def test_subcatchments_dataframe_from_rpt(test_model_02):
sub_ro_summary = dataframe_from_rpt(test_model_02.rpt.path,
'Subcatchment Runoff Summary')
tot_ro = sub_ro_summary['TotalRunoffMG'].sum()
assert tot_ro == pytest.approx(0.28, 0.001)
# test retrieving timeseries results
s1_results = dataframe_from_rpt(test_model_02.rpt.path,
'Subcatchment Results', 'S1')
assert s1_results['RunoffCFS'].max() == pytest.approx(0.5526, 0.00001)
def conduits(self):
"""
collect all useful and available data related model conduits and
organize in one dataframe.
"""
# check if this has been done already and return that data accordingly
if self._conduits_df is not None:
return self._conduits_df
# parse out the main objects of this model
inp = self.inp
rpt = self.rpt
# create dataframes of relevant sections from the INP
conduits_df = dataframe_from_inp(inp.path, "CONDUITS")
xsections_df = dataframe_from_inp(inp.path, "XSECTIONS")
conduits_df = conduits_df.join(xsections_df)
if rpt:
# create a dictionary holding data from an rpt file, if provided
link_flow_df = dataframe_from_rpt(rpt.path, "Link Flow Summary")
conduits_df = conduits_df.join(link_flow_df)
# add conduit coordinates
xys = conduits_df.apply(lambda r: get_link_coords(
r, self.inp.coordinates, self.inp.vertices),
axis=1)
df = conduits_df.assign(coords=xys.map(lambda x: x[0]))
# add conduit up/down inverts and calculate slope
elevs = self.nodes()[['InvertElev']]
df = pd.merge(df,
elevs,
left_on='InletNode',
right_index=True,
how='left')
df = df.rename(index=str, columns={"InvertElev": "InletNodeInvert"})
df = pd.merge(df,
elevs,
left_on='OutletNode',
right_index=True,
how='left')
df = df.rename(index=str, columns={"InvertElev": "OutletNodeInvert"})
df['UpstreamInvert'] = df.InletNodeInvert + df.InOffset
df['DownstreamInvert'] = df.OutletNodeInvert + df.OutOffset
df['SlopeFtPerFt'] = (df.UpstreamInvert -
df.DownstreamInvert) / df.Length
df.InletNode = df.InletNode.astype(str)
df.OutletNode = df.OutletNode.astype(str)
self._conduits_df = df
return df
def test_links_dataframe_from_rpt(test_model_02):
link_flow_summary = dataframe_from_rpt(test_model_02.rpt.path,
'Link Flow Summary')
# print(f'\n{link_flow_summary.to_string()}')
s = '''
Name Type MaxQ MaxDay MaxHr MaxV MaxQPerc MaxDPerc
C1:C2 CONDUIT 2.45 0 10:19 6.23 1.32 0.50
C2.1 CONDUIT 0.00 0 00:00 0.00 0.00 0.50
1 CONDUIT 2.54 0 10:00 3.48 1.10 0.89
2 CONDUIT 2.38 0 10:03 3.64 1.03 0.85
3 CONDUIT 1.97 0 09:59 2.92 0.67 1.00
4 CONDUIT 0.16 0 10:05 0.44 0.10 0.63
5 CONDUIT 0.00 0 00:00 0.00 0.00 0.50
C2 PUMP 4.33 0 10:00 0.22 NaN NaN
C3 WEIR 7.00 0 10:00 0.33 NaN NaN
'''
lfs_df = pd.read_csv(StringIO(s),
index_col=0,
delim_whitespace=True,
skiprows=[0])
assert (lfs_df.equals(link_flow_summary))
def __call__(self):
"""
collect all useful and available data related to the conduits and
organize in one dataframe.
>>> model = swmmio.Model(MODEL_FULL_FEATURES__NET_PATH)
>>> conduits_section = ModelSection(model, 'conduits')
>>> conduits_section()
"""
# concat inp sections with unique element IDs
headers = get_inp_sections_details(self.inp.path)
dfs = [
dataframe_from_inp(self.inp.path, sect)
for sect in self.inp_sections if sect.upper() in headers
]
# return empty df if no inp sections found
if len(dfs) == 0:
return pd.DataFrame()
df = pd.concat(dfs, axis=0, sort=False)
# join to this any sections with matching IDs (e.g. XSECTIONS)
for sect in self.join_sections:
rsuffix = f"_{sect.replace(' ', '_')}"
df = df.join(dataframe_from_inp(self.inp.path, sect),
rsuffix=rsuffix)
if df.empty:
return df
# if there is an RPT available, grab relevant sections
if self.rpt:
for rpt_sect in self.rpt_sections:
df = df.join(dataframe_from_rpt(self.rpt.path, rpt_sect))
# add coordinates
if self.geomtype == 'point':
df = df.join(self.inp.coordinates[['X', 'Y']])
xys = df.apply(lambda r: nodexy(r), axis=1)
df = df.assign(coords=xys)
elif self.geomtype == 'linestring':
# add conduit coordinates
xys = df.apply(lambda r: get_link_coords(r, self.inp.coordinates,
self.inp.vertices),
axis=1)
df = df.assign(coords=xys.map(lambda x: x[0]))
# make inlet/outlet node IDs string type
df.InletNode = df.InletNode.astype(str)
df.OutletNode = df.OutletNode.astype(str)
elif self.geomtype == 'polygon':
p = self.inp.polygons
# take stacked coordinates and orient in list of tuples,
xys = p.groupby(by=p.index).apply(
lambda r: [(xy['X'], xy['Y']) for ix, xy in r.iterrows()])
# copy the first point to the last position
xys = xys.apply(lambda r: r + [r[0]])
df = df.assign(coords=xys)
# confirm index name is string
df = df.rename(index=str)
# trim to desired columns
if self.columns is not None:
df = df[[c for c in self.columns if c in df.columns]]
self._df = df
return df
def fn(self):
if private_df_name not in self.__dict__:
self.__dict__[private_df_name] = dataframe_from_rpt(
self.path, rpt_section)
return self.__dict__[private_df_name]
