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Fixed networkx import
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@micheles
micheles committed May 19, 2022
1 parent d2634e0 commit 679d013
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2 changes: 1 addition & 1 deletion .github/workflows/engine_test.yml
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Expand Up @@ -23,7 +23,7 @@ jobs:
run: |
python -m pip install --upgrade pip wheel
pip install pytest
pip install fiona pyshp
pip install fiona pyshp networkx
pip install -r requirements-py39-linux64.txt && pip install -e .
python -c'import shapely as s; print("shapely=%s" % s.__version__)'
- name: Test with pytest
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257 changes: 129 additions & 128 deletions openquake/risklib/connectivity.py
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@@ -1,128 +1,129 @@
import networkx as nx
import pandas as pd


# function provided by Astha Poudel and Anirudh Rao
def analysis(dstore):
"""
Performs a connectivity analysis. Assumes the datastore contains
an exposure with `supply_or_demand` and all the needed stuff.
:returns: (functional_demand_nodes, avg_connectivity_loss)
"""
oq = dstore["oqparam"]
calculation_mode = oq.calculation_mode
assert calculation_mode in ("event_based_damage", "scenario_damage")

assetcol = dstore["assetcol"]
tagnames = sorted(tn for tn in assetcol.tagnames if tn != "id")
tags = {t: getattr(assetcol.tagcol, t) for t in tagnames}
exposure_df = assetcol.to_dframe().replace(
{
tagname: {i: tag for i, tag in enumerate(tags[tagname])}
for tagname in tagnames
}
).assign(id=lambda df: df.id.str.decode("utf-8")).set_index("id")

source_nodes = exposure_df.loc[
exposure_df.supply_or_demand == "source"].index.to_list()
demand_nodes = exposure_df.loc[
exposure_df.supply_or_demand == "demand"].index.to_list()

# Create the graph and add edge and node attributes
G_original = nx.from_pandas_edgelist(
exposure_df.loc[exposure_df.type == "edge"],
source="start_node",
target="end_node",
edge_attr=True,
)
nx.set_node_attributes(
G_original,
exposure_df.loc[exposure_df.type == "node"].to_dict("index"))

agg_keys = pd.DataFrame(
{"id": [key.decode() for key in dstore["agg_keys"][:]]})
damage_df = (
dstore.read_df("risk_by_event", "event_id")
.join(agg_keys.id, on="agg_id")
.dropna(subset=["id"])
.set_index("id", append=True)
.drop(columns=["agg_id", "loss_id"])
.sort_index(level=["event_id", "id"])
.astype(int)
.join(exposure_df)
.assign(is_functional=lambda x: x.collapsed == 0)
)[["type", "start_node", "end_node", "is_functional"]]
full_damage_df = (
pd.DataFrame(
index=pd.MultiIndex.from_product(
[damage_df.index.levels[0], exposure_df.index]
),
)
.join(exposure_df.supply_or_demand)
.join(damage_df.is_functional)
.fillna(True)
)

for event_id, event_damage_df in damage_df.groupby("event_id"):
G = G_original.copy()
nodes_damage_df = event_damage_df.loc[
event_damage_df.type == "node"].droplevel(level=0)
edges_damage_df = event_damage_df.loc[
event_damage_df.type == "edge"].droplevel(level=0)

# Updating the graph to remove damaged edges and nodes
nonfunctional_edges_df = edges_damage_df.loc[
edges_damage_df.is_functional == 0]
nonfunctional_nodes_df = nodes_damage_df.loc[
nodes_damage_df.is_functional == 0]
nonfunctional_edge_tuples = list(
zip(nonfunctional_edges_df.start_node,
nonfunctional_edges_df.end_node))
G.remove_edges_from(nonfunctional_edge_tuples)
G.remove_nodes_from(nonfunctional_nodes_df.index.to_list())

# Checking if there is a path between any souce to each demand node
# Some demand nodes and source nodes may have been eliminated from
# the network due to damage, so we do not need to check them
extant_demand_nodes = sorted(set(demand_nodes) & set(G.nodes))
extant_source_nodes = set(source_nodes) & set(G.nodes)
full_damage_df.loc[
(event_id, extant_demand_nodes), "is_functional"] = [
any(
[
nx.has_path(G, demand_node, source_node)
for source_node in extant_source_nodes
]
)
for demand_node in extant_demand_nodes
]

demand_nodes_is_functional = (
full_damage_df.loc[full_damage_df.supply_or_demand == "demand"]
.groupby("id")
.sum()
.is_functional
) # a Series
num_ok_nodes = pd.DataFrame(
{'id': demand_nodes_is_functional.index,
'number': list(demand_nodes_is_functional)}
)
sum_connectivity_loss = sum(
1
- full_damage_df.loc[full_damage_df.supply_or_demand == "demand"]
.groupby("event_id")
.sum()
.is_functional
/ len(demand_nodes)
)
if calculation_mode == "event_based_damage":
inv_time = oq.investigation_time
ses_per_ltp = oq.ses_per_logic_tree_path
num_lt_samples = oq.number_of_logic_tree_samples
eff_inv_time = inv_time * ses_per_ltp * num_lt_samples
avg_connectivity_loss = sum_connectivity_loss / eff_inv_time
elif calculation_mode == "scenario_damage":
num_events = len(damage_df.reset_index().event_id.unique())
avg_connectivity_loss = sum_connectivity_loss / num_events
return num_ok_nodes, avg_connectivity_loss
import pandas as pd


# function provided by Astha Poudel and Anirudh Rao
def analysis(dstore):
"""
Performs a connectivity analysis. Assumes the datastore contains
an exposure with `supply_or_demand` and all the needed stuff.
:returns: (functional_demand_nodes, avg_connectivity_loss)
"""
import networkx as nx # imported only if used

oq = dstore["oqparam"]
calculation_mode = oq.calculation_mode
assert calculation_mode in ("event_based_damage", "scenario_damage")

assetcol = dstore["assetcol"]
tagnames = sorted(tn for tn in assetcol.tagnames if tn != "id")
tags = {t: getattr(assetcol.tagcol, t) for t in tagnames}
exposure_df = assetcol.to_dframe().replace(
{
tagname: {i: tag for i, tag in enumerate(tags[tagname])}
for tagname in tagnames
}
).assign(id=lambda df: df.id.str.decode("utf-8")).set_index("id")

source_nodes = exposure_df.loc[
exposure_df.supply_or_demand == "source"].index.to_list()
demand_nodes = exposure_df.loc[
exposure_df.supply_or_demand == "demand"].index.to_list()

# Create the graph and add edge and node attributes
G_original = nx.from_pandas_edgelist(
exposure_df.loc[exposure_df.type == "edge"],
source="start_node",
target="end_node",
edge_attr=True,
)
nx.set_node_attributes(
G_original,
exposure_df.loc[exposure_df.type == "node"].to_dict("index"))

agg_keys = pd.DataFrame(
{"id": [key.decode() for key in dstore["agg_keys"][:]]})
damage_df = (
dstore.read_df("risk_by_event", "event_id")
.join(agg_keys.id, on="agg_id")
.dropna(subset=["id"])
.set_index("id", append=True)
.drop(columns=["agg_id", "loss_id"])
.sort_index(level=["event_id", "id"])
.astype(int)
.join(exposure_df)
.assign(is_functional=lambda x: x.collapsed == 0)
)[["type", "start_node", "end_node", "is_functional"]]
full_damage_df = (
pd.DataFrame(
index=pd.MultiIndex.from_product(
[damage_df.index.levels[0], exposure_df.index]
),
)
.join(exposure_df.supply_or_demand)
.join(damage_df.is_functional)
.fillna(True)
)

for event_id, event_damage_df in damage_df.groupby("event_id"):
G = G_original.copy()
nodes_damage_df = event_damage_df.loc[
event_damage_df.type == "node"].droplevel(level=0)
edges_damage_df = event_damage_df.loc[
event_damage_df.type == "edge"].droplevel(level=0)

# Updating the graph to remove damaged edges and nodes
nonfunctional_edges_df = edges_damage_df.loc[
edges_damage_df.is_functional == 0]
nonfunctional_nodes_df = nodes_damage_df.loc[
nodes_damage_df.is_functional == 0]
nonfunctional_edge_tuples = list(
zip(nonfunctional_edges_df.start_node,
nonfunctional_edges_df.end_node))
G.remove_edges_from(nonfunctional_edge_tuples)
G.remove_nodes_from(nonfunctional_nodes_df.index.to_list())

# Checking if there is a path between any souce to each demand node
# Some demand nodes and source nodes may have been eliminated from
# the network due to damage, so we do not need to check them
extant_demand_nodes = sorted(set(demand_nodes) & set(G.nodes))
extant_source_nodes = set(source_nodes) & set(G.nodes)
full_damage_df.loc[
(event_id, extant_demand_nodes), "is_functional"] = [
any(
[
nx.has_path(G, demand_node, source_node)
for source_node in extant_source_nodes
]
)
for demand_node in extant_demand_nodes
]

demand_nodes_is_functional = (
full_damage_df.loc[full_damage_df.supply_or_demand == "demand"]
.groupby("id")
.sum()
.is_functional
) # a Series
num_ok_nodes = pd.DataFrame(
{'id': demand_nodes_is_functional.index,
'number': list(demand_nodes_is_functional)}
)
sum_connectivity_loss = sum(
1
- full_damage_df.loc[full_damage_df.supply_or_demand == "demand"]
.groupby("event_id")
.sum()
.is_functional
/ len(demand_nodes)
)
if calculation_mode == "event_based_damage":
inv_time = oq.investigation_time
ses_per_ltp = oq.ses_per_logic_tree_path
num_lt_samples = oq.number_of_logic_tree_samples
eff_inv_time = inv_time * ses_per_ltp * num_lt_samples
avg_connectivity_loss = sum_connectivity_loss / eff_inv_time
elif calculation_mode == "scenario_damage":
num_events = len(damage_df.reset_index().event_id.unique())
avg_connectivity_loss = sum_connectivity_loss / num_events
return num_ok_nodes, avg_connectivity_loss

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