diff --git a/testing_sophia/duration_scaling.R b/testing_sophia/duration_scaling.R
index b6a0973..dd240e0 100644
--- a/testing_sophia/duration_scaling.R
+++ b/testing_sophia/duration_scaling.R
@@ -1,4 +1,8 @@
+library(tidyr)
+library(tidyverse)
 library(dplyr)
+library(mvMAPIT)
+library(ggplot2)
 
 set.seed(1234)
 
@@ -43,8 +47,8 @@ for(i in 1:nrow(pairs)) {
         skipProjection = TRUE
     )
     
-    df <- rbind(df, mvmapit_normal$duration %>% mutate(n_samples = n_samples, n_snps = n_snps, projections = "False")) 
-    df <- rbind(df, mvmapit_normal_projection$duration %>% mutate(n_samples = n_samples, n_snps = n_snps, projections = "True")) 
+    df <- rbind(df, mvmapit_normal$duration %>% mutate(n_samples = n_samples, n_snp = n_snp, projection = "False")) 
+    df <- rbind(df, mvmapit_normal_projection$duration %>% mutate(n_samples = n_samples, n_snp = n_snp, projection = "True")) 
     
     #df <- rbind(df, c(mvmapit_normal$duration$duration_ms, n_samples = n_samples,
                       #n_snps = n_snps, projections = "False"))
@@ -59,5 +63,7 @@ for(i in 1:nrow(pairs)) {
 
 #df$total_duration <- rowSums(df[ , c(1, 2, 3, 4, 5, 6)], na.rm=TRUE)
 
-result <- df %>% group_by(n_samples, n_snps, projections) %>% summarise(total = sum(duration_ms))
-view(result)
+result <- df %>% group_by(n_sample, n_snp, projection) %>% summarise(total = sum(duration_ms))
+
+saveRDS(result, "/oscar/data/lcrawfo1/sli347/duration_scaling.RDS")
+
diff --git a/testing_sophia/repeated_trials.R b/testing_sophia/repeated_trials.R
index d61d813..e153719 100644
--- a/testing_sophia/repeated_trials.R
+++ b/testing_sophia/repeated_trials.R
@@ -11,21 +11,22 @@ mvmapit_simulation <- function(data_type = c("null", "alternative")) {
     
     df <- NULL
     df_statistics <- data.frame(trial = c(), correlation_p = c()) #add heretibility and hypo 
+    genotype_data <- readRDS("/oscar/data/lcrawfo1/sli347/biobank_1_10000.rds")
     
     if (data_type == "null"){
         n_pleiotropic = 0
         n_trait_specific = 0
-        print("null")
     } else {
         n_pleiotropic = 10
         n_trait_specific = 10
-        print("alternative")
     }
     
     for (x in 1:5) {
         
+        sample_ids <- sample(1:10000, 5000, replace = FALSE) 
+
         simulated_data <- simulate_traits(
-            genotype_data,
+            genotype_data[sample_ids, 1:5000],
             n_causal = 1000,
             n_trait_specific,
             n_pleiotropic, 
@@ -44,16 +45,18 @@ mvmapit_simulation <- function(data_type = c("null", "alternative")) {
         
         #run on smaller data set -- to test
         #might want to randomize columns
+       
+
         mvmapit <- mvmapit(
-            t(simulated_data$genotype[1:100, 1:100]),
-            t(simulated_data$trait[1:100, ]),
+            t(simulated_data$genotype),
+            t(simulated_data$trait),
             test = "normal", #could add into method inputs the type of test we want to run - matcharg
             skipProjection = FALSE
         )
         
         mvmapit_projection <- mvmapit(
-            t(simulated_data$genotype[1:100, 1:100]),
-            t(simulated_data$trait[1:100, ]),
+            t(simulated_data$genotype),
+            t(simulated_data$trait),
             test = "normal", 
             skipProjection = TRUE
         )
@@ -74,7 +77,6 @@ mvmapit_simulation <- function(data_type = c("null", "alternative")) {
     di <- "exp"
     de <- FALSE # enabling the detrend option
     
-    # TODO: change causal snp facet labels
     gg <- df %>% ggplot(mapping = aes(
         sample = -log10(p)
     )) +
@@ -89,22 +91,21 @@ mvmapit_simulation <- function(data_type = c("null", "alternative")) {
         labs(x = bquote("Theoretical Quantiles " -log[10](p)),
              y = bquote("Sample Quantiles " -log[10](p))) + 
         facet_wrap(~projections) # check this one
-    show(gg)
+    ggsave("/oscar/data/lcrawfo1/sli347/repeated_qqplot.png", plot = gg, width = 6, height = 4, unit = "in", dpi = 300)
+
     
     #evaluate distribution of p-value correlations
     colnames(df_statistics) <- c('trial', 'correlation_p')
     
-    box<- ggplot(df_statistics, aes(y = correlation_p)) +
+    box <- ggplot(df_statistics, aes(y = correlation_p)) +
         geom_boxplot() + scale_x_discrete() +
         labs(title = "Projection correlation distribution",
              y = "correlation coefficent")
-    show(box)
-    return(box)
+    ggsave("/oscar/data/lcrawfo1/sli347/repeated_boxplot.png", plot = box, width = 6, height = 4, unit = "in", dpi = 300)
+    
 }   
 
-box <- mvmapit_simulation("null")
-show(box)
-
+mvmapit_simulation("normal")
 
 
 
diff --git a/testing_sophia/slurmjob.sh b/testing_sophia/slurmjob.sh
new file mode 100644
index 0000000..91aefb2
--- /dev/null
+++ b/testing_sophia/slurmjob.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+#SBATCH --time=0-01:00 # Runtime in D-HH:MM
+#SBATCH --job-name="duration"
+#SBATCH --mem-per-cpu=10G
+#SBATCH --cpus-per-task=2
+#SBATCH -n 1
+#SBATCH -p "batch"
+
+
+NPROC=$((SLURM_JOB_CPUS_PER_NODE * SLURM_NNODES))
+echo "${NPROC} threads"
+export OMP_NUM_THREADS=$NPROC
+
+module load r/4.3.1
+
+Rscript --vanilla "duration_scaling.R"
\ No newline at end of file
diff --git a/testing_sophia/slurmjob_repeated.sh b/testing_sophia/slurmjob_repeated.sh
new file mode 100644
index 0000000..3b48e23
--- /dev/null
+++ b/testing_sophia/slurmjob_repeated.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+#SBATCH --time=0-01:00 # Runtime in D-HH:MM
+#SBATCH --job-name="simulating trials"
+#SBATCH --mem-per-cpu=10G
+#SBATCH --cpus-per-task=2
+#SBATCH -n 1
+#SBATCH -p "batch"
+
+
+NPROC=$((SLURM_JOB_CPUS_PER_NODE * SLURM_NNODES))
+echo "${NPROC} threads"
+export OMP_NUM_THREADS=$NPROC
+
+module load r/4.3.1
+
+Rscript --vanilla "repeated_trials.R"
\ No newline at end of file