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import Image, ImageDraw

def get_colors(infile, outfile, numcolors=10, swatchsize=20, resize=150):

    image = Image.open(infile)
    image = image.resize((resize, resize))
    result = image.convert('P', palette=Image.ADAPTIVE, colors=numcolors)
    result.putalpha(0)
    colors = result.getcolors(resize*resize)

000Nelson000

Automate Chunks of Analysis in R Markdown 
========================================================

```{r setup, echo=FALSE}
library(knitr)
library(DT)
```

```{r create-datasets, echo=FALSE}

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library(proto)

stat_smooth_func <- function (mapping = NULL, data = NULL, geom = "smooth", position = "identity",
                         method = "auto", formula = y ~ x, se = TRUE, n = 80, fullrange = FALSE,
                         level = 0.95, na.rm = FALSE, ...) {
  StatSmoothFunc$new(mapping = mapping, data = data, geom = geom, position = position,
                 method = method, formula = formula, se = se, n = n, fullrange = fullrange,
                 level = level, na.rm = na.rm, ...)
}

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#plot.xts with horizons
#install.packages("xtsExtra", repos="http://R-Forge.R-project.org")

require(PerformanceAnalytics)
require(quantmod)
require(xtsExtra)  #if you get error, install xtsExtra from r-forge

horizon.panel <- function(index,x,...) {
  #get some decent colors from RColorBrewer
  #we will use colors on the edges so 2:4 for red and 7:9 for blue

000Nelson000

import os

def split(filehandler, delimiter=',', row_limit=10000, 
    output_name_template='output_%s.csv', output_path='.', keep_headers=True):
    """
    Splits a CSV file into multiple pieces.
    
    A quick bastardization of the Python CSV library.

    Arguments: