infotroph

dat = data.frame(
	userID=c("one", "two", "three", "four", "five"),
	start_date=as.Date(c("2015-09-01", "2015-09-02", "2015-09-02", "2015-09-03", "2015-09-03")),
	end_date=as.Date(c("2015-09-02", NA, "2015-09-03", NA, "2015-09-03")))

n_start = 100 # number of active users on day zero

days = as.Date("2015-08-25")+1:10

n_new = sapply(days, function(x)length(which(dat$start_date == x)))

infotroph

#!/usr/bin/env python3

lst = [{'one':1,'two':2,'three':3}, {'one':100,'two':200,'three':300}]

def wrapper(x, fun):
    return fun(x)

def this_works():
    def local_inner(d): 
        return d[key]

infotroph

#!/usr/bin/env python3

'''
Pandoc filter to convert inline math subscripts to text sbscripts.
Written for a very specific problem:
	Bibtex entries with "CO_{2}" are rendered by the Pandoc parser as
	[Str "CO",Math InlineMath "_{2}"],
	which is then rendered in OOXML as an inline equation that looks like
		"CO   2", with the 2 subscripted but an empty equation field between the subscript and the previous letters.
This filter solves this problem by replacing

infotroph

# Testing a soft-left-censored Stan model:
# Values above some minimum are detected normally, 
# values less than minimum go detected with some probability > 0.

set.seed(2345767)
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = 7)

sim_mu = 3

infotroph

library(ggplot2)
library(dplyr)
library(zoo) # for rollmean()

d_mean = (diamonds 
	%>% mutate(price=round(price, -2))
	%>% group_by(price)
	%>% mutate(mean_x = mean(x)))

d_roll = (diamonds

infotroph

set.seed(254469)
n=100
xlo=0
xhi=20
loc_logis=5
scale_logis=3


x = runif(n, xlo, xhi)
p_detect = plogis(x, location=loc_logis, scale=scale_logis)

infotroph

sim_mixdist = function(
		n_tubes=1,
		depths=1:3,
		intercept=1, 			# E[y] at depth=0 (log scale)
		b_depth=-1, 		# slope for depth (log scale)
		sig_tube=1, 			# sd for N(0) tube offsets (log scale)
		detect_loc=1,			# intercept for detection logistic. (scale... same as mu?)
		detect_scale=1, 		# slope for detection logistic. (scale?)
		sigma=1){ 			# residual (log scale)
	

infotroph

#!/bin/bash

trtary=(a b c)

echo -e '1\tfoo.out\n0\tunwanted.out\n1\tbar.out\n1\tbaz.out' > tocsv.files

for t in ${trtary[*]}; do
	# generate sample data. Each treatment overwrites previous .out files.
	echo -e 'col1 col2 col3\n1 2 3\n4 5 6\n7 8 9' > foo.out 
	echo -e 'extrajunk\ncol1 col2 col3 col4\n1 2 3 4\n5 6 7 8\n9 10 11 12' > bar.out 

infotroph

# /usr/local/bin/xmlzip.sh:
#!/bin/bash

for i in `unzip -Z -1 "$1"`; do
  echo "$i"
  unzip -a -p "$1" "$i" | xmllint --format -
done

# ~/.gitconfig:
[diff "xmlzip"]

infotroph

#Exim filter

logfile $home/eximfilter.log

logwrite "orig_local: $original_local_part"

if error_message then 
        logwrite "Error message; not filtering"
        finish 
endif