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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 1,
  6.    "metadata": {
  7.     "collapsed": true
  8.    },
  9.    "outputs": [],
  10.    "source": [
  11.     "from sympy import *"
  12.    ]
  13.   },
  14.   {
  15.    "cell_type": "code",
  16.    "execution_count": 4,
  17.    "metadata": {
  18.     "collapsed": true
  19.    },
  20.    "outputs": [],
  21.    "source": [
  22.     "f = 2 * x ** 2 - 3 * x + 1"
  23.    ]
  24.   },
  25.   {
  26.    "cell_type": "code",
  27.    "execution_count": 3,
  28.    "metadata": {
  29.     "collapsed": true
  30.    },
  31.    "outputs": [],
  32.    "source": [
  33.     "x, y = symbols(\"x y\")"
  34.    ]
  35.   },
  36.   {
  37.    "cell_type": "code",
  38.    "execution_count": 5,
  39.    "metadata": {},
  40.    "outputs": [
  41.     {
  42.      "data": {
  43.       "text/plain": [
  44.        "(x - 1)*(2*x - 1)"
  45.       ]
  46.      },
  47.      "execution_count": 5,
  48.      "metadata": {},
  49.      "output_type": "execute_result"
  50.     }
  51.    ],
  52.    "source": [
  53.     "f.factor()"
  54.    ]
  55.   },
  56.   {
  57.    "cell_type": "code",
  58.    "execution_count": 6,
  59.    "metadata": {
  60.     "collapsed": true
  61.    },
  62.    "outputs": [],
  63.    "source": [
  64.     "g=x**3+y**3+1-3*x*y"
  65.    ]
  66.   },
  67.   {
  68.    "cell_type": "code",
  69.    "execution_count": 7,
  70.    "metadata": {},
  71.    "outputs": [
  72.     {
  73.      "data": {
  74.       "text/plain": [
  75.        "(x + y + 1)*(x**2 - x*y - x + y**2 - y + 1)"
  76.       ]
  77.      },
  78.      "execution_count": 7,
  79.      "metadata": {},
  80.      "output_type": "execute_result"
  81.     }
  82.    ],
  83.    "source": [
  84.     "g.factor()"
  85.    ]
  86.   },
  87.   {
  88.    "cell_type": "code",
  89.    "execution_count": 8,
  90.    "metadata": {},
  91.    "outputs": [
  92.     {
  93.      "data": {
  94.       "text/plain": [
  95.        "2*x**3/3 - 3*x**2/2 + x"
  96.       ]
  97.      },
  98.      "execution_count": 8,
  99.      "metadata": {},
  100.      "output_type": "execute_result"
  101.     }
  102.    ],
  103.    "source": [
  104.     "integrate(f, x)"
  105.    ]
  106.   },
  107.   {
  108.    "cell_type": "code",
  109.    "execution_count": 9,
  110.    "metadata": {
  111.     "collapsed": true
  112.    },
  113.    "outputs": [],
  114.    "source": [
  115.     "h=x*(x**2-1)*(x**2-4)*(x**2-9)-y*(y**2-1)*(y**2-4)*(y**2-9)"
  116.    ]
  117.   },
  118.   {
  119.    "cell_type": "code",
  120.    "execution_count": 10,
  121.    "metadata": {},
  122.    "outputs": [
  123.     {
  124.      "data": {
  125.       "text/plain": [
  126.        "(x - y)*(x**6 + x**5*y + x**4*y**2 - 14*x**4 + x**3*y**3 - 14*x**3*y + x**2*y**4 - 14*x**2*y**2 + 49*x**2 + x*y**5 - 14*x*y**3 + 49*x*y + y**6 - 14*y**4 + 49*y**2 - 36)"
  127.       ]
  128.      },
  129.      "execution_count": 10,
  130.      "metadata": {},
  131.      "output_type": "execute_result"
  132.     }
  133.    ],
  134.    "source": [
  135.     "h.factor()"
  136.    ]
  137.   },
  138.   {
  139.    "cell_type": "code",
  140.    "execution_count": 11,
  141.    "metadata": {
  142.     "collapsed": true
  143.    },
  144.    "outputs": [],
  145.    "source": [
  146.     "from sympy.plotting import plot"
  147.    ]
  148.   },
  149.   {
  150.    "cell_type": "code",
  151.    "execution_count": 21,
  152.    "metadata": {},
  153.    "outputs": [
  154.     {
  155.      "data": {
  156.       "image/png": 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TmZ4AuGFZowaXrz4+OXjhhXsxdOjdmD59erl2GNVB55ORwRTN779zqvXddztHW+bXX4Ef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\n/h8z+mIiZ7BlNQAAAABJRU5ErkJggg==\n",
  157.       "text/plain": [
  158.        "<matplotlib.figure.Figure at 0x10edb0f98>"
  159.       ]
  160.      },
  161.      "metadata": {},
  162.      "output_type": "display_data"
  163.     },
  164.     {
  165.      "data": {
  166.       "text/plain": [
  167.        "<sympy.plotting.plot.Plot at 0x10edb06d8>"
  168.       ]
  169.      },
  170.      "execution_count": 21,
  171.      "metadata": {},
  172.      "output_type": "execute_result"
  173.     }
  174.    ],
  175.    "source": [
  176.     "plot_implicit(h, (x, -4, 4), (y, -4, 4), aspect_ratio = (1.,1.))"
  177.    ]
  178.   },
  179.   {
  180.    "cell_type": "code",
  181.    "execution_count": 3,
  182.    "metadata": {},
  183.    "outputs": [
  184.     {
  185.      "ename": "NameError",
  186.      "evalue": "name 'matplotlib' is not defined",
  187.      "output_type": "error",
  188.      "traceback": [
  189.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  190.       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
  191.       "\u001b[0;32m<ipython-input-3-8e64ebe803fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  192.       "\u001b[0;31mNameError\u001b[0m: name 'matplotlib' is not defined"
  193.      ]
  194.     }
  195.    ],
  196.    "source": [
  197.     "matplotlib.use('PS')"
  198.    ]
  199.   },
  200.   {
  201.    "cell_type": "code",
  202.    "execution_count": 2,
  203.    "metadata": {
  204.     "collapsed": true
  205.    },
  206.    "outputs": [],
  207.    "source": [
  208.     "import matplotlib.pyplot as plt"
  209.    ]
  210.   },
  211.   {
  212.    "cell_type": "code",
  213.    "execution_count": 4,
  214.    "metadata": {
  215.     "collapsed": true
  216.    },
  217.    "outputs": [],
  218.    "source": [
  219.     "import numpy as np"
  220.    ]
  221.   },
  222.   {
  223.    "cell_type": "code",
  224.    "execution_count": 5,
  225.    "metadata": {},
  226.    "outputs": [
  227.     {
  228.      "ename": "NameError",
  229.      "evalue": "name 'matplotlib' is not defined",
  230.      "output_type": "error",
  231.      "traceback": [
  232.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  233.       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
  234.       "\u001b[0;32m<ipython-input-5-8e64ebe803fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  235.       "\u001b[0;31mNameError\u001b[0m: name 'matplotlib' is not defined"
  236.      ]
  237.     }
  238.    ],
  239.    "source": [
  240.     "matplotlib.use('PS')"
  241.    ]
  242.   },
  243.   {
  244.    "cell_type": "code",
  245.    "execution_count": null,
  246.    "metadata": {
  247.     "collapsed": true
  248.    },
  249.    "outputs": [],
  250.    "source": []
  251.   }
  252.  ],
  253.  "metadata": {
  254.   "kernelspec": {
  255.    "display_name": "Python 3",
  256.    "language": "python",
  257.    "name": "python3"
  258.   },
  259.   "language_info": {
  260.    "codemirror_mode": {
  261.     "name": "ipython",
  262.     "version": 3
  263.    },
  264.    "file_extension": ".py",
  265.    "mimetype": "text/x-python",
  266.    "name": "python",
  267.    "nbconvert_exporter": "python",
  268.    "pygments_lexer": "ipython3",
  269.    "version": "3.6.1"
  270.   }
  271.  },
  272.  "nbformat": 4,
  273.  "nbformat_minor": 2
  274. }
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