diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/.Rhistory b/CRA_Updated_Version/CRA_Toolbox_bash/.Rhistory
new file mode 100644
index 0000000..9192d4c
--- /dev/null
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/.Rhistory
@@ -0,0 +1,512 @@
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_oldARACNeAP_string_A549_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H522_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_A549_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_A549_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_string_H522_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H522_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_NOdDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_NOdDPI_pc_A549_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_A549_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_pc_H522_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_A549_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_Calu3_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H1734_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H1838_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H2122_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H23_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/SelPlusTam/FGSEA/Sel/gsea_Sel_VIPER_dDPI_string_H522_10000.RData")
+source('~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/script_dotplot_gsea.R')
+p
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/mapDIA_GSEA_April2019/FGSEA/Sel/pc/FGSEA/gsea_Sel_mapDIA_H358_pc.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/mapDIA_GSEA_April2019/FGSEA/Sel/pc/FGSEA/gsea_Sel_mapDIA_H358_pc_FDR.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_dDPI_allreplicas_600bootstraps/VIPER/Selumetinib/FGSEA/PC/gsea_Sel_VIPER_dDPI_pc_H358_10000.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/ARACNE_HPARACNE_OK/NEW_ARACNe_eachreplica_April2019/network_oldARACNeAP_allreplicas_600bootstraps/VIPER_regulon_oldARACNeAP_MEKcorrected/Selumetinib/FGSEA/PC/fgsea_oldARACNeAP_MEKcorrected_H358_Selumetinib_pc.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/Colon_network_George/VIPER_regulon_colon_George/Selumetinib/FGSEA/fgsea_COAD_H358_Selumetinib_pc.RData")
+load("~/Scrivania/Tutto/NSCLC_CellLines_Private/CellLines/PathwayCommonsData/VIPER_regulon_PC_May2019/Selumetinib/FGSEA/fgsea_PC_H358_Selumetinib_pc.RData")
+library(ggplot2)
+cell_line=c("A549","Calu3","H23","H358","H522","H1734","H1838","H2122")
+cell_type=c("KRASMUT","KRASWT","KRASMUT","KRASMUT","KRASWT","KRASMUT","KRASWT","KRASMUT")
+View(Fgsea_dDPI_Sel24h_pc_H358_10000)
+View(Fgsea_COAD_Sel24h_pc_H358_10000)
+View(Fgsea_dDPI_Sel6h_pc_H358_10000)
+View(Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000)
+View(Fgsea_oldARACNeAP_MEKcorrected_Sel6h_pc_H358_10000)
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",3),"NES"=c(Fgsea_mapDIA_H358_Sel5min_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES))
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_mapDIA_H358_Sel1h_pc$NES,Fgsea_mapDIA_H358_Sel1h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES))
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",3),"NES"=c(Fgsea_mapDIA_H358_Sel6h_pc$NES,Fgsea_mapDIA_H358_Sel6h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel6h_pc_H358_10000$NES))
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_mapDIA_H358_Sel24h_pc$NES,Fgsea_mapDIA_H358_Sel24h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES))
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",3),"NES"=c(Fgsea_mapDIA_H358_Sel5min_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES))
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_mapDIA_H358_Sel1h_pc$NES,Fgsea_mapDIA_H358_Sel1h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES))
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_mapDIA_H358_Sel24h_pc$NES,Fgsea_mapDIA_H358_Sel24h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES))
+Fgsea_mapDIA_H358_Sel1h_pc_FDR
+Fgsea_mapDIA_H358_Sel6h_pc_FDR
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_mapDIA_H358_Sel1h_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES))
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",3),"NES"=c(Fgsea_mapDIA_H358_Sel6h_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel6h_pc_H358_10000$NES))
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_mapDIA_H358_Sel24h_pc$NES,Fgsea_mapDIA_H358_Sel24h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES))
+Fgsea_all<-rbind(Fgsea_5min_mapDIA,Fgsea_1h_mapDIA,Fgsea_6h_mapDIA,Fgsea_24h_mapDIA)
+method=c("log2FC","mapDIA","VIPER")
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",3),"NES"=c(Fgsea_mapDIA_H358_Sel5min_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES),"method"=method)
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_mapDIA_H358_Sel1h_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES),"method"=method)
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",3),"NES"=c(Fgsea_mapDIA_H358_Sel6h_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel6h_pc_H358_10000$NES),"method"=method)
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_mapDIA_H358_Sel24h_pc$NES,Fgsea_mapDIA_H358_Sel24h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES),"method"=method)
+Fgsea_all<-rbind(Fgsea_5min_mapDIA,Fgsea_1h_mapDIA,Fgsea_6h_mapDIA,Fgsea_24h_mapDIA)
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES))+geom_line(aes(color=method))+geom_point(aes(color=method))+
+ggtitle()
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES))+geom_line(aes(color=method))+geom_point(aes(color=method))
+p
+View(Fgsea_oldARACNeAP_MEKcorrected_Sel6h_pc_H358_10000)
+?geom_line
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES))+geom_line(aes(color=method))+geom_point(aes(color=method))
+p
+View(Fgsea_dDPI_Sel5min_pc_H358_10000)
+View(Fgsea_dDPI_Sel1h_pc_H358_10000)
+View(Fgsea_dDPI_Sel6h_pc_H358_10000)
+View(Fgsea_dDPI_Sel24h_pc_H358_10000)
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES))+geom_line(aes(color=method,linetype='dashed'))+geom_point(aes(color=method))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,linetype='dashed'))+geom_point(aes(color=method))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method))
+p
+View(Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000)
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",3),"NES"=c(Fgsea_mapDIA_H358_Sel5min_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES),"method"=method)
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_mapDIA_H358_Sel1h_pc$NES,NA,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES),"method"=method)
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",3),"NES"=c(Fgsea_mapDIA_H358_Sel6h_pc$NES,NA,1.53),"method"=method)
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_mapDIA_H358_Sel24h_pc$NES,Fgsea_mapDIA_H358_Sel24h_pc_FDR$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES),"method"=method)
+Fgsea_all<-rbind(Fgsea_5min_mapDIA,Fgsea_1h_mapDIA,Fgsea_6h_mapDIA,Fgsea_24h_mapDIA)
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method))
+p
+View(Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000)
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method))
+ggtitle("GSEA H358 Selumetinib")
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size="5"))
+ggtitle("GSEA H358 Selumetinib")
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size="10"))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size="20"))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size=20))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size=10))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size=5))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size=5))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=5))+geom_point(aes(color=method,shape=method,size=5))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=5))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3)) + ggtitle("H358 Selumetinib")
+p
+method=c("hpARACNe (dDPI)","COAD","ARACNe (MEK regulon updated)", "PC interactome")
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",3),"NES"=c(Fgsea_dDPI_Sel5min_pc_H358_10000$NES,Fgsea_COAD_Sel5min_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES,Fgsea_PC_Sel5min_pc_H358_10000$NES),"method"=method)
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",3),"NES"=c(Fgsea_dDPI_Sel1h_pc_H358_10000$NES,Fgsea_COAD_Sel1h_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES,Fgsea_PC_Sel1h_pc_H358_10000$NES),"method"=method)
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",3),"NES"=c(Fgsea_dDPI_Sel6h_pc_H358_10000$NES,Fgsea_COAD_Sel6h_pc_H358_10000$NES,1.53,Fgsea_PC_Sel6h_pc_H358_10000$NES),"method"=method)
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",3),"NES"=c(Fgsea_dDPI_Sel24h_pc_H358_10000$NES,Fgsea_COAD_Sel24h_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES,Fgsea_PC_Sel24h_pc_H358_10000$NES),"method"=method)
+Fgsea_all<-rbind(Fgsea_5min_mapDIA,Fgsea_1h_mapDIA,Fgsea_6h_mapDIA,Fgsea_24h_mapDIA)
+Fgsea_5min_mapDIA<-data.frame("time"=rep("5min",4),"NES"=c(Fgsea_dDPI_Sel5min_pc_H358_10000$NES,Fgsea_COAD_Sel5min_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel5min_pc_H358_10000$NES,Fgsea_PC_Sel5min_pc_H358_10000$NES),"method"=method)
+Fgsea_1h_mapDIA<-data.frame("time"=rep("1h",4),"NES"=c(Fgsea_dDPI_Sel1h_pc_H358_10000$NES,Fgsea_COAD_Sel1h_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel1h_pc_H358_10000$NES,Fgsea_PC_Sel1h_pc_H358_10000$NES),"method"=method)
+Fgsea_6h_mapDIA<-data.frame("time"=rep("6h",4),"NES"=c(Fgsea_dDPI_Sel6h_pc_H358_10000$NES,Fgsea_COAD_Sel6h_pc_H358_10000$NES,1.53,Fgsea_PC_Sel6h_pc_H358_10000$NES),"method"=method)
+Fgsea_24h_mapDIA<-data.frame("time"=rep("24h",4),"NES"=c(Fgsea_dDPI_Sel24h_pc_H358_10000$NES,Fgsea_COAD_Sel24h_pc_H358_10000$NES,Fgsea_oldARACNeAP_MEKcorrected_Sel24h_pc_H358_10000$NES,Fgsea_PC_Sel24h_pc_H358_10000$NES),"method"=method)
+Fgsea_all<-rbind(Fgsea_5min_mapDIA,Fgsea_1h_mapDIA,Fgsea_6h_mapDIA,Fgsea_24h_mapDIA)
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method))+geom_point(aes(color=method,shape=method,size="5"))
+ggtitle("GSEA H358 Selumetinib")
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=5))
++ ggtitle("GSEA H358 Selumetinib - interactomes")
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=5))+ ggtitle("GSEA H358 Selumetinib - interactomes")
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")+scale_color_manual(values=c("orange", "pink", "blue","gray"))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")+scale_color_manual(values=c("orange", "pink", "cornflowerblue","gray"))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")+scale_color_manual(values=c("cornflowerblue", "blueviolet", "orange","azure4"))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")+scale_color_manual(values=c("cornflowerblue", "blueviolet", "orange","azure4"))+scale_shape_manual(values=c(0,5,6,7))
+p
+p<-ggplot(data=Fgsea_all,aes(x=time,y=NES,group=method))+geom_line(aes(color=method,size=2))+geom_point(aes(color=method,shape=method,size=3))+ ggtitle("GSEA H358 Selumetinib - interactomes")+scale_color_manual(values=c("cornflowerblue", "blueviolet", "orange","azure4"))+scale_shape_manual(values=c(15,17,18,19))
+p
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/metaVIPER_results/regulon_pruned/BASELINE_asnull_pruned/metaviper_baselineasnull_eachdrug_pruned.RData")
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/metaVIPER_results/regulon_pruned/BASELINE_asnull_pruned/metaviper_baselineasnull_eachdrug_STOUFFER_pruned.RData")
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/metaVIPER_results/regulon_pruned/BASELINE_asnull_pruned/metaviper_baselineasnull_pruned.RData")
+View(vpres_baseline)
+install.packages("BiocManager")
+BiocManager::install("WGCNA")
+library(vipe)
+library(viper)
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/DP_GP_cluster-master/DPGP_results_mapDIA_SCALED/pea_on_cluster_DPGP_mapDIA_SCALED.RData")
+View(all_pea)
+A<-all_pea$A549_SelEve_
+View(A)
+A<-subset(A,p.adjust<0.05)
+View(A)
+View(A)
+A<-subset(A,A$p.adjust<0.05)
+B<-subset(A,A$p.adjust<0.05)
+B<-select(A,A$p.adjust<0.05)
+library(dplyr)
+B<-select(A,A$p.adjust<0.05)
+B<-select(A,p.adjust<0.05)
+library(ReactomePA)
+?enrichPathway
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/metaVIPER_results/no_phosph_site/regulon_pruned/BASELINE_asnull_pruned/pea_on_cluster_DPGP_metaviper_nophosphsite_defaultsampling.RData")
+View(all_pea)
+all_pea
+all_pea$A549_SelTam_
+all_pea$A549_Sel_
+A<-as.data.frame(all_pea$A549_Sel_)
+View(A)
+library(ReactomePA)
+?enrichPathway
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/VIPER_results/regulon_RPPA_ARACNe/BASELINE_asnull/FGSEA/fgsea_Selumetinib.RData")
+View(res_gsea_subs_Sel)
+View(res_gsea_subs_Sel)
+A549<-subset(res_gsea_subs_Sel,res_gsea_subs_Sel$cl_tag=='A549')
+View(A549)
+27-16
+68-55
+load("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_GIUGNO_OK/VIPER_results/regulon_RPPA_ARACNe/BASELINE_asnull/pea_on_cluster_DPGP_viper_defaultsampling.RData")
+h2122<-all_pea$H2122_SelTam_
+View(h2122)
+h2122_1<-subset(h2122,h2122$module==1)
+View(h2122_1)
+h2122_1<-subset(h2122_1,h2122$p.adjust<0.05)
+h2122_1<-subset(h2122_1,h2122$qvalue<0.05)
+h2122_2<-subset(h2122,h2122$module==2)
+View(h2122_2)
+h2122_11<-subset(h2122,h2122$module==11)
+View(h2122_11)
+h2122_9<-subset(h2122,h2122$module==9)
+View(h2122_9)
+h522<-all_pea$H522_SelTam_
+h522_5<-subset(h522,h522$module==5)
+h522_6<-subset(h522,h522$module==6)
+h522_8<-subset(h522,h522$module==8)
+View(h522_5)
+View(h522_6)
+View(h522_8)
+library(pheatmap)
+pheatmap
+library(pheatmap)
+library(RColorBrewer)
+library(ggplot2)
+library(tidyverse)
+library(data.table)
+install.packages("tidyverse")
+library(tidyverse)
+install.packages(c("backports", "BiocManager", "callr", "curl", "data.table", "devtools", "digest", "dtwclust", "ellipsis", "gridSVG", "haven", "Hmisc", "hms", "htmlTable", "htmltools", "htmlwidgets", "httpuv", "knitr", "lambda.r", "later", "matrixStats", "pkgbuild", "pkgconfig", "promises", "purrr", "Rcpp", "RcppArmadillo", "RcppParallel", "RcppThread", "rlang", "rmarkdown", "roxygen2", "rvcheck", "rvest", "shiny", "survival", "testthat", "tidyr", "tinytex", "xfun"))
+install.packages("devtools")
+install.packages("RCurl")
+install.packages("RCurl")
+install.packages("devtools")
+install.packages("rversions")
+install.packages("devtools")
+install.packages("devtools")
+setwd("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_JUNE2019_OK")
+library(data.table)
+#library(fgsea)
+library(tidyr)
+library(plyr)
+library(dplyr)
+library(ReactomePA)
+library(org.Hs.eg.db)
+library(clusterProfiler)
+library(UniProt.ws)
+#source('../gsea_function.R')
+#load metaVIPER results
+load('../VIPER_results/regulon_RPPA_ARACNe/DMSO_asnull/viper_dmsoasnull_eachdrug_STOUFFER.RData')
+setwd("~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_JUNE2019_OK/DP_GP_cluster-master")
+library(data.table)
+#library(fgsea)
+library(tidyr)
+library(plyr)
+library(dplyr)
+library(ReactomePA)
+library(org.Hs.eg.db)
+library(clusterProfiler)
+library(UniProt.ws)
+#source('../gsea_function.R')
+#load metaVIPER results
+load('../VIPER_results/regulon_RPPA_ARACNe/DMSO_asnull/viper_dmsoasnull_eachdrug_STOUFFER.RData')
+source('~/Scrivania/Tutto/NSCL-KRAS-cell-lines-creo-drugs/Califano_collaboration/metaVIPER_JUNE2019_OK/my_DPGP_analysis.R', echo=TRUE)
+View(vpres_dmso_Sel_stouffer)
+tag2='A549_Sel_'
+tag<-tag2[i]
+input<-paste0('txt_viper_DMSO_asnull/viper_DMSO_asnull',tag,'.txt')
+newdir<-paste0('DPGP_results_viper_DMSO_asnull_defaultsampling/',tag)
+dir.create(newdir)
+newdir<-paste0('DPGP_results_viper_DMSO_asnull_defaultsampling/',tag)
+tag=tag2
+input<-paste0('txt_viper_DMSO_asnull/viper_DMSO_asnull',tag,'.txt')
+newdir<-paste0('DPGP_results_viper_DMSO_asnull_defaultsampling/',tag)
+dir.create(newdir)
+dir.create(newdir)
+output<-paste0(newdir,'/',tag)
+command<-paste(DPGP.call,' -i ',input,' -o ',output,' -p pdf --plot --cluster_uncertainty_estimate',sep="")
+system(command)
+command<-paste(python3 DPGP.call,' -i ',input,' -o ',output,' -p pdf --plot --cluster_uncertainty_estimate',sep="")
+system(command)
+system(command)
+system(command)
+library(demand)
+BiocManager::install("DeMAND")
+library(DeMAND)
+data(inputExample)
+dobj <- demandClass(exp=bcellExp, anno=bcellAnno, network=bcellNetwork)
+printDeMAND(dobj)
+dobj <- runDeMAND(dobj, fgIndex=caseIndex, bgIndex=controlIndex)
+printDeMAND(dobj)
+View(bcellExp)
+View(bcellExp)
+View(bcellAnno)
+View(bcellNetwork)
+knitr::opts_chunk$set(echo = TRUE)
+library(rmarkdown)
+library(ggplot2)
+library("scales")
+library(R0)
+library(xts)
+library(EpiEstim)
+library(EpiNow)
+library(rmarkdown)
+library(ggplot2)
+library("scales")
+knitr::opts_chunk$set(echo = TRUE)
+library(rmarkdown)
+library(ggplot2)
+library("scales")
+library(R0)
+library(xts)
+library(EpiEstim)
+#library(EpiNow)
+#library(EpiSoon)
+library(incidence)
+library(tidyverse)
+covid19_regioni <- read.csv("~/COVID-19/dati-regioni/dpc-covid19-ita-regioni.csv")
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+knitr::opts_chunk$set(echo = TRUE)
+library(rmarkdown)
+library(ggplot2)
+library("scales")
+library(R0)
+library(xts)
+library(EpiEstim)
+#library(EpiNow)
+#library(EpiSoon)
+library(incidence)
+library(tidyverse)
+covid19_regioni <- read.csv("~/COVID-19/dati-regioni/dpc-covid19-ita-regioni.csv")
+#PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+covid19_regioni_pop$Osp_100k<-covid19_regioni_pop$totale_ospedalizzati/covid19_regioni_pop$popolazione*100000
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+covid19_regioni_pop$Osp_100k<-covid19_regioni_pop$totale_ospedalizzati/covid19_regioni_pop$popolazione*100000
+View(covid19_regioni)
+View(PopolazioniRegione)
+colnames(PopolazioniRegione)<-'codice_regione'
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+covid19_regioni_pop$Osp_100k<-covid19_regioni_pop$totale_ospedalizzati/covid19_regioni_pop$popolazione*100000
+colnames(PopolazioniRegione)<-'codice_regione'
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+covid19_regioni_pop$Osp_100k<-covid19_regioni_pop$totale_ospedalizzati/covid19_regioni_pop$popolazione*100000
+nord_centro=appoggio<-subset(covid19_regioni_pop,codice_regione==3 | codice_regione==10 | codice_regione==11 | codice_regione==12 |codice_regione==8 | codice_regione==9 | codice_regione==1 | codice_regione==5)
+nord_centro=appoggio<-subset(covid19_regioni_pop,codice_regione==3 | codice_regione==10 | codice_regione==12 |codice_regione==8 | codice_regione==1 | codice_regione==5)
+umbria_confini=appoggio<-subset(covid19_regioni_pop,codice_regione==9 | codice_regione==10 | codice_regione==12 |codice_regione==11)
+prova_tamponi<-subset(nord_centro,as.Date(substr(data,1,10),format("%Y-%m-%d"))>=as.Date("2020-03-05"))
+dataset_scatter<-subset(covid19_regioni_pop,as.Date(substr(data,1,10),format("%Y-%m-%d"))>=as.Date("2020-03-06"))
+```
+```{r plot9, echo=FALSE,warning=FALSE,message=FALSE,error=FALSE}
+dataset_ordinato_data_regione=nord_centro[order(nord_centro$denominazione_regione,nord_centro$data),]
+umbria=appoggio_u<-subset(dataset_ordinato_data_regione,denominazione_regione== "Umbria")
+umbria$data= as.Date(substr(umbria$data,1,10),format("%Y-%m-%d"))
+lombardia=appoggio_l<-subset(dataset_ordinato_data_regione,denominazione_regione== "Lombardia")
+veneto=appoggio_v<-subset(dataset_ordinato_data_regione,denominazione_regione== "Veneto")
+piemonte=appoggio_p<-subset(dataset_ordinato_data_regione,denominazione_regione== "Piemonte")
+emilia_romagna=appoggio_e<-subset(dataset_ordinato_data_regione,denominazione_regione== "Emilia-Romagna")
+#dataset deceduti e data
+heatmap_deceduti <- data.frame("lombardia" = lombardia$deceduti, "umbria" = umbria$deceduti,"veneto"=veneto$deceduti,"piemonte" = piemonte$deceduti, "emilia_romagna"=emilia_romagna$deceduti)
+rownames(heatmap_deceduti ) <- umbria$data
+#dataset totale casi
+heatmap_totalecasi <- data.frame("lombardia" = lombardia$totale_casi, "umbria" = umbria$totale_casi,"veneto"=veneto$totale_casi,"piemonte" = piemonte$totale_casi, "emilia_romagna"=emilia_romagna$totale_casi)
+rownames(heatmap_totalecasi ) <- umbria$data
+#dataset nuovi positivi
+heatmap_nuovipositivi <- data.frame("lombardia" = lombardia$nuovi_positivi, "umbria" = umbria$nuovi_positivi,"veneto"=veneto$nuovi_positivi,"piemonte" = piemonte$nuovi_positivi, "emilia_romagna"=emilia_romagna$nuovi_positivi)
+rownames(heatmap_nuovipositivi ) <- umbria$data
+#totale ospedalizzati
+heatmap_totaleosp <- data.frame("lombardia" = lombardia$totale_ospedalizzati, "umbria" = umbria$totale_ospedalizzati,"veneto"=veneto$totale_ospedalizzati,"piemonte" = piemonte$totale_ospedalizzati, "emilia_romagna"=emilia_romagna$totale_ospedalizzati)
+rownames(heatmap_totaleosp ) <- umbria$data
+```
+dataset_ordinato_data_regione=nord_centro[order(nord_centro$denominazione_regione,nord_centro$data),]
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=";")
+PopolazioniRegione <- read.csv("~/PopolazioniRegione.csv", sep=",")
+covid19_regioni_pop <- merge(covid19_regioni,PopolazioniRegione,by=c("codice_regione"))
+covid19_regioni_pop$Osp_100k<-covid19_regioni_pop$totale_ospedalizzati/covid19_regioni_pop$popolazione*100000
+nord_centro=appoggio<-subset(covid19_regioni_pop,codice_regione==3 | codice_regione==10 | codice_regione==11 | codice_regione==12 |codice_regione==8 | codice_regione==9 | codice_regione==1 | codice_regione==5)
+nord_centro=appoggio<-subset(covid19_regioni_pop,codice_regione==3 | codice_regione==10 | codice_regione==12 |codice_regione==8 | codice_regione==1 | codice_regione==5)
+umbria_confini=appoggio<-subset(covid19_regioni_pop,codice_regione==9 | codice_regione==10 | codice_regione==12 |codice_regione==11)
+prova_tamponi<-subset(nord_centro,as.Date(substr(data,1,10),format("%Y-%m-%d"))>=as.Date("2020-03-05"))
+dataset_scatter<-subset(covid19_regioni_pop,as.Date(substr(data,1,10),format("%Y-%m-%d"))>=as.Date("2020-03-06"))
+dataset_ordinato_data_regione=nord_centro[order(nord_centro$denominazione_regione,nord_centro$data),]
+umbria=appoggio_u<-subset(dataset_ordinato_data_regione,denominazione_regione== "Umbria")
+umbria$data= as.Date(substr(umbria$data,1,10),format("%Y-%m-%d"))
+lombardia=appoggio_l<-subset(dataset_ordinato_data_regione,denominazione_regione== "Lombardia")
+veneto=appoggio_v<-subset(dataset_ordinato_data_regione,denominazione_regione== "Veneto")
+piemonte=appoggio_p<-subset(dataset_ordinato_data_regione,denominazione_regione== "Piemonte")
+emilia_romagna=appoggio_e<-subset(dataset_ordinato_data_regione,denominazione_regione== "Emilia-Romagna")
+#dataset deceduti e data
+heatmap_deceduti <- data.frame("lombardia" = lombardia$deceduti, "umbria" = umbria$deceduti,"veneto"=veneto$deceduti,"piemonte" = piemonte$deceduti, "emilia_romagna"=emilia_romagna$deceduti)
+rownames(heatmap_deceduti ) <- umbria$data
+heatmap_totalecasi <- data.frame("lombardia" = lombardia$totale_casi, "umbria" = umbria$totale_casi,"veneto"=veneto$totale_casi,"piemonte" = piemonte$totale_casi, "emilia_romagna"=emilia_romagna$totale_casi)
+rownames(heatmap_totalecasi ) <- umbria$data
+#dataset nuovi positivi
+heatmap_nuovipositivi <- data.frame("lombardia" = lombardia$nuovi_positivi, "umbria" = umbria$nuovi_positivi,"veneto"=veneto$nuovi_positivi,"piemonte" = piemonte$nuovi_positivi, "emilia_romagna"=emilia_romagna$nuovi_positivi)
+rownames(heatmap_nuovipositivi ) <- umbria$data
+#totale ospedalizzati
+heatmap_totaleosp <- data.frame("lombardia" = lombardia$totale_ospedalizzati, "umbria" = umbria$totale_ospedalizzati,"veneto"=veneto$totale_ospedalizzati,"piemonte" = piemonte$totale_ospedalizzati, "emilia_romagna"=emilia_romagna$totale_ospedalizzati)
+rownames(heatmap_totaleosp ) <- umbria$data
+```
+heatmap_totalecasi <- data.frame("lombardia" = lombardia$totale_casi, "umbria" = umbria$totale_casi,"veneto"=veneto$totale_casi,"piemonte" = piemonte$totale_casi, "emilia_romagna"=emilia_romagna$totale_casi)
+rownames(heatmap_totalecasi ) <- umbria$data
+#dataset nuovi positivi
+heatmap_nuovipositivi <- data.frame("lombardia" = lombardia$nuovi_positivi, "umbria" = umbria$nuovi_positivi,"veneto"=veneto$nuovi_positivi,"piemonte" = piemonte$nuovi_positivi, "emilia_romagna"=emilia_romagna$nuovi_positivi)
+rownames(heatmap_nuovipositivi ) <- umbria$data
+#totale ospedalizzati
+heatmap_totaleosp <- data.frame("lombardia" = lombardia$totale_ospedalizzati, "umbria" = umbria$totale_ospedalizzati,"veneto"=veneto$totale_ospedalizzati,"piemonte" = piemonte$totale_ospedalizzati, "emilia_romagna"=emilia_romagna$totale_ospedalizzati)
+rownames(heatmap_totaleosp ) <- umbria$data
+View(heatmap_totaleosp)
+matrix_totaleospedalizzati <- data.matrix(heatmap_totaleosp)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = rev(heat.colors(256)), scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = rev(heat.colors(256)), scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9, breaks=Breaks)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = heat.colors(256), scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9, breaks=Breaks)
+?heatmap
+heat.colors
+heat.colors(256)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = rev(heat.colors(256)), scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9)
+library(RColorBrewer)
+colortime<-colorRampPalette(c('white','red'))
+colortime<-colortime(256)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9)
+colortime<-colorRampPalette(c('white','yellow','red'))
+colortime<-colortime(256)
+colortime<-colorRampPalette(c('white','yellow','red'))
+colortime<-colortime(256)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9)
+View(totaleospedalizzati_heatmap)
+help heatmap
+?heatmap
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9, legend='col')
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9, legend=c('col')
+)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10,'col'),cexCol = 0.9,cexRow = 0.9)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10,13),cexCol = 0.9,cexRow = 0.9)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- heatmap.2(t(matrix_totaleospedalizzati), Rowv=NA, Colv=NA, col = colortime, scale="row", margins=c(5,10),cexCol = 0.9,cexRow = 0.9)
+library(pheatmap)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), show_rownames=FALSE,show_colnames = FALSE)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), show_rownames=FALSE,show_colnames = FALSE, breaks=Breaks)
+?pheatmap
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), show_rownames=FALSE,show_colnames = FALSE, breaks=Breaks, cluster_rows = FALSE, cluster_cols = FALSE, col=colortime)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), show_rownames=FALSE,show_colnames = TRUE, breaks=Breaks, cluster_rows = FALSE, cluster_cols = FALSE, col=colortime)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), breaks=Breaks, cluster_rows = FALSE, cluster_cols = FALSE, col=colortime)
+colortime<-colortime(356)
+colortime<-colorRampPalette(c('white','yellow','red'))
+colortime<-colortime(100)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=257)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), breaks=Breaks, cluster_rows = FALSE, cluster_cols = FALSE, col=colortime)
+Breaks<-seq(from=min(matrix_totaleospedalizzati),to=max(matrix_totaleospedalizzati),length.out=100)
+matrix_totaleospedalizzati <- data.matrix(matrix_totaleospedalizzati)
+totaleospedalizzati_heatmap <- pheatmap(t(matrix_totaleospedalizzati), breaks=Breaks, cluster_rows = FALSE, cluster_cols = FALSE, col=colortime)
+View(totaleospedalizzati_heatmap)
+totaleospedalizzati_heatmap
+totaleospedalizzati
+View(matrix_totaleospedalizzati)
+setwd("~/Scrivania/Tutto/CRA-Matlab/CRA_Updated_Version/CRA_Toolbox_bash/covid19_area_interv_bande_tpo100_3may_8realiz/I3")
+setwd("~/Scrivania/Tutto/CRA-Matlab/CRA_Updated_Version/CRA_Toolbox_bash")
+source('~/Scrivania/Tutto/COVID19_gitlab/epi-model-umbria/COVID-19/Figures_R/plot_pdf.R', echo=TRUE)
+source('~/Scrivania/Tutto/COVID19_gitlab/epi-model-umbria/COVID-19/Figures_R/plot_pdf.R', echo=TRUE)
+source('~/Scrivania/Tutto/COVID19_gitlab/epi-model-umbria/COVID-19/Figures_R/plot_pdf.R', echo=TRUE)
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/init_italia.m b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/init_italia.m
new file mode 100644
index 0000000..9f6314f
--- /dev/null
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/init_italia.m
@@ -0,0 +1,34 @@
+
+region_name='Italy_pred';
+
+%define initial conditions
+N = 60e6; %population Italy
+
+%define time
+%time = 0:1:110;
+
+E00 = (1/N)*10^5;
+S0 = 10^5-E00;
+x0 = [S0 E00 0 0 0 0 0 0 0];
+
+%inizio epidemia: 27 Gennaio
+%dopo 24 giorni (20 Febbraio): paziente 1
+%dopo 25 giorni (21 Febbraio): zona rossa in Lombardia e Veneto
+%dopo 28 giorni (24 Febbraio): chiusura scuole in Emilia-Romagna, Friuli-Venezia Giulia,
+%Liguria, Lombardia, parte delle Marche, Piemonte, Veneto
+%dopo 25 giorni (21 Febbraio) DPI
+%dopo 38 giorni (5 Marzo): chiusura scuole nel resto d'Italia
+%dopo 40 giorni (7 Marzo): fuga verso sud
+%dopo 41 giorni (8 Marzo): lockdown nord Italia + social distancing +
+%eventi vietati
+%dopo 43 giorni (10 Marzo): total lockdown
+
+%RILASCIO DEL LOCKDOWN:
+%dopo 98 giorni (4 Maggio 2020)
+%dopo 112 giorni (18 Maggio 2020)
+
+Tlock=[25 28 38 43 98 112];
+
+%s0 = [0.8 0.75 0.7 0.2]; %reduction of 30%, 70%, 80%
+%s1=[0.7 0.65 0.6 0.15]; %reduction of 40%, 75%, 85%
+%s2=0.4; %reduction of 60%
\ No newline at end of file
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/interv_param_90perc_ITALIA_4Nr.mat b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/interv_param_90perc_ITALIA_4Nr.mat
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/italia_moda_nr4.mat b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ITALIA/italia_moda_nr4.mat
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/compute_intervention.m b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/compute_intervention.m
new file mode 100644
index 0000000..6f45029
--- /dev/null
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/compute_intervention.m
@@ -0,0 +1,143 @@
+
+
+function [s0,s1,s2]=compute_intervention(t,Tlock,s00,s11,s22,region_name)
+
+
+ if(strcmp(region_name,'Italy'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2)) * (t<Tlock(3)) + s00(3)*(t>=Tlock(3))*(t<Tlock(4)) + s00(4)*(t>=Tlock(4));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Italy_pred'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2)) * (t<Tlock(3)) + s00(3)*(t>=Tlock(3))*(t<Tlock(4)) + s00(4)*(t>=Tlock(4))*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Umbria'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2)) * (t<Tlock(3)) + s00(3)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Campania'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3)); %*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'Lazio'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'Lombardia'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2)) * (t<Tlock(3)) + s00(3)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(1)) + s11 * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Veneto'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2)) * (t<Tlock(3)) + s00(3)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11 * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'EmiliaRomagna'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11 * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'Calabria'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'Abruzzo'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Basilicata'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Marche'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Piemonte'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Toscana'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Puglia'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Liguria'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(2)) + s00(2)*(t>=Tlock(2));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Sicilia'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Sardegna'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+ if(strcmp(region_name,'Friuli'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(2)) + s22 * (t>=Tlock(2));
+ end
+
+ if(strcmp(region_name,'Aosta'))
+ s0 = (t<Tlock(1)) + s00(1) * (t>=Tlock(1)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(2)) + s22 * (t>=Tlock(2));
+ end
+
+ if(strcmp(region_name,'Molise'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+
+
+ if(strcmp(region_name,'Trentino'))
+ s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3));%*(t<Tlock(5)) + s00(5)*(t>=Tlock(5))*(t<Tlock(6)) + s00(6)*(t>=Tlock(6));
+ s1 = (t<Tlock(3)) + s11(1) * (t>=Tlock(3));
+ s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
+ end
+end
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/interv_bande_8realizzazione_perCRA.mat b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/interv_bande_8realizzazione_perCRA.mat
new file mode 100644
index 0000000..dddf7f9
Binary files /dev/null and b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/interv_bande_8realizzazione_perCRA.mat differ
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3.m b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3_NO.m
index cb4babf..cfcc224 100644
--- a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3.m
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3_NO.m
@@ -53,7 +53,7 @@ u=(1/TimeICUDeath)*(CFR/FracCritical);
g3=(1/TimeICUDeath)-u;
-s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3)) * (t<Tlock(4)) + s00(3)*(t>=Tlock(4));
+s0 = (t<Tlock(2)) + s00(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s00(2)*(t>=Tlock(3)) * (t<Tlock(4)) + s00(3)*(t>=Tlock(4))* (t<Tlock(5)) + s00(4)*(t>=Tlock(5))* (t<Tlock(6)) + s00(5)* (t>=Tlock(6));
s1 = (t<Tlock(2)) + s11(1) * (t>=Tlock(2)) * (t<Tlock(3)) + s11(2)*(t>=Tlock(3))* (t<Tlock(4)) + s11(3)*(t>=Tlock(4));
s2 = (t<Tlock(1)) + s22 * (t>=Tlock(1));
%s3 = (t<Tlock(1)) + s33 * (t>=Tlock(1));
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3_OK.m b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3_OK.m
new file mode 100644
index 0000000..4eb5c10
--- /dev/null
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/ode_covid19_v3_OK.m
@@ -0,0 +1,95 @@
+%ODE model to describe the spread and clinical progression of COVID-19
+
+function dx=ode_covid19_v3_OK(t,x,p,Tlock,region_name) %s00,s11,s22,region_name)%,Tlock2,s02,s12)
+
+be = p(1);
+b0=p(2);
+b1=p(3);
+b2=p(4);
+b3=p(5);
+
+Norm=10^5;
+
+be=be/(Norm);
+b0=b0/(Norm);
+b1=b1/(Norm);
+b2=b2/(Norm);
+b3=b3/(Norm);
+
+FracSevere = p(6);
+FracCritical = p(7);
+FracMild = 1-FracSevere-FracCritical;
+FracAsym = p(8);
+
+IncubPeriod = p(9);
+DurMildInf = p(10);
+
+DurAsym = p(11);
+
+DurHosp = p(12);
+TimeICUDeath = p(13);
+ProbDeath=p(14);
+
+PresymPeriod=p(15)*IncubPeriod;
+
+CFR=ProbDeath*FracCritical/100;
+
+a1=1/PresymPeriod; %presymptomatic period of transmission
+a0=1/(IncubPeriod-PresymPeriod); %true latent period
+
+f=FracAsym;
+
+g0=1/DurAsym;
+
+g1=(1/DurMildInf)*FracMild;
+p1=(1/DurMildInf)-g1;
+
+p2=(1/DurHosp)*(FracCritical/(FracSevere+FracCritical));
+g2=(1/DurHosp)-p2;
+
+u=(1/TimeICUDeath)*(CFR/FracCritical);
+
+g3=(1/TimeICUDeath)-u;
+
+s00=[p(16) p(17) p(18) p(19) p(20) p(21)];
+s11=p(22);
+s22=p(23);
+
+[s0,s1,s2]=compute_intervention(t,Tlock,s00,s11,s22,region_name);
+
+
+%susceptibles individuals (not infected)
+S = x(1);
+%exposed individuals, infected but no symptoms and no transmission
+E0 = x(2);
+%exposed individuals, infected but no symptoms and can transmit
+E1 = x(3);
+%infected individuals but asymptomatic infection
+I0 = x(4);
+%infected individuals, mild infection
+I1 = x(5);
+%infected individuals, severe infection
+I2 = x(6);
+%infected individuals, critical infection
+I3 = x(7);
+%recovered
+R = x(8);
+%dead
+D = x(9);
+
+
+%dS = - (be *(1/(1+s0^n))* E1 + b0*(1/(1+s0^n))*I0 + b1*(1/(1+s1^n))*I1 + b2*(1/(1+s2^n))*I2 + b3*(1/(1+s2^n))*I3)*S; % + k_e0s * E0;
+%dE0 = (be *(1/(1+s0^n))* E1 + b0*(1/(1+s0^n))*I0 + b1*(1/(1+s1^n))*I1 + b2*(1/(1+s2^n))*I2 + b3*(1/(1+s2^n))*I3)*S -a0*E0; % - k_e0s * E0;
+dS = - ((be * s0)* E1 + (b0*s0)*I0 + (b1*s1)*I1 + (b2*s2)*I2 + (b3*s2)*I3)*S; % + k_e0s * E0;
+dE0 = ((be *s0)* E1 + (b0*s0)*I0 + (b1*s1)*I1 + (b2*s2)*I2 + (b3*s2)*I3)*S -a0*E0; % - k_e0s * E0;
+dE1 = a0 * E0 - a1*E1; % -g4*E1;
+dI0 = f * a1 * E1 - g0*I0;
+dI1 = (1-f) * a1 * E1 - g1*I1 - p1*I1;
+dI2 = p1 * I1 - g2*I2 - p2*I2; % -u2*I2;
+dI3 = p2 * I2 - g3 *I3 - u * I3 ;
+dR = g0*I0 + g1*I1 + g2*I2 + g3*I3; % +g4 * E1;
+dD = u * I3; % + u2 * I2;
+
+
+
+dx=[dS;dE0;dE1;dI0;dI1;dI2;dI3;dR;dD];
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/Examples/param_9step_8realizzazione.mat b/CRA_Updated_Version/CRA_Toolbox_bash/Examples/param_9step_8realizzazione.mat
new file mode 100644
index 0000000..ba37972
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/compute_MIRI.m b/CRA_Updated_Version/CRA_Toolbox_bash/compute_MIRI.m
index 8cd8cae..ae993fa 100644
--- a/CRA_Updated_Version/CRA_Toolbox_bash/compute_MIRI.m
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/compute_MIRI.m
@@ -88,7 +88,7 @@ try
%values of the evaluation function are computed and stored in the
%TimeBehavior object Results
Results.currentEvalFunc=current_func;
- Results.computeEvalFunc();
+ Results.computeEvalFunc();
samples=Results.evalFuncValues;
@@ -124,6 +124,7 @@ try
Results.currentTailMethod=current_tm;
[XiMax,XiMin]=Results.compute_tail(AllPerturbations{k,1},tail_size);
+ [XiMaxD,XiMinD]=Results.compute_tail(AllDerivedParam{k,1},tail_size);
%[XiMax,XiMin]=handles.Results.compute_tail(handles1.perturbation,handles.tail_size);
NcloudMax=size(XiMax,1);
@@ -139,7 +140,7 @@ try
if(isfield(model,'derived_parameters'))
obj_MIRI_derived=MIRI();
- [all_xbin_max_d,all_xbin_min_d,all_ks_max_d,all_ks_min_d]=obj_MIRI_derived.estimate_parampdfs(derived_parameters, AllDerivedParam{k,1}, XiMax, XiMin);
+ [all_xbin_max_d,all_xbin_min_d,all_ks_max_d,all_ks_min_d]=obj_MIRI_derived.estimate_parampdfs(derived_parameters, AllDerivedParam{k,1}, XiMaxD, XiMinD);
%[all_xbin_max,all_xbin_min,all_ks_max,all_ks_min]=obj_MIRI.estimate_parampdfs(handles1.nominal_p, handles1.perturbation, Ncloud, handles.XiMax, handles.XiMin);
[MIRI_param_d,CloudCondMin_d, CloudCondMax_d]=obj_MIRI_derived.MIRI_computation(all_ks_max_d,all_ks_min_d,all_xbin_max_d,all_xbin_min_d);
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deleted file mode 100644
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m b/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m
index 719d11d..c985715 100644
--- a/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m
@@ -47,9 +47,9 @@
tic;
-model_name='ode_covid19_v3';
+model_name='ode_covid19_v3_OK';
-stop_time=80;
+stop_time=300;
step_size=1;
ode_solver='ode15s';
@@ -57,51 +57,24 @@ ode_solver='ode15s';
time_axis=[0:step_size:stop_time]';
%parameters and initial conditions of the model
+init_italia
+load('Examples/ITALIA/italia_moda_nr4.mat');
+nominal_parameters=[italia_moda_nr4(1,1:19) 1 1 italia_moda_nr4(1,20:21)];
+%nominal_parameters=italia_moda_nr4;
-% N = 882000;
-% InitInf=1;
-%
-% E00 = InitInf;
-% S0 = N-E00;
-% x0 = [S0 E00 0 0 0 0 0 0 0];
-%
-N = 882000; %population Umbria
-
-E00 = (1/N)*10^5;
-S0 = 10^5-E00;
-x0 = [S0 E00 0 0 0 0 0 0 0];
-
-%Tlock=23;
-
-%inizio epidemia: 21 Febbraio
-%dopo 3 giorni (24 Febbraio) DPI
-%dopo 14 giorni (5 Marzo): chiusura scuole
-%dopo 17 giorni (8 Marzo): social distancing + no events
-%dopo 19 giorni (10 Marzo): total lockdown
-Tlock=[3 14 17 19];
-
-s00 = [0.7 0.3 0.2]; %reduction of 30%, 70%, 80%
-s11=[0.6 0.25 0.2]; %reduction of 40%, 75%, 80%
-s22=0.4; %reduction of 60%
-
-
-load('Examples/moda_parametri_inizio9step_LINLOG_100000_UMBRIA_I2I3DE_3may.mat');
-nominal_parameters=moda_parametri_inizio9step_LINLOG;
-%s01=moda_parametri_inizio14step_LOG(16);
-%s11=moda_parametri_inizio14step_LOG(17);
-
-%nominal_parameters=[be b0 b1 b2 b3 a0 a1 f g0 g1 p1 g2 p2 g3 u];
-nominal_parameters_name={'be', 'b0', 'b1', 'b2','b3', 'FracSevere', 'FracCritical', 'FracAsym', 'IncubPeriod', 'DurMildInf', 'DurAsym', 'DurHosp', 'TimeICUDeath', 'ProbDeath', 'PresymPercentage'};
+nominal_parameters_name={'be', 'b0', 'b1', 'b2','b3', 'FracSevere', 'FracCritical', 'FracAsym', 'IncubPeriod', 'DurMildInf', 'DurAsym', 'DurHosp', 'TimeICUDeath', 'ProbDeath', 'PresymPeriod','s01','s02','s03','s04','s05','s06','s1','s2'};
+s00=[nominal_parameters(16) nominal_parameters(17) nominal_parameters(18) nominal_parameters(19) nominal_parameters(20) nominal_parameters(21)];
+s11=nominal_parameters(22);
+s22=nominal_parameters(23);
derived_parameters=1;
derived_parameters_name={'R0'};
-%Tlock1=23;
num_observables=9;
observables_name={'S','E0','E1','I0','I1','I2','I3','R','D'};
-model=struct('name',model_name,'odesolver',ode_solver,'time',time_axis,'stop',stop_time,'step',step_size,'nominal_parameters',nominal_parameters,'nominal_parameters_name',{nominal_parameters_name},'derived_parameters',derived_parameters,'derived_parameters_name',{derived_parameters_name},'num_observables',num_observables,'observables_name',{observables_name},'initial_conditions',x0,'Tlock',Tlock,'s00',s00,'s11',s11,'s22',s22);
+model=struct('name',model_name,'odesolver',ode_solver,'time',time_axis,'stop',stop_time,'step',step_size,'nominal_parameters',nominal_parameters,'nominal_parameters_name',{nominal_parameters_name},'derived_parameters',derived_parameters,'derived_parameters_name',{derived_parameters_name},'num_observables',num_observables,'observables_name',{observables_name},'initial_conditions',x0,'Tlock',Tlock,'region_name',region_name);
Nr=10;
@@ -109,32 +82,36 @@ Nr=10;
%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 1 0.4311 0.6988 0.1504 0.9213 0.5 0.2995];
%UBpi=[2 4 4 4 4 32 16 10 2.81 2.8741 6.9881 1.5042 9.2128 2 1.1980];
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
+%LBpi=[0.125 0.25 0.25 0.25 0.25 0.125 0.125 0.25 1 0.5 0.6988 0.5556 0.4 0.25 0.25];
+%UBpi=[1.02 4 4 4 4 1.1 1.1 1.5 3 2 1.5 2.7778 4 1.5 1.02];
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
+%LBpi=[0.125 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.5 0.6988 0.5556 0.4 0.25 0.25];
+%UBpi=[1.02 4 4 4 4 1.1 1.1 1.5 2 2 1.5 2.7778 4 1.5 1.02];
-%load('intervalli_param_perCRA_stessi_delle_bande_stretti.mat')
-%LBpi=interv_low;
-%UBpi=interv_high;
+load('Examples/ITALIA/intervalli_figura_bande_90perc_CON_PRCTILE_ITALIA_4Nr.mat')
+%LBpi=parameters{1,1}(1,:)./nominal_parameters;
+%UBpi=parameters{1,1}(2,:)./nominal_parameters;
-%LBpi=[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5];
-%UBpi=[2 2 2 2 2 2 2 2 2 2 2 2 2 2 1.3235];
-LBpi=0.5;
-UBpi=1.3;
+LBpi=[parameters{1,1}(1,1:19) 0.1 0.1 parameters{1,1}(1,20:21)]./nominal_parameters;
+UBpi=[parameters{1,1}(2,1:19) 0.9 0.9 parameters{1,1}(2,20:21)]./nominal_parameters;
+%LBpi=[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6396 0.7018 0.5403 0.7539 0.6150 0.6177 0.5640 0.6903 0.6754 0.5958 0.5023 0.4850 0.1 0.1 0.6887 0.5429];
+%UBpi=[2 2 2 2 2 1.5 1.38 1.2792 1.2281 1.6210 1.2565 1.5376 3.0885 1.0152 1.2426 1.3509 1.3903 1.5068 1.4549 0.9 0.9 2.4105 2.1716];
-Ns=1000;
+%LBpi=[0.99 0.5 0.5 0.5 0.5 0.5 0.5 0.6396 0.7018 0.5403 0.7539 0.6150 0.6177 0.5640 0.99 0.6754 0.5958 0.5023 0.4850 0.6887 0.5429];
+%UBpi=[1.05 2 2 2 2 1.5 1.38 1.2792 1.2281 1.6210 1.2565 1.5376 3.0885 1.0152 1.05 1.3509 1.3903 1.5068 1.4549 2.4105 2.1716];
+
+
+Ns=10000;
variable_name='I2';
current_func=Area(); %current evaluation function
-tail_size=100; %number of samples for the lower and upper tail
+tail_size=1000; %number of samples for the lower and upper tail
step_size=0.001; %this parameter needs to be defined only when current_tm=tmp_sum(step_size)
current_tm=sorted();
-folder='parfor_p_2_altraprova';
+folder='covid19_area_10Nr_1000sample_time300_ITALIA_4Nr_90perc_predizione';
%model simulation for each sample of the Latin Hypercube
disp('Starting model simulation with perturbed parameters');
@@ -146,8 +123,7 @@ disp('All done! Model simulation completed!');
disp('Starting computation of the MIRI for each parameter...');
try
compute_MIRI(model,variable_name,current_func,tail_size,current_tm,Nr,Ns,AllResults,AllPerturbations,AllDerivedParam,folder)
-catch ME
- %break
+catch ME
return
end
%plot and save probability density function of the evaluation function
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m~ b/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m~
deleted file mode 100644
index 2baab19..0000000
--- a/CRA_Updated_Version/CRA_Toolbox_bash/main_covid19.m~
+++ /dev/null
@@ -1,161 +0,0 @@
-%SCRIPT FOR RUNNING THE CRA TOOLBOX WITHOUT USING THE GUI
-%The model is written as a Matlab function.
-
-%%%% THE FIRST THING TO DO FOR RUNNING THE SCRIPT IS TO ADD THE FOLDER
-%%%% CLASSES AND THE FOLDER Examples TO THE PATH %%%%
-
-%Parameters to be set by the user are:
-% 1-model_name: name of the model in .xml format. A struct is created to
-% store the following characteristics of an ODE model: nominal values and
-% names of the parameters, initial conditions and names of variables, input
-% values.
-% 2-stop_time: final time point for the model simulation
-% 3-step_size: time interval for the vector of time points
-% 4- ode_solver: type of ode_solver for simulating the model (possible
-% choices are: ode45, ode15s, ode23t, sundials, stochastic, explicit tau
-% and implicit tau
-% 5- Nr: number of independent realizations to perform
-% 6- LBpi: lower boundary of the Latin Hypercube Sampling for perturbation
-% of the model parameter space
-% 7- UBpi: upper boundary of the Latin Hypercube Sampling
-% 8- Ns: number of samples of the Latin Hypercube (parameters n of the
-% lhsdesign function)
-% 9- variable_name: variable of the model to set as reference node to be
-% measured
-% 10- current_func: is the type of evaluation function. Currently, it is
-% possible to choose among
-% three evaluation functions: area under the curve, maximum value and time
-% of maximum for the time behavior of the selected variables. The user can
-% also define his evaluation function in a .m file by extending the
-% abstract class EvaluationFunction
-% 11- tail_size: number of samples to include in the upper and lower tail
-% when computing the probability density function of the evaluation
-% function
-% 12- current_tm: is the method for computing the tails of the pdf of the
-% evaluation function. Right now, it is possible to choose between two
-% methods: sorted() which sorts the values of the evaluation function and
-% selects the first and last samples according to tail_size; tmp_sum()
-% computes the tails by selecting the upper and lower quartile. When using
-% tmp_sum(), the parameter step_size needs also to be specified. Step_size
-% is the step for computing the lower and upper quartile of the pdf in an
-% iterative way, i.e. when the upper and lower tails do not have the number of
-% samples specified by the user, the threshold is increased of a quantity
-% equal to step_size and the calculation of the tails is repeated.
-% The user can also define his own method for the tails computation by
-% extending the abstract class TailMethod().
-% 13- folder: name of the folder to create where the results are saved
-
-tic;
-
-model_name='ode_covid19_v3';
-
-stop_time=80;
-step_size=1;
-ode_solver='ode15s';
-
-%time axis for model simulation
-time_axis=[0:step_size:stop_time]';
-
-%parameters and initial conditions of the model
-
-% N = 882000;
-% InitInf=1;
-%
-% E00 = InitInf;
-% S0 = N-E00;
-% x0 = [S0 E00 0 0 0 0 0 0 0];
-%
-N = 882000; %population Umbria
-
-E00 = (1/N)*10^5;
-S0 = 10^5-E00;
-x0 = [S0 E00 0 0 0 0 0 0 0];
-
-%Tlock=23;
-
-%inizio epidemia: 21 Febbraio
-%dopo 3 giorni (24 Febbraio) DPI
-%dopo 14 giorni (5 Marzo): chiusura scuole
-%dopo 17 giorni (8 Marzo): social distancing + no events
-%dopo 19 giorni (10 Marzo): total lockdown
-Tlock=[3 14 17 19];
-
-s00 = [0.7 0.3 0.2]; %reduction of 30%, 70%, 80%
-s1=[0.6 0.25 0.15]; %reduction of 40%, 75%, 85%
-s2=0.4; %reduction of 60%
-
-
-load('Examples/moda_parametri_inizio9step_LINLOG_100000_UMBRIA_I2I3DE_3may.mat');
-nominal_parameters=moda_parametri_inizio9step_LINLOG;
-%s01=moda_parametri_inizio14step_LOG(16);
-%s11=moda_parametri_inizio14step_LOG(17);
-
-%nominal_parameters=[be b0 b1 b2 b3 a0 a1 f g0 g1 p1 g2 p2 g3 u];
-nominal_parameters_name={'be', 'b0', 'b1', 'b2','b3', 'FracSevere', 'FracCritical', 'FracAsym', 'IncubPeriod', 'DurMildInf', 'DurAsym', 'DurHosp', 'TimeICUDeath', 'ProbDeath', 'PresymPercentage'};
-
-derived_parameters=1;
-derived_parameters_name={'R0'};
-
-%Tlock1=23;
-
-num_observables=9;
-observables_name={'S','E0','E1','I0','I1','I2','I3','R','D'};
-
-model=struct('name',model_name,'odesolver',ode_solver,'time',time_axis,'stop',stop_time,'step',step_size,'nominal_parameters',nominal_parameters,'nominal_parameters_name',{nominal_parameters_name},'derived_parameters',derived_parameters,'derived_parameters_name',{derived_parameters_name},'num_observables',num_observables,'observables_name',{observables_name},'initial_conditions',x0,'Tlock',Tlock,'s00',s0,'s11',s1,'s22',s2);
-
-Nr=10;
-
-%define specific boundaries for each parameter
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 1 0.4311 0.6988 0.1504 0.9213 0.5 0.2995];
-%UBpi=[2 4 4 4 4 32 16 10 2.81 2.8741 6.9881 1.5042 9.2128 2 1.1980];
-
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
-
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
-
-
-%load('intervalli_param_perCRA_stessi_delle_bande_stretti.mat')
-%LBpi=interv_low;
-%UBpi=interv_high;
-
-%LBpi=[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5];
-%UBpi=[2 2 2 2 2 2 2 2 2 2 2 2 2 2 1.3235];
-LBpi=0.5;
-UBpi=1.3;
-
-
-Ns=10000;
-
-variable_name='I2';
-current_func=Area(); %current evaluation function
-tail_size=1000; %number of samples for the lower and upper tail
-step_size=0.001; %this parameter needs to be defined only when current_tm=tmp_sum(step_size)
-current_tm=sorted();
-
-folder='2014parfor_p_2_10';
-
-%model simulation for each sample of the Latin Hypercube
-disp('Starting model simulation with perturbed parameters');
-[AllResults,AllPerturbations,AllDerivedParam]=start_simulation_v2_apr2020(model,Nr,LBpi,UBpi,Ns);
-disp('All done! Model simulation completed!');
-
-%computation of the MIRI for the chosen evaluation function and model
-%variable
-disp('Starting computation of the MIRI for each parameter...');
-try
- compute_MIRI(model,variable_name,current_func,tail_size,current_tm,Nr,Ns,AllResults,AllPerturbations,AllDerivedParam,folder)
-catch ME
- %break
- return
-end
-%plot and save probability density function of the evaluation function
-disp('Plot of the probability density function of the chosen evaluation function');
-plotpdf_evalfunc(folder,variable_name);
-
-%plot and save conditional probability density functions of the parameters
-disp('Plot of the parameter probability density functions');
-plotpdf_param(folder,variable_name,model,Nr);
-
-toc;
\ No newline at end of file
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/parfor_p_2_altraprova/I2/pdf_param_derived.mat b/CRA_Updated_Version/CRA_Toolbox_bash/parfor_p_2_altraprova/I2/pdf_param_derived.mat
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diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/start_simulation_v2_apr2020.m b/CRA_Updated_Version/CRA_Toolbox_bash/start_simulation_v2_apr2020.m
index 6f9ff74..1c43d2d 100644
--- a/CRA_Updated_Version/CRA_Toolbox_bash/start_simulation_v2_apr2020.m
+++ b/CRA_Updated_Version/CRA_Toolbox_bash/start_simulation_v2_apr2020.m
@@ -24,22 +24,20 @@ AllDerivedParam=cell(Nr,1);
h = waitbar(0,'Please wait...');
+parpool(4)
for k=1:Nr
waitbar(k/Nr,h,['Realization number ',num2str(k)]);
- hypercube=LatinHypercube(LBpi,UBpi,Ns);
- perturbation=hypercube.generate_sample(num_parameters);
+ %hypercube=LatinHypercube(LBpi,UBpi,Ns);
+ %perturbation=hypercube.generate_sample(num_parameters);
-% perturbation=zeros(Ns,num_parameters);
-% for b=1:num_parameters
-% %LBpi(i)
-% %UBpi(i)
-% b
-% hypercube=LatinHypercube(LBpi(b),UBpi(b),Ns);
-% perturbation_i=hypercube.generate_sample(1);
-% perturbation(:,b)=perturbation_i;
-% end
+ perturbation=zeros(Ns,num_parameters);
+ for b=1:num_parameters
+ hypercube=LatinHypercube(LBpi(b),UBpi(b),Ns);
+ perturbation_i=hypercube.generate_sample(1);
+ perturbation(:,b)=perturbation_i;
+ end
perturbed_p=repmat(nominal_parameters,length(perturbation),1).*perturbation;
size(perturbed_p)
AllPerturbations{k,1}=perturbation;
@@ -48,10 +46,10 @@ for k=1:Nr
psim_all=cell(Ns,1);
R0_all=zeros(Ns,1);
lastwarn('')
- parfor i=1:Ns %parfor
- pi=perturbed_p(i,:);
+ parfor l=1:Ns %parfor
+ pi=perturbed_p(l,:);
%modelsim=@(t,x)feval(model.name,t,x,pi,model.input,model.total_proteins);
- modelsim=@(t,x)feval(model.name,t,x,pi,model.Tlock,model.s00,model.s11,model.s22);
+ modelsim=@(t,x)feval(model.name,t,x,pi,model.Tlock,model.region_name);
%modelsim=@(t,x)feval(model.name,t,x,pi,model.Tlock1,model.s01,model.s11);
[tsimi,psimi]=feval(model.odesolver,modelsim,model.time,model.initial_conditions);
@@ -62,13 +60,13 @@ for k=1:Nr
% for t=1:model.num_observables
% psim_all{i,t}=psimi(:,t);
%end
- msgstr=lastwarn
+ msgstr=lastwarn;
if isequal(msgstr,'')
- psim_all{i,1}=psimi;
- R0_all(i,1)=R0i;
+ psim_all{l,1}=psimi;
+ R0_all(l,1)=R0i;
else
- psim_all{i,1}=NaN(length(model.time),model.num_observables);
- R0_all(i,1)=NaN;
+ psim_all{l,1}=NaN(length(model.time),model.num_observables);
+ R0_all(l,1)=NaN;
end
lastwarn('')
end
diff --git a/CRA_Updated_Version/CRA_Toolbox_bash/untitled.m b/CRA_Updated_Version/CRA_Toolbox_bash/untitled.m
deleted file mode 100644
index 8f42eaf..0000000
--- a/CRA_Updated_Version/CRA_Toolbox_bash/untitled.m
+++ /dev/null
@@ -1,162 +0,0 @@
-%SCRIPT FOR RUNNING THE CRA TOOLBOX WITHOUT USING THE GUI
-%The model is written as a Matlab function.
-
-%%%% THE FIRST THING TO DO FOR RUNNING THE SCRIPT IS TO ADD THE FOLDER
-%%%% CLASSES AND THE FOLDER Examples TO THE PATH %%%%
-
-%Parameters to be set by the user are:
-% 1-model_name: name of the model in .xml format. A struct is created to
-% store the following characteristics of an ODE model: nominal values and
-% names of the parameters, initial conditions and names of variables, input
-% values.
-% 2-stop_time: final time point for the model simulation
-% 3-step_size: time interval for the vector of time points
-% 4- ode_solver: type of ode_solver for simulating the model (possible
-% choices are: ode45, ode15s, ode23t, sundials, stochastic, explicit tau
-% and implicit tau
-% 5- Nr: number of independent realizations to perform
-% 6- LBpi: lower boundary of the Latin Hypercube Sampling for perturbation
-% of the model parameter space
-% 7- UBpi: upper boundary of the Latin Hypercube Sampling
-% 8- Ns: number of samples of the Latin Hypercube (parameters n of the
-% lhsdesign function)
-% 9- variable_name: variable of the model to set as reference node to be
-% measured
-% 10- current_func: is the type of evaluation function. Currently, it is
-% possible to choose among
-% three evaluation functions: area under the curve, maximum value and time
-% of maximum for the time behavior of the selected variables. The user can
-% also define his evaluation function in a .m file by extending the
-% abstract class EvaluationFunction
-% 11- tail_size: number of samples to include in the upper and lower tail
-% when computing the probability density function of the evaluation
-% function
-% 12- current_tm: is the method for computing the tails of the pdf of the
-% evaluation function. Right now, it is possible to choose between two
-% methods: sorted() which sorts the values of the evaluation function and
-% selects the first and last samples according to tail_size; tmp_sum()
-% computes the tails by selecting the upper and lower quartile. When using
-% tmp_sum(), the parameter step_size needs also to be specified. Step_size
-% is the step for computing the lower and upper quartile of the pdf in an
-% iterative way, i.e. when the upper and lower tails do not have the number of
-% samples specified by the user, the threshold is increased of a quantity
-% equal to step_size and the calculation of the tails is repeated.
-% The user can also define his own method for the tails computation by
-% extending the abstract class TailMethod().
-% 13- folder: name of the folder to create where the results are saved
-
-tic;
-
-model_name='ode_covid19_v2';
-
-stop_time=70;
-step_size=1;
-ode_solver='ode15s';
-
-%time axis for model simulation
-time_axis=[0:step_size:stop_time]';
-
-%parameters and initial conditions of the model
-
-N = 882000;
-InitInf=1;
-
-E00 = InitInf;
-S0 = N-E00;
-x0 = [S0 E00 0 0 0 0 0 0 0];
-
-%N = 882000; %population Umbria
-
-%E00 = (1/N)*10^5;
-%S0 = 10^5-E00;
-%x0 = [S0 E00 0 0 0 0 0 0 0];
-
-Tlock=23;
-
-%inizio epidemia: 21 Febbraio
-%dopo 3 giorni (24 Febbraio) DPI
-%dopo 14 giorni (5 Marzo): chiusura scuole
-%dopo 17 giorni (8 Marzo): social distancing + no events
-%dopo 19 giorni (10 Marzo): total lockdown
-%Tlock=[3 14 17 19];
-
-%s0 = [0.7 0.3 0.2]; %reduction of 30%, 70%, 80%
-%s1=[0.6 0.25 0.15]; %reduction of 40%, 75%, 85%
-%s2=0.4; %reduction of 60%
-
-
-load('Examples/moda_parametri_inizio14step_LOG_100000_UMBRIA_I2I3DE.mat');
-nominal_parameters=moda_parametri_inizio14step_LOG(1:15);
-s01=moda_parametri_inizio14step_LOG(16);
-s11=moda_parametri_inizio14step_LOG(17);
-
-%nominal_parameters=[be b0 b1 b2 b3 a0 a1 f g0 g1 p1 g2 p2 g3 u];
-nominal_parameters_name={'be', 'b0', 'b1', 'b2','b3', 'FracSevere', 'FracCritical', 'FracAsym', 'IncubPeriod', 'DurMildInf', 'DurAsym', 'DurHosp', 'TimeICUDeath', 'ProbDeath', 'PresymPeriod'};
-
-derived_parameters=1;
-derived_parameters_name={'R0'};
-
-%Tlock1=23;
-
-num_observables=9;
-observables_name={'S','E0','E1','I0','I1','I2','I3','R','D'};
-
-model=struct('name',model_name,'odesolver',ode_solver,'time',time_axis,'stop',stop_time,'step',step_size,'nominal_parameters',nominal_parameters,'nominal_parameters_name',{nominal_parameters_name},'derived_parameters',derived_parameters,'derived_parameters_name',{derived_parameters_name},'num_observables',num_observables,'observables_name',{observables_name},'initial_conditions',x0,'Tlock1',Tlock,'s01',s01,'s11',s11);
-
-Nr=5;
-
-%define specific boundaries for each parameter
-LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 1 0.4311 0.6988 0.1504 0.9213 0.5 0.2995];
-UBpi=[2 4 4 4 4 32 16 10 2.81 2.8741 6.9881 1.5042 9.2128 2 1.1980];
-
-%LBpi=[0.5 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1 0.9 0.9 0.9 0.9 0.9 0.9];
-%UBpi=[2 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1];
-
-
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
-
-%LBpi=[0.5 0.25 0.25 0.25 0.25 0.125 0.125 0.25 0.5 0.4311 0.6988 0.5556 0.4 0.5 0.7321];
-%UBpi=[2 4 4 4 4 3 3 2 2 2.8741 3.5532 2.7778 4 2 1.3];
-
-
-%load('intervalli_param_perCRA_stessi_delle_bande_stretti.mat')
-%LBpi=interv_low;
-%UBpi=interv_high;
-
-%LBpi=[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5];
-%UBpi=[2 2 2 2 2 2 2 2 2 2 2 2 2 2 1.3235];
-
-Ns=1000;
-
-variable_name='I2';
-current_func=Area(); %current evaluation function
-tail_size=100; %number of samples for the lower and upper tail
-step_size=0.001; %this parameter needs to be defined only when current_tm=tmp_sum(step_size)
-current_tm=sorted();
-
-folder='2014provaparfor_v2';
-
-%model simulation for each sample of the Latin Hypercube
-disp('Starting model simulation with perturbed parameters');
-[AllResults,AllPerturbations,AllDerivedParam]=start_simulation_v2_apr2020(model,Nr,LBpi,UBpi,Ns);
-disp('All done! Model simulation completed!');
-
-%computation of the MIRI for the chosen evaluation function and model
-%variable
-disp('Starting computation of the MIRI for each parameter...');
-try
- compute_MIRI(model,variable_name,current_func,tail_size,current_tm,Nr,Ns,AllResults,AllPerturbations,AllDerivedParam,folder)
-catch ME
- %break
- return
-end
-%plot and save probability density function of the evaluation function
-disp('Plot of the probability density function of the chosen evaluation function');
-plotpdf_evalfunc(folder,variable_name);
-
-%plot and save conditional probability density functions of the parameters
-disp('Plot of the parameter probability density functions');
-plotpdf_param(folder,variable_name,model,Nr);
-
-toc;
\ No newline at end of file
diff --git a/CRA_Updated_Version/Classes/DistanceMaximum.m b/CRA_Updated_Version/Classes/DistanceMaximum.m
new file mode 100644
index 0000000..5148cc5
--- /dev/null
+++ b/CRA_Updated_Version/Classes/DistanceMaximum.m
@@ -0,0 +1,22 @@
+%Class computing the maximum point of a model variable curve. It extends
+%the abstract class EvaluationFunction. The method compute_evalfunc takes in input
+%an object TimeBehavior and returns in output the maximum point.
+
+classdef DistanceMaximum < EvaluationFunction
+
+ properties
+ end
+
+ methods
+
+ function EvalFuncValue=compute_ef(obj,obj_TimeBehavior)
+ [maximum1, timeOfMaximum1]=max(obj_TimeBehavior.values(:,0:100),[],2)
+ [maximum2, timeOfMaximum2]=max(obj_TimeBehavior.values(:,101:end),[],2)
+ %distance=obj_TimeBehavior.time(timeOfMaximum2) - obj_TimeBehavior.time(timeOfMaximum1)
+ distance=abs(maximum2 - maximum1);
+ EvalFuncValue=distance
+ end
+
+ end
+
+end
\ No newline at end of file
diff --git a/CRA_Updated_Version/Classes/MIRI.m b/CRA_Updated_Version/Classes/MIRI.m
index 8e4095c..ad74b08 100644
--- a/CRA_Updated_Version/Classes/MIRI.m
+++ b/CRA_Updated_Version/Classes/MIRI.m
@@ -32,7 +32,7 @@ classdef MIRI
step=(max(p(ip).*Perturbation(:,ip)) - min(p(ip).*Perturbation(:,ip)))/size(Perturbation(:,ip),1);
BinEdgesMax=min(p(ip).*Perturbation(:,ip)):step:max(p(ip).*Perturbation(:,ip));
- BinEdgesMin=min(p(ip).*Perturbation(:,ip)):step:max(p(ip).*Perturbation(:,ip));
+ BinEdgesMin=min(p(ip).*Perturbation(:,ip)):step:max(p(ip).*Perturbation(:,ip)) ;
[ks_max, xbin_max]=pdf_obj.evaluate_pdf(p(ip).*XiMax(:,ip)',BinEdgesMax);
[ks_min, xbin_min]=pdf_obj.evaluate_pdf(p(ip).*XiMin(:,ip)',BinEdgesMin);