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while true
do 
  rsync -avz ./ user@host:remote/directory/
  inotifywait -r ./
done

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"""
Progress bar for rsync
========================

Shows file progress and total progress as a progress bar.

Usage
---------
Run rsync with -P and pipe into this program. Example::

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format pal quality=1 aspect=4:3 mpeg2sound=ac3 mpeg2=3,192
# later: quality=1 interlaced 
set dur=sec
set resize=resize
set title_offset=20%,80% title_area=15%

# http://computoser.com/track/722
# http://computoser.com/track/6340
audio spacy.mp3
#set label=green

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Method decision tree

• Write down your problem mathematically/statistically; stripping out astronomy-specific details from your model (e.g. pre-compute redshifts, weights, etc.)
• Ideally, shorten and simplify it down to primitives of math and statistics, which can be implemented in any language.

For the following decision tree, keep in mind that typically, one first thinks

• I just want to quickly find the best fit on this.

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Enumerated List Bug Demo

First

1. foo (should be 1.)
2. bar (should be 2.)

Second

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ArXiV minimal statistics checklist

This checklist help you identify and fix common errors/misinterpretation in your analysis, or of a paper you are refereeing.

1. If you use p-values (from a KS test, Pearson correlation, etc.).

1. What do you think a low p-value says?

1. You have absolutely disproved the null hypothesis (e.g. "no correlation" is ruled out, the data are not sampled from this model, there is no difference between the population means).

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"""
SYNOPSIS: ./myprog | python console-progress.py

example for myprog: 

#!/bin/bash
echo 100
for i in $(seq 1 100)
do
   sleep 1

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import matplotlib.pyplot as plt
import numpy
import scipy.stats

# http://www.medpagetoday.com/Blogs/TheMethodsMan/52171

def calc_reliability(p, power=0.8, frac_true=0.1):
	"""
	Given this p-value, power of the test and fraction of hypotheses that 
	are actually true.

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import numpy
import matplotlib.pyplot as plt
import scipy.stats

# two gaussian uncertainties with width sigma
# at distance delta
# what is the probability that they actually have the same value?

def compute_bayes(delta, border=5):
	a = scipy.stats.norm()

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"""
Memoizes a given function, given its code dependencies (loaded modules and 
additional data files)

Example::

  import douglasadams
  def costlyfunction():
  	# compute answer to the universe and everything
  	return douglasadams.compute_answer() == 42