Plot_SIMPLEST_101518.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sympy import *\n",
    "init_printing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = symbols(\"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGMAAAAXCAYAAAAfiPFCAAAABHNCSVQICAgIfAhkiAAAAm9JREFU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\nAAAAAElFTkSuQmCC\n",
      "text/latex": [
       "$$x^{2} + 2 x + 2$$"
      ],
      "text/plain": [
       " 2          \n",
       "x  + 2⋅x + 2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x**2 + 2*x + 2\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKEAAAAWCAYAAAConvx7AAAABHNCSVQICAgIfAhkiAAAAhRJREFU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\nkhpbCi3S0dHR0dHR8YffLJRvcI/EHMAAAAAASUVORK5CYII=\n",
      "text/latex": [
       "$$\\left [ -1 - i, \\quad -1 + i\\right ]$$"
      ],
      "text/plain": [
       "[-1 - ⅈ, -1 + ⅈ]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nFfbBIiICH6BfP2jfHm6+Gf73P9fViIiEr7Vr4c477fToDh1cVyP7RUzgGwPT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\n4IuIiEQABb6IiEgE+H/Jd4mGDSGt/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20aa5759898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x20aa53b00b8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  Reference for sympy.plot is https://docs.sympy.org/latest/modules/plotting.html?highlight=plot#sympy.plotting.plot.plot\n",
    "plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZKGk4pV49acYM6bXXpAULbKcJv7lzpSVLpJkzzfVoAPgxShrOGTDATP/OmCHt2GE7Tfh8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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20aa598a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x20aa598a8d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(y,(x,-5,5), ylim = (-3,3))  # simple added features"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}