schain Commit - r1732:8e0a97a787ce · Jicamarca Repository

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from schainpy.model.graphics.jroplot_base import Plot, plt, log

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from schainpy.model.graphics.jroplot_base import Plot, plt, log

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from matplotlib import __version__ as plt_version

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if plt_version >='3.3.4':

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EXTRA_POINTS = 0

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else:

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EXTRA_POINTS = 1

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class SpectraPlot(Plot):

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class SpectraPlot(Plot):

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'''

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'''

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data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor)

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data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor)

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extrapoints = spc.shape[1] % dataOut.nFFTPoints

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extrapoints = spc.shape[1] % dataOut.nFFTPoints

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extrapoints=1

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extrapoints=1

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meta['xrange'] = (dataOut.getFreqRange(~~extrapoints~~) / 1000., dataOut.getAcfRange(~~extrapoints~~), dataOut.getVelRange(~~extrapoints~~))

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meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))

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if self.CODE == 'spc_moments':

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if self.CODE == 'spc_moments':

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data['moments'] = dataOut.moments

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data['moments'] = dataOut.moments

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if self.CODE == 'gaussian_fit':

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if self.CODE == 'gaussian_fit':

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data['spc'] = spc

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data['spc'] = spc

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data['rti'] = dataOut.getPower()

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data['rti'] = dataOut.getPower()

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data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)

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data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)

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meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))

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meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))

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data['shift1'] = dataOut.Dop_EEJ_T1[0]

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data['shift1'] = dataOut.Dop_EEJ_T1[0]

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data['shift2'] = dataOut.Dop_EEJ_T2[0]

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data['shift2'] = dataOut.Dop_EEJ_T2[0]

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spc = dataOut.data_spc

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spc = dataOut.data_spc

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cspc = dataOut.data_cspc

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cspc = dataOut.data_cspc

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extrapoints = spc.shape[1] % dataOut.nFFTPoints

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extrapoints = spc.shape[1] % dataOut.nFFTPoints

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meta['xrange'] = (dataOut.getFreqRange(~~extrapoints~~) / 1000., dataOut.getAcfRange(~~extrapoints~~), dataOut.getVelRange(~~extrapoints~~))

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meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))

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meta['pairs'] = dataOut.pairsList

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meta['pairs'] = dataOut.pairsList

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tmp = []

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tmp = []

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z = numpy.where(numpy.isfinite(z), z, numpy.NAN)

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z = numpy.where(numpy.isfinite(z), z, numpy.NAN)

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#buffer = 10 * numpy.log10(z)

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#buffer = 10 * numpy.log10(z)

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meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))

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meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))

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#self.hei = hei

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#self.hei = hei

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meta = {}

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meta = {}

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spc = 10 * numpy.log10(dataOut.data_pre[0] / dataOut.normFactor)

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spc = 10 * numpy.log10(dataOut.data_pre[0] / dataOut.normFactor)

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data['spc'] = spc

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data['spc'] = spc

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meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))

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meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))

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if self.CODE == 'cut_gaussian_fit':

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if self.CODE == 'cut_gaussian_fit':

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data['gauss_fit0'] = 10 * numpy.log10(dataOut.GaussFit0 / dataOut.normFactor)

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data['gauss_fit0'] = 10 * numpy.log10(dataOut.GaussFit0 / dataOut.normFactor)

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data['gauss_fit1'] = 10 * numpy.log10(dataOut.GaussFit1 / dataOut.normFactor)

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data['gauss_fit1'] = 10 * numpy.log10(dataOut.GaussFit1 / dataOut.normFactor)

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             from schainpy.model.graphics.jroplot_base import Plot, plt, log
+            from matplotlib import __version__ as plt_version
+            if plt_version >='3.3.4':
+                EXTRA_POINTS = 0
+            else:
+                EXTRA_POINTS = 1
             class SpectraPlot(Plot):
                 '''
                         data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor)
                     extrapoints = spc.shape[1] % dataOut.nFFTPoints
                     extrapoints=1
-                    meta['xrange'] = (dataOut.getFreqRange(extrapoints) / 1000., dataOut.getAcfRange(extrapoints), dataOut.getVelRange(extrapoints))
+                    meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                     if self.CODE == 'spc_moments':
                         data['moments'] = dataOut.moments
                     if self.CODE == 'gaussian_fit':
                     data['spc'] = spc
                     data['rti'] = dataOut.getPower()
                     data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
-                    meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                    meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                     data['shift1'] = dataOut.Dop_EEJ_T1[0]
                     data['shift2'] = dataOut.Dop_EEJ_T2[0]
                     spc = dataOut.data_spc
                     cspc = dataOut.data_cspc
                     extrapoints = spc.shape[1] % dataOut.nFFTPoints
-                    meta['xrange'] = (dataOut.getFreqRange(extrapoints) / 1000., dataOut.getAcfRange(extrapoints), dataOut.getVelRange(extrapoints))
+                    meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                     meta['pairs'] = dataOut.pairsList
                     tmp = []
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     #buffer = 10 * numpy.log10(z)
-                    meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                    meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                     #self.hei = hei
                     meta = {}
                     spc = 10 * numpy.log10(dataOut.data_pre[0] / dataOut.normFactor)
                     data['spc'] = spc
-                    meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                    meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                     if self.CODE == 'cut_gaussian_fit':
                         data['gauss_fit0'] = 10 * numpy.log10(dataOut.GaussFit0 / dataOut.normFactor)
                         data['gauss_fit1'] = 10 * numpy.log10(dataOut.GaussFit1 / dataOut.normFactor)