aristhera/zyklen.ipynb

## zyklen.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "zyklen.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNjpjc3Uv2JkP/Z/M34mf97",
      "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/aristhera/7581fcb434ab35b04aca4a60aa29ade3/zyklen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i12Q87m1W142",
        "colab_type": "code",
        "outputId": "e1ca0faf-ddba-4e7e-fa6a-ad9330e23be1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 59
        }
      },
      "source": [
        "from collections import defaultdict\n",
        "result=defaultdict(int)\n",
        "x=1000000\n",
        "def zyklen(z):    \n",
        "    infected=1000\n",
        "    days=1\n",
        "    while infected<x:\n",
        "        infected*=2\n",
        "        days+=z\n",
        "        result[days]+=infected\n",
        "    return result\n",
        "print(zyklen(5.5))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "defaultdict(<class 'int'>, {6.5: 2000, 12.0: 4000, 17.5: 8000, 23.0: 16000, 28.5: 32000, 34.0: 64000, 39.5: 128000, 45.0: 256000, 50.5: 512000, 56.0: 1024000})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twfADiI7Xq_5",
        "colab_type": "code",
        "outputId": "3c75b0c0-d832-4962-8e2b-344cf5e1935e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "result\n",
        "plt.plot(list(result.keys()), list(result.values()))\n",
        "plt.ylabel(\"infected\")\n",
        "plt.xlabel(\"days\")\n",
        "plt.title(\"growth infections\")\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7MC3vHUimIBAROUkLKqqI5RiXTRwQdSlviYJAROQkJLqFqnn7aX3pW1QQdTlvSahBYGaX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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "zyklen.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"authorship_tag": "ABX9TyNjpjc3Uv2JkP/Z/M34mf97",
	"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/aristhera/7581fcb434ab35b04aca4a60aa29ade3/zyklen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "i12Q87m1W142",
	"colab_type": "code",
	"outputId": "e1ca0faf-ddba-4e7e-fa6a-ad9330e23be1",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 59
	}
	},
	"source": [
	"from collections import defaultdict\n",
	"result=defaultdict(int)\n",
	"x=1000000\n",
	"def zyklen(z): \n",
	" infected=1000\n",
	" days=1\n",
	" while infected<x:\n",
	" infected*=2\n",
	" days+=z\n",
	" result[days]+=infected\n",
	" return result\n",
	"print(zyklen(5.5))"
	],
	"execution_count": 1,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"defaultdict(<class 'int'>, {6.5: 2000, 12.0: 4000, 17.5: 8000, 23.0: 16000, 28.5: 32000, 34.0: 64000, 39.5: 128000, 45.0: 256000, 50.5: 512000, 56.0: 1024000})\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "twfADiI7Xq_5",
	"colab_type": "code",
	"outputId": "3c75b0c0-d832-4962-8e2b-344cf5e1935e",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 295
	}
	},
	"source": [
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline\n",
	"\n",
	"result\n",
	"plt.plot(list(result.keys()), list(result.values()))\n",
	"plt.ylabel(\"infected\")\n",
	"plt.xlabel(\"days\")\n",
	"plt.title(\"growth infections\")\n",
	"plt.show()"
	],
	"execution_count": 3,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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8cfpo8mIaJiLTKQhEstj6nXU8vKySR5dvZ+f+Roq75TJzyiCuOXMYk4f2iro8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\nTkSuQmCC\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"tags": []
	}
	}
	]
	}
	]
	}