223 lines
8 KiB
R
223 lines
8 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: Get formatted data for plots
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#########################################################
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# working dir and loading libraries
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getwd()
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source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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# cmd args passed
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# in from other scripts
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# to call this
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# set drug and gene name
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#==========================================
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# variables for lig:
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# comes from functions/plotting_globals.R
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#==========================================
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cat("\nGlobal variables for Ligand:"
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, "\nligand distance colname:", LigDist_colname
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, "\nligand distance cut off:", LigDist_cutoff)
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#===========
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# input
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#===========
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#--------------------------------------------
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# call: import_dirs()
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# comes from functions/plotting_globals.R
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#--------------------------------------------
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import_dirs(drug, gene)
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#---------------------------
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# call: plotting_data()
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#---------------------------
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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# Input 1: read <gene>_comb_afor.csv
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cat("\nReading mcsm combined data file: ", infile_params)
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mcsm_df = read.csv(infile_params, header = T)
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pd_df = plotting_data(mcsm_df
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, lig_dist_colname = LigDist_colname
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, lig_dist_cutoff = LigDist_cutoff)
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my_df = pd_df[[1]]
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my_df_u = pd_df[[2]] # this forms one of the input for combining_dfs_plotting()
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max_ang <- round(max(my_df_u[LigDist_colname]))
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min_ang <- round(min(my_df_u[LigDist_colname]))
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cat("\nLigand distance colname:", LigDist_colname
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, "\nThe max distance", gene, "structure df" , ":", max_ang, "\u212b"
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, "\nThe min distance", gene, "structure df" , ":", min_ang, "\u212b")
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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# Input 2: read <gene>_meta data.csv
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cat("\nReading meta data file: ", infile_metadata)
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gene_metadata <- read.csv(infile_metadata
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, stringsAsFactors = F
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, header = T)
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cat("\nDim of meta data file: ", dim(gene_metadata))
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all_plot_dfs = combining_dfs_plotting(my_df_u
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, gene_metadata
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, lig_dist_colname = LigDist_colname
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, lig_dist_cutoff = LigDist_cutoff)
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merged_df2 = all_plot_dfs[[1]]
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merged_df3 = all_plot_dfs[[2]]
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#merged_df2_comp = all_plot_dfs[[3]]
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#merged_df3_comp = all_plot_dfs[[4]]
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#======================================================================
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####################################################################
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# Data for subcols barplot (~heatmap)
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####################################################################
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#source("coloured_bp_data.R")
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# Repurposed function so that params can be passed instead to generate
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# data required for plotting.
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# Moved "coloured_bp_data.R" to redundant/
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####################################################################
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# Data for logoplots
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####################################################################
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source(paste0(plot_script_path, "logo_data_msa.R"))
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s1 = c("\nSuccessfully sourced logo_data_msa.R")
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cat(s1)
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####################################################################
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# Data for DM OM Plots: WF and LF dfs
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# My function: dm_om_wf_lf_data()
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# location: scripts/functions/dm_om_data.R
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#source("other_plots_data.R")
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####################################################################
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#source(paste0(plot_script_path, "dm_om_data.R")) # calling the function directly instead
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geneL_normal = c("pnca")
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geneL_na = c("gid", "rpob")
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geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene_name = gene)
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wf_duet = all_dm_om_df[['wf_duet']]
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lf_duet = all_dm_om_df[['lf_duet']]
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wf_mcsm_lig = all_dm_om_df[['wf_mcsm_lig']]
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lf_mcsm_lig = all_dm_om_df[['lf_mcsm_lig']]
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wf_foldx = all_dm_om_df[['wf_foldx']]
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lf_foldx = all_dm_om_df[['lf_foldx']]
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wf_deepddg = all_dm_om_df[['wf_deepddg']]
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lf_deepddg = all_dm_om_df[['lf_deepddg']]
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wf_dynamut2 = all_dm_om_df[['wf_dynamut2']]
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lf_dynamut2 = all_dm_om_df[['lf_dynamut2']]
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wf_consurf = all_dm_om_df[['wf_consurf']]
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lf_consurf = all_dm_om_df[['lf_consurf']]
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wf_snap2 = all_dm_om_df[['wf_snap2']]
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lf_snap2 = all_dm_om_df[['lf_snap2']]
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if (tolower(gene)%in%geneL_na){
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wf_mcsm_na = all_dm_om_df[['wf_mcsm_na']]
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lf_mcsm_na = all_dm_om_df[['lf_mcsm_na']]
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}
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if (tolower(gene)%in%geneL_ppi2){
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wf_mcsm_ppi2 = all_dm_om_df[['wf_mcsm_ppi2']]
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lf_mcsm_ppi2 = all_dm_om_df[['lf_mcsm_ppi2']]
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}
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s2 = c("\nSuccessfully sourced other_plots_data.R")
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cat(s2)
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####################################################################
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# Data for Lineage barplots: WF and LF dfs
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# My function: lineage_plot_data()
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# location: scripts/functions/lineage_plot_data.R
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####################################################################
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#source(paste0(plot_script_path, "lineage_data.R"))
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# converted to a function. Moved lineage_data.R to redundant/
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lineage_dfL = lineage_plot_data(merged_df2
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, lineage_column_name = "lineage"
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, remove_empty_lineage = F
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, lineage_label_col_name = "lineage_labels"
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, id_colname = "id"
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, snp_colname = "mutationinformation"
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)
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lin_wf = lineage_dfL[['lin_wf']]
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lin_lf = lineage_dfL[['lin_lf']]
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s3 = c("\nSuccessfully sourced lineage_data.R")
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cat(s3)
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####################################################################
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# Data for corr plots:
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# My function: corr_data_extract()
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# location: scripts/functions/corr_plot_data.R
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####################################################################
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# make sure the above script works because merged_df2_combined is needed
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merged_df3 = as.data.frame(merged_df3)
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corr_df_m3_f = corr_data_extract(merged_df3, extract_scaled_cols = F)
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head(corr_df_m3_f)
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corr_df_m2_f = corr_data_extract(merged_df2, extract_scaled_cols = F)
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head(corr_df_m2_f)
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s4 = c("\nSuccessfully sourced Corr_data.R")
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cat(s4)
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########################################################################
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# End of script
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########################################################################
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if ( all( length(s1), length(s2), length(s3), length(s4) ) > 0 ){
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cat(
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"\n##################################################"
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, "\nSuccessful: get_plotting_dfs.R worked!"
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, "\n###################################################\n")
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} else {
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cat(
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"\n#################################################"
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, "\nFAIL: get_plotting_dfs.R didn't complete fully!Please check"
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, "\n###################################################\n" )
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}
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########################################################################
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# clear excess variables: from the global enviornment
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vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
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vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
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vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
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vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
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rm( infile_metadata
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, infile_params
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, vars0
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, vars1
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, vars2
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, vars3)
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