diff --git a/climax/main/config.py b/climax/main/config.py
index 67a1406c9704c26e44908f3687ec29d28573fd83..a8e78c2b91eb98be9e7c28216b8be44f46514802 100644
--- a/climax/main/config.py
+++ b/climax/main/config.py
@@ -92,6 +92,9 @@ VALID_SIZE = 0.2
# number of folds for training with KFold cross-validation
CV = 5
+# number of bootstrapped model trainings
+BOOTSTRAP = 10
+
# -----------------------------------------------------------------------------
# Observations ----------------------------------------------------------------
# -----------------------------------------------------------------------------
diff --git a/climax/main/downscale.py b/climax/main/downscale.py
index 528062a33938872d9085848de5c2e2c970d45496..d8f37d8e036d1bb710d2fa601ffb74bdbc08321d 100644
--- a/climax/main/downscale.py
+++ b/climax/main/downscale.py
@@ -60,6 +60,10 @@ if __name__ == '__main__':
state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
target = TARGET_PATH.joinpath(PREDICTAND)
+ # check if output path exists
+ if not target.exists():
+ target.mkdir(parents=True, exist_ok=True)
+
# initialize logging
log_file = state_file.parent.joinpath(
state_file.name.replace(state_file.suffix, '_log.txt'))
@@ -82,12 +86,6 @@ if __name__ == '__main__':
LogConfig.init_log('Initializing downscaling for period: {}'.format(
' - '.join([str(CALIB_PERIOD[0]), str(CALIB_PERIOD[-1])])))
- # check if model exists
- if state_file.exists() and not OVERWRITE:
- # load pretrained network
- LogConfig.init_log('{} already exists.'.format(state_file))
- sys.exit()
-
# initialize ERA5 predictor dataset
LogConfig.init_log('Initializing ERA5 predictors.')
Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
@@ -131,15 +129,17 @@ if __name__ == '__main__':
# initialize network and optimizer
LogConfig.init_log('Initializing network and optimizer.')
- # define number of output fields
+ # define number of output fields:
# check whether modelling pr with probabilistic approach
outputs = len(Obs_ds.data_vars)
if PREDICTAND == 'pr':
outputs = (1 if (isinstance(LOSS, MSELoss) or
isinstance(LOSS, L1Loss)) else 3)
- # instanciate network
+ # define number of input fields
inputs = len(Era5_ds.data_vars) + 2 if DOY else len(Era5_ds.data_vars)
+
+ # instanciate network
net = NET(state_file, inputs, outputs, filters=FILTERS)
# initialize optimizer
@@ -208,10 +208,9 @@ if __name__ == '__main__':
# merge predictions for entire validation period
LOGGER.info('Merging reference periods ...')
trg_ds = xr.concat(trg_ds, dim='time')
+ trg_ds = trg_ds.sortby(trg_ds.time) # sort predictions chronologically
# save model predictions as NetCDF file
- if not target.parent.exists():
- target.parent.mkdir(parents=True, exist_ok=True)
LOGGER.info('Saving network predictions: {}.'.format(target))
trg_ds.to_netcdf(target, engine='h5netcdf')
diff --git a/climax/main/downscale_bootstrap.py b/climax/main/downscale_bootstrap.py
new file mode 100644
index 0000000000000000000000000000000000000000..53116b4baa459dab88a7a4f59ca9a1666efdf767
--- /dev/null
+++ b/climax/main/downscale_bootstrap.py
@@ -0,0 +1,226 @@
+"""Dynamical climate downscaling using deep convolutional neural networks."""
+
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# builtins
+import sys
+import time
+import logging
+from datetime import timedelta
+from logging.config import dictConfig
+
+# externals
+import xarray as xr
+from sklearn.model_selection import train_test_split
+from torch.utils.data import DataLoader
+
+# locals
+from pysegcnn.core.utils import search_files
+from pysegcnn.core.models import Network
+from pysegcnn.core.trainer import NetworkTrainer, LogConfig
+from pysegcnn.core.logging import log_conf
+from climax.core.dataset import ERA5Dataset, NetCDFDataset
+from climax.core.loss import MSELoss, L1Loss
+from climax.core.predict import predict_ERA5
+from climax.core.utils import split_date_range
+from climax.main.config import (ERA5_PLEVELS, ERA5_PREDICTORS, PREDICTAND,
+ CALIB_PERIOD, DOY, SHUFFLE, BATCH_SIZE, OPTIM,
+ NORM, TRAIN_CONFIG, NET, LOSS, FILTERS,
+ OVERWRITE, DEM, DEM_FEATURES, STRATIFY,
+ WET_DAY_THRESHOLD, VALID_SIZE, ANOMALIES,
+ OPTIM_PARAMS, LR_SCHEDULER, SENSITIVITY,
+ LR_SCHEDULER_PARAMS, CHUNKS, VALID_PERIOD,
+ NYEARS, BOOTSTRAP)
+from climax.main.io import (ERA5_PATH, OBS_PATH, DEM_PATH, MODEL_PATH,
+ TARGET_PATH)
+
+# module level logger
+LOGGER = logging.getLogger(__name__)
+
+
+if __name__ == '__main__':
+
+ # initialize timing
+ start_time = time.monotonic()
+
+ # initialize network filename
+ state_file = ERA5Dataset.state_file(
+ NET, PREDICTAND, ERA5_PREDICTORS, ERA5_PLEVELS, dem=DEM,
+ dem_features=DEM_FEATURES, doy=DOY, loss=LOSS, anomalies=ANOMALIES,
+ decay=OPTIM_PARAMS['weight_decay'], optim=OPTIM, lr=OPTIM_PARAMS['lr'],
+ lr_scheduler=LR_SCHEDULER)
+
+ # models trained with bootstrapping
+ state_file = MODEL_PATH.joinpath('bootstrap', PREDICTAND, state_file)
+ target = TARGET_PATH.joinpath('bootstrap', PREDICTAND, state_file.stem)
+
+ # check if output path exists
+ if not target.exists():
+ target.mkdir(parents=True, exist_ok=True)
+
+ # initialize logging
+ log_file = state_file.parent.joinpath(
+ state_file.name.replace(state_file.suffix, '_log.txt'))
+ if log_file.exists():
+ log_file.unlink()
+ dictConfig(log_conf(log_file))
+
+ # initialize downscaling
+ LogConfig.init_log('Initializing downscaling for period: {}'.format(
+ ' - '.join([str(CALIB_PERIOD[0]), str(CALIB_PERIOD[-1])])))
+
+ # initialize ERA5 predictor dataset
+ LogConfig.init_log('Initializing ERA5 predictors.')
+ Era5 = ERA5Dataset(ERA5_PATH.joinpath('ERA5'), ERA5_PREDICTORS,
+ plevels=ERA5_PLEVELS)
+ Era5_ds = Era5.merge(chunks=CHUNKS)
+
+ # initialize OBS predictand dataset
+ LogConfig.init_log('Initializing observations for predictand: {}'
+ .format(PREDICTAND))
+
+ # check whether to joinlty train tasmin and tasmax
+ if PREDICTAND == 'tas':
+ # read both tasmax and tasmin
+ tasmax = xr.open_dataset(
+ search_files(OBS_PATH.joinpath('tasmax'), '.nc$').pop())
+ tasmin = xr.open_dataset(
+ search_files(OBS_PATH.joinpath('tasmin'), '.nc$').pop())
+ Obs_ds = xr.merge([tasmax, tasmin])
+ else:
+ # read in-situ gridded observations
+ Obs_ds = search_files(OBS_PATH.joinpath(PREDICTAND),
+ '.nc$').pop()
+ Obs_ds = xr.open_dataset(Obs_ds)
+
+ # whether to use digital elevation model
+ if DEM:
+ # digital elevation model: Copernicus EU-Dem v1.1
+ dem = search_files(DEM_PATH, '^eu_dem_v11_stt.nc$').pop()
+
+ # read elevation and compute slope and aspect
+ dem = ERA5Dataset.dem_features(
+ dem, {'y': Era5_ds.y, 'x': Era5_ds.x},
+ add_coord={'time': Era5_ds.time})
+
+ # check whether to use slope and aspect
+ if not DEM_FEATURES:
+ dem = dem.drop_vars(['slope', 'aspect']).chunk(
+ Era5_ds.chunks)
+
+ # add dem to set of predictor variables
+ Era5_ds = xr.merge([Era5_ds, dem])
+
+ # define number of output fields:
+ # check whether modelling pr with probabilistic approach
+ outputs = len(Obs_ds.data_vars)
+ if PREDICTAND == 'pr':
+ outputs = (1 if (isinstance(LOSS, MSELoss) or
+ isinstance(LOSS, L1Loss)) else 3)
+
+ # define number of input fields
+ inputs = len(Era5_ds.data_vars) + 2 if DOY else len(Era5_ds.data_vars)
+
+ # initialize training data
+ LogConfig.init_log('Initializing training data.')
+
+ # split calibration period into training and validation period
+ if PREDICTAND == 'pr' and STRATIFY:
+ # stratify training and validation dataset by number of
+ # observed wet days for precipitation
+ wet_days = (Obs_ds.sel(time=CALIB_PERIOD).mean(dim=('y', 'x'))
+ >= WET_DAY_THRESHOLD).to_array().values.squeeze()
+ train, valid = train_test_split(
+ CALIB_PERIOD, stratify=wet_days, test_size=VALID_SIZE)
+
+ # sort chronologically
+ train, valid = sorted(train), sorted(valid)
+ else:
+ train, valid = train_test_split(CALIB_PERIOD, shuffle=False,
+ test_size=VALID_SIZE)
+
+ # training and validation dataset
+ Era5_train, Obs_train = Era5_ds.sel(time=train), Obs_ds.sel(time=train)
+ Era5_valid, Obs_valid = Era5_ds.sel(time=valid), Obs_ds.sel(time=valid)
+
+ # create PyTorch compliant dataset and dataloader instances for model
+ # training
+ train_ds = NetCDFDataset(Era5_train, Obs_train, normalize=NORM, doy=DOY,
+ anomalies=ANOMALIES)
+ valid_ds = NetCDFDataset(Era5_valid, Obs_valid, normalize=NORM, doy=DOY,
+ anomalies=ANOMALIES)
+ train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=SHUFFLE,
+ drop_last=False)
+ valid_dl = DataLoader(valid_ds, batch_size=BATCH_SIZE, shuffle=SHUFFLE,
+ drop_last=False)
+
+ # initialize bootstrapped model training
+ for i in range(BOOTSTRAP):
+
+ # add suffix for n'th bootstrap
+ state_file = state_file.parent.joinpath(state_file.name.replace(
+ state_file.suffix, '_{}.pt'.format(i + 1)))
+
+ # check if target dataset already exists
+ target_ds = target.joinpath(state_file.name.replace(
+ state_file.suffix, '.nc'))
+ if target_ds.exists() and not OVERWRITE:
+ LogConfig.init_log('{} already exists.'.format(target_ds))
+ continue
+
+ # load pretrained model
+ if state_file.exists() and not OVERWRITE:
+ # load pretrained network
+ net, _ = Network.load_pretrained_model(state_file, NET)
+ else:
+ # initialize network and optimizer
+ LogConfig.init_log('Initializing network and optimizer.')
+
+ # instanciate network
+ net = NET(state_file, inputs, outputs, filters=FILTERS)
+
+ # initialize optimizer
+ optimizer = OPTIM(net.parameters(), **OPTIM_PARAMS)
+
+ # initialize learning rate scheduler
+ if LR_SCHEDULER is not None:
+ LR_SCHEDULER = LR_SCHEDULER(optimizer, **LR_SCHEDULER_PARAMS)
+
+
+ # initialize network trainer
+ trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
+ valid_dl, loss_function=LOSS,
+ lr_scheduler=LR_SCHEDULER, **TRAIN_CONFIG)
+
+ # train model
+ state = trainer.train()
+
+ # predict reference period
+ LogConfig.init_log('Predicting reference period: {}'.format(
+ ' - '.join([str(VALID_PERIOD[0]), str(VALID_PERIOD[-1])])))
+
+ # subset to reference period and predict in NYEAR intervals
+ trg_ds = []
+ for dates in split_date_range(VALID_PERIOD[0], VALID_PERIOD[-1],
+ years=NYEARS):
+ LogConfig.init_log('Predicting period: {}'.format(
+ ' - '.join([str(dates[0]), str(dates[-1])])))
+ ref_ds = Era5_ds.sel(time=dates)
+ trg_ds.append(predict_ERA5(net, ref_ds, PREDICTAND, LOSS,
+ normalize=NORM, batch_size=BATCH_SIZE,
+ doy=DOY, anomalies=ANOMALIES))
+
+ # merge predictions for entire validation period
+ LOGGER.info('Merging reference periods ...')
+ trg_ds = xr.concat(trg_ds, dim='time')
+ trg_ds = trg_ds.sortby(trg_ds.time) # sort predictions chronologically
+
+ # save model predictions as NetCDF file
+ LOGGER.info('Saving network predictions: {}.'.format(target_ds))
+ trg_ds.to_netcdf(target_ds, engine='h5netcdf')
+
+ # log execution time of script
+ LogConfig.init_log('Execution time of script {}: {}'
+ .format(__file__, timedelta(seconds=time.monotonic() -
+ start_time)))