noamross

#!/bin/bash

text=`cat`

echo -E $text | grep -o '@\w*' | sort -n | uniq | sed -n 's/@\(.*\)/\\citation\{\1\}/p' >> ~/smallbib.aux
bibtool --preserve.key.case=on -s -d -x ~/smallbib.aux /Users/noamross/Dropbox/Public/library.bib -o ~/smallbib.bib

echo -E "$text" | pandoc -f markdown -t html --smart --mathjax --bibliography=/Users/noamross/smallbib.bib --csl=/Users/noamross/Dropbox/Public/pd/ecology.csl --template=/Users/noamross/Dropbox/Public/pd/noam.html --base-header-level=2

rm ~/smallbib.*

noamross

---

$if(title)$ title: $title$ $endif$ $if(author)$ author: $author$ $endif$ $if(tags)$ tags: $tags$ $endif$

noamross

#!/bin/bash

filename="$TM_FILEPATH"
basename="${filename%.*}"

text=`cat `

echo -E $text | grep -o '@\w*' | sort -n | uniq | sed -n 's/@\(.*\)/\\citation\{\1\}/p' >> ~/smallbib.aux
bibtool --preserve.key.case=on -s -d -x ~/smallbib.aux /Users/noamross/Dropbox/Public/library.bib -o ~/smallbib.bib

noamross

javascript:(function() {window.location=window.location.toString().replace(/^http:\/\/www\.jstor\.org\/stable\/.*\/(.*?)$/,'http://www.jstor.org/stable/pdfplus/$1.pdf?acceptTC=true');}())

noamross

#!/bin/bash

# This script is set to run any time a new file is added to my "Papers" folder, using launchd

# 1 - Find papers with JSTOR or Science cover sheets and remove the first page

text1="kind:pdf Your use of the JSTOR archive indicates your acceptance of"  #JSTOR papers
text2="kind:pdf is published weekly, except the last week in December"   #Science papers

#Add more of these as you find text snippets that identify cover sheets from different publishers.  Then add more loops belo

noamross

using terms from application "Quicksilver"
	on process text _bashcom
		do shell script "/bin/bash -ic '" & _bashcom & "'"
	end process text
end using terms from

noamross

# Save this in ~/Libary/Application Support/Quicksilver/Actions and relaunch
# Quicksilver.  Then just enter a text bash command and activate "BashGrowl" in
# the second pane.  I like it for todo.txt and system status calls.

using terms from application "Quicksilver"
	on process text theCommand
		set _text to do shell script "/bin/bash -ic " & quoted form of theCommand
		try
			
			tell application "System Events"

noamross

proftable <- function(file) {
  require(plyr)
  sample.interval <- as.numeric(strsplit(readLines(file, 1), "=")[[1L]][2L])/1e+06
  profdata <- as.matrix(read.table(file, header=FALSE, sep=" ", colClasses="character", skip=1, fill=TRUE, na.strings=""))
  total.time <- nrow(profdata)*sample.interval
  stacktable <- data.frame(table(aaply(profdata, 1, function(x) paste(rev(na.omit(x)), collapse=" > "))))
  names(stacktable) <- c("Stack","PctTime")
  stacktable$PctTime <- 100*stacktable$PctTime/nrow(profdata)
  stacktable <- stacktable[order(stacktable$PctTime, decreasing=TRUE), c("PctTime", "Stack")]
  rownames(stacktable) <- NULL

noamross

#' Use the data editor for a CSV file
#' 
#' This function loads a CSV file, lets the user edit it in the native data
#' editor, then re-saves it, prompting the user for a new name if desired.
#' 
fix.csv <- function(file, new.name=TRUE, sep=",", comment.char="") {
  tmpframe <- read.csv(file, sep=sep,quote="", colClasses="character",
                       stringsAsFactors=FALSE, comment.char="",
                       blank.lines.skip=FALSE, na.strings="")
  tmpframe <- edit(tmpframe)

noamross

Noam Ross
---------

### 13-01-22 14:50:15

Abstract
========

Forest disease spreads through plant communities structured by species composition, age distribution, and spatial arrangement. I propose to examine the consequences of the interaction of these components of population structure on a model systems of *Phytophthora ramorum* invasion in California redwood forests. First, I will compare the dynamic behavior of a series of epidemiological models that include different configurations of population structures. Then I will fit these models to time-series data from a network of disease monitoring plots to determine what components of forest population structure are most important for prediction of disease spread. Using the most parsimoniuous models, I will determine optimal schedules of treatment to minimize the probability of disease outbreak.