ToledoEM

Setting Flightradar 24 and Flightaware simultaneously in a Raspberry pi.

Before start - make note

1. Account Flightradar 24 https://www.flightradar24.com
2. Account Flightaware http://flightaware.com/
3. GPS coordinated - https://maps.google.com
4. Find altitud https://www.daftlogic.com/sandbox-google-maps-find-altitude.htm

Install and set up Flightradar24

ToledoEM

library(parallel)

saveRDS.xz <- function(object,file,threads=parallel::detectCores()) {
  pxzAvail <- any(grepl("(XZ Utils)",system("pxz -V",intern=TRUE)))
  if (pxzAvail) {
    con <- pipe(paste0("pxz -T",threads," > ",file),"wb")
    base::saveRDS(object, file = con)
    close(con)
  } else {
    saveRDS(object,file=file,compress="xz")

ToledoEM

#' Allows to add another scale 
#' 
#' @param new_aes character with the aesthetic for which new scales will be 
#' created
#'
new_scale <- function(new_aes) {
  structure(ggplot2::standardise_aes_names(new_aes), class = "new_aes")
}

#' Convenient functions

ToledoEM

#!/bin/bash
for file in $(ls SS_1797/*.fastq.gz)

do
start=`date +%s`
outname=$(echo $file | cut -c 22- |rev | cut -c 17- | rev | uniq) 
outname=SS_1797/$outname
kallisto quant -i hs_GRCh38_kallisto_ncRNA.idx  --single -l 76 -s 3 -b 50 -t 4 -o $outname  $file
end=`date +%s`
runtime=$((end-start))

ToledoEM

#!/bin/bash
# edited https://benchtobioinformatics.wordpress.com/2015/07/10/using-kallisto-for-gene-expression-analysis-of-published-rnaseq-data/
# and https://www.biostars.org/p/191018/
#
# foldername for output -> filename, revert, cut 17 chr, revert, check if it is unique 
# outname=$(echo $file | rev | cut -c 17- | rev | uniq) 

for file in $(ls folder/*.fastq.gz)   #all files fastq.gz in folder

do

ToledoEM

# Modified from https://raunakms.wordpress.com/2014/02/22/visualize-cluster-one-mode-projection-of-a-bi-partite-graph/
# Add threshole to function
# Bass, Juan I. Fuxman, et al. "Using networks to measure similarity between genes: association index selection." 
# Nature methods 10.12 (2013): 1169-1176.
#
# Variable threshole to avoid nonspecific interactions depending of the data
#

get.csi.t <- function(dat,t) {
  mat <- matrix(0, nrow = nrow(dat), ncol = ncol(dat))