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 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 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 ThisOne in label_list:
# parse the control file for information on how the user wants to do the gap filling
qcgf.GapFillParseControlFile(cf,ds5,ThisOne,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
qcgf.GapFillUsingSOLO(cf,ds4,ds5)
if ds5.returncodes["solo"]=="quit": return ds5
## gap fill using marginal distribution sampling
#qcgf.GapFillFluxUsingMDS(cf,ds5)
## gap fill using ratios
#qcgf.GapFillFluxFromDayRatio(cf,ds5)
# gap fill using climatology
qcgf.GapFillFromClimatology(ds5)
# merge the gap filled drivers into a single series
qcts.MergeSeriesUsingDict(ds5,merge_order="standard")
# 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 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)
# filter Fc for night time and ustar threshold, write to ds as "Fre"
#qcrp.GetFreIndicator(cf,ds6)
qcrp.GetFreFromFc(cf,ds6)
# estimate Reco using SOLO
qcrp.FreUsingSOLO(cf,ds6)
# estimate Reco using FFNET
qcrp.FreUsingFFNET(cf,ds6)
# estimate Reco using Lloyd-Taylor
qcrp.FreUsingLloydTaylor(cf,ds6)
# estimate Reco using Lasslop et al
qcrp.FreUsingLasslop(cf,ds6)
# merge the estimates of Reco with the observations
qcts.MergeSeriesUsingDict(ds6,merge_order="standard")
# calculate NEE from Fc and Fre
qcrp.CalculateNEE(cf,ds6)
# calculate NEP from NEE
qcrp.CalculateNEP(cf,ds6)
# calculate ET from Fe
qcrp.CalculateET(ds6)
# partition NEE into GPP and Reco
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 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 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