def l2qc(cf, ds1):
"""
Perform initial QA/QC on flux data
Generates L2 from L1 data
* check parameters specified in control file
Functions performed:
pfp_ck.do_rangecheck*
pfp_ck.do_CSATcheck
pfp_ck.do_7500check
pfp_ck.do_diurnalcheck*
pfp_ck.do_excludedates*
pfp_ck.do_excludehours*
pfp_ts.albedo
"""
# make a copy of the L1 data
ds2 = copy.deepcopy(ds1)
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds2, "L2")
# apply linear corrections to the data
pfp_ck.do_linear(cf, ds2)
# apply the quality control checks (range, diurnal, exclude dates and exclude hours
pfp_ck.do_qcchecks(cf, ds2)
# do the CSAT diagnostic check
pfp_ck.do_SONICcheck(cf, ds2)
# do the IRGA diagnostic check
pfp_ck.do_IRGAcheck(cf, ds2)
# check missing data and QC flags are consistent
pfp_utils.CheckQCFlags(ds2)
# write series statistics to file
pfp_io.get_seriesstats(cf, ds2)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds2)
return ds2
def l5qc(main_gui, cf, ds4):
ds5 = pfp_io.copy_datastructure(cf, ds4)
# ds5 will be empty (logical false) if an error occurs in copy_datastructure
# return from this routine if this is the case
if not ds5:
return ds5
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds5, "L5")
# parse the control file for information on how the user wants to do the gap filling
l5_info = pfp_gf.ParseL5ControlFile(cf, ds5)
if ds5.returncodes["value"] != 0:
return ds5
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds5)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds5)
pfp_gf.CheckL5Drivers(ds5, l5_info)
if ds5.returncodes["value"] != 0:
return ds5
# now do the flux gap filling methods
# *** start of the section that does the gap filling of the fluxes ***
pfp_gf.CheckGapLengths(cf, ds5, l5_info)
if ds5.returncodes["value"] != 0:
return ds5
# apply the turbulence filter (if requested)
pfp_ck.ApplyTurbulenceFilter(cf, ds5, l5_info)
# fill short gaps using interpolation
pfp_gf.GapFillUsingInterpolation(cf, ds5)
# gap fill using marginal distribution sampling
if "GapFillUsingMDS" in l5_info:
pfp_gfMDS.GapFillUsingMDS(ds5, l5_info, "GapFillUsingMDS")
# do the gap filling using SOLO
if "GapFillUsingSOLO" in l5_info:
pfp_gfSOLO.GapFillUsingSOLO(main_gui, ds5, l5_info, "GapFillUsingSOLO")
if ds5.returncodes["value"] != 0:
return ds5
# fill long gaps using SOLO
if "GapFillLongSOLO" in l5_info:
pfp_gfSOLO.GapFillUsingSOLO(main_gui, ds5, l5_info, "GapFillLongSOLO")
if ds5.returncodes["value"] != 0:
return ds5
# merge the gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds5, l5_info, merge_order="standard")
# check that all targets were gap filled
pfp_gf.CheckL5Targets(ds5, l5_info)
if ds5.returncodes["value"] != 0:
return ds5
# calculate Monin-Obukhov length
pfp_ts.CalculateMoninObukhovLength(ds5)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds5)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds5)
# remove intermediate series from the data structure
pfp_ts.RemoveIntermediateSeries(ds5, l5_info)
return ds5
def l4qc(main_gui, cf, ds3):
ds4 = pfp_io.copy_datastructure(cf, ds3)
# ds4 will be empty (logical false) if an error occurs in copy_datastructure
# return from this routine if this is the case
if not ds4:
return ds4
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds4, "L4")
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds4)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds4)
# now do the meteorological driver gap filling
# parse the control file for information on how the user wants to do the gap filling
l4_info = pfp_gf.ParseL4ControlFile(cf, ds4)
if ds4.returncodes["value"] != 0:
return ds4
# *** start of the section that does the gap filling of the drivers ***
# read the alternate data files
ds_alt = pfp_gf.ReadAlternateFiles(ds4, l4_info)
# fill short gaps using interpolation
pfp_gf.GapFillUsingInterpolation(cf, ds4)
# gap fill using climatology
if "GapFillFromClimatology" in l4_info:
pfp_gf.GapFillFromClimatology(ds4, l4_info, "GapFillFromClimatology")
# do the gap filling using the ACCESS output
if "GapFillFromAlternate" in l4_info:
pfp_gfALT.GapFillFromAlternate(main_gui, ds4, ds_alt, l4_info, "GapFillFromAlternate")
if ds4.returncodes["value"] != 0:
return ds4
# merge the first group of gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds4, l4_info, merge_order="prerequisite")
# re-calculate the ground heat flux but only if requested in control file
opt = pfp_utils.get_keyvaluefromcf(cf,["Options"], "CorrectFgForStorage", default="No", mode="quiet")
if opt.lower() != "no":
pfp_ts.CorrectFgForStorage(cf, ds4, Fg_out='Fg', Fg_in='Fg_Av', Ts_in='Ts', Sws_in='Sws')
# re-calculate the net radiation
pfp_ts.CalculateNetRadiation(cf, ds4, Fn_out='Fn', Fsd_in='Fsd', Fsu_in='Fsu', Fld_in='Fld', Flu_in='Flu')
# re-calculate the available energy
pfp_ts.CalculateAvailableEnergy(ds4, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# merge the second group of gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds4, l4_info, merge_order="standard")
# re-calculate the water vapour concentrations
pfp_ts.CalculateHumiditiesAfterGapFill(ds4, l4_info)
# re-calculate the meteorological variables
pfp_ts.CalculateMeteorologicalVariables(ds4, l4_info)
# check for any missing data
pfp_utils.get_missingingapfilledseries(ds4, l4_info)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds4)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds4)
# remove intermediate series from the data structure
pfp_ts.RemoveIntermediateSeries(ds4, l4_info)
return ds4
def l6qc(main_gui, cf, ds5):
ds6 = pfp_io.copy_datastructure(cf, ds5)
# ds6 will be empty (logical false) if an error occurs in copy_datastructure
# return from this routine if this is the case
if not ds6:
return ds6
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds6, "L6")
# parse the control file
l6_info = pfp_rp.ParseL6ControlFile(cf, ds6)
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds6)
# check units of Fc
Fc_list = [label for label in ds6.series.keys() if label[0:2] == "Fc"]
pfp_utils.CheckUnits(ds6, Fc_list, "umol/m2/s", convert_units=True)
# get ER from the observed Fc
pfp_rp.GetERFromFc(cf, ds6)
# return code will be non-zero if turbulance filter not applied to CO2 flux
if ds6.returncodes["value"] != 0:
return ds6
# estimate ER using SOLO
if "ERUsingSOLO" in l6_info:
pfp_rp.ERUsingSOLO(main_gui, ds6, l6_info, "ERUsingSOLO")
if ds6.returncodes["value"] != 0:
return ds6
# estimate ER using FFNET
#pfp_rp.ERUsingFFNET(cf, ds6, l6_info)
# estimate ER using Lloyd-Taylor
pfp_rp.ERUsingLloydTaylor(cf, ds6, l6_info)
# estimate ER using Lasslop et al
pfp_rp.ERUsingLasslop(ds6, l6_info)
# merge the estimates of ER with the observations
pfp_ts.MergeSeriesUsingDict(ds6, l6_info, merge_order="standard")
# calculate NEE from Fc and ER
pfp_rp.CalculateNEE(cf, ds6, l6_info)
# calculate NEP from NEE
pfp_rp.CalculateNEP(cf, ds6)
# calculate ET from Fe
pfp_rp.CalculateET(ds6)
# partition NEE into GPP and ER
pfp_rp.PartitionNEE(ds6, l6_info)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds6)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds6)
# remove intermediate series from the data structure
pfp_ts.RemoveIntermediateSeries(ds6, l6_info)
# do the L6 summary
pfp_rp.L6_summary(cf, ds6)
return ds6
def l5qc(cf, ds4):
ds5 = pfp_io.copy_datastructure(cf, ds4)
# ds4 will be empty (logical false) if an error occurs in copy_datastructure
# return from this routine if this is the case
if not ds5:
return ds5
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds5, "L5")
ds5.cf = cf
# create a dictionary to hold the gap filling data
ds_alt = {}
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds5)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds5)
# now do the flux gap filling methods
label_list = pfp_utils.get_label_list_from_cf(cf)
for label in label_list:
# parse the control file for information on how the user wants to do the gap filling
pfp_gf.GapFillParseControlFile(cf, ds5, label, ds_alt)
# *** start of the section that does the gap filling of the fluxes ***
# apply the turbulence filter (if requested)
pfp_ck.ApplyTurbulenceFilter(cf, ds5)
# fill short gaps using interpolation
pfp_gf.GapFillUsingInterpolation(cf, ds5)
# do the gap filling using SOLO
pfp_gfSOLO.GapFillUsingSOLO(cf, ds4, ds5)
if ds5.returncodes["solo"] == "quit":
return ds5
# gap fill using marginal distribution sampling
pfp_gfMDS.GapFillFluxUsingMDS(cf, ds5)
# gap fill using climatology
pfp_gf.GapFillFromClimatology(ds5)
# merge the gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds5, merge_order="standard")
# calculate Monin-Obukhov length
pfp_ts.CalculateMoninObukhovLength(ds5)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds5)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds5)
return ds5
def l4qc(cf, ds3):
# !!! code here to use existing L4 file
# logic
# if the L4 doesn't exist
# - create ds4 by using copy.deepcopy(ds3)
# if the L4 does exist and the "UseExistingL4File" option is False
# - create ds4 by using copy.deepcopy(ds3)
# if the L4 does exist and the "UseExistingL4File" option is True
# - read the contents of the L4 netCDF file
# - check the start and end dates of the L3 and L4 data
# - if these are the same then tell the user there is nothing to do
# - copy the L3 data to the L4 data structure
# - replace the L3 data with the L4 data
#ds4 = copy.deepcopy(ds3)
ds4 = pfp_io.copy_datastructure(cf, ds3)
# ds4 will be empty (logical false) if an error occurs in copy_datastructure
# return from this routine if this is the case
if not ds4: return ds4
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds4, "L4")
ds4.cf = cf
## calculate the available energy
#if "Fa" not in ds4.series.keys():
#pfp_ts.CalculateAvailableEnergy(ds4,Fa_out='Fa',Fn_in='Fn',Fg_in='Fg')
# create a dictionary to hold the gap filling data
ds_alt = {}
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds4)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds4)
# now do the meteorological driver gap filling
for ThisOne in cf["Drivers"].keys():
if ThisOne not in ds4.series.keys():
logger.warning("Series " + ThisOne + " not in data structure")
continue
# parse the control file for information on how the user wants to do the gap filling
pfp_gf.GapFillParseControlFile(cf, ds4, ThisOne, ds_alt)
# *** start of the section that does the gap filling of the drivers ***
# fill short gaps using interpolation
pfp_gf.GapFillUsingInterpolation(cf, ds4)
# gap fill using climatology
pfp_gf.GapFillFromClimatology(ds4)
# do the gap filling using the ACCESS output
pfp_gfALT.GapFillFromAlternate(cf, ds4, ds_alt)
if ds4.returncodes["alternate"] == "quit": return ds4
# gap fill using SOLO
pfp_gfSOLO.GapFillUsingSOLO(cf, ds3, ds4)
if ds4.returncodes["solo"] == "quit": return ds4
# merge the first group of gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds4, merge_order="prerequisite")
# re-calculate the ground heat flux but only if requested in control file
opt = pfp_utils.get_keyvaluefromcf(cf, ["Options"],
"CorrectFgForStorage",
default="No",
mode="quiet")
if opt.lower() != "no":
pfp_ts.CorrectFgForStorage(cf,
ds4,
Fg_out='Fg',
Fg_in='Fg_Av',
Ts_in='Ts',
Sws_in='Sws')
# re-calculate the net radiation
pfp_ts.CalculateNetRadiation(cf,
ds4,
Fn_out='Fn',
Fsd_in='Fsd',
Fsu_in='Fsu',
Fld_in='Fld',
Flu_in='Flu')
# re-calculate the available energy
pfp_ts.CalculateAvailableEnergy(ds4, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# merge the second group of gap filled drivers into a single series
pfp_ts.MergeSeriesUsingDict(ds4, merge_order="standard")
# re-calculate the water vapour concentrations
pfp_ts.CalculateHumiditiesAfterGapFill(ds4)
# re-calculate the meteorological variables
pfp_ts.CalculateMeteorologicalVariables(ds4)
# the Tumba rhumba
pfp_ts.CalculateComponentsFromWsWd(ds4)
# check for any missing data
pfp_utils.get_missingingapfilledseries(ds4)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds4)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds4)
return ds4
def l3qc(cf, ds2):
"""
"""
# make a copy of the L2 data
ds3 = copy.deepcopy(ds2)
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds3, "L3")
# put the control file name into the global attributes
ds3.globalattributes['controlfile_name'] = cf['controlfile_name']
# check to see if we have any imports
pfp_gf.ImportSeries(cf, ds3)
# apply linear corrections to the data
pfp_ck.do_linear(cf, ds3)
# ************************
# *** Merge humidities ***
# ************************
# merge whatever humidities are available
pfp_ts.MergeHumidities(cf, ds3, convert_units=True)
# **************************
# *** Merge temperatures ***
# **************************
# get the air temperature from the CSAT virtual temperature
pfp_ts.TaFromTv(cf, ds3)
# merge the HMP and corrected CSAT data
pfp_ts.MergeSeries(cf, ds3, "Ta", convert_units=True)
pfp_utils.CheckUnits(ds3, "Ta", "C", convert_units=True)
# ***************************
# *** Calcuate humidities ***
# ***************************
# calculate humidities (absolute, specific and relative) from whatever is available
pfp_ts.CalculateHumidities(ds3)
# ********************************
# *** Merge CO2 concentrations ***
# ********************************
# merge the 7500 CO2 concentration
# PRI 09/08/2017 possibly the ugliest thing I have done yet
# This needs to be abstracted to a general alias checking routine at the
# start of the L3 processing so that possible aliases are mapped to a single
# set of variable names.
if "CO2" in cf["Variables"]:
CO2 = "CO2"
elif "Cc" in cf["Variables"]:
CO2 = "Cc"
else:
msg = "Label for CO2 ('CO2','Cc') not found in control file"
logger.error(msg)
return
pfp_ts.MergeSeries(cf, ds3, CO2, convert_units=True)
# ******************************************
# *** Calculate meteorological variables ***
# ******************************************
# Update meteorological variables
pfp_ts.CalculateMeteorologicalVariables(ds3)
# *************************************************
# *** Calculate fluxes from covariances section ***
# *************************************************
# check to see if the user wants to use the fluxes in the L2 file
if not pfp_utils.cfoptionskeylogical(cf, Key="UseL2Fluxes", default=False):
# check the covariance units and change if necessary
pfp_ts.CheckCovarianceUnits(ds3)
# do the 2D coordinate rotation
pfp_ts.CoordRotation2D(cf, ds3)
# do the Massman frequency attenuation correction
pfp_ts.MassmanStandard(cf, ds3)
# calculate the fluxes
pfp_ts.CalculateFluxes(cf, ds3)
# approximate wT from virtual wT using wA (ref: Campbell OPECSystem manual)
pfp_ts.FhvtoFh(cf, ds3)
# correct the H2O & CO2 flux due to effects of flux on density measurements
pfp_ts.Fe_WPL(cf, ds3)
pfp_ts.Fc_WPL(cf, ds3)
# **************************************
# *** Calculate Monin-Obukhov length ***
# **************************************
pfp_ts.CalculateMoninObukhovLength(ds3)
# **************************
# *** CO2 and Fc section ***
# **************************
# convert CO2 units if required
pfp_utils.ConvertCO2Units(cf, ds3, CO2=CO2)
# calculate Fc storage term - single height only at present
pfp_ts.CalculateFcStorageSinglePoint(cf,
ds3,
Fc_out='Fc_single',
CO2_in=CO2)
# convert Fc and Fc_storage units if required
pfp_utils.ConvertFcUnits(cf, ds3)
# merge Fc and Fc_storage series if required
merge_list = [
label for label in cf["Variables"].keys() if label[0:2] == "Fc"
and "MergeSeries" in cf["Variables"][label].keys()
]
for label in merge_list:
pfp_ts.MergeSeries(cf, ds3, label, save_originals=True)
# correct Fc for storage term - only recommended if storage calculated from profile available
pfp_ts.CorrectFcForStorage(cf, ds3)
# *************************
# *** Radiation section ***
# *************************
# merge the incoming shortwave radiation
pfp_ts.MergeSeries(cf, ds3, 'Fsd')
# calculate the net radiation from the Kipp and Zonen CNR1
pfp_ts.CalculateNetRadiation(cf,
ds3,
Fn_out='Fn_KZ',
Fsd_in='Fsd',
Fsu_in='Fsu',
Fld_in='Fld',
Flu_in='Flu')
pfp_ts.MergeSeries(cf, ds3, 'Fn')
# ****************************************
# *** Wind speed and direction section ***
# ****************************************
# combine wind speed from the Wind Sentry and the SONIC
pfp_ts.MergeSeries(cf, ds3, 'Ws')
# combine wind direction from the Wind Sentry and the SONIC
pfp_ts.MergeSeries(cf, ds3, 'Wd')
# ********************
# *** Soil section ***
# ********************
# correct soil heat flux for storage
# ... either average the raw ground heat flux, soil temperature and moisture
# and then do the correction (OzFlux "standard")
pfp_ts.AverageSeriesByElements(cf, ds3, 'Ts')
pfp_ts.AverageSeriesByElements(cf, ds3, 'Sws')
if pfp_utils.cfoptionskeylogical(cf, Key='CorrectIndividualFg'):
# ... or correct the individual ground heat flux measurements (James' method)
pfp_ts.CorrectIndividualFgForStorage(cf, ds3)
pfp_ts.AverageSeriesByElements(cf, ds3, 'Fg')
else:
pfp_ts.AverageSeriesByElements(cf, ds3, 'Fg')
pfp_ts.CorrectFgForStorage(cf,
ds3,
Fg_out='Fg',
Fg_in='Fg',
Ts_in='Ts',
Sws_in='Sws')
# calculate the available energy
pfp_ts.CalculateAvailableEnergy(ds3, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# create new series using MergeSeries or AverageSeries
pfp_ck.CreateNewSeries(cf, ds3)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds3)
# coordinate gaps in the three main fluxes
pfp_ck.CoordinateFluxGaps(cf, ds3)
# coordinate gaps in Ah_7500_Av with Fc
pfp_ck.CoordinateAh7500AndFcGaps(cf, ds3)
# check missing data and QC flags are consistent
pfp_utils.CheckQCFlags(ds3)
# get the statistics for the QC flags and write these to an Excel spreadsheet
pfp_io.get_seriesstats(cf, ds3)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds3)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds3)
return ds3
def l3qc(cf, ds2):
"""
"""
# make a copy of the L2 data
ds3 = copy.deepcopy(ds2)
# set some attributes for this level
pfp_utils.UpdateGlobalAttributes(cf, ds3, "L3")
# check to see if we have any imports
pfp_gf.ImportSeries(cf,ds3)
# apply linear corrections to the data
pfp_ck.do_linear(cf,ds3)
# parse the control file for information on how the user wants to do the gap filling
l3_info = pfp_compliance.ParseL3ControlFile(cf, ds3)
if l3_info["status"]["value"] != 0:
logger.error(l3_info["status"]["message"])
return ds3
# ************************
# *** Merge humidities ***
# ************************
# merge whatever humidities are available
pfp_ts.MergeHumidities(cf, ds3, convert_units=True)
# **************************
# *** Merge temperatures ***
# **************************
# get the air temperature from the CSAT virtual temperature
pfp_ts.TaFromTv(cf, ds3)
# merge the HMP and corrected CSAT data
pfp_ts.CombineSeries(cf, ds3, "Ta", convert_units=True)
pfp_utils.CheckUnits(ds3, "Ta", "degC", convert_units=True)
# ***************************
# *** Calcuate humidities ***
# ***************************
# calculate humidities (absolute, specific and relative) from whatever is available
pfp_ts.CalculateHumidities(ds3)
# ********************************
# *** Merge CO2 concentrations ***
# ********************************
# merge the CO2 concentration
pfp_ts.CombineSeries(cf, ds3, l3_info["CO2"]["label"], convert_units=True)
# ******************************************
# *** Calculate meteorological variables ***
# ******************************************
# Update meteorological variables
pfp_ts.CalculateMeteorologicalVariables(ds3, l3_info)
# *************************************************
# *** Calculate fluxes from covariances section ***
# *************************************************
# check to see if the user wants to use the fluxes in the L2 file
if not pfp_utils.get_optionskeyaslogical(cf, "UseL2Fluxes", default=False):
# check the covariance units and change if necessary
pfp_ts.CheckCovarianceUnits(ds3)
# do the 2D coordinate rotation
pfp_ts.CoordRotation2D(cf, ds3)
# do the Massman frequency attenuation correction
pfp_ts.MassmanStandard(cf, ds3)
# calculate the fluxes
pfp_ts.CalculateFluxes(cf, ds3)
# approximate wT from virtual wT using wA (ref: Campbell OPECSystem manual)
pfp_ts.FhvtoFh(cf, ds3)
# correct the H2O & CO2 flux due to effects of flux on density measurements
if pfp_ts.Fe_WPL(cf, ds3):
return ds3
if pfp_ts.Fco2_WPL(cf, ds3):
return ds3
# **************************
# *** CO2 and Fc section ***
# **************************
# convert CO2 units if required
pfp_utils.ConvertCO2Units(cf, ds3)
# calculate Fco2 storage term - single height only at present
pfp_ts.CalculateFco2StorageSinglePoint(cf, ds3, l3_info["CO2"]["label"])
# convert Fco2 units if required
pfp_utils.ConvertFco2Units(cf, ds3)
# merge Fco2 and Fco2_storage series if required
pfp_ts.CombineSeries(cf, ds3, l3_info["Fco2"]["combine_list"], save_originals=True)
# correct Fco2 for storage term - only recommended if storage calculated from profile available
pfp_ts.CorrectFco2ForStorage(cf, ds3)
# *************************
# *** Radiation section ***
# *************************
# merge the incoming shortwave radiation
pfp_ts.CombineSeries(cf, ds3, "Fsd")
# calculate the net radiation from the Kipp and Zonen CNR1
pfp_ts.CalculateNetRadiation(cf, ds3)
pfp_ts.CombineSeries(cf, ds3, "Fn")
# ****************************************
# *** Wind speed and direction section ***
# ****************************************
# combine wind speed from the Wind Sentry and the SONIC
pfp_ts.CombineSeries(cf,ds3, "Ws")
# combine wind direction from the Wind Sentry and the SONIC
pfp_ts.CombineSeries(cf,ds3, "Wd")
# ********************
# *** Soil section ***
# ********************
# correct soil heat flux for storage
# ... either average the raw ground heat flux, soil temperature and moisture
# and then do the correction (OzFlux "standard")
pfp_ts.CombineSeries(cf, ds3, "Ts")
pfp_ts.CombineSeries(cf, ds3, "Sws")
if pfp_utils.get_optionskeyaslogical(cf, "CorrectIndividualFg"):
# ... or correct the individual ground heat flux measurements (James' method)
pfp_ts.CorrectIndividualFgForStorage(cf, ds3)
pfp_ts.CombineSeries(cf, ds3, "Fg")
else:
pfp_ts.CombineSeries(cf, ds3, "Fg")
pfp_ts.CorrectFgForStorage(cf, ds3)
# calculate the available energy
pfp_ts.CalculateAvailableEnergy(ds3)
# create new series using MergeSeries or AverageSeries
pfp_ck.CreateNewSeries(cf, ds3)
# Calculate Monin-Obukhov length
pfp_ts.CalculateMoninObukhovLength(ds3)
# re-apply the quality control checks (range, diurnal and rules)
pfp_ck.do_qcchecks(cf, ds3)
# check missing data and QC flags are consistent
pfp_utils.CheckQCFlags(ds3)
# get the statistics for the QC flags and write these to an Excel spreadsheet
pfp_io.get_seriesstats(cf, ds3)
# write the percentage of good data as a variable attribute
pfp_utils.get_coverage_individual(ds3)
# write the percentage of good data for groups
pfp_utils.get_coverage_groups(ds3)
# remove intermediate series from the data structure
pfp_ts.RemoveIntermediateSeries(ds3, l3_info)
return ds3