Merge remote-tracking branch 'origin/more-infra-refactoring'

doc/manual/risk.rst

@@ -600,13 +600,11 @@ References
    network structure, dynamics, and function using NetworkX, in Proceedings of
    the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis
    Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15
-2. Poudel A, Pitilakis K, Silva V, Rao A (2023) Infrastructure Seismic Risk
-   Assessment: An Overview and Integration to Contemporary Open Tool Towards
-   Global Usage, Bulletin of Earthquake Engineering, Under Review
-3. Poudel A, Pitilakis K (2023) Systemic Vulnerability and Risk Assessment of
-   Critical Infrastructures and Systems at Urban Scale: Prospect towards Global
-   Usage, Deliverable ESR 4.5, URBASIS-EU project, Under Review
-4. Pitilakis, K, Franchin P, Khazai B, & Wenzel H, (Eds.) (2014) SYNER-G:
+2. Poudel, A., Pitilakis, K., Silva, V. and Rao, A., (2023). Infrastructure
+   seismic risk assessment: an overview and integration to contemporary open
+   tool towards global usage. Bulletin of Earthquake Engineering.
+   DOI: https://doi.org/10.1007/s10518-023-01693-z
+3. Pitilakis, K, Franchin P, Khazai B, & Wenzel H, (Eds.) (2014) SYNER-G:
    systemic seismic vulnerability and risk assessment of complex urban,
    utility, lifeline systems and critical facilities: methodology and
    applications (Vol. 31), Springer,

openquake/risklib/connectivity.py

@@ -55,7 +55,7 @@ def get_exposure_df(dstore):
 
 def classify_nodes(exposure_df):
     # Classifying the nodes accodingly to compute performance indicator in
-    # global and local scale
+    # global and local level
 
     # TAZ is the acronym of "Traffic Analysis Zone"
     # user can write both as well
@@ -304,13 +304,7 @@ def cleanup_graph(G_original, event_damage_df, g_type):
             (u, v) for (u, v, data) in G.edges(data=True)
             if data['id'] in nonfunctional_edges_df.index.to_list()]
 
-    # nonfunctional_edge_tuples = list(
-    #     zip(nonfunctional_edges_df.start_node,
-    #         nonfunctional_edges_df.end_node)
-    # )
-
     G.remove_edges_from(edges_to_remove)
-    # G.remove_edges_from(nonfunctional_edge_tuples)
     G.remove_nodes_from(nonfunctional_nodes_df.index.to_list())
 
     return G
@@ -561,7 +555,6 @@ def ELWCLPCLloss_TAZ(exposure_df, G_original, TAZ_nodes,
     # For example, traffic analysis zone in transportation network. This
     # calculates, efficiency loss (EL),
     # weighted connectivity loss (WCL),partial connectivity loss(PCL).
-    # Simple connectivity loss (SCL) doesnt make any sense in this case.
 
     # To store the information of the performance indicators at connectivity
     # level
@@ -594,13 +587,6 @@ def ELWCLPCLloss_TAZ(exposure_df, G_original, TAZ_nodes,
                     count = count + 1
         pcl_table.at[i, 'NS0'] = count
 
-    # Code below is not giving correct answer because the path is checked from
-    # a TAZ to every other TAZ
-    # but this will include itself as well. in the above code it is done by
-    # specifying (if i !=j)
-    # pcl_table['NS0'] = [sum(nx.has_path(G_original, j, i) for j in TAZ_nodes)
-    # for i in TAZ_nodes]
-
     att = nx.get_edge_attributes(G_original, 'weight')
     wcl_table = calc_weighted_connectivity_loss(
         G_original, att, TAZ_nodes, TAZ_nodes, wcl_table, pcl_table, 'WS0',