hirotsuru314/IPython勉強ノート.ipynb

## IPython勉強ノート.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython Notebookで何ができるのか"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython NotebookとはPythonをブラウザ上でインタラクティブ（対話的）に実行できるツール。ユーザは従来の、編集ーコンパイルー実行のサイクルから脱却し、実行ーデータ探索サイクルに乗ってデータ分析を進めることができる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "インストールはAnacondaで行うと、Numpy、SciPy、pandas、matplotlibなどのパッケージを一括インストール・管理できるため、便利。\n",
    "https://www.continuum.io/downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 電卓のように使える"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "四則演算や累乗だってできるし、Mathパッケージを読み込めば、いろんな関数も使える。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from math import sin, pi\n",
    "\n",
    "sin(pi/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 表示機能が充実"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Markdown記法が使えるので、計算やコードの実行とメモを同時に残すことができる。\n",
    "また、LaTeX記法が使えるので、ドルマークで囲ってやると数式が書ける。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 見出し１\n",
    "## 見出し２\n",
    "### 見出し３"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ \\sqrt{a+b} $\n",
    "\n",
    "$x^2 - 6x +1 = 0$\n",
    "\n",
    "$\\displaystyle \\lim_{x \\to \\infty} f(x)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### グラフも簡単に挿入できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f1307b8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f049ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "x = np.arange(0, 10, 0.1)\n",
    "y = np.cos(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## IPython Notebookで何ができるのか"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"IPython NotebookとはPythonをブラウザ上でインタラクティブ（対話的）に実行できるツール。ユーザは従来の、編集ーコンパイルー実行のサイクルから脱却し、実行ーデータ探索サイクルに乗ってデータ分析を進めることができる。"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"インストールはAnacondaで行うと、Numpy、SciPy、pandas、matplotlibなどのパッケージを一括インストール・管理できるため、便利。\n",
	"https://www.continuum.io/downloads"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"#### 電卓のように使える"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"5"
	]
	},
	"execution_count": 1,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"2+3"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"32"
	]
	},
	"execution_count": 2,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"2**5"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"四則演算や累乗だってできるし、Mathパッケージを読み込めば、いろんな関数も使える。"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"1.0"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"import math\n",
	"from math import sin, pi\n",
	"\n",
	"sin(pi/2)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"#### 表示機能が充実"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"collapsed": false
	},
	"source": [
	"Markdown記法が使えるので、計算やコードの実行とメモを同時に残すことができる。\n",
	"また、LaTeX記法が使えるので、ドルマークで囲ってやると数式が書ける。"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# 見出し１\n",
	"## 見出し２\n",
	"### 見出し３"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"$ \\sqrt{a+b} $\n",
	"\n",
	"$x^2 - 6x +1 = 0$\n",
	"\n",
	"$\\displaystyle \\lim_{x \\to \\infty} f(x)$"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"collapsed": true
	},
	"source": [
	"#### グラフも簡単に挿入できる。"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x10f1307b8>]"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XeYVeXV/vHvookCKvESew1YQH0JGBVLHAvSmxAF22vX\nGH9qTEyMMRHzatQkpBqjiQ1RxIIyNBXpYkUBAZFiVEAERAUVlSLz/P5YMxGRqac8+5x9f66LKzPD\n4ezbyTnr7L32UyyEgIiIpEu92AFERCT/VPxFRFJIxV9EJIVU/EVEUkjFX0QkhVT8RURSKCvF38zu\nMbOVZja7isf8zcwWmdksM2ubjeOKiEjdZOvM/z6gU2V/aWZdgO+GEFoBlwB3Zum4IiJSB1kp/iGE\nacDqKh7SC3ig/LEvAzuY2S7ZOLaIiNRevnr+ewBLN/t+WfnPREQkAt3wFRFJoQZ5Os4yYK/Nvt+z\n/GffYmZabEhEpJZCCFabx2fzzN/K/2zNSOAcADM7ClgTQlhZ2ROFEAgh8PHHgRtuCOy0U+BHPwp8\n+WX479+l4c8NN9wQPUMS/uj38O3fxauvBvbbL9CtW2DChEBZWeX/pqwsMHp0oHXrQElJYObM+P8d\nel1k909dZGuo51DgBeAAM1tiZueZ2SVmdnF5MR8LvGNmbwF3AZfV5HmbN4eBA+Gtt+Cjj6CkBJYv\nz0ZikcIUAvzrX9C5M9x2G4weDSeeCFbFOZ8ZdOsGr78O/ftDx47w5JP5yyzJlJW2TwjhjBo85vK6\nPv+OO8KwYXDzzXDEEf7CPfzwuj6bSGHauBFKS+Grr2DaNDjwwNr9+wYN4JJL/L3TsycsXQpXXJGb\nrJJ8+er5Z8wMrr8eWreGLl1g+HD4wQ9ip8qtkpKS2BESQb8HKCuD886Dxo1LmDABmjSp+3O1bw/P\nP+/vo8WL4Y9/rPrKIan0usiM1bVflCtmFqrL9OyzcNZZMGUKHHRQnoKJRBIC/OQn8NprMG4cbLtt\ndp539Wo45RTo1ctPrKRwmRkh4g3fvOnY0fudXbvCykpvG4sUh9/9DiZNglGjslf4we+pjRzp9xAe\nfzx7zyuFoWDaPls691y/ZO3eHSZPzuwyWCSpHnwQ7r3Xe/w77pj9599tN7+PcMopsN9+3hKSdCjI\ntk+FEPxDoKwMhgzJbS6RfHvrLejQASZOhEMPze2xnngCrrwSXn4Zdt89t8eS7EtN26eCGdxxh79g\nddkqxWTjRjjjDPj1r3Nf+AFOPRUuuADOP99PqqT4FfSZf4WXXvKbVrNm+WWsSKH71a9g5kwYMyZ/\nI3E2boSjjoLLLvMPAikcdTnzL4riD36GNGOGT3opxGFrIhWmTPHJWLNmwS55Xvt2zhyfNDZjBuy1\nV/WPl2RIXdtnc7/+NaxYAf/+d+wkInW3bp2P57/77vwXfvAW05VXwkUXqf1T7IrmzB/gjTfg+ONh\n3jxo0SLLwUTy4P/+z8/4hw+Pl6Gi/fOjH8GFF8bLITWX6rZPhauvhrVrfeyySCFZvBjatfPJXPvu\nGzfL7Nlw8smwYIHPB5BkU/EH1qzxWb9PPw1ttVOwFJB+/bztcsMNsZO4Sy6Bpk1h0KDYSaQ6Kv7l\n7rzTF4KbNEk3f6UwTJjgLZZ587I7izcTK1dCmzY+lPq7342dRqqS6hu+m7vwQvj447h9U5Ga2rjR\nV9f805+SU/jBbzhffTVce23sJJILRVn8GzSAv/wFrrnGR0+IJNmQIT5AoXfv2Em+7Sc/8TP/adNi\nJ5FsK8riDz5W+ZBDNPRTkm3DBh/h89vfJrNFue22vrDcT3/qy6hI8Sja4g++C9htt+nsX5Jr8GBo\n1QqOOy52ksqdcYZ/SI0eHTuJZFNRF//27X3Ezz33xE4i8m3r18NNN8GNN8ZOUrV69XwS5U03aeJX\nMSnq4g8+bO7WW/2NJpIk997ro2k6dIidpHq9e8Pnn/tGSlIcinKo55a6dvU9Sy+9NKtPK1Jn69ZB\ny5a+lPIRR8ROUzNDh8I//wlTpybz/kSaaahnJW64AW65xfuWIklwzz3ekiyUwg9w2mm+ftbUqbGT\nSDakovgfeSQcfLDfXBOJbdMmH9P/y1/GTlI7DRp45ptuip1EsiEVxR98osqgQRquJvGVlsLOO8PR\nR8dOUntnnQWLFvkeGlLYUlP8jz/exyw/80zsJJJ2gwb5uPlC7Js3auTZtd5P4UvFDd8KDzzgG2KP\nG5eTpxep1ksvwYABfvbcoEHsNHXz2We+6ujMmbD33rHTCOiGb7VOP913Kpo7N3YSSas//Qmuuqpw\nCz9As2Zwzjnwj3/ETiKZSNWZP/hU+iVLtOyD5N8778Dhh8O773oBLWT/+Y8PpFiyBLbbLnYa0Zl/\nDVx6KTz+OKxaFTuJpM1f/+oboxd64Qdf4vnoo72NKoUpdWf+4PuT7r23T1kXyYe1a/019/rrxbMx\n+oQJvt/vnDmFefO6mOjMv4auvNJnKm7cGDuJpMWwYb54W7EUfvCVc8E/BKTwpLL4H3KIX7ZqlULJ\nlzvvLL7lRcx8E5q//S12EqmLVBZ/gIsv1ibvkh+vvgoffQSnnBI7SfadeaZv9PLee7GTSG2ltvj3\n6wfTp/vIC5FcuusuP9moXz92kuxr0sSHUN93X+wkUlupvOFb4aqroGlTrVUiufPJJz4hav583xO3\nGM2YAaeeCm+/7Wv/S/7phm8tXXSRr6muG7+SKw8+CB07Fm/hB2jXDr7zHRg/PnYSqY1UF/82bWD/\n/WHMmNhJpBiF4C2fYrvRuzUXXaSJk4Um1cUfdONXcueVV+DLL+GEE2Inyb0zzvAz/w8+iJ1Eair1\nxf+HP/Q36eLFsZNIsbn/fjjvvHRMgNphB9/qUXtmFI5U3/Ct8OMfw267wfXX5/WwUsTWrYM99oBZ\ns4prYldVXnjBP+zmz0/HB16S6IZvHZ1zji/3nLDPQSlgpaV+IzQthR98I/r69eH552MnkZpQ8cf3\nUa1XT7sTSfYMHgznnhs7RX6Z+YnUkCGxk0hNqO1T7ne/8+Vp77wz74eWIvP++76EyHvvpW+546VL\nfWP6ZcugcePYadJDbZ8MnH02PPaY92pFMvHggz7pKW2FH7zN1bathk8XAhX/cnvtBd/7HowaFTuJ\nFLIQfJRP2lo+mzv7bL+HJsmm4r+Zc87RUDXJzKuvwoYNcMwxsZPE07cvTJkCH34YO4lURcV/M6ee\n6isUrlwZO4kUqgce8JOINA91bNYMunWDRx6JnUSqouK/maZNoVcvGDo0dhIpRF99BY8+6rNd006t\nn+TLSvE3s85mNt/MFprZL7by98eb2Rozm1H+J7HTqc46S8Vf6mbiRNhnH2jZMnaS+E4+2UfPLVgQ\nO4lUJuPib2b1gNuBTkAbYICZHbSVh04NIbQr/5PYRZRPOMFftG+9FTuJFJqHH4YBA2KnSIYGDfx3\n8dBDsZNIZbJx5n8EsCiEsDiEsBEYBvTayuMKogvaoIGv9zNsWOwkUkjWrYMRI3xjE3EDBvj7KGFT\niaRcNor/HsDSzb5/r/xnW+pgZrPMbIyZtc7CcXOm4kUrUlNjx/pQ4d13j50kOQ4/HDZtgpkzYyeR\nrWmQp+O8BuwdQvjCzLoAI4ADKnvwwIED//t1SUkJJSUluc73DR06wKefwpw5cOiheT20FKiHH9aN\n3i2ZQf/+PuqnXbvYaYrL5MmTmTx5ckbPkfHyDmZ2FDAwhNC5/PtrgRBCuK2Kf/MO0D6E8PFW/i7K\n8g5b+vnPoWFDuPnm2Ekk6T791CcJvvsuNG8eO02yzJ4NPXr47ybNw19zLdbyDtOBlma2j5k1AvoD\nI7cItstmXx+Bf+h8q/AnifqVUlMjRsDxx6vwb82hh/om71o0MXkyLv4hhE3A5cA44A1gWAjhTTO7\nxMwuLn9YPzOba2Yzgb8Aib8t1rat3/ydPj12Ekk6tXwqV9H60T205NGqnlUYOBA++QT+/OfYSSSp\nPv4Y9tvPV/Js0iR2mmRasABKSnyV0/r1Y6cpTlrVM8v69/cZm2VlsZNIUo0YAR07qvBX5cADYddd\n4bnnYieRzan4V+Ggg2CnneDFF2MnkaR67DGfFyJVU+snedT2qcZvfwurV6v1I99W0fJZtszXhZLK\nvfOO75i3fLnfS5PsUtsnB/r1g8cfV+tHvq201NewUeGv3n77+bpHU6fGTiIVVPyr0bq1L1GrUT+y\nJbV8aqdvXxg+PHYKqaC2Tw385jfwxRfwxz/GTiJJsXo17Luvj2Bp1ix2msKwcKHPh9Con+xT2ydH\nKlo/CftMkohGjoQTT1Thr40DDoAWLeCFF2InEVDxr5FDD4VGjeC112InkaRQy6du1PpJDrV9aui6\n6/ym7623xk4isa1Z4zcv1fKpvXnzoFMnWLwY6unUM2vU9smhfv38bC+Bn0uSZ6NHe+9ahb/2Wrf2\n0VEaQBGfin8Nfe97/r+zZsXNIfE9+SScemrsFIWr4h6axKXiX0Nm0KePv/Elvb74AsaP92WKpW40\ngCIZVPxrQcVfxo3zHap22il2ksJ12GE+1FM7fMWl4l8LHTrAqlXa3D3NnnjCTwKk7iquokeMiJ0k\n3VT8a6FePejVS2f/abVxI4wZA717x05S+HQVHZ+Kfy3pRZteU6ZAy5aw556xkxS+o47SVXRsKv61\ndOKJ8OabvjqhpItaPtlTcRWt1k88Kv611KgRdOniKzpKepSVeaHSEM/s6d1bxT8mFf86UOsnfV5+\n2TdoP+CA2EmKx4knwty5sHJl7CTppOJfB126+O5ea9bETiL58uSTavlk2zbbQOfOvkie5J+Kfx00\nberT+8eMiZ1E8qW0VKN8ckFX0fGo+NeRXrTpMX8+fP45tG8fO0nx6dIFpk2DTz+NnSR9VPzrqHt3\nePZZWLcudhLJtdJS6NnTJydJdm2/PRx7LDz1VOwk6aPiX0ctWvg6/5MmxU4iuVZa6sMSJTc06icO\nFf8M9OqlIZ/FbsUKX4O+pCR2kuLVowc884zPoJb8UfHPQM+eMGqUjwGX4jRqlG8+ss02sZMUr912\ng1atYOrU2EnSRcU/Awce6Bt6aHvH4qWWT3706qUhn/mm4p8htX6K19q1vp5P166xkxS/nj39faQ1\n/vNHxT9DKv7Fa9w4OPJI2HHH2EmKX5s2vsb/nDmxk6SHin+GjjwSPvgA3n47dhLJNrV88sfs67N/\nyQ8V/wzVr+9j/tWvLC5ffeUzuHv2jJ0kPdT3zy8V/yxQ66f4vPiir9u/zz6xk6THMcf4FfSyZbGT\npIOKfxacfLKP+Pn449hJJFtGjdJZf741bOjLPYwaFTtJOqj4Z8F22/kkoKefjp1EsmXkSBX/GHQV\nnT8q/lnSo4f6lcVi4UJfaKxdu9hJ0qdTJ3j+eR9mK7ml4p8l3btrinqxGDXKP8zr6d2Rd9tv7/v7\njhsXO0nx08s7SyqmqD/3XOwkkqmK4i9x9Oihvn8+qPhnUc+eav0Uuo8/hhkz4KSTYidJrx49fJjt\npk2xkxQ3Ff8squj7a4p64XrqKTjhBNh229hJ0mvffWHXXX3fZMkdFf8sOuwwnxw0b17sJFJXGuWT\nDGr95J6KfxZVTFHXi7YwbdjgNxq7d4+dRFT8c0/FP8v0oi1cU6f6Mt277BI7iRxxBKxapTWzcknF\nP8tKSuCNN3yxNyksGuWTHPXqQbduOpHKJRX/LNtmG1/uYezY2EmkNkJQ8U8aXUXnlop/DuhFW3jm\nzfOhhYceGjuJVOjY0Uf8fPJJ7CTFScU/B7p2hfHjYf362EmkpirO+s1iJ5EKTZvCccdpzaxcUfHP\ngZ13hkMO8S0ApTCMGqVRPknUvTuMHh07RXHKSvE3s85mNt/MFprZLyp5zN/MbJGZzTKzttk4bpJ1\n767WT6H48EOYO9dv1kuydO/uE++++ip2kuKTcfE3s3rA7UAnoA0wwMwO2uIxXYDvhhBaAZcAd2Z6\n3KSr6Ptrtm/yjR3ryzk0bhw7iWxp7719U50XX4ydpPhk48z/CGBRCGFxCGEjMAzYcufTXsADACGE\nl4EdzKyoR1O3aeP947lzYyeR6miUT7JpAEVuZKP47wEs3ez798p/VtVjlm3lMUXFTC/aQrBhAzz7\nrI8pl2TS+yg3GsQOsDUDBw7879clJSWUFGgztkcP+M1v4LrrYieRykyZAgcfDC1axE4ilTn8cFiz\nBt56C1q2jJ0mGSZPnszkyZMzeg4LGTalzewoYGAIoXP599cCIYRw22aPuROYFEJ4pPz7+cDxIYSV\nW3m+kGmmpNiwwYvKwoUqLkl1xRW+gqQ+oJPtwgt9BN1VV8VOkkxmRgihVgOVs9H2mQ60NLN9zKwR\n0B/YclX7kcA55SGPAtZsrfAXm0aNNNs3yTSrt3Co9ZN9GRf/EMIm4HJgHPAGMCyE8KaZXWJmF5c/\nZizwjpm9BdwFXJbpcQuFXrTJ9cYb/gFwyCGxk0h1Tj4Zpk/XbN9syrjtk23F1PYBX5mwZUtf6G2b\nbWKnkc3dcgu8/z78/e+xk0hNdOsG55wDp58eO0nyxGr7SBUqZvtmeG9GckCzeguLrqKzS8U/D/Si\nTZ4PPvC2T4EOJEslzfbNLhX/PNBs3+QZO9b7yGrFFY4994R99oEXXoidpDio+OdB69bQoAHMnh07\niVTQKJ/CpKvo7FHxzwPN9k2W9et9ye2uXWMnkdrq2RNGbjmQXOpExT9PVPyTY/JkX3tJE+8KT7t2\nsHatT5yUzKj458lxx/kLdsWK2ElELZ/CZabl0rNFxT9PGjWCU06BMWNiJ0m3EHxzEBX/wqWr6OxQ\n8c8jvWjjmzMH6tXzto8UppNOghkzYPXq2EkKm4p/HnXpApMmwZdfxk6SXiNH+k1D7dVbuLbd1udn\nPPVU7CSFTcU/j3baCdq2hYkTYydJr4riL4VNo34yp+KfZ3rRxvP++74m/HHHxU4imerWDZ55xpdN\nl7pR8c+znj29719WFjtJ+owZA507Q8OGsZNIpnbbDQ44AJ57LnaSwqXin2etWsEOO8Brr8VOkj4j\nR2qUTzHp1QtKS2OnKFwq/hGo9ZN/n3/uWzZ27hw7iWRLxftIa2bVjYp/BCr++Td+PHz/+9C8eewk\nki1t2viw3TlzYicpTCr+ERx1FCxfDu++GztJemiUT/Ex8/9P1fqpGxX/COrX99EKmvCVH2VlmtVb\nrHr10lV0Xan4R6IXbf68/LLvqLb//rGTSLYdeyz85z+wbFnsJIVHxT+Sjh29KGlD6twrLfUPWyk+\nDRv6zHldRdeein8kTZrAD37gO0pJbo0YAb17x04huaKr6LpR8Y9I45Rzb/58X/+9ffvYSSRXOnXy\nyV5r18ZOUlhU/CPq0QOeftp3lpLcKC31ESH19EovWjvsAB06+HtJak5viYh23dXHKk+aFDtJ8Sot\nVcsnDXr31lV0ban4R9a7t/ekJfuWL4c33/Tlf6W49erlazdt3Bg7SeFQ8Y+sd2+/WaWF3rJv1Chf\nzqFRo9hJJNf22MPXzZoyJXaSwqHiH1mrVr7kwPTpsZMUH7V80kVX0bWj4p8AetFm32ef+QiQLl1i\nJ5F86dPH30da6K1mVPwTQMU/+555Bo4+GrbfPnYSyZeDDoKmTeHVV2MnKQwq/gnQvr2fqc6fHztJ\n8RgxQrN600gnUjWn4p8A9eppwlc2bdjgIz/U708fFf+aU/FPiN694YknYqcoDhMmQOvWvtWfpMsR\nR8Dq1bBwYewkyafinxAlJb65+NKlsZMUvieegL59Y6eQGCquonX2Xz0V/4Ro2NCXe3jyydhJCtum\nTd4+69MndhKJpU8fXUXXhIp/gvTtqxdtpqZNgz33hP32i51EYjnhBFi0SFfR1VHxT5COHWHWLPjg\ng9hJCtcTT8Cpp8ZOITE1bOiL+elEqmoq/gnSuLEvR6B+Zd2Ulan4i+vbF4YPj50i2VT8E0atn7p7\n9VXfJOfgg2Mnkdg6doQ5c2DFithJkkvFP2G6dIEXXvDhalI7FWf9ZrGTSGzbbANdu2oARVVU/BOm\naVM48UTtSVpbIajlI9+k1k/VVPwTSK2f2ps922f2artGqdC5s6+W++GHsZMkk4p/AnXvDhMn+no/\nUjOPPgqnnaaWj3xtu+3glFO0bEplVPwTqHlzOO44tX5qKoSvi7/I5tT6qZyKf0KddpoXNKne66/7\nzF61fGRL3brB889rAMXWqPgnVO/evrH7J5/ETpJ8jz4KP/yhWj7ybc2awckna9TP1qj4J9QOO/hi\nb+pXVk0tH6nO6afDI4/ETpE8Kv4JptZP9WbO9A+Adu1iJ5Gk6tYNXn4ZVq2KnSRZMir+ZtbczMaZ\n2QIze8bMdqjkce+a2etmNtPMXsnkmGnSs6fvQ6t+ZeU0ykeq06SJT57Ujd9vyvTM/1pgfAjhQGAi\n8MtKHlcGlIQQvhdCOCLDY6ZGs2Zw0knqV1YmBHjsMbV8pHpq/XxbpsW/FzC4/OvBQGUb51kWjpVK\np5+u1k9lZszwzTvato2dRJKuc2dfMXf58thJkiPTgtwihLASIISwAmhRyeMC8KyZTTezizI8Zqp0\n7w4vvqhZilszdKh/OKrlI9Vp3NjbqI89FjtJcjSo7gFm9iywy+Y/wov59Vt5eKjkaY4JISw3s53x\nD4E3QwjTKjvmwIED//t1SUkJJSUl1cUsWk2aQKdO3q+85JLYaZJj0yZ4+GHfr1ekJk4/HW6+Ga64\nInaSzE2ePJnJkydn9BwWQmX1ugb/2OxNvJe/0sx2BSaFEKpcUNfMbgA+CyH8qZK/D5lkKkalpTBo\nEEydGjtJckyYANdc460fkZrYsAF2391fM3vvHTtNdpkZIYRaXQNn2vYZCZxb/vX/At8alW5m25lZ\n0/KvmwCnAHMzPG6qdOkC8+bB4sWxkyTH0KFw5pmxU0ghadTIV30dNix2kmTItPjfBnQ0swXAScCt\nAGa2m5mNLn/MLsA0M5sJvASMCiGMy/C4qdKokc9gHTo0dpJkWLfOR0D17x87iRSas8+GIUN8pFja\nZdT2yQW1fbbu+efhoovgjTd0g3P4cLjjDvX7pfbKymD//X2r1GIaJRaj7SN5cvTR8OWXPlwt7R56\nCM44I3YKKUT16sFZZ/nZf9rpzL+AXH+9fwAMGhQ7STxr1sA++/j9jx13jJ1GCtGCBb5u1tKl0KDa\n8Y6FQWf+Re7MM31446ZNsZPEM3y4r9Kowi91deCBsNdeahuq+BeQgw/2oWqTJsVOEs+QIRrlI5mr\nuPGbZmr7FJi//MVXshw8uPrHFpv//Ac6dID33vMRUCJ1tWoVtGrlr6WmTWOnyZzaPikwYIBP+vr0\n09hJ8u/++/2sX4VfMrXzznDsseleNFHFv8DssguccEL6FnvbtMmvds47L3YSKRbnnOMnFGml4l+A\nLrgA7rkndor8mjABWrSAww6LnUSKRa9eMHs2vP127CRxqPgXoM6dYckSX/IhLe67T2f9kl3bbONj\n/u+9N3aSOHTDt0Bddx2sX5+OMf+rV8N++/kZ2ne+EzuNFJO5c33V3MWLC3vMv274psj558ODD/pK\nhcXu4Yf9akeFX7LtkEN8hc+nnoqdJP9U/AtUy5Zw0EEwenT1jy10996rlo/kzkUXwd13x06Rfyr+\nBSwNN35few0++MBn9Yrkwmmn+V4Z778fO0l+qfgXsH794KWXfI2SYvXPf8Kll0L9+rGTSLFq2tSX\nTE/bxEnd8C1wV14JzZrBTTfFTpJ9q1f78rsLFvgwT5FceeUVn0C5aJGv/FlodMM3hS67DP79bx/5\nU2zuvx+6dlXhl9z7/vdh++3hmWdiJ8kfFf8Cd+CBvinFY4/FTpJdZWXe8rnssthJJA3MfGP3v/89\ndpL8UfEvApdfDrffHjtFdo0fD9tt55vYiOTDgAHw6quwcGHsJPmh4l8EunaFlSth+vTYSbLnjjv8\nrD/tW1ZK/jRu7MM+i+1EqjK64Vsk/vAH39+3GBaqWrLEW1lLlhTHcrtSON57z9ePevddvwdQKHTD\nN8XOP9+Xel61KnaSzN1+u6+4qMIv+bbnntCxYzqGferMv4hccIGvgXP99bGT1N0nn/jwzhkzfK9e\nkXybNs1PpubPL5xhnzrzT7mf/tRHK3zxRewkdXfXXdCliwq/xHPMMX7VWezr/aj4F5HWrX10TKEu\nUbt+Pfz1r3DNNbGTSJqZ+WvwllugmJsQKv5F5tpr4Y9/hI0bYyepvYcegkMPhf/5n9hJJO1OO81H\n0E2dGjtJ7qj4F5kjj/Se+bBhsZPUTlmZj1j6+c9jJxHxtaSuvRZuvjl2ktxR8S9C114Lt97qBbVQ\njB7tfdYTToidRMSdfbbf9C2m+TObU/EvQh07+oSVQlnrPwT43e/8rF+TuiQpGjXy3n+xnv2r+Bch\nMz/7v+mmwrhhNXIkfPkl9O0bO4nIN114Ibz8MsyZEztJ9qn4F6m+ff2m7xNPxE5StU2bfF7CzTcX\nzphqSY9tt4WrrirOJdP1ditS9ep53/+66+Crr2KnqdzDD/s0+m7dYicR2brLL/eJX8XW+1fxL2Kn\nnOLT1ZM67n/DBrjhBu/3q9cvSdWkCdx4I/zsZ4XRRq0pFf8iZuZn/zfeCJ9/HjvNt91zj29Ef/zx\nsZOIVO288+Cjj/z+VLHQ2j4pcNppvkrmddfFTvK1tWt9I5qRI6F9+9hpRKo3dixcfbXf/G3YMHaa\nb6rL2j4q/imwaBF06ADz5iVnS8Sf/xyWL4chQ2InEamZEHwYdd++8KMfxU7zTSr+Uqmf/QxWrIAH\nH4ydxM+cTjoJ5s5NzoeRSE3MnOkLD775JjRvHjvN11T8pVKff+7r5vzzn9CpU7wcZWVw3HG+Xv8l\nl8TLIVJXP/6xz0tJ0kAKLekslWrSBO68Ey69NO7N33vv9Q+Aiy6Kl0EkE7feChMnwrhx8TKsXQvr\n1mX2HDrzT5mzz/ZWy6BB+T/2qlVwyCH+ptHKnVLIxo2Diy/2FmazZvk9dghw1llw0EHw61/7z9T2\nkWpVFOAxY+Dww/N33BCgd28f4fP73+fvuCK5csEFvobWP/6R3+MOHuzvoenTYbvt/Gcq/lIjjz7q\nwz6nT8+hakCdAAAGXUlEQVTfTatBg/y4zz3nC2aJFLo1a/xEavBgH8CQDwsX+k5jEyf6PbwKKv5S\nY1dd5UNAR43K/Zo6L7wAffrAK69oe0YpLhMmeAvmhRd8/+xcWr/eh2xfeCFcdtk3/043fKXG/vAH\n+Owz+O1vc3ucDz+E/v19Nq8KvxSbk07yq+hevfwmbC794hf+HsrWHAOd+afYihXe97/rrtwsrLZu\nHXTvDu3aqc8vxSsEv/n70Ufw+OO5uZIeNAj+/W+/wvjOd7799zrzl1rZdVfvw597LkyenN3nXr/e\nZ0LutJMv3CZSrMz8pu8HH8CvfpX9xd/uvhv+/nd49tmtF/66UvFPuaOPhkcegR/+EJ5+OjvPuX49\n9OvnIxEeeggaNMjO84okVaNGvnfG2LFw5ZXZ20L1kUd85dtnn4W99srOc1bIqPibWT8zm2tmm8ys\nXRWP62xm881soZn9IpNjSvadeCKUlvqs2xEjMnuuL77wheQaNYKhQ1X4JT1atIApU2D2bBgwwE+C\n6ioEn5R5xRXw1FPQqlX2clbI9Mx/DtAHmFLZA8ysHnA70AloAwwws4MyPG4qTM52L6YKRx/tL7JL\nL4Xf/KZuswdfeslXD23e3DdpydbKh/n8PSSdfhdfS+LvYscd/Qp60yZfA2jZsto/xyefwOmne/F/\n7jk47LDs54QMi38IYUEIYRFQ1Y2GI4BFIYTFIYSNwDCgVybHTYt8v7jbt4fXXvMF19q2halTa/bv\nNmzwmYa9e3t///77szuWP4lv8lj0u/haUn8XjRt7u+bYY71w33xzzU6mQoDx432AxM47+8nUAQfk\nLmc+ev57AEs3+/698p9JAu2xh/cub7kFzjgDevTwYZorV37zcSH4uP0rrvDdwmbP9hUP+/WLk1sk\nSerX92HU06fDjBlw8ME+YmfGDL8q2Nynn8Ltt0ObNn6/4Pe/9xvIjRvnNmO1HVkzexbYZfMfAQH4\nVQhhVK6CSVx9+sDJJ/sksJEjfUno3Xf3v1u/3ucIbL/91xNcWraMm1ckifbfH4YP93sBjzziJ1LL\nl/uHwerVPkJo7VqfJ3DHHb6rXb62NM3KOH8zmwT8NIQwYyt/dxQwMITQufz7a4EQQritkufSIH8R\nkVqq7Tj/bI7FqOzA04GWZrYPsBzoDwyo7Elq+x8gIiK1l+lQz95mthQ4ChhtZk+V/3w3MxsNEELY\nBFwOjAPeAIaFEN7MLLaIiGQiccs7iIhI7iVmhq8mgjkz29PMJprZG2Y2x8yuiJ0pNjOrZ2YzzGxk\n7CwxmdkOZvaYmb1Z/vo4MnamWMzsJ+UTTGeb2UNmlpqFws3sHjNbaWazN/tZczMbZ2YLzOwZM9uh\nuudJRPHXRLBv+Aq4OoTQBugA/DjFv4sKVwLzYodIgL8CY0MIBwP/A6SyfWpmuwP/D2gXQjgMv3fZ\nP26qvLoPr5WbuxYYH0I4EJgI/LK6J0lE8UcTwf4rhLAihDCr/Ou1+Bs8tfMizGxPoCtwd+wsMZnZ\n9sBxIYT7AEIIX4UQPo0cK6b6QBMzawBsB7wfOU/ehBCmAau3+HEvYHD514OB3tU9T1KKvyaCbYWZ\n7Qu0BV6OmySqPwPX4HNL0mw/4EMzu6+8BfYvM9s2dqgYQgjvA4OAJcAyYE0IYXzcVNG1CCGsBD+B\nBFpU9w+SUvxlC2bWFHgcuLL8CiB1zKwbsLL8SsioehmRYtcAaAf8I4TQDvgCv9RPHTPbET/T3QfY\nHWhqZmfETZU41Z4sJaX4LwP23uz7Pct/lkrll7KPA0NCCKWx80R0DNDTzN4GHgZOMLMHImeK5T1g\naQjh1fLvH8c/DNLoZODtEMLH5UPJnwCOjpwptpVmtguAme0KfFDdP0hK8f/vRLDyu/b9gTSP7LgX\nmBdC+GvsIDGFEK4LIewdQtgff01MDCGcEztXDOWX9EvNrGKpr5NI703wJcBRZtbYzAz/XaTt5veW\nV8IjgXPLv/5foNqTxkSsth5C2GRmFRPB6gH3pHUimJkdA5wJzDGzmfjl23UhhCxttSIF7ArgITNr\nCLwNnBc5TxQhhFfM7HFgJrCx/H//FTdV/pjZUKAE2MnMlgA3ALcCj5nZ+cBi4LRqn0eTvERE0icp\nbR8REckjFX8RkRRS8RcRSSEVfxGRFFLxFxFJIRV/EZEUUvEXEUkhFX8RkRT6/6ad/CkQCSMrAAAA\nAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x10f049ac8>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"% matplotlib inline\n",
	"\n",
	"x = np.arange(0, 10, 0.1)\n",
	"y = np.cos(x)\n",
	"plt.plot(x,y)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"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": "3.5.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}