{
 "metadata": {
  "language": "Julia",
  "name": "",
  "signature": "sha256:103e429db35f1dea82a8c4a06fa2a6c5f5da4822a14c5681668a7b0da9962af1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Julia Basics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Julia is a dynamic language.  You don't need type declarations, and can change variable types dynamically and interactively.\n",
      "\n",
      "For working with simple numbers, arrays, and strings, its syntax is *superficially* similar to Matlab, Python, and other popular languages. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = rand(10,300)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "10x300 Array{Float64,2}:\n",
        " 0.375636   0.482139   0.361992  0.257166  \u2026  0.754869  0.120599   0.893813\n",
        " 0.0686832  0.671072   0.999855  0.286798     0.425177  0.382689   0.920532\n",
        " 0.447454   0.0964471  0.311746  0.606187     0.941434  0.125957   0.625727\n",
        " 0.730125   0.227747   0.577157  0.91215      0.194293  0.209349   0.196251\n",
        " 0.323599   0.625352   0.834394  0.662423     0.48925   0.955718   0.510419\n",
        " 0.0350443  0.926088   0.385119  0.398782  \u2026  0.715289  0.549668   0.486071\n",
        " 0.980887   0.427284   0.445409  0.352723     0.482097  0.747813   0.143269\n",
        " 0.0323402  0.766844   0.637901  0.136801     0.778503  0.5898     0.557505\n",
        " 0.962389   0.788106   0.258881  0.18815      0.693301  0.754942   0.42183 \n",
        " 0.830188   0.917995   0.822675  0.96834      0.253111  0.0893574  0.638984"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It has all of the usual built-in Matlab/Numpy-like linear-algebra and vector functions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = rand(10)\n",
      "x = A \\ b\n",
      "B = A' * A\n",
      "erf(eigvals(B)) - 2x.^2 + 4x - 6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "300-element Array{Float64,1}:\n",
        " -5.99331\n",
        " -5.96398\n",
        " -5.96022\n",
        " -5.96506\n",
        " -5.98213\n",
        " -5.99655\n",
        " -5.98238\n",
        " -5.98213\n",
        " -5.93154\n",
        " -5.97005\n",
        " -5.97269\n",
        " -5.97308\n",
        " -5.9354 \n",
        "  \u22ee      \n",
        " -5.96845\n",
        " -5.99352\n",
        " -4.99281\n",
        " -4.95339\n",
        " -4.94527\n",
        " -4.9767 \n",
        " -5.00616\n",
        " -5.01051\n",
        " -5.01131\n",
        " -5.04615\n",
        " -5.03316\n",
        " -5.00371"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Complex numbers and arbitrary-precision arithmetic (via MPFR), of course."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cos(big(3 + 4im))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "-2.703494560307422464769480266827091348467753695567661661019265514673434246483996e+01 with 256 bits of precision - 3.851153334811777536563337123053124569704160846091637003157728595256494186490506e+00 with 256 bits of precisionim"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Unicode"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All strings are UTF-8 encoded Unicode by default (UTF-16 and UTF-32 also supported):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matchall(r\"\\s[a-z]+\", \"\u03b1 is a Gr\u00eb\u00e9k letter\") # regex search of a Unicode string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "3-element Array{SubString{UTF8String},1}:\n",
        " \" is\"    \n",
        " \" a\"     \n",
        " \" letter\""
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Like Python 3, variable names can be Unicode, but Julia allows a somewhat wider range of codepoints in identifiers, which can be typed by LaTeX-like tab-completion *\\alpha[TAB]\\hat[TAB]\\_2[TAB]\\prime*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u03b1\u0302\u2082\u2032 = 7\n",
      "\u0127 = 6.62606957e-34 / 2\u03c0\n",
      "x\u0307 = \u0127 * \u03b1\u0302\u2082\u2032"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "7.3820020773540256e-34"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unlike Python 3, Unicode math operators are parsed as infix operators, which are available for user-defined meanings:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\u226a(x,y) = x < 0.1*y\n",
      "50 \u226a 100, 5 \u226a 100, 5 \u2264 50"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(false,true,true)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "const \u2297 = kron\n",
      "eye(2,2) \u2297 rand(2,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "4x4 Array{Float64,2}:\n",
        " 0.556894  0.861108  0.0       0.0     \n",
        " 0.344389  0.960697  0.0       0.0     \n",
        " 0.0       0.0       0.556894  0.861108\n",
        " 0.0       0.0       0.344389  0.960697"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Functions and JIT-compilation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Functions can be defined in several ways, and *don't require type-declarations*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# verbose form:\n",
      "function foo(x)\n",
      "    return x + 1\n",
      "end\n",
      "\n",
      "# one-line form:\n",
      "foo(x) = x + 1\n",
      "\n",
      "# anonymous function\n",
      "x -> x + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(anonymous function)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The *first time you call* a function with arguments of a particular type, Julia JIT-compiles that function *specialized for that type* with LLVM, which is then cached for subsequent calls for the *same types*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo(3) # compiles foo for Int arguments"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo(7) # re-uses compiled foo(Int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "8"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo(7.3) # compiles a different version for Float64 arguments"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "8.3"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo([1,2,7,9]) # compiles a different version for Array{Int,1} arguments"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "4-element Array{Int64,1}:\n",
        "  2\n",
        "  3\n",
        "  8\n",
        " 10"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can *inspect* the generated code easily at various compilation stages, to see that Julia **uses the type of the arguments to infer the types of expressions** and **can produce C-like fast code**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "code_typed(foo, (Int,)) # julia expressions with type annotations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "1-element Array{Any,1}:\n",
        " :($(Expr(:lambda, {:x}, {{},{{:x,Int64,0}},{}}, :(begin  # In[10], line 7:\n",
        "        return (top(box))(Int64,(top(add_int))(x::Int64,1))::Int64\n",
        "    end::Int64))))"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "code_native(foo, (Int,))  # assembly code"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\t.section\t__TEXT,__text,regular,pure_instructions\n",
        "Filename: In[10]\n",
        "Source line: 7\n",
        "\tpush\tRBP\n",
        "\tmov\tRBP, RSP\n",
        "Source line: 7\n",
        "\tlea\tRAX, QWORD PTR [RDI + 1]\n",
        "\tpop\tRBP\n",
        "\tret\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}