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_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_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 do_audit_analysis(base_path): sites = sorted(os.listdir(base_path)) for item in sites: if not os.path.isdir(os.path.join(base_path, item)): sites.remove(item) site_info = OrderedDict() all_sites = {"start_date":datetime.datetime(3000,1,1,0,0), "end_date": datetime.datetime(2000,1,1,0,0)} n = 0 for site in sites: portal_dir = os.path.join(base_path, site, "Data", "Portal") l3_name = os.path.join(portal_dir, site + "_L3.nc") if os.path.isfile(l3_name): print "Processing ", site site_info[site] = {"file_name":l3_name} ds = qcio.nc_read_series(l3_name) site_info[site]["site_name"] = ds.globalattributes["site_name"] start_date = dateutil.parser.parse(ds.globalattributes["start_date"]) site_info[site]["start_date"] = start_date end_date = dateutil.parser.parse(ds.globalattributes["end_date"]) site_info[site]["end_date"] = end_date site_info[site]["X"] = numpy.array([start_date, end_date]) site_info[site]["Y"] = numpy.array([n+1, n+1]) n = n + 1 all_sites["start_date"] = min([all_sites["start_date"], site_info[site]["start_date"]]) all_sites["end_date"] = max([all_sites["end_date"], site_info[site]["end_date"]]) with open('audit_analysis.pickle', 'wb') as handle: pickle.dump([all_sites, site_info], handle, protocol=pickle.HIGHEST_PROTOCOL) return all_sites, site_info
def mpt_main(cf): base_file_path = cf["Files"]["file_path"] nc_file_name = cf["Files"]["in_filename"] nc_file_path = os.path.join(base_file_path, nc_file_name) ds = qcio.nc_read_series(nc_file_path) out_file_paths = run_mpt_code(ds, nc_file_name) ustar_results = read_mpt_output(out_file_paths) mpt_file_path = nc_file_path.replace(".nc", "_MPT.xls") xl_write_mpt(mpt_file_path, ustar_results) return
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 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 ImportSeries(cf, ds): # check to see if there is an Imports section if "Imports" not in cf.keys(): return # number of records nRecs = int(ds.globalattributes["nc_nrecs"]) # get the start and end datetime ldt = ds.series["DateTime"]["Data"] start_date = ldt[0] end_date = ldt[-1] # loop over the series in the Imports section for label in cf["Imports"].keys(): import_filename = qcutils.get_keyvaluefromcf(cf, ["Imports", label], "file_name", default="") if import_filename == "": msg = " ImportSeries: import filename not found in control file, skipping ..." logger.warning(msg) continue var_name = qcutils.get_keyvaluefromcf(cf, ["Imports", label], "var_name", default="") if var_name == "": msg = " ImportSeries: variable name not found in control file, skipping ..." logger.warning(msg) continue ds_import = qcio.nc_read_series(import_filename) ts_import = ds_import.globalattributes["time_step"] ldt_import = ds_import.series["DateTime"]["Data"] si = qcutils.GetDateIndex(ldt_import, str(start_date), ts=ts_import, default=0, match="exact") ei = qcutils.GetDateIndex(ldt_import, str(end_date), ts=ts_import, default=len(ldt_import) - 1, match="exact") data = numpy.ma.ones(nRecs) * float(c.missing_value) flag = numpy.ma.ones(nRecs) data_import, flag_import, attr_import = qcutils.GetSeriesasMA( ds_import, var_name, si=si, ei=ei) ldt_import = ldt_import[si:ei + 1] index = qcutils.FindIndicesOfBInA(ldt_import, ldt) data[index] = data_import flag[index] = flag_import qcutils.CreateSeries(ds, label, data, flag, attr_import)
def compare_eddypro(): epname = qcio.get_filename_dialog(title='Choose an EddyPro full output file') ofname = qcio.get_filename_dialog(title='Choose an L3 output file') ds_ep = qcio.read_eddypro_full(epname) ds_of = qcio.nc_read_series(ofname) dt_ep = ds_ep.series['DateTime']['Data'] dt_of = ds_of.series['DateTime']['Data'] start_datetime = max([dt_ep[0],dt_of[0]]) end_datetime = min([dt_ep[-1],dt_of[-1]]) si_of = qcutils.GetDateIndex(dt_of, str(start_datetime), ts=30, default=0, match='exact') ei_of = qcutils.GetDateIndex(dt_of, str(end_datetime), ts=30, default=len(dt_of), match='exact') si_ep = qcutils.GetDateIndex(dt_ep, str(start_datetime), ts=30, default=0, match='exact') ei_ep = qcutils.GetDateIndex(dt_ep, str(end_datetime), ts=30, default=len(dt_ep), match='exact') us_of = qcutils.GetVariableAsDictionary(ds_of,'ustar',si=si_of,ei=ei_of) us_ep = qcutils.GetVariableAsDictionary(ds_ep,'ustar',si=si_ep,ei=ei_ep) Fh_of = qcutils.GetVariableAsDictionary(ds_of,'Fh',si=si_of,ei=ei_of) Fh_ep = qcutils.GetVariableAsDictionary(ds_ep,'Fh',si=si_ep,ei=ei_ep) Fe_of = qcutils.GetVariableAsDictionary(ds_of,'Fe',si=si_of,ei=ei_of) Fe_ep = qcutils.GetVariableAsDictionary(ds_ep,'Fe',si=si_ep,ei=ei_ep) Fc_of = qcutils.GetVariableAsDictionary(ds_of,'Fc',si=si_of,ei=ei_of) Fc_ep = qcutils.GetVariableAsDictionary(ds_ep,'Fc',si=si_ep,ei=ei_ep) # copy the range check values from the OFQC attributes to the EP attributes for of, ep in zip([us_of, Fh_of, Fe_of, Fc_of], [us_ep, Fh_ep, Fe_ep, Fc_ep]): for item in ["rangecheck_upper", "rangecheck_lower"]: if item in of["Attr"]: ep["Attr"][item] = of["Attr"][item] # apply QC to the EddyPro data qcck.ApplyRangeCheckToVariable(us_ep) qcck.ApplyRangeCheckToVariable(Fc_ep) qcck.ApplyRangeCheckToVariable(Fe_ep) qcck.ApplyRangeCheckToVariable(Fh_ep) # plot the comparison plt.ion() fig = plt.figure(1,figsize=(8,8)) qcplot.xyplot(us_ep["Data"],us_of["Data"],sub=[2,2,1],regr=2,xlabel='u*_EP (m/s)',ylabel='u*_OF (m/s)') qcplot.xyplot(Fh_ep["Data"],Fh_of["Data"],sub=[2,2,2],regr=2,xlabel='Fh_EP (W/m2)',ylabel='Fh_OF (W/m2)') qcplot.xyplot(Fe_ep["Data"],Fe_of["Data"],sub=[2,2,3],regr=2,xlabel='Fe_EP (W/m2)',ylabel='Fe_OF (W/m2)') qcplot.xyplot(Fc_ep["Data"],Fc_of["Data"],sub=[2,2,4],regr=2,xlabel='Fc_EP (umol/m2/s)',ylabel='Fc_OF (umol/m2/s)') plt.tight_layout() plt.draw() plt.ioff()
def do_plotL6_summary(self): """ Plot L6 summary. """ cf = qcio.load_controlfile(path='controlfiles') if len(cf)==0: self.do_progress(text='Waiting for input ...') return if "Options" not in cf: cf["Options"]={} cf["Options"]["call_mode"] = "interactive" l6filename = qcio.get_outfilenamefromcf(cf) if not qcutils.file_exists(l6filename): self.do_progress(text='An error occurred, check the console ...'); return ds6 = qcio.nc_read_series(l6filename) if len(ds6.series.keys())==0: self.do_progress(text='An error occurred, check the console ...'); del ds6; return self.update_startenddate(str(ds6.series['DateTime']['Data'][0]), str(ds6.series['DateTime']['Data'][-1])) self.do_progress(text='Plotting L6 summary ...') qcgf.ImportSeries(cf,ds6) qcrp.L6_summary(cf,ds6) self.do_progress(text='Finished plotting L6 summary') logging.info(' Finished plotting L6 summary, check the GUI')
def compare_eddypro(): epname = qcio.get_filename_dialog(title='Choose an EddyPro full output file') ofname = qcio.get_filename_dialog(title='Choose an L3 output file') ds_ep = qcio.read_eddypro_full(epname) ds_of = qcio.nc_read_series(ofname) dt_ep = ds_ep.series['DateTime']['Data'] dt_of = ds_of.series['DateTime']['Data'] si = dt_of.index(dt_ep[0]) ei = dt_of.index(dt_ep[-1]) us_of,f,a = qcutils.GetSeriesasMA(ds_of,'ustar',si=si,ei=ei) us_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'ustar') Fh_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fh',si=si,ei=ei) Fh_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fh') Fe_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fe',si=si,ei=ei) Fe_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fe') Fc_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fc',si=si,ei=ei) Fc_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fc') us_of.mask = numpy.ma.mask_or(us_of.mask,us_ep.mask) us_ep.mask = numpy.ma.mask_or(us_of.mask,us_ep.mask) Fh_of.mask = numpy.ma.mask_or(Fh_of.mask,Fh_ep.mask) Fh_ep.mask = numpy.ma.mask_or(Fh_of.mask,Fh_ep.mask) Fe_of.mask = numpy.ma.mask_or(Fe_of.mask,Fe_ep.mask) Fe_ep.mask = numpy.ma.mask_or(Fe_of.mask,Fe_ep.mask) Fc_of.mask = numpy.ma.mask_or(Fc_of.mask,Fc_ep.mask) Fc_ep.mask = numpy.ma.mask_or(Fc_of.mask,Fc_ep.mask) plt.ion() fig = plt.figure(1,figsize=(8,8)) qcplot.xyplot(us_ep,us_of,sub=[2,2,1],regr=1,xlabel='u*_EP (m/s)',ylabel='u*_OF (m/s)') qcplot.xyplot(Fh_ep,Fh_of,sub=[2,2,2],regr=1,xlabel='Fh_EP (W/m2)',ylabel='Fh_OF (W/m2)') qcplot.xyplot(Fe_ep,Fe_of,sub=[2,2,3],regr=1,xlabel='Fe_EP (W/m2)',ylabel='Fe_OF (W/m2)') qcplot.xyplot(Fc_ep,Fc_of,sub=[2,2,4],regr=1,xlabel='Fc_EP (umol/m2/s)',ylabel='Fc_OF (umol/m2/s)') plt.tight_layout() plt.draw() plt.ioff()
def do_plotL3L3(self): """ Plot L3 (QA/QC and Corrected) data Control File for do_l3qc function used. If L3 Control File not loaded, requires control file selection. """ if 'ds3' 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_outfilenamefromcf(self.cf) 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 self.update_startenddate(str(self.ds3.series['DateTime']['Data'][0]), str(self.ds3.series['DateTime']['Data'][-1])) self.do_progress(text='Plotting L3 QC ...') cfname = self.ds3.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') 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 L3 XY ...') qcplot.plotxy(self.cf,nFig,plt_cf,self.ds3,self.ds3,si,ei) else: self.do_progress(text='Plotting L3 QC ...') SeriesList = ast.literal_eval(plt_cf['Variables']) qcplot.plottimeseries(self.cf,nFig,self.ds3,self.ds3,si,ei) else: self.do_progress(text='Plotting L3 QC ...') qcplot.plottimeseries(self.cf,nFig,self.ds3,self.ds3,si,ei) self.do_progress(text='Finished plotting L3') logging.info(' Finished plotting L3, check the GUI')
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 # find the start index of the first whole day (time=00:30) si = qcutils.GetDateIndex(DateTime,str(DateTime[0]),ts=ts,default=0,match='startnextday') # find the end index of the last whole day (time=00:00) ei = qcutils.GetDateIndex(DateTime,str(DateTime[-1]),ts=ts,default=-1,match='endpreviousday') # clip the datetime series to a whole number of days DateTime = DateTime[si:ei+1] StartDate = DateTime[0]
import numpy import os import sys # check the scripts directory is present if not os.path.exists("../scripts/"): print("erai2nc: the scripts directory is missing") sys.exit() # since the scripts directory is there, try importing the modules sys.path.append('../scripts') import qcio import qcutils aws_name = qcio.get_filename_dialog(path="/mnt/OzFlux/Sites") ds_aws_30minute = qcio.nc_read_series(aws_name) has_gaps = qcutils.CheckTimeStep(ds_aws_30minute) if has_gaps: print("Problems found with time step") qcutils.FixTimeStep(ds_aws_30minute) qcutils.get_ymdhmsfromdatetime(ds_aws_30minute) dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"] ddt = [ dt_aws_30minute[i + 1] - dt_aws_30minute[i] for i in range(0, len(dt_aws_30minute) - 1) ] print("Minimum time step is", min(ddt), " Maximum time step is", max(ddt)) dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"] start_date = dt_aws_30minute[0] end_date = dt_aws_30minute[-1]
Fscore['Fmax'] = float(numpy.ma.maximum(Fscore['values'])) Fscore['iatFmax'] = int(numpy.ma.where(Fscore['values']==Fscore['Fmax'])[0]) Fscore['usatFmax'] = float(us[Fscore['iatFmax']]) return Fscore # initialise some constants nFig = 0 nTemp = 4 nustarbins = 50 nustarpointsperbin = 5 npointsperseason = nustarpointsperbin*nustarbins*nTemp npointsperjump = npointsperseason/2 ncname = '../../Sites/HowardSprings/Data/Processed/2011/HowardSprings_2011_L3.nc' # read the netCDF file ds = qcio.nc_read_series(ncname) nRecs = int(ds.globalattributes['nc_nrecs']) # get the data from the data structure Fsd,f = qcutils.GetSeriesasMA(ds,'Fsd') Ta,f = qcutils.GetSeriesasMA(ds,'Ta') ustar,f = qcutils.GetSeriesasMA(ds,'ustar') # uncomment following line to use real data Fc,f = qcutils.GetSeriesasMA(ds,'Fc') # uncomment following 3 lines to use synthetic data #Fc = numpy.ma.ones(nRecs)*float(5) #index = numpy.ma.where(ustar<0.25)[0] #Fc[index] = float(20)*ustar[index] dt = ds.series['DateTime']['Data'] # get the night time values where Fc is not masked index = numpy.ma.where((Fsd<10)&(Fc.mask==False)&(ustar.mask==False)&(Ta.mask==False))[0] Fc_n = Fc[index]
import matplotlib.pyplot as plt import meteorologicalfunctions as mf import statsmodels.api as sm import qcio import qcutils # open the logging file log = qcutils.startlog('compare_access','../logfiles/compare_access.log') # load the control file cf = qcio.load_controlfile(path='../controlfiles') if len(cf)==0: sys.exit() tow_name = cf["Files"]["tower_filename"] acc_name = cf["Files"]["access_filename"] # read the data series ds_tow = qcio.nc_read_series(tow_name) ds_acc = qcio.nc_read_series(acc_name) # get the time step and the site name ts = int(ds_tow.globalattributes["time_step"]) site_name = str(ds_tow.globalattributes["site_name"]) # get the start and end indices for the first and last whole days dt_acc = ds_acc.series["DateTime"]["Data"] si_acc = qcutils.GetDateIndex(dt_acc,str(dt_acc[0]),ts=ts,match="startnextday") ei_acc = qcutils.GetDateIndex(dt_acc,str(dt_acc[0]),ts=ts,match="endpreviousday") dt_acc = dt_acc[si_acc:ei_acc+1] nrecs = len(dt_acc) nperhr = int(float(60)/ts+0.5) nperday = int(float(24)*nperhr+0.5) ndays = nrecs/nperday nrecs=ndays*nperday
def CPD_run(cf): # Set input file and output path and create directories for plots and results path_out = cf['Files']['file_path'] file_in = os.path.join(cf['Files']['file_path'], cf['Files']['in_filename']) # if "out_filename" in cf['Files']: file_out = os.path.join(cf['Files']['file_path'], cf['Files']['out_filename']) else: file_out = os.path.join( cf['Files']['file_path'], cf['Files']['in_filename'].replace(".nc", "_CPD.xls")) plot_path = "plots/" if "plot_path" in cf["Files"]: plot_path = os.path.join(cf["Files"]["plot_path"], "CPD/") if not os.path.isdir(plot_path): os.makedirs(plot_path) results_path = path_out if not os.path.isdir(results_path): os.makedirs(results_path) # get a dictionary of the variable names var_list = cf["Variables"].keys() names = {} for item in var_list: if "AltVarName" in cf["Variables"][item].keys(): names[item] = cf["Variables"][item]["AltVarName"] else: names[item] = item # add the xlDateTime names["xlDateTime"] = "xlDateTime" names["Year"] = "Year" # read the netcdf file logger.info(' Reading netCDF file ' + file_in) ds = qcio.nc_read_series(file_in) dates_list = ds.series["DateTime"]["Data"] nrecs = int(ds.globalattributes["nc_nrecs"]) # now get the data d = {} f = {} for item in names.keys(): data, flag, attr = qcutils.GetSeries(ds, names[item]) d[item] = np.where(data == c.missing_value, np.nan, data) f[item] = flag # set all data to NaNs if any flag not 0 or 10 for item in f.keys(): for f_OK in [0, 10]: idx = np.where(f[item] != 0)[0] if len(idx) != 0: for itemd in d.keys(): d[itemd][idx] = np.nan df = pd.DataFrame(d, index=dates_list) # replace missing values with NaN df.replace(c.missing_value, np.nan) # Build dictionary of additional configs d = {} d['radiation_threshold'] = int(cf['Options']['Fsd_threshold']) d['num_bootstraps'] = int(cf['Options']['Num_bootstraps']) d['flux_period'] = int(ds.globalattributes["time_step"]) d['site_name'] = ds.globalattributes["site_name"] d["call_mode"] = qcutils.get_keyvaluefromcf(cf, ["Options"], "call_mode", default="interactive", mode="quiet") d["show_plots"] = qcutils.get_keyvaluefromcf(cf, ["Options"], "show_plots", default=True, mode="quiet") d['plot_tclass'] = False if cf['Options']['Plot_TClass'] == 'True': d['plot_tclass'] = True if cf['Options']['Output_plots'] == 'True': d['plot_path'] = plot_path if cf['Options']['Output_results'] == 'True': d['results_path'] = results_path d["file_out"] = file_out return df, d
# coding: utf-8 import qcio import qcutils import statsmodels.api as sm duname=qcio.get_filename_dialog() drname=qcio.get_filename_dialog() ds_du=qcio.nc_read_series(duname) ds_dr=qcio.nc_read_series(drname) dt_du=ds.series["DateTime"]["Data"] dt_du=ds_du.series["DateTime"]["Data"] dt_dr=ds_dr.series["DateTime"]["Data"] print dt_du[0],dt_dr[0] print dt_du[-1],dt_dr[-1] ts=ds_du.globalattributes["time_step"] si=qcutils.GetDateIndex(dt_du,str(dt_du[0]),ts=ts,match="startnextday") ei=qcutils.GetDateIndex(dt_du,str(dt_dr[-1]),ts=ts,match="endpreviousday") print si,ei Fsd_du,f=qcutils.GetSeriesasMA(ds_du,'Fsd',si=si,ei=ei) Fsd_dr,f=qcutils.GetSeriesasMA(ds_dr,'Fsd',si=si,ei=ei) plot(Fsd_dr,Fsd_du) ei=qcutils.GetDateIndex(ds_du,"2009-01-01 00:00",ts=ts,match="endpreviousday") ei=qcutils.GetDateIndex(dt_du,"2009-01-01 00:00",ts=ts,match="endpreviousday") fig=figure(1) Fsd_du_2008,f=qcutils.GetSeriesasMA(ds_du,'Fsd',si=si,ei=ei) Fsd_dr_2008,f=qcutils.GetSeriesasMA(ds_dr,'Fsd',si=si,ei=ei) plot(Fsd_du_2008,Fsd_dr_2008,'b.') Fsd_du_2008.mask=(Fsd_du_2008.mask==True)|(Fsd_dr_2008.mask==True) Fsd_dr_2008.mask=(Fsd_du_2008.mask==True)|(Fsd_dr_2008.mask==True) Fsd_du_2008=numpy.ma.compressed(Fsd_du_2008) Fsd_dr_2008=numpy.ma.compressed(Fsd_dr_2008) x=Fsd_du_2008
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 # find the start index of the first whole day (time=00:30) si = qcutils.GetDateIndex(DateTime, str(DateTime[0]), ts=ts, default=0,
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)
def gfalternate_createdict(cf, ds, series, ds_alt): """ Purpose: Creates a dictionary in ds to hold information about the alternate data used to gap fill the tower data. Usage: Side effects: Author: PRI Date: August 2014 """ # get the section of the control file containing the series section = qcutils.get_cfsection(cf, series=series, mode="quiet") # return without doing anything if the series isn't in a control file section if len(section) == 0: logger.error( "GapFillFromAlternate: Series %s not found in control file, skipping ...", series) return # create the alternate directory in the data structure if "alternate" not in dir(ds): ds.alternate = {} # name of alternate output series in ds output_list = cf[section][series]["GapFillFromAlternate"].keys() # loop over the outputs listed in the control file for output in output_list: # create the dictionary keys for this output ds.alternate[output] = {} ds.alternate[output]["label_tower"] = series # source name ds.alternate[output]["source"] = cf[section][series][ "GapFillFromAlternate"][output]["source"] # site name ds.alternate[output]["site_name"] = ds.globalattributes["site_name"] # alternate data file name # first, look in the [Files] section for a generic file name file_list = cf["Files"].keys() lower_file_list = [item.lower() for item in file_list] if ds.alternate[output]["source"].lower() in lower_file_list: # found a generic file name i = lower_file_list.index(ds.alternate[output]["source"].lower()) ds.alternate[output]["file_name"] = cf["Files"][file_list[i]] else: # no generic file name found, look for a file name in the variable section ds.alternate[output]["file_name"] = cf[section][series][ "GapFillFromAlternate"][output]["file_name"] # if the file has not already been read, do it now if ds.alternate[output]["file_name"] not in ds_alt: ds_alternate = qcio.nc_read_series( ds.alternate[output]["file_name"], fixtimestepmethod="round") gfalternate_matchstartendtimes(ds, ds_alternate) ds_alt[ds.alternate[output]["file_name"]] = ds_alternate # get the type of fit ds.alternate[output]["fit_type"] = "OLS" if "fit" in cf[section][series]["GapFillFromAlternate"][output]: if cf[section][series]["GapFillFromAlternate"][output][ "fit"].lower() in [ "ols", "ols_thru0", "mrev", "replace", "rma", "odr" ]: ds.alternate[output]["fit_type"] = cf[section][series][ "GapFillFromAlternate"][output]["fit"] else: logger.info( "gfAlternate: unrecognised fit option for series %s, used OLS", output) # correct for lag? if "lag" in cf[section][series]["GapFillFromAlternate"][output]: if cf[section][series]["GapFillFromAlternate"][output][ "lag"].lower() in ["no", "false"]: ds.alternate[output]["lag"] = "no" elif cf[section][series]["GapFillFromAlternate"][output][ "lag"].lower() in ["yes", "true"]: ds.alternate[output]["lag"] = "yes" else: logger.info( "gfAlternate: unrecognised lag option for series %s", output) else: ds.alternate[output]["lag"] = "yes" # choose specific alternate variable? if "usevars" in cf[section][series]["GapFillFromAlternate"][output]: ds.alternate[output]["usevars"] = ast.literal_eval( cf[section][series]["GapFillFromAlternate"][output]["usevars"]) # alternate data variable name if different from name used in control file if "alternate_name" in cf[section][series]["GapFillFromAlternate"][ output]: ds.alternate[output]["alternate_name"] = cf[section][series][ "GapFillFromAlternate"][output]["alternate_name"] else: ds.alternate[output]["alternate_name"] = series # results of best fit for plotting later on ds.alternate[output]["results"] = { "startdate": [], "enddate": [], "No. points": [], "No. filled": [], "r": [], "Bias": [], "RMSE": [], "Frac Bias": [], "NMSE": [], "Avg (Tower)": [], "Avg (Alt)": [], "Var (Tower)": [], "Var (Alt)": [], "Var ratio": [] } # create an empty series in ds if the alternate output series doesn't exist yet if output not in ds.series.keys(): data, flag, attr = qcutils.MakeEmptySeries(ds, output) qcutils.CreateSeries(ds, output, data, flag, attr) qcutils.CreateSeries(ds, series + "_composite", data, flag, attr)
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 numpy import os import sys # check the scripts directory is present if not os.path.exists("../scripts/"): print "erai2nc: the scripts directory is missing" sys.exit() # since the scripts directory is there, try importing the modules sys.path.append('../scripts') import qcio import qcutils aws_name=qcio.get_filename_dialog(path="/mnt/OzFlux/Sites") ds_aws_30minute = qcio.nc_read_series(aws_name) has_gaps = qcutils.CheckTimeStep(ds_aws_30minute) if has_gaps: print "Problems found with time step" qcutils.FixTimeStep(ds_aws_30minute) qcutils.get_ymdhmsfromdatetime(ds_aws_30minute) dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"] ddt=[dt_aws_30minute[i+1]-dt_aws_30minute[i] for i in range(0,len(dt_aws_30minute)-1)] print "Minimum time step is",min(ddt)," Maximum time step is",max(ddt) dt_aws_30minute = ds_aws_30minute.series["DateTime"]["Data"] start_date = dt_aws_30minute[0] end_date = dt_aws_30minute[-1] si_wholehour = qcutils.GetDateIndex(dt_aws_30minute,str(start_date),ts=30,match="startnexthour") ei_wholehour = qcutils.GetDateIndex(dt_aws_30minute,str(end_date),ts=30,match="endprevioushour") start_date = dt_aws_30minute[si_wholehour] end_date = dt_aws_30minute[ei_wholehour]
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)
def compare_eddypro(): epname = qcio.get_filename_dialog( title='Choose an EddyPro full output file') ofname = qcio.get_filename_dialog(title='Choose an L3 output file') ds_ep = qcio.read_eddypro_full(epname) ds_of = qcio.nc_read_series(ofname) dt_ep = ds_ep.series['DateTime']['Data'] dt_of = ds_of.series['DateTime']['Data'] start_datetime = max([dt_ep[0], dt_of[0]]) end_datetime = min([dt_ep[-1], dt_of[-1]]) si_of = qcutils.GetDateIndex(dt_of, str(start_datetime), ts=30, default=0, match='exact') ei_of = qcutils.GetDateIndex(dt_of, str(end_datetime), ts=30, default=len(dt_of), match='exact') si_ep = qcutils.GetDateIndex(dt_ep, str(start_datetime), ts=30, default=0, match='exact') ei_ep = qcutils.GetDateIndex(dt_ep, str(end_datetime), ts=30, default=len(dt_ep), match='exact') us_of = qcutils.GetVariableAsDictionary(ds_of, 'ustar', si=si_of, ei=ei_of) us_ep = qcutils.GetVariableAsDictionary(ds_ep, 'ustar', si=si_ep, ei=ei_ep) Fh_of = qcutils.GetVariableAsDictionary(ds_of, 'Fh', si=si_of, ei=ei_of) Fh_ep = qcutils.GetVariableAsDictionary(ds_ep, 'Fh', si=si_ep, ei=ei_ep) Fe_of = qcutils.GetVariableAsDictionary(ds_of, 'Fe', si=si_of, ei=ei_of) Fe_ep = qcutils.GetVariableAsDictionary(ds_ep, 'Fe', si=si_ep, ei=ei_ep) Fc_of = qcutils.GetVariableAsDictionary(ds_of, 'Fc', si=si_of, ei=ei_of) Fc_ep = qcutils.GetVariableAsDictionary(ds_ep, 'Fc', si=si_ep, ei=ei_ep) # copy the range check values from the OFQC attributes to the EP attributes for of, ep in zip([us_of, Fh_of, Fe_of, Fc_of], [us_ep, Fh_ep, Fe_ep, Fc_ep]): for item in ["rangecheck_upper", "rangecheck_lower"]: if item in of["Attr"]: ep["Attr"][item] = of["Attr"][item] # apply QC to the EddyPro data qcck.ApplyRangeCheckToVariable(us_ep) qcck.ApplyRangeCheckToVariable(Fc_ep) qcck.ApplyRangeCheckToVariable(Fe_ep) qcck.ApplyRangeCheckToVariable(Fh_ep) # plot the comparison plt.ion() fig = plt.figure(1, figsize=(8, 8)) qcplot.xyplot(us_ep["Data"], us_of["Data"], sub=[2, 2, 1], regr=2, xlabel='u*_EP (m/s)', ylabel='u*_OF (m/s)') qcplot.xyplot(Fh_ep["Data"], Fh_of["Data"], sub=[2, 2, 2], regr=2, xlabel='Fh_EP (W/m2)', ylabel='Fh_OF (W/m2)') qcplot.xyplot(Fe_ep["Data"], Fe_of["Data"], sub=[2, 2, 3], regr=2, xlabel='Fe_EP (W/m2)', ylabel='Fe_OF (W/m2)') qcplot.xyplot(Fc_ep["Data"], Fc_of["Data"], sub=[2, 2, 4], regr=2, xlabel='Fc_EP (umol/m2/s)', ylabel='Fc_OF (umol/m2/s)') plt.tight_layout() plt.draw() plt.ioff()
def l4to6qc(cf, ds3, AttrLevel, InLevel, OutLevel): """ Fill gaps in met data from other sources Integrate SOLO-ANN gap filled fluxes performed externally Generates L4 from L3 data Generates daily sums excel workbook Variable Series: Meteorological (MList): Ah_EC, Cc_7500_Av, ps, Ta_EC, Ws_CSAT, Wd_CSAT Radiation (RList): Fld, Flu, Fn, Fsd, Fsu Soil water content (SwsList): all variables containing Sws in variable name Soil (SList): Fg, Ts, SwsList Turbulent fluxes (FList): Fc_wpl, Fe_wpl, Fh, ustar Output (OList): MList, RList, SList, FList Parameters loaded from control file: zmd: z-d z0: roughness height Functions performed: qcts.AddMetVars qcts.ComputeDailySums qcts.InterpolateOverMissing (OList for gaps shorter than 3 observations, OList gaps shorter than 7 observations) qcts.GapFillFromAlternate (MList, RList) qcts.GapFillFromClimatology (Ah_EC, Fn, Fg, ps, Ta_EC, Ws_CSAT, OList) qcts.GapFillFromRatios (Fe, Fh, Fc) qcts.ReplaceOnDiff (Ws_CSAT, ustar) qcts.UstarFromFh qcts.ReplaceWhereMissing (Ustar) qcck.do_qcchecks """ if AttrLevel == 'False': ds3.globalattributes['Functions'] = '' AttrLevel = InLevel # check to ensure L4 functions are defined in controlfile if qcutils.cfkeycheck(cf, Base='Functions'): x = 0 y = 0 z = 0 else: log.error('FunctionList not found in control file') ds3x = copy.deepcopy(ds3) ds3x.globalattributes['nc_level'] = 'L3' ds3x.globalattributes['L4Functions'] = 'No L4-L6 functions applied' return ds3x # handle meta-data and import L4-L6 from external process if InLevel == 'L3': ds3x = copy.deepcopy(ds3) else: infilename = qcio.get_infilename_from_cf(cf, InLevel) ds3x = qcio.nc_read_series(infilename) for ThisOne in ds3.globalattributes.keys(): if ThisOne not in ds3x.globalattributes.keys(): ds3x.globalattributes[ThisOne] = ds3.globalattributes[ThisOne] for ThisOne in ds3.series.keys(): if ThisOne in ds3x.series.keys(): for attr in ds3.series[ThisOne]['Attr'].keys(): if attr not in [ 'ancillary_variables', 'long_name', 'standard_name', 'units' ]: ds3x.series[ThisOne]['Attr'][attr] = ds3.series[ ThisOne]['Attr'][attr] ds3x.globalattributes['nc_level'] = AttrLevel ds3x.globalattributes['EPDversion'] = sys.version ds3x.globalattributes['QC_version_history'] = cfg.__doc__ # put the control file name into the global attributes ds3x.globalattributes['controlfile_name'] = cf['controlfile_name'] if OutLevel == 'L6': ds3x.globalattributes['xlL6_datemode'] = ds3x.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xlL6_filename'] = ds3x.globalattributes[ 'xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes[ 'xl_filename'] ds3x.globalattributes['xlL6_moddatetime'] = ds3x.globalattributes[ 'xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes[ 'xl_moddatetime'] elif OutLevel == 'L5': ds3x.globalattributes['xlL5_datemode'] = ds3x.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xlL5_filename'] = ds3x.globalattributes[ 'xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes[ 'xl_filename'] ds3x.globalattributes['xlL5_moddatetime'] = ds3x.globalattributes[ 'xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes[ 'xl_moddatetime'] elif OutLevel == 'L4': ds3x.globalattributes['xlL4_datemode'] = ds3x.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes[ 'xl_datemode'] ds3x.globalattributes['xlL4_filename'] = ds3x.globalattributes[ 'xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes[ 'xl_filename'] ds3x.globalattributes['xlL4_moddatetime'] = ds3x.globalattributes[ 'xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes[ 'xl_moddatetime'] qcutils.prepOzFluxVars(cf, ds3x) # convert Fc [mgCO2 m-2 s-1] to Fc_co2 [mgCO2 m-2 s-1], Fc_c [mgC m-2 s-1], NEE [umol m-2 s-1] and NEP = - NEE if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='convertFc' ) and cf['Functions']['convertFc'] == 'True': try: ds3x.globalattributes['L4Functions'] = ds3x.globalattributes[ 'L4Functions'] + ', convertFc' except: ds3x.globalattributes['L4Functions'] = 'convertFc' if 'Fc_co2' in ds3x.series.keys(): qcts.ConvertFc(cf, ds3x, Fco2_in='Fc_co2') else: qcts.ConvertFc(cf, ds3x) ds4x = copy.deepcopy(ds3x) for ThisOne in ['NEE', 'NEP', 'Fc', 'Fc_co2', 'Fc_c', 'Fe', 'Fh']: if ThisOne in ds4x.series.keys() and ThisOne in ds3.series.keys(): ds4x.series[ThisOne] = ds3.series[ThisOne].copy() for ThisOne in [ 'GPP', 'CE', 'ER_night', 'ER_dark', 'CE_day', 'CE_NEEmax', 'ER_bio', 'PD', 'ER_n', 'ER_LRF' ]: if ThisOne in ds4x.series.keys(): ds4x.series[ThisOne]['Data'] = numpy.ones( len(ds4x.series[ThisOne]['Data']), dtype=numpy.float64) * numpy.float64(c.missing_value) ds4x.series[ThisOne]['Flag'] = numpy.ones(len( ds4x.series[ThisOne]['Data']), dtype=numpy.int32) if InLevel == 'L4' or AttrLevel == 'L3': ds4, x = l4qc(cf, ds4x, InLevel, x) qcutils.get_coverage_individual(ds4) qcutils.get_coverage_groups(ds4) if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='FlagStats' ) and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf, ds4) if OutLevel == 'L5' or OutLevel == 'L6': try: ds4y = copy.deepcopy(ds4) except: ds4y = copy.deepcopy(ds4x) for ThisOne in [ 'NEE', 'NEP', 'Fc', 'Fc_c', 'Fc_co2', 'Fc_c', 'Fe', 'Fh' ]: var, var_flag, var_attr = qcutils.GetSeriesasMA(ds3x, ThisOne) qcutils.CreateSeries(ds4y, ThisOne, var, Flag=var_flag, Attr=var_attr) ds4y.series[ThisOne]['Attr']['long_name'] = var_attr['long_name'] ds5, y = l5qc(cf, ds4y, y) qcutils.get_coverage_individual(ds5) qcutils.get_coverage_groups(ds5) if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='FlagStats' ) and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf, ds5) if OutLevel == 'L6': ds5z = copy.deepcopy(ds5) for ThisOne in [ 'GPP', 'CE', 'ER_night', 'ER_dark', 'CE_day', 'CE_NEEmax', 'ER_bio', 'PD', 'ER_n', 'ER_LRF' ]: if ThisOne in ds3x.series.keys(): ds5z.series[ThisOne] = ds3x.series[ThisOne].copy() ds6, z = l6qc(cf, ds5z, z) qcutils.get_coverage_individual(ds6) qcutils.get_coverage_groups(ds6) if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='FlagStats' ) and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf, ds6) # calculate daily statistics if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='Sums'): if cf['Functions']['Sums'] == 'L6': ds6.globalattributes[ 'Functions'] = ds6.globalattributes['Functions'] + ', Sums' try: ds6.globalattributes['L6Functions'] = ds6.globalattributes[ 'L6Functions'] + ', Sums' except: ds6.globalattributes['L6Functions'] = 'Sums' qcts.do_sums(cf, ds6) elif cf['Functions']['Sums'] == 'L5': ds5.globalattributes[ 'Functions'] = ds5.globalattributes['Functions'] + ', Sums' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes[ 'L5Functions'] + ', Sums' except: ds5.globalattributes['L5Functions'] = 'Sums' qcts.do_sums(cf, ds5) elif cf['Functions']['Sums'] == 'L4': ds4.globalattributes[ 'Functions'] = ds4.globalattributes['Functions'] + ', Sums' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes[ 'L5Functions'] + ', Sums' except: ds4.globalattributes['L4Functions'] = 'Sums' qcts.do_sums(cf, ds4) # compute climatology if qcutils.cfkeycheck(cf, Base='Functions', ThisOne='climatology'): if cf['Functions']['climatology'] == 'L6': ds6.globalattributes['Functions'] = ds6.globalattributes[ 'Functions'] + ', climatology' try: ds6.globalattributes['L6Functions'] = ds6.globalattributes[ 'L6Functions'] + ', climatology' except: ds6.globalattributes['L6Functions'] = 'climatology' qcts.do_climatology(cf, ds6) elif cf['Functions']['climatology'] == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes[ 'Functions'] + ', climatology' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes[ 'L5Functions'] + ', climatology' except: ds5.globalattributes['L5Functions'] = 'climatology' qcts.do_climatology(cf, ds5) elif cf['Functions']['climatology'] == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes[ 'Functions'] + ', climatology' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes[ 'L4Functions'] + ', climatology' except: ds4.globalattributes['L4Functions'] = 'climatology' qcts.do_climatology(cf, ds4) if OutLevel == 'L4' and (InLevel == 'L3' or InLevel == 'L4'): if x == 0: ds4.globalattributes['Functions'] = ds4.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') return ds4 elif OutLevel == 'L5': if x == 0: if InLevel == 'L3' or InLevel == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') ds5.globalattributes['Functions'] = ds5.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds5.globalattributes['L4Functions'] = ds5.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds5.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' if y == 0: ds5.globalattributes['Functions'] = ds5.globalattributes[ 'Functions'] + ', No further L5 gapfilling' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes[ 'L5Functions'] + ', No further L5 gapfilling' except: ds5.globalattributes[ 'L5Functions'] = 'No further L5 gapfilling' log.warn(' L5: no record of gapfilling functions') return ds4, ds5 elif OutLevel == 'L6': if x == 0: if InLevel == 'L3' or InLevel == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') if InLevel == 'L3' or InLevel == 'L4' or InLevel == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds5.globalattributes['L4Functions'] = ds5.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds5.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') ds6.globalattributes['Functions'] = ds6.globalattributes[ 'Functions'] + ', No further L4 gapfilling' try: ds6.globalattributes['L4Functions'] = ds6.globalattributes[ 'L4Functions'] + ', No further L4 gapfilling' except: ds6.globalattributes[ 'L4Functions'] = 'No further L4 gapfilling' if y == 0: if InLevel == 'L3' or InLevel == 'L4' or InLevel == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes[ 'Functions'] + ', No further L5 gapfilling' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes[ 'L5Functions'] + ', No further L5 gapfilling' except: ds5.globalattributes[ 'L5Functions'] = 'No further L5 gapfilling' log.warn(' L5: no record of gapfilling functions') ds6.globalattributes['Functions'] = ds6.globalattributes[ 'Functions'] + ', No further L5 gapfilling' try: ds6.globalattributes['L5Functions'] = ds6.globalattributes[ 'L5Functions'] + ', No further L5 gapfilling' except: ds6.globalattributes[ 'L5Functions'] = 'No further L5 gapfilling' if z == 0: ds6.globalattributes['Functions'] = ds6.globalattributes[ 'Functions'] + ', No further L6 partitioning' try: ds6.globalattributes['L6Functions'] = ds5.globalattributes[ 'L6Functions'] + ', No further L6 partitioning' except: ds6.globalattributes[ 'L6Functions'] = 'No further L6 partitioning' log.warn(' L6: no record of gapfilling functions') return ds4, ds5, ds6
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) outfilename = qcio.get_outfilenamefromcf(cf)
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) ds3 = qcls.l3qc(cf,ds2)
a, _, _, _ = numpy.linalg.lstsq(x, y) eqnstr = 'y = %.3fx'%(a) plt.text(0.5,0.875,eqnstr,fontsize=8,horizontalalignment='center',transform=ax.transAxes) plt.subplots_adjust(wspace=wspace,hspace=hspace) # get the control file cf = qcio.load_controlfile(path='../controlfiles') if len(cf)==0: sys.exit() # get the netCDF filename ncfilename = qcio.get_infilenamefromcf(cf) # get the Fsdand ustar thresholds Fsd_lower = float(cf['Params']['Fsd_lower']) Fsd_upper = float(cf['Params']['Fsd_upper']) ustar_threshold = float(cf['Params']['ustar_threshold']) # read the netCDF file ds3 = qcio.nc_read_series(ncfilename) if len(ds3.series.keys())==0: print time.strftime('%X')+' netCDF file '+ncfilename+' not found'; sys.exit() SiteName = ds3.globalattributes['site_name'] DateTime = ds3.series['DateTime']['Data'] PlotTitle = SiteName + ': ' + str(DateTime[0]) + ' to ' + str(DateTime[-1]) # first figure is general plots of Fc as a function of ustar, Ts and Sws # get the data as masked arrays Fc,f,a=qcutils.GetSeriesasMA(ds3,'Fc') Fc_units = ds3.series['Fc']['Attr']['units'] us,f,a=qcutils.GetSeriesasMA(ds3,'ustar') Fsd,f,a=qcutils.GetSeriesasMA(ds3,'Fsd') nFig = 1 fig = plt.figure(nFig,figsize=(8,8)) plt.figtext(0.5,0.95,PlotTitle,horizontalalignment='center',size=16) # scatter plot of Fc versus ustar, night time
def l4to6qc(cf,ds3,AttrLevel,InLevel,OutLevel): """ Fill gaps in met data from other sources Integrate SOLO-ANN gap filled fluxes performed externally Generates L4 from L3 data Generates daily sums excel workbook Variable Series: Meteorological (MList): Ah_EC, Cc_7500_Av, ps, Ta_EC, Ws_CSAT, Wd_CSAT Radiation (RList): Fld, Flu, Fn, Fsd, Fsu Soil water content (SwsList): all variables containing Sws in variable name Soil (SList): Fg, Ts, SwsList Turbulent fluxes (FList): Fc_wpl, Fe_wpl, Fh, ustar Output (OList): MList, RList, SList, FList Parameters loaded from control file: zmd: z-d z0: roughness height Functions performed: qcts.AddMetVars qcts.ComputeDailySums qcts.InterpolateOverMissing (OList for gaps shorter than 3 observations, OList gaps shorter than 7 observations) qcts.GapFillFromAlternate (MList, RList) qcts.GapFillFromClimatology (Ah_EC, Fn, Fg, ps, Ta_EC, Ws_CSAT, OList) qcts.GapFillFromRatios (Fe, Fh, Fc) qcts.ReplaceOnDiff (Ws_CSAT, ustar) qcts.UstarFromFh qcts.ReplaceWhereMissing (Ustar) qcck.do_qcchecks """ if AttrLevel == 'False': ds3.globalattributes['Functions'] = '' AttrLevel = InLevel # check to ensure L4 functions are defined in controlfile if qcutils.cfkeycheck(cf,Base='Functions'): x=0 y=0 z=0 else: log.error('FunctionList not found in control file') ds3x = copy.deepcopy(ds3) ds3x.globalattributes['nc_level'] = 'L3' ds3x.globalattributes['L4Functions'] = 'No L4-L6 functions applied' return ds3x # handle meta-data and import L4-L6 from external process if InLevel == 'L3': ds3x = copy.deepcopy(ds3) else: infilename = qcio.get_infilename_from_cf(cf,InLevel) ds3x = qcio.nc_read_series(infilename) for ThisOne in ds3.globalattributes.keys(): if ThisOne not in ds3x.globalattributes.keys(): ds3x.globalattributes[ThisOne] = ds3.globalattributes[ThisOne] for ThisOne in ds3.series.keys(): if ThisOne in ds3x.series.keys(): for attr in ds3.series[ThisOne]['Attr'].keys(): if attr not in ['ancillary_variables','long_name','standard_name','units']: ds3x.series[ThisOne]['Attr'][attr] = ds3.series[ThisOne]['Attr'][attr] ds3x.globalattributes['nc_level'] = AttrLevel ds3x.globalattributes['EPDversion'] = sys.version ds3x.globalattributes['QC_version_history'] = cfg.__doc__ # put the control file name into the global attributes ds3x.globalattributes['controlfile_name'] = cf['controlfile_name'] if OutLevel == 'L6': ds3x.globalattributes['xlL6_datemode'] = ds3x.globalattributes['xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes['xl_datemode'] ds3x.globalattributes['xlL6_filename'] = ds3x.globalattributes['xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes['xl_filename'] ds3x.globalattributes['xlL6_moddatetime'] = ds3x.globalattributes['xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes['xl_moddatetime'] elif OutLevel == 'L5': ds3x.globalattributes['xlL5_datemode'] = ds3x.globalattributes['xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes['xl_datemode'] ds3x.globalattributes['xlL5_filename'] = ds3x.globalattributes['xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes['xl_filename'] ds3x.globalattributes['xlL5_moddatetime'] = ds3x.globalattributes['xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes['xl_moddatetime'] elif OutLevel == 'L4': ds3x.globalattributes['xlL4_datemode'] = ds3x.globalattributes['xl_datemode'] ds3x.globalattributes['xl_datemode'] = ds3.globalattributes['xl_datemode'] ds3x.globalattributes['xlL4_filename'] = ds3x.globalattributes['xl_filename'] ds3x.globalattributes['xl_filename'] = ds3.globalattributes['xl_filename'] ds3x.globalattributes['xlL4_moddatetime'] = ds3x.globalattributes['xl_moddatetime'] ds3x.globalattributes['xl_moddatetime'] = ds3.globalattributes['xl_moddatetime'] qcutils.prepOzFluxVars(cf,ds3x) # convert Fc [mgCO2 m-2 s-1] to Fc_co2 [mgCO2 m-2 s-1], Fc_c [mgC m-2 s-1], NEE [umol m-2 s-1] and NEP = - NEE if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='convertFc') and cf['Functions']['convertFc'] == 'True': try: ds3x.globalattributes['L4Functions'] = ds3x.globalattributes['L4Functions']+', convertFc' except: ds3x.globalattributes['L4Functions'] = 'convertFc' if 'Fc_co2' in ds3x.series.keys(): qcts.ConvertFc(cf,ds3x,Fco2_in='Fc_co2') else: qcts.ConvertFc(cf,ds3x) ds4x = copy.deepcopy(ds3x) for ThisOne in ['NEE','NEP','Fc','Fc_co2','Fc_c','Fe','Fh']: if ThisOne in ds4x.series.keys() and ThisOne in ds3.series.keys(): ds4x.series[ThisOne] = ds3.series[ThisOne].copy() for ThisOne in ['GPP','CE','ER_night','ER_dark','CE_day','CE_NEEmax','ER_bio','PD','ER_n','ER_LRF']: if ThisOne in ds4x.series.keys(): ds4x.series[ThisOne]['Data'] = numpy.ones(len(ds4x.series[ThisOne]['Data']),dtype=numpy.float64) * numpy.float64(c.missing_value) ds4x.series[ThisOne]['Flag'] = numpy.ones(len(ds4x.series[ThisOne]['Data']), dtype=numpy.int32) if InLevel == 'L4' or AttrLevel == 'L3': ds4,x = l4qc(cf,ds4x,InLevel,x) qcutils.get_coverage_individual(ds4) qcutils.get_coverage_groups(ds4) if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='FlagStats') and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf,ds4) if OutLevel == 'L5' or OutLevel == 'L6': try: ds4y = copy.deepcopy(ds4) except: ds4y = copy.deepcopy(ds4x) for ThisOne in ['NEE','NEP','Fc','Fc_c','Fc_co2','Fc_c','Fe','Fh']: var, var_flag, var_attr = qcutils.GetSeriesasMA(ds3x,ThisOne) qcutils.CreateSeries(ds4y,ThisOne,var,Flag=var_flag,Attr=var_attr) ds4y.series[ThisOne]['Attr']['long_name'] = var_attr['long_name'] ds5,y = l5qc(cf,ds4y,y) qcutils.get_coverage_individual(ds5) qcutils.get_coverage_groups(ds5) if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='FlagStats') and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf,ds5) if OutLevel == 'L6': ds5z = copy.deepcopy(ds5) for ThisOne in ['GPP','CE','ER_night','ER_dark','CE_day','CE_NEEmax','ER_bio','PD','ER_n','ER_LRF']: if ThisOne in ds3x.series.keys(): ds5z.series[ThisOne] = ds3x.series[ThisOne].copy() ds6,z = l6qc(cf,ds5z,z) qcutils.get_coverage_individual(ds6) qcutils.get_coverage_groups(ds6) if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='FlagStats') and cf['Functions']['FlagStats'] == 'True': qcio.get_seriesstats(cf,ds6) # calculate daily statistics if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='Sums'): if cf['Functions']['Sums'] == 'L6': ds6.globalattributes['Functions'] = ds6.globalattributes['Functions']+', Sums' try: ds6.globalattributes['L6Functions'] = ds6.globalattributes['L6Functions']+', Sums' except: ds6.globalattributes['L6Functions'] = 'Sums' qcts.do_sums(cf,ds6) elif cf['Functions']['Sums'] == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes['Functions']+', Sums' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes['L5Functions']+', Sums' except: ds5.globalattributes['L5Functions'] = 'Sums' qcts.do_sums(cf,ds5) elif cf['Functions']['Sums'] == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes['Functions']+', Sums' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes['L5Functions']+', Sums' except: ds4.globalattributes['L4Functions'] = 'Sums' qcts.do_sums(cf,ds4) # compute climatology if qcutils.cfkeycheck(cf,Base='Functions',ThisOne='climatology'): if cf['Functions']['climatology'] == 'L6': ds6.globalattributes['Functions'] = ds6.globalattributes['Functions']+', climatology' try: ds6.globalattributes['L6Functions'] = ds6.globalattributes['L6Functions']+', climatology' except: ds6.globalattributes['L6Functions'] = 'climatology' qcts.do_climatology(cf,ds6) elif cf['Functions']['climatology'] == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes['Functions']+', climatology' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes['L5Functions']+', climatology' except: ds5.globalattributes['L5Functions'] = 'climatology' qcts.do_climatology(cf,ds5) elif cf['Functions']['climatology'] == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes['Functions']+', climatology' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes['L4Functions']+', climatology' except: ds4.globalattributes['L4Functions'] = 'climatology' qcts.do_climatology(cf,ds4) if OutLevel == 'L4' and (InLevel == 'L3' or InLevel == 'L4'): if x == 0: ds4.globalattributes['Functions'] = ds4.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes['L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') return ds4 elif OutLevel == 'L5': if x == 0: if InLevel == 'L3' or InLevel == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes['L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') ds5.globalattributes['Functions'] = ds5.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds5.globalattributes['L4Functions'] = ds5.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds5.globalattributes['L4Functions'] = 'No further L4 gapfilling' if y == 0: ds5.globalattributes['Functions'] = ds5.globalattributes['Functions'] + ', No further L5 gapfilling' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes['L5Functions'] + ', No further L5 gapfilling' except: ds5.globalattributes['L5Functions'] = 'No further L5 gapfilling' log.warn(' L5: no record of gapfilling functions') return ds4,ds5 elif OutLevel == 'L6': if x == 0: if InLevel == 'L3' or InLevel == 'L4': ds4.globalattributes['Functions'] = ds4.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds4.globalattributes['L4Functions'] = ds4.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds4.globalattributes['L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') if InLevel == 'L3' or InLevel == 'L4' or InLevel == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds5.globalattributes['L4Functions'] = ds5.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds5.globalattributes['L4Functions'] = 'No further L4 gapfilling' log.warn(' L4: no record of gapfilling functions') ds6.globalattributes['Functions'] = ds6.globalattributes['Functions'] + ', No further L4 gapfilling' try: ds6.globalattributes['L4Functions'] = ds6.globalattributes['L4Functions'] + ', No further L4 gapfilling' except: ds6.globalattributes['L4Functions'] = 'No further L4 gapfilling' if y == 0: if InLevel == 'L3' or InLevel == 'L4' or InLevel == 'L5': ds5.globalattributes['Functions'] = ds5.globalattributes['Functions'] + ', No further L5 gapfilling' try: ds5.globalattributes['L5Functions'] = ds5.globalattributes['L5Functions'] + ', No further L5 gapfilling' except: ds5.globalattributes['L5Functions'] = 'No further L5 gapfilling' log.warn(' L5: no record of gapfilling functions') ds6.globalattributes['Functions'] = ds6.globalattributes['Functions'] + ', No further L5 gapfilling' try: ds6.globalattributes['L5Functions'] = ds6.globalattributes['L5Functions'] + ', No further L5 gapfilling' except: ds6.globalattributes['L5Functions'] = 'No further L5 gapfilling' if z == 0: ds6.globalattributes['Functions'] = ds6.globalattributes['Functions'] + ', No further L6 partitioning' try: ds6.globalattributes['L6Functions'] = ds5.globalattributes['L6Functions'] + ', No further L6 partitioning' except: ds6.globalattributes['L6Functions'] = 'No further L6 partitioning' log.warn(' L6: no record of gapfilling functions') return ds4,ds5,ds6