CnrLwlss

getReps = function(df,avedef){
  dfA = df[df$RepeatNumber%in%c(1,3,5),]
  dfB = df[df$RepeatNumber%in%c(2,4,6),]
  dfaveA = aggregate(dfA,by=list(dfA$Gene),FUN=avedef)
  dfaveA$Gene = dfaveA$Group.1
  dfaveB = aggregate(dfB,by=list(dfB$Gene),FUN=avedef)
  dfaveB$Gene = dfaveB$Group.1
  dfreps = merge(dfaveA,dfaveB,by="Gene")
  return(dfreps)
}

CnrLwlss

Sguais Uibhist - East Camp, Balivanich

A community run squash club

We have renovated the old RAF squash facilities at East Camp. We now have a nice, bright, warm court with changing rooms and great shower facilities available for the community. For the past 12 years, the nearest publicly available squash facilities have been in Portree and Stornoway.

An Caladh (charitable organisation supporting people struggling with addiction) kindly gave us permission to renovate the building and to use it for our club. Without their support, the project would never have gotten off the ground.

After a round of fundraising, we were awarded a large Scottish Landfill Community Fund grant from An Treas Roinn Innse Gall as well as grants from our local councillors, Sealladh na Beinne Mòire and Benbecula Community Council. We have also secured sponsorship from

CnrLwlss

import requests
import json

authDict = {'client_id':'#####',
          'client_secret':'#####',
          'grant_type':'client_credentials'}

authRes = requests.post('https://connect.devicewebmanager.com/oauth/token',json=authDict)
print 'Authorisation response from server: ' + authRes.text
authFromServer = authRes.json()

CnrLwlss

distance=seq(0.2,4,0.2)
pace=rep(9,length(distance))
op=par(mfrow=c(2,2))
plot(distance,pace,type="b",main="Sensible")
plot(distance,pace/distance,type="b",main="A bit odd")
plot(distance,pace/(distance^2),type="b",main="WTF?!")
par(op)

CnrLwlss

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

CnrLwlss

 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)

CnrLwlss

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]

CnrLwlss

# 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)

CnrLwlss

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

CnrLwlss

# 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)
}