Conor Lawless CnrLwlss

## parallelJuliaThreads.jl
# Multithreading is the most straightforward type of parallel processing supported by Julia
# https://docs.julialang.org/en/v1/manual/multi-threading/#man-multithreading
# If your parallel computing problem doesn't involve shared memory state
# then this is probably the way to go

Nthreads = Threads.nthreads();
Ncalcs = Nthreads * 5

# Let's distribute Ncalcs operations across Nthreads threads
# We can have a look at which thread carries out each operation

## ConorsWayIsBest.R
# Simulating two schemes for irregular sampling from regular timeseries

deltasim = 0.1
deltarec = 1/3
tmax = 10.0

simtimes = seq(0,tmax,0.1)
rectargets = seq(0,tmax,deltarec)

recindex = 1

## stochPlots.R
flength = function(fname) length(readLines(fname))

for(droot in c("agent","gillespie")){
 dirname = paste0(droot,"_csv_files")
 fnames = file.path(dirname,list.files(dirname,pattern="*.csv"))

 nfiles = length(fnames)
 ntimes = flength(fnames[1])-1
 times = 0:(ntimes-1) # You might want to change this to get the right units

## WarrenPriors.R
# Warren et al. (2020) https://doi.org/10.1038/s41598-020-70885-3

op = par(mfrow=c(2,2))
 # Healthy control subjects (all proteins)
 # C01, C02, C03
 plot(function(x) dbeta(x,1,75),from=0,to=1.0,xlab="pi",ylab="Density",lwd=2, main="Controls")
 abline(v=c(0,1),col="red",lwd=3)
 # Note that we would expect RC defect to manifest in old age, so, in principle,
 # this prior could change with age of patient at biopsy

## separate_plots.R
# How to separate plotting from CPU intensive analysis
plotfuns = list()
Npts = 1000
Nsims = 12

# Visualising a 1D random walk, for example
makewalk = function(Npts){
    # Do some like real heavy computing man
    deltas = sample(c(-1,1),Npts,replace=TRUE)
    walk = cumsum(deltas)

## QuantileLevelSetsContours.R
library(MASS)
xdat = rnorm(1000)
ydat = rnorm(1000)

# https://stackoverflow.com/questions/16225530/contours-of-percentiles-on-level-plot
dens = kde2d(xdat, ydat, n=200); ## estimate the z counts

prob = c(0.95, 0.5)
dx = diff(dens$x[1:2])
dy = diff(dens$y[1:2])

## random_walk_noise.R
N=10000

noise = rnorm(N,mean=0,sd=0.5)
random_walk=cumsum(noise)
ylim = range(random_walk)

op = par(mfrow=c(1,2))
 plot(noise,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75)
 plot(random_walk,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75)
par(op)

## zscore2Dmito.R
# Make some synthetic data, like patient with v-shaped 2Dmito scatterplot
N = 500
mu_x = 5
sd_x = 2.5
intercept = 0.1
slope_normal = 1.0
slope_deficient = 0.0
sd_err = 1.0

x_normal = rnorm(N/2,mu_x,sd_x)

## gillespie.txt
				RATE OR		STOICHIOMETRY
		REACTIONS	HAZARD		(W)	(M)	PROBABILITIES
Synthesis	W -> 2W		ks*W		+1	 0	ks*W/CH
		M -> 2M		ks*M		 0	+1	ks*M/CH

Degradation	W -> NULL	kd*W		-1	 0	kd*W/CH
		M -> NULL	kd*M		 0	-1	kd*M/CH
						__________
Combined Hazard (CH):	(ks+kd)*(W+M)

## index.html
<!DOCTYPE html>
<html>
 <head>
  <meta charset="utf-8" />
  <title>Annotation|IMC images</title>
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@recogito/annotorious@2.4.0/dist/annotorious.min.css">
  <script src="https://cdn.jsdelivr.net/npm/@recogito/annotorious@2.4.0/dist/annotorious.min.js"></script>
  <script language="javascript" type="text/javascript">
   var key = 2;
	# Multithreading is the most straightforward type of parallel processing supported by Julia
	# https://docs.julialang.org/en/v1/manual/multi-threading/#man-multithreading
	# If your parallel computing problem doesn't involve shared memory state
	# then this is probably the way to go

	Nthreads = Threads.nthreads();
	Ncalcs = Nthreads * 5

	# Let's distribute Ncalcs operations across Nthreads threads
	# We can have a look at which thread carries out each operation
	# Simulating two schemes for irregular sampling from regular timeseries

	deltasim = 0.1
	deltarec = 1/3
	tmax = 10.0

	simtimes = seq(0,tmax,0.1)
	rectargets = seq(0,tmax,deltarec)

	recindex = 1
	flength = function(fname) length(readLines(fname))

	for(droot in c("agent","gillespie")){
	dirname = paste0(droot,"_csv_files")
	fnames = file.path(dirname,list.files(dirname,pattern="*.csv"))

	nfiles = length(fnames)
	ntimes = flength(fnames[1])-1
	times = 0:(ntimes-1) # You might want to change this to get the right units
	# Warren et al. (2020) https://doi.org/10.1038/s41598-020-70885-3

	op = par(mfrow=c(2,2))
	# Healthy control subjects (all proteins)
	# C01, C02, C03
	plot(function(x) dbeta(x,1,75),from=0,to=1.0,xlab="pi",ylab="Density",lwd=2, main="Controls")
	abline(v=c(0,1),col="red",lwd=3)
	# Note that we would expect RC defect to manifest in old age, so, in principle,
	# this prior could change with age of patient at biopsy
	# How to separate plotting from CPU intensive analysis
	plotfuns = list()
	Npts = 1000
	Nsims = 12

	# Visualising a 1D random walk, for example
	makewalk = function(Npts){
	# Do some like real heavy computing man
	deltas = sample(c(-1,1),Npts,replace=TRUE)
	walk = cumsum(deltas)
	library(MASS)
	xdat = rnorm(1000)
	ydat = rnorm(1000)

	# https://stackoverflow.com/questions/16225530/contours-of-percentiles-on-level-plot
	dens = kde2d(xdat, ydat, n=200); ## estimate the z counts

	prob = c(0.95, 0.5)
	dx = diff(dens$x[1:2])
	dy = diff(dens$y[1:2])
	N=10000

	noise = rnorm(N,mean=0,sd=0.5)
	random_walk=cumsum(noise)
	ylim = range(random_walk)

	op = par(mfrow=c(1,2))
	plot(noise,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75)
	plot(random_walk,type="l",ylim=ylim,xlab="time",cex.lab=1.75,cex.axis=1.75)
	par(op)
	# Make some synthetic data, like patient with v-shaped 2Dmito scatterplot
	N = 500
	mu_x = 5
	sd_x = 2.5
	intercept = 0.1
	slope_normal = 1.0
	slope_deficient = 0.0
	sd_err = 1.0

	x_normal = rnorm(N/2,mu_x,sd_x)
	RATE OR STOICHIOMETRY
	REACTIONS HAZARD (W) (M) PROBABILITIES
	Synthesis W -> 2W ksW +1 0 ksW/CH
	M -> 2M ksM 0 +1 ksM/CH

	Degradation W -> NULL kdW -1 0 kdW/CH
	M -> NULL kdM 0 -1 kdM/CH
	__________
	Combined Hazard (CH): (ks+kd)*(W+M)
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8" />
	<title>Annotation\|IMC images</title>
	<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@recogito/annotorious@2.4.0/dist/annotorious.min.css">
	<script src="https://cdn.jsdelivr.net/npm/@recogito/annotorious@2.4.0/dist/annotorious.min.js"></script>
	<script language="javascript" type="text/javascript">
	var key = 2;