{
 "metadata": {
  "language": "haskell",
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- First of all, we can evaluate simple expressions.\n",
      "3 + 5\n",
      "\"Hello, \" ++ \"World!\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "8"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\"Hello, World!\""
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- Unlike in GHCi, we can have multi-line expressions.\n",
      "concat [\n",
      "  \"Hello,\",\n",
      "  \", \",\n",
      "  \"World!\"\n",
      "  ] :: String\n",
      "  \n",
      "-- We can also have normal Haskell declarations, without `let`.\n",
      "-- As long as you group type signatures and declarations together,\n",
      "-- you can use pattern matching and add type signatures.\n",
      "thing :: String -> Int -> Int\n",
      "thing \"no\" _ = 100\n",
      "thing str int = int + length str\n",
      "\n",
      "thing \"no\" 10\n",
      "thing \"ah\" 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\"Hello,, World!\""
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "100"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "12"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can also do IO.\n",
      "print \"What's going on?\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\"What's going on?\""
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can disable extensions.\n",
      ":ext NoEmptyDataDecls\n",
      "data Thing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<span style='color: red; font-style: italic;'>`Thing' has no constructors<br/>  (-XEmptyDataDecls permits this)</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "`Thing' has no constructors\n",
        "  (-XEmptyDataDecls permits this)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- And enable extensions.\n",
      ":ext EmptyDataDecls\n",
      "data Thing"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- Various data declarations work fine.\n",
      "data One\n",
      "     = A String\n",
      "     | B Int\n",
      "     deriving Show\n",
      "\n",
      "print [A \"Hello\", B 10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "[A \"Hello\",B 10]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can look at types like in GHCi.\n",
      ":ty 3 + 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<span style='font-weight: bold; color: green;'>forall a. Num a => a</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "forall a. Num a => a"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- And we can inspect info of things!\n",
      ":info Integral"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "class (Real a, Enum a) => Integral a where\n",
        "  quot :: a -> a -> a\n",
        "  rem :: a -> a -> a\n",
        "  div :: a -> a -> a\n",
        "  mod :: a -> a -> a\n",
        "  quotRem :: a -> a -> (a, a)\n",
        "  divMod :: a -> a -> (a, a)\n",
        "  toInteger :: a -> Integer\n",
        "  \t-- Defined in `GHC.Real'\n",
        "instance Integral Integer -- Defined in `GHC.Real'\n",
        "instance Integral Int -- Defined in `GHC.Real'"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- Results are printed as we go, even from a single expression.\n",
      "import Control.Monad\n",
      "import Control.Concurrent\n",
      "\n",
      "forM_ [1..5] $ \\x -> do\n",
      "  print x\n",
      "  threadDelay $ 100 * 1000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "1\n",
        "2\n",
        "3\n",
        "4\n",
        "5"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can display Maybes fancily for Show-able types.\n",
      "Just ()\n",
      "Nothing\n",
      "\n",
      "-- But it dies if it's not showable.\n",
      "data NoShow = X Int\n",
      "Just (X 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<span style='color: green; font-weight: bold;'>Just</span><span style='font-family: monospace;'>()</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Just ()"
       ]
      },
      {
       "html": [
        "<span style='color: red; font-weight: bold;'>Nothing</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Nothing"
       ]
      },
      {
       "html": [
        "<span style='color: red; font-style: italic;'>No instance for (Show NoShow)<br/>  arising from a use of `print'<br/>Possible fix:<br/>  add an instance declaration for (Show NoShow)</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "No instance for (Show NoShow)\n",
        "  arising from a use of `print'\n",
        "Possible fix:\n",
        "  add an instance declaration for (Show NoShow)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- Aeson JSON data types are displayed nicely.\n",
      ":ext OverloadedStrings\n",
      "\n",
      "import Data.Aeson\n",
      "\n",
      "data Coord  = Coord { x :: Double, y :: Double }\n",
      "instance ToJSON Coord where\n",
      "   toJSON (Coord x y) = object [\"x\" .= x, \"y\" .= y]\n",
      "\n",
      "Null\n",
      "Bool True\n",
      "toJSON (Coord 3 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div class=\"highlight-code\" id=\"javascript\">null</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "null"
       ]
      },
      {
       "html": [
        "<div class=\"highlight-code\" id=\"javascript\">true</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "true"
       ]
      },
      {
       "html": [
        "<div class=\"highlight-code\" id=\"javascript\">{\n",
        "    \"x\": 3.0,\n",
        "    \"y\": 2.0\n",
        "}</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "{\n",
        "    \"x\": 3.0,\n",
        "    \"y\": 2.0\n",
        "}"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- Small bits of HTML generated via Blaze are displayed.\n",
      "import Prelude hiding (div, id)\n",
      "import Text.Blaze.Html4.Strict hiding (map, style)\n",
      "import Text.Blaze.Html4.Strict.Attributes\n",
      "\n",
      "div ! style \"color: red\" $ do\n",
      "    p \"This is an example of BlazeMarkup syntax.\"\n",
      "    b \"Hello\"\n",
      "    \n",
      "forM [1..5] $ \\size -> do\n",
      "  let s = toValue $ size * 80\n",
      "  img ! src \"/static/base/images/ipynblogo.png\" ! width s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"color: red\">\n",
        "    <p>\n",
        "        This is an example of BlazeMarkup syntax.\n",
        "    </p>\n",
        "    <b>\n",
        "        Hello\n",
        "    </b>\n",
        "</div>\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<div style=\"color: red\">\n",
        "    <p>\n",
        "        This is an example of BlazeMarkup syntax.\n",
        "    </p>\n",
        "    <b>\n",
        "        Hello\n",
        "    </b>\n",
        "</div>"
       ]
      },
      {
       "html": [
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"80\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"160\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"240\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"320\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"400\">\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"80\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"160\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"240\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"320\">\n",
        "<img src=\"/static/base/images/ipynblogo.png\" width=\"400\">"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can draw diagrams, right in the notebook.\n",
      ":extension NoMonomorphismRestriction\n",
      "import Diagrams.Prelude\n",
      "\n",
      "-- By Brent Yorgey\n",
      "-- Draw a Sierpinski triangle!\n",
      "sierpinski 1 = eqTriangle 1\n",
      "sierpinski n =     s\n",
      "                  ===\n",
      "               (s ||| s) # centerX\n",
      "  where s = sierpinski (n-1)\n",
      "\n",
      "-- The `diagram` function is used to display them in the notebook.\n",
      "diagram $ sierpinski 4\n",
      "            # centerXY\n",
      "            # fc black\n",
      "          `atop` square 10\n",
      "                   # fc white"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "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"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- We can draw small charts in the notebook.\n",
      "-- This example is taken from the haskell-chart documentation.\n",
      "import Graphics.Rendering.Chart \n",
      "import Data.Default.Class\n",
      "import Control.Lens\n",
      "\n",
      "let values = [\n",
      "     (\"Mexico City\"  , 19.2, 0),\n",
      "     (\"Mumbai\"       , 12.9, 10), \n",
      "     (\"Sydney\"       , 4.3,  0),\n",
      "     (\"London\"       , 8.3,  0), \n",
      "     (\"New York\"     , 8.2,  25)]\n",
      "     \n",
      "pitem (s, v, o) = pitem_value .~ v\n",
      "                $ pitem_label .~ s\n",
      "                $ pitem_offset .~ o\n",
      "                $ def  \n",
      "\n",
      "-- Convert to a renderable in order to display it.\n",
      "toRenderable \n",
      "  $ pie_title .~ \"Relative Population\"\n",
      "  $ pie_plot . pie_data .~ map pitem values\n",
      "  $ def"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "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"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- There is also hlint integration enabled by default.\n",
      "-- If you write sketchy code, it will tell you:\n",
      "f :: Int -> Int\n",
      "f x = x + 1\n",
      "\n",
      "-- Most warnings are orange...\n",
      "f $ 3\n",
      "\n",
      "-- But more severe warnings are red.\n",
      "putStrLn (show 3)\n",
      "do\n",
      "  return 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        " <table class=\"suggestion-table\"> <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-name\"> Redundant $</div> </td> </tr>  <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-warning\">Found:</div>  <div class=\"highlight-code\" id=\"haskell\">  f $ 3\n",
        "</div> </td>  <td class=\"suggestion-cell\"> <div class=\"suggestion-warning\">Why Not:</div>  <div class=\"highlight-code\" id=\"haskell\">  f 3\n",
        "</div> </td> </tr>  <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-name\"> Use print</div> </td> </tr>  <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-error\">Found:</div>  <div class=\"highlight-code\" id=\"haskell\">  putStrLn (show 3)\n",
        "</div> </td>  <td class=\"suggestion-cell\"> <div class=\"suggestion-error\">Why Not:</div>  <div class=\"highlight-code\" id=\"haskell\">  print 3\n",
        "</div> </td> </tr>  <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-name\"> Redundant do</div> </td> </tr>  <tr class=\"suggestion-row\"> <td class=\"suggestion-cell\"> <div class=\"suggestion-error\">Found:</div>  <div class=\"highlight-code\" id=\"haskell\">  do return 3\n",
        "</div> </td>  <td class=\"suggestion-cell\"> <div class=\"suggestion-error\">Why Not:</div>  <div class=\"highlight-code\" id=\"haskell\">  return 3\n",
        "</div> </td> </tr> </table> "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Line 3:  Redundant $\n",
        "Found:\n",
        "  f $ 3\n",
        "\n",
        "Why not:\n",
        "  f 3\n",
        "Line 4:  Use print\n",
        "Found:\n",
        "  putStrLn (show 3)\n",
        "\n",
        "Why not:\n",
        "  print 3\n",
        "Line 5:  Redundant do\n",
        "Found:\n",
        "  do return 3\n",
        "\n",
        "Why not:\n",
        "  return 3"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "4"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "3"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- If hlint annoys you, though, you can turn it off.\n",
      "-- Note that this only takes effect in the next cell execution.\n",
      ":hlint off"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- You could similarly use `:hlint on` to turn it back on.\n",
      "f $ 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- If your code isn't running fast enough, you can just put it into a module.\n",
      "module A.B where\n",
      "\n",
      "fib 0 = 1\n",
      "fib 1 = 1\n",
      "fib n = fib (n-1) + fib (n-2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- The module is automatically imported unqualified.\n",
      "-- Note that since a new module is imported, all previous\n",
      "-- bound identifiers are now unbound.\n",
      "print $ A.B.fib 20\n",
      "print $ fib 20"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "10946"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "10946"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "-- But if you re-import it qualified, the previous import goes away.\n",
      "import qualified A.B as Fib\n",
      "\n",
      "Fib.fib 20\n",
      "fib 20"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "10946"
       ]
      },
      {
       "html": [
        "<span style='color: red; font-style: italic;'>Not in scope: `fib'<br/>Perhaps you meant `Fib.fib' (imported from A.B)</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Not in scope: `fib'\n",
        "Perhaps you meant `Fib.fib' (imported from A.B)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thanks!\n",
      "---\n",
      "\n",
      "I hope you've enjoyed this little demo of **IHaskell**!\n",
      "\n",
      "All of this is running using [IPython notebook][http://ipython.org], communicating asynchronously with a language evaluator kernel written in pure Haskell. \n",
      "\n",
      "In addition to code cells, you can also type Markdown, and have it be displayed as HTML - just like I'm doing with this cell.\n",
      "\n",
      "I hope you find IHaskell useful, and please report any bugs or features requests [on Github](https://github.com/gibiansky/IHaskell/issues). If you have any comments, want to contribute, or just want to get in touch, don't hesitate to contact me at Andrew dot Gibiansky at Gmail."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}