Edit name of spore_rate paramenter and parse it as a editable parameter

R/one_day.R

@@ -6,6 +6,7 @@
 #'
 #' @param daily_vals `list` of model variables which have been calculated for days prior to the `i_date`
 #' @param weather_dat `data.table` of weather observations which includes the query date `i_date`
+#' @param spores_per_gp_per_wet_hour
 #'
 #' @return a `data.table` of values generated for the day `i_date`
 #'
@@ -17,7 +18,8 @@ one_day <- function(i_date,
                     gp_rr,
                     max_gp,
                     max_new_gp,
-                    spore_interception_parameter) {
+                    spore_interception_parameter,
+                    spores_per_gp_per_wet_hour) {
 
   # expand time to be hourly
   i_time <- rep(i_date, 24) + lubridate::dhours(0:23)
@@ -28,7 +30,7 @@ one_day <- function(i_date,
 
   # obtain summary weather for i_day
   i_mean_air_temp <- mean(weather_day[, temp])
-  i_wet_hours <- weather_day[1, wet_hours]
+  i_wet_hours <- weather_day[2, wet_hours]
   i_rainfall <- sum(weather_day[, rain], na.rm = TRUE)
 
   # Start building a list of values for 'i'
@@ -61,7 +63,8 @@ if(any(is.na(daily_vals[["paddock"]][,sporulating_gp]))){
           weather_hourly = weather_day[rain >= 0.2, ],
           paddock = daily_vals[["paddock"]],
           max_interception_probability = max_interception_probability,
-          spore_interception_parameter = spore_interception_parameter
+          spore_interception_parameter = spore_interception_parameter,
+          spores_per_gp_per_wet_hour = spores_per_gp_per_wet_hour
         )
       )
     newly_infected_dt[, cdd_at_infection := daily_vals[["cdd"]]]

R/potentially_effective_spores.R

@@ -1,14 +1,14 @@
 #' Estimate the potentially effective spore
 #'
 #' @param infected_source_address vector of length two giving the paddock coordinates with ascochyta infection
-#' @param sporesPerInfectiveGPPerWetHour estimated number of spores produced in one hour from a single chickpea growing point
+#' @param spores_per_gp_per_wet_hour estimated number of spores produced in one hour from a single chickpea growing point
 #' @param max_interception_probability maximum interception probability
 #' @param paddock_infected_gp integer value giving paddockInfectiveGrowingPoints at paddock coordinates
 #'
 #' @return a double providing the potentially infective spores at the paddock_infected_gp coordinates
 #'
 #' @examples
-potentially_effective_spores <- function(sporesPerInfectiveGPPerWetHour,
+potentially_effective_spores <- function(spores_per_gp_per_wet_hour,
                                          max_interception_probability,
                                          paddock_infected_gp) {
 
@@ -17,13 +17,13 @@ potentially_effective_spores <- function(sporesPerInfectiveGPPerWetHour,
     return(0)
   } else{
     expected_effective_spores <-
-      sporesPerInfectiveGPPerWetHour *
+      spores_per_gp_per_wet_hour *
       max_interception_probability *
       paddock_infected_gp
 
     if(expected_effective_spores < 0) {
       stop("Function 'potentially_effective_spores() returning negative values\n
-           Check parameters sporesPerInfectiveGPPerWetHour and max_interception_probability")
+           Check parameters spores_per_gp_per_wet_hour and max_interception_probability")
     }else{
 
     p_e_s <-

R/spores_each_wet_hour.R

@@ -8,6 +8,8 @@
 #' @param paddock data.table of paddock coordinates detailing the growing points and infections at each location
 #' @param max_interception_probability double with length of one
 #' @param spore_interception_parameter double with length of one
+#' @param spores_per_gp_per_wet_hour Number of spores produced per sporulating growing point each wet hour.
+#'   Also known as the 'spore_rate'. Value is dependent on the susceptibility of the host genotype.
 #' @keywords internal
 #' @noRd
 
@@ -16,7 +18,8 @@ spores_each_wet_hour <- function(h,
                                  weather_hourly,
                                  paddock,
                                  max_interception_probability,
-                                 spore_interception_parameter) {
+                                 spore_interception_parameter,
+                                 spores_per_gp_per_wet_hour) {
 
   # obtain weather data for hour_i
 
@@ -50,6 +53,7 @@ spores_each_wet_hour <- function(h,
 
   newly_infected_dt <-
     apply(paddock_infective, 1, spores_from_1_element,
+          spores_per_gp_per_wet_hour = spores_per_gp_per_wet_hour,
           max_interception_probability = max_interception_probability,
           wind_direction_in_hour = wind_direction_in_hour,
           average_wind_speed_in_hour = average_wind_speed_in_hour,

R/spores_from_1_element.R

@@ -25,7 +25,7 @@
 #' @noRd
 spores_from_1_element <-
   function(paddock_source,
-           sporesPerInfectiveGPPerWetHour = 0.15,
+           spores_per_gp_per_wet_hour = 0.15,
            max_interception_probability,
            wind_direction_in_hour,
            average_wind_speed_in_hour,
@@ -37,7 +37,7 @@ spores_from_1_element <-
 
     # this might be able to be calculated at the spread_spores level, and If statement should come first
     # given that it is if == 0
-    spore_packets <- potentially_effective_spores(sporesPerInfectiveGPPerWetHour = sporesPerInfectiveGPPerWetHour,
+    spore_packets <- potentially_effective_spores(spores_per_gp_per_wet_hour = spores_per_gp_per_wet_hour,
                                                   max_interception_probability = max_interception_probability,
                                                   paddock_source["sporulating_gp"])
 

R/trace_asco.R

@@ -34,7 +34,8 @@
 #'  @param initial_infection refers to initial or primary infection on seedlings,
 #'  resulting in the production of infected growing points
 #'  @param time_zone refers to time in Coordinated Universal Time (UTC)
-#'
+#'  @param spores_per_gp_per_wet_hour Number of spores produced per sporulating growing point each wet hour.
+#'   Also known as the 'spore_rate'. Value is dependent on the susceptibility of the host genotype.
 #'
 #' @return a x y `data.frame` simulating the spread of Ascochyta blight in a
 #' chickpea paddock
@@ -61,8 +62,8 @@ trace_asco <- function(weather,
                        time_zone = "UTC",
                        primary_infection_foci = "random",
                        primary_infection_intensity = 1,
-                       n_foci = 1
-                       ){
+                       n_foci = 1,
+                       spores_per_gp_per_wet_hour = 0.5){
 
 
   # check date inputs for validity -----------------------------------------
@@ -256,7 +257,8 @@ trace_asco <- function(weather,
                        gp_rr = gp_rr,
                        max_gp = max_gp,
                        max_new_gp = max_new_gp,
-                       spore_interception_parameter = spore_interception_parameter)
+                       spore_interception_parameter = spore_interception_parameter,
+                       spores_per_gp_per_wet_hour = spores_per_gp_per_wet_hour)
 
     # temporary line of code to test building of daily_vals in loop
     #daily_vals_list <- day_out

tests/testthat/test-potentially_effective_spores.R

@@ -2,7 +2,7 @@ context("Estimate the potentially effective spores")
 
 test1 <-
   potentially_effective_spores(
-    sporesPerInfectiveGPPerWetHour = 0.15,
+    spores_per_gp_per_wet_hour = 0.15,
     # default parameter of the model
     max_interception_probability = 1,
     # calculated in spread_spores and almost always 1
@@ -17,7 +17,7 @@ test_that("test1 returns zero", {
 
 test2 <-
   potentially_effective_spores(
-    sporesPerInfectiveGPPerWetHour = 0.15,
+    spores_per_gp_per_wet_hour = 0.15,
     # default parameter of the model
     max_interception_probability = 1,
     # calculated in spread_spores and almost always 1
@@ -33,7 +33,7 @@ test_that("test2 returns in range of 0 -2", {
 
 test3 <- sapply(rep(1, 1e5), function(x) {
   potentially_effective_spores(
-    sporesPerInfectiveGPPerWetHour = 0.15,
+    spores_per_gp_per_wet_hour = 0.15,
     max_interception_probability = 1,
     paddock_infected_gp = x
   )
@@ -50,7 +50,7 @@ test_that("test3 returns in range of 100000 iterations", {
 
 test4 <- sapply(rep(40, 1e5), function(x) {
   potentially_effective_spores(
-    sporesPerInfectiveGPPerWetHour = 0.15,
+    spores_per_gp_per_wet_hour = 0.15,
     max_interception_probability = 1,
     paddock_infected_gp = x
   )
@@ -67,7 +67,7 @@ test_that("test3 returns in range of 100000 iterations", {
 test_that("test5 returns an error", {
   expect_error(
     potentially_effective_spores(
-      sporesPerInfectiveGPPerWetHour = 0.15,
+      spores_per_gp_per_wet_hour = 0.15,
       max_interception_probability = -1,
       paddock_infected_gp = 1
     )

tests/testthat/test-spores_from_1_element.R

@@ -35,7 +35,7 @@ test1 <- apply(
   X = paddock_infected,
   MARGIN = 1,
   FUN = spores_from_1_element,
-  sporesPerInfectiveGPPerWetHour = 0.15, # default parameter for the function
+  spores_per_gp_per_wet_hour = 0.15, # default parameter for the function
   max_interception_probability = 1, # parameter in potentially_effective_spores defined in spread_spores
   wind_direction_in_hour = w_dat[1, wd],
   average_wind_speed_in_hour = w_dat[1, ws],
@@ -55,7 +55,7 @@ test2 <- apply(
   X = paddock_infected,
   MARGIN = 1,
   FUN = spores_from_1_element,
-  sporesPerInfectiveGPPerWetHour = 0.15, # default parameter for the function
+  spores_per_gp_per_wet_hour = 0.15, # default parameter for the function
   max_interception_probability = 1, # parameter in potentially_effective_spores defined in spread_spores
   wind_direction_in_hour = w_dat[1, wd],
   average_wind_speed_in_hour = w_dat[1, ws],
@@ -82,7 +82,7 @@ test3 <- apply(
   X = paddock_infected,
   MARGIN = 1,
   FUN = spores_from_1_element,
-  sporesPerInfectiveGPPerWetHour = 0.15, # default parameter for the function
+  spores_per_gp_per_wet_hour = 0.15, # default parameter for the function
   max_interception_probability = 1, # parameter in potentially_effective_spores defined in spread_spores
   wind_direction_in_hour = w_dat[1, wd],
   average_wind_speed_in_hour = w_dat[1, ws],
@@ -119,7 +119,7 @@ test4 <- apply(
   X = paddock_infected,
   MARGIN = 1,
   FUN = spores_from_1_element,
-  sporesPerInfectiveGPPerWetHour = 0.15, # default parameter for the function
+  spores_per_gp_per_wet_hour = 0.15, # default parameter for the function
   max_interception_probability = 1, # parameter in potentially_effective_spores defined in spread_spores
   wind_direction_in_hour = w_dat[1, wd],
   average_wind_speed_in_hour = w_dat[1, ws],