if not os.path.exists(output_path):
os.makedirs(output_path)
print(input_path)
# # NOTE:
# ALL DATA FALL INTO THE historical ARGUMENT WITH THIS DOWNSCALING DUE TO NOT USING A CLIMATOLOGY TO
# GENERATE ANOMALIES WE ARE GENERATING DELTAS OF MIN/MAX FROM MEAN OF THE SAME VARIABLE GROUP ie. tas/tasmax/tasmin
# list files for this set of downscaling -- one per folder
fn, = glob.glob(os.path.join(input_path, '*.nc'))
historical = downscale.Dataset(fn,
variable,
model,
scenario,
project=project,
units=units,
metric=metric,
begin=begin,
end=end)
# mean data -- hacky...
mean_fn, = glob.glob(
os.path.join(input_path.replace(variable, mean_variable), '*.nc'))
mean_ds = downscale.Dataset(mean_fn,
mean_variable,
model,
scenario,
project=project,
units=units,
metric=metric,
begin=begin,
filelist = [ i for i in filelist if '_14_' not in i ] # remove the GD ANNUAL _14_ file. baseline = downscale.Baseline( filelist ) input_path = os.path.join( base_path, model, scenario, variable ) output_path = os.path.join( output_dir, model, scenario, variable ) if not os.path.exists( output_path ): os.makedirs( output_path ) print( input_path ) # list files for this set of downscaling -- one per folder fn, = glob.glob( os.path.join( input_path, '*.nc' ) ) if 'historical' in scenario: historical = downscale.Dataset( fn, variable, model, scenario, project=project, units=units, metric=metric, begin=1860, end=2005 ) future = None # no need for futures here.... else: # get the historical data for anomalies historical_fn, = glob.glob( os.path.join( os.path.dirname( fn ).replace( scenario, 'historical' ), '*.nc' ) ) historical = downscale.Dataset( historical_fn, variable, model, scenario, project=project, units=units, metric=metric, begin=1860, end=2005 ) future = downscale.Dataset( fn, variable, model, scenario, project=project, units=units, metric=metric, begin=2006, end=2100 ) # convert from Kelvin to Celcius if variable != 'pr': if historical: historical.ds[ variable ] = historical.ds[ variable ] - 273.15 historical.ds[ variable ][ 'units' ] = units if future: future.ds[ variable ] = future.ds[ variable ] - 273.15
if not os.path.exists(output_path):
os.makedirs(output_path)
print(input_path)
# list files for this set of downscaling -- one per folder
fn, = glob.glob(os.path.join(input_path, '*.nc'))
if 'historical' in scenario:
historical = downscale.Dataset(fn,
variable,
model,
scenario,
project=project,
units=units,
metric=metric,
begin=1900,
end=2005,
level_name=level_name,
level=level)
future = None
else:
# get the historical data for anomalies
historical_fn, = glob.glob(
os.path.join(
os.path.dirname(fn).replace(scenario, 'historical'),
'*.nc'))
historical = downscale.Dataset(historical_fn,
variable,
model,
# SETUP BASELINE
clim_path = '/workspace/Shared/Tech_Projects/ALFRESCO_Inputs/project_data/TEM_Data/cru_october_final/cru_cl20/cld/akcan'
filelist = glob.glob(os.path.join(clim_path, '*.tif'))
baseline = downscale.Baseline(filelist)
# SETUP DATASET
output_dir = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/test'
future_fn = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/snap_prepped_data/IPSL-CM5A-LR/hur/hur_Amon_IPSL-CM5A-LR_rcp26_r1i1p1_200601_210012.nc'
historical_fn = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/snap_prepped_data/IPSL-CM5A-LR/hur/hur_Amon_IPSL-CM5A-LR_historical_r1i1p1_185001_200512.nc'
variable = 'hur'
model = 'IPSL-CM5A-LR'
scenario = 'rcp26'
historical = downscale.Dataset(historical_fn,
variable,
model,
scenario,
units=None)
future = downscale.Dataset(future_fn, variable, model, scenario, units=None)
# DOWNSCALE
mask = rasterio.open(baseline.filelist[0]).read_masks(1)
clim_begin = '1961'
clim_end = '1990'
ar5 = downscale.DeltaDownscale( baseline, clim_begin, clim_end, historical, future, \
metric='mean', ds_type='absolute', level=1000, level_name='plev' )# add in the mask!
ar5.downscale(output_dir=output_dir)
# CRU historical
output_dir = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/test'
historical_fn = '/Data/Base_Data/Climate/World/CRU_grids/CRU_TS323/cru_ts3.23.1901.2014.cld.dat.nc'
import downscale
# minimum required arguments
ar5_modeled = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/data/prepped/clt_prepped/IPSL-CM5A-LR/clt/clt_Amon_IPSL-CM5A-LR_rcp26_r1i1p1_200601_210012.nc'
ar5_historical = '/workspace/Shared/Tech_Projects/ESGF_Data_Access/project_data/data/prepped/clt_prepped/IPSL-CM5A-LR/clt/clt_Amon_IPSL-CM5A-LR_historical_r1i1p1_185001_200512.nc'
clim_path = '/workspace/Shared/Tech_Projects/ALFRESCO_Inputs/project_data/TEM_Data/cru_october_final/cru_cl20/cld/akcan'
template_raster_fn = '/workspace/Shared/Tech_Projects/ALFRESCO_Inputs/project_data/TEM_Data/templates/tas_mean_C_AR5_GFDL-CM3_historical_01_1860.tif'
base_path = '/atlas_scratch/malindgren/CMIP5/TEST_AR5'
# run
# down = DownscaleAR5.DownscaleAR5( ar5_modeled, ar5_historical, base_path, clim_path, template_raster_fn=template_raster_fn, ncores=32 ) #, climatology_begin, climatology_end, plev, absolute, metric, ncores )
# output = down.downscale_ar5_ts()
down = downscale.Dataset(
ar5_modeled,
ar5_historical,
base_path,
clim_path,
template_raster_fn=template_raster_fn,
ncores=32
) #, climatology_begin, climatology_end, plev, absolute, metric, ncores )
output = down.downscale_ar5_ts()
# CRU
if __name__ == '__main__':
# import modules
from downscale import DownscaleCRU
# example of post_downscale_function - pass in at DownscaleCRU()
def clamp_vals(x):
''' clamp the values following the relative humidity downscaling '''
x[(x > 100) & (x < 500)] = 95
baseline = downscale.Baseline( filelist )
# DOWNSCALE
mask = rasterio.open( baseline.filelist[0] ).read_masks( 1 )
# make round/trunc function for post_downscale_function
if variable == 'pr' or variable == 'pre':
rounder = np.rint
downscaling_operation = 'mult'
else:
rounder = partial( np.around, decimals=1 )
downscaling_operation = 'add'
def round_it( arr ):
return rounder( arr )
historical = Dataset( cru_ts, variable, model, scenario, project, units, metric,
method='linear', ncpus=ncpus, interp=True )
mean_fn = cru_ts.replace( variable, mean_variable_cru )
mean_ds = downscale.Dataset( mean_fn, mean_variable_cru, model, scenario, project=project, units=units, metric=metric, begin=begin, end=end, interp=True )
# FOR CRU WE PASS THE interp=True so we interpolate across space first when creating the Dataset()
ar5 = DeltaDownscaleMinMax( baseline=baseline, clim_begin=clim_begin, clim_end=clim_end, historical=historical, future=None,
downscaling_operation=downscaling_operation, mask=mask, mask_value=0, ncpus=ncpus,
src_crs={'init':'epsg:4326'}, src_nodata=None, dst_nodata=None,
post_downscale_function=round_it, varname=out_varname, modelname=None, anom=anom,
mean_ds=mean_ds, mean_variable=mean_variable_cru, interp=interp )
ar5.downscale( output_dir=output_path )
input_path = os.path.join(base_dir, model, scenario, variable)
output_path = os.path.join(output_dir, model, scenario, variable)
if not os.path.exists(output_path):
os.makedirs(output_path)
print(input_path)
# list files for this set of downscaling -- one per folder
fn, = glob.glob(os.path.join(input_path, '*.nc'))
if 'historical' in scenario:
historical = downscale.Dataset(fn,
variable,
model,
scenario,
project=project,
units=units)
future = None # no need for futures here....
else:
# get the historical data for anomalies
historical_fn, = glob.glob(
os.path.join(
os.path.dirname(fn).replace(scenario, 'historical'), '*.nc'))
historical = downscale.Dataset(historical_fn,
variable,
model,
scenario,
project=project,
units=units)
future = downscale.Dataset(fn,