Conor Lawless CnrLwlss

## Focus.py
from PIL import Image
from scipy import ndimage
import numpy as np

def getVar(im, sigma = 4.0):
  '''Identifies & highlights areas of image fname with high pixel intensity variance (focus)'''
  imbw = im.convert("F") # Convert im to greyscale array
  arrbw = np.array(imbw)

  sub_mean = ndimage.gaussian_filter(arrbw, sigma)  # Calculate variance in area around each pixel

## RectangularArray.R
 fdefpairs=t(combn(fdefs,2))
 npairs=dim(fdefpairs)[1]
 a=round(sqrt(npairs))
 if((a+1)*(a+1)>=npairs) mfrow=c(a+1,a+1)
 if((a+1)*a>=npairs) mfrow=c(a,a+1)
 if(a*a>=npairs) mfrow=c(a,a)

## CustomErrorBars.py
import seaborn as sns
tips = sns.load_dataset("tips")
print(sns.__version__)
print(tips.head())

ax=sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips,split=True)
sns.barplot(x="day", y="total_bill", hue="sex", data=tips,capsize=0.1,errwidth=1.25,alpha=0.25,ci=None)
xcentres=[0,1,2,3]
delt=0.2
xneg=[x-delt for x in xcentres]

## Limited_logistic_mixture.R
# Install and load ODE solver package
#install.packages("deSolve")
library(deSolve)

# Named vector of parameter values
parameters=c(r_1=1.0,r_2=1.1,K=1)

# Named vector of initial conditions
state=c(x_1=0.01,x_2=0.01)

## miniQFA.JAGS

model {
  tau_min <- 10000
  tau_max <- 1000000
  x0_min <- 0.0
  x0_max <- 0.05
  r_min <- 0.0
  r_max <- 10.0
  K_min <- 0.0
  K_max <- 0.5

## ColourContrast.R
# Visual comparison of the contrast between colours and different solid backgrounds

colourGrid=function(dimval=20,background="gray",colourfun=rainbow,ptsize=1){
  cols=colourfun(dimval^2)
  yvals=rep(seq(0,1,length.out=dimval),each=dimval)
  xvals=rep(seq(0,1,length.out=dimval),dimval)
  plot(xvals,yvals,type="n",xaxt="n",yaxt="n",ann=FALSE, bty="n")
  rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = background,border=NA)
  points(xvals,yvals,col=cols,pch=16,cex=ptsize)
}

## RankPlots.py
# Plot comparing ranks

import string
import random
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np

def experiment(genes,vmin=0.0,vmax=1.0):
    '''Simulate an experiment with (average) measurement for a set of genotypes'''

## plotDemo.py
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
import string

# Set font for figure according to journal specs
mp.rcParams['font.family'] = "Arial"

# Gene deletion names read from file might not necessarily be in lower case
gnames=["EXO1","RAD9","TMA20"]

## Colormap.py
#http://matplotlib.org/examples/color/colormaps_reference.html
#http://matplotlib.org/users/colormaps.html

import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np

N = 200
x = np.random.randn(N)
y = np.random.randn(N)

## hierarchies.R
# Hierarchical uniform distributions
Nsamps=500000
r_min=1
r_max=10
trunc=FALSE

makePlot=function(){
  xlim=c(-5,15)
  op=par(mfrow=c(1,3))
  hist(r,breaks=100,freq=FALSE,xlim=xlim)
	from PIL import Image
	from scipy import ndimage
	import numpy as np

	def getVar(im, sigma = 4.0):
	'''Identifies & highlights areas of image fname with high pixel intensity variance (focus)'''
	imbw = im.convert("F") # Convert im to greyscale array
	arrbw = np.array(imbw)

	sub_mean = ndimage.gaussian_filter(arrbw, sigma) # Calculate variance in area around each pixel
	fdefpairs=t(combn(fdefs,2))
	npairs=dim(fdefpairs)[1]
	a=round(sqrt(npairs))
	if((a+1)*(a+1)>=npairs) mfrow=c(a+1,a+1)
	if((a+1)*a>=npairs) mfrow=c(a,a+1)
	if(a*a>=npairs) mfrow=c(a,a)
	import seaborn as sns
	tips = sns.load_dataset("tips")
	print(sns.__version__)
	print(tips.head())

	ax=sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips,split=True)
	sns.barplot(x="day", y="total_bill", hue="sex", data=tips,capsize=0.1,errwidth=1.25,alpha=0.25,ci=None)
	xcentres=[0,1,2,3]
	delt=0.2
	xneg=[x-delt for x in xcentres]
	# Install and load ODE solver package
	#install.packages("deSolve")
	library(deSolve)

	# Named vector of parameter values
	parameters=c(r_1=1.0,r_2=1.1,K=1)

	# Named vector of initial conditions
	state=c(x_1=0.01,x_2=0.01)

	model {
	tau_min <- 10000
	tau_max <- 1000000
	x0_min <- 0.0
	x0_max <- 0.05
	r_min <- 0.0
	r_max <- 10.0
	K_min <- 0.0
	K_max <- 0.5
	# Visual comparison of the contrast between colours and different solid backgrounds

	colourGrid=function(dimval=20,background="gray",colourfun=rainbow,ptsize=1){
	cols=colourfun(dimval^2)
	yvals=rep(seq(0,1,length.out=dimval),each=dimval)
	xvals=rep(seq(0,1,length.out=dimval),dimval)
	plot(xvals,yvals,type="n",xaxt="n",yaxt="n",ann=FALSE, bty="n")
	rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = background,border=NA)
	points(xvals,yvals,col=cols,pch=16,cex=ptsize)
	}
	# Plot comparing ranks

	import string
	import random
	import matplotlib.pyplot as plt
	from matplotlib import cm
	import numpy as np

	def experiment(genes,vmin=0.0,vmax=1.0):
	'''Simulate an experiment with (average) measurement for a set of genotypes'''
	import matplotlib as mp
	import matplotlib.pyplot as plt
	import numpy as np
	import string

	# Set font for figure according to journal specs
	mp.rcParams['font.family'] = "Arial"

	# Gene deletion names read from file might not necessarily be in lower case
	gnames=["EXO1","RAD9","TMA20"]
	#http://matplotlib.org/examples/color/colormaps_reference.html
	#http://matplotlib.org/users/colormaps.html

	import matplotlib.pyplot as plt
	from matplotlib import cm
	import numpy as np

	N = 200
	x = np.random.randn(N)
	y = np.random.randn(N)
	# Hierarchical uniform distributions
	Nsamps=500000
	r_min=1
	r_max=10
	trunc=FALSE

	makePlot=function(){
	xlim=c(-5,15)
	op=par(mfrow=c(1,3))
	hist(r,breaks=100,freq=FALSE,xlim=xlim)