@@ -407,6 +407,8 class WeatherPlot(Plot): | |||||
407 | data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) |
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407 | data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor)) | |
408 | ####print("weather",data['weather']) |
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408 | ####print("weather",data['weather']) | |
409 | data['azi'] = dataOut.data_azi |
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409 | data['azi'] = dataOut.data_azi | |
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410 | ||||
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411 | data['ele'] = dataOut.data_ele | |||
410 | return data, meta |
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412 | return data, meta | |
411 |
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413 | |||
412 | def get2List(self,angulos): |
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414 | def get2List(self,angulos): | |
@@ -456,7 +458,7 class WeatherPlot(Plot): | |||||
456 | condition , value = self.search_pos(i,list1_) |
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458 | condition , value = self.search_pos(i,list1_) | |
457 | if condition: |
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459 | if condition: | |
458 | pos = tmp + c + 1 |
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460 | pos = tmp + c + 1 | |
459 | for k in range(list2_[value]-1): |
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461 | for k in range(round(list2_[value])-1): | |
460 | ang_new[pos+k] = ang_new[pos+k-1]+1 |
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462 | ang_new[pos+k] = ang_new[pos+k-1]+1 | |
461 | ang_new2[pos+k] = numpy.nan |
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463 | ang_new2[pos+k] = numpy.nan | |
462 | tmp = pos +k |
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464 | tmp = pos +k | |
@@ -521,16 +523,20 class WeatherPlot(Plot): | |||||
521 | return data |
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523 | return data | |
522 |
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524 | |||
523 | def replaceNAN(self,data_weather,data_azi,val): |
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525 | def replaceNAN(self,data_weather,data_azi,val): | |
524 | ####print("----------------activeNEWFUNCTION") |
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525 | data= data_azi |
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526 | data= data_azi | |
526 | data_T= data_weather |
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527 | data_T= data_weather | |
527 | ####print("data_azi",data_azi) |
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528 | if data.shape[0]> data_T.shape[0]: | |
528 | ####print("VAL:",val) |
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529 | data_N = numpy.ones( [data.shape[0],data_T.shape[1]]) | |
529 | ####print("SHAPE",data_T.shape) |
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530 | c = 0 | |
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531 | for i in range(len(data)): | |||
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532 | if numpy.isnan(data[i]): | |||
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533 | data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan | |||
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534 | else: | |||
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535 | data_N[i,:]=data_T[c,:] | |||
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536 | sc=c+1 | |||
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537 | else: | |||
530 | for i in range(len(data)): |
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538 | for i in range(len(data)): | |
531 | if numpy.isnan(data[i]): |
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539 | if numpy.isnan(data[i]): | |
532 | ####print("NAN") |
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533 | #data_T[i,:]=numpy.ones(data_T.shape[1])*val |
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534 | data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan |
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540 | data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan | |
535 | return data_T |
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541 | return data_T | |
536 |
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542 | |||
@@ -544,7 +550,6 class WeatherPlot(Plot): | |||||
544 | #------------------------ |
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550 | #------------------------ | |
545 | ####data_azi_new = self.fixDATA(data_azi) |
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551 | ####data_azi_new = self.fixDATA(data_azi) | |
546 | #ata_azi_new = self.fixDATANEW(data_azi) |
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552 | #ata_azi_new = self.fixDATANEW(data_azi) | |
547 |
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548 | start = data_azi_new[-1] + res |
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553 | start = data_azi_new[-1] + res | |
549 | end = data_azi_new[0] - res |
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554 | end = data_azi_new[0] - res | |
550 | ##### new |
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555 | ##### new | |
@@ -566,6 +571,9 class WeatherPlot(Plot): | |||||
566 | start_azi = self.res_azi[0] |
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571 | start_azi = self.res_azi[0] | |
567 | #-----------new------------ |
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572 | #-----------new------------ | |
568 | data_azi ,data_azi_old= self.globalCheckPED(data_azi) |
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573 | data_azi ,data_azi_old= self.globalCheckPED(data_azi) | |
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574 | print("---------------------------------------------------") | |||
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575 | print("data_azi",data_azi) | |||
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576 | print("data_azi_old",data_azi_old) | |||
569 | data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean) |
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577 | data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean) | |
570 | #-------------------------- |
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578 | #-------------------------- | |
571 | ####data_azi_old = data_azi |
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579 | ####data_azi_old = data_azi | |
@@ -657,6 +665,7 class WeatherPlot(Plot): | |||||
657 | self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res) |
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665 | self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res) | |
658 | #numpy.set_printoptions(suppress=True) |
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666 | #numpy.set_printoptions(suppress=True) | |
659 | #print("resultado",self.res_azi) |
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667 | #print("resultado",self.res_azi) | |
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668 | self.res_ele =numpy.mean(data['ele']) | |||
660 | ###########################/DATA_RM/10_tmp/ch0############################### |
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669 | ###########################/DATA_RM/10_tmp/ch0############################### | |
661 | ################# PLOTEO ################### |
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670 | ################# PLOTEO ################### | |
662 | ########################################################## |
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671 | ########################################################## | |
@@ -673,6 +682,6 class WeatherPlot(Plot): | |||||
673 | cbar = plt.gcf().colorbar(pm, pad=0.075) |
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682 | cbar = plt.gcf().colorbar(pm, pad=0.075) | |
674 | caax.set_xlabel('x_range [km]') |
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683 | caax.set_xlabel('x_range [km]') | |
675 | caax.set_ylabel('y_range [km]') |
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684 | caax.set_ylabel('y_range [km]') | |
676 |
plt.text(1.0, 1.05, ' |
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685 | plt.text(1.0, 1.05, 'Azimuth '+str(thisDatetime)+" Step "+str(self.ini)+ " Elev: "+str(round(self.res_ele,2)), transform=caax.transAxes, va='bottom',ha='right') | |
677 |
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686 | |||
678 | self.ini= self.ini+1 |
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687 | self.ini= self.ini+1 |
@@ -54,9 +54,9 n= int(1/(VEL*ipp_sec)) | |||||
54 | print("N° Profiles : ", n) |
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54 | print("N° Profiles : ", n) | |
55 | #--------------------------------------------------------------------------------------- |
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55 | #--------------------------------------------------------------------------------------- | |
56 | plot_rti = 0 |
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56 | plot_rti = 0 | |
57 |
plot_ppi = |
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57 | plot_ppi = 1 | |
58 | integration = 1 |
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58 | integration = 1 | |
59 |
save = |
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59 | save = 0 | |
60 | #---------------------------RANGO DE PLOTEO---------------------------------- |
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60 | #---------------------------RANGO DE PLOTEO---------------------------------- | |
61 | dBmin = '1' |
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61 | dBmin = '1' | |
62 | dBmax = '85' |
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62 | dBmax = '85' |
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