ConfigTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "No module named jp_mpl",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-da6fc537ba15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'pylab inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mjp_mpl\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mjplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: No module named jp_mpl"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import jp_mpl as jplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gUkn+OGk1PcYsZNt+NX0TyQv+HPGnA88652KBFkAfM4u9YMw2IME5Vx94FRh9wfqbnXON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\nEmIU/CIiIeb/A7tDnA88g5hXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7a8b05850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot([3,2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95061e7790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}