python_example_for_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\LaTeX$ Math\n",
    "This is just markdown that can include latex math.\n",
    "\n",
    "\\begin{align}\n",
    "\\dot{x} & = \\sigma(y-x) \\\\\n",
    "\\dot{y} & = \\rho x - y - xz \\\\\n",
    "\\dot{z} & = -\\beta z + xy\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'commit_hash': '7f10f7bb3',\n",
      " 'commit_source': 'installation',\n",
      " 'default_encoding': 'UTF-8',\n",
      " 'ipython_path': '/Users/sk/anaconda3/envs/class/lib/python3.6/site-packages/IPython',\n",
      " 'ipython_version': '6.4.0',\n",
      " 'os_name': 'posix',\n",
      " 'platform': 'Darwin-17.2.0-x86_64-i386-64bit',\n",
      " 'sys_executable': '/Users/sk/anaconda3/envs/class/bin/python',\n",
      " 'sys_platform': 'darwin',\n",
      " 'sys_version': '3.6.5 |Anaconda custom (64-bit)| (default, Apr 26 2018, '\n",
      "                '08:42:37) \\n'\n",
      "                '[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]'}\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "print(IPython.sys_info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.062791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02</td>\n",
       "      <td>1.125333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>1.187381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.04</td>\n",
       "      <td>1.248690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.309017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.368125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.425779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.481754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.535827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1.587785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.11</td>\n",
       "      <td>1.637424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.12</td>\n",
       "      <td>1.684547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.728969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.14</td>\n",
       "      <td>1.770513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.809017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.844328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.17</td>\n",
       "      <td>1.876307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.904827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.19</td>\n",
       "      <td>1.929776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1.951057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1.968583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.22</td>\n",
       "      <td>1.982287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1.992115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.24</td>\n",
       "      <td>1.998027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.25</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.26</td>\n",
       "      <td>1.998027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.27</td>\n",
       "      <td>1.992115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.28</td>\n",
       "      <td>1.982287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.29</td>\n",
       "      <td>1.968583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0.048943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1.71</td>\n",
       "      <td>0.031417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1.72</td>\n",
       "      <td>0.017713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>1.73</td>\n",
       "      <td>0.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1.74</td>\n",
       "      <td>0.001973</td>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1.75</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1.76</td>\n",
       "      <td>0.001973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1.77</td>\n",
       "      <td>0.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1.78</td>\n",
       "      <td>0.017713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1.79</td>\n",
       "      <td>0.031417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1.80</td>\n",
       "      <td>0.048943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1.81</td>\n",
       "      <td>0.070224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>1.82</td>\n",
       "      <td>0.095173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1.83</td>\n",
       "      <td>0.123693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1.84</td>\n",
       "      <td>0.155672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1.85</td>\n",
       "      <td>0.190983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1.86</td>\n",
       "      <td>0.229487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1.87</td>\n",
       "      <td>0.271031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>1.88</td>\n",
       "      <td>0.315453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.89</td>\n",
       "      <td>0.362576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1.90</td>\n",
       "      <td>0.412215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1.91</td>\n",
       "      <td>0.464173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1.92</td>\n",
       "      <td>0.518246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1.93</td>\n",
       "      <td>0.574221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>1.94</td>\n",
       "      <td>0.631875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1.95</td>\n",
       "      <td>0.690983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1.96</td>\n",
       "      <td>0.751310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1.97</td>\n",
       "      <td>0.812619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.98</td>\n",
       "      <td>0.874667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1.99</td>\n",
       "      <td>0.937209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        t         x\n",
       "0    0.00  1.000000\n",
       "1    0.01  1.062791\n",
       "2    0.02  1.125333\n",
       "3    0.03  1.187381\n",
       "4    0.04  1.248690\n",
       "5    0.05  1.309017\n",
       "6    0.06  1.368125\n",
       "7    0.07  1.425779\n",
       "8    0.08  1.481754\n",
       "9    0.09  1.535827\n",
       "10   0.10  1.587785\n",
       "11   0.11  1.637424\n",
       "12   0.12  1.684547\n",
       "13   0.13  1.728969\n",
       "14   0.14  1.770513\n",
       "15   0.15  1.809017\n",
       "16   0.16  1.844328\n",
       "17   0.17  1.876307\n",
       "18   0.18  1.904827\n",
       "19   0.19  1.929776\n",
       "20   0.20  1.951057\n",
       "21   0.21  1.968583\n",
       "22   0.22  1.982287\n",
       "23   0.23  1.992115\n",
       "24   0.24  1.998027\n",
       "25   0.25  2.000000\n",
       "26   0.26  1.998027\n",
       "27   0.27  1.992115\n",
       "28   0.28  1.982287\n",
       "29   0.29  1.968583\n",
       "..    ...       ...\n",
       "170  1.70  0.048943\n",
       "171  1.71  0.031417\n",
       "172  1.72  0.017713\n",
       "173  1.73  0.007885\n",
       "174  1.74  0.001973\n",
       "175  1.75  0.000000\n",
       "176  1.76  0.001973\n",
       "177  1.77  0.007885\n",
       "178  1.78  0.017713\n",
       "179  1.79  0.031417\n",
       "180  1.80  0.048943\n",
       "181  1.81  0.070224\n",
       "182  1.82  0.095173\n",
       "183  1.83  0.123693\n",
       "184  1.84  0.155672\n",
       "185  1.85  0.190983\n",
       "186  1.86  0.229487\n",
       "187  1.87  0.271031\n",
       "188  1.88  0.315453\n",
       "189  1.89  0.362576\n",
       "190  1.90  0.412215\n",
       "191  1.91  0.464173\n",
       "192  1.92  0.518246\n",
       "193  1.93  0.574221\n",
       "194  1.94  0.631875\n",
       "195  1.95  0.690983\n",
       "196  1.96  0.751310\n",
       "197  1.97  0.812619\n",
       "198  1.98  0.874667\n",
       "199  1.99  0.937209\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "curve = pd.DataFrame(np.arange(0.0, 2.0, 0.01), columns=list('t'))\n",
    "curve['x'] = 1 + np.sin(2 * np.pi * curve['t'])\n",
    "curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119058518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [8, 4]\n",
    "curve.plot.line(x = 't', y = 'x')"
   ]
  }
 ],
 "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": "3.6.5"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 2
}