Calorie counting

calories.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Food:\n",
    "    \"\"\"Something that can be eaten.\n",
    "\n",
    "    Units are per 100g or 100ml.\n",
    "    \"\"\"\n",
    "\n",
    "    carbs: int\n",
    "    fats: int\n",
    "    proteins: int\n",
    "\n",
    "    @property\n",
    "    def kcal(self):\n",
    "        return round(self.carbs * 4 + self.fats * 9 + self.proteins * 4)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return f\"Food(carbs={round(self.carbs)}, fats={round(self.fats)}, proteins={round(self.proteins)}) -> {self.kcal} kcal\"\n",
    "\n",
    "    def __mul__(self, other):\n",
    "        assert isinstance(other, (int, float))\n",
    "        return Food(\n",
    "            self.carbs * other,\n",
    "            self.fats * other,\n",
    "            self.proteins * other,\n",
    "        )\n",
    "\n",
    "    def __add__(self, other):\n",
    "        assert isinstance(other, Food)\n",
    "        return Food(\n",
    "            self.carbs + other.carbs,\n",
    "            self.fats + other.fats,\n",
    "            self.proteins + other.proteins,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_rice_g_to_compensate_for_kcal(rice: Food, other_kcal: int) -> int:\n",
    "    return round(other_kcal / rice.calories * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food(carbs=7, fats=4, proteins=1) -> 71 kcal"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweet_potato = Food(20.7, 0.15, 2.0)\n",
    "yasmin_rice = Food(85, 1.1, 6.4)\n",
    "ms_pitta = Food(24.7, 1.3, 6)\n",
    "\n",
    "banana = Food(23, 0.3, 1.2)\n",
    "banana_skinless = Food(20.3, 0.1, 1.2)\n",
    "blueberries = Food(14.5, 0.3, 0.7)\n",
    "conference_pear = Food(10, 0.1, 0.3)\n",
    "kiwi = Food(6.2, 0.7, 0.6)\n",
    "lidl_conference_pear = Food(10.9, 0.1, 0.3)\n",
    "lidl_red_apple = Food(11.5, 0.5, 0.5)\n",
    "morrisons_market_st_large_orange_200g = Food(16.4, 0.4, 1.6)\n",
    "orange = Food(11.8, 0.1, 1.2)\n",
    "# orange_peeled = Food(8.2, 0.2, 0.8)\n",
    "pineapple = Food(13, 0.1, 0.5)\n",
    "pink_lady_apple = Food(12, 0.5, 0.6)\n",
    "strawberries = Food(7.7, 0.3, 0.7)\n",
    "red_grapes = Food(17, 0.1, 0.6)\n",
    "green_grapes = Food(15.2, 0.2, 0.7)\n",
    "\n",
    "salted_butter = Food(0.6, 82.2, 0.6)\n",
    "skimmed_milk = Food(4.8, 1.7, 3.6)\n",
    "whole_milk = Food(4.8, 3.6, 3.6)\n",
    "yoguhrt = Food(6.4, 1.8, 4.3)\n",
    "fage_5p_yogurt = Food(3, 5, 9)\n",
    "ms_organic_hummous = Food(8.8, 12.2, 6.5)\n",
    "pure_nature_greek_yoghurt = Food(4.6, 10, 4)\n",
    "\n",
    "cashew = Food(20, 48, 21)\n",
    "oatmeal = Food(59, 8, 13)\n",
    "mc_dougalls_self_raising_flour = Food(67.9, 1.4, 9.9)\n",
    "sainsburys_baking_powder = Food(1.6, 0, 0) * 20\n",
    "sainsburys_golden_caster_cane_sugar = Food(100, 0, 0)\n",
    "\n",
    "lidl_beef_10p = Food(0, 9.4, 19.8)\n",
    "morrisons_beef_12p = Food(0, 11.8, 20.9)\n",
    "morrisons_beef_15p = Food(0.2, 15.6, 18.6)\n",
    "sainsburys_beef_12p = Food(0, 11.8, 25)\n",
    "chicken_breast = Food(0, 1.5, 30)\n",
    "sainsbury_scottish_salmon_fillets = Food(1.2, 17.3, 22.3)\n",
    "morrison_large_egg = Food(0, 5.4, 7.5)\n",
    "lean_beef_casserole_steak = Food(0.8, 4.3, 33.9)\n",
    "tesco_salmon_pieces = Food(0, 13.1, 19.4)\n",
    "tesco_beef_15p = Food(0, 14.5, 19.7)\n",
    "medium_egg = Food(0.3, 4.7, 6.8)\n",
    "casserole_steak = Food(0.8, 4.3, 33.9)\n",
    "sainsburys_ranch_steak = Food(0, 5.1, 22.5)\n",
    "\n",
    "spinach = Food(0.2, 0.7, 3.2)\n",
    "chantenay_carrots = Food(6, 0.5, 0.5)\n",
    "carrot = Food(9.6, 0.2, 0.9)\n",
    "zucchini = Food(2.3, 0.2, 1.3)\n",
    "sweet_pepper = Food(4.1, 0.2, 0.8)\n",
    "frozen_mixed_veg = Food(5, 0.6, 2.1)\n",
    "celery = Food(1.4, 0.1, 0.5)\n",
    "peas = Food(11.2, 0.7, 5.5)\n",
    "\n",
    "bulk_dextrose = Food(91, 0, 0)\n",
    "bulk_dextrose_serving = bulk_dextrose * 0.25\n",
    "myprotein_whey_powder = Food(6, 7.6, 72)\n",
    "myprotein_whey_powder_serving = myprotein_whey_powder * 0.25\n",
    "pbn_serving = Food(2.3, 1.8, 23)\n",
    "philippines_whey_serving = Food(4, 1, 24)\n",
    "serious_gainz_serving = Food(77.7, 4.7, 26.4)\n",
    "\n",
    "kitkat_2fingers = Food(12.4, 5.1, 1.4)\n",
    "schokobons_piece = Food(3, 2.1, 0.5)\n",
    "kinder_chocolate_piece = Food(6.7, 4.4, 1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breakfast: Food(carbs=120, fats=21, proteins=23) -> 760 kcal\n",
      "Lunch:     Food(carbs=161, fats=42, proteins=61) -> 1264 kcal\n",
      "Shake:     Food(carbs=48, fats=4, proteins=9) -> 266 kcal\n",
      "Dinner:    Food(carbs=161, fats=42, proteins=61) -> 1264 kcal\n",
      "Snacks:    Food(carbs=21, fats=0, proteins=2) -> 95 kcal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Food(carbs=513, fats=108, proteins=157) -> 3649 kcal"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Beef day\n",
    "\n",
    "breakfast = (\n",
    "    banana_skinless * 1.2 + oatmeal * 1.3 + pure_nature_greek_yoghurt + blueberries * 1\n",
    ")\n",
    "\n",
    "lunch = (\n",
    "    morrisons_beef_15p * 2.5\n",
    "    + sweet_potato * 1\n",
    "    + yasmin_rice * 1.5\n",
    "    + spinach * 0.5\n",
    "    + sweet_pepper * 0.75\n",
    "    + carrot * 1\n",
    "    # + salted_butter * 0.025\n",
    ")\n",
    "\n",
    "shake = blueberries + banana_skinless * 1.2 + skimmed_milk * 2\n",
    "\n",
    "dinner = (\n",
    "    morrisons_beef_15p * 2.5\n",
    "    + sweet_potato * 1\n",
    "    + yasmin_rice * 1.5\n",
    "    + spinach * 0.5\n",
    "    + sweet_pepper * 0.75\n",
    "    + carrot * 1\n",
    "    # + salted_butter * 0.025\n",
    ")\n",
    "\n",
    "snacks = (\n",
    "    orange * 1.8\n",
    "    # + medium_egg * 2\n",
    "    # + kiwi * 1\n",
    "    # + green_grapes * 0.5\n",
    "    # + kiwi * 1.1\n",
    "    # + tesco_salmon_pieces * 1\n",
    "    # + sweet_potato * 0.2\n",
    "    # + Food(3.4, 1.7, 1.9) *2# Prawn Tempura\n",
    "    # + banana\n",
    "    # + kinder_chocolate_piece\n",
    "    # + conference_pear\n",
    "    # + schokobons_piece * 2\n",
    "    # + morrisons_market_st_large_orange_200g * 1\n",
    "    # + ms_pitta\n",
    "    # + kitkat_2fingers\n",
    "    # + strawberries * 1.4\n",
    "    # + conference_pear\n",
    "    # + bulk_dextrose_serving\n",
    "    # + pineapple * 0.4\n",
    ")\n",
    "\n",
    "print(\"Breakfast:\", breakfast)\n",
    "print(\"Lunch:    \", lunch)\n",
    "print(\"Shake:    \", shake)\n",
    "print(\"Dinner:   \", dinner)\n",
    "print(\"Snacks:   \", snacks)\n",
    "breakfast + lunch + shake + dinner + snacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food(carbs=0, fats=26, proteins=115) -> 693 kcal"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sainsburys_ranch_steak * 2.55 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food(carbs=476, fats=85, proteins=196) -> 3449 kcal"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Salmon day\n",
    "\n",
    "(\n",
    "    # Breakfast\n",
    "    (banana + oatmeal * 1.2 + whole_milk * 2 + blueberries * 1)\n",
    "    # Lunch\n",
    "    + (\n",
    "        tesco_salmon_pieces * 2.2\n",
    "        + sweet_potato * 1.0\n",
    "        + yasmin_rice * 1.5\n",
    "        + spinach * 0.5\n",
    "        + salted_butter * 0.035\n",
    "    )\n",
    "    # Shake\n",
    "    + (blueberries + pbn_serving + whole_milk * 2)\n",
    "    # Dinner\n",
    "    + (\n",
    "        lean_beef_casserole_steak * 2\n",
    "        + sweet_potato * 1.0\n",
    "        + yasmin_rice * 1.5\n",
    "        + spinach * 0.5\n",
    "        + chantenay_carrots * 1.07\n",
    "        + sweet_pepper * 0.5\n",
    "        + salted_butter * 0.035\n",
    "    )\n",
    "    # Snacks\n",
    "    + lidl_red_apple * 0.5\n",
    "    + kitkat_2fingers\n",
    "    + kinder_chocolate_piece\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food(carbs=23, fats=10, proteins=10) -> 221 kcal"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pancakes\n",
    "\n",
    "pancakes = (\n",
    "    mc_dougalls_self_raising_flour * 2\n",
    "    + whole_milk * 2.05\n",
    "    + morrison_large_egg * 3\n",
    "    + salted_butter * 0.3\n",
    "    + sainsburys_baking_powder * 0.075\n",
    "    + sainsburys_golden_caster_cane_sugar * 0.15\n",
    "    + morrison_large_egg * 3\n",
    ")\n",
    "\n",
    "one_pan_pancake = pancakes * (1 / 7)"
   ]
  }
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