Res1D - Long Term Statistics (LTS) statistics Res1D files¶

In [1]:

import mikeio1d

LTS event statistics results¶

Read entire LTS event statistics file into a data frame.

In [2]:

res1d_events = mikeio1d.open("../tests/testdata/lts_event_statistics.res1d")
df_events = res1d_events.read()

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. 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.

In [3]:

df_events

Out[3]:

	WaterLevelMaximum:A0.0327	WaterLevelMaximumTime:A0.0327	DischargeIntegrated:A0.0327	DischargeIntegratedTime:A0.0327	DischargeMaximum:A0.0327	DischargeMaximumTime:A0.0327	DischargeDuration:A0.0327	DischargeDurationTime:A0.0327	Component_1Maximum:A0.0327	Component_1MaximumTime:A0.0327	...	Component_1MaximumTime:Pump:B4.1510p2:0	Component_1MaximumTime:Pump:B4.1510p2:80.0006	Component_2Maximum:Pump:B4.1510p2:0	Component_2Maximum:Pump:B4.1510p2:80.0006	Component_2MaximumTime:Pump:B4.1510p2:0	Component_2MaximumTime:Pump:B4.1510p2:80.0006	Component_1TransportIntegrated:Pump:B4.1510p2:40.0003	Component_1TransportIntegratedTime:Pump:B4.1510p2:40.0003	Component_2TransportIntegrated:Pump:B4.1510p2:40.0003	Component_2TransportIntegratedTime:Pump:B4.1510p2:40.0003
0	-1.000000e-35	1957-01-01	4684.849609	1959-08-15 09:36:24	1.256756	1961-06-13 15:58:24	9.108004	1957-07-20 09:39:20	-1.000000e-35	1957-01-01	...	1961-09-05 15:54:40	1957-06-11 10:27:52	5.000000e+01	50.0	1961-09-05 15:54:40	1957-06-11 10:27:52	67.307213	1961-09-05 14:31:28	33.653606	1961-09-05 14:31:28
1	-1.000000e-35	1957-01-01	4608.099609	1961-09-05 12:17:20	1.236605	1961-09-05 15:02:24	6.788862	1957-06-11 04:12:20	-1.000000e-35	1957-01-01	...	1957-07-20 17:42:00	1957-07-20 16:33:04	5.000000e+01	50.0	1957-07-20 17:42:00	1957-07-20 16:33:04	45.068405	1961-06-13 15:44:16	22.534203	1961-06-13 15:44:16
2	-1.000000e-35	1957-01-01	3744.287842	1961-06-13 15:38:24	0.958780	1959-08-15 13:38:08	5.560763	1959-08-15 09:36:24	-1.000000e-35	1957-01-01	...	1958-05-26 00:36:16	1958-05-26 00:44:24	5.000000e+01	50.0	1958-05-26 00:36:16	1958-05-26 00:44:24	43.389675	1958-05-25 23:16:16	21.694838	1958-05-25 23:16:16
3	-1.000000e-35	1957-01-01	3631.110596	1958-05-25 21:11:20	0.941482	1958-05-25 23:35:16	4.831801	1961-09-05 12:17:20	-1.000000e-35	1957-01-01	...	1961-06-13 18:35:28	1961-09-05 15:54:56	5.000000e+01	50.0	1961-06-13 18:35:28	1961-09-05 15:54:56	35.251358	1959-08-15 13:26:56	17.625679	1959-08-15 13:26:56
4	-1.000000e-35	1957-01-01	3070.262939	1957-07-20 09:39:20	0.878175	1957-07-27 16:30:04	4.549961	1958-05-25 21:11:20	-1.000000e-35	1957-01-01	...	1959-08-15 16:23:04	1959-08-15 16:04:08	5.000000e+01	50.0	1959-08-15 16:23:04	1959-08-15 16:04:08	23.928904	1957-06-04 15:09:51	11.964452	1957-06-04 15:09:51
5	-1.000000e-35	1957-01-01	2337.491455	1957-06-11 04:12:20	0.830876	1962-07-21 15:19:28	4.169593	1958-05-24 19:07:20	-1.000000e-35	1957-01-01	...	1957-01-01 00:00:00	1961-06-13 18:10:56	-1.000000e-35	50.0	1957-01-01 00:00:00	1961-06-13 18:10:56	23.925802	1962-07-21 14:31:28	11.962901	1962-07-21 14:31:28
6	-1.000000e-35	1957-01-01	2319.675293	1957-06-04 14:28:20	0.763936	1959-08-15 10:02:00	4.119199	1958-11-10 13:35:20	-1.000000e-35	1957-01-01	...	1957-01-01 00:00:00	1958-11-10 16:18:44	-1.000000e-35	50.0	1957-01-01 00:00:00	1958-11-10 16:18:44	21.306870	1957-07-27 16:17:58	10.653435	1957-07-27 16:17:58
7	-1.000000e-35	1957-01-01	2229.712158	1962-07-21 13:50:24	0.742085	1957-06-04 15:22:01	3.769463	1957-09-07 22:18:20	-1.000000e-35	1957-01-01	...	1957-01-01 00:00:00	1962-06-19 15:37:52	-1.000000e-35	50.0	1957-01-01 00:00:00	1962-06-19 15:37:52	18.159843	1959-08-15 09:49:36	9.079922	1959-08-15 09:49:36
8	-1.000000e-35	1957-01-01	1884.727661	1962-06-19 12:18:24	0.710324	1957-06-11 09:53:49	3.661735	1959-07-02 03:15:20	-1.000000e-35	1957-01-01	...	1957-01-01 00:00:00	1957-06-04 16:38:19	-1.000000e-35	50.0	1957-01-01 00:00:00	1957-06-04 16:38:19	15.088912	1957-07-20 15:23:58	7.544456	1957-07-20 15:23:58
9	-1.000000e-35	1957-01-01	1610.750977	1958-11-10 13:35:20	0.669762	1961-08-22 06:47:12	3.626211	1962-06-19 12:18:24	-1.000000e-35	1957-01-01	...	1957-01-01 00:00:00	1962-07-21 15:56:16	-1.000000e-35	50.0	1957-01-01 00:00:00	1962-07-21 15:56:16	13.787300	1959-07-28 06:34:48	6.893650	1959-07-28 06:34:48

10 rows × 1562 columns

The available quantities in LTS event statistics result file are

In [4]:

res1d_events.quantities

Out[4]:

['WaterLevelMaximum',
 'WaterLevelMaximumTime',
 'DischargeIntegrated',
 'DischargeIntegratedTime',
 'DischargeMaximum',
 'DischargeMaximumTime',
 'DischargeDuration',
 'DischargeDurationTime',
 'Component_1Maximum',
 'Component_1MaximumTime',
 'Component_2Maximum',
 'Component_2MaximumTime',
 'Component_1TransportIntegrated',
 'Component_1TransportIntegratedTime',
 'Component_2TransportIntegrated',
 'Component_2TransportIntegratedTime',
 'SurchargeMaximum',
 'SurchargeMaximumTime',
 'SurchargeIntegrated',
 'SurchargeIntegratedTime',
 'SurchargeDuration',
 'SurchargeDurationTime',
 'FlowVelocityMaximum',
 'FlowVelocityMaximumTime']

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:

In [5]:

df_events[["WaterLevelMaximum:B4.1200", "WaterLevelMaximumTime:B4.1200"]]

Out[5]:

	WaterLevelMaximum:B4.1200	WaterLevelMaximumTime:B4.1200
0	20.357243	1961-06-13 15:55:44
1	20.273409	1961-09-05 14:48:00
2	20.067089	1959-08-15 13:30:48
3	19.239136	1957-07-27 16:22:48
4	19.165405	1962-07-21 15:15:28
5	19.103199	1961-08-22 06:40:48
6	18.928940	1958-05-25 23:21:04
7	18.692797	1959-07-11 19:06:48
8	18.399147	1961-04-06 20:36:16
9	18.359352	1959-08-15 09:56:00

Additionally, for flow type quantities like Discharge there are these LTS event quantities:

DischargeMaximum - maximum discharge of an event
DischargeMaximumTime - time of the maximum discharge
DischargeDuration - duration of the flow event
DischargeDurationTime - time when the duration is started to be calculated
DischargeIntegrated - event integrated over the whole duration
DischargeIntegratedTime - start time of the flow event

Let's see the discharge statistics at the start of the pipe (reach) B4.1200l1:0

In [6]:

quantities = [c for c in df_events.columns if "Discharge" in c and "B4.1200l1:26.666" in c]
df_events[quantities]

Out[6]:

	DischargeIntegrated:B4.1200l1:26.6666	DischargeIntegratedTime:B4.1200l1:26.6666	DischargeMaximum:B4.1200l1:26.6666	DischargeMaximumTime:B4.1200l1:26.6666	DischargeDuration:B4.1200l1:26.6666	DischargeDurationTime:B4.1200l1:26.6666
0	1081.991333	1961-09-05 12:17:20	0.464411	1961-06-13 15:44:16	9.108004	1957-07-20 09:39:20
1	1048.237183	1959-08-15 09:36:24	0.436320	1959-08-15 13:27:52	6.788862	1957-06-11 04:12:20
2	908.647827	1961-06-13 15:38:24	0.377782	1961-09-05 14:41:20	5.560763	1959-08-15 09:36:24
3	815.226196	1958-05-25 21:11:20	0.352194	1961-08-22 06:39:12	4.831801	1961-09-05 12:17:20
4	664.019165	1957-07-20 09:39:20	0.328294	1959-07-11 19:05:04	4.549961	1958-05-25 21:11:20
5	508.447784	1957-06-04 14:28:20	0.325543	1957-07-27 16:21:48	4.169593	1958-05-24 19:07:20
6	501.339294	1957-06-11 04:12:20	0.314239	1958-05-25 23:18:24	4.119199	1958-11-10 13:35:20
7	490.517639	1962-07-21 13:50:24	0.312885	1962-07-21 15:14:56	3.769463	1957-09-07 22:18:20
8	418.507965	1962-06-19 12:18:24	0.298825	1961-04-06 20:34:24	3.661735	1959-07-02 03:15:20
9	356.224335	1957-07-27 16:09:20	0.286779	1961-09-25 00:51:28	3.626211	1962-06-19 12:18:24

Note that entries with the same integer label for DischargeIntegrated, DischargeMaximum, DischargeDuration does not represent the same event.

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

In [7]:

df_events[
    [
        "WaterLevelMaximum:B4.1200",
        "WaterLevelMaximum:B4.1200l1:0",
        "WaterLevelMaximum:B4.1200l1:479.999",
        "WaterLevelMaximum:B4.1485",
    ]
].plot();

LTS chronological statistics results¶

Read entire LTS monthly statistics file into a data frame.

In [8]:

res1d_monthly = mikeio1d.open("../tests/testdata/lts_monthly_statistics.res1d")
df_monthly = res1d_monthly.read()

For monthly/yearly statistics the time index has value for every month/year.

In [9]:

df_monthly.head(10)

Out[9]:

	DischargeIntegratedMonthly:A0.0327	DischargeIntegratedMonthlyCount:A0.0327	DischargeIntegratedMonthlyDuration:A0.0327	Component_1TransportIntegratedMonthly:A0.0327	Component_1TransportIntegratedMonthlyCount:A0.0327	Component_1TransportIntegratedMonthlyDuration:A0.0327	Component_2TransportIntegratedMonthly:A0.0327	Component_2TransportIntegratedMonthlyCount:A0.0327	Component_2TransportIntegratedMonthlyDuration:A0.0327	...	Component_2TransportIntegratedMonthlyDuration:Pump:B4.1510p2:40.0003	DischargeIntegratedMonthlyOutlets	DischargeIntegratedMonthlyWeirs	DischargeIntegratedMonthlyTotalOutflow	Component_1TransportIntegratedMonthlyTotalEmission	Component_2TransportIntegratedMonthlyTotalEmission	Component_1TransportIntegratedMonthlyOutlets	Component_2TransportIntegratedMonthlyOutlets	Component_1TransportIntegratedMonthlyWeirs	Component_2TransportIntegratedMonthlyWeirs
1957-01-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-02-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-03-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-04-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-05-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-06-01	4657.166504	2.0	9.395469	454.666199	2.0	9.395469	227.333099	2.0	9.395469	...	1.265970	4657.166504	31.408979	4688.575684	458.651001	229.325500	455.510712	227.755356	3.140271	1.570136
1957-07-01	4643.333496	2.0	10.702984	453.083099	2.0	10.702984	226.541550	2.0	10.702984	...	1.124598	4643.335449	64.702248	4708.037598	460.440796	230.220398	454.080963	227.040482	6.359827	3.179913
1957-08-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1957-09-01	1432.653320	1.0	3.769463	137.650818	1.0	3.769463	68.825409	1.0	3.769463	...	0.261507	1432.653320	0.000000	1432.653320	138.132156	69.066078	138.132156	69.066078	0.000000	0.000000
1957-10-01	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

10 rows × 759 columns

For flow type quantities like Discharge there are these LTS monthly (similary to yearly statistics) event quantities:

DischargeIntegratedMonthly - discharge integrated over the whole month
DischargeIntegratedMonthlyCount - number of events within a month
DischargeIntegratedMonthlyDuration - duration of all events in a month

Let's see the discharge chronological statistics at the start of the pipe (reach) B4.1200l1:0

In [10]:

quantities = [c for c in df_monthly.columns if "Discharge" in c and "B4.1200l1:26.666" in c]
df_monthly[quantities].head(10)

Out[10]:

	DischargeIntegratedMonthly:B4.1200l1:26.6666	DischargeIntegratedMonthlyCount:B4.1200l1:26.6666	DischargeIntegratedMonthlyDuration:B4.1200l1:26.6666
1957-01-01	0.000000	0.0	0.000000
1957-02-01	0.000000	0.0	0.000000
1957-03-01	0.000000	0.0	0.000000
1957-04-01	0.000000	0.0	0.000000
1957-05-01	0.000000	0.0	0.000000
1957-06-01	1009.787048	2.0	9.395469
1957-07-01	1020.243530	2.0	10.702984
1957-08-01	0.000000	0.0	0.000000
1957-09-01	315.122833	1.0	3.769463
1957-10-01	0.000000	0.0	0.000000

Some plots of LTS dsicharge monthly statistics

In [11]:

print(quantities[0])
df_monthly[quantities[0]].plot();

DischargeIntegratedMonthly:B4.1200l1:26.6666

In [12]:

print(quantities[1])
df_monthly[quantities[1]].plot();

DischargeIntegratedMonthlyCount:B4.1200l1:26.6666

In [13]:

print(quantities[2])
df_monthly[quantities[2]].plot();

DischargeIntegratedMonthlyDuration:B4.1200l1:26.6666