def l6qc(cf, ds5):
ds6 = qcio.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
qcutils.UpdateGlobalAttributes(cf, ds6, "L6")
# parse the control file
qcrp.ParseL6ControlFile(cf, ds6)
# check to see if we have any imports
qcgf.ImportSeries(cf, ds6)
# check units
qcutils.CheckUnits(ds6, "Fc", "umol/m2/s", convert_units=True)
## filter Fc for night time and ustar threshold, write to ds as "ER"
#result = qcrp.GetERFromFc(cf,ds6)
#if result==0: return
# apply the turbulence filter (if requested)
qcck.ApplyTurbulenceFilter(cf, ds6)
qcrp.GetERFromFc2(cf, ds6)
# estimate ER using SOLO
qcrp.ERUsingSOLO(cf, ds6)
# estimate ER using FFNET
qcrp.ERUsingFFNET(cf, ds6)
# estimate ER using Lloyd-Taylor
qcrp.ERUsingLloydTaylor(cf, ds6)
# estimate ER using Lasslop et al
qcrp.ERUsingLasslop(cf, ds6)
# merge the estimates of ER with the observations
qcts.MergeSeriesUsingDict(ds6, merge_order="standard")
# calculate NEE from Fc and ER
qcrp.CalculateNEE(cf, ds6)
# calculate NEP from NEE
qcrp.CalculateNEP(cf, ds6)
# calculate ET from Fe
qcrp.CalculateET(ds6)
# partition NEE into GPP and ER
qcrp.PartitionNEE(cf, ds6)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds6)
# write the percentage of good data for groups
qcutils.get_coverage_groups(ds6)
# do the L6 summary
qcrp.L6_summary(cf, ds6)
return ds6
def l5qc(cf, ds4):
ds5 = qcio.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
qcutils.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
qcgf.ImportSeries(cf, ds5)
# re-apply the quality control checks (range, diurnal and rules)
qcck.do_qcchecks(cf, ds5)
# now do the flux gap filling methods
label_list = qcutils.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
qcgf.GapFillParseControlFile(cf, ds5, label, ds_alt)
# *** start of the section that does the gap filling of the fluxes ***
# apply the turbulence filter (if requested)
qcck.ApplyTurbulenceFilter(cf, ds5)
# fill short gaps using interpolation
qcgf.GapFillUsingInterpolation(cf, ds5)
# do the gap filling using SOLO
qcgfSOLO.GapFillUsingSOLO(cf, ds4, ds5)
if ds5.returncodes["solo"] == "quit":
return ds5
# gap fill using marginal distribution sampling
qcgfMDS.GapFillFluxUsingMDS(cf, ds5)
# gap fill using climatology
qcgf.GapFillFromClimatology(ds5)
# merge the gap filled drivers into a single series
qcts.MergeSeriesUsingDict(ds5, merge_order="standard")
# calculate Monin-Obukhov length
qcts.CalculateMoninObukhovLength(ds5)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds5)
# write the percentage of good data for groups
qcutils.get_coverage_groups(ds5)
return ds5
def l2qc(cf, ds1):
"""
Perform initial QA/QC on flux data
Generates L2 from L1 data
* check parameters specified in control file
Functions performed:
qcck.do_rangecheck*
qcck.do_CSATcheck
qcck.do_7500check
qcck.do_diurnalcheck*
qcck.do_excludedates*
qcck.do_excludehours*
qcts.albedo
"""
# make a copy of the L1 data
ds2 = copy.deepcopy(ds1)
# set some attributes for this level
qcutils.UpdateGlobalAttributes(cf, ds2, "L2")
ds2.globalattributes['Functions'] = ''
# put the control file name into the global attributes
ds2.globalattributes['controlfile_name'] = cf['controlfile_name']
# apply the quality control checks (range, diurnal, exclude dates and exclude hours
qcck.do_qcchecks(cf, ds2)
# do the CSAT diagnostic check
qcck.do_SONICcheck(cf, ds2)
# do the IRGA diagnostic check
qcck.do_IRGAcheck(cf, ds2)
# constrain albedo estimates to full sun angles
#qcts.albedo(cf,ds2)
#log.info(' Finished the albedo constraints') # apply linear corrections to the data
#log.info(' Applying linear corrections ...')
qcck.do_linear(cf, ds2)
# check missing data and QC flags are consistent
qcutils.CheckQCFlags(ds2)
# write series statistics to file
qcio.get_seriesstats(cf, ds2)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds2)
return ds2
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 = qcio.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
qcutils.UpdateGlobalAttributes(cf, ds4, "L4")
ds4.cf = cf
# calculate the available energy
if "Fa" not in ds4.series.keys():
qcts.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
qcgf.ImportSeries(cf, ds4)
# re-apply the quality control checks (range, diurnal and rules)
qcck.do_qcchecks(cf, ds4)
# now do the meteorological driver gap filling
for ThisOne in cf["Drivers"].keys():
if ThisOne not in ds4.series.keys():
log.error("Series " + ThisOne + " not in data structure")
continue
# parse the control file for information on how the user wants to do the gap filling
qcgf.GapFillParseControlFile(cf, ds4, ThisOne, ds_alt)
# *** start of the section that does the gap filling of the drivers ***
# fill short gaps using interpolation
qcgf.GapFillUsingInterpolation(cf, ds4)
# gap fill using climatology
qcgf.GapFillFromClimatology(ds4)
# do the gap filling using the ACCESS output
qcgf.GapFillFromAlternate(cf, ds4, ds_alt)
if ds4.returncodes["alternate"] == "quit": return ds4
# gap fill using SOLO
qcgf.GapFillUsingSOLO(cf, ds3, ds4)
if ds4.returncodes["solo"] == "quit": return ds4
# merge the first group of gap filled drivers into a single series
qcts.MergeSeriesUsingDict(ds4, merge_order="prerequisite")
# re-calculate the ground heat flux but only if requested in control file
opt = qcutils.get_keyvaluefromcf(cf, ["Options"],
"CorrectFgForStorage",
default="No",
mode="quiet")
if opt.lower() != "no":
qcts.CorrectFgForStorage(cf,
ds4,
Fg_out='Fg',
Fg_in='Fg_Av',
Ts_in='Ts',
Sws_in='Sws')
# re-calculate the net radiation
qcts.CalculateNetRadiation(cf,
ds4,
Fn_out='Fn',
Fsd_in='Fsd',
Fsu_in='Fsu',
Fld_in='Fld',
Flu_in='Flu')
# re-calculate the available energy
qcts.CalculateAvailableEnergy(ds4, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# merge the second group of gap filled drivers into a single series
qcts.MergeSeriesUsingDict(ds4, merge_order="standard")
# re-calculate the water vapour concentrations
qcts.CalculateHumiditiesAfterGapFill(ds4)
# re-calculate the meteorological variables
qcts.CalculateMeteorologicalVariables(ds4)
# the Tumba rhumba
qcts.CalculateComponentsFromWsWd(ds4)
# check for any missing data
qcutils.get_missingingapfilledseries(ds4)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds4)
# write the percentage of good data for groups
qcutils.get_coverage_groups(ds4)
return ds4
def l3qc(cf, ds2):
"""
Corrections
Generates L3 from L2 data
Functions performed:
qcts.AddMetVars (optional)
qcts.CorrectSWC (optional*)
qcck.do_linear (all sites)
qcutils.GetMergeList + qcts.MergeSeries Ah_EC (optional)x
qcts.TaFromTv (optional)
qcutils.GetMergeList + qcts.MergeSeries Ta_EC (optional)x
qcts.CoordRotation2D (all sites)
qcts.MassmanApprox (optional*)y
qcts.Massman (optional*)y
qcts.CalculateFluxes (used if Massman not optioned)x
qcts.CalculateFluxesRM (used if Massman optioned)y
qcts.FhvtoFh (all sites)
qcts.Fe_WPL (WPL computed on fluxes, as with Campbell algorithm)+x
qcts.Fc_WPL (WPL computed on fluxes, as with Campbell algorithm)+x
qcts.Fe_WPLcov (WPL computed on kinematic fluxes (ie, covariances), as with WPL80)+y
qcts.Fc_WPLcov (WPL computed on kinematic fluxes (ie, covariances), as with WPL80)+y
qcts.CalculateNetRadiation (optional)
qcutils.GetMergeList + qcts.MergeSeries Fsd (optional)
qcutils.GetMergeList + qcts.MergeSeries Fn (optional*)
qcts.InterpolateOverMissing (optional)
AverageSeriesByElements (optional)
qcts.CorrectFgForStorage (all sites)
qcts.Average3SeriesByElements (optional)
qcts.CalculateAvailableEnergy (optional)
qcck.do_qcchecks (all sites)
qcck.gaps (optional)
*: requires ancillary measurements for paratmerisation
+: each site requires one pair, either Fe_WPL & Fc_WPL (default) or Fe_WPLCov & FcWPLCov
x: required together in option set
y: required together in option set
"""
# make a copy of the L2 data
ds3 = copy.deepcopy(ds2)
# set some attributes for this level
qcutils.UpdateGlobalAttributes(cf, ds3, "L3")
# initialise the global attribute to document the functions used
ds3.globalattributes['Functions'] = ''
# put the control file name into the global attributes
ds3.globalattributes['controlfile_name'] = cf['controlfile_name']
# check to see if we have any imports
qcgf.ImportSeries(cf, ds3)
# correct measured soil water content using empirical relationship to collected samples
qcts.CorrectSWC(cf, ds3)
# apply linear corrections to the data
qcck.do_linear(cf, ds3)
# merge whatever humidities are available
qcts.MergeHumidities(cf, ds3, convert_units=True)
# get the air temperature from the CSAT virtual temperature
qcts.TaFromTv(cf, ds3)
# merge the HMP and corrected CSAT data
qcts.MergeSeries(cf, ds3, 'Ta', [0, 10], convert_units=True)
qcutils.CheckUnits(ds3, "Ta", "C", convert_units=True)
# calculate humidities (absolute, specific and relative) from whatever is available
qcts.CalculateHumidities(ds3)
# merge the 7500 CO2 concentration
qcts.MergeSeries(cf, ds3, 'Cc', [0, 10], convert_units=True)
qcutils.CheckUnits(ds3, "Cc", "mg/m3", convert_units=True)
# add relevant meteorological values to L3 data
qcts.CalculateMeteorologicalVariables(ds3)
# check to see if the user wants to use the fluxes in the L2 file
if not qcutils.cfoptionskeylogical(cf, Key="UseL2Fluxes", default=False):
# check the covariancve units and change if necessary
qcts.CheckCovarianceUnits(ds3)
# do the 2D coordinate rotation
qcts.CoordRotation2D(cf, ds3)
# do the Massman frequency attenuation correction
qcts.MassmanStandard(cf, ds3)
# calculate the fluxes
qcts.CalculateFluxes(cf, ds3)
# approximate wT from virtual wT using wA (ref: Campbell OPECSystem manual)
qcts.FhvtoFh(cf, ds3)
# correct the H2O & CO2 flux due to effects of flux on density measurements
qcts.Fe_WPL(cf, ds3)
qcts.Fc_WPL(cf, ds3)
# convert CO2 units if required
qcutils.ConvertCO2Units(cf, ds3, Cc='Cc')
# calculate Fc storage term - single height only at present
qcts.CalculateFcStorage(cf, ds3)
# convert Fc and Fc_storage units if required
qcutils.ConvertFcUnits(cf, ds3, Fc='Fc', Fc_storage='Fc_storage')
# correct Fc for storage term - only recommended if storage calculated from profile available
qcts.CorrectFcForStorage(cf, ds3)
# merge the incoming shortwave radiation
qcts.MergeSeries(cf, ds3, 'Fsd', [0, 10])
# calculate the net radiation from the Kipp and Zonen CNR1
qcts.CalculateNetRadiation(cf,
ds3,
Fn_out='Fn_KZ',
Fsd_in='Fsd',
Fsu_in='Fsu',
Fld_in='Fld',
Flu_in='Flu')
qcts.MergeSeries(cf, ds3, 'Fn', [0, 10])
# combine wind speed from the Wind Sentry and the CSAT
qcts.MergeSeries(cf, ds3, 'Ws', [0, 10])
# combine wind direction from the Wind Sentry and the CSAT
qcts.MergeSeries(cf, ds3, 'Wd', [0, 10])
# correct soil heat flux for storage
# ... either average the raw ground heat flux, soil temperature and moisture
# and then do the correction (OzFlux "standard")
qcts.AverageSeriesByElements(cf, ds3, 'Ts')
qcts.AverageSeriesByElements(cf, ds3, 'Sws')
if qcutils.cfoptionskeylogical(cf, Key='CorrectIndividualFg'):
# ... or correct the individual ground heat flux measurements (James' method)
qcts.CorrectIndividualFgForStorage(cf, ds3)
qcts.AverageSeriesByElements(cf, ds3, 'Fg')
else:
qcts.AverageSeriesByElements(cf, ds3, 'Fg')
qcts.CorrectFgForStorage(cf,
ds3,
Fg_out='Fg',
Fg_in='Fg',
Ts_in='Ts',
Sws_in='Sws')
# calculate the available energy
qcts.CalculateAvailableEnergy(ds3, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# create new series using MergeSeries or AverageSeries
qcck.CreateNewSeries(cf, ds3)
# create a series of daily averaged soil moisture interpolated back to the time step
#qcts.DailyAverageSws_Interpolated(cf,ds3,Sws_out='Sws_daily',Sws_in='Sws')
# re-apply the quality control checks (range, diurnal and rules)
qcck.do_qcchecks(cf, ds3)
# coordinate gaps in the three main fluxes
qcck.CoordinateFluxGaps(cf, ds3)
# coordinate gaps in Ah_7500_Av with Fc
qcck.CoordinateAh7500AndFcGaps(cf, ds3)
# get the statistics for the QC flags and write these to an Excel spreadsheet
qcio.get_seriesstats(cf, ds3)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds3)
# write the percentage of good data for groups
qcutils.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
qcutils.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
qcgf.ImportSeries(cf, ds3)
# apply linear corrections to the data
qcck.do_linear(cf, ds3)
# ************************
# *** Merge humidities ***
# ************************
# merge whatever humidities are available
qcts.MergeHumidities(cf, ds3, convert_units=True)
# **************************
# *** Merge temperatures ***
# **************************
# get the air temperature from the CSAT virtual temperature
qcts.TaFromTv(cf, ds3)
# merge the HMP and corrected CSAT data
qcts.MergeSeries(cf, ds3, "Ta", convert_units=True)
qcutils.CheckUnits(ds3, "Ta", "C", convert_units=True)
# ***************************
# *** Calcuate humidities ***
# ***************************
# calculate humidities (absolute, specific and relative) from whatever is available
qcts.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
qcts.MergeSeries(cf, ds3, CO2, convert_units=True)
# ******************************************
# *** Calculate meteorological variables ***
# ******************************************
# Update meteorological variables
qcts.CalculateMeteorologicalVariables(ds3)
# *************************************************
# *** Calculate fluxes from covariances section ***
# *************************************************
# check to see if the user wants to use the fluxes in the L2 file
if not qcutils.cfoptionskeylogical(cf, Key="UseL2Fluxes", default=False):
# check the covariance units and change if necessary
qcts.CheckCovarianceUnits(ds3)
# do the 2D coordinate rotation
qcts.CoordRotation2D(cf, ds3)
# do the Massman frequency attenuation correction
qcts.MassmanStandard(cf, ds3)
# calculate the fluxes
qcts.CalculateFluxes(cf, ds3)
# approximate wT from virtual wT using wA (ref: Campbell OPECSystem manual)
qcts.FhvtoFh(cf, ds3)
# correct the H2O & CO2 flux due to effects of flux on density measurements
qcts.Fe_WPL(cf, ds3)
qcts.Fc_WPL(cf, ds3)
# **************************************
# *** Calculate Monin-Obukhov length ***
# **************************************
qcts.CalculateMoninObukhovLength(ds3)
# **************************
# *** CO2 and Fc section ***
# **************************
# convert CO2 units if required
qcutils.ConvertCO2Units(cf, ds3, CO2=CO2)
# calculate Fc storage term - single height only at present
qcts.CalculateFcStorageSinglePoint(cf, ds3, Fc_out='Fc_single', CO2_in=CO2)
# convert Fc and Fc_storage units if required
qcutils.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:
qcts.MergeSeries(cf, ds3, label, save_originals=True)
# correct Fc for storage term - only recommended if storage calculated from profile available
qcts.CorrectFcForStorage(cf, ds3)
# *************************
# *** Radiation section ***
# *************************
# merge the incoming shortwave radiation
qcts.MergeSeries(cf, ds3, 'Fsd')
# calculate the net radiation from the Kipp and Zonen CNR1
qcts.CalculateNetRadiation(cf,
ds3,
Fn_out='Fn_KZ',
Fsd_in='Fsd',
Fsu_in='Fsu',
Fld_in='Fld',
Flu_in='Flu')
qcts.MergeSeries(cf, ds3, 'Fn')
# ****************************************
# *** Wind speed and direction section ***
# ****************************************
# combine wind speed from the Wind Sentry and the SONIC
qcts.MergeSeries(cf, ds3, 'Ws')
# combine wind direction from the Wind Sentry and the SONIC
qcts.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")
qcts.AverageSeriesByElements(cf, ds3, 'Ts')
qcts.AverageSeriesByElements(cf, ds3, 'Sws')
if qcutils.cfoptionskeylogical(cf, Key='CorrectIndividualFg'):
# ... or correct the individual ground heat flux measurements (James' method)
qcts.CorrectIndividualFgForStorage(cf, ds3)
qcts.AverageSeriesByElements(cf, ds3, 'Fg')
else:
qcts.AverageSeriesByElements(cf, ds3, 'Fg')
qcts.CorrectFgForStorage(cf,
ds3,
Fg_out='Fg',
Fg_in='Fg',
Ts_in='Ts',
Sws_in='Sws')
# calculate the available energy
qcts.CalculateAvailableEnergy(ds3, Fa_out='Fa', Fn_in='Fn', Fg_in='Fg')
# create new series using MergeSeries or AverageSeries
qcck.CreateNewSeries(cf, ds3)
# re-apply the quality control checks (range, diurnal and rules)
qcck.do_qcchecks(cf, ds3)
# coordinate gaps in the three main fluxes
qcck.CoordinateFluxGaps(cf, ds3)
# coordinate gaps in Ah_7500_Av with Fc
qcck.CoordinateAh7500AndFcGaps(cf, ds3)
# check missing data and QC flags are consistent
qcutils.CheckQCFlags(ds3)
# get the statistics for the QC flags and write these to an Excel spreadsheet
qcio.get_seriesstats(cf, ds3)
# write the percentage of good data as a variable attribute
qcutils.get_coverage_individual(ds3)
# write the percentage of good data for groups
qcutils.get_coverage_groups(ds3)
return ds3
