Open Notebook: family tree

Showing posts with label family tree. Show all posts

Thursday, March 11, 2021

leaf nodes, branching nodes in tree-graph, and RLS estimation

in a tree-graph: leaf nodes have degree=1, branching nodes have degree >= 3, and continuous nodes have degree=2. So, we can tabulate the leaf nodes, branching nodes, their time-intervals to estimate RLS, --Hong

Friday, November 2, 2018

likelihood family tree based CNN image classification

the best image label always as maximum Prob, so penalty need to be given to change from image label to another image label.

Thursday, January 18, 2018

all trap family tree

setup the systems

rm(list=ls())
debug = 1;

read null distribution from single cell data

moving_buffer = read.csv("data/moving_buffer.csv")

p_dist = function(x, input_buffer) {
  input_buffer = input_buffer[ !is.na(input_buffer)];
  lower = input_buffer[ input_buffer < x]; lower = lower[!is.na(lower)];
  upper = input_buffer[input_buffer > x]; upper = upper[!is.na(upper)];
  
  LowerP =  (length(lower) / length(input_buffer))
  UpperP =  (length(upper) / length(input_buffer)) 
  
  return(list(LowerP,UpperP, mean(input_buffer, na.rm=T), sd(input_buffer, na.rm=T)))
}

my_dist = function(x1, y1, x2, y2) {sqrt( (x1 - x2)^2 + (y1-y2)^2 ) };

Load the segmented data

DataTrap <- read.csv("data/DataSet_v6.csv",header=TRUE,stringsAsFactors = FALSE)

Take one trap for the time beling to develop family tree

tb.alltraps = DataTrap;
tb.alltraps$predecessorID = NA;
tb.alltraps$successorID = NA;

traps = sort(unique(tb.alltraps$trap_num));

Work from bottom to top to build a family tree using trace-back.

for( cur_trap in 1:length(traps)){
# cur_trap = 10;
#for( cur_trap in c(20,30,50)){
 tb = tb.alltraps[tb.alltraps$trap_num==traps[cur_trap], ];
 timpoints = sort(unique(tb$Image_num))
 #plot( timpoints, 1:length(timpoints), type='l') #visual check timepoints.
 for (i in length(timpoints):2){
   if(debug > 1) { print(paste("i=",i)); }
   current_frame  = tb[ tb$Image_num == timpoints[i], ];
   previous_frame = tb[ tb$Image_num == timpoints[i-1], ];
   d.tb = p.d.tb = data.frame(matrix(nrow=length(current_frame[,1]), ncol=length(previous_frame[,1]) ));
   for ( i.cur in 1:length(current_frame[,1]) ) {
     for ( j.pre in 1:length(previous_frame[,1]) ) {
       d.tb[i.cur, j.pre] = my_dist(current_frame$cell_X[i.cur], current_frame$cell_Y[i.cur],
                       previous_frame$cell_X[j.pre], previous_frame$cell_Y[j.pre]);
       #my_dist = function(x1, y1, x2, y2) {sqrt( (x1 - x2)^2 + (y1-y2)^2 ) }; 
       p.d.tb[i.cur, j.pre] = p_dist(d.tb[i.cur, j.pre], moving_buffer$d )[[2]];
     }
     current_frame$predecessorID[i.cur] = which.max( p.d.tb[i.cur, ]); #pick max, greedy algorithm. 
   }
   tb$predecessorID[tb$Image_num == timpoints[i]] = current_frame$predecessorID; #assume order unchanged. 
 }
 tb.alltraps$predecessorID[tb.alltraps$trap_num==traps[cur_trap]] = tb$predecessorID;
}

 tb.alltraps$predecessorID[tb.alltraps$trap_num==traps[30]];

##    [1] NA  1  1  1  1  1  1  2  1  2  1  2  1  2  2  1  1  2  1  2  2  1  1
##   [24]  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2
##   [47]  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  2  1  2  1  1  2  1
##   [70]  2  1  2  1  2  1  2  1  2  1  2  1  2  1  1  1  1  1  2  1  2  1  2
##   [93]  1  2  1  2  2  1  2  1  1  1  1  1  2  1  2  1  2  1  2  1  1  1  1
##  [116]  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  2  1  1
##  [139]  2  2  1  1  1  2  1  2  1  2  1  2  1  2  2  1  2  1  1  2  1  2  1
##  [162]  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  2  1  2  1
##  [185]  1  1  2  1  2  1  2  2  1  1  2  2  1  1  1  2  1  2  1  2  1  2  1
##  [208]  1  1  1  1  2  1  2  1  2  2  1  2  1  1  1  2  1  2  1  2  1  2  1
##  [231]  2  1  2  1  1  1  1  2  1  2  1  2  1  2  1  1  1  1  1  2  1  2  1
##  [254]  2  1  2  1  2  1  1  1  1  2  1  2  1  2  1  2  1  1  1  1  1  2  1
##  [277]  2  1  2  1  1  1  1  1  1  2  1  2  1  2  1  2  1  1  1  1  1  2  1
##  [300]  2  1  2  1  2  1  1  1  1  1  1  2  1  2  1  2  1  2  1  2  1  2  1
##  [323]  1  1  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  1  1  1  1  2  1
##  [346]  2  1  2  1  2  1  2  1  1  1  1  1  1  2  1  2  1  2  1  2  1  2  1
##  [369]  2  1  2  1  2  1  1  1  1  1  1  1  1  2  1  2  1  2  1  2  1  2  1
##  [392]  2  1  2  1  2  1  1  1  1  1  1  1  1  2  1  2  1  2  1  2  1  2  1
##  [415]  2  1  2  1  2  1  1  1  1  1  1  2  1  2  3  1  2  3  1  2  3  1  2
##  [438]  3  1  2  3  1  2  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [461]  1  1  1  1  1  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3
##  [484]  1  2  3  1  2  1  2  1  1  2  1  2  1  1  2  3  1  2  1  2  1  2  1
##  [507]  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  1  2  2  1  1  2  2  1
##  [530]  1  1  2  1  3  4  1  2  3  4  2  1  3  4  2  1  3  4  1  2  3  4  1
##  [553]  3  2  4  1  3  2  2  1  3  1  2  3  2  1  3  1  2  3  1  2  3  1  2
##  [576]  3  1  1  2  3  4  2  1  3  4  2  1  3  4  1  2  3  4  1  2  3  4  1
##  [599]  2  3  1  4  2  1  3  4  2  1  3  4  1  2  3  4  1  2  3  1  2  3  1
##  [622]  1  2  3  1  2  3  1  2  3  1  1  2  3  4  1  2  3  4  2  1  3  4  2
##  [645]  1  3  2  1  3  2  1  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3
##  [668]  1  2  3  1  2  3  1  2  3  1  1  2  3  4  1  2  3  4  1  2  3  4  1
##  [691]  2  3  3  4  1  2  1  3  4  6  5  2  1  4  6  1  2  3  3  4  1  2  4
##  [714]  5  1  2  3  3  4  1  2  4  5  1  2  1  3  4  1  2  3  4  5  1  2  3
##  [737]  4  5  1  2  4  5  1  2  3  4  1  2  3  4  1  2  3  3  4  1  2  4  5
##  [760]  1  2  3  4  1  2  3  1  4  1  2  3  5  1  2  3  3  4  1  2  4  5  1
##  [783]  2  1  3  4  1  2  3  4  5  1  2  3  4  5  1  2  4  5  1  2  3  4  1
##  [806]  2  3  4  1  2  3  3  4  1  2  4  5  1  2  3  4  1  2  3  1  4  1  2
##  [829]  3  5  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3  1  2  3
##  [852]  1  2  3  1  1  1  1  1  2  3  4  5  6  7  1  2  3  4  4  3  5  7  1
##  [875]  1  2  3  4  5  6  8  9  7  1  2  3  4  6  7  9  8  1  2  3  4  4  7
##  [898]  6  1  2  3  5  6  7  1  2  3  4  4  5  6  1  2  3  5  4  6  7  1  2
##  [921]  4  3  5  7  6  1  2  3  4  5  7  6  1  2  5  4  4  3  6  1  7  1  2
##  [944]  3  4  6  7  9  1  2  3  4  4  1  6  7  1  2  3  4  3  7  8  1  2  3
##  [967]  4  5  6  7  1  2  5  4  3  6  7  1  2  5  4  3  6  7  1  2  3  4  5
##  [990]  6  7  1  2  4  3  5  4  7  1  6  1  2  3  4  5  6  7  9  8

write.csv(tb.alltraps, "data/alltraps_withtree.csv", quote=F)

Tuesday, January 16, 2018

single trap, family tree for segmented cells