text sort_equations

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "x = np.linspace(0, 10, 1000)\n",
    "\n",
    "ax.plot(x, np.sin(x));\n",
    "ax.plot(x, np.cos(x));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y1 = 1 + x * 0\n",
    "\n",
    "\n",
    "ax.plot(x, y1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y2 = x\n",
    "\n",
    "\n",
    "ax.plot(x, y2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y3 = x ** 2\n",
    "\n",
    "\n",
    "ax.plot(x, y3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y4 = x ** 3\n",
    "\n",
    "\n",
    "ax.plot(x, y4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y5 = (3/2) ** x\n",
    "\n",
    "\n",
    "ax.plot(x, y5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y6 = 2 ** x\n",
    "\n",
    "\n",
    "ax.plot(x, y6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "y1 = 1 + x * 0\n",
    "y2 = x\n",
    "y3 = x ** 2\n",
    "y4 = x ** 3\n",
    "y5 = (3/2) ** x\n",
    "y6 = 2 ** x\n",
    "\n",
    "\n",
    "ax.plot(x, y1);\n",
    "ax.plot(x, y2);\n",
    "ax.plot(x, y3);\n",
    "ax.plot(x, y4);\n",
    "ax.plot(x, y5);\n",
    "ax.plot(x, y6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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