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#!/usr/bin/env python3
#
# This script will BLAST a series of GenBank IDs representing SSR sequences from
# Sirjusingh, C., Kohn, L.M., 2001. Characterization of microsatellites in the 
# fungal plant pathogen, Sclerotinia sclerotiorum. Molecular Ecology 1, 267e269.
#
# This uses biopython to download the XML, save it to a file in the working 
# directory, and then looks for matches that have Sclerotinia chromosome in the
# title and has a score of > 100. If there's a better way to do this, I would
# like to know it. Currently, it can download a few results at a time and then

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# shruggie
# Source: https://twitter.com/climagic/status/672862397015658496
function shrug(){ echo -n "¯\_(ツ)_/¯" | pbcopy;echo "¯\_(ツ)_/¯ copied to your clipboard"; }
export -f shrug

# strollie
function stroll(){ echo -n "ᕕ( ᐛ )ᕗ" | pbcopy; echo "ᕕ( ᐛ )ᕗ copied to your clipboard"; }
export -f stroll

# flippy

zkamvar

# This is from @drob on twitter:
#
# https://twitter.com/drob/status/915378838078722048
#
library("purrr")
transition_mc <- function(steps, start, mat){
  i <- seq_len(nrow(mat))
  transition <- ~ sample(i, prob = (i == .) %*% mat)
  accumulate(seq_len(steps), transition, .init = start)
}

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#' paste function that replaces NA characters with blanks
#'
#' All arguments are passed to paste.
#'
#' Usage: 
#' pastena(letters, sample(c(1:10, NA), 26, replace = TRUE))
pastena <- function(..., collapse = NULL, sep = " ") {
  dots <- list(...)
  dots <- lapply(dots, function(i) ifelse(is.na(i), "", i))
  res  <- do.call("paste", c(dots, list(collapse = collapse, sep = sep)))

zkamvar

#!/usr/bin/env bash
# This ridiculous script will take in a file as input and spit out comma-separated text for easier parsing.
# Usage: 
# $ gvcf2csv.sh GVCF.err | column -t -s,
grep ProgressMeter $1 | \
awk -F" - " '{print $2}' | \
tail -n +2 | \
sed -E 's/[ ]\|[ ]/,/g' | \
sed -E "s/([0-9hmsw%])[ ]{3,}([0-9])/\1,\2/g"

zkamvar

# This will take a list of dois and convert them
# to a table containing the names of coauthers to
# populate a conflict of interest list.
# 
# It's not perfect, but it's better than nothing.
#
library("rcrossref")
library("tidyverse")
#
# Change these to YOUR dois.

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# Note: this assumes that you have a custom library at the top of your .libPaths()
fields <- c("Package", "LibPath", "Version", "Priority", "Depends", "Imports", "LinkingTo", "Suggests", "Enhances", "OS_type", "Built", "RemoteSha", "Packaged", "biocViews")
x <- installed.packages(.libPaths()[1], fields = fields)
needs_update <- x[x[, "Built"] != "3.4.0", ]
bicond <- !is.na(needs_update[, "biocViews"])
ghub   <- !is.na(needs_update[, "RemoteSha"])
remote <- !is.na(needs_update[, "Packaged"])
bioconductor <- needs_update[bicond, ]
cran         <- needs_update[!bicond & !ghub & remote, ]
install.packages(rownames(cran))

zkamvar

Name this damn pattern.

\binom{N}{0}, \binom{N}{1}, \binom{N}{2}, ..., \binom{N}{floor(N/2)}, ... N!

zkamvar

# Replacing multilocus lineages with one representative sample
library(poppr)
data(monpop)
mlg.filter(monpop) <- 0 + .Machine$double.eps
mondf  <- genind2df(monpop, usepop = FALSE)
mid    <- mlg.id(monpop)
orders <- unlist(mid, use.names = FALSE)
monpop <- monpop[orders, ]
mondif <- mondf[orders, ]
n      <- vapply(mid, length, integer(1))

zkamvar

# poppr.plot.phylo definition:
# https://github.com/grunwaldlab/poppr/blob/9170398f6552f27ab69b294172c97c8ee7a97ce0/R/internal.r#L1529-L1545
# You can also get it by simply typing poppr:::poppr.plot.phylo in your R console
library("ape")
library("phangorn")
poppr.plot.phylo <- function(tree, type = "nj", root = FALSE){
  barlen <- min(median(tree$edge.length), 0.1)
  if (barlen < 0.1) barlen <- 0.01
  if (!root && type != "upgma"){
    tree <- ladderize(tree)