{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "slide" } }, "source": [ "IPython-fu: el camino de la \"nbconversi\u00f3n\"." ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "subslide" } }, "source": [ " [Dami\u00e1n Avila](http://www.damian.oquanta.info)\n", "\n", " [@damian_avila](http://twitter.com/damian_avila)\n", "\n", " [damianavila](https://github.com/damianavila)\n", "\n", " info@oquanta.info" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## CHARLA:\n", "\n", "## [http://goo.gl/lwQtB0](http://goo.gl/lwQtB0)" ] }, { "cell_type": "heading", "level": 2, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "1. El notebook the IPython. " ] }, { "cell_type": "markdown", "metadata": { "internals": {}, "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [http://ipython.org/](http://ipython.org/)" ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "\u00bfQu\u00e9 es el notebook the IPython?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ ">The goal of IPython is to create a comprehensive environment for interactive and exploratory computing." ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "subslide" } }, "source": [ "Una herramienta para contar **historias**:\n", "\n", "* metodolog\u00edas\n", "* procedimientos\n", "* an\u00e1lisis" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "T\u00e9cnicamente es una documento basado de JSON:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.nbformat import current\n", "with open('IPython_fu_talk.ipynb') as f:\n", " nb = current.read(f,'json')\n", " \n", "nb.worksheets[0].cells[15:20]" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "[{u'cell_type': u'markdown',\n", " u'metadata': {u'slideshow': {u'slide_type': u'fragment'}},\n", " u'source': u'* Colaborativo'},\n", " {u'cell_type': u'markdown',\n", " u'metadata': {u'internals': {u'slide_helper': u'subslide_end'},\n", " u'slide_helper': u'slide_end',\n", " u'slideshow': {u'slide_type': u'fragment'}},\n", " u'source': u'* Abierto'},\n", " {u'cell_type': u'heading',\n", " u'level': 2,\n", " u'metadata': {u'internals': {u'slide_helper': u'subslide_end',\n", " u'slide_type': u'subslide'},\n", " u'slide_helper': u'subslide_end',\n", " u'slideshow': {u'slide_type': u'slide'}},\n", " u'source': u'2. Instalando el notebook de IPython.'},\n", " {u'cell_type': u'markdown',\n", " u'metadata': {u'internals': {u'slide_helper': u'subslide_end',\n", " u'slide_type': u'subslide'},\n", " u'slide_helper': u'subslide_end',\n", " u'slideshow': {u'slide_type': u'subslide'}},\n", " u'source': u'**\\xbfQu\\xe9 necesito para hacerlo andar?**\\n\\n* Tornado\\n* ZeroMQ/PyZMQ\\n* Jinja2\\n* Chrome, Firefox, Safari (WebSockets, Flexible Box Model)\\n* Matplotlib para hacer figuras\\n* En linux, mac `pip install ipython[all]` o desde el `source` en **github**.\\n* Instalaci\\xf3n f\\xe1cil (incluyendo instaladores para win): Canopy, Anaconda, Python XY, WinPython, Pyzo\\n* Online: Wakari'},\n", " {u'cell_type': u'markdown',\n", " u'metadata': {u'slideshow': {u'slide_type': u'subslide'}},\n", " u'source': u\"**\\xbfC\\xf3mo lo hago andar?**\\n\\n* `cd` en el directorio conteniendo los archivos `ipynb` y simplemente tipeamos:\\n\\n```\\n(ipython_dev)damian@damian-Inspiron-1110:~$ ipython notebook\\n\\n2013-05-10 12:11:26.912 [NotebookApp] Using existing profile dir: u'/home/damian/.config/ipython/profile_default'\\n2013-05-10 12:11:26.937 [NotebookApp] Using MathJax from CDN: http://cdn.mathjax.org/mathjax/latest/MathJax.js\\n2013-05-10 12:11:26.984 [NotebookApp] The port 8888 is already in use, trying another random port.\\n2013-05-10 12:11:26.985 [NotebookApp] Serving notebooks from local directory: /home/damian\\n2013-05-10 12:11:26.985 [NotebookApp] The IPython Notebook is running at: http://127.0.0.1:8888/\\n2013-05-10 12:11:26.986 [NotebookApp] Use Control-C to stop this server and shut down all kernels.\\nSe ha abierto una nueva ventana en la sesi\\xf3n actual del navegador.\\n```\"}]" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "subslide" } }, "source": [ "* Genera archivos con la extensi\u00f3n `.ipynb` que se guardan en el directorio local.\n", "* En el notebook podemos almacenar:\n", " * C\u00f3digo\n", " * Texto (Markdown)\n", " * Equaciones (LaTeX)\n", " * Im\u00e1genes\n", " * Videos\n", " * HTML\n", "* Puede ser controlado por versiones.\n", "* Puede verse sin tener IPython instalado usando NBViewer: http://nbviewer.ipython.org/.\n", "* Puede ser exportado a otros formatos: Slides, HTML, Markdown, reStructured Text, LaTeX y PDF." ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "\u00bfCu\u00e1les son sus caracter\u00edsticas principales?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Interactivo" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Exploratorio" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Colaborativo" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "slide_end", "slideshow": { "slide_type": "fragment" } }, "source": [ "* Abierto" ] }, { "cell_type": "heading", "level": 2, "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "slide" } }, "source": [ "2. Instalando el notebook de IPython." ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "subslide" } }, "source": [ "**\u00bfQu\u00e9 necesito para hacerlo andar?**\n", "\n", "* Tornado\n", "* ZeroMQ/PyZMQ\n", "* Jinja2\n", "* Chrome, Firefox, Safari (WebSockets, Flexible Box Model)\n", "* Matplotlib para hacer figuras\n", "* En linux, mac `pip install ipython[all]` o desde el `source` en **github**.\n", "* Instalaci\u00f3n f\u00e1cil (incluyendo instaladores para win): Canopy, Anaconda, Python XY, WinPython, Pyzo\n", "* Online: Wakari" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**\u00bfC\u00f3mo lo hago andar?**\n", "\n", "* `cd` en el directorio conteniendo los archivos `ipynb` y simplemente tipeamos:\n", "\n", "```\n", "(ipython_dev)damian@damian-Inspiron-1110:~$ ipython notebook\n", "\n", "2013-05-10 12:11:26.912 [NotebookApp] Using existing profile dir: u'/home/damian/.config/ipython/profile_default'\n", "2013-05-10 12:11:26.937 [NotebookApp] Using MathJax from CDN: http://cdn.mathjax.org/mathjax/latest/MathJax.js\n", "2013-05-10 12:11:26.984 [NotebookApp] The port 8888 is already in use, trying another random port.\n", "2013-05-10 12:11:26.985 [NotebookApp] Serving notebooks from local directory: /home/damian\n", "2013-05-10 12:11:26.985 [NotebookApp] The IPython Notebook is running at: http://127.0.0.1:8888/\n", "2013-05-10 12:11:26.986 [NotebookApp] Use Control-C to stop this server and shut down all kernels.\n", "Se ha abierto una nueva ventana en la sesi\u00f3n actual del navegador.\n", "```" ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Interfase de usuario." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "* Dashboard\n", "* Menu\n", "* Barra de herramientas\n", "* \u00c1rea del notebook y celdas" ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Tipo de celdas" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "source": [ "* Code\n", "* Markdown\n", "* Raw text\n", "* Heading" ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "\"Shortcuts\" del teclado" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "slide_end", "slideshow": { "slide_type": "-" } }, "source": [ "* `Shift-Enter` para ejecutar una celda\n", "* `Ctrl-Enter` para ejecutar una celda y *quedarse* en esa misma celda.\n", "* `Alt-Enter` para ejecutar una celda e insertar una nueva celda por debajo de la *ejecutada*.\n", "* Todas las otras \"shortcuts\" tienen la forma: `Ctrl-m ?`\n", "* Si quer\u00e9s ver las \"shortcuts\" disponibles: `Ctrl-m h`" ] }, { "cell_type": "heading", "level": 2, "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "slide" } }, "source": [ "3. Usando el notebook de IPython." ] }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Trabajar con c\u00f3dido y correrlo:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El cl\u00e1sico `hola mundo`:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Hola PyConAr 2013\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Hola PyConAr 2013\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "La representaci\u00f3n de los objetos es m\u00e1s legible:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy.random import randn\n", "data = {i : randn() for i in range(7)}\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "{0: -0.6259264547607343,\n", " 1: -1.9360844606050993,\n", " 2: -0.6221479543727583,\n", " 3: -0.7468880564729741,\n", " 4: 0.1075606061430754,\n", " 5: 0.1633988022645425,\n", " 6: 0.25538102234980953}" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "y contrasten contra la siguiente representaci\u00f3n:" ] }, { "cell_type": "code", "collapsed": false, "input": [ ">>> from numpy.random import randn\n", ">>> data = {i : randn() for i in range(7)}\n", ">>> print data # peque\u00f1a trampa para verlo como se ver\u00eda en consola" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{0: 0.42552225254665826, 1: -0.5582616032586492, 2: -0.5835851552004485, 3: 1.708468060753579, 4: 0.363181597782086, 5: -0.33378775450148057, 6: 0.6321959132378161}\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Puedo probar \"pedacitos\" de c\u00f3digo (por ejemplo de tutoriales):" ] }, { "cell_type": "code", "collapsed": false, "input": [ ">>> the_world_is_flat = 1\n", ">>> if the_world_is_flat:\n", "... print \"Be careful not to fall off!\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Be careful not to fall off!\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "In [5]: [x*x for x in range(5)]" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[0, 1, 4, 9, 16]" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Puedo ver los errores o *tracebacks* de una manera simple y visualmente informativa:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%run non_existent_file" ], "language": "python", "metadata": { "internals": {}, "slideshow": { "slide_type": "-" } }, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR: File `u'non_existent_file.py'` not found.\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "x = 1\n", "y = 4\n", "z = y/(1-x)" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "integer division or modulo by zero", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Otras ventajas a la hora de trabajar con c\u00f3digo:\n", "\n", "* Completado por Tab" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy as sp" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "sp." ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* Ayuda integrada" ] }, { "cell_type": "code", "collapsed": false, "input": [ "?" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "def suma(a, b):\n", " \"\"\"\n", " Demo de una funci\u00f3n que suma cosas...\n", " \"\"\"\n", " return a + b" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "suma?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "suma??" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* \"Atajos\" interactivos (aliases, magics)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%magic" ], "language": "python", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "%lsmagic" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "json": [ "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"ruby\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"pypy\": \"Other\", \"python3\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"load\": \"CodeMagics\", \"psource\": \"NamespaceMagics\", \"lsmagic\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"ed\": \"Other\", \"pycat\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"clear\": \"KernelMagics\", \"colors\": \"BasicMagics\", \"prun\": \"ExecutionMagics\", \"pushd\": \"OSMagics\", \"rep\": \"Other\", \"config\": \"ConfigMagics\", \"dirs\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"who_ls\": \"NamespaceMagics\", \"install_profiles\": \"DeprecatedMagics\", \"macro\": \"ExecutionMagics\", \"autocall\": \"AutoMagics\", \"alias\": \"OSMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"rehashx\": \"OSMagics\", \"pprint\": \"BasicMagics\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"hist\": \"Other\", \"install_default_config\": \"DeprecatedMagics\", \"logoff\": \"LoggingMagics\", \"env\": \"OSMagics\", \"qtconsole\": \"KernelMagics\", \"load_ext\": \"ExtensionMagics\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"profile\": \"BasicMagics\", \"doctest_mode\": \"KernelMagics\", \"pylab\": \"PylabMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"pfile\": \"NamespaceMagics\", \"pinfo2\": \"NamespaceMagics\", \"pdef\": \"NamespaceMagics\", \"killbgscripts\": \"ScriptMagics\", \"who\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"matplotlib\": \"PylabMagics\", \"quickref\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"autosave\": \"KernelMagics\", \"less\": \"KernelMagics\", \"sc\": \"OSMagics\", \"automagic\": \"AutoMagics\", \"reset\": \"NamespaceMagics\", \"sx\": \"OSMagics\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"notebook\": \"BasicMagics\", \"pdb\": \"ExecutionMagics\", \"recall\": \"HistoryMagics\", \"unalias\": \"OSMagics\", \"unload_ext\": \"ExtensionMagics\", \"alias_magic\": \"BasicMagics\", \"reload_ext\": \"ExtensionMagics\", \"man\": \"KernelMagics\", \"rerun\": \"HistoryMagics\", \"debug\": \"ExecutionMagics\", \"logon\": \"LoggingMagics\", \"page\": \"BasicMagics\", \"pdoc\": \"NamespaceMagics\", \"history\": \"HistoryMagics\"}}" ], "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "Available line magics:\n", "%alias %alias_magic %autocall %automagic %autosave %bookmark %cd %clear %colors %config %connect_info %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %less %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %lsmagic %macro %magic %man %matplotlib %more %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %run %save %sc %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "%quickref" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "Pero tambien tenemos *cell magics* (`%%`) a trav\u00e9s de las cuales el notebook soporta correr c\u00f3digo en otros lenguajes (entre otras tareas:\n", "\n", "* R\n", "* Octave\n", "* Cython\n", "* Bash\n", "* Perl\n", "* Ruby\n", "* etc." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "y combinando *magics* las tareas son muy simples:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "mkdir temporal/\n", "touch temporal/mi_programa.py\n", "cat temporal/mi_programa.py" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile temporal/mi_programa.py\n", "def mi_funcion(x, y):\n", " \"\"\"\n", " Demo\n", " \"\"\"\n", " return x / y\n", "\n", "x = 4\n", "y = 2\n", "\n", "resultado = mi_funcion(x, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting temporal/mi_programa.py\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "%run temporal/mi_programa.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "resultado" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "slide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "2" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Puedo interaccionar f\u00e1cilmente con el sistema operativo:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!pwd" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/media/datos/Desarrollos/pyconar2013_talks\r\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "files = !ls\n", "print \"My current directory's files:\"\n", "print files" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "My current directory's files:\n", "['figs', 'IPython_fu_talk.ipynb', 'IPython_fu_talk.slides.html', 'README.md', 'soln', 'temporal', 'toggle.tpl']\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "!echo $files" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[figs, IPython_fu_talk.ipynb, IPython_fu_talk.slides.html, README.md, soln, temporal, toggle.tpl]\r\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "!echo {files[0].upper()}" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "FIGS\r\n" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "internals": {}, "slideshow": { "slide_type": "subslide" } }, "source": [ "Por ejemplo, puedo generar figuras program\u00e1ticamente utilizando matplotlib:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = np.linspace(0, 3*np.pi, 500)\n", "y = np.sin(x**2)\n", "\n", "fig = plt.figure()\n", "\n", "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n", "\n", "axes.plot(x, y, 'r')\n", "\n", "axes.set_xlabel('x')\n", "axes.set_ylabel('sin x^2')\n", "axes.set_title('Una figura simple');" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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QP7x1KM7xiQTfmSoy6cZhKRYVY0SywngUTgSI7O4dHR24+eabsWbNGhQXF+Pk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\noLy83LrggguiXkZs8MEHH1jTp0+3pkyZYl188cVWXV1d1Eu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"text": [ "" ] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "Documentar el flujo de trabajo." ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "source": [ "El texto reendea v\u00eda `Markdown`, por ejemplo puedo tener *italica*, **enf\u00e1sis** o linkear a [google](http://www.google.com).\n", "\n", "Puedo hacer listas:\n", "\n", "1. primero\n", "2. segundo\n", "3. tercero\n", "\n", "\n", "* auto\n", "* biblicleta\n", "* moto" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end", "slide_type": "subslide" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "subslide" } }, "source": [ "Puedo embeber c\u00f3digo para demostraci\u00f3n, por ejemplo, para Python:\n", "\n", " def f(x):\n", " \"\"\"a docstring\"\"\"\n", " return x**2\n", "\n", "o para otros lenguajes:\n", "\n", " if (i=0; i" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import SVG\n", "SVG(filename='pyconar2013_talks/figs/python-logo.svg')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "svg": [ "\n", " \n", " \n", " \n", " image/svg+xml\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "" ], "text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "* Video:" ] }, { "cell_type": "markdown", "metadata": { "internals": {}, "slideshow": { "slide_type": "-" } }, "source": [ "Puedo cargar y mostrar videos de Youtube, Vimeo, etc..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('MIAKOMzRl1I')" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "html": [ "\n", " \n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* HTML" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", "\n", "s = \"\"\"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Header 1Header 2
row 1, cell 1row 1, cell 2
row 2, cell 1row 2, cell 2
\"\"\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "h = HTML(s)\n", "display(h)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Header 1Header 2
row 1, cell 1row 1, cell 2
row 2, cell 1row 2, cell 2
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "subslide" } }, "source": [ "* Sitios web:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import IFrame\n", "IFrame('http://www.damian.oquanta.info',1024,768)" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "html": [ "\n", " \n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* M\u00fasica:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load soln/soundcloud.py\n", "\n", "from IPython.display import HTML\n", "h = HTML(\"\"\"\"\"\")\n", "display(h)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* Javascript" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Javascript\n", "\n", "# fetch d3 from cloudflare\n", "Javascript(\"\"\"$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')\"\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "%%html\n", "" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "%%javascript\n", "\n", "// This unhides the output area\n", "container.show();\n", "\n", "// element is the jQuery element we will append to\n", "var e = element.get(0);\n", " \n", "var diameter = 600,\n", " format = d3.format(\",d\");\n", "\n", "var pack = d3.layout.pack()\n", " .size([diameter - 4, diameter - 4])\n", " .value(function(d) { return d.size; });\n", "\n", "var svg = d3.select(e).append(\"svg\")\n", " .attr(\"width\", diameter)\n", " .attr(\"height\", diameter)\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(2,2)\");\n", "\n", "d3.json(\"files/flare.json\", function(error, root) {\n", " var node = svg.datum(root).selectAll(\".node\")\n", " .data(pack.nodes)\n", " .enter().append(\"g\")\n", " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n", " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n", "\n", " node.append(\"title\")\n", " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n", "\n", " node.append(\"circle\")\n", " .attr(\"r\", function(d) { return d.r; });\n", "\n", " node.filter(function(d) { return !d.children; }).append(\"text\")\n", " .attr(\"dy\", \".3em\")\n", " .style(\"text-anchor\", \"middle\")\n", " .text(function(d) { return d.name.substring(0, d.r / 3); });\n", "});\n", "\n", "d3.select(self.frameElement).style(\"height\", diameter + \"px\");" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "javascript": [ "\n", "// This unhides the output area\n", "container.show();\n", "\n", "// element is the jQuery element we will append to\n", "var e = element.get(0);\n", " \n", "var diameter = 600,\n", " format = d3.format(\",d\");\n", "\n", "var pack = d3.layout.pack()\n", " .size([diameter - 4, diameter - 4])\n", " .value(function(d) { return d.size; });\n", "\n", "var svg = d3.select(e).append(\"svg\")\n", " .attr(\"width\", diameter)\n", " .attr(\"height\", diameter)\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(2,2)\");\n", "\n", "d3.json(\"files/flare.json\", function(error, root) {\n", " var node = svg.datum(root).selectAll(\".node\")\n", " .data(pack.nodes)\n", " .enter().append(\"g\")\n", " .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n", " .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n", "\n", " node.append(\"title\")\n", " .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n", "\n", " node.append(\"circle\")\n", " .attr(\"r\", function(d) { return d.r; });\n", "\n", " node.filter(function(d) { return !d.children; }).append(\"text\")\n", " .attr(\"dy\", \".3em\")\n", " .style(\"text-anchor\", \"middle\")\n", " .text(function(d) { return d.name.substring(0, d.r / 3); });\n", "});\n", "\n", "d3.select(self.frameElement).style(\"height\", diameter + \"px\");" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 37 }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "Cargar c\u00f3digo remoto" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load http://matplotlib.sourceforge.net/mpl_examples/api/collections_demo.py" ], "language": "python", "metadata": { "internals": {}, "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "#!/usr/bin/env python\n", "'''Demonstration of LineCollection, PolyCollection, and\n", "RegularPolyCollection with autoscaling.\n", "\n", "For the first two subplots, we will use spirals. Their\n", "size will be set in plot units, not data units. Their positions\n", "will be set in data units by using the \"offsets\" and \"transOffset\"\n", "kwargs of the LineCollection and PolyCollection.\n", "\n", "The third subplot will make regular polygons, with the same\n", "type of scaling and positioning as in the first two.\n", "\n", "The last subplot illustrates the use of \"offsets=(xo,yo)\",\n", "that is, a single tuple instead of a list of tuples, to generate\n", "successively offset curves, with the offset given in data\n", "units. This behavior is available only for the LineCollection.\n", "\n", "'''\n", "\n", "import matplotlib.pyplot as plt\n", "from matplotlib import collections, transforms\n", "from matplotlib.colors import colorConverter\n", "import numpy as np\n", "\n", "nverts = 50\n", "npts = 100\n", "\n", "# Make some spirals\n", "r = np.array(range(nverts))\n", "theta = np.array(range(nverts)) * (2*np.pi)/(nverts-1)\n", "xx = r * np.sin(theta)\n", "yy = r * np.cos(theta)\n", "spiral = list(zip(xx,yy))\n", "\n", "# Make some offsets\n", "rs = np.random.RandomState([12345678])\n", "xo = rs.randn(npts)\n", "yo = rs.randn(npts)\n", "xyo = list(zip(xo, yo))\n", "\n", "# Make a list of colors cycling through the rgbcmyk series.\n", "colors = [colorConverter.to_rgba(c) for c in ('r','g','b','c','y','m','k')]\n", "\n", "fig, axes = plt.subplots(2,2)\n", "((ax1, ax2), (ax3, ax4)) = axes # unpack the axes\n", "\n", "\n", "col = collections.LineCollection([spiral], offsets=xyo,\n", " transOffset=ax1.transData)\n", "trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)\n", "col.set_transform(trans) # the points to pixels transform\n", " # Note: the first argument to the collection initializer\n", " # must be a list of sequences of x,y tuples; we have only\n", " # one sequence, but we still have to put it in a list.\n", "ax1.add_collection(col, autolim=True)\n", " # autolim=True enables autoscaling. For collections with\n", " # offsets like this, it is neither efficient nor accurate,\n", " # but it is good enough to generate a plot that you can use\n", " # as a starting point. If you know beforehand the range of\n", " # x and y that you want to show, it is better to set them\n", " # explicitly, leave out the autolim kwarg (or set it to False),\n", " # and omit the 'ax1.autoscale_view()' call below.\n", "\n", "# Make a transform for the line segments such that their size is\n", "# given in points:\n", "col.set_color(colors)\n", "\n", "ax1.autoscale_view() # See comment above, after ax1.add_collection.\n", "ax1.set_title('LineCollection using offsets')\n", "\n", "\n", "# The same data as above, but fill the curves.\n", "col = collections.PolyCollection([spiral], offsets=xyo,\n", " transOffset=ax2.transData)\n", "trans = transforms.Affine2D().scale(fig.dpi/72.0)\n", "col.set_transform(trans) # the points to pixels transform\n", "ax2.add_collection(col, autolim=True)\n", "col.set_color(colors)\n", "\n", "\n", "ax2.autoscale_view()\n", "ax2.set_title('PolyCollection using offsets')\n", "\n", "# 7-sided regular polygons\n", "\n", "col = collections.RegularPolyCollection(7,\n", " sizes = np.fabs(xx)*10.0, offsets=xyo,\n", " transOffset=ax3.transData)\n", "trans = transforms.Affine2D().scale(fig.dpi/72.0)\n", "col.set_transform(trans) # the points to pixels transform\n", "ax3.add_collection(col, autolim=True)\n", "col.set_color(colors)\n", "ax3.autoscale_view()\n", "ax3.set_title('RegularPolyCollection using offsets')\n", "\n", "\n", "# Simulate a series of ocean current profiles, successively\n", "# offset by 0.1 m/s so that they form what is sometimes called\n", "# a \"waterfall\" plot or a \"stagger\" plot.\n", "\n", "nverts = 60\n", "ncurves = 20\n", "offs = (0.1, 0.0)\n", "\n", "yy = np.linspace(0, 2*np.pi, nverts)\n", "ym = np.amax(yy)\n", "xx = (0.2 + (ym-yy)/ym)**2 * np.cos(yy-0.4) * 0.5\n", "segs = []\n", "for i in range(ncurves):\n", " xxx = xx + 0.02*rs.randn(nverts)\n", " curve = list(zip(xxx, yy*100))\n", " segs.append(curve)\n", "\n", "col = collections.LineCollection(segs, offsets=offs)\n", "ax4.add_collection(col, autolim=True)\n", "col.set_color(colors)\n", "ax4.autoscale_view()\n", "ax4.set_title('Successive data offsets')\n", "ax4.set_xlabel('Zonal velocity component (m/s)')\n", "ax4.set_ylabel('Depth (m)')\n", "# Reverse the y-axis so depth increases downward\n", "ax4.set_ylim(ax4.get_ylim()[::-1])\n", "\n", "\n", "plt.show()\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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UnL9hGGjAenC6Cep/NsRLYgSk5GDFXmzZgoQ8ZBTfir9kDcPbQLAxpD/TTfv3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UVHD06FF69uxJUFAQhw8fxtTUlJKS32u1jYsLrPYH/Bl/pPDyPTAd0MLVMjhmAl2VssL8HaJ7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\n2dkMDwzERaEgJGQsxcXbKC93OHVVXir7RnzkyBFCQkJ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sy/LGjRvlDh06XHJ+Nen3zz//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\nD7qg+sBqiE5ZAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "Reendeo de LaTeX:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Latex\n", "Latex(r\"\"\"\\begin{eqnarray}\n", "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n", "\\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \n", "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", "\\end{eqnarray}\"\"\")" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "latex": [ "\\begin{eqnarray}\n", "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \n", "\\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \n", "\\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n", "\\end{eqnarray}" ], "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "" ] } ], "prompt_number": 40 }, { "cell_type": "heading", "level": 3, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "Pandas" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas\n", "pandas.set_option('display.notebook_repr_html', True)" ], "language": "python", "metadata": { "internals": {}, "slideshow": { "slide_type": "-" } }, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile data.csv\n", "Date,Open,High,Low,Close,Volume,Adj Close\n", "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n", "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n", "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n", "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n", "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n", "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53" ], "language": "python", "metadata": { "internals": {}, "slideshow": { "slide_type": "-" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing data.csv\n" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pandas.read_csv('data.csv')\n", "df" ], "language": "python", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "slide_end", "slideshow": { "slide_type": "-" } }, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateOpenHighLowCloseVolumeAdj Close
0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50
1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26
2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48
3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99
4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12
5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ " Date Open High Low Close Volume Adj Close\n", "0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n", "1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n", "2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n", "3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n", "4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n", "5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The IPython kernel/client model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%connect_info" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{\n", " \"stdin_port\": 48475, \n", " \"ip\": \"127.0.0.1\", \n", " \"control_port\": 52500, \n", " \"hb_port\": 33886, \n", " \"signature_scheme\": \"hmac-sha256\", \n", " \"key\": \"5e9a6aa9-a8df-4dbf-b84c-f95749413f33\", \n", " \"shell_port\": 43361, \n", " \"transport\": \"tcp\", \n", " \"iopub_port\": 33941\n", "}\n", "\n", "Paste the above JSON into a file, and connect with:\n", " $> ipython --existing \n", "or, if you are local, you can connect with just:\n", " $> ipython --existing kernel-a70891d2-bb7d-4c82-82d4-829a9883aa7b.json \n", "or even just:\n", " $> ipython --existing \n", "if this is the most recent IPython session you have started.\n" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "%qtconsole" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "heading", "level": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "4. `nbconvert`" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Intro..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `NBConvert` es una biblioteca de *reciente* (`tech preview`) incorporaci\u00f3n al n\u00facleo de IPython, y que permite la *nbconversi\u00f3n* de los notebooks de IPython a otros formatos tales como: `html`, `latex`, `markdown`, `python`, `rst` y `slides`.\n", "\n", "* Esta biblioteca esta basada en el sistema de `templates` de Jinja, por lo que modificar los nbconversores (o escribir el propio) no deber\u00eda generar mayores dificultades." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Para utilizarlo desde la linea de comandos:\n", "\n", "`$ ipython nbconvert `\n", "\n", "y para solicitar ayuda: \n", "\n", "`$ ipython nbconvert --help` (y si quer\u00e9s m\u00e1s detalle: `--help-all`)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "ipython nbconvert IPython_fu_talk.ipynb --to html" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "[NbConvertApp] Using existing profile dir: u'/home/damian/.config/ipython/profile_default'\n", "[NbConvertApp] Converting notebook IPython_fu_talk.ipynb to html\n", "[NbConvertApp] Support files will be in IPython_fu_talk_files/\n", "[NbConvertApp] Loaded template html_full.tpl\n", "[NbConvertApp] Writing 480087 bytes to IPython_fu_talk.html\n" ] } ], "prompt_number": 47 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Slides" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Este nbconversor genera una presentacion HTML basada en la popular bibloteca Reveal.js\n", "\n", "* Slides (horizontales).\n", "* Subslides (verticales).\n", "* Fragmentos.\n", "* Transiciones.\n", "* Temas.\n", "* Notas.\n", "* Exportacion a pdf." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%bash\n", "ipython nbconvert IPython_fu_talk.ipynb --to slides" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "[NbConvertApp] Using existing profile dir: u'/home/damian/.config/ipython/profile_default'\n", "[NbConvertApp] Converting notebook IPython_fu_talk.ipynb to slides\n", "[NbConvertApp] Support files will be in IPython_fu_talk_files/\n", "[NbConvertApp] Loaded template slides_reveal.tpl\n", "[NbConvertApp] Writing 485430 bytes to IPython_fu_talk.slides.html\n" ] } ], "prompt_number": 48 }, { "cell_type": "markdown", "metadata": {}, "source": [ "M\u00e1s info: [http://www.damian.oquanta.info/categories/reveal.html](http://www.damian.oquanta.info/categories/reveal.html)" ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Usando la api de `nbconvert`... para bloguear con IPython y Nikola." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos tomar como ejemplo el plugin `ipynb` presente en Nikola:\n", "\n", "```\n", "def compile_html(self, source, dest, is_two_file=True):\n", " if flag is None:\n", " req_missing(['ipython>=1.0.0'], 'build this site (compile ipynb)')\n", " makedirs(os.path.dirname(dest))\n", " HTMLExporter.default_template = 'basic'\n", " c = Config(self.site.config['IPYNB_CONFIG'])\n", " exportHtml = HTMLExporter(config=c)\n", " with codecs.open(dest, \"w+\", \"utf8\") as out_file:\n", " with codecs.open(source, \"r\", \"utf8\") as in_file:\n", " nb = in_file.read()\n", " nb_json = nbformat.reads_json(nb)\n", " (body, resources) = exportHtml.from_notebook_node(nb_json)\n", " out_file.write(body)\n", "``` " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "" ] }, { "cell_type": "heading", "level": 2, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "5. Discusi\u00f3n." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El notebook de IPython provee toda la infraestructura para:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "1- Obtener **datos**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "2- **Procesar** los datos obtenidos." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "3- **Visualizar** los datos procesados" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "4- **Elaborar** una **historia** que se **sustenta** en los datos analizados." ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "subslide_end", "slideshow": { "slide_type": "fragment" } }, "source": [ "5- **Comunicar** dicha **historia** (junto con los **datos** y las **visualizaciones** obtenidas)." ] }, { "cell_type": "heading", "level": 2, "metadata": { "internals": { "slide_type": "subslide" }, "slideshow": { "slide_type": "slide" } }, "source": [ "6. El fututo del notebook the IPython" ] }, { "cell_type": "markdown", "metadata": { "internals": { "slide_helper": "subslide_end" }, "slide_helper": "slide_end", "slideshow": { "slide_type": "-" } }, "source": [ "Para conocer en forma detallada lo lineamientos a futuro: \n", "\n", "[https://github.com/ipython/ipython/wiki/Roadmap:-IPython](https://github.com/ipython/ipython/wiki/Roadmap:-IPython)\n", "\n", "* Soporte para multiusuarios\n", "* Soporte para m\u00faltiples directorios\n", "* Widgets interactivos (js)\n", "* Exportaci\u00f3n mejorada de los notebook a otros formatos\n", "* Modo presentaci\u00f3n\n", "\n", "Y no se olviden de pasear por la galer\u00eda de notebooks de IPython disponible en: \n", "\n", "[https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ " [\u00a1GRACIAS!](\u00a1GRACIAS!)\n", "\n", " [Dami\u00e1n Avila](http://www.damian.oquanta.info)\n", "\n", " [@damian_avila](http://twitter.com/damian_avila)\n", "\n", " [damianavila](https://github.com/damianavila)\n", "\n", " info@oquanta.info" ] } ], "metadata": {} } ] }