replacing verbose with quiet

src/model_fitting/abundance_model.R

@@ -35,8 +35,8 @@ option_list <- list (
               default=FALSE,      help="include aerial transects"),
   make_option("--stability",       action="store_true", default=FALSE,
               help="do stability analysis"),
-  make_option(c("-v", "--verbose"), dest = "verbose_script", action = "store_true",
-              default = TRUE, help = "print all intermediate results")
+  make_option(c("-q", "--quiet"), dest = "quiet_script", action = "store_true",
+              default = FALSE, help = "don't print all intermediate results")
 )
 
 options <- parse_args(OptionParser(option_list=option_list))
@@ -55,26 +55,26 @@ if (!is.na(options$exclude_year) && !(options$exclude_year %in% exclude_year_pos
   stop(paste("exclude year must be between", min(exclude_year_possibilities), "and", max(exclude_year_possibilities)))
 }
 
-is_verbose <- options$verbose_script
+is_quiet <- options$quiet_script
 
-if(is_verbose){print(paste("Reminder: Include aerial is ", options$include_aerial, ". Please
+if(is_quiet){print(paste("Reminder: Include aerial is ", options$include_aerial, ". Please
             pay attention that the same is the case for post-processing."))}
 # input directory
 indir <- options$input_directory
-if(is_verbose){print(paste("indir", indir))}
+if(is_quiet){print(paste("indir", indir))}
 
 # directory in which output is written
 outdir <- options$output_directory
-if(is_verbose){print(paste("outdir", outdir))}
+if(is_quiet){print(paste("outdir", outdir))}
 
 do_stability <- options$stability
-if(is_verbose){print(paste("stability", do_stability))}
+if(is_quiet){print(paste("stability", do_stability))}
 
 include_aerial <- options$include_aerial
-if(is_verbose){print(paste("include_aerial", include_aerial))}
+if(is_quiet){print(paste("include_aerial", include_aerial))}
 
 exclude_year <- options$exclude_year
-if(is_verbose){print(paste("exclude year", exclude_year))}
+if(is_quiet){print(paste("exclude year", exclude_year))}
 
 
 
@@ -83,7 +83,7 @@ if(is_verbose){print(paste("exclude year", exclude_year))}
 #---------#
 
 indir_fun <- "../functions"
-if(is_verbose){print(paste("indir_fun", indir_fun))}
+if(is_quiet){print(paste("indir_fun", indir_fun))}
 
 cl <- makeForkCluster(outfile = "")
 registerDoParallel(cl)
@@ -107,16 +107,16 @@ options("scipen" = 100, "digits" = 4)
 #---------------#
 
 geography_path <- path.to.current(indir, "geography_observation", "rds")
-if(is_verbose){print(paste("geography-path", geography_path))}
+if(is_quiet){print(paste("geography-path", geography_path))}
 geography <- readRDS(geography_path)
 
 transects_path <- path.to.current(indir, "transects", "rds")
-if(is_verbose){print(paste("transect_path", transects_path))}
+if(is_quiet){print(paste("transect_path", transects_path))}
 transects <- readRDS(transects_path)
 
 
 predictors_path <- path.to.current(indir, "predictors_observation", "rds")
-if(is_verbose){print(paste("predictors-path", predictors_path))}
+if(is_quiet){print(paste("predictors-path", predictors_path))}
 predictors <- readRDS(predictors_path)
 
 
@@ -129,7 +129,7 @@ predictor_names <- c("year", "temp_mean", "rain_var", "rain_dry", "dom_T_OC",
 
 geography <- dplyr::select(geography, -year)
 
-if(is_verbose){print("how many rows with na in scaled_value")
+if(is_quiet){print("how many rows with na in scaled_value")
 nrow(predictors[is.na(predictors$scaled_value),  ])}
 # deleting is.na values here
 predictors <- predictors[!is.na(predictors$scaled_value), ]
@@ -176,7 +176,7 @@ aerial_predictors_obs$nr_nests <- round(exp(4.7297 + 0.9796 *
 
   predictors_obs <- aerial_predictors_obs %>%
     dplyr::select(id:length_km, nr_nests, nest_decay, ou_dens, offset_term)
-  if(is_verbose){ print("This has to be true:")
+  if(is_quiet){ print("This has to be true:")
   unique(names(predictors_obs) == names(other_predictors_obs))}
   # HAS TO BE TRUE
   predictors_obs <- predictors_obs %>%
@@ -203,7 +203,7 @@ predictors_obs$y_center <- rowMeans(cbind(predictors_obs$y_start, predictors_obs
 predictors_obs$nr_ou_per_km2 <- predictors_obs$nr_nests /
   (predictors_obs$length_km * ESW * 2 * predictors_obs$nest_decay  * NCS * PNB )
 
-if(is_verbose){print("look at predictors_obs")
+if(is_quiet){print("look at predictors_obs")
 str(predictors_obs)
 summary(predictors_obs)}
 
@@ -213,7 +213,7 @@ if (!is.na(exclude_year)){
     predictors_excluded_year <- predictors_obs[predictors_obs$unscaled_year == exclude_year, ] }
     predictors_obs <- predictors_obs[predictors_obs$unscaled_year != exclude_year, ]
 
-if(is_verbose){ print(paste("3. start making all_model_terms", Sys.time()))}
+if(is_quiet){ print(paste("3. start making all_model_terms", Sys.time()))}
 
  # #build models needed for analysis with a function
 all_model_terms <- built.all.models(env.cov.names =
@@ -273,11 +273,11 @@ names(predictor_estimates) <- c("intercept", predictor_names,
 
 # calculate stability of the full model if desired
 if(do_stability){
-  if(is_verbose){print(paste("Start stability calculation", Sys.time()))}
+  if(is_quiet){print(paste("Start stability calculation", Sys.time()))}
 full_model <- paste(
   m_terms[all_model_terms[nrow(all_model_terms), ] == 1],
   collapse = "+")
-if(is_verbose){print(paste("This is the full-model", full_model))}
+if(is_quiet){print(paste("This is the full-model", full_model))}
 model <- as.formula(
   paste("nr_nests ~", full_model, "+ offset(offset_term)"))
 
@@ -295,7 +295,7 @@ write.csv(dfbeta_frame, file.path(outdir,
 }
 
 # #run models
-if(is_verbose){print(paste("8. Start running models", Sys.time()))}
+if(is_quiet){print(paste("8. Start running models", Sys.time()))}
 
 
 if (is.na(exclude_year)){
@@ -430,4 +430,4 @@ write.csv(summary_mean_coefficients,
 save.image(file.path(outdir, paste0("abundance_model_fitting_",
                                     name_suffix,
                                     Sys.Date(), ".RData")))
-if(is_verbose){print(paste("11. finished script, finally, at", Sys.time()))}
+if(is_quiet){print(paste("11. finished script, finally, at", Sys.time()))}

src/prediction/abundance_prediction.R

@@ -47,8 +47,8 @@ option_list <- list (
               type = "integer",
               help = "year of the survey years (1994:2015) to predict abundance to",
               metavar = "2015"),
-  make_option(c("-v", "--verbose"), dest = "verbose_script", action = "store_true",
-              default = TRUE, help = "print all intermediate results")
+  make_option(c("-q", "--quiet"), dest = "quiet_script", action = "store_true",
+              default = FALSE, help = "don't print all intermediate results")
 )
 
 options <- parse_args(OptionParser(option_list=option_list))
@@ -84,30 +84,30 @@ if (!(options$year_to_predict %in% year_to_predict_possibilities)) {
 }
 
 
-# is verbose?
-is_verbose <- options$verbose_script
+# is quiet?
+is_quiet <- options$quiet_script
 # input directory
 
 
 indir <- options$input_directory
-if(is_verbose){print(paste("indir", indir))}
+if(is_quiet){print(paste("indir", indir))}
 
 # input directory
 indir_predictors <- options$pred_input_directory
-if(is_verbose){print(paste("indir_predictors", indir_predictors))}
+if(is_quiet){print(paste("indir_predictors", indir_predictors))}
 
 # directory in which output is written
 outdir <- options$output_directory
-if(is_verbose){print(paste("outdir", outdir))}
+if(is_quiet){print(paste("outdir", outdir))}
 
 year_to_predict <- as.numeric(options$year_to_predict )
-if(is_verbose){print(paste("year to predict " , year_to_predict))}
+if(is_quiet){print(paste("year to predict " , year_to_predict))}
 
 
 exclude_year <- as.numeric(options$exclude_year)
-if(is_verbose){print(paste("exclude year", exclude_year))}
+if(is_quiet){print(paste("exclude year", exclude_year))}
 
-if(is_verbose){print(paste(Sys.time(), "0. start run"))}
+if(is_quiet){print(paste(Sys.time(), "0. start run"))}
 #------------------------#
 # command line arguments #
 #------------------------#
@@ -126,7 +126,7 @@ source(file.path(indir_fun, "generic/path.to.current.R"))
 
 # Load coefficients and weights
 abundMod_results_path <- path.to.current(indir, "abundMod_results", "rds" )
-if(is_verbose){print(paste("this is abundMod_results_path:", abundMod_results_path))}
+if(is_quiet){print(paste("this is abundMod_results_path:", abundMod_results_path))}
 abundMod_results <- readRDS(abundMod_results_path)
 # exclude the first column, which contains models, exclude the coefficient of the
 # autocorellation term and the weighted aic of the model
@@ -160,19 +160,19 @@ predictor_names <- predictor_names_coeffs[!grepl("I(*)", predictor_names_coeffs)
 #----------------------------#
 # Load and prepare estimates #
 #----------------------------#
-if(is_verbose){print(paste("these are predictor names: ", predictor_names))}
+if(is_quiet){print(paste("these are predictor names: ", predictor_names))}
 predictors_path <- path.to.current(indir_predictors, paste0("predictors_abundance_",
                                                  year_to_predict),"rds")
-if(is_verbose){print(paste("this is predictors path", predictors_path))}
+if(is_quiet){print(paste("this is predictors path", predictors_path))}
 predictors <- readRDS(predictors_path) %>%
  dplyr::filter(predictor %in% predictor_names) %>%
     dcast(id + z_year ~ predictor,  value.var = "scaled_value")
 
 predictors$year <- predictors$z_year
 predictors$z_year <- NULL
-if(is_verbose){str(predictors)}
+if(is_quiet){str(predictors)}
 
-if(is_verbose){print(paste("this is nrow predictors", nrow(predictors)))}
+if(is_quiet){print(paste("this is nrow predictors", nrow(predictors)))}
 
 #--------------------------#
 # PREDICTION FOR each year #
@@ -196,7 +196,7 @@ names(predictor_estimates) <- c("intercept", predictor_names,
 # but takes a bit longer (52s, to 35s for 100 rows)
 ## PLUS PAY ATTENTION, IF PREDICTIONS NOT SAME NROW--> VALUES GET RECYCLED
 
-if(is_verbose){print(paste("1. start pred_per_cell", Sys.time()))}
+if(is_quiet){print(paste("1. start pred_per_cell", Sys.time()))}
 pred_per_cell <- foreach(i = 1:nrow(predictor_estimates), .combine = c)  %dopar% {
 # pred_per_cell <- foreach(i = 1:100, .combine = c)  %dopar% {
 t_predictor_estimates <- t( predictor_estimates[i, ])
@@ -207,7 +207,7 @@ pred_estimates_sum <- apply(pred_estimates_wcoeffs, 1, sum)
 return(exp(pred_estimates_calc))
 }
 
-if(is_verbose){print(paste(Sys.time(), "2. finished dopar loop"))}
+if(is_quiet){print(paste(Sys.time(), "2. finished dopar loop"))}
 
 # is this correct -> ????
 pred_per_cell <- as.data.frame(cbind(predictors$id, pred_per_cell))
@@ -252,7 +252,7 @@ save.image(file.path(outdir, paste0("abundance_pred_image_", name_suffix,
 #-----------------------#
 # convert output to map #
 #-----------------------#
-if(is_verbose){print(paste(Sys.time(), "3. Start making map"))}
+if(is_quiet){print(paste(Sys.time(), "3. Start making map"))}
 
 geography_grid_path <- path.to.current(indir_predictors,
                                        paste0("geography_", year_to_predict), "rds")
@@ -302,4 +302,4 @@ save.image(file.path(outdir, paste0("abundance_pred_image_", name_suffix,
 
 
 
-if(is_verbose){print(paste(Sys.time(), "4. wrote results and done :-)"))}
+if(is_quiet){print(paste(Sys.time(), "4. wrote results and done :-)"))}