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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
394 394 def update(self, dataOut):
395 395
396 396 data = {}
397 397 meta = {}
398 data['weather'] = 10*numpy.log10(dataOut.data_360[0]/(250.0))
398 if hasattr(dataOut, 'dataPP_POWER'):
399 factor = 1
400
401 if hasattr(dataOut, 'nFFTPoints'):
402 factor = dataOut.normFactor
403
404 print("factor",factor)
405 data['weather'] = 10*numpy.log10(dataOut.data_360[0]/(factor))
406 print("weather",data['weather'])
399 407 data['azi'] = dataOut.data_azi
400 408 return data, meta
401 409
402 410 def const_ploteo(self,data_weather,data_azi,step,res):
403 411 if self.ini==0:
404 412 #------- AZIMUTH
405 413 n = (360/res)-len(data_azi)
406 414 start = data_azi[-1] + res
407 415 end = data_azi[0] - res
408 416 if start>end:
409 417 end = end + 360
410 418 azi_vacia = numpy.linspace(start,end,int(n))
411 419 azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
412 420 data_azi = numpy.hstack((data_azi,azi_vacia))
413 421 # RADAR
414 422 val_mean = numpy.mean(data_weather[:,0])
415 423 data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
416 424 data_weather = numpy.vstack((data_weather,data_weather_cmp))
417 425 else:
418 426 # azimuth
419 427 flag=0
420 428 start_azi = self.res_azi[0]
421 429 start = data_azi[0]
422 430 end = data_azi[-1]
423 431 print("start",start)
424 432 print("end",end)
425 433 if start< start_azi:
426 434 start = start +360
427 435 if end <start_azi:
428 436 end = end +360
429 437
430 438 print("start",start)
431 439 print("end",end)
432 440 #### AQUI SERA LA MAGIA
433 441 pos_ini = int((start-start_azi)/res)
434 442 len_azi = len(data_azi)
435 443 if (360-pos_ini)<len_azi:
436 444 if pos_ini+1==360:
437 445 pos_ini=0
438 446 else:
439 447 flag=1
440 448 dif= 360-pos_ini
441 449 comp= len_azi-dif
442 450
443 451 print(pos_ini)
444 452 print(len_azi)
445 453 print("shape",self.res_azi.shape)
446 454 if flag==0:
447 455 # AZIMUTH
448 456 self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
449 457 # RADAR
450 458 self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
451 459 else:
452 460 # AZIMUTH
453 461 self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
454 462 self.res_azi[0:comp] = data_azi[dif:]
455 463 # RADAR
456 464 self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
457 465 self.res_weather[0:comp,:] = data_weather[dif:,:]
458 466 flag=0
459 467 data_azi = self.res_azi
460 468 data_weather = self.res_weather
461 469
462 470 return data_weather,data_azi
463 471
464 472 def plot(self):
465 473 print("--------------------------------------",self.ini,"-----------------------------------")
466 474 #numpy.set_printoptions(suppress=True)
467 475 #print(self.data.times)
468 476 thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1])
469 477 data = self.data[-1]
470 478 # ALTURA altura_tmp_h
471 479 altura_h = (data['weather'].shape[1])/10.0
472 480 stoprange = float(altura_h*1.5)#stoprange = float(33*1.5) por ahora 400
473 481 rangestep = float(0.15)
474 482 r = numpy.arange(0, stoprange, rangestep)
475 483 self.y = 2*r
476 484 # RADAR
477 485 #data_weather = data['weather']
478 486 # PEDESTAL
479 487 #data_azi = data['azi']
480 488 res = 1
481 489 # STEP
482 490 step = (360/(res*data['weather'].shape[0]))
483 491 #print("shape wr_data", wr_data.shape)
484 492 #print("shape wr_azi",wr_azi.shape)
485 493 #print("step",step)
486 494 print("Time---->",self.data.times[-1],thisDatetime)
487 495 #print("alturas", len(self.y))
488 496 self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'],data_azi=data['azi'],step=step,res=res)
489 497 #numpy.set_printoptions(suppress=True)
490 498 #print("resultado",self.res_azi)
491 499 ##########################################################
492 500 ################# PLOTEO ###################
493 501 ##########################################################
494 502
495 503 for i,ax in enumerate(self.axes):
496 504 if ax.firsttime:
497 505 plt.clf()
498 506 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=1, vmax=60)
499 507 else:
500 508 plt.clf()
501 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=0, vmax=60)
509 cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=1, vmax=60)
502 510 caax = cgax.parasites[0]
503 511 paax = cgax.parasites[1]
504 512 cbar = plt.gcf().colorbar(pm, pad=0.075)
505 513 caax.set_xlabel('x_range [km]')
506 514 caax.set_ylabel('y_range [km]')
507 515 plt.text(1.0, 1.05, 'azimuth '+str(thisDatetime)+"step"+str(self.ini), transform=caax.transAxes, va='bottom',ha='right')
508 516
509 517 self.ini= self.ini+1
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1 1 # Copyright (c) 2012-2020 Jicamarca Radio Observatory
2 2 # All rights reserved.
3 3 #
4 4 # Distributed under the terms of the BSD 3-clause license.
5 5 """Spectra processing Unit and operations
6 6
7 7 Here you will find the processing unit `SpectraProc` and several operations
8 8 to work with Spectra data type
9 9 """
10 10
11 11 import time
12 12 import itertools
13 13
14 14 import numpy
15 15
16 16 from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation
17 17 from schainpy.model.data.jrodata import Spectra
18 18 from schainpy.model.data.jrodata import hildebrand_sekhon
19 19 from schainpy.utils import log
20 20
21 21
22 22 class SpectraProc(ProcessingUnit):
23 23
24 24 def __init__(self):
25 25
26 26 ProcessingUnit.__init__(self)
27 27
28 28 self.buffer = None
29 29 self.firstdatatime = None
30 30 self.profIndex = 0
31 31 self.dataOut = Spectra()
32 32 self.id_min = None
33 33 self.id_max = None
34 34 self.setupReq = False #Agregar a todas las unidades de proc
35 35
36 36 def __updateSpecFromVoltage(self):
37 37
38 38 self.dataOut.timeZone = self.dataIn.timeZone
39 39 self.dataOut.dstFlag = self.dataIn.dstFlag
40 40 self.dataOut.errorCount = self.dataIn.errorCount
41 41 self.dataOut.useLocalTime = self.dataIn.useLocalTime
42 42 try:
43 43 self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
44 44 except:
45 45 pass
46 46 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
47 47 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
48 48 self.dataOut.channelList = self.dataIn.channelList
49 49 self.dataOut.heightList = self.dataIn.heightList
50 50 self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
51 51 self.dataOut.nProfiles = self.dataOut.nFFTPoints
52 52 self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
53 53 self.dataOut.utctime = self.firstdatatime
54 54 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData
55 55 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData
56 56 self.dataOut.flagShiftFFT = False
57 57 self.dataOut.nCohInt = self.dataIn.nCohInt
58 58 self.dataOut.nIncohInt = 1
59 59 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
60 60 self.dataOut.frequency = self.dataIn.frequency
61 61 self.dataOut.realtime = self.dataIn.realtime
62 62 self.dataOut.azimuth = self.dataIn.azimuth
63 63 self.dataOut.zenith = self.dataIn.zenith
64 64 self.dataOut.beam.codeList = self.dataIn.beam.codeList
65 65 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
66 66 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
67 67
68 68 def __getFft(self):
69 69 """
70 70 Convierte valores de Voltaje a Spectra
71 71
72 72 Affected:
73 73 self.dataOut.data_spc
74 74 self.dataOut.data_cspc
75 75 self.dataOut.data_dc
76 76 self.dataOut.heightList
77 77 self.profIndex
78 78 self.buffer
79 79 self.dataOut.flagNoData
80 80 """
81 81 fft_volt = numpy.fft.fft(
82 82 self.buffer, n=self.dataOut.nFFTPoints, axis=1)
83 83 fft_volt = fft_volt.astype(numpy.dtype('complex'))
84 84 dc = fft_volt[:, 0, :]
85 85
86 86 # calculo de self-spectra
87 87 fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
88 88 spc = fft_volt * numpy.conjugate(fft_volt)
89 89 spc = spc.real
90 90
91 91 blocksize = 0
92 92 blocksize += dc.size
93 93 blocksize += spc.size
94 94
95 95 cspc = None
96 96 pairIndex = 0
97 97 if self.dataOut.pairsList != None:
98 98 # calculo de cross-spectra
99 99 cspc = numpy.zeros(
100 100 (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
101 101 for pair in self.dataOut.pairsList:
102 102 if pair[0] not in self.dataOut.channelList:
103 103 raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % (
104 104 str(pair), str(self.dataOut.channelList)))
105 105 if pair[1] not in self.dataOut.channelList:
106 106 raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % (
107 107 str(pair), str(self.dataOut.channelList)))
108 108
109 109 cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \
110 110 numpy.conjugate(fft_volt[pair[1], :, :])
111 111 pairIndex += 1
112 112 blocksize += cspc.size
113 113
114 114 self.dataOut.data_spc = spc
115 115 self.dataOut.data_cspc = cspc
116 116 self.dataOut.data_dc = dc
117 117 self.dataOut.blockSize = blocksize
118 118 self.dataOut.flagShiftFFT = False
119 119
120 120 def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False):
121
121
122 122 if self.dataIn.type == "Spectra":
123 123 self.dataOut.copy(self.dataIn)
124 124 if shift_fft:
125 125 #desplaza a la derecha en el eje 2 determinadas posiciones
126 126 shift = int(self.dataOut.nFFTPoints/2)
127 127 self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1)
128 128
129 129 if self.dataOut.data_cspc is not None:
130 130 #desplaza a la derecha en el eje 2 determinadas posiciones
131 131 self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1)
132 132 if pairsList:
133 133 self.__selectPairs(pairsList)
134 134
135 135 elif self.dataIn.type == "Voltage":
136 136
137 137 self.dataOut.flagNoData = True
138 138
139 139 if nFFTPoints == None:
140 140 raise ValueError("This SpectraProc.run() need nFFTPoints input variable")
141 141
142 142 if nProfiles == None:
143 143 nProfiles = nFFTPoints
144 144
145 145 if ippFactor == None:
146 146 self.dataOut.ippFactor = 1
147
147
148 148 self.dataOut.nFFTPoints = nFFTPoints
149 149
150 150 if self.buffer is None:
151 151 self.buffer = numpy.zeros((self.dataIn.nChannels,
152 152 nProfiles,
153 153 self.dataIn.nHeights),
154 154 dtype='complex')
155 155
156 156 if self.dataIn.flagDataAsBlock:
157 157 nVoltProfiles = self.dataIn.data.shape[1]
158 158
159 159 if nVoltProfiles == nProfiles:
160 160 self.buffer = self.dataIn.data.copy()
161 161 self.profIndex = nVoltProfiles
162 162
163 163 elif nVoltProfiles < nProfiles:
164 164
165 165 if self.profIndex == 0:
166 166 self.id_min = 0
167 167 self.id_max = nVoltProfiles
168 168
169 169 self.buffer[:, self.id_min:self.id_max,
170 170 :] = self.dataIn.data
171 171 self.profIndex += nVoltProfiles
172 172 self.id_min += nVoltProfiles
173 173 self.id_max += nVoltProfiles
174 174 else:
175 175 raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % (
176 176 self.dataIn.type, self.dataIn.data.shape[1], nProfiles))
177 177 self.dataOut.flagNoData = True
178 178 else:
179 179 self.buffer[:, self.profIndex, :] = self.dataIn.data.copy()
180 180 self.profIndex += 1
181 181
182 182 if self.firstdatatime == None:
183 183 self.firstdatatime = self.dataIn.utctime
184 184
185 185 if self.profIndex == nProfiles:
186 186 self.__updateSpecFromVoltage()
187 187 if pairsList == None:
188 188 self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)]
189 189 else:
190 190 self.dataOut.pairsList = pairsList
191 191 self.__getFft()
192 192 self.dataOut.flagNoData = False
193 193 self.firstdatatime = None
194 194 self.profIndex = 0
195 195 else:
196 196 raise ValueError("The type of input object '%s' is not valid".format(
197 197 self.dataIn.type))
198 198
199 199 def __selectPairs(self, pairsList):
200 200
201 201 if not pairsList:
202 202 return
203 203
204 204 pairs = []
205 205 pairsIndex = []
206 206
207 207 for pair in pairsList:
208 208 if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList:
209 209 continue
210 210 pairs.append(pair)
211 211 pairsIndex.append(pairs.index(pair))
212 212
213 213 self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex]
214 214 self.dataOut.pairsList = pairs
215 215
216 216 return
217
217
218 218 def selectFFTs(self, minFFT, maxFFT ):
219 219 """
220 Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango
220 Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango
221 221 minFFT<= FFT <= maxFFT
222 222 """
223
223
224 224 if (minFFT > maxFFT):
225 225 raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT))
226 226
227 227 if (minFFT < self.dataOut.getFreqRange()[0]):
228 228 minFFT = self.dataOut.getFreqRange()[0]
229 229
230 230 if (maxFFT > self.dataOut.getFreqRange()[-1]):
231 231 maxFFT = self.dataOut.getFreqRange()[-1]
232 232
233 233 minIndex = 0
234 234 maxIndex = 0
235 235 FFTs = self.dataOut.getFreqRange()
236 236
237 237 inda = numpy.where(FFTs >= minFFT)
238 238 indb = numpy.where(FFTs <= maxFFT)
239 239
240 240 try:
241 241 minIndex = inda[0][0]
242 242 except:
243 243 minIndex = 0
244 244
245 245 try:
246 246 maxIndex = indb[0][-1]
247 247 except:
248 248 maxIndex = len(FFTs)
249 249
250 250 self.selectFFTsByIndex(minIndex, maxIndex)
251 251
252 252 return 1
253
253
254 254 def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None):
255 255 newheis = numpy.where(
256 256 self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex])
257 257
258 258 if hei_ref != None:
259 259 newheis = numpy.where(self.dataOut.heightList > hei_ref)
260 260
261 261 minIndex = min(newheis[0])
262 262 maxIndex = max(newheis[0])
263 263 data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
264 264 heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
265 265
266 266 # determina indices
267 267 nheis = int(self.dataOut.radarControllerHeaderObj.txB /
268 268 (self.dataOut.heightList[1] - self.dataOut.heightList[0]))
269 269 avg_dB = 10 * \
270 270 numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0))
271 271 beacon_dB = numpy.sort(avg_dB)[-nheis:]
272 272 beacon_heiIndexList = []
273 273 for val in avg_dB.tolist():
274 274 if val >= beacon_dB[0]:
275 275 beacon_heiIndexList.append(avg_dB.tolist().index(val))
276 276
277 277 #data_spc = data_spc[:,:,beacon_heiIndexList]
278 278 data_cspc = None
279 279 if self.dataOut.data_cspc is not None:
280 280 data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
281 281 #data_cspc = data_cspc[:,:,beacon_heiIndexList]
282 282
283 283 data_dc = None
284 284 if self.dataOut.data_dc is not None:
285 285 data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
286 286 #data_dc = data_dc[:,beacon_heiIndexList]
287 287
288 288 self.dataOut.data_spc = data_spc
289 289 self.dataOut.data_cspc = data_cspc
290 290 self.dataOut.data_dc = data_dc
291 291 self.dataOut.heightList = heightList
292 292 self.dataOut.beacon_heiIndexList = beacon_heiIndexList
293 293
294 294 return 1
295 295
296 296 def selectFFTsByIndex(self, minIndex, maxIndex):
297 297 """
298
298
299 299 """
300 300
301 301 if (minIndex < 0) or (minIndex > maxIndex):
302 302 raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))
303 303
304 304 if (maxIndex >= self.dataOut.nProfiles):
305 305 maxIndex = self.dataOut.nProfiles-1
306 306
307 307 #Spectra
308 308 data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:]
309 309
310 310 data_cspc = None
311 311 if self.dataOut.data_cspc is not None:
312 312 data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:]
313 313
314 314 data_dc = None
315 315 if self.dataOut.data_dc is not None:
316 316 data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:]
317 317
318 318 self.dataOut.data_spc = data_spc
319 319 self.dataOut.data_cspc = data_cspc
320 320 self.dataOut.data_dc = data_dc
321
321
322 322 self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1])
323 323 self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1]
324 324 self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1]
325 325
326 326 return 1
327 327
328 328 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
329 329 # validacion de rango
330 330 if minHei == None:
331 331 minHei = self.dataOut.heightList[0]
332 332
333 333 if maxHei == None:
334 334 maxHei = self.dataOut.heightList[-1]
335 335
336 336 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
337 337 print('minHei: %.2f is out of the heights range' % (minHei))
338 338 print('minHei is setting to %.2f' % (self.dataOut.heightList[0]))
339 339 minHei = self.dataOut.heightList[0]
340 340
341 341 if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
342 342 print('maxHei: %.2f is out of the heights range' % (maxHei))
343 343 print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1]))
344 344 maxHei = self.dataOut.heightList[-1]
345 345
346 346 # validacion de velocidades
347 347 velrange = self.dataOut.getVelRange(1)
348 348
349 349 if minVel == None:
350 350 minVel = velrange[0]
351 351
352 352 if maxVel == None:
353 353 maxVel = velrange[-1]
354 354
355 355 if (minVel < velrange[0]) or (minVel > maxVel):
356 356 print('minVel: %.2f is out of the velocity range' % (minVel))
357 357 print('minVel is setting to %.2f' % (velrange[0]))
358 358 minVel = velrange[0]
359 359
360 360 if (maxVel > velrange[-1]) or (maxVel < minVel):
361 361 print('maxVel: %.2f is out of the velocity range' % (maxVel))
362 362 print('maxVel is setting to %.2f' % (velrange[-1]))
363 363 maxVel = velrange[-1]
364 364
365 365 # seleccion de indices para rango
366 366 minIndex = 0
367 367 maxIndex = 0
368 368 heights = self.dataOut.heightList
369 369
370 370 inda = numpy.where(heights >= minHei)
371 371 indb = numpy.where(heights <= maxHei)
372 372
373 373 try:
374 374 minIndex = inda[0][0]
375 375 except:
376 376 minIndex = 0
377 377
378 378 try:
379 379 maxIndex = indb[0][-1]
380 380 except:
381 381 maxIndex = len(heights)
382 382
383 383 if (minIndex < 0) or (minIndex > maxIndex):
384 384 raise ValueError("some value in (%d,%d) is not valid" % (
385 385 minIndex, maxIndex))
386 386
387 387 if (maxIndex >= self.dataOut.nHeights):
388 388 maxIndex = self.dataOut.nHeights - 1
389 389
390 390 # seleccion de indices para velocidades
391 391 indminvel = numpy.where(velrange >= minVel)
392 392 indmaxvel = numpy.where(velrange <= maxVel)
393 393 try:
394 394 minIndexVel = indminvel[0][0]
395 395 except:
396 396 minIndexVel = 0
397 397
398 398 try:
399 399 maxIndexVel = indmaxvel[0][-1]
400 400 except:
401 401 maxIndexVel = len(velrange)
402 402
403 403 # seleccion del espectro
404 404 data_spc = self.dataOut.data_spc[:,
405 405 minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1]
406 406 # estimacion de ruido
407 407 noise = numpy.zeros(self.dataOut.nChannels)
408 408
409 409 for channel in range(self.dataOut.nChannels):
410 410 daux = data_spc[channel, :, :]
411 411 sortdata = numpy.sort(daux, axis=None)
412 412 noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt)
413 413
414 414 self.dataOut.noise_estimation = noise.copy()
415 415
416 416 return 1
417 417
418 418 class removeDC(Operation):
419 419
420 420 def run(self, dataOut, mode=2):
421 421 self.dataOut = dataOut
422 422 jspectra = self.dataOut.data_spc
423 423 jcspectra = self.dataOut.data_cspc
424 424
425 425 num_chan = jspectra.shape[0]
426 426 num_hei = jspectra.shape[2]
427 427
428 428 if jcspectra is not None:
429 429 jcspectraExist = True
430 430 num_pairs = jcspectra.shape[0]
431 431 else:
432 432 jcspectraExist = False
433 433
434 434 freq_dc = int(jspectra.shape[1] / 2)
435 435 ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
436 436 ind_vel = ind_vel.astype(int)
437 437
438 438 if ind_vel[0] < 0:
439 439 ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof
440 440
441 441 if mode == 1:
442 442 jspectra[:, freq_dc, :] = (
443 443 jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION
444 444
445 445 if jcspectraExist:
446 446 jcspectra[:, freq_dc, :] = (
447 447 jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2
448 448
449 449 if mode == 2:
450 450
451 451 vel = numpy.array([-2, -1, 1, 2])
452 452 xx = numpy.zeros([4, 4])
453 453
454 454 for fil in range(4):
455 455 xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
456 456
457 457 xx_inv = numpy.linalg.inv(xx)
458 458 xx_aux = xx_inv[0, :]
459 459
460 for ich in range(num_chan):
460 for ich in range(num_chan):
461 461 yy = jspectra[ich, ind_vel, :]
462 462 jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
463 463
464 464 junkid = jspectra[ich, freq_dc, :] <= 0
465 465 cjunkid = sum(junkid)
466 466
467 467 if cjunkid.any():
468 468 jspectra[ich, freq_dc, junkid.nonzero()] = (
469 469 jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
470 470
471 471 if jcspectraExist:
472 472 for ip in range(num_pairs):
473 473 yy = jcspectra[ip, ind_vel, :]
474 474 jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy)
475 475
476 476 self.dataOut.data_spc = jspectra
477 477 self.dataOut.data_cspc = jcspectra
478 478
479 479 return self.dataOut
480 480
481 481 class removeInterference(Operation):
482 482
483 483 def removeInterference2(self):
484
484
485 485 cspc = self.dataOut.data_cspc
486 486 spc = self.dataOut.data_spc
487 Heights = numpy.arange(cspc.shape[2])
487 Heights = numpy.arange(cspc.shape[2])
488 488 realCspc = numpy.abs(cspc)
489
489
490 490 for i in range(cspc.shape[0]):
491 491 LinePower= numpy.sum(realCspc[i], axis=0)
492 492 Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)]
493 493 SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ]
494 494 InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 )
495 495 InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)]
496 496 InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)]
497
498
497
498
499 499 InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) )
500 500 #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax]))
501 501 if len(InterferenceRange)<int(cspc.shape[1]*0.3):
502 502 cspc[i,InterferenceRange,:] = numpy.NaN
503
503
504 504 self.dataOut.data_cspc = cspc
505
505
506 506 def removeInterference(self, interf = 2, hei_interf = None, nhei_interf = None, offhei_interf = None):
507 507
508 508 jspectra = self.dataOut.data_spc
509 509 jcspectra = self.dataOut.data_cspc
510 510 jnoise = self.dataOut.getNoise()
511 511 num_incoh = self.dataOut.nIncohInt
512 512
513 513 num_channel = jspectra.shape[0]
514 514 num_prof = jspectra.shape[1]
515 515 num_hei = jspectra.shape[2]
516 516
517 517 # hei_interf
518 518 if hei_interf is None:
519 519 count_hei = int(num_hei / 2)
520 520 hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei
521 521 hei_interf = numpy.asarray(hei_interf)[0]
522 522 # nhei_interf
523 523 if (nhei_interf == None):
524 524 nhei_interf = 5
525 525 if (nhei_interf < 1):
526 526 nhei_interf = 1
527 527 if (nhei_interf > count_hei):
528 528 nhei_interf = count_hei
529 529 if (offhei_interf == None):
530 530 offhei_interf = 0
531 531
532 532 ind_hei = list(range(num_hei))
533 533 # mask_prof = numpy.asarray(range(num_prof - 2)) + 1
534 534 # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
535 535 mask_prof = numpy.asarray(list(range(num_prof)))
536 536 num_mask_prof = mask_prof.size
537 537 comp_mask_prof = [0, num_prof / 2]
538 538
539 539 # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
540 540 if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
541 541 jnoise = numpy.nan
542 542 noise_exist = jnoise[0] < numpy.Inf
543 543
544 544 # Subrutina de Remocion de la Interferencia
545 545 for ich in range(num_channel):
546 546 # Se ordena los espectros segun su potencia (menor a mayor)
547 547 power = jspectra[ich, mask_prof, :]
548 548 power = power[:, hei_interf]
549 549 power = power.sum(axis=0)
550 550 psort = power.ravel().argsort()
551 551
552 552 # Se estima la interferencia promedio en los Espectros de Potencia empleando
553 553 junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range(
554 554 offhei_interf, nhei_interf + offhei_interf))]]]
555 555
556 556 if noise_exist:
557 557 # tmp_noise = jnoise[ich] / num_prof
558 558 tmp_noise = jnoise[ich]
559 559 junkspc_interf = junkspc_interf - tmp_noise
560 560 #junkspc_interf[:,comp_mask_prof] = 0
561 561
562 562 jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf
563 563 jspc_interf = jspc_interf.transpose()
564 564 # Calculando el espectro de interferencia promedio
565 565 noiseid = numpy.where(
566 566 jspc_interf <= tmp_noise / numpy.sqrt(num_incoh))
567 567 noiseid = noiseid[0]
568 568 cnoiseid = noiseid.size
569 569 interfid = numpy.where(
570 570 jspc_interf > tmp_noise / numpy.sqrt(num_incoh))
571 571 interfid = interfid[0]
572 572 cinterfid = interfid.size
573 573
574 574 if (cnoiseid > 0):
575 575 jspc_interf[noiseid] = 0
576 576
577 577 # Expandiendo los perfiles a limpiar
578 578 if (cinterfid > 0):
579 579 new_interfid = (
580 580 numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof
581 581 new_interfid = numpy.asarray(new_interfid)
582 582 new_interfid = {x for x in new_interfid}
583 583 new_interfid = numpy.array(list(new_interfid))
584 584 new_cinterfid = new_interfid.size
585 585 else:
586 586 new_cinterfid = 0
587 587
588 588 for ip in range(new_cinterfid):
589 589 ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort()
590 590 jspc_interf[new_interfid[ip]
591 591 ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]]
592 592
593 593 jspectra[ich, :, ind_hei] = jspectra[ich, :,
594 594 ind_hei] - jspc_interf # Corregir indices
595 595
596 596 # Removiendo la interferencia del punto de mayor interferencia
597 597 ListAux = jspc_interf[mask_prof].tolist()
598 598 maxid = ListAux.index(max(ListAux))
599 599
600 600 if cinterfid > 0:
601 601 for ip in range(cinterfid * (interf == 2) - 1):
602 602 ind = (jspectra[ich, interfid[ip], :] < tmp_noise *
603 603 (1 + 1 / numpy.sqrt(num_incoh))).nonzero()
604 604 cind = len(ind)
605 605
606 606 if (cind > 0):
607 607 jspectra[ich, interfid[ip], ind] = tmp_noise * \
608 608 (1 + (numpy.random.uniform(cind) - 0.5) /
609 609 numpy.sqrt(num_incoh))
610 610
611 611 ind = numpy.array([-2, -1, 1, 2])
612 612 xx = numpy.zeros([4, 4])
613 613
614 614 for id1 in range(4):
615 615 xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
616 616
617 617 xx_inv = numpy.linalg.inv(xx)
618 618 xx = xx_inv[:, 0]
619 619 ind = (ind + maxid + num_mask_prof) % num_mask_prof
620 620 yy = jspectra[ich, mask_prof[ind], :]
621 621 jspectra[ich, mask_prof[maxid], :] = numpy.dot(
622 622 yy.transpose(), xx)
623 623
624 624 indAux = (jspectra[ich, :, :] < tmp_noise *
625 625 (1 - 1 / numpy.sqrt(num_incoh))).nonzero()
626 626 jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \
627 627 (1 - 1 / numpy.sqrt(num_incoh))
628 628
629 629 # Remocion de Interferencia en el Cross Spectra
630 630 if jcspectra is None:
631 631 return jspectra, jcspectra
632 632 num_pairs = int(jcspectra.size / (num_prof * num_hei))
633 633 jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
634 634
635 635 for ip in range(num_pairs):
636 636
637 637 #-------------------------------------------
638 638
639 639 cspower = numpy.abs(jcspectra[ip, mask_prof, :])
640 640 cspower = cspower[:, hei_interf]
641 641 cspower = cspower.sum(axis=0)
642 642
643 643 cspsort = cspower.ravel().argsort()
644 644 junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range(
645 645 offhei_interf, nhei_interf + offhei_interf))]]]
646 646 junkcspc_interf = junkcspc_interf.transpose()
647 647 jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf
648 648
649 649 ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
650 650
651 651 median_real = int(numpy.median(numpy.real(
652 652 junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
653 653 median_imag = int(numpy.median(numpy.imag(
654 654 junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
655 655 comp_mask_prof = [int(e) for e in comp_mask_prof]
656 656 junkcspc_interf[comp_mask_prof, :] = numpy.complex(
657 657 median_real, median_imag)
658 658
659 659 for iprof in range(num_prof):
660 660 ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()
661 661 jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]]
662 662
663 663 # Removiendo la Interferencia
664 664 jcspectra[ip, :, ind_hei] = jcspectra[ip,
665 665 :, ind_hei] - jcspc_interf
666 666
667 667 ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
668 668 maxid = ListAux.index(max(ListAux))
669 669
670 670 ind = numpy.array([-2, -1, 1, 2])
671 671 xx = numpy.zeros([4, 4])
672 672
673 673 for id1 in range(4):
674 674 xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
675 675
676 676 xx_inv = numpy.linalg.inv(xx)
677 677 xx = xx_inv[:, 0]
678 678
679 679 ind = (ind + maxid + num_mask_prof) % num_mask_prof
680 680 yy = jcspectra[ip, mask_prof[ind], :]
681 681 jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx)
682 682
683 683 # Guardar Resultados
684 684 self.dataOut.data_spc = jspectra
685 685 self.dataOut.data_cspc = jcspectra
686 686
687 687 return 1
688 688
689 689 def run(self, dataOut, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None, mode=1):
690 690
691 691 self.dataOut = dataOut
692 692
693 693 if mode == 1:
694 694 self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None)
695 695 elif mode == 2:
696 696 self.removeInterference2()
697 697
698 698 return self.dataOut
699 699
700 700
701 701 class IncohInt(Operation):
702 702
703 703 __profIndex = 0
704 704 __withOverapping = False
705 705
706 706 __byTime = False
707 707 __initime = None
708 708 __lastdatatime = None
709 709 __integrationtime = None
710 710
711 711 __buffer_spc = None
712 712 __buffer_cspc = None
713 713 __buffer_dc = None
714 714
715 715 __dataReady = False
716 716
717 717 __timeInterval = None
718 718
719 719 n = None
720 720
721 721 def __init__(self):
722 722
723 723 Operation.__init__(self)
724 724
725 725 def setup(self, n=None, timeInterval=None, overlapping=False):
726 726 """
727 727 Set the parameters of the integration class.
728 728
729 729 Inputs:
730 730
731 731 n : Number of coherent integrations
732 732 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
733 733 overlapping :
734 734
735 735 """
736 736
737 737 self.__initime = None
738 738 self.__lastdatatime = 0
739 739
740 740 self.__buffer_spc = 0
741 741 self.__buffer_cspc = 0
742 742 self.__buffer_dc = 0
743 743
744 744 self.__profIndex = 0
745 745 self.__dataReady = False
746 746 self.__byTime = False
747 747
748 748 if n is None and timeInterval is None:
749 749 raise ValueError("n or timeInterval should be specified ...")
750 750
751 751 if n is not None:
752 752 self.n = int(n)
753 753 else:
754
754
755 755 self.__integrationtime = int(timeInterval)
756 756 self.n = None
757 757 self.__byTime = True
758 758
759 759 def putData(self, data_spc, data_cspc, data_dc):
760 760 """
761 761 Add a profile to the __buffer_spc and increase in one the __profileIndex
762 762
763 763 """
764 764
765 765 self.__buffer_spc += data_spc
766 766
767 767 if data_cspc is None:
768 768 self.__buffer_cspc = None
769 769 else:
770 770 self.__buffer_cspc += data_cspc
771 771
772 772 if data_dc is None:
773 773 self.__buffer_dc = None
774 774 else:
775 775 self.__buffer_dc += data_dc
776 776
777 777 self.__profIndex += 1
778 778
779 779 return
780 780
781 781 def pushData(self):
782 782 """
783 783 Return the sum of the last profiles and the profiles used in the sum.
784 784
785 785 Affected:
786 786
787 787 self.__profileIndex
788 788
789 789 """
790 790
791 791 data_spc = self.__buffer_spc
792 792 data_cspc = self.__buffer_cspc
793 793 data_dc = self.__buffer_dc
794 794 n = self.__profIndex
795 795
796 796 self.__buffer_spc = 0
797 797 self.__buffer_cspc = 0
798 798 self.__buffer_dc = 0
799 799 self.__profIndex = 0
800 800
801 801 return data_spc, data_cspc, data_dc, n
802 802
803 803 def byProfiles(self, *args):
804 804
805 805 self.__dataReady = False
806 806 avgdata_spc = None
807 807 avgdata_cspc = None
808 808 avgdata_dc = None
809 809
810 810 self.putData(*args)
811 811
812 812 if self.__profIndex == self.n:
813 813
814 814 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
815 815 self.n = n
816 816 self.__dataReady = True
817 817
818 818 return avgdata_spc, avgdata_cspc, avgdata_dc
819 819
820 820 def byTime(self, datatime, *args):
821 821
822 822 self.__dataReady = False
823 823 avgdata_spc = None
824 824 avgdata_cspc = None
825 825 avgdata_dc = None
826 826
827 827 self.putData(*args)
828 828
829 829 if (datatime - self.__initime) >= self.__integrationtime:
830 830 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
831 831 self.n = n
832 832 self.__dataReady = True
833 833
834 834 return avgdata_spc, avgdata_cspc, avgdata_dc
835 835
836 836 def integrate(self, datatime, *args):
837 837
838 838 if self.__profIndex == 0:
839 839 self.__initime = datatime
840 840
841 841 if self.__byTime:
842 842 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(
843 843 datatime, *args)
844 844 else:
845 845 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
846 846
847 847 if not self.__dataReady:
848 848 return None, None, None, None
849 849
850 850 return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc
851 851
852 852 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
853 853 if n == 1:
854 854 return dataOut
855
855
856 856 dataOut.flagNoData = True
857 857
858 858 if not self.isConfig:
859 859 self.setup(n, timeInterval, overlapping)
860 860 self.isConfig = True
861 861
862 862 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
863 863 dataOut.data_spc,
864 864 dataOut.data_cspc,
865 865 dataOut.data_dc)
866 866
867 867 if self.__dataReady:
868 868
869 869 dataOut.data_spc = avgdata_spc
870 870 dataOut.data_cspc = avgdata_cspc
871 dataOut.data_dc = avgdata_dc
871 dataOut.data_dc = avgdata_dc
872 872 dataOut.nIncohInt *= self.n
873 873 dataOut.utctime = avgdatatime
874 874 dataOut.flagNoData = False
875 875
876 876 return dataOut
877 877
878 878 class dopplerFlip(Operation):
879
879
880 880 def run(self, dataOut):
881 881 # arreglo 1: (num_chan, num_profiles, num_heights)
882 self.dataOut = dataOut
882 self.dataOut = dataOut
883 883 # JULIA-oblicua, indice 2
884 884 # arreglo 2: (num_profiles, num_heights)
885 885 jspectra = self.dataOut.data_spc[2]
886 886 jspectra_tmp = numpy.zeros(jspectra.shape)
887 887 num_profiles = jspectra.shape[0]
888 888 freq_dc = int(num_profiles / 2)
889 889 # Flip con for
890 890 for j in range(num_profiles):
891 891 jspectra_tmp[num_profiles-j-1]= jspectra[j]
892 892 # Intercambio perfil de DC con perfil inmediato anterior
893 893 jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1]
894 894 jspectra_tmp[freq_dc]= jspectra[freq_dc]
895 895 # canal modificado es re-escrito en el arreglo de canales
896 896 self.dataOut.data_spc[2] = jspectra_tmp
897 897
898 return self.dataOut No newline at end of file
898 return self.dataOut
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@@ -1,216 +1,216
1 1 # Ing. AVP
2 2 # 06/10/2021
3 3 # ARCHIVO DE LECTURA
4 4 import os, sys
5 5 import datetime
6 6 import time
7 7 from schainpy.controller import Project
8 8 #### NOTA###########################################
9 9 # INPUT :
10 10 # VELOCIDAD PARAMETRO : V = 2Β°/seg
11 11 # MODO PULSE PAIR O MOMENTOS: 0 : Pulse Pair ,1 : Momentos
12 12 ######################################################
13 13 ##### PROCESAMIENTO ##################################
14 14 ##### OJO TENER EN CUENTA EL n= para el Pulse Pair ##
15 15 ##### O EL n= nFFTPoints ###
16 16 ######################################################
17 17 ######## BUSCAMOS EL numero de IPP equivalente 1Β°#####
18 18 ######## Sea V la velocidad del Pedestal en Β°/seg#####
19 19 ######## 1Β° sera Recorrido en un tiempo de 1/V ######
20 20 ######## IPP del Radar 400 useg --> 60 Km ############
21 21 ######## n = 1/(V(Β°/seg)*IPP(Km)) , NUMERO DE IPP ##
22 22 ######## n = 1/(V*IPP) #############################
23 23 ######## VELOCIDAD DEL PEDESTAL ######################
24 24 print("SETUP- RADAR METEOROLOGICO")
25 25 V = 10
26 26 mode = 1
27 27 #path = '/DATA_RM/23/6v'
28 28 ####path = '/DATA_RM/TEST_INTEGRACION_2M'
29 29 #path = '/DATA_RM/TEST_19OCTUBRE/10MHZ'
30 30 path = '/DATA_RM/WR_20_OCT'
31 31 #### path_ped='/DATA_RM/TEST_PEDESTAL/P20211012-082745'
32 #### path_ped='/DATA_RM/TEST_PEDESTAL/P20211019-192244'
32 ####path_ped='/DATA_RM/TEST_PEDESTAL/P20211019-192244'
33 33 figpath_pp = "/home/soporte/Pictures/TEST_PP"
34 34 figpath_spec = "/home/soporte/Pictures/TEST_MOM"
35 plot = 1
36 integration = 0
35 plot = 0
36 integration = 1
37 37 save = 0
38 38 if save == 1:
39 39 if mode==0:
40 40 path_save = '/DATA_RM/TEST_HDF5_PP_23/6v'
41 41 path_save = '/DATA_RM/TEST_HDF5_PP'
42 42 path_save = '/DATA_RM/TEST_HDF5_PP_100'
43 43 else:
44 44 path_save = '/DATA_RM/TEST_HDF5_SPEC_23_V2/6v'
45 45
46 46 print("* PATH data ADQ :", path)
47 47 print("* Velocidad Pedestal :",V,"Β°/seg")
48 48 ############################ NRO Perfiles PROCESAMIENTO ###################
49 49 V=V
50 50 IPP=400*1e-6
51 51 n= int(1/(V*IPP))
52 52 print("* n - NRO Perfiles Proc:", n )
53 53 ################################## MODE ###################################
54 54 print("* Modo de Operacion :",mode)
55 55 if mode ==0:
56 56 print("* Met. Seleccionado : Pulse Pair")
57 57 else:
58 58 print("* Met. Momentos : Momentos")
59 59
60 60 ################################## MODE ###################################
61 61 print("* Grabado de datos :",save)
62 62 if save ==1:
63 63 if mode==0:
64 64 ope= "Pulse Pair"
65 65 else:
66 66 ope= "Momentos"
67 67 print("* Path-Save Data -", ope , path_save)
68 68
69 69 print("* Integracion de datos :",integration)
70 70
71 71 time.sleep(15)
72 72 #remotefolder = "/home/wmaster/graficos"
73 73 #######################################################################
74 74 ################# RANGO DE PLOTEO######################################
75 75 dBmin = '1'
76 76 dBmax = '65'
77 77 xmin = '13.2'
78 78 xmax = '13.5'
79 79 ymin = '0'
80 80 ymax = '60'
81 81 #######################################################################
82 82 ########################FECHA##########################################
83 83 str = datetime.date.today()
84 84 today = str.strftime("%Y/%m/%d")
85 85 str2 = str - datetime.timedelta(days=1)
86 86 yesterday = str2.strftime("%Y/%m/%d")
87 87 #######################################################################
88 88 ########################SIGNAL CHAIN ##################################
89 89 #######################################################################
90 90 desc = "USRP_test"
91 91 filename = "USRP_processing.xml"
92 92 controllerObj = Project()
93 93 controllerObj.setup(id = '191', name='Test_USRP', description=desc)
94 94 #######################################################################
95 95 ######################## UNIDAD DE LECTURA#############################
96 96 #######################################################################
97 97 readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader',
98 98 path=path,
99 99 startDate="2021/01/01",#today,
100 100 endDate="2021/12/30",#today,
101 101 startTime='00:00:00',
102 102 endTime='23:59:59',
103 103 delay=0,
104 104 #set=0,
105 105 online=0,
106 106 walk=1,
107 107 ippKm = 60)
108 108
109 109 opObj11 = readUnitConfObj.addOperation(name='printInfo')
110 110
111 111 procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())
112 112
113 113 if mode ==0:
114 114 ####################### METODO PULSE PAIR ######################################################################
115 115 opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other')
116 116 opObj11.addParameter(name='n', value=int(n), format='int')#10 VOY A USAR 250 DADO QUE LA VELOCIDAD ES 10 GRADOS
117 117 #opObj11.addParameter(name='removeDC', value=1, format='int')
118 118 ####################### METODO Parametros ######################################################################
119 119 procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId())
120 120 if plot==1:
121 121 opObj11 = procUnitConfObjB.addOperation(name='GenericRTIPlot',optype='external')
122 122 opObj11.addParameter(name='attr_data', value='dataPP_POWER')
123 123 opObj11.addParameter(name='colormap', value='jet')
124 124 opObj11.addParameter(name='xmin', value=xmin)
125 125 opObj11.addParameter(name='xmax', value=xmax)
126 126 opObj11.addParameter(name='zmin', value=dBmin)
127 127 opObj11.addParameter(name='zmax', value=dBmax)
128 128 opObj11.addParameter(name='save', value=figpath_pp)
129 129 opObj11.addParameter(name='showprofile', value=0)
130 130 opObj11.addParameter(name='save_period', value=10)
131 131
132 132 ####################### METODO ESCRITURA #######################################################################
133 133 if save==1:
134 134 opObj10 = procUnitConfObjB.addOperation(name='HDFWriter')
135 135 opObj10.addParameter(name='path',value=path_save)
136 136 #opObj10.addParameter(name='mode',value=0)
137 137 opObj10.addParameter(name='blocksPerFile',value='100',format='int')
138 138 opObj10.addParameter(name='metadataList',value='utctimeInit,timeZone,paramInterval,profileIndex,channelList,heightList,flagDataAsBlock',format='list')
139 139 opObj10.addParameter(name='dataList',value='dataPP_POWER,dataPP_DOP,utctime',format='list')#,format='list'
140 140 if integration==1:
141 141 V=10
142 142 blocksPerfile=360
143 143 print("* Velocidad del Pedestal:",V)
144 144 tmp_blocksPerfile = 100
145 145 f_a_p= int(tmp_blocksPerfile/V)
146 146
147 147 opObj11 = procUnitConfObjB.addOperation(name='PedestalInformation')
148 148 opObj11.addParameter(name='path_ped', value=path_ped)
149 149 #opObj11.addParameter(name='path_adq', value=path_adq)
150 150 opObj11.addParameter(name='t_Interval_p', value='0.01', format='float')
151 151 opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int')
152 152 opObj11.addParameter(name='n_Muestras_p', value='100', format='float')
153 153 opObj11.addParameter(name='f_a_p', value=f_a_p, format='int')
154 154 opObj11.addParameter(name='online', value='0', format='int')
155 155
156 156 opObj11 = procUnitConfObjB.addOperation(name='Block360')
157 157 opObj11.addParameter(name='n', value='10', format='int')
158 158 opObj11.addParameter(name='mode', value=mode, format='int')
159 159
160 160 # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180
161 161
162 162 opObj11= procUnitConfObjB.addOperation(name='WeatherPlot',optype='other')
163 163
164 164
165 165 else:
166 166 ####################### METODO SPECTROS ######################################################################
167 167 procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId())
168 168 procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int')
169 169 procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int')
170 170
171 171 procUnitConfObjC = controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId())
172 172 procUnitConfObjC.addOperation(name='SpectralMoments')
173 173 if plot==1:
174 174 dBmin = '1'
175 175 dBmax = '65'
176 176 opObj11 = procUnitConfObjC.addOperation(name='PowerPlot',optype='external')
177 177 opObj11.addParameter(name='xmin', value=xmin)
178 178 opObj11.addParameter(name='xmax', value=xmax)
179 179 opObj11.addParameter(name='zmin', value=dBmin)
180 180 opObj11.addParameter(name='zmax', value=dBmax)
181 181 opObj11.addParameter(name='save', value=figpath_spec)
182 182 opObj11.addParameter(name='showprofile', value=0)
183 183 opObj11.addParameter(name='save_period', value=10)
184 184
185 185 if save==1:
186 186 opObj10 = procUnitConfObjC.addOperation(name='HDFWriter')
187 187 opObj10.addParameter(name='path',value=path_save)
188 188 #opObj10.addParameter(name='mode',value=0)
189 189 opObj10.addParameter(name='blocksPerFile',value='360',format='int')
190 190 #opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex
191 191 opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex
192 192 opObj10.addParameter(name='dataList',value='data_pow,data_dop,utctime',format='list')#,format='list'
193 193
194 194 if integration==1:
195 195 V=10
196 196 blocksPerfile=360
197 197 print("* Velocidad del Pedestal:",V)
198 198 tmp_blocksPerfile = 100
199 199 f_a_p= int(tmp_blocksPerfile/V)
200 200
201 201 opObj11 = procUnitConfObjC.addOperation(name='PedestalInformation')
202 202 opObj11.addParameter(name='path_ped', value=path_ped)
203 203 #opObj11.addParameter(name='path_adq', value=path_adq)
204 204 opObj11.addParameter(name='t_Interval_p', value='0.01', format='float')
205 205 opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int')
206 206 opObj11.addParameter(name='n_Muestras_p', value='100', format='float')
207 207 opObj11.addParameter(name='f_a_p', value=f_a_p, format='int')
208 208 opObj11.addParameter(name='online', value='0', format='int')
209 209
210 210 opObj11 = procUnitConfObjC.addOperation(name='Block360')
211 211 opObj11.addParameter(name='n', value='10', format='int')
212 212 opObj11.addParameter(name='mode', value=mode, format='int')
213 213
214 214 # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180
215 215 opObj11= procUnitConfObjC.addOperation(name='WeatherPlot',optype='other')
216 216 controllerObj.start()
Show file before | Show file after | Hide comments
@@ -1,217 +1,217
1 1 # Ing. AVP
2 2 # 06/10/2021
3 3 # ARCHIVO DE LECTURA
4 4 import os, sys
5 5 import datetime
6 6 import time
7 7 from schainpy.controller import Project
8 8 #### NOTA###########################################
9 9 # INPUT :
10 10 # VELOCIDAD PARAMETRO : V = 2Β°/seg
11 11 # MODO PULSE PAIR O MOMENTOS: 0 : Pulse Pair ,1 : Momentos
12 12 ######################################################
13 13 ##### PROCESAMIENTO ##################################
14 14 ##### OJO TENER EN CUENTA EL n= para el Pulse Pair ##
15 15 ##### O EL n= nFFTPoints ###
16 16 ######################################################
17 17 ######## BUSCAMOS EL numero de IPP equivalente 1Β°#####
18 18 ######## Sea V la velocidad del Pedestal en Β°/seg#####
19 19 ######## 1Β° sera Recorrido en un tiempo de 1/V ######
20 20 ######## IPP del Radar 400 useg --> 60 Km ############
21 21 ######## n = 1/(V(Β°/seg)*IPP(Km)) , NUMERO DE IPP ##
22 22 ######## n = 1/(V*IPP) #############################
23 23 ######## VELOCIDAD DEL PEDESTAL ######################
24 24 print("SETUP- RADAR METEOROLOGICO")
25 25 V = 10
26 26 mode = 1
27 27 #path = '/DATA_RM/23/6v'
28 28 #path = '/DATA_RM/TEST_INTEGRACION_2M'
29 29 path = '/DATA_RM/WR_20_OCT'
30 30
31 31 #path_ped='/DATA_RM/TEST_PEDESTAL/P20211012-082745'
32 32 path_ped='/DATA_RM/TEST_PEDESTAL/P20211020-131248'
33 33
34 34 figpath_pp = "/home/soporte/Pictures/TEST_PP"
35 35 figpath_mom = "/home/soporte/Pictures/TEST_MOM"
36 36 plot = 0
37 37 integration = 1
38 38 save = 0
39 39 if save == 1:
40 40 if mode==0:
41 41 path_save = '/DATA_RM/TEST_HDF5_PP_23/6v'
42 42 path_save = '/DATA_RM/TEST_HDF5_PP'
43 43 path_save = '/DATA_RM/TEST_HDF5_PP_100'
44 44 else:
45 45 path_save = '/DATA_RM/TEST_HDF5_SPEC_23_V2/6v'
46 46
47 47 print("* PATH data ADQ :", path)
48 48 print("* Velocidad Pedestal :",V,"Β°/seg")
49 49 ############################ NRO Perfiles PROCESAMIENTO ###################
50 50 V=V
51 51 IPP=400*1e-6
52 52 n= int(1/(V*IPP))
53 53 print("* n - NRO Perfiles Proc:", n )
54 54 ################################## MODE ###################################
55 55 print("* Modo de Operacion :",mode)
56 56 if mode ==0:
57 57 print("* Met. Seleccionado : Pulse Pair")
58 58 else:
59 59 print("* Met. Momentos : Momentos")
60 60
61 61 ################################## MODE ###################################
62 62 print("* Grabado de datos :",save)
63 63 if save ==1:
64 64 if mode==0:
65 65 ope= "Pulse Pair"
66 66 else:
67 67 ope= "Momentos"
68 68 print("* Path-Save Data -", ope , path_save)
69 69
70 70 print("* Integracion de datos :",integration)
71 71
72 time.sleep(15)
72 time.sleep(5)
73 73 #remotefolder = "/home/wmaster/graficos"
74 74 #######################################################################
75 75 ################# RANGO DE PLOTEO######################################
76 76 dBmin = '1'
77 77 dBmax = '85'
78 78 xmin = '15'
79 79 xmax = '15.25'
80 80 ymin = '0'
81 81 ymax = '600'
82 82 #######################################################################
83 83 ########################FECHA##########################################
84 84 str = datetime.date.today()
85 85 today = str.strftime("%Y/%m/%d")
86 86 str2 = str - datetime.timedelta(days=1)
87 87 yesterday = str2.strftime("%Y/%m/%d")
88 88 #######################################################################
89 89 ########################SIGNAL CHAIN ##################################
90 90 #######################################################################
91 91 desc = "USRP_test"
92 92 filename = "USRP_processing.xml"
93 93 controllerObj = Project()
94 94 controllerObj.setup(id = '191', name='Test_USRP', description=desc)
95 95 #######################################################################
96 96 ######################## UNIDAD DE LECTURA#############################
97 97 #######################################################################
98 98 readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader',
99 99 path=path,
100 100 startDate="2021/01/01",#today,
101 101 endDate="2021/12/30",#today,
102 102 startTime='00:00:00',
103 103 endTime='23:59:59',
104 104 delay=0,
105 105 #set=0,
106 106 online=0,
107 107 walk=1,
108 108 ippKm = 60)
109 109
110 110 opObj11 = readUnitConfObj.addOperation(name='printInfo')
111 111
112 112 procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())
113 113
114 114 if mode ==0:
115 115 ####################### METODO PULSE PAIR ######################################################################
116 116 opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other')
117 117 opObj11.addParameter(name='n', value=int(n), format='int')#10 VOY A USAR 250 DADO QUE LA VELOCIDAD ES 10 GRADOS
118 118 #opObj11.addParameter(name='removeDC', value=1, format='int')
119 119 ####################### METODO Parametros ######################################################################
120 120 procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId())
121 121 if plot==1:
122 122 opObj11 = procUnitConfObjB.addOperation(name='GenericRTIPlot',optype='external')
123 123 opObj11.addParameter(name='attr_data', value='dataPP_POW')
124 124 opObj11.addParameter(name='colormap', value='jet')
125 125 opObj11.addParameter(name='xmin', value=xmin)
126 126 opObj11.addParameter(name='xmax', value=xmax)
127 127 opObj11.addParameter(name='zmin', value=dBmin)
128 128 opObj11.addParameter(name='zmax', value=dBmax)
129 129 opObj11.addParameter(name='save', value=figpath_pp)
130 130 opObj11.addParameter(name='showprofile', value=0)
131 131 opObj11.addParameter(name='save_period', value=50)
132 132
133 133 ####################### METODO ESCRITURA #######################################################################
134 134 if save==1:
135 135 opObj10 = procUnitConfObjB.addOperation(name='HDFWriter')
136 136 opObj10.addParameter(name='path',value=path_save)
137 137 #opObj10.addParameter(name='mode',value=0)
138 138 opObj10.addParameter(name='blocksPerFile',value='100',format='int')
139 139 opObj10.addParameter(name='metadataList',value='utctimeInit,timeZone,paramInterval,profileIndex,channelList,heightList,flagDataAsBlock',format='list')
140 140 opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,utctime',format='list')#,format='list'
141 141 if integration==1:
142 142 V=10
143 143 blocksPerfile=360
144 144 print("* Velocidad del Pedestal:",V)
145 145 tmp_blocksPerfile = 100
146 146 f_a_p= int(tmp_blocksPerfile/V)
147 147
148 148 opObj11 = procUnitConfObjB.addOperation(name='PedestalInformation')
149 149 opObj11.addParameter(name='path_ped', value=path_ped)
150 150 #opObj11.addParameter(name='path_adq', value=path_adq)
151 151 opObj11.addParameter(name='t_Interval_p', value='0.01', format='float')
152 152 opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int')
153 153 opObj11.addParameter(name='n_Muestras_p', value='100', format='float')
154 154 opObj11.addParameter(name='f_a_p', value=f_a_p, format='int')
155 155 opObj11.addParameter(name='online', value='0', format='int')
156 156
157 157 opObj11 = procUnitConfObjB.addOperation(name='Block360')
158 158 opObj11.addParameter(name='n', value='10', format='int')
159 159 opObj11.addParameter(name='mode', value=mode, format='int')
160 160
161 161 # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180
162 162
163 163 opObj11= procUnitConfObjB.addOperation(name='WeatherPlot',optype='other')
164 164
165 165
166 166 else:
167 167 ####################### METODO SPECTROS ######################################################################
168 168 procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId())
169 169 procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int')
170 170 procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int')
171 171
172 172 procUnitConfObjC = controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId())
173 173 procUnitConfObjC.addOperation(name='SpectralMoments')
174 174 if plot==1:
175 175 dBmin = '1'
176 176 dBmax = '65'
177 177 opObj11 = procUnitConfObjC.addOperation(name='PowerPlot',optype='external')
178 178 opObj11.addParameter(name='xmin', value=xmin)
179 179 opObj11.addParameter(name='xmax', value=xmax)
180 180 opObj11.addParameter(name='zmin', value=dBmin)
181 181 opObj11.addParameter(name='zmax', value=dBmax)
182 182 opObj11.addParameter(name='save', value=figpath_mom)
183 183 opObj11.addParameter(name='showprofile', value=0)
184 184 opObj11.addParameter(name='save_period', value=100)
185 185
186 186 if save==1:
187 187 opObj10 = procUnitConfObjC.addOperation(name='HDFWriter')
188 188 opObj10.addParameter(name='path',value=path_save)
189 189 #opObj10.addParameter(name='mode',value=0)
190 190 opObj10.addParameter(name='blocksPerFile',value='360',format='int')
191 191 #opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex
192 192 opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex
193 193 opObj10.addParameter(name='dataList',value='data_pow,data_dop,utctime',format='list')#,format='list'
194 194
195 195 if integration==1:
196 196 V=10
197 197 blocksPerfile=360
198 198 print("* Velocidad del Pedestal:",V)
199 199 tmp_blocksPerfile = 100
200 200 f_a_p= int(tmp_blocksPerfile/V)
201 201
202 202 opObj11 = procUnitConfObjC.addOperation(name='PedestalInformation')
203 203 opObj11.addParameter(name='path_ped', value=path_ped)
204 204 #opObj11.addParameter(name='path_adq', value=path_adq)
205 205 opObj11.addParameter(name='t_Interval_p', value='0.01', format='float')
206 206 opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int')
207 207 opObj11.addParameter(name='n_Muestras_p', value='100', format='float')
208 208 opObj11.addParameter(name='f_a_p', value=f_a_p, format='int')
209 209 opObj11.addParameter(name='online', value='0', format='int')
210 210
211 211 opObj11 = procUnitConfObjC.addOperation(name='Block360')
212 opObj11.addParameter(name='n', value='30', format='int')
212 opObj11.addParameter(name='n', value='10', format='int')
213 213 opObj11.addParameter(name='mode', value=mode, format='int')
214 214
215 215 # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180
216 216 opObj11= procUnitConfObjC.addOperation(name='WeatherPlot',optype='other')
217 217 controllerObj.start()
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