diff --git a/climax/main/downscale.py b/climax/main/downscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..528062a33938872d9085848de5c2e2c970d45496
--- /dev/null
+++ b/climax/main/downscale.py
@@ -0,0 +1,221 @@
+"""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)
+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)
+
+ # path to model state
+ if SENSITIVITY:
+ # models trained for hyperparameter optimization
+ state_file = MODEL_PATH.joinpath('sensitivity', PREDICTAND, state_file)
+ target = TARGET_PATH.joinpath('sensitivity', PREDICTAND)
+ else:
+ state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
+ target = TARGET_PATH.joinpath(PREDICTAND)
+
+ # 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))
+
+ # check if target dataset already exists
+ target = target.joinpath(state_file.name.replace(state_file.suffix, '.nc'))
+ if target.exists() and not OVERWRITE:
+ LogConfig.init_log('{} already exists.'.format(target))
+ sys.exit()
+
+ # load pretrained model
+ if state_file.exists() and not OVERWRITE:
+ # load pretrained network
+ net, _ = Network.load_pretrained_model(state_file, NET)
+ else:
+ # initialize downscaling
+ 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,
+ 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])
+
+ # initialize network and optimizer
+ LogConfig.init_log('Initializing network and optimizer.')
+
+ # 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
+ inputs = len(Era5_ds.data_vars) + 2 if DOY else len(Era5_ds.data_vars)
+ 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 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 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')
+
+ # 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')
+
+ # log execution time of script
+ LogConfig.init_log('Execution time of script {}: {}'
+ .format(__file__, timedelta(seconds=time.monotonic() -
+ start_time)))
diff --git a/climax/main/downscale_infer.py b/climax/main/downscale_infer.py
deleted file mode 100644
index 0a7945fb1204c6770660e6f6393c383d88e63616..0000000000000000000000000000000000000000
--- a/climax/main/downscale_infer.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""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
-
-# locals
-from pysegcnn.core.trainer import LogConfig
-from pysegcnn.core.models import Network
-from pysegcnn.core.logging import log_conf
-from pysegcnn.core.utils import search_files
-from climax.core.dataset import ERA5Dataset
-from climax.core.predict import predict_ERA5
-from climax.core.utils import split_date_range
-from climax.main.config import (ERA5_PREDICTORS, ERA5_PLEVELS, PREDICTAND, NET,
- VALID_PERIOD, BATCH_SIZE, NORM, DOY, NYEARS,
- DEM, DEM_FEATURES, LOSS, ANOMALIES, OPTIM,
- OPTIM_PARAMS, CHUNKS, LR_SCHEDULER, OVERWRITE,
- SENSITIVITY)
-from climax.main.io import ERA5_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)
-
- # path to model state
- if SENSITIVITY:
- # models trained for hyperparameter optimization
- state_file = MODEL_PATH.joinpath('sensitivity', PREDICTAND, state_file)
- target = TARGET_PATH.joinpath('sensitivity', PREDICTAND)
- else:
- state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
- target = TARGET_PATH.joinpath(PREDICTAND)
-
- # check if target dataset already exists
- target = target.joinpath(state_file.name.replace(state_file.suffix, '.nc'))
- if target.exists() and not OVERWRITE:
- LogConfig.init_log('{} already exists.'.format(target))
- sys.exit()
- else:
- # load pretrained model
- if state_file.exists():
- # load pretrained network
- net, _ = Network.load_pretrained_model(state_file, NET)
- else:
- # initialize OBS predictand dataset
- LOGGER.info('{} does not exist.'.format(state_file))
- sys.exit()
-
- # initialize logging
- log_file = state_file.parent.joinpath(
- state_file.name.replace(state_file.suffix, '_log.txt'))
- dictConfig(log_conf(log_file))
-
- # predict reference period
- LogConfig.init_log('Predicting reference period: {}'.format(
- ' - '.join([str(VALID_PERIOD[0]), str(VALID_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)
-
- # 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'])
-
- # add dem to set of predictor variables
- Era5_ds = xr.merge([Era5_ds, dem]).chunk(Era5_ds.chunks)
-
- # 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')
-
- # 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')
-
- # log execution time of script
- LogConfig.init_log('Execution time of script {}: {}'
- .format(__file__, timedelta(seconds=time.monotonic() -
- start_time)))
diff --git a/climax/main/downscale_train.py b/climax/main/downscale_train.py
deleted file mode 100644
index 87e994bd54c3628e0293307ca0d8dc6088c3e095..0000000000000000000000000000000000000000
--- a/climax/main/downscale_train.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""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.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.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)
-from climax.main.io import ERA5_PATH, OBS_PATH, DEM_PATH, MODEL_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)
-
- # path to model state
- if SENSITIVITY:
- # models trained for hyperparameter optimization
- state_file = MODEL_PATH.joinpath('sensitivity', PREDICTAND, state_file)
- else:
- state_file = MODEL_PATH.joinpath(PREDICTAND, state_file)
-
- # 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])])))
-
- # 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,
- 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])
-
- # initialize network and optimizer
- LogConfig.init_log('Initializing network and optimizer.')
-
- # 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
- inputs = len(Era5_ds.data_vars) + 2 if DOY else len(Era5_ds.data_vars)
- 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 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 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()
-
- # log execution time of script
- LogConfig.init_log('Execution time of script {}: {}'
- .format(__file__, timedelta(seconds=time.monotonic() -
- start_time)))