Split into two files

R/simulation.r

@@ -0,0 +1,80 @@
+
+# Example usage:
+run_complete_simulation <- function(output_dir,
+                                    output_file = "output.csv",
+                                    plot_file = "SIR_plot.png",
+                                    use_toy_example = TRUE,
+                                    simulation_duration,
+                                    initial_infected) {
+  # Initialize environment
+  pe <- initialize_simulation_env()
+
+  pe <- configure_parameters(pe, input_dir, config_parameters)
+
+  # Create all parameter sets
+  pop_params <- list(
+    population_size = as.integer(100),
+    cell_number = as.integer(2),
+    microcell_number = as.integer(2),
+    household_number = as.integer(5),
+    place_number = as.integer(2)
+  )
+
+  sim_params <- list(
+    simulation_start_time = as.integer(0),
+    simulation_end_time = as.integer(simulation_duration),
+    simulation_seed = TRUE,
+    initial_infected_number = as.integer(initial_infected),
+    initial_infect_cell = TRUE,
+    include_waning = TRUE
+  )
+
+  file_params <- list(
+    output_file = output_file,
+    output_dir = output_dir,
+    spatial_output = FALSE,
+    age_stratified = FALSE
+  )
+
+  dem_file_params <- list(
+    output_dir = output_dir,
+    spatial_output = FALSE,
+    age_output = FALSE
+  )
+
+  inf_history_params <- list(
+    output_dir = output_dir,
+    status_output = TRUE,
+    infectiousness_output = TRUE,
+    compress = FALSE,
+    secondary_infections_output = TRUE,
+    generation_time_output =  TRUE
+  )
+
+  # User defined variables - see README for instructions
+  input_dir <- "./data/Andorra/inputs"
+  config_parameters <- ".data/Andorra/inputs/simple_parameters"
+  epigeopop_file <- "./data/Andorra/inputs/Andorra_microcells.csv"
+  seed <- 42
+  output_dir <- "./data/Andorra/simulation_outputs"
+  simulation_duration <- 60
+  initial_infected <- 10
+
+  # Run simulation
+  source(wrapper.R)
+  sim <- run_simulation(pe, sim_params, file_params, dem_file_params,
+                        inf_history_params, pop_params,
+                        epigeopop_file = epigeopop_file, seed,
+                        use_toy_example = use_toy_example)
+
+  # Process data and create plot
+  df_long <- process_simulation_data(file.path(output_dir, output_file))
+  plot <- create_sir_plot(df_long)
+
+  # Save plot
+  save_sir_plot(plot, file.path(output_dir, plot_file))
+
+  return(list(simulation = sim, data = df_long, plot = plot))
+}
+
+results <- run_complete_simulation()

R/wrapper.R

@@ -0,0 +1,132 @@
+library(reticulate)
+library(here)
+library(tidyr)
+library(ggplot2)
+
+# Initialize Python environment
+initialize_simulation_env <- function() {
+  source("R/zzz.R")
+  initialize_python_env()
+  check_python_env()
+
+  # Import Python dependencies
+  os <- import("os", delay_load = TRUE)
+  logging <- import("logging", delay_load = TRUE)
+  pd <- import("pandas", delay_load = TRUE)
+  plt <- import("matplotlib.pyplot", delay_load = TRUE)
+  pe <- import("pyEpiabm", delay_load = TRUE)
+  return(pe)
+}
+
+configure_parameters <- function(pe, input_dir, config_parameters) {
+  pe$Parameters$set_file(here(input_dir, config_parameters))
+  return(pe)
+}
+
+# Wrap python simulation function
+run_simulation <- function(pe, sim_params, file_params, dem_file_params,
+                           inf_history_params, pop_params = NULL,
+                           epigeopop_file = "", seed = 42,
+                           use_toy_example = FALSE) {
+  # Set seed
+  pe$routine$Simulation$set_random_seed(seed = as.integer(seed))
+  # Create population or load from file
+  if (epigeopop_file == "") {
+    population <- pe$routine$ToyPopulationFactory()$
+      make_pop(pop_params)
+  } else {
+    population <- pe$routine$FilePopulationFactory()$
+      make_pop_from_file(epigeopop_file)
+  }
+
+  # Create and configure simulation
+  sim <- pe$routine$Simulation()
+  sim$configure(
+    population,
+    list(
+      pe$sweep$InitialInfectedSweep(),
+      pe$sweep$InitialDemographicsSweep(dem_file_params)
+    ),
+    list(
+      pe$sweep$HouseholdSweep(),
+      pe$sweep$QueueSweep(),
+      pe$sweep$HostProgressionSweep()
+    ),
+    sim_params,
+    file_params,
+    inf_history_params
+  )
+
+  # Run simulation
+  sim$run_sweeps()
+  sim$compress_csv()
+
+  return(sim)
+}
+
+# Process simulation data
+process_simulation_data <- function(output_file) {
+  df <- read.csv(here(output_file))
+
+  status_columns <- c(
+    "InfectionStatus.Susceptible",
+    "InfectionStatus.InfectMild",
+    "InfectionStatus.Recovered",
+    "InfectionStatus.Dead"
+  )
+  df_long <- pivot_longer(
+    df,
+    cols = all_of(status_columns),
+    names_to = "Status",
+    values_to = "Count"
+  )
+  df_long$Status <- factor(
+    df_long$Status,
+    levels = status_columns,
+    labels = c("Susceptible", "Infected", "Recovered", "Dead")
+  )
+
+  return(df_long)
+}
+
+# Create SIR plot
+create_sir_plot <- function(df_long, title = "SIR Model Flow", display = TRUE) {
+  p <- ggplot(df_long, aes(x = time, y = Count, color = Status)) +
+    geom_line() +
+    scale_color_manual(
+      values = c(
+        "Susceptible" = "blue",
+        "Infected" = "red",
+        "Recovered" = "green",
+        "Dead" = "black"
+      )
+    ) +
+    theme_minimal() +
+    labs(
+      title = title,
+      x = "Time",
+      y = "Count"
+    ) +
+    theme(
+      legend.position = "right",
+      plot.title = element_text(hjust = 0.5),
+      panel.grid.minor = element_blank()
+    )
+
+  if (display) {
+    print(p)
+  }
+
+  return(p)
+}
+
+# Save plot
+save_sir_plot <- function(plot, filename, width = 10, height = 6, dpi = 300) {
+  ggsave(
+    filename = here(filename),
+    plot = plot,
+    width = width,
+    height = height,
+    dpi = dpi
+  )
+}

R/zzz.R

@@ -78,7 +78,7 @@ for package in packages:
 # Install pyEpiabm from GitHub
 subprocess.check_call([
     sys.executable, '-m', 'pip', 'install', '--upgrade',
-    'git+https://github.com/SABS-R3-Epidemiology/epiabm.git@main#egg=pyEpiabm&subdirectory=pyEpiabm'
+    'git+https://github.com/SABS-R3-Epidemiology/epiabm.git@main#egg=pyEpiabm&subdirectory=pyEpiabm' # nolint: line_length_linter.
 ])
   ")
   # Verify installations
@@ -108,6 +108,7 @@ load_python_modules <- function() {
   modules <- list(
     os = "os",
     logging = "logging",
+    np = "numpy",
     pd = "pandas",
     plt = "matplotlib.pyplot",
     pe = "pyEpiabm"