예제 #1
0
		return round_data( x )

	def round_data_clamp_clt( x ):
		x[ x < 0.0 ] = 0.0
		x[ x > 100.0 ] = 100.0 # per Stephanie McAfee
		return round_data( x )

	if variable == 'hur':
		post_downscale_function = round_data_clamp_hur
	if variable == 'clt':
		post_downscale_function = round_data_clamp_clt
	else:
		post_downscale_function = round_data

	# FOR CRU WE PASS THE interp=True so we interpolate across space first when creating the Dataset()
	historical = Dataset( cru_ts, variable, model, scenario, project, units, metric, 
							method='linear', ncpus=32 )

	# ar5 = DeltaDownscale( baseline, clim_begin, clim_end, historical, future=None,
	# 			downscaling_operation=downscaling_operation, mask=mask, mask_value=0, ncpus=32,
	# 			src_crs={'init':'epsg:4326'}, src_nodata=None, dst_nodata=None,
	# 			post_downscale_function=round_it, varname=out_varname, modelname=None, anom=True )

	# FOR CRU WE PASS THE interp=True so we interpolate across space first when creating the Dataset()
	ar5 = DeltaDownscale( baseline, clim_begin, clim_end, historical, future=None,
				downscaling_operation=downscaling_operation, mask=mask, mask_value=0, ncpus=32,
				src_crs={'init':'epsg:4326'}, src_nodata=None, dst_nodata=None,
				post_downscale_function=post_downscale_function, varname=out_varname, modelname=None, 
				anom=anom, interp=interp, find_bounds=find_bounds, fix_clim=fix_clim, aoi_mask=aoi_mask )

	if not os.path.exists( output_path ):
		os.makedirs( output_path )
예제 #2
0
# RUN 2.0
filelist = glob.glob(os.path.join(clim_path, '*.tif'))
filelist = [fn for fn in filelist if not '_14_' in fn]
baseline = Baseline(filelist)

# DOWNSCALE
mask = rasterio.open(baseline.filelist[0]).read_masks(1)
clim_begin = '1961'
clim_end = '1990'

# FOR CRU WE PASS THE interp=True so we interpolate across space first when creating the Dataset()
historical = Dataset(cru_ts,
                     variable,
                     model,
                     scenario,
                     project,
                     units,
                     metric=metric,
                     interp=True,
                     method='linear',
                     ncpus=32)

ar5 = DeltaDownscale( baseline, clim_begin, clim_end, historical, future=None, \
  downscaling_operation='add', mask=mask, mask_value=0, ncpus=32, \
  src_crs={'init':'epsg:4326'}, src_nodata=None, dst_nodata=None,
                      post_downscale_function=None, varname=out_varname, modelname=None ) # -9999

if not os.path.exists(output_path):
    os.makedirs(output_path)

ar5.downscale(output_dir=output_path)