def getTargetFile(dataset=None, mode=None, dataargs=None, grid=None, shape=None, station=None, period=None, filetype=None,
lwrite=True):
''' generate filename for target dataset '''
# for CESM & WRF
if grid is None: grid = dataargs.gridstr # also use grid for station/shape type
if period is None: period = dataargs.periodstr
if dataset in ('WRF','CESM') and lwrite:
# prepare some variables
domain = dataargs.domain
if filetype is None: filetype = dataargs.filetype
gstr = '_{}'.format(grid) if grid else ''
# prepend shape or station type before grid
if shape and station: raise ArgumentError
elif shape: gstr = '_{}{}'.format(shape,gstr)
elif station: gstr = '_{}{}'.format(station,gstr)
pstr = '_{}'.format(period) if period else ''
if dataset == 'WRF':
import datasets.WRF as WRF
fileclass = WRF.fileclasses[filetype] if filetype in WRF.fileclasses else WRF.FileType(filetype)
if mode == 'climatology': filename = fileclass.climfile.format(domain,gstr,pstr)
elif mode == 'time-series': filename = fileclass.tsfile.format(domain,gstr)
elif dataset == 'CESM':
import datasets.CESM as CESM
fileclass = CESM.fileclasses[filetype] if filetype in CESM.fileclasses else CESM.FileType(filetype)
if mode == 'climatology': filename = fileclass.climfile.format(gstr,pstr)
elif mode == 'time-series': filename = fileclass.tsfile.format(gstr)
else: raise NotImplementedError, "Unsupported Mode: '{:s}'".format(mode)
elif lwrite: # assume observational datasets
filename = getFileName(grid=grid, shape=shape, station=station, period=period, name=dataargs.obs_res, filetype=mode)
else: raise DatasetError(dataset)
if not os.path.exists(dataargs.avgfolder):
raise IOError, "Dataset folder '{:s}' does not exist!".format(dataargs.avgfolder)
# return filename
return filename
def getTargetFile(dataset=None, mode=None, dataargs=None, lwrite=True, grid=None, period=None, filetype=None):
''' generate filename for target dataset '''
# prepare some variables
domain = dataargs.domain
if filetype is None: filetype = dataargs.filetype
if grid is None: grid = dataargs.gridstr # also use grid for station type
if period is None: period = dataargs.periodstr
gstr = '_{}'.format(grid) if grid else ''
pstr = '_{}'.format(period) if period else ''
# figure out filename
if dataset == 'WRF' and lwrite:
if mode == 'climatology': filename = WRF.clim_file_pattern.format(filetype,domain,gstr,pstr)
elif mode == 'time-series': filename = WRF.ts_file_pattern.format(filetype,domain,gstr)
else: raise NotImplementedError
elif dataset == 'CESM' and lwrite:
if mode == 'climatology': filename = CESM.clim_file_pattern.format(filetype,gstr,pstr)
elif mode == 'time-series': filename = CESM.ts_file_pattern.format(filetype,gstr)
else: raise NotImplementedError
elif ( dataset == dataset.upper() or dataset == 'Unity' ) and lwrite: # observational datasets
filename = getFileName(grid=grid, period=dataargs.period, name=dataargs.obs_res, filetype=mode)
elif not lwrite: raise DatasetError
if not os.path.exists(dataargs.avgfolder):
raise IOError, "Dataset folder '{:s}' does not exist!".format(dataargs.avgfolder)
# return filename
return filename
def getTargetFile(dataset=None,
mode=None,
dataargs=None,
grid=None,
shape=None,
station=None,
period=None,
filetype=None,
lwrite=True):
''' generate filename for target dataset '''
# for CESM & WRF
if grid is None:
grid = dataargs.gridstr # also use grid for station/shape type
if period is None: period = dataargs.periodstr
if dataset in ('WRF', 'CESM') and lwrite:
# prepare some variables
domain = dataargs.domain
if filetype is None: filetype = dataargs.filetype
gstr = '_{}'.format(grid) if grid else ''
# prepend shape or station type before grid
if shape and station: raise ArgumentError
elif shape: gstr = '_{}{}'.format(shape, gstr)
elif station: gstr = '_{}{}'.format(station, gstr)
pstr = '_{}'.format(period) if period else ''
if dataset == 'WRF':
import datasets.WRF as WRF
fileclass = WRF.fileclasses[
filetype] if filetype in WRF.fileclasses else WRF.FileType(
filetype)
if mode == 'climatology':
filename = fileclass.climfile.format(domain, gstr, pstr)
elif mode == 'time-series':
filename = fileclass.tsfile.format(domain, gstr)
elif dataset == 'CESM':
import datasets.CESM as CESM
fileclass = CESM.fileclasses[
filetype] if filetype in CESM.fileclasses else CESM.FileType(
filetype)
if mode == 'climatology':
filename = fileclass.climfile.format(gstr, pstr)
elif mode == 'time-series':
filename = fileclass.tsfile.format(gstr)
else:
raise NotImplementedError, "Unsupported Mode: '{:s}'".format(mode)
elif lwrite: # assume observational datasets
filename = getFileName(grid=grid,
shape=shape,
station=station,
period=period,
name=dataargs.obs_res,
filetype=mode)
else:
raise DatasetError(dataset)
if not os.path.exists(dataargs.avgfolder):
raise IOError, "Dataset folder '{:s}' does not exist!".format(
dataargs.avgfolder)
# return filename
return filename
def getTargetFile(name, dataset, mode, module, dataargs, lwrite):
''' generate filename for target dataset '''
# extract some variables
periodstr = dataargs.periodstr; filetype = dataargs.filetype; domain = dataargs.domain
sstr = '_{}'.format(name) # use name as "grid" designation for station data
pstr = '_{}'.format(periodstr) if periodstr else ''
# figure out filename
if dataset == 'WRF' and lwrite:
if mode == 'climatology': filename = module.clim_file_pattern.format(filetype,domain,sstr,pstr)
elif mode == 'time-series': filename = module.ts_file_pattern.format(filetype,domain,sstr)
else: raise NotImplementedError
elif dataset == 'CESM' and lwrite:
if mode == 'climatology': filename = module.clim_file_pattern.format(filetype,sstr,pstr)
elif mode == 'time-series': filename = module.ts_file_pattern.format(filetype,sstr)
else: raise NotImplementedError
elif ( dataset == dataset.upper() or dataset == 'Unity' ) and lwrite: # observational datasets
filename = getFileName(grid=name, period=dataargs.period, name=dataargs.obs_res, filetype=mode)
elif not lwrite: raise DatasetError
if not os.path.exists(dataargs.avgfolder):
raise IOError, "Dataset folder '{:s}' does not exist!".format(dataargs.avgfolder)
# return filename
return filename
def getTargetFile(name, dataset, mode, module, dataargs, lwrite):
''' generate filename for target dataset '''
# extract some variables
periodstr = dataargs.periodstr
filetype = dataargs.filetype
domain = dataargs.domain
sstr = '_{}'.format(
name) # use name as "grid" designation for station data
pstr = '_{}'.format(periodstr) if periodstr else ''
# figure out filename
if dataset == 'WRF' and lwrite:
if mode == 'climatology':
filename = module.clim_file_pattern.format(filetype, domain, sstr,
pstr)
elif mode == 'time-series':
filename = module.ts_file_pattern.format(filetype, domain, sstr)
else:
raise NotImplementedError
elif dataset == 'CESM' and lwrite:
if mode == 'climatology':
filename = module.clim_file_pattern.format(filetype, sstr, pstr)
elif mode == 'time-series':
filename = module.ts_file_pattern.format(filetype, sstr)
else:
raise NotImplementedError
elif (dataset == dataset.upper()
or dataset == 'Unity') and lwrite: # observational datasets
filename = getFileName(grid=name,
period=dataargs.period,
name=dataargs.obs_res,
filetype=mode)
elif not lwrite:
raise DatasetError
if not os.path.exists(dataargs.avgfolder):
raise IOError, "Dataset folder '{:s}' does not exist!".format(
dataargs.avgfolder)
# return filename
return filename
dataset = loadGPCC_LTM(varlist=['stations','precip'],resolution=res)
# change meta-data
dataset.name = 'GPCC'
dataset.title = 'GPCC Long-term Climatology'
dataset.atts.resolution = res
# load data into memory
dataset.load()
# add landmask
addLandMask(dataset) # create landmask from precip mask
dataset.mask(dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid=res, period=None, name='GPCC', filepattern=avgfile)
print('\n'+filename+'\n')
if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder+filename, close=False)
# add_var(ncset,'name_of_month', name_of_month, 'time', # add names of month
# atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# close...
ncset.close()
dataset.close()
# print dataset before
print(dataset)
print('')
def getMetaData(dataset, mode, dataargs, lone=True):
''' determine dataset type and meta data, as well as path to main source file '''
# determine dataset mode
lclim = False
lts = False
if mode == 'climatology': lclim = True
elif mode == 'time-series': lts = True
elif mode[-5:] == '-mean':
lclim = True
mode = 'climatology' # only for export to seasonal means (load entire monthly climatology)
else:
raise NotImplementedError, "Unrecognized Mode: '{:s}'".format(mode)
# general arguments (dataset independent)
varlist = dataargs.get('varlist', None)
resolution = dataargs.get('resolution', None)
grid = dataargs.get('grid', None) # get grid
period = dataargs.get('period', None)
# determine meta data based on dataset type
if dataset == 'WRF':
import datasets.WRF as WRF
# WRF datasets
obs_res = None # only for datasets (not used here)
exp = dataargs['experiment'] # need that one
dataset_name = exp.name
avgfolder = exp.avgfolder
filetypes = dataargs['filetypes']
fileclasses = WRF.fileclasses.copy()
for filetype in filetypes:
if filetype not in fileclasses:
fileclasses[filetype] = WRF.FileType(filetype)
domain = dataargs.get('domain', None)
periodstr, gridstr = getPeriodGridString(period, grid, exp=exp)
# check arguments
if period is None and lclim:
raise DatasetError, "A 'period' argument is required to load climatologies!"
if lone and len(filetypes) > 1:
raise DatasetError # process only one file at a time
if not isinstance(domain, (np.integer, int)): raise DatasetError
# construct dataset message
if lone:
datamsgstr = "Processing WRF '{:s}'-file from Experiment '{:s}' (d{:02d})".format(
filetypes[0], dataset_name, domain)
else:
datamsgstr = "Processing WRF dataset from Experiment '{:s}' (d{:02d})".format(
dataset_name, domain)
# figure out age of source file(s)
srcage = getSourceAge(fileclasses=fileclasses,
filetypes=filetypes,
exp=exp,
domain=domain,
periodstr=periodstr,
gridstr=gridstr,
lclim=lclim,
lts=lts)
# load source data
if lclim:
loadfct = partial(WRF.loadWRF,
experiment=exp,
name=None,
domains=domain,
grid=grid,
varlist=varlist,
period=period,
filetypes=filetypes,
varatts=None,
lconst=True,
ltrimT=False) # still want topography...
elif lts:
loadfct = partial(WRF.loadWRF_TS,
experiment=exp,
name=None,
domains=domain,
grid=grid,
varlist=varlist,
filetypes=filetypes,
varatts=None,
lconst=True,
ltrimT=False) # still want topography...
elif dataset == 'CESM':
import datasets.CESM as CESM
# CESM datasets
obs_res = None # only for datasets (not used here)
domain = None # only for WRF
exp = dataargs['experiment']
avgfolder = exp.avgfolder
dataset_name = exp.name
periodstr, gridstr = getPeriodGridString(period, grid, exp=exp)
filetypes = dataargs['filetypes']
fileclasses = CESM.fileclasses.copy()
for filetype in filetypes:
if filetype not in fileclasses:
fileclasses[filetype] = CESM.FileType(filetype)
# check arguments
if period is None and lclim:
raise DatasetError, "A 'period' argument is required to load climatologies!"
if lone and len(filetypes) > 1:
raise DatasetError # process only one file at a time
# construct dataset message
if lone:
datamsgstr = "Processing CESM '{:s}'-file from Experiment '{:s}'".format(
filetypes[0], dataset_name)
else:
datamsgstr = "Processing CESM dataset from Experiment '{:s}'".format(
dataset_name)
# figure out age of source file(s)
srcage = getSourceAge(fileclasses=fileclasses,
filetypes=filetypes,
exp=exp,
domain=None,
periodstr=periodstr,
gridstr=gridstr,
lclim=lclim,
lts=lts)
# load source data
load3D = dataargs.pop(
'load3D', None) # if 3D fields should be loaded (default: False)
if lclim:
loadfct = partial(CESM.loadCESM,
experiment=exp,
name=None,
grid=grid,
period=period,
varlist=varlist,
filetypes=filetypes,
varatts=None,
load3D=load3D,
translateVars=None)
elif lts:
loadfct = partial(CESM.loadCESM_TS,
experiment=exp,
name=None,
grid=grid,
varlist=varlist,
filetypes=filetypes,
varatts=None,
load3D=load3D,
translateVars=None)
else:
# assume observational datasets
filetypes = [None] # only for CESM & WRF
domain = None # only for WRF
try:
module = import_module('datasets.{0:s}'.format(dataset))
except ImportError:
raise DatasetError(
"Error loading dataset module '{:s}' from 'datasets' package!".
format(dataset))
dataset_name = module.dataset_name
resolution = dataargs['resolution']
if resolution: obs_res = '{0:s}_{1:s}'.format(dataset_name, resolution)
else: obs_res = dataset_name
# figure out period
periodstr, gridstr = getPeriodGridString(period, grid, beginyear=1979)
if period is None and lclim: periodstr = 'LTM'
datamsgstr = "Processing Dataset '{:s}'".format(dataset_name)
# assemble filename to check modification dates (should be only one file)
filename = getFileName(grid=grid,
period=period,
name=obs_res,
filetype=mode)
avgfolder = module.avgfolder
filepath = '{:s}/{:s}'.format(avgfolder, filename)
# load pre-processed climatology
kwargs = dict(name=dataset_name,
grid=grid,
varlist=varlist,
resolution=resolution,
varatts=None)
if dataset == 'Unity': kwargs['unity_grid'] = dataargs['unity_grid']
if lclim and module.loadClimatology is not None:
loadfct = partial(module.loadClimatology, period=period, **kwargs)
elif lts and module.loadTimeSeries is not None:
loadfct = partial(module.loadTimeSeries, **kwargs)
else:
raise DatasetError(
"Unable to identify time aggregation mode; the dataset " +
"'{}' may not support selected mode '{}'.".format(
dataset, mode))
# check if the source file is actually correct
if os.path.exists(filepath): filelist = [filepath]
else:
source = loadfct(
) # don't load dataset, just construct the file list
filelist = source.filelist
# figure out age of source file(s)
srcage = getSourceAge(filelist=filelist, lclim=lclim, lts=lts)
# N.B.: it would be nice to print a message, but then we would have to make the logger available,
# which would be too much trouble
## assemble and return meta data
dataargs = namedTuple(dataset_name=dataset_name,
period=period,
periodstr=periodstr,
avgfolder=avgfolder,
filetypes=filetypes,
filetype=filetypes[0],
domain=domain,
obs_res=obs_res,
varlist=varlist,
grid=grid,
gridstr=gridstr,
resolution=resolution)
# return meta data
return dataargs, loadfct, srcage, datamsgstr
# load dataset
dataset = loadNARR_LTM()
# change meta-data
dataset.name = 'NARR'
dataset.title = 'NARR Long-term Climatology'
# load data into memory
dataset.load()
# # add landmask
# addLandMask(dataset) # create landmask from precip mask
# dataset.mask(dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid='NARR', period=None, name='NARR', filepattern=avgfile)
print('\n'+filename+'\n')
if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder+filename, close=False)
add_strvar(ncset,'name_of_month', name_of_month, 'time', # add names of month
atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# close...
ncset.close()
dataset.close()
# print dataset before
print(dataset)
print('')
time = dataset.time
time.load(data=np.arange(
1, 13, dtype=time.dtype)) # 1 to 12 (incl.) for climatology
time.units = 'month'
time.atts.long_name = 'Month of the Year'
print(time)
# print diagnostic
print(dataset)
print('')
for var in dataset:
#print(var)
if not var.strvar:
print('Mean {0:s}: {1:s} {2:s}'.format(var.atts.long_name,
str(var.mean()),
var.units))
#print('')
print('')
## create new NetCDF file
# figure out a different filename
filename = getFileName(name='PCIC', filepattern=avgfile)
if os.path.exists(avgfolder + filename):
os.remove(avgfolder + filename)
# write data and some annotation
sink = writeNetCDF(dataset, avgfolder + filename, close=False)
# add_strvar(sink,'name_of_month', name_of_month, 'time', # add names of month
# atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
sink.close() # close...
print('Saving Climatology to: ' + filename)
print(avgfolder)
def getMetaData(dataset, mode, dataargs, lone=True):
''' determine dataset type and meta data, as well as path to main source file '''
# determine dataset mode
lclim = False; lts = False
if mode == 'climatology': lclim = True
elif mode == 'time-series': lts = True
else: raise NotImplementedError, "Unrecognized Mode: '{:s}'".format(mode)
# general arguments (dataset independent)
varlist = dataargs.get('varlist',None)
grid = dataargs.get('grid',None) # get grid
period = dataargs.get('period',None)
# determine meta data based on dataset type
if dataset == 'WRF':
# WRF datasets
obs_res = None # only for datasets (not used here)
exp = dataargs['experiment'] # need that one
dataset_name = exp.name
avgfolder = exp.avgfolder
filetypes = dataargs['filetypes']
domain = dataargs.get('domain',None)
periodstr, gridstr = getPeriodGridString(period, grid, exp=exp)
# check arguments
if period is None and lclim: raise DatasetError, "A 'period' argument is required to load climatologies!"
if lone and len(filetypes) > 1: raise DatasetError # process only one file at a time
if not isinstance(domain, (np.integer,int)): raise DatasetError
# construct dataset message
if lone:
datamsgstr = "Processing WRF '{:s}'-file from Experiment '{:s}' (d{:02d})".format(filetypes[0], dataset_name, domain)
else: datamsgstr = "Processing WRF dataset from Experiment '{:s}' (d{:02d})".format(dataset_name, domain)
# figure out age of source file(s)
srcage = getSourceAge(fileclasses=WRF.fileclasses, filetypes=filetypes, exp=exp, domain=domain,
periodstr=periodstr, gridstr=gridstr, lclim=lclim, lts=lts)
# load source data
if lclim:
loadfct = partial(WRF.loadWRF, experiment=exp, name=None, domains=domain, grid=grid, varlist=varlist,
period=period, filetypes=filetypes, varatts=None, lconst=True) # still want topography...
elif lts:
loadfct = partial(WRF.loadWRF_TS, experiment=exp, name=None, domains=domain, grid=grid, varlist=varlist,
filetypes=filetypes, varatts=None, lconst=True) # still want topography...
elif dataset == 'CESM':
# CESM datasets
obs_res = None # only for datasets (not used here)
domain = None # only for WRF
exp = dataargs['experiment']
avgfolder = exp.avgfolder
dataset_name = exp.name
periodstr, gridstr = getPeriodGridString(period, grid, exp=exp)
filetypes = dataargs['filetypes']
# check arguments
if period is None and lclim: raise DatasetError, "A 'period' argument is required to load climatologies!"
if lone and len(filetypes) > 1: raise DatasetError # process only one file at a time
# construct dataset message
if lone:
datamsgstr = "Processing CESM '{:s}'-file from Experiment '{:s}'".format(filetypes[0], dataset_name)
else: datamsgstr = "Processing CESM dataset from Experiment '{:s}'".format(dataset_name)
# figure out age of source file(s)
srcage = getSourceAge(fileclasses=CESM.fileclasses, filetypes=filetypes, exp=exp, domain=None,
periodstr=periodstr, gridstr=gridstr, lclim=lclim, lts=lts)
# load source data
load3D = dataargs.pop('load3D',None) # if 3D fields should be loaded (default: False)
if lclim:
loadfct = partial(CESM.loadCESM, experiment=exp, name=None, grid=grid, period=period, varlist=varlist,
filetypes=filetypes, varatts=None, load3D=load3D, translateVars=None)
elif lts:
loadfct = partial(CESM.loadCESM_TS, experiment=exp, name=None, grid=grid, varlist=varlist,
filetypes=filetypes, varatts=None, load3D=load3D, translateVars=None)
elif dataset == dataset.upper() or dataset == 'Unity':
# observational datasets
filetypes = [None] # only for CESM & WRF
domain = None # only for WRF
module = import_module('datasets.{0:s}'.format(dataset))
dataset_name = module.dataset_name
resolution = dataargs['resolution']
if resolution: obs_res = '{0:s}_{1:s}'.format(dataset_name,resolution)
else: obs_res = dataset_name
# figure out period
periodstr, gridstr = getPeriodGridString(period, grid, beginyear=1979)
if period is None and lclim: periodstr = 'LTM'
datamsgstr = "Processing Dataset '{:s}'".format(dataset_name)
# assemble filename to check modification dates (should be only one file)
filename = getFileName(grid=grid, period=period, name=obs_res, filetype=mode)
avgfolder = module.avgfolder
filepath = '{:s}/{:s}'.format(avgfolder,filename)
# load pre-processed climatology
if lclim:
loadfct = partial(module.loadClimatology, name=dataset_name, period=period, grid=grid,
varlist=varlist, resolution=resolution, varatts=None)
elif lts:
loadfct = partial(module.loadTimeSeries, name=dataset_name, grid=grid, varlist=varlist,
resolution=resolution, varatts=None)
# check if the source file is actually correct
if os.path.exists(filepath): filelist = [filepath]
else:
source = loadfct() # don't load dataset, just construct the file list
filelist = source.filelist
# figure out age of source file(s)
srcage = getSourceAge(filelist=filelist, lclim=lclim, lts=lts)
# N.B.: it would be nice to print a message, but then we would have to make the logger available,
# which would be too much trouble
else:
raise DatasetError, "Dataset '{:s}' not found!".format(dataset)
## assemble and return meta data
dataargs = namedTuple(dataset_name=dataset_name, period=period, periodstr=periodstr, avgfolder=avgfolder,
filetypes=filetypes,filetype=filetypes[0], domain=domain, obs_res=obs_res,
varlist=varlist, grid=grid, gridstr=gridstr)
# return meta data
return dataargs, loadfct, srcage, datamsgstr
def getMetaData(dataset, mode, dataargs):
''' determine dataset type and meta data, as well as path to main source file '''
# determine dataset mode
lclim = False
lts = False
if mode == 'climatology': lclim = True
elif mode == 'time-series': lts = True
else: raise NotImplementedError, "Unrecognized Mode: '{:s}'".format(mode)
# defaults for specific variables
obs_res = None
domain = None
filetype = None
varlist = dataargs.get('varlist', None)
# determine meta data based on dataset type
if dataset == 'WRF':
# WRF datasets
module = import_module('datasets.WRF')
exp = dataargs['experiment']
dataset_name = exp.name
domain = dataargs['domain']
grid = dataargs.get('grid', None)
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period, (int, np.integer)):
beginyear = int(exp.begindate[0:4])
period = (beginyear, beginyear + period)
elif len(period) != 2 and all(isInt(period)):
raise DateError
if period is None: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
gridstr = grid if grid is not None else ''
# identify file and domain
if len(dataargs['filetypes']) > 1:
raise DatasetError # process only one file at a time
filetype = dataargs['filetypes'][0]
if isinstance(domain, (list, tuple)): domain = domain[0]
if not isinstance(domain, (np.integer, int)): raise DatasetError
datamsgstr = "Processing WRF '{:s}'-file from Experiment '{:s}' (d{:02d})".format(
filetype, dataset_name, domain)
# assemble filename to check modification dates (should be only one file)
fileclass = module.fileclasses[
filetype] # avoid WRF & CESM name collision
pstr = '_' + periodstr if periodstr else ''
gstr = '_' + gridstr if gridstr else ''
if lclim:
filename = fileclass.climfile.format(
domain, gstr, pstr) # insert domain number, grid, and period
elif lts:
filename = fileclass.tsfile.format(
domain, gstr) # insert domain number, and grid
avgfolder = exp.avgfolder
# load source data
if lclim:
loadfct = functools.partial(
loadWRF,
experiment=exp,
name=None,
domains=domain,
grid=None,
varlist=varlist,
period=period,
filetypes=[filetype],
varatts=None,
lconst=True) # still want topography...
elif lts:
loadfct = functools.partial(
loadWRF_TS,
experiment=exp,
name=None,
domains=domain,
grid=None,
varlist=varlist,
filetypes=[filetype],
varatts=None,
lconst=True) # still want topography...
filepath = '{:s}/{:s}'.format(avgfolder, filename)
elif dataset == 'CESM':
# CESM datasets
module = import_module('datasets.CESM')
exp = dataargs['experiment']
dataset_name = exp.name
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period, (int, np.integer)):
beginyear = int(exp.begindate[0:4])
period = (beginyear, beginyear + period)
elif len(period) != 2 and all(isInt(period)):
raise DateError
# identify file
if len(dataargs['filetypes']) > 1:
raise DatasetError # process only one file at a time
filetype = dataargs['filetypes'][0]
# check period
if period is None: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
datamsgstr = "Processing CESM '{:s}'-file from Experiment '{:s}'".format(
filetype, dataset_name)
# assemble filename to check modification dates (should be only one file)
fileclass = module.fileclasses[
filetype] # avoid WRF & CESM name collision
pstr = '_' + periodstr if periodstr else ''
if lclim:
filename = fileclass.climfile.format(
'', pstr) # insert domain number, grid, and period
elif lts:
filename = fileclass.tsfile.format(
'') # insert domain number, and grid
avgfolder = exp.avgfolder
# load source data
load3D = dataargs.pop(
'load3D', None) # if 3D fields should be loaded (default: False)
if lclim:
loadfct = functools.partial(loadCESM,
experiment=exp,
name=None,
grid=None,
period=period,
varlist=varlist,
filetypes=[filetype],
varatts=None,
load3D=load3D,
translateVars=None)
elif lts:
loadfct = functools.partial(loadCESM_TS,
experiment=exp,
name=None,
grid=None,
varlist=varlist,
filetypes=[filetype],
varatts=None,
load3D=load3D,
translateVars=None)
filepath = '{:s}/{:s}'.format(avgfolder, filename)
elif dataset == dataset.upper() or dataset == 'Unity':
# observational datasets
module = import_module('datasets.{0:s}'.format(dataset))
dataset_name = module.dataset_name
resolution = dataargs['resolution']
if resolution: obs_res = '{0:s}_{1:s}'.format(dataset_name, resolution)
else: obs_res = dataset_name
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period, (int, np.integer)):
period = (1979, 1979 + period) # they all begin in 1979
elif len(period) != 2 and not all(isInt(period)):
raise DateError
datamsgstr = "Processing Dataset '{:s}'".format(dataset_name)
# check period
if period is None:
if mode == 'climatology': periodstr = 'Long-Term Mean'
else: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
# assemble filename to check modification dates (should be only one file)
filename = getFileName(grid=None,
period=period,
name=obs_res,
filetype=mode)
avgfolder = module.avgfolder
# load pre-processed climatology
if lclim:
loadfct = functools.partial(module.loadClimatology,
name=dataset_name,
period=period,
grid=None,
varlist=varlist,
resolution=resolution,
varatts=None,
folder=module.avgfolder,
filelist=None)
elif lts:
loadfct = functools.partial(module.loadTimeSeries,
name=dataset_name,
grid=None,
varlist=varlist,
resolution=resolution,
varatts=None,
folder=None,
filelist=None)
# check if the source file is actually correct
filepath = '{:s}/{:s}'.format(avgfolder, filename)
if not os.path.exists(filepath):
source = loadfct(
) # no varlist - obs don't have many variables anyways
filepath = source.filelist[0]
# N.B.: it would be nice to print a message, but then we would have to make the logger available,
# which would be too much trouble
else:
raise DatasetError, "Dataset '{:s}' not found!".format(dataset)
## assemble and return meta data
if not os.path.exists(filepath):
raise IOError, "Source file '{:s}' does not exist!".format(filepath)
dataargs = namedTuple(dataset_name=dataset_name,
period=period,
periodstr=periodstr,
avgfolder=avgfolder,
filetype=filetype,
domain=domain,
obs_res=obs_res,
varlist=varlist)
# return meta data
return module, dataargs, loadfct, filepath, datamsgstr
if grid in ('shpavg', ):
# regional averages: shape index as grid
uclim = loadUnity_Shp(shape=pntset, period=period)
cruclim = loadCRU_Shp(shape=grid, period=period)
cruts = loadCRU_ShpTS(shape=grid)
else:
raise NotImplementedError
grid_name = grid
periodstr = '{0:4d}-{1:4d}'.format(*period)
print(
'\n *** Merging Shape-Averaged Time-Series on {:s} Grid *** \n'
.format(grid, ))
## prepare target dataset
filename = getFileName(grid=grid_name,
period=None,
name=None,
filepattern=tsfile)
filepath = avgfolder + filename
print(' Saving data to: \'{0:s}\'\n'.format(filepath))
assert os.path.exists(avgfolder)
if os.path.exists(filepath): os.remove(filepath) # remove old file
# set attributes
atts = dict() # collect attributes, but add prefixes
atts = uclim.atts.copy()
atts['title'] = 'Corrected Time-sries on {:s} Grid'.format(
grid_name)
# make new dataset
sink = DatasetNetCDF(folder=avgfolder,
filelist=[filename],
atts=atts,
mode='w')
## load source datasets
# period = (1979,2009)
period = (1979,1994)
if grid in ('shpavg',):
# regional averages: shape index as grid
uclim = loadUnity_Shp(shape=pntset, period=period)
cruclim = loadCRU_Shp(shape=grid, period=period)
cruts = loadCRU_ShpTS(shape=grid)
else:
raise NotImplementedError
grid_name = grid
periodstr = '{0:4d}-{1:4d}'.format(*period)
print('\n *** Merging Shape-Averaged Time-Series on {:s} Grid *** \n'.format(grid,))
## prepare target dataset
filename = getFileName(grid=grid_name, period=None, name=None, filepattern=tsfile)
filepath = avgfolder + filename
print(' Saving data to: \'{0:s}\'\n'.format(filepath))
assert os.path.exists(avgfolder)
if os.path.exists(filepath): os.remove(filepath) # remove old file
# set attributes
atts=dict() # collect attributes, but add prefixes
atts = uclim.atts.copy()
atts['title'] = 'Corrected Time-sries on {:s} Grid'.format(grid_name)
# make new dataset
sink = DatasetNetCDF(folder=avgfolder, filelist=[filename], atts=atts, mode='w')
# sync and write data so far
sink.sync()
## correct data (create variables)
for varname,var in uclim.variables.iteritems():
T2 = dataset.Tmin + dataset.Tmax # average temperature is just the average between min and max
T2 /= 2.
T2.name = 'T2'; T2.atts.long_name='Average 2m Temperature'
print(T2)
dataset += T2 # add to dataset
# rewrite time axis
time = dataset.time
time.load(data=np.arange(1,13))
time.units = 'month'; time.atts.long_name='Month of the Year'
print(time)
# print diagnostic
print(dataset)
print('')
for var in dataset:
#print(var)
print('Mean {0:s}: {1:s} {2:s}'.format(var.atts.long_name, str(var.mean()), var.units))
#print('')
print('')
## create new NetCDF file
# figure out a different filename
filename = getFileName(name='PCIC', filepattern=avgfile)
if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)
# write data and some annotation
sink = writeNetCDF(dataset, avgfolder+filename, close=False)
add_strvar(sink,'name_of_month', name_of_month, 'time', # add names of month
atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
sink.close() # close...
print('Saving Climatology to: '+filename)
print(avgfolder)
dataset.name = 'GPCC'
dataset.title = 'GPCC Long-term Climatology'
dataset.atts.resolution = res
# load data into memory
dataset.load()
# add landmask
addLandMask(dataset) # create landmask from precip mask
dataset.mask(
dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid=res,
period=None,
name='GPCC',
filepattern=avgfile)
print('\n' + filename + '\n')
if os.path.exists(avgfolder + filename):
os.remove(avgfolder + filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder + filename, close=False)
# add_var(ncset,'name_of_month', name_of_month, 'time', # add names of month
# atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# close...
ncset.close()
dataset.close()
# print dataset before
print(dataset)
print('')
def getMetaData(dataset, mode, dataargs):
''' determine dataset type and meta data, as well as path to main source file '''
# determine dataset mode
lclim = False; lts = False
if mode == 'climatology': lclim = True
elif mode == 'time-series': lts = True
else: raise NotImplementedError, "Unrecognized Mode: '{:s}'".format(mode)
# defaults for specific variables
obs_res = None; domain = None; filetype = None
varlist = dataargs.get('varlist',None)
# determine meta data based on dataset type
if dataset == 'WRF':
# WRF datasets
module = import_module('datasets.WRF')
exp = dataargs['experiment']
dataset_name = exp.name
domain = dataargs['domain']
grid = dataargs.get('grid',None)
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period,(int,np.integer)):
beginyear = int(exp.begindate[0:4])
period = (beginyear, beginyear+period)
elif len(period) != 2 and all(isInt(period)): raise DateError
if period is None: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
gridstr = grid if grid is not None else ''
# identify file and domain
if len(dataargs['filetypes']) > 1: raise DatasetError # process only one file at a time
filetype = dataargs['filetypes'][0]
if isinstance(domain,(list,tuple)): domain = domain[0]
if not isinstance(domain, (np.integer,int)): raise DatasetError
datamsgstr = "Processing WRF '{:s}'-file from Experiment '{:s}' (d{:02d})".format(filetype, dataset_name, domain)
# assemble filename to check modification dates (should be only one file)
fileclass = module.fileclasses[filetype] # avoid WRF & CESM name collision
pstr = '_'+periodstr if periodstr else ''
gstr = '_'+gridstr if gridstr else ''
if lclim: filename = fileclass.climfile.format(domain,gstr,pstr) # insert domain number, grid, and period
elif lts: filename = fileclass.tsfile.format(domain,gstr) # insert domain number, and grid
avgfolder = exp.avgfolder
# load source data
if lclim:
loadfct = functools.partial(loadWRF, experiment=exp, name=None, domains=domain, grid=None, varlist=varlist,
period=period, filetypes=[filetype], varatts=None, lconst=True) # still want topography...
elif lts:
loadfct = functools.partial(loadWRF_TS, experiment=exp, name=None, domains=domain, grid=None, varlist=varlist,
filetypes=[filetype], varatts=None, lconst=True) # still want topography...
filepath = '{:s}/{:s}'.format(avgfolder,filename)
elif dataset == 'CESM':
# CESM datasets
module = import_module('datasets.CESM')
exp = dataargs['experiment']
dataset_name = exp.name
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period,(int,np.integer)):
beginyear = int(exp.begindate[0:4])
period = (beginyear, beginyear+period)
elif len(period) != 2 and all(isInt(period)): raise DateError
# identify file
if len(dataargs['filetypes']) > 1: raise DatasetError # process only one file at a time
filetype = dataargs['filetypes'][0]
# check period
if period is None: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
datamsgstr = "Processing CESM '{:s}'-file from Experiment '{:s}'".format(filetype, dataset_name)
# assemble filename to check modification dates (should be only one file)
fileclass = module.fileclasses[filetype] # avoid WRF & CESM name collision
pstr = '_'+periodstr if periodstr else ''
if lclim: filename = fileclass.climfile.format('',pstr) # insert domain number, grid, and period
elif lts: filename = fileclass.tsfile.format('') # insert domain number, and grid
avgfolder = exp.avgfolder
# load source data
load3D = dataargs.pop('load3D',None) # if 3D fields should be loaded (default: False)
if lclim:
loadfct = functools.partial(loadCESM, experiment=exp, name=None, grid=None, period=period, varlist=varlist,
filetypes=[filetype], varatts=None, load3D=load3D, translateVars=None)
elif lts:
loadfct = functools.partial(loadCESM_TS, experiment=exp, name=None, grid=None, varlist=varlist,
filetypes=[filetype], varatts=None, load3D=load3D, translateVars=None)
filepath = '{:s}/{:s}'.format(avgfolder,filename)
elif dataset == dataset.upper() or dataset == 'Unity':
# observational datasets
module = import_module('datasets.{0:s}'.format(dataset))
dataset_name = module.dataset_name
resolution = dataargs['resolution']
if resolution: obs_res = '{0:s}_{1:s}'.format(dataset_name,resolution)
else: obs_res = dataset_name
# figure out period
period = dataargs['period']
if period is None: pass
elif isinstance(period,(int,np.integer)):
period = (1979, 1979+period) # they all begin in 1979
elif len(period) != 2 and not all(isInt(period)): raise DateError
datamsgstr = "Processing Dataset '{:s}'".format(dataset_name)
# check period
if period is None:
if mode == 'climatology': periodstr = 'Long-Term Mean'
else: periodstr = ''
else: periodstr = '{0:4d}-{1:4d}'.format(*period)
# assemble filename to check modification dates (should be only one file)
filename = getFileName(grid=None, period=period, name=obs_res, filetype=mode)
avgfolder = module.avgfolder
# load pre-processed climatology
if lclim:
loadfct = functools.partial(module.loadClimatology, name=dataset_name, period=period, grid=None, varlist=varlist,
resolution=resolution, varatts=None, folder=module.avgfolder, filelist=None)
elif lts:
loadfct = functools.partial(module.loadTimeSeries, name=dataset_name, grid=None, varlist=varlist,
resolution=resolution, varatts=None, folder=None, filelist=None)
# check if the source file is actually correct
filepath = '{:s}/{:s}'.format(avgfolder,filename)
if not os.path.exists(filepath):
source = loadfct() # no varlist - obs don't have many variables anyways
filepath = source.filelist[0]
# N.B.: it would be nice to print a message, but then we would have to make the logger available,
# which would be too much trouble
else:
raise DatasetError, "Dataset '{:s}' not found!".format(dataset)
## assemble and return meta data
if not os.path.exists(filepath): raise IOError, "Source file '{:s}' does not exist!".format(filepath)
dataargs = namedTuple(dataset_name=dataset_name, period=period, periodstr=periodstr, avgfolder=avgfolder,
filetype=filetype, domain=domain, obs_res=obs_res, varlist=varlist)
# return meta data
return module, dataargs, loadfct, filepath, datamsgstr