| blob | a7fccd65407dee1b2d5fdc88ec6c864a03b54e29 |
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"
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 "def read_pkl(file_path):\n",
74 " with open(file_path, 'rb') as file:\n",
75 " print(f\"loading file {file_path}\")\n",
76 " d = pickle.load(file)\n",
77 " return d"
78 ]
79 },
80 {
81 "cell_type": "code",
82 "execution_count": null,
83 "id": "14f03aa9-a39c-4547-8c8c-52e30acff136",
84 "metadata": {},
85 "outputs": [],
86 "source": [
87 "def read_yml(yaml_path, subkey=None):\n",
88 " with open(yaml_path, 'r') as file:\n",
89 " d = yaml.safe_load(file)\n",
90 " if subkey is not None:\n",
91 " d = d[subkey]\n",
92 " return d"
93 ]
94 },
95 {
96 "cell_type": "code",
97 "execution_count": null,
98 "id": "e69e37b9-73ef-45a1-9738-844f26dc3323",
99 "metadata": {},
100 "outputs": [],
101 "source": [
102 "data_params = read_yml(\"params_data.yaml\")\n",
103 "data_params"
104 ]
105 },
106 {
107 "cell_type": "code",
108 "execution_count": null,
109 "id": "6b5c3c82-84ba-426c-b8d9-f540b5026158",
110 "metadata": {},
111 "outputs": [],
112 "source": [
113 "dat = read_pkl(\"data/test_CA_202401.pkl\")"
114 ]
115 },
116 {
117 "cell_type": "code",
118 "execution_count": null,
119 "id": "5b2aeecb-89e6-41d0-af88-59a07b929edc",
120 "metadata": {},
121 "outputs": [],
122 "source": [
123 "dat['NV040_202401']['RAWS']['fm']"
124 ]
125 },
126 {
127 "cell_type": "markdown",
128 "id": "dae0e47b-02eb-4759-9b95-3cc1b281d41e",
129 "metadata": {},
130 "source": [
131 "## Filters"
132 ]
133 },
134 {
135 "cell_type": "code",
136 "execution_count": null,
137 "id": "8d6fbd18-adad-4370-add7-164275fc010d",
138 "metadata": {},
139 "outputs": [],
140 "source": [
141 "def filter_fmc(x, data_params=data_params):\n",
142 " # Vector to track which values of input x are set to na\n",
143 " na_vec = np.zeros_like(x)\n",
144 " \n",
145 " # Filter extreme lows\n",
146 " condition = x<= data_params['min_fm']\n",
147 " x[condition] = np.nan\n",
148 " na_vec[np.where(condition)] = 1\n",
149 " \n",
150 " # Filter extreme highs\n",
151 " condition = x>= data_params['max_fm']\n",
152 " x[condition] = np.nan\n",
153 " na_vec[condition] = 1\n",
154 " \n",
155 " return x, na_vec"
156 ]
157 },
158 {
159 "cell_type": "code",
160 "execution_count": null,
161 "id": "e49e7951-228c-44ee-ae54-d941e9a350f2",
162 "metadata": {},
163 "outputs": [],
164 "source": [
165 "def filter_rain(r, data_params=data_params):\n",
166 " # Vector to track which values of input x are set to na\n",
167 " na_vec = np.zeros_like(r)\n",
168 " \n",
169 " # Filter extreme lows\n",
170 " condition = r< data_params['min_rain']\n",
171 " r[condition] = np.nan\n",
172 " na_vec[np.where(condition)] = 1\n",
173 " \n",
174 " # Filter extreme highs\n",
175 " condition = r>= data_params['max_rain']\n",
176 " r[condition] = np.nan\n",
177 " na_vec[condition] = 1 \n",
178 "\n",
179 " return r, na_vec"
180 ]
181 },
182 {
183 "cell_type": "code",
184 "execution_count": null,
185 "id": "957c0a4f-874c-4cf7-ad73-45053c2fdecc",
186 "metadata": {},
187 "outputs": [],
188 "source": [
189 "def filter_wind(w, data_params=data_params):\n",
190 " # Vector to track which values of input x are set to na\n",
191 " na_vec = np.zeros_like(w)\n",
192 " \n",
193 " # Filter extreme lows\n",
194 " condition = w< data_params['min_wind']\n",
195 " w[condition] = np.nan\n",
196 " na_vec[np.where(condition)] = 1\n",
197 " \n",
198 " # Filter extreme highs\n",
199 " condition = w>= data_params['max_wind']\n",
200 " w[condition] = np.nan\n",
201 " na_vec[condition] = 1 \n",
202 "\n",
203 " return w, na_vec"
204 ]
205 },
206 {
207 "cell_type": "code",
208 "execution_count": null,
209 "id": "7b6b4347-6abe-4c21-8318-06a766d67d21",
210 "metadata": {},
211 "outputs": [],
212 "source": [
213 "# Useful Cases:\n",
214 " # NV040_202401: more raws observations than HRRR, interp should shorten\n",
215 " # NV026_202401: raws 10min obs, interp should shorten\n",
216 " # CGVC1_202401: missing only a few observations, interp should lengthen\n",
217 " # YNWC1_202401: only 2 observations, should be filtered entirely"
218 ]
219 },
220 {
221 "cell_type": "code",
222 "execution_count": null,
223 "id": "74d3b25a-6803-41c5-855b-92ed980d3fcb",
224 "metadata": {},
225 "outputs": [],
226 "source": [
227 "def time_intp(t1, v1, t2):\n",
228 " # Check if t1 v1 t2 are 1D arrays\n",
229 " if t1.ndim != 1:\n",
230 " logging.error(\"Error: t1 is not a 1D array. Dimension: %s\", t1.ndim)\n",
231 " return None\n",
232 " if v1.ndim != 1:\n",
233 " logging.error(\"Error: v1 is not a 1D array. Dimension %s:\", v1.ndim)\n",
234 " return None\n",
235 " if t2.ndim != 1:\n",
236 " logging.errorr(\"Error: t2 is not a 1D array. Dimension: %s\", t2.ndim)\n",
237 " return None\n",
238 " # Check if t1 and v1 have the same length\n",
239 " if len(t1) != len(v1):\n",
240 " logging.error(\"Error: t1 and v1 have different lengths: %s %s\",len(t1),len(v1))\n",
241 " return None\n",
242 " t1_no_nan, v1_no_nan = filter_nan_values(t1, v1)\n",
243 " # print('t1_no_nan.dtype=',t1_no_nan.dtype)\n",
244 " # Convert datetime objects to timestamps\n",
245 " t1_stamps = np.array([t.timestamp() for t in t1_no_nan])\n",
246 " t2_stamps = np.array([t.timestamp() for t in t2])\n",
247 " "
248 ]
249 },
250 {
251 "cell_type": "code",
252 "execution_count": null,
253 "id": "2e1a9438-3bfa-4c6d-8f4e-2888409ecb75",
254 "metadata": {},
255 "outputs": [],
256 "source": [
257 "len(dat.keys())"
258 ]
259 },
260 {
261 "cell_type": "code",
262 "execution_count": null,
263 "id": "f7e77394-dea3-4b24-82ed-6ce3c7dab21e",
264 "metadata": {},
265 "outputs": [],
266 "source": [
267 "def filter_nan_cases(d, data_params=data_params):\n",
268 " # Remove cases with too many NAN relative to interp target. Use to avoid over-interpolating\n",
269 " ks = [] # accumulate keys to remove\n",
270 " for k in dat:\n",
271 " n_fm = dat[k]['RAWS']['fm'].shape[0]\n",
272 " n_obs = dat[k]['HRRR']['time'].shape[0]\n",
273 " pct_na = (1 - n_fm / n_obs)\n",
274 " if pct_na > data_params['max_pct_na']:\n",
275 " print(f\"Removing key {k} due to extensive missing data. Percent NA relative to HRRR: {np.round(pct_na, 3)}\")\n",
276 " ks.append(k)\n",
277 " for k in ks: d.pop(k, None)"
278 ]
279 },
280 {
281 "cell_type": "code",
282 "execution_count": null,
283 "id": "faffc4ea-1b96-4ab4-af44-e2555eba39e1",
284 "metadata": {},
285 "outputs": [],
286 "source": [
287 "filter_nan_cases(dat)"
288 ]
289 },
290 {
291 "cell_type": "code",
292 "execution_count": null,
293 "id": "ac4c695e-bcc6-4e53-b934-f3837dac70ca",
294 "metadata": {},
295 "outputs": [],
296 "source": [
297 "len(dat.keys())"
298 ]
299 },
300 {
301 "cell_type": "code",
302 "execution_count": null,
303 "id": "c3fb7817-82b4-49ee-8c62-ef7308648c02",
304 "metadata": {},
305 "outputs": [],
306 "source": []
307 },
308 {
309 "cell_type": "code",
310 "execution_count": null,
311 "id": "a9f0b53b-b291-4592-ab0a-d05a6d2f770c",
312 "metadata": {},
313 "outputs": [],
314 "source": []
315 },
316 {
317 "cell_type": "code",
318 "execution_count": null,
319 "id": "9b7af652-5836-480f-861c-09bacaf035ea",
320 "metadata": {},
321 "outputs": [],
322 "source": [
323 "d = dat['NV026_202401']"
324 ]
325 },
326 {
327 "cell_type": "code",
328 "execution_count": null,
329 "id": "c5d7d2d6-cbae-402c-8585-a48ceba8f431",
330 "metadata": {},
331 "outputs": [],
332 "source": [
333 "d['RAWS']['fm'].shape"
334 ]
335 },
336 {
337 "cell_type": "code",
338 "execution_count": null,
339 "id": "ceb44038-b166-4827-a9ab-f62f51971c39",
340 "metadata": {},
341 "outputs": [],
342 "source": [
343 "d['RAWS']['time_raws'].shape"
344 ]
345 },
346 {
347 "cell_type": "code",
348 "execution_count": null,
349 "id": "481bb0b6-b5de-4244-89bc-24b6073e5cd4",
350 "metadata": {},
351 "outputs": [],
352 "source": [
353 "d['HRRR']['f01']['Ed'].shape"
354 ]
355 },
356 {
357 "cell_type": "code",
358 "execution_count": null,
359 "id": "88d3d4b6-c482-4158-ae13-daf729c2a496",
360 "metadata": {},
361 "outputs": [],
362 "source": [
363 "d['HRRR']['time'].shape"
364 ]
365 },
366 {
367 "cell_type": "code",
368 "execution_count": null,
369 "id": "d834581e-eb4a-494f-894a-5c81ff92bf85",
370 "metadata": {},
371 "outputs": [],
372 "source": [
373 "from utils import str2time\n",
374 "d = dat['NV040_202401']\n",
375 "time_raws=str2time(d['RAWS']['time_raws']) \n",
376 "time_hrrr=str2time(d['HRRR']['time'])\n",
377 "\n",
378 "t1 = np.array([t.timestamp() for t in time_raws])\n",
379 "t2 = np.array([t.timestamp() for t in time_hrrr])"
380 ]
381 },
382 {
383 "cell_type": "code",
384 "execution_count": null,
385 "id": "266ac8f6-119b-4424-b162-9077ab76b8e4",
386 "metadata": {},
387 "outputs": [],
388 "source": [
389 "tnew = np.interp(t2, t1, t1)"
390 ]
391 },
392 {
393 "cell_type": "code",
394 "execution_count": null,
395 "id": "1950759a-a332-4054-aee1-b086fbf4c39c",
396 "metadata": {},
397 "outputs": [],
398 "source": [
399 "np.isin(tnew, t1).mean()"
400 ]
401 },
402 {
403 "cell_type": "code",
404 "execution_count": null,
405 "id": "8b93c8ae-cfbb-4344-beda-5d3f60ed8ed9",
406 "metadata": {},
407 "outputs": [],
408 "source": []
409 },
410 {
411 "cell_type": "code",
412 "execution_count": null,
413 "id": "1c9a2263-9ec2-4c7e-9c79-90f8209a4c87",
414 "metadata": {},
415 "outputs": [],
416 "source": []
417 },
418 {
419 "cell_type": "code",
420 "execution_count": null,
421 "id": "9e2a78e5-330a-49ce-9dc9-babcb580d0d4",
422 "metadata": {},
423 "outputs": [],
424 "source": [
425 "d = dat['LIB03_202401']"
426 ]
427 },
428 {
429 "cell_type": "code",
430 "execution_count": null,
431 "id": "08b4c474-4332-4b45-9110-d8a21432cdf9",
432 "metadata": {},
433 "outputs": [],
434 "source": [
435 "d['RAWS']['fm'].shape"
436 ]
437 },
438 {
439 "cell_type": "code",
440 "execution_count": null,
441 "id": "1c12c514-37db-4b13-a35d-bd28651c00b4",
442 "metadata": {},
443 "outputs": [],
444 "source": [
445 "d['RAWS']['time_raws'].shape"
446 ]
447 },
448 {
449 "cell_type": "code",
450 "execution_count": null,
451 "id": "9846b2ae-38a8-4f0b-96a6-1c21d0ecb616",
452 "metadata": {},
453 "outputs": [],
454 "source": [
455 "d['HRRR']['f01']['Ed'].shape"
456 ]
457 },
458 {
459 "cell_type": "code",
460 "execution_count": null,
461 "id": "614fe317-8233-4dbf-97c8-c2d8d8923fea",
462 "metadata": {},
463 "outputs": [],
464 "source": [
465 "d['HRRR']['time'].shape"
466 ]
467 },
468 {
469 "cell_type": "code",
470 "execution_count": null,
471 "id": "40be5a62-06ec-4623-9684-bf186c3bcd1d",
472 "metadata": {},
473 "outputs": [],
474 "source": []
475 },
476 {
477 "cell_type": "code",
478 "execution_count": null,
479 "id": "081e0202-2bc0-4bd8-b4ab-f5eec289b70c",
480 "metadata": {},
481 "outputs": [],
482 "source": []
483 },
484 {
485 "cell_type": "code",
486 "execution_count": null,
487 "id": "31a1d814-9028-4213-ad9d-fabf1e35b554",
488 "metadata": {},
489 "outputs": [],
490 "source": []
491 },
492 {
493 "cell_type": "code",
494 "execution_count": null,
495 "id": "42de9659-3725-4f92-b17c-455126e89c47",
496 "metadata": {},
497 "outputs": [],
498 "source": []
499 },
500 {
501 "cell_type": "code",
502 "execution_count": null,
503 "id": "37cb45e7-b46b-429b-a9be-6c3a35677a28",
504 "metadata": {},
505 "outputs": [],
506 "source": []
507 },
508 {
509 "cell_type": "code",
510 "execution_count": null,
511 "id": "3f007941-6682-4022-9b78-48c214152635",
512 "metadata": {},
513 "outputs": [],
514 "source": []
515 },
516 {
517 "cell_type": "code",
518 "execution_count": null,
519 "id": "20687996-8267-4e93-9e24-b9515259e256",
520 "metadata": {},
521 "outputs": [],
522 "source": []
523 },
524 {
525 "cell_type": "code",
526 "execution_count": null,
527 "id": "55ce3b0e-7135-43ff-86ab-379c50b349db",
528 "metadata": {},
529 "outputs": [],
530 "source": [
531 "time_raws = str2time(dat['NV040_202401']['RAWS']['time_raws'])\n",
532 "time_hrrr = str2time(dat['NV040_202401']['HRRR']['time'])\n",
533 "fm = dat['NV040_202401']['RAWS']['fm']\n",
534 "rain = dat['NV040_202401']['HRRR']['f01']['rain']"
535 ]
536 },
537 {
538 "cell_type": "code",
539 "execution_count": null,
540 "id": "cc4fd0b0-286b-4cd9-88c4-59e996348235",
541 "metadata": {},
542 "outputs": [],
543 "source": [
544 "len(time_raws) == len(fm)"
545 ]
546 },
547 {
548 "cell_type": "code",
549 "execution_count": null,
550 "id": "1680ed39-a021-4631-a764-40f354cf6a09",
551 "metadata": {},
552 "outputs": [],
553 "source": [
554 "filter_fmc(fm)"
555 ]
556 },
557 {
558 "cell_type": "code",
559 "execution_count": null,
560 "id": "8a3c405a-f976-4756-aaec-63998d58203d",
561 "metadata": {},
562 "outputs": [],
563 "source": [
564 "filter_rain(rain)"
565 ]
566 },
567 {
568 "cell_type": "code",
569 "execution_count": null,
570 "id": "5f49d9d4-2edf-434e-8812-c8c6ef3fbb2f",
571 "metadata": {},
572 "outputs": [],
573 "source": [
574 "filter_wind(dat['NV040_202401']['HRRR']['f01']['wind'])"
575 ]
576 },
577 {
578 "cell_type": "code",
579 "execution_count": null,
580 "id": "3d6a8fe7-1864-475a-a4f3-0fe04806ad50",
581 "metadata": {},
582 "outputs": [],
583 "source": [
584 "dat['NV040_202401']['RAWS']['wind']"
585 ]
586 },
587 {
588 "cell_type": "code",
589 "execution_count": null,
590 "id": "25ad1ce4-2060-4ece-932a-396813e56dc6",
591 "metadata": {},
592 "outputs": [],
593 "source": [
594 "dat['CNFC1_202401']['RAWS'].keys()"
595 ]
596 },
597 {
598 "cell_type": "code",
599 "execution_count": null,
600 "id": "a58ca663-074d-4eb4-a644-c9e05a415e65",
601 "metadata": {},
602 "outputs": [],
603 "source": [
604 "dat['CNFC1_202401']['RAWS']['solar']"
605 ]
606 },
607 {
608 "cell_type": "code",
609 "execution_count": null,
610 "id": "c2f27bfb-73fc-4bc6-9abc-ac1f5ab46acd",
611 "metadata": {},
612 "outputs": [],
613 "source": [
614 "dat['CNFC1_202401']['HRRR']['f01']['wind']"
615 ]
616 },
617 {
618 "cell_type": "code",
619 "execution_count": null,
620 "id": "74b98be4-55b1-4db2-81de-5f650aa00bb2",
621 "metadata": {},
622 "outputs": [],
623 "source": [
624 "dat['CNFC1_202401']['HRRR']['f01']['soilm']"
625 ]
626 },
627 {
628 "cell_type": "code",
629 "execution_count": null,
630 "id": "915f9534-ff84-4844-bde4-dce567d222c5",
631 "metadata": {},
632 "outputs": [],
633 "source": []
634 },
635 {
636 "cell_type": "code",
637 "execution_count": null,
638 "id": "cd2f357d-c813-4aa0-b3f7-cdc72f1f660c",
639 "metadata": {},
640 "outputs": [],
641 "source": [
642 "for k in dat:\n",
643 " print(\"~\"*50)\n",
644 " print(k)\n",
645 " print(f\"HRRR Shape: {dat[k]['HRRR']['f01']['Ew'].shape}\")\n",
646 " print(f\"RAWS Shape: {dat[k]['RAWS']['fm'].shape}\")"
647 ]
648 },
649 {
650 "cell_type": "code",
651 "execution_count": null,
652 "id": "1ab4cb8b-1aa8-4541-a5cc-041c493d8894",
653 "metadata": {},
654 "outputs": [],
655 "source": [
656 "dat['LIB03_202401']['RAWS']['soil_moisture']"
657 ]
658 },
659 {
660 "cell_type": "code",
661 "execution_count": null,
662 "id": "f62a897e-eeb8-4951-b64f-8b6ee11bc92a",
663 "metadata": {},
664 "outputs": [],
665 "source": [
666 "dat['LIB03_202401']['HRRR']['f01']['soilm']"
667 ]
668 },
669 {
670 "cell_type": "code",
671 "execution_count": null,
672 "id": "5037b7f3-a749-4b27-9e4c-f6c8d4fb8b6d",
673 "metadata": {},
674 "outputs": [],
675 "source": [
676 "for k in dat:\n",
677 " print(\"~\"*50)\n",
678 " print(k)\n",
679 " # Filter RAWS\n",
680 " fm, filter_vec = filter_fmc(dat[k][\"RAWS\"]['fm'])\n",
681 " print(f\"Percent FMC Observations Filtered: {np.mean(filter_vec)}\")\n",
682 " if 'rain' in dat[k][\"RAWS\"].keys():\n",
683 " rain, filter_vec = filter_rain(dat[k][\"RAWS\"]['rain'])\n",
684 " print(f\"Percent Rain Observations Filtered: {np.mean(filter_vec)}\")\n",
685 " if 'wind' in dat[k][\"RAWS\"].keys():\n",
686 " wind, filter_vec = filter_rain(dat[k][\"RAWS\"]['wind'])\n",
687 " print(f\"Percent Wind Observations Filtered: {np.mean(filter_vec)}\")\n",
688 " # Filter HRRR unncessesary? "
689 ]
690 },
691 {
692 "cell_type": "code",
693 "execution_count": null,
694 "id": "abd5985c-c727-44fb-a1d6-b212dc24d3ac",
695 "metadata": {},
696 "outputs": [],
697 "source": []
698 },
699 {
700 "cell_type": "code",
701 "execution_count": null,
702 "id": "3e6849f7-e983-446e-9d1b-26a1b4c693fd",
703 "metadata": {},
704 "outputs": [],
705 "source": []
706 },
707 {
708 "cell_type": "code",
709 "execution_count": null,
710 "id": "3b307ae1-980d-487a-aad4-f2fc0a87cbcf",
711 "metadata": {},
712 "outputs": [],
713 "source": []
714 },
715 {
716 "cell_type": "code",
717 "execution_count": null,
718 "id": "6d7b7335-589e-49bd-88bd-20b7b2a40611",
719 "metadata": {},
720 "outputs": [],
721 "source": []
722 },
723 {
724 "cell_type": "code",
725 "execution_count": null,
726 "id": "982de3ce-e883-4eab-88dd-41295da16e7f",
727 "metadata": {},
728 "outputs": [],
729 "source": []
730 },
731 {
732 "cell_type": "code",
733 "execution_count": null,
734 "id": "940e36a7-1597-4bac-8a82-3ecb602e124c",
735 "metadata": {},
736 "outputs": [],
737 "source": []
738 },
739 {
740 "cell_type": "code",
741 "execution_count": null,
742 "id": "b34546f9-5c28-4c29-9148-0473fa9e00c1",
743 "metadata": {},
744 "outputs": [],
745 "source": []
746 },
747 {
748 "cell_type": "code",
749 "execution_count": null,
750 "id": "81a421c8-ed47-40f2-83c2-d258bce764f4",
751 "metadata": {},
752 "outputs": [],
753 "source": []
754 },
755 {
756 "cell_type": "code",
757 "execution_count": null,
758 "id": "897f84d1-4ac8-478a-82f1-ed59052b0199",
759 "metadata": {},
760 "outputs": [],
761 "source": [
762 "def foo():\n",
763 " d = read_pkl()\n",
764 " dat = format_fmda_data()\n",
765 " dat = filter_data(dat)\n",
766 " return dat"
767 ]
768 },
769 {
770 "cell_type": "code",
771 "execution_count": null,
772 "id": "ed4112af-2b76-4b57-8d2e-1e5e0cba3b42",
773 "metadata": {},
774 "outputs": [],
775 "source": [
776 "def format_fmda_data(d):\n",
777 " # Inputs:\n",
778 " # d: (dict) fmda dictionary, output of process in wrfxpy\n",
779 "\n",
780 " return d"
781 ]
782 },
783 {
784 "cell_type": "code",
785 "execution_count": null,
786 "id": "b0ebb758-d1ec-477f-b6af-207dbe2a01d1",
787 "metadata": {},
788 "outputs": [],
789 "source": []
790 },
791 {
792 "cell_type": "code",
793 "execution_count": null,
794 "id": "e4d704f9-cb10-4b3a-a65d-cf1a26ddb9d4",
795 "metadata": {},
796 "outputs": [],
797 "source": []
798 },
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800 "cell_type": "code",
801 "execution_count": null,
802 "id": "c5972b2b-eab2-4705-8401-6aa746203ac1",
803 "metadata": {},
804 "outputs": [],
805 "source": []
806 },
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813 "source": []
814 },
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817 "execution_count": null,
818 "id": "94ad78d5-47a0-4c9e-abe8-9f3cfc67208f",
819 "metadata": {},
820 "outputs": [],
821 "source": []
822 }
823 ],
824 "metadata": {
825 "kernelspec": {
826 "display_name": "Python 3 (ipykernel)",
827 "language": "python",
828 "name": "python3"
829 },
830 "language_info": {
831 "codemirror_mode": {
832 "name": "ipython",
833 "version": 3
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835 "file_extension": ".py",
836 "mimetype": "text/x-python",
837 "name": "python",
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839 "pygments_lexer": "ipython3",
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