Typewriter_Monkeys.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Typewriter Monkeys",
      "provenance": [],
      "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/github/jasonmhead/orderFromChaos/blob/master/Typewriter_Monkeys.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sns0Gts3ZVUG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import secrets\n",
        "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXR2-JEuahWB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def random_alpha():\n",
        "  #Getting systemRandom class instance out of secrets module\n",
        "  secretsGenerator = secrets.SystemRandom()\n",
        "\n",
        "  #Secure Random choice using secrets\n",
        "  alpha_list = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n",
        "  secure_choice = secretsGenerator.choice(alpha_list)\n",
        "  return secure_choice"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5I8ANRBIcaqI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## match any alpha string ##\n",
        "## returns the count of number of match attempts to success ##\n",
        "def random_match(match_what):\n",
        "  \n",
        "  # make sure the input is a string\n",
        "  match_what = str(match_what)\n",
        "  \n",
        "  # make sure the string is lower case\n",
        "  match_what = match_what.lower()\n",
        "  \n",
        "  # if the input isn't alpha, return 0\n",
        "  if(match_what.isalpha() == False):\n",
        "    return 0;\n",
        "  \n",
        "  # set limit to match attempts to prevent un-ending loop\n",
        "  limit_int = 10000000\n",
        "  \n",
        "  # keep track of the attempts\n",
        "  attempt_count = 0\n",
        "  \n",
        "  # try to randomly match\n",
        "  for lp in range(limit_int):\n",
        "    \n",
        "    # get random letter / word\n",
        "    rand_selection = ''\n",
        "    for lttr in match_what:\n",
        "      rand_letter = random_alpha()\n",
        "      rand_selection = rand_selection + str(rand_letter)\n",
        "    \n",
        "    # count the attempts\n",
        "    attempt_count = attempt_count + 1\n",
        "    \n",
        "    # if it matches return the attempt count\n",
        "    if(rand_selection == match_what):\n",
        "      return attempt_count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyH0lyUTdF_0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## attempt matches for x times ##\n",
        "def attempt_matches(match_what, how_many_times):\n",
        "  # try to randomly match for x times\n",
        "  attempt_counts = []\n",
        "  for lp in range(how_many_times):\n",
        "    attempt_count = random_match(match_what)\n",
        "    attempt_counts.append(attempt_count)\n",
        "  return attempt_counts"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9452OQ2P6Krz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def start_monkeys(match_this, attempts):\n",
        "  match_data = attempt_matches(match_this, attempts)\n",
        "\n",
        "  count_obj = range(1,len(match_data)+1)\n",
        "  y_pos = np.arange(len(match_data))\n",
        "  performance = match_data\n",
        "\n",
        "  plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
        "  plt.xticks(y_pos, count_obj)\n",
        "  plt.ylabel('Usage')\n",
        "  plt.title('Monkeys Banging on Keys Matching \"'+match_this+'\" -- '+str(attempts)+' independant attempts')\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57QhYQ56m4mK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "923d0c8b-8a8b-461c-f3a2-5f634a75448a"
      },
      "source": [
        "#####################\n",
        "##  what to match  ##\n",
        "## WARNING: four letter words start to take a lot longer\n",
        "## and 3 letter words generated a number of times as well\n",
        "## keep your numbers and letters low at first\n",
        "match_this = \"and\"\n",
        "attempts = 20;\n",
        "####################\n",
        "\n",
        "start_monkeys(match_this, attempts)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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99tsYNmwYMjMzsWLFCjRq1Kjc/SY1Zs+ejdjYWHTo0AHvvfee9KWlefPmFa5T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\nQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myUhbv30o1RN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}