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1 1 import os
2 2 import datetime
3 3 import numpy
4 4
5 5 from schainpy.model.graphics.jroplot_base import Plot, plt
6 6 from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
7 7 from schainpy.utils import log
8 8 # libreria wradlib
9 9 import wradlib as wrl
10 10
11 11 EARTH_RADIUS = 6.3710e3
12 12
13 13
14 14 def ll2xy(lat1, lon1, lat2, lon2):
15 15
16 16 p = 0.017453292519943295
17 17 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
18 18 numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
19 19 r = 12742 * numpy.arcsin(numpy.sqrt(a))
20 20 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
21 21 * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
22 22 theta = -theta + numpy.pi/2
23 23 return r*numpy.cos(theta), r*numpy.sin(theta)
24 24
25 25
26 26 def km2deg(km):
27 27 '''
28 28 Convert distance in km to degrees
29 29 '''
30 30
31 31 return numpy.rad2deg(km/EARTH_RADIUS)
32 32
33 33
34 34
35 35 class SpectralMomentsPlot(SpectraPlot):
36 36 '''
37 37 Plot for Spectral Moments
38 38 '''
39 39 CODE = 'spc_moments'
40 40 # colormap = 'jet'
41 41 # plot_type = 'pcolor'
42 42
43 43 class DobleGaussianPlot(SpectraPlot):
44 44 '''
45 45 Plot for Double Gaussian Plot
46 46 '''
47 47 CODE = 'gaussian_fit'
48 48 # colormap = 'jet'
49 49 # plot_type = 'pcolor'
50 50
51 51 class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
52 52 '''
53 53 Plot SpectraCut with Double Gaussian Fit
54 54 '''
55 55 CODE = 'cut_gaussian_fit'
56 56
57 57 class SnrPlot(RTIPlot):
58 58 '''
59 59 Plot for SNR Data
60 60 '''
61 61
62 62 CODE = 'snr'
63 63 colormap = 'jet'
64 64
65 65 def update(self, dataOut):
66 66
67 67 data = {
68 68 'snr': 10*numpy.log10(dataOut.data_snr)
69 69 }
70 70
71 71 return data, {}
72 72
73 73 class DopplerPlot(RTIPlot):
74 74 '''
75 75 Plot for DOPPLER Data (1st moment)
76 76 '''
77 77
78 78 CODE = 'dop'
79 79 colormap = 'jet'
80 80
81 81 def update(self, dataOut):
82 82
83 83 data = {
84 84 'dop': 10*numpy.log10(dataOut.data_dop)
85 85 }
86 86
87 87 return data, {}
88 88
89 89 class PowerPlot(RTIPlot):
90 90 '''
91 91 Plot for Power Data (0 moment)
92 92 '''
93 93
94 94 CODE = 'pow'
95 95 colormap = 'jet'
96 96
97 97 def update(self, dataOut):
98 98 data = {
99 99 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
100 100 }
101 101 return data, {}
102 102
103 103 class SpectralWidthPlot(RTIPlot):
104 104 '''
105 105 Plot for Spectral Width Data (2nd moment)
106 106 '''
107 107
108 108 CODE = 'width'
109 109 colormap = 'jet'
110 110
111 111 def update(self, dataOut):
112 112
113 113 data = {
114 114 'width': dataOut.data_width
115 115 }
116 116
117 117 return data, {}
118 118
119 119 class SkyMapPlot(Plot):
120 120 '''
121 121 Plot for meteors detection data
122 122 '''
123 123
124 124 CODE = 'param'
125 125
126 126 def setup(self):
127 127
128 128 self.ncols = 1
129 129 self.nrows = 1
130 130 self.width = 7.2
131 131 self.height = 7.2
132 132 self.nplots = 1
133 133 self.xlabel = 'Zonal Zenith Angle (deg)'
134 134 self.ylabel = 'Meridional Zenith Angle (deg)'
135 135 self.polar = True
136 136 self.ymin = -180
137 137 self.ymax = 180
138 138 self.colorbar = False
139 139
140 140 def plot(self):
141 141
142 142 arrayParameters = numpy.concatenate(self.data['param'])
143 143 error = arrayParameters[:, -1]
144 144 indValid = numpy.where(error == 0)[0]
145 145 finalMeteor = arrayParameters[indValid, :]
146 146 finalAzimuth = finalMeteor[:, 3]
147 147 finalZenith = finalMeteor[:, 4]
148 148
149 149 x = finalAzimuth * numpy.pi / 180
150 150 y = finalZenith
151 151
152 152 ax = self.axes[0]
153 153
154 154 if ax.firsttime:
155 155 ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
156 156 else:
157 157 ax.plot.set_data(x, y)
158 158
159 159 dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
160 160 dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
161 161 title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
162 162 dt2,
163 163 len(x))
164 164 self.titles[0] = title
165 165
166 166
167 167 class GenericRTIPlot(Plot):
168 168 '''
169 169 Plot for data_xxxx object
170 170 '''
171 171
172 172 CODE = 'param'
173 173 colormap = 'viridis'
174 174 plot_type = 'pcolorbuffer'
175 175
176 176 def setup(self):
177 177 self.xaxis = 'time'
178 178 self.ncols = 1
179 179 self.nrows = self.data.shape('param')[0]
180 180 self.nplots = self.nrows
181 181 self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
182 182
183 183 if not self.xlabel:
184 184 self.xlabel = 'Time'
185 185
186 186 self.ylabel = 'Range [km]'
187 187 if not self.titles:
188 188 self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
189 189
190 190 def update(self, dataOut):
191 191
192 192 data = {
193 193 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
194 194 }
195 195
196 196 meta = {}
197 197
198 198 return data, meta
199 199
200 200 def plot(self):
201 201 # self.data.normalize_heights()
202 202 self.x = self.data.times
203 203 self.y = self.data.yrange
204 204 self.z = self.data['param']
205 205 self.z = 10*numpy.log10(self.z)
206 206 self.z = numpy.ma.masked_invalid(self.z)
207 207
208 208 if self.decimation is None:
209 209 x, y, z = self.fill_gaps(self.x, self.y, self.z)
210 210 else:
211 211 x, y, z = self.fill_gaps(*self.decimate())
212 212
213 213 for n, ax in enumerate(self.axes):
214 214
215 215 self.zmax = self.zmax if self.zmax is not None else numpy.max(
216 216 self.z[n])
217 217 self.zmin = self.zmin if self.zmin is not None else numpy.min(
218 218 self.z[n])
219 219
220 220 if ax.firsttime:
221 221 if self.zlimits is not None:
222 222 self.zmin, self.zmax = self.zlimits[n]
223 223
224 224 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
225 225 vmin=self.zmin,
226 226 vmax=self.zmax,
227 227 cmap=self.cmaps[n]
228 228 )
229 229 else:
230 230 if self.zlimits is not None:
231 231 self.zmin, self.zmax = self.zlimits[n]
232 232 ax.collections.remove(ax.collections[0])
233 233 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
234 234 vmin=self.zmin,
235 235 vmax=self.zmax,
236 236 cmap=self.cmaps[n]
237 237 )
238 238
239 239
240 240 class PolarMapPlot(Plot):
241 241 '''
242 242 Plot for weather radar
243 243 '''
244 244
245 245 CODE = 'param'
246 246 colormap = 'seismic'
247 247
248 248 def setup(self):
249 249 self.ncols = 1
250 250 self.nrows = 1
251 251 self.width = 9
252 252 self.height = 8
253 253 self.mode = self.data.meta['mode']
254 254 if self.channels is not None:
255 255 self.nplots = len(self.channels)
256 256 self.nrows = len(self.channels)
257 257 else:
258 258 self.nplots = self.data.shape(self.CODE)[0]
259 259 self.nrows = self.nplots
260 260 self.channels = list(range(self.nplots))
261 261 if self.mode == 'E':
262 262 self.xlabel = 'Longitude'
263 263 self.ylabel = 'Latitude'
264 264 else:
265 265 self.xlabel = 'Range (km)'
266 266 self.ylabel = 'Height (km)'
267 267 self.bgcolor = 'white'
268 268 self.cb_labels = self.data.meta['units']
269 269 self.lat = self.data.meta['latitude']
270 270 self.lon = self.data.meta['longitude']
271 271 self.xmin, self.xmax = float(
272 272 km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
273 273 self.ymin, self.ymax = float(
274 274 km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
275 275 # self.polar = True
276 276
277 277 def plot(self):
278 278
279 279 for n, ax in enumerate(self.axes):
280 280 data = self.data['param'][self.channels[n]]
281 281
282 282 zeniths = numpy.linspace(
283 283 0, self.data.meta['max_range'], data.shape[1])
284 284 if self.mode == 'E':
285 285 azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
286 286 r, theta = numpy.meshgrid(zeniths, azimuths)
287 287 x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
288 288 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
289 289 x = km2deg(x) + self.lon
290 290 y = km2deg(y) + self.lat
291 291 else:
292 292 azimuths = numpy.radians(self.data.yrange)
293 293 r, theta = numpy.meshgrid(zeniths, azimuths)
294 294 x, y = r*numpy.cos(theta), r*numpy.sin(theta)
295 295 self.y = zeniths
296 296
297 297 if ax.firsttime:
298 298 if self.zlimits is not None:
299 299 self.zmin, self.zmax = self.zlimits[n]
300 300 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
301 301 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
302 302 vmin=self.zmin,
303 303 vmax=self.zmax,
304 304 cmap=self.cmaps[n])
305 305 else:
306 306 if self.zlimits is not None:
307 307 self.zmin, self.zmax = self.zlimits[n]
308 308 ax.collections.remove(ax.collections[0])
309 309 ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
310 310 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
311 311 vmin=self.zmin,
312 312 vmax=self.zmax,
313 313 cmap=self.cmaps[n])
314 314
315 315 if self.mode == 'A':
316 316 continue
317 317
318 318 # plot district names
319 319 f = open('/data/workspace/schain_scripts/distrito.csv')
320 320 for line in f:
321 321 label, lon, lat = [s.strip() for s in line.split(',') if s]
322 322 lat = float(lat)
323 323 lon = float(lon)
324 324 # ax.plot(lon, lat, '.b', ms=2)
325 325 ax.text(lon, lat, label.decode('utf8'), ha='center',
326 326 va='bottom', size='8', color='black')
327 327
328 328 # plot limites
329 329 limites = []
330 330 tmp = []
331 331 for line in open('/data/workspace/schain_scripts/lima.csv'):
332 332 if '#' in line:
333 333 if tmp:
334 334 limites.append(tmp)
335 335 tmp = []
336 336 continue
337 337 values = line.strip().split(',')
338 338 tmp.append((float(values[0]), float(values[1])))
339 339 for points in limites:
340 340 ax.add_patch(
341 341 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
342 342
343 343 # plot Cuencas
344 344 for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
345 345 f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
346 346 values = [line.strip().split(',') for line in f]
347 347 points = [(float(s[0]), float(s[1])) for s in values]
348 348 ax.add_patch(Polygon(points, ec='b', fc='none'))
349 349
350 350 # plot grid
351 351 for r in (15, 30, 45, 60):
352 352 ax.add_artist(plt.Circle((self.lon, self.lat),
353 353 km2deg(r), color='0.6', fill=False, lw=0.2))
354 354 ax.text(
355 355 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
356 356 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
357 357 '{}km'.format(r),
358 358 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
359 359
360 360 if self.mode == 'E':
361 361 title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
362 362 label = 'E{:02d}'.format(int(self.data.meta['elevation']))
363 363 else:
364 364 title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
365 365 label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
366 366
367 367 self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
368 368 self.titles = ['{} {}'.format(
369 369 self.data.parameters[x], title) for x in self.channels]
370 370
371 371 class WeatherPlot(Plot):
372 372 CODE = 'weather'
373 373 plot_name = 'weather'
374 374 plot_type = 'ppistyle'
375 375 buffering = False
376 376
377 377 def setup(self):
378 378 self.ncols = 1
379 379 self.nrows = 1
380 380 self.nplots= 1
381 381 self.ylabel= 'Range [Km]'
382 382 self.titles= ['Weather']
383 383 self.colorbar=False
384 384 self.width =8
385 385 self.height =8
386 386 self.ini =0
387 387 self.len_azi =0
388 388 self.buffer_ini = None
389 389 self.buffer_azi = None
390 390 self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
391 391 self.flag =0
392 392 self.indicador= 0
393 393 self.last_data_azi = None
394 394 self.val_mean = None
395 395
396 396 def update(self, dataOut):
397 397
398 398 data = {}
399 399 meta = {}
400 400 if hasattr(dataOut, 'dataPP_POWER'):
401 401 factor = 1
402 402
403 403 if hasattr(dataOut, 'nFFTPoints'):
404 404 factor = dataOut.normFactor
405 405
406 406 ####print("factor",factor)
407 407 data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
408 408 ####print("weather",data['weather'])
409 409 data['azi'] = dataOut.data_azi
410 410 return data, meta
411 411
412 412 def get2List(self,angulos):
413 413 list1=[]
414 414 list2=[]
415 415 for i in reversed(range(len(angulos))):
416 416 diff_ = angulos[i]-angulos[i-1]
417 417 if diff_ >1.5:
418 418 list1.append(i-1)
419 419 list2.append(diff_)
420 420 return list(reversed(list1)),list(reversed(list2))
421 421
422 422 def fixData360(self,list_,ang_):
423 423 if list_[0]==-1:
424 424 vec = numpy.where(ang_<ang_[0])
425 425 ang_[vec] = ang_[vec]+360
426 426 return ang_
427 427 return ang_
428 428
429 429
430 430 def fixData360HL(self,angulos):
431 431 vec = numpy.where(angulos>=360)
432 432 angulos[vec]=angulos[vec]-360
433 433 return angulos
434 434
435 435 def search_pos(self,pos,list_):
436 436 for i in range(len(list_)):
437 437 if pos == list_[i]:
438 438 return True,i
439 439 i=None
440 440 return False,i
441 441
442 442 def fixDataComp(self,ang_,list1_,list2_):
443 443 size = len(ang_)
444 444 size2 = 0
445 445 for i in range(len(list2_)):
446 446 size2=size2+list2_[i]-1
447 447 new_size= size+size2
448 448 ang_new = numpy.zeros(new_size)
449 449 ang_new2 = numpy.zeros(new_size)
450 450
451 451 tmp = 0
452 452 c = 0
453 453 for i in range(len(ang_)):
454 454 ang_new[tmp +c] = ang_[i]
455 455 ang_new2[tmp+c] = ang_[i]
456 456 condition , value = self.search_pos(i,list1_)
457 457 if condition:
458 458 pos = tmp + c + 1
459 459 for k in range(list2_[value]-1):
460 460 ang_new[pos+k] = ang_new[pos+k-1]+1
461 461 ang_new2[pos+k] = numpy.nan
462 462 tmp = pos +k
463 463 c = 0
464 464 c=c+1
465 465 return ang_new,ang_new2
466 466
467 467
468 468 def globalCheckPED(self,angulos):
469 469 l1,l2 = self.get2List(angulos)
470 470 if len(l1)>0:
471 471 angulos2 = self.fixData360(list_=l1,ang_=angulos)
472 472 l1,l2 = self.get2List(angulos2)
473 473
474 474 ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2)
475 475 ang1_ = self.fixData360HL(ang1_)
476 476 ang2_ = self.fixData360HL(ang2_)
477 477
478 478 else:
479 479 ang1_= angulos
480 480 ang2_= angulos
481 481 return ang1_,ang2_
482 482
483 483 def analizeDATA(self,data_azi):
484 484 list1 = []
485 485 list2 = []
486 486 dat = data_azi
487 487 for i in reversed(range(1,len(dat))):
488 488 if dat[i]>dat[i-1]:
489 489 diff = int(dat[i])-int(dat[i-1])
490 490 else:
491 491 diff = 360+int(dat[i])-int(dat[i-1])
492 492 if diff > 1:
493 493 list1.append(i-1)
494 494 list2.append(diff-1)
495 495 return list1,list2
496 496
497 497 def fixDATANEW(self,data_azi,data_weather):
498 498 list1,list2 = self.analizeDATA(data_azi)
499 499 if len(list1)== 0:
500 500 return data_azi,data_weather
501 501 else:
502 502 resize = 0
503 503 for i in range(len(list2)):
504 504 resize= resize + list2[i]
505 505 new_data_azi = numpy.resize(data_azi,resize)
506 506 new_data_weather= numpy.resize(date_weather,resize)
507 507
508 508 for i in range(len(list2)):
509 509 j=0
510 510 position=list1[i]+1
511 511 for j in range(list2[i]):
512 512 new_data_azi[position+j]=new_data_azi[position+j-1]+1
513 513
514 514 return new_data_azi
515 515
516 516 def fixDATA(self,data_azi):
517 517 data=data_azi
518 518 for i in range(len(data)):
519 519 if numpy.isnan(data[i]):
520 520 data[i]=data[i-1]+1
521 521 return data
522 522
523 523 def replaceNAN(self,data_weather,data_azi,val):
524 524 ####print("----------------activeNEWFUNCTION")
525 525 data= data_azi
526 526 data_T= data_weather
527 527 ####print("data_azi",data_azi)
528 528 ####print("VAL:",val)
529 529 ####print("SHAPE",data_T.shape)
530 530 for i in range(len(data)):
531 531 if numpy.isnan(data[i]):
532 532 ####print("NAN")
533 data_T[i,:]=numpy.ones(data_T.shape[1])*val
534 #data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
533 #data_T[i,:]=numpy.ones(data_T.shape[1])*val
534 data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
535 535 return data_T
536 536
537 537 def const_ploteo(self,data_weather,data_azi,step,res):
538 538 if self.ini==0:
539 539 #------- AZIMUTH
540 540 n = (360/res)-len(data_azi)
541 541 #--------------------- new -------------------------
542 542 ####data_azi_old = data_azi
543 543 data_azi_new ,data_azi_old= self.globalCheckPED(data_azi)
544 544 #------------------------
545 545 ####data_azi_new = self.fixDATA(data_azi)
546 546 #ata_azi_new = self.fixDATANEW(data_azi)
547 547
548 548 start = data_azi_new[-1] + res
549 549 end = data_azi_new[0] - res
550 550 ##### new
551 551 self.last_data_azi = end
552 552 if start>end:
553 553 end = end + 360
554 554 azi_vacia = numpy.linspace(start,end,int(n))
555 555 azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
556 556 data_azi = numpy.hstack((data_azi_new,azi_vacia))
557 557 # RADAR
558 558 val_mean = numpy.mean(data_weather[:,-1])
559 559 self.val_mean = val_mean
560 560 data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
561 561 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
562 562 data_weather = numpy.vstack((data_weather,data_weather_cmp))
563 563 else:
564 564 # azimuth
565 565 flag=0
566 566 start_azi = self.res_azi[0]
567 567 #-----------new------------
568 568 data_azi ,data_azi_old= self.globalCheckPED(data_azi)
569 569 data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
570 570 #--------------------------
571 571 ####data_azi_old = data_azi
572 572 ### weather ###
573 573 ####data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
574 574
575 575 ####if numpy.isnan(data_azi[0]):
576 576 #### data_azi[0]=self.last_data_azi+1
577 577 ####data_azi = self.fixDATA(data_azi)
578 578 start = data_azi[0]
579 579 end = data_azi[-1]
580 580 self.last_data_azi= end
581 581 ####print("start",start)
582 582 ####print("end",end)
583 583 if start< start_azi:
584 584 start = start +360
585 585 if end <start_azi:
586 586 end = end +360
587 587 ####print("start",start)
588 588 ####print("end",end)
589 589 #### AQUI SERA LA MAGIA
590 590 pos_ini = int((start-start_azi)/res)
591 591 len_azi = len(data_azi)
592 592 if (360-pos_ini)<len_azi:
593 593 if pos_ini+1==360:
594 594 pos_ini=0
595 595 else:
596 596 flag=1
597 597 dif= 360-pos_ini
598 598 comp= len_azi-dif
599 599
600 600 #-----------------
601 601 ####print(pos_ini)
602 602 ####print(len_azi)
603 603 ####print("shape",self.res_azi.shape)
604 604 if flag==0:
605 605 # AZIMUTH
606 606 self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
607 607 # RADAR
608 608 self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
609 609 else:
610 610 # AZIMUTH
611 611 self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
612 612 self.res_azi[0:comp] = data_azi[dif:]
613 613 # RADAR
614 614 self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
615 615 self.res_weather[0:comp,:] = data_weather[dif:,:]
616 616 flag=0
617 617 data_azi = self.res_azi
618 618 data_weather = self.res_weather
619 619
620 620 return data_weather,data_azi
621 621
622 622 def plot(self):
623 623 #print("--------------------------------------",self.ini,"-----------------------------------")
624 624 #numpy.set_printoptions(suppress=True)
625 625 ####print("times: ",self.data.times)
626 626 thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
627 627 #print("times: ",thisDatetime)
628 628 data = self.data[-1]
629 629 ####ALTURA altura_tmp_h
630 630 ###print("Y RANGES",self.data.yrange,len(self.data.yrange))
631 631 ###altura_h = (data['weather'].shape[1])/10.0
632 632 ###stoprange = float(altura_h*0.3)#stoprange = float(33*1.5) por ahora 400
633 633 ###rangestep = float(0.03)
634 634 ###r = numpy.arange(0, stoprange, rangestep)
635 635 ###print("r",r,len(r))
636 636 #-----------------------------update----------------------
637 637 r= self.data.yrange
638 638 delta_height = r[1]-r[0]
639 639 #print("1",r)
640 640 r_mask= numpy.where(r>=0)[0]
641 641 r = numpy.arange(len(r_mask))*delta_height
642 642 #print("2",r)
643 643 self.y = 2*r
644 644 ######self.y = self.data.yrange
645 645 # RADAR
646 646 #data_weather = data['weather']
647 647 # PEDESTAL
648 648 #data_azi = data['azi']
649 649 res = 1
650 650 # STEP
651 651 step = (360/(res*data['weather'].shape[0]))
652 652 #print("shape wr_data", wr_data.shape)
653 653 #print("shape wr_azi",wr_azi.shape)
654 654 #print("step",step)
655 655 ####print("Time---->",self.data.times[-1],thisDatetime)
656 656 #print("alturas", len(self.y))numpy.where(r>=0)
657 657 self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res)
658 658 #numpy.set_printoptions(suppress=True)
659 659 #print("resultado",self.res_azi)
660 660 ###########################/DATA_RM/10_tmp/ch0###############################
661 661 ################# PLOTEO ###################
662 662 ##########################################################
663 663
664 664 for i,ax in enumerate(self.axes):
665 665 if ax.firsttime:
666 666 plt.clf()
667 667 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
668 668 else:
669 669 plt.clf()
670 670 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=8, vmax=35)
671 671 caax = cgax.parasites[0]
672 672 paax = cgax.parasites[1]
673 673 cbar = plt.gcf().colorbar(pm, pad=0.075)
674 674 caax.set_xlabel('x_range [km]')
675 675 caax.set_ylabel('y_range [km]')
676 676 plt.text(1.0, 1.05, 'azimuth '+str(thisDatetime)+" step "+str(self.ini), transform=caax.transAxes, va='bottom',ha='right')
677 677
678 678 self.ini= self.ini+1
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