swmmio.elements#

Objects encapsulating model elements

class swmmio.elements.Links(model, inp_sections, join_sections=None, rpt_sections=None, columns=None)[source]#

Bases: ModelSection

Generalized links object for working with link-like SWMM objects.

Parameters:

model – swmmio.Model object
inp_sections – list of link-related sections from the inp file to concatenate in the object
join_sections – list of link-related sections from the inp file to join to the object
rpt_sections – list of link-related sections from the rpt file to join to the object
columns – optional subset of columns used to exclude unwanted columns in the resulting object

>>> from swmmio.examples import spruce
>>> conduits = Links(
...     spruce,
...     inp_sections=['conduits'],
...     rpt_sections=['Link Flow Summary'],
...     columns=['MaxQ', 'MaxQPerc', 'coords']
... )
>>> conduits.dataframe
       MaxQ  MaxQPerc                                             coords
Name
C1:C2  2.45      1.32                    [(0.0, 0.0), (238.75, -53.332)]
C2.1   0.00      0.00  [(238.75, -53.332), (295.63599999999997, -159....
1      2.54      1.10  [(-77.021, -78.321), (-67.28399999999999, -37....
2      2.38      1.03  [(-67.28399999999999, -37.603), (-56.662, 15.5...
3      1.97      0.67                    [(-56.662, 15.507), (0.0, 0.0)]
4      0.16      0.10  [(-18.6, -71.23899999999999), (-23.91099999999...
5      0.00      0.00  [(-84.988, 43.833), (-85.87299999999999, 19.93...
>>> # access data as geojson
>>> conduits.geojson['features'][0]['geometry']
{"coordinates": [[0.0, 0.0], [238.75, -53.332]], "type": "LineString"}
>>> conduits.geojson['features'][0]['properties']
{'MaxQ': 2.45, 'MaxQPerc': 1.32, 'Name': 'C1:C2'}

Attributes:

dataframe: Return a Pandas.Dataframe representation of the group
geodataframe: Return a GeoPandas.GeoDataFrame representation of the group
geojson: Return a GeoJSON representation of the group

Methods

__call__()

collect all useful and available data related to the conduits and organize in one dataframe.

class swmmio.elements.ModelSection(model, inp_sections, join_sections=None, rpt_sections=None, columns=None, geomtype='point')[source]#

Bases: object

Base class of a group of model elements.

Parameters:

model – swmmio.Model object
inp_sections – list of node-related sections from the inp file to concatenate in the object
join_sections – list of node-related sections from the inp file to join to the object
rpt_sections – list of node-related sections from the rpt file to join to the object
columns – optional subset of columns used to exclude unwanted columns in the resulting object
geomtype – type of geometry for section [point, linestring, polygon]

Attributes:

dataframe: Return a Pandas.Dataframe representation of the group
geodataframe: Return a GeoPandas.GeoDataFrame representation of the group
geojson: Return a GeoJSON representation of the group

Methods

__call__()

collect all useful and available data related to the conduits and organize in one dataframe.

property dataframe#

Return a Pandas.Dataframe representation of the group

Returns:: pd.Dataframe

property geodataframe#

Return a GeoPandas.GeoDataFrame representation of the group

Returns:: GeoPandas.GeoDataFrame

property geojson#

Return a GeoJSON representation of the group

Returns:: GeoJSON string

class swmmio.elements.Nodes(model, inp_sections, join_sections=None, rpt_sections=None, columns=None)[source]#

Bases: ModelSection

Generalized nodes object for working with node-like SWMM objects.

Parameters:

model – swmmio.Model object
inp_sections – list of node-related sections from the inp file to concatenate in the object
join_sections – list of node-related sections from the inp file to join to the object
rpt_sections – list of node-related sections from the rpt file to join to the object
columns – optional subset of columns used to exclude unwanted columns in the resulting object

>>> from swmmio.examples import spruce
>>> nodes = Nodes(
...     spruce,
...     inp_sections=['junctions'],
...     rpt_sections=['Node Depth Summary'],
...     columns=['InvertElev', 'MaxHGL', 'coords']
... )
>>> nodes.dataframe
      InvertElev  MaxHGL                            coords
Name
J3         6.547    8.19  [(459.05800000000005, -113.145)]
1         17.000   17.94              [(-77.021, -78.321)]
2         17.000   17.00               [(-84.988, 43.833)]
3         16.500   16.89     [(-18.6, -71.23899999999999)]
4         16.000   16.87   [(-67.28399999999999, -37.603)]
5         15.000   16.00               [(-56.662, 15.507)]
J2        13.000   13.00               [(238.75, -53.332)]
>>> # access data as geojson
>>> nodes.geojson['features'][0]['geometry']
{"coordinates": [[459.058, -113.145]], "type": "Point"}
>>> nodes.geojson['features'][0]['properties']
{'InvertElev': 6.547, 'MaxHGL': 8.19, 'Name': 'J3'}

Attributes:

dataframe: Return a Pandas.Dataframe representation of the group
geodataframe: Return a GeoPandas.GeoDataFrame representation of the group
geojson: Return a GeoJSON representation of the group

Methods

__call__()

collect all useful and available data related to the conduits and organize in one dataframe.