|
7 | 7 | "metadata": {}, |
8 | 8 | "outputs": [], |
9 | 9 | "source": [ |
10 | | - "import pandas as pd\n", |
11 | 10 | "import os\n", |
12 | | - "from pathlib import Path" |
| 11 | + "from pathlib import Path\n", |
| 12 | + "\n", |
| 13 | + "import pandas as pd" |
13 | 14 | ] |
14 | 15 | }, |
15 | 16 | { |
|
98 | 99 | "outputs": [], |
99 | 100 | "source": [ |
100 | 101 | "reference_df = pd.read_csv(\n", |
101 | | - " Path(TUTORIAL_DIR) / \"resources/study_df.csv\",\n", |
102 | | - " index_col=0,\n", |
103 | | - " parse_dates=True\n", |
104 | | - ") " |
| 102 | + " Path(TUTORIAL_DIR) / \"resources/study_df.csv\", index_col=0, parse_dates=True\n", |
| 103 | + ")" |
105 | 104 | ] |
106 | 105 | }, |
107 | 106 | { |
|
137 | 136 | "source": [ |
138 | 137 | "df_to_combitimetable(\n", |
139 | 138 | " df=reference_df.loc[\"2018-03-22\":\"2018-03-23\"],\n", |
140 | | - " filename=\"resources/boundary_temp.txt\"\n", |
| 139 | + " filename=\"resources/boundary_temp.txt\",\n", |
141 | 140 | ")" |
142 | 141 | ] |
143 | 142 | }, |
|
215 | 214 | "outputs": [], |
216 | 215 | "source": [ |
217 | 216 | "simulation_opt = {\n", |
218 | | - " \"startTime\": second_index[0],\n", |
219 | | - " \"stopTime\": second_index[-1],\n", |
220 | | - " \"stepSize\": 300,\n", |
221 | | - " \"tolerance\": 1e-06,\n", |
222 | | - " \"solver\": \"dassl\",\n", |
223 | | - " \"outputFormat\": \"csv\"\n", |
| 217 | + " \"startTime\": second_index[0],\n", |
| 218 | + " \"stopTime\": second_index[-1],\n", |
| 219 | + " \"stepSize\": 300,\n", |
| 220 | + " \"tolerance\": 1e-06,\n", |
| 221 | + " \"solver\": \"dassl\",\n", |
| 222 | + " \"outputFormat\": \"csv\",\n", |
224 | 223 | "}" |
225 | 224 | ] |
226 | 225 | }, |
|
239 | 238 | "metadata": {}, |
240 | 239 | "outputs": [], |
241 | 240 | "source": [ |
242 | | - "output_list = [\n", |
243 | | - " \"T_coat_ins.T\",\n", |
244 | | - " \"T_ins_ins.T\",\n", |
245 | | - " \"Tw_out.T\"\n", |
246 | | - "]" |
| 241 | + "output_list = [\"T_coat_ins.T\", \"T_ins_ins.T\", \"Tw_out.T\"]" |
247 | 242 | ] |
248 | 243 | }, |
249 | 244 | { |
|
308 | 303 | " \"Tins1_init\": 19.70 + 273.15,\n", |
309 | 304 | " \"Tins2_init\": 10.56 + 273.15,\n", |
310 | 305 | " \"Tcoat_init\": 6.4 + 273.15,\n", |
311 | | - " 'Lambda_ins.k': 0.04,\n", |
| 306 | + " \"Lambda_ins.k\": 0.04,\n", |
312 | 307 | "}" |
313 | 308 | ] |
314 | 309 | }, |
|
333 | 328 | "outputs": [], |
334 | 329 | "source": [ |
335 | 330 | "init_res_OM = simu_OM.simulate(\n", |
336 | | - " simflags = \"-initialStepSize=60 -maxStepSize=3600 -w -lv=LOG_STATS\",\n", |
| 331 | + " simflags=\"-initialStepSize=60 -maxStepSize=3600 -w -lv=LOG_STATS\",\n", |
337 | 332 | " parameter_dict=parameter_dict_OM,\n", |
338 | 333 | " year=2024,\n", |
339 | 334 | ")" |
|
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