dwivedys · February 3, 2021 04:56
diff --git a/untitled14.ipynb b/untitled14.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled14.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN4aoXViZdEkBeZlf5+avAw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/dwivedys/97e9e9394a1cb20c3c4bed094499cc4e/untitled14.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "id": "ovw9k2aTkneT",
        "outputId": "0cf9bbb8-3b6e-4652-d284-aed5331c5210"
      },
      "source": [
        "import random\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# List sizes of the lists to be sorted\n",
        "nums = [100, 1000, 10000, 20000, 30000]\n",
        "\n",
        "# Declare a dictionary data structure to hold the list size the times it took to sort the list in ascending order\n",
        "perf_dict = {}\n",
        "\n",
        "# This block of code runs for each differently sized list, sorts the list, records the run time of the sort and then publishes the output in a tabular format\n",
        "\n",
        "\n",
        "# The program should run once for each list hence we use a while loop to control the number of runs\n",
        "z = 0\n",
        "while(z < len(nums)): \n",
        "    my_list = []\n",
        "\n",
        "# Create the list with desired number of elements (uses random numbers to generate the list) \n",
        "    print(f'\\nPass: {z+1} - Sorting a list of {nums[z]} elements')   \n",
        "    for x in range(nums[z]):\n",
        "        my_list.append(random.randint(0,x))\n",
        "\n",
        "    print(f'First 20 elements of the list to be sorted is: {my_list[0:20]}')\n",
        "\n",
        "    # The Sorting Process begins\n",
        "    start = time.perf_counter()\n",
        "    l = len(my_list)\n",
        "    if l == 0: \n",
        "        print(f'The list is empty')\n",
        "    else:    \n",
        "        for i in range(0, l):\n",
        "            for j in range(i+1, l):\n",
        "                if my_list[j] >= my_list[i]:\n",
        "                    continue\n",
        "                else:\n",
        "                    swap_var = my_list[i]\n",
        "                    my_list[i] = my_list[j]\n",
        "                    my_list[j] = swap_var\n",
        "        print(f'First 20 elements of the sorted List = {my_list[0:20]}')\n",
        "    end = time.perf_counter()\n",
        "    #print(f'For a list of {l} elements, it took {end - start} seconds to sort')\n",
        "    perf_dict[len(my_list)] = end-start    \n",
        "    z = z + 1\n",
        "\n",
        "# Print the output results \n",
        "print(f'List_Size\\t\\tTime (in seconds)')\n",
        "for m in perf_dict:\n",
        "    print(f'{m}\\t\\t{perf_dict[m]}')\n",
        "\n",
        "x = []\n",
        "y = []\n",
        "for key in perf_dict:\n",
        "  x.append(key)\n",
        "  y.append(perf_dict[key])\n",
        "\n",
        "print(x)\n",
        "print(y)\n",
        "\n",
        "plt.plot(x, y, 'r')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Pass: 1 - Sorting a list of 100 elements\n",
            "First 20 elements of the list to be sorted is: [0, 0, 0, 2, 1, 3, 5, 1, 0, 3, 6, 5, 2, 11, 3, 10, 6, 7, 14, 11]\n",
            "First 20 elements of the sorted List = [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4]\n",
            "\n",
            "Pass: 2 - Sorting a list of 1000 elements\n",
            "First 20 elements of the list to be sorted is: [0, 1, 1, 0, 3, 4, 6, 0, 0, 7, 8, 10, 7, 2, 6, 4, 9, 13, 11, 15]\n",
            "First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4]\n",
            "\n",
            "Pass: 3 - Sorting a list of 10000 elements\n",
            "First 20 elements of the list to be sorted is: [0, 0, 0, 2, 0, 0, 2, 6, 5, 4, 5, 3, 11, 4, 13, 2, 14, 10, 7, 3]\n",
            "First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]\n",
            "\n",
            "Pass: 4 - Sorting a list of 20000 elements\n",
            "First 20 elements of the list to be sorted is: [0, 0, 2, 2, 2, 0, 5, 6, 4, 2, 7, 7, 12, 12, 2, 6, 12, 15, 14, 7]\n",
            "First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "\n",
            "Pass: 5 - Sorting a list of 30000 elements\n",
            "First 20 elements of the list to be sorted is: [0, 0, 2, 3, 4, 5, 0, 1, 1, 4, 10, 8, 12, 7, 9, 6, 1, 15, 4, 12]\n",
            "First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "List_Size\t\tTime (in seconds)\n",
            "100\t\t0.0013394249999691965\n",
            "1000\t\t0.08856283600016468\n",
            "10000\t\t8.083250383000177\n",
            "20000\t\t32.44908605500041\n",
            "30000\t\t73.21550271900014\n",
            "[100, 1000, 10000, 20000, 30000]\n",
            "[0.0013394249999691965, 0.08856283600016468, 8.083250383000177, 32.44908605500041, 73.21550271900014]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5ce70aa438>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIfEwYDolsMd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
 }
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "Untitled14.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"authorship_tag": "ABX9TyN4aoXViZdEkBeZlf5+avAw",
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/dwivedys/97e9e9394a1cb20c3c4bed094499cc4e/untitled14.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 758
	},
	"id": "ovw9k2aTkneT",
	"outputId": "0cf9bbb8-3b6e-4652-d284-aed5331c5210"
	},
	"source": [
	"import random\n",
	"import time\n",
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline\n",
	"\n",
	"# List sizes of the lists to be sorted\n",
	"nums = [100, 1000, 10000, 20000, 30000]\n",
	"\n",
	"# Declare a dictionary data structure to hold the list size the times it took to sort the list in ascending order\n",
	"perf_dict = {}\n",
	"\n",
	"# This block of code runs for each differently sized list, sorts the list, records the run time of the sort and then publishes the output in a tabular format\n",
	"\n",
	"\n",
	"# The program should run once for each list hence we use a while loop to control the number of runs\n",
	"z = 0\n",
	"while(z < len(nums)): \n",
	" my_list = []\n",
	"\n",
	"# Create the list with desired number of elements (uses random numbers to generate the list) \n",
	" print(f'\\nPass: {z+1} - Sorting a list of {nums[z]} elements') \n",
	" for x in range(nums[z]):\n",
	" my_list.append(random.randint(0,x))\n",
	"\n",
	" print(f'First 20 elements of the list to be sorted is: {my_list[0:20]}')\n",
	"\n",
	" # The Sorting Process begins\n",
	" start = time.perf_counter()\n",
	" l = len(my_list)\n",
	" if l == 0: \n",
	" print(f'The list is empty')\n",
	" else: \n",
	" for i in range(0, l):\n",
	" for j in range(i+1, l):\n",
	" if my_list[j] >= my_list[i]:\n",
	" continue\n",
	" else:\n",
	" swap_var = my_list[i]\n",
	" my_list[i] = my_list[j]\n",
	" my_list[j] = swap_var\n",
	" print(f'First 20 elements of the sorted List = {my_list[0:20]}')\n",
	" end = time.perf_counter()\n",
	" #print(f'For a list of {l} elements, it took {end - start} seconds to sort')\n",
	" perf_dict[len(my_list)] = end-start \n",
	" z = z + 1\n",
	"\n",
	"# Print the output results \n",
	"print(f'List_Size\\t\\tTime (in seconds)')\n",
	"for m in perf_dict:\n",
	" print(f'{m}\\t\\t{perf_dict[m]}')\n",
	"\n",
	"x = []\n",
	"y = []\n",
	"for key in perf_dict:\n",
	" x.append(key)\n",
	" y.append(perf_dict[key])\n",
	"\n",
	"print(x)\n",
	"print(y)\n",
	"\n",
	"plt.plot(x, y, 'r')\n"
	],
	"execution_count": 10,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"\n",
	"Pass: 1 - Sorting a list of 100 elements\n",
	"First 20 elements of the list to be sorted is: [0, 0, 0, 2, 1, 3, 5, 1, 0, 3, 6, 5, 2, 11, 3, 10, 6, 7, 14, 11]\n",
	"First 20 elements of the sorted List = [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4]\n",
	"\n",
	"Pass: 2 - Sorting a list of 1000 elements\n",
	"First 20 elements of the list to be sorted is: [0, 1, 1, 0, 3, 4, 6, 0, 0, 7, 8, 10, 7, 2, 6, 4, 9, 13, 11, 15]\n",
	"First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4]\n",
	"\n",
	"Pass: 3 - Sorting a list of 10000 elements\n",
	"First 20 elements of the list to be sorted is: [0, 0, 0, 2, 0, 0, 2, 6, 5, 4, 5, 3, 11, 4, 13, 2, 14, 10, 7, 3]\n",
	"First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]\n",
	"\n",
	"Pass: 4 - Sorting a list of 20000 elements\n",
	"First 20 elements of the list to be sorted is: [0, 0, 2, 2, 2, 0, 5, 6, 4, 2, 7, 7, 12, 12, 2, 6, 12, 15, 14, 7]\n",
	"First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
	"\n",
	"Pass: 5 - Sorting a list of 30000 elements\n",
	"First 20 elements of the list to be sorted is: [0, 0, 2, 3, 4, 5, 0, 1, 1, 4, 10, 8, 12, 7, 9, 6, 1, 15, 4, 12]\n",
	"First 20 elements of the sorted List = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
	"List_Size\t\tTime (in seconds)\n",
	"100\t\t0.0013394249999691965\n",
	"1000\t\t0.08856283600016468\n",
	"10000\t\t8.083250383000177\n",
	"20000\t\t32.44908605500041\n",
	"30000\t\t73.21550271900014\n",
	"[100, 1000, 10000, 20000, 30000]\n",
	"[0.0013394249999691965, 0.08856283600016468, 8.083250383000177, 32.44908605500041, 73.21550271900014]\n"
	],
	"name": "stdout"
	},
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7f5ce70aa438>]"
	]
	},
	"metadata": {
	"tags": []
	},
	"execution_count": 10
	},
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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C7ZBDYNgwuOUWmDVLBbzApdKdsgk40d3bA4cDp5nZT4C/Ave4+4HAl4CupIhkyrJlYfrYCy6Atm1h5kz405+gYcO4k0nMqi3iHnwdPW0Q/ThwIjAy2j8Y6J6RhCKFrLQUHnoI2rWDCRPCIg5vvQWHHRZ3MskRKV3YNLN6ZjYbWA2MB5YAa919S/SSZcBeVby3t5kVm1lxSUlJOjKLFIYPPoCf/Sysf3nUUfDuu9CnD9SrF3cyySEpFXF33+ruhwN7A0cDB6f6Ae4+yN2L3L2oZcuWtYwpUkA2b4Y77git7Tlz4LHH4LXX4IAD4k4mOahGd6e4+1ozmwh0ApqaWf2oNb43sDwTAUUKyqxZYaDOrFlhxOXAgdC6ddypJIelcndKSzNrGm3vBJwMLAQmAj2il/UERmUqpEje+/Zb6NcvdJt89hmMHAnPPqsCLtVKpSXeGhhsZvUIRX+4u79kZguAZ8zsdmAW8FgGc4rkrzffhMsug0WLoFcvuPNOaNYs7lSSENUWcXefC3SoZP+HhP5xEamNdevgxhvD3Sdt28L48fDzn8edShJGw+5F4vDii+G2wUGD4IYbwp0nKuBSCyriItm0ejWcf34YuNO0KUydCnffDY0bx51MEkpFXCQb3OGpp0Lr+9ln4bbb4J13oGPHuJNJwmkCLJFM++QT+M1v4JVX4Cc/gUcfDbMPiqSBWuIimVJaCvffHwr2//4v3HsvTJ6sAi5ppZa4SCa89164bXDKFDjlFHj4YWjTJu5UkofUEhdJp82b4fbboX17WLAA/vnPsNqOCrhkiFriIulSXByGzM+dC7/8Jdx3H7RqFXcqyXNqiYvU1YYNYV3Ljh2hpASefz4sWKwCLlmglrhIXUycCJdfDkuWhMcBA8L93yJZopa4SG2sXRuK9oknhuevvx5GX6qAS5apiIvU1AsvhEE7jz8eulHmzg2LN4jEQN0pIqlatQquuQZGjAgLNoweDUVFcaeSAqeWuEh13MOtgoccAqNGhVsIi4tVwCUnqCUusj0ffwy9e4dpYo89NgyZPzjl1QlFMk4tcZHKbN0ahskfemhYXX7gwLB4gwq45Bi1xEW2NX9+GLQzbRp06RIWbdh337hTiVRKLXGRMt99F6aI7dABFi+GJ5+El19WAZeclspCyfuY2UQzW2Bm883sumh/czMbb2aLokctCijJNW0aHHkk9O8PPXqEeU8uugjM4k4msl2ptMS3AL9193bAT4CrzKwd0A+Y4O4HAROi5yLJ8s030KcPdOoEX34Zlk0bMgR23z3uZCIpqbaIu/sKd38n2l4PLAT2AroBg6OXDQa6ZyqkSEa89hr8+Mdwzz1h0YYFC6Br17hTidRIjfrEzawN0AGYBrRy9xXRoZVApbP9mFlvMys2s+KSkpI6RBVJky+/hF694OSToX79sGDDAw/ArrvGnUykxlIu4ma2C/AscL27r6t4zN0d8Mre5+6D3L3I3YtatmxZp7Aidfbss2HQzr/+Bf36wZw5cPzxcacSqbWUbjE0swaEAv60uz8X7V5lZq3dfYWZtQZWZyqkSJ2tWAFXXRWmie3QIax32aFD3KlE6iyVu1MMeAxY6O53Vzg0GugZbfcERqU/nkgducNjj4XW95gxcMcd4U4UFXDJE6m0xI8FLgbeNbPZ0b4/AHcAw83sUmApcE5mIorU0pIlYcj866+HLpNHHoH/+I+4U4mkVbVF3N0nA1XdLHtSeuOIpMHWrfD3v8Mtt4QLlw8+GIr5DhrbJvlHw+4lv7z7bhgyP2NGuF3wwQdh773jTiWSMWqaSH7YtAluvRWOOCLMPDh0aJjvWwVc8pxa4pJ8U6fCZZfBwoVhqPw990CLFnGnEskKtcQlub7+Gq69Fjp3DttjxoRJq1TApYCoiEsyjR0b5voeODDc/z1/fpg2VqTAqIhLsnzxBfznf4aCvfPOMGkS3HcfNGkSdzKRWKiISzK4w7BhYdDO0KFw880wa1ZYMk2kgOnCpuS+5cvhyivD3SZHHhnWu2zfPu5UIjlBLXHJXaWlMGgQtGsXCvff/gZvv60CLlKBWuKSmxYtgssvD9PE/uxnYcj8AQfEnUok56glLrllyxYYMAAOOwxmzw7Fe8IEFXCRKqglLrlj9uwwZP6dd6B7d7j/fthzz7hTieQ0tcQlfhs3wh/+AEVFsGwZjBgBzz2nAi6SArXEJV6TJoUh8x98AJdcAnfdBc2bx51KJDHUEpd4rFsXRloefzx89x28+io88YQKuEgNqYhL9r38chgy/+CDcN11YfrYU06JO5VIIqmIS/aUlMCFF4Z5vnfdFaZMCYs37LJL3MlEEktFXDLPHYYMCYN2RoyAP/4x3IHSqVPcyUQSL5WFkh83s9VmNq/CvuZmNt7MFkWPzTIbUxLr00/hzDNDC3z//UPx7t8fGjaMO5lIXkilJf5P4LRt9vUDJrj7QcCE6LlIudJSeOCB0Pc9cWJYqGHqVPjRj+JOJpJXqi3i7v4msGab3d2AwdH2YKB7mnNJkr3/Pvz0p+Huk44dYd48uP56qFcv7mQieae2feKt3H1FtL0SaFXVC82st5kVm1lxSUlJLT9OEmHzZvjLX8IEVfPmweOPw7hx0LZt3MlE8ladL2y6uwO+neOD3L3I3YtatmxZ14+TXDVzJhx1VJjnu2vXsN7lr34FZnEnE8lrtS3iq8ysNUD0uDp9kSRRvv0WbrwxdJusWgXPPgsjR8Iee8SdTKQg1LaIjwZ6Rts9gVHpiSOJ8sYbYbbBAQPCkPkFC+Dss+NOJVJQqp07xcyGAicALcxsGfBH4A5guJldCiwFzslkSMkR7vDRRzB5MrzyCjzzTLht8LXX4KST4k4nUpCqLeLufn4Vh/SvNt9t3Qpz54aiXfbz2WfhWNOm0Lcv3HZbWLBYRGKhWQyl3LffwvTpYWbByZPDfd3r14dj++wDJ5wAnTuHn0MPhR004FckbiriheyLL8L8JZMnh8I9c2a4TRDCoJyLLiov2vvuG29WEamUinihcIelS8tb2ZMmhdsAAXbcMdwe2KcPHHccHHMMNNNMCiJJoCKer7ZuDQNuyor25MmwfHk4tttucOyxcPHFoZV91FHQqFG8eUWkVlTE88W338KMGeWt7KlTw8ILAHvtFVrYxx1X3p+tIfAieUFFPKnWrCnvz548GYqLwwo5EIr0+eeXF+1999XISZE8pSKeBO7wySflrezJk2H+/HCsQYPQHXL99aFgH3usljgTKSAq4rmotDT0Z5e1sidNCqvAQ1gR55hj4IILyvuzd9op3rwiEhsV8VywcWPoDilrZU+ZAl99FY7tuWd5t8hxx4Vb/9SfLSIRFfE4fPlluPBYVrRnzCjvz27XDs49t/z+7DZt1J8tIlVSEc+Gsv7ssp9580I/d/36UFQE115bfn92ixZxpxWRBFERT7fS0jCbX8X7sz/5JBxr0iQU6nPOCUX7qKM074iI1ImKeF1t2hT6s8suQE6ZAmvXhmOtW4di3bdv6Bo57DD1Z4tIWqmI19TataE/u6yVPX16KOQABx8MPXqUX4hs21b92SKSUSri1Vm27Pv3Z7/7bnl/9pFHwtVXl/dna/k5EckyFfGKSkvDpFAV789eujQc22WXUKh79Ait7I4d1Z8tIrEr7CL+3Xdh+tWK92evWROO7bFHKNZ9+pT3Z9cv7L8uEck9hVWVvvoK3nqrvGhPnx4G2gD88Ifwi1+UD6rZf3/1Z4tIzqtTETez04B7gXrAo+5+R1pSpcvy5d/vGpk7N/Rn16sX+rOvvLJ8vpHdd487rYhIjdW6iJtZPeB+4GRgGTDDzEa7+4J0hUuJe+gW2bSp/CJk2c9HH4XXNG4MnTpB//7l/dmNG2c1pohIJtSlJX40sNjdPwQws2eAbkD6i/gVV8Drr4dCXVawyx7LlhOraPfdQ5dI2UjI9u3Vny0ieakulW0v4NMKz5cBHbd9kZn1BnoD7FvbdRr33ReOOAIaNgxLiVX12KJFuIPkwAPVny0iBSHjzVN3HwQMAigqKvJa/SE33ZTOSCIieWOHOrx3ObBPhed7R/tERCRL6lLEZwAHmVlbM9sROA8YnZ5YIiKSilp3p7j7FjO7GniVcIvh4+4+P23JRESkWnXqE3f3McCYNGUREZEaqkt3ioiIxExFXEQkwVTERUQSTEVcRCTBzL12429q9WFmJcDSWry1BfB5muPEReeSm3QuuSlfzqWu57Gfu1e66kxWi3htmVmxuxfFnSMddC65SeeSm/LlXDJ5HupOERFJMBVxEZEES0oRHxR3gDTSueQmnUtuypdzydh5JKJPXEREKpeUlriIiFRCRVxEJMFyvoib2Wlm9r6ZLTazfnHnqYyZfWxm75rZbDMrjvY1N7PxZrYoemwW7Tcz+0d0PnPN7IgKf07P6PWLzKxnlrI/bmarzWxehX1py25mR0Z/N4uj92ZsyaUqzqW/mS2PvpvZZnZ6hWM3RbneN7NTK+yv9HcumnZ5WrR/WDQFc6bOZR8zm2hmC8xsvpldF+1P3HeznXNJ3HdjZo3MbLqZzYnO5bbtfb6ZNYyeL46Ot6ntOVbJ3XP2hzDF7RJgf2BHYA7QLu5cleT8GGixzb4BQL9oux/w12j7dOAVwICfANOi/c2BD6PHZtF2syxkPx44ApiXiezA9Oi1Fr23S5bPpT/Qt5LXtot+nxoCbaPfs3rb+50DhgPnRdsPAVdk8FxaA0dE202AD6LMiftutnMuiftuor+rXaLtBsC06O+w0s8HrgQeirbPA4bV9hyr+sn1lvi/F2N29++AssWYk6AbMDjaHgx0r7D/Xx68DTQ1s9bAqcB4d1/j7l8C44HTMh3S3d8E1mQie3RsV3d/28Nv7r8q/FnZOpeqdAOecfdN7v4RsJjw+1bp71zUSj0RGBm9v+LfS9q5+wp3fyfaXg8sJKxrm7jvZjvnUpWc/W6iv9+vo6cNoh/fzudX/L5GAidFeWt0jtvLlOtFvLLFmLf35cfFgXFmNtPCwtAArdx9RbS9EmgVbVd1Trl0runKvle0ve3+bLs66mJ4vKz7gZqfyw+Ate6+ZZv9GRf9L3gHQqsv0d/NNucCCfxuzKyemc0GVhP+o7hkO5//78zR8a+ivGmrA7lexJOis7sfAXQBrjKz4ysejFo6ibyXM8nZIw8CBwCHAyuAu+KNUzNmtgvwLHC9u6+reCxp300l55LI78bdt7r74YR1hY8GDo4zT64X8UQsxuzuy6PH1cDzhC92VfS/rESPq6OXV3VOuXSu6cq+PNredn/WuPuq6B9dKfAI4buBmp/LF4Quivrb7M8YM2tAKHpPu/tz0e5EfjeVnUuSvxsAd18LTAQ6befz/505Or5blDd9dSATnf/p+iEsH/choeO/rJP/0LhzbZOxMdCkwvZUQl/23/j+BagB0fYZfP8C1PRof3PgI8LFp2bRdvMsnUMbvn8xMG3Z+f8Xz07P8rm0rrB9A6EfEuBQvn9h6UPCRaUqf+eAEXz/4tWVGTwPI/RT/32b/Yn7brZzLon7boCWQNNoeydgEtC1qs8HruL7FzaH1/Ycq8yUyX9QafpLO51wNXsJcHPceSrJt3/0Fz0HmF+WkdDvNQFYBLxW4R+OAfdH5/MuUFThz+pFuMCxGPhVlvIPJfyv7GZC/9ul6cwOFAHzovcMJBolnMVzeTLKOhcYvU3huDnK9T4V7syo6ncu+q6nR+c4AmiYwXPpTOgqmQvMjn5OT+J3s51zSdx3AxwGzIoyzwNu3d7nA42i54uj4/vX9hyr+tGwexGRBMv1PnEREdkOFXERkQRTERcRSTAVcRGRBFMRFxFJMBVxEZEEUxEXEUmw/wMF0lgwh63VsAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": [],
	"needs_background": "light"
	}
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "JIfEwYDolsMd"
	},
	"source": [
	""
	],
	"execution_count": null,
	"outputs": []
	}
	]
	}