| blob | 206de0d0da6a5de7ecebf785ebca8db89dc1d3cd |
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": "8ef14e43-030f-422e-a3a6-e9802a3df3ac",
40 "metadata": {},
41 "outputs": [],
42 "source": [
43 "def retrieve_url(url, dest_path, force_download=False):\n",
44 " if not osp.exists(dest_path) or force_download:\n",
45 " target_extension = osp.splitext(dest_path)[1]\n",
46 " url_extension = osp.splitext(urlparse(url).path)[1]\n",
47 " if target_extension != url_extension:\n",
48 " print(\"Warning: file extension from url does not match destination file extension\")\n",
49 " subprocess.call(f\"wget -O {dest_path} {url}\", shell=True)\n",
50 " assert osp.exists(dest_path)\n",
51 " print(f\"Successfully downloaded {url} to {dest_path}\")\n",
52 " else:\n",
53 " print(f\"Target data already exists at {dest_path}\")"
54 ]
55 },
56 {
57 "cell_type": "code",
58 "execution_count": null,
59 "id": "41b0d403-7d6b-44f4-963f-8dc492ae0126",
60 "metadata": {},
61 "outputs": [],
62 "source": [
63 "retrieve_url(\"https://demo.openwfm.org/web/data/fmda/dicts/fmda_nw_202401-05_f05.pkl\", \"data/fmda_nw_202401-05_f05.pkl\")"
64 ]
65 },
66 {
67 "cell_type": "code",
68 "execution_count": null,
69 "id": "5a2972e2-e360-43b8-9706-01aee586096c",
70 "metadata": {},
71 "outputs": [],
72 "source": []
73 },
74 {
75 "cell_type": "code",
76 "execution_count": null,
77 "id": "e69e37b9-73ef-45a1-9738-844f26dc3323",
78 "metadata": {},
79 "outputs": [],
80 "source": [
81 "data_params = read_yml(\"params_data.yaml\")\n",
82 "data_params"
83 ]
84 },
85 {
86 "cell_type": "code",
87 "execution_count": null,
88 "id": "6b5c3c82-84ba-426c-b8d9-f540b5026158",
89 "metadata": {},
90 "outputs": [],
91 "source": [
92 "dat = read_pkl(\"data/test_CA_202401.pkl\")"
93 ]
94 },
95 {
96 "cell_type": "code",
97 "execution_count": null,
98 "id": "5b2aeecb-89e6-41d0-af88-59a07b929edc",
99 "metadata": {},
100 "outputs": [],
101 "source": [
102 "dat['NV040_202401']['RAWS']['fm']"
103 ]
104 },
105 {
106 "cell_type": "markdown",
107 "id": "dae0e47b-02eb-4759-9b95-3cc1b281d41e",
108 "metadata": {},
109 "source": [
110 "## Filters"
111 ]
112 },
113 {
114 "cell_type": "code",
115 "execution_count": null,
116 "id": "8d6fbd18-adad-4370-add7-164275fc010d",
117 "metadata": {},
118 "outputs": [],
119 "source": [
120 "def filter_fmc(x, data_params=data_params):\n",
121 " # Vector to track which values of input x are set to na\n",
122 " na_vec = np.zeros_like(x)\n",
123 " \n",
124 " # Filter extreme lows\n",
125 " condition = x<= data_params['min_fm']\n",
126 " x[condition] = np.nan\n",
127 " na_vec[np.where(condition)] = 1\n",
128 " \n",
129 " # Filter extreme highs\n",
130 " condition = x>= data_params['max_fm']\n",
131 " x[condition] = np.nan\n",
132 " na_vec[condition] = 1\n",
133 " \n",
134 " return x, na_vec"
135 ]
136 },
137 {
138 "cell_type": "code",
139 "execution_count": null,
140 "id": "e49e7951-228c-44ee-ae54-d941e9a350f2",
141 "metadata": {},
142 "outputs": [],
143 "source": [
144 "def filter_rain(r, data_params=data_params):\n",
145 " # Vector to track which values of input x are set to na\n",
146 " na_vec = np.zeros_like(r)\n",
147 " \n",
148 " # Filter extreme lows\n",
149 " condition = r< data_params['min_rain']\n",
150 " r[condition] = np.nan\n",
151 " na_vec[np.where(condition)] = 1\n",
152 " \n",
153 " # Filter extreme highs\n",
154 " condition = r>= data_params['max_rain']\n",
155 " r[condition] = np.nan\n",
156 " na_vec[condition] = 1 \n",
157 "\n",
158 " return r, na_vec"
159 ]
160 },
161 {
162 "cell_type": "code",
163 "execution_count": null,
164 "id": "957c0a4f-874c-4cf7-ad73-45053c2fdecc",
165 "metadata": {},
166 "outputs": [],
167 "source": [
168 "def filter_wind(w, data_params=data_params):\n",
169 " # Vector to track which values of input x are set to na\n",
170 " na_vec = np.zeros_like(w)\n",
171 " \n",
172 " # Filter extreme lows\n",
173 " condition = w< data_params['min_wind']\n",
174 " w[condition] = np.nan\n",
175 " na_vec[np.where(condition)] = 1\n",
176 " \n",
177 " # Filter extreme highs\n",
178 " condition = w>= data_params['max_wind']\n",
179 " w[condition] = np.nan\n",
180 " na_vec[condition] = 1 \n",
181 "\n",
182 " return w, na_vec"
183 ]
184 },
185 {
186 "cell_type": "code",
187 "execution_count": null,
188 "id": "7b6b4347-6abe-4c21-8318-06a766d67d21",
189 "metadata": {},
190 "outputs": [],
191 "source": [
192 "# Useful Cases:\n",
193 " # NV040_202401: more raws observations than HRRR, interp should shorten\n",
194 " # NV026_202401: raws 10min obs, interp should shorten\n",
195 " # CGVC1_202401: missing only a few observations, interp should lengthen\n",
196 " # YNWC1_202401: only 2 observations, should be filtered entirely"
197 ]
198 },
199 {
200 "cell_type": "code",
201 "execution_count": null,
202 "id": "74d3b25a-6803-41c5-855b-92ed980d3fcb",
203 "metadata": {},
204 "outputs": [],
205 "source": [
206 "def time_intp(t1, v1, t2):\n",
207 " # Check if t1 v1 t2 are 1D arrays\n",
208 " if t1.ndim != 1:\n",
209 " logging.error(\"Error: t1 is not a 1D array. Dimension: %s\", t1.ndim)\n",
210 " return None\n",
211 " if v1.ndim != 1:\n",
212 " logging.error(\"Error: v1 is not a 1D array. Dimension %s:\", v1.ndim)\n",
213 " return None\n",
214 " if t2.ndim != 1:\n",
215 " logging.errorr(\"Error: t2 is not a 1D array. Dimension: %s\", t2.ndim)\n",
216 " return None\n",
217 " # Check if t1 and v1 have the same length\n",
218 " if len(t1) != len(v1):\n",
219 " logging.error(\"Error: t1 and v1 have different lengths: %s %s\",len(t1),len(v1))\n",
220 " return None\n",
221 " t1_no_nan, v1_no_nan = filter_nan_values(t1, v1)\n",
222 " # print('t1_no_nan.dtype=',t1_no_nan.dtype)\n",
223 " # Convert datetime objects to timestamps\n",
224 " t1_stamps = np.array([t.timestamp() for t in t1_no_nan])\n",
225 " t2_stamps = np.array([t.timestamp() for t in t2])\n",
226 " "
227 ]
228 },
229 {
230 "cell_type": "code",
231 "execution_count": null,
232 "id": "2e1a9438-3bfa-4c6d-8f4e-2888409ecb75",
233 "metadata": {},
234 "outputs": [],
235 "source": [
236 "len(dat.keys())"
237 ]
238 },
239 {
240 "cell_type": "code",
241 "execution_count": null,
242 "id": "f7e77394-dea3-4b24-82ed-6ce3c7dab21e",
243 "metadata": {},
244 "outputs": [],
245 "source": [
246 "def filter_nan_cases(d, data_params=data_params):\n",
247 " # Remove cases with too many NAN relative to interp target. Use to avoid over-interpolating\n",
248 " ks = [] # accumulate keys to remove\n",
249 " for k in dat:\n",
250 " n_fm = dat[k]['RAWS']['fm'].shape[0]\n",
251 " n_obs = dat[k]['HRRR']['time'].shape[0]\n",
252 " pct_na = (1 - n_fm / n_obs)\n",
253 " if pct_na > data_params['max_pct_na']:\n",
254 " print(f\"Removing key {k} due to extensive missing data. Percent NA relative to HRRR: {np.round(pct_na, 3)}\")\n",
255 " ks.append(k)\n",
256 " for k in ks: d.pop(k, None)"
257 ]
258 },
259 {
260 "cell_type": "code",
261 "execution_count": null,
262 "id": "faffc4ea-1b96-4ab4-af44-e2555eba39e1",
263 "metadata": {},
264 "outputs": [],
265 "source": [
266 "filter_nan_cases(dat)"
267 ]
268 },
269 {
270 "cell_type": "code",
271 "execution_count": null,
272 "id": "ac4c695e-bcc6-4e53-b934-f3837dac70ca",
273 "metadata": {},
274 "outputs": [],
275 "source": [
276 "len(dat.keys())"
277 ]
278 },
279 {
280 "cell_type": "code",
281 "execution_count": null,
282 "id": "c3fb7817-82b4-49ee-8c62-ef7308648c02",
283 "metadata": {},
284 "outputs": [],
285 "source": []
286 },
287 {
288 "cell_type": "code",
289 "execution_count": null,
290 "id": "a9f0b53b-b291-4592-ab0a-d05a6d2f770c",
291 "metadata": {},
292 "outputs": [],
293 "source": []
294 },
295 {
296 "cell_type": "code",
297 "execution_count": null,
298 "id": "9b7af652-5836-480f-861c-09bacaf035ea",
299 "metadata": {},
300 "outputs": [],
301 "source": [
302 "d = dat['NV026_202401']"
303 ]
304 },
305 {
306 "cell_type": "code",
307 "execution_count": null,
308 "id": "c5d7d2d6-cbae-402c-8585-a48ceba8f431",
309 "metadata": {},
310 "outputs": [],
311 "source": [
312 "d['RAWS']['fm'].shape"
313 ]
314 },
315 {
316 "cell_type": "code",
317 "execution_count": null,
318 "id": "ceb44038-b166-4827-a9ab-f62f51971c39",
319 "metadata": {},
320 "outputs": [],
321 "source": [
322 "d['RAWS']['time_raws'].shape"
323 ]
324 },
325 {
326 "cell_type": "code",
327 "execution_count": null,
328 "id": "481bb0b6-b5de-4244-89bc-24b6073e5cd4",
329 "metadata": {},
330 "outputs": [],
331 "source": [
332 "d['HRRR']['f01']['Ed'].shape"
333 ]
334 },
335 {
336 "cell_type": "code",
337 "execution_count": null,
338 "id": "88d3d4b6-c482-4158-ae13-daf729c2a496",
339 "metadata": {},
340 "outputs": [],
341 "source": [
342 "d['HRRR']['time'].shape"
343 ]
344 },
345 {
346 "cell_type": "code",
347 "execution_count": null,
348 "id": "d834581e-eb4a-494f-894a-5c81ff92bf85",
349 "metadata": {},
350 "outputs": [],
351 "source": [
352 "from utils import str2time\n",
353 "d = dat['NV040_202401']\n",
354 "time_raws=str2time(d['RAWS']['time_raws']) \n",
355 "time_hrrr=str2time(d['HRRR']['time'])\n",
356 "\n",
357 "t1 = np.array([t.timestamp() for t in time_raws])\n",
358 "t2 = np.array([t.timestamp() for t in time_hrrr])"
359 ]
360 },
361 {
362 "cell_type": "code",
363 "execution_count": null,
364 "id": "266ac8f6-119b-4424-b162-9077ab76b8e4",
365 "metadata": {},
366 "outputs": [],
367 "source": [
368 "tnew = np.interp(t2, t1, t1)"
369 ]
370 },
371 {
372 "cell_type": "code",
373 "execution_count": null,
374 "id": "1950759a-a332-4054-aee1-b086fbf4c39c",
375 "metadata": {},
376 "outputs": [],
377 "source": [
378 "np.isin(tnew, t1).mean()"
379 ]
380 },
381 {
382 "cell_type": "code",
383 "execution_count": null,
384 "id": "8b93c8ae-cfbb-4344-beda-5d3f60ed8ed9",
385 "metadata": {},
386 "outputs": [],
387 "source": []
388 },
389 {
390 "cell_type": "code",
391 "execution_count": null,
392 "id": "1c9a2263-9ec2-4c7e-9c79-90f8209a4c87",
393 "metadata": {},
394 "outputs": [],
395 "source": []
396 },
397 {
398 "cell_type": "code",
399 "execution_count": null,
400 "id": "9e2a78e5-330a-49ce-9dc9-babcb580d0d4",
401 "metadata": {},
402 "outputs": [],
403 "source": [
404 "d = dat['LIB03_202401']"
405 ]
406 },
407 {
408 "cell_type": "code",
409 "execution_count": null,
410 "id": "08b4c474-4332-4b45-9110-d8a21432cdf9",
411 "metadata": {},
412 "outputs": [],
413 "source": [
414 "d['RAWS']['fm'].shape"
415 ]
416 },
417 {
418 "cell_type": "code",
419 "execution_count": null,
420 "id": "1c12c514-37db-4b13-a35d-bd28651c00b4",
421 "metadata": {},
422 "outputs": [],
423 "source": [
424 "d['RAWS']['time_raws'].shape"
425 ]
426 },
427 {
428 "cell_type": "code",
429 "execution_count": null,
430 "id": "9846b2ae-38a8-4f0b-96a6-1c21d0ecb616",
431 "metadata": {},
432 "outputs": [],
433 "source": [
434 "d['HRRR']['f01']['Ed'].shape"
435 ]
436 },
437 {
438 "cell_type": "code",
439 "execution_count": null,
440 "id": "614fe317-8233-4dbf-97c8-c2d8d8923fea",
441 "metadata": {},
442 "outputs": [],
443 "source": [
444 "d['HRRR']['time'].shape"
445 ]
446 },
447 {
448 "cell_type": "code",
449 "execution_count": null,
450 "id": "40be5a62-06ec-4623-9684-bf186c3bcd1d",
451 "metadata": {},
452 "outputs": [],
453 "source": []
454 },
455 {
456 "cell_type": "code",
457 "execution_count": null,
458 "id": "081e0202-2bc0-4bd8-b4ab-f5eec289b70c",
459 "metadata": {},
460 "outputs": [],
461 "source": []
462 },
463 {
464 "cell_type": "code",
465 "execution_count": null,
466 "id": "31a1d814-9028-4213-ad9d-fabf1e35b554",
467 "metadata": {},
468 "outputs": [],
469 "source": []
470 },
471 {
472 "cell_type": "code",
473 "execution_count": null,
474 "id": "42de9659-3725-4f92-b17c-455126e89c47",
475 "metadata": {},
476 "outputs": [],
477 "source": []
478 },
479 {
480 "cell_type": "code",
481 "execution_count": null,
482 "id": "37cb45e7-b46b-429b-a9be-6c3a35677a28",
483 "metadata": {},
484 "outputs": [],
485 "source": []
486 },
487 {
488 "cell_type": "code",
489 "execution_count": null,
490 "id": "3f007941-6682-4022-9b78-48c214152635",
491 "metadata": {},
492 "outputs": [],
493 "source": []
494 },
495 {
496 "cell_type": "code",
497 "execution_count": null,
498 "id": "20687996-8267-4e93-9e24-b9515259e256",
499 "metadata": {},
500 "outputs": [],
501 "source": []
502 },
503 {
504 "cell_type": "code",
505 "execution_count": null,
506 "id": "55ce3b0e-7135-43ff-86ab-379c50b349db",
507 "metadata": {},
508 "outputs": [],
509 "source": [
510 "time_raws = str2time(dat['NV040_202401']['RAWS']['time_raws'])\n",
511 "time_hrrr = str2time(dat['NV040_202401']['HRRR']['time'])\n",
512 "fm = dat['NV040_202401']['RAWS']['fm']\n",
513 "rain = dat['NV040_202401']['HRRR']['f01']['rain']"
514 ]
515 },
516 {
517 "cell_type": "code",
518 "execution_count": null,
519 "id": "cc4fd0b0-286b-4cd9-88c4-59e996348235",
520 "metadata": {},
521 "outputs": [],
522 "source": [
523 "len(time_raws) == len(fm)"
524 ]
525 },
526 {
527 "cell_type": "code",
528 "execution_count": null,
529 "id": "1680ed39-a021-4631-a764-40f354cf6a09",
530 "metadata": {},
531 "outputs": [],
532 "source": [
533 "filter_fmc(fm)"
534 ]
535 },
536 {
537 "cell_type": "code",
538 "execution_count": null,
539 "id": "8a3c405a-f976-4756-aaec-63998d58203d",
540 "metadata": {},
541 "outputs": [],
542 "source": [
543 "filter_rain(rain)"
544 ]
545 },
546 {
547 "cell_type": "code",
548 "execution_count": null,
549 "id": "5f49d9d4-2edf-434e-8812-c8c6ef3fbb2f",
550 "metadata": {},
551 "outputs": [],
552 "source": [
553 "filter_wind(dat['NV040_202401']['HRRR']['f01']['wind'])"
554 ]
555 },
556 {
557 "cell_type": "code",
558 "execution_count": null,
559 "id": "3d6a8fe7-1864-475a-a4f3-0fe04806ad50",
560 "metadata": {},
561 "outputs": [],
562 "source": [
563 "dat['NV040_202401']['RAWS']['wind']"
564 ]
565 },
566 {
567 "cell_type": "code",
568 "execution_count": null,
569 "id": "25ad1ce4-2060-4ece-932a-396813e56dc6",
570 "metadata": {},
571 "outputs": [],
572 "source": [
573 "dat['CNFC1_202401']['RAWS'].keys()"
574 ]
575 },
576 {
577 "cell_type": "code",
578 "execution_count": null,
579 "id": "a58ca663-074d-4eb4-a644-c9e05a415e65",
580 "metadata": {},
581 "outputs": [],
582 "source": [
583 "dat['CNFC1_202401']['RAWS']['solar']"
584 ]
585 },
586 {
587 "cell_type": "code",
588 "execution_count": null,
589 "id": "c2f27bfb-73fc-4bc6-9abc-ac1f5ab46acd",
590 "metadata": {},
591 "outputs": [],
592 "source": [
593 "dat['CNFC1_202401']['HRRR']['f01']['wind']"
594 ]
595 },
596 {
597 "cell_type": "code",
598 "execution_count": null,
599 "id": "74b98be4-55b1-4db2-81de-5f650aa00bb2",
600 "metadata": {},
601 "outputs": [],
602 "source": [
603 "dat['CNFC1_202401']['HRRR']['f01']['soilm']"
604 ]
605 },
606 {
607 "cell_type": "code",
608 "execution_count": null,
609 "id": "915f9534-ff84-4844-bde4-dce567d222c5",
610 "metadata": {},
611 "outputs": [],
612 "source": []
613 },
614 {
615 "cell_type": "code",
616 "execution_count": null,
617 "id": "cd2f357d-c813-4aa0-b3f7-cdc72f1f660c",
618 "metadata": {},
619 "outputs": [],
620 "source": [
621 "for k in dat:\n",
622 " print(\"~\"*50)\n",
623 " print(k)\n",
624 " print(f\"HRRR Shape: {dat[k]['HRRR']['f01']['Ew'].shape}\")\n",
625 " print(f\"RAWS Shape: {dat[k]['RAWS']['fm'].shape}\")"
626 ]
627 },
628 {
629 "cell_type": "code",
630 "execution_count": null,
631 "id": "1ab4cb8b-1aa8-4541-a5cc-041c493d8894",
632 "metadata": {},
633 "outputs": [],
634 "source": [
635 "dat['LIB03_202401']['RAWS']['soil_moisture']"
636 ]
637 },
638 {
639 "cell_type": "code",
640 "execution_count": null,
641 "id": "f62a897e-eeb8-4951-b64f-8b6ee11bc92a",
642 "metadata": {},
643 "outputs": [],
644 "source": [
645 "dat['LIB03_202401']['HRRR']['f01']['soilm']"
646 ]
647 },
648 {
649 "cell_type": "code",
650 "execution_count": null,
651 "id": "5037b7f3-a749-4b27-9e4c-f6c8d4fb8b6d",
652 "metadata": {},
653 "outputs": [],
654 "source": [
655 "for k in dat:\n",
656 " print(\"~\"*50)\n",
657 " print(k)\n",
658 " # Filter RAWS\n",
659 " fm, filter_vec = filter_fmc(dat[k][\"RAWS\"]['fm'])\n",
660 " print(f\"Percent FMC Observations Filtered: {np.mean(filter_vec)}\")\n",
661 " if 'rain' in dat[k][\"RAWS\"].keys():\n",
662 " rain, filter_vec = filter_rain(dat[k][\"RAWS\"]['rain'])\n",
663 " print(f\"Percent Rain Observations Filtered: {np.mean(filter_vec)}\")\n",
664 " if 'wind' in dat[k][\"RAWS\"].keys():\n",
665 " wind, filter_vec = filter_rain(dat[k][\"RAWS\"]['wind'])\n",
666 " print(f\"Percent Wind Observations Filtered: {np.mean(filter_vec)}\")\n",
667 " # Filter HRRR unncessesary? "
668 ]
669 },
670 {
671 "cell_type": "code",
672 "execution_count": null,
673 "id": "abd5985c-c727-44fb-a1d6-b212dc24d3ac",
674 "metadata": {},
675 "outputs": [],
676 "source": []
677 },
678 {
679 "cell_type": "code",
680 "execution_count": null,
681 "id": "3e6849f7-e983-446e-9d1b-26a1b4c693fd",
682 "metadata": {},
683 "outputs": [],
684 "source": []
685 },
686 {
687 "cell_type": "code",
688 "execution_count": null,
689 "id": "3b307ae1-980d-487a-aad4-f2fc0a87cbcf",
690 "metadata": {},
691 "outputs": [],
692 "source": []
693 },
694 {
695 "cell_type": "code",
696 "execution_count": null,
697 "id": "6d7b7335-589e-49bd-88bd-20b7b2a40611",
698 "metadata": {},
699 "outputs": [],
700 "source": []
701 },
702 {
703 "cell_type": "code",
704 "execution_count": null,
705 "id": "982de3ce-e883-4eab-88dd-41295da16e7f",
706 "metadata": {},
707 "outputs": [],
708 "source": []
709 },
710 {
711 "cell_type": "code",
712 "execution_count": null,
713 "id": "940e36a7-1597-4bac-8a82-3ecb602e124c",
714 "metadata": {},
715 "outputs": [],
716 "source": []
717 },
718 {
719 "cell_type": "code",
720 "execution_count": null,
721 "id": "b34546f9-5c28-4c29-9148-0473fa9e00c1",
722 "metadata": {},
723 "outputs": [],
724 "source": []
725 },
726 {
727 "cell_type": "code",
728 "execution_count": null,
729 "id": "81a421c8-ed47-40f2-83c2-d258bce764f4",
730 "metadata": {},
731 "outputs": [],
732 "source": []
733 },
734 {
735 "cell_type": "code",
736 "execution_count": null,
737 "id": "897f84d1-4ac8-478a-82f1-ed59052b0199",
738 "metadata": {},
739 "outputs": [],
740 "source": [
741 "def foo():\n",
742 " d = read_pkl()\n",
743 " dat = format_fmda_data()\n",
744 " dat = filter_data(dat)\n",
745 " return dat"
746 ]
747 },
748 {
749 "cell_type": "code",
750 "execution_count": null,
751 "id": "ed4112af-2b76-4b57-8d2e-1e5e0cba3b42",
752 "metadata": {},
753 "outputs": [],
754 "source": [
755 "def format_fmda_data(d):\n",
756 " # Inputs:\n",
757 " # d: (dict) fmda dictionary, output of process in wrfxpy\n",
758 "\n",
759 " return d"
760 ]
761 },
762 {
763 "cell_type": "code",
764 "execution_count": null,
765 "id": "b0ebb758-d1ec-477f-b6af-207dbe2a01d1",
766 "metadata": {},
767 "outputs": [],
768 "source": []
769 },
770 {
771 "cell_type": "code",
772 "execution_count": null,
773 "id": "e4d704f9-cb10-4b3a-a65d-cf1a26ddb9d4",
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775 "outputs": [],
776 "source": []
777 },
778 {
779 "cell_type": "code",
780 "execution_count": null,
781 "id": "c5972b2b-eab2-4705-8401-6aa746203ac1",
782 "metadata": {},
783 "outputs": [],
784 "source": []
785 },
786 {
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788 "execution_count": null,
789 "id": "0818c399-be45-4520-9b9e-d847a73ea1bf",
790 "metadata": {},
791 "outputs": [],
792 "source": []
793 },
794 {
795 "cell_type": "code",
796 "execution_count": null,
797 "id": "94ad78d5-47a0-4c9e-abe8-9f3cfc67208f",
798 "metadata": {},
799 "outputs": [],
800 "source": []
801 }
802 ],
803 "metadata": {
804 "kernelspec": {
805 "display_name": "Python 3 (ipykernel)",
806 "language": "python",
807 "name": "python3"
808 },
809 "language_info": {
810 "codemirror_mode": {
811 "name": "ipython",
812 "version": 3
813 },
814 "file_extension": ".py",
815 "mimetype": "text/x-python",
816 "name": "python",
817 "nbconvert_exporter": "python",
818 "pygments_lexer": "ipython3",
819 "version": "3.9.12"
820 }
821 },
822 "nbformat": 4,
823 "nbformat_minor": 5
824 }