| @@ -377,12 +377,12 class WeatherPlot(Plot): | |||
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377 | 377 | def setup(self): |
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378 | 378 | self.ncols = 1 |
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379 | 379 | self.nrows = 1 |
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380 | self.width =8 | |
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381 | self.height =8 | |
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380 | 382 | self.nplots= 1 |
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381 | 383 | self.ylabel= 'Range [Km]' |
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382 | 384 | self.titles= ['Weather'] |
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383 | 385 | self.colorbar=False |
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384 | self.width =8 | |
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385 | self.height =8 | |
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386 | 386 | self.ini =0 |
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387 | 387 | self.len_azi =0 |
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388 | 388 | self.buffer_ini = None |
| @@ -643,11 +643,26 class WeatherRHIPlot(Plot): | |||
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643 | 643 | data_ele_tmp = None |
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644 | 644 | |
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645 | 645 | def setup(self): |
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646 | print("********************") | |
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647 | print("********************") | |
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648 | print("********************") | |
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649 | print("SETUP WEATHER PLOT") | |
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646 | 650 | self.ncols = 1 |
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647 | 651 | self.nrows = 1 |
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648 | 652 | self.nplots= 1 |
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649 | 653 | self.ylabel= 'Range [Km]' |
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650 | 654 | self.titles= ['Weather'] |
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655 | if self.channels is not None: | |
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656 | self.nplots = len(self.channels) | |
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657 | self.nrows = len(self.channels) | |
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658 | else: | |
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659 | self.nplots = self.data.shape(self.CODE)[0] | |
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660 | self.nrows = self.nplots | |
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661 | self.channels = list(range(self.nplots)) | |
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662 | print("channels",self.channels) | |
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663 | print("que saldra", self.data.shape(self.CODE)[0]) | |
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664 | self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)] | |
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665 | print("self.titles",self.titles) | |
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651 | 666 | self.colorbar=False |
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652 | 667 | self.width =8 |
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653 | 668 | self.height =8 |
| @@ -669,9 +684,16 class WeatherRHIPlot(Plot): | |||
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669 | 684 | factor = 1 |
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670 | 685 | if hasattr(dataOut, 'nFFTPoints'): |
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671 | 686 | factor = dataOut.normFactor |
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672 | data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) | |
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687 | print("dataOut",dataOut.data_360.shape) | |
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688 | # | |
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689 | data['weather'] = 10*numpy.log10(dataOut.data_360/(factor)) | |
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690 | # | |
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691 | #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) | |
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673 | 692 | data['azi'] = dataOut.data_azi |
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674 | 693 | data['ele'] = dataOut.data_ele |
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694 | print("UPDATE") | |
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695 | print("data[weather]",data['weather'].shape) | |
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696 | print("data[azi]",data['azi']) | |
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675 | 697 | return data, meta |
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676 | 698 | |
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677 | 699 | def get2List(self,angulos): |
| @@ -788,10 +810,11 class WeatherRHIPlot(Plot): | |||
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788 | 810 | return 3 |
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789 | 811 | |
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790 | 812 | |
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791 | def const_ploteo(self,data_weather,data_ele,step,res,ang_max,ang_min): | |
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813 | def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min): | |
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792 | 814 | ang_max= ang_max |
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793 | 815 | ang_min= ang_min |
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794 | 816 | data_weather=data_weather |
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817 | val_ch=val_ch | |
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795 | 818 | ##print("*********************DATA WEATHER**************************************") |
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796 | 819 | ##print(data_weather) |
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797 | 820 | if self.ini==0: |
| @@ -805,6 +828,7 class WeatherRHIPlot(Plot): | |||
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805 | 828 | #print("data_ele",data_ele) |
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806 | 829 | #---------------------------------------------------------- |
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807 | 830 | tipo_case = self.check_case(data_ele,ang_max,ang_min) |
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831 | print("check_case",tipo_case) | |
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808 | 832 | #--------------------- new ------------------------- |
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809 | 833 | data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case) |
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810 | 834 | |
| @@ -845,7 +869,9 class WeatherRHIPlot(Plot): | |||
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845 | 869 | self.end_data_ele = data_ele_new[-1] |
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846 | 870 | |
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847 | 871 | n1= round(self.start_data_ele)- start |
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848 | n2= end - round(self.end_data_ele) | |
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872 | n2= end - round(self.end_data_ele)-1 | |
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873 | print(self.start_data_ele) | |
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874 | print(self.end_data_ele) | |
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849 | 875 | if n1>0: |
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850 | 876 | ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1) |
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851 | 877 | ele1_nan= numpy.ones(n1)*numpy.nan |
| @@ -861,7 +887,7 class WeatherRHIPlot(Plot): | |||
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861 | 887 | val_mean = numpy.mean(data_weather[:,-1]) |
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862 | 888 | self.val_mean = val_mean |
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863 | 889 | data_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) |
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864 | self.data_ele_tmp= data_ele_old | |
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890 | self.data_ele_tmp[val_ch]= data_ele_old | |
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865 | 891 | else: |
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866 | 892 | #print("**********************************************") |
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867 | 893 | #print("****************VARIABLE**********************") |
| @@ -897,13 +923,13 class WeatherRHIPlot(Plot): | |||
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897 | 923 | #print(data_ele,len(data_ele)) |
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898 | 924 | #print(data_ele_old,len(data_ele_old)) |
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899 | 925 | if new_i_ele< 2: |
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900 | self.data_ele_tmp = numpy.ones(ang_max-ang_min)*numpy.nan | |
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901 | self.res_weather = self.replaceNAN(data_weather=self.res_weather,data_ele=self.data_ele_tmp,val=self.val_mean) | |
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902 | self.data_ele_tmp[new_i_ele:new_i_ele+len(data_ele)]=data_ele_old | |
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926 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan | |
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927 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) | |
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928 | self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old | |
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903 | 929 | self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele |
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904 | self.res_weather[new_i_ele:new_i_ele+len(data_ele),:]= data_weather | |
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930 | self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather | |
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905 | 931 | data_ele = self.res_ele |
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906 | data_weather = self.res_weather | |
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932 | data_weather = self.res_weather[val_ch] | |
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907 | 933 | |
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908 | 934 | elif tipo_case==1 : #BAJADA |
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909 | 935 | data_ele = data_ele[::-1] # reversa |
| @@ -927,13 +953,13 class WeatherRHIPlot(Plot): | |||
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927 | 953 | if new_i_ele <= 1: |
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928 | 954 | new_i_ele = 1 |
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929 | 955 | if round(data_ele[-1])>=ang_max-1: |
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930 | self.data_ele_tmp = numpy.ones(ang_max-ang_min)*numpy.nan | |
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931 | self.res_weather = self.replaceNAN(data_weather=self.res_weather,data_ele=self.data_ele_tmp,val=self.val_mean) | |
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932 | self.data_ele_tmp[new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old | |
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956 | self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan | |
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957 | self.res_weather[val_ch] = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean) | |
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958 | self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old | |
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933 | 959 | self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele |
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934 | self.res_weather[new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather | |
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960 | self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather | |
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935 | 961 | data_ele = self.res_ele |
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936 | data_weather = self.res_weather | |
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962 | data_weather = self.res_weather[val_ch] | |
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937 | 963 | |
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938 | 964 | elif tipo_case==2: #bajada |
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939 | 965 | vec = numpy.where(data_ele<ang_max) |
| @@ -959,11 +985,11 class WeatherRHIPlot(Plot): | |||
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959 | 985 | data_ele = numpy.hstack((data_ele,ele2)) |
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960 | 986 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
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961 | 987 | |
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962 | self.data_ele_tmp = data_ele_old | |
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988 | self.data_ele_tmp[val_ch] = data_ele_old | |
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963 | 989 | self.res_ele = data_ele |
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964 | self.res_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) | |
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990 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) | |
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965 | 991 | data_ele = self.res_ele |
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966 | data_weather = self.res_weather | |
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992 | data_weather = self.res_weather[val_ch] | |
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967 | 993 | |
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968 | 994 | elif tipo_case==3:#subida |
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969 | 995 | vec = numpy.where(0<data_ele) |
| @@ -996,11 +1022,11 class WeatherRHIPlot(Plot): | |||
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996 | 1022 | data_ele = numpy.hstack((data_ele,ele2)) |
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997 | 1023 | data_ele_old = numpy.hstack((data_ele_old,ele2_nan)) |
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998 | 1024 | |
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999 | self.data_ele_tmp = data_ele_old | |
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1025 | self.data_ele_tmp[val_ch] = data_ele_old | |
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1000 | 1026 | self.res_ele = data_ele |
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1001 | self.res_weather = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) | |
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1027 | self.res_weather[val_ch] = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean) | |
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1002 | 1028 | data_ele = self.res_ele |
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1003 | data_weather = self.res_weather | |
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1029 | data_weather = self.res_weather[val_ch] | |
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1004 | 1030 | #print("self.data_ele_tmp",self.data_ele_tmp) |
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1005 | 1031 | return data_weather,data_ele |
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1006 | 1032 | |
| @@ -1026,22 +1052,31 class WeatherRHIPlot(Plot): | |||
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1026 | 1052 | ##print('weather',data['weather'].shape) |
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1027 | 1053 | ##print('ele',data['ele'].shape) |
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1028 | 1054 | |
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1029 | self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) | |
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1030 | self.res_azi = numpy.mean(data['azi']) | |
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1055 | ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) | |
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1056 | ###self.res_azi = numpy.mean(data['azi']) | |
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1031 | 1057 | ###print("self.res_ele",self.res_ele) |
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1058 | plt.clf() | |
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1059 | subplots = [121, 122] | |
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1060 | if self.ini==0: | |
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1061 | self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan | |
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1062 | self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan | |
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1063 | print("SHAPE",self.data_ele_tmp.shape) | |
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1064 | ||
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1032 | 1065 | for i,ax in enumerate(self.axes): |
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1066 | self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min) | |
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1067 | self.res_azi = numpy.mean(data['azi']) | |
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1033 | 1068 | if ax.firsttime: |
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1034 | plt.clf() | |
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1035 |
cgax, pm = wrl.vis.plot_rhi(self.res_weather,r=r,th=self.res_ele, |
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1069 | #plt.clf() | |
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1070 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80) | |
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1071 | #fig=self.figures[0] | |
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1036 | 1072 | else: |
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1037 | plt.clf() | |
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1038 |
cgax, pm = wrl.vis.plot_rhi(self.res_weather,r=r,th=self.res_ele, |
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1039 | caax = cgax.parasites[0] | |
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1040 | paax = cgax.parasites[1] | |
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1041 | cbar = plt.gcf().colorbar(pm, pad=0.075) | |
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1042 | caax.set_xlabel('x_range [km]') | |
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1043 | caax.set_ylabel('y_range [km]') | |
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1044 | plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right') | |
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1045 | ||
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1046 | #print("***************************self.ini****************************",self.ini) | |
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1073 | #plt.clf() | |
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1074 | cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80) | |
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1075 | caax = cgax.parasites[0] | |
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1076 | paax = cgax.parasites[1] | |
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1077 | cbar = plt.gcf().colorbar(pm, pad=0.075) | |
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1078 | caax.set_xlabel('x_range [km]') | |
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1079 | caax.set_ylabel('y_range [km]') | |
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1080 | plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+" Step "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right') | |
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1081 | print("***************************self.ini****************************",self.ini) | |
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1047 | 1082 | self.ini= self.ini+1 |
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