def rotate( dat, lons, to_pacific=False ): '''rotate longitudes in WGS84 Global Extent''' if to_pacific == True: # to 0 - 360 dat, lons = utils.shiftgrid( 0., dat, lons ) elif to_pacific == False: # to -180.0 - 180.0 dat, lons = utils.shiftgrid( 180., dat, lons, start=False ) else: raise AttributeError( 'to_pacific must be boolean True:False' ) return dat, lons
def rotate( dat, lons, to_pacific=False ): '''rotate longitudes in WGS84 Global Extent''' if to_pacific == False: # to -180.0 - 180.0 dat, lons = utils.shiftgrid( 180., dat, lons, start=False ) elif to_pacific == True: # to 0 - 360 dat, lons = utils.shiftgrid( 0., dat, lons ) else: raise AttributeError( 'to_pacific can be only one of True or False' ) return dat, lons
def downscale( self, output_dir, prefix=None, ncpus=32 ):
import affine
import itertools
from functools import partial
# import multiprocessing
from pathos import multiprocessing
# output_filenames
time = self.anomalies.time.to_pandas()
time_suffix = [ '_'.join([str(t.month), str(t.year)]) for t in time ]
if prefix:
output_filenames = [ os.path.join( output_dir, '_'.join([prefix, ts]) + '.tif' ) for ts in time_suffix ]
else:
if not self.historical.units:
units = 'units'
else:
units = self.historical.units
output_filenames = [ os.path.join( output_dir, '_'.join([self.historical.variable, units, \
self.metric, self.historical.model, ts]) + '.tif') for ts in time_suffix ]
# rotate
if ( self.anomalies.lon > 200.0 ).any() == True:
dat, lons = utils.shiftgrid( 180., self.anomalies, self.anomalies.lon, start=False )
a,b,c,d,e,f,g,h,i = self.affine
# flip it to the greenwich-centering
src_transform = affine.Affine( a, b, -180.0, d, e, 180.0 )
else:
dat, lons = ( self.anomalies, self.anomalies.lon )
src_transform = self.affine
# run and output
rstlist = self.baseline.filelist * (self.anomalies.shape[0] / 12)
args = zip( self.anomalies, rstlist, output_filenames )
args = [{'anom':i.data, 'base':j, 'output_filename':k} for i,j,k in args ]
# determine operation type
downscaling_operation_switch = {'absolute':'add', 'relative':'mult'}
downscaling_operation = downscaling_operation_switch[ self.ds_type ]
# partial and wrapper
f = partial( self.interp_ds, src_transform=src_transform, downscaling_operation=downscaling_operation, \
post_downscale_function=None, mask=None, mask_value=0 )
def wrap( d ):
return f( **d )
pool = multiprocessing.Pool( ncpus )
# out = pool.map( lambda x: f( **x ), args )
out = pool.map( wrap, args )
# pool.join()
pool.close()
return output_dir
def downscale( self, output_dir, prefix=None ):
import affine
from affine import Affine
import itertools
from functools import partial
from pathos.mp_map import mp_map
operation_switch = { 'add':self.add, 'mult':self.mult }
def two_digit_month( x ):
''' make 1 digit month a standard 2-digit for output filenames '''
month = str( x )
if len(month) == 1:
month = '0'+month
return month
time_suffix = [ '_'.join([two_digit_month( t.month ), str(t.year)]) for t in self.anomalies.time.to_pandas() ]
# handle missing variable / model names
if self.varname != None:
variable = self.varname
elif self.historical.variable != None:
variable = self.historical.variable
else:
variable = 'variable'
if self.modelname != None:
model = self.modelname
elif self.historical.model != None:
model = self.historical.model
else:
model = 'model'
output_filenames = [ os.path.join( output_dir, '_'.join([variable, self.historical.metric, self.historical.units, \
self.historical.project, model, self.historical.scenario, ts]) + '.tif') for ts in time_suffix ]
# if there is a specific name prefix, use it
if prefix != None:
output_filenames = [ os.path.join( output_dir, '_'.join([prefix, ts]) + '.tif' ) for ts in time_suffix ]
# rotate to pacific-centered
if ( self.anomalies.lon.data > 200.0 ).any() == True:
dat, lons = ( self.anomalies, self.anomalies.lon )
self.anomalies_rot = dat
src_transform = self.historical.transform_from_latlon( self.historical.ds.lat, lons )
print( 'anomalies NOT rotated!' )
else:
dat, lons = utils.shiftgrid( 0., self.anomalies, self.anomalies.lon )
self.anomalies_rot = dat
src_transform = self.historical.transform_from_latlon( self.historical.ds.lat, lons )
print( src_transform )
print( 'anomalies rotated!' )
# run and output
rstlist = self.baseline.filelist * (self.anomalies_rot.shape[0] / 12)
if isinstance( self.anomalies_rot, xr.Dataset ):
self.anomalies_rot = self.anomalies_rot[ self.historical.variable ].data
elif isinstance( self.anomalies_rot, xr.DataArray ):
self.anomalies_rot = self.anomalies_rot.data
else:
self.anomalies_rot = self.anomalies_rot
args = zip( self.anomalies_rot, rstlist, output_filenames )
args = [{'anom':i, 'base':j, 'output_filename':k,\
'downscaling_operation':self.downscaling_operation, \
'post_downscale_function':self.post_downscale_function,\
'mask':self.mask, 'mask_value':self.mask_value } for i,j,k in args ]
# partial and wrapper
f = partial( self.interp_ds, src_crs=self.src_crs, src_nodata=self.src_nodata, \
dst_nodata=self.dst_nodata, src_transform=src_transform, resample_type=self.resample_type )
run = partial( self._run_ds, f=f, operation_switch=operation_switch, anom=self.anom, mask_value=self.mask_value )
# run it
out = mp_map( run, args, nproc=self.ncpus )
return output_dir
