Rlang-QQ-0.1.1.0: examples/ihaskell1.ipynb

{
 "metadata": {
  "language": "haskell",
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Rlang-QQ is a way to call the R language interpreter from haskell.\n",
      "This demo shows how to plot things with it, and include those plots\n",
      "in the IHaskell notebook. Ideally this example will become a 4 liner\n",
      "when a new variation of the `r` quasiquote is written."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "{-# LANGUAGE QuasiQuotes #-} -- should enable the extension, but it doesn't for now\n",
      ":extension QuasiQuotes\n",
      "import RlangQQ\n",
      "import IHaskell.Display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "let fn = \"tmp1\" -- should come from a global variable...\n",
      "let npts = 100 :: Double -- cannot leave out the above let\n",
      "let sins = map sin [ 2*pi * x / npts | x <- [1 .. npts] ]\n",
      "     \n",
      "     \n",
      "[r| # an R function to make png and svg plots,\n",
      "    # ideally this part is hidden and we replay plots\n",
      "    # just like knitr (evaluate package) with code\n",
      "    # the user does not have to write out\n",
      "    pp <- function(p) {\n",
      "        svg(paste0($(fn), \".svg\"))\n",
      "        p()\n",
      "        dev.off()\n",
      "        png(paste0($(fn), \".png\"))\n",
      "        p()\n",
      "        dev.off()\n",
      "    }\n",
      "\n",
      "    pp(function() plot( $(sins), type ='l'))\n",
      " |]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": []
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import qualified Data.ByteString as B\n",
      "import qualified Data.ByteString.Base64 as Base64\n",
      "import qualified Data.ByteString.Char8 as Char"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "do\n",
      "  pp <- B.readFile (fn++\".png\")\n",
      "  ps <- hGetContents =<< openBinaryFile (fn++\".svg\") ReadMode\n",
      "  length ps `seq` return () -- lazy IO?\n",
      "  return [png 800 400 (Char.unpack (Base64.encode pp)), svg ps]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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