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#!/usr/bin/python
"""
python resize_files.py folder_of_input_images output_file.gif
"""
import os, sys, re

RESIZE_PERCENT = 12
QUALITY = 40

folder = sys.argv[1]

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values <- c('a', 'b', 'c')
probs <- c(.2, .5, .3)

sample(values, 10, replace=TRUE, prob=probs)

# [1] "a" "b" "a" "a" "c" "a" "c" "b" "c" "b"

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{block:Posts}
    <script language="JavaScript">
    var source_url = '{block:ContentSource}{SourceURL}{/block:ContentSource}';
    var reblogged_from = '{block:RebloggedFrom}{ReblogParentName}{/block:RebloggedFrom}' + '.tumblr.com';
    if (source_url.indexOf(reblogged_from) == -1) {
        document.write('Your attribution string!');
    }
    </script>
{/block:Posts}

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"""
Input is one number per line

Output is json of the form:
[
    {
        'bin' : bin1,
        'value' : value1
    },
    {

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my.reduce <- function(df.in, f1, f2){
  x <- ddply(df.in, .(f1, f2), summarize,
             value=sum(value)
             )
  x
}

df <- data.frame(
  state=rep(c("MA", "NY", "CA"), each=10),
  year=rep((2001:2010), 3),

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import numpy as np
import random

# dict to hold results                                                                    
counts = {}

# a hack to generate random key, value pairs.                                             
# 5k keys, 100k values                                                                    
x=[i%5000 for i in xrange(100000)]
random.shuffle(x)

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from numpy.random import uniform
import numpy
import random

def slice_sampler(px, N = 1, x = None):
    """
    Provides samples from a user-defined distribution.
    
    slice_sampler(px, N = 1, x = None)
    

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plotmatrix2 <- function (data, mapping = aes())
{
    grid <- expand.grid(x = 1:ncol(data), y = 1:ncol(data))
    grid <- subset(grid, x != y)
    all <- do.call("rbind", lapply(1:nrow(grid), function(i) {
        xcol <- grid[i, "x"]
        ycol <- grid[i, "y"]
        data.frame(xvar = names(data)[ycol], yvar = names(data)[xcol], 
            x = data[, xcol], y = data[, ycol], data)
    }))

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from __future__ import division
import numpy as np
from pylab import *
from warnings import warn

class OutlierDetector(object):
    
    def __init__(self, N=10):
        """
        Generates some simple statistics on the previous N data points