dgrtwo

"""
DrupalPasswordHasher

To use, put this in any app and add to your settings.py, something like this:

PASSWORD_HASHERS = (
    'django.contrib.auth.hashers.PBKDF2PasswordHasher',
    'myproject.myapp.drupal_hasher.DrupalPasswordHasher',
    'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
    'django.contrib.auth.hashers.BCryptPasswordHasher',

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"""
Usage: python minimal_Python.py infile outfile

Programs output by this translator will run the same way as the original and
use only the characters:

()+1cehrx

The code is for entertainment only. Converting your code base to minimal Python
is not recommended.

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"""
barcode_splitter.py

Barcode splitter for fastq sequencing files, that matches using Levenshtein
distance.

USAGE:

python barcode_splitter.py reads.fastq index_reads.fastq barcodes.txt

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library(ggplot2)
library(shiny)

shinyServer(function(input, output) {
    output$main_plot <- renderPlot({
        set.seed(205)
        means = t(replicate(input$clusters, c(runif(1), runif(1))))
        means = cbind(1:input$clusters, means)
        means = apply(means, 2, function(col) rep(col, input$points))
        means = data.frame(means)

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

# used the query here:
# http://data.stackexchange.com/stackoverflow/query/213011/metrics-of-question-activity-over-time

dat = read.csv("permonth_data.csv")

dat = dat %>% mutate(Date=as.Date(paste(Year, Month, "15", sep="-")))

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## "mer" methods for lme4.0, which couldn't be included in CRAN version
## of broom because it suggested lme4.0. See ?broom::lme4_tidiers for
## documentation.


tidy.mer <- function(x, effects = "random", ...) {
    # pass directly on to lme4 method
    effects <- match.arg(effects, c("random", "fixed"))
    if (effects == "fixed") {
        # return tidied fixed effects rather than random

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GID	YORF	NAME	GWEIGHT	G0.05	G0.1	G0.15	G0.2	G0.25	G0.3	N0.05	N0.1	N0.15	N0.2	N0.25	N0.3	P0.05	P0.1	P0.15	P0.2	P0.25	P0.3	S0.05	S0.1	S0.15	S0.2	S0.25	S0.3	L0.05	L0.1	L0.15	L0.2	L0.25	L0.3	U0.05	U0.1	U0.15	U0.2	U0.25	U0.3
GENE1331X	A_06_P5820	SFB2       || ER to Golgi transport || molecular function unknown || YNL049C || 1082129	1	-0.24	-0.13	-0.21	-0.15	-0.05	-0.05	0.2	0.24	-0.2	-0.42	-0.14	0.09	-0.26	-0.2	-0.22	-0.31	0.04	0.34	-0.51	-0.12	0.09	0.09	0.2	0.08	0.18	0.18	0.13	0.2	0.17	0.11	-0.06	-0.26	-0.05	-0.28	-0.19	0.09
GENE4924X	A_06_P5866	          || biological process unknown || molecular function unknown || YNL095C || 1086222	1	0.28	0.13	-0.4	-0.48	-0.11	0.17	0.31	0	-0.63	-0.44	-0.26	0.21	-0.09	-0.04	-0.1	0.15	0.2	0.63	0.53	0.15	-0.01	0.12	-0.15	0.32	0.16	0.09	0.02	0.04	0.03	0.01	-1.02	-0.91	-0.59	-0.61	-0.17	0.18
GENE4690X	A_06_P1834	QRI7       || proteolysis and peptidolysis || metalloendopeptidase activity || YDL104C || 1085955	1	-0.02	-0.27	-0.27	-0.02	0.24	0.25	0.23	0.06	-0.66	-0.4	-0.46	-0.43	0.18	

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snippet S3
	${1:funcname} <- function(${2:args}, ...) UseMethod("${1:funcname}")

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---
title: "Effect of compression type and file complexity on saveRDS size and speed"
author: "David Robinson"
date: "April 20, 2015"
output: html_document
---

```{r echo = FALSE}
knitr::opts_chunk$set(cache = TRUE, message = FALSE)
```

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library(plyr)
library(dplyr)
library(tidyr)
library(reshape2)

library(predpipe)

pipes_tidy <- pipes %>%
  ldply(melt, .id = "path") %>%
  rename(func = value, chain = L1) %>%