https://github.com/maartenpaul/DBD_tracking
Revision fe1884d1a8f83faa68c789c30a839b3874986713 authored by Maarten Paul on 24 November 2022, 07:54:07 UTC, committed by GitHub on 24 November 2022, 07:54:07 UTC
fix hard coded variable for track length
1 parent 36f032f
Tip revision: fe1884d1a8f83faa68c789c30a839b3874986713 authored by Maarten Paul on 24 November 2022, 07:54:07 UTC
Update MSD_fit.R
Update MSD_fit.R
Tip revision: fe1884d
ML_py.R
library(reticulate)
library(tidyverse)
library(reshape2)
path_to_model<-"python/Model_Bidirectional_NoShape_3state_Tr10000"
rm(py)
#py$MLfolder <- 'D:/Imaging_data/ML_test/'
cat("Loading Python functions and ML model")
source_python(file.path(getwd(),"python/MLE_functions.py"))
py$path_to_model <- path_to_model
source_python(file.path(getwd(),"python/Load_model.py"))
ML_segment_tracks <- function(tracks,directory=NULL){
#source_python("python/Load_data.py")
x <- dlply(tracks,.variables = c("cellID","track"), function(x){
return(x$X)
})
names(x) <- NULL
py$x <- x
y <- dlply(tracks,.variables = c("cellID","track"), function(x){
return(x$Y)
})
names(y) <- NULL
py$y <- y
indices <- cumsum(daply(tracks,.variables = "cellID", function(x){
return(length(unique(x$track)))
}))
indices <- c(0,indices-1)
cat("import R data")
source_python("python/Load_Rdata.py")
cat("Get ML predictions")
source_python("python/Get_predictions.py")
cat("Return data")
#source_python('python/make_table.py')
if(!is.null(directory)){
py_save_object(object = x,filename = file.path(directory,tracks$condition[1],"x.pydata"))
py_save_object(object = y,filename = file.path(directory,tracks$condition[1],"y.pydata"))
py_save_object(object = allStates,filename = file.path(directory,tracks$condition[1],"allStates.pydata"))
}
out <- py$getTrackPiecesForInfo(x, y, allStates)
x0 <- out[[1]]
y0 <- out[[2]]
x1 <- out[[3]]
y1 <- out[[4]]
x2 <- out[[5]]
y2 <- out[[6]]
trnums0 <- out[[7]]
trnums1 <- out[[8]]
trnums2 <- out[[9]]
names(x0) <- trnums0
names(x1) <- trnums1
names(x2) <- trnums2
names(y0) <- trnums0
names(y1) <- trnums1
names(y2) <- trnums2
x0 <- melt(x0)
y0 <- melt(y0)
x0$y <- y0$value
x0$state <- 0
x1 <- melt(x1)
y1 <- melt(y1)
x1$y <- y1$value
x1$state <- 1
x2 <- melt(x2)
y2 <- melt(y2)
x2$y <- y2$value
x2$state <- 2
all <- rbind(x0,x1,x2)
all$L1 <- as.numeric(all$L1)
all <- plyr::arrange(df = all, L1)
tracks$state <- all$state
return(tracks)
}
#plotting
#source_python('python/mss.py')
source_python('python/SMMsplot.py')
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