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Merge branch 'main' into 91-design-output-metrics-as-in-metrics-for-t…
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…he-networks-design-parameters
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Dobson committed Mar 18, 2024
2 parents 1373c61 + 4376a9b commit 0b68e19
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2 changes: 1 addition & 1 deletion dev-requirements.txt
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Expand Up @@ -25,7 +25,7 @@ build==1.0.3
# via pip-tools
cdsapi==0.6.1
# via swmmanywhere (pyproject.toml)
certifi==2023.11.17
certifi==2024.2.2
# via
# fiona
# netcdf4
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2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -22,7 +22,7 @@ attrs==23.2.0
# referencing
cdsapi==0.6.1
# via swmmanywhere (pyproject.toml)
certifi==2023.11.17
certifi==2024.2.2
# via
# fiona
# netcdf4
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127 changes: 113 additions & 14 deletions swmmanywhere/metric_utilities.py
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Expand Up @@ -172,6 +172,59 @@ def nse(y: np.ndarray,
"""Calculate Nash-Sutcliffe efficiency (NSE)."""
return 1 - np.sum((y - yhat)**2) / np.sum((y - np.mean(y))**2)

def median_nse_by_group(results: pd.DataFrame,
gb_key: str) -> float:
"""Median NSE by group.
Calculate the median Nash-Sutcliffe efficiency (NSE) of a variable over time
for each group in the results dataframe, and return the median of these
values.
Args:
results (pd.DataFrame): The results dataframe.
gb_key (str): The column to group by.
Returns:
float: The median NSE.
"""
val = (
results
.groupby(['date',gb_key])
.sum()
.reset_index()
.groupby(gb_key)
.apply(lambda x: nse(x.value_real, x.value_sim))
.median()
)
return val


def nodes_to_subs(G: nx.Graph,
subs: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
"""Nodes to subcatchments.
Classify the nodes of the graph to the subcatchments of the subs dataframe.
Args:
G (nx.Graph): The graph.
subs (gpd.GeoDataFrame): The subcatchments.
Returns:
gpd.GeoDataFrame: A dataframe from the nodes and data, and the
subcatchment information, distinguished by the column 'sub_id'.
"""
nodes_df = pd.DataFrame([{'id' :x, **d} for x,d in G.nodes(data=True)])
nodes_joined = (
gpd.GeoDataFrame(nodes_df,
geometry=gpd.points_from_xy(nodes_df.x,
nodes_df.y),
crs = G.graph['crs'])
.sjoin(subs.rename(columns = {'id' : 'sub_id'}),
how="inner",
predicate="within")
)
return nodes_joined

def best_outlet_match(synthetic_G: nx.Graph,
real_subs: gpd.GeoDataFrame) -> tuple[nx.Graph,int]:
"""Best outlet match.
Expand All @@ -188,19 +241,8 @@ def best_outlet_match(synthetic_G: nx.Graph,
most nodes within the real_subs.
int: The id of the outlet.
"""
# Identify which nodes fall within real_subs
nodes_df = pd.DataFrame([d for x,d in synthetic_G.nodes(data=True)],
index = synthetic_G.nodes)
nodes_joined = (
gpd.GeoDataFrame(nodes_df,
geometry=gpd.points_from_xy(nodes_df.x,
nodes_df.y),
crs = synthetic_G.graph['crs'])
.sjoin(real_subs,
how="right",
predicate="within")
)

nodes_joined = nodes_to_subs(synthetic_G, real_subs)

# Select the most common outlet
outlet = nodes_joined.outlet.value_counts().idxmax()

Expand Down Expand Up @@ -271,6 +313,40 @@ def edge_betweenness_centrality(G: nx.Graph,
bt_c[n] += v
return bt_c

def align_by_subcatchment(var,
synthetic_results: pd.DataFrame,
real_results: pd.DataFrame,
real_subs: gpd.GeoDataFrame,
synthetic_G: nx.Graph,
real_G: nx.Graph) -> pd.DataFrame:
"""Align by subcatchment.
Align synthetic and real results by subcatchment and return the results.
"""
synthetic_joined = nodes_to_subs(synthetic_G, real_subs)
real_joined = nodes_to_subs(real_G, real_subs)

# Extract data
real_results = extract_var(real_results, var)
synthetic_results = extract_var(synthetic_results, var)

# Align data
synthetic_results = pd.merge(synthetic_results,
synthetic_joined[['id','sub_id']],
left_on='object',
right_on = 'id')
real_results = pd.merge(real_results,
real_joined[['id','sub_id']],
left_on='object',
right_on = 'id')

results = pd.merge(real_results[['date','sub_id','value']],
synthetic_results[['date','sub_id','value']],
on = ['date','sub_id'],
suffixes = ('_real', '_sim')
)
return results

@metrics.register
def nc_deltacon0(synthetic_G: nx.Graph,
real_G: nx.Graph,
Expand Down Expand Up @@ -524,4 +600,27 @@ def outlet_pbias_npipes(real_G: nx.Graph,
sg_real, _ = dominant_outlet(real_G, real_results)

return (sg_syn.number_of_edges() - sg_real.number_of_edges()) \
/ sg_real.number_of_edges()
/ sg_real.number_of_edges()


@metrics.register
def subcatchment_nse_flooding(synthetic_G: nx.Graph,
real_G: nx.Graph,
synthetic_results: pd.DataFrame,
real_results: pd.DataFrame,
real_subs: gpd.GeoDataFrame,
**kwargs) -> float:
"""Subcatchment NSE flooding.
Classify synthetic nodes to real subcatchments and calculate the NSE of
flooding over time for each subcatchment. The metric produced is the median
NSE across all subcatchments.
"""
results = align_by_subcatchment('flooding',
synthetic_results = synthetic_results,
real_results = real_results,
real_subs = real_subs,
synthetic_G = synthetic_G,
real_G = real_G)

return median_nse_by_group(results, 'sub_id')
86 changes: 85 additions & 1 deletion tests/test_metric_utilities.py
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Expand Up @@ -361,4 +361,88 @@ def test_netcomp_iterate():
metric_list = netcomp_results.keys())
for metric, val in metrics.items():
assert metric in netcomp_results
assert np.isclose(val, netcomp_results[metric])
assert np.isclose(val, netcomp_results[metric])

def test_subcatchment_nse_flooding():
"""Test the outlet_nse_flow metric."""
# Load data
G = load_graph(Path(__file__).parent / 'test_data' / 'graph_topo_derived.json')
subs = get_subs()

# Mock results
results = pd.DataFrame([{'object' : 4253560,
'variable' : 'flow',
'value' : 10,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 4253560,
'variable' : 'flow',
'value' : 5,
'date' : pd.to_datetime('2021-01-01 00:00:05')},
{'object' : 1696030874,
'variable' : 'flooding',
'value' : 4.5,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 770549936,
'variable' : 'flooding',
'value' : 5,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 107736,
'variable' : 'flooding',
'value' : 10,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 107733,
'variable' : 'flooding',
'value' : 1,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 107737,
'variable' : 'flooding',
'value' : 2,
'date' : pd.to_datetime('2021-01-01 00:00:00')},
{'object' : 1696030874,
'variable' : 'flooding',
'value' : 0,
'date' : pd.to_datetime('2021-01-01 00:00:05')},
{'object' : 770549936,
'variable' : 'flooding',
'value' : 5,
'date' : pd.to_datetime('2021-01-01 00:00:05')},
{'object' : 107736,
'variable' : 'flooding',
'value' : 15,
'date' : pd.to_datetime('2021-01-01 00:00:05')},
{'object' : 107733,
'variable' : 'flooding',
'value' : 2,
'date' : pd.to_datetime('2021-01-01 00:00:05')},
{'object' : 107737,
'variable' : 'flooding',
'value' : 2,
'date' : pd.to_datetime('2021-01-01 00:00:05')}])

# Calculate NSE (perfect results)
val = mu.metrics.subcatchment_nse_flooding(synthetic_G = G,
real_G = G,
synthetic_results = results,
real_results = results,
real_subs = subs)
assert val == 1.0

# Calculate NSE (remapped node)

G_ = G.copy()
# Create a mapping from the old name to the new name
mapping = {1696030874: 'new_name',
107737 : 'new_name2'}

# Rename the node
G_ = nx.relabel_nodes(G_, mapping)

results_ = results.copy()
results_.object = results_.object.replace(mapping)

val = mu.metrics.subcatchment_nse_flooding(synthetic_G = G_,
synthetic_results = results_,
real_G = G,
real_results = results,
real_subs = subs)
assert val == 1.0

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