add GHA to run pollock tests

.github/workflows/run-pollock-tests-by-fleet.yml

@@ -0,0 +1,39 @@
+on:
+  workflow_dispatch:
+
+name: Run pollock tests (by fleet)
+
+jobs:
+  build-deploy:
+    runs-on: macos-latest
+    env:
+          GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v2 
+
+      - name: Set up R (needed for Rmd)
+        uses: r-lib/actions/setup-r@v2
+
+      - name: Run pollock tests 
+        run: |
+          # Names of required packages
+          packages <- c("dplyr", "tidyr", "ggplot2", "TMB", "reshape2", "here", "remotes", "lubridate")
+
+          # Install packages not yet installed
+          installed_packages <- packages %in% rownames(installed.packages())
+          if (any(installed_packages == FALSE)) {
+            install.packages(packages[!installed_packages], repos = "http://cran.us.r-project.org")
+          }
+          
+          install.packages("FIMS", repos = c("https://noaa-fims.r-universe.dev", "https://cloud.r-project.org"))
+
+          # Load packages
+          invisible(lapply(packages, library, character.only = TRUE))
+
+          source(file.path(getwd(), "content", "run_pollock_tests_by_fleet.R"))
+        shell: Rscript {0}
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3

.github/workflows/run-pollock-tests-by-process.yml

@@ -0,0 +1,39 @@
+on:
+  workflow_dispatch:
+
+name: Run pollock tests (by process)
+
+jobs:
+  build-deploy:
+    runs-on: macos-latest
+    env:
+          GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v2 
+
+      - name: Set up R (needed for Rmd)
+        uses: r-lib/actions/setup-r@v2
+
+      - name: Run pollock tests 
+        run: |
+          # Names of required packages
+          packages <- c("TMB")
+
+          # Install packages not yet installed
+          installed_packages <- packages %in% rownames(installed.packages())
+          if (any(installed_packages == FALSE)) {
+            install.packages(packages[!installed_packages], repos = "http://cran.us.r-project.org")
+          }
+          
+          install.packages("FIMS", repos = c("https://noaa-fims.r-universe.dev", "https://cloud.r-project.org"))
+
+          # Load packages
+          invisible(lapply(packages, library, character.only = TRUE))
+
+          source(file.path(getwd(), "content", "run_pollock_tests.R"))
+        shell: Rscript {0}
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3

content/R/pk_prepare_dat.R

@@ -2,14 +2,12 @@
 
 ## build a FIMS and PK data set that match
 
-pkfitfinal <- readRDS("data_files/pkfitfinal.RDS")
-pkfit0 <- readRDS("data_files/pkfit0.RDS")
+pkfitfinal <- readRDS(file.path(getwd(), "content", "data_files", "pkfitfinal.RDS"))
+pkfit0 <- readRDS(file.path(getwd(), "content", "data_files", "pkfit0.RDS"))
 parfinal <- pkfitfinal$obj$env$parList()
-pkinput0 <- readRDS('data_files/pkinput0.RDS')
+pkinput0 <- readRDS(file.path(getwd(), "content", "data_files", "pkinput0.RDS"))
 fimsdat <- pkdat0 <- pkinput0$dat
-pkinput <- readRDS('data_files/pkinput.RDS')
-
-
+pkinput <- readRDS(file.path(getwd(), "content", "data_files", "pkinput.RDS"))
 
 ##  need to fill missing years with -999 so it's ignored in FIMS
 ind2 <- 0 * pkfit0$rep$Eindxsurv2 - 999

content/run_pollock_tests.R

@@ -13,7 +13,8 @@ ages <- 1:nages
 ## This will fit the models bridging to FIMS (simplifying)
 ## source("fit_bridge_models.R")
 ## compare changes to model
-source("R/pk_prepare_dat.R")
+# source("R/pk_prepare_dat.R")
+source(file.path(getwd(), "content", "R", "pk_prepare_dat.R"))
 ## Some global settings which I Think we can ignore for now
 estimate_fish_selex <- TRUE
 estimate_survey_selex <- TRUE
@@ -27,7 +28,8 @@ estimate_recdevs <- TRUE
 
 clear()
 clear_logs()
-source("R/pk_prepare_FIMS_inputs.R")
+# source("R/pk_prepare_FIMS_inputs.R")
+source(file.path(getwd(), "content", "R", "pk_prepare_FIMS_inputs.R"))
 ## make FIMS model
 success <- CreateTMBModel()
 parameters <- list(p = get_fixed())
@@ -38,7 +40,8 @@ rep1 <- obj1$report()
 ## Organize input lists by process
 clear()
 clear_logs()
-source("R/pk_prepare_FIMS_inputs_by_process.R")
+# source("R/pk_prepare_FIMS_inputs_by_process.R")
+source(file.path(getwd(), "content", "R", "pk_prepare_FIMS_inputs_by_process.R"))
 ## make FIMS model
 success <- CreateTMBModel()
 parameters <- list(p = get_fixed())

content/run_pollock_tests_by_fleet.R

@@ -1,14 +1,14 @@
 ## define the dimensions and global variables
 library(FIMS)
 
-source("R/pk_prepare_FIMS_inputs_by_fleet.R")
+source(file.path(getwd(), "content", "R", "pk_prepare_FIMS_inputs_by_fleet.R"))
 
 years <- 1970:2023
 nyears <- length(years)
 nseasons <- 1
 nages <- 10
 ages <- 1:nages
-source("R/pk_prepare_dat.R")
+source(file.path(getwd(), "content", "R", "pk_prepare_dat.R"))
 
 
 # test R functions: approach 1 --------------------------------------------
@@ -194,115 +194,115 @@ clear_logs()
 
 
 # test R functions: approach 2 --------------------------------------------
-selectivity_double_logistic <- set_selectivity(
-  form = "DoubleLogisticSelectivity",
-  initial_values = c(
-    parfinal$inf1_fsh_mean, exp(parfinal$log_slp1_fsh_mean),
-    parfinal$inf2_fsh_mean, exp(parfinal$log_slp2_fsh_mean)
-  ),
-  is_estimated = rep(TRUE, 4),
-  is_random_effect = rep(FALSE, 4)
-)
-
-selectivity_logistic <- set_selectivity(
-  form = "LogisticSelectivity",
-  initial_values = c(
-    parfinal$inf1_fsh_mean, exp(parfinal$log_slp1_fsh_mean)
-  ),
-  is_estimated = rep(TRUE, 2),
-  is_random_effect = rep(FALSE, 2)
-)
-
-selectivity <- vector(mode = "list", length = 4)
-selectivity[[1]] <- selectivity[[2]] <- selectivity_double_logistic
-selectivity[[3]] <- selectivity_logistic
-selectivity[[4]] <- selectivity_double_logistic # Order matters because the selectivity modules assign unique IDs.
-
-fleet <- set_fleet(
-  data = age_frame,
-  is_estimated_obs_error = list(FALSE, FALSE, FALSE, FALSE),
-  selectivity_ctl = NULL,
-  selectivity_module_list = selectivity,
-  Fmort_ctl = list(
-    is_survey = list(FALSE, TRUE, TRUE, TRUE),
-    estimate_F = list(TRUE, FALSE, FALSE, FALSE),
-    random_F = list(FALSE, FALSE, FALSE, FALSE)
-  ),
-  catchability_ctl = list(
-    log_q = list(
-      0, parfinal$log_q2_mean,
-      parfinal$log_q3_mean, parfinal$inf1_srv6
-    ),
-    estimate_q = list(FALSE, TRUE, TRUE, TRUE),
-    random_q = list(FALSE, FALSE, FALSE, FALSE)
-  )
-)
-
-# Population module
-# recruitment
-recruitment <- set_recruitment(
-  form = "BevertonHoltRecruitment",
-  initial_values = list(
-    log_rzero = parfinal$mean_log_recruit + log(1e9),
-    logit_steep = -log(1.0 - .99999) + log(.99999 - 0.2),
-    log_sigma_recruit = log(parfinal$sigmaR),
-    log_devs = parfinal$dev_log_recruit[-1]
-  ),
-  is_estimated = list(
-    log_rzero = TRUE,
-    logit_steep = FALSE,
-    log_sigma_recruit = FALSE,
-    log_devs = TRUE
-  ),
-  is_random_effect = list(
-    log_rzero = FALSE,
-    logit_steep = FALSE,
-    log_sigma_recruit = FALSE
-  )
-)
-
-## growth  -- assumes single WAA vector for everything, based on
-## Srv1 above
-growth <- set_growth(
-  data = population_data,
-  form = "EWAAgrowth",
-  initial_values = list(weights = pkinput$dat$wt_srv1)
-)
-## NOTE: FIMS assumes SSB calculated at the start of the year, so
-## need to adjust ASAP to do so as well for now, need to make
-## timing of SSB calculation part of FIMS later
-## maturity
-## NOTE: for now tricking FIMS into thinking age 0 is age 1, so need to adjust A50 for maturity because FIMS calculations use ages 0-5+ instead of 1-6
-maturity <- set_maturity(
-  form = maturity_ctl$form,
-  initial_values = maturity_ctl$initial_values,
-  is_estimated = maturity_ctl$is_estimated,
-  is_random_effect = maturity_ctl$is_random_effect
-)
-
-# population
-population <- set_population(
-  data = population_data,
-  initial_values = population_initial_values,
-  is_estimated = population_is_estimated,
-  maturity = maturity,
-  growth = growth,
-  recruitment = recruitment
-)
-
-## make FIMS model
-success <- CreateTMBModel()
-parameters <- list(p = get_fixed())
-obj4 <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE)
-## report values for the two models
-rep4 <- obj4$report()
-
-## Parameters are in a different order but seem to match
-opt4 <- with(obj4, nlminb(par, fn, gr))
-clear()
-clear_logs()
-# rep4
-# rm(list = ls())
-
-## Test that the two models are identical
-all.equal(rep3, rep4)
+# selectivity_double_logistic <- set_selectivity(
+#   form = "DoubleLogisticSelectivity",
+#   initial_values = c(
+#     parfinal$inf1_fsh_mean, exp(parfinal$log_slp1_fsh_mean),
+#     parfinal$inf2_fsh_mean, exp(parfinal$log_slp2_fsh_mean)
+#   ),
+#   is_estimated = rep(TRUE, 4),
+#   is_random_effect = rep(FALSE, 4)
+# )
+# 
+# selectivity_logistic <- set_selectivity(
+#   form = "LogisticSelectivity",
+#   initial_values = c(
+#     parfinal$inf1_fsh_mean, exp(parfinal$log_slp1_fsh_mean)
+#   ),
+#   is_estimated = rep(TRUE, 2),
+#   is_random_effect = rep(FALSE, 2)
+# )
+# 
+# selectivity <- vector(mode = "list", length = 4)
+# selectivity[[1]] <- selectivity[[2]] <- selectivity_double_logistic
+# selectivity[[3]] <- selectivity_logistic
+# selectivity[[4]] <- selectivity_double_logistic # Order matters because the selectivity modules assign unique IDs.
+# 
+# fleet <- set_fleet(
+#   data = age_frame,
+#   is_estimated_obs_error = list(FALSE, FALSE, FALSE, FALSE),
+#   selectivity_ctl = NULL,
+#   selectivity_module_list = selectivity,
+#   Fmort_ctl = list(
+#     is_survey = list(FALSE, TRUE, TRUE, TRUE),
+#     estimate_F = list(TRUE, FALSE, FALSE, FALSE),
+#     random_F = list(FALSE, FALSE, FALSE, FALSE)
+#   ),
+#   catchability_ctl = list(
+#     log_q = list(
+#       0, parfinal$log_q2_mean,
+#       parfinal$log_q3_mean, parfinal$inf1_srv6
+#     ),
+#     estimate_q = list(FALSE, TRUE, TRUE, TRUE),
+#     random_q = list(FALSE, FALSE, FALSE, FALSE)
+#   )
+# )
+# 
+# # Population module
+# # recruitment
+# recruitment <- set_recruitment(
+#   form = "BevertonHoltRecruitment",
+#   initial_values = list(
+#     log_rzero = parfinal$mean_log_recruit + log(1e9),
+#     logit_steep = -log(1.0 - .99999) + log(.99999 - 0.2),
+#     log_sigma_recruit = log(parfinal$sigmaR),
+#     log_devs = parfinal$dev_log_recruit[-1]
+#   ),
+#   is_estimated = list(
+#     log_rzero = TRUE,
+#     logit_steep = FALSE,
+#     log_sigma_recruit = FALSE,
+#     log_devs = TRUE
+#   ),
+#   is_random_effect = list(
+#     log_rzero = FALSE,
+#     logit_steep = FALSE,
+#     log_sigma_recruit = FALSE
+#   )
+# )
+# 
+# ## growth  -- assumes single WAA vector for everything, based on
+# ## Srv1 above
+# growth <- set_growth(
+#   data = population_data,
+#   form = "EWAAgrowth",
+#   initial_values = list(weights = pkinput$dat$wt_srv1)
+# )
+# ## NOTE: FIMS assumes SSB calculated at the start of the year, so
+# ## need to adjust ASAP to do so as well for now, need to make
+# ## timing of SSB calculation part of FIMS later
+# ## maturity
+# ## NOTE: for now tricking FIMS into thinking age 0 is age 1, so need to adjust A50 for maturity because FIMS calculations use ages 0-5+ instead of 1-6
+# maturity <- set_maturity(
+#   form = maturity_ctl$form,
+#   initial_values = maturity_ctl$initial_values,
+#   is_estimated = maturity_ctl$is_estimated,
+#   is_random_effect = maturity_ctl$is_random_effect
+# )
+# 
+# # population
+# population <- set_population(
+#   data = population_data,
+#   initial_values = population_initial_values,
+#   is_estimated = population_is_estimated,
+#   maturity = maturity,
+#   growth = growth,
+#   recruitment = recruitment
+# )
+# 
+# ## make FIMS model
+# success <- CreateTMBModel()
+# parameters <- list(p = get_fixed())
+# obj4 <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS", silent = TRUE)
+# ## report values for the two models
+# rep4 <- obj4$report()
+# 
+# ## Parameters are in a different order but seem to match
+# opt4 <- with(obj4, nlminb(par, fn, gr))
+# clear()
+# clear_logs()
+# # rep4
+# # rm(list = ls())
+# 
+# ## Test that the two models are identical
+# all.equal(rep3, rep4)