| blob | 0affdb1835770cabd367f0663b791cacce13f398 |
1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "id": "04617c3b-c110-4c6d-b6e5-d0611bf42181",
6 "metadata": {},
7 "source": [
8 "# Create Test Dictionary\n",
9 "\n",
10 "The purpose of this notebook is to create a dictionary of fuel moisture scenarios for model testing.\n",
11 "\n",
12 "[USGS](https://water.usgs.gov/edu/activity-howmuchrain-metric.html#:~:text=Slight%20rain%3A%20Less%20than%200.5,than%208%20mm%20per%20hour.) classifies rainfall as:\n",
13 "* Slight: $\\text{rain}<.5$ mm/hr\n",
14 "* Moderate: $.5<\\text{rain}<4$ mm/hr\n",
15 "* Heavy: $4<\\text{rain}<8$ mm/hr\n",
16 "* Very Heavy: $8<\\text{rain}$ mm/hr\n",
17 "\n",
18 "See available Mesonet variables [here](https://developers.synopticdata.com/mesonet/v2/api-variables/)."
19 ]
20 },
21 {
22 "cell_type": "markdown",
23 "id": "18361bd2-6ff7-44c2-8e41-7a02f546f7c2",
24 "metadata": {},
25 "source": [
26 "## Setup"
27 ]
28 },
29 {
30 "cell_type": "code",
31 "execution_count": null,
32 "id": "4d27bc2c-c134-48b0-8be9-cad80044a6ae",
33 "metadata": {},
34 "outputs": [],
35 "source": [
36 "# Change path for module imports\n",
37 "import os\n",
38 "os.chdir('..')\n",
39 "\n",
40 "import numpy as np, random\n",
41 "from numpy.random import rand\n",
42 "import matplotlib.pyplot as plt\n",
43 "from data_funcs import synthetic_data, plot_data, to_json, from_json, format_raws\n",
44 "\n",
45 "import moisture_models as mod\n",
46 "from moisture_models import model_decay, model_moisture\n",
47 "from datetime import datetime, timedelta\n",
48 "import json\n",
49 "from utils import hash2\n",
50 "\n",
51 "from MesoPy import Meso\n",
52 "meso_token=\"4192c18707b848299783d59a9317c6e1\"\n",
53 "m=Meso(meso_token)\n",
54 "\n",
55 "import reproducibility"
56 ]
57 },
58 {
59 "cell_type": "code",
60 "execution_count": null,
61 "id": "399206cf-32d9-4017-89e7-39d208517248",
62 "metadata": {},
63 "outputs": [],
64 "source": [
65 "# Change directory to data\n",
66 "os.chdir('data')"
67 ]
68 },
69 {
70 "cell_type": "markdown",
71 "id": "31a6243d-d894-4061-9027-1d8e4c812c10",
72 "metadata": {},
73 "source": [
74 "---\n",
75 "\n",
76 "**Scenarios 1-6**: simulated moisture with default equilibrium parameters. Vary rain from none up to very heavy as described above."
77 ]
78 },
79 {
80 "cell_type": "code",
81 "execution_count": null,
82 "id": "0dc68c44-1c07-4704-8e4d-a824bad3a4d5",
83 "metadata": {},
84 "outputs": [],
85 "source": [
86 "# Dictionary to be saved for testing\n",
87 "test_dict = {}"
88 ]
89 },
90 {
91 "cell_type": "code",
92 "execution_count": null,
93 "id": "aba3e81a-824c-4454-8696-4f4b2d6b4fa8",
94 "metadata": {},
95 "outputs": [],
96 "source": [
97 "## Case 1: no rain\n",
98 "synt_dat=synthetic_data(max_rain = 0) \n",
99 "synt_dat['id'] = 'case1'\n",
100 "synt_dat['title'] = 'Synthetic Data (no rain)'\n",
101 "synt_dat['descr'] = 'Max rain: 0 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
102 "\n",
103 "test_dict['case1'] = synt_dat # save to test dictionary"
104 ]
105 },
106 {
107 "cell_type": "code",
108 "execution_count": null,
109 "id": "2a2e36da-b047-454c-a000-1f34460f5738",
110 "metadata": {},
111 "outputs": [],
112 "source": [
113 "## Case 2: light rain\n",
114 "synt_dat=synthetic_data(max_rain = 0.4) \n",
115 "synt_dat['id'] = 'case2'\n",
116 "synt_dat['title'] = 'Synthetic Data (light rain)'\n",
117 "synt_dat['descr'] = 'Max rain: .4 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
118 "\n",
119 "test_dict['case2'] = synt_dat # save to test dictionary"
120 ]
121 },
122 {
123 "cell_type": "code",
124 "execution_count": null,
125 "id": "1defc608-4ace-4f62-8ad0-80eb3f4a3779",
126 "metadata": {},
127 "outputs": [],
128 "source": [
129 "## Case 3: moderate rain\n",
130 "synt_dat=synthetic_data(max_rain = 3) \n",
131 "synt_dat['id'] = 'case3'\n",
132 "synt_dat['title'] = 'Synthetic Data (med. rain)'\n",
133 "synt_dat['descr'] = 'Max rain: 3 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
134 "\n",
135 "test_dict['case3'] = synt_dat # save to test dictionary"
136 ]
137 },
138 {
139 "cell_type": "code",
140 "execution_count": null,
141 "id": "a2fc842d-4c60-484d-b9ec-408ac8cd5984",
142 "metadata": {},
143 "outputs": [],
144 "source": [
145 "## Case 4: heavy rain\n",
146 "synt_dat=synthetic_data(max_rain = 6) \n",
147 "synt_dat['id'] = 'case4'\n",
148 "synt_dat['title'] = 'Synthetic Data (heavy rain)'\n",
149 "synt_dat['descr'] = 'Max rain: 6 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
150 "\n",
151 "test_dict['case4'] = synt_dat # save to test dictionary"
152 ]
153 },
154 {
155 "cell_type": "code",
156 "execution_count": null,
157 "id": "1307e2b1-f907-4396-be97-ea8069efcd5a",
158 "metadata": {},
159 "outputs": [],
160 "source": [
161 "## Case 5: very heavy rain 1\n",
162 "synt_dat=synthetic_data(max_rain = 10) \n",
163 "synt_dat['id'] = 'case5'\n",
164 "synt_dat['title'] = 'Synthetic Data (very heavy rain 1)'\n",
165 "synt_dat['descr'] = 'Max rain: 10 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
166 "\n",
167 "test_dict['case5'] = synt_dat # save to test dictionary"
168 ]
169 },
170 {
171 "cell_type": "code",
172 "execution_count": null,
173 "id": "0b7d68cf-85d1-418e-935f-016c776c7f77",
174 "metadata": {},
175 "outputs": [],
176 "source": [
177 "## Case 6: very heavy rain 2\n",
178 "synt_dat=synthetic_data(max_rain = 15) \n",
179 "synt_dat['id'] = 'case6'\n",
180 "synt_dat['title'] = 'Synthetic Data (very heavy rain 2)'\n",
181 "synt_dat['descr'] = 'Max rain: 15 mm/hr; Emin: 5; Emax: 30; 20 days'\n",
182 "\n",
183 "test_dict['case6'] = synt_dat # save to test dictionary"
184 ]
185 },
186 {
187 "cell_type": "code",
188 "execution_count": null,
189 "id": "f96c5be8-b237-4ddd-8ebe-0d34161ffa39",
190 "metadata": {
191 "tags": []
192 },
193 "outputs": [],
194 "source": [
195 "plot_data(test_dict['case6'])"
196 ]
197 },
198 {
199 "cell_type": "markdown",
200 "id": "c065be74-3fef-4165-adaf-be2b1f36348d",
201 "metadata": {},
202 "source": [
203 "---\n",
204 "\n",
205 "**Scenario 7-8:** RAWS Data, multiple time slices"
206 ]
207 },
208 {
209 "cell_type": "code",
210 "execution_count": null,
211 "id": "34f38801-aa57-423e-9191-db18a863b49d",
212 "metadata": {},
213 "outputs": [],
214 "source": [
215 "## Read RAWS data with MesoPy\n",
216 "\n",
217 "time_start = \"201806010800\" # June 1 2018 08:00 in format yyyymmddHHMM\n",
218 "time_end = \"201907200900\" # June 20 2018 09:00 in format yyyymmddHHMM\n",
219 "\n",
220 "vars='air_temp,relative_humidity,precip_accum,fuel_moisture,wind_speed,solar_radiation'"
221 ]
222 },
223 {
224 "cell_type": "code",
225 "execution_count": null,
226 "id": "3f7b5ff0-9233-4a69-b4de-f7dfdb50f029",
227 "metadata": {},
228 "outputs": [],
229 "source": [
230 "meso_ts = m.timeseries(time_start, time_end, stid=\"CPTC2\", showemptystations = '0', vars=vars) # ask the object for data"
231 ]
232 },
233 {
234 "cell_type": "code",
235 "execution_count": null,
236 "id": "09df9f1e-bde1-446d-b86d-398548182754",
237 "metadata": {},
238 "outputs": [],
239 "source": [
240 "raws1 = format_raws(meso_ts['STATION'][0])"
241 ]
242 },
243 {
244 "cell_type": "code",
245 "execution_count": null,
246 "id": "0a0ebfde-e790-4800-955c-6a2636b8f1e5",
247 "metadata": {},
248 "outputs": [],
249 "source": [
250 "# Scenario 7: time 0-1200 for station\n",
251 "## Heavy rain at end of time period\n",
252 "dict1={\n",
253 " 'id': 'case7',\n",
254 " 'time': raws1['time'][0:1200],\n",
255 " 'rain': raws1['rain'][0:1200],\n",
256 " 'fm' : raws1['fm'][0:1200],\n",
257 " 'rh' : raws1['rh'][0:1200],\n",
258 " 'temp' : raws1['temp'][0:1200],\n",
259 " 'Ed' : raws1['Ed'][0:1200],\n",
260 " 'Ew' : raws1['Ew'][0:1200],\n",
261 " 'wind' : raws1['wind_speed'][0:1200],\n",
262 " 'solar' : raws1['solar_radiation'][0:1200],\n",
263 " 'STID' : raws1['STID'],\n",
264 " 'title' : 'RAWS Station CPTC2 #1',\n",
265 " 'descr' : 'Real surface level data, very heavy rain at end',\n",
266 " 'hours':1200,\n",
267 " 'h2':300,\n",
268 " 'other': {'lon': raws1['lon'], 'lat': raws1['lat']}\n",
269 "}\n",
270 "\n",
271 "test_dict['case7'] = dict1 # save to test dictionary"
272 ]
273 },
274 {
275 "cell_type": "code",
276 "execution_count": null,
277 "id": "8e2b7707-0c05-471e-a679-df11a1ba2047",
278 "metadata": {},
279 "outputs": [],
280 "source": [
281 "plot_data(dict1)"
282 ]
283 },
284 {
285 "cell_type": "code",
286 "execution_count": null,
287 "id": "5f9957cb-a858-406d-9fde-c8c615b2f262",
288 "metadata": {},
289 "outputs": [],
290 "source": [
291 "# Scenario 8: time 800-2000 for station\n",
292 "## Heavy rain at beginning of time period\n",
293 "dict1={\n",
294 " 'id': 'case8',\n",
295 " 'time': raws1['time'][800:2000],\n",
296 " 'rain': raws1['rain'][800:2000],\n",
297 " 'fm' : raws1['fm'][800:2000],\n",
298 " 'rh' : raws1['rh'][800:2000],\n",
299 " 'temp' : raws1['temp'][800:2000],\n",
300 " 'Ed' : raws1['Ed'][800:2000],\n",
301 " 'Ew' : raws1['Ew'][800:2000],\n",
302 " 'wind' : raws1['wind_speed'][800:2000],\n",
303 " 'solar' : raws1['solar_radiation'][800:2000],\n",
304 " 'STID' : raws1['STID'],\n",
305 " 'title' : 'RAWS Station CPTC2 #2',\n",
306 " 'descr' : 'Real surface level data, very heavy rain at beginning',\n",
307 " 'hours':1200,\n",
308 " 'h2':300,\n",
309 " 'other': {'lon': raws1['lon'], 'lat': raws1['lat']}\n",
310 "}\n",
311 "\n",
312 "test_dict['case8'] = dict1 # save to test dictionary"
313 ]
314 },
315 {
316 "cell_type": "code",
317 "execution_count": null,
318 "id": "7d97200d-604f-4092-8150-326f230e71d6",
319 "metadata": {},
320 "outputs": [],
321 "source": [
322 "plot_data(test_dict['case8'])"
323 ]
324 },
325 {
326 "cell_type": "markdown",
327 "id": "a9e424a2-16d7-4c5d-a012-f4d0def14ae4",
328 "metadata": {},
329 "source": [
330 "---\n",
331 "\n",
332 "**Scenario 9-10:** RTMA Data, multiple time slices at station BKCU1"
333 ]
334 },
335 {
336 "cell_type": "code",
337 "execution_count": null,
338 "id": "b046d58b-f88c-42dc-8322-eb390dacdc3a",
339 "metadata": {},
340 "outputs": [],
341 "source": [
342 "rtma = from_json('rtma.json')"
343 ]
344 },
345 {
346 "cell_type": "code",
347 "execution_count": null,
348 "id": "6aabbf30-452a-401e-a0dc-ad325bf9474d",
349 "metadata": {},
350 "outputs": [],
351 "source": [
352 "## Read RAWS data with MesoPy\n",
353 "\n",
354 "time_start = \"201807041600\" # '2018-07-04 16:00'\n",
355 "time_end = \"201810040900\" # '2018-10-04 08:00', 1 hr buffer\n",
356 "\n",
357 "vars='fuel_moisture'\n",
358 "\n",
359 "meso_ts = m.timeseries(time_start, time_end, stid=\"BKCU1\", showemptystations = '0', vars=vars) # ask the object for data"
360 ]
361 },
362 {
363 "cell_type": "code",
364 "execution_count": null,
365 "id": "0184bf0e-d529-4ac9-8839-bef03eafb4fc",
366 "metadata": {},
367 "outputs": [],
368 "source": [
369 "from data_funcs import format_rtma"
370 ]
371 },
372 {
373 "cell_type": "code",
374 "execution_count": null,
375 "id": "01653876-970a-4347-82c3-a0e8c21bc78c",
376 "metadata": {},
377 "outputs": [],
378 "source": [
379 "rtma1 = format_rtma(rtma)\n",
380 "fm = np.array(meso_ts['STATION'][0]['OBSERVATIONS']['fuel_moisture_set_1'])"
381 ]
382 },
383 {
384 "cell_type": "code",
385 "execution_count": null,
386 "id": "2298a66d-429a-4347-9746-bd91ac38de31",
387 "metadata": {},
388 "outputs": [],
389 "source": [
390 "# Scenario 9: time 800:2000 for rtma location\n",
391 "## moderate rain towards end of time period\n",
392 "dict1={\n",
393 " 'id': 'case9',\n",
394 " 'time': rtma1['time'][800:2000],\n",
395 " 'rain': rtma1['rain'][800:2000],\n",
396 " 'fm' : fm[0:1200], # Note: time vectors need to be offset here\n",
397 " 'rh' : rtma1['rh'][800:2000],\n",
398 " 'temp' : rtma1['temp'][800:2000],\n",
399 " 'Ed' : rtma1['Ed'][800:2000],\n",
400 " 'Ew' : rtma1['Ew'][800:2000],\n",
401 " 'title' : 'RTMA Data, Fuel from RAWS Station BKCU1 #1',\n",
402 " 'descr' : 'rtma weather, surface level fuel, moderate rain at end',\n",
403 " 'hours':1200,\n",
404 " 'h2':300,\n",
405 " 'other': {'lon': rtma1['lon'], 'lat': rtma1['lat']}\n",
406 "}\n",
407 "\n",
408 "test_dict['case9'] = dict1 # save to test dictionary"
409 ]
410 },
411 {
412 "cell_type": "code",
413 "execution_count": null,
414 "id": "7b28db79-f79e-41ae-ac90-96ee7922a85f",
415 "metadata": {},
416 "outputs": [],
417 "source": [
418 "plot_data(test_dict['case9'])"
419 ]
420 },
421 {
422 "cell_type": "code",
423 "execution_count": null,
424 "id": "84693079-e468-494d-afee-76cef4198aba",
425 "metadata": {},
426 "outputs": [],
427 "source": [
428 "# Scenario 10: time 1000-2200 for same location\n",
429 "## moderate rain towards end of time period\n",
430 "dict1={\n",
431 " 'id': 'case10',\n",
432 " 'time': rtma1['time'][1800:3000],\n",
433 " 'rain': rtma1['rain'][1800:3000],\n",
434 " 'fm' : fm[1000:2200],\n",
435 " 'rh' : rtma1['rh'][1800:3000],\n",
436 " 'temp' : rtma1['temp'][1800:3000],\n",
437 " 'Ed' : rtma1['Ed'][1800:3000],\n",
438 " 'Ew' : rtma1['Ew'][1800:3000],\n",
439 " 'title' : 'RTMA Data, Fuel from RAWS Station BKCU1 #2',\n",
440 " 'descr' : 'rtma weather, surface level fuel, heavy rain at end',\n",
441 " 'hours':1200,\n",
442 " 'h2':300,\n",
443 " 'other': {'lon': rtma1['lon'], 'lat': rtma1['lat']}\n",
444 "}\n",
445 "\n",
446 "test_dict['case10'] = dict1 # save to test dictionary"
447 ]
448 },
449 {
450 "cell_type": "code",
451 "execution_count": null,
452 "id": "12803e8c-4024-4108-a808-9768cde9b2f3",
453 "metadata": {},
454 "outputs": [],
455 "source": [
456 "plot_data(test_dict['case10'])"
457 ]
458 },
459 {
460 "cell_type": "markdown",
461 "id": "f9c5d8e4-a43a-4465-a4b1-0dc5c016b879",
462 "metadata": {},
463 "source": [
464 "### Scenario 11: Original RNN\n",
465 "\n",
466 "RNN generated from notebook to a json file, used for reproducibility since the beginning. RTMA data from same station."
467 ]
468 },
469 {
470 "cell_type": "code",
471 "execution_count": null,
472 "id": "8d055b4f-47f4-4627-9e23-a3d6114dd22d",
473 "metadata": {},
474 "outputs": [],
475 "source": [
476 "rnn = from_json('rnn_orig.json')"
477 ]
478 },
479 {
480 "cell_type": "code",
481 "execution_count": null,
482 "id": "5bc86c64-29f8-495f-81cf-bcd4ab1e57fd",
483 "metadata": {},
484 "outputs": [],
485 "source": [
486 "rnn.keys()"
487 ]
488 },
489 {
490 "cell_type": "code",
491 "execution_count": null,
492 "id": "79277920-3d10-4fb9-85a3-5f59a1a84213",
493 "metadata": {},
494 "outputs": [],
495 "source": [
496 "# Scenario 11:\n",
497 "N = rnn['Ed'].shape[0]\n",
498 "\n",
499 "dict1={\n",
500 " 'id': 'case11',\n",
501 " 'time': None,\n",
502 " 'rain': rnn['rain'][0:N],\n",
503 " 'fm' : rnn['fm'][0:N],\n",
504 " 'Ed' : rnn['Ed'][0:N],\n",
505 " 'Ew' : rnn['Ew'][0:N],\n",
506 " 'rain' : rnn['rain'][0:N],\n",
507 " 'title' : 'RNN Orig',\n",
508 " 'descr' : 'rtma weather, surface level fuel, moderate rain at end',\n",
509 " 'hours':N,\n",
510 " 'h2':300,\n",
511 " 'other': {'lon': rtma1['lon'], 'lat': rtma1['lat']}\n",
512 "}\n",
513 "\n",
514 "test_dict['case11'] = dict1 # save to test dictionary"
515 ]
516 },
517 {
518 "cell_type": "code",
519 "execution_count": null,
520 "id": "3fb874a9-3604-4940-b99f-70cb5fcd1482",
521 "metadata": {},
522 "outputs": [],
523 "source": [
524 "plot_data(test_dict['case11'])"
525 ]
526 },
527 {
528 "cell_type": "markdown",
529 "id": "ca31c0f3-aebb-4e94-8892-82ddfa252701",
530 "metadata": {},
531 "source": [
532 "### Scenario 12-13 More Rainy RAWS cases\n",
533 "\n",
534 "These cases utilize stations from Washington state (since I wanted to find rain...)"
535 ]
536 },
537 {
538 "cell_type": "code",
539 "execution_count": null,
540 "id": "4671215c-990f-4437-9b08-e8bac5f2c181",
541 "metadata": {},
542 "outputs": [],
543 "source": [
544 "## Read RAWS data with MesoPy\n",
545 "\n",
546 "time_start = \"202209010800\"\n",
547 "time_end = \"202210300900\"\n",
548 "\n",
549 "vars='air_temp,relative_humidity,precip_accum,fuel_moisture,wind_speed,solar_radiation'"
550 ]
551 },
552 {
553 "cell_type": "code",
554 "execution_count": null,
555 "id": "9bf6d7ef-956c-4165-84a8-73184dc564b5",
556 "metadata": {},
557 "outputs": [],
558 "source": [
559 "meso_ts = m.timeseries(time_start, time_end, stid=\"BMFW1\", showemptystations = '0', vars=vars) # ask the object for data"
560 ]
561 },
562 {
563 "cell_type": "code",
564 "execution_count": null,
565 "id": "c2ea3ebd-393b-467f-af50-722bbefaca3e",
566 "metadata": {},
567 "outputs": [],
568 "source": [
569 "raws1 = format_raws(meso_ts['STATION'][0])"
570 ]
571 },
572 {
573 "cell_type": "code",
574 "execution_count": null,
575 "id": "2e3208ce-8b06-44f9-b35d-d6819638f025",
576 "metadata": {},
577 "outputs": [],
578 "source": [
579 "# Scenario 12: \n",
580 "## \n",
581 "dict1={\n",
582 " 'id': 'case12',\n",
583 " 'time': raws1['time'][0:1200],\n",
584 " 'rain': raws1['rain'][0:1200],\n",
585 " 'fm' : raws1['fm'][0:1200],\n",
586 " 'rh' : raws1['rh'][0:1200],\n",
587 " 'temp' : raws1['temp'][0:1200],\n",
588 " 'Ed' : raws1['Ed'][0:1200],\n",
589 " 'Ew' : raws1['Ew'][0:1200],\n",
590 " 'wind' : raws1['wind_speed'][0:1200],\n",
591 " 'solar' : raws1['solar_radiation'][0:1200],\n",
592 " 'STID' : raws1['STID'],\n",
593 " 'title' : 'RAWS Station BMFW1 #1',\n",
594 " 'descr' : 'Real surface level data, heave rain in train',\n",
595 " 'hours':1200,\n",
596 " 'h2':300,\n",
597 " 'other': {'lon': raws1['lon'], 'lat': raws1['lat']}\n",
598 "}\n",
599 "\n",
600 "test_dict['case12'] = dict1 # save to test dictionary"
601 ]
602 },
603 {
604 "cell_type": "code",
605 "execution_count": null,
606 "id": "5cc14349-f6d5-42bb-9d79-bd63662e217a",
607 "metadata": {},
608 "outputs": [],
609 "source": [
610 "plot_data(test_dict['case12'])"
611 ]
612 },
613 {
614 "cell_type": "code",
615 "execution_count": null,
616 "id": "af68c139-df05-416f-b66e-080baf744377",
617 "metadata": {},
618 "outputs": [],
619 "source": [
620 "## Read RAWS data with MesoPy\n",
621 "\n",
622 "time_start = \"202205010800\"\n",
623 "time_end = \"202206300900\"\n",
624 "\n",
625 "vars='air_temp,relative_humidity,precip_accum,fuel_moisture,wind_speed,solar_radiation'\n",
626 "\n",
627 "meso_ts = m.timeseries(time_start, time_end, stid=\"CMFW1\", showemptystations = '0', vars=vars) # ask the object for data\n",
628 "raws1 = format_raws(meso_ts['STATION'][0])"
629 ]
630 },
631 {
632 "cell_type": "code",
633 "execution_count": null,
634 "id": "ab8f065d-a586-475a-b675-4eead66c68db",
635 "metadata": {},
636 "outputs": [],
637 "source": [
638 "# Scenario 13: \n",
639 "## \n",
640 "dict1={\n",
641 " 'id': 'case13',\n",
642 " 'time': raws1['time'][0:1200],\n",
643 " 'rain': raws1['rain'][0:1200],\n",
644 " 'fm' : raws1['fm'][0:1200],\n",
645 " 'rh' : raws1['rh'][0:1200],\n",
646 " 'temp' : raws1['temp'][0:1200],\n",
647 " 'Ed' : raws1['Ed'][0:1200],\n",
648 " 'Ew' : raws1['Ew'][0:1200],\n",
649 " 'wind' : raws1['wind_speed'][0:1200],\n",
650 " 'solar' : raws1['solar_radiation'][0:1200],\n",
651 " 'STID' : raws1['STID'],\n",
652 " 'title' : 'RAWS Station CMFW1 #1',\n",
653 " 'descr' : 'Real surface level data, moderate rain in train and test',\n",
654 " 'hours':1200,\n",
655 " 'h2':300,\n",
656 " 'other': {'lon': raws1['lon'], 'lat': raws1['lat']}\n",
657 "}\n",
658 "\n",
659 "test_dict['case13'] = dict1 # save to test dictionary"
660 ]
661 },
662 {
663 "cell_type": "code",
664 "execution_count": null,
665 "id": "f1fa9304-4091-4924-805d-1f3f5f770851",
666 "metadata": {},
667 "outputs": [],
668 "source": [
669 "plot_data(test_dict['case13'])"
670 ]
671 },
672 {
673 "cell_type": "markdown",
674 "id": "1eb3cc2c-3ccc-4b11-8ae5-6faa61099b57",
675 "metadata": {},
676 "source": [
677 "## Save Output"
678 ]
679 },
680 {
681 "cell_type": "code",
682 "execution_count": null,
683 "id": "681e5f96-fc3d-4b94-9fed-5cf5eb42e287",
684 "metadata": {},
685 "outputs": [],
686 "source": [
687 "import pickle\n",
688 "with open('testing_dict.pickle', 'wb') as handle:\n",
689 " pickle.dump(test_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
690 ]
691 },
692 {
693 "cell_type": "markdown",
694 "id": "ae83a1de-dd2d-4a97-89a5-5e3e65497c83",
695 "metadata": {
696 "tags": []
697 },
698 "source": [
699 "## Sample KF Loop on Dict\n",
700 "\n",
701 "**Needs update to calculate rmse with new data funcs**"
702 ]
703 },
704 {
705 "cell_type": "code",
706 "execution_count": null,
707 "id": "ad3b5cd2-f929-4b87-bc33-d44274533e93",
708 "metadata": {},
709 "outputs": [],
710 "source": [
711 "with open('testing_dict.pickle', 'rb') as handle:\n",
712 " test_dict = pickle.load(handle)"
713 ]
714 },
715 {
716 "cell_type": "code",
717 "execution_count": null,
718 "id": "3fbda713-01d5-4bd7-917c-e60f4c27c488",
719 "metadata": {},
720 "outputs": [],
721 "source": [
722 "err_dict = {} # dictionary to save validation error for cases"
723 ]
724 },
725 {
726 "cell_type": "code",
727 "execution_count": null,
728 "id": "aa6d38af-67f8-44d2-ac66-3d05d45dd6b2",
729 "metadata": {},
730 "outputs": [],
731 "source": [
732 "# %%capture cap --no-stderr\n",
733 "\n",
734 "# # Loop through Test dictionary and run model\n",
735 "# # Save Print output as text file for quick reference\n",
736 "# with open(\"errors_KF.txt\", \"a\") as f:\n",
737 "# for key in [*test_dict.keys()]:\n",
738 "# print(key, ':', test_dict[key]['title'])\n",
739 "\n",
740 "# dict1 = test_dict[key]\n",
741 "# m,Ec = mod.run_augmented_kf(dict1)\n",
742 "# dict1['m']=m\n",
743 "\n",
744 "# errs = mse_data(dict1)\n",
745 "\n",
746 "# print('-'*25)\n",
747 "\n",
748 "# err_dict[key] = {\n",
749 "# 'title' : test_dict[key]['title'],\n",
750 "# 'train' : errs[0],\n",
751 "# 'test' : errs[1],\n",
752 "# 'model_type' : 'augmented KF',\n",
753 "# 'm_hash' : int(hash2(m)),\n",
754 "# 'm' : m,\n",
755 "# 'Ec' : Ec # equil. correction learned by training\n",
756 "# }"
757 ]
758 },
759 {
760 "cell_type": "code",
761 "execution_count": null,
762 "id": "d1dcf674-edb4-4353-add6-b8436ae0dde5",
763 "metadata": {},
764 "outputs": [],
765 "source": [
766 "# # Print above output for visualization, save to text file\n",
767 "\n",
768 "# print(cap.stdout)\n",
769 "\n",
770 "# with open('errors_KF.txt', 'w') as f:\n",
771 "# f.write(cap.stdout)"
772 ]
773 },
774 {
775 "cell_type": "code",
776 "execution_count": null,
777 "id": "fee3b8a1-7f0a-418e-8282-a7885c7cc39d",
778 "metadata": {},
779 "outputs": [],
780 "source": [
781 "## Save Validation Outputs as pickle file with all data\n",
782 "\n",
783 "# with open('errors_KF.pickle', 'wb') as handle:\n",
784 "# pickle.dump(err_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
785 ]
786 },
787 {
788 "cell_type": "code",
789 "execution_count": null,
790 "id": "344ae468-afa5-47f3-a18f-b6eb605df09e",
791 "metadata": {},
792 "outputs": [],
793 "source": []
794 }
795 ],
796 "metadata": {
797 "kernelspec": {
798 "display_name": "Python 3 (ipykernel)",
799 "language": "python",
800 "name": "python3"
801 },
802 "language_info": {
803 "codemirror_mode": {
804 "name": "ipython",
805 "version": 3
806 },
807 "file_extension": ".py",
808 "mimetype": "text/x-python",
809 "name": "python",
810 "nbconvert_exporter": "python",
811 "pygments_lexer": "ipython3",
812 "version": "3.11.3"
813 }
814 },
815 "nbformat": 4,
816 "nbformat_minor": 5
817 }