├── Explorer.ipynb
├── Explorer2.ipynb
├── FDC_API_Test.ipynb
├── LICENSE
├── README.md
├── SR_Food_Lookup.ipynb
├── convert_sr.py
├── daily_intake.csv
├── example.txt
├── fnb_dri_elements.csv
├── fnb_dri_macro.csv
├── fnb_dri_vitamins.csv
├── fnb_ul_elements.csv
├── fnb_ul_vitamins.csv
├── fndds_nutrients.csv
├── foods.csv
├── my_intake_requirements.csv
├── program.py
├── run.sh
├── test_log.csv
├── test_nutrient_profiles.csv
└── to.do


/Explorer.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "id": "47c000f9",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "import program\n",
  11 |     "import pulp as pl"
  12 |    ]
  13 |   },
  14 |   {
  15 |    "cell_type": "code",
  16 |    "execution_count": 2,
  17 |    "id": "6e4fdeae",
  18 |    "metadata": {},
  19 |    "outputs": [],
  20 |    "source": [
  21 |     "problem = program.test()\n",
  22 |     "problem.solve()"
  23 |    ]
  24 |   },
  25 |   {
  26 |    "cell_type": "code",
  27 |    "execution_count": 3,
  28 |    "id": "ca58c344",
  29 |    "metadata": {},
  30 |    "outputs": [
  31 |     {
  32 |      "data": {
  33 |       "text/html": [
  34 |        "<div>\n",
  35 |        "<style scoped>\n",
  36 |        "    .dataframe tbody tr th:only-of-type {\n",
  37 |        "        vertical-align: middle;\n",
  38 |        "    }\n",
  39 |        "\n",
  40 |        "    .dataframe tbody tr th {\n",
  41 |        "        vertical-align: top;\n",
  42 |        "    }\n",
  43 |        "\n",
  44 |        "    .dataframe thead th {\n",
  45 |        "        text-align: right;\n",
  46 |        "    }\n",
  47 |        "</style>\n",
  48 |        "<table border=\"1\" class=\"dataframe\">\n",
  49 |        "  <thead>\n",
  50 |        "    <tr style=\"text-align: right;\">\n",
  51 |        "      <th></th>\n",
  52 |        "      <th>amount</th>\n",
  53 |        "    </tr>\n",
  54 |        "    <tr>\n",
  55 |        "      <th>name</th>\n",
  56 |        "      <th></th>\n",
  57 |        "    </tr>\n",
  58 |        "  </thead>\n",
  59 |        "  <tbody>\n",
  60 |        "    <tr>\n",
  61 |        "      <th>Broccoli (Cooked)</th>\n",
  62 |        "      <td>0.518573</td>\n",
  63 |        "    </tr>\n",
  64 |        "    <tr>\n",
  65 |        "      <th>Cooked Celery</th>\n",
  66 |        "      <td>13.191981</td>\n",
  67 |        "    </tr>\n",
  68 |        "    <tr>\n",
  69 |        "      <th>Cooked Tomatoes</th>\n",
  70 |        "      <td>21.014955</td>\n",
  71 |        "    </tr>\n",
  72 |        "    <tr>\n",
  73 |        "      <th>Cucumber</th>\n",
  74 |        "      <td>6.096698</td>\n",
  75 |        "    </tr>\n",
  76 |        "    <tr>\n",
  77 |        "      <th>Dry Roasted Pistachio Nuts</th>\n",
  78 |        "      <td>2.421314</td>\n",
  79 |        "    </tr>\n",
  80 |        "    <tr>\n",
  81 |        "      <th>White Button Mushrooms</th>\n",
  82 |        "      <td>4.076494</td>\n",
  83 |        "    </tr>\n",
  84 |        "  </tbody>\n",
  85 |        "</table>\n",
  86 |        "</div>"
  87 |       ],
  88 |       "text/plain": [
  89 |        "                               amount\n",
  90 |        "name                                 \n",
  91 |        "Broccoli (Cooked)            0.518573\n",
  92 |        "Cooked Celery               13.191981\n",
  93 |        "Cooked Tomatoes             21.014955\n",
  94 |        "Cucumber                     6.096698\n",
  95 |        "Dry Roasted Pistachio Nuts   2.421314\n",
  96 |        "White Button Mushrooms       4.076494"
  97 |       ]
  98 |      },
  99 |      "execution_count": 3,
 100 |      "metadata": {},
 101 |      "output_type": "execute_result"
 102 |     }
 103 |    ],
 104 |    "source": [
 105 |     "problem.recipe"
 106 |    ]
 107 |   },
 108 |   {
 109 |    "cell_type": "code",
 110 |    "execution_count": 4,
 111 |    "id": "d81552d7",
 112 |    "metadata": {},
 113 |    "outputs": [
 114 |     {
 115 |      "data": {
 116 |       "text/html": [
 117 |        "<div>\n",
 118 |        "<style scoped>\n",
 119 |        "    .dataframe tbody tr th:only-of-type {\n",
 120 |        "        vertical-align: middle;\n",
 121 |        "    }\n",
 122 |        "\n",
 123 |        "    .dataframe tbody tr th {\n",
 124 |        "        vertical-align: top;\n",
 125 |        "    }\n",
 126 |        "\n",
 127 |        "    .dataframe thead th {\n",
 128 |        "        text-align: right;\n",
 129 |        "    }\n",
 130 |        "</style>\n",
 131 |        "<table border=\"1\" class=\"dataframe\">\n",
 132 |        "  <thead>\n",
 133 |        "    <tr style=\"text-align: right;\">\n",
 134 |        "      <th></th>\n",
 135 |        "      <th>min</th>\n",
 136 |        "      <th>max</th>\n",
 137 |        "      <th>intake</th>\n",
 138 |        "    </tr>\n",
 139 |        "    <tr>\n",
 140 |        "      <th>name</th>\n",
 141 |        "      <th></th>\n",
 142 |        "      <th></th>\n",
 143 |        "      <th></th>\n",
 144 |        "    </tr>\n",
 145 |        "  </thead>\n",
 146 |        "  <tbody>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>calcium_mg</th>\n",
 149 |        "      <td>1000.0</td>\n",
 150 |        "      <td>2500.0</td>\n",
 151 |        "      <td>{Almonds: 269.0, Almonds_Unsalted: 260.0, Appl...</td>\n",
 152 |        "    </tr>\n",
 153 |        "    <tr>\n",
 154 |        "      <th>calories_kcal</th>\n",
 155 |        "      <td>1800.0</td>\n",
 156 |        "      <td>2200.0</td>\n",
 157 |        "      <td>{Almonds: 579.0, Almonds_Unsalted: 607.0, Appl...</td>\n",
 158 |        "    </tr>\n",
 159 |        "    <tr>\n",
 160 |        "      <th>choline_mg</th>\n",
 161 |        "      <td>550.0</td>\n",
 162 |        "      <td>3500.0</td>\n",
 163 |        "      <td>{Almonds: 52.1, Almonds_Unsalted: 50.5, Apples...</td>\n",
 164 |        "    </tr>\n",
 165 |        "    <tr>\n",
 166 |        "      <th>copper_mg</th>\n",
 167 |        "      <td>0.9</td>\n",
 168 |        "      <td>10.0</td>\n",
 169 |        "      <td>{Almonds: 1.031, Almonds_Unsalted: 1.065999999...</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>fiber_g</th>\n",
 173 |        "      <td>38.0</td>\n",
 174 |        "      <td>NaN</td>\n",
 175 |        "      <td>{Almonds: 12.5, Almonds_Unsalted: 10.6, Apples...</td>\n",
 176 |        "    </tr>\n",
 177 |        "    <tr>\n",
 178 |        "      <th>fluoride_mcg</th>\n",
 179 |        "      <td>4.0</td>\n",
 180 |        "      <td>10.0</td>\n",
 181 |        "      <td>{Apples: 3.3, Bananas: 2.2, Broccoli_(Cooked):...</td>\n",
 182 |        "    </tr>\n",
 183 |        "    <tr>\n",
 184 |        "      <th>folate_mcg</th>\n",
 185 |        "      <td>400.0</td>\n",
 186 |        "      <td>1000.0</td>\n",
 187 |        "      <td>{Almonds: 44.0, Almonds_Unsalted: 53.0, Apples...</td>\n",
 188 |        "    </tr>\n",
 189 |        "    <tr>\n",
 190 |        "      <th>histidine_mg_kg</th>\n",
 191 |        "      <td>14.0</td>\n",
 192 |        "      <td>NaN</td>\n",
 193 |        "      <td>{Almonds: 539.0, Apples: 5.0, Baked_Potato_(No...</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>iron_mg</th>\n",
 197 |        "      <td>8.0</td>\n",
 198 |        "      <td>45.0</td>\n",
 199 |        "      <td>{Almonds: 3.71, Almonds_Unsalted: 3.62, Apples...</td>\n",
 200 |        "    </tr>\n",
 201 |        "    <tr>\n",
 202 |        "      <th>isoleucine_mg_kg</th>\n",
 203 |        "      <td>19.0</td>\n",
 204 |        "      <td>NaN</td>\n",
 205 |        "      <td>{Almonds: 751.0, Apples: 6.0, Baked_Potato_(No...</td>\n",
 206 |        "    </tr>\n",
 207 |        "    <tr>\n",
 208 |        "      <th>leucine_mg_kg</th>\n",
 209 |        "      <td>42.0</td>\n",
 210 |        "      <td>NaN</td>\n",
 211 |        "      <td>{Almonds: 1473.0, Apples: 13.0, Baked_Potato_(...</td>\n",
 212 |        "    </tr>\n",
 213 |        "    <tr>\n",
 214 |        "      <th>lysine_mg_kg</th>\n",
 215 |        "      <td>38.0</td>\n",
 216 |        "      <td>NaN</td>\n",
 217 |        "      <td>{Almonds: 568.0, Apples: 12.0, Baked_Potato_(N...</td>\n",
 218 |        "    </tr>\n",
 219 |        "    <tr>\n",
 220 |        "      <th>magnesium_mg</th>\n",
 221 |        "      <td>400.0</td>\n",
 222 |        "      <td>NaN</td>\n",
 223 |        "      <td>{Almonds: 270.0, Almonds_Unsalted: 271.0, Appl...</td>\n",
 224 |        "    </tr>\n",
 225 |        "    <tr>\n",
 226 |        "      <th>manganese_mg</th>\n",
 227 |        "      <td>2.3</td>\n",
 228 |        "      <td>11.0</td>\n",
 229 |        "      <td>{Almonds: 2.1790000000000003, Apples: 0.035, B...</td>\n",
 230 |        "    </tr>\n",
 231 |        "    <tr>\n",
 232 |        "      <th>methionine_and_cysteine_mg_kg</th>\n",
 233 |        "      <td>19.0</td>\n",
 234 |        "      <td>NaN</td>\n",
 235 |        "      <td>{Almonds: 372.0, Apples: 2.0, Baked_Potato_(No...</td>\n",
 236 |        "    </tr>\n",
 237 |        "    <tr>\n",
 238 |        "      <th>niacin_mg</th>\n",
 239 |        "      <td>16.0</td>\n",
 240 |        "      <td>35.0</td>\n",
 241 |        "      <td>{Almonds: 3.618, Almonds_Unsalted: 3.528, Appl...</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>pantothenic_acid_mg</th>\n",
 245 |        "      <td>5.0</td>\n",
 246 |        "      <td>NaN</td>\n",
 247 |        "      <td>{Almonds: 0.471, Apples: 0.061, Baked_Potato_(...</td>\n",
 248 |        "    </tr>\n",
 249 |        "    <tr>\n",
 250 |        "      <th>phenylalanine_and_tyrosine_mg_kg</th>\n",
 251 |        "      <td>33.0</td>\n",
 252 |        "      <td>NaN</td>\n",
 253 |        "      <td>{Almonds: 1582.0, Apples: 7.0, Baked_Potato_(N...</td>\n",
 254 |        "    </tr>\n",
 255 |        "    <tr>\n",
 256 |        "      <th>phosphorus_mg</th>\n",
 257 |        "      <td>700.0</td>\n",
 258 |        "      <td>4000.0</td>\n",
 259 |        "      <td>{Almonds: 481.0, Almonds_Unsalted: 457.0, Appl...</td>\n",
 260 |        "    </tr>\n",
 261 |        "    <tr>\n",
 262 |        "      <th>potassium_mg</th>\n",
 263 |        "      <td>3400.0</td>\n",
 264 |        "      <td>NaN</td>\n",
 265 |        "      <td>{Almonds: 733.0, Almonds_Unsalted: 692.0, Appl...</td>\n",
 266 |        "    </tr>\n",
 267 |        "    <tr>\n",
 268 |        "      <th>protein_g_kg</th>\n",
 269 |        "      <td>0.8</td>\n",
 270 |        "      <td>NaN</td>\n",
 271 |        "      <td>{Almonds: 21.15, Almonds_Unsalted: 20.33, Appl...</td>\n",
 272 |        "    </tr>\n",
 273 |        "    <tr>\n",
 274 |        "      <th>riboflavin_mg</th>\n",
 275 |        "      <td>1.3</td>\n",
 276 |        "      <td>NaN</td>\n",
 277 |        "      <td>{Almonds: 1.138, Almonds_Unsalted: 1.161, Appl...</td>\n",
 278 |        "    </tr>\n",
 279 |        "    <tr>\n",
 280 |        "      <th>selenium_mcg</th>\n",
 281 |        "      <td>55.0</td>\n",
 282 |        "      <td>400.0</td>\n",
 283 |        "      <td>{Almonds: 4.1, Almonds_Unsalted: 1.9, Baked_Po...</td>\n",
 284 |        "    </tr>\n",
 285 |        "    <tr>\n",
 286 |        "      <th>sodium_mg</th>\n",
 287 |        "      <td>1500.0</td>\n",
 288 |        "      <td>NaN</td>\n",
 289 |        "      <td>{Almonds: 1.0, Almonds_Unsalted: 3.0, Apples: ...</td>\n",
 290 |        "    </tr>\n",
 291 |        "    <tr>\n",
 292 |        "      <th>thiamin_mg</th>\n",
 293 |        "      <td>1.2</td>\n",
 294 |        "      <td>NaN</td>\n",
 295 |        "      <td>{Almonds: 0.205, Almonds_Unsalted: 0.075, Appl...</td>\n",
 296 |        "    </tr>\n",
 297 |        "    <tr>\n",
 298 |        "      <th>threonine_mg_kg</th>\n",
 299 |        "      <td>20.0</td>\n",
 300 |        "      <td>NaN</td>\n",
 301 |        "      <td>{Almonds: 601.0, Apples: 6.0, Baked_Potato_(No...</td>\n",
 302 |        "    </tr>\n",
 303 |        "    <tr>\n",
 304 |        "      <th>tryptophan_mg_kg</th>\n",
 305 |        "      <td>5.0</td>\n",
 306 |        "      <td>NaN</td>\n",
 307 |        "      <td>{Almonds: 211.0, Apples: 1.0, Baked_Potato_(No...</td>\n",
 308 |        "    </tr>\n",
 309 |        "    <tr>\n",
 310 |        "      <th>valine_mg_kg</th>\n",
 311 |        "      <td>24.0</td>\n",
 312 |        "      <td>NaN</td>\n",
 313 |        "      <td>{Almonds: 855.0, Apples: 12.0, Baked_Potato_(N...</td>\n",
 314 |        "    </tr>\n",
 315 |        "    <tr>\n",
 316 |        "      <th>vitamin_a_mcg</th>\n",
 317 |        "      <td>900.0</td>\n",
 318 |        "      <td>3000.0</td>\n",
 319 |        "      <td>{Apples: 3.0, Bananas: 3.0, Broccoli: 31.0, Br...</td>\n",
 320 |        "    </tr>\n",
 321 |        "    <tr>\n",
 322 |        "      <th>vitamin_b6_mg</th>\n",
 323 |        "      <td>1.3</td>\n",
 324 |        "      <td>100.0</td>\n",
 325 |        "      <td>{Almonds: 0.1369999999999999, Almonds_Unsalted...</td>\n",
 326 |        "    </tr>\n",
 327 |        "    <tr>\n",
 328 |        "      <th>vitamin_c_mg</th>\n",
 329 |        "      <td>90.0</td>\n",
 330 |        "      <td>2000.0</td>\n",
 331 |        "      <td>{Apples: 4.6, Baked_Potato_(No_Skin): 12.8, Ba...</td>\n",
 332 |        "    </tr>\n",
 333 |        "    <tr>\n",
 334 |        "      <th>vitamin_e_mg</th>\n",
 335 |        "      <td>15.0</td>\n",
 336 |        "      <td>1000.0</td>\n",
 337 |        "      <td>{Almonds: 25.63, Almonds_Unsalted: 23.54, Appl...</td>\n",
 338 |        "    </tr>\n",
 339 |        "    <tr>\n",
 340 |        "      <th>vitamin_k_mcg</th>\n",
 341 |        "      <td>120.0</td>\n",
 342 |        "      <td>NaN</td>\n",
 343 |        "      <td>{Almonds_Unsalted: 3.5, Apples: 2.2, Baked_Pot...</td>\n",
 344 |        "    </tr>\n",
 345 |        "    <tr>\n",
 346 |        "      <th>zinc_mg</th>\n",
 347 |        "      <td>11.0</td>\n",
 348 |        "      <td>40.0</td>\n",
 349 |        "      <td>{Almonds: 3.12, Almonds_Unsalted: 3.21, Apples...</td>\n",
 350 |        "    </tr>\n",
 351 |        "  </tbody>\n",
 352 |        "</table>\n",
 353 |        "</div>"
 354 |       ],
 355 |       "text/plain": [
 356 |        "                                     min     max  \\\n",
 357 |        "name                                               \n",
 358 |        "calcium_mg                        1000.0  2500.0   \n",
 359 |        "calories_kcal                     1800.0  2200.0   \n",
 360 |        "choline_mg                         550.0  3500.0   \n",
 361 |        "copper_mg                            0.9    10.0   \n",
 362 |        "fiber_g                             38.0     NaN   \n",
 363 |        "fluoride_mcg                         4.0    10.0   \n",
 364 |        "folate_mcg                         400.0  1000.0   \n",
 365 |        "histidine_mg_kg                     14.0     NaN   \n",
 366 |        "iron_mg                              8.0    45.0   \n",
 367 |        "isoleucine_mg_kg                    19.0     NaN   \n",
 368 |        "leucine_mg_kg                       42.0     NaN   \n",
 369 |        "lysine_mg_kg                        38.0     NaN   \n",
 370 |        "magnesium_mg                       400.0     NaN   \n",
 371 |        "manganese_mg                         2.3    11.0   \n",
 372 |        "methionine_and_cysteine_mg_kg       19.0     NaN   \n",
 373 |        "niacin_mg                           16.0    35.0   \n",
 374 |        "pantothenic_acid_mg                  5.0     NaN   \n",
 375 |        "phenylalanine_and_tyrosine_mg_kg    33.0     NaN   \n",
 376 |        "phosphorus_mg                      700.0  4000.0   \n",
 377 |        "potassium_mg                      3400.0     NaN   \n",
 378 |        "protein_g_kg                         0.8     NaN   \n",
 379 |        "riboflavin_mg                        1.3     NaN   \n",
 380 |        "selenium_mcg                        55.0   400.0   \n",
 381 |        "sodium_mg                         1500.0     NaN   \n",
 382 |        "thiamin_mg                           1.2     NaN   \n",
 383 |        "threonine_mg_kg                     20.0     NaN   \n",
 384 |        "tryptophan_mg_kg                     5.0     NaN   \n",
 385 |        "valine_mg_kg                        24.0     NaN   \n",
 386 |        "vitamin_a_mcg                      900.0  3000.0   \n",
 387 |        "vitamin_b6_mg                        1.3   100.0   \n",
 388 |        "vitamin_c_mg                        90.0  2000.0   \n",
 389 |        "vitamin_e_mg                        15.0  1000.0   \n",
 390 |        "vitamin_k_mcg                      120.0     NaN   \n",
 391 |        "zinc_mg                             11.0    40.0   \n",
 392 |        "\n",
 393 |        "                                                                             intake  \n",
 394 |        "name                                                                                 \n",
 395 |        "calcium_mg                        {Almonds: 269.0, Almonds_Unsalted: 260.0, Appl...  \n",
 396 |        "calories_kcal                     {Almonds: 579.0, Almonds_Unsalted: 607.0, Appl...  \n",
 397 |        "choline_mg                        {Almonds: 52.1, Almonds_Unsalted: 50.5, Apples...  \n",
 398 |        "copper_mg                         {Almonds: 1.031, Almonds_Unsalted: 1.065999999...  \n",
 399 |        "fiber_g                           {Almonds: 12.5, Almonds_Unsalted: 10.6, Apples...  \n",
 400 |        "fluoride_mcg                      {Apples: 3.3, Bananas: 2.2, Broccoli_(Cooked):...  \n",
 401 |        "folate_mcg                        {Almonds: 44.0, Almonds_Unsalted: 53.0, Apples...  \n",
 402 |        "histidine_mg_kg                   {Almonds: 539.0, Apples: 5.0, Baked_Potato_(No...  \n",
 403 |        "iron_mg                           {Almonds: 3.71, Almonds_Unsalted: 3.62, Apples...  \n",
 404 |        "isoleucine_mg_kg                  {Almonds: 751.0, Apples: 6.0, Baked_Potato_(No...  \n",
 405 |        "leucine_mg_kg                     {Almonds: 1473.0, Apples: 13.0, Baked_Potato_(...  \n",
 406 |        "lysine_mg_kg                      {Almonds: 568.0, Apples: 12.0, Baked_Potato_(N...  \n",
 407 |        "magnesium_mg                      {Almonds: 270.0, Almonds_Unsalted: 271.0, Appl...  \n",
 408 |        "manganese_mg                      {Almonds: 2.1790000000000003, Apples: 0.035, B...  \n",
 409 |        "methionine_and_cysteine_mg_kg     {Almonds: 372.0, Apples: 2.0, Baked_Potato_(No...  \n",
 410 |        "niacin_mg                         {Almonds: 3.618, Almonds_Unsalted: 3.528, Appl...  \n",
 411 |        "pantothenic_acid_mg               {Almonds: 0.471, Apples: 0.061, Baked_Potato_(...  \n",
 412 |        "phenylalanine_and_tyrosine_mg_kg  {Almonds: 1582.0, Apples: 7.0, Baked_Potato_(N...  \n",
 413 |        "phosphorus_mg                     {Almonds: 481.0, Almonds_Unsalted: 457.0, Appl...  \n",
 414 |        "potassium_mg                      {Almonds: 733.0, Almonds_Unsalted: 692.0, Appl...  \n",
 415 |        "protein_g_kg                      {Almonds: 21.15, Almonds_Unsalted: 20.33, Appl...  \n",
 416 |        "riboflavin_mg                     {Almonds: 1.138, Almonds_Unsalted: 1.161, Appl...  \n",
 417 |        "selenium_mcg                      {Almonds: 4.1, Almonds_Unsalted: 1.9, Baked_Po...  \n",
 418 |        "sodium_mg                         {Almonds: 1.0, Almonds_Unsalted: 3.0, Apples: ...  \n",
 419 |        "thiamin_mg                        {Almonds: 0.205, Almonds_Unsalted: 0.075, Appl...  \n",
 420 |        "threonine_mg_kg                   {Almonds: 601.0, Apples: 6.0, Baked_Potato_(No...  \n",
 421 |        "tryptophan_mg_kg                  {Almonds: 211.0, Apples: 1.0, Baked_Potato_(No...  \n",
 422 |        "valine_mg_kg                      {Almonds: 855.0, Apples: 12.0, Baked_Potato_(N...  \n",
 423 |        "vitamin_a_mcg                     {Apples: 3.0, Bananas: 3.0, Broccoli: 31.0, Br...  \n",
 424 |        "vitamin_b6_mg                     {Almonds: 0.1369999999999999, Almonds_Unsalted...  \n",
 425 |        "vitamin_c_mg                      {Apples: 4.6, Baked_Potato_(No_Skin): 12.8, Ba...  \n",
 426 |        "vitamin_e_mg                      {Almonds: 25.63, Almonds_Unsalted: 23.54, Appl...  \n",
 427 |        "vitamin_k_mcg                     {Almonds_Unsalted: 3.5, Apples: 2.2, Baked_Pot...  \n",
 428 |        "zinc_mg                           {Almonds: 3.12, Almonds_Unsalted: 3.21, Apples...  "
 429 |       ]
 430 |      },
 431 |      "execution_count": 4,
 432 |      "metadata": {},
 433 |      "output_type": "execute_result"
 434 |     }
 435 |    ],
 436 |    "source": [
 437 |     "problem.nutrient_table"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 5,
 443 |    "id": "a48bef6d",
 444 |    "metadata": {},
 445 |    "outputs": [
 446 |     {
 447 |      "data": {
 448 |       "text/html": [
 449 |        "<div>\n",
 450 |        "<style scoped>\n",
 451 |        "    .dataframe tbody tr th:only-of-type {\n",
 452 |        "        vertical-align: middle;\n",
 453 |        "    }\n",
 454 |        "\n",
 455 |        "    .dataframe tbody tr th {\n",
 456 |        "        vertical-align: top;\n",
 457 |        "    }\n",
 458 |        "\n",
 459 |        "    .dataframe thead th {\n",
 460 |        "        text-align: right;\n",
 461 |        "    }\n",
 462 |        "</style>\n",
 463 |        "<table border=\"1\" class=\"dataframe\">\n",
 464 |        "  <thead>\n",
 465 |        "    <tr style=\"text-align: right;\">\n",
 466 |        "      <th></th>\n",
 467 |        "      <th>food_group</th>\n",
 468 |        "      <th>calories_kcal</th>\n",
 469 |        "      <th>fat_g</th>\n",
 470 |        "      <th>protein_g_kg</th>\n",
 471 |        "      <th>carbohydrate_g</th>\n",
 472 |        "      <th>sugars_g</th>\n",
 473 |        "      <th>fiber_g</th>\n",
 474 |        "      <th>cholesterol_mg</th>\n",
 475 |        "      <th>saturated_fats_g</th>\n",
 476 |        "      <th>calcium_mg</th>\n",
 477 |        "      <th>...</th>\n",
 478 |        "      <th>alcohol_g</th>\n",
 479 |        "      <th>caffeine_mg</th>\n",
 480 |        "      <th>theobromine_mg</th>\n",
 481 |        "      <th>200_calorie_weight_g</th>\n",
 482 |        "      <th>phenylalanine_and_tyrosine_mg_kg</th>\n",
 483 |        "      <th>methionine_and_cysteine_mg_kg</th>\n",
 484 |        "      <th>max</th>\n",
 485 |        "      <th>cost</th>\n",
 486 |        "      <th>amount_var</th>\n",
 487 |        "      <th>amount</th>\n",
 488 |        "    </tr>\n",
 489 |        "    <tr>\n",
 490 |        "      <th>name</th>\n",
 491 |        "      <th></th>\n",
 492 |        "      <th></th>\n",
 493 |        "      <th></th>\n",
 494 |        "      <th></th>\n",
 495 |        "      <th></th>\n",
 496 |        "      <th></th>\n",
 497 |        "      <th></th>\n",
 498 |        "      <th></th>\n",
 499 |        "      <th></th>\n",
 500 |        "      <th></th>\n",
 501 |        "      <th></th>\n",
 502 |        "      <th></th>\n",
 503 |        "      <th></th>\n",
 504 |        "      <th></th>\n",
 505 |        "      <th></th>\n",
 506 |        "      <th></th>\n",
 507 |        "      <th></th>\n",
 508 |        "      <th></th>\n",
 509 |        "      <th></th>\n",
 510 |        "      <th></th>\n",
 511 |        "      <th></th>\n",
 512 |        "    </tr>\n",
 513 |        "  </thead>\n",
 514 |        "  <tbody>\n",
 515 |        "    <tr>\n",
 516 |        "      <th>Almonds</th>\n",
 517 |        "      <td>Nuts and Seeds</td>\n",
 518 |        "      <td>579</td>\n",
 519 |        "      <td>49.93</td>\n",
 520 |        "      <td>21.15</td>\n",
 521 |        "      <td>21.55</td>\n",
 522 |        "      <td>4.35</td>\n",
 523 |        "      <td>12.5</td>\n",
 524 |        "      <td>0.0</td>\n",
 525 |        "      <td>3.802</td>\n",
 526 |        "      <td>269.0</td>\n",
 527 |        "      <td>...</td>\n",
 528 |        "      <td>0.0</td>\n",
 529 |        "      <td>0.0</td>\n",
 530 |        "      <td>0.0</td>\n",
 531 |        "      <td>34.542</td>\n",
 532 |        "      <td>1582.0</td>\n",
 533 |        "      <td>372.0</td>\n",
 534 |        "      <td>10000</td>\n",
 535 |        "      <td>1</td>\n",
 536 |        "      <td>Almonds</td>\n",
 537 |        "      <td>0.000000</td>\n",
 538 |        "    </tr>\n",
 539 |        "    <tr>\n",
 540 |        "      <th>Almonds Unsalted</th>\n",
 541 |        "      <td>Nuts and Seeds</td>\n",
 542 |        "      <td>607</td>\n",
 543 |        "      <td>53.96</td>\n",
 544 |        "      <td>20.33</td>\n",
 545 |        "      <td>20.38</td>\n",
 546 |        "      <td>4.71</td>\n",
 547 |        "      <td>10.6</td>\n",
 548 |        "      <td>0.0</td>\n",
 549 |        "      <td>4.380</td>\n",
 550 |        "      <td>260.0</td>\n",
 551 |        "      <td>...</td>\n",
 552 |        "      <td>0.0</td>\n",
 553 |        "      <td>0.0</td>\n",
 554 |        "      <td>0.0</td>\n",
 555 |        "      <td>32.949</td>\n",
 556 |        "      <td>0.0</td>\n",
 557 |        "      <td>0.0</td>\n",
 558 |        "      <td>10000</td>\n",
 559 |        "      <td>1</td>\n",
 560 |        "      <td>Almonds_Unsalted</td>\n",
 561 |        "      <td>0.000000</td>\n",
 562 |        "    </tr>\n",
 563 |        "    <tr>\n",
 564 |        "      <th>Apples</th>\n",
 565 |        "      <td>Fruits</td>\n",
 566 |        "      <td>52</td>\n",
 567 |        "      <td>0.17</td>\n",
 568 |        "      <td>0.26</td>\n",
 569 |        "      <td>13.81</td>\n",
 570 |        "      <td>10.39</td>\n",
 571 |        "      <td>2.4</td>\n",
 572 |        "      <td>0.0</td>\n",
 573 |        "      <td>0.028</td>\n",
 574 |        "      <td>6.0</td>\n",
 575 |        "      <td>...</td>\n",
 576 |        "      <td>0.0</td>\n",
 577 |        "      <td>0.0</td>\n",
 578 |        "      <td>0.0</td>\n",
 579 |        "      <td>384.615</td>\n",
 580 |        "      <td>7.0</td>\n",
 581 |        "      <td>2.0</td>\n",
 582 |        "      <td>10000</td>\n",
 583 |        "      <td>1</td>\n",
 584 |        "      <td>Apples</td>\n",
 585 |        "      <td>0.000000</td>\n",
 586 |        "    </tr>\n",
 587 |        "    <tr>\n",
 588 |        "      <th>Baked Potato (No Skin)</th>\n",
 589 |        "      <td>Vegetables</td>\n",
 590 |        "      <td>93</td>\n",
 591 |        "      <td>0.10</td>\n",
 592 |        "      <td>1.96</td>\n",
 593 |        "      <td>21.55</td>\n",
 594 |        "      <td>1.70</td>\n",
 595 |        "      <td>1.5</td>\n",
 596 |        "      <td>0.0</td>\n",
 597 |        "      <td>0.026</td>\n",
 598 |        "      <td>5.0</td>\n",
 599 |        "      <td>...</td>\n",
 600 |        "      <td>0.0</td>\n",
 601 |        "      <td>0.0</td>\n",
 602 |        "      <td>0.0</td>\n",
 603 |        "      <td>215.054</td>\n",
 604 |        "      <td>160.0</td>\n",
 605 |        "      <td>56.0</td>\n",
 606 |        "      <td>10000</td>\n",
 607 |        "      <td>1</td>\n",
 608 |        "      <td>Baked_Potato_(No_Skin)</td>\n",
 609 |        "      <td>0.000000</td>\n",
 610 |        "    </tr>\n",
 611 |        "    <tr>\n",
 612 |        "      <th>Bananas</th>\n",
 613 |        "      <td>Fruits</td>\n",
 614 |        "      <td>89</td>\n",
 615 |        "      <td>0.33</td>\n",
 616 |        "      <td>1.09</td>\n",
 617 |        "      <td>22.84</td>\n",
 618 |        "      <td>12.23</td>\n",
 619 |        "      <td>2.6</td>\n",
 620 |        "      <td>0.0</td>\n",
 621 |        "      <td>0.112</td>\n",
 622 |        "      <td>5.0</td>\n",
 623 |        "      <td>...</td>\n",
 624 |        "      <td>0.0</td>\n",
 625 |        "      <td>0.0</td>\n",
 626 |        "      <td>0.0</td>\n",
 627 |        "      <td>224.719</td>\n",
 628 |        "      <td>58.0</td>\n",
 629 |        "      <td>17.0</td>\n",
 630 |        "      <td>10000</td>\n",
 631 |        "      <td>1</td>\n",
 632 |        "      <td>Bananas</td>\n",
 633 |        "      <td>0.000000</td>\n",
 634 |        "    </tr>\n",
 635 |        "    <tr>\n",
 636 |        "      <th>...</th>\n",
 637 |        "      <td>...</td>\n",
 638 |        "      <td>...</td>\n",
 639 |        "      <td>...</td>\n",
 640 |        "      <td>...</td>\n",
 641 |        "      <td>...</td>\n",
 642 |        "      <td>...</td>\n",
 643 |        "      <td>...</td>\n",
 644 |        "      <td>...</td>\n",
 645 |        "      <td>...</td>\n",
 646 |        "      <td>...</td>\n",
 647 |        "      <td>...</td>\n",
 648 |        "      <td>...</td>\n",
 649 |        "      <td>...</td>\n",
 650 |        "      <td>...</td>\n",
 651 |        "      <td>...</td>\n",
 652 |        "      <td>...</td>\n",
 653 |        "      <td>...</td>\n",
 654 |        "      <td>...</td>\n",
 655 |        "      <td>...</td>\n",
 656 |        "      <td>...</td>\n",
 657 |        "      <td>...</td>\n",
 658 |        "    </tr>\n",
 659 |        "    <tr>\n",
 660 |        "      <th>Walnuts</th>\n",
 661 |        "      <td>Nuts and Seeds</td>\n",
 662 |        "      <td>654</td>\n",
 663 |        "      <td>65.21</td>\n",
 664 |        "      <td>15.23</td>\n",
 665 |        "      <td>13.71</td>\n",
 666 |        "      <td>2.61</td>\n",
 667 |        "      <td>6.7</td>\n",
 668 |        "      <td>0.0</td>\n",
 669 |        "      <td>6.126</td>\n",
 670 |        "      <td>98.0</td>\n",
 671 |        "      <td>...</td>\n",
 672 |        "      <td>0.0</td>\n",
 673 |        "      <td>0.0</td>\n",
 674 |        "      <td>0.0</td>\n",
 675 |        "      <td>30.581</td>\n",
 676 |        "      <td>1117.0</td>\n",
 677 |        "      <td>444.0</td>\n",
 678 |        "      <td>10000</td>\n",
 679 |        "      <td>1</td>\n",
 680 |        "      <td>Walnuts</td>\n",
 681 |        "      <td>0.000000</td>\n",
 682 |        "    </tr>\n",
 683 |        "    <tr>\n",
 684 |        "      <th>Watermelon</th>\n",
 685 |        "      <td>Fruits</td>\n",
 686 |        "      <td>30</td>\n",
 687 |        "      <td>0.15</td>\n",
 688 |        "      <td>0.61</td>\n",
 689 |        "      <td>7.55</td>\n",
 690 |        "      <td>6.20</td>\n",
 691 |        "      <td>0.4</td>\n",
 692 |        "      <td>0.0</td>\n",
 693 |        "      <td>0.016</td>\n",
 694 |        "      <td>7.0</td>\n",
 695 |        "      <td>...</td>\n",
 696 |        "      <td>0.0</td>\n",
 697 |        "      <td>0.0</td>\n",
 698 |        "      <td>0.0</td>\n",
 699 |        "      <td>666.667</td>\n",
 700 |        "      <td>27.0</td>\n",
 701 |        "      <td>8.0</td>\n",
 702 |        "      <td>10000</td>\n",
 703 |        "      <td>1</td>\n",
 704 |        "      <td>Watermelon</td>\n",
 705 |        "      <td>0.000000</td>\n",
 706 |        "    </tr>\n",
 707 |        "    <tr>\n",
 708 |        "      <th>White Button Mushrooms</th>\n",
 709 |        "      <td>Vegetables</td>\n",
 710 |        "      <td>22</td>\n",
 711 |        "      <td>0.34</td>\n",
 712 |        "      <td>3.09</td>\n",
 713 |        "      <td>3.26</td>\n",
 714 |        "      <td>1.98</td>\n",
 715 |        "      <td>1.0</td>\n",
 716 |        "      <td>0.0</td>\n",
 717 |        "      <td>0.050</td>\n",
 718 |        "      <td>3.0</td>\n",
 719 |        "      <td>...</td>\n",
 720 |        "      <td>0.0</td>\n",
 721 |        "      <td>0.0</td>\n",
 722 |        "      <td>0.0</td>\n",
 723 |        "      <td>909.091</td>\n",
 724 |        "      <td>129.0</td>\n",
 725 |        "      <td>43.0</td>\n",
 726 |        "      <td>10000</td>\n",
 727 |        "      <td>1</td>\n",
 728 |        "      <td>White_Button_Mushrooms</td>\n",
 729 |        "      <td>4.076494</td>\n",
 730 |        "    </tr>\n",
 731 |        "    <tr>\n",
 732 |        "      <th>White Rice</th>\n",
 733 |        "      <td>Grains and Pasta</td>\n",
 734 |        "      <td>130</td>\n",
 735 |        "      <td>0.28</td>\n",
 736 |        "      <td>2.69</td>\n",
 737 |        "      <td>28.17</td>\n",
 738 |        "      <td>0.05</td>\n",
 739 |        "      <td>0.4</td>\n",
 740 |        "      <td>0.0</td>\n",
 741 |        "      <td>0.077</td>\n",
 742 |        "      <td>10.0</td>\n",
 743 |        "      <td>...</td>\n",
 744 |        "      <td>0.0</td>\n",
 745 |        "      <td>0.0</td>\n",
 746 |        "      <td>0.0</td>\n",
 747 |        "      <td>153.846</td>\n",
 748 |        "      <td>234.0</td>\n",
 749 |        "      <td>118.0</td>\n",
 750 |        "      <td>10000</td>\n",
 751 |        "      <td>1</td>\n",
 752 |        "      <td>White_Rice</td>\n",
 753 |        "      <td>0.000000</td>\n",
 754 |        "    </tr>\n",
 755 |        "    <tr>\n",
 756 |        "      <th>Zucchini</th>\n",
 757 |        "      <td>Vegetables</td>\n",
 758 |        "      <td>17</td>\n",
 759 |        "      <td>0.32</td>\n",
 760 |        "      <td>1.21</td>\n",
 761 |        "      <td>3.11</td>\n",
 762 |        "      <td>2.50</td>\n",
 763 |        "      <td>1.0</td>\n",
 764 |        "      <td>0.0</td>\n",
 765 |        "      <td>0.084</td>\n",
 766 |        "      <td>16.0</td>\n",
 767 |        "      <td>...</td>\n",
 768 |        "      <td>0.0</td>\n",
 769 |        "      <td>0.0</td>\n",
 770 |        "      <td>0.0</td>\n",
 771 |        "      <td>1176.471</td>\n",
 772 |        "      <td>75.0</td>\n",
 773 |        "      <td>30.0</td>\n",
 774 |        "      <td>10000</td>\n",
 775 |        "      <td>1</td>\n",
 776 |        "      <td>Zucchini</td>\n",
 777 |        "      <td>0.000000</td>\n",
 778 |        "    </tr>\n",
 779 |        "  </tbody>\n",
 780 |        "</table>\n",
 781 |        "<p>68 rows × 99 columns</p>\n",
 782 |        "</div>"
 783 |       ],
 784 |       "text/plain": [
 785 |        "                              food_group  calories_kcal  fat_g  protein_g_kg  \\\n",
 786 |        "name                                                                           \n",
 787 |        "Almonds                   Nuts and Seeds            579  49.93         21.15   \n",
 788 |        "Almonds Unsalted          Nuts and Seeds            607  53.96         20.33   \n",
 789 |        "Apples                            Fruits             52   0.17          0.26   \n",
 790 |        "Baked Potato (No Skin)        Vegetables             93   0.10          1.96   \n",
 791 |        "Bananas                           Fruits             89   0.33          1.09   \n",
 792 |        "...                                  ...            ...    ...           ...   \n",
 793 |        "Walnuts                   Nuts and Seeds            654  65.21         15.23   \n",
 794 |        "Watermelon                        Fruits             30   0.15          0.61   \n",
 795 |        "White Button Mushrooms        Vegetables             22   0.34          3.09   \n",
 796 |        "White Rice              Grains and Pasta            130   0.28          2.69   \n",
 797 |        "Zucchini                      Vegetables             17   0.32          1.21   \n",
 798 |        "\n",
 799 |        "                        carbohydrate_g  sugars_g  fiber_g  cholesterol_mg  \\\n",
 800 |        "name                                                                        \n",
 801 |        "Almonds                          21.55      4.35     12.5             0.0   \n",
 802 |        "Almonds Unsalted                 20.38      4.71     10.6             0.0   \n",
 803 |        "Apples                           13.81     10.39      2.4             0.0   \n",
 804 |        "Baked Potato (No Skin)           21.55      1.70      1.5             0.0   \n",
 805 |        "Bananas                          22.84     12.23      2.6             0.0   \n",
 806 |        "...                                ...       ...      ...             ...   \n",
 807 |        "Walnuts                          13.71      2.61      6.7             0.0   \n",
 808 |        "Watermelon                        7.55      6.20      0.4             0.0   \n",
 809 |        "White Button Mushrooms            3.26      1.98      1.0             0.0   \n",
 810 |        "White Rice                       28.17      0.05      0.4             0.0   \n",
 811 |        "Zucchini                          3.11      2.50      1.0             0.0   \n",
 812 |        "\n",
 813 |        "                        saturated_fats_g  calcium_mg  ...  alcohol_g  \\\n",
 814 |        "name                                                  ...              \n",
 815 |        "Almonds                            3.802       269.0  ...        0.0   \n",
 816 |        "Almonds Unsalted                   4.380       260.0  ...        0.0   \n",
 817 |        "Apples                             0.028         6.0  ...        0.0   \n",
 818 |        "Baked Potato (No Skin)             0.026         5.0  ...        0.0   \n",
 819 |        "Bananas                            0.112         5.0  ...        0.0   \n",
 820 |        "...                                  ...         ...  ...        ...   \n",
 821 |        "Walnuts                            6.126        98.0  ...        0.0   \n",
 822 |        "Watermelon                         0.016         7.0  ...        0.0   \n",
 823 |        "White Button Mushrooms             0.050         3.0  ...        0.0   \n",
 824 |        "White Rice                         0.077        10.0  ...        0.0   \n",
 825 |        "Zucchini                           0.084        16.0  ...        0.0   \n",
 826 |        "\n",
 827 |        "                        caffeine_mg  theobromine_mg  200_calorie_weight_g  \\\n",
 828 |        "name                                                                        \n",
 829 |        "Almonds                         0.0             0.0                34.542   \n",
 830 |        "Almonds Unsalted                0.0             0.0                32.949   \n",
 831 |        "Apples                          0.0             0.0               384.615   \n",
 832 |        "Baked Potato (No Skin)          0.0             0.0               215.054   \n",
 833 |        "Bananas                         0.0             0.0               224.719   \n",
 834 |        "...                             ...             ...                   ...   \n",
 835 |        "Walnuts                         0.0             0.0                30.581   \n",
 836 |        "Watermelon                      0.0             0.0               666.667   \n",
 837 |        "White Button Mushrooms          0.0             0.0               909.091   \n",
 838 |        "White Rice                      0.0             0.0               153.846   \n",
 839 |        "Zucchini                        0.0             0.0              1176.471   \n",
 840 |        "\n",
 841 |        "                        phenylalanine_and_tyrosine_mg_kg  \\\n",
 842 |        "name                                                       \n",
 843 |        "Almonds                                           1582.0   \n",
 844 |        "Almonds Unsalted                                     0.0   \n",
 845 |        "Apples                                               7.0   \n",
 846 |        "Baked Potato (No Skin)                             160.0   \n",
 847 |        "Bananas                                             58.0   \n",
 848 |        "...                                                  ...   \n",
 849 |        "Walnuts                                           1117.0   \n",
 850 |        "Watermelon                                          27.0   \n",
 851 |        "White Button Mushrooms                             129.0   \n",
 852 |        "White Rice                                         234.0   \n",
 853 |        "Zucchini                                            75.0   \n",
 854 |        "\n",
 855 |        "                        methionine_and_cysteine_mg_kg    max  cost  \\\n",
 856 |        "name                                                                 \n",
 857 |        "Almonds                                         372.0  10000     1   \n",
 858 |        "Almonds Unsalted                                  0.0  10000     1   \n",
 859 |        "Apples                                            2.0  10000     1   \n",
 860 |        "Baked Potato (No Skin)                           56.0  10000     1   \n",
 861 |        "Bananas                                          17.0  10000     1   \n",
 862 |        "...                                               ...    ...   ...   \n",
 863 |        "Walnuts                                         444.0  10000     1   \n",
 864 |        "Watermelon                                        8.0  10000     1   \n",
 865 |        "White Button Mushrooms                           43.0  10000     1   \n",
 866 |        "White Rice                                      118.0  10000     1   \n",
 867 |        "Zucchini                                         30.0  10000     1   \n",
 868 |        "\n",
 869 |        "                                    amount_var    amount  \n",
 870 |        "name                                                      \n",
 871 |        "Almonds                                Almonds  0.000000  \n",
 872 |        "Almonds Unsalted              Almonds_Unsalted  0.000000  \n",
 873 |        "Apples                                  Apples  0.000000  \n",
 874 |        "Baked Potato (No Skin)  Baked_Potato_(No_Skin)  0.000000  \n",
 875 |        "Bananas                                Bananas  0.000000  \n",
 876 |        "...                                        ...       ...  \n",
 877 |        "Walnuts                                Walnuts  0.000000  \n",
 878 |        "Watermelon                          Watermelon  0.000000  \n",
 879 |        "White Button Mushrooms  White_Button_Mushrooms  4.076494  \n",
 880 |        "White Rice                          White_Rice  0.000000  \n",
 881 |        "Zucchini                              Zucchini  0.000000  \n",
 882 |        "\n",
 883 |        "[68 rows x 99 columns]"
 884 |       ]
 885 |      },
 886 |      "execution_count": 5,
 887 |      "metadata": {},
 888 |      "output_type": "execute_result"
 889 |     }
 890 |    ],
 891 |    "source": [
 892 |     "problem.food_table.df"
 893 |    ]
 894 |   },
 895 |   {
 896 |    "cell_type": "code",
 897 |    "execution_count": 6,
 898 |    "id": "f1206e24",
 899 |    "metadata": {},
 900 |    "outputs": [
 901 |     {
 902 |      "data": {
 903 |       "text/plain": [
 904 |        "['Broccoli (Cooked)',\n",
 905 |        " 'Cooked Celery',\n",
 906 |        " 'Cooked Tomatoes',\n",
 907 |        " 'Cucumber',\n",
 908 |        " 'Dry Roasted Pistachio Nuts',\n",
 909 |        " 'White Button Mushrooms']"
 910 |       ]
 911 |      },
 912 |      "execution_count": 6,
 913 |      "metadata": {},
 914 |      "output_type": "execute_result"
 915 |     }
 916 |    ],
 917 |    "source": [
 918 |     "list(problem.recipe.index)"
 919 |    ]
 920 |   },
 921 |   {
 922 |    "cell_type": "code",
 923 |    "execution_count": 7,
 924 |    "id": "ebba2300",
 925 |    "metadata": {},
 926 |    "outputs": [
 927 |     {
 928 |      "data": {
 929 |       "text/html": [
 930 |        "<div>\n",
 931 |        "<style scoped>\n",
 932 |        "    .dataframe tbody tr th:only-of-type {\n",
 933 |        "        vertical-align: middle;\n",
 934 |        "    }\n",
 935 |        "\n",
 936 |        "    .dataframe tbody tr th {\n",
 937 |        "        vertical-align: top;\n",
 938 |        "    }\n",
 939 |        "\n",
 940 |        "    .dataframe thead th {\n",
 941 |        "        text-align: right;\n",
 942 |        "    }\n",
 943 |        "</style>\n",
 944 |        "<table border=\"1\" class=\"dataframe\">\n",
 945 |        "  <thead>\n",
 946 |        "    <tr style=\"text-align: right;\">\n",
 947 |        "      <th></th>\n",
 948 |        "      <th>food_group</th>\n",
 949 |        "      <th>calories_kcal</th>\n",
 950 |        "      <th>fat_g</th>\n",
 951 |        "      <th>protein_g_kg</th>\n",
 952 |        "      <th>carbohydrate_g</th>\n",
 953 |        "      <th>sugars_g</th>\n",
 954 |        "      <th>fiber_g</th>\n",
 955 |        "      <th>cholesterol_mg</th>\n",
 956 |        "      <th>saturated_fats_g</th>\n",
 957 |        "      <th>calcium_mg</th>\n",
 958 |        "      <th>...</th>\n",
 959 |        "      <th>alcohol_g</th>\n",
 960 |        "      <th>caffeine_mg</th>\n",
 961 |        "      <th>theobromine_mg</th>\n",
 962 |        "      <th>200_calorie_weight_g</th>\n",
 963 |        "      <th>phenylalanine_and_tyrosine_mg_kg</th>\n",
 964 |        "      <th>methionine_and_cysteine_mg_kg</th>\n",
 965 |        "      <th>max</th>\n",
 966 |        "      <th>cost</th>\n",
 967 |        "      <th>amount_var</th>\n",
 968 |        "      <th>amount</th>\n",
 969 |        "    </tr>\n",
 970 |        "    <tr>\n",
 971 |        "      <th>name</th>\n",
 972 |        "      <th></th>\n",
 973 |        "      <th></th>\n",
 974 |        "      <th></th>\n",
 975 |        "      <th></th>\n",
 976 |        "      <th></th>\n",
 977 |        "      <th></th>\n",
 978 |        "      <th></th>\n",
 979 |        "      <th></th>\n",
 980 |        "      <th></th>\n",
 981 |        "      <th></th>\n",
 982 |        "      <th></th>\n",
 983 |        "      <th></th>\n",
 984 |        "      <th></th>\n",
 985 |        "      <th></th>\n",
 986 |        "      <th></th>\n",
 987 |        "      <th></th>\n",
 988 |        "      <th></th>\n",
 989 |        "      <th></th>\n",
 990 |        "      <th></th>\n",
 991 |        "      <th></th>\n",
 992 |        "      <th></th>\n",
 993 |        "    </tr>\n",
 994 |        "  </thead>\n",
 995 |        "  <tbody>\n",
 996 |        "    <tr>\n",
 997 |        "      <th>Broccoli (Cooked)</th>\n",
 998 |        "      <td>Vegetables</td>\n",
 999 |        "      <td>35</td>\n",
1000 |        "      <td>0.41</td>\n",
1001 |        "      <td>2.38</td>\n",
1002 |        "      <td>7.18</td>\n",
1003 |        "      <td>1.39</td>\n",
1004 |        "      <td>3.3</td>\n",
1005 |        "      <td>0.0</td>\n",
1006 |        "      <td>0.079</td>\n",
1007 |        "      <td>40.0</td>\n",
1008 |        "      <td>...</td>\n",
1009 |        "      <td>0.0</td>\n",
1010 |        "      <td>0.0</td>\n",
1011 |        "      <td>0.0</td>\n",
1012 |        "      <td>571.429</td>\n",
1013 |        "      <td>176.0</td>\n",
1014 |        "      <td>74.0</td>\n",
1015 |        "      <td>10000</td>\n",
1016 |        "      <td>1</td>\n",
1017 |        "      <td>Broccoli_(Cooked)</td>\n",
1018 |        "      <td>0.518573</td>\n",
1019 |        "    </tr>\n",
1020 |        "    <tr>\n",
1021 |        "      <th>Cooked Celery</th>\n",
1022 |        "      <td>Vegetables</td>\n",
1023 |        "      <td>18</td>\n",
1024 |        "      <td>0.16</td>\n",
1025 |        "      <td>0.83</td>\n",
1026 |        "      <td>4.00</td>\n",
1027 |        "      <td>2.37</td>\n",
1028 |        "      <td>1.6</td>\n",
1029 |        "      <td>0.0</td>\n",
1030 |        "      <td>0.040</td>\n",
1031 |        "      <td>42.0</td>\n",
1032 |        "      <td>...</td>\n",
1033 |        "      <td>0.0</td>\n",
1034 |        "      <td>0.0</td>\n",
1035 |        "      <td>0.0</td>\n",
1036 |        "      <td>1111.111</td>\n",
1037 |        "      <td>35.0</td>\n",
1038 |        "      <td>12.0</td>\n",
1039 |        "      <td>10000</td>\n",
1040 |        "      <td>1</td>\n",
1041 |        "      <td>Cooked_Celery</td>\n",
1042 |        "      <td>13.191981</td>\n",
1043 |        "    </tr>\n",
1044 |        "    <tr>\n",
1045 |        "      <th>Cooked Tomatoes</th>\n",
1046 |        "      <td>Vegetables</td>\n",
1047 |        "      <td>18</td>\n",
1048 |        "      <td>0.11</td>\n",
1049 |        "      <td>0.95</td>\n",
1050 |        "      <td>4.01</td>\n",
1051 |        "      <td>2.49</td>\n",
1052 |        "      <td>0.7</td>\n",
1053 |        "      <td>0.0</td>\n",
1054 |        "      <td>0.015</td>\n",
1055 |        "      <td>11.0</td>\n",
1056 |        "      <td>...</td>\n",
1057 |        "      <td>0.0</td>\n",
1058 |        "      <td>0.0</td>\n",
1059 |        "      <td>0.0</td>\n",
1060 |        "      <td>1111.111</td>\n",
1061 |        "      <td>46.0</td>\n",
1062 |        "      <td>23.0</td>\n",
1063 |        "      <td>10000</td>\n",
1064 |        "      <td>1</td>\n",
1065 |        "      <td>Cooked_Tomatoes</td>\n",
1066 |        "      <td>21.014955</td>\n",
1067 |        "    </tr>\n",
1068 |        "    <tr>\n",
1069 |        "      <th>Cucumber</th>\n",
1070 |        "      <td>Vegetables</td>\n",
1071 |        "      <td>15</td>\n",
1072 |        "      <td>0.11</td>\n",
1073 |        "      <td>0.65</td>\n",
1074 |        "      <td>3.63</td>\n",
1075 |        "      <td>1.67</td>\n",
1076 |        "      <td>0.5</td>\n",
1077 |        "      <td>0.0</td>\n",
1078 |        "      <td>0.037</td>\n",
1079 |        "      <td>16.0</td>\n",
1080 |        "      <td>...</td>\n",
1081 |        "      <td>0.0</td>\n",
1082 |        "      <td>0.0</td>\n",
1083 |        "      <td>0.0</td>\n",
1084 |        "      <td>1333.333</td>\n",
1085 |        "      <td>30.0</td>\n",
1086 |        "      <td>10.0</td>\n",
1087 |        "      <td>10000</td>\n",
1088 |        "      <td>1</td>\n",
1089 |        "      <td>Cucumber</td>\n",
1090 |        "      <td>6.096698</td>\n",
1091 |        "    </tr>\n",
1092 |        "    <tr>\n",
1093 |        "      <th>Dry Roasted Pistachio Nuts</th>\n",
1094 |        "      <td>Nuts and Seeds</td>\n",
1095 |        "      <td>572</td>\n",
1096 |        "      <td>45.82</td>\n",
1097 |        "      <td>21.05</td>\n",
1098 |        "      <td>28.28</td>\n",
1099 |        "      <td>7.74</td>\n",
1100 |        "      <td>10.3</td>\n",
1101 |        "      <td>0.0</td>\n",
1102 |        "      <td>5.645</td>\n",
1103 |        "      <td>107.0</td>\n",
1104 |        "      <td>...</td>\n",
1105 |        "      <td>0.0</td>\n",
1106 |        "      <td>0.0</td>\n",
1107 |        "      <td>0.0</td>\n",
1108 |        "      <td>34.965</td>\n",
1109 |        "      <td>1671.0</td>\n",
1110 |        "      <td>680.0</td>\n",
1111 |        "      <td>10000</td>\n",
1112 |        "      <td>1</td>\n",
1113 |        "      <td>Dry_Roasted_Pistachio_Nuts</td>\n",
1114 |        "      <td>2.421314</td>\n",
1115 |        "    </tr>\n",
1116 |        "    <tr>\n",
1117 |        "      <th>White Button Mushrooms</th>\n",
1118 |        "      <td>Vegetables</td>\n",
1119 |        "      <td>22</td>\n",
1120 |        "      <td>0.34</td>\n",
1121 |        "      <td>3.09</td>\n",
1122 |        "      <td>3.26</td>\n",
1123 |        "      <td>1.98</td>\n",
1124 |        "      <td>1.0</td>\n",
1125 |        "      <td>0.0</td>\n",
1126 |        "      <td>0.050</td>\n",
1127 |        "      <td>3.0</td>\n",
1128 |        "      <td>...</td>\n",
1129 |        "      <td>0.0</td>\n",
1130 |        "      <td>0.0</td>\n",
1131 |        "      <td>0.0</td>\n",
1132 |        "      <td>909.091</td>\n",
1133 |        "      <td>129.0</td>\n",
1134 |        "      <td>43.0</td>\n",
1135 |        "      <td>10000</td>\n",
1136 |        "      <td>1</td>\n",
1137 |        "      <td>White_Button_Mushrooms</td>\n",
1138 |        "      <td>4.076494</td>\n",
1139 |        "    </tr>\n",
1140 |        "  </tbody>\n",
1141 |        "</table>\n",
1142 |        "<p>6 rows × 99 columns</p>\n",
1143 |        "</div>"
1144 |       ],
1145 |       "text/plain": [
1146 |        "                                food_group  calories_kcal  fat_g  \\\n",
1147 |        "name                                                               \n",
1148 |        "Broccoli (Cooked)               Vegetables             35   0.41   \n",
1149 |        "Cooked Celery                   Vegetables             18   0.16   \n",
1150 |        "Cooked Tomatoes                 Vegetables             18   0.11   \n",
1151 |        "Cucumber                        Vegetables             15   0.11   \n",
1152 |        "Dry Roasted Pistachio Nuts  Nuts and Seeds            572  45.82   \n",
1153 |        "White Button Mushrooms          Vegetables             22   0.34   \n",
1154 |        "\n",
1155 |        "                            protein_g_kg  carbohydrate_g  sugars_g  fiber_g  \\\n",
1156 |        "name                                                                          \n",
1157 |        "Broccoli (Cooked)                   2.38            7.18      1.39      3.3   \n",
1158 |        "Cooked Celery                       0.83            4.00      2.37      1.6   \n",
1159 |        "Cooked Tomatoes                     0.95            4.01      2.49      0.7   \n",
1160 |        "Cucumber                            0.65            3.63      1.67      0.5   \n",
1161 |        "Dry Roasted Pistachio Nuts         21.05           28.28      7.74     10.3   \n",
1162 |        "White Button Mushrooms              3.09            3.26      1.98      1.0   \n",
1163 |        "\n",
1164 |        "                            cholesterol_mg  saturated_fats_g  calcium_mg  ...  \\\n",
1165 |        "name                                                                      ...   \n",
1166 |        "Broccoli (Cooked)                      0.0             0.079        40.0  ...   \n",
1167 |        "Cooked Celery                          0.0             0.040        42.0  ...   \n",
1168 |        "Cooked Tomatoes                        0.0             0.015        11.0  ...   \n",
1169 |        "Cucumber                               0.0             0.037        16.0  ...   \n",
1170 |        "Dry Roasted Pistachio Nuts             0.0             5.645       107.0  ...   \n",
1171 |        "White Button Mushrooms                 0.0             0.050         3.0  ...   \n",
1172 |        "\n",
1173 |        "                            alcohol_g  caffeine_mg  theobromine_mg  \\\n",
1174 |        "name                                                                 \n",
1175 |        "Broccoli (Cooked)                 0.0          0.0             0.0   \n",
1176 |        "Cooked Celery                     0.0          0.0             0.0   \n",
1177 |        "Cooked Tomatoes                   0.0          0.0             0.0   \n",
1178 |        "Cucumber                          0.0          0.0             0.0   \n",
1179 |        "Dry Roasted Pistachio Nuts        0.0          0.0             0.0   \n",
1180 |        "White Button Mushrooms            0.0          0.0             0.0   \n",
1181 |        "\n",
1182 |        "                            200_calorie_weight_g  \\\n",
1183 |        "name                                               \n",
1184 |        "Broccoli (Cooked)                        571.429   \n",
1185 |        "Cooked Celery                           1111.111   \n",
1186 |        "Cooked Tomatoes                         1111.111   \n",
1187 |        "Cucumber                                1333.333   \n",
1188 |        "Dry Roasted Pistachio Nuts                34.965   \n",
1189 |        "White Button Mushrooms                   909.091   \n",
1190 |        "\n",
1191 |        "                            phenylalanine_and_tyrosine_mg_kg  \\\n",
1192 |        "name                                                           \n",
1193 |        "Broccoli (Cooked)                                      176.0   \n",
1194 |        "Cooked Celery                                           35.0   \n",
1195 |        "Cooked Tomatoes                                         46.0   \n",
1196 |        "Cucumber                                                30.0   \n",
1197 |        "Dry Roasted Pistachio Nuts                            1671.0   \n",
1198 |        "White Button Mushrooms                                 129.0   \n",
1199 |        "\n",
1200 |        "                            methionine_and_cysteine_mg_kg    max  cost  \\\n",
1201 |        "name                                                                     \n",
1202 |        "Broccoli (Cooked)                                    74.0  10000     1   \n",
1203 |        "Cooked Celery                                        12.0  10000     1   \n",
1204 |        "Cooked Tomatoes                                      23.0  10000     1   \n",
1205 |        "Cucumber                                             10.0  10000     1   \n",
1206 |        "Dry Roasted Pistachio Nuts                          680.0  10000     1   \n",
1207 |        "White Button Mushrooms                               43.0  10000     1   \n",
1208 |        "\n",
1209 |        "                                            amount_var     amount  \n",
1210 |        "name                                                               \n",
1211 |        "Broccoli (Cooked)                    Broccoli_(Cooked)   0.518573  \n",
1212 |        "Cooked Celery                            Cooked_Celery  13.191981  \n",
1213 |        "Cooked Tomatoes                        Cooked_Tomatoes  21.014955  \n",
1214 |        "Cucumber                                      Cucumber   6.096698  \n",
1215 |        "Dry Roasted Pistachio Nuts  Dry_Roasted_Pistachio_Nuts   2.421314  \n",
1216 |        "White Button Mushrooms          White_Button_Mushrooms   4.076494  \n",
1217 |        "\n",
1218 |        "[6 rows x 99 columns]"
1219 |       ]
1220 |      },
1221 |      "execution_count": 7,
1222 |      "metadata": {},
1223 |      "output_type": "execute_result"
1224 |     }
1225 |    ],
1226 |    "source": [
1227 |     "problem.food_table.df[problem.food_table.df.index.isin(problem.recipe.index)]"
1228 |    ]
1229 |   },
1230 |   {
1231 |    "cell_type": "code",
1232 |    "execution_count": 8,
1233 |    "id": "f1a5c0b5",
1234 |    "metadata": {},
1235 |    "outputs": [],
1236 |    "source": [
1237 |     "problem.nutrient_table['intake_value'] = problem.nutrient_matrix[problem.recipe.index] @ problem.recipe.amount"
1238 |    ]
1239 |   },
1240 |   {
1241 |    "cell_type": "code",
1242 |    "execution_count": 9,
1243 |    "id": "88164ca3",
1244 |    "metadata": {},
1245 |    "outputs": [
1246 |     {
1247 |      "data": {
1248 |       "text/html": [
1249 |        "<div>\n",
1250 |        "<style scoped>\n",
1251 |        "    .dataframe tbody tr th:only-of-type {\n",
1252 |        "        vertical-align: middle;\n",
1253 |        "    }\n",
1254 |        "\n",
1255 |        "    .dataframe tbody tr th {\n",
1256 |        "        vertical-align: top;\n",
1257 |        "    }\n",
1258 |        "\n",
1259 |        "    .dataframe thead th {\n",
1260 |        "        text-align: right;\n",
1261 |        "    }\n",
1262 |        "</style>\n",
1263 |        "<table border=\"1\" class=\"dataframe\">\n",
1264 |        "  <thead>\n",
1265 |        "    <tr style=\"text-align: right;\">\n",
1266 |        "      <th></th>\n",
1267 |        "      <th>min</th>\n",
1268 |        "      <th>max</th>\n",
1269 |        "      <th>intake</th>\n",
1270 |        "      <th>intake_value</th>\n",
1271 |        "    </tr>\n",
1272 |        "    <tr>\n",
1273 |        "      <th>name</th>\n",
1274 |        "      <th></th>\n",
1275 |        "      <th></th>\n",
1276 |        "      <th></th>\n",
1277 |        "      <th></th>\n",
1278 |        "    </tr>\n",
1279 |        "  </thead>\n",
1280 |        "  <tbody>\n",
1281 |        "    <tr>\n",
1282 |        "      <th>calcium_mg</th>\n",
1283 |        "      <td>1000.0</td>\n",
1284 |        "      <td>2500.0</td>\n",
1285 |        "      <td>{Almonds: 269.0, Almonds_Unsalted: 260.0, Appl...</td>\n",
1286 |        "      <td>1174.827914</td>\n",
1287 |        "    </tr>\n",
1288 |        "    <tr>\n",
1289 |        "      <th>calories_kcal</th>\n",
1290 |        "      <td>1800.0</td>\n",
1291 |        "      <td>2200.0</td>\n",
1292 |        "      <td>{Almonds: 579.0, Almonds_Unsalted: 607.0, Appl...</td>\n",
1293 |        "      <td>2200.000034</td>\n",
1294 |        "    </tr>\n",
1295 |        "    <tr>\n",
1296 |        "      <th>choline_mg</th>\n",
1297 |        "      <td>550.0</td>\n",
1298 |        "      <td>3500.0</td>\n",
1299 |        "      <td>{Almonds: 52.1, Almonds_Unsalted: 50.5, Apples...</td>\n",
1300 |        "      <td>550.000006</td>\n",
1301 |        "    </tr>\n",
1302 |        "    <tr>\n",
1303 |        "      <th>copper_mg</th>\n",
1304 |        "      <td>0.9</td>\n",
1305 |        "      <td>10.0</td>\n",
1306 |        "      <td>{Almonds: 1.031, Almonds_Unsalted: 1.065999999...</td>\n",
1307 |        "      <td>6.759715</td>\n",
1308 |        "    </tr>\n",
1309 |        "    <tr>\n",
1310 |        "      <th>fiber_g</th>\n",
1311 |        "      <td>38.0</td>\n",
1312 |        "      <td>NaN</td>\n",
1313 |        "      <td>{Almonds: 12.5, Almonds_Unsalted: 10.6, Apples...</td>\n",
1314 |        "      <td>69.593310</td>\n",
1315 |        "    </tr>\n",
1316 |        "    <tr>\n",
1317 |        "      <th>fluoride_mcg</th>\n",
1318 |        "      <td>4.0</td>\n",
1319 |        "      <td>10.0</td>\n",
1320 |        "      <td>{Apples: 3.3, Bananas: 2.2, Broccoli_(Cooked):...</td>\n",
1321 |        "      <td>10.000000</td>\n",
1322 |        "    </tr>\n",
1323 |        "    <tr>\n",
1324 |        "      <th>folate_mcg</th>\n",
1325 |        "      <td>400.0</td>\n",
1326 |        "      <td>1000.0</td>\n",
1327 |        "      <td>{Almonds: 44.0, Almonds_Unsalted: 53.0, Apples...</td>\n",
1328 |        "      <td>1000.000014</td>\n",
1329 |        "    </tr>\n",
1330 |        "    <tr>\n",
1331 |        "      <th>histidine_mg_kg</th>\n",
1332 |        "      <td>14.0</td>\n",
1333 |        "      <td>NaN</td>\n",
1334 |        "      <td>{Almonds: 539.0, Apples: 5.0, Baked_Potato_(No...</td>\n",
1335 |        "      <td>2142.327436</td>\n",
1336 |        "    </tr>\n",
1337 |        "    <tr>\n",
1338 |        "      <th>iron_mg</th>\n",
1339 |        "      <td>8.0</td>\n",
1340 |        "      <td>45.0</td>\n",
1341 |        "      <td>{Almonds: 3.71, Almonds_Unsalted: 3.62, Apples...</td>\n",
1342 |        "      <td>33.681465</td>\n",
1343 |        "    </tr>\n",
1344 |        "    <tr>\n",
1345 |        "      <th>isoleucine_mg_kg</th>\n",
1346 |        "      <td>19.0</td>\n",
1347 |        "      <td>NaN</td>\n",
1348 |        "      <td>{Almonds: 751.0, Apples: 6.0, Baked_Potato_(No...</td>\n",
1349 |        "      <td>3678.939105</td>\n",
1350 |        "    </tr>\n",
1351 |        "    <tr>\n",
1352 |        "      <th>leucine_mg_kg</th>\n",
1353 |        "      <td>42.0</td>\n",
1354 |        "      <td>NaN</td>\n",
1355 |        "      <td>{Almonds: 1473.0, Apples: 13.0, Baked_Potato_(...</td>\n",
1356 |        "      <td>6131.985784</td>\n",
1357 |        "    </tr>\n",
1358 |        "    <tr>\n",
1359 |        "      <th>lysine_mg_kg</th>\n",
1360 |        "      <td>38.0</td>\n",
1361 |        "      <td>NaN</td>\n",
1362 |        "      <td>{Almonds: 568.0, Apples: 12.0, Baked_Potato_(N...</td>\n",
1363 |        "      <td>4814.037326</td>\n",
1364 |        "    </tr>\n",
1365 |        "    <tr>\n",
1366 |        "      <th>magnesium_mg</th>\n",
1367 |        "      <td>400.0</td>\n",
1368 |        "      <td>NaN</td>\n",
1369 |        "      <td>{Almonds: 270.0, Almonds_Unsalted: 271.0, Appl...</td>\n",
1370 |        "      <td>738.197184</td>\n",
1371 |        "    </tr>\n",
1372 |        "    <tr>\n",
1373 |        "      <th>manganese_mg</th>\n",
1374 |        "      <td>2.3</td>\n",
1375 |        "      <td>11.0</td>\n",
1376 |        "      <td>{Almonds: 2.1790000000000003, Apples: 0.035, B...</td>\n",
1377 |        "      <td>7.388452</td>\n",
1378 |        "    </tr>\n",
1379 |        "    <tr>\n",
1380 |        "      <th>methionine_and_cysteine_mg_kg</th>\n",
1381 |        "      <td>19.0</td>\n",
1382 |        "      <td>NaN</td>\n",
1383 |        "      <td>{Almonds: 372.0, Apples: 2.0, Baked_Potato_(No...</td>\n",
1384 |        "      <td>2562.772116</td>\n",
1385 |        "    </tr>\n",
1386 |        "    <tr>\n",
1387 |        "      <th>niacin_mg</th>\n",
1388 |        "      <td>16.0</td>\n",
1389 |        "      <td>35.0</td>\n",
1390 |        "      <td>{Almonds: 3.618, Almonds_Unsalted: 3.528, Appl...</td>\n",
1391 |        "      <td>35.000000</td>\n",
1392 |        "    </tr>\n",
1393 |        "    <tr>\n",
1394 |        "      <th>pantothenic_acid_mg</th>\n",
1395 |        "      <td>5.0</td>\n",
1396 |        "      <td>NaN</td>\n",
1397 |        "      <td>{Almonds: 0.471, Apples: 0.061, Baked_Potato_(...</td>\n",
1398 |        "      <td>16.135919</td>\n",
1399 |        "    </tr>\n",
1400 |        "    <tr>\n",
1401 |        "      <th>phenylalanine_and_tyrosine_mg_kg</th>\n",
1402 |        "      <td>33.0</td>\n",
1403 |        "      <td>NaN</td>\n",
1404 |        "      <td>{Almonds: 1582.0, Apples: 7.0, Baked_Potato_(N...</td>\n",
1405 |        "      <td>6274.461059</td>\n",
1406 |        "    </tr>\n",
1407 |        "    <tr>\n",
1408 |        "      <th>phosphorus_mg</th>\n",
1409 |        "      <td>700.0</td>\n",
1410 |        "      <td>4000.0</td>\n",
1411 |        "      <td>{Almonds: 481.0, Almonds_Unsalted: 457.0, Appl...</td>\n",
1412 |        "      <td>2585.458343</td>\n",
1413 |        "    </tr>\n",
1414 |        "    <tr>\n",
1415 |        "      <th>potassium_mg</th>\n",
1416 |        "      <td>3400.0</td>\n",
1417 |        "      <td>NaN</td>\n",
1418 |        "      <td>{Almonds: 733.0, Almonds_Unsalted: 692.0, Appl...</td>\n",
1419 |        "      <td>13110.528043</td>\n",
1420 |        "    </tr>\n",
1421 |        "    <tr>\n",
1422 |        "      <th>protein_g_kg</th>\n",
1423 |        "      <td>0.8</td>\n",
1424 |        "      <td>NaN</td>\n",
1425 |        "      <td>{Almonds: 21.15, Almonds_Unsalted: 20.33, Appl...</td>\n",
1426 |        "      <td>99.675643</td>\n",
1427 |        "    </tr>\n",
1428 |        "    <tr>\n",
1429 |        "      <th>riboflavin_mg</th>\n",
1430 |        "      <td>1.3</td>\n",
1431 |        "      <td>NaN</td>\n",
1432 |        "      <td>{Almonds: 1.138, Almonds_Unsalted: 1.161, Appl...</td>\n",
1433 |        "      <td>3.856082</td>\n",
1434 |        "    </tr>\n",
1435 |        "    <tr>\n",
1436 |        "      <th>selenium_mcg</th>\n",
1437 |        "      <td>55.0</td>\n",
1438 |        "      <td>400.0</td>\n",
1439 |        "      <td>{Almonds: 4.1, Almonds_Unsalted: 1.9, Baked_Po...</td>\n",
1440 |        "      <td>88.482726</td>\n",
1441 |        "    </tr>\n",
1442 |        "    <tr>\n",
1443 |        "      <th>sodium_mg</th>\n",
1444 |        "      <td>1500.0</td>\n",
1445 |        "      <td>NaN</td>\n",
1446 |        "      <td>{Almonds: 1.0, Almonds_Unsalted: 3.0, Apples: ...</td>\n",
1447 |        "      <td>1500.000031</td>\n",
1448 |        "    </tr>\n",
1449 |        "    <tr>\n",
1450 |        "      <th>thiamin_mg</th>\n",
1451 |        "      <td>1.2</td>\n",
1452 |        "      <td>NaN</td>\n",
1453 |        "      <td>{Almonds: 0.205, Almonds_Unsalted: 0.075, Appl...</td>\n",
1454 |        "      <td>3.270244</td>\n",
1455 |        "    </tr>\n",
1456 |        "    <tr>\n",
1457 |        "      <th>threonine_mg_kg</th>\n",
1458 |        "      <td>20.0</td>\n",
1459 |        "      <td>NaN</td>\n",
1460 |        "      <td>{Almonds: 601.0, Apples: 6.0, Baked_Potato_(No...</td>\n",
1461 |        "      <td>3214.634927</td>\n",
1462 |        "    </tr>\n",
1463 |        "    <tr>\n",
1464 |        "      <th>tryptophan_mg_kg</th>\n",
1465 |        "      <td>5.0</td>\n",
1466 |        "      <td>NaN</td>\n",
1467 |        "      <td>{Almonds: 211.0, Apples: 1.0, Baked_Potato_(No...</td>\n",
1468 |        "      <td>1138.408060</td>\n",
1469 |        "    </tr>\n",
1470 |        "    <tr>\n",
1471 |        "      <th>valine_mg_kg</th>\n",
1472 |        "      <td>24.0</td>\n",
1473 |        "      <td>NaN</td>\n",
1474 |        "      <td>{Almonds: 855.0, Apples: 12.0, Baked_Potato_(N...</td>\n",
1475 |        "      <td>5313.991476</td>\n",
1476 |        "    </tr>\n",
1477 |        "    <tr>\n",
1478 |        "      <th>vitamin_a_mcg</th>\n",
1479 |        "      <td>900.0</td>\n",
1480 |        "      <td>3000.0</td>\n",
1481 |        "      <td>{Apples: 3.0, Bananas: 3.0, Broccoli: 31.0, Br...</td>\n",
1482 |        "      <td>949.241139</td>\n",
1483 |        "    </tr>\n",
1484 |        "    <tr>\n",
1485 |        "      <th>vitamin_b6_mg</th>\n",
1486 |        "      <td>1.3</td>\n",
1487 |        "      <td>100.0</td>\n",
1488 |        "      <td>{Almonds: 0.1369999999999999, Almonds_Unsalted...</td>\n",
1489 |        "      <td>6.282944</td>\n",
1490 |        "    </tr>\n",
1491 |        "    <tr>\n",
1492 |        "      <th>vitamin_c_mg</th>\n",
1493 |        "      <td>90.0</td>\n",
1494 |        "      <td>2000.0</td>\n",
1495 |        "      <td>{Apples: 4.6, Baked_Potato_(No_Skin): 12.8, Ba...</td>\n",
1496 |        "      <td>626.162795</td>\n",
1497 |        "    </tr>\n",
1498 |        "    <tr>\n",
1499 |        "      <th>vitamin_e_mg</th>\n",
1500 |        "      <td>15.0</td>\n",
1501 |        "      <td>1000.0</td>\n",
1502 |        "      <td>{Almonds: 25.63, Almonds_Unsalted: 23.54, Appl...</td>\n",
1503 |        "      <td>22.615417</td>\n",
1504 |        "    </tr>\n",
1505 |        "    <tr>\n",
1506 |        "      <th>vitamin_k_mcg</th>\n",
1507 |        "      <td>120.0</td>\n",
1508 |        "      <td>NaN</td>\n",
1509 |        "      <td>{Almonds_Unsalted: 3.5, Apples: 2.2, Baked_Pot...</td>\n",
1510 |        "      <td>762.616630</td>\n",
1511 |        "    </tr>\n",
1512 |        "    <tr>\n",
1513 |        "      <th>zinc_mg</th>\n",
1514 |        "      <td>11.0</td>\n",
1515 |        "      <td>40.0</td>\n",
1516 |        "      <td>{Almonds: 3.12, Almonds_Unsalted: 3.21, Apples...</td>\n",
1517 |        "      <td>14.027321</td>\n",
1518 |        "    </tr>\n",
1519 |        "  </tbody>\n",
1520 |        "</table>\n",
1521 |        "</div>"
1522 |       ],
1523 |       "text/plain": [
1524 |        "                                     min     max  \\\n",
1525 |        "name                                               \n",
1526 |        "calcium_mg                        1000.0  2500.0   \n",
1527 |        "calories_kcal                     1800.0  2200.0   \n",
1528 |        "choline_mg                         550.0  3500.0   \n",
1529 |        "copper_mg                            0.9    10.0   \n",
1530 |        "fiber_g                             38.0     NaN   \n",
1531 |        "fluoride_mcg                         4.0    10.0   \n",
1532 |        "folate_mcg                         400.0  1000.0   \n",
1533 |        "histidine_mg_kg                     14.0     NaN   \n",
1534 |        "iron_mg                              8.0    45.0   \n",
1535 |        "isoleucine_mg_kg                    19.0     NaN   \n",
1536 |        "leucine_mg_kg                       42.0     NaN   \n",
1537 |        "lysine_mg_kg                        38.0     NaN   \n",
1538 |        "magnesium_mg                       400.0     NaN   \n",
1539 |        "manganese_mg                         2.3    11.0   \n",
1540 |        "methionine_and_cysteine_mg_kg       19.0     NaN   \n",
1541 |        "niacin_mg                           16.0    35.0   \n",
1542 |        "pantothenic_acid_mg                  5.0     NaN   \n",
1543 |        "phenylalanine_and_tyrosine_mg_kg    33.0     NaN   \n",
1544 |        "phosphorus_mg                      700.0  4000.0   \n",
1545 |        "potassium_mg                      3400.0     NaN   \n",
1546 |        "protein_g_kg                         0.8     NaN   \n",
1547 |        "riboflavin_mg                        1.3     NaN   \n",
1548 |        "selenium_mcg                        55.0   400.0   \n",
1549 |        "sodium_mg                         1500.0     NaN   \n",
1550 |        "thiamin_mg                           1.2     NaN   \n",
1551 |        "threonine_mg_kg                     20.0     NaN   \n",
1552 |        "tryptophan_mg_kg                     5.0     NaN   \n",
1553 |        "valine_mg_kg                        24.0     NaN   \n",
1554 |        "vitamin_a_mcg                      900.0  3000.0   \n",
1555 |        "vitamin_b6_mg                        1.3   100.0   \n",
1556 |        "vitamin_c_mg                        90.0  2000.0   \n",
1557 |        "vitamin_e_mg                        15.0  1000.0   \n",
1558 |        "vitamin_k_mcg                      120.0     NaN   \n",
1559 |        "zinc_mg                             11.0    40.0   \n",
1560 |        "\n",
1561 |        "                                                                             intake  \\\n",
1562 |        "name                                                                                  \n",
1563 |        "calcium_mg                        {Almonds: 269.0, Almonds_Unsalted: 260.0, Appl...   \n",
1564 |        "calories_kcal                     {Almonds: 579.0, Almonds_Unsalted: 607.0, Appl...   \n",
1565 |        "choline_mg                        {Almonds: 52.1, Almonds_Unsalted: 50.5, Apples...   \n",
1566 |        "copper_mg                         {Almonds: 1.031, Almonds_Unsalted: 1.065999999...   \n",
1567 |        "fiber_g                           {Almonds: 12.5, Almonds_Unsalted: 10.6, Apples...   \n",
1568 |        "fluoride_mcg                      {Apples: 3.3, Bananas: 2.2, Broccoli_(Cooked):...   \n",
1569 |        "folate_mcg                        {Almonds: 44.0, Almonds_Unsalted: 53.0, Apples...   \n",
1570 |        "histidine_mg_kg                   {Almonds: 539.0, Apples: 5.0, Baked_Potato_(No...   \n",
1571 |        "iron_mg                           {Almonds: 3.71, Almonds_Unsalted: 3.62, Apples...   \n",
1572 |        "isoleucine_mg_kg                  {Almonds: 751.0, Apples: 6.0, Baked_Potato_(No...   \n",
1573 |        "leucine_mg_kg                     {Almonds: 1473.0, Apples: 13.0, Baked_Potato_(...   \n",
1574 |        "lysine_mg_kg                      {Almonds: 568.0, Apples: 12.0, Baked_Potato_(N...   \n",
1575 |        "magnesium_mg                      {Almonds: 270.0, Almonds_Unsalted: 271.0, Appl...   \n",
1576 |        "manganese_mg                      {Almonds: 2.1790000000000003, Apples: 0.035, B...   \n",
1577 |        "methionine_and_cysteine_mg_kg     {Almonds: 372.0, Apples: 2.0, Baked_Potato_(No...   \n",
1578 |        "niacin_mg                         {Almonds: 3.618, Almonds_Unsalted: 3.528, Appl...   \n",
1579 |        "pantothenic_acid_mg               {Almonds: 0.471, Apples: 0.061, Baked_Potato_(...   \n",
1580 |        "phenylalanine_and_tyrosine_mg_kg  {Almonds: 1582.0, Apples: 7.0, Baked_Potato_(N...   \n",
1581 |        "phosphorus_mg                     {Almonds: 481.0, Almonds_Unsalted: 457.0, Appl...   \n",
1582 |        "potassium_mg                      {Almonds: 733.0, Almonds_Unsalted: 692.0, Appl...   \n",
1583 |        "protein_g_kg                      {Almonds: 21.15, Almonds_Unsalted: 20.33, Appl...   \n",
1584 |        "riboflavin_mg                     {Almonds: 1.138, Almonds_Unsalted: 1.161, Appl...   \n",
1585 |        "selenium_mcg                      {Almonds: 4.1, Almonds_Unsalted: 1.9, Baked_Po...   \n",
1586 |        "sodium_mg                         {Almonds: 1.0, Almonds_Unsalted: 3.0, Apples: ...   \n",
1587 |        "thiamin_mg                        {Almonds: 0.205, Almonds_Unsalted: 0.075, Appl...   \n",
1588 |        "threonine_mg_kg                   {Almonds: 601.0, Apples: 6.0, Baked_Potato_(No...   \n",
1589 |        "tryptophan_mg_kg                  {Almonds: 211.0, Apples: 1.0, Baked_Potato_(No...   \n",
1590 |        "valine_mg_kg                      {Almonds: 855.0, Apples: 12.0, Baked_Potato_(N...   \n",
1591 |        "vitamin_a_mcg                     {Apples: 3.0, Bananas: 3.0, Broccoli: 31.0, Br...   \n",
1592 |        "vitamin_b6_mg                     {Almonds: 0.1369999999999999, Almonds_Unsalted...   \n",
1593 |        "vitamin_c_mg                      {Apples: 4.6, Baked_Potato_(No_Skin): 12.8, Ba...   \n",
1594 |        "vitamin_e_mg                      {Almonds: 25.63, Almonds_Unsalted: 23.54, Appl...   \n",
1595 |        "vitamin_k_mcg                     {Almonds_Unsalted: 3.5, Apples: 2.2, Baked_Pot...   \n",
1596 |        "zinc_mg                           {Almonds: 3.12, Almonds_Unsalted: 3.21, Apples...   \n",
1597 |        "\n",
1598 |        "                                  intake_value  \n",
1599 |        "name                                            \n",
1600 |        "calcium_mg                         1174.827914  \n",
1601 |        "calories_kcal                      2200.000034  \n",
1602 |        "choline_mg                          550.000006  \n",
1603 |        "copper_mg                             6.759715  \n",
1604 |        "fiber_g                              69.593310  \n",
1605 |        "fluoride_mcg                         10.000000  \n",
1606 |        "folate_mcg                         1000.000014  \n",
1607 |        "histidine_mg_kg                    2142.327436  \n",
1608 |        "iron_mg                              33.681465  \n",
1609 |        "isoleucine_mg_kg                   3678.939105  \n",
1610 |        "leucine_mg_kg                      6131.985784  \n",
1611 |        "lysine_mg_kg                       4814.037326  \n",
1612 |        "magnesium_mg                        738.197184  \n",
1613 |        "manganese_mg                          7.388452  \n",
1614 |        "methionine_and_cysteine_mg_kg      2562.772116  \n",
1615 |        "niacin_mg                            35.000000  \n",
1616 |        "pantothenic_acid_mg                  16.135919  \n",
1617 |        "phenylalanine_and_tyrosine_mg_kg   6274.461059  \n",
1618 |        "phosphorus_mg                      2585.458343  \n",
1619 |        "potassium_mg                      13110.528043  \n",
1620 |        "protein_g_kg                         99.675643  \n",
1621 |        "riboflavin_mg                         3.856082  \n",
1622 |        "selenium_mcg                         88.482726  \n",
1623 |        "sodium_mg                          1500.000031  \n",
1624 |        "thiamin_mg                            3.270244  \n",
1625 |        "threonine_mg_kg                    3214.634927  \n",
1626 |        "tryptophan_mg_kg                   1138.408060  \n",
1627 |        "valine_mg_kg                       5313.991476  \n",
1628 |        "vitamin_a_mcg                       949.241139  \n",
1629 |        "vitamin_b6_mg                         6.282944  \n",
1630 |        "vitamin_c_mg                        626.162795  \n",
1631 |        "vitamin_e_mg                         22.615417  \n",
1632 |        "vitamin_k_mcg                       762.616630  \n",
1633 |        "zinc_mg                              14.027321  "
1634 |       ]
1635 |      },
1636 |      "execution_count": 9,
1637 |      "metadata": {},
1638 |      "output_type": "execute_result"
1639 |     }
1640 |    ],
1641 |    "source": [
1642 |     "problem.nutrient_table"
1643 |    ]
1644 |   },
1645 |   {
1646 |    "cell_type": "code",
1647 |    "execution_count": 10,
1648 |    "id": "edeb0e69",
1649 |    "metadata": {},
1650 |    "outputs": [
1651 |     {
1652 |      "data": {
1653 |       "text/plain": [
1654 |        "1*Almonds + 1*Almonds_Unsalted + 1*Apples + 1*Baked_Potato_(No_Skin) + 1*Bananas + 1*Beans_Pinto_Mature_Seeds_Raw + 1*Boiled_Red_Kidney_Beans + 1*Broccoli + 1*Broccoli_(Cooked) + 1*Canola_Oil + 1*Carrots + 1*Cashews_(Raw) + 1*Cashews_Unsalted + 1*Celery + 1*Chia_Seeds + 1*Chickpeas_(Garbanzo_Beans)_(Cooked) + 1*Cooked_Carrots + 1*Cooked_Celery + 1*Cooked_Eggplant + 1*Cooked_Green_Bell_Peppers + 1*Cooked_Green_Peas + 1*Cooked_Oatmeal + 1*Cooked_Onions + 1*Cooked_Red_Kidney_Beans + 1*Cooked_Tomatoes + 1*Cooked_White_Button_Mushrooms + 1*Cooked_Zucchini + 1*Cucumber + 1*Cucumber_Raw + 1*Dates_(Deglet_Noor) + 1*Dried_Figs + 1*Dried_Sunflower_Seeds + 1*Dry_Roasted_Almonds + 1*Dry_Roasted_Pistachio_Nuts + 1*Dry_Roasted_Sunflower_Seeds + 1*Eggplant + 1*Flax_Seeds + 1*Grapes + 1*Green_Bell_Peppers + 1*Hazelnuts + 1*Hemp_Seeds + 1*Kidney_Beans + 1*Lentils_(Cooked) + 1*Medjool_Dates + 1*Onions + 1*Peanut_Butter_(Chunk_Style) + 1*Peas + 1*Pinto_Beans_(Cooked) + 1*Pistachio_Nuts + 1*Pistachio_Nuts_Unsalted + 1*Potatoes_Baked_Skin_Without_Salt + 1*Raw_Peanuts + 1*Red_Or_Green_Grapes_(European) + 1*Rice_Cooked_Nfs + 1*Rice_White_Cooked_Fat_Added_In_Cooking_Made_With_Oil + 1*Seeds_Sesame_Seeds_Whole_Dried + 1*Seeds_Sunflower_Seed_Kernels_Toasted_Without_Salt + 1*Sesame_Oil + 1*Sesame_Seeds_(Toasted) + 1*Sweet_Potato_Fries_From_Fresh_Fried + 1*Tomatoes + 1*Uncooked_Oats + 1*Unsalted_Peanut_Butter_(Smooth) + 1*Walnuts + 1*Watermelon + 1*White_Button_Mushrooms + 1*White_Rice + 1*Zucchini + 0"
1655 |       ]
1656 |      },
1657 |      "execution_count": 10,
1658 |      "metadata": {},
1659 |      "output_type": "execute_result"
1660 |     }
1661 |    ],
1662 |    "source": [
1663 |     "problem.cost"
1664 |    ]
1665 |   },
1666 |   {
1667 |    "cell_type": "code",
1668 |    "execution_count": 11,
1669 |    "id": "166af31c",
1670 |    "metadata": {},
1671 |    "outputs": [
1672 |     {
1673 |      "data": {
1674 |       "text/plain": [
1675 |        "47.32001572"
1676 |       ]
1677 |      },
1678 |      "execution_count": 11,
1679 |      "metadata": {},
1680 |      "output_type": "execute_result"
1681 |     }
1682 |    ],
1683 |    "source": [
1684 |     "pl.value(problem.cost)"
1685 |    ]
1686 |   },
1687 |   {
1688 |    "cell_type": "code",
1689 |    "execution_count": 12,
1690 |    "id": "f676ac5c",
1691 |    "metadata": {},
1692 |    "outputs": [],
1693 |    "source": [
1694 |     "x = pl.LpVariable('something')"
1695 |    ]
1696 |   },
1697 |   {
1698 |    "cell_type": "code",
1699 |    "execution_count": 13,
1700 |    "id": "8bc24b25",
1701 |    "metadata": {},
1702 |    "outputs": [
1703 |     {
1704 |      "data": {
1705 |       "text/plain": [
1706 |        "something"
1707 |       ]
1708 |      },
1709 |      "execution_count": 13,
1710 |      "metadata": {},
1711 |      "output_type": "execute_result"
1712 |     }
1713 |    ],
1714 |    "source": [
1715 |     "x"
1716 |    ]
1717 |   },
1718 |   {
1719 |    "cell_type": "code",
1720 |    "execution_count": 14,
1721 |    "id": "af899b1c",
1722 |    "metadata": {},
1723 |    "outputs": [],
1724 |    "source": [
1725 |     "pl.value(x)"
1726 |    ]
1727 |   },
1728 |   {
1729 |    "cell_type": "code",
1730 |    "execution_count": null,
1731 |    "id": "bf5e497a",
1732 |    "metadata": {},
1733 |    "outputs": [],
1734 |    "source": []
1735 |   }
1736 |  ],
1737 |  "metadata": {
1738 |   "kernelspec": {
1739 |    "display_name": "Python 3",
1740 |    "language": "python",
1741 |    "name": "python3"
1742 |   },
1743 |   "language_info": {
1744 |    "codemirror_mode": {
1745 |     "name": "ipython",
1746 |     "version": 3
1747 |    },
1748 |    "file_extension": ".py",
1749 |    "mimetype": "text/x-python",
1750 |    "name": "python",
1751 |    "nbconvert_exporter": "python",
1752 |    "pygments_lexer": "ipython3",
1753 |    "version": "3.8.10"
1754 |   }
1755 |  },
1756 |  "nbformat": 4,
1757 |  "nbformat_minor": 5
1758 | }
1759 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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675 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # mealsolver
 2 | Logic-based meal optimizer
 3 | 
 4 | # Running examples:
 5 | Open any of the .ipynb files in Jupyter Notebook.
 6 | 
 7 | # Nutrients list
 8 | fndds_nutrients.csv comes from:
 9 | * https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/fndds/2017_2018_FNDDS_Doc.pdf
10 | ** appendix K
11 | 
12 | fnb_dri_elements.csv comes from:
13 | * https://www.ncbi.nlm.nih.gov/books/NBK545442/table/appJ_tab3/?report=objectonly
14 | 
15 | fnb_dri_vitamins.csv comes from:
16 | * https://www.ncbi.nlm.nih.gov/books/NBK56068/table/summarytables.t2/?report=objectonly
17 | 
18 | fnb_dri_macro.csv comes from:
19 | * https://www.ncbi.nlm.nih.gov/books/NBK56068/table/summarytables.t4/?report=objectonly
20 | 
21 | fnb_ul_vitamins.csv comes from:
22 | * https://www.ncbi.nlm.nih.gov/books/NBK56068/table/summarytables.t7/?report=objectonly
23 | 
24 | fnb_ul_elements.csv comes from:
25 | * https://www.ncbi.nlm.nih.gov/books/NBK545442/table/appJ_tab9/?report=objectonly
26 | 
27 | 
28 | # Types of nutrient recommendations:
29 | Dietary Reference Intakes (DRI):
30 | * DRI is the general term for a set of reference values used to plan and assess nutrient intakes of healthy people.
31 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
32 | 
33 | 
34 | Recommended Dietary Allowance (RDA)
35 | * Average daily level of intake sufficient to meet the nutrient requirements of nearly all (97–98%) healthy individuals; often used to plan nutritionally adequate diets for individuals.
36 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
37 | 
38 | 
39 | Adequate Intake (AI):
40 | * Intake at this level is assumed to ensure nutritional adequacy; established when evidence is insufficient to develop an RDA.
41 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
42 | 
43 | Estimated Average Requirements (EAR):
44 | * Average daily level of intake estimated to meet the requirements of 50% of healthy individuals; usually used to assess the nutrient intakes of groups of people and to plan nutritionally adequate diets for them; can also be used to assess the nutrient intakes of individuals.
45 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
46 | 
47 | Tolerable Upper Intake Levels (UL)
48 | * Maximum daily intake unlikely to cause adverse health effects.
49 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
50 | 
51 | 
52 | Daily Value (DV):
53 | * Recommended intakes of nutrients vary by age and sex and are known as Recommended Dietary Allowances (RDAs) and Adequate Intakes (AIs). However, one value for each nutrient, known as the Daily Value (DV), is selected for the labels of dietary supplements and foods. A DV is often, but not always, similar to one’s RDA or AI for that nutrient. DVs were developed by the U.S. Food and Drug Administration (FDA) to help consumers determine the level of various nutrients in a standard serving of food in relation to their approximate requirement for it. The label actually provides the %DV so that you can see how much (what percentage) a serving of the product contributes to reaching the DV.
54 | * https://ods.od.nih.gov/HealthInformation/nutrientrecommendations.aspx
55 | 


--------------------------------------------------------------------------------
/convert_sr.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | 
 3 | def read_sr():
 4 |     return pd.read_csv(
 5 |         "USDA_SR/NUTR_DEF.txt",
 6 |         delimiter = "^",
 7 |         quotechar = "~",
 8 |         names=[
 9 |             "Nutr_No",
10 |             "Units",
11 |             "Tagname",
12 |             "NutrDesc",
13 |             "Num_Dec",
14 |             "SR_Order"
15 |         ]
16 |     )
17 | 
18 | def convert_sr():
19 |     df = read_sr()
20 |     df.to_csv("USDA_SR_Converted/nutrients.csv")
21 | 


--------------------------------------------------------------------------------
/daily_intake.csv:
--------------------------------------------------------------------------------
 1 | name,min,max
 2 | calories_kcal,1800.0,2200.0
 3 | calcium_mg,1000.0,2500.0
 4 | iron_mg,8.0,45.0
 5 | manganese_mg,2.3,11.0
 6 | phosphorus_mg,700.0,4000.0
 7 | zinc_mg,11.0,40.0
 8 | vitamin_b6_mg,1.3,100.0
 9 | vitamin_c_mg,90.0,2000.0
10 | choline_mg,550.0,3500.0
11 | vitamin_e_mg,15.0,1000.0
12 | niacin_mg,16.0,35.0
13 | copper_mg,0.9,10.0
14 | fluoride_mcg,4.0,10.0
15 | iodine_mcg,150.0,1100.0
16 | molybdenum_mcg,45.0,2000.0
17 | chloride_g,2.3,3.6
18 | selenium_mcg,55.0,400.0
19 | folate_mcg,400.0,1000.0
20 | vitamin_a_mcg,900.0,3000.0
21 | vitamin_d_iu,600.0,4000.0
22 | chromium_mcg,35.0,
23 | magnesium_mg,400.0,
24 | potassium_mg,3400.0,
25 | sodium_mg,1500.0,
26 | biotin_mcg,30.0,
27 | vitamin_b12_mcg,2.4,
28 | vitamin_k_mcg,120.0,
29 | thiamin_mg,1.2,
30 | riboflavin_mg,1.3,
31 | pantothenic_acid_mg,5.0,
32 | alpha_linolenic_acid_g,1.6,
33 | linoleic_acid_g,17.0,
34 | histidine_mg_kg,14.0,
35 | isoleucine_mg_kg,19.0,
36 | leucine_mg_kg,42.0,
37 | lysine_mg_kg,38.0,
38 | methionine_and_cysteine_mg_kg,19.0,
39 | phenylalanine_and_tyrosine_mg_kg,33.0,
40 | threonine_mg_kg,20.0,
41 | tryptophan_mg_kg,5.0,
42 | valine_mg_kg,24.0,
43 | fiber_g,38.0,
44 | protein_g_kg,0.8,


--------------------------------------------------------------------------------
/example.txt:
--------------------------------------------------------------------------------
 1 | andrewdo@ai2:~/meal-planner/new$ python3
 2 | Python 3.7.3 (default, Jul 25 2020, 13:03:44) 
 3 | [GCC 8.3.0] on linux
 4 | Type "help", "copyright", "credits" or "license" for more information.
 5 | >>> import program
 6 | 1: calcium_mg: Optimal
 7 | 2: calories_kcal: Optimal
 8 | 3: choline_mg: Optimal
 9 | 4: copper_mg: Optimal
10 | 5: fiber_g: Optimal
11 | 6: fluoride_mcg: Optimal
12 | 7: folate_mcg: Optimal
13 | 8: histidine_mg_kg: Optimal
14 | 9: iron_mg: Optimal
15 | 10: isoleucine_mg_kg: Optimal
16 | 11: leucine_mg_kg: Optimal
17 | 12: lysine_mg_kg: Optimal
18 | 13: magnesium_mg: Optimal
19 | 14: manganese_mg: Optimal
20 | 15: methionine_and_cysteine_mg_kg: Optimal
21 | 16: niacin_mg: Optimal
22 | 17: pantothenic_acid_mg: Optimal
23 | 18: phenylalanine_and_tyrosine_mg_kg: Optimal
24 | 19: phosphorus_mg: Optimal
25 | 20: potassium_mg: Optimal
26 | 21: protein_g_kg: Optimal
27 | 22: riboflavin_mg: Optimal
28 | 23: selenium_mcg: Optimal
29 | 24: sodium_mg: Optimal
30 | 25: thiamin_mg: Optimal
31 | 26: threonine_mg_kg: Optimal
32 | 27: tryptophan_mg_kg: Optimal
33 | 28: valine_mg_kg: Optimal
34 | 29: vitamin_a_mcg: Optimal
35 | 30: vitamin_b6_mg: Optimal
36 | 31: vitamin_c_mg: Optimal
37 | 32: vitamin_e_mg: Optimal
38 | 33: vitamin_k_mcg: Optimal
39 | >>> program.full_example()
40 |                           name     amount
41 | 32  Dry Roasted Pistachio Nuts   2.421314
42 | 45           Broccoli (Cooked)   0.518573
43 | 49               Cooked Celery  13.191981
44 | 54             Cooked Tomatoes  21.014955
45 | 57                    Cucumber   6.096698
46 | 66      White Button Mushrooms   4.076494
47 | >>> 
48 | 


--------------------------------------------------------------------------------
/fnb_dri_elements.csv:
--------------------------------------------------------------------------------
 1 | Life-Stage Group,Group,Calcium (mg/d),Chromium (μg/d),Copper (μg/d),Fluoride (mg/d),Iodine (μg/d),Iron (mg/d),Magnesium (mg/d),Manganese (mg/d),Molybdenum (μg/d),Phosphorus (mg/d),Selenium (μg/d),Zinc (mg/d),Potassium (mg/d),Sodium (mg/d),Chloride (g/d)
 2 | 0–6 mo,Infants,200*a,0.2*,200*,0.01*,110*,0.27*,30*,0.003*,2*,100*,15*,2*,400*,110*,0.18*
 3 | 7–12 mo,Infants,260*a,5.5*,220*,0.5*,130*,11,75*,0.6*,3*,275*,20*,3,860*,370*,0.57*
 4 | 1–3 y,Children,700,11*,340,0.7*,90,7,80,1.2*,17,460,20,3,"2,000*",800*,1.5*
 5 | 4–8 y,Children,1000,15*,440,1*,90,10,130,1.5*,22,500,30,5,"2,300*","1,000*",1.9*
 6 | 9–13 y,Males,1300,25*,700,2*,120,8,240,1.9*,34,1250,40,8,"2,500*","1,200*",2.3*
 7 | 14–18 y,Males,1300,35*,890,3*,150,11,410,2.2*,43,1250,55,11,"3,000*","1,500*",2.3*
 8 | 19–30 y,Males,1000,35*,900,4*,150,8,400,2.3*,45,700,55,11,"3,400*","1,500*",2.3*
 9 | 31–50 y,Males,1000,35*,900,4*,150,8,420,2.3*,45,700,55,11,"3,400*","1,500*",2.3*
10 | 51–70 y,Males,1000,30*,900,4*,150,8,420,2.3*,45,700,55,11,"3,400*","1,500*",2.0*
11 | > 70 y,Males,1200,30*,900,4*,150,8,420,2.3*,45,700,55,11,"3,400*","1,500*",1.8*
12 | 9–13 y,Females,1300,21*,700,2*,120,8,240,1.6*,34,1250,40,8,"2,300*","1,200*",2.3*
13 | 14–18 y,Females,1300,24*,890,3*,150,15,360,1.6*,43,1250,55,9,"2,300*","1,500*",2.3*
14 | 19–30 y,Females,1000,25*,900,3*,150,18,310,1.8*,45,700,55,8,"2,600*","1,500*",2.3*
15 | 31–50 y,Females,1000,25*,900,3*,150,18,320,1.8*,45,700,55,8,"2,600*","1,500*",2.3*
16 | 51–70 y,Females,1200,20*,900,3*,150,8,320,1.8*,45,700,55,8,"2,600*","1,500*",2.0*
17 | > 70 y,Females,1200,20*,900,3*,150,8,320,1.8*,45,700,55,8,"2,600*","1,500*",1.8*
18 | 14–18 y,"Females, pregnancy",1300,29*,1000,3*,220,27,400,2.0*,50,1250,60,12,"2,600*","1,500*",2.3*
19 | 19–30 y,"Females, pregnancy",1000,30*,1000,3*,220,27,350,2.0*,50,700,60,11,"2,900*","1,500*",2.3*
20 | 31–50 y,"Females, pregnancy",1000,30*,1000,3*,220,27,360,2.0*,50,700,60,11,"2,900*","1,500*",2.3*
21 | 14–18 y,"Females, lactation",1300,44*,1300,3*,290,10,360,2.6*,50,1250,70,13,"2,500*","1,500*",2.3*
22 | 19–30 y,"Females, lactation",1000,45*,1300,3*,290,9,310,2.6*,50,700,70,12,"2,800*","1,500*",2.3*
23 | 31–50 y,"Females, lactation",1000,45*,1300,3*,290,9,320,2.6*,50,700,70,12,"2,800*","1,500*",2.3*
24 | 


--------------------------------------------------------------------------------
/fnb_dri_macro.csv:
--------------------------------------------------------------------------------
 1 | "Life Stage Group",Group,"Total Watera(L/d)","Carbohydrate (g/d)","Total Fiber(g/d)","Fat(g/d)","Linoleic Acid(g/d)","α-Linolenic Acid(g/d)","Proteinb(g/d)"
 2 |  0–6 mo,Infants,0.7*,60*,ND,31*,4.4*,0.5*,9.1*
 3 |  6–12 mo,Infants,0.8*,95*,ND,30*,4.6*,0.5*,11
 4 |  1–3 y,Children,1.3*,130,19*,NDc,7*,0.7*,13
 5 |  4–8 y,Children,1.7*,130,25*,ND,10*,0.9*,19
 6 |  9–13 y,Males,2.4*,130,31*,ND,12*,1.2*,34
 7 |  14–18 y,Males,3.3*,130,38*,ND,16*,1.6*,52
 8 |  19–30 y,Males,3.7*,130,38*,ND,17*,1.6*,56
 9 |  31–50 y,Males,3.7*,130,38*,ND,17*,1.6*,56
10 |  51–70 y,Males,3.7*,130,30*,ND,14*,1.6*,56
11 |  > 70 y,Males,3.7*,130,30*,ND,14*,1.6*,56
12 |  9–13 y,Females,2.1*,130,26*,ND,10*,1.0*,34
13 |  14–18 y,Females,2.3*,130,26*,ND,11*,1.1*,46
14 |  19–30 y,Females,2.7*,130,25*,ND,12*,1.1*,46
15 |  31–50 y,Females,2.7*,130,25*,ND,12*,1.1*,46
16 |  51–70 y,Females,2.7*,130,21*,ND,11*,1.1*,46
17 |  > 70 y,Females,2.7*,130,21*,ND,11*,1.1*,46
18 |  14–18 y,"Females, pregnancy",3.0*,175,28*,ND,13*,1.4*,71
19 |  19–30 y,"Females, pregnancy",3.0*,175,28*,ND,13*,1.4*,71
20 |  31–50 y,"Females, pregnancy",3.0*,175,28*,ND,13*,1.4*,71
21 |  14–18,"Females, lactation",3.8*,210,29*,ND,13*,1.3*,71
22 |  19–30 y,"Females, lactation",3.8*,210,29*,ND,13*,1.3*,71
23 |  31–50 y,"Females, lactation",3.8*,210,29*,ND,13*,1.3*,71
24 | 


--------------------------------------------------------------------------------
/fnb_dri_vitamins.csv:
--------------------------------------------------------------------------------
 1 | "Life Stage Group",Group,"Vitamin A(μg/d)a","Vitamin C(mg/d)","Vitamin D(μg/d)b,c","Vitamin E(mg/d)d","Vitamin K(μg/d)","Thiamin(mg/d)","Riboflavin(mg/d)","Niacin(mg/d)e","Vitamin B6(mg/d)","Folate(μg/d)f","Vitamin B12(μg/d)","Pantothenic Acid(mg/d)","Biotin(μg/d)","Choline(mg/d)g"
 2 |  0–6 mo,Infants,400*,40*,10*,4*,2.0*,0.2*,0.3*,2*,0.1*,65*,0.4*,1.7*,5*,125*
 3 |  6–12 mo,Infants,500*,50*,10*,5*,2.5*,0.3*,0.4*,4*,0.3*,80*,0.5*,1.8*,6*,150*
 4 |  1–3 y,Children,300,15,15,6,30*,0.5,0.5,6,0.5,150,0.9,2*,8*,200*
 5 |  4–8 y,Children,400,25,15,7,55*,0.6,0.6,8,0.6,200,1.2,3*,12*,250*
 6 |  9–13 y,Males,600,45,15,11,60*,0.9,0.9,12,1,300,1.8,4*,20*,375*
 7 |  14–18 y,Males,900,75,15,15,75*,1.2,1.3,16,1.3,400,2.4,5*,25*,550*
 8 |  19–30 y,Males,900,90,15,15,120*,1.2,1.3,16,1.3,400,2.4,5*,30*,550*
 9 |  31–50 y,Males,900,90,15,15,120*,1.2,1.3,16,1.3,400,2.4,5*,30*,550*
10 |  51–70 y,Males,900,90,15,15,120*,1.2,1.3,16,1.7,400,2.4h,5*,30*,550*
11 |  > 70 y,Males,900,90,20,15,120*,1.2,1.3,16,1.7,400,2.4h,5*,30*,550*
12 |  9–13 y,Females,600,45,15,11,60*,0.9,0.9,12,1,300,1.8,4*,20*,375*
13 |  14–18 y,Females,700,65,15,15,75*,1,1,14,1.2,400i,2.4,5*,25*,400*
14 |  19–30 y,Females,700,75,15,15,90*,1.1,1.1,14,1.3,400i,2.4,5*,30*,425*
15 |  31–50 y,Females,700,75,15,15,90*,1.1,1.1,14,1.3,400i,2.4,5*,30*,425*
16 |  51–70 y,Females,700,75,15,15,90*,1.1,1.1,14,1.5,400,2.4h,5*,30*,425*
17 |  > 70 y,Females,700,75,20,15,90*,1.1,1.1,14,1.5,400,2.4h,5*,30*,425*
18 |  14–18 y,"Females, pregnancy",750,80,15,15,75*,1.4,1.4,18,1.9,600j,2.6,6*,30*,450*
19 |  19–30 y,"Females, pregnancy",770,85,15,15,90*,1.4,1.4,18,1.9,600j,2.6,6*,30*,450*
20 |  31–50 y,"Females, pregnancy",770,85,15,15,90*,1.4,1.4,18,1.9,600j,2.6,6*,30*,450*
21 |  14–18 y,"Females, lactation",1200,115,15,19,75*,1.4,1.6,17,2,500,2.8,7*,35*,550*
22 |  19–30 y,"Females, lactation",1300,120,15,19,90*,1.4,1.6,17,2,500,2.8,7*,35*,550*
23 |  31–50 y,"Females, lactation",1300,120,15,19,90*,1.4,1.6,17,2,500,2.8,7*,35*,550*
24 | 


--------------------------------------------------------------------------------
/fnb_ul_elements.csv:
--------------------------------------------------------------------------------
 1 | Life-Stage Group,Group,Arsenica,Boron (mg/d),Calcium (mg/d),Chromium,Copper (μg/d),Fluoride (mg/d),Iodine (μg/d),Iron (mg/d),Magnesium (mg/d)b,Manganese (mg/d),Molybdenum (μg/d),Nickel (mg/d),Phosphorus (g/d),Potassium,Selenium (μg/d),Siliconc,Sulfate,Vanadium (mg/d)d,Zinc (mg/d),Sodiume,Chloride (g/d)
 2 | 0–6 mo,Infants,NDf,ND,"1,000g",ND,ND,0.7,ND,40,ND,ND,ND,ND,ND,NDh,45,ND,ND,ND,4,NDh,ND
 3 | 7–12 mo,Infants,ND,ND,"1,500g",ND,ND,0.9,ND,40,ND,ND,ND,ND,ND,NDh,60,ND,ND,ND,5,NDh,ND
 4 | 1–3 y,Children,ND,3,2500,ND,1000,1.3,200,40,65,2,300,0.2,3,NDh,90,ND,ND,ND,7,NDh,2.3
 5 | 4–8 y,Children,ND,6,2500,ND,3000,2.2,300,40,110,3,600,0.3,3,NDh,150,ND,ND,ND,12,NDh,2.9
 6 | 9–13 y,Males,ND,11,3000,ND,5000,10,600,40,350,6,1100,0.6,4,NDh,280,ND,ND,ND,23,NDh,3.4
 7 | 14–18 y,Males,ND,17,3 000,ND,8 000,10,900,45,350,9,1 700,1,4,NDh,400,ND,ND,ND,34,NDh,3.6
 8 | 19–30 y,Males,ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
 9 | 31–50 y,Males,ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
10 | 51–70 y,Males,ND,20,2000,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
11 | > 70 y,Males,ND,20,2000,ND,10000,10,1100,45,350,11,2000,1,3,NDh,400,ND,ND,1.8,40,NDh,3.6
12 | 9–13 y,Females,ND,11,3000,ND,5000,10,600,40,350,6,1100,0.6,4,NDh,280,ND,ND,ND,23,NDh,3.4
13 | 14–18 y,Females,ND,17,3000,ND,8000,10,900,45,350,9,1700,1,4,NDh,400,ND,ND,ND,34,NDh,3.6
14 | 19–30 y,Females,ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
15 | 31–50 y,Females,ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
16 | 51–70 y,Females,ND,20,2000,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,1.8,40,NDh,3.6
17 | > 70 y,Females,ND,20,2000,ND,10000,10,1100,45,350,11,2000,1,3,NDh,400,ND,ND,1.8,40,NDh,3.6
18 | 14–18 y,"Females, pregnancy",ND,17,3000,ND,8000,10,900,45,350,9,1700,1,3.5,NDh,400,ND,ND,ND,34,NDh,3.6
19 | 19–30 y,"Females, pregnancy",ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,3.5,NDh,400,ND,ND,ND,40,NDh,3.6
20 | 31–50 y,"Females, pregnancy",ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,3.5,NDh,400,ND,ND,ND,40,NDh,3.6
21 | 14–18 y,"Females, lactation",ND,17,3000,ND,8000,10,900,45,350,9,1700,1,4,NDh,400,ND,ND,ND,34,NDh,3.6
22 | 19–30 y,"Females, lactation",ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,ND,40,NDh,3.6
23 | 31−50 y,"Females, lactation",ND,20,2500,ND,10000,10,1100,45,350,11,2000,1,4,NDh,400,ND,ND,ND,40,NDh,3.6
24 | 


--------------------------------------------------------------------------------
/fnb_ul_vitamins.csv:
--------------------------------------------------------------------------------
 1 | "Life Stage Group",Group,"Vitamin A(μg/d)a","Vitamin C(mg/d)","Vitamin D(μg/d)","Vitamin E(mg/d)b,c",Vitamin K,Thiamin,Riboflavin,"Niacin(mg/d)c","Vitamin B6(mg/d)","Folate(μg/d)c",Vitamin B12,Pantothenic Acid,Biotin,"Choline(g/d)",Carotenoidsd
 2 |  0–6 mo,Infants,600,NDe,25,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND
 3 |  6–12 mo,Infants,600,ND,38,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND,ND
 4 |  1–3 y,Children,600,400,63,200,ND,ND,ND,10,30,300,ND,ND,ND,1,ND
 5 |  4–8 y,Children,900,650,75,300,ND,ND,ND,15,40,400,ND,ND,ND,1,ND
 6 |  9–13 y,Males,1700,1200,100,600,ND,ND,ND,20,60,600,ND,ND,ND,2,ND
 7 |  14–18 y,Males,2800,1800,100,800,ND,ND,ND,30,80,800,ND,ND,ND,3,ND
 8 |  19–30 y,Males,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
 9 |  31–50 y,Males,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
10 |  51–70 y,Males,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
11 |  > 70 y,Males,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
12 |  9–13 y,Females,1700,1200,100,600,ND,ND,ND,20,60,600,ND,ND,ND,2,ND
13 |  14–18 y,Females,2800,1800,100,800,ND,ND,ND,30,80,800,ND,ND,ND,3,ND
14 |  19–30 y,Females,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
15 |  31–50 y,Females,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
16 |  51–70 y,Females,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
17 |  > 70 y,Females,3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
18 |  14–18 y,"Females, pregnancy",2800,1800,100,800,ND,ND,ND,30,80,800,ND,ND,ND,3,ND
19 |  19–30 y,"Females, pregnancy",3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
20 |  31–50 y,"Females, pregnancy",3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
21 |  14–18 y,"Females, lactation",2800,1800,100,800,ND,ND,ND,30,80,800,ND,ND,ND,3,ND
22 |  19–30 y,"Females, lactation",3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
23 |  31–50 y,"Females, lactation",3000,2000,100,1000,ND,ND,ND,35,100,1000,ND,ND,ND,3.5,ND
24 | 


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/fndds_nutrients.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/aindilis/mealsolver/7e7ef27d675f83ca3a6360a56a287ae2d044ae08/fndds_nutrients.csv


--------------------------------------------------------------------------------
/foods.csv:
--------------------------------------------------------------------------------
 1 | name,food_group,calories_kcal,fat_g,protein_g_kg,carbohydrate_g,sugars_g,fiber_g,cholesterol_mg,saturated_fats_g,calcium_mg,iron_mg,potassium_mg,magnesium_mg,vitamin_a_iu,vitamin_a_mcg,vitamin_c_mg,vitamin_b12_mcg,vitamin_d_mcg,vitamin_e_mg,added_sugar_g,net_carbs_g,water_g,omega_3s_mg,omega_6s_mg,pral_score,trans_fatty_acids_g,soluble_fiber_g,insoluble_fiber_g,sucrose_g,glucose_dextrose_g,fructose_g,lactose_g,maltose_g,galactose_g,starch_g,total_sugar_alcohols_g,phosphorus_mg,sodium_mg,zinc_mg,copper_mg,manganese_mg,selenium_mcg,fluoride_mcg,molybdenum_mcg,chlorine_mg,thiamin_mg,riboflavin_mg,niacin_mg,pantothenic_acid_mg,vitamin_b6_mg,biotin_mcg,folate_mcg,folic_acid_mcg,food_folate_mcg,folate_mcg_1,choline_mg,betaine_mg,retinol_mcg,carotene_beta_mcg,carotene_alpha_mcg,lycopene_mcg,lutein_and_zeaxanthin_mcg,vitamin_d2_mcg,vitamin_d3_mcg,vitamin_d_iu,vitamin_k_mcg,dihydrophylloquinone_mcg,menaquinone_4_mcg,fatty_acids_total_monounsaturated_mg,fatty_acids_total_polyunsaturated_mg,ala_mg,epa_mg,dpa_mg,dha_mg,tryptophan_mg_kg,threonine_mg_kg,isoleucine_mg_kg,leucine_mg_kg,lysine_mg_kg,valine_mg_kg,arginine_mg,histidine_mg_kg,alanine_mg,aspartic_acid_mg,glutamic_acid_mg,glycine_mg,proline_mg,serine_mg,hydroxyproline_mg,alcohol_g,caffeine_mg,theobromine_mg,200_calorie_weight_g,phenylalanine_and_tyrosine_mg_kg,methionine_and_cysteine_mg_kg
 2 | Beans Pinto Mature Seeds Raw,Beans and Lentils,347,1.23,21.42,62.55,2.11,15.5,0.0,0.235,113.0,5.07,1393.0,176.0,0.0,0.0,6.3,0.0,0.0,0.21,,47.05,11.33,237.0,170.0,-9.787,0.0,,,1.98,0.13,0.0,0.0,0.0,0.0,34.17,,411.0,12.0,2.28,0.893,1.148,27.9,,,,0.713,0.212,1.1740000000000002,0.785,0.474,,525.0,0.0,525.0,525.0,66.2,0.4,0.0,0.0,0.0,0.0,0.0,,,0.0,5.6,0.0,,229.0,407.0,,0.0,0.0,0.0,237.0,810.0,871.0,1558.0,1356.0,998.0,1096.0,556.0,872.0,2268.0,3027.0,796.0,1072.0,1171.0,,0.0,0.0,0.0,57.637,1522.0,446.0
 3 | Boiled Red Kidney Beans,Beans and Lentils,123,0.17,9.49,21.85,,9.3,0.0,0.024,44.0,2.77,378.0,42.0,3.0,0.0,1.2,0.0,0.0,,,12.55,66.99,57.0,36.0,0.252,0.0,,,,,,,,,,,142.0,5.0,0.9,0.262,0.255,1.2,,,,0.095,0.067,0.552,0.219,0.104,,74.0,0.0,74.0,74.0,,,0.0,,,,,,,0.0,,,,13.0,93.0,,,,,112.0,399.0,419.0,757.0,651.0,496.0,587.0,264.0,398.0,1148.0,1447.0,370.0,402.0,516.0,,,,,162.602,780.0,246.0
 4 | Chickpeas (Garbanzo Beans) (Cooked),Beans and Lentils,164,2.59,8.86,27.42,4.8,7.6,0.0,0.269,49.0,2.89,291.0,48.0,27.0,1.0,1.3,0.0,0.0,0.35,,19.82,60.21,43.0,1113.0,2.518,0.0,,,,,,,,,,,168.0,7.0,1.53,0.35200000000000004,1.03,3.7,,,,0.116,0.063,0.526,0.28600000000000003,0.139,,172.0,0.0,172.0,172.0,42.8,,0.0,16.0,0.0,0.0,0.0,,,0.0,4.0,,,583.0,1156.0,,0.0,0.0,0.0,85.0,329.0,380.0,631.0,593.0,372.0,835.0,244.0,380.0,1042.0,1550.0,369.0,366.0,447.0,,0.0,0.0,0.0,121.951,695.0,235.0
 5 | Cooked Red Kidney Beans,Beans and Lentils,127,0.5,8.67,22.8,0.32,7.4,0.0,0.07200000000000001,28.0,2.94,403.0,45.0,0.0,0.0,1.2,0.0,0.0,0.03,,15.4,66.94,168.0,107.0,-0.5489999999999999,0.0,,,,,,,,,,,142.0,2.0,1.07,0.242,0.477,1.2,,,,0.16,0.058,0.578,0.22,0.12,,130.0,0.0,130.0,130.0,30.5,,0.0,0.0,0.0,0.0,0.0,,,0.0,8.4,,,39.0,275.0,,0.0,0.0,0.0,103.0,365.0,383.0,693.0,595.0,454.0,537.0,242.0,364.0,1049.0,1323.0,339.0,368.0,472.0,,0.0,0.0,0.0,157.48,713.0,224.0
 6 | Kidney Beans,Beans and Lentils,127,0.5,8.67,22.8,0.32,6.4,0.0,0.073,35.0,2.22,405.0,42.0,0.0,0.0,1.2,0.0,0.0,0.03,,16.4,66.94,170.0,108.0,-0.7509999999999999,0.0,,,,,,,,,,,138.0,1.0,1.0,0.21600000000000005,0.43,1.1,,,,0.16,0.058,0.578,0.22,0.12,,130.0,0.0,130.0,130.0,30.5,0.1,0.0,0.0,0.0,0.0,0.0,,,0.0,8.4,,,39.0,278.0,,0.0,0.0,0.0,104.0,319.0,410.0,736.0,607.0,500.0,475.0,238.0,394.0,1086.0,1393.0,351.0,496.0,544.0,,0.0,0.0,0.0,157.48,716.0,194.0
 7 | Lentils (Cooked),Beans and Lentils,116,0.38,9.02,20.13,1.8,7.9,0.0,0.053,19.0,3.33,369.0,36.0,8.0,0.0,1.5,0.0,0.0,0.11,,12.23,69.64,37.0,137.0,2.1,0.0,,,,,,,,,,,180.0,2.0,1.27,0.251,0.494,2.8,,,,0.16899999999999998,0.073,1.06,0.638,0.17800000000000002,,181.0,0.0,181.0,181.0,32.7,,0.0,5.0,0.0,0.0,0.0,,,0.0,1.7,,,64.0,175.0,,0.0,0.0,0.0,81.0,323.0,390.0,654.0,630.0,448.0,697.0,254.0,377.0,998.0,1399.0,367.0,377.0,416.0,,0.0,0.0,0.0,172.41400000000004,686.0,195.0
 8 | Peanut Butter (Chunk Style),Beans and Lentils,589,49.94,24.06,21.57,8.41,8.0,0.0,7.607,45.0,1.9,745.0,160.0,0.0,0.0,0.0,0.0,0.0,6.3,,13.57,1.14,78.0,13854.0,3.08,,,,4.19,4.22,0.0,0.0,0.0,0.0,4.93,,319.0,17.0,2.79,0.578,1.8,8.2,3.1,,,0.106,0.111,13.696,1.118,0.418,,92.0,0.0,92.0,92.0,61.3,1.0,0.0,0.0,0.0,0.0,0.0,,,0.0,0.5,0.5,,23123.0,13932.0,,0.0,0.0,0.0,228.0,518.0,608.0,1525.0,671.0,772.0,2731.0,550.0,903.0,3012.0,5023.0,1417.0,1389.0,1461.0,40.0,0.0,0.0,0.0,33.956,2003.0,488.0
 9 | Pinto Beans (Cooked),Beans and Lentils,143,0.65,9.01,26.22,0.34,9.0,0.0,0.136,46.0,2.09,436.0,50.0,0.0,0.0,0.8,0.0,0.0,0.94,,17.22,62.95,137.0,98.0,-1.259,0.0,,,0.34,0.0,0.0,0.0,0.0,0.0,15.15,,147.0,1.0,0.98,0.219,0.453,6.2,2.2,,,0.193,0.062,0.318,0.21,0.229,,172.0,0.0,172.0,172.0,35.3,0.1,0.0,0.0,0.0,0.0,0.0,,,0.0,3.5,0.0,,133.0,235.0,,0.0,0.0,0.0,108.0,331.0,426.0,765.0,630.0,519.0,494.0,247.0,409.0,1128.0,1447.0,365.0,515.0,565.0,,0.0,0.0,0.0,139.86,744.0,201.0
10 | Raw Peanuts,Beans and Lentils,567,49.24,25.8,16.13,4.72,8.5,0.0,6.279,92.0,4.58,705.0,168.0,0.0,0.0,0.0,0.0,0.0,8.33,,7.63,6.5,3.0,15555.0,6.064,0.0,,,,,,,,,,,376.0,18.0,3.27,1.144,1.934,7.2,,,,0.64,0.135,12.066,1.767,0.348,,240.0,0.0,240.0,240.0,52.5,0.6,0.0,0.0,0.0,0.0,0.0,,,0.0,0.0,,,24426.0,15558.0,,0.0,0.0,0.0,250.0,883.0,907.0,1672.0,926.0,1082.0,3085.0,652.0,1025.0,3146.0,5390.0,1554.0,1138.0,1271.0,,0.0,0.0,0.0,35.273,2426.0,648.0
11 | Unsalted Peanut Butter (Smooth),Beans and Lentils,598,51.36,22.21,22.31,10.49,5.0,0.0,10.325,49.0,1.74,558.0,168.0,0.0,0.0,0.0,0.0,0.0,9.1,,17.31,1.23,37.0,12284.0,6.449,0.075,,,10.25,0.13,0.12,0.0,0.0,0.0,3.56,,335.0,17.0,2.51,0.422,1.665,4.1,,,,0.15,0.192,13.112,1.137,0.441,,87.0,0.0,87.0,87.0,63.0,0.8,0.0,0.0,0.0,0.0,0.0,,,0.0,0.3,,,25941.0,12535.0,27.0,0.0,0.0,0.0,231.0,525.0,616.0,1546.0,681.0,782.0,2768.0,557.0,916.0,3054.0,5092.0,1437.0,1408.0,1481.0,,0.0,0.0,0.0,33.445,2031.0,494.0
12 | Canola Oil,Fats and Oils,884,100.0,0.0,0.0,0.0,0.0,0.0,7.365,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.46,,0.0,0.0,9137.0,19005.0,0.0,0.395,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,,,,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,,,0.0,71.3,0.0,0.0,63276.0,28142.0,9137.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,22.624,0.0,0.0
13 | Sesame Oil,Fats and Oils,884,100.0,0.0,0.0,0.0,0.0,0.0,14.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.4,,0.0,0.0,300.0,41300.0,0.0,,,,,,,,,,,,0.0,0.0,0.0,0.0,,0.0,,,,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.2,,0.0,0.0,0.0,0.0,0.0,,,0.0,13.6,,,39700.0,41700.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,22.624,0.0,0.0
14 | Apples,Fruits,52,0.17,0.26,13.81,10.39,2.4,0.0,0.028,6.0,0.12,107.0,5.0,54.0,3.0,4.6,0.0,0.0,0.18,,11.41,85.56,9.0,43.0,-1.933,0.0,,,2.07,2.43,5.9,0.0,0.0,0.0,0.05,,11.0,1.0,0.04,0.027000000000000003,0.035,0.0,3.3,,,0.017,0.026,0.091,0.061,0.041,,3.0,0.0,3.0,3.0,3.4,0.1,0.0,27.0,0.0,0.0,29.0,,,0.0,2.2,0.0,,7.0,51.0,,0.0,0.0,0.0,1.0,6.0,6.0,13.0,12.0,12.0,6.0,5.0,11.0,70.0,25.0,9.0,6.0,10.0,,0.0,0.0,0.0,384.615,7.0,2.0
15 | Bananas,Fruits,89,0.33,1.09,22.84,12.23,2.6,0.0,0.112,5.0,0.26,358.0,27.0,64.0,3.0,8.7,0.0,0.0,0.1,,20.24,74.91,27.0,46.0,-6.981,0.0,,,2.39,4.98,4.85,0.0,0.01,0.0,5.38,,22.0,1.0,0.15,0.078,0.27,1.0,2.2,,,0.031,0.073,0.665,0.334,0.3670000000000001,,20.0,0.0,20.0,20.0,9.8,0.1,0.0,26.0,25.0,0.0,22.0,,,0.0,0.5,0.0,,32.0,73.0,,0.0,0.0,0.0,9.0,28.0,28.0,68.0,50.0,47.0,49.0,77.0,40.0,124.0,152.0,38.0,28.0,40.0,,0.0,0.0,0.0,224.719,58.0,17.0
16 | Dates (Deglet Noor),Fruits,282,0.39,2.45,75.03,63.35,8.0,0.0,0.032,39.0,1.02,656.0,43.0,10.0,0.0,0.4,0.0,0.0,0.05,,67.03,20.53,3.0,16.0,-11.985,0.0,,,23.84,19.87,19.56,0.0,0.12,0.0,,,62.0,2.0,0.29,0.206,0.262,3.0,,,,0.052000000000000005,0.066,1.274,0.589,0.165,,19.0,0.0,19.0,19.0,6.3,0.4,0.0,6.0,0.0,0.0,75.0,,,0.0,2.7,0.0,,36.0,19.0,,0.0,0.0,0.0,12.0,43.0,49.0,84.0,66.0,71.0,136.0,32.0,83.0,213.0,359.0,101.0,130.0,57.0,,0.0,0.0,0.0,70.922,65.0,89.0
17 | Dried Figs,Fruits,249,0.93,3.3,63.87,47.92,9.8,0.0,0.14400000000000002,162.0,2.03,680.0,68.0,10.0,0.0,1.2,0.0,0.0,0.35,,54.07,30.05,0.0,345.0,-14.146,,,,0.07,24.79,22.93,0.0,0.0,0.13,5.07,,67.0,10.0,0.55,0.287,0.51,0.6,,,,0.085,0.08199999999999999,0.619,0.434,0.106,,9.0,0.0,9.0,9.0,15.8,0.7,0.0,6.0,0.0,0.0,32.0,,,0.0,15.6,0.0,,159.0,345.0,,0.0,0.0,0.0,20.0,85.0,89.0,128.0,88.0,122.0,77.0,37.0,134.0,645.0,295.0,108.0,610.0,128.0,,0.0,0.0,0.0,80.321,117.0,70.0
18 | Grapes,Fruits,67,0.35,0.63,17.15,16.25,0.9,0.0,0.114,14.0,0.29,191.0,5.0,100.0,5.0,4.0,0.0,0.0,0.19,,16.25,81.3,24.0,79.0,-3.665,0.0,,,,,,,,,,,10.0,2.0,0.04,0.04,0.718,0.1,,,,0.092,0.057,0.3,0.024,0.11,,4.0,0.0,4.0,4.0,5.6,,0.0,59.0,1.0,0.0,72.0,,,0.0,14.6,,,14.0,102.0,,0.0,0.0,0.0,3.0,17.0,5.0,13.0,14.0,17.0,46.0,23.0,26.0,77.0,131.0,19.0,21.0,30.0,,0.0,0.0,0.0,298.507,24.0,31.0
19 | Medjool Dates,Fruits,277,0.15,1.81,74.97,66.47,6.7,,,64.0,0.9,696.0,54.0,149.0,7.0,0.0,,0.0,,,68.27,21.32,,,-13.757,0.0,,,0.53,33.68,31.95,0.0,0.3,0.0,,,62.0,1.0,0.44,0.362,0.29600000000000004,,,,,0.05,0.06,1.61,0.805,0.249,,15.0,0.0,15.0,15.0,9.9,0.4,,89.0,0.0,0.0,23.0,,,0.0,2.7,0.0,,,,,,,,7.0,42.0,45.0,82.0,54.0,66.0,60.0,29.0,78.0,220.0,265.0,90.0,111.0,62.0,,,,,72.202,64.0,63.0
20 | Red Or Green Grapes (European),Fruits,69,0.16,0.72,18.1,15.48,0.9,0.0,0.054000000000000006,10.0,0.36,191.0,7.0,66.0,3.0,3.2,0.0,0.0,0.19,,17.2,80.54,11.0,37.0,-3.2510000000000003,0.0,,,0.15,7.2,8.13,0.0,0.0,0.0,0.0,,20.0,2.0,0.07,0.127,0.071,0.1,7.8,,,0.069,0.07,0.188,0.05,0.086,,2.0,0.0,2.0,2.0,5.6,0.1,0.0,39.0,1.0,0.0,72.0,,,0.0,14.6,0.0,,7.0,48.0,,0.0,0.0,0.0,11.0,22.0,11.0,22.0,27.0,22.0,130.0,22.0,22.0,38.0,81.0,16.0,80.0,22.0,,0.0,0.0,0.0,289.855,29.0,19.0
21 | Watermelon,Fruits,30,0.15,0.61,7.55,6.2,0.4,0.0,0.016,7.0,0.24,112.0,10.0,569.0,28.0,8.1,0.0,0.0,0.05,,7.15,91.45,0.0,50.0,-2.011,0.0,,,1.21,1.58,3.36,0.0,0.06,0.0,0.0,,11.0,1.0,0.1,0.042,0.038,0.4,1.5,,,0.033,0.021,0.17800000000000002,0.221,0.045,,3.0,0.0,3.0,3.0,4.1,0.3,0.0,303.0,0.0,4532.0,8.0,,,0.0,0.1,0.0,,37.0,50.0,,0.0,0.0,0.0,7.0,27.0,19.0,18.0,62.0,16.0,59.0,6.0,17.0,39.0,63.0,10.0,24.0,16.0,,0.0,0.0,0.0,666.6669999999998,27.0,8.0
22 | Cooked Oatmeal,Grains and Pasta,71,1.52,2.54,12.0,0.27,1.7,0.0,0.31,9.0,0.9,70.0,27.0,0.0,0.0,0.0,0.0,0.0,0.08,,10.3,83.61,18.0,541.0,1.79,0.001,,,0.25,0.0,0.0,0.0,0.0,0.02,11.6,,77.0,4.0,1.0,0.07400000000000001,0.58,5.4,71.6,,,0.076,0.016,0.225,0.311,0.005,,6.0,0.0,6.0,6.0,7.4,3.1,0.0,0.0,0.0,0.0,180.0,,,0.0,0.3,0.0,,435.0,559.0,18.0,0.0,0.0,0.0,40.0,96.0,116.0,216.0,135.0,160.0,167.0,54.0,124.0,302.0,623.0,147.0,96.0,151.0,,0.0,0.0,0.0,281.69,243.0,143.0
23 | Rice Cooked Nfs,Grains and Pasta,129,0.28,2.67,27.99,0.05,0.4,0.0,0.077,10.0,1.2,35.0,12.0,,0.0,0.0,0.0,0.0,0.04,,27.59,68.01,13.0,62.0,1.715,,,,,,,,,,,,43.0,245.0,0.49,0.069,,7.5,,,,0.162,0.013,1.467,,0.092,,58.0,55.0,3.0,96.0,2.1,,0.0,0.0,0.0,0.0,0.0,,,,0.0,,,87.0,76.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,155.039,0.0,0.0
24 | Rice White Cooked Fat Added In Cooking Made With Oil,Grains and Pasta,150,3.05,2.6,27.22,0.05,0.4,0.0,0.454,10.0,1.17,34.0,12.0,,0.0,0.0,0.0,0.0,0.36,,26.82,66.12,153.0,1063.0,1.665,,,,,,,,,,,,42.0,238.0,0.47,0.067,,7.2,,,,0.157,0.013,1.426,,0.09,,56.0,53.0,3.0,94.0,2.0,,0.0,0.0,0.0,0.0,0.0,,,,3.3,,,1248.0,1217.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,133.333,0.0,0.0
25 | Uncooked Oats,Grains and Pasta,389,6.9,16.89,66.27,,10.6,0.0,1.217,54.0,4.72,429.0,177.0,0.0,0.0,0.0,0.0,0.0,,,55.67,8.22,111.0,2424.0,13.218,,,,,,,,,,,,523.0,2.0,3.97,0.626,4.916,,,,,0.763,0.139,0.961,1.349,0.119,,56.0,0.0,56.0,56.0,,,0.0,,,,,,,0.0,,,,2178.0,2535.0,,,,,234.0,575.0,694.0,1284.0,701.0,937.0,1192.0,405.0,881.0,1448.0,3712.0,841.0,934.0,750.0,,0.0,,,51.414,1468.0,720.0
26 | White Rice,Grains and Pasta,130,0.28,2.69,28.17,0.05,0.4,0.0,0.077,10.0,1.2,35.0,12.0,0.0,0.0,0.0,0.0,0.0,0.04,,27.77,68.44,13.0,62.0,1.725,,,,,,,,,,,,43.0,1.0,0.49,0.069,0.472,7.5,41.1,,,0.163,0.013,1.476,0.39,0.093,,58.0,55.0,3.0,97.0,2.1,0.3,0.0,0.0,0.0,0.0,0.0,,,0.0,0.0,,,88.0,76.0,,0.0,0.0,0.0,31.0,96.0,116.0,222.0,97.0,164.0,224.0,63.0,156.0,253.0,524.0,122.0,127.0,141.0,,0.0,0.0,0.0,153.846,234.0,118.0
27 | Almonds,Nuts and Seeds,579,49.93,21.15,21.55,4.35,12.5,0.0,3.802,269.0,3.71,733.0,270.0,2.0,0.0,0.0,0.0,0.0,25.63,,9.05,4.41,3.0,12328.0,2.096,0.015,,,3.95,0.17,0.11,0.0,0.04,0.07,0.72,,481.0,1.0,3.12,1.031,2.1790000000000003,4.1,,,,0.205,1.138,3.618,0.471,0.13699999999999998,,44.0,0.0,44.0,44.0,52.1,0.5,0.0,1.0,0.0,0.0,1.0,,,0.0,0.0,0.0,,31551.0,12329.0,3.0,0.0,0.0,0.0,211.0,601.0,751.0,1473.0,568.0,855.0,2465.0,539.0,999.0,2639.0,6206.0,1429.0,969.0,912.0,,0.0,0.0,0.0,34.542,1582.0,372.0
28 | Almonds Unsalted,Nuts and Seeds,607,53.96,20.33,20.38,4.71,10.6,0.0,4.38,260.0,3.62,692.0,271.0,,0.0,0.0,0.0,0.0,23.54,,9.78,2.34,162.0,13640.0,1.7619999999999998,,,,,,,,,,,,457.0,3.0,3.21,1.0659999999999998,,1.9,,,,0.075,1.161,3.528,,0.132,,53.0,0.0,53.0,53.0,50.5,,0.0,1.0,0.0,0.0,1.0,,,,3.5,,,33340.0,13802.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,32.949,0.0,0.0
29 | Cashews (Raw),Nuts and Seeds,553,43.85,18.22,30.19,5.91,3.3,0.0,7.782999999999999,37.0,6.68,660.0,292.0,0.0,0.0,0.5,0.0,0.0,0.9,,26.89,5.2,62.0,7782.0,8.782,,,,5.81,0.05,0.05,0.0,0.0,0.0,23.49,,593.0,12.0,5.78,2.195,1.655,19.9,,,,0.423,0.058,1.062,0.8640000000000001,0.417,,25.0,0.0,25.0,25.0,,,0.0,0.0,0.0,0.0,22.0,,,0.0,34.1,,,23797.0,7845.0,,0.0,0.0,0.0,287.0,688.0,789.0,1472.0,928.0,1094.0,2123.0,456.0,837.0,1795.0,4506.0,937.0,812.0,1079.0,,,,,36.166,1459.0,755.0
30 | Cashews Unsalted,Nuts and Seeds,583,47.96,14.85,31.71,4.86,2.9,0.0,9.293,44.0,5.82,548.0,252.0,,0.0,0.0,0.0,0.0,1.24,,28.81,1.65,308.0,8514.0,6.089,,,,,,,,,,,,475.0,16.0,5.43,2.153,,11.3,,,,0.194,0.194,1.358,,0.248,,67.0,0.0,67.0,67.0,59.2,,0.0,0.0,0.0,0.0,22.0,,,,37.2,,,27754.0,8837.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,34.305,0.0,0.0
31 | Chia Seeds,Nuts and Seeds,486,30.74,16.54,42.12,,34.4,0.0,3.33,631.0,7.72,407.0,335.0,54.0,,1.6,0.0,,0.5,,7.72,5.8,17830.0,5835.0,14.323,0.14,,,,,,,,,,,860.0,16.0,4.58,0.924,2.7230000000000003,55.2,,,,0.62,0.17,8.83,,,,49.0,,49.0,,,,,,,,,,,,,,,2309.0,23665.0,17830.0,,,,436.0,709.0,801.0,1371.0,970.0,950.0,2143.0,531.0,1044.0,1689.0,3500.0,943.0,776.0,1049.0,,,,,41.152,1579.0,995.0
32 | Dried Sunflower Seeds,Nuts and Seeds,584,51.46,20.78,20.0,2.62,8.6,0.0,4.455,78.0,5.25,645.0,325.0,50.0,3.0,1.4,0.0,0.0,35.17,,11.4,4.73,28.0,23050.0,11.431,,,,2.5,,,,,,,,660.0,9.0,5.0,1.8,1.95,53.0,,,,1.48,0.355,8.335,1.13,1.345,,227.0,0.0,227.0,227.0,55.1,35.4,0.0,30.0,0.0,0.0,0.0,,,0.0,0.0,0.0,0.0,18528.0,23137.0,,14.0,0.0,0.0,348.0,928.0,1139.0,1659.0,937.0,1315.0,2403.0,632.0,1117.0,2446.0,5579.0,1461.0,1182.0,1075.0,,0.0,0.0,0.0,34.247,1835.0,945.0
33 | Dry Roasted Almonds,Nuts and Seeds,598,52.54,20.96,21.01,4.86,10.9,0.0,4.092,268.0,3.73,713.0,279.0,1.0,0.0,0.0,0.0,0.0,23.9,,10.11,2.41,6.0,12945.0,1.831,0.006999999999999999,,,4.75,0.01,0.01,0.0,0.09,0.0,0.73,,471.0,3.0,3.31,1.099,2.232,2.0,,,,0.077,1.197,3.637,0.321,0.136,,55.0,0.0,55.0,55.0,52.1,,0.0,1.0,0.0,0.0,1.0,,,0.0,0.0,,,33076.0,12955.0,6.0,0.0,0.0,0.0,209.0,595.0,745.0,1461.0,563.0,848.0,2444.0,534.0,991.0,2616.0,6153.0,1417.0,961.0,904.0,40.0,0.0,0.0,0.0,33.445,1568.0,369.0
34 | Dry Roasted Pistachio Nuts,Nuts and Seeds,572,45.82,21.05,28.28,7.74,10.3,0.0,5.645,107.0,4.03,1007.0,109.0,266.0,13.0,3.0,0.0,0.0,2.17,,17.98,1.85,212.0,13125.0,2.162,0.0,,,7.09,0.25,0.22,0.0,0.13,0.05,1.38,,469.0,6.0,2.34,1.293,1.243,10.0,,,,0.695,0.234,1.3730000000000002,0.513,1.122,,51.0,0.0,51.0,51.0,71.4,0.8,0.0,159.0,0.0,0.0,1160.0,,,0.0,13.2,0.0,,24534.0,13346.0,212.0,0.0,0.0,0.0,262.0,714.0,957.0,1675.0,1189.0,1305.0,2228.0,535.0,1016.0,1968.0,4490.0,1054.0,980.0,1340.0,96.0,0.0,0.0,0.0,34.965,1671.0,680.0
35 | Dry Roasted Sunflower Seeds,Nuts and Seeds,582,49.8,19.33,24.07,2.73,11.1,0.0,5.2189999999999985,70.0,3.8,850.0,129.0,9.0,0.0,1.4,0.0,0.0,26.1,,12.97,1.2,69.0,32782.0,29.969,,,,,,,,,,,,1155.0,3.0,5.29,1.83,2.11,79.3,,,,0.106,0.246,7.042000000000002,7.042000000000002,0.804,,237.0,0.0,237.0,237.0,55.1,,0.0,5.0,0.0,0.0,0.0,,,0.0,2.7,,,9505.0,32884.0,,0.0,0.0,0.0,295.0,788.0,967.0,1408.0,795.0,1116.0,2039.0,536.0,948.0,2076.0,4735.0,1240.0,1003.0,912.0,,0.0,0.0,0.0,34.364000000000004,1557.0,803.0
36 | Flax Seeds,Nuts and Seeds,534,42.16,18.29,28.88,1.55,27.3,0.0,3.663,255.0,5.73,813.0,392.0,0.0,0.0,0.6,0.0,0.0,0.31,,1.58,6.96,22813.0,5910.0,1.937,,,,1.15,0.4,0.0,0.0,0.0,0.0,,,642.0,30.0,4.34,1.22,2.482,25.4,,,,1.644,0.161,3.08,0.985,0.473,,87.0,0.0,87.0,87.0,78.7,3.1,0.0,0.0,0.0,0.0,651.0,,,0.0,4.3,0.0,,7527.0,28730.0,,0.0,0.0,0.0,297.0,766.0,896.0,1235.0,862.0,1072.0,1925.0,472.0,925.0,2046.0,4039.0,1248.0,806.0,970.0,175.0,0.0,0.0,0.0,37.453,1450.0,710.0
37 | Hazelnuts,Nuts and Seeds,628,60.75,14.95,16.7,4.34,9.7,0.0,4.464,114.0,4.7,680.0,163.0,20.0,1.0,6.3,0.0,0.0,15.03,,7.0,5.31,87.0,7833.0,-2.061,,,,4.2,0.07,0.07,0.0,0.0,,0.48,,290.0,0.0,2.45,1.725,6.175,2.4,,,,0.643,0.113,1.8,0.918,0.563,,113.0,0.0,113.0,113.0,45.6,0.4,0.0,11.0,3.0,0.0,92.0,,,0.0,14.2,0.0,,45652.0,7920.0,,0.0,0.0,0.0,193.0,497.0,545.0,1063.0,420.0,701.0,2211.0,432.0,730.0,1679.0,3710.0,724.0,561.0,735.0,,0.0,0.0,0.0,31.847,1025.0,498.0
38 | Hemp Seeds,Nuts and Seeds,553,48.75,31.56,8.67,1.5,4.0,0.0,4.6,70.0,7.95,1200.0,700.0,11.0,1.0,0.5,,,0.8,,4.67,4.96,9301.0,29001.0,31.874,0.0,,,0.85,0.2,0.31,0.07,0.07,,,,1650.0,5.0,9.9,1.6,7.6,,,,,1.275,0.285,9.2,,0.6,,110.0,0.0,110.0,110.0,,,,7.0,,,,,,,,,,5400.0,38100.0,8684.0,,,,369.0,1269.0,1286.0,2163.0,1276.0,1777.0,4550.0,969.0,1528.0,3662.0,6269.0,1611.0,1597.0,1713.0,,,,,36.166,2710.0,1605.0
39 | Pistachio Nuts,Nuts and Seeds,560,45.32,20.16,27.17,7.66,10.6,0.0,5.907,105.0,3.92,1025.0,121.0,516.0,26.0,5.6,0.0,0.0,2.86,,16.57,4.37,289.0,14091.0,1.834,0.0,,,6.87,0.32,0.24,0.0,0.17,,1.67,,490.0,1.0,2.2,1.3,1.2,7.0,3.4,,,0.87,0.16,1.3,0.52,1.7,,51.0,0.0,51.0,51.0,,,0.0,305.0,10.0,,2903.0,,,0.0,,,,23257.0,14380.0,,0.0,0.0,0.0,251.0,684.0,917.0,1604.0,1138.0,1249.0,2134.0,512.0,973.0,1884.0,4300.0,1009.0,938.0,1283.0,,0.0,0.0,0.0,35.714,1601.0,652.0
40 | Pistachio Nuts Unsalted,Nuts and Seeds,581,47.44,20.42,27.43,7.51,10.0,0.0,5.886,104.0,3.91,977.0,106.0,,13.0,2.9,0.0,0.0,2.46,,17.43,1.79,358.0,13815.0,2.086,,,,,,,,,,,,455.0,6.0,2.27,1.254,,9.7,,,,0.674,0.227,1.332,,1.088,,49.0,0.0,49.0,49.0,69.3,,0.0,154.0,0.0,0.0,1125.0,,,,16.3,,,25054.0,14181.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,34.423,0.0,0.0
41 | Seeds Sesame Seeds Whole Dried,Nuts and Seeds,573,49.67,17.73,23.45,0.3,11.8,0.0,6.957000000000002,975.0,14.55,468.0,351.0,9.0,0.0,0.0,0.0,0.0,0.25,,11.65,4.69,376.0,21375.0,0.18,,,,,,,,,,,,629.0,11.0,7.75,4.082,2.46,34.4,,,,0.7909999999999999,0.247,4.515,0.05,0.79,,97.0,0.0,97.0,97.0,25.6,,0.0,5.0,0.0,0.0,0.0,,,0.0,0.0,,,18759.0,21773.0,,0.0,0.0,0.0,388.0,736.0,763.0,1358.0,569.0,990.0,2630.0,522.0,927.0,1646.0,3955.0,1215.0,810.0,967.0,,0.0,0.0,0.0,34.904,1683.0,944.0
42 | Seeds Sunflower Seed Kernels Toasted Without Salt,Nuts and Seeds,619,56.8,17.21,20.59,,11.5,0.0,5.9529999999999985,57.0,6.81,491.0,129.0,0.0,0.0,1.4,0.0,0.0,,,9.09,1.0,79.0,37390.0,36.785,,,,,,,,,,,,1158.0,3.0,5.3,1.834,2.114,,,,,0.325,0.285,4.198,7.056,0.805,,238.0,0.0,238.0,238.0,,,0.0,,,,,,,0.0,,,,10841.0,37507.0,,,,,263.0,702.0,861.0,1254.0,708.0,994.0,1816.0,477.0,844.0,1848.0,4216.0,1104.0,893.0,812.0,,,,,32.31,1386.0,715.0
43 | Sesame Seeds (Toasted),Nuts and Seeds,565,48.0,16.96,25.74,,14.0,0.0,6.722,989.0,14.76,475.0,356.0,9.0,0.0,0.0,0.0,0.0,,,11.74,3.3,363.0,20654.0,-0.326,,,,,,,,,,,,638.0,11.0,7.16,2.47,2.496,34.4,,,,0.8029999999999999,0.251,4.581,0.051,0.802,,98.0,0.0,98.0,98.0,,,0.0,,,,,,,0.0,,,,18127.0,21039.0,,,,,371.0,704.0,730.0,1299.0,544.0,947.0,2515.0,499.0,886.0,1574.0,3782.0,1162.0,774.0,925.0,,,,,35.398,1609.0,902.0
44 | Walnuts,Nuts and Seeds,654,65.21,15.23,13.71,2.61,6.7,0.0,6.126,98.0,2.91,441.0,158.0,20.0,1.0,1.3,0.0,0.0,0.7,,7.01,4.07,9080.0,38093.0,5.53,,,,2.43,0.08,0.09,0.0,0.0,,0.06,,346.0,2.0,3.09,1.586,3.414,4.9,,,,0.341,0.15,1.125,0.57,0.537,,98.0,0.0,98.0,98.0,39.2,0.3,0.0,12.0,0.0,0.0,9.0,,,0.0,2.7,0.0,,8933.0,47174.0,,0.0,0.0,0.0,170.0,596.0,625.0,1170.0,424.0,753.0,2278.0,391.0,696.0,1829.0,2816.0,816.0,706.0,934.0,,0.0,0.0,0.0,30.581,1117.0,444.0
45 | Baked Potato (No Skin),Vegetables,93,0.1,1.96,21.55,1.7,1.5,0.0,0.026,5.0,0.35,391.0,25.0,0.0,0.0,12.8,0.0,0.0,0.04,,20.05,75.42,10.0,32.0,-6.162000000000001,0.0,,,,,,,,,,,50.0,5.0,0.29,0.215,0.161,0.3,,,,0.105,0.021,1.395,0.555,0.301,,9.0,0.0,9.0,9.0,14.5,0.2,0.0,0.0,0.0,0.0,0.0,,,0.0,0.3,,,2.0,43.0,,0.0,0.0,0.0,30.0,71.0,80.0,118.0,119.0,110.0,90.0,43.0,60.0,479.0,328.0,58.0,70.0,85.0,,0.0,0.0,0.0,215.054,160.0,56.0
46 | Broccoli,Vegetables,34,0.37,2.82,6.64,1.7,2.6,0.0,0.114,47.0,0.73,316.0,21.0,623.0,31.0,89.2,0.0,0.0,0.78,,4.04,89.3,63.0,49.0,-4.007,0.0,,,0.1,0.49,0.68,0.21,0.21,0.0,0.0,,66.0,33.0,0.41,0.049,0.21,2.5,,,,0.071,0.117,0.639,0.573,0.175,,63.0,0.0,63.0,63.0,18.7,0.1,0.0,361.0,25.0,0.0,1403.0,,,0.0,101.6,0.0,,31.0,112.0,,0.0,0.0,0.0,33.0,88.0,79.0,129.0,135.0,125.0,191.0,59.0,104.0,325.0,542.0,89.0,110.0,121.0,,0.0,0.0,0.0,588.235,167.0,66.0
47 | Broccoli (Cooked),Vegetables,35,0.41,2.38,7.18,1.39,3.3,0.0,0.079,40.0,0.67,293.0,21.0,1548.0,77.0,64.9,0.0,0.0,1.45,,3.88,89.25,119.0,51.0,-3.609,0.0,,,0.08,0.49,0.74,0.0,0.0,0.0,0.0,,67.0,41.0,0.45,0.061,0.194,1.6,4.0,,,0.063,0.123,0.5529999999999999,0.616,0.2,,108.0,0.0,108.0,108.0,40.1,0.1,0.0,929.0,0.0,0.0,1080.0,,,0.0,141.1,0.0,,40.0,170.0,,0.0,0.0,0.0,34.0,96.0,92.0,147.0,155.0,138.0,200.0,63.0,114.0,329.0,549.0,101.0,111.0,129.0,,0.0,0.0,0.0,571.429,176.0,74.0
48 | Carrots,Vegetables,41,0.24,0.93,9.58,4.74,2.8,0.0,0.032,33.0,0.3,320.0,12.0,16706.0,835.0,5.9,0.0,0.0,0.66,,6.78,88.29,2.0,100.0,-5.746,0.0,,,3.59,0.59,0.55,0.0,0.0,0.0,1.43,,35.0,69.0,0.24,0.045,0.14300000000000002,0.1,3.2,,,0.066,0.058,0.983,0.273,0.138,,19.0,0.0,19.0,19.0,8.8,0.4,0.0,8285.0,3477.0,1.0,256.0,,,0.0,13.2,0.0,,12.0,102.0,,0.0,0.0,0.0,12.0,191.0,77.0,102.0,101.0,69.0,91.0,40.0,113.0,190.0,366.0,47.0,54.0,54.0,,0.0,0.0,0.0,487.805,104.0,103.0
49 | Celery,Vegetables,14,0.17,0.69,2.97,1.34,1.6,0.0,0.042,40.0,0.2,260.0,11.0,449.0,22.0,3.1,0.0,0.0,0.27,,1.37,95.43,0.0,79.0,-5.069,0.0,,,0.08,0.4,0.37,0.0,0.0,0.48,0.0,,24.0,80.0,0.13,0.035,0.103,0.4,4.0,,,0.021,0.057,0.32,0.246,0.07400000000000001,,36.0,0.0,36.0,36.0,6.1,0.1,0.0,270.0,0.0,0.0,283.0,,,0.0,29.3,0.0,,32.0,79.0,,0.0,0.0,0.0,9.0,20.0,21.0,32.0,27.0,27.0,20.0,12.0,23.0,117.0,90.0,21.0,18.0,20.0,,0.0,0.0,0.0,1428.571,29.0,9.0
50 | Cooked Carrots,Vegetables,35,0.18,0.76,8.22,3.45,3.0,0.0,0.03,30.0,0.34,235.0,10.0,17033.0,852.0,3.6,0.0,0.0,1.03,,5.22,90.17,1.0,87.0,-4.129,0.0,,,2.7,0.4,0.36,0.0,0.0,0.0,0.17,,30.0,58.0,0.2,0.017,0.155,0.7,47.5,,,0.066,0.044,0.645,0.232,0.153,,14.0,0.0,14.0,14.0,8.8,0.1,0.0,8332.0,3776.0,0.0,687.0,,,0.0,13.7,0.0,,6.0,89.0,,0.0,0.0,0.0,10.0,157.0,63.0,84.0,83.0,56.0,75.0,33.0,93.0,156.0,301.0,38.0,44.0,44.0,,0.0,0.0,0.0,571.429,85.0,85.0
51 | Cooked Celery,Vegetables,18,0.16,0.83,4.0,2.37,1.6,0.0,0.04,42.0,0.42,284.0,12.0,521.0,26.0,6.1,0.0,0.0,0.35,,2.4,94.11,0.0,75.0,-5.522,0.0,,,0.14,0.71,0.66,0.0,0.0,0.85,,,25.0,91.0,0.14,0.036000000000000004,0.106,1.0,,,,0.023,0.07,0.3720000000000001,0.317,0.086,,33.0,0.0,33.0,33.0,7.9,,0.0,313.0,0.0,0.0,329.0,,,0.0,37.8,,,30.0,75.0,,0.0,0.0,0.0,11.0,24.0,25.0,39.0,32.0,33.0,24.0,14.0,27.0,141.0,107.0,26.0,22.0,24.0,,0.0,0.0,0.0,1111.111,35.0,12.0
52 | Cooked Eggplant,Vegetables,35,0.23,0.83,8.73,3.2,2.5,0.0,0.044,6.0,0.25,123.0,11.0,37.0,2.0,1.3,0.0,0.0,0.41,,6.23,89.67,15.0,78.0,-2.001,0.0,,,,,,,,,,,15.0,1.0,0.12,0.059,0.113,0.1,,,,0.076,0.02,0.6,0.075,0.086,,14.0,0.0,14.0,14.0,9.4,,0.0,22.0,0.0,0.0,0.0,,,0.0,2.9,,,20.0,93.0,,0.0,0.0,0.0,8.0,30.0,36.0,52.0,39.0,43.0,46.0,19.0,42.0,134.0,152.0,33.0,34.0,34.0,,0.0,0.0,0.0,571.429,57.0,13.0
53 | Cooked Green Bell Peppers,Vegetables,28,0.2,0.92,6.7,3.19,1.2,0.0,0.029,9.0,0.46,166.0,10.0,468.0,23.0,74.4,0.0,0.0,0.5,,5.5,91.87,10.0,96.0,-2.766,0.0,,,0.15,1.55,1.5,0.0,0.0,0.0,,,18.0,2.0,0.12,0.065,0.115,0.3,,,,0.059,0.03,0.477,0.079,0.233,,16.0,0.0,16.0,16.0,7.4,,0.0,264.0,26.0,0.0,431.0,,,0.0,9.8,,,13.0,106.0,,0.0,0.0,0.0,12.0,34.0,30.0,48.0,41.0,39.0,44.0,19.0,38.0,132.0,122.0,34.0,40.0,37.0,,0.0,0.0,0.0,714.2860000000002,48.0,29.0
54 | Cooked Green Peas,Vegetables,84,0.22,5.36,15.63,5.93,5.5,0.0,0.039,27.0,1.54,271.0,39.0,801.0,40.0,14.2,0.0,0.0,0.14,,10.13,77.87,19.0,82.0,-0.139,0.0,,,5.22,0.13,0.41,0.0,0.18,0.0,,,117.0,3.0,1.19,0.173,0.525,1.9,,,,0.259,0.149,2.021,0.153,0.21600000000000005,,63.0,0.0,63.0,63.0,29.7,0.2,0.0,470.0,22.0,0.0,2593.0,,,0.0,25.9,,,19.0,102.0,,0.0,0.0,0.0,37.0,201.0,193.0,320.0,314.0,232.0,423.0,105.0,237.0,490.0,733.0,182.0,171.0,179.0,,0.0,0.0,0.0,238.095,310.0,113.0
55 | Cooked Onions,Vegetables,44,0.19,1.36,10.15,4.73,1.4,0.0,0.031,22.0,0.24,166.0,11.0,2.0,0.0,5.2,0.0,0.0,0.02,,8.75,87.86,4.0,70.0,-2.117,0.0,,,1.1,2.19,1.44,0.0,0.0,0.0,,,35.0,3.0,0.21,0.067,0.153,0.6,,,,0.042,0.023,0.165,0.113,0.129,,15.0,0.0,15.0,15.0,6.8,,0.0,1.0,0.0,0.0,4.0,,,0.0,0.5,,,27.0,73.0,,0.0,0.0,0.0,20.0,33.0,48.0,48.0,65.0,31.0,183.0,22.0,38.0,74.0,220.0,57.0,42.0,40.0,,0.0,0.0,0.0,454.545,69.0,35.0
56 | Cooked Tomatoes,Vegetables,18,0.11,0.95,4.01,2.49,0.7,0.0,0.015,11.0,0.68,218.0,9.0,489.0,24.0,22.8,0.0,0.0,0.56,,3.31,94.34,2.0,42.0,-3.478,0.0,,,0.0,1.18,1.31,0.0,0.0,0.0,,,28.0,11.0,0.14,0.075,0.105,0.5,,,,0.036000000000000004,0.022,0.532,0.129,0.079,,13.0,0.0,13.0,13.0,6.9,,0.0,293.0,0.0,3041.0,94.0,,,0.0,2.8,0.0,,16.0,44.0,,0.0,0.0,0.0,8.0,27.0,26.0,39.0,39.0,27.0,26.0,16.0,30.0,148.0,393.0,26.0,20.0,28.0,,0.0,0.0,0.0,1111.111,46.0,23.0
57 | Cooked White Button Mushrooms,Vegetables,28,0.47,2.17,5.29,2.34,2.2,0.0,0.061,6.0,1.74,356.0,12.0,0.0,0.0,4.0,0.0,0.2,0.01,,3.09,91.08,1.0,179.0,-3.623,0.0,,,,,,,,,,,87.0,2.0,0.87,0.504,0.115,11.9,,,,0.073,0.3,4.46,2.16,0.095,,18.0,0.0,18.0,18.0,20.4,,0.0,0.0,0.0,0.0,0.0,0.2,0.0,8.0,0.0,,,8.0,183.0,,0.0,0.0,0.0,24.0,75.0,53.0,84.0,75.0,163.0,55.0,40.0,140.0,137.0,240.0,64.0,53.0,66.0,,0.0,0.0,0.0,714.2860000000002,91.0,31.0
58 | Cooked Zucchini,Vegetables,15,0.36,1.14,2.69,1.71,1.0,0.0,0.07200000000000001,18.0,0.37,264.0,19.0,1117.0,56.0,12.9,0.0,0.0,0.12,,1.69,95.22,94.0,58.0,-4.377,0.0,,,,,,,,,0.0,,37.0,3.0,0.33,0.052000000000000005,0.173,0.2,,,,0.035,0.024,0.51,0.28800000000000003,0.08,,28.0,0.0,28.0,28.0,9.4,0.3,0.0,670.0,0.0,0.0,1150.0,,,0.0,4.2,,,29.0,151.0,,0.0,0.0,0.0,6.0,15.0,23.0,37.0,35.0,29.0,27.0,14.0,33.0,78.0,68.0,24.0,20.0,26.0,,0.0,0.0,0.0,1333.333,39.0,16.0
59 | Cucumber,Vegetables,15,0.11,0.65,3.63,1.67,0.5,0.0,0.037000000000000005,16.0,0.28,147.0,13.0,105.0,5.0,2.8,0.0,0.0,0.03,,3.13,95.23,5.0,28.0,-2.445,0.0,,,0.03,0.76,0.87,0.0,0.01,0.0,0.83,,24.0,2.0,0.2,0.041,0.079,0.3,1.3,,,0.027000000000000003,0.033,0.098,0.259,0.04,,7.0,0.0,7.0,7.0,6.0,0.1,0.0,45.0,11.0,0.0,23.0,,,0.0,16.4,0.0,,5.0,32.0,,0.0,0.0,0.0,5.0,19.0,21.0,29.0,29.0,22.0,44.0,10.0,24.0,41.0,196.0,24.0,15.0,20.0,,0.0,0.0,0.0,1333.333,30.0,10.0
60 | Cucumber Raw,Vegetables,12,0.16,0.59,2.16,1.38,0.7,0.0,0.013,14.0,0.22,136.0,12.0,,4.0,3.2,0.0,0.0,0.03,,1.46,96.73,2.0,2.0,-2.301,,,,,,,,,,,,21.0,2.0,0.17,0.071,,0.1,,,,0.031,0.025,0.037000000000000005,,0.051,,14.0,0.0,14.0,14.0,5.7,,0.0,31.0,8.0,0.0,16.0,,,,7.2,,,2.0,3.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,1666.667,0.0,0.0
61 | Eggplant,Vegetables,25,0.18,0.98,5.88,3.53,3.0,0.0,0.034,9.0,0.23,229.0,14.0,23.0,1.0,2.2,0.0,0.0,0.3,,2.88,92.3,13.0,63.0,-3.949,0.0,,,0.26,1.58,1.54,,,,,,24.0,2.0,0.16,0.081,0.232,0.3,,,,0.039,0.037000000000000005,0.649,0.281,0.084,,22.0,0.0,22.0,22.0,6.9,,0.0,14.0,0.0,0.0,36.0,,,0.0,3.5,,,16.0,76.0,,0.0,0.0,0.0,9.0,37.0,45.0,64.0,47.0,53.0,57.0,23.0,51.0,164.0,186.0,41.0,43.0,42.0,,0.0,0.0,0.0,800.0,70.0,17.0
62 | Green Bell Peppers,Vegetables,20,0.17,0.86,4.64,2.4,1.7,0.0,0.058,10.0,0.34,175.0,10.0,370.0,18.0,80.4,0.0,0.0,0.37,,2.94,93.89,8.0,54.0,-2.924,0.0,,,0.11,1.16,1.12,0.0,0.0,0.0,0.0,,20.0,3.0,0.13,0.066,0.122,0.0,2.0,,,0.057,0.028,0.48,0.099,0.224,,10.0,0.0,10.0,10.0,5.5,0.1,0.0,208.0,21.0,0.0,341.0,,,0.0,7.4,0.0,,8.0,62.0,,0.0,0.0,0.0,12.0,36.0,24.0,36.0,39.0,36.0,27.0,10.0,36.0,208.0,194.0,30.0,24.0,54.0,,0.0,0.0,0.0,1000.0,104.0,19.0
63 | Onions,Vegetables,40,0.1,1.1,9.34,4.24,1.7,0.0,0.042,23.0,0.21,146.0,10.0,2.0,0.0,7.4,0.0,0.0,0.02,,7.64,89.11,4.0,13.0,-2.031,0.0,,,0.99,1.97,1.29,0.0,0.0,0.0,0.0,,29.0,4.0,0.17,0.039,0.129,0.5,1.1,,,0.046,0.027000000000000003,0.116,0.123,0.12,,19.0,0.0,19.0,19.0,6.1,0.1,0.0,1.0,0.0,0.0,4.0,,,0.0,0.4,0.0,,13.0,17.0,,0.0,0.0,0.0,14.0,21.0,14.0,25.0,39.0,21.0,104.0,14.0,21.0,91.0,258.0,25.0,12.0,21.0,,0.0,0.0,0.0,500.0,39.0,6.0
64 | Peas,Vegetables,81,0.4,5.42,14.45,5.67,5.7,0.0,0.071,25.0,1.47,244.0,33.0,765.0,38.0,40.0,0.0,0.0,0.13,,8.75,78.86,35.0,152.0,0.311,0.0,,,4.99,0.12,0.39,0.0,0.17,0.0,,,108.0,5.0,1.24,0.17600000000000002,0.41,1.8,,,,0.266,0.132,2.09,0.104,0.16899999999999998,,65.0,0.0,65.0,65.0,28.4,0.2,0.0,449.0,21.0,0.0,2477.0,,,0.0,24.8,,,35.0,187.0,,0.0,0.0,0.0,37.0,203.0,195.0,323.0,317.0,235.0,428.0,107.0,240.0,496.0,741.0,184.0,173.0,181.0,,0.0,0.0,0.0,246.914,314.0,114.0
65 | Potatoes Baked Skin Without Salt,Vegetables,198,0.1,4.29,46.06,1.4,7.9,0.0,0.026,34.0,7.04,573.0,43.0,10.0,1.0,13.5,0.0,0.0,0.04,,38.16,47.31,10.0,32.0,-7.824,0.0,,,,,,,,,,,101.0,21.0,0.49,0.8170000000000001,0.616,0.7,,,,0.122,0.106,3.065,0.857,0.614,,22.0,0.0,22.0,22.0,31.0,0.5,0.0,6.0,0.0,0.0,30.0,,,0.0,1.7,,,2.0,43.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,101.01,0.0,0.0
66 | Sweet Potato Fries From Fresh Fried,Vegetables,193,13.13,1.74,17.95,5.62,2.9,0.0,1.823,33.0,0.62,412.0,23.0,,833.0,17.0,0.0,0.0,2.14,,15.05,65.67,664.0,4773.0,-7.136,,,,,,,,,,,,47.0,166.0,0.28,0.14,,0.2,,,,0.093,0.092,1.289,,0.248,,5.0,0.0,5.0,5.0,11.4,,0.0,9973.0,37.0,0.0,0.0,,,,17.3,,,5446.0,5434.0,,0.0,0.0,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,103.627,0.0,0.0
67 | Tomatoes,Vegetables,18,0.2,0.88,3.89,2.63,1.2,0.0,0.028,10.0,0.27,237.0,11.0,833.0,42.0,13.7,0.0,0.0,0.54,,2.69,94.52,3.0,80.0,-4.101,0.0,,,0.0,1.25,1.37,0.0,0.0,0.0,0.0,,24.0,5.0,0.17,0.059,0.114,0.0,2.3,,,0.037000000000000005,0.019,0.594,0.08900000000000001,0.08,,15.0,0.0,15.0,15.0,6.7,0.1,0.0,449.0,101.0,2573.0,123.0,,,0.0,7.9,0.0,,31.0,83.0,,0.0,0.0,0.0,6.0,27.0,18.0,25.0,27.0,18.0,21.0,14.0,27.0,135.0,431.0,19.0,15.0,26.0,,0.0,0.0,0.0,1111.111,41.0,15.0
68 | White Button Mushrooms,Vegetables,22,0.34,3.09,3.26,1.98,1.0,0.0,0.05,3.0,0.5,318.0,9.0,0.0,0.0,2.1,0.04,0.2,0.01,,2.26,92.45,0.0,160.0,-2.289,0.0,,,0.0,1.48,0.17,0.0,0.0,0.0,0.0,,86.0,5.0,0.52,0.318,0.047,9.3,,,,0.081,0.402,3.607,1.497,0.104,,17.0,0.0,17.0,17.0,17.3,9.4,0.0,0.0,0.0,0.0,0.0,0.2,0.0,7.0,0.0,1.0,0.0,0.0,160.0,,0.0,0.0,0.0,35.0,107.0,76.0,120.0,107.0,232.0,78.0,57.0,199.0,195.0,343.0,92.0,76.0,94.0,,0.0,0.0,0.0,909.091,129.0,43.0
69 | Zucchini,Vegetables,17,0.32,1.21,3.11,2.5,1.0,0.0,0.084,16.0,0.37,261.0,18.0,200.0,10.0,17.9,0.0,0.0,0.12,,2.11,94.79,61.0,30.0,-4.19,0.0,,,0.05,1.07,1.38,0.0,0.0,0.0,0.0,,38.0,8.0,0.32,0.053,0.177,0.2,,,,0.045,0.094,0.451,0.204,0.163,,24.0,0.0,24.0,24.0,9.5,,0.0,120.0,0.0,0.0,2125.0,,,0.0,4.3,,,11.0,91.0,61.0,0.0,0.0,0.0,10.0,29.0,44.0,71.0,67.0,54.0,51.0,26.0,63.0,147.0,129.0,46.0,37.0,49.0,,0.0,0.0,0.0,1176.471,75.0,30.0


--------------------------------------------------------------------------------
/my_intake_requirements.csv:
--------------------------------------------------------------------------------
 1 | "name","internal_id","fdc_id","unit","min","upper_limit"
 2 | "Calories",1,,,,
 3 | "Water",2,255,,,
 4 | "Protein",3,203,"g",100,200
 5 | "Carbs",4,205,"g",,
 6 | "Dietary fiber",5,,,,
 7 | "Soluble fiber",6,,,,
 8 | "Insoluble fiber",7,,,,
 9 | "Total sugars",8,,,,
10 | "Added sugars",9,,,,
11 | "Fat",10,204,"g",,
12 | "Saturated fat",11,606,,,
13 | "Trans fat",12,605,,,
14 | "Polyunsaturated fat",13,646,,,
15 | "Monounsaturated fat",14,645,,,
16 | "Cholesterol",15,601,,,
17 | "Vitamin A, IU",16,318,"ug",900,3000
18 | "Vitamin A, RAE",17,320,,,
19 | "Vitamin C",18,401,"mg",,2000
20 | "Vitamin D",19,324,"ug",15,100
21 | "Vitamin D2",20,325,,,
22 | "Vitamin D3",21,326,,,
23 | "Vitamin E",22,323,"mg",15,1000
24 | "Vitamin K",23,,"ug",120,
25 | "Vitamin K1 (phylloquinone)",24,430,,,
26 | "Vitamin K2, MK-4 (menaquinone-4)",25,428,,,
27 | "Niacin",26,406,"mg",16,35
28 | "Riboflavin",27,405,"mg",1.3,
29 | "Thiamin",28,404,"mg",1.2,
30 | "Vitamin B6",29,415,"mg",1.3,100
31 | "DFE, Dietary folate equivalents",30,435,,,
32 | "Folate",31,417,"ug",400,1000
33 | "Folic Acid",32,431,,,
34 | "Methylated folate",33,,,,
35 | "Vitamin B12",34,418,"ug",2.4,
36 | "Pantothenic acid",35,410,"mg",5,
37 | "Biotin",36,,"ug",30,
38 | "Choline",37,421,"mg",550,3500
39 | "Carotenoids",38,,,,
40 | "Calcium",39,301,"mg",1000,2500
41 | "Chromium",40,,"ug",35,
42 | "Copper",41,312,"ug",900,10000
43 | "Fluoride",42,313,"mg",4,10
44 | "Iodine",43,,"mg",8,1100
45 | "Iron",44,303,"mg",,45
46 | "Magnesium",45,304,"mg",420,
47 | "Magnesium (from pharmocological agents)",46,,"mg",,350
48 | "Manganese",47,315,"mg",2.3,11
49 | "Molybdenum",48,,"ug",45,2000
50 | "Nickel",49,,"mg",,1
51 | "Phosphorus",50,305,"mg",700,4000
52 | "Potassium",51,306,"mg",3400,
53 | "Selenium",52,317,"ug",55,400
54 | "Zinc",53,309,"mg",11,
55 | "Sodium",54,307,"mg",1500,
56 | "Chloride",55,,"g",2.3,3.6
57 | "Arsenic",56,,,,
58 | "Boron",57,,"mg",,20
59 | "Silicon",58,,,,
60 | "Sulfate",59,,,,
61 | "Vanadium",60,,"mg",,1.8
62 | "Mercury",61,,,,
63 | "Omega 3 fatty acids",62,,,,
64 | "Omega 6 fatty acids",63,,,,
65 | "Caffeine",64,262,,,
66 | "Alcohol",65,221,,,
67 | 


--------------------------------------------------------------------------------
/program.py:
--------------------------------------------------------------------------------
  1 | import pandas as pd
  2 | import pulp as pl
  3 | 
  4 | class NutrientRequirements:
  5 |     def __init__(self, csv, unnecessary = []):
  6 |         self.df = pd.read_csv(csv)          \
  7 |                     .sort_values(by='name') \
  8 |                     .set_index('name')
  9 | 
 10 |         self.unnecessary = unnecessary
 11 | 
 12 |     def parse_units(self):
 13 |         pass
 14 | 
 15 | class FoodNutrients:
 16 |     def __init__(self,csv):
 17 |         self.df = pd.read_csv(csv)          \
 18 |                     .sort_values(by='name') \
 19 |                     .set_index('name')      \
 20 |                     .fillna(0)
 21 |         self.df['max'] = 10000 # for now, let's say max value is 1kg for all foods
 22 |         self.df['cost'] = 1
 23 |         self.df['amount_var'] = self.df.apply(
 24 |             lambda food: pl.LpVariable(food.name, 0, food['max']),
 25 |             axis=1
 26 |         )
 27 | 
 28 |     def missing_nutrients(self):
 29 |         non_nutrient_columns = ['name', 'food_group']
 30 |         return {
 31 |             nutrient
 32 |             for nutrient in self.df
 33 |             if nutrient not in non_nutrient_columns
 34 |             and (self.df[nutrient] == 0).all()
 35 |         }
 36 | 
 37 | class Log:
 38 |     def __init__(self,csv):
 39 |         self.df = pd.read_csv(csv)
 40 | 
 41 | class Problem:
 42 |     def __init__(self, nutrient_table, food_table):
 43 |         self.excluded = nutrient_table.unnecessary | food_table.missing_nutrients()
 44 |         self.included = set(nutrient_table.df.index) - self.excluded
 45 |         self.nutrient_table = nutrient_table.df[nutrient_table.df.index.isin(self.included)].copy()
 46 |         self.food_table = food_table
 47 |         self.nutrient_matrix = self.food_table.df[self.included].T
 48 |         self.add_constraints()
 49 | 
 50 |     def add_constraints(self):
 51 |         self.prob = pl.LpProblem("meal_planner", pl.LpMinimize)
 52 | 
 53 |         N = self.nutrient_matrix
 54 |         f = self.food_table.df['amount_var']
 55 |         c = self.food_table.df['cost']
 56 | 
 57 |         # @ is Pandas operator for matrix multiplication / dot product
 58 |         self.nutrient_table['intake']   = N @ f
 59 |         self.cost                       = c @ f
 60 | 
 61 |         # constraints:
 62 |         # should be basically just:
 63 |         #   daily['intake'] >= daily['min']
 64 |         #   daily['intake'] <= daily['max']
 65 | 
 66 |         # or:
 67 |         # daily['min'] <= N @ f <= daily['max']
 68 |         # minimize(c @ f)
 69 |         for min_val, max_val, intake in self.nutrient_table.values.tolist():
 70 |             self.prob += intake >= min_val
 71 | 
 72 |             # if we could do inf for max_val then we wouldn't need the conditional
 73 |             if not pd.isna(max_val):
 74 |                 self.prob += intake <= max_val
 75 | 
 76 |         # objective:
 77 |         self.prob += self.cost
 78 | 
 79 |     def solve(self):
 80 |         status = self.prob.solve(pl.PULP_CBC_CMD(msg=0))
 81 |         foods_df = self.food_table.df
 82 |         foods_df['amount'] = foods_df.apply(
 83 |               lambda food: pl.value(food['amount_var']),
 84 |               axis = 1
 85 |         )
 86 |         included = foods_df[foods_df['amount'] > 0]
 87 |         self.recipe = included[['amount']].copy()
 88 | 
 89 | 
 90 | def test():
 91 |     nutrient_table = NutrientRequirements(
 92 |         'daily_intake.csv',
 93 |         {
 94 |             "chromium_mcg",
 95 |             "linoleic_acid_g",
 96 |             "iodine_mcg",
 97 |             "alpha_linolenic_acid_g",
 98 |             "chloride_g",
 99 |             "vitamin_b12_mcg",
100 |             "vitamin_d_iu"
101 |         }
102 |     )
103 |     food_table = FoodNutrients('foods.csv')
104 |     return Problem(nutrient_table, food_table)
105 | 
106 | def test2():
107 |     return Log('test_log.csv').df
108 | 
109 | 
110 | def test3():
111 |     return pd.read_csv("test_nutrient_profiles.csv")
112 | 


--------------------------------------------------------------------------------
/run.sh:
--------------------------------------------------------------------------------
1 | #!/bin/sh
2 | 
3 | python3 -c 'import program; program.full_example()'
4 | 


--------------------------------------------------------------------------------
/test_log.csv:
--------------------------------------------------------------------------------
 1 | "date","meal","food","id","unit","amount","servings"
 2 | 08/01/21,"breakfast","eggs",4,"egg",2,2
 3 | 08/01/21,"breakfast","banana",10,"banana",1,1
 4 | 08/01/21,"breakfast","oatmeal",13,"packet",1,1
 5 | 08/01/21,"breakfast","honey nut cheerios",30,"bowl",1,1
 6 | 08/01/21,"breakfast","whole milk",3,"bowl",1,1
 7 | 08/01/21,"breakfast","coffee, nguyen, light roast",33,"mug",1,1
 8 | 08/01/21,"lunch","hummus",28,"serving",1,1
 9 | 08/01/21,"lunch","pita chips",29,"cracker",12,2
10 | 08/01/21,"Lunch 2","bran flakes",35,"bowl",1,1
11 | 08/01/21,"Lunch 2","Milk, 2%",31,"bowl",1,1
12 | 08/01/21,"dinner","pork fried rice, The Orient",36,"container",0.5,0.5
13 | 08/01/21,"dinner","avocado roll, The Orient",21,"roll",1,1
14 | 08/01/21,"dinner","pepsi",37,"can",1,1
15 | 08/01/21,"dinner","dogfish head 60 minute ipa",38,"can",2,2
16 | 08/01/21,"dinner","tea",39,"mug",1,1
17 | 08/02/21,"breakfast","eggs",4,"egg",2,2
18 | 08/02/21,"breakfast","banana",10,"banana",1,1
19 | 08/02/21,"breakfast","honey nut cheerios",30,"bowl",1,1
20 | 08/02/21,"breakfast","whole milk",3,"bowl",1,1
21 | 08/02/21,"breakfast","brownie",40,"piece",1,1
22 | 08/02/21,"breakfast","coffee, nguyen, dark roast",34,"mug",1,1
23 | 08/02/21,"lunch","pork fried rice, The Orient",36,"container",0.5,0.5
24 | 08/02/21,"dinner","broccoli",15,"cup",2,2
25 | 08/02/21,"dinner","white rice",17,"cup",0.5,1
26 | 08/02/21,"dinner","teriyaki chicken",43,"oz",4,1
27 | 08/02/21,"dinner","coffee, nguyen, dark roast",34,"mug",1,1
28 | 08/02/21,"dessert","blueberry scone, Ashley",53,"scone",1,1
29 | 08/02/21,"dessert","coffee, nguyen, dark roast",34,"mug",1,1
30 | 08/03/21,"breakfast","french toast, home",54,"slice",4,4
31 | 08/03/21,"breakfast","eggs",4,"egg",2,2
32 | 08/03/21,"breakfast","coffee, nguyen, dark roast",34,"mug",1,1
33 | 08/03/21,"lunch","Strawberries (in smoothie)",55,"cup",1,1
34 | 08/03/21,"lunch","banana (in smoothie)",10,"banana",1,1
35 | 08/03/21,"lunch","honey nut cheerios",30,"bowl",1,1
36 | 08/03/21,"lunch","whole milk",3,"bowl",1,1
37 | 08/03/21,"lunch","tostitos, hint of lime",60,"servings",2,2
38 | 08/03/21,"lunch","salsa",61,"servings",1,1
39 | 08/03/21,"lunch","guacamole",22,"servings",1,1
40 | 08/03/21,"dinner","barbacoa quesadilla, chipotle",56,"quesadilla",0.25,0.25
41 | 08/03/21,"dinner","steak taco, chipotle",57,"taco",1,1
42 | 08/03/21,"dinner","beer, dogfishead 60 minute ipa",38,"can",1,1
43 | 08/03/21,"dinner","beer, flying dog",58,"bottle",1,1
44 | 08/03/21,"dessert","brownie, neighbor",40,"piece",1,1
45 | 08/03/21,"dessert","ice cream, vanilla, breyer’s",59,"scoop",1,1
46 | 08/04/21,"breakfast","honey nut cheerios",30,"bowl",1,1
47 | 08/04/21,"breakfast","whole milk",3,"bowl",1,1
48 | 08/04/21,"breakfast","coffee, nguyen, dark roast",34,"mug",1,1
49 | 08/04/21,"lunch","eggs",4,"egg",2,2
50 | 08/04/21,"lunch","tostitos, hint of lime",60,"servings",1,1
51 | 08/04/21,"lunch","salsa",61,"servings",1,1
52 | 


--------------------------------------------------------------------------------
/test_nutrient_profiles.csv:
--------------------------------------------------------------------------------
 1 | "id","food","unit","serving size","Weight","Volume","Calories","Water","Protein","Carbs","Dietary fiber","Soluble fiber","Insoluble fiber","Total sugars","Added sugars","Total fat","Saturated fat","Trans fat","Polyunsaturated fat","Monounsaturated fat","Cholesterol","Vitamin A","Vitamin C","Vitamin D","Vitamin E","Vitamin K","Vitamin K1","Vitamin K2, MK-4","Niacin","Riboflavin","Thiamin","Vitamin B6","DFE (Dietary folate equivalents)","Folate","Folic Acid","Methylated folate","Vitamin B12","Pantothenic acid","Biotin","Choline","Carotenoids","Calcium","Chromium","Copper","Fluoride","Iodine","Iron","Magnesium","Manganese","Molybdenum","Nickel","Phosphorus","Potassium","Selenium","Zinc","Sodium","Chloride","Arsenic","Boron","Silicon","Sulfate","Vanadium","Omega-3","Caffeine","Alcohol","Mercury"
 2 | 1,"Strawberry & banana blend","cup",1.25,,,80,,1,22,4,,,11,0,0,0,0,,,0,,46,0,,,,,,,,,,,,,,,,,,20,,,,,0.6,,,,,,340,,,0,,,,,,,,,,
 3 | 2,"chobani oat milk","cup",1,,,,,2,11,2,,,7,7,6,0,0,,,0,135,,3,,,,,,,,,,,,,,,,,,313,,,,,,,,,,,360,,,100,,,,,,,,,,
 4 | 3,"whole milk, horizon organic, organic grassfed","cup",1,,,,,8,13,0,,,12,0,9,5,0,0,2.5,35,90,,2.5,,,,,,0.5,,,,,,,1.3,,,,,330,,,,,,,,,,250,430,,,,,,,,,,,,,
 5 | 4,"eggs, large, grade A, whole, organic, pete & gerry’s, Kurtz Farm","egg",1,,,70,,6,0,0,,,0,0,5,1.5,0,,,185,,,1,,,,,,,,,,,,,,,,,,30,,,,,0.9,,,,,,70,,,70,,,,,,,70,,,
 6 | 5,"martin’s potato bread","slice",1,32,,90,,3,15,1,,,3,2,1.5,0,0,,,0,,,0,,,,,1,0.1,0.1,,49,,,,,,,,,53,,,,,1,,,,,,59,,,105,,,,,,,,,,
 7 | 6,"turkey, sliced",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 8 | 7,"ritz crackers","cracker",5,,,80,,,10,0,,,1,1,4.5,1,0,,,0,,,0,,,,,,,,,,,,,,,,,,20,,,,,0.5,,,,,,10,,,130,,,,,,,,,,
 9 | 8,"peanut butter, creamy, jif","tbsp",2,,,190,,7,8,2,,,3,2,16,3.5,0,,,0,,,0,2,,,,30,,,,,,,,,,,,,17,,,,,1,,,,,,185,,,140,,,,,,,,,,
10 | 9,"wise guys cheese steak",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
11 | 10,"banana, Chiquita","banana",1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
12 | 11,"Strawberry & mango blend","cup",1,,,70,,1,17,2,,,13,0,0,0,0,,,0,,56,0,,,,,,,,,,,,,,,,,,20,,,,,0.5,,,,,,190,,,,,,,,,,,,,
13 | 12,"Cran-blackberry juice, ocean spray","cup",1,,,100,,0,27,,,,25,24,0,,,,,,,90,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,,,,,,,,,,
14 | 13,"oatmeal, instant, Quaker, maple & brown sugar","packet",1,43,,160,,4,33,3,1,,12,12,2,0.5,0,0.5,1,0,,,0,,,,,,,,,,,,,,,,,,20,,,,,1.1,,,,,,110,,,260,,,,,,,,,,
15 | 14,"oatmeal, instant, Quaker, bluberry strawberry","packet",1,39,,140,,4,29,3,1,,8,7,2,0.5,0,0.5,1,0,,,0,,,,,,,,,,,,,,,,,,20,,,,,1.2,,,,,,130,,,120,,,,,,,,,,
16 | 15,"broccoli, Good & Gather","cup",1,85,,30,,3,4,2,,,1,0,0,0,0,,,0,,,0,,,,,,,,,,,,,,,,,,41,,,,,6,,,,,,276,,,25,,,,,,,,,,
17 | 16,"brussels sprouts",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
18 | 17,"white rice",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
19 | 18,"potatoes, mini red",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
20 | 19,"salmon",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
21 | 20,"avocado",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
22 | 21,"avocado roll, The Orient",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
23 | 22,"guacamole",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
24 | 23,"lime juice",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
25 | 24,"lime",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
26 | 25,"ground turkey",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
27 | 26,"flour tortilla, Mission","tortilla",1,49,,140,,4,24,1,,,2,1,3,1,0,,,0,,,0,,,,,,,,,,,,,,,,,,90,,,,,2,,,,,,50,,,410,,,,,,,,,,
28 | 27,"black beans",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
29 | 28,"hummus, Good & Gather, Roasted Artichoke & Spinach","tbsp",2,30,,80,,2,5,1,,,1,0,6,0.5,0,,,0,,,0.00%,,,,,,,,,,,,,,,,,,2.00%,,,,,6.00%,,,,,,2.00%,,,140,,,,,,,,,,
30 | 29,"pita crackers, sea salt","cracker",6,15,,70,,1,11,,,,,,2.5,0,0,1,1,0,,,0,,,,,,,,,,,,,,,,,,0,,,,,0.5,,,,,,20,,,140,,,,,,,,,,
31 | 30,"honey nut cheerios","cup",1,37,,140,,3,30,3,,,12,12,2,0,0,0.5,0.5,0,10.00%,10.00%,2,,,,,10.00%,10.00%,20.00%,20.00%,,20.00%,45,,20.00%,,,,,130,,,,,3.6,8.00%,,,,8.00%,150,,20.00%,210,,,,,,,,,,
32 | 31,"2% milk",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
33 | 32,"french toast",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
34 | 33,"coffee, nguyen, light roast",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
35 | 34,"coffee, nguyen, dark roast",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
36 | 35,"bran flakes",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
37 | 36,"pork fried rice, The Orient",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
38 | 37,"pepsi",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
39 | 38,"beer, dogfishhead, 60 minute IPA",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
40 | 39,"tea, mint (brand?)",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
41 | 40,"brownie (brand?)",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
42 | 41,"olive oil, California Olive Ranch, extra virgin, everyday blend, cold pressed",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
43 | 42,"chicken breast, thin sliced, Perdue Fresh Cuts","oz",4,112,,120,,25,0,0,,,0,,2,0,0,,,80,0.00%,0.00%,,,,,,,,,,,,,,,,,,,0.00%,,,,,2.00%,,,,,,,,,75,,,,,,,,,,
44 | 43,"teriyaki chicken, Megan’s recipe","oz",4,112,,120,,25,0,0,,,0,,2,0,0,,,80,0.00%,0.00%,,,,,,,,,,,,,,,,,,,0.00%,,,,,2.00%,,,,,,,,,75,,,,,,,,,,
45 | 44,"teriyaki sauce, Veri Veri",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
46 | 45,"salt",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
47 | 46,"garlic powder",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
48 | 47,"onion powder",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
49 | 48,"cumin",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
50 | 49,"smoked paprika",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
51 | 50,"pepper",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
52 | 51,"chipotle powder",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
53 | 52,"shishito peppers",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
54 | 53,"blueberry scone, Ashley","scone",1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
55 | 54,"french toast, my recipe","slice",1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
56 | 55,"strawberries","cup",1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
57 | 56,"barbacoa quesadilla, chipotle",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
58 | 57,"steak taco, chipotle",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
59 | 58,"beer, flying dog",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
60 | 59,"ice cream, vanilla, breyer’s",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
61 | 60,"Tortilla chips, tostitos, hint of lime","chips",6,28,,150,,2,18,1,,,"<1g",,7,1,0,,,0,,,0,,,,,,,,,,,,,,,,,,30,,,,,0.3,,,,,,40,,,130,,,,,,,,,,
62 | 61,"salsa, medium, green mountain gringo","tbsp",2,31,,10,,0,2,1,,,1,0,0,0,0,,,0,,,0.00%,,,,,,,,,,,,,,,,,,11,,,,,0,,,,,,107,,,80,,,,,,,,,,
63 | 


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/to.do:
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1 | # incorporate recipes
2 | # use official sources for daily nutrient requirements
3 | # use official sources for food-nutrient database
4 | # incorporate units
5 | #  * minimum units, i.e. foods/ingredients should be treated as having
6 | #    finite granularity; can't arbitrarily subdivide, ex. you might include
7 | #    half a grapefruit in a recipe but nobody's doing 0.3423232344 of a grapefruit
8 | 


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