kmader

# check the status of your jobs
qstat

# check the status of all jobs
qstat -u '*'

# check the status of the merlin cluster
qhost

# run an interactive analysis (default is one core and no memory)

kmader

// Load the data in
imageStackDirectory=getDirectory("Select the folder where the images should loaded from");
run("Image Sequence...", "open="+imageStackDirectory+" sort");
run("Set Measurements...", "area mean min centroid center perimeter bounding fit shape redirect=None decimal=3");
// Apply a threshold
setThreshold(129, 255);
setOption("BlackBackground", false);
run("Convert to Mask", "method=Default background=Dark black");

imgDirectory=getInfo("image.directory");

kmader

% this function (will) performs the ellipsoid based shape analysis on a 3D image
% the input is a labeled image probably from the bwlabel function
% the output is an array formatted like such
% labelId, volume, centerX, centerY, centerZ, extentsX, extentsY, extentsZ, pca1X, pca1Y, pca1Z, score1, score2, score3
function [out_table]=ellipsoid_analysis(in_image,out_file)
% create an array the same size as the image of x,y, and z points. This way we can calculate
% center of volume and other parameters by using multiplication
[xgrid,ygrid,zgrid]=meshgrid(1:size(in_image,1),1:size(in_image,2),1:size(in_image,3));

num_of_obj=max(in_image(:));

kmader

##################################################################
##
## Mandelbrot pictures with MATLAB and Condor
##	Filename: mandelbrot.condor
##
##################################################################

#
# For this example, variables not used by Condor are marked my_*
#

kmader

##################################################################
##
## Run a parameter sweep with threshold and filter using condor
##	Filename: batchimage.condor
##
##################################################################


universe	= vanilla
getenv		= True		# MATLAB needs local environment

kmader

// taken from https://gist.github.com/kmader/10871528
// Load the data in
imageStackDirectory=getDirectory("Select the folder where the images should loaded from");
run("Image Sequence...", "open="+imageStackDirectory+" sort use");
// crop the image
makeRectangle(1089, 993, 675, 702);
run("Crop");
// clear the meaningless pixel sizes and set them to 0
run("Properties...", "unit=pixel pixel_width=1 pixel_height=1 voxel_depth=1 global");
// set which metrics to record for each analysis

kmader

library(igraph)
library(plyr)
node.names<-c("Gene 104","Gene 105", "Gene 106","Cellular Structure","Cellular Viability","Turnover Rate","Mechanosensitivity","AB Signaling","BC Signaling","Temperature","pH","Mixing Rate","Cooling Rate","Catalysts")
c.mat<-matrix(0,length(node.names),length(node.names))
colnames(c.mat)<-node.names
rownames(c.mat)<-node.names
rr<-function(x=length(node.names),n=1) round(runif(n,min=0,max=x)+.5)
edge.count<-round(3*length(node.names))
for(i in c(1:edge.count)) {
  rpos<-rr(n=2)

kmader

library(ggplot2)
library(reshape2)
parm.table<-expand.grid(Temperature=seq(20,80,length.out=20),Gene=c(1,2,3),pH=seq(3,6,length.out=10))
rf<-function(amplitude=0.1) runif(nrow(parm.table),min=1/(1+amplitude),max=1+amplitude)
parm.table$Thickness<-with(parm.table,10^(pH+as.numeric(Gene))-10*Temperature)*rf(.1)
parm.table$Count<-with(parm.table,10^(as.numeric(Gene))+100*Temperature)*rf(0.01)
parm.table$Connections<-with(parm.table,2^(as.numeric(Gene))-5*as.numeric(Gene)+12)*rf(0.2)
parm.table$GrowthRate<-with(parm.table,log(Thickness/Count)*Gene)*rf(0.2)
parm.table$Strength<-with(parm.table,Count/Connections)*rf(0.1)
parm.table$Viability<-with(parm.table,10^(10-Connections))*rf(0.2)

kmader

"""
Touches all files in the image folder based on a list of sample names to keep (prevent from being autodeleted
"""

start_dir='/sls/X02DA/data/e14570/Data10/'
from subprocess import call,Popen,PIPE
import os
fancyExec=lambda cmd: Popen(cmd.split(' '), stdin=PIPE, stdout=PIPE, stderr=PIPE).communicate()[0].split('\n')
clean_line = lambda lines: dict(filter(lambda cline: len(cline[0])>0,map(lambda cline: (cline.upper().strip(),cline.strip()),lines)))
keeplines = clean_line(open('keepfiles.txt').readlines()).keys()

kmader

val inData=sc.textFile("tiny.csv")
// Read the first line and split by commas
import scala.collection.immutable.StringOps
val header= inData.first
val headerLine = header.split(",").map(_.trim.filter(_ != '"'))
// Count lines in the file
inData.count
// Remove the header from the data
val rowData = inData.filter(_ != header).cache
// Divide each row into columns by seperating by commas