|
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 | }, |
|
164 | 163 | "metadata": {}, |
165 | 164 | "outputs": [], |
166 | 165 | "source": [ |
167 | | - "output_list = [\n", |
168 | | - " \"T_coat_ins.T\",\n", |
169 | | - " \"T_ins_ins.T\",\n", |
170 | | - " \"Tw_out.T\"\n", |
171 | | - "]" |
| 166 | + "output_list = [\"T_coat_ins.T\", \"T_ins_ins.T\", \"Tw_out.T\"]" |
172 | 167 | ] |
173 | 168 | }, |
174 | 169 | { |
|
277 | 272 | "outputs": [], |
278 | 273 | "source": [ |
279 | 274 | "simulation_opt = {\n", |
280 | | - " \"startTime\": second_index[0],\n", |
281 | | - " \"stopTime\": second_index[-1],\n", |
282 | | - " \"stepSize\": 300,\n", |
283 | | - " \"tolerance\": 1e-06,\n", |
284 | | - " \"solver\": \"dassl\",\n", |
285 | | - " \"outputFormat\": \"csv\"\n", |
| 275 | + " \"startTime\": second_index[0],\n", |
| 276 | + " \"stopTime\": second_index[-1],\n", |
| 277 | + " \"stepSize\": 300,\n", |
| 278 | + " \"tolerance\": 1e-06,\n", |
| 279 | + " \"solver\": \"dassl\",\n", |
| 280 | + " \"outputFormat\": \"csv\",\n", |
286 | 281 | "}" |
287 | 282 | ] |
288 | 283 | }, |
|
314 | 309 | " \"Tins1_init\": 19.70 + 273.15,\n", |
315 | 310 | " \"Tins2_init\": 10.56 + 273.15,\n", |
316 | 311 | " \"Tcoat_init\": 6.4 + 273.15,\n", |
317 | | - " 'Lambda_ins.k': 0.04,\n", |
| 312 | + " \"Lambda_ins.k\": 0.04,\n", |
318 | 313 | "}" |
319 | 314 | ] |
320 | 315 | }, |
|
341 | 336 | "outputs": [], |
342 | 337 | "source": [ |
343 | 338 | "init_res_OM = simu_OM.simulate(\n", |
344 | | - " simflags = \"-initialStepSize=60 -maxStepSize=3600 -w -lv=LOG_STATS\",\n", |
| 339 | + " simflags=\"-initialStepSize=60 -maxStepSize=3600 -w -lv=LOG_STATS\",\n", |
345 | 340 | " parameter_dict=parameter_dict_OM,\n", |
346 | 341 | " x=reference_df,\n", |
347 | 342 | " year=2024,\n", |
|
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