{
 "metadata": {
  "language": "Julia",
  "name": "",
  "signature": "sha256:73ae5751599103704021a661e15a085d80d81cc8cdee9c763c05411a37d37267"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Here is a few nice things that Matlab user can do in Julia"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "array of ints"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x1 = [1:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "10-element Array{Int32,1}:\n",
        "  1\n",
        "  2\n",
        "  3\n",
        "  4\n",
        "  5\n",
        "  6\n",
        "  7\n",
        "  8\n",
        "  9\n",
        " 10"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "array of floats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x1 = [1.:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "10-element Array{Float64,1}:\n",
        "  1.0\n",
        "  2.0\n",
        "  3.0\n",
        "  4.0\n",
        "  5.0\n",
        "  6.0\n",
        "  7.0\n",
        "  8.0\n",
        "  9.0\n",
        " 10.0"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comprehensions\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    \n",
      "    A = [ F(x,y,...) for x=rx, y=ry, ... ]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[i+j for i=1:5, j=1:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "5x5 Array{Int32,2}:\n",
        " 2  3  4  5   6\n",
        " 3  4  5  6   7\n",
        " 4  5  6  7   8\n",
        " 5  6  7  8   9\n",
        " 6  7  8  9  10"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = rand(8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "8-element Array{Float64,1}:\n",
        " 0.604484\n",
        " 0.392061\n",
        " 0.159006\n",
        " 0.700496\n",
        " 0.016461\n",
        " 0.990571\n",
        " 0.56842 \n",
        " 0.249582"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[ 0.25*x[i-1] + 0.5*x[i] + 0.25*x[i+1] for i=2:length(x)-1 ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "6-element Array{Any,1}:\n",
        " 0.386903\n",
        " 0.352642\n",
        " 0.394115\n",
        " 0.430997\n",
        " 0.641506\n",
        " 0.594249"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Pkg.add(\"Winston\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using Winston"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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",
       "prompt_number": 4,
       "text": [
        "FramedPlot(...)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x1, rand(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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",
       "prompt_number": 6,
       "text": [
        "FramedPlot(...)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x1, rand(10),\"b-o\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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",
       "prompt_number": 7,
       "text": [
        "FramedPlot(...)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x2 = rand(10,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "10x10 Array{Float64,2}:\n",
        " 0.470593   0.745078   0.00507957  0.55476    \u2026  0.849684  0.61146   0.177914\n",
        " 0.0176863  0.854575   0.292973    0.416571      0.89902   0.336281  0.139129\n",
        " 0.559851   0.610792   0.42808     0.118449      0.267995  0.935479  0.234999\n",
        " 0.250439   0.0527366  0.838703    0.712658      0.561449  0.378895  0.317819\n",
        " 0.463184   0.629851   0.417069    0.544282      0.159377  0.844525  0.299531\n",
        " 0.873786   0.860217   0.0142058   0.236449   \u2026  0.789597  0.668284  0.503   \n",
        " 0.195273   0.179327   0.450923    0.14229       0.263772  0.132346  0.31263 \n",
        " 0.89076    0.543465   0.0208126   0.922975      0.177622  0.988187  0.931534\n",
        " 0.236742   0.564753   0.299484    0.0745965     0.111413  0.209411  0.293134\n",
        " 0.608397   0.684601   0.846688    0.675908      0.613112  0.332819  0.422293"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imagesc(x2)\n",
      "title(\"random matrix\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "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",
       "prompt_number": 16,
       "text": [
        "FramedPlot(...)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Nice way to express equations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 5; y = 3;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "3x + 2y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "21"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "3x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "15"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Unicode for everyone"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u03c1(T,S) = \u03c10*(1-\u03b1*(T-T0)+\u03b2*(S-S0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "\u03c1 (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u03c10=1025\n",
      "\u03b1 = 2.5E-4\n",
      "T0 = 20.\n",
      "\u03b2 = 8.E-4\n",
      "S0 = 35."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "35.0"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u03c1(10,35)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "1027.5625"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = [10:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "6-element Array{Int32,1}:\n",
        " 10\n",
        " 11\n",
        " 12\n",
        " 13\n",
        " 14\n",
        " 15"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "S = [35.0:0.1:35.5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "6-element Array{Float64,1}:\n",
        " 35.0\n",
        " 35.1\n",
        " 35.2\n",
        " 35.3\n",
        " 35.4\n",
        " 35.5"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Function is already vectorized"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u03c1(T,S)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "6-element Array{Float64,1}:\n",
        " 1027.56\n",
        " 1027.39\n",
        " 1027.21\n",
        " 1027.04\n",
        " 1026.87\n",
        " 1026.69"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(\u03c1(T,S))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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",
       "prompt_number": 46,
       "text": [
        "FramedPlot(...)"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = \"Hello there\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "\"Hello there\""
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "string(st[1:5],\" \",st[7:9])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "\"Hello the\""
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "loops"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for letter in st\n",
      "    print(letter, \"\\n\")\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "H\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "e\n",
        "l\n",
        "l\n",
        "o\n",
        " \n",
        "t\n",
        "h\n",
        "e\n",
        "r\n",
        "e\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for letter=st\n",
      "    print(letter, \"\\n\")\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "H\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "e\n",
        "l\n",
        "l\n",
        "o\n",
        " \n",
        "t\n",
        "h\n",
        "e\n",
        "r\n",
        "e\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Evaluation of expressions inside strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"1 + 2 = $(1 + 2 + S[1])\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "\"1 + 2 = 38.0\""
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"First salinity is $(S[1]) psu\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "\"First salinity is 35.0 psu\""
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"All salinities are: $S\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "\"All salinities are: [35.0,35.1,35.2,35.3,35.4,35.5]\""
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Operators are functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "+(1,2,3,4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "10"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "*(1,2,3,4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "24"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Single expression which evaluates several subexpressions in order"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = (x = 1+T[1]; y = 2*S[1]; x + y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "81.0"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "a ? b : c "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`a ? b : c` - The expression `a`, before the `?`, is a condition expression, and the ternary operation evaluates the expression `b`, before the `:`, if the condition `a` is `true` or the expression `c`, after the `:`, if it is `false`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1 < 2 ? print(\"yes\") : print(\"no\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "yes"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "danet(x) = (x>0) ? 1 : -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "danet (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "danet(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "danet(-10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "-1"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Multiple nested for loops can be combined into a single outer loop, forming the cartesian product of its iterables:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i = 1:2, j = 3:4\n",
      "         println((i, j))\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ",3)\n",
        "(1,4)\n",
        "(2,3)\n",
        "(2,4)\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "System cals"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "run(`ls`|`grep U`)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Untitled0.ipynb\n",
        "Untitled1.ipynb\n"
       ]
      }
     ],
     "prompt_number": 92
    }
   ],
   "metadata": {}
  }
 ]
}