change calls to repeated print to cat

R/auto_crop_fast.R

@@ -108,11 +108,7 @@ auto_crop_fast <- function(img_path,  max_cell_area = 20000, min_cell_area = 700
       antibody3_store <- 0
     }
   }
-print("out of")
-print(image_count)
-print("images, we got")
-print(cell_count)
-print("viable cells")
+cat("out of",image_count,"images, we got",cell_count,"viable cells \n", sep = " ")
 }
 
 

R/count_foci.R

@@ -42,9 +42,7 @@ count_foci <- function(img_path, stage = "none", offset_px = 0.2, offset_factor
   else{
     img_path_new <- paste0(img_path,"/crops/")
   }
-
   file_list <- list.files(img_path_new)
-  print(file_list)
   df_cols <- c("filename","cell_no","genotype","stage","foci_count", "sd_foci","mean_foci","median_foci","mean_px","median_px", "percent_on","sd_px","lone_foci")
   df_cells <- data.frame(matrix(ncol = length(df_cols), nrow = 0))
   colnames(df_cells) <- df_cols
@@ -124,17 +122,14 @@ count_foci <- function(img_path, stage = "none", offset_px = 0.2, offset_factor
       }
       ### multiply strands by foci_label
       if(annotation == "on"){
-        print("at file")
-        print(img_file)
-        print("cell counter is")
-        print(cell_count)
+        cat("at file",img_file, sep = " ")
+        cat("cell counter is", cell_count, sep= " ")
         print("original images")
         plot(new_img)
         plot(img_orig_foci)
-        print("displaying resulting foci count")
-        print("Overlay two channels")
+        print("displaying resulting foci count plots. Overlay two channels:")
         plot(rgbImage(strands,foci_label,0*foci_label))
-        print("coincident foci")
+        print("coincident foci:")
         plot(colorLabels(coincident_foci))
         print("two channels, only coincident foci")
         plot(rgbImage(strands,coincident_foci,coincident_foci))
@@ -142,8 +137,7 @@ count_foci <- function(img_path, stage = "none", offset_px = 0.2, offset_factor
       overlap_no <- table(coincident_foci)
       foci_per_cell <-  length(overlap_no)
       if(annotation=="on"){
-        print("which counts this many foci:")
-        print(foci_per_cell)
+        cat("which counts this many foci:",foci_per_cell, sep = " ")
       }
       image_mat <- as.matrix(foci_mask_crop)
       image_mat <- image_mat[image_mat > 1e-06]
@@ -161,17 +155,16 @@ count_foci <- function(img_path, stage = "none", offset_px = 0.2, offset_factor
       ### number of foci NOT on an SC
       alone_foci <- nrow(foci_candidates) - foci_per_cell
       if(annotation == "on"){
-        print("number of alone foci")
-        print(alone_foci)
+        cat("number of alone foci",alone_foci, sep = " ")
         if(alone_foci < 0){
           print("this one had a negative lone foci amount. Suspect overcounting of foci in this one:")
           plot(rgbImage(strands,foci_label,0*foci_label))
+          alone_foci <- 0
         }
       }
       percent_px <- sum(overlap)/sum(foci_areas)
       if(annotation == "on"){
-        print("percentage of foci channel coincident:")
-        print(percent_px*100)
+        cat("percentage of foci channel coincident:", percent_px*100, sep = " ")
       }
       tryCatch({
         ### data frame stuff

R/get_pachytene.R

@@ -121,8 +121,7 @@ get_pachytene <- function(img_path, species_num = 20, offset = 0.2,ecc_thresh =
       }
     }
   }
-print("number of cells kept")
-print(pachytene_count)
+cat("number of cells kept",pachytene_count, sep = " ")
 colnames(df_cells) <- df_cols
 return(df_cells)
 }

R/measure_distances_general.R

@@ -101,8 +101,7 @@ measure_distances_general <- function(img_path,offset_px = 0.2, offset_factor =
       foci_candidates <- data.frame(foci_candidates)
       foci_areas <- foci_candidates$s.area
       if(annotation == "on"){
-        print("looking at cell number:")
-        print(cell_count)
+        cat("\n looking at cell number:", cell_count, sep = " ")
       }
       dimensionless_dist <- get_distance_general(strands,num_strands,new_img,foci_label, foci_count_strand, strand_iter,img_file,annotation,eccentricity_min, max_strand_area,cell_count,KO_str ,WT_str,KO_out, WT_out,target_foci_number, max_dist_sq,SC_intens_stop)
       df_lengths <- rbind(df_lengths, dimensionless_dist)
@@ -403,8 +402,7 @@ get_distances_along <- function(distance_strand,distance_strand_2,per_strand,foc
   strand_info <- as.data.frame(strand_info)
   #### turn this into a table/ data frame
   no_foci <- nrow(strand_info)
-  print("the number of foci in this strand is")
-  print(no_foci)
+  cat("\n the number of foci in this strand is", no_foci, sep = " ")
   df_col <- c("file","cell_id","genotype","strand_iter","foci_per_strand","iteration_on_strand","foci_x","foci_y","foci_x_line","foci_y_line","total_length","distance_squared","distance_along","SC_pass_fail")
   foci_df <- data.frame(matrix(ncol = length(df_col), nrow = 0))
   my_walkers_matrix <- t(as.matrix(walkers))
@@ -412,8 +410,7 @@ get_distances_along <- function(distance_strand,distance_strand_2,per_strand,foc
   iter <- 0
   while(iter<= no_foci-1){
     iter <- iter + 1
-    print("looking at position data of foci number")
-    print(iter)
+    cat("\n looking at position data of foci number",iter, sep = " ")
     foci_x <- strand_info$m.cx[iter]
     foci_y <- strand_info$m.cy[iter]
     #### finding closest point
@@ -465,13 +462,10 @@ get_distances_along <- function(distance_strand,distance_strand_2,per_strand,foc
       colnames(foci_df) <- df_col
       if(x_curr == as.numeric(foci_df$foci_x_line[iter]) && y_curr == as.numeric(foci_df$foci_y_line[iter])){
         ### record the distance along
-        print("I found a focus, which is this many pixels along:")
         foci_dist_along <- dist_along
-        print(dist_along)
-        print("and its distance (squared) to the actual foci centre was")
-        print(distance_fi)
-        print("and this was a pass/fail")
-        print(strand_test)
+        cat("\n I found a focus, which is this many pixels along:", dist_along, sep = " ")
+        cat("\n and its distance (squared) to the actual foci centre was", distance_fi, sep = " ")
+        cat("\n and this was a pass/fail", strand_test,  sep = " ")
         if(annotation == "on"){
           ch1 <- bwlabel(noise_gone)
           ch1 <-channel(noise_gone,"grey")