def do_l6qc(self):
"""
Call qcls.l6qc function to partition NEE into GPP and ER.
"""
logging.info(" Starting L6 processing ...")
cf = qcio.load_controlfile(path='controlfiles')
if len(cf)==0: self.do_progress(text='Waiting for input ...'); return
infilename = qcio.get_infilenamefromcf(cf)
if len(infilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
if not qcutils.file_exists(infilename): self.do_progress(text='An error occurred, check the console ...'); return
ds5 = qcio.nc_read_series(infilename)
if len(ds5.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del ds5; return
ds5.globalattributes['controlfile_name'] = cf['controlfile_name']
self.update_startenddate(str(ds5.series['DateTime']['Data'][0]),
str(ds5.series['DateTime']['Data'][-1]))
sitename = ds5.globalattributes['site_name']
self.do_progress(text='Doing L6 partitioning: '+sitename+' ...')
if "Options" not in cf: cf["Options"]={}
cf["Options"]["call_mode"] = "interactive"
ds6 = qcls.l6qc(cf,ds5)
self.do_progress(text='Finished L6: '+sitename)
logging.info(' Finished L6: '+sitename)
self.do_progress(text='Saving L6 partitioned data ...') # put up the progress message
outfilename = qcio.get_outfilenamefromcf(cf)
if len(outfilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
ncFile = qcio.nc_open_write(outfilename)
outputlist = qcio.get_outputlistfromcf(cf,'nc')
qcio.nc_write_series(ncFile,ds6,outputlist=outputlist) # save the L6 data
self.do_progress(text='Finished saving L6 partitioned data') # tell the user we are done
logging.info(' Finished saving L6 partitioned data')
logging.info("")
def do_plotL3L4(self):
"""
Plot L3 (QA/QC and Corrected) and L4 (Gap Filled) data in blue and
red, respectively
Control File for do_l4qc function used.
If L4 Control File not loaded, requires control file selection.
"""
if 'ds3' not in dir(self) or 'ds4' not in dir(self):
self.cf = qcio.load_controlfile(path='controlfiles')
if len(self.cf)==0:
self.do_progress(text='Waiting for input ...')
return
l3filename = qcio.get_infilenamefromcf(self.cf)
if not qcutils.file_exists(l3filename): self.do_progress(text='An error occurred, check the console ...'); return
self.ds3 = qcio.nc_read_series(l3filename)
if len(self.ds3.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds3; return
l4filename = qcio.get_outfilenamefromcf(self.cf)
self.ds4 = qcio.nc_read_series(l4filename)
if len(self.ds4.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds4; return
self.update_startenddate(str(self.ds3.series['DateTime']['Data'][0]),
str(self.ds3.series['DateTime']['Data'][-1]))
self.do_progress(text='Plotting L3 and L4 QC ...')
cfname = self.ds4.globalattributes['controlfile_name']
self.cf = qcio.get_controlfilecontents(cfname)
for nFig in self.cf['Plots'].keys():
si = qcutils.GetDateIndex(self.ds3.series['DateTime']['Data'],self.plotstartEntry.get(),
ts=self.ds3.globalattributes['time_step'],default=0,match='exact')
ei = qcutils.GetDateIndex(self.ds3.series['DateTime']['Data'],self.plotendEntry.get(),
ts=self.ds3.globalattributes['time_step'],default=-1,match='exact')
qcplot.plottimeseries(self.cf,nFig,self.ds3,self.ds4,si,ei)
self.do_progress(text='Finished plotting L4')
logging.info(' Finished plotting L4, check the GUI')
def do_l4qc(self):
"""
Call qcls.l4qc_gapfill function
Performs L4 gap filling on L3 met data
or
Ingests L4 gap filled fluxes performed in external SOLO-ANN and c
omputes daily sums
Outputs L4 netCDF file to ncData folder
Outputs L4 netCDF file to OzFlux folder
ControlFiles:
L4_year.txt
or
L4b.txt
ControlFile contents (see ControlFile/Templates/L4.txt and
ControlFile/Templates/L4b.txt for examples):
[General]:
Python control parameters (SOLO)
Site characteristics parameters (Gap filling)
[Files]:
L3 input file name and path (Gap filling)
L4 input file name and path (SOLO)
L4 output file name and ncData folder path (both)
L4 OzFlux output file name and OzFlux folder path
[Variables]:
Variable subset list for OzFlux output file (where
available)
"""
logging.info(" Starting L4 processing ...")
cf = qcio.load_controlfile(path='controlfiles')
if len(cf)==0: self.do_progress(text='Waiting for input ...'); return
infilename = qcio.get_infilenamefromcf(cf)
if len(infilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
if not qcutils.file_exists(infilename): self.do_progress(text='An error occurred, check the console ...'); return
ds3 = qcio.nc_read_series(infilename)
if len(ds3.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del ds3; return
ds3.globalattributes['controlfile_name'] = cf['controlfile_name']
self.update_startenddate(str(ds3.series['DateTime']['Data'][0]),
str(ds3.series['DateTime']['Data'][-1]))
sitename = ds3.globalattributes['site_name']
self.do_progress(text='Doing L4 gap filling drivers: '+sitename+' ...')
if "Options" not in cf: cf["Options"]={}
cf["Options"]["call_mode"] = "interactive"
ds4 = qcls.l4qc(cf,ds3)
if ds4.returncodes["alternate"]=="quit" or ds4.returncodes["solo"]=="quit":
self.do_progress(text='Quitting L4: '+sitename)
logging.info(' Quitting L4: '+sitename)
else:
self.do_progress(text='Finished L4: '+sitename)
logging.info(' Finished L4: '+sitename)
self.do_progress(text='Saving L4 gap filled data ...') # put up the progress message
outfilename = qcio.get_outfilenamefromcf(cf)
if len(outfilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
ncFile = qcio.nc_open_write(outfilename)
outputlist = qcio.get_outputlistfromcf(cf,'nc')
qcio.nc_write_series(ncFile,ds4,outputlist=outputlist) # save the L4 data
self.do_progress(text='Finished saving L4 gap filled data') # tell the user we are done
logging.info(' Finished saving L4 gap filled data')
logging.info("")
def l1qc(cf):
# get the data series from the Excel file
in_filename = qcio.get_infilenamefromcf(cf)
if not qcutils.file_exists(in_filename, mode="quiet"):
msg = " Input file " + in_filename + " not found ..."
logger.error(msg)
ds1 = qcio.DataStructure()
ds1.returncodes = {"value": 1, "message": msg}
return ds1
file_name, file_extension = os.path.splitext(in_filename)
if "csv" in file_extension.lower():
ds1 = qcio.csv_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Excel datetime from the Python datetime objects
qcutils.get_xldatefromdatetime(ds1)
else:
ds1 = qcio.xl_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Python datetime objects from the Excel datetime
qcutils.get_datetimefromxldate(ds1)
# get the netCDF attributes from the control file
qcts.do_attributes(cf, ds1)
# round the Python datetime to the nearest second
qcutils.round_datetime(ds1, mode="nearest_second")
#check for gaps in the Python datetime series and fix if present
fixtimestepmethod = qcutils.get_keyvaluefromcf(cf, ["options"],
"FixTimeStepMethod",
default="round")
if qcutils.CheckTimeStep(ds1):
qcutils.FixTimeStep(ds1, fixtimestepmethod=fixtimestepmethod)
# recalculate the Excel datetime
qcutils.get_xldatefromdatetime(ds1)
# get the Year, Month, Day etc from the Python datetime
qcutils.get_ymdhmsfromdatetime(ds1)
# write the processing level to a global attribute
ds1.globalattributes['nc_level'] = str("L1")
# get the start and end date from the datetime series unless they were
# given in the control file
if 'start_date' not in ds1.globalattributes.keys():
ds1.globalattributes['start_date'] = str(
ds1.series['DateTime']['Data'][0])
if 'end_date' not in ds1.globalattributes.keys():
ds1.globalattributes['end_date'] = str(
ds1.series['DateTime']['Data'][-1])
# calculate variances from standard deviations and vice versa
qcts.CalculateStandardDeviations(cf, ds1)
# create new variables using user defined functions
qcts.DoFunctions(cf, ds1)
# create a series of synthetic downwelling shortwave radiation
qcts.get_synthetic_fsd(ds1)
# check missing data and QC flags are consistent
qcutils.CheckQCFlags(ds1)
return ds1
def do_l2qc(self):
"""
Call qcls.l2qc function
Performs L2 QA/QC processing on raw data
Outputs L2 netCDF file to ncData folder
ControlFiles:
L2_year.txt
or
L2.txt
ControlFile contents (see ControlFile/Templates/L2.txt for example):
[General]:
Enter list of functions to be performed
[Files]:
L1 input file name and path
L2 output file name and path
[Variables]:
Variable names and parameters for:
Range check to set upper and lower rejection limits
Diurnal check to reject observations by time of day that
are outside specified standard deviation limits
Timestamps for excluded dates
Timestamps for excluded hours
[Plots]:
Variable lists for plot generation
"""
logging.info(" Starting L2 processing ...")
self.do_progress(text='Load L2 Control File ...')
self.cf = qcio.load_controlfile(path='controlfiles')
if len(self.cf)==0:
logging.info( " L2: no control file chosen")
self.do_progress(text='Waiting for input ...')
return
infilename = qcio.get_infilenamefromcf(self.cf)
if not qcutils.file_exists(infilename): self.do_progress(text='An error occurred, check the console ...'); return
self.do_progress(text='Doing L2 QC ...')
self.ds1 = qcio.nc_read_series(infilename)
if len(self.ds1.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds1; return
self.update_startenddate(str(self.ds1.series['DateTime']['Data'][0]),
str(self.ds1.series['DateTime']['Data'][-1]))
self.ds2 = qcls.l2qc(self.cf,self.ds1)
logging.info(' Finished L2 QC process')
self.do_progress(text='Finished L2 QC process')
self.do_progress(text='Saving L2 QC ...') # put up the progress message
outfilename = qcio.get_outfilenamefromcf(self.cf)
if len(outfilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
ncFile = qcio.nc_open_write(outfilename)
qcio.nc_write_series(ncFile,self.ds2) # save the L2 data
self.do_progress(text='Finished saving L2 QC data') # tdo_progressell the user we are done
logging.info(' Finished saving L2 QC data')
logging.info("")
def l1qc(cf):
# get the data series from the Excel file
in_filename = qcio.get_infilenamefromcf(cf)
if not qcutils.file_exists(in_filename,mode="quiet"):
msg = " Input file "+in_filename+" not found ..."
log.error(msg)
ds1 = qcio.DataStructure()
ds1.returncodes = {"value":1,"message":msg}
return ds1
file_name,file_extension = os.path.splitext(in_filename)
if "csv" in file_extension.lower():
ds1 = qcio.csv_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Excel datetime from the Python datetime objects
qcutils.get_xldatefromdatetime(ds1)
else:
ds1 = qcio.xl_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Python datetime objects from the Excel datetime
qcutils.get_datetimefromxldate(ds1)
# get the netCDF attributes from the control file
qcts.do_attributes(cf,ds1)
# round the Python datetime to the nearest second
qcutils.round_datetime(ds1,mode="nearest_second")
#check for gaps in the Python datetime series and fix if present
fixtimestepmethod = qcutils.get_keyvaluefromcf(cf,["options"],"FixTimeStepMethod",default="round")
if qcutils.CheckTimeStep(ds1): qcutils.FixTimeStep(ds1,fixtimestepmethod=fixtimestepmethod)
# recalculate the Excel datetime
qcutils.get_xldatefromdatetime(ds1)
# get the Year, Month, Day etc from the Python datetime
qcutils.get_ymdhmsfromdatetime(ds1)
# write the processing level to a global attribute
ds1.globalattributes['nc_level'] = str("L1")
# get the start and end date from the datetime series unless they were
# given in the control file
if 'start_date' not in ds1.globalattributes.keys():
ds1.globalattributes['start_date'] = str(ds1.series['DateTime']['Data'][0])
if 'end_date' not in ds1.globalattributes.keys():
ds1.globalattributes['end_date'] = str(ds1.series['DateTime']['Data'][-1])
# calculate variances from standard deviations and vice versa
qcts.CalculateStandardDeviations(cf,ds1)
# create new variables using user defined functions
qcts.DoFunctions(cf,ds1)
# create a series of synthetic downwelling shortwave radiation
qcts.get_synthetic_fsd(ds1)
return ds1
def do_plotL1L2(self):
"""
Plot L1 (raw) and L2 (QA/QC) data in blue and red, respectively
Control File for do_l2qc function used.
If L2 Control File not loaded, requires control file selection.
"""
if 'ds1' not in dir(self) or 'ds2' not in dir(self):
self.cf = qcio.load_controlfile(path='controlfiles')
if len(self.cf)==0: self.do_progress(text='Waiting for input ...'); return
l1filename = qcio.get_infilenamefromcf(self.cf)
if not qcutils.file_exists(l1filename): self.do_progress(text='An error occurred, check the console ...'); return
self.ds1 = qcio.nc_read_series(l1filename)
if len(self.ds1.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds1; return
l2filename = qcio.get_outfilenamefromcf(self.cf)
self.ds2 = qcio.nc_read_series(l2filename)
if len(self.ds2.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds2; return
self.update_startenddate(str(self.ds1.series['DateTime']['Data'][0]),
str(self.ds1.series['DateTime']['Data'][-1]))
self.do_progress(text='Plotting L1 & L2 QC ...')
cfname = self.ds2.globalattributes['controlfile_name']
self.cf = qcio.get_controlfilecontents(cfname)
for nFig in self.cf['Plots'].keys():
si = qcutils.GetDateIndex(self.ds1.series['DateTime']['Data'],self.plotstartEntry.get(),
ts=self.ds1.globalattributes['time_step'],default=0,match='exact')
ei = qcutils.GetDateIndex(self.ds1.series['DateTime']['Data'],self.plotendEntry.get(),
ts=self.ds1.globalattributes['time_step'],default=-1,match='exact')
plt_cf = self.cf['Plots'][str(nFig)]
if 'Type' in plt_cf.keys():
if str(plt_cf['Type']).lower() =='xy':
self.do_progress(text='Plotting L1 and L2 XY ...')
qcplot.plotxy(self.cf,nFig,plt_cf,self.ds1,self.ds2,si,ei)
else:
self.do_progress(text='Plotting L1 and L2 QC ...')
qcplot.plottimeseries(self.cf,nFig,self.ds1,self.ds2,si,ei)
else:
self.do_progress(text='Plotting L1 and L2 QC ...')
qcplot.plottimeseries(self.cf,nFig,self.ds1,self.ds2,si,ei)
self.do_progress(text='Finished plotting L1 and L2')
logging.info(' Finished plotting L1 and L2, check the GUI')
def l1qc_read_files(cf):
# get the data series from the Excel file
in_filename = qcio.get_infilenamefromcf(cf)
if not qcutils.file_exists(in_filename, mode="quiet"):
msg = " Input file " + in_filename + " not found ..."
log.error(msg)
ds1 = qcio.DataStructure()
ds1.returncodes = {"value": 1, "message": msg}
return ds1
file_name, file_extension = os.path.splitext(in_filename)
if "csv" in file_extension.lower():
ds1 = qcio.csv_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Excel datetime from the Python datetime objects
qcutils.get_xldatefromdatetime(ds1)
else:
ds1 = qcio.xl_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Python datetime objects from the Excel datetime
qcutils.get_datetimefromxldate(ds1)
return ds1
def climatology(cf):
nc_filename = qcio.get_infilenamefromcf(cf)
if not qcutils.file_exists(nc_filename): return
xl_filename = nc_filename.replace(".nc","_Climatology.xls")
xlFile = xlwt.Workbook()
ds = qcio.nc_read_series(nc_filename)
# calculate Fa if it is not in the data structure
if "Fa" not in ds.series.keys():
if "Fn" in ds.series.keys() and "Fg" in ds.series.keys():
qcts.CalculateAvailableEnergy(ds,Fa_out='Fa',Fn_in='Fn',Fg_in='Fg')
else:
log.warning(" Climatology: Fn or Fg not in data struicture")
# get the time step
ts = int(ds.globalattributes['time_step'])
# get the site name
SiteName = ds.globalattributes['site_name']
# get the datetime series
dt = ds.series['DateTime']['Data']
Hdh = ds.series['Hdh']['Data']
Month = ds.series['Month']['Data']
# get the initial start and end dates
StartDate = str(dt[0])
EndDate = str(dt[-1])
# find the start index of the first whole day (time=00:30)
si = qcutils.GetDateIndex(dt,StartDate,ts=ts,default=0,match='startnextday')
# find the end index of the last whole day (time=00:00)
ei = qcutils.GetDateIndex(dt,EndDate,ts=ts,default=-1,match='endpreviousday')
# get local views of the datetime series
ldt = dt[si:ei+1]
Hdh = Hdh[si:ei+1]
Month = Month[si:ei+1]
# get the number of time steps in a day and the number of days in the data
ntsInDay = int(24.0*60.0/float(ts))
nDays = int(len(ldt))/ntsInDay
for ThisOne in cf['Variables'].keys():
if "AltVarName" in cf['Variables'][ThisOne].keys(): ThisOne = cf['Variables'][ThisOne]["AltVarName"]
if ThisOne in ds.series.keys():
log.info(" Doing climatology for "+ThisOne)
data,f,a = qcutils.GetSeriesasMA(ds,ThisOne,si=si,ei=ei)
if numpy.ma.count(data)==0:
log.warning(" No data for "+ThisOne+", skipping ...")
continue
fmt_str = get_formatstring(cf,ThisOne,fmt_def='')
xlSheet = xlFile.add_sheet(ThisOne)
Av_all = do_diurnalstats(Month,Hdh,data,xlSheet,format_string=fmt_str,ts=ts)
# now do it for each day
# we want to preserve any data that has been truncated by the use of the "startnextday"
# and "endpreviousday" match options used above. Here we revisit the start and end indices
# and adjust these backwards and forwards respectively if data has been truncated.
nDays_daily = nDays
ei_daily = ei
si_daily = si
sdate = ldt[0]
edate = ldt[-1]
# is there data after the current end date?
if dt[-1]>ldt[-1]:
# if so, push the end index back by 1 day so it is included
ei_daily = ei + ntsInDay
nDays_daily = nDays_daily + 1
edate = ldt[-1]+datetime.timedelta(days=1)
# is there data before the current start date?
if dt[0]<ldt[0]:
# if so, push the start index back by 1 day so it is included
si_daily = si - ntsInDay
nDays_daily = nDays_daily + 1
sdate = ldt[0]-datetime.timedelta(days=1)
# get the data and use the "pad" option to add missing data if required to
# complete the extra days
data,f,a = qcutils.GetSeriesasMA(ds,ThisOne,si=si_daily,ei=ei_daily,mode="pad")
data_daily = data.reshape(nDays_daily,ntsInDay)
xlSheet = xlFile.add_sheet(ThisOne+'(day)')
write_data_1columnpertimestep(xlSheet, data_daily, ts, startdate=sdate, format_string=fmt_str)
data_daily_i = do_2dinterpolation(data_daily)
xlSheet = xlFile.add_sheet(ThisOne+'i(day)')
write_data_1columnpertimestep(xlSheet, data_daily_i, ts, startdate=sdate, format_string=fmt_str)
elif ThisOne=="EF":
log.info(" Doing evaporative fraction")
EF = numpy.ma.zeros([48,12]) + float(c.missing_value)
Hdh,f,a = qcutils.GetSeriesasMA(ds,'Hdh',si=si,ei=ei)
Fa,f,a = qcutils.GetSeriesasMA(ds,'Fa',si=si,ei=ei)
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
for m in range(1,13):
mi = numpy.where(Month==m)[0]
Fa_Num,Hr,Fa_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fa[mi],ts)
Fe_Num,Hr,Fe_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fe[mi],ts)
index = numpy.ma.where((Fa_Num>4)&(Fe_Num>4))
EF[:,m-1][index] = Fe_Av[index]/Fa_Av[index]
# reject EF values greater than upper limit or less than lower limit
upr, lwr = get_rangecheck_limit(cf,'EF')
EF = numpy.ma.filled(numpy.ma.masked_where((EF>upr)|(EF<lwr),EF),float(c.missing_value))
# write the EF to the Excel file
xlSheet = xlFile.add_sheet('EF')
write_data_1columnpermonth(xlSheet, EF, ts, format_string='0.00')
# do the 2D interpolation to fill missing EF values
EFi = do_2dinterpolation(EF)
xlSheet = xlFile.add_sheet('EFi')
write_data_1columnpermonth(xlSheet, EFi, ts, format_string='0.00')
# now do EF for each day
Fa,f,a = qcutils.GetSeriesasMA(ds,'Fa',si=si,ei=ei)
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
EF = Fe/Fa
EF = numpy.ma.filled(numpy.ma.masked_where((EF>upr)|(EF<lwr),EF),float(c.missing_value))
EF_daily = EF.reshape(nDays,ntsInDay)
xlSheet = xlFile.add_sheet('EF(day)')
write_data_1columnpertimestep(xlSheet, EF_daily, ts, startdate=ldt[0], format_string='0.00')
EFi = do_2dinterpolation(EF_daily)
xlSheet = xlFile.add_sheet('EFi(day)')
write_data_1columnpertimestep(xlSheet, EFi, ts, startdate=ldt[0], format_string='0.00')
elif ThisOne=="BR":
log.info(" Doing Bowen ratio")
BR = numpy.ma.zeros([48,12]) + float(c.missing_value)
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
Fh,f,a = qcutils.GetSeriesasMA(ds,'Fh',si=si,ei=ei)
for m in range(1,13):
mi = numpy.where(Month==m)[0]
Fh_Num,Hr,Fh_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fh[mi],ts)
Fe_Num,Hr,Fe_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fe[mi],ts)
index = numpy.ma.where((Fh_Num>4)&(Fe_Num>4))
BR[:,m-1][index] = Fh_Av[index]/Fe_Av[index]
# reject BR values greater than upper limit or less than lower limit
upr,lwr = get_rangecheck_limit(cf,'BR')
BR = numpy.ma.filled(numpy.ma.masked_where((BR>upr)|(BR<lwr),BR),float(c.missing_value))
# write the BR to the Excel file
xlSheet = xlFile.add_sheet('BR')
write_data_1columnpermonth(xlSheet, BR, ts, format_string='0.00')
# do the 2D interpolation to fill missing EF values
BRi = do_2dinterpolation(BR)
xlSheet = xlFile.add_sheet('BRi')
write_data_1columnpermonth(xlSheet, BRi, ts, format_string='0.00')
# now do BR for each day ...
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
Fh,f,a = qcutils.GetSeriesasMA(ds,'Fh',si=si,ei=ei)
BR = Fh/Fe
BR = numpy.ma.filled(numpy.ma.masked_where((BR>upr)|(BR<lwr),BR),float(c.missing_value))
BR_daily = BR.reshape(nDays,ntsInDay)
xlSheet = xlFile.add_sheet('BR(day)')
write_data_1columnpertimestep(xlSheet, BR_daily, ts, startdate=ldt[0], format_string='0.00')
BRi = do_2dinterpolation(BR_daily)
xlSheet = xlFile.add_sheet('BRi(day)')
write_data_1columnpertimestep(xlSheet, BRi, ts, startdate=ldt[0], format_string='0.00')
elif ThisOne=="WUE":
log.info(" Doing ecosystem WUE")
WUE = numpy.ma.zeros([48,12]) + float(c.missing_value)
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
Fc,f,a = qcutils.GetSeriesasMA(ds,'Fc',si=si,ei=ei)
for m in range(1,13):
mi = numpy.where(Month==m)[0]
Fc_Num,Hr,Fc_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fc[mi],ts)
Fe_Num,Hr,Fe_Av,Sd,Mx,Mn = get_diurnalstats(Hdh[mi],Fe[mi],ts)
index = numpy.ma.where((Fc_Num>4)&(Fe_Num>4))
WUE[:,m-1][index] = Fc_Av[index]/Fe_Av[index]
# reject WUE values greater than upper limit or less than lower limit
upr,lwr = get_rangecheck_limit(cf,'WUE')
WUE = numpy.ma.filled(numpy.ma.masked_where((WUE>upr)|(WUE<lwr),WUE),float(c.missing_value))
# write the WUE to the Excel file
xlSheet = xlFile.add_sheet('WUE')
write_data_1columnpermonth(xlSheet, WUE, ts, format_string='0.00000')
# do the 2D interpolation to fill missing EF values
WUEi = do_2dinterpolation(WUE)
xlSheet = xlFile.add_sheet('WUEi')
write_data_1columnpermonth(xlSheet, WUEi, ts, format_string='0.00000')
# now do WUE for each day ...
Fe,f,a = qcutils.GetSeriesasMA(ds,'Fe',si=si,ei=ei)
Fc,f,a = qcutils.GetSeriesasMA(ds,'Fc',si=si,ei=ei)
WUE = Fc/Fe
WUE = numpy.ma.filled(numpy.ma.masked_where((WUE>upr)|(WUE<lwr),WUE),float(c.missing_value))
WUE_daily = WUE.reshape(nDays,ntsInDay)
xlSheet = xlFile.add_sheet('WUE(day)')
write_data_1columnpertimestep(xlSheet, WUE_daily, ts, startdate=ldt[0], format_string='0.00000')
WUEi = do_2dinterpolation(WUE_daily)
xlSheet = xlFile.add_sheet('WUEi(day)')
write_data_1columnpertimestep(xlSheet, WUEi, ts, startdate=ldt[0], format_string='0.00000')
else:
log.warning(" qcclim.climatology: requested variable "+ThisOne+" not in data structure")
continue
log.info(" Saving Excel file "+xl_filename)
xlFile.save(xl_filename)
for level in level_list:
if level.lower()=="l1":
# L1 processing
for i in cf_batch["Levels"][level].keys():
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L1 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
qcio.xl2nc(cf,'L1')
logging.info('Finished L1 processing with '+cfname)
elif level.lower()=="l2":
# L2 processing
for i in cf_batch["Levels"][level].keys():
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L2 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
infilename = qcio.get_infilenamefromcf(cf)
ds1 = qcio.nc_read_series(infilename)
ds2 = qcls.l2qc(cf,ds1)
outfilename = qcio.get_outfilenamefromcf(cf)
ncFile = qcio.nc_open_write(outfilename)
qcio.nc_write_series(ncFile,ds2)
logging.info('Finished L2 processing with '+cfname)
elif level.lower()=="l3":
# L3 processing
for i in cf_batch["Levels"][level].keys():
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L3 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
infilename = qcio.get_infilenamefromcf(cf)
ds2 = qcio.nc_read_series(infilename)
ds3 = qcls.l3qc(cf,ds2)
def climatology(cf):
nc_filename = qcio.get_infilenamefromcf(cf)
if not qcutils.file_exists(nc_filename): return
xl_filename = nc_filename.replace(".nc", "_Climatology.xls")
xlFile = xlwt.Workbook()
ds = qcio.nc_read_series(nc_filename)
# calculate Fa if it is not in the data structure
if "Fa" not in ds.series.keys():
if "Fn" in ds.series.keys() and "Fg" in ds.series.keys():
qcts.CalculateAvailableEnergy(ds,
Fa_out='Fa',
Fn_in='Fn',
Fg_in='Fg')
else:
log.warning(" Climatology: Fn or Fg not in data struicture")
# get the time step
ts = int(ds.globalattributes['time_step'])
# get the site name
SiteName = ds.globalattributes['site_name']
# get the datetime series
dt = ds.series['DateTime']['Data']
Hdh = ds.series['Hdh']['Data']
Month = ds.series['Month']['Data']
# get the initial start and end dates
StartDate = str(dt[0])
EndDate = str(dt[-1])
# find the start index of the first whole day (time=00:30)
si = qcutils.GetDateIndex(dt,
StartDate,
ts=ts,
default=0,
match='startnextday')
# find the end index of the last whole day (time=00:00)
ei = qcutils.GetDateIndex(dt,
EndDate,
ts=ts,
default=-1,
match='endpreviousday')
# get local views of the datetime series
ldt = dt[si:ei + 1]
Hdh = Hdh[si:ei + 1]
Month = Month[si:ei + 1]
# get the number of time steps in a day and the number of days in the data
ntsInDay = int(24.0 * 60.0 / float(ts))
nDays = int(len(ldt)) / ntsInDay
for ThisOne in cf['Variables'].keys():
if "AltVarName" in cf['Variables'][ThisOne].keys():
ThisOne = cf['Variables'][ThisOne]["AltVarName"]
if ThisOne in ds.series.keys():
log.info(" Doing climatology for " + ThisOne)
data, f, a = qcutils.GetSeriesasMA(ds, ThisOne, si=si, ei=ei)
if numpy.ma.count(data) == 0:
log.warning(" No data for " + ThisOne + ", skipping ...")
continue
fmt_str = get_formatstring(cf, ThisOne, fmt_def='')
xlSheet = xlFile.add_sheet(ThisOne)
Av_all = do_diurnalstats(Month,
Hdh,
data,
xlSheet,
format_string=fmt_str,
ts=ts)
# now do it for each day
# we want to preserve any data that has been truncated by the use of the "startnextday"
# and "endpreviousday" match options used above. Here we revisit the start and end indices
# and adjust these backwards and forwards respectively if data has been truncated.
nDays_daily = nDays
ei_daily = ei
si_daily = si
sdate = ldt[0]
edate = ldt[-1]
# is there data after the current end date?
if dt[-1] > ldt[-1]:
# if so, push the end index back by 1 day so it is included
ei_daily = ei + ntsInDay
nDays_daily = nDays_daily + 1
edate = ldt[-1] + datetime.timedelta(days=1)
# is there data before the current start date?
if dt[0] < ldt[0]:
# if so, push the start index back by 1 day so it is included
si_daily = si - ntsInDay
nDays_daily = nDays_daily + 1
sdate = ldt[0] - datetime.timedelta(days=1)
# get the data and use the "pad" option to add missing data if required to
# complete the extra days
data, f, a = qcutils.GetSeriesasMA(ds,
ThisOne,
si=si_daily,
ei=ei_daily,
mode="pad")
data_daily = data.reshape(nDays_daily, ntsInDay)
xlSheet = xlFile.add_sheet(ThisOne + '(day)')
write_data_1columnpertimestep(xlSheet,
data_daily,
ts,
startdate=sdate,
format_string=fmt_str)
data_daily_i = do_2dinterpolation(data_daily)
xlSheet = xlFile.add_sheet(ThisOne + 'i(day)')
write_data_1columnpertimestep(xlSheet,
data_daily_i,
ts,
startdate=sdate,
format_string=fmt_str)
elif ThisOne == "EF":
log.info(" Doing evaporative fraction")
EF = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Hdh, f, a = qcutils.GetSeriesasMA(ds, 'Hdh', si=si, ei=ei)
Fa, f, a = qcutils.GetSeriesasMA(ds, 'Fa', si=si, ei=ei)
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
for m in range(1, 13):
mi = numpy.where(Month == m)[0]
Fa_Num, Hr, Fa_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fa[mi], ts)
Fe_Num, Hr, Fe_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fe[mi], ts)
index = numpy.ma.where((Fa_Num > 4) & (Fe_Num > 4))
EF[:, m - 1][index] = Fe_Av[index] / Fa_Av[index]
# reject EF values greater than upper limit or less than lower limit
upr, lwr = get_rangecheck_limit(cf, 'EF')
EF = numpy.ma.filled(
numpy.ma.masked_where((EF > upr) | (EF < lwr), EF),
float(c.missing_value))
# write the EF to the Excel file
xlSheet = xlFile.add_sheet('EF')
write_data_1columnpermonth(xlSheet, EF, ts, format_string='0.00')
# do the 2D interpolation to fill missing EF values
EFi = do_2dinterpolation(EF)
xlSheet = xlFile.add_sheet('EFi')
write_data_1columnpermonth(xlSheet, EFi, ts, format_string='0.00')
# now do EF for each day
Fa, f, a = qcutils.GetSeriesasMA(ds, 'Fa', si=si, ei=ei)
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
EF = Fe / Fa
EF = numpy.ma.filled(
numpy.ma.masked_where((EF > upr) | (EF < lwr), EF),
float(c.missing_value))
EF_daily = EF.reshape(nDays, ntsInDay)
xlSheet = xlFile.add_sheet('EF(day)')
write_data_1columnpertimestep(xlSheet,
EF_daily,
ts,
startdate=ldt[0],
format_string='0.00')
EFi = do_2dinterpolation(EF_daily)
xlSheet = xlFile.add_sheet('EFi(day)')
write_data_1columnpertimestep(xlSheet,
EFi,
ts,
startdate=ldt[0],
format_string='0.00')
elif ThisOne == "BR":
log.info(" Doing Bowen ratio")
BR = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fh, f, a = qcutils.GetSeriesasMA(ds, 'Fh', si=si, ei=ei)
for m in range(1, 13):
mi = numpy.where(Month == m)[0]
Fh_Num, Hr, Fh_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fh[mi], ts)
Fe_Num, Hr, Fe_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fe[mi], ts)
index = numpy.ma.where((Fh_Num > 4) & (Fe_Num > 4))
BR[:, m - 1][index] = Fh_Av[index] / Fe_Av[index]
# reject BR values greater than upper limit or less than lower limit
upr, lwr = get_rangecheck_limit(cf, 'BR')
BR = numpy.ma.filled(
numpy.ma.masked_where((BR > upr) | (BR < lwr), BR),
float(c.missing_value))
# write the BR to the Excel file
xlSheet = xlFile.add_sheet('BR')
write_data_1columnpermonth(xlSheet, BR, ts, format_string='0.00')
# do the 2D interpolation to fill missing EF values
BRi = do_2dinterpolation(BR)
xlSheet = xlFile.add_sheet('BRi')
write_data_1columnpermonth(xlSheet, BRi, ts, format_string='0.00')
# now do BR for each day ...
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fh, f, a = qcutils.GetSeriesasMA(ds, 'Fh', si=si, ei=ei)
BR = Fh / Fe
BR = numpy.ma.filled(
numpy.ma.masked_where((BR > upr) | (BR < lwr), BR),
float(c.missing_value))
BR_daily = BR.reshape(nDays, ntsInDay)
xlSheet = xlFile.add_sheet('BR(day)')
write_data_1columnpertimestep(xlSheet,
BR_daily,
ts,
startdate=ldt[0],
format_string='0.00')
BRi = do_2dinterpolation(BR_daily)
xlSheet = xlFile.add_sheet('BRi(day)')
write_data_1columnpertimestep(xlSheet,
BRi,
ts,
startdate=ldt[0],
format_string='0.00')
elif ThisOne == "WUE":
log.info(" Doing ecosystem WUE")
WUE = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fc, f, a = qcutils.GetSeriesasMA(ds, 'Fc', si=si, ei=ei)
for m in range(1, 13):
mi = numpy.where(Month == m)[0]
Fc_Num, Hr, Fc_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fc[mi], ts)
Fe_Num, Hr, Fe_Av, Sd, Mx, Mn = get_diurnalstats(
Hdh[mi], Fe[mi], ts)
index = numpy.ma.where((Fc_Num > 4) & (Fe_Num > 4))
WUE[:, m - 1][index] = Fc_Av[index] / Fe_Av[index]
# reject WUE values greater than upper limit or less than lower limit
upr, lwr = get_rangecheck_limit(cf, 'WUE')
WUE = numpy.ma.filled(
numpy.ma.masked_where((WUE > upr) | (WUE < lwr), WUE),
float(c.missing_value))
# write the WUE to the Excel file
xlSheet = xlFile.add_sheet('WUE')
write_data_1columnpermonth(xlSheet,
WUE,
ts,
format_string='0.00000')
# do the 2D interpolation to fill missing EF values
WUEi = do_2dinterpolation(WUE)
xlSheet = xlFile.add_sheet('WUEi')
write_data_1columnpermonth(xlSheet,
WUEi,
ts,
format_string='0.00000')
# now do WUE for each day ...
Fe, f, a = qcutils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fc, f, a = qcutils.GetSeriesasMA(ds, 'Fc', si=si, ei=ei)
WUE = Fc / Fe
WUE = numpy.ma.filled(
numpy.ma.masked_where((WUE > upr) | (WUE < lwr), WUE),
float(c.missing_value))
WUE_daily = WUE.reshape(nDays, ntsInDay)
xlSheet = xlFile.add_sheet('WUE(day)')
write_data_1columnpertimestep(xlSheet,
WUE_daily,
ts,
startdate=ldt[0],
format_string='0.00000')
WUEi = do_2dinterpolation(WUE_daily)
xlSheet = xlFile.add_sheet('WUEi(day)')
write_data_1columnpertimestep(xlSheet,
WUEi,
ts,
startdate=ldt[0],
format_string='0.00000')
else:
log.warning(" qcclim.climatology: requested variable " + ThisOne +
" not in data structure")
continue
log.info(" Saving Excel file " + xl_filename)
xlFile.save(xl_filename)
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L1 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
ds1 = qcls.l1qc(cf)
outfilename = qcio.get_outfilenamefromcf(cf)
ncFile = qcio.nc_open_write(outfilename)
qcio.nc_write_series(ncFile,ds1)
logging.info('Finished L1 processing with '+cfname)
logging.info('')
elif level.lower()=="l2":
# L2 processing
for i in cf_batch["Levels"][level].keys():
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L2 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
infilename = qcio.get_infilenamefromcf(cf)
ds1 = qcio.nc_read_series(infilename)
ds2 = qcls.l2qc(cf,ds1)
outfilename = qcio.get_outfilenamefromcf(cf)
ncFile = qcio.nc_open_write(outfilename)
qcio.nc_write_series(ncFile,ds2)
logging.info('Finished L2 processing with '+cfname)
logging.info('')
elif level.lower()=="l3":
# L3 processing
for i in cf_batch["Levels"][level].keys():
cfname = cf_batch["Levels"][level][i]
logging.info('Starting L3 processing with '+cfname)
cf = qcio.get_controlfilecontents(cfname)
infilename = qcio.get_infilenamefromcf(cf)
ds2 = qcio.nc_read_series(infilename)
def do_l3qc(self):
"""
Call qcls.l3qc_sitename function
Performs L3 Corrections and QA/QC processing on L2 data
Outputs L3 netCDF file to ncData folder
Outputs L3 netCDF file to OzFlux folder
Available corrections:
* corrections requiring ancillary measurements or samples
marked with an asterisk
Linear correction
fixed slope
linearly shifting slope
Conversion of virtual temperature to actual temperature
2D Coordinate rotation
Massman correction for frequency attenuation*
Webb, Pearman and Leuning correction for flux effects on density
measurements
Conversion of virtual heat flux to actual heat flux
Correction of soil moisture content to empirical calibration
curve*
Addition of soil heat storage to ground ground heat flux*
ControlFiles:
L3_year.txt
or
L3a.txt
ControlFile contents (see ControlFile/Templates/L3.txt for example):
[General]:
Python control parameters
[Files]:
L2 input file name and path
L3 output file name and ncData folder path
L3 OzFlux output file name and OzFlux folder path
[Massman] (where available):
Constants used in frequency attenuation correction
zmd: instrument height (z) less zero-plane displacement
height (d), m
z0: aerodynamic roughness length, m
angle: angle from CSAT mounting point between CSAT and
IRGA mid-path, degrees
CSATarm: distance from CSAT mounting point to CSAT
mid-path, m
IRGAarm: distance from CSAT mounting point to IRGA
mid-path, m
[Soil]:
Constants used in correcting Fg for storage and in empirical
corrections of soil water content
FgDepth: Heat flux plate depth, m
BulkDensity: Soil bulk density, kg/m3
OrganicContent: Soil organic content, fraction
SwsDefault
Constants for empirical corrections using log(sensor)
and exp(sensor) functions (SWC_a0, SWC_a1, SWC_b0,
SWC_b1, SWC_t, TDR_a0, TDR_a1, TDR_b0, TDR_b1,
TDR_t)
Variable and attributes lists (empSWCin, empSWCout,
empTDRin, empTDRout, linTDRin, SWCattr, TDRattr)
[Output]:
Variable subset list for OzFlux output file
[Variables]:
Variable names and parameters for:
Range check to set upper and lower rejection limits
Diurnal check to reject observations by time of day that
are outside specified standard deviation limits
Timestamps, slope, and offset for Linear correction
[Plots]:
Variable lists for plot generation
"""
logging.info(" Starting L3 processing ...")
self.cf = qcio.load_controlfile(path='controlfiles')
if len(self.cf)==0:
logging.info( " L3: no control file chosen")
self.do_progress(text='Waiting for input ...')
return
infilename = qcio.get_infilenamefromcf(self.cf)
if not qcutils.file_exists(infilename): self.do_progress(text='An error occurred, check the console ...'); return
self.ds2 = qcio.nc_read_series(infilename)
if len(self.ds2.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del self.ds2; return
self.update_startenddate(str(self.ds2.series['DateTime']['Data'][0]),
str(self.ds2.series['DateTime']['Data'][-1]))
self.do_progress(text='Doing L3 QC & Corrections ...')
self.ds3 = qcls.l3qc(self.cf,self.ds2)
self.do_progress(text='Finished L3')
txtstr = ' Finished L3: Standard processing for site: '
txtstr = txtstr+self.ds3.globalattributes['site_name'].replace(' ','')
logging.info(txtstr)
self.do_progress(text='Saving L3 QC & Corrected NetCDF data ...') # put up the progress message
outfilename = qcio.get_outfilenamefromcf(self.cf)
if len(outfilename)==0: self.do_progress(text='An error occurred, check the console ...'); return
ncFile = qcio.nc_open_write(outfilename)
outputlist = qcio.get_outputlistfromcf(self.cf,'nc')
qcio.nc_write_series(ncFile,self.ds3,outputlist=outputlist) # save the L3 data
self.do_progress(text='Finished saving L3 QC & Corrected NetCDF data') # tell the user we are done
logging.info(' Finished saving L3 QC & Corrected NetCDF data')
logging.info("")
import qcplot
import qcutils
import statsmodels.api as sm
import sys
import time
nfig = 0
plotwidth = 10.9
plotheight = 7.5
# load the control file
cf = qcio.load_controlfile(path='../controlfiles')
if len(cf) == 0: sys.exit()
min_n = int(cf["General"]["minimum_number"])
min_r = float(cf["General"]["minimum_correlation"])
# get the input file name
fname = qcio.get_infilenamefromcf(cf)
if not os.path.exists(fname):
print " compare_ah: Input netCDF file " + fname + " doesn't exist"
sys.exit()
# read the input file and return the data structure
ds = qcio.nc_read_series(fname)
if len(ds.series.keys()) == 0:
print time.strftime('%X') + ' netCDF file ' + fname + ' not found'
sys.exit()
# get the site name
SiteName = ds.globalattributes['site_name']
# get the time step
ts = int(ds.globalattributes['time_step'])
# get the datetime series
DateTime = ds.series['DateTime']['Data']
# get the initial start and end dates
