Nick Waters nickp60

## cumulative_domination_example.R
library(tidyverse)
if (!"speedyseq" %in% installed.packages()) remotes::install_github("mikemc/speedyseq")
library(speedyseq)

download.file("https://figshare.com/ndownloader/files/33076496", "samples.tsv")
#this file has duplicate rows, and has multiple rows per pool
samples <- read.csv("samples.tsv") %>% distinct() %>%
  select(-Pool, -Run, -ShotgunBatchID) %>% distinct()
#download.file("https://figshare.com/ndownloader/files/26770931", "metadata.tsv")
#metadata <-  read.csv("metadata.tsv")

## nrw-microbiome.R
# Source this for a handy discrete color palette and various helper functions for microbiome analyses.
# not complex enough for an actual package. For now...  #featurecreep
mycolors=c(
  "#511087", "#4ecdc4", "#ffd447", "#13c01f", "#b78900", "#e66d17",
  "#8e3400", "#729b79", "#bf0000", "#907ad6", "#3f6356", "#ffbfb7",
  "#92bccc", "#c0e8ab", "#890466", "#382438", "#dad2bc", "#0048ff",
  "#f5f749", "#146d25", "#c08552", "#fc60a8", "#89fc00", "#3df8ff",
  "#ef2917", "#101f75", "#bfcccc"
)

## gist:9f55d17234f21392172c3d74904e367b
searchres <- "~/Downloads/SearchResults.csv"
outdir <- "~/Downloads/springercovid/"
dir.create(outdir)

resdf <- read.csv(searchres, stringsAsFactors = FALSE)

resdf$dloadurl <- paste0("http://link.springer.com/content/pdf/",
                         gsub("\\/", "%2F", fixed=TRUE, resdf$Item.DOI), ".pdf")
resdf$local_file <- paste0(gsub(" +", "_", gsub("[^[:alnum:][:space:]]", "",  resdf$Item.Title)), ".pdf")

## Priz
----------begin_max5_patcher---------- 8209.3oc68r1iiabjed8uBBgb.w41YR+lj2GBNm3CFFw9BfMt6vAGiEZj3LC8pgbhD0tdufje6W+hRjyPotjX0TxAw.ViVIpppt5pptppqt5+5m8lY2U+yEalk7uk7CIu4M+0O6Muw9QlO3M9+8al8z7edwp4arO1rphOVe2OM6stupo3mare7u4u29Q0aaVUzz7omKbfc1lxGpluZVxO5efmWWronpYdSYc06VWrnw8brb4sj2ljSxL+gkeqT+J4VxteW01mJqzf1RGL+Gto4SqrHZVK9eddyhGKqd3DAsirsvl5+zxkV.qGt2PE7YlO7u8Yel4k2NNl07kaVGje81tuy9+g3fblxL9xRsCSE0NXOHGTdJbPHftCGTL.GjmeNbvmJ1rY9CEuhERNH6KrfFM0JMPsCII43roSSPC.nCHnQnmCaZQ8SOoGpuhM0TroIon5CyNNKglyrSubKcScDNM6P7D5ovS.A6NLEx.LEI6bXJqJVdPojRMyJjfRpJ0Qx1oQQ6q6+YyWO+ohlh0uqnZ9cNNAY.P1kkc97QllNz1sxjpCPN.Lhkior02EPnRJ32xzTbpkTcZF8l2wmGIU14pr7yWTSnvjG88g3Q5kjlZdDm3LkOBdj.SdzWFfGIT42R6vjxsbrHykDN9SNYDbIFlbouHDWhmN0RRBdtyx94yi3oH5VEB9fJsikLJAeePA.5PluoHxr1L+iimcQsB.obq+fbIpdREFzgXWjyxogxOTb6lUkKKV2RdeX95J8B8u7q+A9OdXmP09qe+p54gcxPj6Vk7FqWRNMpTV.uLnmiN8.bYkjZjCuI+.3tOSlMjBrpkGrY9GJV9t4MMqKuaaSw92swys8raC+b01h56a+31Ou6PsrprozHAZ8fjrih58PadrdcytYl1kZ68HsSIjA9tsZbrSVjNvCrpt5g8fe6ll4kUCgDOw1c9w8PVQu1+fqb32UrpX9lBDD.k7Ko.XtXrB

## dates.R
# I made some slighlty bigger test data to make sure we had some we weren't interested in
# here are all the dates in the two years between dec 2011 and 2013
all_dates <- seq(as.Date("2011-12-15"), as.Date("2013-12-15"), "day")

# like you did, randomly fill in value, and randomly sample for the available nodes
bigtest <- data.frame(
  time = all_dates,
  node = sample(c(206,211,301,108), size = length(all_dates), replace=T),
  value = rnorm(length(all_dates), 21, 2)
)

## Microbiology_Society_Annual_Meeting_Program.md

      
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                For those tired of clicking and scrolling...
              
          
    Session View
Monday 09 April, Morning

Pre-Conference session: Teaching microbiology in higher education

This half-day symposium will present several interactive sessions which aim to
bring delegates together to discuss and share innovative ways of teaching.

  
## plotMSA.py
#!/usr/bin/env python
"""
Gist of how to make similar plots to the entropy plots in riboSeed from an MSA.
This assumes you want to annotate rDNAs, but you probably don't
 care about those.  Adjust as needed.

"""
import os
import datetime
import time

## kptrk.sh
# The purpose of this script it to make working from the terminal a bit more reprodicible.
# It is very simple
# A boolean value (KPTRK_ON) is set to true when kptrk is invoked, and the current directory is recorded.
# when kptrk is invoked again, logging is turned off.

#  it is built on top of https://github.com/rcaloras/bash-preexec

# Installation
#    - install bash-pre-exec, see instructions here https://github.com/rcaloras/bash-preexec, or, in short:
#     -- Pull down our file from GitHub and write it to our home directory as a hidden file.

## quickplot.py
#!/usr/bin/env python3
#-*- coding: utf-8 -*-
import math
import random
import sys
from bisect import bisect

def printPlot(data, line=None, y=30, x=60, tick=.2,  title="test", logger=None):
    yax = "|"
    xax = "_"

## gist:dea34d056b840b24d223f0cb7ca601d7
<?
/////////////////////
// slack2html
// by @levelsio
/////////////////////
//
/////////////////////
// WHAT DOES THIS DO?
/////////////////////
//
	library(tidyverse)
	if (!"speedyseq" %in% installed.packages()) remotes::install_github("mikemc/speedyseq")
	library(speedyseq)

	download.file("https://figshare.com/ndownloader/files/33076496", "samples.tsv")
	#this file has duplicate rows, and has multiple rows per pool
	samples <- read.csv("samples.tsv") %>% distinct() %>%
	select(-Pool, -Run, -ShotgunBatchID) %>% distinct()
	#download.file("https://figshare.com/ndownloader/files/26770931", "metadata.tsv")
	#metadata <- read.csv("metadata.tsv")
	# Source this for a handy discrete color palette and various helper functions for microbiome analyses.
	# not complex enough for an actual package. For now... #featurecreep
	mycolors=c(
	"#511087", "#4ecdc4", "#ffd447", "#13c01f", "#b78900", "#e66d17",
	"#8e3400", "#729b79", "#bf0000", "#907ad6", "#3f6356", "#ffbfb7",
	"#92bccc", "#c0e8ab", "#890466", "#382438", "#dad2bc", "#0048ff",
	"#f5f749", "#146d25", "#c08552", "#fc60a8", "#89fc00", "#3df8ff",
	"#ef2917", "#101f75", "#bfcccc"
	)
	searchres <- "~/Downloads/SearchResults.csv"
	outdir <- "~/Downloads/springercovid/"
	dir.create(outdir)

	resdf <- read.csv(searchres, stringsAsFactors = FALSE)

	resdf$dloadurl <- paste0("http://link.springer.com/content/pdf/",
	gsub("\\/", "%2F", fixed=TRUE, resdf$Item.DOI), ".pdf")
	resdf$local_file <- paste0(gsub(" +", "_", gsub("[^[:alnum:][:space:]]", "", resdf$Item.Title)), ".pdf")
	# I made some slighlty bigger test data to make sure we had some we weren't interested in
	# here are all the dates in the two years between dec 2011 and 2013
	all_dates <- seq(as.Date("2011-12-15"), as.Date("2013-12-15"), "day")

	# like you did, randomly fill in value, and randomly sample for the available nodes
	bigtest <- data.frame(
	time = all_dates,
	node = sample(c(206,211,301,108), size = length(all_dates), replace=T),
	value = rnorm(length(all_dates), 21, 2)
	)
	#!/usr/bin/env python
	"""
	Gist of how to make similar plots to the entropy plots in riboSeed from an MSA.
	This assumes you want to annotate rDNAs, but you probably don't
	care about those. Adjust as needed.

	"""
	import os
	import datetime
	import time
	# The purpose of this script it to make working from the terminal a bit more reprodicible.
	# It is very simple
	# A boolean value (KPTRK_ON) is set to true when kptrk is invoked, and the current directory is recorded.
	# when kptrk is invoked again, logging is turned off.

	# it is built on top of https://github.com/rcaloras/bash-preexec

	# Installation
	# - install bash-pre-exec, see instructions here https://github.com/rcaloras/bash-preexec, or, in short:
	# -- Pull down our file from GitHub and write it to our home directory as a hidden file.
	#!/usr/bin/env python3
	#-- coding: utf-8 --
	import math
	import random
	import sys
	from bisect import bisect

	def printPlot(data, line=None, y=30, x=60, tick=.2, title="test", logger=None):
	yax = "\|"
	xax = "_"
	<?
	/////////////////////
	// slack2html
	// by @levelsio
	/////////////////////
	//
	/////////////////////
	// WHAT DOES THIS DO?
	/////////////////////
	//