| @@ -220,6 +220,158 class SpectraPlot(Figure): | |||
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220 | 220 | wr_period=wr_period, |
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221 | 221 | thisDatetime=thisDatetime) |
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222 | 222 | |
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223 | class ACFPlot(Figure): | |
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224 | ||
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225 | isConfig = None | |
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226 | __nsubplots = None | |
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227 | ||
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228 | WIDTHPROF = None | |
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229 | HEIGHTPROF = None | |
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230 | PREFIX = 'acf' | |
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231 | ||
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232 | def __init__(self, **kwargs): | |
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233 | Figure.__init__(self, **kwargs) | |
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234 | self.isConfig = False | |
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235 | self.__nsubplots = 1 | |
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236 | ||
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237 | self.PLOT_CODE = POWER_CODE | |
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238 | ||
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239 | self.WIDTH = 700 | |
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240 | self.HEIGHT = 500 | |
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241 | self.counter_imagwr = 0 | |
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242 | ||
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243 | def getSubplots(self): | |
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244 | ncol = 1 | |
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245 | nrow = 1 | |
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246 | ||
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247 | return nrow, ncol | |
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248 | ||
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249 | def setup(self, id, nplots, wintitle, show): | |
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250 | ||
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251 | self.nplots = nplots | |
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252 | ||
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253 | ncolspan = 1 | |
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254 | colspan = 1 | |
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255 | ||
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256 | self.createFigure(id = id, | |
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257 | wintitle = wintitle, | |
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258 | widthplot = self.WIDTH, | |
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259 | heightplot = self.HEIGHT, | |
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260 | show=show) | |
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261 | ||
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262 | nrow, ncol = self.getSubplots() | |
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263 | ||
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264 | counter = 0 | |
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265 | for y in range(nrow): | |
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266 | for x in range(ncol): | |
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267 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) | |
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268 | ||
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269 | def run(self, dataOut, id, wintitle="", channelList=None, | |
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270 | xmin=None, xmax=None, ymin=None, ymax=None, | |
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271 | save=False, figpath='./', figfile=None, show=True, | |
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272 | ftp=False, wr_period=1, server=None, | |
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273 | folder=None, username=None, password=None, | |
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274 | xaxis="frequency"): | |
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275 | ||
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276 | ||
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277 | if channelList == None: | |
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278 | channelIndexList = dataOut.channelIndexList | |
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279 | channelList = dataOut.channelList | |
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280 | else: | |
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281 | channelIndexList = [] | |
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282 | for channel in channelList: | |
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283 | if channel not in dataOut.channelList: | |
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284 | raise ValueError, "Channel %d is not in dataOut.channelList" | |
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285 | channelIndexList.append(dataOut.channelList.index(channel)) | |
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286 | ||
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287 | factor = dataOut.normFactor | |
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288 | ||
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289 | y = dataOut.getHeiRange() | |
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290 | ||
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291 | #z = dataOut.data_spc/factor | |
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292 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
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293 | print deltaHeight | |
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294 | z = dataOut.data_spc | |
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295 | ||
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296 | #z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
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297 | shape = dataOut.data_spc.shape | |
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298 | for i in range(shape[0]): | |
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299 | for j in range(shape[2]): | |
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300 | z[i,:,j]= (z[i,:,j]+1.0)*deltaHeight*5/2.0 + j*deltaHeight | |
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301 | #z[i,:,j]= (z[i,:,j]+1.0)*deltaHeight*dataOut.step/2.0 + j*deltaHeight*dataOut.step | |
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302 | ||
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303 | hei_index = numpy.arange(shape[2]) | |
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304 | #print hei_index.shape | |
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305 | #b = [] | |
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306 | #for i in range(hei_index.shape[0]): | |
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307 | # if hei_index[i]%30 == 0: | |
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308 | # b.append(hei_index[i]) | |
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309 | ||
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310 | #hei_index= numpy.array(b) | |
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311 | hei_index = hei_index[300:320] | |
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312 | #hei_index = numpy.arange(20)*30+80 | |
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313 | hei_index= numpy.arange(20)*5 | |
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314 | if xaxis == "frequency": | |
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315 | x = dataOut.getFreqRange()/1000. | |
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316 | zdB = 10*numpy.log10(z[0,:,hei_index]) | |
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317 | xlabel = "Frequency (kHz)" | |
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318 | ylabel = "Power (dB)" | |
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319 | ||
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320 | elif xaxis == "time": | |
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321 | x = dataOut.getAcfRange() | |
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322 | zdB = z[0,:,hei_index] | |
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323 | xlabel = "Time (ms)" | |
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324 | ylabel = "ACF" | |
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325 | ||
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326 | else: | |
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327 | x = dataOut.getVelRange() | |
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328 | zdB = 10*numpy.log10(z[0,:,hei_index]) | |
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329 | xlabel = "Velocity (m/s)" | |
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330 | ylabel = "Power (dB)" | |
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331 | ||
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332 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) | |
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333 | title = wintitle + " ACF Plot %s" %(thisDatetime.strftime("%d-%b-%Y")) | |
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334 | ||
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335 | if not self.isConfig: | |
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336 | ||
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337 | nplots = 1 | |
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338 | ||
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339 | self.setup(id=id, | |
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340 | nplots=nplots, | |
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341 | wintitle=wintitle, | |
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342 | show=show) | |
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343 | ||
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344 | if xmin == None: xmin = numpy.nanmin(x)*0.9 | |
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345 | if xmax == None: xmax = numpy.nanmax(x)*1.1 | |
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346 | if ymin == None: ymin = numpy.nanmin(zdB) | |
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347 | if ymax == None: ymax = numpy.nanmax(zdB) | |
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348 | ||
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349 | self.isConfig = True | |
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350 | ||
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351 | self.setWinTitle(title) | |
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352 | ||
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353 | title = "Spectra Cuts: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
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354 | axes = self.axesList[0] | |
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355 | ||
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356 | legendlabels = ["Range = %dKm" %y[i] for i in hei_index] | |
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357 | ||
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358 | axes.pmultilineyaxis( x, zdB, | |
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359 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
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360 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, | |
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361 | ytick_visible=True, nxticks=5, | |
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362 | grid='x') | |
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363 | ||
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364 | self.draw() | |
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365 | ||
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366 | self.save(figpath=figpath, | |
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367 | figfile=figfile, | |
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368 | save=save, | |
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369 | ftp=ftp, | |
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370 | wr_period=wr_period, | |
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371 | thisDatetime=thisDatetime) | |
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372 | ||
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373 | ||
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374 | ||
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223 | 375 | class CrossSpectraPlot(Figure): |
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224 | 376 | |
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225 | 377 | isConfig = None |
| @@ -12,6 +12,7 TOTAL_CODE = 6 #Total Power. | |||
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12 | 12 | DRIFT_CODE = 7 #Drifts graphics. |
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13 | 13 | HEIGHT_CODE = 8 #Height profile. |
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14 | 14 | PHASE_CODE = 9 #Signal Phase. |
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15 | AFC_CODE = 10 #Autocorrelation function. | |
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15 | 16 | |
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16 | 17 | POWER_CODE = 16 |
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17 | 18 | NOISE_CODE = 17 |
| @@ -65,6 +65,8 class SpectraProc(ProcessingUnit): | |||
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65 | 65 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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66 | 66 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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67 | 67 | |
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68 | self.dataOut.step = self.dataIn.step | |
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69 | ||
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68 | 70 | def __getFft(self): |
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69 | 71 | """ |
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70 | 72 | Convierte valores de Voltaje a Spectra |
| @@ -149,11 +149,18 class SpectraAFCProc(ProcessingUnit): | |||
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149 | 149 | data = numpy.fft.ifft(spc, axis=1) |
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150 | 150 | data = numpy.fft.fftshift(data, axes=(1,)) |
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151 | 151 | spc = data.real |
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152 | spc = spc[0,:,0] / numpy.max(numpy.abs(spc[0,:,0])) | |
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153 | print spc | |
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154 | import matplotlib.pyplot as plt | |
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155 | #plt.plot(spc[10:]) | |
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156 | plt.show() | |
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152 | shape = spc.shape #nchannels, nprofiles, nsamples | |
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153 | ||
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154 | #print spc.shape | |
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155 | for i in range(shape[0]): | |
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156 | for j in range(shape[2]): | |
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157 | spc[i,:,j]= spc[i,:,j] / numpy.max(numpy.abs(spc[i,:,j])) | |
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158 | #spc = spc[0,:,250] / numpy.max(numpy.abs(spc[0,:,250])) | |
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159 | #print spc.shape | |
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160 | #import matplotlib.pyplot as plt | |
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161 | #print spc[0:10] | |
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162 | #plt.plot(spc[0,:,350]) | |
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163 | #plt.show() | |
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157 | 164 | |
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158 | 165 | |
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159 | 166 | self.dataOut.data_spc = spc |
| @@ -1209,7 +1209,7 class SSheightProfiles(Operation): | |||
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1209 | 1209 | self.__nProfiles = dataOut.nProfiles |
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1210 | 1210 | self.__nHeis = dataOut.nHeights |
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1211 | 1211 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
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1212 | print "shape",shape | |
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1212 | #print "shape",shape | |
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1213 | 1213 | #last test |
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1214 | 1214 | residue = (shape[1] - self.nsamples) % self.step |
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1215 | 1215 | if residue != 0: |
| @@ -1248,6 +1248,8 class SSheightProfiles(Operation): | |||
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1248 | 1248 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
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1249 | 1249 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
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1250 | 1250 | |
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1251 | ||
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1252 | ||
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1251 | 1253 | dataOut.data = self.sshProfiles |
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1252 | 1254 | dataOut.flagNoData = False |
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1253 | 1255 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
| @@ -1255,7 +1257,7 class SSheightProfiles(Operation): | |||
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1255 | 1257 | dataOut.profileIndex = profileIndex |
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1256 | 1258 | dataOut.flagDataAsBlock = True |
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1257 | 1259 | dataOut.ippSeconds = ippSeconds |
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1258 | ||
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1260 | dataOut.step = self.step | |
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1259 | 1261 | |
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1260 | 1262 | |
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1261 | 1263 | # import collections |
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