def _init_from_model_run(self, model_run, debug_data):
self._model_run = model_run
self._debug_data = debug_data
log_time(self._timings, 'model_run_creation')
self._model_data_original = build_model_data(model_run)
log_time(self._timings, 'model_data_original_creation')
random_seed = self._model_run.get_key('model.random_seed', None)
if random_seed:
np.random.seed(seed=random_seed)
# After setting the random seed, time clustering can take place
time_config = model_run.model.get('time', None)
if not time_config:
_model_data = self._model_data_original
else:
_model_data = apply_time_clustering(self._model_data_original,
model_run)
self._model_data = final_timedimension_processing(_model_data)
log_time(self._timings, 'model_data_creation', time_since_start=True)
for var in self._model_data.data_vars:
self._model_data[var].attrs['is_result'] = 0
self.inputs = self._model_data.filter_by_attrs(is_result=0)
def _init_from_model_run(self, model_run, debug_data):
self._model_run = model_run
self._debug_data = debug_data
log_time(self._timings, 'model_run_creation', comment='Model: preprocessing stage 1 (model_run)')
self._model_data_original = build_model_data(model_run)
log_time(self._timings, 'model_data_original_creation', comment='Model: preprocessing stage 2 (model_data)')
random_seed = self._model_run.get_key('model.random_seed', None)
if random_seed:
np.random.seed(seed=random_seed)
# After setting the random seed, time clustering can take place
time_config = model_run.model.get('time', None)
if not time_config:
_model_data = self._model_data_original
else:
_model_data = apply_time_clustering(
self._model_data_original, model_run
)
self._model_data = final_timedimension_processing(_model_data)
log_time(
self._timings, 'model_data_creation',
comment='Model: preprocessing complete',
time_since_start=True
)
for var in self._model_data.data_vars:
self._model_data[var].attrs['is_result'] = 0
self.inputs = self._model_data.filter_by_attrs(is_result=0)
def _init_from_model_run(self, model_run, debug_data):
self._model_run = model_run
self._debug_data = debug_data
log_time(logger,
self._timings,
'model_run_creation',
comment='Model: preprocessing stage 1 (model_run)')
self._model_data_original = build_model_data(model_run)
log_time(logger,
self._timings,
'model_data_original_creation',
comment='Model: preprocessing stage 2 (model_data)')
random_seed = self._model_run.get_key('model.random_seed', None)
if random_seed:
np.random.seed(seed=random_seed)
# After setting the random seed, time clustering can take place
time_config = model_run.model.get('time', None)
if not time_config:
_model_data = self._model_data_original
else:
_model_data = apply_time_clustering(self._model_data_original,
model_run)
self._model_data = final_timedimension_processing(_model_data)
log_time(logger,
self._timings,
'model_data_creation',
comment='Model: preprocessing complete')
# Ensure model and run attributes of _model_data update themselves
for var in self._model_data.data_vars:
self._model_data[var].attrs['is_result'] = 0
self.inputs = self._model_data.filter_by_attrs(is_result=0)
model_config = {
k: v
for k, v in model_run.get('model', {}).items()
if k != 'file_allowed'
}
self.model_config = UpdateObserverDict(initial_dict=model_config,
name='model_config',
observer=self._model_data)
self.run_config = UpdateObserverDict(initial_dict=model_run.get(
'run', {}),
name='run_config',
observer=self._model_data)