jupyter-shortcut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python course on Classes and Functional Programming\n",
    "\n",
    "### *J.A. Hernando, USC, 2016*\n",
    "\n",
    "## Apendix: About Jypiter notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Last Execution  Tue Oct  8 15:16:25 2019\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "print(' Last Execution ', time.asctime())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupiter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupiter notebook is a document (with .ipypy extensions) that is a notebook to preserve text, images, links, code (in particular Python code). It can be bwroser and edit with your favorite web browser!\n",
    "\n",
    "The notebook is composed of cells, each cell can contain text, code. The code is executable.\n",
    "The notebook has a kernel (i.e Python -see top right corner on the browser-) that is the engine that executes the code.\n",
    "\n",
    "You can open a notebook in the console with:\n",
    "\n",
    "jupyter notebook\n",
    "\n",
    "It will open your browser and display the documents in the current directory, including the notebooks. You can reopen or run a notebook or you can create a new notebook from the meny on the top row of your browser.\n",
    "\n",
    "To add a new cell click the '+' symbol, and select in the meny display the type of cell: Markdown, heading, code, etc. You can now edit the cell.\n",
    "\n",
    "Notebooks are automatically saved every few minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Image\n",
    "display(Image('img/classes_car.png',width=300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding web frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"https://ipython.org/ipython-doc/3/interactive/magics.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x110b53668>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('https://ipython.org/ipython-doc/3/interactive/magics.html', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding latex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markdown (https://guides.github.com/features/mastering-markdown/#intro) cells allow you to write *Latex* formulas. \n",
    "> Edit and check the next cell to see its output!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(A \\mid B) = \\frac{P(B \\mid A) \\, P(A)}{P(B)} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\n",
       "This is a latex equation: $x_i = g_{ij} x^j$\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\n",
    "This is a latex equation: $x_i = g_{ij} x^j$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell using the %%latex symbol, process the text in *Latex*\n",
    "\n",
    "But what are these %% symbols? This is the magic!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The magic "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some magic commands that can be executed in a code cell that extend the simple python code. \n",
    "\n",
    "This commands are preceeding by the % (line magic) or %% (cell magic) symbols.\n",
    "\n",
    "See the documentation of all magic commands at (http://ipython.readthedocs.io/en/stable/interactive/magics.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %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  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this list the magic commands\n",
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### interacting with the shell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FunctProg-example.ipynb           complex_test.py\r\n",
      "FunctProg-iterators.ipynb         \u001b[34mdata\u001b[m\u001b[m/\r\n",
      "Functions-intro.ipynb             expert_level_miscellaneous.ipynb\r\n",
      "Object-Oriented.ipynb             functprog_iterators.ipynb\r\n",
      "README.md                         \u001b[34mimg\u001b[m\u001b[m/\r\n",
      "\u001b[34m__pycache__\u001b[m\u001b[m/                      jupyter-shortcut.ipynb\r\n",
      "classes-complex.ipynb             panda-shortcut.ipynb\r\n",
      "classes-exercises.ipynb           plt-I-shortcut.ipynb\r\n",
      "classes-inheritance.ipynb         plt-II-shortcut.ipynb\r\n",
      "classes-intro.ipynb               plt-shortcut.ipynb\r\n",
      "classes-more.ipynb                python-intro.ipynb\r\n",
      "classes.ipynb                     shapes.py\r\n",
      "complex.py\r\n"
     ]
    }
   ],
   "source": [
    "# list the files in the current directoy\n",
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FunctProg-example.ipynb          complex_test.py\r\n",
      "FunctProg-iterators.ipynb        \u001b[34mdata\u001b[m\u001b[m\r\n",
      "Functions-intro.ipynb            expert_level_miscellaneous.ipynb\r\n",
      "Object-Oriented.ipynb            functprog_iterators.ipynb\r\n",
      "README.md                        \u001b[34mimg\u001b[m\u001b[m\r\n",
      "\u001b[34m__pycache__\u001b[m\u001b[m                      jupyter-shortcut.ipynb\r\n",
      "classes-complex.ipynb            panda-shortcut.ipynb\r\n",
      "classes-exercises.ipynb          plt-I-shortcut.ipynb\r\n",
      "classes-inheritance.ipynb        plt-II-shortcut.ipynb\r\n",
      "classes-intro.ipynb              plt-shortcut.ipynb\r\n",
      "classes-more.ipynb               python-intro.ipynb\r\n",
      "classes.ipynb                    shapes.py\r\n",
      "complex.py\r\n"
     ]
    }
   ],
   "source": [
    "# equivalent\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYCOURSE=/Users/hernando/Docencia/Master/Fisica_Computacional/notebooks\n",
      "PYTHONPATH =  /Users/hernando/investigacion/NEXT/work/IC:/Users/hernando/Docencia/Master/Fisica_Computacional/notebooks\n"
     ]
    }
   ],
   "source": [
    "# setting an enviroment variable\n",
    "%env PYCOURSE=/Users/hernando/Docencia/Master/Fisica_Computacional/notebooks\n",
    "\n",
    "import os\n",
    "os.environ['PYTHONPATH'] = os.environ['PYTHONPATH']+':'+os.environ['PYCOURSE']\n",
    "print('PYTHONPATH = ', os.environ['PYTHONPATH'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load, write, run python code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load complex.py\n",
    "\n",
    "import math\n",
    "\n",
    "\n",
    "class Complex:\n",
    "\n",
    "    def __init__(self, real, img):\n",
    "        \"\"\" constructor of a complex number.\n",
    "        real is the real part\n",
    "        img is the imaginary part\n",
    "        \"\"\"\n",
    "        self.real = real\n",
    "        self.img = img\n",
    "\n",
    "    def module(self):\n",
    "        \"\"\" return the module of the complex number\n",
    "        \"\"\"\n",
    "        return math.sqrt(self.real*self.real + self.img*self.img)\n",
    "\n",
    "    def phase(self):\n",
    "        \"\"\" return the phase of the complex number\n",
    "        \"\"\"\n",
    "        m = self.module()\n",
    "        xcos = self.real/m\n",
    "        phas = math.acos(xcos)\n",
    "        return phas\n",
    "\n",
    "    def __add__(self, x):\n",
    "        \"\"\" return a complex number that is this complex + x\n",
    "        \"\"\"\n",
    "        real = self.real+x.real\n",
    "        img = self.img+x.img\n",
    "        return Complex(real, img)\n",
    "\n",
    "    def __sub__(self, x):\n",
    "        \"\"\" return a complex number that is this complex - x\n",
    "        \"\"\"\n",
    "        real = self.real - x.real\n",
    "        img = self.img - x.img\n",
    "        return Complex(real, img)\n",
    "\n",
    "    def __mul__(self, x):\n",
    "        \"\"\" return a complex number that is this complex * x\n",
    "        \"\"\"\n",
    "        real = self.real*x.real - self.img*x.img\n",
    "        img = self.real*x.img + self.img*x.real\n",
    "        return Complex(real, img)\n",
    "\n",
    "    def __str__(self):\n",
    "        \"\"\" convert this complex into a string\n",
    "        \"\"\"\n",
    "        if (self.img >= 0):\n",
    "            s = str(self.real) + '+' + str(self.img) + 'j'\n",
    "        else:\n",
    "            s = str(self.real) + str(self.img) + 'j'\n",
    "        return s\n",
    "\n",
    "    def __repr__(self):\n",
    "        return str(self)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting complex_test.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile complex_test.py\n",
    "#writting this code into the module complex_tex.py\n",
    "\n",
    "from complex import Complex\n",
    "\n",
    "a = Complex(1, 1)\n",
    "b = Complex(1, -1)\n",
    "\n",
    "print('a', a, '|a|', a.module())\n",
    "print(a, ' + ', b, ' = ', a+b)\n",
    "print(a, ' * ', b, ' = ', a*b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note*: When using %% magic cell, they should be the first line in the cell otherwise you get a syntax error!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1+1j |a| 1.4142135623730951\n",
      "1+1j  +  1-1j  =  2+0j\n",
      "1+1j  *  1-1j  =  2+0j\n"
     ]
    }
   ],
   "source": [
    "# runing the code\n",
    "%run ./complex_test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 µs, sys: 1 µs, total: 9 µs\n",
      "Wall time: 12.2 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2+0j"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# time the operator * (one time only)\n",
    "%time a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "944 ns ± 41.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# time the operator * (several times)\n",
    "%timeit a*b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note*: when running %%timeit the notebook got frozen for a moment. it showed a [] with a * and on the top of the web page, close to Python [ROOT] the cicle was dark. That indicates that the kernel is running. \n",
    "*Note*: You can stop the kernil clicking on the dark square on the menu on top! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting: Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is the standard module to plot, histogram and display data (http://matplotlib.org).\n",
    "To use it you need to add the following magic line (%matplotlib inline):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-colorblind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'probability to be the next USA president')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'Clinton':88, 'Trump':12}\n",
    "x = np.arange(len(data))\n",
    "bar_width = 0.4\n",
    "plt.bar(range(len(data)), data.values())\n",
    "plt.xticks(x + bar_width, data.keys());\n",
    "plt.title('probability to be the next USA president')\n",
    "# notice the ';' at the end. It captures the output of plt!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}