGRGkyUiIiIiNRgokRERESkBhMlIiIiIjWYKBERERGpwUSJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEaTJSIiIiI1GCiRERERKQGEyUiIiIiNfS1PQAiIiKSh6L1Lxrdvtg7SKPbL46K5RGlY8eOoV27dihbtiwUCgW2b9+u0i6EwOTJk+Hs7AwTExP4+vrixo0bKn2ePHmC7t27w9LSEtbW1ujfvz/S0tJU+ly6dAmffPIJjI2N4eLigtmzZ78yli1btqBGjRowNjZGnTp1sGfPHtnnS0RERMVTsUyU0tPTUa9ePSxevLjA9tmzZ2PhwoVYtmwZTp8+DTMzM/j5+SEjI0Pq0717d0RFReHgwYPYtWsXjh07hkGD/i8TTklJQatWrVChQgWEh4djzpw5mDJlCn755f+y8ZMnT+LLL79E//79ceHCBQQEBCAgIACRkZGamzwREREVG8Xy1Fvr1q3RunXrAtuEEJg/fz4mTZqEDh06AADWr18PR0dHbN++HV27dsXVq1exb98+nD17Fh988AEA4Oeff0abNm3w448/omzZsvj999+RlZWF1atXw9DQELVq1UJERAR++uknKaFasGABPv30U4wfPx4A8P333+PgwYNYtGgRli1bVgSfBBEREWlTsTyi9DoxMTGIi4uDr6+vtM7Kygqenp4ICwsDAISFhcHa2lpKkgDA19cXSqUSp0+flvo0bdoUhoaGUh8/Pz9cu3YNiYmJUp8X4+T3yY9TkMzMTKSkpKi8iIiIqGQqcYlSXFwcAMDR0VFlvaOjo9QWFxeHMmXKqLTr6+vD1tZWpU9B23gxhro++e0FmTFjBqysrKSXi4vLu06RiIiIiokSlygVdxMnTkRycrL0unfvnraHRERERO+pxCVKTk5OAID4+HiV9fHx8VKbk5MTHj16pNKek5ODJ0+eqPQpaBsvxlDXJ7+9IEZGRrC0tFR5ERERUclU4hKlihUrwsnJCYcOHZLWpaSk4PTp0/Dy8gIAeHl5ISkpCeHh4VKfw4cPIy8vD56enlKfY8eOITs7W+pz8OBBVK9eHTY2NlKfF+Pk98mPQ0RERLqtWCZKaWlpiIiIQEREBIDnF3BHREQgNjYWCoUCY8aMwQ8//IAdO3bg8uXL6NWrF8qWLYuAgAAAQM2aNfHpp59i4MCBOHPmDE6cOIERI0aga9euKFu2LACgW7duMDQ0RP/+/REVFYVNmzZhwYIFCAwMlMYxevRo7Nu3D3PnzkV0dDSmTJmCc+fOYcSIEUX9kRAREZEWFMvyAOfOnYOPj4+0nJ+89O7dG2vXrsWECROQnp6OQYMGISkpCR9//DH27dsHY2Nj6T2///47RowYgRYtWkCpVKJTp05YuHCh1G5lZYUDBw5g+PDh8PDwgL29PSZPnqxSa6lJkybYsGEDJk2ahG+++QZVq1bF9u3bUbt27SL4FIiIiEjbFEIIoe1B6LKUlBRYWVkhOTmZ1yvRO+GjCIgKr7T9HmlrvoN21tNo3F/aXZR9m2/773OxPPVGREREVBwwUSIiIiJSg4kSERERkRpMlIiIiIjUYKJEREREpAYTJSIiIiI1mCgRERERqcFEiYiIiEgNJkpEREREajBRIiIiIlKDiRIRERGRGkyUiIiIiNRgokRERESkBhMlIiIiIjWYKBERERGpwUSJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEa+toeABER6T5F6180un2xd5BGt0+lF48oEREREanBRImIiIhIDSZKRERERGowUSIiIiJSg4kSERERkRpMlIiIiIjUYKJEREREpAYTJSIiIiI1mCgRERERqcHK3ERvwIrCRESlF48oEREREanBI0pvYfHixZgzZw7i4uJQr149/Pzzz2jUqJG2h0VEMuARQyJ6HR5ReoNNmzYhMDAQwcHBOH/+POrVqwc/Pz88evRI20MjIiIiDWOi9AY//fQTBg4ciL59+8Ld3R3Lli2DqakpVq9ere2hERERkYbx1NtrZGVlITw8HBMnTpTWKZVK+Pr6IiwsTIsjI9I9pe0UWGmbL1FJxUTpNR4/fozc3Fw4OjqqrHd0dER0dHSB78nMzERmZqa0nJycDABISUnR3EBJs3KeaXTzar8b2oqrLaXtc+bPV1b8nP8/Lc0362muVuLKsU0hxOs7ClLr/v37AoA4efKkyvrx48eLRo0aFfie4OBgAYAvvvjiiy+++CoBr3v37r02F+ARpdewt7eHnp4e4uPjVdbHx8fDycmpwPdMnDgRgYGB0nJeXh6ePHkCOzs7KBQKjY73dVJSUuDi4oJ79+7B0tKScRmXcRmXcRm3VMV9mRACqampKFu27Gv7MVF6DUNDQ3h4eODQoUMICAgA8DzxOXToEEaMGFHge4yMjGBkZKSyztraWsMjfXuWlpZa+WIyLuMyLuMyLuMWh7gvsrKyemMfJkpvEBgYiN69e+ODDz5Ao0aNMH/+fKSnp6Nv377aHhoRERFpGBOlN/jiiy+QkJCAyZMnIy4uDvXr18e+ffteucCbiIiIdA8TpbcwYsQItafaSgojIyMEBwe/clqQcRmXcRmXcRm3NMR9Xwoh3nRfHBEREVHpxMrcRERERGowUSIiIiJSg4kSERERkRpMlIiIiIjU4F1vREREJKsdO3a8dd/27dtrcCSFx7veSHbHjx/H8uXLcevWLWzduhXlypXDr7/+iooVK+Ljjz+WNdbChQsLXG9lZYVq1arBy8tL1njqJCUlYcuWLYiNjUWFChXQpUuXt6r4Su8nOzsbBgYGGo9z+/ZthIaG4uHDh1AqlahUqRJatmxZpNWEs7OzcefOHZQpU0Zj36nw8HB4eHhoZNtv8ujRI0RGRsLDwwNWVlaIj4/HunXrkJeXB39/f9SpU0cr49K0W7duYc2aNbh16xYWLFiAMmXKYO/evXB1dUWtWrWKdCw5OTl48OABXF1dZdumUvl2J6wUCgVyczX7QN1Ck+PhsVR81K5dW3z33XciNjZWK/G3bt0qTExMxIABA4SRkZG4deuWEEKIn3/+WbRu3Vr2eG5ubgW+rK2thUKhEB999JH477//ZI/72WefiS1btgghhIiMjBT29vbCwcFBeHp6CkdHR+Hk5CSuXLkie1whhFi8eLFo0aKF6NKli/jnn39U2hISEkTFihVlj2ltbS1sbGxeebm5uYlWrVqJAwcOyB5TCCE2bdokMjMzpeWff/5ZuLq6CqVSKezs7MTUqVM1EjctLU107txZKBQKoVAohFKpFE5OTkJPT0+Ym5uLRYsWaSTurFmzxNOnT4UQQuTk5IivvvpKGBoaCqVSKfT19UXfvn1FVlaW7HEVCoWoXLmymDZtmrh//77s21fnyJEjwszMTCgUCuHk5CQiIiJE+fLlRdWqVUX16tWFkZGR2L9/f5GNJ19ERIRQKpUa235ISIgwMTERvr6+wtDQUNpPzpgxQ3Tq1EljcdXR9HxLOiZKOkahUAg7Ozuhp6cn/Pz8xNatW0V2dnaRxa9fv75Yt26dEEIIc3NzaQdw/vx54ejoKEuMoUOHioSEhDf2u3XrlvDy8hJDhw6VJe6LbGxsxNWrV4UQQrRu3Vp069ZN+gc9KytL9O/fX7Rq1Ur2uAsWLBCmpqZi+PDhokePHsLQ0FBMnz5dao+Li5Nth3fv3j2Rm5srhBBi7dq1Bb7mz58vevbsKQwNDcWOHTtkifsipVIp4uPjhRBCrF69WhgbG4vJkyeL3bt3ix9++EGYmZmJFStWyB530KBB4qOPPhKXL18WN27cEJ07dxYTJkwQ6enpYtWqVcLU1FT8/vvvssd9cb5z5swRNjY2YvXq1SIqKkr89ttvokyZMmLWrFmyx1UoFGLgwIGiTJkyQl9fX/j7+4tt27aJnJwc2WO96OOPPxbDhw8XqampYs6cOaJcuXJi+PDhUvu4ceNEkyZNNDqGgkRERAiFQqGx7Tdu3FjMnTtXCKG6nzx9+rQoV66cxuKqw0Tp9Zgo6RiFQiHu378vtm3bJtq1ayf09fWFg4OD+OqrrzR2hONFJiYmIiYmRgihugO4deuWMDIykiXGl19+Kf1j8iZHjx4VlStXliXui0xMTMTNmzeFEEI4OzuL8+fPq7Rfu3ZNWFlZyR7X3d1d5R/oEydOCAcHB/Htt98KIeRNlF78+b3J3LlzhZeXlyxxX6RQKKSfdaNGjcTs2bNV2pcsWSIaNGgge1x7e3tx7tw5afnJkyfC2NhYpKenCyGEWLRokahfv77scV+cb4MGDcTy5ctV2n/77TdRq1YtjcXNzs4WW7duFW3atBF6enrC0dFRTJgwQVy7dk32mEIIYWlpKf0eZWdnC319fXHhwgWp/fr16xr5Pfrss89e+2revLlGEwczMzNx+/ZtIYTq71lMTIxs+8kXNWjQ4LWvGjVqaDxRSktLE7t37xZLly4VCxYsUHkVd7yYWwfp6+sjICAAAQEBePjwIdauXYs1a9Zg3rx58PT0xIABA9CvXz+NxHZycsLNmzfh5uamsj40NBSVKlWSJcaGDRveuq+rqyvi4uJkifuiunXr4vDhw6hcuTKcnJxw9+5dNGjQQGq/e/cuTExMZI8bExODJk2aSMtNmjTB4cOH4evri+zsbIwZM0a2WGFhYW99zULbtm3xww8/yBb7RQqFAsDz64VatWql0taqVSsEBQXJHjMnJ0flOiRzc3Pk5OQgPT0dpqamaNWqFcaNGyd7XOD/5hsbG6vyswae/7xjYmI0Ehd4vu/o1KkTOnXqhPv372P16tVYu3YtfvzxR3z00Uc4duyYrPEMDQ2RkZEBAMjKykJeXp60DADPnj3TyLVoO3fuRMuWLdU+s1PT18xYW1vj4cOHqFixosr6CxcuoFy5crLHu3LlCrp27fpKvHwPHz7E9evXZY+b78KFC2jTpg2ePn2K9PR02Nra4vHjxzA1NUWZMmUwatQojcWWhbYzNZLXi4fuX3bkyBHRo0cPYWZmprH406dPF+7u7uLUqVPCwsJCHD9+XPz222/CwcFBLFy4UJYYLi4u4s6dO2/Vd8eOHcLd3V2WuC/atWuXsLW1FWvWrBFr1qwRbm5uYuXKleLEiRNi9erVwsXFRYwfP172uC4uLuLYsWOvrI+KihKOjo6iV69esv1luHTpUpGSkvJWfS9duiTbqdUXKRQKsX79evH333+L8uXLi5MnT6q0R0ZGCktLS9njtmzZUuUU0Jw5c4Szs7O0fP78eWFvby97XIVCIaZNmyYWLFggnJ2dxdGjR1XaL168KGxsbGSP+7r9hhBC/PPPP6Jbt26yx+3QoYNo27atCA0NFYMGDRIffPCB8Pf3F2lpaSI9PV107txZfPrpp7LHrVOnjli5cqXa9gsXLmj0CMtXX30lPv74Y/Hw4UNhYWEhbty4IUJDQ0WlSpXElClTZI/n4eEhlixZorZd0/P19vYWAwcOFLm5udIRtNjYWNG0aVPx559/aiyuXJgo6ZgXD92rk5ycrLH4eXl50rUj+RfCGhsbi0mTJskW4/fffxdpaWlCiOdzKegVGxsrtm3bJipVqqSxC363bt0qypcvL5RKpTTX/PmOGTNGI9d3fPnll2LMmDEFtkVGRgoHBwfZdni1atV665sCRo8eLfz8/GSJ+6IXP1eFQiF++OEHlfaVK1dq5NRbeHi4sLW1FU5OTsLV1VUYGhqKjRs3Su2LFi0SvXr1kj1uhQoVVG5KmDdvnkr7/PnzRePGjWWP+zb7DU24fv26qFq1qlAoFKJmzZri33//Fe3btxf6+vrSZQPh4eGyx+3Tp48YNmyY2vYrV64INzc32ePmy8zMFAMGDBD6+vpCoVAIAwMDoVQqRY8ePTSy3xg1apQYPXq02vabN2+KZs2ayR43n5WVlYiOjpb+P/8ykFOnTonq1atrLK5cWB5Ax/Tt2xcLFy6EhYWFVseRlZWFmzdvIi0tDe7u7jA3N9dIHKVSKZ2qeJlCocCAAQOwcOFCGBoaaiR+bm4uzp8/j9u3byMvLw/Ozs7w8PDQ2Od/6dIlhIeHo2/fvgW2R0ZG4s8//0RwcLCscQMDAwtcn5ycjPPnz+P69es4duxYkd9ivmvXLhgYGMDPz0/2bT98+BC7du1CZmYmmjdvDnd3d9ljvKtTp07ByMhI5TSvHI4ePYqPPvoI+vrauRrjv//+g52dnbR86NAhPHv2DF5eXirr5ZKZmYnc3FyYmprKvu13ERsbi8jISKSlpaFBgwaoWrWqVsejKQ4ODjh58iSqVq2KatWq4eeff4afnx+io6Ph4eGB9PR0bQ/xtZgoUYl29OjRAtdbWlqiatWqGkvQShsfH58C11taWqJ69eoYOnSo2usfiEq6mTNnYsiQIbC2ttb2UIrEsGHD8N1338He3l6W7bVq1Qp9+vRBt27dMHDgQFy6dAmjRo3Cr7/+isTERJw+fVqWOJrCRKmUiI+PR2ZmpqwFxfJ17Njxrfv+9ddfssd/F3LvANRJTEzEzp070atXL41sPy8vr8CCbnl5efj333818nPWJnXzFULg3r17Gpnvf//9h0uXLqFevXrSxaerVq1CZmYmunTpgpo1a8oeU51KlSph//79RX7EobTFVcfS0hIRERGFuiFF3VHZgvz000/vHUcOcsz3RefOnUNqaip8fHzw6NEj9OrVSzrCtGrVKtSvX1+WOJrCu950TGpqKoYOHYrjx4+jWbNmWLFiBcaOHYulS5dCoVDg448/xs6dO2WtLPxitWAhBLZt2wYrKyt88MEHAJ5X/U1KSnqnhEpTfvvtN4wbN07jiVJsbCz69u0re6KUkpKCAQMGSD/DwYMHIzg4GHp6egCAhIQEVKxYsfhXun1Lb5rvo0ePNDLfM2fOoFWrVkhJSYG1tTUOHjyILl26QF9fH3l5eZg5cyZCQ0PRsGFDWeOqqzQfGxuLNWvWwMnJCQBkv0uotMV9V3IcT7hw4YLK8vnz55GTk4Pq1asDAK5fvw49PT2tVUh/kdzHT/L/LQCAMmXKYN++fbJuX+O0dXEUacaIESNEjRo1xMKFC0WzZs1Ehw4dRO3atUVoaKg4evSocHd3F998843G4k+YMEEMGDBA5YLEnJwcMWjQIDFu3DiNxX1b71Ib6HXUXUSe/zp+/LhG7iIZNWqUqFatmtiyZYtYsWKFqFChgvD395eKXcbFxWmsUN7hw4fFjz/+KEJDQ4UQQixbtky4uLgIe3t7MWDAAKmitJy0NV9fX18xYMAAkZKSIubMmSPKly8vBgwYILX37dtXBAQEyB5XoVCI8uXLv1JpXqFQiHLlygk3NzeNVF4vbXHflVz7jXxz584V7dq1E0+ePJHWPXnyRHTo0EH8+OOPssV5X3LP18fHRyQmJr6yPjk5Wfj4+MgWR1OYKOkYFxcXcfjwYSGEEPfv3xcKhULs3LlTat+1a5dG7zKwt7eX7m54UXR0tLC1tdVY3Lcl1w4g/7EW6l757XJzdXUVR44ckZYTEhJEo0aNRKtWrURGRoasBSdf9Msvvwg9PT1RpUoVYWRkJKZPny7MzMzEkCFDxLBhw4SlpaUICgqSPa625mtjYyPdmZOVlSWUSqU4ffq01B4eHq6RCsqDBw8W9evXf6U4rL6+voiKipI9XmmN+67kThzKli0rIiMjX1l/+fJllTIU2iL3fNXdVRkfHy/09fVli6Mpb/fUOioxHj16hCpVqgAAypYtCxMTE1SrVk1qr127Nu7du6ex+Dk5OYiOjn5lfXR0NPLy8jQWt6hZWFhgxowZOHz4cIGvX375RSNxExISUKFCBWnZ3t4e//zzD1JTU6WCbpqwYMECzJs3Dzdu3MD27dsxefJkLF68GEuXLsXixYuxcuVKbN26Vfa42ppvVlaWVDDUwMAApqamKqdr7e3t8d9//8ked9myZZg8eTL8/PywaNEi2bfPuMVDSkoKEhISXlmfkJCA1NRULYxIMy5duoRLly4BeF70Mn/50qVLuHDhAlatWqWRApuy03amRvIqW7asSt2Rlx/3ERkZqZGCdfnGjh0r7OzsxNy5c8Xx48fF8ePHxY8//ijs7e3F2LFjNRb3bcn1l1KzZs1e+8wtTT0rqnr16mL37t2vrE9NTRVeXl6iXr16GjnCYmJiolLk08DAQOUowN27d4WhoaHscbU13xo1aohDhw5Jy7t27VI5tXjq1ClRvnx52ePm+/fff0Xz5s3Fp59+Kh4+fFhkR1hKW9y3JfcRlp49ewo3Nzfx559/inv37ol79+6JrVu3iooVK2qkPte70sSR95droikUCmFqaipWrVolw4g1ixdz65i6devi7Nmz0kWmLz/u4+zZsxq9W+fHH3+Ek5MT5s6di4cPHwIAnJ2dMX78eHz11Vcai1vUunXrhmfPnqltd3Jykr2WEfD8Nts1a9agTZs2KuvNzc2xf/9+tGzZUvaYAJCRkaHySBYjIyMYGRmpLOfk5MgeV1vz7dq1Kx49eiQt+/v7q7Tv2LEDjRo10khsAChXrhz++ecfzJw5Ew0aNJD94lrGfTeffPKJrI8kWrZsGcaNG4du3bohOzsbwPPHx/Tv3x9z5syRLc776tGjhyw3/MTExEAIgUqVKuHMmTNwcHCQ2gwNDVGmTBnpxozijOUBdMyTJ0+gVCrV1vvYu3cvTExM0KxZM42PJSUlBQBkvcOusIYOHYrvv/9e43e9aUpiYiIePHiAWrVqFdiempqK8+fPw9vbW9a4enp6uH79OhwcHCCEgIuLC0JDQ6Vn+sXHx6NGjRqy332mrfm+ydOnT6Gnp6eSLGpKeHg4QkND0atXL9jY2Gg8XmmKm5ubq/IP9enTp5GZmQkvLy+NPGPuZenp6bh16xYAoHLlyjAzM9N4THXjCA8PR9OmTbUSv7hjokQ6jTsAebxcAV0IUeCyrpQlIN328OFDdOnSBadOncJHH32E7du3o2fPntizZw8AoGrVqggJCYGzs7OWR1o0Ll68iIYNG2r09/fWrVuYP38+rl69CgBwd3fH6NGjUblyZY3FlAtPvekgIQTu3LkDFxcX6OvrIysrC9u2bUNmZibatGmj8aMpW7duxebNmxEbG4usrCyVtvPnz2s09stu3rwJHx8fjewAnj17hvDwcNja2r7yeIuMjAxs3rxZIwUntRH3yJEjsm7vXWjrc36de/fuITg4GKtXr2bcEhg3KChIqvn2+++/o23bttDT08O9e/eQm5uLbt26Ydq0aRq9wPzcuXNq95PaLswrt/3796N9+/aoX78+PvroIwDAiRMnUKtWLezcuVNjp9Blo5Uro0hjoqOjRYUKFYRSqRRVqlQRt2/fFh4eHsLMzEyYmpoKe3t7cf36dY3FX7BggTA3NxcjRowQhoaGYvDgwcLX11dYWVlptH6TOhERERq52PfatWuiQoUK0sWKTZs2FQ8ePJDaNXXburbivqsZM2YUWDflXRXX+Wrqe8W4RRPX2dlZhIWFCSGE+O+//4RCoRD//POP1H7o0CFRqVIl2ePm27hxozAwMBBt27YVhoaGom3btqJatWrCyspK9OnTR/Z4NjY2r31ZWlpq9Odbv379AsuHBAUFaeSh1nLjESUdExQUhHr16mHnzp1YvXo1/P39Ua1aNYSFhSEvLw9dunTBd999h19//VUj8ZcsWYJffvkFX375JdauXYsJEyagUqVKmDx5Mp48eSJ7PFtb29e2a+pQclBQEGrXro1z584hKSkJY8aMwUcffYSQkBCNPj5EW3Hf1fTp0/H5558X+tlY2prvjh07Xtt++/Ztxi3BcRMTE6Xb0m1tbWFqaqpShqJKlSrSzSiaMH36dMybNw/Dhw+HhYUFFixYgIoVK2Lw4MEaOd2XmZmJoUOHok6dOgW23717F1OnTpU9br6rV69i8+bNr6zv168f5s+fr7G4stF2pkbycnBwEBcuXBBCCJGWliYUCoU4fvy41H7ixAnh6uqqsfgv3kbu4OAgIiIihBBCXL9+XSMFJ01NTcVXX30l1q5dW+Br6tSpGvlLqUyZMuLSpUvScl5enhgyZIhwdXUVt27d0tiRDm3FfVdy3V6srfnmH8Eq6Jbm/Bfjlty4rq6uKgVEg4KCxH///SctR0RECHt7e9nj5jM1NRUxMTFCCCFsbW2l7/iVK1eEk5OT7PGaNGki5s+fr7Zd00cMy5cvLzZv3vzK+k2bNgkXFxeNxZULC07qmLS0NOkoi5mZGczMzFT+QnFxcUF8fLzG4js5OUlHjlxdXXHq1CkA/3ebqNzq168PFxcX9O7du8BXhw4dZI8JPL9uRl///w7IKhQKLF26FO3atYO3tzeuX7+uU3G1RVvzdXZ2xl9//YW8vLwCX5q61o5xiyZu/fr1ERYWJi3PnDlT5eh0aGgo6tatq5HYAGBjYyMVlixXrhwiIyMBAElJSRopourv74+kpCS17ba2thq5zu+7777D06dPMXDgQAwaNAizZs3C8ePHcfz4ccycORODBw/GwIEDZY8rN5560zFly5ZFbGysdFpi9uzZKFOmjNSekJCg0Vtumzdvjh07dqBBgwbo27cvxo4di61bt+LcuXMaeSiutnYANWrUwLlz516pSZV/8Wf79u1lj6nNuNqirfl6eHggPDxcbaKtUCg0kvgzbtHE/fvvv1/b/uGHH2q05ETTpk1x8OBB1KlTB126dMHo0aNx+PBhHDx4EC1atJA93jfffPPadhcXF6xZs0b2uFOnTsWQIUPw7bffwsLCAnPnzsXEiRMBPP+3asqUKVp/4PFb0erxLJLd4MGDxYoVK9S2z5gxQ7Rp00Zj8XNzc0V2dra0vHHjRjFy5EixcOFC6UGmumD69OmidevWatuHDh2qkcrc2or7ruQ69aat+R47dkzs3btXbXtaWpoICQlh3BIaV9v+++8/cf/+fSHE833mjBkzRLt27URgYKDKg3JLuoKe8ZaSkiJSUlK0NKL3wzpKpUxMTAyMjY01csFgTk4Opk+fjn79+qF8+fKyb59KDgsLC1y8eBGVKlXS9lCICvTff//h0qVLqFevHmxtbfH48WOsWrUKmZmZ6NKli8aeYJCTk4MNGzbAz88Pjo6OGolRkKysLGzfvh1hYWGIi4sD8PxSiSZNmqBDhw4wNDSUPaZSqUR8fLxKRe6SiIkSycrc3ByRkZFSxeaioI0dwMsyMzMBoEgqNReHuG/Spk0brFq1SvaEvLR9zoyrGWfOnEGrVq2QkpICa2trHDx4EF26dIG+vj7y8vLw4MEDhIaGSo+CkpupqSmuXr2qcqedJt28eRN+fn548OABPD09pQQtPj4ep0+fRvny5bF3717pgepyUSqVsLKyUilOWxBN3BEtK+0e0CJNSEhIELNmzRIBAQGicePGonHjxiIgIEDMnj1bPHr0SKOx27dvL9auXavRGC+6ceOGqFSpkjA2Nhbe3t7i888/F59//rnw9vYWxsbGokqVKuLGjRsaiX3gwAHRunVrYW1tLT340draWrRu3VocPHhQIzG1GTcnJ0fcunVL5ObmCiGEyMjIEJs2bRIbN24UcXFxGotb2j5nxtV8XF9fXzFgwACRkpIi5syZI8qXLy8GDBggtfft21cEBARoJLYQQnh7e4vt27drbPsv8/X1FR06dBDJycmvtCUnJ4sOHTqIVq1ayR5XoVCIBQsWqL0rOf9V3PGIko45e/Ys/Pz8YGpqCl9fX5W/HA4dOoSnT59i//79+OCDDzQSf9myZZg6dSq6d+8ODw+PV55dJPfFty1btoSZmRnWr1//yjPlUlJS0KtXLzx79gz79++XNe66deswYMAAdO7cWeUQenx8PA4cOICtW7di1apV6Nmzp07EvXTpEj799FPEx8fD3d0de/bsQZs2bRATEwOFQgEDAwPs378fH374oaxxS9vnzLhFE9fW1hYnTpxAzZo1kZ2dDWNjY4SFhUkPOj5//jzat2+Pf//9V9a4+TZv3oyJEydi7NixBe4n5b7jztTUFGfOnEHt2rULbL98+TI8PT1lv+NOqVQiLi5O5YaiEknbmRrJy9PTUwwaNEjk5eW90paXlycGDRokGjdurLH4RV0PxcTERFy+fFlt+6VLl4SJiYnscatWrSoWLVqktn3x4sWiSpUqOhPXz89PdO7cWVy+fFmMHj1a1KxZU3Tp0kVkZWWJ7Oxs0aNHD+Hr6yt73NL2OTNu0cQ1MzOT6hgJ8erNB3fv3hXGxsayx82nbv+oqf2ks7Oz2Llzp9r2HTt2CGdnZ9njKpXKVy7mLomYKOkYY2NjcfXqVbXtV69e1egOoKhpawdgZGQkoqOj1bZHR0dr5HPWVlwbGxtx5coVIYQQT58+FXp6eioF+yIjI4WdnZ3scUvb58y4RRO3Ro0a4tChQ9Lyrl27xNOnT6XlU6dOifLly8seN9+dO3de+5Lbt99+K2xsbMRPP/0kLl68KOLi4kRcXJy4ePGi+Omnn4Stra0IDg6WPW5Bd72VRCw4qWOcnJxw5swZte1nzpzRyJ0Wx44de2OfkSNHyh53wIAB6NWrF+bNm4dLly4hPj4e8fHxuHTpEubNm4c+ffpg0KBBssetVasWVq1apbZ99erVrzzAtSTHFUJIhR9f/i8A6OnpIS8vT/a4pe1zZtyiidu1a1c8evRIWvb394eJiYm0vGPHDuk0nJyuXLkCAKhQoYLaV0GP+iis7777DkFBQZgzZw7q16+PsmXLomzZsqhfvz7mzJmDoKAgTJkyRfa4eXl5Jf+0G3jXm85ZvHgxvvrqKwwePBgtWrR45RqlFStW4Mcff8SwYcNkjWttbY2QkBDUr1+/wPaRI0di3bp1SElJkTUuAMyaNQsLFixAXFycdHeFEAJOTk4YM2YMJkyYIHvMkJAQtG3bFpUqVSrwWrDbt29j9+7daNq0qU7E9fX1hZubG6ZOnYpVq1bh119/xSeffCI91X348OG4fPnyWyXM76K0fc6MWzRx3+Tp06fQ09OT/e47FxcXnDhxQu1zCn/88Ud88803yMrKkjXui2JiYlTuDq5YsaLGYukKJko6aNOmTZg3bx7Cw8Olh8Lq6enBw8MDgYGB+Pzzz2WPOW7cOPz2228IDQ195RbT0aNHY9WqVdi9e7dGq90W9Q7gzp07WLp0KU6dOqUS18vLC0OGDNFYiQRtxD179ixat26NxMRE2NnZ4ciRI+jfvz/u3r0LpVKJxMRE7Ny5UyNVhUvT58y4RRdXG7788kuEh4fjxIkTr9QWmjt3Lr7++mv8+uuv6Nq1q5ZGSAVhoqTDsrOz8fjxYwCAvb09DAwMNBqvX79+OHz4ME6ePImyZcsCAMaMGYMVK1Zg165d8PHx0Wh80qz09HRER0ejevXqMDc3R0ZGBn7//Xc8e/YMLVu2RPXq1bU9RCJZ3Lt3D8HBwdIRU7nk5OSgXbt2iI+PR0hIiHSn7rx58zBhwgSsW7cO3bp1kzVmvocPH2Lp0qUIDQ3Fw4cPoVQqUalSJQQEBKBPnz7Q09PTSFxdwESJZJOXl4fOnTsjOjoax48fx7Rp07Bs2TKNHWnIp40dgLpTiGZmZhrd4WgrrrZoa75nzpyBh4eH2hiZmZn4+++/ZT86q624/D6runjxIho2bCgdkZfTs2fP4OvrCz09PRw4cADLli3DuHHjsHbtWvTo0UP2eABw7tw5+Pr6okqVKjAxMUFYWBi6deuGrKws7N+/H+7u7ti3bx8sLCw0Er/E09ZV5KQ5hw8fFj/++KMIDQ0VQgixbNky4eLiIuzt7cWAAQNU7u6QW2ZmpvD19RUODg7C1NRUo4XqhBDi7NmzwsrKSnh4eIiPP/5Y6OnpiZ49e4ovvvhCWFtbiyZNmmjkuUL5t/G+/DIwMBDVqlUTv/zyi+wxtRk3X36xyYLW3717V/Z42prvy7c1W1hYqNw+HhcXp5HbuLUVt7R9n//+++/XvubNm6eRzzlfUlKSqFevnnB3dxf6+vpi/fr1GoslhBAfffSRmDJlirT866+/Ck9PTyGEEE+ePBH169cXo0aN0ugYSjL9N6dSVJKsWLECQ4cORcWKFfG///0PwcHBmDZtGnr27AmlUonffvsNdnZ2mDlzpqxxFy5cKP1/s2bNcPz4cfj5+eHKlSvSnR4AZH9S9JgxYzB27FgEBwcDAH777TcsWrQIp06dQmJiIpo3b45JkyZhwYIFssY9cuRIgeuTkpIQHh6O8ePHQ19fH3379tWJuCkpKRgwYAB27twJS0tLDB48GMHBwdJf/QkJCahYsaLsf4Fra77ipQPtLy+rW1dS45a273NAQAAUCsVrP8s3PXbjfezYsUP6/6FDh2L06NEICAiAlZWVSpvchXnPnz+P9evXS8vdunVDv379EB8fD0dHR8yePRt9+vSRfT+pM7SYpJEG1KpVSyxcuFAIIcTevXuFvr6+Son4zZs3i8qVK8se183N7Y2vihUryh7XxMRE5S/u3NxcYWBgID1S48CBA6Js2bKyx32TVatWiQYNGuhM3FGjRolq1aqJLVu2iBUrVogKFSoIf39/kZmZKYR4fqRDoVDIHvdNNDXfl+u/vFyQUJNHdrQR90107ftctmzZ1z5C5MKFCxr7+b7ppYm4FSpUkM4wCCHEgwcPhEKhkM4uxMTE6FR9PbmxjpKOuX37tvTXyKeffgqFQqFSD8TT0xP37t2TPW5MTMwbX7dv35Y9bpkyZfDw4UNpOT4+Hjk5OdJFklWrVtXKAxe9vb1x8+ZNnYm7fft2LF++HJ07d8aAAQNw7tw5JCQkoF27dtKDTDXxF/ibaOtzLm107fvs4eGB8PBwte1vOtr0vvLy8t740sR1UQEBARgyZAj27duHI0eOoHv37vD29pZqR127dg3lypWTPa6u4Kk3HZORkaFSOM3IyEilFoiRkRFycnK0MTSNyN8BzJkzB0ZGRvj++++LxQ4gOTkZVlZWOhM3ISFB5Unn9vb2+Oeff+Dn54c2bdpg5cqVssd8G5r8nK9cuSLdri6EQHR0NNLS0gBAuptUl+K+jq59n8ePH4/09HS17VWqVFF7WrAk+uGHH/Dw4UO0a9cOubm58PLywq+//iq1KxQKzJgxQ4sjLN5415uO0dPTw/Xr1+Hg4AAhBFxcXBAaGirVIomPj0eNGjU08ldLamoqrl+/Lt0+fv78ecyfPx/Pnj1DQEAAunfvLnvMtLQ09O/fH3/99ZfKDqBSpUoAgAMHDiA5ORldunSRPbY62dnZ6NWrF7Kzs7F161adiFujRg389NNPaNOmjcr6tLQ0tGrVCk+fPsXly5c18r1SR5PzVSqVao8q5K9XKBSyz1dbcV9HF7/P2vbff//Bzs4OwPNSBCtWrMCzZ8/Qrl07jRbXzMjIQE5ODszNzTUWQxcxUdIx+TvafPk71peX5d7RHjt2DG3btkVaWhpsbGywceNGdO7cGeXKlYOenh6uXr2KZcuWYeDAgbLGzVfUO4COHTsWuD45ORlRUVFQKBQ4fvz4K8U3S2rcUaNG4eHDh9iyZcsrbampqWjZsiXOnj0r+/dKW/O9e/fuW/V78ShbSY5b2r7PL8s/fSx3Je6XXb58Ge3atcO9e/dQtWpV/PHHH/j000+Rnp4OpVKJ9PR0bN26FQEBARodR1HNV1cwUdIxR48efat+clfIbtq0KapWrYrvvvsOq1evxk8//YShQ4di+vTpAJ4f+t26dSsiIiJkjfuyotoBqLsLx9LSEtWrV0f37t01cspAW3ETExPx4MED1KpVq8D21NRUnD9/XvbvlbbmW9qUtu8zABw8eBDz5s1DWFiYVM/J0tISXl5eCAwMhK+vr+wxW7duDX19fakC965du+Dn54cVK1YAeP6op/DwcJw6dUr22NqYr84o+uvHSZPepmZQSEiI7HGtrKzE1atXhRDPaykplUoREREhtd+4cUOYm5vLHleI53e2tW7dWlhbW0t1WKytrUXr1q01Vsfp/v37b+yzceNGnYmrLdqar7r6OiEhIeLBgweyx8vn6uoq+vTpI9atWydiY2M1Fudlpe37vHbtWqGvry+6du0q1qxZI/bs2SP27Nkj1qxZI7788kthYGCgkdpGdnZ24uLFi0IIIVJTU4VCoRDnzp2T2q9evSqsrKxkj6ut+eoKJko6xtvbW2RkZKhtDwkJ0UjCoq3bmrW1A6hVq5ZITExU275x40ZhYGCgM3GFEOLp06fi+PHjIioq6pW2Z8+eiXXr1skeU1vzfdPt2926dRPp6emyxw0ODhbe3t7C2NhYKJVKUblyZTFgwACxYcMG8fDhQ9nj5Stt3+eqVauKRYsWqW1fvHixqFKliuxxtbWf1NZ8dQUTJR1Tu3Zt0b59+wIrKB89elSYmZmJESNGyB5XqVSKR48eScsWFhbi9u3b0rKu7QCaNWsmGjduXOA/lps2bRL6+vpi9uzZOhP32rVrokKFClKi0LRpU5UjK5r6+WprvuokJSWJQ4cOiRo1aoiJEydqLE5GRoY4dOiQmDx5smjatKkwMjISSqVS1KhRQwwbNkz2eKXt+2xkZCSio6PVtkdHR2ukrpBCoVDZT5qbmxfJflJb89UVTJR0zP3790WlSpVEz549VdYfO3ZMWFhYaGQnK8TzHUCdOnVEgwYNRIMGDYSenp6oVauWtFynTh2d2gGkpqYKDw8P0bJlS5GVlSWt37x5szA0NBQzZ86UPaY24wYEBAh/f3+RkJAgbty4Ifz9/UXFihWlx5Zoagevrfm+yd69e0X16tWLLN6TJ0/E//73P2FpaalTn7O24jZs2FCMHz9ebfuECRNEw4YNZY+rUChEmzZtxGeffSY+++wzoa+vL1q1aiUtt2nTRiM/X23NV1fwYm4ddOvWLXzyySfo0qULFixYgNDQULRu3Rrdu3fHsmXLNBJz6tSpb9Uv/1EjcvHw8ECLFi0we/bsAtuDgoLwzz//vLa43PtKSEhA06ZNUbt2bWzevBl//vknunXrhilTpuCbb76RPZ424zo6OuKff/5BnTp1ADy/e3LYsGHYs2cPjhw5AjMzM5QtW1Yjt61r63N+nTt37qB27dpSfSO5ZWVlISwsDCEhIQgJCcHp06dRrlw5NG3aFN7e3ujVq5fsMUvT9zkkJARt27ZFpUqV4OvrC0dHRwDPy6ccOnQIt2/fxu7du2W/Vf9tH8WyZs0aWeNqa746Q8uJGmnIxYsXhY2Njejdu7ewtLQUAwcO1PaQNOLIkSPCzMxM1KlTR4wdO1bMnDlTzJw5U4wdO1bUrVtXmJubi6NHj2osfmxsrHB1dRUtWrQQhoaG4vvvv9dYLG3GtbCwEFeuXHll/fDhw0X58uXFsWPHNPpoDW19zuocOnRIVK1aVfbtTp06Vfj4+AhTU1NRs2ZNMXjwYLFhw4a3uuhZDqXl+yzE88d2TJgwQTRt2lRUq1ZNVKtWTTRt2lQEBQWJmJgYjccvaqVtvnLiESUdk3/bJwCcOHECn332GQICArB8+XKVekr5j/iQy6NHj1CmTBm17Tk5OTh//rzK41TkcufOHSxduhSnTp2SKho7OTnBy8sLQ4YMkYptyunSpUvS/0dHR6NXr17o0KED/ve//6n0q1u3rk7EbdSoEUaOHImePXu+0jZixAj8/vvvSElJkf2Ikrbm+zoRERHo168fvL29MW/ePFm3rVQq4erqiq+//hpdunSRihJqWmn7Phdnb9qXUtFjoqRjCio4Cfzfc7iEhgpO6unp4eHDh9IveJ06dbBnzx64uLgAeH6IV1OnZrThxQrKL1ZSfvn/NVm5uSjjzpgxA8ePH8eePXsKbB82bBiWLVuGvLw8WeNqa742NjYFPrsuPT0dOTk5aNmyJTZv3iz7Hxz79+/HkSNHEBISggsXLqBatWpo1qwZvL294e3tDQcHB1nj5Stt3+cX/6B8kZmZGfT09GSN9SJTU1PcvXtX+jn6+/tj5cqVcHZ2BqC5/aS25qsrmCjpGG0VnFQqlYiLi5MSJQsLC1y8eFF6lEh8fDycnZ1l/4dUWzuA0la5WVu0Nd9169YVuD6/EKK7u7us8QqSmpqK48eP4+jRozhy5AguXryIKlWqwMfHB4sWLZI1Vmn7Pr/8B2U+PT09VKxYEePGjdPIUwS0tZ/U1nx1BRMlksXb7AA08ZcSdwDawUcgFJ3c3FycOXMGO3bswJIlS5CWlqYzR2a1Rd0flElJSQgPD8fChQsxb968t774+m1paz+prfnqCn1tD4CK1vnz5zF58mTs2rVL20ORhbonfOfvAMaPHw99fX3ZdwCPHz9Genq6yl+6UVFR+PHHH5Geno6AgAB069ZN1pjajAto5xEI2pzv62jy9ygvLw/nzp2TTsGdOHEC6enpKF++PD777DP4+PjIHrO0fZ9fd0S9Q4cOcHNzw88//6wziUNpm6/siuyycSoy+/btE1999ZWYOHGiVPX16tWrokOHDkKpVIrWrVvLHlOpVIqbN2+K5ORkkZSUJCwsLMTFixdFcnKySE5OFtevX9foXVHqrFq1SjRo0ED27Xbt2lUEBgZKy/Hx8cLGxkbUqlVLtG/fXmMVwbUVV1sV0LU1XyG083v06aefCktLS6FQKES5cuVEjx49xMqVK1WqN2tCafs+v8nNmzeFhYWF7NvVVmHeN9HUfHUFEyUds3LlSqFQKISdnZ1QKpXCwcFB/Prrr8La2loMHjy4wFu85ZBfsTn/pW65qGlqB+Dm5qbyzLw5c+aIypUri+zsbGnZ09NTZ+JqqwK6tuarrd+jrl27iuXLl4vr169rZPvqlLbv85uEh4eL8uXLy75dhUIhrK2thY2NjbCxsREKhUJYWVlJy/nPqyxqmpqvruCpNx2zYMECzJo1C+PHj8eff/6JLl26YMmSJbh8+TLKly+vsbjqToFpW3JyskaePh4XF6dSduDw4cPo2LEj9PWf/0q1b98eM2bM0Jm4sbGxrz211qJFC3z11Veyx9XWfLX1e7Rx40aNbft1Stv3+XWys7MxZ84ceHp6yr5tuQtJykGT89UVTJR0zK1bt9ClSxcAkHY4c+bM0ejOHZD/Ljo5aHIHYGlpiaSkJOnaijNnzqB///5Su0KhkC541oW4tWrVwqpVq9RWQF+9erVG7gTT1ny19Xu0fv36t+ond2Xu0vZ97tixY4Hrk5OTERUVBYVCgePHj8set3fv3rJv821oa766gomSjnn27BlMTU0BPN/JGBkZSTU6NOnFwnEvsrKygqura4F3pslBWzuAxo0bY+HChVixYgX++usvpKamonnz5lL79evXpRpSuhB37ty5aNu2Lfbt2/faRyDITVvz1dbvUZ8+fWBubg59fX2pjtDLFAqF7IlSafs+qzvK7OLigk6dOqF79+4aORKtrXIm2pqvrmCipINWrlwJc3NzAM8rYq9duxb29vYqfUaNGiVrzPr166sUicunUChgbGyMMWPG4LvvvpN9Z6CtHcD333+PFi1a4LfffkNOTg6++eYb2NjYSO1//PGHRo6yaStus2bNEBkZWWAF9NatW2usArq25gto5/eoZs2aiI+PR48ePdCvX78iq0hd2r7P06ZNQ9myZV/b548//kDXrl1ljWttba2Vcibamq+uYB0lHePm5vbGozcKhQK3b9+WNa66wnH5t+l/++23GDt2LMaNGydr3AcPHmhtB/D48WOcOHECTk5Or5ze2717N9zd3VGxYkWdiast2pivtn6PAOD06dNYvXo1Nm3ahCpVqqB///7o3r277FXAX1aavs+1a9dGaGgorK2tC2z/448/0KtXL2RlZckaV1v1jLQ1X52h3WvJqbTYsmWLqF27tuzbrVWrlkhMTFTbvnHjRmFgYCB73HdVu3ZtERsbW2Lj5pd5ePmVk5Mjwyjlo63PWROePn0q1q1bJ5o1ayZMTU1Ft27dREZGhraHJYQo+d/nZs2aicaNG4v09PRX2jZt2iT09fXF7NmzCx3nXWmqnElxnW9JwUSplCuqHd7t27eFmZmZ7NstKTsAc3NzjdfC0WTcl8s95L8MDAxEtWrVxC+//CLDaAtPW5+zJn+Pjh49Kpo1ayaUSqV48uSJRmK8q5L+fU5NTRUeHh6iZcuWIisrS1q/efNmYWhoKGbOnFnoGO9DU+VMiut8Swpeo1TK3blzB9nZ2RqPExcXp5EHeu7cuRPNmjVDQEAAdu/eDQMDAwDAli1b0LNnT/zwww8YP3687HFLG21VQC8p5P49un//PtatW4c1a9YgPT0dPXr0wNKlS1Wu36H3Z25ujr1796Jp06bo1q0bNm/ejD///BPdu3fHlClTEBQUpJVxaaqcSXGdb4mh7UyNtKso/jJ89OiRaNGihejbt6/Gtl+jRg3RuXNnkZeXJ7Zs2SIMDAzEtGnTNBLvfZT0v8DfRFOnDN5VSf+cN23aJD799FNhYmIiAgICxN9//13sTm8KUfI/53yxsbHC1dVVtGjRQhgaGorvv/9etm2/q6ysLNG1a1fRqVMnjcUoTvMtSXhEiWTRoEGDAi9+TU5Oxr///ovq1avjt99+00hsBwcHHDhwAB9//DFatmyJ48ePY/Lkyfjmm280Eo9e5e3tjTFjxmh7GCVe165d4erqirFjx8LR0RF37tzB4sWLX+kn9912pc2L5UzmzJmDXr16ISAgAO3bt1dpk/uuQ22VM9HWfHUFEyWSRUBAQIHrLS0tUb16dfj5+WmkTgh3AMWDpk4ZlDb5Ncc2bNigto9CoWCiVEgvljPJ/++WLVuwdetWqcSJQqFAbm6urHG1Vc5EW/PVFUyUSBbBwcFaiautHUBMTIxWbsHXVtzX0WQF9OI4X026c+eOVuKWtu9zTExMkccEtPcIE23NV1cwUdIxxfUflocPH2LatGlYtGiRrNvV1g6gcuXKqFChAnx8fKTX6x5vsXz5cqmadUmMq61TBtqab3H9PXpZnTp1sGfPnkJXry5t3+f8R6YUNxkZGVi0aJHs9eaK63xLChac1DFKpfKddjwbNmxAhw4dYGZmVujYUVFROHLkCAwNDfH555/D2toajx8/xrRp07Bs2TJUqlQJUVFRhY5THISEhEiv06dPIysrC5UqVULz5s2lz12OHXpxiavubrb8U6uaOmWgrflq8/foXVhYWODixYuoVKlSobZT2r7Pjx8/Rnp6ukoCERUVhR9//BHp6ekICAhAt27dZI8LAAkJCTh9+jQMDQ3RokUL6OnpITs7G0uWLMGMGTOQk5ODx48fyxpTm/PVCVq6iJw05MiRIyI4OFh4e3sLY2NjoVQqRZUqVcSgQYPExo0bRVxcnEbi/v3338LAwEAoFAqhUChE5cqVxeHDh4W9vb3w8/MTe/fu1UjchIQEcefOHZV1kZGRok+fPqJLly7i999/10jcFz179kwcOnRIfPvtt+KTTz4RRkZGQqlUCnd3d52Je//+/Tf22bhxo+xxX1SU89XW79G70sTdZ6Xh+9y1a1cRGBgoLcfHxwsbGxtRq1Yt0b59e2FgYCDWr18ve9zjx48LKysrqS5Zo0aNRFRUlKhataqoWbOmWLp0qXj69KnscbU1X13BREmHFeWO58MPPxRjxowRqampYt68eUKhUIjatWuLM2fOyB7rRcVpB5CZmSkOHz4sxo8fLywtLYVSqdSZuMWpAnpRf87aShzehiZv09fl77Obm5sICQmRlufMmSMqV64ssrOzpWVPT0/Z43p7e4svv/xSXL58WYwbN04oFApRrVo1sWXLFtljvUhb89UVTJRKgaLY8VhaWoobN24IIYTIyckRenp64uDBg7LHeZk2dwCZmZni6NGjYsqUKaJZs2bCxMREVKtWTQwYMECsX79e3L17V2fiarMCurY+54LGoY3E4XXkTJRK0/fZ2NhY5Uh069atxfjx46Xla9euCVtbW9nj2traiqioKCHE80fUKJVKsX37dtnjvExb89UVTJR0kDZ2PAqFQsTHx0vLRVWQTls7AB8fH2Fqaipq1aolhg0bJjZu3CgePHgge5ziEldbj0DQ1nyFKD4J2uvI9XtW2r7PZcqUEREREdKynZ2d2Lp1q7R8/fp1jTxyqaD95M2bN2WP8zJtzVdX8K43HdO8eXOcPn0aFStWhLe3NwYPHowNGzbA2dlZ47H3798vXdCbl5eHQ4cOITIyUqVP+/btZY1paWmJpKQk6SLFM2fOoH///lK7QqFAZmamrDEB4Pjx43B2dkbz5s3RrFkzeHt7w87OTvY4xSWuth6BoK35auv3SFt325W273Pjxo2xcOFCrFixAn/99RdSU1PRvHlzqf369euFvpNQnStXriAuLg4AIITAtWvXkJ6ertJH7rpv2pyvTtB2pkby0tfXFy4uLmLkyJHizz//FI8fPy6SuPkXcb/upYlTFe3btxf9+vUTubm5YsuWLcLQ0FDlwaG7du0SNWrUkD1uWlqa2Lt3rwgKChKNGjUShoaGonbt2mL48OFiy5Yt4tGjR7LH1GbcfEX9CARtzVebv0dubm6ib9++Yv369eLevXuv7f/777+LtLS0Qsctbd/nixcvCnt7e2FoaCiUSqWYNGmSSnuPHj3E4MGDZY+bvx9Ut3/U1H5SW/PVFSwPoGPS09Nx/PhxhISE4MiRI4iIiEC1atXg7e0t/cWmiYfTasulS5fQokULpKSkICcnB9988w2+//57qb1nz54wMzPDsmXLNDqO1NRUhIaG4siRIwgJCcHFixdRtWrVV46oldS4L1Y5j46ORq9evdChQwf873//U+mn6QroRTVfbf0eaet2+Zfp+vcZeH7L/IkTJ+Dk5PRKsdTdu3fD3d1d9qN7d+/efat+mqh7pI356gomSjpOWzu8N/H398fKlStlOZVRHHYAeXl5OHv2LI4cOYIjR44gNDQUGRkZGn8kQFHFVSqVr1RAB/DK/+vKfF+mjd+jjIwMnDx5Ukqczpw5g+zsbNSoUUPj9ch0/fv8ruQq7Pmuhg0bhu+++w729vZFGldb8y2umCjpuOK645GrUN67kmsHkJeXh3PnzklHHE6cOIH09HSUK1dOpUih3H8Zaiuutv4S1tZ8CxqHtn6PsrKycOLECezduxfLly9HWlqa7HFL2/f5XWlrf2VpaYmIiIgij6ut+RZXvJhbx7xpx7N48WL4+Phoe5hac+fOHWRnZxd6O9bW1khPT4eTkxN8fHwwb948NGvWDJUrV5ZhlMUvrrb+odLWfLX5e5SVlYVTp05JR69Onz4NFxcXNG3aFIsWLYK3t7fsMUvb97mk4HGM4oGJko7hjqdozJkzBz4+PqhWrVqpiKutRyBoa77a+j3S1t12pe37TPQueOpNxyxfvrxE7Hi0dWiXh5Tfz5dffomyZcti7ty5AIBHjx6hRo0aKFu2LCpXroy9e/di1apV6Nmzp5ZHKg9t/R4ZGBjA2dkZAQEBRXq7PL1eadtfcT+pSqntAZC8Bg8eXOyTJCp5Tp06pVIDa/369bC1tUVERAT+/vtvTJ8+HYsXL9biCOWlrd+jpKQk/PLLLzA1NcWsWbNQtmxZ1KlTByNGjMDWrVuRkJBQ5GMiKu2YKBHRG8XFxcHNzU1aPnz4MDp27Ah9/edn79u3b48bN25oaXS6w8zMDJ9++ilmzpyJ06dP4/Hjx5g9ezZMTU0xe/ZslC9fHrVr19b2MEu8mJgYbQ+hSJW2+cqNiRJpxTfffANbW9tCb4c7gKKRXwE935kzZ1RKMWiqAnppZ2ZmBltbW9ja2sLGxgb6+vq4evWqtodV4lWuXBkVK1ZEv3798Ouvv+Lff/99bf/ly5cXSf2ql/Xo0QOWlpaF3k5JmW9xxWuUqEgkJiZi586d6NWrl6zbVSqVqFChgsqtxOXLl1fbf8OGDejQoQPMzMxkHYeu69ChA+zt7aVHIHTv3h1xcXGwsbEB8Lxe1bhx4/iPeCGVlNvlSzptF/bMy8uDUvnqcYq8vDz8+++/cHV1lTWetudb0jFRoiJx8eJFNGzYUPb6L9wBFI3iUgFd11laWqrcbefj48O7VjWsKAt7pqSkYMCAAdi5cycsLS0xePBgBAcHQ09PDwAQHx+PsmXLarQ+lzYLmZZUTJRIFikpKa9tv3TpEry9vbkDKMGKQwV0XVdS7lrVRUVR2HP06NHYt28fpk2bhqSkJPzwww+oXbs2/vrrLxgaGiI+Ph7Ozs7Iy8uTNW5BimK+uoKJEski/xEX6uQ/+kJXKhnT6/ERCFTcva6wZ9OmTeHt7S37KbAKFSpg3bp1aNasGYDnf3z4+/vD2toaO3bsQFJSksaOKGljvrqCiRLJwsrKCv/73/9eOdKQ78aNGxg8eDB3AKUE67BQcfZyYc9PPvkE3t7eGi/saWpqiqioKJUjr6mpqfDz84OJiQlWrlyJKlWqyL6f1NZ8dQUrc5MsGjZsCABqH69gbW2tkXL82qpkTEQl1/Hjx+Hs7IzmzZsXaWFPV1dXXL16VSVRsrCwwIEDB9CqVSt89tlnGomrrfnqCpYHIFl069YNRkZGatudnJwQHBwse9zjx4/Dzs4OzZs3R4sWLdCyZUsmSUT0Wtoq7NmyZUusWbPmlfXm5ubYv38/jI2NNRKXhUwLh6feSBYxMTFauZA3PT0dx48fl26njoiIQLVq1eDt7S395eTg4FDk4yrteOqNSpLU1FSEhoZKp+8vXryIqlWrIjIyUtY4Fy5cgJGREdzd3dWO4/z58xp58PHLcYpivrqCiRLJ4l3rGWkKdwDFAxMlKkny8vJw9uxZHDlyBEeOHEFoaCgyMjJkv1aouOwni2q+uoLXKJEsDh8+LN2Wv3HjRq3VM2IlY83Q1hFDIk14U2HPxYsXw8fHR/a42tpPamu+uoJHlEh2RVnPiJWMiwYroJMuKQ6FPYtyP1kc5luSMVEijSmKekbcARQNVkAnXVKcCnsWxX6yOM23JGKiRLLRRj0j7gCKHiugE70/1n0reZgokSxY0Kz0YQV0onfD/WTJxESJZGFgYABnZ2cEBASwoJmO4l/CRIXD/WTJxESJZMF6RrqNfwkTFR73kyUTEyXSCNYz0i38S5hIftxPlgx8hAlpBOsZ6RY+AoFIftxPlgw8okSyYD2j0oV/CRO9O+4nSyZW5iZZWFtbq9QzmjdvHusZ6TD+JUz07rifLJl4RIlkwXpGuo1/CRMVHveTJRMTJSJ6I1ZAJ6LSiokSEb0R/xImotKKiRIRERGRGiwPQERERKQGEyUiIiIiNZgoEREREanBRImIiIhIDSZKRFTiXLt2De3atYO9vT0sLS1Ro0YNzJo1CwDg5uaG7du3azS+QqFARESERmMQUfHARImIShx/f3/Uq1cPsbGxSExMxJ9//olKlSq91XtzcnKg7Zt9s7OztRqfiN4eEyUiKlEeP36MW7duYfDgwTA1NYWenh5q1aqFLl26oEuXLoiNjcWXX34Jc3NzDBkyBMDzI0CLFi1C7dq1YWZmhrS0tFeOCs2fPx/NmjWTluPi4tCjRw84OzvD2toaTZs2xbNnz9CoUSMAQJMmTWBubo7p06cjJCQE1tbWKuMMCAjAlClTAEBqX7p0KVxdXdGkSRMAwD///INGjRrB2toatWrVwo4dO6T3Hzx4EHXr1oWFhQUcHR0xdOhQ+T9MInojPuuNiEoUOzs7VK9eHX379sWgQYPg6ekpPTply5YtcHNzw/z58xEQEKDyvg0bNuDAgQOws7ODgYHBa2Pk5eWhXbt2qFWrFq5cuQILCwucOnUKSqUSZ86cgUKhwMmTJ1G/fn0AzxOhN0lNTcXFixcRHR0NALh06RK6dOmCP//8E82aNcPJkyfh7++PM2fOoHr16ujduzdmzZqFnj17Ij09HRcvXnznz4qICo9HlIioRFEoFAgJCUG9evUwdepUVKpUCe7u7jh48OBr3zdhwgSULVsWRkZGUCpfv+s7e/Ysrl69iqVLl0oP/f34449hZGT03uPOy8vDzJkzYWpqClNTUyxfvhx9+vRB8+bNoVQq8fHHH6Nt27bYvHkzAMDAwAA3b95EQkICzMzMpKNQRFS0mCgRUYnj5OSEuXPnIioqCgkJCWjdujU+++wzPHnyRO17XF1d33r7d+/eRbly5WBiYiLHcAEAFhYWKqfn7ty5g2XLlsHa2lp6/f3333jw4AEAYNu2bYiMjET16tXRoEEDKYEioqLFRImISjRbW1tMmTIF6enpiImJUXu06OX1ZmZmePr0qbT88OFD6f8rVKiA+/fvIyMjo8BtKRQKlWVzc3M8e/ZM5SLxF7dXUHwXFxeMHj0aSUlJ0istLQ1Lly4FADRs2BB//vknHj9+jG+//RbdunVDfHy8uo+BiDSEiRIRlSiJiYmYNGkSoqOjkZubi6dPn+Knn36Cra0tatSoAUdHR9y6deuN22nYsCF+/fVX5OTkICIiAr/++qvU9uGHH6J69eoYNmwYkpKSkJOTg9DQUGRmZgLAKzGqVasGAwMDbNiwAbm5udi4cSMuXLjw2viDBw/GmjVrcOTIEeTm5iIzMxNhYWG4evUqsrKy8OuvvyIxMRFKpVI6EqWvz8tKiYoaEyUiKlEMDQ1x//59tGnTBlZWVnB1dcWJEyewd+9emJmZ4ZtvvsGiRYtgbW2NYcOGqd3Ozz//jLCwMFhbWyMoKAi9e/eW2pRKJXbu3ImnT5+ievXqsLe3x6RJk5CXlwcA+P777zFq1CjY2Nhg5syZsLS0xIoVK/D111/Dzs4OJ06cgJ+f32vn0aBBA2zcuBGTJk2Cg4MDypUrh2+//VZKxjZs2IAqVarAwsICI0eOxIYNG2BnZyfDJ0hE70IhtF1QhIiIiKiY4hElIiIiIjWYKBERERGpwUSJiIiISA0mSkRERERqMFEiIiIiUoOJEhEREZEaTJSIiIiI1GCiRERERKQGEyUiIiIiNZgoEREREanBRImIiIhIDSZKRERERGr8P9A7o4u1+0okAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 629.921x314.961 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"df = pd.read_excel(folder / \"Overflow_Results.xlsx\")\n",
"structure_columns = df.columns[4:18]\n",
diff --git a/Optimization of control rules/readme.md b/Optimization of control rules/readme.md
index 8403a43ee1861feadea210574f79b2153e320134..25ab6bdccaabf97e8f043ae3e0de5848b2f45194 100644
--- a/Optimization of control rules/readme.md
+++ b/Optimization of control rules/readme.md
@@ -13,6 +13,10 @@ a list of the parameters to be optimised (config_parameters.xlsx)
and a list of the structures (structures.xlsx) for which overflows are to be minimised.
The files should be saved in the same folder as this script, as relative paths are used.
+Evalutation_resuts.ipynb can be used to read the res1d result files of the optimisation.
+The script needs information about the Wiers (Retention_storage.xlsx) and Events (Events.xlsx).
+The discharge over structures is saved in an Excel file and visualised in a figure.
+
# equal-filling-degree (EQF)
no optimization was used for the implemented EQF model but a script was used to get the storage curves.