schain Commit - r1545:af752b8a14b4 · Jicamarca Repository

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r1545:af752b8a14b4

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schainpy/model/proc/jroproc_spectra.py +1 -1

                     #print("2: ",10*numpy.log10(self.dataOut.noise_estimation/64))
                     #print(self.dataOut.flagNoData)
-                    print("getNoise Done")
+                    print("getNoise Done", noise)
                     return self.dataOut
                 def getNoiseByMean(self,data):

schainpy/model/proc/jroproc_voltage.py +37 -31

             from schainpy.utils import log
             from schainpy.model.io.utils import getHei_index
             from time import time
-            #import datetime
             import numpy
             #import copy
             from schainpy.model.data import _noise
-            #import skimage.color
-            #import skimage.io
-            #import matplotlib.pyplot as plt
             class removeProfileByFaradayHS(Operation):
                 '''
                     return data
                 def cleanOutliersByBlock(self):
+                    import matplotlib.pyplot as plt
+                    import datetime
                     #print(self.__buffer_data[0].shape)
                     data = self.__buffer_data#.copy()
-                    #print("cleaning shape inpt: ",data.shape)
+                    print("cleaning shape inpt: ",data.shape)
-                    '''
                     self.__buffer_data = []
-                    spectrum = numpy.fft.fft2(data, axes=(0,2))
+                    spectrum = numpy.fft.fft2(data[:,:,self.minHei_idx:], axes=(0,2))
-                    #print("spc : ",spectrum.shape)
+                    print("spc : ",spectrum.shape)
                     (nch,nsamples, nh) = spectrum.shape
                     data2 = None
                     #print(data.shape)
                         x, y = numpy.meshgrid(numpy.sort(freqh),numpy.sort(freqv))
                         z = numpy.abs(spectrum[ch,:,:])
+                        phase = numpy.angle(spectrum[ch,:,:])
                         # Find all peaks higher than the 98th percentile
-                        peaks = z < numpy.percentile(z, 98)
+                        peaks = z < numpy.percentile(z, 99)
                         #print(peaks)
                         # Set those peak coefficients to zero
                         spectrum2 = spectrum2 * peaks.astype(int)
-                        data2 = numpy.fft.ifft2(spectrum2)
+                        data2 = numpy.fft.ifft2(spectrum2,axes=(0,2))
                         dat = numpy.log10(z.T)
-                        dat2 = numpy.log10(spectrum2.T)
+                        pdat = numpy.log10(phase.T)
+                        dat2 = numpy.log10(numpy.abs(spectrum2.T))
-                        # m = numpy.mean(dat)
-                        # o = numpy.std(dat)
+                        m = numpy.mean(dat)
-                        # fig, ax = plt.subplots(2,1,figsize=(8, 6))
+                        o = numpy.std(dat)
+                        fig, ax = plt.subplots(1,2,figsize=(12, 6))
-                        # c = ax[0].pcolormesh(x, y, dat, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o))
-                        # #c = ax.pcolor( z.T , cmap ='gray', vmin = (m-2*o), vmax = (m+2*o))
+                        colormap = 'jet'
-                        # date_time = datetime.datetime.fromtimestamp(self.__buffer_times[0]).strftime('%Y-%m-%d %H:%M:%S.%f')
+                        #c = ax[0].pcolormesh(x, y, dat, cmap =colormap, vmin = (m-2*o)/2, vmax = (m+2*o))
-                        # #strftime('%Y-%m-%d %H:%M:%S')
+                        c = ax[0].pcolormesh(x, y, dat, cmap =colormap, vmin = 4.2, vmax = 5.0)
+                        fig.colorbar(c, ax=ax[0])
+                        #print("aqui estoy", dat.shape)
+                        #c = ax.pcolor( z.T , cmap ='gray', vmin = (m-2*o), vmax = (m+2*o))
+                        date_time = datetime.datetime.fromtimestamp(self.__buffer_times[0]).strftime('%Y-%m-%d %H:%M:%S.%f')
+                        #strftime('%Y-%m-%d %H:%M:%S')
                         #ax[0].set_title('Spectrum magnitude '+date_time)
-                        # fig.canvas.set_window_title('Spectrum magnitude {} '.format(self.n)+date_time)
+                        fig.canvas.set_window_title('Spectrum magnitude {} '.format(self.n)+date_time)
+                        #print("aqui estoy2",dat2[:,:,0].shape)
+                        c = ax[1].pcolormesh(x, y, pdat, cmap =colormap, vmin = -0.0, vmax = 0.5)
-                        # c = ax[1].pcolormesh(x, y, dat, cmap ='YlGnBu', vmin = (m-2*o), vmax = (m+2*o))
+                        #c = ax[1].pcolormesh(x, y, dat2[:,:,0], cmap =colormap, vmin = (m-2*o)/2, vmax = (m+2*o)-1)
-                        # fig.colorbar(c)
+                        #print("aqui estoy3")
-                        # plt.show()
+                        fig.colorbar(c, ax=ax[1])
+                        plt.show()
                     #print(data2.shape)
-                    data = data2
+                    #data = data2
+                    #cleanBlock = numpy.fft.ifft2(data, axes=(0,2)).reshape()
-                    #cleanBlock = numpy.fft.ifft2(spectrum, axes=(0,2)).reshape()
-                    '''
                     #print("cleanOutliersByBlock Done")
-                    return self.filterSatsProfiles()
+                    return data
                     else:
                         self.__buffer_data = numpy.concatenate((self.__buffer_data,data), axis=1)#en perfiles
                     self.__profIndex += 1
-                    #self.__buffer_times.append(datatime)
+                    self.__buffer_times.append(datatime)
                 def getData(self, data, datatime=None):

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