swmm.pandas.Output.node_series¶
-
Output.node_series(node, attribute=
('invert_depth', 'flooding_losses', 'total_inflow'), start=None, end=None, columns='attr', asframe=True)[source]¶ Get one or more time series for one or more node attributes. Specify series start index and end index to get desired time range.
- Parameters
- node: Union[int, str, Sequence[Union[int, str]], None]
The node index or name.
- attribute: Union[int, str, Enum, Sequence[Union[int, str, Enum]], None],
The attribute index or name.
On of:
invert_depth, hydraulic_head, ponded_volume, lateral_inflow, total_inflow, flooding_losses.
defaults to: (‘invert_depth’,’flooding_losses’,’total_inflow’)
Can also input the integer index of the attribute you would like to pull or the actual enum from Output.node_attributes.
Setting to None indicates all attributes.
- start: Union[str, int, datetime, None], optional
The start datetime or index of from which to return series, defaults to None.
Setting to None indicates simulation start.
- end: Union[str, int, datetime, None], optional
The end datetime or index of from which to return series, defaults to None.
Setting to None indicates simulation end.
- columns: Optional[str], optional
Decide whether or not to break out elements or attributes as columns. May be one of:
None: Return long-form data with one column for each data point
‘elem’: Return data with a column for each element. If more than one attribute are given, attribute names are added to the index.
‘attr’: Return data with a column for each attribute. If more than one element are given, element names are added to the index.
defaults to ‘attr’.
- asframe: bool
A switch to return an indexed DataFrame. Set to False to get an array of values only, defaults to True.
- Returns
- Union[pd.DataFrame,np.ndarray]
A DataFrame or ndarray of attribute values in each column for requested date range and nodes.
Examples
Pull single time series for a single node
>>> from swmm.pandas import Output,test_out_path >>> out = Output(test_out_path) >>> out.node_series('JUNC2', 'invert_depth') invert_depth datetime 1900-01-01 00:05:00 0.334742 1900-01-01 00:10:00 0.509440 1900-01-01 00:15:00 0.562722 1900-01-01 00:20:00 0.602668 1900-01-01 00:25:00 0.631424 ... ... 1900-01-01 23:40:00 0.766949 1900-01-01 23:45:00 0.766949 1900-01-01 23:50:00 0.766949 1900-01-01 23:55:00 0.766949 1900-01-02 00:00:00 0.766949 [288 rows x 1 columns]Pull a wide-form dataframe for all parameters for a node
>>> out.node_series('JUNC2', out.node_attributes) invert_depth hydraulic_head ponded_volume lateral_inflow total_inflow flooding_losses groundwater pol_rainfall sewage datetime 1900-01-01 00:05:00 0.334742 -0.705258 0.0 0.185754 0.185785 0.0 3.935642 0.000000 95.884094 1900-01-01 00:10:00 0.509440 -0.530560 0.0 0.196764 0.197044 0.0 8.902034 0.000000 90.335831 1900-01-01 00:15:00 0.562722 -0.477278 0.0 0.198615 0.199436 0.0 9.038609 0.000000 89.253334 1900-01-01 00:20:00 0.602668 -0.437332 0.0 0.200802 0.202462 0.0 9.259741 0.000000 87.919571 1900-01-01 00:25:00 0.631424 -0.408576 0.0 0.203108 0.205802 0.0 9.523322 0.000000 86.492836 ... ... ... ... ... ... ... ... ... ... 1900-01-01 23:40:00 0.766949 -0.273052 0.0 0.314470 0.352183 0.0 15.293419 39.303375 45.430920 1900-01-01 23:45:00 0.766949 -0.273052 0.0 0.314499 0.352183 0.0 15.313400 39.292118 45.430920 1900-01-01 23:50:00 0.766949 -0.273052 0.0 0.314530 0.352183 0.0 15.333243 39.281300 45.430920 1900-01-01 23:55:00 0.766949 -0.273052 0.0 0.314559 0.352183 0.0 15.352408 39.271194 45.430920 1900-01-02 00:00:00 0.766949 -0.273052 0.0 0.314590 0.352183 0.0 15.371475 39.261478 45.430920 [288 rows x 9 columns]Pull a long-form dataframe of all nodes and attributes
>>> out.node_series('JUNC2', out.node_attributes, columns=None) result datetime element attribute 1900-01-01 00:05:00 JUNC1 invert_depth 0.002143 1900-01-01 00:10:00 JUNC1 invert_depth 0.010006 1900-01-01 00:15:00 JUNC1 invert_depth 0.017985 1900-01-01 00:20:00 JUNC1 invert_depth 0.025063 1900-01-01 00:25:00 JUNC1 invert_depth 0.031329 ... ... 1900-01-01 23:40:00 STOR1 sewage 51.502193 1900-01-01 23:45:00 STOR1 sewage 51.164684 1900-01-01 23:50:00 STOR1 sewage 50.905445 1900-01-01 23:55:00 STOR1 sewage 50.715385 1900-01-02 00:00:00 STOR1 sewage 50.574486 [23328 rows x 1 columns]Pull flow timeseries and calculate the total flow volume for all nodes
>>> from swmm.pandas.constants import gal_per_cf >>> df = out.node_series(out.nodes, ['lateral_inflow','total_inflow','flooding_losses']) lateral_inflow total_inflow flooding_losses datetime element 1900-01-01 00:05:00 JUNC1 0.002362 0.002362 0.0 1900-01-01 00:10:00 JUNC1 0.005792 0.005792 0.0 1900-01-01 00:15:00 JUNC1 0.006524 0.006524 0.0 1900-01-01 00:20:00 JUNC1 0.007306 0.007306 0.0 1900-01-01 00:25:00 JUNC1 0.008039 0.008039 0.0 ... ... ... ... 1900-01-01 23:40:00 STOR1 0.000000 1.455056 0.0 1900-01-01 23:45:00 STOR1 0.000000 1.455056 0.0 1900-01-01 23:50:00 STOR1 0.000000 1.455056 0.0 1900-01-01 23:55:00 STOR1 0.000000 1.455056 0.0 1900-01-02 00:00:00 STOR1 0.000000 1.455056 0.0 [2592 rows x 3 columns] #---------------------------------------------------------------------------- # group by element name and sum, # then multiply by reporting timestep in seconds # then convert to millions of gallons >>> df.groupby('element').sum() * out.report * gal_per_cf / 1e6 lateral_inflow total_inflow flooding_losses element JUNC1 0.101562 0.101898 0.000053 JUNC2 0.544891 0.857012 0.000000 JUNC3 0.000000 0.502078 0.080634 JUNC4 1.813826 2.096243 0.317929 JUNC5 0.000000 1.870291 0.073878 JUNC6 0.000000 1.701455 0.000000 OUT1 0.000000 1.698081 0.000000 OUT2 0.000000 0.575617 0.000000 STOR1 0.000000 1.862843 0.172482