def loadEnsembleFit(lfit=True, dist=None, dist_args=None, reference=None, target=None,
lglobalScale=False, lrescale=False, lflatten=True, sample_axis=None,
lcrossval=False, ncv=0.2, lbootstrap=False, nbs=100,
WRF_exps=None, CESM_exps=None, WRF_ens=None, CESM_ens=None, variable_list=None,
load_list=None, lproduct='outer', lshort=True, datatype=None, **kwargs):
''' convenience function to load ensemble time-series data and compute associated distribution ensembles '''
if lrescale and not lfit: raise ArgumentError
load_list = [] if load_list is None else load_list[:] # use a copy, since the list may be modified
# load shape ensemble
if datatype.lower() == 'shape':
ensemble = loadShapeEnsemble(variable_list=variable_list, WRF_exps=WRF_exps, CESM_exps=CESM_exps,
WRF_ens=WRF_ens, CESM_ens=CESM_ens, load_list=load_list, lproduct=lproduct, **kwargs)
if datatype.lower() == 'station':
ensemble = loadStationEnsemble(variable_list=variable_list, WRF_exps=WRF_exps, CESM_exps=CESM_exps,
WRF_ens=WRF_ens, CESM_ens=CESM_ens, load_list=load_list, lproduct=lproduct, **kwargs)
# N.B.: kwargs are first passed on to loadShapeEnsemble/loadStationEnsemble and then to loadEnsembleTS
# generate matching datasets with fitted distributions
fitens, sclens = addDistFit(ensemble=ensemble, lfit=lfit, dist=dist, dist_args=dist_args, lflatten=lflatten,
lrescale=lrescale, reference=reference, target=target, lglobalScale=lglobalScale,
lbootstrap=lbootstrap, nbs=nbs, lcrossval=lcrossval, ncv=ncv,
sample_axis=sample_axis, load_list=load_list, lproduct=lproduct, **kwargs)
# N.B.: kwargs are mainly needed to infer the expanded shape of the ensemble list
# return ensembles (will be wrapped in a list, if BatchLoad is used)
if lshort:
if lfit and lrescale: return ensemble, fitens, sclens
elif lfit: return ensemble, fitens
else: return ensemble
else:
return ensemble, fitens, sclens
def loadShapeEnsemble(variable_list=None, **kwargs):
''' wrapper for clim.load.loadShapeEnsemble that sets experiment and variable lists '''
if variable_list is None: variable_list = variables_rc
return clim_load.loadShapeEnsemble(variable_list=variable_list,
WRF_exps=WRF_exps,
CESM_exps=CESM_exps,
WRF_ens=WRF_ens,
CESM_ens=CESM_ens,
**kwargs)
def loadShapeEnsemble(variable_list=None, **kwargs):
""" wrapper for clim.load.loadShapeEnsemble that sets experiment and variable lists """
if variable_list is None:
variable_list = variables_rc
return clim_load.loadShapeEnsemble(
variable_list=variable_list,
WRF_exps=WRF_exps,
CESM_exps=CESM_exps,
WRF_ens=WRF_ens,
CESM_ens=CESM_ens,
**kwargs
)
def loadShapeEnsemble(variable_list=variables_rc, shapetype=default_shapetype, basin_list=wsc_basins.basin_list, **kwargs):
''' wrapper for clim.load.loadShapeEnsemble that sets experiment and variable lists '''
return clim_load.loadShapeEnsemble(variable_list=variable_list, shapetype=shapetype, WRF_exps=WRF_exps,
CESM_exps=CESM_exps, WRF_ens=WRF_ens, CESM_ens=CESM_ens, basin_list=basin_list,
obs_clim='NRCan', obs_ts='CRU', **kwargs)
def loadEnsembleFit(lfit=True,
dist=None,
dist_args=None,
reference=None,
target=None,
lglobalScale=False,
lrescale=False,
lflatten=True,
sample_axis=None,
lcrossval=False,
ncv=0.2,
lbootstrap=False,
nbs=100,
WRF_exps=None,
CESM_exps=None,
WRF_ens=None,
CESM_ens=None,
variable_list=None,
load_list=None,
lproduct='outer',
lshort=True,
datatype=None,
**kwargs):
''' convenience function to load ensemble time-series data and compute associated distribution ensembles '''
if lrescale and not lfit: raise ArgumentError
load_list = [] if load_list is None else load_list[:] # use a copy, since the list may be modified
# load shape ensemble
if datatype.lower() == 'shape':
ensemble = loadShapeEnsemble(variable_list=variable_list,
WRF_exps=WRF_exps,
CESM_exps=CESM_exps,
WRF_ens=WRF_ens,
CESM_ens=CESM_ens,
load_list=load_list,
lproduct=lproduct,
**kwargs)
if datatype.lower() == 'station':
ensemble = loadStationEnsemble(variable_list=variable_list,
WRF_exps=WRF_exps,
CESM_exps=CESM_exps,
WRF_ens=WRF_ens,
CESM_ens=CESM_ens,
load_list=load_list,
lproduct=lproduct,
**kwargs)
# N.B.: kwargs are first passed on to loadShapeEnsemble/loadStationEnsemble and then to loadEnsembleTS
# generate matching datasets with fitted distributions
fitens, sclens = addDistFit(ensemble=ensemble,
lfit=lfit,
dist=dist,
dist_args=dist_args,
lflatten=lflatten,
lrescale=lrescale,
reference=reference,
target=target,
lglobalScale=lglobalScale,
lbootstrap=lbootstrap,
nbs=nbs,
lcrossval=lcrossval,
ncv=ncv,
sample_axis=sample_axis,
load_list=load_list,
lproduct=lproduct,
**kwargs)
# N.B.: kwargs are mainly needed to infer the expanded shape of the ensemble list
# return ensembles (will be wrapped in a list, if BatchLoad is used)
if lshort:
if lfit and lrescale: return ensemble, fitens, sclens
elif lfit: return ensemble, fitens
else: return ensemble
else:
return ensemble, fitens, sclens
def loadShapeEnsemble(variable_list=variables_rc, shapetype=default_shapetype, basin_list=None, **kwargs):
''' wrapper for clim.load.loadShapeEnsemble that sets experiment and variable lists '''
return clim_load.loadShapeEnsemble(variable_list=variable_list, shapetype=shapetype, WRF_exps=None,
CESM_exps=None, WRF_ens=None, CESM_ens=None, basin_list=None, **kwargs)
