Nothing
ndpis <<- 72
ncor <<- 1
Scaling_cor <<- matrix()
rr_is <<- c()
peran_stocsy_is <<- 0
chkzoom_stocsy_is <<- 1
idb_stocsy_is <<- 0
ysup_stocsy_is <- max(NMRData_plot[1,])
yinf_stocsy_is <- ysup_stocsy_is*-0.03
ysup_stocsy_is <- ysup_stocsy_is + ysup_stocsy_is*0.03
testy_stocsy_is <- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_is <- reactiveValues(x = c((min(testy_stocsy_is$Chemical_Shift)), max(testy_stocsy_is$Chemical_Shift)), y = c(yinf_stocsy_is,ysup_stocsy_is))
normalize <- (NMRData[1,] - min(NMRData[1,]))
spectrums_stocsy_is <- reactiveValues(dat = data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_plot[1,])) #, facs = data.frame(fac_stocsy_is = rr_is[])
facts_is <<- reactiveValues(fac_stocsy_is = c())
output$plot_stocsy_is <- renderPlot({
ggplot2::ggplot(spectrums_stocsy_is$dat,ggplot2::aes(Chemical_Shift,Spectrum)) + ggplot2::geom_line(color='blue') +
ggplot2::geom_line(ggplot2::aes(colour=facts_is$fac_stocsy_is,group=1)) + ggplot2::guides(colour = FALSE) + ggplot2::scale_colour_manual(values = c("red","blue")) +
ggplot2::coord_cartesian(xlim = c(ranges_stocsy_is$x[2],ranges_stocsy_is$x[1]), ylim = ranges_stocsy_is$y, expand = FALSE) +
ggplot2::scale_x_reverse() +
ggplot2::theme(axis.text.x = ggplot2::element_text(size = 12, color = "#000000"),
axis.text.y = ggplot2::element_text(size = 12, color = "#000000"),
title = ggplot2::element_text(face = "bold", color = "#000000", size = 17),
axis.title = ggplot2::element_text(face = "bold", color = "#000000", size = 15)
) +
ggplot2::labs(x = "Chemical Shift", y = "Intensity")
})
observeEvent(input$stocsy_s, {
for (k in 1:dim(cor_pearson)[2]) {
Scaling_cor <<- (1 - (cor_pearson[drv_pk,k]*cor_pearson[drv_pk,k]))
s <<- col_select[k]
if(drv_pk != k) {
spectrums_stocsy_is$dat$Spectrum[s] <<- spectrums_stocsy_is$dat$Spectrum[s]*Scaling_cor
}
else {
spectrums_stocsy_is$dat$Spectrum[s] <<- spectrums_stocsy_is$dat$Spectrum[s]*0.02
}
}
})
observeEvent(input$radio_is, {
value <<- (input$radio_is)
if (value == 1) {
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= cor_cutoff) {
rr_is[k] <<- "A"
}
else {
rr_is[k] <<- "B"
}
}
}
facts_is$fac_stocsy_is <<- rr_is[]
}
if (value == 2) {
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_spearman[drv_pk,z] >= cor_cutoff) {
rr_is[k] <<- "A"
}
else {
rr_is[k] <<- "B"
}}
else {
rr_is[k] <<- "B" #falta transformar 'rr_is' em variavel reativa
}
}
facts_is$fac_stocsy_is <<- rr_is[]
}
if (value == 3) {
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= cor_cutoff) {
rr_is[k] <<- "A"
}
else { }
if (cor_spearman[drv_pk,z] >= cor_cutoff) {
rr_is[k] <<- "A"
}
else {
}
}
else { }
}
facts_is$fac_stocsy_is <<- rr_is[]
}
})
observeEvent(input$plot_brush_stocsy_is,{
brush <- input$plot_brush_stocsy_is
if (!is.null(brush)) {
ranges_stocsy_is$x <- c(brush$xmin, brush$xmax)
ranges_stocsy_is$y <- c(brush$ymin, brush$ymax)
idb_stocsy_is <<- 1
peran_stocsy_is <<- (ranges_stocsy_is$x[2] - ranges_stocsy_is$x[1])*0.2
}
else {
ranges_stocsy_is$x <- NULL
}
})
observeEvent(input$x2_stocsy_is, {
tryton <<- facts_is$fac_stocsy_is
chkzoom_stocsy_is <<- chkzoom_stocsy_is*2
spectrums_stocsy_is$dat$Spectrum <- spectrums_stocsy_is$dat$Spectrum*2
})
observeEvent(input$x8_stocsy_is, {
chkzoom_stocsy_is <<- chkzoom_stocsy_is*8
spectrums_stocsy_is$dat$Spectrum <- spectrums_stocsy_is$dat$Spectrum*8
})
observeEvent(input$q2_stocsy_is, {
chkzoom_stocsy_is <<- chkzoom_stocsy_is/2
spectrums_stocsy_is$dat$Spectrum <- spectrums_stocsy_is$dat$Spectrum/2
})
observeEvent(input$q8_stocsy_is, {
chkzoom_stocsy_is <<- chkzoom_stocsy_is/8
spectrums_stocsy_is$dat$Spectrum <- spectrums_stocsy_is$dat$Spectrum/8
})
observeEvent(input$all_stocsy_is, {
ranges_stocsy_is$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
freshnum_stocsy_is <- which(file_names[] == input$spectrum_list_stocsy_is)
ysup_stocsy_is <- max (NMRData_plot[freshnum_stocsy_is,])
yinf_stocsy_is <- ysup_stocsy_is*-0.03
ysup_stocsy_is <- ysup_stocsy_is + ysup_stocsy_is*0.03
ranges_stocsy_is$y <- c(yinf_stocsy_is,ysup_stocsy_is)
spectrums_stocsy_is$dat$Spectrum <- spectrums_stocsy_is$dat$Spectrum/chkzoom_stocsy_is
chkzoom_stocsy_is <<- 1
idb_stocsy_is <<- 0
})
observeEvent(input$s_left_stocsy_is, {
if (!(ranges_stocsy_is$x[1] <= min(testy_stocsy_is$Chemical_Shift))) {
ranges_stocsy_is$x[1] <<- (ranges_stocsy_is$x[1] - peran_stocsy_is)
ranges_stocsy_is$x[2] <<- (ranges_stocsy_is$x[2] - peran_stocsy_is)
}
})
observeEvent(input$s_right_stocsy_is, {
if (!(ranges_stocsy_is$x[2] >= max(testy_stocsy_is$Chemical_Shift))) {
ranges_stocsy_is$x[1] <<- (ranges_stocsy_is$x[1] + peran_stocsy_is)
ranges_stocsy_is$x[2] <<- (ranges_stocsy_is$x[2] + peran_stocsy_is)
}
})
observeEvent(input$s_left_f_stocsy_is, {
if (!(ranges_stocsy_is$x[1] <= min(testy_stocsy_is$Chemical_Shift))) {
das_stocsy_is <<- (ranges_stocsy_is$x[2] - ranges_stocsy_is$x[1])
ranges_stocsy_is$x[1] <<- min(testy_stocsy_is$Chemical_Shift)
ranges_stocsy_is$x[2] <<- (min(testy_stocsy_is$Chemical_Shift) + das_stocsy_is)
}
})
observeEvent(input$s_right_f_stocsy_is, {
if (!(ranges_stocsy_is$x[2] >= max(testy_stocsy_is$Chemical_Shift))) {
das_stocsy_is <<- (ranges_stocsy_is$x[2] - ranges_stocsy_is$x[1])
ranges_stocsy_is$x[2] <<- max(testy_stocsy_is$Chemical_Shift)
ranges_stocsy_is$x[1] <<- (max(testy_stocsy_is$Chemical_Shift) - das_stocsy_is)
}
})
observeEvent(input$spectrum_list_stocsy_is,{
freshnum_stocsy_is <- which(file_names[] == input$spectrum_list_stocsy_is)
normalize <<- (NMRData_plot[freshnum_stocsy_is,] - min(NMRData_plot[freshnum_stocsy_is,]))
spectrums_stocsy_is$dat <- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=normalize)
if (!idb_stocsy_is && chkzoom_stocsy_is == 1) {
ysup_stocsy_is <- max (normalize[])
yinf_stocsy_is <- ysup_stocsy_is*-0.03
ysup_stocsy_is <- ysup_stocsy_is + ysup_stocsy_is*0.03
ranges_stocsy_is$y <- c(yinf_stocsy_is, ysup_stocsy_is)
}
else {
spectrums_stocsy_is$dat$Spectrum_ <- spectrums_stocsy_is$dat$Spectrum*chkzoom_stocsy_is
}
})
observeEvent(input$dblclick_stocsy_is, {
drv_pk_ois <<- which(abs(CS_values_real[1,]-input$dblclick_stocsy_is$x)==min(abs(CS_values_real[1,]-input$dblclick_stocsy_is$x)))
if (drv_pk_ois %in% col_select) {
drv_pk <<- which(col_select[] == drv_pk_ois)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= input$cutoff_stocsy_is) {
rr_is[k] <<- "A"
}
else {
rr_is[k] <<- "B"
}
}
else {
rr_is[k] <<- "B"
}
}
facts_is$fac_stocsy_is <<- rr_is[]
}
else {
showModal(modalDialog(
title = "Warning!!!",
"The selected point is not inside the previously loaded regions. Please, click on another point or load a new group of signals!",
easyClose = TRUE,
footer = modalButton("Close"),
size = "l",
drv_pk <- drv_pk_ois
))
}
})
## Download plot
# DPI
observeEvent(input$slide_dpi_is, {
n_dpi <<- input$slide_dpi_is
})
# Botton download plot
output$plot_download_is <- downloadHandler(
filename = function() {
paste0('stocsy-i.',input$data_input_is)
},
content = function(file1) {
ggplot2::ggsave(file1,width=295, device = input$data_input,height=205, units="mm", dpi = n_dpi)
}
)
observeEvent(input$cutoff_stocsy_is, {
cor_cutoff <<- input$cutoff_stocsy_is
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= cor_cutoff) {
rr_is[k] <<- "A"
}
else {
rr_is[k] <<- "B"
}
}
else {
rr_is[k] <<- "B" #falta transformar 'rr_is' em variavel reativa
}
}
facts_is$fac_stocsy_is <<- rr_is[]
})
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