Read SWMM result files¶

In this notebook we give examples how to read SWMM result files out. Most of the functionality is the same as reading res1d files, which is described in res1d.ipynb.

In [1]:

from mikeio1d import Res1D
from mikeio1d.query import QueryDataNode, QueryDataReach, QueryDataCatchment

SWMM out file¶

Read entire res file into a data frame. Here we use results from a standard Example1.inp distributed together with EPA SWMM 5.1.

In [2]:

file_path = "../tests/testdata/Swmm.out"
res = Res1D(file_path)
df = res.read()

Let's peek at the full data frame coming from EPANET result.

In [3]:

df.head()

Out[3]:

	SWMM_NODE_DEPTH:9	SWMM_NODE_HEAD:9	SWMM_NODE_LATFLOW:9	SWMM_NODE_INFLOW:9	SWMM_NODE_QUAL:9	SWMM_NODE_QUAL:9	SWMM_NODE_DEPTH:10	SWMM_NODE_HEAD:10	...	SWMM_SYS_INFLOW:	SWMM_SYS_FLOODING:	SWMM_SYS_OUTFLOW:	SWMM_SYS_STORAGE:
1998-01-01 01:00:00	0.000000	1000.000000	0.000000	0.000000	0.000000	0.000000	0.000000	995.000000	...	0.000000	0.000000	0.000000	0.000000
1998-01-01 02:00:00	0.288774	1000.288757	1.239633	1.239633	15.720075	3.144015	0.520309	995.520325	...	6.216826	0.000000	6.128909	1656.397217
1998-01-01 03:00:00	0.418453	1000.418457	2.595429	2.595429	15.389509	3.077902	3.000000	998.000000	...	13.030855	0.521967	12.470544	2796.561035
1998-01-01 04:00:00	0.567633	1000.567627	4.645479	4.645479	14.810841	2.962168	3.000000	998.000000	...	24.252975	4.511855	19.521162	3780.683594
1998-01-01 05:00:00	0.425047	1000.425049	2.675368	2.675368	14.338782	2.867756	3.000000	998.000000	...	14.172027	0.555154	13.623661	2913.430420

5 rows × 298 columns

For SWMM there quite a lot of available quantities:

In [4]:

res.quantities

Out[4]:

['SWMM_NODE_DEPTH',
 'SWMM_NODE_HEAD',
 'SWMM_NODE_VOLUME',
 'SWMM_NODE_LATFLOW',
 'SWMM_NODE_INFLOW',
 'SWMM_NODE_OVERFLOW',
 'SWMM_NODE_QUAL',
 'SWMM_LINK_FLOW',
 'SWMM_LINK_DEPTH',
 'SWMM_LINK_VELOCITY',
 'SWMM_LINK_Froude_Number',
 'SWMM_LINK_CAPACITY',
 'SWMM_LINK_QUAL',
 'SWMM_SUBCATCH_RAINFALL',
 'SWMM_SUBCATCH_SNOWDEPTH',
 'SWMM_SUBCATCH_EVAP',
 'SWMM_SUBCATCH_INFIL',
 'SWMM_SUBCATCH_RUNOFF',
 'SWMM_SUBCATCH_GW_FLOW',
 'SWMM_SUBCATCH_GW_ELEV',
 'SWMM_SUBCATCH_SOIL_MOIST',
 'SWMM_SUBCATCH_WASHOFF',
 'SWMM_SYS_TEMPERATURE',
 'SWMM_SYS_RAINFALL',
 'SWMM_SYS_SNOWDEPTH',
 'SWMM_SYS_INFIL',
 'SWMM_SYS_RUNOFF',
 'SWMM_SYS_DWFLOW',
 'SWMM_SYS_GWFLOW',
 'SWMM_SYS_INFLOW',
 'SWMM_SYS_EXFLOW',
 'SWMM_SYS_FLOODING',
 'SWMM_SYS_OUTFLOW',
 'SWMM_SYS_STORAGE',
 'SWMM_SYS_EVAP',
 'SWMM_SYS_PET']

Here we retrieve flow in a pipe with ID 10, which has connecting junctions 17 and 18. Note that in order to have network connectivity the corresponding setup inp file is needed, which has to be called the same as the result file, i.e., for Swmm.out we need to have Swmm.inp with the relevant network information.

In [5]:

pipe_id = '10'
pipe_data_item = res.reaches[pipe_id]
node_start = res.data.Nodes[pipe_data_item.StartNodeIndex]
node_end = res.data.Nodes[pipe_data_item.EndNodeIndex]
print(f'Start node ID: {node_start.ID}')
print(f'End node ID: {node_end.ID}')

Start node ID: 17
End node ID: 18

In [6]:

df[['SWMM_LINK_FLOW:10', 'SWMM_NODE_DEPTH:17', 'SWMM_NODE_DEPTH:18']].head()

Out[6]:

	SWMM_LINK_FLOW:10	SWMM_NODE_DEPTH:17	SWMM_NODE_DEPTH:18
1998-01-01 01:00:00	0.000000	0.000000	0.000000
1998-01-01 02:00:00	5.876935	0.607021	0.572612
1998-01-01 03:00:00	11.923845	0.886635	0.833084
1998-01-01 04:00:00	18.203714	1.139519	1.063005
1998-01-01 05:00:00	12.643229	0.916358	0.860591

Let's plot the above results, but this time we will retrieve the data using QueryDataReach and QueryDataNode.

In [7]:

pipe_query = QueryDataReach('SWMM_LINK_FLOW', pipe_id)
df_pipe = res.read(queries=[pipe_query])
df_pipe.plot();

node_start_query = QueryDataNode('SWMM_NODE_DEPTH', node_start.ID)
node_end_query = QueryDataNode('SWMM_NODE_DEPTH', node_end.ID)
df_nodes = res.read(queries=[node_start_query, node_end_query])
df_nodes.plot();

Finally, the catchment results are also stored in SWMM out file. The current result file has the following catchments:

In [8]:

res.catchments

Out[8]:

{'1': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A4B63100>,
 '2': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A6CD5540>,
 '3': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D401C0>,
 '4': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D40E40>,
 '5': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D40F80>,
 '6': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D40FC0>,
 '7': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D40200>,
 '8': <DHI.Mike1D.ResultDataAccess.Res1DCatchment object at 0x000001E3A7D3F9C0>}

Available quantities for catchments can be obtained from one of the catchments (for example, ID='5') as

In [9]:

catchment_id = '5'
swmm_catchment = res.catchments[catchment_id]
catchment_quantities = [data_item.Quantity.Id for data_item in swmm_catchment.DataItems]
print(catchment_quantities)

['SWMM_SUBCATCH_RAINFALL', 'SWMM_SUBCATCH_SNOWDEPTH', 'SWMM_SUBCATCH_EVAP', 'SWMM_SUBCATCH_INFIL', 'SWMM_SUBCATCH_RUNOFF', 'SWMM_SUBCATCH_GW_FLOW', 'SWMM_SUBCATCH_GW_ELEV', 'SWMM_SUBCATCH_SOIL_MOIST', 'SWMM_SUBCATCH_WASHOFF', 'SWMM_SUBCATCH_WASHOFF']

We can create quantity 'SWMM_SUBCATCH_RUNOFF' queries for all catchments as

In [10]:

catchment_queries = [QueryDataCatchment('SWMM_SUBCATCH_RUNOFF', catchment_id) for catchment_id in res.catchments]
df_catchments = res.read(queries=catchment_queries)

In [11]:

df_catchments.head()

Out[11]:

	SWMM_SUBCATCH_RUNOFF:1	SWMM_SUBCATCH_RUNOFF:2	SWMM_SUBCATCH_RUNOFF:3	SWMM_SUBCATCH_RUNOFF:4	SWMM_SUBCATCH_RUNOFF:5	SWMM_SUBCATCH_RUNOFF:6	SWMM_SUBCATCH_RUNOFF:7	SWMM_SUBCATCH_RUNOFF:8
1998-01-01 01:00:00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
1998-01-01 02:00:00	1.243824	1.243824	0.629613	0.629613	1.814816	0.302342	0.100822	0.251970
1998-01-01 03:00:00	2.597976	2.563968	1.302650	1.302650	3.821663	0.649161	0.244563	0.548222
1998-01-01 04:00:00	4.656198	4.524055	2.449128	2.449128	6.562110	1.495427	0.789465	1.327465
1998-01-01 05:00:00	2.675617	2.511513	1.369764	1.369764	3.585666	1.082219	0.596153	0.981333

Let's plot the runoff for catchment ID='5'

In [12]:

df_catchments['SWMM_SUBCATCH_RUNOFF:5'].plot();

In [ ]: