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