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library(parallel)

cores = 5
bigN = 50000000

# Function which can take a noticeable time to run for large N
sumnorm = function(N) sum(rnorm(N))
 
cl = makeCluster(cores)

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makeBoundsQFA<-function(inocguess,d,minK=0,fixG=FALSE,globalOpt=FALSE){

	if(is.null(inocguess)){
		# Without a sensible independent estimate for inoculum density, the best we can do is to estimate it based on observed data.
		# This strategy will only work well if the inoculum density is high enough to be measurable (e.g. pinned cultures or 
		# conc. spotted) and is clearly observed.  Clearly observed means: no condensation on plates immediately after they are 
		# placed in incubator for example.
		if(length(d$Growth)>0) {candidate=min(d$Growth)}else{candidate=0}
		inocguess=max(0.001,candidate)
	}

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fnames = list.files(pattern="*.out")
datlist = lapply(fnames, read.delim,sep="\t",header=TRUE,stringsAsFactors=FALSE)
dat = do.call("rbind", datlist)
dat$ID=paste(dat$Barcode,dat$Row,dat$Column,sep="_")
dat$time=as.numeric(sapply(strsplit(dat$Filename,'_'), "[", 4))
dat=dat[order(dat$time),]

pdf("GrowthCurves.pdf")
by(dat,dat$ID,function(x) plot(x$time,x$Intensity,type="b",xlab="Time",ylab="Intensity",ylim=c(0,0.1),main=unique(x$ID)))
dev.off()

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# To execute this script manually, in parallel on multiple CPUs:
# Rscript Scripts/QFA_ANALYSIS_INDIVIDUAL.R QFA0028 QFA0029 &
# Rscript Scripts/QFA_ANALYSIS_INDIVIDUAL.R QFA0030 QFA0031 &
library(parallel)
source("Scripts/QFA_ANALYSIS.R")
PLOSGenetics=sprintf("QFA%04d",c(28,29,30,31,35,36))

# NCPUs is the maximum number of CPUs the script can use on this node (should be less than 48 on cisban cluster)
NCPUs=47
cisbanNodes=TRUE

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from colonyzer2.functions import *
import time, sys, os, numpy, PIL

print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print("Note that this script requires a Colonyzer.txt file (as generated by ColonyzerParametryzer) describing initial guess for culture array")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")

# Lydall lab file naming convention
# First 15 characters in filename identify unique plates
# Remaining charaters can be used to store date, time etc.

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library(qfa)

numerical_r=function(obsdat,mkPlots=FALSE,span=0.5,nBrute=1000,cDiffDelta=0.0001){
	# Generate numerical (model-free) estimate for maximum intrinsic growth rate
	tims=obsdat$Expt.Time
	gdat=obsdat$Growth
	tmax=max(tims)

	# Smooth data, find slope as function of time and maximum slope
	gdat=log(gdat)

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import Control.Parallel (par, pseq)
import Control.DeepSeq
import System.Environment (getArgs)
import Data.Time.Clock (diffUTCTime, getCurrentTime)

data Tree x = Empty | Node x (Tree x) (Tree x) deriving (Show, Read, Eq) 

-- Create instance of NFData for Tree data type (defining "normal form")
instance NFData a => NFData (Tree a) where
	rnf Empty = ()

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import Control.Parallel (par, pseq)
import Control.DeepSeq
import System.Environment (getArgs)
import Data.Time.Clock (diffUTCTime, getCurrentTime)

data Tree x = Empty | Node x (Tree x) (Tree x) deriving (Show, Read, Eq) 

-- Create instance of NFData for Tree data type (defining "normal form")
instance NFData a => NFData (Tree a) where
	rnf Empty = ()

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data Cell = Cell {teloLength :: Double, birthDate :: Double, lifeSpan :: Double, seedCell :: Integer}
instance Eq Cell where (Cell t1 _ _ _) == (Cell t2 _ _ _) = t1 == t2
instance Ord Cell where (Cell t1 _ _ _) `compare` (Cell t2 _ _ _) = t1 `compare` t2
instance Show Cell where show (Cell t b _ _) = show t ++ " " ++ show b

data CellLine x = Senescent | Mother x (CellLine x) (CellLine x) deriving (Show, Read, Eq) 

-- Functions modelling cell biology
shortenTelo :: Cell -> Cell
shortenTelo (Cell telo x y oldseed) = Cell (telo - delta) x y newseed

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# Demonstrating significant differences between a
# vector of measurements and a single value
# Using the statsmodels package for doing test
# Using numpy to generate some fake data

from statsmodels.stats import weightstats as stests
import numpy as np

data=np.random.normal(loc=3.4,scale=0.1,size=100)
singleValue=3.3