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schain-jc
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Fix SpectralMoments Plot
Juan C. Espinoza -
r1207:15feaa6e0b57
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@@ -1163,9 +1163,11 class PlotterData(object):
1163 1163 for plot in self.plottypes:
1164 1164 if 'snr' in plot:
1165 1165 plot = 'snr'
1166 elif 'spc_moments' == plot:
1167 plot = 'moments'
1166 1168 self.data[plot] = {}
1167 1169
1168 if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data:
1170 if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data:
1169 1171 self.data['noise'] = {}
1170 1172 if 'noise' not in self.plottypes:
1171 1173 self.plottypes.append('noise')
@@ -1199,7 +1201,7 class PlotterData(object):
1199 1201 self.channels = dataOut.channelList
1200 1202 self.interval = dataOut.getTimeInterval()
1201 1203 self.localtime = dataOut.useLocalTime
1202 if 'spc' in self.plottypes or 'cspc' in self.plottypes:
1204 if 'spc' in self.plottypes or 'cspc' in self.plottypes or 'spc_moments' in self.plottypes:
1203 1205 self.xrange = (dataOut.getFreqRange(1)/1000.,
1204 1206 dataOut.getAcfRange(1), dataOut.getVelRange(1))
1205 1207 self.factor = dataOut.normFactor
@@ -1208,7 +1210,7 class PlotterData(object):
1208 1210 self.__times.append(tm)
1209 1211
1210 1212 for plot in self.plottypes:
1211 if plot == 'spc':
1213 if plot in ('spc', 'spc_moments'):
1212 1214 z = dataOut.data_spc/dataOut.normFactor
1213 1215 buffer = 10*numpy.log10(z)
1214 1216 if plot == 'cspc':
@@ -1246,6 +1248,9 class PlotterData(object):
1246 1248 elif plot == 'cspc':
1247 1249 self.data['spc'] = buffer[0]
1248 1250 self.data['cspc'] = buffer[1]
1251 elif plot == 'spc_moments':
1252 self.data['spc'] = buffer
1253 self.data['moments'][tm] = dataOut.moments
1249 1254 else:
1250 1255 if self.buffering:
1251 1256 self.data[plot][tm] = buffer
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@@ -66,7 +66,7 class SpectraPlot(Plot):
66 66 x = self.data.xrange[2]
67 67 self.xlabel = "Velocity (m/s)"
68 68
69 if self.CODE == 'spc_mean':
69 if self.CODE == 'spc_moments':
70 70 x = self.data.xrange[2]
71 71 self.xlabel = "Velocity (m/s)"
72 72
@@ -78,8 +78,8 class SpectraPlot(Plot):
78 78
79 79 for n, ax in enumerate(self.axes):
80 80 noise = self.data['noise'][n][-1]
81 if self.CODE == 'spc_mean':
82 mean = self.data['mean'][n][-1]
81 if self.CODE == 'spc_moments':
82 mean = self.data['moments'][n, :, 1, :][-1]
83 83 if ax.firsttime:
84 84 self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
85 85 self.xmin = self.xmin if self.xmin else -self.xmax
@@ -96,16 +96,15 class SpectraPlot(Plot):
96 96 self.data['rti'][n][-1], y)[0]
97 97 ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y,
98 98 color="k", linestyle="dashed", lw=1)[0]
99 if self.CODE == 'spc_mean':
99 if self.CODE == 'spc_moments':
100 100 ax.plt_mean = ax.plot(mean, y, color='k')[0]
101 101 else:
102 102 ax.plt.set_array(z[n].T.ravel())
103 103 if self.showprofile:
104 104 ax.plt_profile.set_data(self.data['rti'][n][-1], y)
105 105 ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y)
106 if self.CODE == 'spc_mean':
106 if self.CODE == 'spc_moments':
107 107 ax.plt_mean.set_data(mean, y)
108
109 108 self.titles.append('CH {}: {:3.2f}dB'.format(n, noise))
110 109
111 110
@@ -206,11 +205,11 class CrossSpectraPlot(Plot):
206 205 self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1]))
207 206
208 207
209 class SpectraMeanPlot(SpectraPlot):
208 class SpectralMomentsPlot(SpectraPlot):
210 209 '''
211 Plot for Spectra and Mean
210 Plot for Spectral Moments
212 211 '''
213 CODE = 'spc_mean'
212 CODE = 'spc_moments'
214 213 colormap = 'jro'
215 214
216 215
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@@ -290,10 +290,10 class SpcParamPlot_(Figure):
290 290 raise ValueError("Channel %d is not in dataOut.channelList" %channel)
291 291 channelIndexList.append(dataOut.channelList.index(channel))
292 292
293 # if normFactor is None:
294 # factor = dataOut.normFactor
295 # else:
296 # factor = normFactor
293 # if normFactor is None:
294 # factor = dataOut.normFactor
295 # else:
296 # factor = normFactor
297 297 if xaxis == "frequency":
298 298 x = dataOut.spcparam_range[0]
299 299 xlabel = "Frequency (kHz)"
@@ -594,8 +594,8 class SkyMapPlot_(Figure):
594 594 self.isConfig = False
595 595 self.__nsubplots = 1
596 596
597 # self.WIDTH = 280
598 # self.HEIGHT = 250
597 # self.WIDTH = 280
598 # self.HEIGHT = 250
599 599 self.WIDTH = 600
600 600 self.HEIGHT = 600
601 601 self.WIDTHPROF = 120
@@ -828,11 +828,11 class WindProfilerPlot_(Figure):
828 828 if dataOut.flagNoData:
829 829 return dataOut
830 830
831 # if timerange is not None:
832 # self.timerange = timerange
833 #
834 # tmin = None
835 # tmax = None
831 # if timerange is not None:
832 # self.timerange = timerange
833 #
834 # tmin = None
835 # tmax = None
836 836
837 837 x = dataOut.getTimeRange1(dataOut.paramInterval)
838 838 y = dataOut.heightList
@@ -1082,7 +1082,7 class ParametersPlot_(Figure):
1082 1082 z[ind] = numpy.nan
1083 1083
1084 1084 thisDatetime = dataOut.datatime
1085 # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0])
1085 # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0])
1086 1086 title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y"))
1087 1087 xlabel = ""
1088 1088 ylabel = "Range (km)"
@@ -1129,24 +1129,24 class ParametersPlot_(Figure):
1129 1129
1130 1130 self.setWinTitle(title)
1131 1131
1132 # for i in range(self.nchan):
1133 # index = channelIndexList[i]
1134 # title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S"))
1135 # axes = self.axesList[i*self.plotFact]
1136 # z1 = z[i,:].reshape((1,-1))
1137 # axes.pcolorbuffer(x, y, z1,
1138 # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
1139 # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
1140 # ticksize=9, cblabel='', cbsize="1%",colormap=colormap)
1141 #
1142 # if showSNR:
1143 # title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S"))
1144 # axes = self.axesList[i*self.plotFact + 1]
1145 # SNRdB1 = SNRdB[i,:].reshape((1,-1))
1146 # axes.pcolorbuffer(x, y, SNRdB1,
1147 # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax,
1148 # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
1149 # ticksize=9, cblabel='', cbsize="1%",colormap='jet')
1132 # for i in range(self.nchan):
1133 # index = channelIndexList[i]
1134 # title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S"))
1135 # axes = self.axesList[i*self.plotFact]
1136 # z1 = z[i,:].reshape((1,-1))
1137 # axes.pcolorbuffer(x, y, z1,
1138 # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax,
1139 # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
1140 # ticksize=9, cblabel='', cbsize="1%",colormap=colormap)
1141 #
1142 # if showSNR:
1143 # title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S"))
1144 # axes = self.axesList[i*self.plotFact + 1]
1145 # SNRdB1 = SNRdB[i,:].reshape((1,-1))
1146 # axes.pcolorbuffer(x, y, SNRdB1,
1147 # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax,
1148 # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,
1149 # ticksize=9, cblabel='', cbsize="1%",colormap='jet')
1150 1150
1151 1151 i=0
1152 1152 index = channelIndexList[i]
@@ -1587,11 +1587,11 class SpectralFittingPlot_(Figure):
1587 1587 for f in range(nGroups):
1588 1588 groupChann = groupArray[f,:]
1589 1589 p = dataOut.data_param[f,:,heightindex]
1590 # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167])
1590 # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167])
1591 1591 fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles
1592 1592 fitLineAux = fitLineAux.reshape((nChannels,nProfiles))
1593 1593 spcFitLine[groupChann,:] = fitLineAux
1594 # spcFitLine = spcFitLine/factor
1594 # spcFitLine = spcFitLine/factor
1595 1595
1596 1596 z = z[listChannels,:]
1597 1597 spcFitLine = spcFitLine[listChannels,:]
@@ -1623,7 +1623,7 class SpectralFittingPlot_(Figure):
1623 1623
1624 1624 self.setWinTitle(title)
1625 1625 for i in range(self.nplots):
1626 # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i])
1626 # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i])
1627 1627 title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i])
1628 1628 axes = self.axesList[i*self.__nsubplots]
1629 1629 if fit == False:
@@ -1747,7 +1747,7 class EWDriftsPlot_(Figure):
1747 1747 tmax = None
1748 1748
1749 1749 x = dataOut.getTimeRange1(dataOut.outputInterval)
1750 # y = dataOut.heightList
1750 # y = dataOut.heightList
1751 1751 y = dataOut.heightList
1752 1752
1753 1753 z = dataOut.data_output
@@ -1774,7 +1774,7 class EWDriftsPlot_(Figure):
1774 1774
1775 1775
1776 1776 showprofile = False
1777 # thisDatetime = dataOut.datatime
1777 # thisDatetime = dataOut.datatime
1778 1778 thisDatetime = datetime.datetime.utcfromtimestamp(x[1])
1779 1779 title = wintitle + " EW Drifts"
1780 1780 xlabel = ""
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@@ -228,6 +228,7 class SpectraPlot_(Figure):
228 228
229 229
230 230 return dataOut
231
231 232 @MPDecorator
232 233 class CrossSpectraPlot_(Figure):
233 234
@@ -358,7 +359,7 class CrossSpectraPlot_(Figure):
358 359 #thisDatetime = dataOut.datatime
359 360 thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0])
360 361 title = wintitle + " Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
361 # xlabel = "Velocity (m/s)"
362 # xlabel = "Velocity (m/s)"
362 363 ylabel = "Range (Km)"
363 364
364 365 if xaxis == "frequency":
@@ -428,7 +429,7 class CrossSpectraPlot_(Figure):
428 429
429 430 coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:] / numpy.sqrt( dataOut.data_spc[chan_index0,:,:]*dataOut.data_spc[chan_index1,:,:] )
430 431 coherence = numpy.abs(coherenceComplex)
431 # phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi
432 # phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi
432 433 phase = numpy.arctan2(coherenceComplex.imag, coherenceComplex.real)*180/numpy.pi
433 434
434 435 title = "Coherence Ch%d * Ch%d" %(pair[0], pair[1])
@@ -1461,8 +1462,8 class BeaconPhase_(Figure):
1461 1462 if pairsIndexList == []:
1462 1463 return
1463 1464
1464 # if len(pairsIndexList) > 4:
1465 # pairsIndexList = pairsIndexList[0:4]
1465 # if len(pairsIndexList) > 4:
1466 # pairsIndexList = pairsIndexList[0:4]
1466 1467
1467 1468 hmin_index = None
1468 1469 hmax_index = None
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@@ -2,37 +2,7 import os
2 2 import sys
3 3 import datetime
4 4 import numpy
5 import matplotlib
6
7 if 'BACKEND' in os.environ:
8 matplotlib.use(os.environ['BACKEND'])
9 elif 'linux' in sys.platform:
10 matplotlib.use("TkAgg")
11 elif 'darwin' in sys.platform:
12 matplotlib.use('TkAgg')
13 else:
14 from schainpy.utils import log
15 log.warning('Using default Backend="Agg"', 'INFO')
16 matplotlib.use('Agg')
17 # Qt4Agg', 'GTK', 'GTKAgg', 'ps', 'agg', 'cairo', 'MacOSX', 'GTKCairo', 'WXAgg', 'template', 'TkAgg', 'GTK3Cairo', 'GTK3Agg', 'svg', 'WebAgg', 'CocoaAgg', 'emf', 'gdk', 'WX'
18 import matplotlib.pyplot
19
20 from mpl_toolkits.axes_grid1 import make_axes_locatable
21 from matplotlib.ticker import FuncFormatter, LinearLocator
22
23 ###########################################
24 # Actualizacion de las funciones del driver
25 ###########################################
26
27 # create jro colormap
28
29 jet_values = matplotlib.pyplot.get_cmap("jet", 100)(numpy.arange(100))[10:90]
30 blu_values = matplotlib.pyplot.get_cmap(
31 "seismic_r", 20)(numpy.arange(20))[10:15]
32 ncmap = matplotlib.colors.LinearSegmentedColormap.from_list(
33 "jro", numpy.vstack((blu_values, jet_values)))
34 matplotlib.pyplot.register_cmap(cmap=ncmap)
35
5 from .jroplot_base import matplotlib, make_axes_locatable, FuncFormatter, LinearLocator
36 6
37 7 def createFigure(id, wintitle, width, height, facecolor="w", show=True, dpi=80):
38 8
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@@ -1197,8 +1197,8 class SpectralMoments(Operation):
1197 1197 self.dataOut.noise : Noise level per channel
1198 1198
1199 1199 Affected:
1200 self.dataOut.moments : Parameters per channel
1201 self.dataOut.data_SNR : SNR per channel
1200 self.dataOut.moments : Parameters per channel
1201 self.dataOut.data_SNR : SNR per channel
1202 1202
1203 1203 '''
1204 1204
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