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\ No newline at end of file diff --git a/docs/_modules/pyrad/io/config.html b/docs/_modules/pyrad/io/config.html index b48cb8b..4346b54 100644 --- a/docs/_modules/pyrad/io/config.html +++ b/docs/_modules/pyrad/io/config.html @@ -14,7 +14,6 @@ - @@ -470,7 +469,7 @@

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\ No newline at end of file diff --git a/docs/_modules/pyrad/io/io_aux.html b/docs/_modules/pyrad/io/io_aux.html index cfd2c6f..51a5ff6 100644 --- a/docs/_modules/pyrad/io/io_aux.html +++ b/docs/_modules/pyrad/io/io_aux.html @@ -14,7 +14,6 @@ - @@ -86,8 +85,6 @@

Source code for pyrad.io.io_aux

     find_hzt_file
     find_rad4alpcosmo_file
     _get_datetime
-    find_date_in_file_name
-
 
 """
 
@@ -382,392 +379,60 @@ 
Source code for pyrad.io.io_aux
         corresponding Py-ART field name
 
     """
-    if datatype == 'dBZ':
-        field_name = 'reflectivity'
-        datatype_odim = 'DBZH'
-    elif datatype == 'dBuZ':
-        field_name = 'unfiltered_reflectivity'
+    if datatype == 'udBZ':
         datatype_odim = 'TH'
-    elif datatype == 'dBZc':
-        field_name = 'corrected_reflectivity'
-        datatype_odim = 'DBZHC'
-    elif datatype == 'dBuZc':
-        field_name = 'corrected_unfiltered_reflectivity'
-        datatype_odim = 'THC'
+        field_name = 'uncorrected_reflectivity'
+    elif datatype == 'udBZv':
+        datatype_odim = 'TV'
+        field_name = 'uncorrected_reflectivity_vv'
+    elif datatype == 'dBZ':
+        datatype_odim = 'DBZH'
+        field_name = 'reflectivity'
     elif datatype == 'dBZv':
-        field_name = 'reflectivity_vv'
         datatype_odim = 'DBZV'
+        field_name = 'reflectivity_vv'
+    elif datatype == 'dBZc':
+        datatype_odim = 'DBZH'
+        field_name = 'corrected_reflectivity'
     elif datatype == 'dBZvc':
+        datatype_odim = 'DBZV'
         field_name = 'corrected_reflectivity_vv'
-        datatype_odim = 'DBZVC'
-    elif datatype == 'dBuZv':
-        field_name = 'unfiltered_reflectivity_vv'
-        datatype_odim = 'TV'
-    elif datatype == 'dBuZvc':
-        field_name = 'corrected_unfiltered_reflectivity_vv'
-        datatype_odim = 'TVC'
-    elif datatype == 'dBZ_bias':
-        field_name = 'reflectivity_bias'
-        datatype_odim = 'ZBIAS'
-    elif datatype == 'eta_h':
-        field_name = 'volumetric_reflectivity'
-        datatype_odim = 'etah'
-    elif datatype == 'eta_v':
-        field_name = 'volumetric_reflectivity_vv'
-        datatype_odim = 'etav'
-    elif datatype == 'rcs_h':
-        field_name = 'radar_cross_section_hh'
-        datatype_odim = 'RCSH'
-    elif datatype == 'rcs_v':
-        field_name = 'radar_cross_section_vv'
-        datatype_odim = 'RCSV'
-
     elif datatype == 'ZDR':
-        field_name = 'differential_reflectivity'
         datatype_odim = 'ZDR'
-    elif datatype == 'ZDRu':
-        field_name = 'unfiltered_differential_reflectivity'
-        datatype_odim = 'ZDRU'
-    elif datatype == 'ZDRc':
-        field_name = 'corrected_differential_reflectivity'
-        datatype_odim = 'ZDRC'
-    elif datatype == 'ZDRuc':
-        field_name = 'corrected_unfiltered_differential_reflectivity'
-        datatype_odim = 'ZDRUC'
-    elif datatype == 'ZDR_prec':
-        field_name = 'differential_reflectivity_in_precipitation'
-        datatype_odim = 'ZDRPREC'
-    elif datatype == 'ZDR_snow':
-        field_name = 'differential_reflectivity_in_snow'
-        datatype_odim = 'ZDRSNOW'
-
-    elif datatype == 'dBm':
-        field_name = 'signal_power_hh'
-        datatype_odim = 'DBMH'
-    elif datatype == 'dBmv':
-        field_name = 'signal_power_vv'
-        datatype_odim = 'DBMV'
-    elif datatype == 'Nh':
-        field_name = 'noisedBZ_hh'
-        datatype_odim = 'NDBZH'
-    elif datatype == 'Nv':
-        field_name = 'noisedBZ_vv'
-        datatype_odim = 'NDBZV'
-    elif datatype == 'SNRh':
-        field_name = 'signal_to_noise_ratio_hh'
-        datatype_odim = 'SNRH'
-    elif datatype == 'SNRv':
-        field_name = 'signal_to_noise_ratio_vv'
-        datatype_odim = 'SNRV'
-    elif datatype == 'SQI':
-        field_name = 'normalized_coherent_power'
-        datatype_odim = 'SQIH'
-    elif datatype == 'SQIv':
-        field_name = 'normalized_coherent_power_vv'
-        datatype_odim = 'SQIV'
-
-    elif datatype == 'dBm_sun_hit':
-        field_name = 'sun_hit_power_h'
-        datatype_odim = 'DBM_SUNHIT'
-    elif datatype == 'dBmv_sun_hit':
-        field_name = 'sun_hit_power_v'
-        datatype_odim = 'DBMV_SUNHIT'
-    elif datatype == 'ZDR_sun_hit':
-        field_name = 'sun_hit_differential_reflectivity'
-        datatype_odim = 'ZDR_SUNHIT'
-    elif datatype == 'dBm_sun_est':
-        field_name = 'sun_est_power_h'
-        datatype_odim = 'DBM_SUNEST'
-    elif datatype == 'dBmv_sun_est':
-        field_name = 'sun_est_power_v'
-        datatype_odim = 'DBMV_SUNEST'
-    elif datatype == 'ZDR_sun_est':
-        field_name = 'sun_est_differential_reflectivity'
-        datatype_odim = 'ZDR_SUNEST'
-    elif datatype == 'sun_pos_h':
-        field_name = 'sun_hit_h'
-        datatype_odim = 'POSH_SUNHIT'
-    elif datatype == 'sun_pos_v':
-        field_name = 'sun_hit_v'
-        datatype_odim = 'POSV_SUNHIT'
-    elif datatype == 'sun_pos_zdr':
-        field_name = 'sun_hit_zdr'
-        datatype_odim = 'POSZDR_SUNHIT'
-
+        field_name = 'differential_reflectivity'
     elif datatype == 'RhoHV':
-        field_name = 'cross_correlation_ratio'
         datatype_odim = 'RHOHV'
-    elif datatype == 'uRhoHV':
-        field_name = 'uncorrected_cross_correlation_ratio'
-        datatype_odim = 'URHOHV'
-    elif datatype == 'RhoHVc':
-        field_name = 'corrected_cross_correlation_ratio'
-        datatype_odim = 'RHOHVC'
-    elif datatype == 'RhoHV_rain':
-        field_name = 'cross_correlation_ratio_in_rain'
-        datatype_odim = 'RHOHVRAIN'
-    elif datatype == 'L':
-        field_name = 'logarithmic_cross_correlation_ratio'
-        datatype_odim = 'LRHOHV'
-    elif datatype == 'CDR':
-        field_name = 'circular_depolarization_ratio'
-        datatype_odim = 'CDR'
+        field_name = 'cross_correlation_ratio'
     elif datatype == 'LDR':
-        field_name = 'linear_polarization_ratio'
         datatype_odim = 'LDR'
-
+        field_name = 'linear_polarization_ratio'
     elif datatype == 'PhiDP':
-        field_name = 'differential_phase'
         datatype_odim = 'PHIDP'
-    elif datatype == 'uPhiDP':
-        field_name = 'uncorrected_differential_phase'
-        datatype_odim = 'UPHIDP'
-    elif datatype == 'PhiDPc':
-        field_name = 'corrected_differential_phase'
-        datatype_odim = 'PHIDPC'
-    elif datatype == 'PhiDP0':
-        field_name = 'system_differential_phase'
-        datatype_odim = 'PHIDP0'
-    elif datatype == 'PhiDP0_bin':
-        field_name = 'first_gate_differential_phase'
-        datatype_odim = 'PHIDP0_BIN'
+        field_name = 'differential_phase'
     elif datatype == 'KDP':
-        field_name = 'specific_differential_phase'
         datatype_odim = 'KDP'
-    elif datatype == 'KDPc':
-        field_name = 'corrected_specific_differential_phase'
-        datatype_odim = 'KDPC'
-
+        field_name = 'specific_differential_phase'
+    elif datatype == 'SQI':
+        datatype_odim = 'SQI'
+        field_name = 'normalized_coherent_power'
+    elif datatype == 'SNR':
+        datatype_odim = 'SNR'
+        field_name = 'signal_to_noise_ratio'
     elif datatype == 'V':
+        datatype_odim = 'VRAD'
         field_name = 'velocity'
-        datatype_odim = 'VRADH'
     elif datatype == 'Vh':
-        field_name = 'velocity'
         datatype_odim = 'VRADH'
-    elif datatype == 'dealV':
-        field_name = 'dealiased_velocity'
-        datatype_odim = 'VRADDH'
-    elif datatype == 'Vc':
-        field_name = 'corrected_velocity'
-        datatype_odim = 'VRADHC'
-    elif datatype == 'dealVc':
-        field_name = 'dealiased_corrected_velocity'
-        datatype_odim = 'VRADDHC'
-    elif datatype == 'estV':
-        field_name = 'retrieved_velocity'
-        datatype_odim = 'VRADEST'
-    elif datatype == 'stdV':
-        field_name = 'retrieved_velocity_std'
-        datatype_odim = 'sd_vvp'
-    elif datatype == 'diffV':
-        field_name = 'velocity_difference'
-        datatype_odim = 'VDIFF'
+        field_name = 'velocity'
     elif datatype == 'Vv':
-        field_name = 'velocity_vv'
         datatype_odim = 'VRADV'
-    elif datatype == 'dealVv':
-        field_name = 'dealiased_velocity_vv'
-        datatype_odim = 'VRADDV'
+        field_name = 'velocity_vv'
     elif datatype == 'W':
+        datatype_odim = 'WRAD'
         field_name = 'spectrum_width'
-        datatype_odim = 'WRADH'
-    elif datatype == 'Wc':
-        field_name = 'corrected_spectrum_width'
-        datatype_odim = 'WRADHC'
-    elif datatype == 'Wv':
-        field_name = 'spectrum_width_vv'
-        datatype_odim = 'WRADV'
-    elif datatype == 'wind_vel_h_az':
-        field_name = 'azimuthal_horizontal_wind_component'
-        datatype_odim = 'AHWND'
-    elif datatype == 'wind_vel_v':
-        field_name = 'vertical_wind_component'
-        datatype_odim = 'w'
-    elif datatype == 'wind_vel_h_u':
-        field_name = 'eastward_wind_component'
-        datatype_odim = 'UWND'
-    elif datatype == 'wind_vel_h_v':
-        field_name = 'northward_wind_component'
-        datatype_odim = 'VWND'
-    elif datatype == 'windshear_v':
-        field_name = 'vertical_wind_shear'
-        datatype_odim = 'VSHR'
-    elif datatype == 'WIND_SPEED':
-        field_name = 'wind_speed'
-        datatype_odim = 'ff'
-    elif datatype == 'WIND_DIRECTION':
-        field_name = 'wind_direction'
-        datatype_odim = 'dd'
-
-    elif datatype == 'Ah':
-        field_name = 'specific_attenuation'
-        datatype_odim = 'AH'
-    elif datatype == 'Ahc':
-        field_name = 'corrected_specific_attenuation'
-        datatype_odim = 'AHC'
-    elif datatype == 'PIA':
-        field_name = 'path_integrated_attenuation'
-        datatype_odim = 'PIA'
-    elif datatype == 'PIAc':
-        field_name = 'corrected_path_integrated_attenuation'
-        datatype_odim = 'PIAC'
-    elif datatype == 'Adp':
-        field_name = 'specific_differential_attenuation'
-        datatype_odim = 'ADP'
-    elif datatype == 'Adpc':
-        field_name = 'corrected_specific_differential_attenuation'
-        datatype_odim = 'ADPC'
-    elif datatype == 'PIDA':
-        field_name = 'path_integrated_differential_attenuation'
-        datatype_odim = 'PIDA'
-    elif datatype == 'PIDAc':
-        field_name = 'corrected_path_integrated_differential_attenuation'
-        datatype_odim = 'PIDAC'
-
-    elif datatype == 'TEMP':
-        field_name = 'temperature'
-        datatype_odim = 'TEMP'
-    elif datatype == 'ISO0':
-        field_name = 'iso0'
-        datatype_odim = 'ISO0'
-    elif datatype == 'H_ISO0':
-        field_name = 'height_over_iso0'
-        datatype_odim = 'HISO0'
-    elif datatype == 'cosmo_index':
-        field_name = 'cosmo_index'
-        datatype_odim = 'COSMOIND'
-    elif datatype == 'hzt_index':
-        field_name = 'hzt_index'
-        datatype_odim = 'HZTIND'
-    elif datatype == 'ml':
-        field_name = 'melting_layer'
-        datatype_odim = 'ML'
-
-    elif datatype == 'VIS':
-        field_name = 'visibility'
-        datatype_odim = 'VIS'
-
-    elif datatype == 'echoID':
-        field_name = 'radar_echo_id'
-        datatype_odim = 'ECHOID'
-    elif datatype == 'CLT':
-        field_name = 'clutter_exit_code'
-        datatype_odim = 'CLT'
-    elif datatype == 'occurrence':
-        field_name = 'occurrence'
-        datatype_odim = 'OCC'
-    elif datatype == 'freq_occu':
-        field_name = 'frequency_of_occurrence'
-        datatype_odim = 'OCCFREQ'
-    elif datatype == 'RR':
-        field_name = 'radar_estimated_rain_rate'
-        datatype_odim = 'RATE'
-
-    elif datatype == 'hydro':
-        field_name = 'radar_echo_classification'
-        datatype_odim = 'CLASS'
-    elif datatype == 'entropy':
-        field_name = 'hydroclass_entropy'
-        datatype_odim = 'ENTROPY'
-    elif datatype == 'propAG':
-        field_name = 'proportion_AG'
-        datatype_odim = 'propAG'
-    elif datatype == 'propCR':
-        field_name = 'proportion_CR'
-        datatype_odim = 'propCR'
-    elif datatype == 'propLR':
-        field_name = 'proportion_LR'
-        datatype_odim = 'propLR'
-    elif datatype == 'propRP':
-        field_name = 'proportion_RP'
-        datatype_odim = 'propRP'
-    elif datatype == 'propRN':
-        field_name = 'proportion_RN'
-        datatype_odim = 'propRN'
-    elif datatype == 'propVI':
-        field_name = 'proportion_VI'
-        datatype_odim = 'propVI'
-    elif datatype == 'propWS':
-        field_name = 'proportion_WS'
-        datatype_odim = 'propWS'
-    elif datatype == 'propMH':
-        field_name = 'proportion_MH'
-        datatype_odim = 'propMH'
-    elif datatype == 'propIH':
-        field_name = 'proportion_IH'
-        datatype_odim = 'propIH'
-
-    elif datatype == 'time_avg_flag':
-        field_name = 'time_avg_flag'
-        datatype_odim = 'TAFLAG'
-    elif datatype == 'colocated_gates':
-        field_name = 'colocated_gates'
-        datatype_odim = 'COLGATES'
-    elif datatype == 'nsamples':
-        field_name = 'number_of_samples'
-        datatype_odim = 'ns'
-    elif datatype == 'bird_density':
-        field_name = 'bird_density'
-        datatype_odim = 'dens'
-    elif datatype == 'std':
-        field_name = 'standard_deviation'
-        datatype_odim = 'STD'
-    elif datatype == 'sum':
-        field_name = 'sum'
-        datatype_odim = 'SUM'
-    elif datatype == 'sum2':
-        field_name = 'sum_squared'
-        datatype_odim = 'SUM2'
-
-    # vol2bird field names
-    elif datatype == 'ff':
-        field_name = 'wind_speed'
-        datatype_odim = 'ff'
-    elif datatype == 'dd':
-        field_name = 'wind_direction'
-        datatype_odim = 'dd'
-    elif datatype == 'u':
-        field_name = 'eastward_wind_component'
-        datatype_odim = 'UWND'
-    elif datatype == 'v':
-        field_name = 'northward_wind_component'
-        datatype_odim = 'VWND'
-    elif datatype == 'w':
-        field_name = 'vertical_wind_component'
-        datatype_odim = 'w'
-    elif datatype == 'width':
-        field_name = 'height_resolution'
-        datatype_odim = 'width'
-    elif datatype == 'gap':
-        field_name = 'gap'
-        datatype_odim = 'gap'
-    elif datatype == 'dbz':
-        field_name = 'bird_reflectivity'
-        datatype_odim = 'eta'
-    elif datatype == 'eta':
-        field_name = 'volumetric_reflectivity'
-        datatype_odim = 'etah'
-    elif datatype == 'dens':
-        field_name = 'bird_density'
-        datatype_odim = 'dens'
-    elif datatype == 'n':
-        field_name = 'number_of_samples_velocity'
-        datatype_odim = 'n'
-    elif datatype == 'n_dbz':
-        field_name = 'number_of_samples_reflectivity'
-        datatype_odim = 'n_dbz'
-    elif datatype == 'sd_vvp':
-        field_name = 'retrieved_velocity_std'
-        datatype_odim = 'sd_vvp'
-    elif datatype == 'DBZH':
-        field_name = 'reflectivity'
-        datatype_odim = 'DBZH'
-    elif datatype == 'n_all':
-        field_name = 'number_of_samples_velocity_all'
-        datatype_odim = 'n_all'
-    elif datatype == 'n_dbz_all':
-        field_name = 'number_of_samples_reflectivity_all'
-        datatype_odim = 'n_dbz_all'
+    elif datatype == 'QIND':
+        datatype_odim = 'QIND'
+        field_name = 'signal_quality_index'
     else:
         raise ValueError(
             'ERROR: ODIM fields do not contain datatype '+datatype)
@@ -1001,12 +666,6 @@ 
Source code for pyrad.io.io_aux
         field_name = 'number_of_samples'
     elif datatype == 'bird_density':
         field_name = 'bird_density'
-    elif datatype == 'std':
-        field_name = 'standard_deviation'
-    elif datatype == 'sum':
-        field_name = 'sum'
-    elif datatype == 'sum2':
-        field_name = 'sum_squared'
 
     # vol2bird field names
     elif datatype == 'ff':
@@ -1107,7 +766,7 @@ 
Source code for pyrad.io.io_aux
         datadescriptor)
     ind_rad = int(radarnr[5:8])-1
 
-    if datatype in ('Nh', 'Nv'):
+    if (datatype == 'Nh') or (datatype == 'Nv'):
         datatype = 'dBZ'
 
     t_filelist = []
@@ -1204,7 +863,7 @@ 
Source code for pyrad.io.io_aux
                     starttime+datetime.timedelta(days=i)).strftime(
                         fpath_strf)
                 datapath = (cfg['datapath'][ind_rad] + daydir+'/')
-                dayfilelist = glob.glob(datapath+'*'+scan+'*.h5')
+                dayfilelist = glob.glob(datapath+'*'+scan)
             else:
                 dayinfo = (starttime+datetime.timedelta(days=i)).strftime('%y%j')
                 basename = ('M'+cfg['RadarRes'][ind_rad] +
@@ -1227,11 +886,7 @@ 
Source code for pyrad.io.io_aux
                 continue
             for filename in dayfilelist:
                 t_filelist.append(filename)
-        elif datagroup in ('CFRADIAL', 'ODIMPYRAD'):
-            termination = '.nc'
-            if datagroup == 'ODIMPYRAD':
-                termination = '.h5'
-
+        elif datagroup == 'CFRADIAL':
             daydir = (
                 starttime+datetime.timedelta(days=i)).strftime('%Y-%m-%d')
             dayinfo = (starttime+datetime.timedelta(days=i)).strftime('%Y%m%d')
@@ -1241,7 +896,7 @@ 
Source code for pyrad.io.io_aux
             if not os.path.isdir(datapath):
                 warn("WARNING: Unknown datapath '%s'" % datapath)
                 continue
-            dayfilelist = glob.glob(datapath+dayinfo+'*'+datatype+termination)
+            dayfilelist = glob.glob(datapath+dayinfo+'*'+datatype+'.nc')
             for filename in dayfilelist:
                 t_filelist.append(filename)
         elif datagroup == 'MXPOL':
@@ -1274,9 +929,8 @@ 
Source code for pyrad.io.io_aux
     for filename in t_filelist:
         filenamestr = str(filename)
         fdatetime = get_datetime(filenamestr, datadescriptor)
-        if fdatetime is not None:
-            if (fdatetime >= starttime) and (fdatetime <= endtime):
-                filelist.append(filenamestr)
+        if (fdatetime >= starttime) and (fdatetime <= endtime):
+            filelist.append(filenamestr)
 
     return sorted(filelist)

 
@@ -1424,7 +1078,7 @@ 
Source code for pyrad.io.io_aux
             product = None
         else:
             datagroup = descrfields[1]
-            if datagroup in ('CFRADIAL', 'ODIMPYRAD'):
+            if datagroup == 'CFRADIAL':
                 descrfields2 = descrfields[2].split(',')
                 datatype = descrfields2[0]
                 dataset = descrfields2[1]
@@ -1447,7 +1101,7 @@ 
Source code for pyrad.io.io_aux
     else:
         radarnr = 'RADAR001'
         datagroup = descrfields[0]
-        if datagroup in ('CFRADIAL', 'ODIMPYRAD'):
+        if datagroup == 'CFRADIAL':
             descrfields2 = descrfields[1].split(',')
             datatype = descrfields2[0]
             dataset = descrfields2[1]
@@ -1770,7 +1424,7 @@ 
Source code for pyrad.io.io_aux
 
     """
     bfile = os.path.basename(fname)
-    if datagroup in ('RAINBOW', 'CFRADIAL', 'ODIMPYRAD'):
+    if datagroup == 'RAINBOW' or datagroup == 'CFRADIAL':
         datetimestr = bfile[0:14]
         fdatetime = datetime.datetime.strptime(datetimestr, '%Y%m%d%H%M%S')
     elif datagroup == 'RAD4ALP':
@@ -1782,8 +1436,19 @@ 
Source code for pyrad.io.io_aux
             datetimestr = bfile[3:12]
             fdatetime = datetime.datetime.strptime(datetimestr, '%y%j%H%M')
         else:
-            return find_date_in_file_name(
-                bfile, date_format=ftime_format[ftime_format.find("F")+2:-1])
+            fdate_strf = ftime_format[ftime_format.find("F")+2:-1]
+            today = datetime.datetime.now()
+            len_datestr = len(today.strftime(fdate_strf))
+            count = 0
+            while True:
+                try:
+                    fdatetime = datetime.datetime.strptime(
+                        os.path.basename(bfile)[count:count+len_datestr], fdate_strf)
+                except ValueError:
+                    count = count + 1
+                else:
+                    # No error, stop the loop
+                    break
     elif datagroup == 'MXPOL':
         datetimestr = re.findall(r"([0-9]{8}-[0-9]{6})", bfile)[0]
         fdatetime = datetime.datetime.strptime(datetimestr, '%Y%m%d-%H%M%S')
@@ -1792,46 +1457,6 @@ 
Source code for pyrad.io.io_aux
         return None
 
     return fdatetime
-
-
-def find_date_in_file_name(filename, date_format='%Y%m%d%H%M%S'):
-    """
-    Find a date with date format defined in date_format in a file name.
-    If no date is found returns None
-
-    Parameters
-    ----------
-    filename : str
-        file name
-    date_format : str
-        The time format
-
-    Returns
-    -------
-    fdatetime : datetime object
-        date and time in file name
-
-    """
-    today = datetime.datetime.now()
-    len_datestr = len(today.strftime(date_format))
-    count = 0
-    bfile = os.path.basename(filename)
-    while True:
-        try:
-            fdatetime = datetime.datetime.strptime(
-                bfile[count:count+len_datestr], date_format)
-        except ValueError:
-            count = count + 1
-            if count+len_datestr >= len(bfile):
-                warn('Unable to find date from string name. ' +
-                     'date format '+date_format+'. File name ' +
-                     bfile)
-                return None
-        else:
-            # No error, stop the loop
-            break
-
-    return fdatetime
 

 
           

@@ -1854,7 +1479,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/read_data_cosmo.html b/docs/_modules/pyrad/io/read_data_cosmo.html
index 2615a2b..27b1b6d 100644
--- a/docs/_modules/pyrad/io/read_data_cosmo.html
+++ b/docs/_modules/pyrad/io/read_data_cosmo.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -580,7 +579,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/read_data_hzt.html b/docs/_modules/pyrad/io/read_data_hzt.html
index 3160f56..d26d315 100644
--- a/docs/_modules/pyrad/io/read_data_hzt.html
+++ b/docs/_modules/pyrad/io/read_data_hzt.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -375,7 +374,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/read_data_other.html b/docs/_modules/pyrad/io/read_data_other.html
index 0749f1a..94477fb 100644
--- a/docs/_modules/pyrad/io/read_data_other.html
+++ b/docs/_modules/pyrad/io/read_data_other.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1572,7 +1571,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/read_data_radar.html b/docs/_modules/pyrad/io/read_data_radar.html
index 966dc97..242a79a 100644
--- a/docs/_modules/pyrad/io/read_data_radar.html
+++ b/docs/_modules/pyrad/io/read_data_radar.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -73,7 +72,7 @@ 
Source code for pyrad.io.read_data_radar
     merge_scans_dem_rad4alp
     merge_scans_hydro_rad4alp
     merge_fields_rainbow
-    merge_fields_pyrad
+    merge_fields_cfradial
     merge_fields_dem
     merge_fields_cosmo
     get_data_rainbow
@@ -100,12 +99,13 @@ 
Source code for pyrad.io.read_data_radar
     _WRADLIB_AVAILABLE = False
 
 import pyart
+from pyart.aux_io.odim_h5 import read_odim_h5 as read_odim
 
 from .read_data_other import read_status, read_rad4alp_cosmo, read_rad4alp_vis
 from .read_data_mxpol import pyrad_MXPOL, pyrad_MCH
 
 from .io_aux import get_datatype_metranet, get_fieldname_pyart, get_file_list
-from .io_aux import get_datatype_odim, find_date_in_file_name
+from .io_aux import get_datatype_odim
 from .io_aux import get_datatype_fields, get_datetime, map_hydro, map_Doppler
 from .io_aux import find_cosmo_file, find_rad4alpcosmo_file
 
@@ -137,9 +137,6 @@ 
Source code for pyrad.io.read_data_radar
     datatype_cfradial = list()
     dataset_cfradial = list()
     product_cfradial = list()
-    datatype_odimpyrad = list()
-    dataset_odimpyrad = list()
-    product_odimpyrad = list()
     datatype_cosmo = list()
     datatype_rad4alpcosmo = list()
     datatype_dem = list()
@@ -161,10 +158,6 @@ 
Source code for pyrad.io.read_data_radar
             datatype_cfradial.append(datatype)
             dataset_cfradial.append(dataset)
             product_cfradial.append(product)
-        elif datagroup == 'ODIMPYRAD':
-            datatype_odimpyrad.append(datatype)
-            dataset_odimpyrad.append(dataset)
-            product_odimpyrad.append(product)
         elif datagroup == 'COSMO':
             datatype_cosmo.append(datatype)
         elif datagroup == 'RAD4ALPCOSMO':
@@ -186,7 +179,6 @@ 
Source code for pyrad.io.read_data_radar
     ndatatypes_rad4alp = len(datatype_rad4alp)
     ndatatypes_odim = len(datatype_odim)
     ndatatypes_cfradial = len(datatype_cfradial)
-    ndatatypes_odimpyrad = len(datatype_odimpyrad)
     ndatatypes_cosmo = len(datatype_cosmo)
     ndatatypes_rad4alpcosmo = len(datatype_rad4alpcosmo)
     ndatatypes_dem = len(datatype_dem)
@@ -224,16 +216,10 @@ 
Source code for pyrad.io.read_data_radar
             datatype_mxpol, cfg)
 
     if ndatatypes_cfradial > 0:
-        radar_aux = merge_fields_pyrad(
+        radar_aux = merge_fields_cfradial(
             cfg['loadbasepath'][ind_rad], cfg['loadname'][ind_rad], voltime,
             datatype_cfradial, dataset_cfradial, product_cfradial,
-            rmax=cfg['rmax'])
-        radar = add_field(radar, radar_aux)
-    if ndatatypes_odimpyrad > 0:
-        radar_aux = merge_fields_pyrad(
-            cfg['loadbasepath'][ind_rad], cfg['loadname'][ind_rad], voltime,
-            datatype_odimpyrad, dataset_odimpyrad, product_odimpyrad,
-            rmax=cfg['rmax'], termination='.h5')
+            cfg['rmax'])
         radar = add_field(radar, radar_aux)
 
     # add COSMO files to the radar field
@@ -612,7 +598,7 @@ 
Source code for pyrad.io.read_data_radar
     radar = None
     dayinfo = voltime.strftime('%y%j')
     timeinfo = voltime.strftime('%H%M')
-    if radar_name is not None and radar_res is not None:
+    if (radar_name != None) or (radar_res != None):
         basename = 'M'+radar_res+radar_name+dayinfo
     if cfg['path_convention'] == 'LTE':
         yy = dayinfo[0:2]
@@ -636,13 +622,7 @@ 
Source code for pyrad.io.read_data_radar
         fpath_strf = dataset_list[0][dataset_list[0].find("D")+2:dataset_list[0].find("F")-2]
         fdate_strf = dataset_list[0][dataset_list[0].find("F")+2:-1]
         datapath = (basepath+voltime.strftime(fpath_strf)+'/')
-        filenames = glob.glob(datapath+'*'+scan_list[0]+'*')
-        filename = []
-        for filename_aux in filenames:
-            fdatetime = find_date_in_file_name(
-                filename_aux, date_format=fdate_strf)
-            if fdatetime == voltime:
-                filename = [filename_aux]
+        filename = glob.glob(datapath+'*'+scan_list[0]+'*')
     else:
         datapath = basepath+'M'+radar_res+radar_name+'/'
         filename = glob.glob(
@@ -663,14 +643,7 @@ 
Source code for pyrad.io.read_data_radar
     # merge the elevations into a single radar instance
     for scan in scan_list[1:]:
         if cfg['path_convention'] == 'ODIM':
-            filenames = glob.glob(datapath+'*'+scan+'*')
-            filename = []
-            for filename_aux in filenames:
-                fdatetime = find_date_in_file_name(
-                    filename_aux, date_format=fdate_strf)
-                if fdatetime == voltime:
-                    filename = [filename_aux]
-                    break
+            filename = glob.glob(datapath+'*'+scan+'*')
         else:
             filename = glob.glob(datapath+basename+timeinfo+'*'+scan+'*')
         if not filename:
@@ -688,7 +661,6 @@ 
Source code for pyrad.io.read_data_radar
 
     return radar
 
-
 def merge_scans_mxpol(basepath, scan_list, voltime, datatype_list, cfg):
     """
     merge rad4alp data.
@@ -1406,7 +1378,7 @@ 
Source code for pyrad.io.read_data_radar
 
     # add other fields in the same scan
     for datatype in datatype_list[1:]:
-        if datatype not in ('Nh', 'Nv'):
+        if (datatype != 'Nh') and (datatype != 'Nv'):
             filename = glob.glob(datapath+fdatetime+datatype+'.*')
         elif datatype == 'Nh':
             filename = glob.glob(datapath+fdatetime+'dBZ.*')
@@ -1433,12 +1405,10 @@ 
Source code for pyrad.io.read_data_radar
     return radar
 
 
-def merge_fields_pyrad(basepath, loadname, voltime, datatype_list,
-                       dataset_list, product_list, rmax=0.,
-                       termination='.nc'):
+def merge_fields_cfradial(basepath, loadname, voltime, datatype_list,
+                          dataset_list, product_list, rmax=0.):
     """
-    merge fields from Pyrad-generated files into a single radar object.
-    Accepted file types are CFRadial and ODIM.
+    merge CF/Radial fields into a single radar object.
 
     Parameters
     ----------
@@ -1457,8 +1427,6 @@ 
Source code for pyrad.io.read_data_radar
         list of products. Used to get path
     rmax : float
         maximum range that will be kept.
-    termination : str
-        file termination type. Can be '.nc' or '.h5'
 
     Returns
     -------
@@ -1469,7 +1437,7 @@ 
Source code for pyrad.io.read_data_radar
     datapath = (basepath+loadname+'/'+voltime.strftime('%Y-%m-%d')+'/' +
                 dataset_list[0]+'/'+product_list[0]+'/')
     fdatetime = voltime.strftime('%Y%m%d%H%M%S')
-    filename = glob.glob(datapath+fdatetime+'*'+datatype_list[0]+termination)
+    filename = glob.glob(datapath+fdatetime+'*'+datatype_list[0]+'.nc')
 
     # create radar object
     radar = None
@@ -1477,10 +1445,7 @@ 
Source code for pyrad.io.read_data_radar
         warn('No file found in '+datapath+fdatetime+'*'+datatype_list[0] +
              '.nc')
     else:
-        if termination == '.nc':
-            radar = pyart.io.read_cfradial(filename[0])
-        else:
-            radar = pyart.aux_io.read_odim_h5(filename[0])
+        radar = pyart.io.read_cfradial(filename[0])
         if rmax > 0.:
             radar.range['data'] = radar.range['data'][
                 radar.range['data'] < rmax]
@@ -1500,16 +1465,13 @@ 
Source code for pyrad.io.read_data_radar
                 basepath+loadname+'/'+voltime.strftime('%Y-%m-%d')+'/' +
                 dataset+'/'+product_list[i]+'/')
             filename = glob.glob(
-                datapath+fdatetime+'*'+datatype_list[i]+termination)
+                datapath+fdatetime+'*'+datatype_list[i]+'.nc')
             if not filename:
                 warn('No file found in '+datapath+fdatetime+'*' +
                      datatype_list[i]+'.nc')
                 continue
 
-            if termination == '.nc':
-                radar_aux = pyart.io.read_cfradial(filename[0])
-            else:
-                radar_aux = pyart.aux_io.read_odim_h5(filename[0])
+            radar_aux = pyart.io.read_cfradial(filename[0])
             if rmax > 0.:
                 radar_aux.range['data'] = radar_aux.range['data'][
                     radar_aux.range['data'] < rmax]
@@ -1661,7 +1623,7 @@ 
Source code for pyrad.io.read_data_radar
     if radar is None:
         return None
 
-    if datatype in ('Nh', 'Nv'):
+    if (datatype == 'Nh') or (datatype == 'Nv'):
         try:
             with open(filename, 'rb') as fid:
                 rbf = wrl.io.read_rainbow(fid, loaddata=True)
@@ -1721,7 +1683,7 @@ 
Source code for pyrad.io.read_data_radar
     """
     metranet_field_names = dict()
     for datatype in datatype_list:
-        if datatype not in ('Nh', 'Nv'):
+        if (datatype != 'Nh') and (datatype != 'Nv'):
             metranet_field_names.update(get_datatype_metranet(datatype))
 
     if cfg['path_convention'] == 'LTE':
@@ -1833,10 +1795,10 @@ 
Source code for pyrad.io.read_data_radar
     """
     odim_field_names = dict()
     for datatype in datatype_list:
-        if datatype not in ('Nh', 'Nv'):
+        if (datatype != 'Nh') and (datatype != 'Nv'):
             odim_field_names.update(get_datatype_odim(datatype))
         try:
-            radar = pyart.aux_io.read_odim_h5(
+            radar = read_odim(
                 filename, field_names=odim_field_names)
         except ValueError as ee:
             warn("Unable to read file '"+filename+": (%s)" % str(ee))
@@ -1935,7 +1897,7 @@ 
Source code for pyrad.io.read_data_radar
     """
     field_names = dict()
     for datatype in datatype_list:
-        if datatype not in ('Nh', 'Nv'):
+        if (datatype != 'Nh') and (datatype != 'Nv'):
             field_names.update(get_datatype_metranet(datatype))
     radar = pyrad_MXPOL(filename, field_names=field_names)
 
@@ -2113,7 +2075,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/read_data_sensor.html b/docs/_modules/pyrad/io/read_data_sensor.html
index c24636f..550df88 100644
--- a/docs/_modules/pyrad/io/read_data_sensor.html
+++ b/docs/_modules/pyrad/io/read_data_sensor.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1305,7 +1304,7 @@ 
Navigation

     

     
   
 
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diff --git a/docs/_modules/pyrad/io/read_data_sun.html b/docs/_modules/pyrad/io/read_data_sun.html
index f3913cf..d691419 100644
--- a/docs/_modules/pyrad/io/read_data_sun.html
+++ b/docs/_modules/pyrad/io/read_data_sun.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -487,7 +486,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/timeseries.html b/docs/_modules/pyrad/io/timeseries.html
index 3f56ac2..c43f40a 100644
--- a/docs/_modules/pyrad/io/timeseries.html
+++ b/docs/_modules/pyrad/io/timeseries.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -317,7 +316,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/io/trajectory.html b/docs/_modules/pyrad/io/trajectory.html
index 0a573c0..5daead7 100644
--- a/docs/_modules/pyrad/io/trajectory.html
+++ b/docs/_modules/pyrad/io/trajectory.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -751,7 +750,7 @@ 
Navigation

     

     
   
 
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diff --git a/docs/_modules/pyrad/io/write_data.html b/docs/_modules/pyrad/io/write_data.html
index 20bf96e..c795177 100644
--- a/docs/_modules/pyrad/io/write_data.html
+++ b/docs/_modules/pyrad/io/write_data.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1908,7 +1907,7 @@ 
Navigation

     

     
   
 
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diff --git a/docs/_modules/pyrad/proc/process_Doppler.html b/docs/_modules/pyrad/proc/process_Doppler.html
index 9275b03..c53f117 100644
--- a/docs/_modules/pyrad/proc/process_Doppler.html
+++ b/docs/_modules/pyrad/proc/process_Doppler.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -551,7 +550,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/proc/process_aux.html b/docs/_modules/pyrad/proc/process_aux.html
index ce660ef..a824368 100644
--- a/docs/_modules/pyrad/proc/process_aux.html
+++ b/docs/_modules/pyrad/proc/process_aux.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -265,9 +264,6 @@ 
Source code for pyrad.proc.process_aux
     elif dataset_type == 'OCCURRENCE':
         func_name = 'process_occurrence'
         dsformat = 'OCCURRENCE'
-    elif dataset_type == 'TIMEAVG_STD':
-        func_name = 'process_time_avg_std'
-        dsformat = 'OCCURRENCE'
     elif dataset_type == 'OCCURRENCE_PERIOD':
         func_name = 'process_occurrence_period'
         dsformat = 'OCCURRENCE'
@@ -756,7 +752,7 @@ 
Source code for pyrad.proc.process_aux
     delta_azi = dscfg.get('delta_azi', None)
     avg_type = dscfg.get('avg_type', 'mean')
     nvalid_min = dscfg.get('nvalid_min', 1)
-    if avg_type not in ('mean', 'median'):
+    if avg_type != 'mean' and avg_type != 'median':
         warn('Unsuported statistics '+avg_type)
         return None, None
 
@@ -851,7 +847,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/proc/process_calib.html b/docs/_modules/pyrad/proc/process_calib.html
index c3b169c..9ba6407 100644
--- a/docs/_modules/pyrad/proc/process_calib.html
+++ b/docs/_modules/pyrad/proc/process_calib.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -67,7 +66,6 @@ 
Source code for pyrad.proc.process_calib
     process_correct_noise_rhohv
     process_gc_monitoring
     process_occurrence
-    process_time_avg_std
     process_occurrence_period
     process_sun_hits
 
@@ -319,7 +317,7 @@ 
Source code for pyrad.proc.process_calib
 
         # filter wet or dry volumes
         filter_prec = dscfg.get('filter_prec', 'None')
-        if filter_prec in ('keep_wet', 'keep_dry'):
+        if filter_prec == 'keep_wet' or filter_prec == 'keep_dry':
             if echoid_field not in radar.fields:
                 warn('Unable to determine if there is precipitation ' +
                      'close to the radar. Missing echoID field.')
@@ -516,7 +514,7 @@ 
Source code for pyrad.proc.process_calib
                 mask, radar.fields[field_name]['data'] < val_min)
 
         filter_prec = dscfg.get('filter_prec', 'None')
-        if filter_prec in ('keep_wet', 'keep_dry'):
+        if filter_prec == 'keep_wet' or filter_prec == 'keep_dry':
             if echoid_field not in radar.fields:
                 warn('Unable to determine if there is precipitation ' +
                      'close to the radar. Missing echoID field.')
@@ -657,253 +655,6 @@ 
Source code for pyrad.proc.process_calib
         return new_dataset, ind_rad

 
 
-
[docs]def process_time_avg_std(procstatus, dscfg, radar_list=None):
-    """
-    computes the average and standard deviation of data. It looks only for
-    gates where data is present.
-
-    Parameters
-    ----------
-    procstatus : int
-        Processing status: 0 initializing, 1 processing volume,
-        2 post-processing
-    dscfg : dictionary of dictionaries
-        data set configuration. Accepted Configuration Keywords::
-
-        datatype : list of string. Dataset keyword
-            The input data types
-        regular_grid : Boolean. Dataset keyword
-            Whether the radar has a Boolean grid or not. Default False
-        rmin, rmax : float. Dataset keyword
-            minimum and maximum ranges where the computation takes place. If
-            -1 the whole range is considered. Default is -1
-        val_min : Float. Dataset keyword
-            Minimum reflectivity value to consider that the gate has signal.
-            Default None
-        filter_prec : str. Dataset keyword
-            Give which type of volume should be filtered. None, no filtering;
-            keep_wet, keep wet volumes; keep_dry, keep dry volumes.
-        rmax_prec : float. Dataset keyword
-            Maximum range to consider when looking for wet gates [m]
-        percent_prec_max : float. Dataset keyword
-            Maxim percentage of wet gates to consider the volume dry
-        lin_trans : Boolean. Dataset keyword
-            If True the data will be transformed into linear units. Default
-            False
-    radar_list : list of Radar objects
-        Optional. list of radar objects
-
-    Returns
-    -------
-    new_dataset : dict
-        dictionary containing the output
-    ind_rad : int
-        radar index
-
-    """
-    echoid_field = None
-    refl_field = None
-    for datatypedescr in dscfg['datatype']:
-        radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-        if datatype == 'echoID':
-            echoid_field = get_fieldname_pyart(datatype)
-        elif (datatype in ('dBZ', 'dBZc', 'dBZv', 'dBZvc', 'dBuZ', 'dBuZc') and
-              refl_field is None):
-            refl_field = get_fieldname_pyart(datatype)
-        else:
-            field_name = get_fieldname_pyart(datatype)
-    ind_rad = int(radarnr[5:8])-1
-
-    lin_trans = dscfg.get('lin_trans', 0)
-
-    if procstatus == 0:
-        return None, None
-
-    if procstatus == 1:
-        if radar_list[ind_rad] is None:
-            warn('No valid radar')
-            return None, None
-        radar = radar_list[ind_rad]
-
-        if field_name not in radar.fields:
-            warn(field_name+' not available.')
-            return None, None
-
-        # filter out low reflectivity values
-        val_min = dscfg.get('val_min', None)
-        mask = np.ma.getmaskarray(radar.fields[field_name]['data'])
-        if val_min is not None and refl_field is not None:
-            mask = np.logical_or(
-                mask, radar.fields[refl_field]['data'] < val_min)
-
-        filter_prec = dscfg.get('filter_prec', 'None')
-        if filter_prec in ('keep_wet', 'keep_dry'):
-            if echoid_field not in radar.fields:
-                warn('Unable to determine if there is precipitation ' +
-                     'close to the radar. Missing echoID field.')
-                return None, None
-
-            # Put invalid values to noise
-            echoid = deepcopy(radar.fields[echoid_field]['data'])
-            echoid[mask] = 1
-
-            rmax_prec = dscfg.get('rmax_prec', 0.)
-            percent_prec_max = dscfg.get('percent_prec_max', 10.)
-
-            ngates = radar.ngates
-            if rmax_prec > 0.:
-                ngates = len(
-                    radar.range['data'][radar.range['data'] < rmax_prec])
-            ngates_total = ngates*radar.nrays
-
-            prec_field = echoid[:, :ngates]
-            ngates_prec = np.size(prec_field[prec_field == 3])
-
-            percent_prec = ngates_prec/ngates_total*100.
-            warn('Percent gates with precipitation: '+str(percent_prec)+'\n')
-            if percent_prec > percent_prec_max:
-                if filter_prec == 'keep_dry':
-                    warn('Radar volume is precipitation contaminated.\n' +
-                         'Maximum percentage allowed: '+str(percent_prec_max))
-                    return None, None
-            else:
-                if filter_prec == 'keep_wet':
-                    warn('Radar volume has not enough precipitation.\n' +
-                         'Minimum percentage required: ' +
-                         str(percent_prec_max))
-                    return None, None
-
-        # filter out out of range data
-        rmin = dscfg.get('rmin', -1.)
-        rmax = dscfg.get('rmax', -1.)
-        if rmin >= 0.:
-            ind_min = np.where(radar.range['data'] < rmin)[0]
-            if ind_min:
-                ind_min = ind_min[-1]
-                mask[:, 0:ind_min+1] = 1
-        if rmax >= 0.:
-            ind_max = np.where(radar.range['data'] > rmax)[0]
-            if ind_max:
-                ind_max = ind_max[0]
-                mask[:, ind_max:radar.ngates] = 1
-
-        # prepare field number of samples and values sum
-        field = deepcopy(radar.fields[field_name]['data'])
-        if lin_trans:
-            field = np.ma.power(10., 0.1*field)
-
-        field = np.ma.masked_where(mask, field)
-        field = np.ma.asarray(field)
-
-        radar_aux = deepcopy(radar)
-        radar_aux.fields = dict()
-
-        sum_dict = pyart.config.get_metadata('sum')
-        sum_dict['data'] = field
-        radar_aux.add_field('sum', sum_dict)
-
-        sum2_dict = pyart.config.get_metadata('sum_squared')
-        sum2_dict['data'] = field*field
-        radar_aux.add_field('sum_squared', sum2_dict)
-
-        npoints_dict = pyart.config.get_metadata('number_of_samples')
-        npoints_dict['data'] = np.ma.asarray(np.logical_not(mask), dtype=int)
-        radar_aux.add_field('number_of_samples', npoints_dict)
-
-        # first volume: initialize radar object
-        if dscfg['initialized'] == 0:
-            new_dataset = {
-                'radar_out': radar_aux,
-                'starttime': dscfg['timeinfo'],
-                'endtime': dscfg['timeinfo'],
-                'occu_final': False}
-
-            dscfg['global_data'] = new_dataset
-            dscfg['initialized'] = 1
-
-            return new_dataset, ind_rad
-
-        # accumulate data
-        regular_grid = False
-        if 'regular_grid' in dscfg:
-            regular_grid = dscfg['regular_grid']
-
-        if not regular_grid:
-            sum_interp = interpol_field(
-                dscfg['global_data']['radar_out'], radar_aux, 'sum')
-            sum2_interp = interpol_field(
-                dscfg['global_data']['radar_out'], radar_aux, 'sum_squared')
-            npoints_interp = interpol_field(
-                dscfg['global_data']['radar_out'], radar_aux,
-                'number_of_samples')
-        else:
-            if radar_aux.nrays != dscfg['global_data']['radar_out'].nrays:
-                warn('Unable to accumulate radar object. ' +
-                     'Number of rays of current radar different from ' +
-                     'reference. nrays current: '+str(radar_aux.nrays) +
-                     ' nrays ref: ' +
-                     str(dscfg['global_data']['radar_out'].nrays))
-                return None, None
-            sum_interp = radar_aux.fields['sum']
-            sum2_interp = radar_aux.fields['sum_squared']
-            npoints_interp = radar_aux.fields['number_of_samples']
-
-        valid = np.logical_not(np.ma.getmaskarray(sum_interp))
-
-        dscfg['global_data']['radar_out'].fields['sum']['data'][valid] += (
-            np.ma.asarray(sum_interp['data'][valid]))
-        dscfg['global_data']['radar_out'].fields['sum_squared'][
-            'data'][valid] += np.ma.asarray(sum2_interp['data'][valid])
-        dscfg['global_data']['radar_out'].fields['number_of_samples'][
-            'data'][valid] += np.ma.asarray(npoints_interp['data'][valid])
-        dscfg['global_data']['endtime'] = dscfg['timeinfo']
-
-        new_dataset = {
-            'radar_out': dscfg['global_data']['radar_out'],
-            'starttime': dscfg['global_data']['starttime'],
-            'endtime': dscfg['global_data']['endtime'],
-            'occu_final': False}
-
-        return new_dataset, ind_rad
-
-    # no more files to process. Compute mean and standard deviation
-    if procstatus == 2:
-        if dscfg['initialized'] == 0:
-            return None, None
-        if 'radar_out' not in dscfg['global_data']:
-            return None, None
-
-        field_mean = (
-            dscfg['global_data']['radar_out'].fields['sum']['data'] /
-            dscfg['global_data']['radar_out'].fields[
-                'number_of_samples']['data'])
-        field_std = np.ma.sqrt(
-            dscfg['global_data']['radar_out'].fields['sum_squared']['data'] /
-            dscfg['global_data']['radar_out'].fields[
-                'number_of_samples']['data']-field_mean*field_mean)
-        if lin_trans:
-            field_mean = 10.*np.ma.log10(field_mean)
-            field_std = 10.*np.ma.log10(field_std)
-
-        radar = dscfg['global_data']['radar_out']
-
-        mean_dict = pyart.config.get_metadata(field_name)
-        mean_dict['data'] = field_mean
-        radar.add_field(field_name, mean_dict)
-
-        std_dict = pyart.config.get_metadata('standard_deviation')
-        std_dict['data'] = field_std
-        radar.add_field('standard_deviation', std_dict)
-
-        new_dataset = {
-            'radar_out': radar,
-            'starttime': dscfg['global_data']['starttime'],
-            'endtime': dscfg['global_data']['endtime'],
-            'occu_final': True}
-
-        return new_dataset, ind_rad

-
-
 
[docs]def process_occurrence_period(procstatus, dscfg, radar_list=None):
     """
     computes the frequency of occurrence over a long period of time by adding
@@ -1478,7 +1229,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_cosmo.html b/docs/_modules/pyrad/proc/process_cosmo.html
index fe06b66..cf767c0 100644
--- a/docs/_modules/pyrad/proc/process_cosmo.html
+++ b/docs/_modules/pyrad/proc/process_cosmo.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -880,7 +879,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_echoclass.html b/docs/_modules/pyrad/proc/process_echoclass.html
index 696924c..366b061 100644
--- a/docs/_modules/pyrad/proc/process_echoclass.html
+++ b/docs/_modules/pyrad/proc/process_echoclass.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -146,7 +145,7 @@ 
Source code for pyrad.proc.process_echoclass
warn('Unable to create radar_echo_id dataset. Missing data')
         return None, None
 
-    echo_id = np.ma.zeros((radar.nrays, radar.ngates), dtype=np.uint8)+3
+    echo_id = np.zeros((radar.nrays, radar.ngates), dtype=np.uint8)+3
 
     # look for clutter
     gatefilter = pyart.filters.moment_and_texture_based_gate_filter(
@@ -166,7 +165,6 @@ 
Source code for pyrad.proc.process_echoclass
id_field = pyart.config.get_metadata('radar_echo_id')
     id_field['data'] = echo_id
-    id_field.update({'_FillValue': 0})
 
     # prepare for exit
     new_dataset = {'radar_out': deepcopy(radar)}
@@ -525,7 +523,7 @@ 
Source code for pyrad.proc.process_echoclass
for datatypedescr in dscfg['datatype']:
         radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-        if datatype in ('SNRh', 'SNRv'):
+        if (datatype == 'SNRh') or (datatype == 'SNRv'):
             snr_field = get_fieldname_pyart(datatype)
             break
 
@@ -549,7 +547,7 @@ 
Source code for pyrad.proc.process_echoclass
for datatypedescr in dscfg['datatype']:
         radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
 
-        if datatype in ('SNRh', 'SNRv'):
+        if (datatype == 'SNRh') or (datatype == 'SNRv'):
             continue
 
         field_name = get_fieldname_pyart(datatype)
@@ -733,7 +731,9 @@ 
Source code for pyrad.proc.process_echoclass
radar_aux.fields[field_name]['data'] = np.ma.masked_where(
             is_lowVIS, radar_aux.fields[field_name]['data'])
 
-        if datatype in ('dBZ', 'dBZc', 'dBuZ', 'dBZv', 'dBZvc', 'dBuZv'):
+        if ((datatype == 'dBZ') or (datatype == 'dBZc') or
+                (datatype == 'dBuZ') or (datatype == 'dBZv') or
+                (datatype == 'dBZvc') or (datatype == 'dBuZv')):
             radar_field = pyart.correct.correct_visibility(
                 radar_aux, vis_field=vis_field, field_name=field_name)
         else:
@@ -930,8 +930,6 @@ 
Source code for pyrad.proc.process_echoclass
zdr_field = 'corrected_differential_reflectivity'
             if datatype == 'RhoHV':
                 rhv_field = 'cross_correlation_ratio'
-            if datatype == 'uRhoHV':
-                rhv_field = 'uncorrected_cross_correlation_ratio'
             if datatype == 'RhoHVc':
                 rhv_field = 'corrected_cross_correlation_ratio'
             if datatype == 'KDP':
@@ -1217,18 +1215,17 @@ 
Source code for pyrad.proc.process_echoclass
radar = radar_list[ind_rad]
 
         # Check which should be the reference field for temperature
-        if iso0_field is not None:
-            if iso0_field not in radar.fields:
-                warn('Unable to detect melting layer. ' +
-                     'Missing height over iso0 field')
-                return None, None
+        if iso0_field is not None and (iso0_field not in radar.fields):
+            warn('Unable to detect melting layer. ' +
+                 'Missing height over iso0 field')
+            return None, None
+        else:
             temp_ref = 'height_over_iso0'
 
-        if temp_field is not None:
-            if temp_field not in radar.fields:
-                warn('Unable to detect melting layer. ' +
-                     'Missing temperature field')
-                return None, None
+        if temp_field is not None and (temp_field not in radar.fields):
+            warn('Unable to detect melting layer. Missing temperature field')
+            return None, None
+        else:
             temp_ref = 'temperature'
             iso0_field = 'height_over_iso0'
 
@@ -1425,6 +1422,7 @@ 
Source code for pyrad.proc.process_echoclass
if procstatus != 1:
         return None, None
 
+    temp_ref = None
     temp_field = None
     iso0_field = None
     for datatypedescr in dscfg['datatype']:
@@ -1453,13 +1451,15 @@ 
Source code for pyrad.proc.process_echoclass
warn('Unable to detect melting layer. ' +
              'Missing height over iso0 field')
         return None, None
-    temp_ref = 'height_over_iso0'
+    else:
+        temp_ref = 'height_over_iso0'
 
     if temp_field is not None and (temp_field not in radar.fields):
         warn('Unable to detect melting layer. Missing temperature field')
         return None, None
-    temp_ref = 'temperature'
-    iso0_field = 'height_over_iso0'
+    else:
+        temp_ref = 'temperature'
+        iso0_field = 'height_over_iso0'
 
     if ((zdr_field not in radar.fields) or
             (rhv_field not in radar.fields)):
@@ -1595,7 +1595,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_intercomp.html b/docs/_modules/pyrad/proc/process_intercomp.html
index f38bd52..0ccf31d 100644
--- a/docs/_modules/pyrad/proc/process_intercomp.html
+++ b/docs/_modules/pyrad/proc/process_intercomp.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -530,7 +529,7 @@ 
Source code for pyrad.proc.process_intercomp
hydro_field = radar.fields[hydro_name]
                 # check where is no rain
                 is_not_rain = np.logical_and(
-                    hydro_field['data'] != 4, hydro_field['data'] != 6)
+                    hydro_field['data'] != 3, hydro_field['data'] != 5)
                 # where is no rain should be precip
                 is_not_rain = np.logical_and(
                     is_not_rain, echo_field['data'] == 3)
@@ -1603,7 +1602,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_monitoring.html b/docs/_modules/pyrad/proc/process_monitoring.html
index 7d2fd29..be24dd7 100644
--- a/docs/_modules/pyrad/proc/process_monitoring.html
+++ b/docs/_modules/pyrad/proc/process_monitoring.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -219,7 +218,7 @@ 
Source code for pyrad.proc.process_monitoring
warn('Unable to retrieve PhiDP and KDP using self-consistency. ' +
                  'No selfconsistency files for the radar elevations.')
 
-            return None, None
+            return None
 
         zdr_kdpzh_dict = {'zdr_kdpzh': zdr_kdpzh_list,
                           'elev': el_list}
@@ -406,7 +405,7 @@ 
Source code for pyrad.proc.process_monitoring
warn('Unable to retrieve PhiDP and KDP using self-consistency. ' +
                  'No selfconsistency files for the radar elevations.')
 
-            return None, None
+            return None
 
         zdr_kdpzh_dict = {'zdr_kdpzh': zdr_kdpzh_list,
                           'elev': el_list}
@@ -920,7 +919,7 @@ 
Source code for pyrad.proc.process_monitoring
            Default None
         hydroclass : list of ints. Dataset keyword
             list of hydrometeor classes to keep for the analysis
-            Default [2] (dry snow)
+            Default [1] (dry snow)
     radar_list : list of Radar objects
         Optional. list of radar objects
 
@@ -980,24 +979,53 @@ 
Source code for pyrad.proc.process_monitoring
(rhohv_field not in radar.fields) or
             (zdr_field not in radar.fields) or
             (phidp_field not in radar.fields) or
+            (snr_field not in radar.fields) or
             (hydro_field not in radar.fields)):
         warn('Unable to estimate ZDR in snow. Missing data')
         return None, None
 
-    # User defined values
-    rmin = dscfg.get('rmin', 1000.)
-    rmax = dscfg.get('rmax', 50000.)
-    zmin = dscfg.get('Zmin', 0.)
-    zmax = dscfg.get('Zmax', 30.)
-    snrmin = dscfg.get('SNRmin', 10.)
-    snrmax = dscfg.get('SNRmax', 50.)
-    rhohvmin = dscfg.get('RhoHVmin', 0.97)
-    phidpmax = dscfg.get('PhiDPmax', 10.)
-    elmax = dscfg.get('elmax', None)
-    kdpmax = dscfg.get('KDPmax', None)
-    tempmin = dscfg.get('TEMPmin', None)
-    tempmax = dscfg.get('TEMPmax', None)
-    hydroclass = dscfg.get('hydroclass', [2])
+    # default values
+    rmin = 1000.
+    rmax = 50000.
+    zmin = 0.
+    zmax = 30.
+    snrmin = 10.
+    snrmax = 50.
+    rhohvmin = 0.97
+    phidpmax = 10.
+    elmax = None
+    kdpmax = None
+    tempmin = None
+    tempmax = None
+    hydroclass = [1]
+
+    # user defined values
+    if 'rmin' in dscfg:
+        rmin = dscfg['rmin']
+    if 'rmax' in dscfg:
+        rmax = dscfg['rmax']
+    if 'Zmin' in dscfg:
+        zmin = dscfg['Zmin']
+    if 'Zmax' in dscfg:
+        zmax = dscfg['Zmax']
+    if 'SNRmin' in dscfg:
+        snrmin = dscfg['SNRmin']
+    if 'SNRmax' in dscfg:
+        snrmax = dscfg['SNRmax']
+    if 'RhoHVmin' in dscfg:
+        rhohvmin = dscfg['RhoHVmin']
+    if 'PhiDPmax' in dscfg:
+        phidpmax = dscfg['PhiDPmax']
+    if 'KDPmax' in dscfg:
+        kdpmax = dscfg['KDPmax']
+    if 'TEMPmin' in dscfg:
+        tempmin = dscfg['TEMPmin']
+    if 'TEMPmax' in dscfg:
+        tempmax = dscfg['TEMPmax']
+    if 'elmax' in dscfg:
+        elmax = dscfg['elmax']
+    if 'hydroclass' in dscfg:
+        hydroclass = dscfg['hydroclass']
 
     ind_rmin = np.where(radar.range['data'] > rmin)[0][0]
     ind_rmax = np.where(radar.range['data'] < rmax)[0][-1]
@@ -1159,7 +1187,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_phase.html b/docs/_modules/pyrad/proc/process_phase.html
index 47e1f78..8fb2cdd 100644
--- a/docs/_modules/pyrad/proc/process_phase.html
+++ b/docs/_modules/pyrad/proc/process_phase.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1224,7 +1223,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_retrieve.html b/docs/_modules/pyrad/proc/process_retrieve.html
index 93eb964..d9cc725 100644
--- a/docs/_modules/pyrad/proc/process_retrieve.html
+++ b/docs/_modules/pyrad/proc/process_retrieve.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1058,7 +1057,7 @@ 
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diff --git a/docs/_modules/pyrad/proc/process_timeseries.html b/docs/_modules/pyrad/proc/process_timeseries.html
index 5e1e031..3a55fb1 100644
--- a/docs/_modules/pyrad/proc/process_timeseries.html
+++ b/docs/_modules/pyrad/proc/process_timeseries.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -71,13 +70,18 @@ 
Source code for pyrad.proc.process_timeseries
    process_time_height
 """
 
+from copy import deepcopy
 from warnings import warn
 import numpy as np
+from scipy.interpolate import interp1d
 from netCDF4 import num2date
 
 import pyart
 
 from ..io.io_aux import get_datatype_fields, get_fieldname_pyart
+from ..util.radar_utils import find_nearest_gate, find_neighbour_gates
+from ..util.radar_utils import project_to_vertical, compute_directional_stats
+from ..util.radar_utils import get_target_elevations
 
 
 
[docs]def process_point_measurement(procstatus, dscfg, radar_list=None):
@@ -330,11 +334,10 @@ 
Source code for pyrad.proc.process_timeseries
return None, None
 
     if procstatus == 1:
-        field_names = []
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-            field_names.append(get_fieldname_pyart(datatype))
-
+            break
+        field_name = get_fieldname_pyart(datatype)
         ind_rad = int(radarnr[5:8])-1
 
         if (radar_list is None) or (radar_list[ind_rad] is None):
@@ -343,6 +346,10 @@ 
Source code for pyrad.proc.process_timeseries
radar = radar_list[ind_rad]
 
+        if field_name not in radar.fields:
+            warn('Field name '+field_name+' not available in radar object')
+            return None, None
+
         # default parameters
         angle = dscfg.get('angle', 0)
         ang_tol = dscfg.get('ang_tol', 1.)
@@ -355,7 +362,7 @@ 
Source code for pyrad.proc.process_timeseries
# initialize dataset
         if not dscfg['initialized']:
             qvp = pyart.retrieve.compute_qvp(
-                radar, field_names, ref_time=dscfg['timeinfo'],
+                radar, [field_name], ref_time=dscfg['timeinfo'],
                 angle=angle, ang_tol=ang_tol, hmax=hmax, hres=hres,
                 avg_type=avg_type, nvalid_min=nvalid_min,
                 interp_kind=interp_kind, qvp=None)
@@ -371,7 +378,7 @@ 
Source code for pyrad.proc.process_timeseries
dscfg['initialized'] = 1
         else:
             qvp = pyart.retrieve.compute_qvp(
-                radar, field_names, ref_time=dscfg['timeinfo'],
+                radar, [field_name], ref_time=dscfg['timeinfo'],
                 angle=angle, ang_tol=ang_tol, hmax=hmax, hres=hres,
                 avg_type=avg_type, nvalid_min=nvalid_min,
                 interp_kind=interp_kind,
@@ -391,15 +398,14 @@ 
Source code for pyrad.proc.process_timeseries
return new_dataset, ind_rad
 
     if procstatus == 2:
-        if not dscfg['initialized']:
-            return None, None
-
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
             break
 
         ind_rad = int(radarnr[5:8])-1
 
+        print(dscfg['global_data'])
+
         qvp = dscfg['global_data']['radar_out']
 
         new_dataset = dict()
@@ -477,11 +483,10 @@ 
Source code for pyrad.proc.process_timeseries
return None, None
 
     if procstatus == 1:
-        field_names = []
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-            field_names.append(get_fieldname_pyart(datatype))
-
+            break
+        field_name = get_fieldname_pyart(datatype)
         ind_rad = int(radarnr[5:8])-1
 
         if (radar_list is None) or (radar_list[ind_rad] is None):
@@ -490,19 +495,23 @@ 
Source code for pyrad.proc.process_timeseries
radar = radar_list[ind_rad]
 
+        if field_name not in radar.fields:
+            warn('Field name '+field_name+' not available in radar object')
+            return None, None
+
         # default parameters
         hmax = dscfg.get('hmax', 10000.)
         hres = dscfg.get('hres', 2.)
         avg_type = dscfg.get('avg_type', 'mean')
         nvalid_min = dscfg.get('nvalid_min', 30)
         interp_kind = dscfg.get('interp_kind', 'nearest')
-        rmax = dscfg.get('rmax', 50000.)
+        rmax = dscfg.get('rmax', 50000.)/1000.  # [Km]
         weight_power = dscfg.get('weight_power', 2.)
 
         # initialize dataset
         if not dscfg['initialized']:
             qvp = pyart.retrieve.compute_rqvp(
-                radar, field_names, ref_time=dscfg['timeinfo'],
+                radar, [field_name], ref_time=dscfg['timeinfo'],
                 hmax=hmax, hres=hres, avg_type=avg_type,
                 nvalid_min=nvalid_min, interp_kind=interp_kind, rmax=rmax,
                 weight_power=weight_power, qvp=None)
@@ -513,12 +522,12 @@ 
Source code for pyrad.proc.process_timeseries
global_dict = dict()
             global_dict.update({'start_time': dscfg['timeinfo']})
-            global_dict.update({'radar_out': qvp})
+            global_dict.update({'radar_out': qvp_aux})
             dscfg['global_data'] = global_dict
             dscfg['initialized'] = 1
         else:
             qvp = pyart.retrieve.compute_rqvp(
-                radar, field_names, ref_time=dscfg['timeinfo'],
+                radar, [field_name], ref_time=dscfg['timeinfo'],
                 hmax=hmax, hres=hres, avg_type=avg_type,
                 nvalid_min=nvalid_min, interp_kind=interp_kind, rmax=rmax,
                 weight_power=weight_power,
@@ -538,9 +547,6 @@ 
Source code for pyrad.proc.process_timeseries
return new_dataset, ind_rad
 
     if procstatus == 2:
-        if not dscfg['initialized']:
-            return None, None
-
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
             break
@@ -612,22 +618,15 @@ 
Source code for pyrad.proc.process_timeseries
    ind_rad : int
         radar index
 
-    Reference
-    ---------
-    Kaltenboeck R., Ryzhkov A. 2016: A freezing rain storm explored with a
-    C-band polarimetric weather radar using the QVP methodology. Meteorologische
-    Zeitschrift vol. 26 pp 207-222
-
     """
     if procstatus == 0:
         return None, None
 
     if procstatus == 1:
-        field_names = []
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-            field_names.append(get_fieldname_pyart(datatype))
-
+            break
+        field_name = get_fieldname_pyart(datatype)
         ind_rad = int(radarnr[5:8])-1
 
         if (radar_list is None) or (radar_list[ind_rad] is None):
@@ -636,6 +635,10 @@ 
Source code for pyrad.proc.process_timeseries
radar = radar_list[ind_rad]
 
+        if field_name not in radar.fields:
+            warn('Field name '+field_name+' not available in radar object')
+            return None, None
+
         # default parameters
         lon = dscfg['lon']
         lat = dscfg['lat']
@@ -647,59 +650,117 @@ 
Source code for pyrad.proc.process_timeseries
avg_type = dscfg.get('avg_type', 'mean')
         nvalid_min = dscfg.get('nvalid_min', 1)
         interp_kind = dscfg.get('interp_kind', 'none')
+        if avg_type != 'mean' and avg_type != 'median':
+            warn('Unsuported statistics '+avg_type)
+            return None, None
 
-        # initialize dataset
-        if not dscfg['initialized']:
-            qvp = pyart.retrieve.compute_evp(
-                radar, field_names, lon, lat, ref_time=dscfg['timeinfo'],
-                latlon_tol=latlon_tol, delta_rng=delta_rng,
-                delta_azi=delta_azi, hmax=hmax, hres=hres, avg_type=avg_type,
-                nvalid_min=nvalid_min, interp_kind=interp_kind, qvp=None)
+        radar_aux = deepcopy(radar)
+        # transform radar into ppi over the required elevation
+        if radar_aux.scan_type == 'rhi':
+            target_elevations, el_tol = get_target_elevations(radar_aux)
+            radar_ppi = pyart.util.cross_section_rhi(
+                radar_aux, target_elevations, el_tol=el_tol)
+        elif radar_aux.scan_type == 'ppi':
+            radar_ppi = radar_aux
+        else:
+            warn('Error: unsupported scan type.')
+            return None, None
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+        radar_aux = radar_ppi.extract_sweeps([0])
+
+        # initialize dataset
+        if dscfg['initialized'] == 0:
+            evp_aux = pyart.retrieve._create_qvp_object(
+                radar_aux, [field_name], qvp_type='evp',
+                start_time=dscfg['timeinfo'], hmax=hmax, hres=hres)
 
             global_dict = dict()
             global_dict.update({'start_time': dscfg['timeinfo']})
-            global_dict.update({'radar_out': qvp})
+            global_dict.update({'radar_out': evp_aux})
             dscfg['global_data'] = global_dict
             dscfg['initialized'] = 1
-        else:
-            qvp = pyart.retrieve.compute_evp(
-                radar, field_names, lon, lat, ref_time=dscfg['timeinfo'],
-                latlon_tol=latlon_tol, delta_rng=delta_rng,
-                delta_azi=delta_azi, hmax=hmax, hres=hres, avg_type=avg_type,
-                nvalid_min=nvalid_min, interp_kind=interp_kind,
-                qvp=dscfg['global_data']['radar_out'])
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+        # modify metadata
+        evp = dscfg['global_data']['radar_out']
+
+        start_time = num2date(0, evp.time['units'], evp.time['calendar'])
+        evp.time['data'] = np.append(
+            evp.time['data'], (dscfg['timeinfo'] - start_time).total_seconds())
+        evp.sweep_end_ray_index['data'][0] += 1
+        evp.rays_per_sweep[0] += 1
+        evp.nrays += 1
+
+        evp.azimuth['data'] = np.ones((evp.nrays, ), dtype='float64')*0.
+        evp.elevation['data'] = np.ones((evp.nrays, ), dtype='float64')*90.
+
+        evp.gate_longitude['data'] = (
+            np.ones((evp.nrays, evp.ngates), dtype='float64') * lon)
+        evp.gate_latitude['data'] = (
+            np.ones((evp.nrays, evp.ngates), dtype='float64') * lat)
+        evp.gate_altitude['data'] = np.broadcast_to(
+            evp.range['data'], (evp.nrays, evp.ngates))
+
+        values = np.ma.array([], dtype=float)
+        height = np.array([], dtype=float)
+        for sweep in range(radar_ppi.nsweeps):
+            radar_aux = deepcopy(radar_ppi)
+            radar_aux = radar_aux.extract_sweeps([sweep])
+
+            # find nearest gate to lat lon point
+            ind_ray, _, azi, rng = find_nearest_gate(
+                radar_aux, lat, lon, latlon_tol=latlon_tol)
+
+            if ind_ray is None:
+                continue
+
+            # find neighbouring gates to be selected
+            inds_ray, inds_rng = find_neighbour_gates(
+                radar_aux, azi, rng, delta_azi=delta_azi, delta_rng=delta_rng)
+
+            # keep only data we are interested in
+            field = radar_aux.fields[field_name]['data'][:, inds_rng]
+            field = field[inds_ray, :]
+
+            vals, _ = compute_directional_stats(
+                field, avg_type=avg_type, nvalid_min=nvalid_min, axis=0)
+            values = np.ma.append(values, vals)
+
+            height = np.append(
+                height,
+                radar_aux.gate_altitude['data'][ind_ray, inds_rng])
+
+        # Project to vertical grid:
+        evp_data = project_to_vertical(
+            values, height, evp.range['data'], interp_kind=interp_kind)
+
+        # Put data in radar object
+        if np.size(evp.fields[field_name]['data']) == 0:
+            evp.fields[field_name]['data'] = evp_data.reshape(1, evp.ngates)
+        else:
+            evp.fields[field_name]['data'] = np.ma.concatenate(
+                (evp.fields[field_name]['data'],
+                 evp_data.reshape(1, evp.ngates)))
 
-        dscfg['global_data']['radar_out'] = qvp
+        dscfg['global_data']['radar_out'] = evp
 
         new_dataset = dict()
-        new_dataset.update({'radar_out': qvp})
+        new_dataset.update({'radar_out': evp})
         new_dataset.update({'radar_type': 'temporal'})
         new_dataset.update({'start_time': dscfg['global_data']['start_time']})
 
         return new_dataset, ind_rad
 
     if procstatus == 2:
-        if not dscfg['initialized']:
-            return None, None
-
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
             break
 
         ind_rad = int(radarnr[5:8])-1
 
-        qvp = dscfg['global_data']['radar_out']
+        evp = dscfg['global_data']['radar_out']
 
         new_dataset = dict()
-        new_dataset.update({'radar_out': qvp})
+        new_dataset.update({'radar_out': evp})
         new_dataset.update({'radar_type': 'final'})
         new_dataset.update({'start_time': dscfg['global_data']['start_time']})
 
@@ -727,7 +788,7 @@ 
Source code for pyrad.proc.process_timeseries
            Default 0.
         ang_tol : float
             If the radar object contains an RHI volume, the tolerance in the
-            elevation angle for the conversion into PPI. Default 1.
+            elevation angle for the conversion into PPI
         lat, lon : float
             latitude and longitude of the point of interest [deg]
         latlon_tol : float
@@ -768,22 +829,15 @@ 
Source code for pyrad.proc.process_timeseries
    ind_rad : int
         radar index
 
-    Reference
-    ---------
-    Bukovcic P., Zrnic D., Zhang G. 2017: Winter Precipitation Liquid-Ice
-    Phase Transitions Revealed with Polarimetric Radar and 2DVD Observations
-    in Central Oklahoma. JTECH vol. 56 pp 1345-1363
-
     """
     if procstatus == 0:
         return None, None
 
     if procstatus == 1:
-        field_names = []
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-            field_names.append(get_fieldname_pyart(datatype))
-
+            break
+        field_name = get_fieldname_pyart(datatype)
         ind_rad = int(radarnr[5:8])-1
 
         if (radar_list is None) or (radar_list[ind_rad] is None):
@@ -792,6 +846,10 @@ 
Source code for pyrad.proc.process_timeseries
radar = radar_list[ind_rad]
 
+        if field_name not in radar.fields:
+            warn('Field name '+field_name+' not available in radar object')
+            return None, None
+
         # default parameters
         angle = dscfg.get('angle', 0)
         ang_tol = dscfg.get('ang_tol', 1.)
@@ -805,51 +863,97 @@ 
Source code for pyrad.proc.process_timeseries
avg_type = dscfg.get('avg_type', 'mean')
         nvalid_min = dscfg.get('nvalid_min', 1)
         interp_kind = dscfg.get('interp_kind', 'none')
+        if avg_type != 'mean' and avg_type != 'median':
+            warn('Unsuported statistics '+avg_type)
+            return None, None
 
-        # initialize dataset
-        if not dscfg['initialized']:
-            qvp = pyart.retrieve.compute_svp(
-                radar, field_names, lon, lat, angle,
-                ref_time=dscfg['timeinfo'], ang_tol=ang_tol,
-                latlon_tol=latlon_tol, delta_rng=delta_rng,
-                delta_azi=delta_azi, hmax=hmax, hres=hres, avg_type=avg_type,
-                nvalid_min=nvalid_min, interp_kind=interp_kind, qvp=None)
+        radar_aux = deepcopy(radar)
+        # transform radar into ppi over the required elevation
+        if radar_aux.scan_type == 'rhi':
+            radar_aux = pyart.util.cross_section_rhi(
+                radar_aux, [angle], el_tol=ang_tol)
+        elif radar_aux.scan_type == 'ppi':
+            radar_aux = radar_aux.extract_sweeps([int(angle)])
+        else:
+            warn('Error: unsupported scan type.')
+            return None, None
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+        # initialize dataset
+        if dscfg['initialized'] == 0:
+            svp_aux = pyart.retrieve._create_qvp_object(
+                radar_aux, [field_name], qvp_type='svp',
+                start_time=dscfg['timeinfo'], hmax=hmax, hres=hres)
 
             global_dict = dict()
             global_dict.update({'start_time': dscfg['timeinfo']})
-            global_dict.update({'radar_out': qvp})
+            global_dict.update({'radar_out': svp_aux})
             dscfg['global_data'] = global_dict
             dscfg['initialized'] = 1
+
+        # modify metadata
+        svp = dscfg['global_data']['radar_out']
+
+        start_time = num2date(0, svp.time['units'], svp.time['calendar'])
+        svp.time['data'] = np.append(
+            svp.time['data'], (dscfg['timeinfo'] - start_time).total_seconds())
+        svp.sweep_end_ray_index['data'][0] += 1
+        svp.rays_per_sweep[0] += 1
+        svp.nrays += 1
+
+        svp.azimuth['data'] = np.ones((svp.nrays, ), dtype='float64')*0.
+        svp.elevation['data'] = np.ones((svp.nrays, ), dtype='float64')*90.
+
+        svp.gate_longitude['data'] = (
+            np.ones((svp.nrays, svp.ngates), dtype='float64') * lon)
+        svp.gate_latitude['data'] = (
+            np.ones((svp.nrays, svp.ngates), dtype='float64') * lat)
+        svp.gate_altitude['data'] = np.broadcast_to(
+            svp.range['data'], (svp.nrays, svp.ngates))
+
+        # find nearest gate to lat lon point
+        ind_ray, _, azi, rng = find_nearest_gate(
+            radar_aux, lat, lon, latlon_tol=latlon_tol)
+
+        if ind_ray is None:
+            values = np.ma.array([], dtype=float)
+            height = np.array([], dtype=float)
         else:
-            qvp = pyart.retrieve.compute_svp(
-                radar, field_names, lon, lat, angle,
-                ref_time=dscfg['timeinfo'], ang_tol=ang_tol,
-                latlon_tol=latlon_tol, delta_rng=delta_rng,
-                delta_azi=delta_azi, hmax=hmax, hres=hres, avg_type=avg_type,
-                nvalid_min=nvalid_min, interp_kind=interp_kind,
-                qvp=dscfg['global_data']['radar_out'])
+            # find neighbouring gates to be selected
+            inds_ray, inds_rng = find_neighbour_gates(
+                radar_aux, azi, rng, delta_azi=delta_azi, delta_rng=delta_rng)
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+            # keep only data we are interested in
+            field = radar_aux.fields[field_name]['data'][:, inds_rng]
+            field = field[inds_ray, :]
+
+            # compute values
+            values, _ = compute_directional_stats(
+                field, avg_type=avg_type, nvalid_min=nvalid_min, axis=0)
+
+            height = radar_aux.gate_altitude['data'][ind_ray, inds_rng]
+
+        # Project to vertical grid:
+        svp_data = project_to_vertical(
+            values, height, svp.range['data'], interp_kind=interp_kind)
+
+        # Put data in radar object
+        if np.size(svp.fields[field_name]['data']) == 0:
+            svp.fields[field_name]['data'] = svp_data.reshape(1, svp.ngates)
+        else:
+            svp.fields[field_name]['data'] = np.ma.concatenate(
+                (svp.fields[field_name]['data'],
+                 svp_data.reshape(1, svp.ngates)))
 
-        dscfg['global_data']['radar_out'] = qvp
+        dscfg['global_data']['radar_out'] = svp
 
         new_dataset = dict()
-        new_dataset.update({'radar_out': qvp})
+        new_dataset.update({'radar_out': svp})
         new_dataset.update({'radar_type': 'temporal'})
         new_dataset.update({'start_time': dscfg['global_data']['start_time']})
 
         return new_dataset, ind_rad
 
     if procstatus == 2:
-        if not dscfg['initialized']:
-            return None, None
-
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
             break
@@ -918,11 +1022,10 @@ 
Source code for pyrad.proc.process_timeseries
return None, None
 
     if procstatus == 1:
-        field_names = []
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
-            field_names.append(get_fieldname_pyart(datatype))
-
+            break
+        field_name = get_fieldname_pyart(datatype)
         ind_rad = int(radarnr[5:8])-1
 
         if (radar_list is None) or (radar_list[ind_rad] is None):
@@ -931,6 +1034,10 @@ 
Source code for pyrad.proc.process_timeseries
radar = radar_list[ind_rad]
 
+        if field_name not in radar.fields:
+            warn('Field name '+field_name+' not available in radar object')
+            return None, None
+
         # default parameters
         lon = dscfg['lon']
         lat = dscfg['lat']
@@ -939,56 +1046,112 @@ 
Source code for pyrad.proc.process_timeseries
hres = dscfg.get('hres', 50.)
         interp_kind = dscfg.get('interp_kind', 'none')
 
+        radar_aux = deepcopy(radar)
+        # transform radar into ppi over the required elevation
+        if radar_aux.scan_type == 'rhi':
+            target_elevations, el_tol = get_target_elevations(radar_aux)
+            radar_ppi = pyart.util.cross_section_rhi(
+                radar_aux, target_elevations, el_tol=el_tol)
+        elif radar_aux.scan_type == 'ppi':
+            radar_ppi = radar_aux
+        else:
+            warn('Error: unsupported scan type.')
+            return None, None
+
         # initialize dataset
-        if not dscfg['initialized']:
-            qvp = pyart.retrieve.compute_vp(
-                radar, field_names, lon, lat, ref_time=dscfg['timeinfo'],
-                latlon_tol=latlon_tol, hmax=hmax, hres=hres,
-                interp_kind=interp_kind, qvp=None)
+        if dscfg['initialized'] == 0:
+            th_aux = pyart.retrieve._create_qvp_object(
+                radar_aux, [field_name], qvp_type='time_height',
+                start_time=dscfg['timeinfo'], hmax=hmax, hres=hres)
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+            th_aux.sweep_number['data'] = np.array([0], dtype='int32')
+            th_aux.nsweeps = 1
+
+            if radar_aux.rays_are_indexed is not None:
+                th_aux.rays_are_indexed['data'] = np.array(
+                    [radar_aux.rays_are_indexed['data'][0]])
+
+            if radar_aux.ray_angle_res is not None:
+                th_aux.ray_angle_res['data'] = np.array(
+                    [radar_aux.ray_angle_res['data'][0]])
 
             global_dict = dict()
             global_dict.update({'start_time': dscfg['timeinfo']})
-            global_dict.update({'radar_out': qvp})
+            global_dict.update({'radar_out': th_aux})
             dscfg['global_data'] = global_dict
             dscfg['initialized'] = 1
-        else:
-            qvp = pyart.retrieve.compute_vp(
-                radar, field_names, lon, lat, ref_time=dscfg['timeinfo'],
-                latlon_tol=latlon_tol, hmax=hmax, hres=hres,
-                interp_kind=interp_kind,
-                qvp=dscfg['global_data']['radar_out'])
 
-            if qvp is None:
-                warn('Unable to compute QVP')
-                return None, None
+        # modify metadata
+        th = dscfg['global_data']['radar_out']
+
+        start_time = num2date(0, th.time['units'], th.time['calendar'])
+        th.time['data'] = np.append(
+            th.time['data'], (dscfg['timeinfo'] - start_time).total_seconds())
+        th.sweep_end_ray_index['data'][0] += 1
+        th.rays_per_sweep[0] += 1
+        th.nrays += 1
+
+        th.azimuth['data'] = np.ones((th.nrays, ), dtype='float64')*0.
+        th.elevation['data'] = np.ones((th.nrays, ), dtype='float64')*90.
+
+        th.gate_longitude['data'] = (
+            np.ones((th.nrays, th.ngates), dtype='float64')*lon)
+        th.gate_latitude['data'] = (
+            np.ones((th.nrays, th.ngates), dtype='float64')*lat)
+        th.gate_altitude['data'] = np.broadcast_to(
+            th.range['data'], (th.nrays, th.ngates))
+
+        values = np.ma.array([], dtype=float)
+        height = np.array([], dtype=float)
+        for sweep in range(radar_ppi.nsweeps):
+            radar_aux = deepcopy(radar_ppi.extract_sweeps([sweep]))
+
+            # find nearest gate to lat lon point
+            ind_ray, ind_rng, _, _ = find_nearest_gate(
+                radar_aux, lat, lon, latlon_tol=latlon_tol)
+
+            if ind_ray is None:
+                continue
+
+            values = np.ma.append(
+                values,
+                radar_aux.fields[field_name]['data'][ind_ray, ind_rng])
+            height = np.append(
+                height,
+                radar_aux.gate_altitude['data'][ind_ray, ind_rng])
+
+        # Project to vertical grid:
+        th_data = project_to_vertical(
+            values, height, th.range['data'], interp_kind=interp_kind)
+
+        # Put data in radar object
+        if np.size(th.fields[field_name]['data']) == 0:
+            th.fields[field_name]['data'] = th_data.reshape(1, th.ngates)
+        else:
+            th.fields[field_name]['data'] = np.ma.concatenate(
+                (th.fields[field_name]['data'],
+                 th_data.reshape(1, th.ngates)))
 
-        dscfg['global_data']['radar_out'] = qvp
+        dscfg['global_data']['radar_out'] = th
 
         new_dataset = dict()
-        new_dataset.update({'radar_out': qvp})
+        new_dataset.update({'radar_out': th})
         new_dataset.update({'radar_type': 'temporal'})
         new_dataset.update({'start_time': dscfg['global_data']['start_time']})
 
         return new_dataset, ind_rad
 
     if procstatus == 2:
-        if not dscfg['initialized']:
-            return None, None
-
         for datatypedescr in dscfg['datatype']:
             radarnr, _, datatype, _, _ = get_datatype_fields(datatypedescr)
             break
 
         ind_rad = int(radarnr[5:8])-1
 
-        qvp = dscfg['global_data']['radar_out']
+        th = dscfg['global_data']['radar_out']
 
         new_dataset = dict()
-        new_dataset.update({'radar_out': qvp})
+        new_dataset.update({'radar_out': th})
         new_dataset.update({'radar_type': 'final'})
         new_dataset.update({'start_time': dscfg['global_data']['start_time']})
 
@@ -1015,7 +1178,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/proc/process_traj.html b/docs/_modules/pyrad/proc/process_traj.html
index 8ea199f..158ae58 100644
--- a/docs/_modules/pyrad/proc/process_traj.html
+++ b/docs/_modules/pyrad/proc/process_traj.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -95,7 +94,7 @@ 
Source code for pyrad.proc.process_traj
 from ..io.read_data_other import read_antenna_pattern
 
 from ..util.stat_utils import quantiles_weighted
-from ..util.radar_utils import belongs_roi_indices, find_nearest_gate
+from ..util.radar_utils import belongs_roi_indices
 
 
 
[docs]def process_trajectory(procstatus, dscfg, radar_list=None, trajectory=None):
@@ -216,13 +215,10 @@ 
Source code for pyrad.proc.process_traj
     time_tol = dscfg.get('TimeTol', 100.)
     alt_min = dscfg.get('alt_min', None)
     alt_max = dscfg.get('alt_max', None)
-    cell_center = dscfg.get('cell_center', False)
-    latlon_tol = dscfg.get('latlon_tol', 0.01)  # aprox. 1 km
 
     inds_ray, inds_rng, lat, lon, alt = _get_gates_trt(
         radar, trajectory, dscfg['timeinfo'], time_tol=time_tol,
-        alt_min=alt_min, alt_max=alt_max, cell_center=cell_center,
-        latlon_tol=latlon_tol)
+        alt_min=alt_min, alt_max=alt_max)
 
     if inds_ray is None:
         return None, None
@@ -1412,7 +1408,7 @@ 
Source code for pyrad.proc.process_traj
 
 
 def _get_gates_trt(radar, trajectory, voltime, time_tol=100., alt_min=None,
-                   alt_max=None, cell_center=False, latlon_tol=0.0005):
+                   alt_max=None):
     """
     Find the gates of the radar object that belong to a TRT cell
 
@@ -1440,105 +1436,55 @@ 
Source code for pyrad.proc.process_traj
         dt[i] = np.abs((voltime - time_traj).total_seconds())
     if dt.min() > time_tol:
         warn('No TRT data for radar volume time')
-        return None, None, None, None, None
+        return None, None
     ind = np.argmin(dt)
+    lon_roi = trajectory.cell_contour[ind]['lon']
+    lat_roi = trajectory.cell_contour[ind]['lat']
+
+    roi_dict = {
+        'lon': lon_roi,
+        'lat': lat_roi,
+        'alt_min': alt_min,
+        'alt_max': alt_max}
+
+    # extract the data within the ROI boundaries
+    inds_ray, inds_rng = np.indices(np.shape(radar.gate_longitude['data']))
+
+    mask = np.logical_and(
+        np.logical_and(
+            radar.gate_latitude['data'] >= roi_dict['lat'].min(),
+            radar.gate_latitude['data'] <= roi_dict['lat'].max()),
+        np.logical_and(
+            radar.gate_longitude['data'] >= roi_dict['lon'].min(),
+            radar.gate_longitude['data'] <= roi_dict['lon'].max()))
+
+    if alt_min is not None:
+        mask[radar.gate_altitude['data'] < alt_min] = 0
+    if alt_max is not None:
+        mask[radar.gate_altitude['data'] > alt_max] = 0
+
+    if np.all(mask == 0):
+        warn('No values within ROI')
+        return None, None, None, None, None
 
-    if not cell_center:
-        lon_roi = trajectory.cell_contour[ind]['lon']
-        lat_roi = trajectory.cell_contour[ind]['lat']
-
-        roi_dict = {
-            'lon': lon_roi,
-            'lat': lat_roi,
-            'alt_min': alt_min,
-            'alt_max': alt_max}
-
-        # extract the data within the ROI boundaries
-        inds_ray, inds_rng = np.indices(np.shape(radar.gate_longitude['data']))
-
-        mask = np.logical_and(
-            np.logical_and(
-                radar.gate_latitude['data'] >= roi_dict['lat'].min(),
-                radar.gate_latitude['data'] <= roi_dict['lat'].max()),
-            np.logical_and(
-                radar.gate_longitude['data'] >= roi_dict['lon'].min(),
-                radar.gate_longitude['data'] <= roi_dict['lon'].max()))
-
-        if alt_min is not None:
-            mask[radar.gate_altitude['data'] < alt_min] = 0
-        if alt_max is not None:
-            mask[radar.gate_altitude['data'] > alt_max] = 0
-
-        if np.all(mask == 0):
-            warn('No values within ROI')
-            return None, None, None, None, None
-
-        inds_ray = inds_ray[mask]
-        inds_rng = inds_rng[mask]
-
-        # extract the data inside the ROI
-        lat = radar.gate_latitude['data'][mask]
-        lon = radar.gate_longitude['data'][mask]
-        inds, is_roi = belongs_roi_indices(lat, lon, roi_dict)
-
-        if is_roi == 'None':
-            warn('No values within ROI')
-            return None, None, None, None, None
-
-        inds_ray = inds_ray[inds]
-        inds_rng = inds_rng[inds]
-
-        lat = lat[inds]
-        lon = lon[inds]
-        alt = radar.gate_altitude['data'][inds_ray, inds_rng]
-    else:
-        radar_aux = deepcopy(radar)
-        # transform radar into ppi over the required elevation
-        if radar_aux.scan_type == 'rhi':
-            target_elevations, el_tol = get_target_elevations(radar_aux)
-            radar_ppi = cross_section_rhi(
-                radar_aux, target_elevations, el_tol=el_tol)
-        elif radar_aux.scan_type == 'ppi':
-            radar_ppi = radar_aux
-        else:
-            warn('Error: unsupported scan type.')
-            return None, None, None, None, None
-
-        inds_ray = np.array([], dtype=int)
-        inds_rng = np.array([], dtype=int)
-        lat = np.array([])
-        lon = np.array([])
-        alt = np.array([])
-        for sweep in range(radar_ppi.nsweeps):
-            radar_aux = deepcopy(radar_ppi.extract_sweeps([sweep]))
-
-            # find nearest gate to lat lon point
-            ind_ray, ind_rng, _, _ = find_nearest_gate(
-                radar_aux, trajectory.wgs84_lat_deg[ind],
-                trajectory.wgs84_lon_deg[ind], latlon_tol=latlon_tol)
-
-            if ind_ray is None:
-                continue
+    inds_ray = inds_ray[mask]
+    inds_rng = inds_rng[mask]
 
-            if alt_min is not None:
-                if radar_aux.gate_altitude['data'][ind_ray][ind_rng] < alt_min:
-                    continue
-            if alt_max is not None:
-                if radar_aux.gate_altitude['data'][ind_ray][ind_rng] > alt_max:
-                    continue
+    # extract the data inside the ROI
+    lat = radar.gate_latitude['data'][mask]
+    lon = radar.gate_longitude['data'][mask]
+    inds, is_roi = belongs_roi_indices(lat, lon, roi_dict)
+
+    if is_roi == 'None':
+        warn('No values within ROI')
+        return None, None, None, None, None
 
-            inds_ray = np.append(inds_ray, ind_ray)
-            inds_rng = np.append(inds_rng, ind_rng)
-            lat = np.append(
-                lat, radar_aux.gate_latitude['data'][ind_ray][ind_rng])
-            lon = np.append(
-                lon, radar_aux.gate_longitude['data'][ind_ray][ind_rng])
-            alt = np.append(
-                alt, radar_aux.gate_altitude['data'][ind_ray][ind_rng])
+    inds_ray = inds_ray[inds]
+    inds_rng = inds_rng[inds]
 
-        if inds_ray.size == 0:
-            warn('No values in center of cell')
-            return None, None, None, None, None
+    lat = lat[inds]
+    lon = lon[inds]
+    alt = radar.gate_altitude['data'][inds_ray, inds_rng]
 
     return inds_ray, inds_rng, lat, lon, alt
 
@@ -1773,7 +1719,7 @@ 
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diff --git a/docs/_modules/pyrad/prod/process_grid_products.html b/docs/_modules/pyrad/prod/process_grid_products.html
index fa1b594..3e760e7 100644
--- a/docs/_modules/pyrad/prod/process_grid_products.html
+++ b/docs/_modules/pyrad/prod/process_grid_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -371,7 +370,7 @@ 
Navigation

     

     
   
 
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diff --git a/docs/_modules/pyrad/prod/process_intercomp_products.html b/docs/_modules/pyrad/prod/process_intercomp_products.html
index 01022c6..8c2fa5b 100644
--- a/docs/_modules/pyrad/prod/process_intercomp_products.html
+++ b/docs/_modules/pyrad/prod/process_intercomp_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -406,7 +405,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/prod/process_monitoring_products.html b/docs/_modules/pyrad/prod/process_monitoring_products.html
index 8b148ea..220d8c9 100644
--- a/docs/_modules/pyrad/prod/process_monitoring_products.html
+++ b/docs/_modules/pyrad/prod/process_monitoring_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -772,7 +771,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/prod/process_product.html b/docs/_modules/pyrad/prod/process_product.html
index 5f4cb74..9052d74 100644
--- a/docs/_modules/pyrad/prod/process_product.html
+++ b/docs/_modules/pyrad/prod/process_product.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -621,7 +620,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/prod/process_timeseries_products.html b/docs/_modules/pyrad/prod/process_timeseries_products.html
index 7842a72..6398a8a 100644
--- a/docs/_modules/pyrad/prod/process_timeseries_products.html
+++ b/docs/_modules/pyrad/prod/process_timeseries_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -613,7 +612,7 @@ 
Navigation

     

     
   
 
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diff --git a/docs/_modules/pyrad/prod/process_traj_products.html b/docs/_modules/pyrad/prod/process_traj_products.html
index be7be1a..65b4e41 100644
--- a/docs/_modules/pyrad/prod/process_traj_products.html
+++ b/docs/_modules/pyrad/prod/process_traj_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -69,7 +68,6 @@ 
Source code for pyrad.prod.process_traj_products

 
 from ..io.io_aux import get_save_dir, make_filename
 from ..io.timeseries import TimeSeries
-from ..graph.plots_vol import plot_pos
 
 
 
[docs]def generate_traj_product(traj, prdcfg):
@@ -179,40 +177,6 @@ 
Source code for pyrad.prod.process_traj_products

 
         return None
 
-    elif prdcfg['type'] == 'TRAJ_MAP': #Trajectory on a map
-    
-        timeinfo = traj.time_vector[0]
-
-        savedir = get_save_dir(prdcfg['basepath'], prdcfg['procname'],
-                               dssavedir, prdcfg['prdname'],
-                               timeinfo=timeinfo)
-
-        
-        fname = make_filename('ts', prdcfg['dstype'], 'TRAJ',
-                                  prdcfg['imgformat'],
-                                  prdcfginfo="MAP", timeinfo=timeinfo,
-                                  timeformat='%Y%m%d%H%M%S',
-                                  runinfo=prdcfg['runinfo'])
-
-        title = "Trajectory Starting at  %s" % \
-                traj.time_vector[0].strftime("%Y-%m-%d")
-
-        fname_list=fname
-        for i,fname in enumerate(fname_list):
-            fname_list[i] = savedir+fname
-
-        # Get traj
-        lat=traj.wgs84_lat_deg
-        lon=traj.wgs84_lon_deg
-        alt=traj.wgs84_alt_m
-
-        fig=plot_pos(
-            lat, lon,
-            alt,
-            fname_list,cb_label='Altitude [m]',
-            titl=title,save_fig=True)
-
-
     else:
         raise Exception("ERROR: Unsupported product type: '%s' of dataset '%s'"
                         % (prdcfg['type'], prdcfg['dsname']))

@@ -238,7 +202,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/prod/process_vol_products.html b/docs/_modules/pyrad/prod/process_vol_products.html
index 564512a..870ab91 100644
--- a/docs/_modules/pyrad/prod/process_vol_products.html
+++ b/docs/_modules/pyrad/prod/process_vol_products.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -151,7 +150,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDOPPI_IMAGE':
+    elif prdcfg['type'] == 'PSEUDOPPI_IMAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -284,7 +283,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDOPPI_CONTOUR_OVERPLOT':
+    elif prdcfg['type'] == 'PSEUDOPPI_CONTOUR_OVERPLOT':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -378,7 +377,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDOPPI_CONTOUR':
+    elif prdcfg['type'] == 'PSEUDOPPI_CONTOUR':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -420,7 +419,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
             return None
 
-    if prdcfg['type'] == 'RHI_IMAGE':
+    elif prdcfg['type'] == 'RHI_IMAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -456,7 +455,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDORHI_IMAGE':
+    elif prdcfg['type'] == 'PSEUDORHI_IMAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -499,7 +498,7 @@ 
Source code for pyrad.prod.process_vol_products
<
                 prdcfg['type'])
             return None
 
-    if prdcfg['type'] == 'RHI_CONTOUR_OVERPLOT':
+    elif prdcfg['type'] == 'RHI_CONTOUR_OVERPLOT':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -551,7 +550,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDORHI_CONTOUR_OVERPLOT':
+    elif prdcfg['type'] == 'PSEUDORHI_CONTOUR_OVERPLOT':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -610,7 +609,7 @@ 
Source code for pyrad.prod.process_vol_products
<
                 prdcfg['type'])
             return None
 
-    if prdcfg['type'] == 'RHI_CONTOUR':
+    elif prdcfg['type'] == 'RHI_CONTOUR':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -645,7 +644,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PSEUDORHI_CONTOUR':
+    elif prdcfg['type'] == 'PSEUDORHI_CONTOUR':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -687,7 +686,7 @@ 
Source code for pyrad.prod.process_vol_products
<
                 prdcfg['type'])
             return None
 
-    if prdcfg['type'] == 'RHI_PROFILE':
+    elif prdcfg['type'] == 'RHI_PROFILE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -835,7 +834,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname
 
-    if prdcfg['type'] == 'PROFILE_STATS':
+    elif prdcfg['type'] == 'PROFILE_STATS':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -847,7 +846,7 @@ 
Source code for pyrad.prod.process_vol_products
<
         # mask unclassified data
         field = deepcopy(dataset['radar_out'].fields[field_name]['data'])
         if prdcfg['voltype'] == 'hydro':
-            field = np.ma.masked_equal(field, 1)
+            field = np.ma.masked_equal(field, 0)
 
         # user defined parameters
         heightResolution = prdcfg.get('heightResolution', 100.)
@@ -963,7 +962,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname
 
-    if prdcfg['type'] == 'WIND_PROFILE':
+    elif prdcfg['type'] == 'WIND_PROFILE':
         # user defined parameters
         heightResolution = prdcfg.get('heightResolution', 100.)
         hmin_user = prdcfg.get('heightMin', None)
@@ -1288,7 +1287,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname
 
-    if prdcfg['type'] == 'PSEUDOPPI_MAP':
+    elif prdcfg['type'] == 'PSEUDOPPI_MAP':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1327,7 +1326,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
             return None
 
-    if prdcfg['type'] == 'CAPPI_IMAGE':
+    elif prdcfg['type'] == 'CAPPI_IMAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1356,7 +1355,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'PLOT_ALONG_COORD':
+    elif prdcfg['type'] == 'PLOT_ALONG_COORD':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1624,7 +1623,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'BSCOPE_IMAGE':
+    elif prdcfg['type'] == 'BSCOPE_IMAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1657,7 +1656,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'TIME_RANGE':
+    elif prdcfg['type'] == 'TIME_RANGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1690,7 +1689,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'HISTOGRAM':
+    elif prdcfg['type'] == 'HISTOGRAM':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1747,7 +1746,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'QUANTILES':
+    elif prdcfg['type'] == 'QUANTILES':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1759,7 +1758,7 @@ 
Source code for pyrad.prod.process_vol_products
<
         # mask unclassified data
         field = deepcopy(dataset['radar_out'].fields[field_name]['data'])
         if prdcfg['voltype'] == 'hydro':
-            field = np.ma.masked_equal(field, 1)
+            field = np.ma.masked_equal(field, 0)
 
         # user defined variables
         quantiles = prdcfg.get('quantiles', None)
@@ -1817,7 +1816,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname_list
 
-    if prdcfg['type'] == 'FIELD_COVERAGE':
+    elif prdcfg['type'] == 'FIELD_COVERAGE':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -1977,7 +1976,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname
 
-    if prdcfg['type'] == 'CDF':
+    elif prdcfg['type'] == 'CDF':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -2162,7 +2161,7 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return fname
 
-    if prdcfg['type'] == 'SAVEVOL':
+    elif prdcfg['type'] == 'SAVEVOL':
         field_name = get_fieldname_pyart(prdcfg['voltype'])
         if field_name not in dataset['radar_out'].fields:
             warn(
@@ -2171,11 +2170,6 @@ 
Source code for pyrad.prod.process_vol_products
<
                 prdcfg['type'])
             return None
 
-        file_type = prdcfg.get('file_type', 'nc')
-        physical = prdcfg.get('physical', True)
-        compression = prdcfg.get('compression', 'gzip')
-        compression_opts = prdcfg.get('compression_opts', 6)
-
         new_dataset = deepcopy(dataset['radar_out'])
         new_dataset.fields = dict()
         new_dataset.add_field(
@@ -2186,77 +2180,33 @@ 
Source code for pyrad.prod.process_vol_products
<
             prdcfg['prdname'], timeinfo=prdcfg['timeinfo'])
 
         fname = make_filename(
-            'savevol', prdcfg['dstype'], prdcfg['voltype'], [file_type],
+            'savevol', prdcfg['dstype'], prdcfg['voltype'], ['nc'],
             timeinfo=prdcfg['timeinfo'], runinfo=prdcfg['runinfo'])[0]
 
         fname = savedir+fname
 
-        if file_type == 'nc':
-            pyart.io.write_cfradial(fname, new_dataset, physical=physical)
-        elif file_type == 'h5':
-            pyart.aux_io.write_odim_h5(
-                fname, new_dataset, physical=physical,
-                compression=compression, compression_opts=compression_opts)
-        else:
-            warn('Data could not be saved. ' +
-                 'Unknown saving file type '+file_type)
-            return None
-
+        pyart.io.cfradial.write_cfradial(fname, new_dataset)
         print('saved file: '+fname)
 
         return fname
 
-    if prdcfg['type'] == 'SAVEALL':
-        file_type = prdcfg.get('file_type', 'nc')
-        datatypes = prdcfg.get('datatypes', None)
-        physical = prdcfg.get('physical', True)
-        compression = prdcfg.get('compression', 'gzip')
-        compression_opts = prdcfg.get('compression_opts', 6)
-
+    elif prdcfg['type'] == 'SAVEALL':
         savedir = get_save_dir(
             prdcfg['basepath'], prdcfg['procname'], dssavedir,
             prdcfg['prdname'], timeinfo=prdcfg['timeinfo'])
 
         fname = make_filename(
-            'savevol', prdcfg['dstype'], 'all_fields', [file_type],
+            'savevol', prdcfg['dstype'], 'all_fields', ['nc'],
             timeinfo=prdcfg['timeinfo'], runinfo=prdcfg['runinfo'])[0]
 
         fname = savedir+fname
 
-        field_names = None
-        if datatypes is not None:
-            field_names = []
-            for datatype in datatypes:
-                field_names.append(get_fieldname_pyart(datatype))
-
-        if file_type == 'nc':
-            if field_names is not None:
-                radar_aux = deepcopy(dataset['radar_out'])
-                radar_aux.fields = dict()
-                for field_name in field_names:
-                    if field_name not in dataset['radar_out'].fields:
-                        warn(field_name+' not in radar object')
-                    else:
-                        radar_aux.add_field(
-                            field_name,
-                            dataset['radar_out'].fields[field_name])
-            else:
-                radar_aux = dataset['radar_out']
-            pyart.io.write_cfradial(fname, radar_aux, physical=physical)
-        elif file_type == 'h5':
-            pyart.aux_io.write_odim_h5(
-                fname, dataset['radar_out'], field_names=field_names,
-                physical=physical, compression=compression,
-                compression_opts=compression_opts)
-        else:
-            warn('Data could not be saved. ' +
-                 'Unknown saving file type '+file_type)
-
+        pyart.io.cfradial.write_cfradial(fname, dataset['radar_out'])
         print('saved file: '+fname)
 
         return fname
 
-    if prdcfg['type'] == 'SAVESTATE':
+    elif prdcfg['type'] == 'SAVESTATE':
         if prdcfg['lastStateFile'] is None:
             warn('Unable to save last state file. File name not specified')
             return None
@@ -2279,8 +2229,9 @@ 
Source code for pyrad.prod.process_vol_products
<
 
         return prdcfg['lastStateFile']
 
-    warn(' Unsupported product type: ' + prdcfg['type'])
-    return None

+    else:
+        warn(' Unsupported product type: ' + prdcfg['type'])
+        return None

 

 
           

@@ -2303,7 +2254,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/prod/product_aux.html b/docs/_modules/pyrad/prod/product_aux.html
index b4cb23c..782981f 100644
--- a/docs/_modules/pyrad/prod/product_aux.html
+++ b/docs/_modules/pyrad/prod/product_aux.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -154,7 +153,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/util/radar_utils.html b/docs/_modules/pyrad/util/radar_utils.html
index 75f318c..c188126 100644
--- a/docs/_modules/pyrad/util/radar_utils.html
+++ b/docs/_modules/pyrad/util/radar_utils.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1509,7 +1508,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/_modules/pyrad/util/stat_utils.html b/docs/_modules/pyrad/util/stat_utils.html
index b87b582..8a19127 100644
--- a/docs/_modules/pyrad/util/stat_utils.html
+++ b/docs/_modules/pyrad/util/stat_utils.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -165,7 +164,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/flow.html b/docs/flow.html
index dbe2fef..63a4d22 100644
--- a/docs/flow.html
+++ b/docs/flow.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
     
      
@@ -203,7 +202,7 @@ 
Navigation

     

     
   
 
\ No newline at end of file
diff --git a/docs/genindex.html b/docs/genindex.html
index db5f5c1..daed2ef 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -15,7 +15,6 @@
     
     
     
-    
     
      
   
@@ -142,6 +141,12 @@ 
_

 
       
+      
  • __init_subclass__() (pyrad.io.TimeSeries method)
+
+      
    • 
       
  • __le__ (pyrad.io.TimeSeries attribute)
@@ -649,8 +654,6 @@ 
    P
    
       
  • process_time_avg() (in module pyrad.proc)
 
    • 
       
  • process_time_avg_flag() (in module pyrad.proc)
-
    • 
-      
  • process_time_avg_std() (in module pyrad.proc)
 
    • 
       
  • process_time_height() (in module pyrad.proc)
 
    • 
@@ -892,7 +895,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/graph.html b/docs/graph.html
index 7cc2d55..97b3204 100644
--- a/docs/graph.html
+++ b/docs/graph.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -1823,7 +1822,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/index.html b/docs/index.html
index cc865cc..409d18b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
     
      
@@ -151,7 +150,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/io.html b/docs/io.html
index 656c457..f87ce10 100644
--- a/docs/io.html
+++ b/docs/io.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -498,7 +497,7 @@ 
    TimeSeries
 
    
-__dict__ = mappingproxy({'add_dataseries': <function TimeSeries.add_dataseries>, '__weakref__': <attribute '__weakref__' of 'TimeSeries' objects>, 'plot': <function TimeSeries.plot>, '__doc__': "\n    Holding timeseries data and metadata.\n\n    Attributes\n    ----------\n    description : array of str\n        Description of the data of the time series.\n    time_vector : array of datetime objects\n    timeformat : how to print the time (default:\n                 'Date, UTC [seconds since midnight]'\n    dataseries : List of _dataSeries object holding the\n                 data\n\n    Methods:\n    --------\n    add_dataseries : Add a data series to the object\n    write : Write time series to a file\n    plot : Plot a figure of a time series\n\n    ", 'write': <function TimeSeries.write>, '__module__': 'pyrad.io.timeseries', 'plot_hist': <function TimeSeries.plot_hist>, '__dict__': <attribute '__dict__' of 'TimeSeries' objects>, '__init__': <function TimeSeries.__init__>, 'add_timesample': <function TimeSeries.add_timesample>})
    
+__dict__ = mappingproxy({'__module__': 'pyrad.io.timeseries', '__doc__': "\n    Holding timeseries data and metadata.\n\n    Attributes\n    ----------\n    description : array of str\n        Description of the data of the time series.\n    time_vector : array of datetime objects\n    timeformat : how to print the time (default:\n                 'Date, UTC [seconds since midnight]'\n    dataseries : List of _dataSeries object holding the\n                 data\n\n    Methods:\n    --------\n    add_dataseries : Add a data series to the object\n    write : Write time series to a file\n    plot : Plot a figure of a time series\n\n    ", '__init__': <function TimeSeries.__init__>, 'add_dataseries': <function TimeSeries.add_dataseries>, 'add_timesample': <function TimeSeries.add_timesample>, 'write': <function TimeSeries.write>, 'plot': <function TimeSeries.plot>, 'plot_hist': <function TimeSeries.plot_hist>, '__dict__': <attribute '__dict__' of 'TimeSeries' objects>, '__weakref__': <attribute '__weakref__' of 'TimeSeries' objects>})
 
    
 
 
    
@@ -569,6 +568,14 @@ 
    TimeSeries
+
    
+__init_subclass__()
    
+
    This method is called when a class is subclassed.
    
+
    The default implementation does nothing. It may be
+overridden to extend subclasses.
    
+
    
+
 
    
 
    
 __le__($self, value, /)
    
@@ -789,7 +796,7 @@ 
    TimeSeries
 
    
-__dict__ = mappingproxy({'calculate_velocities': <function Trajectory.calculate_velocities>, 'get_end_time': <function Trajectory.get_end_time>, '__weakref__': <attribute '__weakref__' of 'Trajectory' objects>, '__doc__': "\n    A class for reading and handling trajectory data from a file.\n\n    Attributes\n    ----------\n    filename : str\n        Path and name of the trajectory definition file\n    starttime : datetime\n        Start time of trajectory processing.\n    endtime : datetime\n        End time of trajectory processing.\n    trajtype : str\n    Type of trajectory. Can be 'plane' or 'lightning'\n    time_vector : Array of datetime objects\n        Array containing the trajectory time samples\n    wgs84_lat_deg : Array of floats\n        WGS84 latitude samples in radian\n    wgs84_lon_deg : Array of floats\n        WGS84 longitude samples in radian\n    wgs84_alt_m : Array of floats\n        WGS84 altitude samples in m\n    nsamples : int\n    Number of samples in the trajectory\n    _swiss_grid_done : Bool\n        Indicates that convertion to Swiss coordinates has been performed\n    swiss_chy, swiss_chx, swiss_chh : Array of floats\n        Swiss coordinates in m\n    radar_list : list\n          List of radars for which trajectories are going to be computed\n    flashnr : int\n          For 'lightning' only. Number of flash for which trajectory data\n          is going to be computed. If 0 all all flashes are going to be\n          considered.\n    time_in_flash : array of floats\n          For 'lightning' only. Time within flash (sec)\n    flashnr_vec : array of ints\n          For 'lightning' only. Flash number of each data sample\n    dBm : array of floats\n          For 'lightning' only. Lightning power (dBm)\n\n\n    Methods:\n    --------\n    add_radar : Add a radar\n    calculate_velocities : Computes velocities\n    get_start_time : Return time of first trajectory sample\n    get_end_time : Return time of last trajectory sample\n    get_samples_in_period : Get indices of samples within period\n    _convert_traj_to_swissgrid : convert data from WGS84 to Swiss coordinates\n    _read_traj : Read plane trajectory from file\n    _read_traj_lightning : Read lightning trajectory from file\n    _get_total_seconds : Get the total time of the trajectory in seconds\n\n    ", 'get_start_time': <function Trajectory.get_start_time>, '_read_traj': <function Trajectory._read_traj>, '__dict__': <attribute '__dict__' of 'Trajectory' objects>, '_convert_traj_to_swissgrid': <function Trajectory._convert_traj_to_swissgrid>, '_get_total_seconds': <function Trajectory._get_total_seconds>, '__init__': <function Trajectory.__init__>, 'get_samples_in_period': <function Trajectory.get_samples_in_period>, '__module__': 'pyrad.io.trajectory', '_read_traj_trt': <function Trajectory._read_traj_trt>, '_read_traj_lightning': <function Trajectory._read_traj_lightning>, 'add_radar': <function Trajectory.add_radar>})
    
+__dict__ = mappingproxy({'__module__': 'pyrad.io.trajectory', '__doc__': "\n    A class for reading and handling trajectory data from a file.\n\n    Attributes\n    ----------\n    filename : str\n        Path and name of the trajectory definition file\n    starttime : datetime\n        Start time of trajectory processing.\n    endtime : datetime\n        End time of trajectory processing.\n    trajtype : str\n    Type of trajectory. Can be 'plane' or 'lightning'\n    time_vector : Array of datetime objects\n        Array containing the trajectory time samples\n    wgs84_lat_deg : Array of floats\n        WGS84 latitude samples in radian\n    wgs84_lon_deg : Array of floats\n        WGS84 longitude samples in radian\n    wgs84_alt_m : Array of floats\n        WGS84 altitude samples in m\n    nsamples : int\n    Number of samples in the trajectory\n    _swiss_grid_done : Bool\n        Indicates that convertion to Swiss coordinates has been performed\n    swiss_chy, swiss_chx, swiss_chh : Array of floats\n        Swiss coordinates in m\n    radar_list : list\n          List of radars for which trajectories are going to be computed\n    flashnr : int\n          For 'lightning' only. Number of flash for which trajectory data\n          is going to be computed. If 0 all all flashes are going to be\n          considered.\n    time_in_flash : array of floats\n          For 'lightning' only. Time within flash (sec)\n    flashnr_vec : array of ints\n          For 'lightning' only. Flash number of each data sample\n    dBm : array of floats\n          For 'lightning' only. Lightning power (dBm)\n\n\n    Methods:\n    --------\n    add_radar : Add a radar\n    calculate_velocities : Computes velocities\n    get_start_time : Return time of first trajectory sample\n    get_end_time : Return time of last trajectory sample\n    get_samples_in_period : Get indices of samples within period\n    _convert_traj_to_swissgrid : convert data from WGS84 to Swiss coordinates\n    _read_traj : Read plane trajectory from file\n    _read_traj_lightning : Read lightning trajectory from file\n    _get_total_seconds : Get the total time of the trajectory in seconds\n\n    ", '__init__': <function Trajectory.__init__>, 'add_radar': <function Trajectory.add_radar>, 'calculate_velocities': <function Trajectory.calculate_velocities>, 'get_samples_in_period': <function Trajectory.get_samples_in_period>, 'get_start_time': <function Trajectory.get_start_time>, 'get_end_time': <function Trajectory.get_end_time>, '_convert_traj_to_swissgrid': <function Trajectory._convert_traj_to_swissgrid>, '_read_traj': <function Trajectory._read_traj>, '_read_traj_lightning': <function Trajectory._read_traj_lightning>, '_read_traj_trt': <function Trajectory._read_traj_trt>, '_get_total_seconds': <function Trajectory._get_total_seconds>, '__dict__': <attribute '__dict__' of 'Trajectory' objects>, '__weakref__': <attribute '__weakref__' of 'Trajectory' objects>})
 
    
 
 
    
@@ -868,6 +875,14 @@ 
    TimeSeries
+
    
+__init_subclass__()
    
+
    This method is called when a class is subclassed.
    
+
    The default implementation does nothing. It may be
+overridden to extend subclasses.
    
+
    
+
 
    
 
    
 __le__($self, value, /)
    
@@ -4082,7 +4097,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/objects.inv b/docs/objects.inv
index 04fbd59..210ef42 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/proc.html b/docs/proc.html
index ecc679f..44c0620 100644
--- a/docs/proc.html
+++ b/docs/proc.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -231,37 +230,34 @@ 
    Monitoring, calibration and noise correctionprocess_selfconsistency_bias(procstatus, dscfg)
 Estimates the reflectivity bias by means of the selfconsistency algorithm by Gourley
 
-process_time_avg_std(procstatus, dscfg[, …])
-computes the average and standard deviation of data.
-
-process_occurrence(procstatus, dscfg[, …])
+process_occurrence(procstatus, dscfg[, …])
 computes the frequency of occurrence of data.
 
-process_occurrence_period(procstatus, dscfg)
+process_occurrence_period(procstatus, dscfg)
 computes the frequency of occurrence over a long period of time by adding together shorter periods
 
-process_monitoring(procstatus, dscfg[, …])
+process_monitoring(procstatus, dscfg[, …])
 computes monitoring statistics
 
-process_gc_monitoring(procstatus, dscfg[, …])
+process_gc_monitoring(procstatus, dscfg[, …])
 computes ground clutter monitoring statistics
 
-process_time_avg(procstatus, dscfg[, radar_list])
+process_time_avg(procstatus, dscfg[, radar_list])
 computes the temporal mean of a field
 
-process_weighted_time_avg(procstatus, dscfg)
+process_weighted_time_avg(procstatus, dscfg)
 computes the temporal mean of a field weighted by the reflectivity
 
-process_time_avg_flag(procstatus, dscfg[, …])
+process_time_avg_flag(procstatus, dscfg[, …])
 computes a flag field describing the conditions of the data used while averaging
 
-process_colocated_gates(procstatus, dscfg[, …])
+process_colocated_gates(procstatus, dscfg[, …])
 Find colocated gates within two radars
 
-process_intercomp(procstatus, dscfg[, …])
+process_intercomp(procstatus, dscfg[, …])
 intercomparison between two radars
 
-process_intercomp_time_avg(procstatus, dscfg)
+process_intercomp_time_avg(procstatus, dscfg)
 intercomparison between the average reflectivity of two radars
 
 
@@ -3931,7 +3927,7 @@ 
    COSMO data: float
 
    If the radar object contains an RHI volume, the tolerance in the
-elevation angle for the conversion into PPI. Default 1.
    
+elevation angle for the conversion into PPI
    
 
    
 
    lat, lon : float
    
 
    latitude and longitude of the point of interest [deg]
    
@@ -4093,73 +4089,6 @@ 
    COSMO data
-
    
-pyrad.proc.process_time_avg_std(procstatus, dscfg, radar_list=None)[source]
    
-
    computes the average and standard deviation of data. It looks only for
-gates where data is present.
    
-
---
-
-
-
-
-
-
    


    Parameters:
    
-
    procstatus : int
    
-
    Processing status: 0 initializing, 1 processing volume,
-2 post-processing
    
-
    
-
    dscfg : dictionary of dictionaries
    
-
    data set configuration. Accepted Configuration Keywords:
    
-
    
-
    datatype : list of string. Dataset keyword
    
-
    The input data types
    
-
    
-
    regular_grid : Boolean. Dataset keyword
    
-
    Whether the radar has a Boolean grid or not. Default False
    
-
    
-
    rmin, rmax : float. Dataset keyword
    
-
    minimum and maximum ranges where the computation takes place. If
--1 the whole range is considered. Default is -1
    
-
    
-
    val_min : Float. Dataset keyword
    
-
    Minimum reflectivity value to consider that the gate has signal.
-Default None
    
-
    
-
    filter_prec : str. Dataset keyword
    
-
    Give which type of volume should be filtered. None, no filtering;
-keep_wet, keep wet volumes; keep_dry, keep dry volumes.
    
-
    
-
    rmax_prec : float. Dataset keyword
    
-
    Maximum range to consider when looking for wet gates [m]
    
-
    
-
    percent_prec_max : float. Dataset keyword
    
-
    Maxim percentage of wet gates to consider the volume dry
    
-
    
-
    lin_trans : Boolean. Dataset keyword
    
-
    If True the data will be transformed into linear units. Default
-False
    
-
    
-
    
-
    
-
    radar_list : list of Radar objects
    
-
    Optional. list of radar objects
    
-
    
-
    
-
    Returns:
    
-
    new_dataset : dict
    
-
    dictionary containing the output
    
-
    
-
    ind_rad : int
    
-
    radar index
    
-
    
-
    
-
    
-
    
-
 
    
 
    
 pyrad.proc.process_time_height(procstatus, dscfg, radar_list=None)[source]
    
@@ -4958,7 +4887,7 @@ 
    COSMO data: list of ints. Dataset keyword
 
    list of hydrometeor classes to keep for the analysis
-Default [2] (dry snow)
    
+Default [1] (dry snow)
    
 
    
 
    
 
    
@@ -5005,7 +4934,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/prod.html b/docs/prod.html
index 6db87ee..d3f5021 100644
--- a/docs/prod.html
+++ b/docs/prod.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
      
   
@@ -559,7 +558,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 4fae876..3e4b8f9 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -14,7 +14,6 @@
     
     
     
-    
     
     
  
@@ -124,7 +123,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/search.html b/docs/search.html
index ded57a7..b239294 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -15,7 +15,6 @@
     
     
     
-    
     
     
     
@@ -92,7 +91,7 @@ 
    Navigation
    
     
    
     
   
 
\ No newline at end of file
diff --git a/docs/searchindex.js b/docs/searchindex.js
index b98cc86..17a29fa 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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