Res1D - Basic¶

Open a network result file.

In [1]:

import mikeio1d

In [2]:

res = mikeio1d.open("../tests/testdata/network.res1d")
res

Out[2]:

<mikeio1d.Res1D>

In [3]:

res.info()

Start time: 1994-08-07 16:35:00
End time: 1994-08-07 18:35:00
# Timesteps: 110
# Catchments: 0
# Nodes: 119
# Reaches: 118
# Globals: 0
0 - Water level (m)
1 - Discharge (m^3/s)

In [4]:

res.quantities

Out[4]:

['WaterLevel', 'Discharge']

In [5]:

res.derived_quantities

Out[5]:

['ReachWaterLevelAboveCritical',
 'ReachFlooding',
 'ReachWaterDepth',
 'NodeWaterDepth',
 'NodeFlooding',
 'ReachFilling',
 'ReachAbsoluteDischarge',
 'ReachQQManning',
 'NodeWaterLevelAboveCritical']

Read all available results

In [6]:

df = res.read()
df.head()

Out[6]:

	WaterLevel:1	WaterLevel:2	WaterLevel:3	WaterLevel:4	WaterLevel:5	WaterLevel:6	WaterLevel:7	WaterLevel:8	WaterLevel:9	WaterLevel:10	...	Discharge:99l1:22.2508	WaterLevel:9l1:0	WaterLevel:9l1:10	Discharge:9l1:5	WaterLevel:Weir:119w1:0	WaterLevel:Weir:119w1:1	WaterLevel:Pump:115p1:0	WaterLevel:Pump:115p1:82.4281
1994-08-07 16:35:00.000	195.052994	195.821503	195.8815	193.604996	193.615005	193.625000	193.675003	193.764999	193.774994	193.804993	...	0.000002	193.774994	193.764999	0.000031	193.550003	188.479996	193.304993	195.005005
1994-08-07 16:36:01.870	195.052994	195.821701	195.8815	193.604996	193.615005	193.625320	193.675110	193.765060	193.775116	193.804993	...	0.000002	193.775070	193.765060	0.000031	193.550003	188.479996	193.306061	195.005005
1994-08-07 16:37:07.560	195.052994	195.821640	195.8815	193.604996	193.615005	193.625671	193.675369	193.765106	193.775513	193.804993	...	0.000002	193.775391	193.765106	0.000033	193.550034	188.479996	193.307144	195.005005
1994-08-07 16:38:55.828	195.052994	195.821503	195.8815	193.604996	193.615005	193.626236	193.675751	193.765228	193.776077	193.804993	...	0.000002	193.775894	193.765228	0.000037	193.550079	188.479996	193.308884	195.005005
1994-08-07 16:39:55.828	195.052994	195.821503	195.8815	193.604996	193.615005	193.626556	193.675949	193.765335	193.776352	193.804993	...	0.000002	193.776154	193.765335	0.000039	193.550095	188.479996	193.309860	195.005005

5 rows × 495 columns

Read subsets of available results

In [7]:

df = res.reaches.read()
df.head()

Out[7]:

	WaterLevel:100l1:0	WaterLevel:100l1:47.6827	WaterLevel:101l1:0	WaterLevel:101l1:66.4361	WaterLevel:102l1:0	WaterLevel:102l1:10.9366	WaterLevel:103l1:0	WaterLevel:103l1:26.0653	WaterLevel:104l1:0	WaterLevel:104l1:34.4131	...	Discharge:93l1:24.5832	Discharge:94l1:21.2852	Discharge:95l1:21.9487	Discharge:96l1:14.9257	Discharge:97l1:5.71207	Discharge:98l1:8.00489	Discharge:99l1:22.2508	Discharge:9l1:5
1994-08-07 16:35:00.000	195.441498	194.661499	195.931503	195.441498	193.550003	193.550003	195.801498	195.701508	197.072006	196.962006	...	0.000004	0.000003	0.000001	0.000005	0.000013	0.000003	0.000002	0.000031
1994-08-07 16:36:01.870	195.441498	194.661621	195.931503	195.441605	193.550140	193.550064	195.801498	195.703171	197.072006	196.962051	...	0.000004	0.000003	0.000001	0.000005	0.000010	0.000003	0.000002	0.000031
1994-08-07 16:37:07.560	195.441498	194.661728	195.931503	195.441620	193.550232	193.550156	195.801498	195.703400	197.072006	196.962082	...	0.000004	0.000003	0.000001	0.000005	0.000010	0.000003	0.000002	0.000033
1994-08-07 16:38:55.828	195.441498	194.661804	195.931503	195.441605	193.550369	193.550308	195.801498	195.703690	197.072006	196.962112	...	0.000004	0.000003	0.000001	0.000005	0.000009	0.000003	0.000002	0.000037
1994-08-07 16:39:55.828	195.441498	194.661972	195.931503	195.441605	193.550430	193.550369	195.801498	195.703827	197.072006	196.962128	...	0.000004	0.000003	0.000001	0.000005	0.000009	0.000003	0.000002	0.000039

5 rows × 376 columns

In [8]:

df = res.reaches["100l1"].read()
df.head()

Out[8]:

	WaterLevel:100l1:0	WaterLevel:100l1:47.6827	Discharge:100l1:23.8414
1994-08-07 16:35:00.000	195.441498	194.661499	0.000006
1994-08-07 16:36:01.870	195.441498	194.661621	0.000006
1994-08-07 16:37:07.560	195.441498	194.661728	0.000006
1994-08-07 16:38:55.828	195.441498	194.661804	0.000006
1994-08-07 16:39:55.828	195.441498	194.661972	0.000006

In [9]:

df = res.reaches["100l1"].WaterLevel.read()
df.head()

Out[9]:

	WaterLevel:100l1:0	WaterLevel:100l1:47.6827
1994-08-07 16:35:00.000	195.441498	194.661499
1994-08-07 16:36:01.870	195.441498	194.661621
1994-08-07 16:37:07.560	195.441498	194.661728
1994-08-07 16:38:55.828	195.441498	194.661804
1994-08-07 16:39:55.828	195.441498	194.661972

In [10]:

# Index by gridpoint number
df = res.reaches["100l1"][0].read()
df.head()

Out[10]:

	WaterLevel:100l1:0
1994-08-07 16:35:00.000	195.441498
1994-08-07 16:36:01.870	195.441498
1994-08-07 16:37:07.560	195.441498
1994-08-07 16:38:55.828	195.441498
1994-08-07 16:39:55.828	195.441498

In [11]:

# Or index by gridpoint chainage
df = res.reaches["100l1"]["23.841"].read()
df.head()

Out[11]:

	Discharge:100l1:23.8414
1994-08-07 16:35:00.000	0.000006
1994-08-07 16:36:01.870	0.000006
1994-08-07 16:37:07.560	0.000006
1994-08-07 16:38:55.828	0.000006
1994-08-07 16:39:55.828	0.000006

Make plots by replacing read() with plot()

In [12]:

ax = res.nodes["101"].WaterLevel.plot()

Dynamically select quantities with add() followed by read()

In [13]:

res.nodes["101"].WaterLevel.add()
res.nodes["102"].WaterLevel.add()
df = res.read()
df.head()

Out[13]:

	WaterLevel:101	WaterLevel:102
1994-08-07 16:35:00.000	195.931503	193.550003
1994-08-07 16:36:01.870	195.931595	193.550140
1994-08-07 16:37:07.560	195.931625	193.550232
1994-08-07 16:38:55.828	195.931656	193.550369
1994-08-07 16:39:55.828	195.931656	193.550430