def generate_model_run(
config,
timeseries_dataframes,
debug_comments,
applied_overrides,
scenario,
subsets,
):
"""
Returns a processed model_run configuration AttrDict and a debug
YAML object with comments attached, ready to write to disk.
Parameters
----------
config : AttrDict
timeseries_dataframes : dict
debug_comments : AttrDict
scenario : str
"""
model_run = AttrDict()
model_run["scenario"] = scenario
model_run["applied_overrides"] = ";".join(applied_overrides)
# 1) Initial checks on model configuration
warning_messages, errors = checks.check_initial(config)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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 nodes
(
model_run["nodes"],
debug_nodes,
warning_messages,
errors,
) = nodes.process_nodes(config, model_run["techs"])
debug_comments.set_key("model_run.nodes", debug_nodes)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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["timeseries_vars"],
) = process_timeseries_data(config, model_run, timeseries_dataframes)
# 6) Grab additional relevant bits from run and model config
model_run["run"] = config["run"]
model_run["model"] = config["model"]
# model_run["sets"] = all_sets
model_run["subsets"] = subsets
# model_run["constraint_sets"] = constraint_sets.generate_constraint_sets(model_run)
# 8) Final sense-checking
final_check_comments, warning_messages, errors = checks.check_final(
model_run)
debug_comments.union(final_check_comments)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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, timeseries_dataframes, 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
timeseries_dataframes : dict
debug_comments : AttrDict
scenario : str
"""
model_run = AttrDict()
model_run["scenario"] = scenario
model_run["applied_overrides"] = ";".join(applied_overrides)
# 1) Initial checks on model configuration
warning_messages, errors = checks.check_initial(config)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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,
warning_messages,
errors,
) = locations.process_locations(config, model_run["techs"])
debug_comments.set_key("model_run.locations", debug_locs)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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,
timeseries_dataframes)
# 6) Grab additional relevant bits from run and model config
model_run["run"] = config["run"]
model_run["model"] = config["model"]
model_run["group_constraints"] = config.get("group_constraints", {})
# 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)
# 7.5) get scaling factors if available
if "scale" in config:
model_run['scale'] = config['scale']
# 8) Final sense-checking
final_check_comments, warning_messages, errors = checks.check_final(
model_run)
debug_comments.union(final_check_comments)
exceptions.print_warnings_and_raise_errors(warnings=warning_messages,
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