BARF.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "efc9f670",
   "metadata": {},
   "source": [
    "# BARF Plan according to Nadine Wolf - Das BARF Buch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install dependencies\n",
    "! pip install -q gamspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d45bc7e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import pandas as pd\n",
    "from gamspy import Equation, Model, Container, Sum, Sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
        "tags": ["hide-input"]
    },
   "outputs": [],
   "source": [
    "# prepare notebook\n",
    "class Product:\n",
    "    def __init__(self, name, price, volume, fat, category,\n",
    "                 muscle=0, lung=0, liver=0, kidney=0, spleen=0, heart=0,\n",
    "                 fish=0, pansen=0, blaettermagen=0, rfk=0, vegetable=0, fruit=0, inventory=0):\n",
    "        self.name = name\n",
    "        self.price = price\n",
    "        self.volume = volume\n",
    "        self.fat = fat\n",
    "        self.category = category\n",
    "        self.inventory = inventory\n",
    "        self.muscle = muscle\n",
    "        self.fish = fish\n",
    "        self.liver = liver\n",
    "        self.lung = lung\n",
    "        self.kidney = kidney\n",
    "        self.spleen = spleen\n",
    "        self.heart = heart\n",
    "        self.pansen = pansen\n",
    "        self.blaettermagen = blaettermagen\n",
    "        self.rfk = rfk\n",
    "        self.vegetable = vegetable\n",
    "        self.fruit = fruit\n",
    "        self.price_per_kg = self.calc_price_per_kg()\n",
    "        self.composition = {\n",
    "            'Muscle': muscle + fish,\n",
    "            'Intestinals': liver + lung + kidney + spleen + heart,\n",
    "            'Pansen': pansen,\n",
    "            'Blättermagen': blaettermagen,\n",
    "            'RFK': rfk,\n",
    "            'Vegetable': vegetable,\n",
    "            'Fruit': fruit\n",
    "        }\n",
    "\n",
    "    def calc_price_per_kg(self):\n",
    "        return round((self.price / self.volume) * 1000, 2)\n",
    "\n",
    "\n",
    "def process_products(products, comp):\n",
    "    df = pd.DataFrame({\n",
    "        'Name': [p.name for p in products], 'Category': [p.category for p in products],\n",
    "        'Inventory': [p.inventory for p in products],\n",
    "        'Price [€/kg]': [p.price_per_kg for p in products], 'Price': [p.price for p in products],\n",
    "        'Volume': [p.volume for p in products], 'Fat': [p.fat for p in products]\n",
    "    })\n",
    "\n",
    "    cont_comp = pd.DataFrame([[c, p.name, p.composition[c]]\n",
    "                             for c in comp.index for p in products], columns=['Component', 'Name', 'Share'])\n",
    "\n",
    "    a = []\n",
    "    for p in products:\n",
    "        a.append(['Liver', p.name, p.liver])\n",
    "        a.append(['Heart', p.name, p.heart])\n",
    "        a.append(['Kidney', p.name, p.kidney])\n",
    "        a.append(['Spleen', p.name, p.spleen])\n",
    "    int_comp = pd.DataFrame(a)\n",
    "\n",
    "    return df, cont_comp, int_comp\n",
    "\n",
    "\n",
    "def check_components(df):\n",
    "    assert df['Share'].sum() == 2, 'Components do not add up to 100%'\n",
    "\n",
    "\n",
    "def calc_demand(mod, comp, weeklydemand):\n",
    "    share = pd.DataFrame([\n",
    "        ['Muscle', mod['Meat']],\n",
    "        ['Intestinals', mod['Meat']],\n",
    "        ['Pansen', mod['Meat']],\n",
    "        ['Blättermagen', mod['Meat']],\n",
    "        ['RFK', mod['Meat']],\n",
    "        ['Vegetable', mod['Veg & Fruit']],\n",
    "        ['Fruit', mod['Veg & Fruit']]\n",
    "    ], columns=['Component', 'Share']).set_index('Component')\n",
    "\n",
    "    comp_pie = share * comp\n",
    "    demand = comp_pie * weeklydemand\n",
    "\n",
    "    return comp_pie, demand\n",
    "\n",
    "\n",
    "# inventory\n",
    "def add_inventory(df, product, inventory):\n",
    "    assert df['Name'].isin([product]).any(), f'{product} not in DataFrame'\n",
    "    df.loc[df['Name'] == product, 'Inventory'] = inventory\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d73275e",
   "metadata": {},
   "source": [
    "## Data\n",
    "### Planning Horizon\n",
    "- `weeks`: number of weeks to account for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3045729",
   "metadata": {},
   "outputs": [],
   "source": [
    "weeks = 6"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e00b8e01",
   "metadata": {},
   "source": [
    "### Daily Food Demand\n",
    "The daily food demand is determined by three components: \n",
    "- body weight (`weight`)\n",
    "- base food ratio (`perc`)\n",
    "- activity level (`activity`).\n",
    "\n",
    "The body weight represents the targeted and ideal body weight of the dog. The base food ratio is usually calculated with 2% from the optimal body weight. The activity level is usually set as follows:\n",
    "\n",
    "| Activity per day | Factor |\n",
    "| --- | --- |\n",
    "| <2 h | 1 |\n",
    "| 2-3 h | 1.25 |\n",
    "| 3-4 h | 1.5 |\n",
    "| 4-5 h | 1.75 |\n",
    "| 5-6 h | 2.5 |\n",
    "| >6 h | 2.5 | \n",
    "\n",
    "A neutered dog requires less food. Usually the activity level is set to 0.9 for neutered dogs.\n",
    "\n",
    "On a side note: The activity level can vary from dog to dog. Adjust the activity level by regularly checking the body weight until you find a configuration where the dog remains around the optimal weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc6a467b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily food demand: 700 g\n",
      "Weekly food demand: 4900 g\n"
     ]
    }
   ],
   "source": [
    "weight = 35\n",
    "perc =  0.02 # percent of body weight \n",
    "activity = 1 # dog activity level \n",
    "\n",
    "daylydemand = weight * perc * activity * 1000\n",
    "weeklydemand = daylydemand * 7\n",
    "\n",
    "print('Daily food demand:', round(daylydemand), 'g')\n",
    "print('Weekly food demand:', round(weeklydemand), 'g')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029933f3",
   "metadata": {},
   "source": [
    "### Fat\n",
    "Fat is the main energy source for a dog. The optimal amount of fat in a food ratio is between 11% and 15%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6084c08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fat_lb = 0.11 # lower bound of fat\n",
    "fat_ub = 0.15 # upper bound of fat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd69bc3",
   "metadata": {},
   "source": [
    "### Food Components\n",
    "An optimal BARF ration withouth grain consists of 80% animal components (`Meat`) and 20% plant-based components (`Veg & Fruit`). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77044883",
   "metadata": {},
   "outputs": [],
   "source": [
    "modules_share = {\n",
    "    'Meat': 0.8,\n",
    "    'Veg & Fruit': 0.2\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20970779",
   "metadata": {},
   "source": [
    "The meat share is ideally composed as:\n",
    "- 50% muscle meat\n",
    "- 20% pansen / blättermagen\n",
    "- 15% intestinals (heart, liver, kidney, spleen)\n",
    "- 15% raw flesh bones (not bare bones - rather 1/2 bone, 1/2 meat) - RFK\n",
    "\n",
    "If the dog does not tolerate pansen, you substitute with muscle meat.\n",
    "\n",
    "RFK can be hard and soft. Hard bones are, e.g., sternum and rips of lamb and cow. Soft bones are, e.g., spine, neck, and chest of chicken and rabbit. \n",
    "\n",
    "Some dogs tend to throw up bigger pieces of hard bones at night. Soft bones are easier to digest. If you only feed soft bones the RFK share is increased to 20% and the muscle share decreased to 45%. \n",
    "\n",
    "The vegtable share is ideally composed as:\n",
    "- 75% vegetables\n",
    "- 25% fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3be7e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "\n",
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       "        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>Share</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Muscle</th>\n",
       "      <td>1764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intestinals</th>\n",
       "      <td>588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pansen</th>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blättermagen</th>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFK</th>\n",
       "      <td>784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegetable</th>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fruit</th>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Share\n",
       "Component           \n",
       "Muscle        1764.0\n",
       "Intestinals    588.0\n",
       "Pansen         392.0\n",
       "Blättermagen   392.0\n",
       "RFK            784.0\n",
       "Vegetable      735.0\n",
       "Fruit          245.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "component_share = pd.DataFrame([\n",
    "    ['Muscle', 0.45],\n",
    "    ['Intestinals', 0.15],\n",
    "    ['Pansen', 0.2 / 2],\n",
    "    ['Blättermagen', 0.2 / 2],\n",
    "    ['RFK', 0.2],\n",
    "    ['Vegetable', 0.75],\n",
    "    ['Fruit', 0.25]\n",
    "], columns=['Component', 'Share']).set_index('Component')\n",
    "\n",
    "# check if components add up to 100%\n",
    "check_components(component_share)\n",
    "\n",
    "# calc demand\n",
    "components_pie_chart, demand_df = calc_demand(modules_share, component_share, weeklydemand)\n",
    "\n",
    "demand_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a689539b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Components'}, ylabel='Share'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot components\n",
    "components_pie_chart.plot(kind='pie', y='Share', title='Components', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeee3241",
   "metadata": {},
   "source": [
    "### Intestinals\n",
    "The optimal composition of intestinals:\n",
    "- $\\frac{2}{5}$ liver\n",
    "- $\\frac{1}{5}$ heart\n",
    "- $\\frac{1}{5}$ kidney\n",
    "- $\\frac{1}{5}$ spleen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3bf52da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Intestinals'}, ylabel='Share'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intestinal_share_df = pd.DataFrame([\n",
    "    ['Liver', 2/5],\n",
    "    ['Heart', 1/5],\n",
    "    ['Kidney', 1/5],\n",
    "    ['Spleen', 1/5]\n",
    "], columns=['Intestinal', 'Share']).set_index('Intestinal')\n",
    "intestinal_share_df.plot(kind='pie', y='Share', title='Intestinals', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ce93e0",
   "metadata": {},
   "source": [
    "### Fish\n",
    "Fisch ist ein wichtiger Bestandteil der BARF Ration, um den Vitamin D Haushalt zu decken. Es wird empfohlen 1x pro Woche den Muskelfleischanteil durch Fisch zu ersetzen. Dazu eignen sich insbesondere Vitamin D haltige Fische wie Sprotten, Hering, Lachs, Sardellen und Forelle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eec36c9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The suggested amount of fish per week is 560g.\n"
     ]
    }
   ],
   "source": [
    "weekly_fish_amount = round(daylydemand * modules_share['Meat'])\n",
    "print(f'The suggested amount of fish per week is {weekly_fish_amount}g.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5b563b",
   "metadata": {},
   "source": [
    "You can manually adjust the amount of fish if desired. If you do not want to feed fish, set `weekly_fish_amount` to 0. In this case, remember to substitute fish with Lebertran to supply enough vitamin D. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "709d0dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjusted fish amount\n",
    "weekly_fish_amount = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9717f859",
   "metadata": {},
   "source": [
    "## Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5bc7bc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "products = [\n",
    "    # Pferd\n",
    "    Product(name='Pferdefleisch', category='Horse', volume=500, price=3.89, muscle=1, fat=0.23),\n",
    "    Product(name='PferdefleischwuerfelFettig', category='Horse', volume=2000, price=13.99, muscle=1, fat=0.254),\n",
    "    Product(name='PferdefleischKlumpen', category='Horse', volume=2000, price=17.49, muscle=1, fat=0.05),\n",
    "    Product(name='Pferdeherz', category='Horse', volume=500, price=4.98, heart=1, fat=0.12),\n",
    "    Product(name='Pferdemilz', category='Horse', volume=500, price=5.69, spleen=1, fat=0.051),\n",
    "    Product(name='Pferdeniere', category='Horse', volume=500, price=4.29, kidney=1, fat=0.085),\n",
    "    Product(name='Pferdeleber', category='Horse', volume=500, price=4.29, liver=1, fat=0.04),\n",
    "    Product(name='Pferdefett', category='Horse', volume=500, price=3.48, muscle=1, fat=0.91),\n",
    "    Product(name='Pferdeknochenbrei', category='Horse', volume=250, price=3.19, rfk=1, fat=0.135),\n",
    "    # Rind\n",
    "    Product(name='Kalbsfleischabschnitte', category='Beef', volume=500, price=3.29, muscle=1, fat=0.35),\n",
    "    Product(name='RindfleischamStueck', category='Beef', volume=100, price=7.88, muscle=1, fat=0.12),\n",
    "    Product(name='RindfleischKlumpen', category='Beef', volume=2000, price=16.99, muscle=1, fat=0.12),\n",
    "    Product(name='Rindermuskelfleischwuerfel', category='Beef', volume=500, price=4.29, muscle=1, fat=0.09),\n",
    "    Product(name='Rinderfett', category='Beef', volume=500, price=2.98, muscle=1, fat=0.91),\n",
    "    Product(name='RinderherzKlumpen', category='Beef', volume=2000, price=10.49, heart=1, fat=0.05),\n",
    "    Product(name='Rinderherzwuerfel', category='Beef', volume=500, price=3.59, heart=1, fat=0.05),\n",
    "    Product(name='RinderniereAmStueck', category='Beef', volume=500, price=2.49, kidney=1, fat=0.085),\n",
    "    Product(name='RindermilzWuerfel', category='Beef', volume=500, price=2.49, spleen=1, fat=0.051),\n",
    "    Product(name='RinderleberAmStueck', category='Beef', volume=500, price=2.49, liver=1, fat=0.085),\n",
    "    # Product(name='RinderLeberNierenMilzMix', category='Beef', volume=500, price=2.78, liver=0.5, kidney=0.25, spleen=0.25, fat=0.039),\n",
    "    Product(name='Rinderpansen', category='Beef', volume=500, price=2.19, pansen=1, fat=0.05),\n",
    "    Product(name='Rinderblaettermagen', category='Beef', volume=500, price=2.29, blaettermagen=1, fat=0.05),\n",
    "    # # Lamm\n",
    "    Product(name='Lammbauchlappen', category='Lamb', volume=1000, price=10.19, muscle=1, fat=0.3),\n",
    "    Product(name='Lammfleisch', category='Lamb', volume=500, price=4.38, muscle=1, fat=0.048),\n",
    "    Product(name='Lammfleischabschnitte', category='Lamb', volume=500, price=5.29, muscle=1, fat=0.15),\n",
    "    Product(name='Lammfett', category='Lamb', volume=500, price=3.98, muscle=1, fat=0.91),\n",
    "    Product(name='Lammpansen', category='Lamb', volume=500, price=3.88, pansen=1, fat=0.14),\n",
    "    Product(name='LammleberStuecke', category='Lamb', volume=500, price=3.28, liver=1, fat=0.05),\n",
    "    Product(name='LammniereStuecke', category='Lamb', volume=500, price=3.78, kidney=1, fat=0.06),\n",
    "    Product(name='LammmilzStuecke', category='Lamb', volume=500, price=4.68, spleen=1, fat=0.027),\n",
    "    # Product(name='LamminnereienMix', category='Lamb', volume=250, price=2.08, heart=0.4, kidney=0.3, liver=0.3, fat=0.07),\n",
    "    Product(name='LammherzStuecke', category='Lamb', volume=500, price=4.18, heart=1, fat=0.05),\n",
    "    # # Wild\n",
    "    Product(name='WildfleischKlumpen', category='Deer', volume=2000, price=16.99, muscle=1, fat=0.16),\n",
    "    # Product(name='Elchfleisch', category='Deer', volume=500, price=3.69, muscle=1, fat=0.16),\n",
    "    # # Geflügel\n",
    "    # Product(name='Truthahnfleisch', category='Chicken', volume=500, price=2.98, muscle=1, fat=0.07), # zu gewolft \n",
    "    Product(name='Haehnchenherz', category='Chicken', volume=500, price=3.49, heart=1, fat=0.05),\n",
    "    Product(name='Huehnerfett', category='Chicken',  volume=1000, price=3.99, muscle=1, fat=0.995),\n",
    "    Product(name='Huehnerfleisch', category='Chicken', volume=1000, price=8.99, muscle=1, fat=0.142),\n",
    "    Product(name='HuehnerfleischStuecke', category='Chicken', volume=500, price=3.79, muscle=1, fat=0.142),\n",
    "    # Product(name='Gefluegelinnereien-Mix', category='Chicken', volume=500, price=2.98, heart=0.5, muscle=0.5, fat=0.05),\n",
    "    # # Fish\n",
    "    Product(name='Lachs', category='Fish', volume=500, price=3.19, fish=1, fat=0.075),\n",
    "    # # Knochen\n",
    "    Product(name='Kaninchenkarkasse', category='Rabbit', volume=1000, price=5.49, rfk=1, fat=0.15),\n",
    "    # Product(name='Rinderbrustbein', category='Beef', volume=1000, price=3.68, rfk=1, fat=0.14),\n",
    "    # Product(name='Lammrippe', category='Lamb', volume=1000, price=6.53, rfk=1, fat=0.24),\n",
    "    # Product(name='Lammbrustbein', category='Lamb', volume=1000, price=5.78, rfk=1, fat=0.17),\n",
    "    # Product(name='Wildrippe', category='Deer', volume=1000, price=5.68, rfk=1, fat=0.24),\n",
    "    Product(name='Huehnerhaelse', category='Chicken', volume=1000, price=3.99, rfk=1, fat=0.22),\n",
    "    Product(name='SpareribsVomPferd', category='Horse', volume=1000, price=7.48, rfk=1, fat=0.16),\n",
    "    # Vegetable & Fruit\n",
    "    Product(name='Vegetables', category='Vegetables', volume=1, price=0.001, vegetable=1, fat=0),\n",
    "    Product(name='Fruit', category='Fruits', volume=1, price=0.001, fruit=1, fat=0)\n",
    "] \n",
    "\n",
    "df, content_components_df, intest_components_df = process_products(products, component_share)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e48c6e1",
   "metadata": {},
   "source": [
    "## Inventory\n",
    "Account for the products you have in your inventory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90d1ec80",
   "metadata": {},
   "outputs": [
    {
     "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>Category</th>\n",
       "      <th>Inventory</th>\n",
       "      <th>Price [€/kg]</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Fat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fruit</th>\n",
       "      <td>Fruits</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegetables</th>\n",
       "      <td>Vegetables</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huehnerhaelse</th>\n",
       "      <td>Chicken</td>\n",
       "      <td>750</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.990</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huehnerfett</th>\n",
       "      <td>Chicken</td>\n",
       "      <td>0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.990</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rinderpansen</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>4.38</td>\n",
       "      <td>2.190</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rinderblaettermagen</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>4.58</td>\n",
       "      <td>2.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RinderniereAmStueck</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>4.98</td>\n",
       "      <td>2.490</td>\n",
       "      <td>500</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RindermilzWuerfel</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>4.98</td>\n",
       "      <td>2.490</td>\n",
       "      <td>500</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RinderleberAmStueck</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>4.98</td>\n",
       "      <td>2.490</td>\n",
       "      <td>500</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RinderherzKlumpen</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>10.490</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kaninchenkarkasse</th>\n",
       "      <td>Rabbit</td>\n",
       "      <td>0</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.490</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rinderfett</th>\n",
       "      <td>Beef</td>\n",
       "      <td>300</td>\n",
       "      <td>5.96</td>\n",
       "      <td>2.980</td>\n",
       "      <td>500</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lachs</th>\n",
       "      <td>Fish</td>\n",
       "      <td>0</td>\n",
       "      <td>6.38</td>\n",
       "      <td>3.190</td>\n",
       "      <td>500</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LammleberStuecke</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>400</td>\n",
       "      <td>6.56</td>\n",
       "      <td>3.280</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalbsfleischabschnitte</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>6.58</td>\n",
       "      <td>3.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdefett</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>3.480</td>\n",
       "      <td>500</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haehnchenherz</th>\n",
       "      <td>Chicken</td>\n",
       "      <td>0</td>\n",
       "      <td>6.98</td>\n",
       "      <td>3.490</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PferdefleischwuerfelFettig</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>13.990</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rinderherzwuerfel</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>7.18</td>\n",
       "      <td>3.590</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpareribsVomPferd</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>7.48</td>\n",
       "      <td>7.480</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LammniereStuecke</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>7.56</td>\n",
       "      <td>3.780</td>\n",
       "      <td>500</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HuehnerfleischStuecke</th>\n",
       "      <td>Chicken</td>\n",
       "      <td>0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>3.790</td>\n",
       "      <td>500</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lammpansen</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>7.76</td>\n",
       "      <td>3.880</td>\n",
       "      <td>500</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdefleisch</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>7.78</td>\n",
       "      <td>3.890</td>\n",
       "      <td>500</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lammfett</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>7.96</td>\n",
       "      <td>3.980</td>\n",
       "      <td>500</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LammherzStuecke</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>8.36</td>\n",
       "      <td>4.180</td>\n",
       "      <td>500</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RindfleischKlumpen</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>8.49</td>\n",
       "      <td>16.990</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WildfleischKlumpen</th>\n",
       "      <td>Deer</td>\n",
       "      <td>0</td>\n",
       "      <td>8.49</td>\n",
       "      <td>16.990</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdeleber</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>8.58</td>\n",
       "      <td>4.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdeniere</th>\n",
       "      <td>Horse</td>\n",
       "      <td>50</td>\n",
       "      <td>8.58</td>\n",
       "      <td>4.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rindermuskelfleischwuerfel</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>8.58</td>\n",
       "      <td>4.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PferdefleischKlumpen</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>8.74</td>\n",
       "      <td>17.490</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lammfleisch</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>8.76</td>\n",
       "      <td>4.380</td>\n",
       "      <td>500</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huehnerfleisch</th>\n",
       "      <td>Chicken</td>\n",
       "      <td>0</td>\n",
       "      <td>8.99</td>\n",
       "      <td>8.990</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LammmilzStuecke</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>9.36</td>\n",
       "      <td>4.680</td>\n",
       "      <td>500</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdeherz</th>\n",
       "      <td>Horse</td>\n",
       "      <td>0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>4.980</td>\n",
       "      <td>500</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lammbauchlappen</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.190</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lammfleischabschnitte</th>\n",
       "      <td>Lamb</td>\n",
       "      <td>0</td>\n",
       "      <td>10.58</td>\n",
       "      <td>5.290</td>\n",
       "      <td>500</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdemilz</th>\n",
       "      <td>Horse</td>\n",
       "      <td>200</td>\n",
       "      <td>11.38</td>\n",
       "      <td>5.690</td>\n",
       "      <td>500</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pferdeknochenbrei</th>\n",
       "      <td>Horse</td>\n",
       "      <td>200</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.190</td>\n",
       "      <td>250</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RindfleischamStueck</th>\n",
       "      <td>Beef</td>\n",
       "      <td>0</td>\n",
       "      <td>78.80</td>\n",
       "      <td>7.880</td>\n",
       "      <td>100</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Category  Inventory  Price [€/kg]   Price  \\\n",
       "Name                                                                      \n",
       "Fruit                           Fruits          0          1.00   0.001   \n",
       "Vegetables                  Vegetables          0          1.00   0.001   \n",
       "Huehnerhaelse                  Chicken        750          3.99   3.990   \n",
       "Huehnerfett                    Chicken          0          3.99   3.990   \n",
       "Rinderpansen                      Beef          0          4.38   2.190   \n",
       "Rinderblaettermagen               Beef          0          4.58   2.290   \n",
       "RinderniereAmStueck               Beef          0          4.98   2.490   \n",
       "RindermilzWuerfel                 Beef          0          4.98   2.490   \n",
       "RinderleberAmStueck               Beef          0          4.98   2.490   \n",
       "RinderherzKlumpen                 Beef          0          5.25  10.490   \n",
       "Kaninchenkarkasse               Rabbit          0          5.49   5.490   \n",
       "Rinderfett                        Beef        300          5.96   2.980   \n",
       "Lachs                             Fish          0          6.38   3.190   \n",
       "LammleberStuecke                  Lamb        400          6.56   3.280   \n",
       "Kalbsfleischabschnitte            Beef          0          6.58   3.290   \n",
       "Pferdefett                       Horse          0          6.96   3.480   \n",
       "Haehnchenherz                  Chicken          0          6.98   3.490   \n",
       "PferdefleischwuerfelFettig       Horse          0          7.00  13.990   \n",
       "Rinderherzwuerfel                 Beef          0          7.18   3.590   \n",
       "SpareribsVomPferd                Horse          0          7.48   7.480   \n",
       "LammniereStuecke                  Lamb          0          7.56   3.780   \n",
       "HuehnerfleischStuecke          Chicken          0          7.58   3.790   \n",
       "Lammpansen                        Lamb          0          7.76   3.880   \n",
       "Pferdefleisch                    Horse          0          7.78   3.890   \n",
       "Lammfett                          Lamb          0          7.96   3.980   \n",
       "LammherzStuecke                   Lamb          0          8.36   4.180   \n",
       "RindfleischKlumpen                Beef          0          8.49  16.990   \n",
       "WildfleischKlumpen                Deer          0          8.49  16.990   \n",
       "Pferdeleber                      Horse          0          8.58   4.290   \n",
       "Pferdeniere                      Horse         50          8.58   4.290   \n",
       "Rindermuskelfleischwuerfel        Beef          0          8.58   4.290   \n",
       "PferdefleischKlumpen             Horse          0          8.74  17.490   \n",
       "Lammfleisch                       Lamb          0          8.76   4.380   \n",
       "Huehnerfleisch                 Chicken          0          8.99   8.990   \n",
       "LammmilzStuecke                   Lamb          0          9.36   4.680   \n",
       "Pferdeherz                       Horse          0          9.96   4.980   \n",
       "Lammbauchlappen                   Lamb          0         10.19  10.190   \n",
       "Lammfleischabschnitte             Lamb          0         10.58   5.290   \n",
       "Pferdemilz                       Horse        200         11.38   5.690   \n",
       "Pferdeknochenbrei                Horse        200         12.76   3.190   \n",
       "RindfleischamStueck               Beef          0         78.80   7.880   \n",
       "\n",
       "                            Volume    Fat  \n",
       "Name                                       \n",
       "Fruit                            1  0.000  \n",
       "Vegetables                       1  0.000  \n",
       "Huehnerhaelse                 1000  0.220  \n",
       "Huehnerfett                   1000  0.995  \n",
       "Rinderpansen                   500  0.050  \n",
       "Rinderblaettermagen            500  0.050  \n",
       "RinderniereAmStueck            500  0.085  \n",
       "RindermilzWuerfel              500  0.051  \n",
       "RinderleberAmStueck            500  0.085  \n",
       "RinderherzKlumpen             2000  0.050  \n",
       "Kaninchenkarkasse             1000  0.150  \n",
       "Rinderfett                     500  0.910  \n",
       "Lachs                          500  0.075  \n",
       "LammleberStuecke               500  0.050  \n",
       "Kalbsfleischabschnitte         500  0.350  \n",
       "Pferdefett                     500  0.910  \n",
       "Haehnchenherz                  500  0.050  \n",
       "PferdefleischwuerfelFettig    2000  0.254  \n",
       "Rinderherzwuerfel              500  0.050  \n",
       "SpareribsVomPferd             1000  0.160  \n",
       "LammniereStuecke               500  0.060  \n",
       "HuehnerfleischStuecke          500  0.142  \n",
       "Lammpansen                     500  0.140  \n",
       "Pferdefleisch                  500  0.230  \n",
       "Lammfett                       500  0.910  \n",
       "LammherzStuecke                500  0.050  \n",
       "RindfleischKlumpen            2000  0.120  \n",
       "WildfleischKlumpen            2000  0.160  \n",
       "Pferdeleber                    500  0.040  \n",
       "Pferdeniere                    500  0.085  \n",
       "Rindermuskelfleischwuerfel     500  0.090  \n",
       "PferdefleischKlumpen          2000  0.050  \n",
       "Lammfleisch                    500  0.048  \n",
       "Huehnerfleisch                1000  0.142  \n",
       "LammmilzStuecke                500  0.027  \n",
       "Pferdeherz                     500  0.120  \n",
       "Lammbauchlappen               1000  0.300  \n",
       "Lammfleischabschnitte          500  0.150  \n",
       "Pferdemilz                     500  0.051  \n",
       "Pferdeknochenbrei              250  0.135  \n",
       "RindfleischamStueck            100  0.120  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add_inventory(df, 'PferdefleischwuerfelFettig', 3 * 2000)\n",
    "add_inventory(df, 'Rinderfett', 300)\n",
    "add_inventory(df, 'Pferdeknochenbrei', 200)\n",
    "add_inventory(df, 'Pferdemilz', 200)\n",
    "add_inventory(df, 'Pferdeniere', 50)\n",
    "# add_inventory(df, 'Lachs', 4 * 500)\n",
    "add_inventory(df, 'Huehnerhaelse', 500 + 250)\n",
    "# add_inventory(df, 'Kaninchenkarkasse', 400)\n",
    "add_inventory(df, 'LammleberStuecke', 400)\n",
    "# add_inventory(df, 'Rinderblaettermagen', 400 + 500 + 600 + 500)\n",
    "# add_inventory(df, 'Rinderpansen', 1000)\n",
    "# add_inventory(df, 'RinderleberAmStueck', 100 + 150)\n",
    "# add_inventory(df, 'RindermilzWuerfel', 50 + 150)\n",
    "# add_inventory(df, 'RinderniereAmStueck', 50)\n",
    "\n",
    "df.sort_values(by='Price [€/kg]').set_index('Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e00738",
   "metadata": {},
   "source": [
    "Validate input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "abce2e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 'Fish' in df['Category'].values, 'No fish in products'\n",
    "for c in component_share.index:\n",
    "    assert content_components_df[['Component', 'Share']].groupby('Component').sum().loc[c]['Share'] > 0, f'No {c} in products'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1c28d3ec",
   "metadata": {},
   "source": [
    "## Model\n",
    "### Symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4729823",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Container()\n",
    "\n",
    "# Sets\n",
    "p = m.addSet('p', description='Products', records=df['Name'])\n",
    "c = m.addSet('c', description='Components', records=component_share.index)\n",
    "i = m.addSet('i', description='Intestinals', records=intestinal_share_df.index)\n",
    "\n",
    "GREENS = m.addSet('GREENS', p, records=df[(df['Category'] == 'Fruits') | (df['Category'] == 'Vegetables')][['Name', 'Category']])\n",
    "FISH = m.addSet('FISH', p, records=df[df['Category'] == 'Fish'][['Name', 'Category']])\n",
    "\n",
    "# create parameters\n",
    "price = m.addParameter('price', p, records=df[['Name', 'Price']], description=\"price of product p\")\n",
    "\n",
    "volume = m.addParameter('volume', p, records=df[['Name', 'Volume']], \n",
    "                        description='volume of product p')\n",
    "\n",
    "inventory = m.addParameter('inventory', p, records=df[['Name', 'Inventory']], \n",
    "                           description='inventory of product p')\n",
    "\n",
    "fat = m.addParameter('fat', p, records=df[['Name', 'Fat']], description='fat share of product p')\n",
    "\n",
    "demand = m.addParameter('demand', c, records=demand_df.reset_index(), description='demand for each component')\n",
    "\n",
    "content_components = m.addParameter('content_components', [c, p], records=content_components_df, \n",
    "                                    description='components of product p')\n",
    "\n",
    "intestinal_components = m.addParameter('intestinal_components', [i, p], records=intest_components_df,\n",
    "                                       description='intestinal components of product p')\n",
    "\n",
    "intestinal_share = m.addParameter('intestinal_share', i, records=intestinal_share_df.reset_index(),\n",
    "                           description='intestinal share')\n",
    "\n",
    "# create scalars\n",
    "m.addParameter('weeks', records=weeks, description=\"number of weeks\")\n",
    "m.addParameter('weeklydemand', records=weeklydemand, description=\"weekly demand\")\n",
    "m.addParameter('weight', records=weight, description=\"dogs desired body weight\")\n",
    "m.addParameter('perc', records=perc, description=\"percent of body weight\")\n",
    "m.addParameter('activity', records=activity, description=\"dog activity level\")\n",
    "m.addParameter('fatlb', records=fat_lb, description=\"lower bound on fat\")\n",
    "m.addParameter('fatub', records=fat_ub, description=\"upper bound on fat\")\n",
    "m.addParameter('fish_amount', records=weekly_fish_amount, description=\"amount of fish\")\n",
    "\n",
    "# create variables\n",
    "X = m.addVariable('X', 'positive', p, description='Amount of product p')\n",
    "Y = m.addVariable('Y', 'integer', p, description='Auxiliary variable to avoid odd numbers for X')\n",
    "Z = m.addVariable('Z', 'integer', p, description='Number of packs to buy from product p')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c8edbb",
   "metadata": {},
   "source": [
    "### Equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fbe5e8c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "totalLB = Equation(m, name='totalLB', description='Minimum total quantity')\n",
    "totalUB = Equation(m, name='totalUB', description='Maximum total quantity')\n",
    "components = Equation(m, name='components', domain=[c], description='Account for the quantity of each component')\n",
    "intests = Equation(m, name='intests', domain=[i], description='Account for the quantity of each intestinal component')\n",
    "fish_day = Equation(m, name='fish_day', description='Include one fish day')\n",
    "fatLB = Equation(m, name='fat_lb',  description='Account for the minimal amount of fat')\n",
    "fatUB = Equation(m, name='fat_ub',  description='Account for the maximal amount of fat')\n",
    "buy = Equation(m, name='buy', domain=[p], description='Determine the number of packs to buy')\n",
    "rounding = Equation(m, name='rounding', domain=[p], description='Prohibit uneven amounts')\n",
    "bones = Equation(m, name='bones')\n",
    "\n",
    "objective = Sum(p, (price[p] / volume[p]) * X[p]) + Sum(p, price[p] * Z[p])\n",
    "totalLB[...] = Sum(p, X[p]) >= 0.98 * weeklydemand * weeks\n",
    "totalUB[...] = Sum(p, X[p]) <= 1.02 * weeklydemand * weeks\n",
    "components[c] = Sum(p, content_components[c, p] * X[p]) >= weeks * demand[c] - 26\n",
    "intests[i] = Sum(p, intestinal_components[i, p] * X[p]) >= intestinal_share[i] * weeks * demand['Intestinals']\n",
    "fish_day[...] = Sum(FISH[p], X[p]) == weekly_fish_amount * weeks\n",
    "fatLB[...] = Sum(p, fat[p] * X[p]) >= fat_lb * Sum(p, X[p])\n",
    "fatUB[...] = Sum(p, fat[p] * X[p]) <= fat_ub * Sum(p, X[p])\n",
    "buy[p] = inventory[p] + volume[p] * Z[p] >= X[p]\n",
    "rounding[p] = X[p] == 50 * Y[p]\n",
    "bones[...] = X['Kaninchenkarkasse'] >= X['Huehnerhaelse']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8055e959",
   "metadata": {},
   "outputs": [],
   "source": [
    "barf = Model(\n",
    "    m,\n",
    "    name=\"BARF\",\n",
    "    equations=m.getEquations(),\n",
    "    problem=\"MIP\",\n",
    "    sense=Sense.MIN,\n",
    "    objective=objective,\n",
    ")\n",
    "\n",
    "barf.solve()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e52ca3d7",
   "metadata": {},
   "source": [
    "# Review Results\n",
    "Amount of Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f4185ad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PferdefleischwuerfelFettig</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kalbsfleischabschnitte</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rinderfett</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RinderniereAmStueck</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RindermilzWuerfel</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RinderleberAmStueck</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rinderpansen</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rinderblaettermagen</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Haehnchenherz</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Huehnerfett</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>HuehnerfleischStuecke</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Lachs</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kaninchenkarkasse</td>\n",
       "      <td>2950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Huehnerhaelse</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Vegetables</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Fruit</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             p   level\n",
       "1   PferdefleischwuerfelFettig  4000.0\n",
       "9       Kalbsfleischabschnitte  2000.0\n",
       "13                  Rinderfett   100.0\n",
       "16         RinderniereAmStueck   750.0\n",
       "17           RindermilzWuerfel   750.0\n",
       "18         RinderleberAmStueck  1450.0\n",
       "19                Rinderpansen  2350.0\n",
       "20         Rinderblaettermagen  2350.0\n",
       "31               Haehnchenherz   750.0\n",
       "32                 Huehnerfett  1000.0\n",
       "34       HuehnerfleischStuecke   500.0\n",
       "35                       Lachs  3000.0\n",
       "36           Kaninchenkarkasse  2950.0\n",
       "37               Huehnerhaelse  1750.0\n",
       "39                  Vegetables  4400.0\n",
       "40                       Fruit  1450.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.records.loc[X.records['level'] > 1][['p', 'level']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57b3a11",
   "metadata": {},
   "source": [
    "Number of packs to buy of product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a0f62f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PferdefleischwuerfelFettig</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kalbsfleischabschnitte</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RinderniereAmStueck</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RindermilzWuerfel</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RinderleberAmStueck</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rinderpansen</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rinderblaettermagen</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Haehnchenherz</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Huehnerfett</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>HuehnerfleischStuecke</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Lachs</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kaninchenkarkasse</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Huehnerhaelse</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Vegetables</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Fruit</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             p   level\n",
       "1   PferdefleischwuerfelFettig     2.0\n",
       "9       Kalbsfleischabschnitte     4.0\n",
       "16         RinderniereAmStueck     2.0\n",
       "17           RindermilzWuerfel     2.0\n",
       "18         RinderleberAmStueck     3.0\n",
       "19                Rinderpansen     5.0\n",
       "20         Rinderblaettermagen     5.0\n",
       "31               Haehnchenherz     2.0\n",
       "32                 Huehnerfett     1.0\n",
       "34       HuehnerfleischStuecke     1.0\n",
       "35                       Lachs     6.0\n",
       "36           Kaninchenkarkasse     3.0\n",
       "37               Huehnerhaelse     1.0\n",
       "39                  Vegetables  4400.0\n",
       "40                       Fruit  1450.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.records.loc[Z.records['level'] > 0][['p', 'level']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4735f91e",
   "metadata": {},
   "source": [
    "## BARF Ratio Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b0bbd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Final Percent [%]</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Targeted Percent [%]</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total Cost [€]</td>\n",
       "      <td>138.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daily Cost [€]</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fat [%]</td>\n",
       "      <td>14.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Daily Demand [g]</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Index  Column\n",
       "0     Final Percent [%]    2.01\n",
       "1  Targeted Percent [%]    2.00\n",
       "2        Total Cost [€]  138.69\n",
       "3        Daily Cost [€]    3.30\n",
       "4               Fat [%]   14.97\n",
       "5      Daily Demand [g]  700.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_percent = X.records['level'].sum() / (7 * weeks) / (weight * 1000) * 100\n",
    "tar_percent = activity * perc * 100\n",
    "total_cost = round((X.records['level'] / 1000 * df['Price [€/kg]']).sum(), 2)\n",
    "daily_cost = total_cost / (7 * weeks)\n",
    "fat = (X.records['level'] * df['Fat']).sum() / X.records['level'].sum() * 100\n",
    "r = pd.DataFrame({\n",
    "    'Index': ['Final Percent [%]', 'Targeted Percent [%]', 'Total Cost [€]', 'Daily Cost [€]', 'Fat [%]', 'Daily Demand [g]'],\n",
    "    'Column': [final_percent, tar_percent, total_cost, daily_cost, fat, daylydemand]\n",
    "}).round(2)\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b510e5",
   "metadata": {},
   "source": [
    "## Supplements\n",
    "Don't forget to add salt, iodine, and oil.\n",
    "\n",
    "### Salt:\n",
    "1-2 Pinches per week\n",
    "\n",
    "### Jod (Seealgenmehl/Ascophyllum nodosum):\n",
    "- [Seealgen Blog](https://www.barf-check.de/seealgen-bei-barf/)\n",
    "- [Seealgen Rechner](https://www.barf-kultur.de/Seealgenrechner)\n",
    "\n",
    "### Oil:\n",
    "- 1ml oil per 100g food (1 tee spoon = 5 ml)\n",
    "- Salmon oil, Linseed oil, and Hemp seed oil are well balanced oils\n",
    "- Stabilize oil with Vitamin E (10IE pro ml oil)\n",
    "- [Öl Blog](https://www.der-barf-blog.de/2015/11/barf-oel.html)"
   ]
  }
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