CnrLwlss

# Testing whether small deletions are under-represented within fibres containing multiple deletion species

# Assume 200 mtDNA molecules per fibre section
Nassume = 200

# P7, P15 and P16 are the proportions of smaller mtDNA species in fibres with two or more mtDNA species
P7 = c(0.6,0.2,0.49,0.33,0.57,0.75,0.47,0.29,0.27,0.23,0.51,0.54)
N7 = rep(Nassume,length(P7))
Ndel7 = c(2,2,3,2,2,2,2,3,2,2,2,2)
P15 = c(0.39,0.73,0.37,0.17,0.43,0.53,0.54,0.57)

CnrLwlss

library(grDevices)
library(gtools)

dat = read.delim("data/RTdata.txt",sep="\t",stringsAsFactors=FALSE)
dat$PNUM = as.numeric(gsub("P","",dat$Patient))
dat$ID = sprintf("P%02d_%04d",dat$PNUM,dat$Cell.number)
dat$PAT = sprintf("P%02d",dat$PNUM)

colfunc = colorRamp(c("blue","yellow","red"),space="Lab")
colfun = function(x, alpha=1.0) {

CnrLwlss

# Read data and rename columns
dat = read.delim("mitocyto_merged_results.csv",sep=",",stringsAsFactors=FALSE)
colnames(dat) = c("value","id","channel","patient_id","patient_type")

# Specify which ids correspond to patients, and which to control
dat$patient_type = ifelse(dat$patient_id=="M1105","patient","control")
dat$patient_id = paste(toupper(substr(dat$patient_type,1,1)),dat$patient_id,sep="_")

# Specify some colours for plotting
dat$colour = "black"

CnrLwlss

mu = 5
stdev = 2
N = 10000
data = rnorm(N,mu,stdev)

pdf = function(x) dnorm(x,mu,stdev)
bestmu = function(N,x) sum(x[1:N])/N

op=par(mfrow=c(1,2))

CnrLwlss

#install.packages(c("mixOmics","RVAideMemoire"))
library(mixOmics)
library(RVAideMemoire)

# Calculate whether measure is greater in control group after scaling
# Used to colour points in VIP plots
direction=function(dt,measure){
  dts = as.data.frame(scale(dt[,-1]))
  dts$Group = dt$Group
  res = median(dts[[measure]][dts$Group=="Control"],na.rm=TRUE) > median(dts[[measure]][dts$Group!="Control"],na.rm=TRUE)

CnrLwlss

from PIL import Image
import numpy as np
import os

for fname in os.listdir("."):
    if fname.endswith(".tiff"):
        im=Image.open(fname)
        arr=np.array(im,dtype=np.uint16)
        maxval = np.max(arr[arr>0])
        minval = np.min(arr[arr>0])

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)