Change load type

Before these tabs only plotted .Rda files

UIs/ui_binning_params.R

@@ -9,7 +9,7 @@ binning_params <- tabPanel(
                  status = "danger",
                  solidHeader = TRUE,
                  title = "Choose a directory of raster data files",
-                 helpText("We only accept .mat and .Rda format"),
+                 helpText("We only accept .mat, .Rda, .csv formats"),
                  shinyFiles::shinyDirButton("bin_chosen_raster", "Browse", ""),
                  helpText("Loaded raster data: "),
                  textOutput("bin_show_chosen_raster")),

servers/server_binning_params.R

@@ -13,9 +13,13 @@ observe({
   req(input$bin_chosen_raster)
   # Set current directory name to the base directory
   rv$selected_rasters <- shinyFiles::parseDirPath(c(wd=rv$raster_base_dir), input$bin_chosen_raster)
-  rv$mat_raster_list <- list.files(rv$selected_rasters, pattern = "\\.mat$")
-  rv$rda_raster_list <- list.files(rv$selected_rasters, pattern = "\\.[rR]da$")
-  rv$raster_num_neuron <- length(rv$rda_raster_list)
+  #rv$mat_raster_list <- list.files(rv$selected_rasters, pattern = "\\.mat$")
+  #rv$rda_raster_list <- list.files(rv$selected_rasters, pattern = "\\.[rR]da$")
+  rv$raster_list <- list.files(rv$selected_rasters,
+                               pattern = "\\.[rR]da$|\\.csv$|\\.mat$")
+
+  rv$raster_num_neuron <- length(rv$raster_list) #- elisa
+  #rv$raster_num_neuron <- length(rv$rda_raster_list)
 })
 
 

servers/server_plot_raster.R

@@ -25,17 +25,19 @@ observeEvent(list(rv$selected_rasters, rv$raster_cur_neuron),{
   req(rv$raster_num_neuron)
   if(rv$raster_num_neuron > 0){
     # Find the current file to view
-    rv$raster_cur_file_name <- rv$rda_raster_list[rv$raster_cur_neuron]
+    #rv$raster_cur_file_name <- rv$rda_raster_list[rv$raster_cur_neuron]
+    rv$raster_cur_file_name <- rv$raster_list[rv$raster_cur_neuron] #elisa
 
     # Load in the data and save time cols into matrix
-    #NeuroDecodeR::read_raster_data() - elisa
-    load(file.path(rv$selected_rasters, rv$raster_cur_file_name))
+    raster_data <- NeuroDecodeR::read_raster_data(file.path(rv$selected_rasters, rv$raster_cur_file_name))
+    #load(file.path(rv$selected_rasters, rv$raster_cur_file_name)) #elisa
     raster_df <- select(raster_data, starts_with("time."))
     raster_matrix <- as.matrix(raster_df)
 
     # Reformat matrix and save as random variable
     rownames(raster_matrix) <- 1:dim(raster_matrix)[1]
     colnames(raster_matrix) <- gsub("time.", "", colnames(raster_matrix))
+    colnames(raster_matrix) <- gsub("^(-?[0-9]+)_.*", "\\1", colnames(raster_matrix)) #Elisa
     rv$cur_raster_matrix <- raster_matrix
   }
 })
@@ -75,6 +77,11 @@ output$bin_PSTH <- renderPlot({
   req(rv$cur_raster_matrix)
   # Create column means and turn into a df to plot
   cur_raster_col_means <- colMeans(rv$cur_raster_matrix, na.rm = FALSE)
+
+  # Removing the ranges
+
+  #raster_means_df <- data.frame(time = time,
+  #                              spike_mean_over_trials = cur_raster_col_means)
   raster_means_df <- data.frame(time = as.numeric(names(cur_raster_col_means)),
                                    spike_mean_over_trials = cur_raster_col_means)