generated ggpairs plots finally
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11 changed files with 657 additions and 939 deletions
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@ -4,22 +4,41 @@ library("ggforce")
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#install.packages("gginference")
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library(gginference)
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library(ggpubr)
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library(svglite)
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##################################################
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#%% read data
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# DOME: read data using gene and drug combination
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# gene must be lowercase
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# tolower(gene)
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#################################################
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############################################################
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#gene="pncA"
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#drug="pyrazinamide"
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#lineage_filename=paste0(tolower(gene),"_merged_df2.csv")
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#lineage_data_path="~/git/Data/pyrazinamide/output"
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df2 = read.csv(paste0(lineage_data_path,"/",lineage_filename))
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#=============
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# Data: Input
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#==============
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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# source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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foo = as.data.frame(colnames(df2))
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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###########################################################
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class(merged_df2)
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foo = as.data.frame(colnames(merged_df2))
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cols_to_subset = c('mutationinformation'
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, 'snp_frequency'
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@ -36,7 +55,7 @@ cols_to_subset = c('mutationinformation'
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#cols_to_subset%in%foo
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my_df = df2[ ,cols_to_subset]
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my_df = merged_df2[ ,cols_to_subset]
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# r24p_embb = df_embb[df_embb$mutationinformation == "R24P",]
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# #tm = c("A102P", "M1T")
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@ -73,10 +92,9 @@ table(my_df2$lineage)
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sel_lineages2 = c("L1", "L2", "L3", "L4")
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my_df2 = my_df2[my_df2$lineage%in%sel_lineages2,]
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table(my_df2$lineage)
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sum(table(my_df2$lineage)) == nrow(my_df2)
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table(my_df2$lineage)
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table(my_df2$lineage, my_df2$sensitivity)
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# %%
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# str(my_df2)
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@ -85,6 +103,7 @@ table(my_df2$lineage)
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#%% get only muts which belong to > 1 lineage and have different sensitivity classifications
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muts = unique(my_df2$mutationinformation)
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cat ("Total unique muts in L1-L4", tolower(gene), ":", length(muts))
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#-----------------------------------------------
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# step 0 : get muts with more than one lineage
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#-----------------------------------------------
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@ -100,7 +119,6 @@ for (i in muts) {
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}
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cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities")
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#-----------------------------------------------
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# step 1 : get other muts that do not have this
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#-----------------------------------------------
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@ -111,7 +129,6 @@ cat("\nGot:", length(consist_muts), "mutations that are consistent")
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# step 2: subset these muts for plotting
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#-----------------------------------------------
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plot_df = my_df2[my_df2$mutationinformation%in%lin_muts,]
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cat("\nnrow of plot_df:", nrow(plot_df))
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#-----------------------------------------------
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@ -125,7 +142,9 @@ for (i in lin_muts) {
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s_tab = table(s_mut$lineage, s_mut$sens2)
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#print(s_tab)
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#ft_pvalue_i = round(fisher.test(s_tab)$p.value, 3)
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ft_pvalue_i = fisher.test(s_tab)$p.value
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ft_pvalue_i = fisher.test(s_tab
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#, workspace=2e9
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, simulate.p.value=TRUE,B=1e7)$p.value
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#print(ft_pvalue_i)
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plot_df$pval[plot_df$mutationinformation == i] <- ft_pvalue_i
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#print(s_tab)
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@ -155,8 +174,6 @@ plot_df
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head(plot_df)
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table(plot_df$pvalR<0.05)
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# format p value
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# TODO: add case statement for correct pvalue formatting
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#plot_df$pvalF = ifelse(plot_df$pval <= 0.0001, paste0(round(plot_df$pval, 3), "**** "), plot_df$pval )
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@ -233,6 +250,7 @@ cat("\nGot:", sig_muts, "mutations that are significant")
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plot_df_ns = plot_df2[plot_df2$pvalR>0.05,]
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ns_muts = length(unique(plot_df_ns$mutationinformation))
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cat("\nGot:", ns_muts, "mutations that are NOT significant")
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p_title = gene
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ts = 8
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gls = 3
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@ -244,7 +262,7 @@ gls = 3
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#3) Add *: Extend yaxis for each plot to allow geom_label to have space (or see
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# if this self resolving with facet_wrap_paginate())
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#================================================
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#svg(paste0(outdir_images, "embb_linDS.svg"), width = 6, height = 10 ) # old-school square 4:3 CRT shape 1.3:1
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#svg(paste0(outdir_images, tolower(gene), "_linDS.svg"), width = 6, height = 10 ) # old-school square 4:3 CRT shape 1.3:1
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ds_s = ggplot(plot_df_sig, aes(x = lineage
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, fill = sens2)) +
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geom_bar(stat = 'count') +
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@ -280,7 +298,7 @@ ds_s = ggplot(plot_df_sig, aes(x = lineage
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###################################
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#ns muts
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#svg(paste0(outdir_images, "embb_linDS_ns.svg"), width =10 , height = 8) # old-school square 4:3 CRT shape 1.3:1
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#svg(paste0(outdir_images, tolower(gene), "_linDS_ns.svg"), width =10 , height = 8) # old-school square 4:3 CRT shape 1.3:1
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ds_ns = ggplot(plot_df_ns, aes(x = lineage
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, fill = sens2)) +
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geom_bar(stat = 'count') +
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@ -309,31 +327,57 @@ ds_ns = ggplot(plot_df_ns, aes(x = lineage
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labs(title = paste0(p_title, ": sensitivity by lineage")
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, y = 'Sample Count')
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#dev.off()
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#####################################################################
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#===================
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# Combine output
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#====================
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# svg(paste0(outdir_images, "embb_linDS_CL.svg")
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# svg(paste0(outdir_images, tolower(gene), "_linDS_CL.svg")
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# , width = 11
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# , height = 8 )
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png(paste0(outdir_images, "embb_linDS_CL.png")
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, width = 11.75
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png(paste0(outdir_images, tolower(gene), "_linDS_CL2.png")
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, width = 11.75*1.15
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, height = 8, units = "in", res = 300 )
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cowplot::plot_grid(ds_s, ds_ns
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, ncol = 2
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,rel_widths = c(1,2)
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#, align = "hv"
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, rel_widths = c(1,2.5)
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, labels = "AUTO")
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dev.off()
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########################################################################
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#==================
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# Summary output
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#==================
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cat ("Total unique muts in ALL samples for", tolower(gene), ":", length(unique(merged_df2$mutationinformation)))
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other_lin_muts = unique(merged_df2$mutationinformation)[!unique(merged_df2$mutationinformation)%in%unique(my_df2$mutationinformation)]
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cat ("Total unique muts NOT in L1-L4:", length(other_lin_muts))
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cat("These are:\n", other_lin_muts)
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other_lin_muts_df = merged_df2[merged_df2$mutationinformation%in%other_lin_muts,]
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if ( length(unique(other_lin_muts_df$mutationinformation)) == length(other_lin_muts)) {
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cat("\nPASS: other lin muts extracted")
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}else{
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stop("\nAbort: other lin muts numbers mismatch")
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}
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table(other_lin_muts_df$mutationinformation, other_lin_muts_df$lineage)
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cat("\n==============================================\n")
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cat ("Total samples L1-L4:", nrow(my_df2))
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table(my_df2$lineage)
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table(my_df2$lineage, my_df2$sensitivity)
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cat ("Total unique muts in L1-L4", tolower(gene), ":", length(muts))
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cat("\nGot:", length(lin_muts), "mutations belonging to >1 lineage with differing drug sensitivities")
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cat("\nGot:", sig_muts, "mutations that are significant"
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, "\nThese are:", unique(plot_df_sig$mutationinformation))
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#geom_text(aes(label = paste0("p=",pvalF), x = 2.5, ypos_label+1))# +
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cat("\nGot:", ns_muts, "mutations that are NOT significant"
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, "\nThese are:", unique(plot_df_ns$mutationinformation))
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#geom_segment(aes(x = 1, y = ypos_label+0.5, xend = 4, yend = ypos_label+0.5))
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#geom_hline(data = lin_muts_dfM, aes(yintercept=ypos_label+0.5))
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#geom_bracket(data=lin_muts_dfM, aes(xmin = 1, xmax = 4, y.position = ypos_label+0.5, label=''))
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cat("\n==============================================\n")
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