def l1qc(cf):
# get the data series from the Excel file
in_filename = pfp_io.get_infilenamefromcf(cf)
if not pfp_utils.file_exists(in_filename, mode="quiet"):
msg = " Input file " + in_filename + " not found ..."
logger.error(msg)
ds1 = pfp_io.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 = pfp_io.csv_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Excel datetime from the Python datetime objects
#pfp_utils.get_xldatefromdatetime(ds1)
else:
ds1 = pfp_io.xl_read_series(cf)
if ds1.returncodes["value"] != 0:
return ds1
# get a series of Python datetime objects from the Excel datetime
#pfp_utils.get_datetime_from_xldate(ds1)
# get the netCDF attributes from the control file
#pfp_ts.do_attributes(cf,ds1)
pfp_utils.get_datetime(cf, ds1)
# round the Python datetime to the nearest second
pfp_utils.round_datetime(ds1, mode="nearest_second")
#check for gaps in the Python datetime series and fix if present
fixtimestepmethod = pfp_utils.get_keyvaluefromcf(cf, ["options"],
"FixTimeStepMethod",
default="round")
if pfp_utils.CheckTimeStep(ds1):
pfp_utils.FixTimeStep(ds1, fixtimestepmethod=fixtimestepmethod)
# recalculate the Excel datetime
#pfp_utils.get_xldatefromdatetime(ds1)
# get the Year, Month, Day etc from the Python datetime
#pfp_utils.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
pfp_ts.CalculateStandardDeviations(cf, ds1)
# create new variables using user defined functions
pfp_ts.DoFunctions(cf, ds1)
# create a series of synthetic downwelling shortwave radiation
#pfp_ts.get_synthetic_fsd(ds1)
# check missing data and QC flags are consistent
pfp_utils.CheckQCFlags(ds1)
return ds1
def do_run_l5(main_gui, cfg):
"""
Purpose:
Top level routine for running the L5 gap filling.
Usage:
pfp_top_level.do_run_l5()
Side effects:
Creates an L5 netCDF file with gap filled meteorology.
Author: PRI
Date: Back in the day
Mods:
December 2017: rewrite for use with new GUI
"""
try:
logger.info("Starting L5 processing")
in_filepath = pfp_io.get_infilenamefromcf(cfg)
if not pfp_utils.file_exists(in_filepath):
in_filename = os.path.split(in_filepath)
logger.error("File " + in_filename[1] + " not found")
return
ds4 = pfp_io.nc_read_series(in_filepath)
if ds4.returncodes["value"] != 0: return
#ds4.globalattributes['controlfile_name'] = cfg['controlfile_name']
sitename = ds4.globalattributes['site_name']
if "Options" not in cfg:
cfg["Options"] = {}
cfg["Options"]["call_mode"] = "interactive"
ds5 = pfp_levels.l5qc(main_gui, cfg, ds4)
# check to see if all went well
if ds5.returncodes["value"] != 0:
# tell the user something went wrong
logger.info("Quitting L5: " + sitename)
# delete the output file if it exists
out_filepath = pfp_io.get_outfilenamefromcf(cfg)
if os.path.isfile(out_filepath):
os.remove(out_filepath)
else:
# tell the user we are finished
logger.info("Finished L5: " + sitename)
# get the output file name from the control file
out_filepath = pfp_io.get_outfilenamefromcf(cfg)
# open it for writing
nc_file = pfp_io.nc_open_write(out_filepath)
if nc_file is None: return
# write the output file
pfp_io.nc_write_series(nc_file, ds5)
logger.info("Finished saving L5 gap filled data")
logger.info("")
except Exception:
msg = " Error running L5, see below for details ..."
logger.error(msg)
error_message = traceback.format_exc()
logger.error(error_message)
return
def do_run_l3(cfg=None):
"""
Purpose:
Top level routine for running the L23 post-processing.
Usage:
pfp_top_level.do_l3()
Side effects:
Creates an L3 netCDF file.
Author: PRI
Date: Back in the day
Mods:
December 2017: rewrite for use with new GUI
"""
try:
logger.info("Starting L3 processing")
if not cfg:
cfg = pfp_io.load_controlfile()
if len(cfg) == 0:
logger.info("Quiting L3 processing (no control file)")
return
in_filepath = pfp_io.get_infilenamefromcf(cfg)
if not pfp_utils.file_exists(in_filepath):
in_filename = os.path.split(in_filepath)
logger.error("File " + in_filename[1] + " not found")
return
ds2 = pfp_io.nc_read_series(in_filepath)
ds3 = pfp_levels.l3qc(cfg, ds2)
if ds3.returncodes["value"] != 0:
logger.error("An error occurred during L3 processing")
logger.error("")
return
out_filepath = pfp_io.get_outfilenamefromcf(cfg)
nc_file = pfp_io.nc_open_write(out_filepath)
pfp_io.nc_write_series(nc_file, ds3)
logger.info("Finished L3 processing")
if "Plots" in list(cfg.keys()):
logger.info("Plotting L3 data")
for nFig in cfg['Plots'].keys():
plt_cf = cfg['Plots'][str(nFig)]
if 'Type' in plt_cf.keys():
if str(plt_cf['Type']).lower() == 'xy':
pfp_plot.plotxy(cfg, nFig, plt_cf, ds2, ds3)
else:
pfp_plot.plottimeseries(cfg, nFig, ds2, ds3)
else:
pfp_plot.plottimeseries(cfg, nFig, ds2, ds3)
logger.info("Finished plotting L3 data")
except Exception:
msg = " Error running L3, see below for details ..."
logger.error(msg)
error_message = traceback.format_exc()
logger.error(error_message)
logger.info("")
return
def do_run_l6(main_gui, cfg=None):
"""
Purpose:
Top level routine for running the L6 gap filling.
Usage:
pfp_top_level.do_run_l6()
Side effects:
Creates an L6 netCDF file with NEE partitioned into GPP and ER.
Author: PRI
Date: Back in the day
Mods:
December 2017: rewrite for use with new GUI
"""
try:
logger.info("Starting L6 processing")
if not cfg:
cfg = pfp_io.load_controlfile(path='controlfiles')
if len(cfg) == 0:
logger.info("Quiting L6 processing (no control file)")
return
in_filepath = pfp_io.get_infilenamefromcf(cfg)
if not pfp_utils.file_exists(in_filepath):
in_filename = os.path.split(in_filepath)
logger.error("File " + in_filename[1] + " not found")
return
ds5 = pfp_io.nc_read_series(in_filepath)
#ds5.globalattributes['controlfile_name'] = cfg['controlfile_name']
sitename = ds5.globalattributes['site_name']
if "Options" not in cfg:
cfg["Options"] = {}
cfg["Options"]["call_mode"] = "interactive"
cfg["Options"]["show_plots"] = "Yes"
ds6 = pfp_levels.l6qc(main_gui, cfg, ds5)
if ds6.returncodes["value"] != 0:
logger.info("Quitting L6: " + sitename)
else:
logger.info("Finished L6: " + sitename)
out_filepath = pfp_io.get_outfilenamefromcf(cfg)
nc_file = pfp_io.nc_open_write(out_filepath)
pfp_io.nc_write_series(nc_file, ds6)
logger.info("Finished saving L6 gap filled data")
logger.info("")
except Exception:
msg = " Error running L6, see below for details ..."
logger.error(msg)
error_message = traceback.format_exc()
logger.error(error_message)
return
def do_run_l4(main_gui, cfg):
"""
Purpose:
Top level routine for running the L4 gap filling.
Usage:
pfp_top_level.do_run_l4()
Side effects:
Creates an L4 netCDF file with gap filled meteorology.
Author: PRI
Date: Back in the day
Mods:
December 2017: rewrite for use with new GUI
"""
try:
logger.info("Starting L4 processing")
in_filepath = pfp_io.get_infilenamefromcf(cfg)
if not pfp_utils.file_exists(in_filepath):
in_filename = os.path.split(in_filepath)
logger.error("File " + in_filename[1] + " not found")
return
ds3 = pfp_io.nc_read_series(in_filepath)
if ds3.returncodes["value"] != 0: return
#ds3.globalattributes['controlfile_name'] = cfg['controlfile_name']
sitename = ds3.globalattributes['site_name']
if "Options" not in cfg:
cfg["Options"] = {}
cfg["Options"]["call_mode"] = "interactive"
ds4 = pfp_levels.l4qc(main_gui, cfg, ds3)
if ds4.returncodes["value"] != 0:
logger.info("Quitting L4: " + sitename)
else:
logger.info("Finished L4: " + sitename)
out_filepath = pfp_io.get_outfilenamefromcf(cfg)
nc_file = pfp_io.nc_open_write(out_filepath)
if nc_file is None: return
pfp_io.nc_write_series(nc_file, ds4) # save the L4 data
logger.info("Finished saving L4 gap filled data")
logger.info("")
except Exception:
msg = " Error running L4, see below for details ..."
logger.error(msg)
error_message = traceback.format_exc()
logger.error(error_message)
return
def climatology(cf):
nc_filename = pfp_io.get_infilenamefromcf(cf)
if not pfp_utils.file_exists(nc_filename): return
xl_filename = nc_filename.replace(".nc","_Climatology.xls")
xlFile = xlwt.Workbook()
ds = pfp_io.nc_read_series(nc_filename)
# calculate Fa if it is not in the data structure
got_Fa = True
if "Fa" not in ds.series.keys():
if "Fn" in ds.series.keys() and "Fg" in ds.series.keys():
pfp_ts.CalculateAvailableEnergy(ds,Fa_out='Fa',Fn_in='Fn',Fg_in='Fg')
else:
got_Fa = False
logger.warning(" 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 = numpy.array([(d.hour + d.minute/float(60)) for d in dt])
Month = numpy.array([d.month for d in dt])
# 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 = pfp_utils.GetDateIndex(dt,StartDate,ts=ts,default=0,match='startnextday')
# find the end index of the last whole day (time=00:00)
ei = pfp_utils.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():
logger.info(" Doing climatology for "+ThisOne)
data,f,a = pfp_utils.GetSeriesasMA(ds,ThisOne,si=si,ei=ei)
if numpy.ma.count(data)==0:
logger.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 = pfp_utils.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)
else:
logger.warning(" Requested variable "+ThisOne+" not in data structure")
continue
logger.info(" Saving Excel file "+os.path.split(xl_filename)[1])
xlFile.save(xl_filename)
def climatology(cf):
nc_filename = pfp_io.get_infilenamefromcf(cf)
if not pfp_utils.file_exists(nc_filename): return
xl_filename = nc_filename.replace(".nc", "_Climatology.xls")
xlFile = xlwt.Workbook()
ds = pfp_io.nc_read_series(nc_filename)
# calculate Fa if it is not in the data structure
got_Fa = True
if "Fa" not in ds.series.keys():
if "Fn" in ds.series.keys() and "Fg" in ds.series.keys():
pfp_ts.CalculateAvailableEnergy(ds,
Fa_out='Fa',
Fn_in='Fn',
Fg_in='Fg')
else:
got_Fa = False
logger.warning(" 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 = numpy.array([(d.hour + d.minute / float(60)) for d in dt])
Month = numpy.array([d.month for d in dt])
# 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 = pfp_utils.GetDateIndex(dt,
StartDate,
ts=ts,
default=0,
match='startnextday')
# find the end index of the last whole day (time=00:00)
ei = pfp_utils.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():
logger.info(" Doing climatology for " + ThisOne)
data, f, a = pfp_utils.GetSeriesasMA(ds, ThisOne, si=si, ei=ei)
if numpy.ma.count(data) == 0:
logger.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 = pfp_utils.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" and got_Fa:
logger.info(" Doing evaporative fraction")
EF = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Hdh, f, a = pfp_utils.GetSeriesasMA(ds, 'Hdh', si=si, ei=ei)
Fa, f, a = pfp_utils.GetSeriesasMA(ds, 'Fa', si=si, ei=ei)
Fe, f, a = pfp_utils.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 = pfp_utils.GetSeriesasMA(ds, 'Fa', si=si, ei=ei)
Fe, f, a = pfp_utils.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":
logger.info(" Doing Bowen ratio")
BR = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Fe, f, a = pfp_utils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fh, f, a = pfp_utils.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 = pfp_utils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fh, f, a = pfp_utils.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":
logger.info(" Doing ecosystem WUE")
WUE = numpy.ma.zeros([48, 12]) + float(c.missing_value)
Fe, f, a = pfp_utils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fc, f, a = pfp_utils.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 = pfp_utils.GetSeriesasMA(ds, 'Fe', si=si, ei=ei)
Fc, f, a = pfp_utils.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:
logger.warning(" Requested variable " + ThisOne +
" not in data structure")
continue
logger.info(" Saving Excel file " + os.path.split(xl_filename)[1])
xlFile.save(xl_filename)