magic-keywords-slides.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Magic Keywords\n",
    "\n",
    "Magic keywords are special commands you can run in cells that let you control the notebook itself or perform system calls such as changing directories. \n",
    "Magic commands are preceded with one or two percent signs `(% or %%)` for line magics and cell magics, respectively. Line magics apply only to the line the magic command is written on, while cell magics apply to the whole cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Timing Code\n",
    "\n",
    "- %timeit allows you to time the instruction in the current line\n",
    "- %%timeit allows youto time the current cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "\n",
    "def fibo_recursive(n):\n",
    "    if n == 0:\n",
    "        return 0\n",
    "    elif n == 1:\n",
    "        return 1\n",
    "    return fibo_recursive(n-1) + fibo_recursive(n - 2)\n",
    "\n",
    "def fibo(n):\n",
    "    return ((1+sqrt(5))**n-(1 - sqrt(5))**n)/(2**n*sqrt(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.07 ms ± 37 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fibo_recursive(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "992 ns ± 16.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit fibo(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137 µs ± 720 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "rolls= [random.randint(1,6) for _ in range(100)]\n",
    "prize = sum(roll if roll%2 == 0 else -1 for roll in rolls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Embedding Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available matplotlib backends: ['ipympl', 'qt4', 'tk', 'wx', 'qt', 'inline', 'nbagg', 'osx', 'notebook', 'gtk3', 'gtk', 'agg', 'qt5', 'widget']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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oeD1dvXZWzUgY/dhxOdBRr/psu5nr5qfQ3NHLu0dr3X4vjXloYdCMn4rdEBB23jDMEZPs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\nngWWAIXN1inElRMF+AMwWYLUKG3jgrZZVYtUtcZ7uQlXAyO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137ca908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,1,300)\n",
    "for w in range(2,6,2):\n",
    "    plt.plot(x, np.sin(np.pi*x)*np.sin(2*w*np.pi*x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Debugging in the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned ON\n"
     ]
    }
   ],
   "source": [
    "%pdb on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-62ac3a1676e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hello'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m<ipython-input-14-62ac3a1676e0>\u001b[0m(2)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m      1 \u001b[0;31m\u001b[0mnumbers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hello'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> q\n"
     ]
    }
   ],
   "source": [
    "numbers = 'hello'\n",
    "sum(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "%pdb off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Changing the current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/fabiofumarola\n"
     ]
    }
   ],
   "source": [
    "%cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## List the Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ARCHFLAGS': '-arch x86_64',\n",
       " 'Apple_PubSub_Socket_Render': '/private/tmp/com.apple.launchd.d2u1DKmAut/Render',\n",
       " 'CLICOLOR': '1',\n",
       " 'COLORFGBG': '7;0',\n",
       " 'COLORTERM': 'truecolor',\n",
       " 'CONDA_DEFAULT_ENV': 'tflow',\n",
       " 'CONDA_PATH_BACKUP': '/Users/fabiofumarola/anaconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/TeX/texbin:/Users/fabiofumarola/.rvm/bin',\n",
       " 'CONDA_PREFIX': '/Users/fabiofumarola/anaconda3/envs/tflow',\n",
       " 'CONDA_PS1_BACKUP': '%{$fg_bold[cyan]%}$ZSH_THEME_CLOUD_PREFIX %{$fg_bold[green]%}%p %{$fg[green]%}%c %{$fg_bold[cyan]%}$(git_prompt_info)%{$fg_bold[blue]%} % %{$reset_color%}',\n",
       " 'GIT_PAGER': 'cat',\n",
       " 'HOME': '/Users/fabiofumarola',\n",
       " 'ITERM_PROFILE': 'Default',\n",
       " 'ITERM_SESSION_ID': 'w0t0p0:79DEE282-EA9E-470F-8A29-C26DCF8D40B6',\n",
       " 'JAVA_HOME': '/Library/Java/JavaVirtualMachines/jdk1.8.0_112.jdk/Contents/Home',\n",
       " 'JPY_PARENT_PID': '2239',\n",
       " 'LANG': 'en_US.UTF-8',\n",
       " 'LC_CTYPE': 'UTF-8',\n",
       " 'LESS': '-R',\n",
       " 'LOGNAME': 'fabiofumarola',\n",
       " 'LSCOLORS': 'Gxfxcxdxbxegedabagacad',\n",
       " 'MPLBACKEND': 'module://ipykernel.pylab.backend_inline',\n",
       " 'OLDPWD': '/Users/fabiofumarola/git',\n",
       " 'PAGER': 'cat',\n",
       " 'PATH': '/Users/fabiofumarola/anaconda3/envs/tflow/bin:/Users/fabiofumarola/anaconda3/envs/tflow/bin:/Users/fabiofumarola/anaconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/TeX/texbin:/Users/fabiofumarola/.rvm/bin',\n",
       " 'PS1': '(tflow) %{$fg_bold[cyan]%}$ZSH_THEME_CLOUD_PREFIX %{$fg_bold[green]%}%p %{$fg[green]%}%c %{$fg_bold[cyan]%}$(git_prompt_info)%{$fg_bold[blue]%} % %{$reset_color%}',\n",
       " 'PWD': '/Users/fabiofumarola/git/jupyter_tutorial',\n",
       " 'SHELL': '/bin/zsh',\n",
       " 'SHLVL': '1',\n",
       " 'SSH_AUTH_SOCK': '/private/tmp/com.apple.launchd.2PPeZ4GDNk/Listeners',\n",
       " 'TERM': 'xterm-color',\n",
       " 'TERM_PROGRAM': 'iTerm.app',\n",
       " 'TERM_PROGRAM_VERSION': '3.1.1',\n",
       " 'TERM_SESSION_ID': 'w0t0p0:79DEE282-EA9E-470F-8A29-C26DCF8D40B6',\n",
       " 'TMPDIR': '/var/folders/hh/r3v5z7qx5_jft6kwzkyxh9_c0000gn/T/',\n",
       " 'USER': 'fabiofumarola',\n",
       " 'XPC_FLAGS': '0x0',\n",
       " 'XPC_SERVICE_NAME': '0',\n",
       " 'ZSH': '/Users/fabiofumarola/.oh-my-zsh',\n",
       " '_': '/Users/fabiofumarola/anaconda3/envs/tflow/bin/jupyter',\n",
       " '__CF_USER_TEXT_ENCODING': '0x1F5:0x0:0x0'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: usage is 'set_env var=val'\n"
     ]
    }
   ],
   "source": [
    "#to the set an env variable\n",
    "%set_env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Call Pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The following command must be run outside of the IPython shell:\n",
      "\n",
      "    $ pip \n",
      "\n",
      "The Python package manager (pip) can only be used from outside of IPython.\n",
      "Please reissue the `pip` command in a separate terminal or command prompt.\n",
      "\n",
      "See the Python documentation for more informations on how to install packages:\n",
      "\n",
      "    https://docs.python.org/3/installing/\n"
     ]
    }
   ],
   "source": [
    "%pip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Bonus: run shell commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/fabiofumarola/git/jupyter_tutorial\n"
     ]
    }
   ],
   "source": [
    "%cd ~/git/jupyter_tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keyboard-shortcuts.ipynb          magic-keywords-slides.slides.html working-with-cells.ipynb\r\n",
      "magic-keywords-slides.ipynb       magic-keywords.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda env:tflow]",
   "language": "python",
   "name": "conda-env-tflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}