{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Res1D - Long Term Statistics (LTS) statistics Res1D files"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mikeio1d"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## LTS event statistics results"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Read entire LTS event statistics file into a data frame."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"res1d_events = mikeio1d.open(\"../tests/testdata/lts_event_statistics.res1d\")\n",
"df_events = res1d_events.read()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the full data frame. Here the integer index of the data frame specifies label of the event. The events are ordered from having the largest value to smallest value.\n",
"So the smallest integer label represents an event with largest value. Note that entries with delete value $-1\\times 10^{-35}$ specifies that there is no event."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WaterLevelMaximum:A0.0327</th>\n",
" <th>WaterLevelMaximumTime:A0.0327</th>\n",
" <th>DischargeIntegrated:A0.0327</th>\n",
" <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",
" <th>Component_1MaximumTime:Pump:B4.1510p2:0</th>\n",
" <th>Component_1MaximumTime:Pump:B4.1510p2:80.0006</th>\n",
" <th>Component_2Maximum:Pump:B4.1510p2:0</th>\n",
" <th>Component_2Maximum:Pump:B4.1510p2:80.0006</th>\n",
" <th>Component_2MaximumTime:Pump:B4.1510p2:0</th>\n",
" <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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The available quantities in LTS event statistics result file are"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The quantities ending with label **'...Time'** give the time of the correspoding quantity **'...'**. For example, quantity **'WaterLevelMaximum'** has the event time quantity **'WaterLevelMaximumTime'**. For example, water level maximum event statistics for node **'B4.1200'** can be retrieved as:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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\"]]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Additionally, for flow type quantities like **Discharge** there are these LTS event quantities:\n",
" * **DischargeMaximum** - maximum discharge of an event\n",
" * **DischargeMaximumTime** - time of the maximum discharge\n",
" * **DischargeDuration** - duration of the flow event\n",
" * **DischargeDurationTime** - time when the duration is started to be calculated\n",
" * **DischargeIntegrated** - event integrated over the whole duration\n",
" * **DischargeIntegratedTime** - start time of the flow event\n",
"\n",
"Let's see the discharge statistics at the start of the pipe (reach) **B4.1200l1:0**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that entries with the same integer label for **DischargeIntegrated**, **DischargeMaximum**, **DischargeDuration** does not represent the same event."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the water level maximum for pipe (reach) **B4.1200l1** having chainages **(0, 479.999)** and connecting nodes start/end nodes **B4.1200**/**B4.1485**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_events[\n",
" [\n",
" \"WaterLevelMaximum:B4.1200\",\n",
" \"WaterLevelMaximum:B4.1200l1:0\",\n",
" \"WaterLevelMaximum:B4.1200l1:479.999\",\n",
" \"WaterLevelMaximum:B4.1485\",\n",
" ]\n",
"].plot();"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## LTS chronological statistics results"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Read entire LTS monthly statistics file into a data frame."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"res1d_monthly = mikeio1d.open(\"../tests/testdata/lts_monthly_statistics.res1d\")\n",
"df_monthly = res1d_monthly.read()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"For monthly/yearly statistics the time index has value for every month/year."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"For flow type quantities like **Discharge** there are these LTS monthly (similary to yearly statistics) event quantities:\n",
" * **DischargeIntegratedMonthly** - discharge integrated over the whole month\n",
" * **DischargeIntegratedMonthlyCount** - number of events within a month\n",
" * **DischargeIntegratedMonthlyDuration** - duration of all events in a month\n",
"\n",
"Let's see the discharge chronological statistics at the start of the pipe (reach) **B4.1200l1:0**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <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)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Some plots of LTS dsicharge monthly statistics"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DischargeIntegratedMonthly:B4.1200l1:26.6666\n"
]
},
{
"data": {
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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();"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DischargeIntegratedMonthlyCount: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[1])\n",
"df_monthly[quantities[1]].plot();"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"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();"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}