def loadGPCC_TS(name=dataset_name, grid=None, varlist=None, resolution='25', varatts=None, filelist=None,
folder=None, lautoregrid=None):
''' Get a properly formatted dataset with the monthly GPCC time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# prepare input
if resolution not in ('05', '10', '25'): raise DatasetError, "Selected resolution '%s' is not available!"%resolution
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = varatts.keys()
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
filelist = []
if 'p' in varlist: filelist.append(orig_ts_file.format('precip',resolution))
if 's' in varlist: filelist.append(orig_ts_file.format('statio',resolution))
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts, multifile=False, ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0,len(dataset.time),1, dtype='int16') + (1901-1979)*12 # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time', units='month', coord=data, atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add GDAL info
dataset = addGDALtoDataset(dataset, projection=None, geotransform=None)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
grid, resolution = checkGridRes(grid, resolution, period=None, lclim=False)
dataset = loadObservations(name=name, folder=folder, projection=None, resolution=resolution, grid=grid,
period=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
# return formatted dataset
return dataset
def loadCFSR_Shp(name=dataset_name,
period=None,
shape=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
lencl=False):
''' Get the pre-processed monthly CFSR climatology averaged over regions as a DatasetNetCDF. '''
grid, resolution = checkGridRes(None, resolution)
del grid
# load standardized climatology dataset with -specific parameters
dataset = loadObservations(name=name,
folder=folder,
period=period,
station=None,
shape=shape,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=avgfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
projection=None,
mode='climatology')
# return formatted dataset
return dataset
def loadPGMN_TS(name=None,
title=None,
well=None,
varlist=None,
varatts=None,
conservation_authority=None,
folder=avgfolder,
filelist=None,
lload=True):
''' Get the pre-processed monthly timeseries for GRCA well heads as a DatasetNetCDF. '''
# load standardized time-series dataset with GRCA-specific parameters
name = name or conservation_authority or dataset_name
dataset = loadObservations(name=name,
title=title,
folder=folder,
station=conservation_authority.lower(),
period=None,
grid=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=False,
mode='climatology')
# post-processing
if well: dataset = sliceWellName(well, dataset)
if lload: dataset.load()
# return formatted dataset
return dataset
def loadCRU_TS(name=dataset_name, grid=None, varlist=None, resolution=None, varatts=None, filelist=None,
folder=None, lautoregrid=None):
''' Get a properly formatted CRU dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = varatts.keys()
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
# assemble filelist
if filelist is None: # generate default filelist
filelist = [orig_ts_file.format(var) for var in varlist if var not in nofile]
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts,
multifile=False, ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0,len(dataset.time),1, dtype='int16') + (1901-1979)*12 # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time', units='month', coord=data, atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
dataset = addGDALtoDataset(dataset, projection=None, geotransform=None, gridfolder=grid_folder)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
dataset = loadObservations(name=name, folder=folder, projection=None, resolution=None, grid=grid,
period=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
# return formatted dataset
return dataset
def loadGPCC_Stn(
name=dataset_name,
period=None,
station=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
):
""" Get the pre-processed monthly GPCC climatology as a DatasetNetCDF at station locations. """
grid, resolution = checkGridRes(None, resolution, period=period, lclim=True)
del grid
# load standardized climatology dataset with GPCC-specific parameters
dataset = loadObservations(
name=name,
folder=folder,
projection=None,
period=period,
station=station,
varlist=varlist,
varatts=varatts,
filepattern=avgfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
mode="climatology",
)
# return formatted dataset
return dataset
def loadNARR(name=dataset_name,
period=None,
grid=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True):
''' Get the pre-processed monthly NARR climatology as a DatasetNetCDF. '''
# load standardized climatology dataset with NARR-specific parameters
dataset = loadObservations(name=name,
folder=folder,
projection=projection,
resolution=resolution,
period=period,
grid=grid,
station=None,
shape=None,
varlist=varlist,
varatts=varatts,
filelist=filelist,
filepattern=avgfile,
lautoregrid=lautoregrid,
mode='climatology')
# return formatted dataset
return dataset
def loadNARR_ShpTS(name=dataset_name,
shape=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
lencl=False):
''' Get the pre-processed monthly NARR time-series averaged over regions as a DatasetNetCDF. '''
# load standardized time-series dataset with NARR-specific parameters
dataset = loadObservations(name=name,
folder=folder,
period=None,
station=None,
shape=shape,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=False,
projection=None,
mode='time-series')
# return formatted dataset
return dataset
def loadPCIC_Shp(name=dataset_name,
period=None,
shape=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lencl=False):
''' Get the pre-processed monthly PCIC PRISM climatology averaged over regions as a DatasetNetCDF. '''
# only the climatology is available
if period is not None:
warn(
'Only the full climatology is currently available: setting \'period\' to None.'
)
period = None
# load standardized climatology dataset with PCIC-specific parameters
dataset = loadObservations(name=name,
folder=folder,
grid=None,
station=None,
shape=shape,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=avgfile,
projection=None,
filelist=filelist,
lautoregrid=False,
period=period,
mode='climatology')
# return formatted dataset
return dataset
def loadCFSR_StnTS(name=dataset_name,
station=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True):
''' Get the pre-processed monthly CFSR time-series at station locations as a DatasetNetCDF. '''
grid, resolution = checkGridRes(None, resolution)
del grid
# load standardized time-series dataset with -specific parameters
dataset = loadObservations(name=name,
folder=folder,
period=None,
station=station,
shape=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
projection=None,
mode='time-series')
# return formatted dataset
return dataset
def loadGPCC_ShpTS(name=dataset_name,
shape=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
lencl=False):
''' Get the pre-processed monthly GPCC time-series as a DatasetNetCDF averaged over regions. '''
grid, resolution = checkGridRes(None, resolution, period=None, lclim=False)
del grid
# load standardized time-series dataset with GPCC-specific parameters
dataset = loadObservations(name=name,
folder=folder,
projection=None,
shape=shape,
station=None,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
mode='time-series',
period=None)
# return formatted dataset
return dataset
def loadGPCC_Stn(name=dataset_name,
period=None,
station=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True):
''' Get the pre-processed monthly GPCC climatology as a DatasetNetCDF at station locations. '''
grid, resolution = checkGridRes(None,
resolution,
period=period,
lclim=True)
del grid
# load standardized climatology dataset with GPCC-specific parameters
dataset = loadObservations(name=name,
folder=folder,
projection=None,
period=period,
station=station,
varlist=varlist,
varatts=varatts,
filepattern=avgfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
mode='climatology')
# return formatted dataset
return dataset
def loadNARR_TS(name=dataset_name, grid=None, varlist=None, resolution=None, varatts=None, filelist=None,
folder=None, lautoregrid=None):
''' Get a properly formatted NARR dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = tsvarlist
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
filelist = [orig_ts_file.format(special[var]) if var in special else orig_ts_file.format(var) for var in varlist
if var not in nofile and var in varatts]
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts,
atts=projdict, multifile=False, ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0,len(dataset.time),1, dtype='int16') # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time', units='month', coord=data, atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
projection = getProjFromDict(projdict, name='{0:s} Coordinate System'.format(name))
dataset = addGDALtoDataset(dataset, projection=projection, geotransform=None, gridfolder=grid_folder)
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
dataset = loadObservations(name=name, folder=folder, projection=None, resolution=None, grid=grid,
period=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
# return formatted dataset
return dataset
def loadGPCC_ShpTS(
name=dataset_name,
shape=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
lencl=False,
):
""" Get the pre-processed monthly GPCC time-series as a DatasetNetCDF averaged over regions. """
grid, resolution = checkGridRes(None, resolution, period=None, lclim=False)
del grid
# load standardized time-series dataset with GPCC-specific parameters
dataset = loadObservations(
name=name,
folder=folder,
projection=None,
shape=shape,
station=None,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
resolution=resolution,
lautoregrid=False,
mode="time-series",
period=None,
)
# return formatted dataset
return dataset
def loadPCIC(name=dataset_name,
period=None,
grid=None,
resolution=None,
varlist=None,
varatts=None,
folder=None,
filelist=None,
lautoregrid=True):
''' Get the pre-processed monthly PCIC PRISM climatology as a DatasetNetCDF. '''
if folder is None: folder = avgfolder
# only the climatology is available
if period is not None:
warn(
'Only the full climatology is currently available: setting \'period\' to None.'
)
period = None
# load standardized climatology dataset with PRISM-specific parameters
dataset = loadObservations(name=name,
folder=folder,
projection=None,
period=period,
grid=grid,
varlist=varlist,
varatts=varatts,
filepattern=avgfile,
filelist=filelist,
lautoregrid=lautoregrid,
mode='climatology')
# # make sure all fields are masked
# dataset.load()
# dataset.mask(dataset.datamask, maskSelf=False)
# return formatted dataset
return dataset
def loadCRU_ShpTS(name=dataset_name, shape=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True, lencl=False):
''' Get the pre-processed monthly CRU time-series as a DatasetNetCDF averaged over regions. '''
dataset = loadObservations(name=name, folder=folder, period=None, station=None, shape=shape, lencl=lencl,
varlist=varlist, varatts=varatts, filepattern=tsfile, filelist=filelist,
lautoregrid=False, projection=None, mode='time-series')
# return formatted dataset
return dataset
def loadCRU_Shp(name=dataset_name, period=None, shape=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True, lencl=False):
''' Get the pre-processed monthly CRU climatology as a DatasetNetCDF averaged over regions. '''
# load standardized climatology dataset with CRU-specific parameters
dataset = loadObservations(name=name, folder=folder, period=period, station=None, shape=shape, lencl=lencl,
varlist=varlist, varatts=varatts, filepattern=avgfile, filelist=filelist,
lautoregrid=False, projection=None, mode='climatology')
# return formatted dataset
return dataset
def loadNARR_Stn(name=dataset_name, period=None, station=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True):
''' Get the pre-processed monthly NARR climatology at station locations as a DatasetNetCDF. '''
# load standardized climatology dataset with NARR-specific parameters
dataset = loadObservations(name=name, folder=folder, period=period, station=station, shape=None,
varlist=varlist, varatts=varatts, filepattern=avgfile, filelist=filelist,
lautoregrid=False, projection=None, mode='climatology')
# return formatted dataset
return dataset
def loadCRU_StnTS(name=dataset_name, station=None, resolution=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=False):
''' Get the pre-processed monthly CRU climatology as a DatasetNetCDF. '''
# load standardized time-series dataset with CRU-specific parameters
dataset = loadObservations(name=name, folder=folder, period=None, station=station, shape=None,
varlist=varlist, varatts=varatts, filepattern=tsfile, filelist=filelist,
lautoregrid=False, projection=None, mode='time-series')
# return formatted dataset
return dataset
def loadCRU(name=dataset_name, period=None, grid=None, resolution=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True):
''' Get the pre-processed monthly CRU climatology as a DatasetNetCDF. '''
# load standardized climatology dataset with CRU-specific parameters
dataset = loadObservations(name=name, folder=folder, projection=None, period=period, grid=grid,
varlist=varlist, varatts=varatts, filepattern=avgfile, filelist=filelist,
lautoregrid=lautoregrid, mode='climatology')
# return formatted dataset
return dataset
def loadNARR_ShpTS(name=dataset_name, shape=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True, lencl=False):
''' Get the pre-processed monthly NARR time-series averaged over regions as a DatasetNetCDF. '''
# load standardized time-series dataset with NARR-specific parameters
dataset = loadObservations(name=name, folder=folder, period=None, station=None, shape=shape, lencl=lencl,
varlist=varlist, varatts=varatts, filepattern=tsfile, filelist=filelist,
lautoregrid=False, projection=None, mode='time-series')
# return formatted dataset
return dataset
def loadGPCC_Shp(name=dataset_name, period=None, shape=None, resolution=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True, lencl=False):
''' Get the pre-processed monthly GPCC climatology as a DatasetNetCDF averaged over regions. '''
grid, resolution = checkGridRes(None, resolution, period=period, lclim=True); del grid
# load standardized climatology dataset with GPCC-specific parameters
dataset = loadObservations(name=name, folder=folder, projection=None, period=period, shape=shape, lencl=lencl,
station=None, varlist=varlist, varatts=varatts, filepattern=avgfile,
filelist=filelist, resolution=resolution, lautoregrid=False, mode='climatology')
# return formatted dataset
return dataset
def loadGPCC_StnTS(name=dataset_name, station=None, resolution=None, varlist=None, varatts=None,
folder=avgfolder, filelist=None, lautoregrid=True):
''' Get the pre-processed monthly GPCC time-series as a DatasetNetCDF at station locations. '''
grid, resolution = checkGridRes(None, resolution, period=None, lclim=False); del grid
# load standardized time-series dataset with GPCC-specific parameters
dataset = loadObservations(name=name, folder=folder, projection=None, period=None, station=station,
varlist=varlist, varatts=varatts, filepattern=tsfile, filelist=filelist,
resolution=resolution, lautoregrid=False, mode='time-series')
# return formatted dataset
return dataset
def loadCFSR_TS(name=dataset_name, grid=None, varlist=None, varatts=None, resolution='hires',
filelist=None, folder=None, lautoregrid=None):
''' Get a properly formatted CFSR dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None:
if resolution == 'hires' or resolution == '03' or resolution == '031': varlist = varlist_hires
elif resolution == 'lowres' or resolution == '05': varlist = varlist_lowres
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
if resolution == 'hires' or resolution == '03' or resolution == '031':
files = [hiresfiles[var] for var in varlist if var in hiresfiles]
elif resolution == 'lowres' or resolution == '05':
files = [lowresfiles[var] for var in varlist if var in lowresfiles]
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=files, varlist=varlist, varatts=varatts,
check_override=['time'], multifile=False, ncformat='NETCDF4_CLASSIC')
# load static data
if filelist is None: # generate default filelist
if resolution == 'hires' or resolution == '03' or resolution == '031':
files = [hiresstatic[var] for var in varlist if var in hiresstatic]
elif resolution == 'lowres' or resolution == '05':
files = [lowresstatic[var] for var in varlist if var in lowresstatic]
# load constants, if any (and with singleton time axis)
if len(files) > 0:
staticdata = DatasetNetCDF(name=name, folder=folder, filelist=files, varlist=varlist, varatts=varatts,
axes=dict(lon=dataset.lon, lat=dataset.lat), multifile=False,
check_override=['time'], ncformat='NETCDF4_CLASSIC')
# N.B.: need to override the axes, so that the datasets are consistent
if len(staticdata.variables) > 0:
for var in staticdata.variables.values():
if not dataset.hasVariable(var.name):
var.squeeze() # remove time dimension
dataset.addVariable(var, copy=False) # no need to copy... but we can't write to the netcdf file!
# replace time axis with number of month since Jan 1979
data = np.arange(0,len(dataset.time),1, dtype='int16') # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time', units='month', coord=data, atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
dataset = addGDALtoDataset(dataset, projection=None, geotransform=None, gridfolder=grid_folder)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
grid, resolution = checkGridRes(grid, resolution)
dataset = loadObservations(name=name, folder=folder, projection=None, resolution=resolution, grid=grid,
period=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
# return formatted dataset
return dataset
def loadPRISM_Shp(name=dataset_name, period=None, shape=None, resolution=None, varlist=None,
varatts=None, folder=avgfolder, filelist=None, lencl=False):
''' Get the pre-processed monthly PRISM climatology averaged over regions as a DatasetNetCDF. '''
# only the climatology is available
if period is not None:
warn('Only the full climatology is currently available: setting \'period\' to None.')
period = None
# load standardized climatology dataset with PRISM-specific parameters
dataset = loadObservations(name=name, folder=folder, grid=None, station=None, shape=shape, lencl=lencl,
varlist=varlist, varatts=varatts, filepattern=avgfile, filelist=filelist,
lautoregrid=False, period=period, mode='climatology')
# return formatted dataset
return dataset
def loadPGMN_TS(name=None, title=None, well=None, varlist=None, varatts=None,
conservation_authority=None, folder=avgfolder, filelist=None, lload=True):
''' Get the pre-processed monthly timeseries for GRCA well heads as a DatasetNetCDF. '''
# load standardized time-series dataset with GRCA-specific parameters
name = name or conservation_authority or dataset_name
dataset = loadObservations(name=name, title=title, folder=folder, station=conservation_authority.lower(),
period=None, grid=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=False, mode='climatology')
# post-processing
if well: dataset = sliceWellName(well, dataset)
if lload: dataset.load()
# return formatted dataset
return dataset
def loadPRISM(name=dataset_name, period=None, grid=None, resolution=None, varlist=None,
varatts=None, folder=avgfolder, filelist=None, lautoregrid=True):
''' Get the pre-processed monthly PRISM climatology as a DatasetNetCDF. '''
# only the climatology is available
if period is not None:
warn('Only the full climatology is currently available: setting \'period\' to None.')
period = None
# load standardized climatology dataset with PRISM-specific parameters
dataset = loadObservations(name=name, folder=folder, period=period, grid=grid, station=None,
varlist=varlist, varatts=varatts, filepattern=avgfile, filelist=filelist,
lautoregrid=lautoregrid, mode='climatology')
# # make sure all fields are masked
# dataset.load()
# dataset.mask(dataset.datamask, maskSelf=False)
# return formatted dataset
return dataset
def loadSpecialObs(varlist=None, **kwargs):
''' wrapper that adds some special/derived variables '''
# parse and edit varlist
if varlist is None:
ldryprec = True
lwetprec = True
lwetdays = True
else:
# cast/copy varlist
if isinstance(varlist, basestring): varlist = [varlist] # cast as list
else: varlist = list(varlist) # make copy to avoid interference
# select options
ldryprec = False
lwetprec = False
lwetdays = False
if 'dryprec' in varlist: # really just an alias for precip
ldryprec = True
varlist.remove('dryprec')
if 'precip' not in varlist: varlist.append('precip')
if 'wetprec' in varlist:
lwetprec = True
varlist.remove('wetprec') # wet-day precip
if 'precip' not in varlist: varlist.append('precip')
if 'wetfrq' not in varlist: varlist.append('wetfrq')
if any([var[:6] == 'wetfrq' for var in varlist]):
lwetdays = True
if 'wetfrq' not in varlist: varlist.append('wetfrq')
# load actual data using standard call to loadObservation
dataset = loadObservations(varlist=varlist, **kwargs)
# add new/special variables
if lwetprec and 'wetprec' not in dataset:
wetprec = dataset.precip.load() / dataset.wetfrq.load()
dataset += wetprec.copy(plot=variablePlotatts['wetprec'],
**varatts['wetprec'])
if ldryprec and 'dryprec' not in dataset:
dataset += dataset.precip.copy(plot=variablePlotatts['dryprec'],
**varatts['dryprec'])
if lwetdays:
from wrfavg.derived_variables import precip_thresholds
for threshold in precip_thresholds:
varname = 'wetfrq_{:03d}'.format(int(threshold * 10))
if varname not in dataset:
dataset += dataset.wetfrq.copy(name=varname)
# return augmented dataset
return dataset
def loadCRU_ShpTS(name=dataset_name,
shape=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=True,
lencl=False):
''' Get the pre-processed monthly CRU time-series as a DatasetNetCDF averaged over regions. '''
dataset = loadObservations(name=name,
folder=folder,
period=None,
station=None,
shape=shape,
lencl=lencl,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=False,
projection=None,
mode='time-series')
# return formatted dataset
return dataset
def loadSpecialObs(varlist=None, **kwargs):
''' wrapper that adds some special/derived variables '''
# parse and edit varlist
if varlist is None:
ldryprec = True; lwetprec = True; lwetdays = True
else:
# cast/copy varlist
if isinstance(varlist,basestring): varlist = [varlist] # cast as list
else: varlist = list(varlist) # make copy to avoid interference
# select options
ldryprec = False; lwetprec = False; lwetdays = False
if 'dryprec' in varlist: # really just an alias for precip
ldryprec = True; varlist.remove('dryprec')
if 'precip' not in varlist: varlist.append('precip')
if 'wetprec' in varlist:
lwetprec = True; varlist.remove('wetprec') # wet-day precip
if 'precip' not in varlist: varlist.append('precip')
if 'wetfrq' not in varlist: varlist.append('wetfrq')
if any([var[:6]=='wetfrq' for var in varlist]):
lwetdays = True
if 'wetfrq' not in varlist: varlist.append('wetfrq')
# load actual data using standard call to loadObservation
dataset = loadObservations(varlist=varlist, **kwargs)
# add new/special variables
if lwetprec and 'wetprec' not in dataset:
wetprec = dataset.precip.load() / dataset.wetfrq.load()
dataset += wetprec.copy(plot=variablePlotatts['wetprec'], **varatts['wetprec'])
if ldryprec and 'dryprec' not in dataset:
dataset += dataset.precip.copy(plot=variablePlotatts['dryprec'], **varatts['dryprec'])
if lwetdays:
for threshold in precip_thresholds:
varname = 'wetfrq_{:03d}'.format(int(threshold*10))
if varname not in dataset:
dataset += dataset.wetfrq.copy(name=varname)
# return augmented dataset
return dataset
def loadCRU_StnTS(name=dataset_name,
station=None,
resolution=None,
varlist=None,
varatts=None,
folder=avgfolder,
filelist=None,
lautoregrid=False):
''' Get the pre-processed monthly CRU climatology as a DatasetNetCDF. '''
# load standardized time-series dataset with CRU-specific parameters
dataset = loadObservations(name=name,
folder=folder,
period=None,
station=station,
shape=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=False,
projection=None,
mode='time-series')
# return formatted dataset
return dataset
def loadCRU_TS(name=dataset_name,
grid=None,
varlist=None,
resolution=None,
varatts=None,
filelist=None,
folder=None,
lautoregrid=None):
''' Get a properly formatted CRU dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = varatts.keys()
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
# assemble filelist
if filelist is None: # generate default filelist
filelist = [
orig_ts_file.format(var) for var in varlist
if var not in nofile
]
# load dataset
dataset = DatasetNetCDF(name=name,
folder=folder,
filelist=filelist,
varlist=varlist,
varatts=varatts,
multifile=False,
ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0, len(dataset.time), 1, dtype='int16') + (
1901 - 1979) * 12 # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time',
units='month',
coord=data,
atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
dataset = addGDALtoDataset(dataset,
projection=None,
geotransform=None,
gridfolder=grid_folder)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
dataset = loadObservations(name=name,
folder=folder,
projection=None,
resolution=None,
grid=grid,
period=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=lautoregrid,
mode='time-series')
# return formatted dataset
return dataset
def loadGPCC_TS(name=dataset_name,
grid=None,
varlist=None,
resolution='25',
varatts=None,
filelist=None,
folder=None,
lautoregrid=None):
''' Get a properly formatted dataset with the monthly GPCC time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# prepare input
if resolution not in ('05', '10', '25'):
raise DatasetError, "Selected resolution '%s' is not available!" % resolution
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = varatts.keys()
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
filelist = []
if 'p' in varlist:
filelist.append(orig_ts_file.format('precip', resolution))
if 's' in varlist:
filelist.append(orig_ts_file.format('statio', resolution))
# load dataset
dataset = DatasetNetCDF(name=name,
folder=folder,
filelist=filelist,
varlist=varlist,
varatts=varatts,
multifile=False,
ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0, len(dataset.time), 1, dtype='int16') + (
1901 - 1979) * 12 # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time',
units='month',
coord=data,
atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add GDAL info
dataset = addGDALtoDataset(dataset, projection=None, geotransform=None)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
grid, resolution = checkGridRes(grid,
resolution,
period=None,
lclim=False)
dataset = loadObservations(name=name,
folder=folder,
projection=None,
resolution=resolution,
grid=grid,
period=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=lautoregrid,
mode='time-series')
# return formatted dataset
return dataset
def loadCFSR_TS(name=dataset_name,
grid=None,
varlist=None,
varatts=None,
resolution='hires',
filelist=None,
folder=None,
lautoregrid=None):
''' Get a properly formatted CFSR dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None:
if resolution == 'hires' or resolution == '03' or resolution == '031':
varlist = varlist_hires
elif resolution == 'lowres' or resolution == '05':
varlist = varlist_lowres
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
if resolution == 'hires' or resolution == '03' or resolution == '031':
files = [
hiresfiles[var] for var in varlist if var in hiresfiles
]
elif resolution == 'lowres' or resolution == '05':
files = [
lowresfiles[var] for var in varlist if var in lowresfiles
]
# load dataset
dataset = DatasetNetCDF(name=name,
folder=folder,
filelist=files,
varlist=varlist,
varatts=varatts,
check_override=['time'],
multifile=False,
ncformat='NETCDF4_CLASSIC')
# load static data
if filelist is None: # generate default filelist
if resolution == 'hires' or resolution == '03' or resolution == '031':
files = [
hiresstatic[var] for var in varlist if var in hiresstatic
]
elif resolution == 'lowres' or resolution == '05':
files = [
lowresstatic[var] for var in varlist if var in lowresstatic
]
# create singleton time axis
staticdata = DatasetNetCDF(name=name,
folder=folder,
filelist=files,
varlist=varlist,
varatts=varatts,
axes=dict(lon=dataset.lon,
lat=dataset.lat),
multifile=False,
check_override=['time'],
ncformat='NETCDF4_CLASSIC')
# N.B.: need to override the axes, so that the datasets are consistent
if len(staticdata.variables) > 0:
for var in staticdata.variables.values():
if not dataset.hasVariable(var.name):
var.squeeze() # remove time dimension
dataset.addVariable(
var, copy=False
) # no need to copy... but we can't write to the netcdf file!
# replace time axis with number of month since Jan 1979
data = np.arange(0, len(dataset.time), 1,
dtype='int16') # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time',
units='month',
coord=data,
atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
dataset = addGDALtoDataset(dataset,
projection=None,
geotransform=None,
gridfolder=grid_folder)
# N.B.: projection should be auto-detected as geographic
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
grid, resolution = checkGridRes(grid, resolution)
dataset = loadObservations(name=name,
folder=folder,
projection=None,
resolution=resolution,
grid=grid,
period=None,
varlist=varlist,
varatts=varatts,
filepattern=tsfile,
filelist=filelist,
lautoregrid=lautoregrid,
mode='time-series')
# return formatted dataset
return dataset
