changed final part of script

josura · Aug 12, 2024 · c49c9c6 · c49c9c6
1 parent 7d3dd7b
commit c49c9c6
Showing 1 changed file with 1 addition and 132 deletions.
diff --git a/scripts/R/euroHPC/createInputsSpeedupAnalysis.R b/scripts/R/euroHPC/createInputsSpeedupAnalysis.R
@@ -201,137 +201,6 @@ create_input_graphs <- function(graph, node_conditions, community_data, output_d
 }
 
 #create the directory to contain the significance analysis input data
-dir.create("significanceAnalysys", showWarnings = FALSE)
-
-# create the directory for the graphs generated with preferential attachment (barabasi model)
-dir.create("significanceAnalysys/preferentialAttachment", showWarnings = FALSE)
-
-# create the directory for the graphs generated with erdos renyi model
-dir.create("significanceAnalysys/erdosRenyi", showWarnings = FALSE)
-
-# create the directory for the graphs generated with lattice model
-dir.create("significanceAnalysys/regular", showWarnings = FALSE)
-
-# create the directory for the graphs generated with preferential attachment and aging model
-dir.create("significanceAnalysys/preferentialAttachmentAging", showWarnings = FALSE)
+dir.create("euroHPCgraphs", showWarnings = FALSE)
 
 nodes.list <- c(100, 1000, 10000)
-
-for(numNodes in nodes.list){
-  # create the directory for the defined nodes in the list nodes.list, with barabasi model, generate 30 graphs
-  dir.create(paste0("significanceAnalysys/preferentialAttachment/",numNodes,"nodes"), showWarnings = FALSE)
-
-  # create the directory for the defined nodes in the list nodes.list, with erdos renyi model, generate 30 graphs
-  dir.create(paste0("significanceAnalysys/erdosRenyi/",numNodes,"nodes"), showWarnings = TRUE)
-
-  # create the directory for the defined nodes in the list nodes.list, with lattice model, generate 30 graphs
-  dir.create(paste0("significanceAnalysys/regular/",numNodes,"nodes"), showWarnings = FALSE)
-
-  # create the directory for the defined nodes in the list nodes.list, with preferential attachment and aging model, generate 30 graphs
-  dir.create(paste0("significanceAnalysys/preferentialAttachmentAging/",numNodes,"nodes"), showWarnings = FALSE)
-
-  for (i in 1:30){
-    ## PREFERENTIAL ATTACHMENT
-    current_dir <- paste0("significanceAnalysys/preferentialAttachment/",numNodes,"nodes/",i,"/")
-    dir.create(current_dir, showWarnings = FALSE)
-    # Generate a graph with preferential attachment (100 nodes, m=2 for preferential attachment)
-    graph <- generate_graph_barabasi(numNodes, m = 2)
-
-    # Assign node conditions to the graph
-    node_conditions <- assign_node_conditions(graph, prob_infectious = 0.1)
-
-    # Create communities based on the graph
-    community_data <- create_communities(graph)
-
-    # Generate edge data with edge weights
-    edge_data <- generate_edge_data(graph)
-
-    # Plot the graph with node colors and edge widths
-    #plot_graph(graph, node_conditions)
-
-    create_input_graphs(graph, node_conditions, community_data, current_dir)
-    # Write data to tsv files
-    write_tsv(edge_data, paste0(current_dir,"edge_data.tsv"))
-    write_tsv(node_conditions, paste0(current_dir,"node_conditions.tsv"))
-    write_tsv(community_data, paste0(current_dir,"communities.tsv"))
-
-    print(paste0("saved graph ",i," for ",numNodes," nodes with preferential attachment model."))
-
-    ## PREFERENTIAL ATTACHMENT AGING
-    current_dir <- paste0("significanceAnalysys/preferentialAttachmentAging/",numNodes,"nodes/",i,"/")
-    dir.create(current_dir, showWarnings = FALSE)
-    # Generate a graph with preferential attachment and aging (100 nodes, m=2 for preferential attachment, aging=1 for aging)
-    graph <- generate_graph_barabasi_aging(numNodes, m = 2, aging = 1)
-
-    # Assign node conditions to the graph
-    node_conditions <- assign_node_conditions(graph, prob_infectious = 0.1)
-
-    # Create communities based on the graph
-    community_data <- create_communities(graph)
-
-    # Generate edge data with edge weights
-    edge_data <- generate_edge_data(graph)
-
-    # Plot the graph with node colors and edge widths
-    #plot_graph(graph, node_conditions)
-
-    create_input_graphs(graph, node_conditions, community_data, current_dir)
-    # Write data to tsv files
-    write_tsv(edge_data, paste0(current_dir,"edge_data.tsv"))
-    write_tsv(node_conditions, paste0(current_dir,"node_conditions.tsv"))
-    write_tsv(community_data, paste0(current_dir,"communities.tsv"))
-
-    print(paste0("saved graph ",i," for ",numNodes," nodes with preferential attachment and aging model."))
-
-
-    ## ERDOS RENYI
-    current_dir <- paste0("significanceAnalysys/erdosRenyi/",numNodes,"nodes/",i,"/")
-    dir.create(current_dir, showWarnings = FALSE)
-    # Generate a graph with erdos renyi model (100 nodes, prob=0.1 for erdos renyi)
-    graph <- generate_graph_erdos(numNodes, prob = 10/numNodes)
-
-    # Assign node conditions to the graph
-    node_conditions <- assign_node_conditions(graph, prob_infectious = 0.1)
-
-    # Create communities based on the graph
-    community_data <- create_communities(graph)
-
-    # Generate edge data with edge weights
-    edge_data <- generate_edge_data(graph)
-
-    # Plot the graph with node colors and edge widths
-    #plot_graph(graph, node_conditions)
-
-    create_input_graphs(graph, node_conditions, community_data, current_dir)
-    # Write data to tsv files
-    write_tsv(edge_data, paste0(current_dir,"edge_data.tsv"))
-    write_tsv(node_conditions, paste0(current_dir,"node_conditions.tsv"))
-    write_tsv(community_data, paste0(current_dir,"communities.tsv"))
-  }
-
-  ## Regular graph
-  current_dir <- paste0("significanceAnalysys/regular/",numNodes,"nodes/")
-  #dir.create(current_dir, showWarnings = FALSE)
-
-  # Generate a graph with regular model (100 nodes, degree=2 for regular)
-  graph <- generate_graph_regular(numNodes, degree = 2)
-
-  # Assign node conditions to the graph
-  node_conditions <- assign_node_conditions(graph, prob_infectious = 0.1)
-
-  # Create communities based on the graph
-  community_data <- create_communities(graph)
-
-  # Generate edge data with edge weights
-  edge_data <- generate_edge_data(graph)
-
-  # Plot the graph with node colors and edge widths
-  #plot_graph(graph, node_conditions)
-
-  create_input_graphs(graph, node_conditions, community_data, current_dir)
-  # Write data to tsv files
-  write_tsv(edge_data, paste0(current_dir,"edge_data.tsv"))
-  write_tsv(node_conditions, paste0(current_dir,"node_conditions.tsv"))
-  write_tsv(community_data, paste0(current_dir,"communities.tsv"))
-}
-