Nopt_xlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adjust variables below\n",
    "\n",
    "# Variable N-Kosten pro kg\n",
    "n_price = 0.96\n",
    "# Variable Roggenpreis 20 pro dt\n",
    "rye_price = 16\n",
    "# Variable Fenster\n",
    "window_size = 50\n",
    "# Letzes kg N soll X Euro Gewinn erzielen (Grenzgewinn = 0)\n",
    "last_kg_n_profit = 0\n",
    "# Menge N in kg pro ha der einzelnen Düngestufen\n",
    "n_amounts = [ 0, 77, 110, 143, 170 ]\n",
    "# Ertrag dt pro ha\n",
    "x_yield = [ 32.9, 57.2, 58.1, 60.6, 59.3 ]\n",
    "# dE Werte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab3f588885a644eba777d8063c64a758",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=0, row_start=0, type='text', value='N1'), Cell(column_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipysheet import sheet, cell, row, column, cell_range\n",
    "n_table = sheet(rows=5, columns=5, column_headers=(\"Stufe\", \"N[kg/ha]\", \"Ertrag[dt/ha]\", \"dE\", \"dDB\"))\n",
    "\n",
    "price_relation = rye_price / n_price\n",
    "\n",
    "row_count = len(n_amounts)\n",
    "\n",
    "dE_values = []\n",
    "dDB_values = []\n",
    "\n",
    "for i in range(row_count):\n",
    "  # put N1..N5\n",
    "  cell(i, 0, \"N\" + str(i + 1))\n",
    "  # add amount N to table\n",
    "  cell(i, 1, n_amounts[i])\n",
    "  # add yield to table\n",
    "  cell(i, 2, x_yield[i])\n",
    "  # calculate dE\n",
    "  # (D3-D2)/(C3-C2)\n",
    "  if i < row_count - 1:\n",
    "      dE = (x_yield[i + 1] - x_yield[i])/(n_amounts[i + 1] - n_amounts[i])\n",
    "      dE_values.append(dE)\n",
    "      cell(i, 3, dE)\n",
    "      # calculate dDB\n",
    "      dDB = dE * rye_price - n_price\n",
    "      dDB_values.append(dDB)\n",
    "      cell(i, 4, dDB)\n",
    "\n",
    "\n",
    "n_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N0= []\n",
    "dE= []\n",
    "dDB= []\n",
    "ENi= []\n",
    "E_= []\n",
    "for i in range(len(n_amounts) - 1):\n",
    "    for j in range(n_amounts[i], n_amounts[i+1]):\n",
    "        N0.append(j)\n",
    "        dE.append(dE_values[i])\n",
    "        dDB.append(dDB_values[i])\n",
    "        ENi.append(n_amounts[i])\n",
    "        E_.append(x_yield[i] + dE_values[i] * (j - n_amounts[i]))\n",
    "\n",
    "# Ggf. noch den letzten n_amounts-Wert hinzufuegen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size2= int(window_size/2)\n",
    "\n",
    "MWDB= dDB.copy()\n",
    "E__= E_.copy()\n",
    "for i in range(window_size2, n_amounts[len(n_amounts)-1]):\n",
    "    _= dDB[i-window_size2:i+window_size2]\n",
    "    MWDB[i]= sum(_)/len(_)\n",
    "    _= E_[i-window_size2:i+window_size2]\n",
    "    E__[i]= sum(_)/len(_)\n",
    "#    print(i, MWDB[i], E__[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_first_under_last_profit():\n",
    "    for i in N0:\n",
    "        if MWDB[i] < last_kg_n_profit:\n",
    "            return i\n",
    "\n",
    "find_first_under_last_profit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "DB_= []\n",
    "for i in N0:\n",
    "    DB_.append(E__[i]*rye_price - i*n_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N0</th>\n",
       "      <th>dE</th>\n",
       "      <th>E(Ni)</th>\n",
       "      <th>E_</th>\n",
       "      <th>dDB</th>\n",
       "      <th>MW DB(Intervall)</th>\n",
       "      <th>E__</th>\n",
       "      <th>DB_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.315584</td>\n",
       "      <td>0</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>32.900000</td>\n",
       "      <td>658.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.315584</td>\n",
       "      <td>0</td>\n",
       "      <td>33.215584</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>33.215584</td>\n",
       "      <td>663.351688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.315584</td>\n",
       "      <td>0</td>\n",
       "      <td>33.531169</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>33.531169</td>\n",
       "      <td>668.703377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.315584</td>\n",
       "      <td>0</td>\n",
       "      <td>33.846753</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>33.846753</td>\n",
       "      <td>674.055065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.315584</td>\n",
       "      <td>0</td>\n",
       "      <td>34.162338</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>5.351688</td>\n",
       "      <td>34.162338</td>\n",
       "      <td>679.406753</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>165</td>\n",
       "      <td>-0.048148</td>\n",
       "      <td>143</td>\n",
       "      <td>59.540741</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>-1.675152</td>\n",
       "      <td>60.021515</td>\n",
       "      <td>1042.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>166</td>\n",
       "      <td>-0.048148</td>\n",
       "      <td>143</td>\n",
       "      <td>59.492593</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>-1.752059</td>\n",
       "      <td>60.009404</td>\n",
       "      <td>1040.828088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>167</td>\n",
       "      <td>-0.048148</td>\n",
       "      <td>143</td>\n",
       "      <td>59.444444</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>-1.834459</td>\n",
       "      <td>59.993723</td>\n",
       "      <td>1039.554459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>168</td>\n",
       "      <td>-0.048148</td>\n",
       "      <td>143</td>\n",
       "      <td>59.396296</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>59.974074</td>\n",
       "      <td>1038.201481</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
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       "      <td>-0.048148</td>\n",
       "      <td>143</td>\n",
       "      <td>59.348148</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>-1.922963</td>\n",
       "      <td>59.950000</td>\n",
       "      <td>1036.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N0        dE  E(Ni)         E_       dDB  MW DB(Intervall)        E__  \\\n",
       "0      0  0.315584      0  32.900000  5.351688          5.351688  32.900000   \n",
       "1      1  0.315584      0  33.215584  5.351688          5.351688  33.215584   \n",
       "2      2  0.315584      0  33.531169  5.351688          5.351688  33.531169   \n",
       "3      3  0.315584      0  33.846753  5.351688          5.351688  33.846753   \n",
       "4      4  0.315584      0  34.162338  5.351688          5.351688  34.162338   \n",
       "..   ...       ...    ...        ...       ...               ...        ...   \n",
       "165  165 -0.048148    143  59.540741 -1.922963         -1.675152  60.021515   \n",
       "166  166 -0.048148    143  59.492593 -1.922963         -1.752059  60.009404   \n",
       "167  167 -0.048148    143  59.444444 -1.922963         -1.834459  59.993723   \n",
       "168  168 -0.048148    143  59.396296 -1.922963         -1.922963  59.974074   \n",
       "169  169 -0.048148    143  59.348148 -1.922963         -1.922963  59.950000   \n",
       "\n",
       "             DB_  \n",
       "0     658.000000  \n",
       "1     663.351688  \n",
       "2     668.703377  \n",
       "3     674.055065  \n",
       "4     679.406753  \n",
       "..           ...  \n",
       "165  1042.030303  \n",
       "166  1040.828088  \n",
       "167  1039.554459  \n",
       "168  1038.201481  \n",
       "169  1036.760000  \n",
       "\n",
       "[170 rows x 8 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'N0': N0,\n",
    "    'dE': dE,\n",
    "    'E(Ni)': ENi,\n",
    "    'E_': E_,\n",
    "    'dDB': dDB,\n",
    "    'MW DB(Intervall)': MWDB,\n",
    "    'E__': E__,\n",
    "    'DB_': DB_,\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:label='516a2b41-042a-40a4-b5e1-3da6e76452a1', xlabel='N0'>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(kind = \"line\", \n",
    "        x = \"N0\", \n",
    "        y = \"MW DB(Intervall)\", \n",
    "        ylim = (0,int(max(MWDB)*1.2)),\n",
    "        color = \"red\")\n",
    "ax2= ax.twinx()\n",
    "df.plot(kind = \"line\", \n",
    "        x = \"N0\", \n",
    "        y = \"DB_\", \n",
    "        color = \"blue\",\n",
    "        ax = ax2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= df.plot(kind = \"line\", \n",
    "        x = \"N0\", \n",
    "        y = \"E__\", \n",
    "        color = \"red\")\n",
    "ax2= ax.twinx()\n",
    "ax2 = df.plot(kind = \"line\", \n",
    "        x = \"N0\", \n",
    "        y = \"E_\", \n",
    "        color = \"blue\",\n",
    "        ax = ax2)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}