1-IntrodJulia-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Como graficar en julia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo primero que hay que hacer es instalar la paquetería que quieren usar. `PyPlot` es una buena opción; también vale la pena checar `Gadfly`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 required packages:\n",
      " - Compat                        0.6.0              master\n",
      " - Convex                        0.0.6\n",
      " - Coverage                      0.2.0\n",
      " - Escher                        0.1.0\n",
      " - FactCheck                     0.3.1\n",
      " - Gadfly                        0.3.13\n",
      " - HTTPClient                    0.1.5\n",
      " - IJulia                        0.2.5\n",
      " - Images                        0.4.43\n",
      " - Interact                      0.1.6+             master\n",
      " - Jewel                         1.0.5\n",
      " - JuMP                          0.9.3\n",
      " - Lint                          0.1.68\n",
      " - PyPlot                        1.5.3\n",
      " - QuDirac                       0.1.3\n",
      " - RandomMatrices                0.1.0\n",
      " - SCS                           0.0.8\n",
      " - TaylorSeries                  0.1.0+             master\n",
      " - TypeCheck                     0.0.3\n",
      " - ValidatedNumerics             0.1.1+             master\n",
      "73 additional packages:\n",
      " - ArgParse                      0.2.11\n",
      " - ArrayViews                    0.6.3\n",
      " - Benchmark                     0.1.0\n",
      " - BinDeps                       0.3.15\n",
      " - Calculus                      0.1.10\n",
      " - Codecs                        0.1.4\n",
      " - Color                         0.4.7\n",
      " - Combinatorics                 0.1.2\n",
      " - Compose                       0.3.13\n",
      " - Contour                       0.0.7\n",
      " - DataArrays                    0.2.17\n",
      " - DataFrames                    0.6.9\n",
      " - DataStructures                0.3.12\n",
      " - Dates                         0.3.2\n",
      " - Distances                     0.2.0\n",
      " - Distributions                 0.8.3\n",
      " - Docile                        0.5.16\n",
      " - DualNumbers                   0.1.3\n",
      " - FixedPointNumbers             0.0.10\n",
      " - FunctionalCollections         0.1.2\n",
      " - GZip                          0.2.17\n",
      " - GnuTLS                        0.0.4\n",
      " - Graphics                      0.1.0\n",
      " - Grid                          0.3.10\n",
      " - Hexagons                      0.0.3\n",
      " - Hiccup                        0.0.1\n",
      " - Homebrew                      0.1.16\n",
      " - HttpCommon                    0.0.12\n",
      " - HttpParser                    0.0.11\n",
      " - HttpServer                    0.0.12\n",
      " - ImmutableArrays               0.0.10\n",
      " - Iterators                     0.1.8\n",
      " - JSON                          0.4.5\n",
      " - JuliaParser                   0.6.2\n",
      " - KernelDensity                 0.1.1\n",
      " - LNR                           0.0.1\n",
      " - LaTeXStrings                  0.1.4\n",
      " - Lazy                          0.10.0\n",
      " - LibCURL                       0.1.4\n",
      " - Loess                         0.0.3\n",
      " - MacroTools                    0.2.0\n",
      " - Markdown                      0.3.0\n",
      " - MathProgBase                  0.3.16\n",
      " - Mux                           0.0.0\n",
      " - NaNMath                       0.1.0\n",
      " - Nettle                        0.1.10\n",
      " - ODE                           0.1.2\n",
      " - Optim                         0.4.2\n",
      " - Options                       0.2.4\n",
      " - PDMats                        0.3.5\n",
      " - Patchwork                     0.1.6\n",
      " - Polynomial                    0.1.1\n",
      " - Polynomials                   0.0.3\n",
      " - PyCall                        0.8.2\n",
      " - REPLCompletions               0.0.3\n",
      " - Reactive                      0.2.2+             master\n",
      " - Reexport                      0.0.2\n",
      " - Requests                      0.0.8\n",
      " - Requires                      0.1.3\n",
      " - ReverseDiffSparse             0.2.10\n",
      " - SHA                           0.1.1\n",
      " - SIUnits                       0.0.5\n",
      " - Showoff                       0.0.4\n",
      " - SortingAlgorithms             0.0.5\n",
      " - StatsBase                     0.7.1\n",
      " - StatsFuns                     0.1.2\n",
      " - TexExtensions                 0.0.2\n",
      " - TextWrap                      0.1.3\n",
      " - URIParser                     0.0.5\n",
      " - WebSockets                    0.0.6\n",
      " - WoodburyMatrices              0.1.1\n",
      " - ZMQ                           0.2.0\n",
      " - Zlib                          0.1.8\n"
     ]
    }
   ],
   "source": [
    "Pkg.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de instalar, *siempre* vale la pena actualizar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Updating METADATA...\n",
      "INFO: Updating cache of Graphics...\n",
      "INFO: Updating cache of Gadfly...\n",
      "INFO: Updating cache of Images...\n",
      "INFO: Updating cache of Patchwork...\n",
      "INFO: Updating cache of Coverage...\n",
      "INFO: Updating cache of FactCheck...\n",
      "INFO: Updating cache of Compose...\n",
      "INFO: Updating cache of Color...\n",
      "INFO: Updating TaylorSeries...\n",
      "INFO: Updating ValidatedNumerics...\n",
      "INFO: Updating Interact...\n",
      "INFO: Updating Reactive...\n",
      "INFO: Updating Compat...\n",
      "INFO: Computing changes...\n",
      "INFO: Cloning cache of ColorTypes from git://github.com/JuliaGraphics/ColorTypes.jl.git\n",
      "INFO: Cloning cache of ColorVectorSpace from git://github.com/JuliaGraphics/ColorVectorSpace.jl.git\n",
      "INFO: Cloning cache of Colors from git://github.com/JuliaGraphics/Colors.jl.git\n",
      "INFO: Upgrading Color: v0.4.7 => v0.4.8\n",
      "INFO: Installing ColorTypes v0.1.3\n",
      "INFO: Installing ColorVectorSpace v0.0.2\n",
      "INFO: Installing Colors v0.5.2\n",
      "INFO: Upgrading Compose: v0.3.13 => v0.3.14\n",
      "INFO: Upgrading FactCheck: v0.3.1 => v0.4.0\n",
      "INFO: Upgrading Gadfly: v0.3.13 => v0.3.14\n",
      "INFO: Upgrading Images: v0.4.43 => v0.4.45\n",
      "INFO: Upgrading Patchwork: v0.1.6 => v0.1.7\n",
      "INFO: Building Homebrew\n",
      "From https://github.com/Homebrew/homebrew\n",
      "   4e8d121..4cad151  master     -> origin/master\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HEAD is now at 4cad151 osquery: update 1.5.2 bottle.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "From https://github.com/staticfloat/homebrew-juliadeps\n",
      "   e641aa5..432b6ff  master     -> origin/master\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HEAD is now at 432b6ff Merge pull request #59 from staticfloat/staging\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Building Images\n"
     ]
    }
   ],
   "source": [
    "Pkg.update() # Esto actualiza METADATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo que hay que hacer para instalar, por ejemplo `PyPlot`, es simplemente:\n",
    "\n",
    "```julia\n",
    "Pkg.add(\"PyPlot\")\n",
    "```\n",
    "\n",
    "Y, una vez instalado, usamos esa paquetería:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Color is deprecated, please use Colors instead. See https://github.com/JuliaGraphics/Colors.jl\n",
      "INFO: Loading help data...\n"
     ]
    }
   ],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25:0.25:4.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.25:1.0/2^2:4  # un ejemplo de una construcción de un FloatRange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FloatRange{Float64} (constructor with 1 method)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16-element Array{Float64,1}:\n",
       " 0.25\n",
       " 0.5 \n",
       " 0.75\n",
       " 1.0 \n",
       " 1.25\n",
       " 1.5 \n",
       " 1.75\n",
       " 2.0 \n",
       " 2.25\n",
       " 2.5 \n",
       " 2.75\n",
       " 3.0 \n",
       " 3.25\n",
       " 3.5 \n",
       " 3.75\n",
       " 4.0 "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = collect(0.25:1.0/2^2:4) # convertimos el FloatRange a vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos un otro vector `y`; en este caso, es una función simple de `x`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16-element Array{Float64,1}:\n",
       "  0.0625\n",
       "  0.25  \n",
       "  0.5625\n",
       "  1.0   \n",
       "  1.5625\n",
       "  2.25  \n",
       "  3.0625\n",
       "  4.0   \n",
       "  5.0625\n",
       "  6.25  \n",
       "  7.5625\n",
       "  9.0   \n",
       " 10.5625\n",
       " 12.25  \n",
       " 14.0625\n",
       " 16.0   "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x.^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ahora, la graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <matplotlib.figure.Figure object at 0x117de6490>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1-element Array{Any,1}:\n",
       " PyObject <matplotlib.lines.Line2D object at 0x1186f2950>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(x,y,\"ro-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <matplotlib.figure.Figure object at 0x120ae6d50>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x120c101d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(x,y,\"go-\")\n",
    "xlabel(L\"x\")\n",
    "ylabel(L\"y = x^2\", fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <matplotlib.figure.Figure object at 0x120e10050>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x120e31990>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loglog(x,y,\"go-\")\n",
    "xlabel(L\"x\")\n",
    "ylabel(L\"y = x^2\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo mejor que se puede hacer para explotar este graficador es ir a la página de [Matplotlib](http://matplotlib.org), *y leer* la documentación."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La documentación sobre [Gadfly](https://github.com/dcjones/Gadfly.jl) está [aquí](http://gadflyjl.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como no lo escribí en ningún lado, déjenmelo escribir aquí: Una pieza básica del curso se puede resumir en:\n",
    "\n",
    "> Learning by doing\n",
    "\n",
    "Piénsenlo y, sobre todo, ¡practíquenlo!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.3.11-debug",
   "language": "julia",
   "name": "julia-0.3-debug"
  },
  "language_info": {
   "name": "julia",
   "version": "0.3.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}