Swarchal

library(raster)

# load images
image_DNA   <- raster('~/BBBC007_v1_images/A9/A9 p5d.tif')
image_actin <- raster('~/BBBC007_v1_images/A9/A9 p5f.tif')

Swarchal

import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.feature import match_template
import matplotlib.pyplot as plt
import numpy as np

image = imread("image.jpg")
wally = imread("wally.png")

# select just the red channel

Swarchal

import matplotlib.pyplot as plt
from skimage.io import imread
from skimage.morphology import disk
from skimage.filters import rank
import numpy as np

im_all = imread("/home/scott/Dropbox/wally/ww2.jpg")
all_red = im_all[:, :, 0]
im_wally = imread("/home/scott/Dropbox/wally/wally2.png")
wally_red = im_wally[:, :, 0]

Swarchal

path <- paste(getwd(), "\\", sep = "")
files <- list.files(path=path, pattern="*.csv")
for(file in files)
{
  perpos <- which(strsplit(file, "")[[1]]==".")
  assign(
    gsub(" ","",substr(file, 1, perpos-1)), 
    read.csv(paste(path,file,sep="")))
}

Swarchal

source_https <- function(url, ...) {
  # load package
  require(RCurl)
 
  # parse and evaluate each .R script
  sapply(c(url, ...), function(u) {
    eval(
        parse(
            text = getURL(u, followlocation = TRUE,
            cainfo = system.file("CurlSSL",