emacs-ipython-notebook/Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.5.1 |Anaconda 2.5.0 (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ae5e649470>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zURBjrqrqF4Ig1BeiLY1ny1a9+/nidj/9NLOv5UYniHxtXb9+loMHM/8eHg7m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\nCoVCcQBQwlyhUCgOAP8/SRqVY/ayDaQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae5dcdd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def plotnormal():\n",
    "    return plt.plot(np.random.randn(1000), np.random.randn(1000), 'o', alpha=0.3)\n",
    "plotnormal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Errors are shown in informative ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR: File `'non_existent_file.py'` not found.\n"
     ]
    }
   ],
   "source": [
    "%run non_existent_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-dc39888fd1d2>\u001b[0m in \u001b[0;36m<module>\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: division by zero"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "y = 4\n",
    "z = y/(1-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "ip = get_ipython()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installed hierarchymagic.py. To use it, type:\n",
      "  %load_ext hierarchymagic\n"
     ]
    }
   ],
   "source": [
    "%install_ext https://raw.github.com/anaderi/ipython-hierarchymagic/master/hierarchymagic.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext hierarchymagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "You need graphviz installed and on your PATH for the following to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 2] The system cannot find the file specified",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-9d6fb4693659>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hierarchy get_ipython()'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\millejoh\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[1;34m(self, arg_s)\u001b[0m\n\u001b[0;32m   2161\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2162\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2163\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2165\u001b[0m     \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line)\u001b[0m\n\u001b[0;32m   2082\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2083\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2084\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2085\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2086\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-126>\u001b[0m in \u001b[0;36mhierarchy\u001b[1;34m(self, parameter_s)\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\.ipython\\extensions\\hierarchymagic.py\u001b[0m in \u001b[0;36mhierarchy\u001b[1;34m(self, parameter_s)\u001b[0m\n\u001b[0;32m    245\u001b[0m                                graph_attrs={'rankdir': args.rankdir,\n\u001b[0;32m    246\u001b[0m                                             'size': '\"{0}\"'.format(args.size)})\n\u001b[1;32m--> 247\u001b[1;33m         \u001b[0mstdout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[0mdisplay_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\.ipython\\extensions\\hierarchymagic.py\u001b[0m in \u001b[0;36mrun_dot\u001b[1;34m(code, options, format)\u001b[0m\n\u001b[0;32m    111\u001b[0m         \u001b[1;31m# * http://stackoverflow.com/a/2935727/727827\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m         p = Popen(dot_args, stdout=PIPE, stdin=PIPE, stderr=PIPE,\n\u001b[1;32m--> 113\u001b[1;33m                   creationflags=0x08000000)\n\u001b[0m\u001b[0;32m    114\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdot_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds)\u001b[0m\n\u001b[0;32m    948\u001b[0m                                 \u001b[0mc2pread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc2pwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    949\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 950\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    951\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    952\u001b[0m             \u001b[1;31m# Cleanup if the child failed starting.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\millejoh\\Anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m   1218\u001b[0m                                          \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1219\u001b[0m                                          \u001b[0mcwd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1220\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m   1221\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1222\u001b[0m                 \u001b[1;31m# Child is launched. Close the parent's copy of those pipe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified"
     ]
    }
   ],
   "source": [
    "%hierarchy get_ipython()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "EIN also supports pretty printing from SymPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from sympy.interactive import printing\n",
    "printing.init_printing()\n",
    "\n",
    "import sympy as sym\n",
    "from sympy import *\n",
    "x, y, z = symbols(\"x y z\")\n",
    "\n",
    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "If you install px can you get the below to work?\n",
    "\n",
    "$a^2=b$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hy_cell": true,
    "ein.hycell": true,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Datas Science Tools (py36)",
   "name": "datascience"
  },
  "name": "Demo.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}