test.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "import os, math\n",
    "import json \n",
    "import random as rd\n",
    "import numpy as np\n",
    "import richardsonpy\n",
    "import richardsonpy.classes.stochastic_el_load_wrapper as wrap\n",
    "import richardsonpy.classes.appliance as app_model\n",
    "import richardsonpy.classes.lighting as light_model\n",
    "from districtgenerator.profils import Profiles\n",
    "import functions.heating_profile_5R1C as heating\n",
    "from districtgenerator import Datahandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rd\n",
    "annual_el_demand = 1100\n",
    " # Write a function, that returns the respective \n",
    "src_path = os.path.dirname(richardsonpy.__file__)\n",
    "path_app = os.path.join(src_path,'inputs','Appliances.csv')\n",
    "path_light = os.path.join(src_path,'inputs','LightBulbs.csv')\n",
    "# annual demand of the elictric appliances (annual demand minus lighting)\n",
    "# source: https://www.umweltbundesamt.de/daten/private-haushalte-konsum/wohnen/energieverbrauch-privater-haushalte#stromverbrauch-mit-einem-anteil-von-rund-einem-funftel\n",
    "# values from diagram for 2018 without heating, dhw and cooling: 8,1 / 81,1 = 10,0%\n",
    "appliancesDemand = 0.9 * annual_el_demand\n",
    "random_nb = rd.random()\n",
    "lighting_index = int(random_nb * 100)\n",
    "\n",
    "#  Create and save appliances object\n",
    "appliances = \\\n",
    "    app_model.Appliances(path_app,\n",
    "                        annual_consumption=appliancesDemand,\n",
    "                        randomize_appliances=True,\n",
    "                        max_iter=15,\n",
    "                        prev_heat_dev=True)\n",
    "\n",
    "# Create and save light configuration object\n",
    "lights = light_model.load_lighting_profile(filename=path_light,\n",
    "                                        index=lighting_index)\n",
    "\n",
    "#  Create wrapper object\n",
    "el_wrapper = wrap.ElectricityProfile(appliances,lights)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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    "vars(el_wrapper.appliances)"
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  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "data = pd.read_excel(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\Non-domestic-multi-zone-average-usage-profiles-for-Germany.xlsx')"
   ]
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       "      <td>Büro-, Verwaltungs- oder Amtsgebäude</td>\n",
       "      <td>Office, Administrative or Government Buildings</td>\n",
       "      <td>Parlaments- oder Gerichtsgebäude</td>\n",
       "      <td>Parliament or Court Building</td>\n",
       "      <td>6.987150</td>\n",
       "      <td>18.048927</td>\n",
       "      <td>11.061778</td>\n",
       "      <td>251.972117</td>\n",
       "      <td>...</td>\n",
       "      <td>3.994206</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.462520</td>\n",
       "      <td>4.034134</td>\n",
       "      <td>1.428428</td>\n",
       "      <td>0.198428</td>\n",
       "      <td>0.850696</td>\n",
       "      <td>22.222648</td>\n",
       "      <td>32.672892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HK_UK_1-2</td>\n",
       "      <td>Büro-, Verwaltungs- oder Amtsgebäude</td>\n",
       "      <td>Office, Administrative or Government Buildings</td>\n",
       "      <td>Öffentliches Verwaltungs- oder Ämtergebäude, R...</td>\n",
       "      <td>Public Administration or Office Building, Town...</td>\n",
       "      <td>7.059232</td>\n",
       "      <td>18.165506</td>\n",
       "      <td>11.106273</td>\n",
       "      <td>253.662834</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.986926</td>\n",
       "      <td>26.008128</td>\n",
       "      <td>0.580558</td>\n",
       "      <td>4.542927</td>\n",
       "      <td>1.618366</td>\n",
       "      <td>0.218665</td>\n",
       "      <td>0.827908</td>\n",
       "      <td>28.508191</td>\n",
       "      <td>38.197157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>HK_UK_1-3</td>\n",
       "      <td>Büro-, Verwaltungs- oder Amtsgebäude</td>\n",
       "      <td>Office, Administrative or Government Buildings</td>\n",
       "      <td>Bürogebäude</td>\n",
       "      <td>Office Building</td>\n",
       "      <td>6.821892</td>\n",
       "      <td>18.280712</td>\n",
       "      <td>11.458820</td>\n",
       "      <td>261.808516</td>\n",
       "      <td>...</td>\n",
       "      <td>3.806054</td>\n",
       "      <td>19.818476</td>\n",
       "      <td>26.068869</td>\n",
       "      <td>0.529982</td>\n",
       "      <td>5.047320</td>\n",
       "      <td>1.645056</td>\n",
       "      <td>0.230376</td>\n",
       "      <td>0.878165</td>\n",
       "      <td>32.925602</td>\n",
       "      <td>66.120123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>HK_UK_1-4</td>\n",
       "      <td>Büro-, Verwaltungs- oder Amtsgebäude</td>\n",
       "      <td>Office, Administrative or Government Buildings</td>\n",
       "      <td>Rundfunk- oder Fernsehanstalt</td>\n",
       "      <td>Broadcasting or Television Station</td>\n",
       "      <td>6.821892</td>\n",
       "      <td>18.280712</td>\n",
       "      <td>11.458820</td>\n",
       "      <td>261.808516</td>\n",
       "      <td>...</td>\n",
       "      <td>3.806054</td>\n",
       "      <td>19.818476</td>\n",
       "      <td>26.068869</td>\n",
       "      <td>0.529982</td>\n",
       "      <td>5.047320</td>\n",
       "      <td>1.645056</td>\n",
       "      <td>0.230376</td>\n",
       "      <td>0.878165</td>\n",
       "      <td>32.925602</td>\n",
       "      <td>66.120123</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>HK_UK_11-1</td>\n",
       "      <td>Verkehrsgebäude</td>\n",
       "      <td>Transport Buildings</td>\n",
       "      <td>Park-/Garagengebäude, Fahrradparkhaus</td>\n",
       "      <td>Park/Garage Building, Bicycle Parking Garage</td>\n",
       "      <td>2.441274</td>\n",
       "      <td>21.812980</td>\n",
       "      <td>19.371706</td>\n",
       "      <td>342.811152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767166</td>\n",
       "      <td>15.214431</td>\n",
       "      <td>27.337398</td>\n",
       "      <td>0.056176</td>\n",
       "      <td>1.883892</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.026872</td>\n",
       "      <td>0.987472</td>\n",
       "      <td>7.912073</td>\n",
       "      <td>20.636915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>HK_UK_11-2</td>\n",
       "      <td>Verkehrsgebäude</td>\n",
       "      <td>Transport Buildings</td>\n",
       "      <td>Halle für sonstige Verkehrsmittel (z.B. für Fl...</td>\n",
       "      <td>Hall for Other Means of Transportation (e.g., ...</td>\n",
       "      <td>2.441274</td>\n",
       "      <td>21.812980</td>\n",
       "      <td>19.371706</td>\n",
       "      <td>342.811152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767166</td>\n",
       "      <td>15.214431</td>\n",
       "      <td>27.337398</td>\n",
       "      <td>0.056176</td>\n",
       "      <td>1.883892</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.026872</td>\n",
       "      <td>0.987472</td>\n",
       "      <td>7.912073</td>\n",
       "      <td>20.636915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>HK_UK_11-3</td>\n",
       "      <td>Verkehrsgebäude</td>\n",
       "      <td>Transport Buildings</td>\n",
       "      <td>Gebäude zur Pflege von Fahrzeugen (z.B. Waschs...</td>\n",
       "      <td>Building for Vehicle Maintenance (e.g., Car Wash)</td>\n",
       "      <td>5.167719</td>\n",
       "      <td>20.057703</td>\n",
       "      <td>14.889984</td>\n",
       "      <td>308.097599</td>\n",
       "      <td>...</td>\n",
       "      <td>3.984652</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.285404</td>\n",
       "      <td>3.411500</td>\n",
       "      <td>1.018000</td>\n",
       "      <td>0.137138</td>\n",
       "      <td>0.946748</td>\n",
       "      <td>36.048453</td>\n",
       "      <td>518.719970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>107</td>\n",
       "      <td>HK_UK_11-4</td>\n",
       "      <td>Verkehrsgebäude</td>\n",
       "      <td>Transport Buildings</td>\n",
       "      <td>Empfangsgebäude (Bahnhof, Busbahnhof, Flughafe...</td>\n",
       "      <td>Terminal Building (Train Station, Bus Station,...</td>\n",
       "      <td>7.444449</td>\n",
       "      <td>18.967596</td>\n",
       "      <td>11.523147</td>\n",
       "      <td>268.656296</td>\n",
       "      <td>...</td>\n",
       "      <td>3.952655</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.798224</td>\n",
       "      <td>6.288050</td>\n",
       "      <td>1.896750</td>\n",
       "      <td>0.386801</td>\n",
       "      <td>0.868702</td>\n",
       "      <td>77.626399</td>\n",
       "      <td>11.116274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>HK_UK_12-0</td>\n",
       "      <td>Sonstiges Nichtwohngebäude</td>\n",
       "      <td>Other non-doemestic buildings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID_Enob_lfd ID_HK_UK_Enob_long                           Bez_HK_Enob  \\\n",
       "0              1          HK_UK_1-0  Büro-, Verwaltungs- oder Amtsgebäude   \n",
       "1              2          HK_UK_1-1  Büro-, Verwaltungs- oder Amtsgebäude   \n",
       "2              3          HK_UK_1-2  Büro-, Verwaltungs- oder Amtsgebäude   \n",
       "3              4          HK_UK_1-3  Büro-, Verwaltungs- oder Amtsgebäude   \n",
       "4              5          HK_UK_1-4  Büro-, Verwaltungs- oder Amtsgebäude   \n",
       "..           ...                ...                                   ...   \n",
       "103          104         HK_UK_11-1                       Verkehrsgebäude   \n",
       "104          105         HK_UK_11-2                       Verkehrsgebäude   \n",
       "105          106         HK_UK_11-3                       Verkehrsgebäude   \n",
       "106          107         HK_UK_11-4                       Verkehrsgebäude   \n",
       "107          108         HK_UK_12-0            Sonstiges Nichtwohngebäude   \n",
       "\n",
       "                                    Bez_HK_Enob_Eng  \\\n",
       "0    Office, Administrative or Government Buildings   \n",
       "1    Office, Administrative or Government Buildings   \n",
       "2    Office, Administrative or Government Buildings   \n",
       "3    Office, Administrative or Government Buildings   \n",
       "4    Office, Administrative or Government Buildings   \n",
       "..                                              ...   \n",
       "103                             Transport Buildings   \n",
       "104                             Transport Buildings   \n",
       "105                             Transport Buildings   \n",
       "106                             Transport Buildings   \n",
       "107                   Other non-doemestic buildings   \n",
       "\n",
       "                                           Bez_UK_Enob  \\\n",
       "0                                                  NaN   \n",
       "1                     Parlaments- oder Gerichtsgebäude   \n",
       "2    Öffentliches Verwaltungs- oder Ämtergebäude, R...   \n",
       "3                                          Bürogebäude   \n",
       "4                        Rundfunk- oder Fernsehanstalt   \n",
       "..                                                 ...   \n",
       "103              Park-/Garagengebäude, Fahrradparkhaus   \n",
       "104  Halle für sonstige Verkehrsmittel (z.B. für Fl...   \n",
       "105  Gebäude zur Pflege von Fahrzeugen (z.B. Waschs...   \n",
       "106  Empfangsgebäude (Bahnhof, Busbahnhof, Flughafe...   \n",
       "107                                                NaN   \n",
       "\n",
       "                                       Bez_UK_Enob_Eng   t_start      t_end  \\\n",
       "0                                                  NaN  6.947645  18.185219   \n",
       "1                         Parliament or Court Building  6.987150  18.048927   \n",
       "2    Public Administration or Office Building, Town...  7.059232  18.165506   \n",
       "3                                      Office Building  6.821892  18.280712   \n",
       "4                   Broadcasting or Television Station  6.821892  18.280712   \n",
       "..                                                 ...       ...        ...   \n",
       "103       Park/Garage Building, Bicycle Parking Garage  2.441274  21.812980   \n",
       "104  Hall for Other Means of Transportation (e.g., ...  2.441274  21.812980   \n",
       "105  Building for Vehicle Maintenance (e.g., Car Wash)  5.167719  20.057703   \n",
       "106  Terminal Building (Train Station, Bus Station,...  7.444449  18.967596   \n",
       "107                                                NaN  0.000000   0.000000   \n",
       "\n",
       "      t_nutz_d    d_nutz_a  ...  deltaTheta_i_NA  theta_i_h_min  \\\n",
       "0    11.237574  256.563179  ...         3.910732      19.900379   \n",
       "1    11.061778  251.972117  ...         3.994206      20.000000   \n",
       "2    11.106273  253.662834  ...         4.000000      19.986926   \n",
       "3    11.458820  261.808516  ...         3.806054      19.818476   \n",
       "4    11.458820  261.808516  ...         3.806054      19.818476   \n",
       "..         ...         ...  ...              ...            ...   \n",
       "103  19.371706  342.811152  ...         0.767166      15.214431   \n",
       "104  19.371706  342.811152  ...         0.767166      15.214431   \n",
       "105  14.889984  308.097599  ...         3.984652      20.000000   \n",
       "106  11.523147  268.656296  ...         3.952655      20.000000   \n",
       "107   0.000000    0.000000  ...         0.000000       0.000000   \n",
       "\n",
       "     theta_i_c_max   Feuchte      Vd_A  Vd_A_Geb     c_RLT     F_RLT  \\\n",
       "0        26.033088  0.581514  5.013155  1.736607  0.230361  0.860952   \n",
       "1        26.000000  0.462520  4.034134  1.428428  0.198428  0.850696   \n",
       "2        26.008128  0.580558  4.542927  1.618366  0.218665  0.827908   \n",
       "3        26.068869  0.529982  5.047320  1.645056  0.230376  0.878165   \n",
       "4        26.068869  0.529982  5.047320  1.645056  0.230376  0.878165   \n",
       "..             ...       ...       ...       ...       ...       ...   \n",
       "103      27.337398  0.056176  1.883892  0.390244  0.026872  0.987472   \n",
       "104      27.337398  0.056176  1.883892  0.390244  0.026872  0.987472   \n",
       "105      26.000000  0.285404  3.411500  1.018000  0.137138  0.946748   \n",
       "106      26.000000  0.798224  6.288050  1.896750  0.386801  0.868702   \n",
       "107       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "         q_I_p     q_I_fac  \n",
       "0    30.744682   56.342308  \n",
       "1    22.222648   32.672892  \n",
       "2    28.508191   38.197157  \n",
       "3    32.925602   66.120123  \n",
       "4    32.925602   66.120123  \n",
       "..         ...         ...  \n",
       "103   7.912073   20.636915  \n",
       "104   7.912073   20.636915  \n",
       "105  36.048453  518.719970  \n",
       "106  77.626399   11.116274  \n",
       "107   0.000000    0.000000  \n",
       "\n",
       "[108 rows x 34 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = data[data['Bez_UK_Enob'].isna()]\n",
    "filtered_data.to_csv(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\multi_zone_average\\Non-domestic-multi-zone-average-usage-profiles-for-Germany.csv', sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not numpy.float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mfelix\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mProgrammieren\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mtecdm\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124msrc\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mdistrictgenerator\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124moccupancy_schedules\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mGarderobeDusche.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      8\u001b[0m occupancy \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m45\u001b[39m \u001b[38;5;241m*\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPeople\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m----> 9\u001b[0m \u001b[43mel_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpower_sim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mirradiation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweekend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mday\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moccupancy\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\richardsonpy-0.2.1-py3.11.egg\\richardsonpy\\classes\\stochastic_el_load_wrapper.py:115\u001b[0m, in \u001b[0;36mElectricityProfile.power_sim\u001b[1;34m(self, irradiation, weekend, day, occupancy)\u001b[0m\n\u001b[0;32m    112\u001b[0m month \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_month(day)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;66;03m# Lighting\u001b[39;00m\n\u001b[1;32m--> 115\u001b[0m demand_lighting \u001b[38;5;241m=\u001b[39m \u001b[43mlighting_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_lighting_simulation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    116\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvOccupancyArray\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moccupancy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    117\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvBulbArray\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightbulbs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvIrradianceArray\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mirradiation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    119\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlight_mod_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlighting_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# Appliances\u001b[39;00m\n\u001b[0;32m    122\u001b[0m fun \u001b[38;5;241m=\u001b[39m appliance_model\u001b[38;5;241m.\u001b[39mrun_application_simulation\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\richardsonpy-0.2.1-py3.11.egg\\richardsonpy\\classes\\lighting.py:213\u001b[0m, in \u001b[0;36mrun_lighting_simulation\u001b[1;34m(vOccupancyArray, vBulbArray, vIrradianceArray, light_mod_config)\u001b[0m\n\u001b[0;32m    209\u001b[0m bLowIrradiance \u001b[38;5;241m=\u001b[39m ((iIrradiance \u001b[38;5;241m<\u001b[39m iIrradianceThreshold) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m    210\u001b[0m         random\u001b[38;5;241m.\u001b[39mrandom() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.05\u001b[39m))\n\u001b[0;32m    212\u001b[0m \u001b[38;5;66;03m# Get the effective occupancy for this number of active occupants to allow for sharing\u001b[39;00m\n\u001b[1;32m--> 213\u001b[0m fEffectiveOccupancy \u001b[38;5;241m=\u001b[39m \u001b[43mlight_mod_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meff_occupancy\u001b[49m\u001b[43m[\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43miActiveOccupants\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    216\u001b[0m \u001b[38;5;66;03m# Check the probability of a switch on at this time\u001b[39;00m\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (bLowIrradiance \u001b[38;5;129;01mand\u001b[39;00m (random\u001b[38;5;241m.\u001b[39mrandom() \u001b[38;5;241m<\u001b[39m (\n\u001b[0;32m    218\u001b[0m         fEffectiveOccupancy \u001b[38;5;241m*\u001b[39m fCalibratedRelativeUseWeighting))):\n\u001b[0;32m    219\u001b[0m \n\u001b[0;32m    220\u001b[0m     \u001b[38;5;66;03m# This is a switch on event\u001b[39;00m\n\u001b[0;32m    221\u001b[0m \n\u001b[0;32m    222\u001b[0m     \u001b[38;5;66;03m# Determine how long this bulb is on for\u001b[39;00m\n",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not numpy.float64"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "test_data = Datahandler()\n",
    "test_data.generateEnvironment()\n",
    "irradiation = test_data.site[\"SunTotal\"] \n",
    "weekend = True\n",
    "day = 6\n",
    "df = pd.read_csv(r\"C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\occupancy_schedules\\GarderobeDusche.csv\", sep=\";\")\n",
    "occupancy = 45 * df[\"People\"]\n",
    "el_wrapper.power_sim(irradiation, weekend, day, occupancy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "test_data = Datahandler()\n",
    "test_data.generateEnvironment()\n",
    "irradiation = test_data.site[\"SunTotal\"] \n",
    "weekend = True\n",
    "day = 6\n",
    "df = pd.read_csv(r\"C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\occupancy_schedules\\GarderobeDusche.csv\", sep=\";\")\n",
    "occupancy = 45 * df[\"People\"]\n",
    "appliances = 1000 * df[\"Appliances\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bldgs = {}\n",
    "with open(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\design_building_data.json') as json_file:\n",
    "            jsonData = json.load(json_file)\n",
    "            for subData in jsonData:\n",
    "                bldgs[subData[\"name\"]] = subData[\"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'T_set_min': 20.0,\n",
       " 'T_set_max': 26.0,\n",
       " 'T_bivalent': -2.0,\n",
       " 'T_heatlimit': 15.0,\n",
       " 'ventilation_rate': 0.5,\n",
       " 'buildings_short': ['SFH', 'MFH', 'TH', 'AB'],\n",
       " 'buildings_long': ['single_family_house',\n",
       "  'multi_family_house',\n",
       "  'terraced_house',\n",
       "  'apartment_block'],\n",
       " 'retrofit_short': [0, 1, 2],\n",
       " 'retrofit_long': ['tabula_standard',\n",
       "  'tabula_retrofit',\n",
       "  'tabula_adv_retrofit'],\n",
       " 'dhwload': [4662.1, 4662.1, 4662.1, 3999.8],\n",
       " 'mean_drawoff_vol_per_day': [40, 40, 40, 40]}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bldgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4662.1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#    building[\"dhwload\"] \n",
    "bldgs[\"dhwload\"][bldgs[\"buildings_short\"].index(\"SFH\")] * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>OCCUPANCY</th>\n",
       "      <th>APPLIANCES</th>\n",
       "      <th>LIGHTING</th>\n",
       "      <th>WATER</th>\n",
       "      <th>HEATING</th>\n",
       "      <th>COOLING</th>\n",
       "      <th>PROCESSES</th>\n",
       "      <th>SERVERS</th>\n",
       "      <th>ELECTROMOBILITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OFF</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OFF</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OFF</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OFF</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OFF</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DAY  HOUR  OCCUPANCY  APPLIANCES  LIGHTING  WATER  HEATING COOLING  \\\n",
       "0      1     1        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "1      1     2        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "2      1     3        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "3      1     4        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "4      1     5        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "..   ...   ...        ...         ...       ...    ...      ...     ...   \n",
       "139    6    20        0.0         0.1       0.1    0.0      OFF     OFF   \n",
       "140    6    21        0.0         0.1       0.1    0.0      OFF     OFF   \n",
       "141    6    22        0.0         0.1       0.1    0.0      OFF     OFF   \n",
       "142    6    23        0.0         0.1       0.1    0.0      OFF     OFF   \n",
       "143    6    24        0.0         0.1       0.1    0.0      OFF     OFF   \n",
       "\n",
       "     PROCESSES  SERVERS  ELECTROMOBILITY  \n",
       "0          0.0      0.0              0.0  \n",
       "1          0.0      0.0              0.0  \n",
       "2          0.0      0.0              0.0  \n",
       "3          0.0      0.0              0.0  \n",
       "4          0.0      0.0              0.0  \n",
       "..         ...      ...              ...  \n",
       "139        0.0      0.0              0.0  \n",
       "140        0.0      0.0              0.0  \n",
       "141        0.0      0.0              0.0  \n",
       "142        0.0      0.0              0.0  \n",
       "143        0.0      0.0              0.0  \n",
       "\n",
       "[144 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functions import schedule_reader as schedule_reader\n",
    "schedule, schedule_type = schedule_reader.get_schedule('oag')\n",
    "schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Benutzerdefinierte Sortierfunktion definieren\n",
    "def custom_sort(val):\n",
    "    if val < 7:\n",
    "        return float('inf')  # Stellt sicher, dass Werte unter 7 am Ende kommen\n",
    "    elif val >= 7:\n",
    "        return -val  # Umkehren der Reihenfolge für Werte ab 7\n",
    "    else:\n",
    "        return val  # Fallback-Fall, falls irgendwie ein ungültiger Wert vorliegt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# schedules_sorted = schedule.sort_values(by=\"DAY\", key=custom_sort)\n",
    "sorter = [ 5 , 6,  0 , 1 , 2, 3 ,4 ]\n",
    "#schedule.sort_values(by=\"DAY\", key=lambda column: column.map(lambda e: sorter.index(e)), inplace=True)\n",
    "schedule.sort_values(by=[\"DAY\", \"HOUR\"], key=lambda x: sorter.index(x) if x.name == \"DAY\" else x, inplace=True)\n",
    "schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorter = [ 5 , 6,  0 , 1 , 2, 3 ,4 ] \n",
    "# Create a custom sorter index\n",
    "sorter_index = {day: index for index, day in enumerate(sorter)}\n",
    "\n",
    "# Apply sorting\n",
    "schedule['DAY'] = pd.Categorical(schedule['DAY'], categories=sorter, ordered=True)\n",
    "schedule.sort_values(by=['DAY', 'HOUR'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def expand_dataframe(df, total_days):\n",
    "    unique_days = df['DAY'].unique()\n",
    "    num_days = len(unique_days)\n",
    "    \n",
    "    # Calculate the number of full weeks and extra days needed\n",
    "    full_cycles = total_days // num_days\n",
    "    extra_days = total_days % num_days\n",
    "    \n",
    "    # Replicate the DataFrame for the number of full cycles\n",
    "    result_df = pd.concat([df] * full_cycles, ignore_index=True)\n",
    "    \n",
    "    # If there are extra days, append the needed days from a new cycle\n",
    "    if extra_days > 0:\n",
    "        extra_data = df[df['DAY'].isin(unique_days[:extra_days])]\n",
    "        result_df = pd.concat([result_df, extra_data], ignore_index=True)\n",
    "    \n",
    "    return result_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule_len = expand_dataframe(schedule, 14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HOUR\n",
       "1     14\n",
       "2     14\n",
       "3     14\n",
       "4     14\n",
       "5     14\n",
       "6     14\n",
       "7     14\n",
       "8     14\n",
       "9     14\n",
       "10    14\n",
       "11    14\n",
       "12    14\n",
       "13    14\n",
       "14    14\n",
       "15    14\n",
       "16    14\n",
       "17    14\n",
       "18    14\n",
       "19    14\n",
       "20    14\n",
       "21    14\n",
       "22    14\n",
       "23    14\n",
       "24    14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule_len.value_counts(\"HOUR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>OCCUPANCY</th>\n",
       "      <th>APPLIANCES</th>\n",
       "      <th>LIGHTING</th>\n",
       "      <th>WATER</th>\n",
       "      <th>HEATING</th>\n",
       "      <th>COOLING</th>\n",
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       "      <th>SERVERS</th>\n",
       "      <th>ELECTROMOBILITY</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
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       "      <td>0.1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SETBACK</td>\n",
       "      <td>OFF</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DAY  HOUR  OCCUPANCY  APPLIANCES  LIGHTING  WATER  HEATING COOLING  \\\n",
       "96    5     1        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "97    5     2        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "98    5     3        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "99    5     4        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "100   5     5        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "..   ..   ...        ...         ...       ...    ...      ...     ...   \n",
       "91    4    20        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "92    4    21        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "93    4    22        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "94    4    23        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "95    4    24        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
       "\n",
       "     PROCESSES  SERVERS  ELECTROMOBILITY  \n",
       "96         0.0      0.0              0.0  \n",
       "97         0.0      0.0              0.0  \n",
       "98         0.0      0.0              0.0  \n",
       "99         0.0      0.0              0.0  \n",
       "100        0.0      0.0              0.0  \n",
       "..         ...      ...              ...  \n",
       "91         0.0      0.0              0.0  \n",
       "92         0.0      0.0              0.0  \n",
       "93         0.0      0.0              0.0  \n",
       "94         0.0      0.0              0.0  \n",
       "95         0.0      0.0              0.0  \n",
       "\n",
       "[144 rows x 11 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tek(building_type):\n",
    "    \"\"\"\n",
    "    Returns the TEK Warmwasser value according to the building type.\n",
    "    \n",
    "    Data is read from 'TEK_districtgenerator.csv' in the specified directory.\n",
    "\n",
    "    Args:\n",
    "    building_type (str): The key representing the building type.\n",
    "\n",
    "    Returns:\n",
    "    float: The TEK Warmwasser value for the specified building type.\n",
    "    str: The descriptive name of the building type.\n",
    "    \"\"\"\n",
    "    tek_assignment = {\n",
    "        \"oag\": \"Bürogebäude\",\n",
    "        \"IWU Research and University Teaching\": \"Hochschule und Forschung (allgemein)\",\n",
    "        \"IWU Health and Care\": \"Beherbergungsstätten (allgemein)\",\n",
    "        \"IWU School, Day Nursery and other Care\": \"Schulen\",\n",
    "        \"IWU Culture and Leisure\": \"Ausstellungsgebäude\",\n",
    "        \"IWU Sports Facilities\": \"Sporthallen\",\n",
    "        \"IWU Hotels, Boarding, Restaurants or Catering\": \"Hotels / Pensionen\",\n",
    "        \"IWU Production, Workshop, Warehouse or Operations\": \"Gewerbliche und industrielle Gebäude – Mischung aus leichter u. schwerer Arbeit\",\n",
    "        \"IWU Trade Buildings\": \"Verkaufsstätten (allgemein)\",\n",
    "        \"IWU Generalized (1) Services building\": \"Verwaltungsgebäude (allgemein)\",\n",
    "        \"IWU Generalized (2) Production buildings\": \"Gewerbliche und industrielle Gebäude – Mischung aus leichter u. schwerer Arbeit\"\n",
    "    }\n",
    "\n",
    "    tek_name = tek_assignment.get(building_type)\n",
    "    if tek_name is None:\n",
    "        print(f\"No schedule for building type {building_type}\")\n",
    "        return None, None\n",
    "\n",
    "    data_path = os.path.join(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\TEKs\\TEK_districtgenerator.csv')\n",
    "\n",
    "    try:\n",
    "        data_schedule = pd.read_csv(data_path, sep=',')\n",
    "        warm_water_value = data_schedule[data_schedule[\"TEK\"] == tek_name][\"TEK Warmwasser\"].iloc[0]\n",
    "        return warm_water_value, tek_name\n",
    "    except FileNotFoundError:\n",
    "        print(f\"File not found: {data_path}\")\n",
    "        return None, None\n",
    "    except IndexError:\n",
    "        print(f\"No data available for {tek_name}\")\n",
    "        return None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = os.path.join(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\TEKs\\TEK_districtgenerator.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.1 Bürogebäude\n"
     ]
    }
   ],
   "source": [
    "data, test = get_tek('oag')\n",
    "print(data, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSchedule(hk_geb, uk_geb):\n",
    "    \"\"\"\n",
    "    Find occupancy schedule from SIA2024, depending on hk_geb, uk_geb\n",
    "    \n",
    "    \n",
    "    :external input data: ../auxiliary/occupancy_schedules/occupancy_schedules_zuweisungen.csv\n",
    "    \n",
    "    :param hk_geb: usage type (main category)\n",
    "    :type hk_geb: string\n",
    "    :param uk_geb: usage type (subcategory)\n",
    "    :type uk_geb: string\n",
    "\n",
    "    :return: df_schedule, schedule_name\n",
    "    :rtype: DataFrame (with floats), string\n",
    "    \"\"\"\n",
    "    \n",
    "    zuweisungen = pd.read_csv(os.path.join('../auxiliary/occupancy_schedules/occupancy_schedules_zuweisungen.csv'), sep = ';', encoding = 'latin')\n",
    "    \n",
    "    if hk_geb in zuweisungen['hk_geb'].values:\n",
    "        \n",
    "        if uk_geb in zuweisungen['uk_geb'].values:\n",
    "            row = zuweisungen[zuweisungen['uk_geb'] == uk_geb]\n",
    "            schedule_name = row['schedule_name'].to_string(index = False).strip()\n",
    "            df_schedule = pd.read_csv(os.path.join('../auxiliary/occupancy_schedules/')+schedule_name+'.csv', sep = ';')\n",
    "            \n",
    "            return df_schedule, schedule_name\n",
    "        \n",
    "        else: \n",
    "            return print('uk_geb unbekannt')\n",
    "        \n",
    "    else:\n",
    "        return print('hk_geb unbekannt')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>People</th>\n",
       "      <th>Appliances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   People  Appliances\n",
       "0     0.0        0.08\n",
       "1     0.0        0.08\n",
       "2     0.0        0.08\n",
       "3     0.0        0.08\n",
       "4     0.0        0.08"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\occupancy_schedules\\Parkhaus.csv', sep=';')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Occupancy_Full_Usage_Hours = data.People.sum()  # in h/a\n",
    "Occupancy_Full_Usage_Hours\n",
    "TEK_dhw_per_Occupancy_Full_Usage_Hour = 48.7 / Occupancy_Full_Usage_Hours\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for hour in range(0, 120):\n",
    "    dhw_water = data.loc[hour, 'People'] * TEK_dhw_per_Occupancy_Full_Usage_Hour * 1000 * 240"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are about to generate a Non Residential building.\n",
      "Test, 3 3.125\n",
      "{'ow1': 0.541796301, 'win1': 0.345399313, 'rt1': 0.506728916, 'gf1': 0.369112228}\n",
      "     DAY  HOUR  OCCUPANCY  APPLIANCES  LIGHTING  WATER  HEATING COOLING  \\\n",
      "0      1     1        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "1      1     2        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "2      1     3        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "3      1     4        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "4      1     5        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "..   ...   ...        ...         ...       ...    ...      ...     ...   \n",
      "139    6    20        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "140    6    21        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "141    6    22        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "142    6    23        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "143    6    24        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "\n",
      "     PROCESSES  SERVERS  ELECTROMOBILITY  \n",
      "0          0.0      0.0              0.0  \n",
      "1          0.0      0.0              0.0  \n",
      "2          0.0      0.0              0.0  \n",
      "3          0.0      0.0              0.0  \n",
      "4          0.0      0.0              0.0  \n",
      "..         ...      ...              ...  \n",
      "139        0.0      0.0              0.0  \n",
      "140        0.0      0.0              0.0  \n",
      "141        0.0      0.0              0.0  \n",
      "142        0.0      0.0              0.0  \n",
      "143        0.0      0.0              0.0  \n",
      "\n",
      "[144 rows x 11 columns]\n",
      "We are about to generate a Non Residential building.\n",
      "Test, 3 3.125\n",
      "{'ow1': 0.288649056, 'win1': 0.298723187, 'rt1': 0.331884826, 'gf1': 0.383291576}\n",
      "     DAY  HOUR  OCCUPANCY  APPLIANCES  LIGHTING  WATER  HEATING COOLING  \\\n",
      "0      1     1        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "1      1     2        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "2      1     3        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "3      1     4        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "4      1     5        0.0         0.1       0.1    0.0  SETBACK     OFF   \n",
      "..   ...   ...        ...         ...       ...    ...      ...     ...   \n",
      "139    6    20        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "140    6    21        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "141    6    22        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "142    6    23        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "143    6    24        0.0         0.1       0.1    0.0      OFF     OFF   \n",
      "\n",
      "     PROCESSES  SERVERS  ELECTROMOBILITY  \n",
      "0          0.0      0.0              0.0  \n",
      "1          0.0      0.0              0.0  \n",
      "2          0.0      0.0              0.0  \n",
      "3          0.0      0.0              0.0  \n",
      "4          0.0      0.0              0.0  \n",
      "..         ...      ...              ...  \n",
      "139        0.0      0.0              0.0  \n",
      "140        0.0      0.0              0.0  \n",
      "141        0.0      0.0              0.0  \n",
      "142        0.0      0.0              0.0  \n",
      "143        0.0      0.0              0.0  \n",
      "\n",
      "[144 rows x 11 columns]\n",
      "Das ist der Wert 8.1\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'power_sim'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 21\u001b[0m\n\u001b[0;32m     19\u001b[0m data\u001b[38;5;241m.\u001b[39minitializeBuildings(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexample_nrb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     20\u001b[0m data\u001b[38;5;241m.\u001b[39mgenerateBuildings()\n\u001b[1;32m---> 21\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerateDemands\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\districtgenerator\\datahandler.py:447\u001b[0m, in \u001b[0;36mDatahandler.generateDemands\u001b[1;34m(self, calcUserProfiles, saveUserProfiles, savePath)\u001b[0m\n\u001b[0;32m    445\u001b[0m \u001b[38;5;66;03m# calculate or load user profiles\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m calcUserProfiles:\n\u001b[1;32m--> 447\u001b[0m     \u001b[43mbuilding\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalcProfiles\u001b[49m\u001b[43m(\u001b[49m\u001b[43msite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    448\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mtime_horizon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdataLength\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    449\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mtime_resolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimeResolution\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    450\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m saveUserProfiles:\n\u001b[0;32m    451\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m savePath \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\districtgenerator\\non_residential_users.py:317\u001b[0m, in \u001b[0;36mcalcProfiles\u001b[1;34m(self, site, time_resolution, time_horizon, initital_day)\u001b[0m\n\u001b[0;32m    315\u001b[0m \n\u001b[0;32m    316\u001b[0m         self.occ = np.zeros(int(time_horizon/time_resolution))\n\u001b[1;32m--> 317\u001b[0m         self.dhw = np.zeros(int(time_horizon/time_resolution))\n\u001b[0;32m    318\u001b[0m         self.elec = np.zeros(int(time_horizon/time_resolution))\n\u001b[0;32m    319\u001b[0m         self.gains = np.zeros(int(time_horizon/time_resolution))\n",
      "File \u001b[1;32mc:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\districtgenerator\\profils.py:660\u001b[0m, in \u001b[0;36mNonResidentialProfiles.generate_el_profile\u001b[1;34m(self, irradiance, el_wrapper, annual_demand, do_normalization)\u001b[0m\n\u001b[0;32m    644\u001b[0m day_of_the_year \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \n\u001b[0;32m    645\u001b[0m \u001b[38;5;66;03m# only necessary for electric heating\u001b[39;00m\n\u001b[0;32m    646\u001b[0m \u001b[38;5;66;03m# Perform lighting and appliance usage simulation for one day\u001b[39;00m\n\u001b[0;32m    647\u001b[0m \u001b[38;5;66;03m# Figure out, how to get el_p, light_p and app_p \u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;66;03m#     power_el_app : array\u001b[39;00m\n\u001b[0;32m    659\u001b[0m \u001b[38;5;66;03m#         Array holding el. power values for appliance usage in Watt\u001b[39;00m\n\u001b[1;32m--> 660\u001b[0m (el_p_curve, light_p_curve, app_p_curve) \u001b[38;5;241m=\u001b[39m \u001b[43mel_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpower_sim\u001b[49m(irradiation\u001b[38;5;241m=\u001b[39mirrad_day_minutewise,\n\u001b[0;32m    661\u001b[0m                                                                 weekend\u001b[38;5;241m=\u001b[39mweekend,\n\u001b[0;32m    662\u001b[0m                                                                 day\u001b[38;5;241m=\u001b[39mi\u001b[38;5;241m+\u001b[39mday_of_the_year,)\n\u001b[0;32m    663\u001b[0m \u001b[38;5;66;03m# Substitution of caluclations \u001b[39;00m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;66;03m# Lighniging \u001b[39;00m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;66;03m# If occupancy -> occupancy electricity                                                                 occupancy=current_occupancy)\u001b[39;00m\n\u001b[0;32m    666\u001b[0m \u001b[38;5;66;03m# Append results\u001b[39;00m\n\u001b[0;32m    667\u001b[0m demand\u001b[38;5;241m.\u001b[39mappend(el_p_curve)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'power_sim'"
     ]
    }
   ],
   "source": [
    "data = Datahandler()\n",
    "\n",
    "data.setWeatherFile(r\"data\\weather\\EPW\\DEU_BE_Berlin-Schonefeld.AP.103850_TMYx.2004-2018.epw\")\n",
    "\n",
    "\n",
    "# Next we generate an environment.\n",
    "# Based on the location of the district this includes outside temperatures and sun radiation.\n",
    "# The location can be changed in the site_data.json file. You find it in \\data.\n",
    "# We create our first district in Aachen. When you open the site_data.json file, you should see, the \"location\"\n",
    "# is [51.0,6.55], the \"climateZone\" 0 and the \"altitude\" 0.\n",
    "# The time resolution can be changed in time_data.json. You also find it in \\data.\n",
    "# For this example it should be 900 seconds, which equals 15 Minutes.\n",
    "# The weather data is taken from a Test Reference Year Database from the DWD.\n",
    "\n",
    "# Generate Environment for the District\n",
    "\n",
    "data.generateEnvironment()\n",
    "\n",
    "data.initializeBuildings('example_nrb')\n",
    "data.generateBuildings()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'U': {'window': 1.08,\n",
       "  'opaque': {'roof': 0.14,\n",
       "   'intWall': [],\n",
       "   'wall': 0.19,\n",
       "   'ceiling': [],\n",
       "   'floor': 0.21,\n",
       "   'intFloor': [],\n",
       "   'window': 1.08}},\n",
       " 'd': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'd_iso': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'rho': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'cp': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'Lambda': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'kappa': {'window': [],\n",
       "  'opaque': {'roof': 405000.0,\n",
       "   'intWall': 405000.0,\n",
       "   'wall': 405000.0,\n",
       "   'ceiling': 405000.0,\n",
       "   'floor': 405000.0,\n",
       "   'intFloor': 405000.0}},\n",
       " 'g_gl': {'window': [],\n",
       "  'opaque': {'roof': [],\n",
       "   'intWall': [],\n",
       "   'wall': [],\n",
       "   'ceiling': [],\n",
       "   'floor': [],\n",
       "   'intFloor': []}},\n",
       " 'R_se': {'window': 0.0476,\n",
       "  'opaque': {'roof': 0.04,\n",
       "   'intWall': [],\n",
       "   'wall': 0.04,\n",
       "   'ceiling': [],\n",
       "   'floor': 0.0,\n",
       "   'intFloor': []}},\n",
       " 'R_si': {'window': 0.13,\n",
       "  'opaque': {'roof': 0.13,\n",
       "   'intWall': [],\n",
       "   'wall': 0.13,\n",
       "   'ceiling': [],\n",
       "   'floor': 0.13,\n",
       "   'intFloor': []}},\n",
       " 'epsilon': {'window': 0.9,\n",
       "  'opaque': {'roof': 0.9,\n",
       "   'intWall': [],\n",
       "   'wall': 0.9,\n",
       "   'ceiling': [],\n",
       "   'floor': 0.9,\n",
       "   'intFloor': []}},\n",
       " 'alpha_Sc': {'window': [],\n",
       "  'opaque': {'roof': 0.6,\n",
       "   'intWall': [],\n",
       "   'wall': 0.6,\n",
       "   'ceiling': [],\n",
       "   'floor': 0.6,\n",
       "   'intFloor': []}},\n",
       " 'id': np.int64(0),\n",
       " 'construction_year': np.int64(2022),\n",
       " 'construction_type': None,\n",
       " 'retrofit': np.int64(1),\n",
       " 'usage_short': 'oag',\n",
       " 'file_path': 'c:\\\\Users\\\\felix\\\\Programmieren\\\\tecdm\\\\src\\\\districtgenerator\\\\data',\n",
       " 'c_p_air': 1000.0,\n",
       " 'rho_air': 1.2,\n",
       " 'T_set_min': 20.0,\n",
       " 'T_set_max': 26.0,\n",
       " 'ventilationRate': 0.5,\n",
       " 'T_bivalent': -2.0,\n",
       " 'T_heatlimit': 15.0,\n",
       " 'attributes': [{'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}},\n",
       "  {'window': 1.08,\n",
       "   'opaque': {'roof': 0.14,\n",
       "    'intWall': [],\n",
       "    'wall': 0.19,\n",
       "    'ceiling': [],\n",
       "    'floor': 0.21,\n",
       "    'intFloor': [],\n",
       "    'window': 1.08}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': 405000.0,\n",
       "    'intWall': 405000.0,\n",
       "    'wall': 405000.0,\n",
       "    'ceiling': 405000.0,\n",
       "    'floor': 405000.0,\n",
       "    'intFloor': 405000.0}},\n",
       "  {'window': 0.0476,\n",
       "   'opaque': {'roof': 0.04,\n",
       "    'intWall': [],\n",
       "    'wall': 0.04,\n",
       "    'ceiling': [],\n",
       "    'floor': 0.0,\n",
       "    'intFloor': []}},\n",
       "  {'window': 0.13,\n",
       "   'opaque': {'roof': 0.13,\n",
       "    'intWall': [],\n",
       "    'wall': 0.13,\n",
       "    'ceiling': [],\n",
       "    'floor': 0.13,\n",
       "    'intFloor': []}},\n",
       "  {'window': 0.9,\n",
       "   'opaque': {'roof': 0.9,\n",
       "    'intWall': [],\n",
       "    'wall': 0.9,\n",
       "    'ceiling': [],\n",
       "    'floor': 0.9,\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': 0.6,\n",
       "    'intWall': [],\n",
       "    'wall': 0.6,\n",
       "    'ceiling': [],\n",
       "    'floor': 0.6,\n",
       "    'intFloor': []}},\n",
       "  {'window': [],\n",
       "   'opaque': {'roof': [],\n",
       "    'intWall': [],\n",
       "    'wall': [],\n",
       "    'ceiling': [],\n",
       "    'floor': [],\n",
       "    'intFloor': []}}],\n",
       " 'opaque_ext': ['wall', 'roof', 'floor'],\n",
       " 'opaque': {'ceiling', 'floor', 'intFloor', 'intWall', 'roof', 'wall'},\n",
       " 'V': 1837.5,\n",
       " 'A': {'f': 196.0,\n",
       "  'opaque': {'south': 26.548018749,\n",
       "   'north': 26.548018749,\n",
       "   'west': 26.548018749,\n",
       "   'east': 26.548018749,\n",
       "   'roof': 99.318867536,\n",
       "   'floor': 72.345996688,\n",
       "   'wall': 106.192074996,\n",
       "   'intFloor': 196.0,\n",
       "   'ceiling': 72.345996688,\n",
       "   'intWall': 159.28811249400002},\n",
       "  'window': {'south': 16.924566336999998,\n",
       "   'north': 16.924566336999998,\n",
       "   'west': 16.924566336999998,\n",
       "   'east': 16.924566336999998,\n",
       "   'roof': 0.0,\n",
       "   'floor': 0.0,\n",
       "   'sum': 67.69826534799999}}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars(data.district[0]['envelope'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Datahandler' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mvars\u001b[39m(\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdistrict\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuildingFeatures\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menvelope\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mTypeError\u001b[0m: 'Datahandler' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "vars(data['district'][0]['buildingFeatures']['envelope'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Error tokenizing data. C error: Expected 14 fields in line 6, saw 16\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mfelix\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mProgrammieren\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mtecdm\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43msrc\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdistrictgenerator\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mconsumption_data\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43minternal_loads.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\io\\parsers\\readers.py:626\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[1;32m--> 626\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\io\\parsers\\readers.py:1923\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1916\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[0;32m   1917\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1918\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[0;32m   1919\u001b[0m     (\n\u001b[0;32m   1920\u001b[0m         index,\n\u001b[0;32m   1921\u001b[0m         columns,\n\u001b[0;32m   1922\u001b[0m         col_dict,\n\u001b[1;32m-> 1923\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[0;32m   1924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[0;32m   1925\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1926\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1927\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\io\\parsers\\c_parser_wrapper.py:234\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[1;32m--> 234\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    235\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[0;32m    236\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[1;32mparsers.pyx:838\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mparsers.pyx:905\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mparsers.pyx:874\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mparsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mparsers.pyx:2061\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 14 fields in line 6, saw 16\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\consumption_data\\internal_loads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "def getTryWeather(file_path: str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Parse a TRY weather data file into a DataFrame with a timestamp index.\n",
    "    Assumes specific file formatting.\n",
    "    \n",
    "    Parameters:\n",
    "    - file_path: str, the path to the TRY weather data file.\n",
    "\n",
    "    Returns:\n",
    "    - weather: pd.DataFrame, a DataFrame with a timestamp.\n",
    "    \"\"\"\n",
    "    if \"2015\" or \"2045\" in file_path:\n",
    "        if \"2015\" in file_path:\n",
    "            year = 2015\n",
    "        elif \"2045\" in file_path:\n",
    "            year = 2015\n",
    "        with open(file_path, \"r\") as file:\n",
    "            for line_number, line in enumerate(file, start=1):\n",
    "                if \"***\" in line:\n",
    "                    header_row = (\n",
    "                        line_number - 1 - 1\n",
    "                    )  # -1 for header above *** and -1 for start to count at 0\n",
    "                    break\n",
    "\n",
    "   \n",
    "\n",
    "    else:\n",
    "        raise ValueError(\"Unsupported format type for TRY files. Only 2015 and 2045 are supported.\")\n",
    "    df = pd.read_table(\n",
    "        filepath_or_buffer=file_path,\n",
    "        header=header_row,\n",
    "        sep='\\s+',\n",
    "        skip_blank_lines=False,\n",
    "        encoding=\"latin\",\n",
    "    )\n",
    "    df = df.iloc[1:]\n",
    "    df[\"YEAR\"] = year \n",
    "    df[\"MONTH\"] = df[\"MM\"].astype(int)\n",
    "    df[\"DAY\"] = df[\"DD\"].astype(int)\n",
    "    df[\"HOUR\"] = df[\"HH\"].astype(int)\n",
    "    df['Timestamp'] = pd.to_datetime(df[[\"YEAR\", 'MONTH', 'DAY', 'HOUR']])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = getTryWeather(r\"C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\weather\\DWD\\TRY2015_Zone8_kalt.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RW</th>\n",
       "      <th>HW</th>\n",
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       "      <th>DD</th>\n",
       "      <th>HH</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>WR</th>\n",
       "      <th>WG</th>\n",
       "      <th>N</th>\n",
       "      <th>...</th>\n",
       "      <th>B</th>\n",
       "      <th>D</th>\n",
       "      <th>A</th>\n",
       "      <th>E</th>\n",
       "      <th>IL</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>265.0</td>\n",
       "      <td>-288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>924.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>-288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>924.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>-287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>924.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>-287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>924.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>-286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-01 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>924.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-293.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>2015-12-31 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>924.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>-292.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>2015-12-31 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>-291.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>2015-12-31 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>925.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>2015-12-31 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8760</th>\n",
       "      <td>4040500</td>\n",
       "      <td>2770500.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>925.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>-289.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           RW         HW    MM    DD    HH    t      p    WR   WG    N  ...  \\\n",
       "1     4040500  2770500.0   1.0   1.0   1.0 -5.7  924.0  50.0  4.0  8.0  ...   \n",
       "2     4040500  2770500.0   1.0   1.0   2.0 -5.8  924.0  50.0  3.6  8.0  ...   \n",
       "3     4040500  2770500.0   1.0   1.0   3.0 -5.9  924.0  60.0  4.9  8.0  ...   \n",
       "4     4040500  2770500.0   1.0   1.0   4.0 -5.9  924.0  70.0  4.4  8.0  ...   \n",
       "5     4040500  2770500.0   1.0   1.0   5.0 -5.8  924.0  50.0  4.0  8.0  ...   \n",
       "...       ...        ...   ...   ...   ...  ...    ...   ...  ...  ...  ...   \n",
       "8756  4040500  2770500.0  12.0  31.0  20.0 -4.6  924.0  60.0  3.5  8.0  ...   \n",
       "8757  4040500  2770500.0  12.0  31.0  21.0 -4.8  924.0  60.0  3.6  7.0  ...   \n",
       "8758  4040500  2770500.0  12.0  31.0  22.0 -5.0  925.0  60.0  3.9  7.0  ...   \n",
       "8759  4040500  2770500.0  12.0  31.0  23.0 -5.2  925.0  70.0  3.5  8.0  ...   \n",
       "8760  4040500  2770500.0  12.0  31.0  24.0 -5.6  925.0  60.0  3.4  8.0  ...   \n",
       "\n",
       "        B    D      A      E   IL  YEAR  MONTH  DAY  HOUR           Timestamp  \n",
       "1     0.0  0.0  265.0 -288.0  0.0  2015      1    1     1 2015-01-01 01:00:00  \n",
       "2     0.0  0.0  265.0 -288.0  0.0  2015      1    1     2 2015-01-01 02:00:00  \n",
       "3     0.0  0.0  265.0 -287.0  0.0  2015      1    1     3 2015-01-01 03:00:00  \n",
       "4     0.0  0.0  266.0 -287.0  0.0  2015      1    1     4 2015-01-01 04:00:00  \n",
       "5     0.0  0.0  267.0 -286.0  0.0  2015      1    1     5 2015-01-01 05:00:00  \n",
       "...   ...  ...    ...    ...  ...   ...    ...  ...   ...                 ...  \n",
       "8756  0.0  0.0  259.0 -293.0  0.0  2015     12   31    20 2015-12-31 20:00:00  \n",
       "8757  0.0  0.0  258.0 -292.0  0.0  2015     12   31    21 2015-12-31 21:00:00  \n",
       "8758  0.0  0.0  249.0 -291.0  0.0  2015     12   31    22 2015-12-31 22:00:00  \n",
       "8759  0.0  0.0  259.0 -290.0  0.0  2015     12   31    23 2015-12-31 23:00:00  \n",
       "8760  0.0  0.0  267.0 -289.0  0.0  2015     12   31    24 2016-01-01 00:00:00  \n",
       "\n",
       "[8760 rows x 22 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEpWeather(file_path:str) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Function gets EPW weather data from a file and returns it as a pandas dataframe. \n",
    "\n",
    "    Information about epw files: https://bigladdersoftware.com/epx/docs/8-3/auxiliary-programs/energyplus-weather-file-epw-data-dictionary.html#field-dry-bulb-temperature \n",
    "    Returns:\n",
    "    weather: pandas dataframe  with the columns: \n",
    "        - DirNormRad\n",
    "        - DiffHorRad\n",
    "        - DryBulbTemp\n",
    "    Index is a timestamp in the yyyy-mm-dd-hh format. \n",
    "    \"\"\"\n",
    "\n",
    "    # Define the column names as per EPW file documentation\n",
    "    col_names = [\"Year\", \"Month\", \"Day\", \"Hour\", \"Minute\", \n",
    "                 \"Data Source and Uncertainty Flags\", \"Dry Bulb Temperature\", \n",
    "                 \"Dew Point Temperature\", \"Relative Humidity\", \"Atmospheric Station Pressure\", \n",
    "                 \"Extraterrestrial Horizontal Radiation\", \"Extraterrestrial Direct Normal Radiation\", \n",
    "                 \"Horizontal Infrared Radiation Intensity\", \"Global Horizontal Radiation\", \n",
    "                 \"Direct Normal Radiation\", \"Diffuse Horizontal Radiation\", \n",
    "                 \"Global Horizontal Illuminance\", \"Direct Normal Illuminance\", \n",
    "                 \"Diffuse Horizontal Illuminance\", \"Zenith Luminance\", \"Wind Direction\", \n",
    "                 \"Wind Speed\", \"Total Sky Cover\", \"Opaque Sky Cover\", \"Visibility\", \n",
    "                 \"Ceiling Height\", \"Present Weather Observation\", \"Present Weather Codes\", \n",
    "                 \"Precipitable Water\", \"Aerosol Optical Depth\", \"Snow Depth\", \n",
    "                 \"Days Since Last Snowfall\", \"Albedo\", \"Liquid Precipitation Depth\", \n",
    "                 \"Liquid Precipitation Quantity\"]\n",
    "    \n",
    "    # Load the file\n",
    "    df = pd.read_csv(file_path, skiprows=8, header=None, names=col_names)\n",
    "    \n",
    "    # Creating a timestamp column in the required format\n",
    "    df['Timestamp'] = pd.to_datetime(df[['Year', 'Month', 'Day', 'Hour']]) - pd.Timedelta(hours=1)\n",
    "    \n",
    "    # Adjust the minute and second to 00:00, as EPW files do not contain this information\n",
    "    #df['Timestamp'] = df['Timestamp'].dt.strftime('%Y-%m-%d-%H-00-00')\n",
    "    \n",
    "    # Select the required columns\n",
    "    df = df[['Timestamp', 'Direct Normal Radiation', 'Diffuse Horizontal Radiation', 'Dry Bulb Temperature', \n",
    "              \"Direct Normal Illuminance\",  \"Diffuse Horizontal Illuminance\",]]\n",
    "    \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      2015-01-01 01:00:00\n",
       "2      2015-01-01 02:00:00\n",
       "3      2015-01-01 03:00:00\n",
       "4      2015-01-01 04:00:00\n",
       "5      2015-01-01 05:00:00\n",
       "               ...        \n",
       "8756   2015-12-31 20:00:00\n",
       "8757   2015-12-31 21:00:00\n",
       "8758   2015-12-31 22:00:00\n",
       "8759   2015-12-31 23:00:00\n",
       "8760   2016-01-01 00:00:00\n",
       "Name: Timestamp, Length: 8760, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Timestamp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8760"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Timestamp\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = getEpWeather(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\weather\\EPW\\DEU_BE_Berlin-Schonefeld.AP.103850_TMYx.2004-2018.epw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Direct Normal Radiation</th>\n",
       "      <th>Diffuse Horizontal Radiation</th>\n",
       "      <th>Dry Bulb Temperature</th>\n",
       "      <th>Direct Normal Illuminance</th>\n",
       "      <th>Diffuse Horizontal Illuminance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>2013-12-31 19:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>2013-12-31 20:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>2013-12-31 21:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>2013-12-31 22:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>2013-12-31 23:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Timestamp  Direct Normal Radiation  \\\n",
       "0    2011-01-01 00:00:00                        0   \n",
       "1    2011-01-01 01:00:00                        0   \n",
       "2    2011-01-01 02:00:00                        0   \n",
       "3    2011-01-01 03:00:00                        0   \n",
       "4    2011-01-01 04:00:00                        0   \n",
       "...                  ...                      ...   \n",
       "8755 2013-12-31 19:00:00                        0   \n",
       "8756 2013-12-31 20:00:00                        0   \n",
       "8757 2013-12-31 21:00:00                        0   \n",
       "8758 2013-12-31 22:00:00                        0   \n",
       "8759 2013-12-31 23:00:00                        0   \n",
       "\n",
       "      Diffuse Horizontal Radiation  Dry Bulb Temperature  \\\n",
       "0                                0                   1.0   \n",
       "1                                0                   1.0   \n",
       "2                                0                   1.0   \n",
       "3                                0                   1.0   \n",
       "4                                0                   2.0   \n",
       "...                            ...                   ...   \n",
       "8755                             0                   1.0   \n",
       "8756                             0                   0.0   \n",
       "8757                             0                   0.0   \n",
       "8758                             0                   0.0   \n",
       "8759                             0                   0.0   \n",
       "\n",
       "      Direct Normal Illuminance  Diffuse Horizontal Illuminance  \n",
       "0                             0                               0  \n",
       "1                             0                               0  \n",
       "2                             0                               0  \n",
       "3                             0                               0  \n",
       "4                             0                               0  \n",
       "...                         ...                             ...  \n",
       "8755                          0                               0  \n",
       "8756                          0                               0  \n",
       "8757                          0                               0  \n",
       "8758                          0                               0  \n",
       "8759                          0                               0  \n",
       "\n",
       "[8760 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Column 'oag' does not exist in the table.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[58], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctions\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschedule_reader\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mschedule_reader\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mschedule_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_schedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuilding_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43moag\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\functions\\schedule_reader.py:58\u001b[0m, in \u001b[0;36mget_schedule\u001b[1;34m(building_type)\u001b[0m\n\u001b[0;32m     41\u001b[0m type_assignment \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     42\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moag\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moffice.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     43\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIWU Research and University Teaching\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHörsaal, Auditorium.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIWU Generalized (2) Production buildings\u001b[39m\u001b[38;5;124m\"\u001b[39m:  \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLagerhalle.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     55\u001b[0m     }\n\u001b[0;32m     57\u001b[0m \u001b[38;5;66;03m#schedule_name = type_assignment.get(building_type)\u001b[39;00m\n\u001b[1;32m---> 58\u001b[0m schedule_name \u001b[38;5;241m=\u001b[39m \u001b[43mget_building_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mterm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSIA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuilding_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m schedule_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     60\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo schedule for building type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbuilding_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\functions\\schedule_reader.py:18\u001b[0m, in \u001b[0;36mget_building_type\u001b[1;34m(term, kind)\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Check if the kind column exists\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m---> 18\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkind\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m does not exist in the table.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# Find the row that matches the term in 'districtgenerator'\u001b[39;00m\n\u001b[0;32m     21\u001b[0m match \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistrictgenerator\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m term]\n",
      "\u001b[1;31mValueError\u001b[0m: Column 'oag' does not exist in the table."
     ]
    }
   ],
   "source": [
    "import functions.schedule_reader as schedule_reader\n",
    "schedule_reader.get_schedule(building_type='oag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "the JSON object must be str, bytes or bytearray, not TextIOWrapper",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC:\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mfelix\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mProgrammieren\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mtecdm\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124msrc\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mdistrictgenerator\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mnon_residential_envelope\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mnon_residential_envelope.json\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m----> 2\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\json\\__init__.py:339\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    337\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    338\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(s, (\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mbytearray\u001b[39m)):\n\u001b[1;32m--> 339\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe JSON object must be str, bytes or bytearray, \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    340\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00ms\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    341\u001b[0m     s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n",
      "\u001b[1;31mTypeError\u001b[0m: the JSON object must be str, bytes or bytearray, not TextIOWrapper"
     ]
    }
   ],
   "source": [
    "with open(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\non_residential_envelope\\non_residential_envelope.json') as f:\n",
    "    data = json.loads(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mfelix\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mProgrammieren\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mtecdm\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43msrc\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdistrictgenerator\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mnon_residential_envelope\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mnon_residential_envelope.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\json\\__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    341\u001b[0m     s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[1;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\json\\decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w\u001b[38;5;241m=\u001b[39mWHITESPACE\u001b[38;5;241m.\u001b[39mmatch):\n\u001b[0;32m    333\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;124;03m    containing a JSON document).\u001b[39;00m\n\u001b[0;32m    335\u001b[0m \n\u001b[0;32m    336\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 337\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    338\u001b[0m     end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m    339\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\json\\decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[1;34m(self, s, idx)\u001b[0m\n\u001b[0;32m    353\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscan_once(s, idx)\n\u001b[0;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
      "\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "data = json.loads(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\non_residential_envelope\\non_residential_envelope.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\non_residential_envelope\\non_residential_envelope.json', 'r', encoding='utf-8') as file:\n",
    "            data = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'oag': {'building_age_group': {'1860 - 1978': {'u_aw': 1.29,\n",
       "    'u_fen': 2.68,\n",
       "    'u_d_opak': 0.44,\n",
       "    'u_d_transp': 1.53,\n",
       "    'u_ug': 1.07,\n",
       "    'u_geb': 1.16,\n",
       "    'wwr_north': 0.3,\n",
       "    'wwr_south': 0.3,\n",
       "    'wwr_east': 0.3,\n",
       "    'wwr_west': 0.3},\n",
       "   '1979 - 2009': {'u_aw': 0.48,\n",
       "    'u_fen': 1.89,\n",
       "    'u_d_opak': 0.24,\n",
       "    'u_d_transp': 2.14,\n",
       "    'u_ug': 0.27,\n",
       "    'u_geb': 0.6,\n",
       "    'wwr_north': 0.39,\n",
       "    'wwr_south': 0.39,\n",
       "    'wwr_east': 0.39,\n",
       "    'wwr_west': 0.39},\n",
       "   '2010 - 2030': {'u_aw': 0.19,\n",
       "    'u_fen': 1.08,\n",
       "    'u_d_opak': 0.14,\n",
       "    'u_d_transp': 1.3,\n",
       "    'u_ug': 0.21,\n",
       "    'u_geb': 0.3,\n",
       "    'wwr_north': 0.37,\n",
       "    'wwr_south': 0.37,\n",
       "    'wwr_east': 0.37,\n",
       "    'wwr_west': 0.37}}},\n",
       " 'IWU Research and University Teaching': {'building_age_group': {'1860 - 1978': {'u_aw': 1.28,\n",
       "    'u_fen': 3.1,\n",
       "    'u_d_opak': 0.4,\n",
       "    'u_d_transp': 2.19,\n",
       "    'u_ug': 0.82,\n",
       "    'u_geb': 1.44,\n",
       "    'wwr_north': 0.37,\n",
       "    'wwr_south': 0.37,\n",
       "    'wwr_east': 0.37,\n",
       "    'wwr_west': 0.37},\n",
       "   '1979 - 2009': {'u_aw': 0.29,\n",
       "    'u_fen': 1.74,\n",
       "    'u_d_opak': 0.13,\n",
       "    'u_d_transp': 0.62,\n",
       "    'u_ug': 0.53,\n",
       "    'u_geb': 0.58,\n",
       "    'wwr_north': 0.4,\n",
       "    'wwr_south': 0.4,\n",
       "    'wwr_east': 0.4,\n",
       "    'wwr_west': 0.4},\n",
       "   '2010 - 2030': {'u_aw': 0.2,\n",
       "    'u_fen': 1.24,\n",
       "    'u_d_opak': 0.14,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.23,\n",
       "    'u_geb': 0.25,\n",
       "    'wwr_north': 0.46,\n",
       "    'wwr_south': 0.46,\n",
       "    'wwr_east': 0.46,\n",
       "    'wwr_west': 0.46}}},\n",
       " 'IWU Health and Care': {'building_age_group': {'1860 - 1978': {'u_aw': 0.95,\n",
       "    'u_fen': 2.72,\n",
       "    'u_d_opak': 0.31,\n",
       "    'u_d_transp': 0.76,\n",
       "    'u_ug': 0.7,\n",
       "    'u_geb': 1.31,\n",
       "    'wwr_north': 0.27,\n",
       "    'wwr_south': 0.27,\n",
       "    'wwr_east': 0.27,\n",
       "    'wwr_west': 0.27},\n",
       "   '1979 - 2009': {'u_aw': 0.44,\n",
       "    'u_fen': 2.24,\n",
       "    'u_d_opak': 0.23,\n",
       "    'u_d_transp': 2.07,\n",
       "    'u_ug': 0.25,\n",
       "    'u_geb': 0.59,\n",
       "    'wwr_north': 0.34,\n",
       "    'wwr_south': 0.34,\n",
       "    'wwr_east': 0.34,\n",
       "    'wwr_west': 0.34},\n",
       "   '2010 - 2030': {'u_aw': 0.21,\n",
       "    'u_fen': 1.25,\n",
       "    'u_d_opak': 0.09,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.14,\n",
       "    'u_geb': 0.31,\n",
       "    'wwr_north': 0.47,\n",
       "    'wwr_south': 0.47,\n",
       "    'wwr_east': 0.47,\n",
       "    'wwr_west': 0.47}}},\n",
       " 'IWU School, Day Nursery and other Care': {'building_age_group': {'1860 - 1978': {'u_aw': 1.09,\n",
       "    'u_fen': 2.22,\n",
       "    'u_d_opak': 0.44,\n",
       "    'u_d_transp': 3.17,\n",
       "    'u_ug': 0.79,\n",
       "    'u_geb': 1.12,\n",
       "    'wwr_north': 0.4,\n",
       "    'wwr_south': 0.4,\n",
       "    'wwr_east': 0.4,\n",
       "    'wwr_west': 0.4},\n",
       "   '1979 - 2009': {'u_aw': 0.51,\n",
       "    'u_fen': 2.05,\n",
       "    'u_d_opak': 0.25,\n",
       "    'u_d_transp': 1.64,\n",
       "    'u_ug': 0.44,\n",
       "    'u_geb': 0.7,\n",
       "    'wwr_north': 0.43,\n",
       "    'wwr_south': 0.43,\n",
       "    'wwr_east': 0.43,\n",
       "    'wwr_west': 0.43},\n",
       "   '2010 - 2030': {'u_aw': 0.23,\n",
       "    'u_fen': 1.17,\n",
       "    'u_d_opak': 0.14,\n",
       "    'u_d_transp': 1.27,\n",
       "    'u_ug': 0.18,\n",
       "    'u_geb': 0.33,\n",
       "    'wwr_north': 0.36,\n",
       "    'wwr_south': 0.36,\n",
       "    'wwr_east': 0.36,\n",
       "    'wwr_west': 0.36}}},\n",
       " 'IWU Culture and Leisure': {'building_age_group': {'1860 - 1978': {'u_aw': 1.39,\n",
       "    'u_fen': 2.56,\n",
       "    'u_d_opak': 0.75,\n",
       "    'u_d_transp': 2.68,\n",
       "    'u_ug': 0.99,\n",
       "    'u_geb': 1.16,\n",
       "    'wwr_north': 0.3,\n",
       "    'wwr_south': 0.3,\n",
       "    'wwr_east': 0.3,\n",
       "    'wwr_west': 0.3},\n",
       "   '1979 - 2009': {'u_aw': 0.53,\n",
       "    'u_fen': 2.04,\n",
       "    'u_d_opak': 0.33,\n",
       "    'u_d_transp': 1.94,\n",
       "    'u_ug': 0.44,\n",
       "    'u_geb': 0.52,\n",
       "    'wwr_north': 0.29,\n",
       "    'wwr_south': 0.29,\n",
       "    'wwr_east': 0.29,\n",
       "    'wwr_west': 0.29},\n",
       "   '2010 - 2030': {'u_aw': 0.51,\n",
       "    'u_fen': 0.96,\n",
       "    'u_d_opak': 0.33,\n",
       "    'u_d_transp': 0.9,\n",
       "    'u_ug': 0.54,\n",
       "    'u_geb': 0.3,\n",
       "    'wwr_north': 0.51,\n",
       "    'wwr_south': 0.51,\n",
       "    'wwr_east': 0.51,\n",
       "    'wwr_west': 0.51}}},\n",
       " 'IWU Sports Facilities': {'building_age_group': {'1860 - 1978': {'u_aw': 1.35,\n",
       "    'u_fen': 3.09,\n",
       "    'u_d_opak': 0.4,\n",
       "    'u_d_transp': 1.3,\n",
       "    'u_ug': 0.57,\n",
       "    'u_geb': 1.06,\n",
       "    'wwr_north': 0.59,\n",
       "    'wwr_south': 0.59,\n",
       "    'wwr_east': 0.59,\n",
       "    'wwr_west': 0.59},\n",
       "   '1979 - 2009': {'u_aw': 0.55,\n",
       "    'u_fen': 2.56,\n",
       "    'u_d_opak': 0.34,\n",
       "    'u_d_transp': 1.05,\n",
       "    'u_ug': 0.57,\n",
       "    'u_geb': 0.66,\n",
       "    'wwr_north': 0.27,\n",
       "    'wwr_south': 0.27,\n",
       "    'wwr_east': 0.27,\n",
       "    'wwr_west': 0.27},\n",
       "   '2010 - 2030': {'u_aw': 0.27,\n",
       "    'u_fen': 1.07,\n",
       "    'u_d_opak': 0.15,\n",
       "    'u_d_transp': 1.3,\n",
       "    'u_ug': 0.99,\n",
       "    'u_geb': 0.39,\n",
       "    'wwr_north': 0.13,\n",
       "    'wwr_south': 0.13,\n",
       "    'wwr_east': 0.13,\n",
       "    'wwr_west': 0.13}}},\n",
       " 'IWU Hotels, Boarding, Restaurants or Catering': {'building_age_group': {'1860 - 1978': {'u_aw': 1.2,\n",
       "    'u_fen': 2.76,\n",
       "    'u_d_opak': 0.8,\n",
       "    'u_d_transp': 2.3,\n",
       "    'u_ug': 0.83,\n",
       "    'u_geb': 1.24,\n",
       "    'wwr_north': 0.3,\n",
       "    'wwr_south': 0.3,\n",
       "    'wwr_east': 0.3,\n",
       "    'wwr_west': 0.3},\n",
       "   '1979 - 2009': {'u_aw': 0.47,\n",
       "    'u_fen': 2.07,\n",
       "    'u_d_opak': 0.29,\n",
       "    'u_d_transp': 2.78,\n",
       "    'u_ug': 0.37,\n",
       "    'u_geb': 0.79,\n",
       "    'wwr_north': 0.33,\n",
       "    'wwr_south': 0.33,\n",
       "    'wwr_east': 0.33,\n",
       "    'wwr_west': 0.33},\n",
       "   '2010 - 2030': {'u_aw': 0.32,\n",
       "    'u_fen': 1.24,\n",
       "    'u_d_opak': 0.18,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.26,\n",
       "    'u_geb': 0.38,\n",
       "    'wwr_north': 0.32,\n",
       "    'wwr_south': 0.32,\n",
       "    'wwr_east': 0.32,\n",
       "    'wwr_west': 0.32}}},\n",
       " 'IWU Production, Workshop, Warehouse or Operations': {'building_age_group': {'1860 - 1978': {'u_aw': 1.03,\n",
       "    'u_fen': 2.99,\n",
       "    'u_d_opak': 0.53,\n",
       "    'u_d_transp': 3.84,\n",
       "    'u_ug': 1.01,\n",
       "    'u_geb': 1.12,\n",
       "    'wwr_north': 0.26,\n",
       "    'wwr_south': 0.26,\n",
       "    'wwr_east': 0.26,\n",
       "    'wwr_west': 0.26},\n",
       "   '1979 - 2009': {'u_aw': 0.51,\n",
       "    'u_fen': 2.17,\n",
       "    'u_d_opak': 0.24,\n",
       "    'u_d_transp': 2.05,\n",
       "    'u_ug': 0.41,\n",
       "    'u_geb': 0.54,\n",
       "    'wwr_north': 0.21,\n",
       "    'wwr_south': 0.21,\n",
       "    'wwr_east': 0.21,\n",
       "    'wwr_west': 0.21},\n",
       "   '2010 - 2030': {'u_aw': 0.24,\n",
       "    'u_fen': 1.2,\n",
       "    'u_d_opak': 0.11,\n",
       "    'u_d_transp': 1.07,\n",
       "    'u_ug': 0.22,\n",
       "    'u_geb': 0.28,\n",
       "    'wwr_north': 0.19,\n",
       "    'wwr_south': 0.19,\n",
       "    'wwr_east': 0.19,\n",
       "    'wwr_west': 0.19}}},\n",
       " 'IWU Trade Buildings': {'building_age_group': {'1860 - 1978': {'u_aw': 0.55,\n",
       "    'u_fen': 3.03,\n",
       "    'u_d_opak': 0.22,\n",
       "    'u_d_transp': 0.07,\n",
       "    'u_ug': 0.37,\n",
       "    'u_geb': 1.32,\n",
       "    'wwr_north': 0.2,\n",
       "    'wwr_south': 0.2,\n",
       "    'wwr_east': 0.2,\n",
       "    'wwr_west': 0.2},\n",
       "   '1979 - 2009': {'u_aw': 0.4,\n",
       "    'u_fen': 2.1,\n",
       "    'u_d_opak': 0.31,\n",
       "    'u_d_transp': 2.35,\n",
       "    'u_ug': 0.5,\n",
       "    'u_geb': 0.5,\n",
       "    'wwr_north': 0.23,\n",
       "    'wwr_south': 0.23,\n",
       "    'wwr_east': 0.23,\n",
       "    'wwr_west': 0.23},\n",
       "   '2010 - 2030': {'u_aw': 0.25,\n",
       "    'u_fen': 1.05,\n",
       "    'u_d_opak': 0.16,\n",
       "    'u_d_transp': 0.9,\n",
       "    'u_ug': 0.12,\n",
       "    'u_geb': 0.33,\n",
       "    'wwr_north': 0.4,\n",
       "    'wwr_south': 0.4,\n",
       "    'wwr_east': 0.4,\n",
       "    'wwr_west': 0.4}}},\n",
       " 'IWU Technical and Utility (supply and disposal)': {'building_age_group': {'1860 - 1978': {'u_aw': 1.98,\n",
       "    'u_fen': 4.2,\n",
       "    'u_d_opak': 0.99,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 1.51,\n",
       "    'u_geb': 1.3,\n",
       "    'wwr_north': 0.23,\n",
       "    'wwr_south': 0.23,\n",
       "    'wwr_east': 0.23,\n",
       "    'wwr_west': 0.23},\n",
       "   '1979 - 2009': {'u_aw': 0.47,\n",
       "    'u_fen': 2.02,\n",
       "    'u_d_opak': 0.22,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.37,\n",
       "    'u_geb': 0.55,\n",
       "    'wwr_north': 0.12,\n",
       "    'wwr_south': 0.12,\n",
       "    'wwr_east': 0.12,\n",
       "    'wwr_west': 0.12},\n",
       "   '2010 - 2030': {'u_aw': 0.16,\n",
       "    'u_fen': 1.3,\n",
       "    'u_d_opak': 0.15,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.24,\n",
       "    'u_geb': 0.24,\n",
       "    'wwr_north': 0.0,\n",
       "    'wwr_south': 0.0,\n",
       "    'wwr_east': 0.0,\n",
       "    'wwr_west': 0.0}}},\n",
       " 'IWU Transport': {'building_age_group': {'1860 - 1978': {'u_aw': 1.78,\n",
       "    'u_fen': 5.0,\n",
       "    'u_d_opak': 1.41,\n",
       "    'u_d_transp': 5.0,\n",
       "    'u_ug': 0.98,\n",
       "    'u_geb': 1.43,\n",
       "    'wwr_north': 0.42,\n",
       "    'wwr_south': 0.42,\n",
       "    'wwr_east': 0.42,\n",
       "    'wwr_west': 0.42},\n",
       "   '1979 - 2009': {'u_aw': 0.63,\n",
       "    'u_fen': 0.41,\n",
       "    'u_d_opak': 0.41,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.61,\n",
       "    'u_geb': 0.68,\n",
       "    'wwr_north': 0.06,\n",
       "    'wwr_south': 0.06,\n",
       "    'wwr_east': 0.06,\n",
       "    'wwr_west': 0.06},\n",
       "   '2010 - 2030': {'u_aw': 0.16,\n",
       "    'u_fen': 1.3,\n",
       "    'u_d_opak': 0.15,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.24,\n",
       "    'u_geb': 0.22,\n",
       "    'wwr_north': 0.0,\n",
       "    'wwr_south': 0.0,\n",
       "    'wwr_east': 0.0,\n",
       "    'wwr_west': 0.0}}},\n",
       " 'IWU Generalized (1) Services building, Includes categories (1) to (7) and (9)': {'building_age_group': {'1860 - 1978': {'u_aw': 1.11,\n",
       "    'u_fen': 2.8,\n",
       "    'u_d_opak': 0.45,\n",
       "    'u_d_transp': 0.66,\n",
       "    'u_ug': 0.64,\n",
       "    'u_geb': 1.2,\n",
       "    'wwr_north': 0.34,\n",
       "    'wwr_south': 0.34,\n",
       "    'wwr_east': 0.34,\n",
       "    'wwr_west': 0.34},\n",
       "   '1979 - 2009': {'u_aw': 0.48,\n",
       "    'u_fen': 2.1,\n",
       "    'u_d_opak': 0.27,\n",
       "    'u_d_transp': 2.03,\n",
       "    'u_ug': 0.41,\n",
       "    'u_geb': 0.62,\n",
       "    'wwr_north': 0.34,\n",
       "    'wwr_south': 0.34,\n",
       "    'wwr_east': 0.34,\n",
       "    'wwr_west': 0.34},\n",
       "   '2010 - 2030': {'u_aw': 0.25,\n",
       "    'u_fen': 1.12,\n",
       "    'u_d_opak': 0.13,\n",
       "    'u_d_transp': 1.13,\n",
       "    'u_ug': 0.2,\n",
       "    'u_geb': 0.31,\n",
       "    'wwr_north': 0.38,\n",
       "    'wwr_south': 0.38,\n",
       "    'wwr_east': 0.38,\n",
       "    'wwr_west': 0.38}}},\n",
       " 'IWU Generalized (2) Production buildings and similar, Includes cat. (8), (10), (11)': {'building_age_group': {'1860 - 1978': {'u_aw': 1.78,\n",
       "    'u_fen': 5.0,\n",
       "    'u_d_opak': 1.41,\n",
       "    'u_d_transp': 5.0,\n",
       "    'u_ug': 0.98,\n",
       "    'u_geb': 1.43,\n",
       "    'wwr_north': 0.26,\n",
       "    'wwr_south': 0.26,\n",
       "    'wwr_east': 0.26,\n",
       "    'wwr_west': 0.26},\n",
       "   '1979 - 2009': {'u_aw': 0.63,\n",
       "    'u_fen': 0.41,\n",
       "    'u_d_opak': 0.41,\n",
       "    'u_d_transp': 0.0,\n",
       "    'u_ug': 0.61,\n",
       "    'u_geb': 0.68,\n",
       "    'wwr_north': 0.2,\n",
       "    'wwr_south': 0.2,\n",
       "    'wwr_east': 0.2,\n",
       "    'wwr_west': 0.2},\n",
       "   '2010 - 2030': {'u_aw': 0.24,\n",
       "    'u_fen': 1.2,\n",
       "    'u_d_opak': 0.11,\n",
       "    'u_d_transp': 1.07,\n",
       "    'u_ug': 0.22,\n",
       "    'u_geb': 0.28,\n",
       "    'wwr_north': 0.19,\n",
       "    'wwr_south': 0.19,\n",
       "    'wwr_east': 0.19,\n",
       "    'wwr_west': 0.19}}}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Values to add based on year range\n",
    "values_to_add = {\n",
    "    '1860 - 1978': 0.78,\n",
    "    '1979 - 2009': 0.78,  # Placeholder value\n",
    "    '2010 - 2030': 0.67   # Placeholder value\n",
    "}\n",
    "\n",
    "# Loop through each key in the dictionary\n",
    "for key in data:\n",
    "    # Check if 'building_age_group' is present\n",
    "    if 'building_age_group' in data[key]:\n",
    "        # Loop through the building_age_group and add the appropriate new value\n",
    "        for year_range, properties in data[key]['building_age_group'].items():\n",
    "            properties['g_gl_fen'] = values_to_add.get(year_range, 0.0)  # Default value if year_range not found\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "JSONEncoder.__init__() got an unexpected keyword argument 'fp'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mfelix\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mProgrammieren\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mtecdm\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43msrc\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdistrictgenerator\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mnon_residential_envelope\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mnon_residential_envelope.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\json\\__init__.py:234\u001b[0m, in \u001b[0;36mdumps\u001b[1;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONEncoder\n\u001b[1;32m--> 234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    235\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskipkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mensure_ascii\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_ascii\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    236\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcheck_circular\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_circular\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    237\u001b[0m \u001b[43m    \u001b[49m\u001b[43mseparators\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseparators\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    238\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mencode(obj)\n",
      "\u001b[1;31mTypeError\u001b[0m: JSONEncoder.__init__() got an unexpected keyword argument 'fp'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\non_residential_envelope\\non_residential_envelope.json', 'w', encoding='utf-8') as file:\n",
    "    json.dump(data, file, indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(r'C:\\Users\\felix\\Programmieren\\CityEnergyAnalyst\\cea\\databases\\DE\\archetypes\\use_types\\OFFICE_1P.csv', skiprows=2)\n",
    "# Filter out weekdays, Saturday, and Sunday\n",
    "weekday_data = df[df['DAY'] == 'WEEKDAY']\n",
    "saturday_data = df[df['DAY'] == 'SATURDAY']\n",
    "sunday_data = df[df['DAY'] == 'SUNDAY']\n",
    "\n",
    "# Ensure each type of day has only 24 hours worth of data if there are duplicates\n",
    "weekday_data = weekday_data.head(24)\n",
    "saturday_data = saturday_data.head(24)\n",
    "sunday_data = sunday_data.head(24)\n",
    "\n",
    "# Repeat the weekday data five times\n",
    "repeated_weekdays = pd.concat([weekday_data] * 5, ignore_index=True)\n",
    "\n",
    "# Modify the 'DAY' column for repeated weekdays\n",
    "day_mappings = {i: f\"weekday_{(i//24)+1}\" for i in range(120)}  # Generate weekday labels\n",
    "repeated_weekdays['DAY'] = [day_mappings[i] for i in range(120)]  # Apply labels\n",
    "\n",
    "# Concatenate all data: five weekdays, one Saturday, one Sunday\n",
    "final_df = pd.concat([repeated_weekdays, saturday_data, sunday_data], ignore_index=True)\n",
    "\n",
    "# Adjust the 'DAY' column to reflect the new day numbering (optional)\n",
    "day_numbering = {**{f\"weekday_{i+1}\": i for i in range(5)}, 'SATURDAY': 5, 'SUNDAY': 6}\n",
    "final_df['DAY'] = final_df['DAY'].map(day_numbering)\n",
    "final_df = final_df.drop(columns=['Unnamed: 11', 'Unnamed: 12'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv(r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\occupancy_schedules\\OFFICE_1P.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\felix\\Programmieren\\CityEnergyAnalyst\\cea\\databases\\DE\\archetypes\\use_types\\OFFICE_1P.csv', skiprows=2)\n",
    "# Filter out weekdays, Saturday, and Sunday\n",
    "weekday_data = df[df['DAY'] == 'WEEKDAY']\n",
    "saturday_data = df[df['DAY'] == 'SATURDAY']\n",
    "sunday_data = df[df['DAY'] == 'SUNDAY']\n",
    "\n",
    "# Ensure each type of day has only 24 hours worth of data if there are duplicates\n",
    "weekday_data = weekday_data.head(24)\n",
    "saturday_data = saturday_data.head(24)\n",
    "sunday_data = sunday_data.head(24)\n",
    "\n",
    "# Repeat the weekday data five times\n",
    "repeated_weekdays = pd.concat([weekday_data] * 5, ignore_index=True)\n",
    "\n",
    "# Modify the 'DAY' column for repeated weekdays\n",
    "day_mappings = {i: f\"weekday_{(i//24)+1}\" for i in range(120)}  # Generate weekday labels\n",
    "repeated_weekdays['DAY'] = [day_mappings[i] for i in range(120)]  # Apply labels\n",
    "\n",
    "# Concatenate all data: five weekdays, one Saturday, one Sunday\n",
    "final_df = pd.concat([repeated_weekdays, saturday_data, sunday_data], ignore_index=True)\n",
    "\n",
    "# Adjust the 'DAY' column to reflect the new day numbering (optional)\n",
    "day_numbering = {**{f\"weekday_{i+1}\": i for i in range(5)}, 'SATURDAY': 5, 'SUNDAY': 6}\n",
    "final_df['DAY'] = final_df['DAY'].map(day_numbering)\n",
    "final_df = final_df.drop(columns=['Unnamed: 11', 'Unnamed: 12'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing file: C:\\Users\\felix\\Programmieren\\CityEnergyAnalyst\\cea\\databases\\DE\\archetypes\\use_types\\COOLROOM.csv\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"['Unnamed: 11', 'Unnamed: 12'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[45], line 34\u001b[0m\n\u001b[0;32m     31\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_path,  skiprows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Process the DataFrame\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m processed_df \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mhead())\n\u001b[0;32m     37\u001b[0m \u001b[38;5;66;03m# Save the processed DataFrame\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[45], line 15\u001b[0m, in \u001b[0;36mprocess_dataframe\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     13\u001b[0m final_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDAY\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m final_df\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m24\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m7\u001b[39m\n\u001b[0;32m     14\u001b[0m final_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDAY\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m final_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDAY\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmap({i: i \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m5\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m5\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m6\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m7\u001b[39m)})\n\u001b[1;32m---> 15\u001b[0m final_df \u001b[38;5;241m=\u001b[39m \u001b[43mfinal_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mUnnamed: 11\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mUnnamed: 12\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m final_df\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\core\\frame.py:5581\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5433\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5434\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5435\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5442\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5443\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5444\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5445\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5446\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5579\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5580\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5582\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5583\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5584\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5585\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5586\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5587\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5588\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5589\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\core\\generic.py:4788\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4786\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4787\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4788\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4790\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4791\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\core\\generic.py:4830\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4828\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4829\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4830\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4831\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4833\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4834\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\felix\\.conda\\envs\\districtgenerator\\Lib\\site-packages\\pandas-2.2.2-py3.11-win-amd64.egg\\pandas\\core\\indexes\\base.py:7070\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   7068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   7069\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 7070\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   7071\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   7072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Unnamed: 11', 'Unnamed: 12'] not found in axis\""
     ]
    }
   ],
   "source": [
    "import os \n",
    "def process_dataframe(df):\n",
    "    # Assuming df is the loaded DataFrame from each CSV\n",
    "    weekday_data = df[df['DAY'] == 'WEEKDAY'].head(24)\n",
    "    saturday_data = df[df['DAY'] == 'SATURDAY'].head(24)\n",
    "    sunday_data = df[df['DAY'] == 'SUNDAY'].head(24)\n",
    "\n",
    "    # Repeat the weekday data five times and append Saturday and Sunday data\n",
    "    repeated_weekdays = pd.concat([weekday_data] * 5, ignore_index=True)\n",
    "    final_df = pd.concat([repeated_weekdays, saturday_data, sunday_data], ignore_index=True)\n",
    "    \n",
    "    # Re-assigning 'DAY' values to ensure continuity in the data (optional)\n",
    "    final_df['DAY'] = final_df.index // 24 % 7\n",
    "    final_df['DAY'] = final_df['DAY'].map({i: i if i < 5 else 5 if i == 5 else 6 for i in range(7)})\n",
    "    final_df = final_df.drop(columns=['Unnamed: 11', 'Unnamed: 12'])\n",
    "    \n",
    "    return final_df\n",
    "\n",
    "# List all files in the directory\n",
    "dir_path = r'C:\\Users\\felix\\Programmieren\\CityEnergyAnalyst\\cea\\databases\\DE\\archetypes\\use_types'\n",
    "files = os.listdir(dir_path)\n",
    "save_folder= r'C:\\Users\\felix\\Programmieren\\tecdm\\src\\districtgenerator\\data\\occupancy_schedules'\n",
    "\n",
    "# Process each file\n",
    "for file in files:\n",
    "    if file.endswith('.csv'):  # Check if the file is a CSV\n",
    "        file_path = os.path.join(dir_path, file)\n",
    "        print(f\"Processing file: {file_path}\")\n",
    "        \n",
    "        # Load the DataFrame from CSV\n",
    "        df = pd.read_csv(file_path,  skiprows=2)\n",
    "        \n",
    "        # Process the DataFrame\n",
    "        processed_df = process_dataframe(df)\n",
    "        print(df.head())\n",
    "        \n",
    "        # Save the processed DataFrame\n",
    "        save_path = os.path.join(save_folder, f\"{file}\")\n",
    "        processed_df.to_csv(save_path, index=False)\n",
    "        \n",
    "        \"\"\" \n",
    "        try: \n",
    "            processed_df.to_csv(save_path, index=False)\n",
    "            print(f\"Saved processed file to: {save_path}\")\n",
    "        except OSError:\n",
    "            print(f\"Could not save {file} in {file_path}\")\n",
    "        \"\"\""
   ]
  }
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