def downscale_wrapper( arr, affine, crs, baseline, output_filename, downscaling_operation, post_downscale_function ):
# rotate
if ( self.data.ds.lon > 200.0 ).any() == True:
dat, lons = utils.shiftgrid( 180., self.data.anomalies, self.historical.ds.lon, start=False )
a,b,c,d,e,f,g,h,i = affine #flip it to the greenwich-centering
src_transform = affine.Affine( a, b, -180.0, d, e, 180.0 )
else:
dat, lons = ( self.data.ds, self.historical.ds.lon )
src_transform = affine
# reproject / resample
src_crs = {'init':'epsg:4326'}
src_nodata = None # DangerTown™
baseline_meta = baseline.meta
baseline_meta.update( compress='lzw' )
output_arr = np.empty_like( baeline.read( 1 ) )
# TODO: make this function available for manipulation if used for different needs
reproject( arr, output_arr, src_transform=src_transform, src_crs=src_crs, src_nodata=src_nodata, \
dst_transform=baseline_meta['affine'], dst_crs=baseline_meta['crs'],\
dst_nodata=None, resampling=RESAMPLING.cubic_spline, SOURCE_EXTRA=1000 )
# downscale
return utils.downscale( arr, output_arr, output_filename, downscaling_operation, \
baseline_meta, post_downscale_function, mask=None, mask_value=0 )
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 = [ rasterio.open( fn ) for fn in self.baseline.filelist ]
# # # # #
# maybe itertools.repeat is needed here? # these are same references
rstlist = itertools.repeat( rstlist, (self.anomalies.shape[0] / 12) )
rstlist = [ j for i in rstlist for j in i ]
# rstlist = rstlist * (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 ]
downscaling_operation_switch = {'absolute':'add', 'relative':'mult'}
downscaling_operation = downscaling_operation_switch[ self.ds_type ]
# anom_arr, baseline_arr, output_filename, downscaling_operation, \
# meta, post_downscale_function, mask=None, mask_value=0
f = partial( self.interp_ds, src_transform=src_transform, downscaling_operation=downscaling_operation, post_downscale_function=None, mask=None, mask_value=0 )
pool = multiprocessing.Pool( ncpus )
out = pool.map( lambda x: f( **x ), args[:11] )
pool.join()
pool.close()
return output_dir
def interp_ds( anom, base, output_filename, src_transform, downscaling_operation, post_downscale_function=None, mask=None, mask_value=0 ):
from rasterio.warp import reproject, RESAMPLING
# reproject / resample
src_crs = {'init':'epsg:4326'}
src_nodata = None # DangerTown™
baseline_meta = base.meta
baseline_meta.update( compress='lzw' )
baseline_arr = base.read( 1 )
output_arr = np.empty_like( base.read( 1 ) ) # base should be a rasterio obj not arr... check this.
# TODO: make this function available for manipulation if used for different needs
reproject( output_arr, baseline_arr, src_transform=src_transform, src_crs=src_crs, src_nodata=src_nodata, \
dst_transform=baseline_meta['affine'], dst_crs=baseline_meta['crs'],\
dst_nodata=None, resampling=RESAMPLING.cubic_spline, SOURCE_EXTRA=1000 )
# downscale
return utils.downscale( anom, output_arr, output_filename, downscaling_operation, \
baseline_meta, post_downscale_function, mask, mask_value )
