def generate_model_run(config, debug_comments, applied_overrides, scenario):
"""
Returns a processed model_run configuration AttrDict and a debug
YAML object with comments attached, ready to write to disk.
Parameters
----------
config : AttrDict
debug_comments : AttrDict
"""
model_run = AttrDict()
model_run['scenario'] = scenario
model_run['applied_overrides'] = ';'.join(applied_overrides)
# 1) Initial checks on model configuration
warnings, errors = checks.check_initial(config)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 2) Fully populate techs
# Raises ModelError if necessary
model_run['techs'], debug_techs, errors = process_techs(config)
debug_comments.set_key('model_run.techs', debug_techs)
exceptions.print_warnings_and_raise_errors(errors=errors)
# 3) Fully populate tech_groups
model_run['tech_groups'] = process_tech_groups(config, model_run['techs'])
# 4) Fully populate locations
model_run['locations'], debug_locs, warnings, errors = locations.process_locations(
config, model_run['techs']
)
debug_comments.set_key('model_run.locations', debug_locs)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 5) Fully populate timeseries data
# Raises ModelErrors if there are problems with timeseries data at this stage
model_run['timeseries_data'], model_run['timesteps'] = (
process_timeseries_data(config, model_run)
)
# 6) Grab additional relevant bits from run and model config
model_run['run'] = config['run']
model_run['model'] = config['model']
# 7) Initialize sets
all_sets = sets.generate_simple_sets(model_run)
all_sets.union(sets.generate_loc_tech_sets(model_run, all_sets))
all_sets = AttrDict({k: list(v) for k, v in all_sets.items()})
model_run['sets'] = all_sets
model_run['constraint_sets'] = constraint_sets.generate_constraint_sets(model_run)
# 8) Final sense-checking
final_check_comments, warnings, errors = checks.check_final(model_run)
debug_comments.union(final_check_comments)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 9) Build a debug data dict with comments and the original configs
debug_data = AttrDict({
'comments': debug_comments,
'config_initial': config,
})
return model_run, debug_data
def generate_model_run(config, debug_comments, applied_overrides, scenario):
"""
Returns a processed model_run configuration AttrDict and a debug
YAML object with comments attached, ready to write to disk.
Parameters
----------
config : AttrDict
debug_comments : AttrDict
"""
model_run = AttrDict()
model_run['scenario'] = scenario
model_run['applied_overrides'] = ';'.join(applied_overrides)
# 1) Initial checks on model configuration
warnings, errors = checks.check_initial(config)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 2) Fully populate techs
# Raises ModelError if necessary
model_run['techs'], debug_techs, errors = process_techs(config)
debug_comments.set_key('model_run.techs', debug_techs)
exceptions.print_warnings_and_raise_errors(errors=errors)
# 3) Fully populate tech_groups
model_run['tech_groups'] = process_tech_groups(config, model_run['techs'])
# 4) Fully populate locations
model_run['locations'], debug_locs, warnings, errors = locations.process_locations(
config, model_run['techs']
)
debug_comments.set_key('model_run.locations', debug_locs)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 5) Fully populate timeseries data
# Raises ModelErrors if there are problems with timeseries data at this stage
model_run['timeseries_data'], model_run['timesteps'] = (
process_timeseries_data(config, model_run)
)
# 6) Grab additional relevant bits from run and model config
model_run['run'] = config['run']
model_run['model'] = config['model']
# 7) Initialize sets
all_sets = sets.generate_simple_sets(model_run)
all_sets.union(sets.generate_loc_tech_sets(model_run, all_sets))
all_sets = AttrDict({k: list(v) for k, v in all_sets.items()})
model_run['sets'] = all_sets
model_run['constraint_sets'] = constraint_sets.generate_constraint_sets(model_run)
# 8) Final sense-checking
final_check_comments, warnings, errors = checks.check_final(model_run)
debug_comments.union(final_check_comments)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# 9) Build a debug data dict with comments and the original configs
debug_data = AttrDict({
'comments': debug_comments,
'config_initial': config,
})
return model_run, debug_data