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Find and use the active logger in the grandparent ensemble classs #141

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Merged
rolfjl merged 1 commit into from
Mar 12, 2026
Merged

Find and use the active logger in the grandparent ensemble classs #141

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19 changes: 11 additions & 8 deletions ensemble/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,20 +81,23 @@ def __init__(self, keys_en: dict, sim, redund_sim=None):
# Save name for (potential) pickle dump/load
self.pickle_restart_file = 'emergency_dump'

# initiallize the restart. Standard is no restart
# Initiallize the restart. Standard is no restart
self.restart = False

# Get the active logger
self.logger = logging.getLogger(__name__)

# If it is a restart run, we do not need to initialize anything, only load the self info. that exists in the
# pickle save file. If it is not a restart run, we initialize everything below.
if ('restart' in self.keys_en) and (self.keys_en['restart'] == 'yes'):
# Initiate a restart run
self.logger('\033[92m--- Restart run initiated! ---\033[92m')
self.logger.info('\033[92m--- Restart run initiated! ---\033[92m')
# Check if the pickle save file exists in folder
try:
assert (self.pickle_restart_file in [
f for f in os.listdir('.') if os.path.isfile(f)])
except AssertionError as err:
self.logger('The restart file "{0}" does not exist in folder. Cannot restart!'.format(
self.logger.info('The restart file "{0}" does not exist in folder. Cannot restart!'.format(
self.pickle_restart_file))
raise err

Expand Down Expand Up @@ -312,7 +315,7 @@ def calc_prediction(self, enX=None, save_prediction=None):
if len(list_crash) > 1:
print(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
self.logger(
self.logger.info(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
sys.exit(1)
return success
Expand All @@ -333,7 +336,7 @@ def calc_prediction(self, enX=None, save_prediction=None):
f"has been replaced by ensemble member {element}! ---\033[92m"
)
print(msg)
self.logger(msg)
self.logger.info(msg)
if enX.shape[1] > 1:
enX[:, list_crash[index]] = deepcopy(self.enX[:, element])
en_pred[list_crash[index]] = deepcopy(en_pred[element])
Expand Down Expand Up @@ -365,7 +368,7 @@ def run_on_HPC(self, enX, batch_size=None, **kwargs):

# Split the ensemble into batches of 500
if batch_size >= 1000:
self.logger(f'Cannot run batch size of {batch_size}. Set to 1000')
self.logger.info(f'Cannot run batch size of {batch_size}. Set to 1000')
batch_size = 1000
en_pred = []
batch_en = [np.arange(start, start + batch_size) for start in
Expand Down Expand Up @@ -499,7 +502,7 @@ def calc_ml_prediction(self, enX=None):
if len(list_crash) > 1:
print(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
self.logger(
self.logger.info(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
sys.exit(1)
return success
Expand All @@ -522,7 +525,7 @@ def calc_ml_prediction(self, enX=None):
f"has been replaced by ensemble member {element}! ---\033[92m"
)
print(msg)
self.logger(msg)
self.logger.info(msg)
if enX[level].shape[1] > 1:
enX[level][:, list_crash[index]] = deepcopy(enX[level][:, element])

Expand Down
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