burrito_simulator.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "burrito_simulator.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNj1jVSTMiOxOHgctKAaUHD",
      "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/wingated/cs180_labs/blob/main/burrito_simulator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzpmpaSaBg7K"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCTT_fR_BiLN"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "plt.rcParams['figure.figsize'] = [12, 10]\n"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfN7pHquBp8n"
      },
      "source": [
        "def sample_burrito_type():\n",
        "  btypes = ['beef','chicken','barbacoa','veggie','asada']\n",
        "  bprobs = [ 0.2,  0.15,     0.3,       0.1,     0.25 ]\n",
        "  return np.random.choice( btypes, p=bprobs )\n",
        "\n",
        "def sample_burrito_calories( btype ):\n",
        "  if btype=='beef':\n",
        "    return 100*np.random.randn()+550\n",
        "  if btype=='chicken':\n",
        "    return 100*np.random.randn()+450\n",
        "  if btype=='barbacoa':\n",
        "    return 100*np.random.randn()+720\n",
        "  if btype=='veggie':\n",
        "    return 100*np.random.randn()+400\n",
        "  if btype=='asada':\n",
        "    return 100*np.random.randn()+500\n",
        "\n",
        "def sample_drink_type():\n",
        "  dtypes = ['coke','sprite','fanta','ibc','pepsi']\n",
        "  dprobs = [ 0.1,  0.3,     0.2,    0.3,   0.1 ]\n",
        "  return np.random.choice( dtypes, p=dprobs )\n",
        "\n",
        "def sample_drink_size():\n",
        "  stypes = ['small','medium','large']\n",
        "  sprobs = [ 0.1,   0.8,     0.1 ]\n",
        "  return np.random.choice( stypes, p=sprobs )\n",
        "\n",
        "def sample_drink_calories( dtype, dsize ):\n",
        "  \n",
        "  if dtype=='coke':\n",
        "    cals_per_oz = 50\n",
        "  if dtype=='sprite':\n",
        "    cals_per_oz = 60\n",
        "  if dtype=='fanta':\n",
        "    cals_per_oz = 65\n",
        "  if dtype=='ibc':\n",
        "    cals_per_oz = 45\n",
        "  if dtype=='pepsi':\n",
        "    cals_per_oz = 50\n",
        "\n",
        "  if dsize=='small':\n",
        "    return 10*np.random.randn() + 8*cals_per_oz\n",
        "  if dsize=='medium':\n",
        "    return 20*np.random.randn() + 16*cals_per_oz\n",
        "  if dsize=='large':\n",
        "    return 40*np.random.randn() + 32*cals_per_oz\n",
        "\n",
        "def sample_meal():\n",
        "  my_burrito = sample_burrito_type()\n",
        "  my_drink = sample_drink_type()\n",
        "  my_drink_size = sample_drink_size()\n",
        "  #print( f'Your meal is a {my_burrito} burrito with a {my_drink_size} {my_drink}.' )\n",
        "  burrito_calories = sample_burrito_calories( my_burrito )\n",
        "  drink_calories = sample_drink_calories( my_drink, my_drink_size )\n",
        "  return { \"burrito\":my_burrito, \"drink\":my_drink, \"size\":my_drink_size, \"calories\":burrito_calories + drink_calories }\n",
        "\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yivjNpXEp2l",
        "outputId": "235a0aa3-657e-40a7-e775-8dd78aa437b4"
      },
      "source": [
        "sample_meal()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'burrito': 'asada',\n",
              " 'calories': 913.4163341712767,\n",
              " 'drink': 'sprite',\n",
              " 'size': 'small'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9EQVdYIE7Gk",
        "outputId": "a0516e07-a732-4ec5-a53e-6dfa802f9d0d"
      },
      "source": [
        "df = pd.DataFrame()\n",
        "tmp = []\n",
        "for s in tqdm( range(10000) ):\n",
        "  meal = sample_meal()\n",
        "  tmp.append(meal)\n",
        "df = df.append(tmp,ignore_index=True)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 10000/10000 [00:01<00:00, 9429.78it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "1rfOvNSSFVYS",
        "outputId": "e10baa97-5630-47cf-afd4-ba64da7c7e8c"
      },
      "source": [
        "df"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>burrito</th>\n",
              "      <th>drink</th>\n",
              "      <th>size</th>\n",
              "      <th>calories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>beef</td>\n",
              "      <td>ibc</td>\n",
              "      <td>medium</td>\n",
              "      <td>1350.864154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>beef</td>\n",
              "      <td>fanta</td>\n",
              "      <td>large</td>\n",
              "      <td>2819.605364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "      <td>1659.448582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chicken</td>\n",
              "      <td>pepsi</td>\n",
              "      <td>medium</td>\n",
              "      <td>1211.463930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>sprite</td>\n",
              "      <td>medium</td>\n",
              "      <td>1488.672483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>beef</td>\n",
              "      <td>ibc</td>\n",
              "      <td>medium</td>\n",
              "      <td>1374.073027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>ibc</td>\n",
              "      <td>medium</td>\n",
              "      <td>1220.612789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>asada</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "      <td>1550.427875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "      <td>1563.705733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>chicken</td>\n",
              "      <td>ibc</td>\n",
              "      <td>large</td>\n",
              "      <td>1975.164909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       burrito   drink    size     calories\n",
              "0         beef     ibc  medium  1350.864154\n",
              "1         beef   fanta   large  2819.605364\n",
              "2     barbacoa   fanta  medium  1659.448582\n",
              "3      chicken   pepsi  medium  1211.463930\n",
              "4     barbacoa  sprite  medium  1488.672483\n",
              "...        ...     ...     ...          ...\n",
              "9995      beef     ibc  medium  1374.073027\n",
              "9996  barbacoa     ibc  medium  1220.612789\n",
              "9997     asada   fanta  medium  1550.427875\n",
              "9998  barbacoa   fanta  medium  1563.705733\n",
              "9999   chicken     ibc   large  1975.164909\n",
              "\n",
              "[10000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "JUE1WXVrFXd3",
        "outputId": "ba39754f-f988-4e3a-c74a-36c0dfa94278"
      },
      "source": [
        "sns.displot( data=df, x='calories', height=8, aspect=10/8)\n",
        "#sns.displot( data=df, x='calories', hue='drink', kind='kde', height=8, aspect=10/8)\n",
        "#sns.displot( data=df, x='calories', hue='size', kind='kde', height=8, aspect=10/8)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f95b170a890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "qzvNly6wHE7I",
        "outputId": "d80199ea-2b18-461b-8bfb-8208e55f514f"
      },
      "source": [
        ""
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>burrito</th>\n",
              "      <th>calories</th>\n",
              "      <th>drink</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>2550.679862</td>\n",
              "      <td>fanta</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>beef</td>\n",
              "      <td>2752.458183</td>\n",
              "      <td>fanta</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>chicken</td>\n",
              "      <td>1424.403855</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>1409.393882</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>chicken</td>\n",
              "      <td>1407.511986</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>969</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>1461.878010</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>979</th>\n",
              "      <td>barbacoa</td>\n",
              "      <td>1470.139051</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>980</th>\n",
              "      <td>chicken</td>\n",
              "      <td>1416.592585</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>chicken</td>\n",
              "      <td>1548.493113</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>987</th>\n",
              "      <td>veggie</td>\n",
              "      <td>1442.202409</td>\n",
              "      <td>fanta</td>\n",
              "      <td>medium</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>173 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      burrito     calories  drink    size\n",
              "6    barbacoa  2550.679862  fanta   large\n",
              "11       beef  2752.458183  fanta   large\n",
              "17    chicken  1424.403855  fanta  medium\n",
              "21   barbacoa  1409.393882  fanta  medium\n",
              "23    chicken  1407.511986  fanta  medium\n",
              "..        ...          ...    ...     ...\n",
              "969  barbacoa  1461.878010  fanta  medium\n",
              "979  barbacoa  1470.139051  fanta  medium\n",
              "980   chicken  1416.592585  fanta  medium\n",
              "985   chicken  1548.493113  fanta  medium\n",
              "987    veggie  1442.202409  fanta  medium\n",
              "\n",
              "[173 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OU3rGB-5JE0W"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}