| blob | 169a7b02dd0ed698620c247c3371d6f50a5dc4c1 |
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": null,
6 "id": "9ddd1d89-abdb-4627-a0ca-23db006b62f4",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import yaml\n",
11 "import pickle\n",
12 "import os.path as osp\n",
13 "import subprocess\n",
14 "from urllib.parse import urlparse\n",
15 "import numpy as np\n",
16 "import matplotlib.pyplot as plt\n",
17 "from utils import time_intp, str2time, filter_nan_values, read_pkl, read_yml"
18 ]
19 },
20 {
21 "cell_type": "code",
22 "execution_count": null,
23 "id": "3c141ad1-b997-485f-a4a7-d0c1ee79eb09",
24 "metadata": {},
25 "outputs": [],
26 "source": []
27 },
28 {
29 "cell_type": "markdown",
30 "id": "609ea544-ed92-40a6-892b-1943e9f6f620",
31 "metadata": {},
32 "source": [
33 "## Setup"
34 ]
35 },
36 {
37 "cell_type": "code",
38 "execution_count": null,
39 "id": "41b0d403-7d6b-44f4-963f-8dc492ae0126",
40 "metadata": {},
41 "outputs": [],
42 "source": [
43 "retrieve_url(\"https://demo.openwfm.org/web/data/fmda/dicts/fmda_nw_202401-05_f05.pkl\", \"data/fmda_nw_202401-05_f05.pkl\")"
44 ]
45 },
46 {
47 "cell_type": "code",
48 "execution_count": null,
49 "id": "5a2972e2-e360-43b8-9706-01aee586096c",
50 "metadata": {},
51 "outputs": [],
52 "source": []
53 },
54 {
55 "cell_type": "code",
56 "execution_count": null,
57 "id": "e69e37b9-73ef-45a1-9738-844f26dc3323",
58 "metadata": {},
59 "outputs": [],
60 "source": [
61 "data_params = read_yml(\"params_data.yaml\")\n",
62 "data_params"
63 ]
64 },
65 {
66 "cell_type": "code",
67 "execution_count": null,
68 "id": "6b5c3c82-84ba-426c-b8d9-f540b5026158",
69 "metadata": {},
70 "outputs": [],
71 "source": [
72 "dat = read_pkl(\"data/test_CA_202401.pkl\")"
73 ]
74 },
75 {
76 "cell_type": "code",
77 "execution_count": null,
78 "id": "5b2aeecb-89e6-41d0-af88-59a07b929edc",
79 "metadata": {},
80 "outputs": [],
81 "source": [
82 "dat['NV040_202401']['RAWS']['fm']"
83 ]
84 },
85 {
86 "cell_type": "markdown",
87 "id": "dae0e47b-02eb-4759-9b95-3cc1b281d41e",
88 "metadata": {},
89 "source": [
90 "## Filters"
91 ]
92 },
93 {
94 "cell_type": "code",
95 "execution_count": null,
96 "id": "8d6fbd18-adad-4370-add7-164275fc010d",
97 "metadata": {},
98 "outputs": [],
99 "source": [
100 "def filter_fmc(x, data_params=data_params):\n",
101 " # Vector to track which values of input x are set to na\n",
102 " na_vec = np.zeros_like(x)\n",
103 " \n",
104 " # Filter extreme lows\n",
105 " condition = x<= data_params['min_fm']\n",
106 " x[condition] = np.nan\n",
107 " na_vec[np.where(condition)] = 1\n",
108 " \n",
109 " # Filter extreme highs\n",
110 " condition = x>= data_params['max_fm']\n",
111 " x[condition] = np.nan\n",
112 " na_vec[condition] = 1\n",
113 " \n",
114 " return x, na_vec"
115 ]
116 },
117 {
118 "cell_type": "code",
119 "execution_count": null,
120 "id": "e49e7951-228c-44ee-ae54-d941e9a350f2",
121 "metadata": {},
122 "outputs": [],
123 "source": [
124 "def filter_rain(r, data_params=data_params):\n",
125 " # Vector to track which values of input x are set to na\n",
126 " na_vec = np.zeros_like(r)\n",
127 " \n",
128 " # Filter extreme lows\n",
129 " condition = r< data_params['min_rain']\n",
130 " r[condition] = np.nan\n",
131 " na_vec[np.where(condition)] = 1\n",
132 " \n",
133 " # Filter extreme highs\n",
134 " condition = r>= data_params['max_rain']\n",
135 " r[condition] = np.nan\n",
136 " na_vec[condition] = 1 \n",
137 "\n",
138 " return r, na_vec"
139 ]
140 },
141 {
142 "cell_type": "code",
143 "execution_count": null,
144 "id": "957c0a4f-874c-4cf7-ad73-45053c2fdecc",
145 "metadata": {},
146 "outputs": [],
147 "source": [
148 "def filter_wind(w, data_params=data_params):\n",
149 " # Vector to track which values of input x are set to na\n",
150 " na_vec = np.zeros_like(w)\n",
151 " \n",
152 " # Filter extreme lows\n",
153 " condition = w< data_params['min_wind']\n",
154 " w[condition] = np.nan\n",
155 " na_vec[np.where(condition)] = 1\n",
156 " \n",
157 " # Filter extreme highs\n",
158 " condition = w>= data_params['max_wind']\n",
159 " w[condition] = np.nan\n",
160 " na_vec[condition] = 1 \n",
161 "\n",
162 " return w, na_vec"
163 ]
164 },
165 {
166 "cell_type": "code",
167 "execution_count": null,
168 "id": "7b6b4347-6abe-4c21-8318-06a766d67d21",
169 "metadata": {},
170 "outputs": [],
171 "source": [
172 "# Useful Cases:\n",
173 " # NV040_202401: more raws observations than HRRR, interp should shorten\n",
174 " # NV026_202401: raws 10min obs, interp should shorten\n",
175 " # CGVC1_202401: missing only a few observations, interp should lengthen\n",
176 " # YNWC1_202401: only 2 observations, should be filtered entirely"
177 ]
178 },
179 {
180 "cell_type": "code",
181 "execution_count": null,
182 "id": "74d3b25a-6803-41c5-855b-92ed980d3fcb",
183 "metadata": {},
184 "outputs": [],
185 "source": [
186 "def time_intp(t1, v1, t2):\n",
187 " # Check if t1 v1 t2 are 1D arrays\n",
188 " if t1.ndim != 1:\n",
189 " logging.error(\"Error: t1 is not a 1D array. Dimension: %s\", t1.ndim)\n",
190 " return None\n",
191 " if v1.ndim != 1:\n",
192 " logging.error(\"Error: v1 is not a 1D array. Dimension %s:\", v1.ndim)\n",
193 " return None\n",
194 " if t2.ndim != 1:\n",
195 " logging.errorr(\"Error: t2 is not a 1D array. Dimension: %s\", t2.ndim)\n",
196 " return None\n",
197 " # Check if t1 and v1 have the same length\n",
198 " if len(t1) != len(v1):\n",
199 " logging.error(\"Error: t1 and v1 have different lengths: %s %s\",len(t1),len(v1))\n",
200 " return None\n",
201 " t1_no_nan, v1_no_nan = filter_nan_values(t1, v1)\n",
202 " # print('t1_no_nan.dtype=',t1_no_nan.dtype)\n",
203 " # Convert datetime objects to timestamps\n",
204 " t1_stamps = np.array([t.timestamp() for t in t1_no_nan])\n",
205 " t2_stamps = np.array([t.timestamp() for t in t2])\n",
206 " "
207 ]
208 },
209 {
210 "cell_type": "code",
211 "execution_count": null,
212 "id": "2e1a9438-3bfa-4c6d-8f4e-2888409ecb75",
213 "metadata": {},
214 "outputs": [],
215 "source": [
216 "len(dat.keys())"
217 ]
218 },
219 {
220 "cell_type": "code",
221 "execution_count": null,
222 "id": "f7e77394-dea3-4b24-82ed-6ce3c7dab21e",
223 "metadata": {},
224 "outputs": [],
225 "source": [
226 "def filter_nan_cases(d, data_params=data_params):\n",
227 " # Remove cases with too many NAN relative to interp target. Use to avoid over-interpolating\n",
228 " ks = [] # accumulate keys to remove\n",
229 " for k in dat:\n",
230 " n_fm = dat[k]['RAWS']['fm'].shape[0]\n",
231 " n_obs = dat[k]['HRRR']['time'].shape[0]\n",
232 " pct_na = (1 - n_fm / n_obs)\n",
233 " if pct_na > data_params['max_pct_na']:\n",
234 " print(f\"Removing key {k} due to extensive missing data. Percent NA relative to HRRR: {np.round(pct_na, 3)}\")\n",
235 " ks.append(k)\n",
236 " for k in ks: d.pop(k, None)"
237 ]
238 },
239 {
240 "cell_type": "code",
241 "execution_count": null,
242 "id": "faffc4ea-1b96-4ab4-af44-e2555eba39e1",
243 "metadata": {},
244 "outputs": [],
245 "source": [
246 "filter_nan_cases(dat)"
247 ]
248 },
249 {
250 "cell_type": "code",
251 "execution_count": null,
252 "id": "ac4c695e-bcc6-4e53-b934-f3837dac70ca",
253 "metadata": {},
254 "outputs": [],
255 "source": [
256 "len(dat.keys())"
257 ]
258 },
259 {
260 "cell_type": "code",
261 "execution_count": null,
262 "id": "c3fb7817-82b4-49ee-8c62-ef7308648c02",
263 "metadata": {},
264 "outputs": [],
265 "source": []
266 },
267 {
268 "cell_type": "code",
269 "execution_count": null,
270 "id": "a9f0b53b-b291-4592-ab0a-d05a6d2f770c",
271 "metadata": {},
272 "outputs": [],
273 "source": []
274 },
275 {
276 "cell_type": "code",
277 "execution_count": null,
278 "id": "9b7af652-5836-480f-861c-09bacaf035ea",
279 "metadata": {},
280 "outputs": [],
281 "source": [
282 "d = dat['NV026_202401']"
283 ]
284 },
285 {
286 "cell_type": "code",
287 "execution_count": null,
288 "id": "c5d7d2d6-cbae-402c-8585-a48ceba8f431",
289 "metadata": {},
290 "outputs": [],
291 "source": [
292 "d['RAWS']['fm'].shape"
293 ]
294 },
295 {
296 "cell_type": "code",
297 "execution_count": null,
298 "id": "ceb44038-b166-4827-a9ab-f62f51971c39",
299 "metadata": {},
300 "outputs": [],
301 "source": [
302 "d['RAWS']['time_raws'].shape"
303 ]
304 },
305 {
306 "cell_type": "code",
307 "execution_count": null,
308 "id": "481bb0b6-b5de-4244-89bc-24b6073e5cd4",
309 "metadata": {},
310 "outputs": [],
311 "source": [
312 "d['HRRR']['f01']['Ed'].shape"
313 ]
314 },
315 {
316 "cell_type": "code",
317 "execution_count": null,
318 "id": "88d3d4b6-c482-4158-ae13-daf729c2a496",
319 "metadata": {},
320 "outputs": [],
321 "source": [
322 "d['HRRR']['time'].shape"
323 ]
324 },
325 {
326 "cell_type": "code",
327 "execution_count": null,
328 "id": "d834581e-eb4a-494f-894a-5c81ff92bf85",
329 "metadata": {},
330 "outputs": [],
331 "source": [
332 "from utils import str2time\n",
333 "d = dat['NV040_202401']\n",
334 "time_raws=str2time(d['RAWS']['time_raws']) \n",
335 "time_hrrr=str2time(d['HRRR']['time'])\n",
336 "\n",
337 "t1 = np.array([t.timestamp() for t in time_raws])\n",
338 "t2 = np.array([t.timestamp() for t in time_hrrr])"
339 ]
340 },
341 {
342 "cell_type": "code",
343 "execution_count": null,
344 "id": "266ac8f6-119b-4424-b162-9077ab76b8e4",
345 "metadata": {},
346 "outputs": [],
347 "source": [
348 "tnew = np.interp(t2, t1, t1)"
349 ]
350 },
351 {
352 "cell_type": "code",
353 "execution_count": null,
354 "id": "1950759a-a332-4054-aee1-b086fbf4c39c",
355 "metadata": {},
356 "outputs": [],
357 "source": [
358 "np.isin(tnew, t1).mean()"
359 ]
360 },
361 {
362 "cell_type": "code",
363 "execution_count": null,
364 "id": "8b93c8ae-cfbb-4344-beda-5d3f60ed8ed9",
365 "metadata": {},
366 "outputs": [],
367 "source": []
368 },
369 {
370 "cell_type": "code",
371 "execution_count": null,
372 "id": "1c9a2263-9ec2-4c7e-9c79-90f8209a4c87",
373 "metadata": {},
374 "outputs": [],
375 "source": []
376 },
377 {
378 "cell_type": "code",
379 "execution_count": null,
380 "id": "9e2a78e5-330a-49ce-9dc9-babcb580d0d4",
381 "metadata": {},
382 "outputs": [],
383 "source": [
384 "d = dat['LIB03_202401']"
385 ]
386 },
387 {
388 "cell_type": "code",
389 "execution_count": null,
390 "id": "08b4c474-4332-4b45-9110-d8a21432cdf9",
391 "metadata": {},
392 "outputs": [],
393 "source": [
394 "d['RAWS']['fm'].shape"
395 ]
396 },
397 {
398 "cell_type": "code",
399 "execution_count": null,
400 "id": "1c12c514-37db-4b13-a35d-bd28651c00b4",
401 "metadata": {},
402 "outputs": [],
403 "source": [
404 "d['RAWS']['time_raws'].shape"
405 ]
406 },
407 {
408 "cell_type": "code",
409 "execution_count": null,
410 "id": "9846b2ae-38a8-4f0b-96a6-1c21d0ecb616",
411 "metadata": {},
412 "outputs": [],
413 "source": [
414 "d['HRRR']['f01']['Ed'].shape"
415 ]
416 },
417 {
418 "cell_type": "code",
419 "execution_count": null,
420 "id": "614fe317-8233-4dbf-97c8-c2d8d8923fea",
421 "metadata": {},
422 "outputs": [],
423 "source": [
424 "d['HRRR']['time'].shape"
425 ]
426 },
427 {
428 "cell_type": "code",
429 "execution_count": null,
430 "id": "40be5a62-06ec-4623-9684-bf186c3bcd1d",
431 "metadata": {},
432 "outputs": [],
433 "source": []
434 },
435 {
436 "cell_type": "code",
437 "execution_count": null,
438 "id": "081e0202-2bc0-4bd8-b4ab-f5eec289b70c",
439 "metadata": {},
440 "outputs": [],
441 "source": []
442 },
443 {
444 "cell_type": "code",
445 "execution_count": null,
446 "id": "31a1d814-9028-4213-ad9d-fabf1e35b554",
447 "metadata": {},
448 "outputs": [],
449 "source": []
450 },
451 {
452 "cell_type": "code",
453 "execution_count": null,
454 "id": "42de9659-3725-4f92-b17c-455126e89c47",
455 "metadata": {},
456 "outputs": [],
457 "source": []
458 },
459 {
460 "cell_type": "code",
461 "execution_count": null,
462 "id": "37cb45e7-b46b-429b-a9be-6c3a35677a28",
463 "metadata": {},
464 "outputs": [],
465 "source": []
466 },
467 {
468 "cell_type": "code",
469 "execution_count": null,
470 "id": "3f007941-6682-4022-9b78-48c214152635",
471 "metadata": {},
472 "outputs": [],
473 "source": []
474 },
475 {
476 "cell_type": "code",
477 "execution_count": null,
478 "id": "20687996-8267-4e93-9e24-b9515259e256",
479 "metadata": {},
480 "outputs": [],
481 "source": []
482 },
483 {
484 "cell_type": "code",
485 "execution_count": null,
486 "id": "55ce3b0e-7135-43ff-86ab-379c50b349db",
487 "metadata": {},
488 "outputs": [],
489 "source": [
490 "time_raws = str2time(dat['NV040_202401']['RAWS']['time_raws'])\n",
491 "time_hrrr = str2time(dat['NV040_202401']['HRRR']['time'])\n",
492 "fm = dat['NV040_202401']['RAWS']['fm']\n",
493 "rain = dat['NV040_202401']['HRRR']['f01']['rain']"
494 ]
495 },
496 {
497 "cell_type": "code",
498 "execution_count": null,
499 "id": "cc4fd0b0-286b-4cd9-88c4-59e996348235",
500 "metadata": {},
501 "outputs": [],
502 "source": [
503 "len(time_raws) == len(fm)"
504 ]
505 },
506 {
507 "cell_type": "code",
508 "execution_count": null,
509 "id": "1680ed39-a021-4631-a764-40f354cf6a09",
510 "metadata": {},
511 "outputs": [],
512 "source": [
513 "filter_fmc(fm)"
514 ]
515 },
516 {
517 "cell_type": "code",
518 "execution_count": null,
519 "id": "8a3c405a-f976-4756-aaec-63998d58203d",
520 "metadata": {},
521 "outputs": [],
522 "source": [
523 "filter_rain(rain)"
524 ]
525 },
526 {
527 "cell_type": "code",
528 "execution_count": null,
529 "id": "5f49d9d4-2edf-434e-8812-c8c6ef3fbb2f",
530 "metadata": {},
531 "outputs": [],
532 "source": [
533 "filter_wind(dat['NV040_202401']['HRRR']['f01']['wind'])"
534 ]
535 },
536 {
537 "cell_type": "code",
538 "execution_count": null,
539 "id": "3d6a8fe7-1864-475a-a4f3-0fe04806ad50",
540 "metadata": {},
541 "outputs": [],
542 "source": [
543 "dat['NV040_202401']['RAWS']['wind']"
544 ]
545 },
546 {
547 "cell_type": "code",
548 "execution_count": null,
549 "id": "25ad1ce4-2060-4ece-932a-396813e56dc6",
550 "metadata": {},
551 "outputs": [],
552 "source": [
553 "dat['CNFC1_202401']['RAWS'].keys()"
554 ]
555 },
556 {
557 "cell_type": "code",
558 "execution_count": null,
559 "id": "a58ca663-074d-4eb4-a644-c9e05a415e65",
560 "metadata": {},
561 "outputs": [],
562 "source": [
563 "dat['CNFC1_202401']['RAWS']['solar']"
564 ]
565 },
566 {
567 "cell_type": "code",
568 "execution_count": null,
569 "id": "c2f27bfb-73fc-4bc6-9abc-ac1f5ab46acd",
570 "metadata": {},
571 "outputs": [],
572 "source": [
573 "dat['CNFC1_202401']['HRRR']['f01']['wind']"
574 ]
575 },
576 {
577 "cell_type": "code",
578 "execution_count": null,
579 "id": "74b98be4-55b1-4db2-81de-5f650aa00bb2",
580 "metadata": {},
581 "outputs": [],
582 "source": [
583 "dat['CNFC1_202401']['HRRR']['f01']['soilm']"
584 ]
585 },
586 {
587 "cell_type": "code",
588 "execution_count": null,
589 "id": "915f9534-ff84-4844-bde4-dce567d222c5",
590 "metadata": {},
591 "outputs": [],
592 "source": []
593 },
594 {
595 "cell_type": "code",
596 "execution_count": null,
597 "id": "cd2f357d-c813-4aa0-b3f7-cdc72f1f660c",
598 "metadata": {},
599 "outputs": [],
600 "source": [
601 "for k in dat:\n",
602 " print(\"~\"*50)\n",
603 " print(k)\n",
604 " print(f\"HRRR Shape: {dat[k]['HRRR']['f01']['Ew'].shape}\")\n",
605 " print(f\"RAWS Shape: {dat[k]['RAWS']['fm'].shape}\")"
606 ]
607 },
608 {
609 "cell_type": "code",
610 "execution_count": null,
611 "id": "1ab4cb8b-1aa8-4541-a5cc-041c493d8894",
612 "metadata": {},
613 "outputs": [],
614 "source": [
615 "dat['LIB03_202401']['RAWS']['soil_moisture']"
616 ]
617 },
618 {
619 "cell_type": "code",
620 "execution_count": null,
621 "id": "f62a897e-eeb8-4951-b64f-8b6ee11bc92a",
622 "metadata": {},
623 "outputs": [],
624 "source": [
625 "dat['LIB03_202401']['HRRR']['f01']['soilm']"
626 ]
627 },
628 {
629 "cell_type": "code",
630 "execution_count": null,
631 "id": "5037b7f3-a749-4b27-9e4c-f6c8d4fb8b6d",
632 "metadata": {},
633 "outputs": [],
634 "source": [
635 "for k in dat:\n",
636 " print(\"~\"*50)\n",
637 " print(k)\n",
638 " # Filter RAWS\n",
639 " fm, filter_vec = filter_fmc(dat[k][\"RAWS\"]['fm'])\n",
640 " print(f\"Percent FMC Observations Filtered: {np.mean(filter_vec)}\")\n",
641 " if 'rain' in dat[k][\"RAWS\"].keys():\n",
642 " rain, filter_vec = filter_rain(dat[k][\"RAWS\"]['rain'])\n",
643 " print(f\"Percent Rain Observations Filtered: {np.mean(filter_vec)}\")\n",
644 " if 'wind' in dat[k][\"RAWS\"].keys():\n",
645 " wind, filter_vec = filter_rain(dat[k][\"RAWS\"]['wind'])\n",
646 " print(f\"Percent Wind Observations Filtered: {np.mean(filter_vec)}\")\n",
647 " # Filter HRRR unncessesary? "
648 ]
649 },
650 {
651 "cell_type": "code",
652 "execution_count": null,
653 "id": "abd5985c-c727-44fb-a1d6-b212dc24d3ac",
654 "metadata": {},
655 "outputs": [],
656 "source": []
657 },
658 {
659 "cell_type": "code",
660 "execution_count": null,
661 "id": "3e6849f7-e983-446e-9d1b-26a1b4c693fd",
662 "metadata": {},
663 "outputs": [],
664 "source": []
665 },
666 {
667 "cell_type": "code",
668 "execution_count": null,
669 "id": "3b307ae1-980d-487a-aad4-f2fc0a87cbcf",
670 "metadata": {},
671 "outputs": [],
672 "source": []
673 },
674 {
675 "cell_type": "code",
676 "execution_count": null,
677 "id": "6d7b7335-589e-49bd-88bd-20b7b2a40611",
678 "metadata": {},
679 "outputs": [],
680 "source": []
681 },
682 {
683 "cell_type": "code",
684 "execution_count": null,
685 "id": "982de3ce-e883-4eab-88dd-41295da16e7f",
686 "metadata": {},
687 "outputs": [],
688 "source": []
689 },
690 {
691 "cell_type": "code",
692 "execution_count": null,
693 "id": "940e36a7-1597-4bac-8a82-3ecb602e124c",
694 "metadata": {},
695 "outputs": [],
696 "source": []
697 },
698 {
699 "cell_type": "code",
700 "execution_count": null,
701 "id": "b34546f9-5c28-4c29-9148-0473fa9e00c1",
702 "metadata": {},
703 "outputs": [],
704 "source": []
705 },
706 {
707 "cell_type": "code",
708 "execution_count": null,
709 "id": "81a421c8-ed47-40f2-83c2-d258bce764f4",
710 "metadata": {},
711 "outputs": [],
712 "source": []
713 },
714 {
715 "cell_type": "code",
716 "execution_count": null,
717 "id": "897f84d1-4ac8-478a-82f1-ed59052b0199",
718 "metadata": {},
719 "outputs": [],
720 "source": [
721 "def foo():\n",
722 " d = read_pkl()\n",
723 " dat = format_fmda_data()\n",
724 " dat = filter_data(dat)\n",
725 " return dat"
726 ]
727 },
728 {
729 "cell_type": "code",
730 "execution_count": null,
731 "id": "ed4112af-2b76-4b57-8d2e-1e5e0cba3b42",
732 "metadata": {},
733 "outputs": [],
734 "source": [
735 "def format_fmda_data(d):\n",
736 " # Inputs:\n",
737 " # d: (dict) fmda dictionary, output of process in wrfxpy\n",
738 "\n",
739 " return d"
740 ]
741 },
742 {
743 "cell_type": "code",
744 "execution_count": null,
745 "id": "b0ebb758-d1ec-477f-b6af-207dbe2a01d1",
746 "metadata": {},
747 "outputs": [],
748 "source": []
749 },
750 {
751 "cell_type": "code",
752 "execution_count": null,
753 "id": "e4d704f9-cb10-4b3a-a65d-cf1a26ddb9d4",
754 "metadata": {},
755 "outputs": [],
756 "source": []
757 },
758 {
759 "cell_type": "code",
760 "execution_count": null,
761 "id": "c5972b2b-eab2-4705-8401-6aa746203ac1",
762 "metadata": {},
763 "outputs": [],
764 "source": []
765 },
766 {
767 "cell_type": "code",
768 "execution_count": null,
769 "id": "0818c399-be45-4520-9b9e-d847a73ea1bf",
770 "metadata": {},
771 "outputs": [],
772 "source": []
773 },
774 {
775 "cell_type": "code",
776 "execution_count": null,
777 "id": "94ad78d5-47a0-4c9e-abe8-9f3cfc67208f",
778 "metadata": {},
779 "outputs": [],
780 "source": []
781 }
782 ],
783 "metadata": {
784 "kernelspec": {
785 "display_name": "Python 3 (ipykernel)",
786 "language": "python",
787 "name": "python3"
788 },
789 "language_info": {
790 "codemirror_mode": {
791 "name": "ipython",
792 "version": 3
793 },
794 "file_extension": ".py",
795 "mimetype": "text/x-python",
796 "name": "python",
797 "nbconvert_exporter": "python",
798 "pygments_lexer": "ipython3",
799 "version": "3.12.5"
800 }
801 },
802 "nbformat": 4,
803 "nbformat_minor": 5
804 }