angelovangel

#!/bin/bash
set -e

usage="$(basename "$0") [-h] [-i PROJECT] [-v VM] [-p PYTHON] [-d NOTEBOOKS]
Make a user provide SSH key and jupyter notebooks (in roles/bootstrap/files/notebooks) to each user listed in var/common.yml
where:
    -h  show this help text
    -i  google cloud project id
    -v  name of instance/virtual machine
    -p  python path

angelovangel

Generate checksums for many files in a directory

```
# make checksums (use md5 on macos)
find ./some_dir -type f -exec md5sum {} \; >> checksums.md5

# check them
md5sum -c checksums.md5

# use gnu parallel for really many files? Much faster

angelovangel

In ksh only (not in bash), this just works:

printf '%#d\n' 105000000
105M

Even this!!!

angelovangel

# demonstrate Leaflet.hotline JS plugin in leaflet for R

library(leaflet)
library(htmlwidgets)
library(htmltools)
#library(mapview)


# the Leaflet.hotline plugin has to be locally downloaded

angelovangel

# this code is about
# reading fcs files (from FACS) and converting them to one dataframe
# plotting is then done normally in ggplot
# installation instructions for the required libraries can be found on the internet:)

library(flowCore)
library(tidyverse)

 
### this function reads a fcs file and returns the data as a tibble, adding a column with the file name #####

angelovangel

# these two functions perform the following:
# do_drm() --> performs drm (from the drc package) on a long dataframe, using the LL.4 log-logistic model for describing dose-response relationships
# do_drm_plot() --> plots the output of do_drm()
# to use the functions just paste this file in your R session and source it.
# try it out with
# do_drm(S.alba, Dose, DryMatter, Herbicide) %>% do_drm_plot(ed50 = TRUE, color = ~Herbicide) + scale_x_log10()

##=====================
# do_drm() usage
# do_drm(df, d, r, x, y, ...)

angelovangel

# the aim is to use the 'drc' package to fit models to data and then extract the data and use for plotting in ggplot
# the data could be growth curves, dose-response, Michaelis-Menten etc.
# here, the S.alba data from drc is used for dose-response

library(tidyverse)
library(broom)
library(drc)
library(modelr)
attach(S.alba) # the data used in this gist
library(egg) 

angelovangel

# a general function for plotting linear models
# makes plots of the data and the model plus the model coefficients

# the function takes a linear model object as argument

ggplotLM <- function(fit) {
  
  require(ggplot2)
  require(broom)

angelovangel

library(ggplot2)
library(dplyr)
library(reshape2)

#df
n <- as.integer(grep("Header", readLines("ASCIIData.txt")) %>% length()) # number of runs, without the system call above

ole <- readline(prompt="Enter number of olefins detected for these runs(including C30): ")
ole <- as.integer(ole)