def rerun_pyomo_model(model_data, backend_model):
"""
Rerun the Pyomo backend, perhaps after updating a parameter value,
(de)activating a constraint/objective or updating run options in the model
model_data object (e.g. `run.solver`).
Returns
-------
run_data : xarray.Dataset
Raw data from this rerun, including both inputs and results.
to filter inputs/results, use `run_data.filter_by_attrs(is_result=...)`
with 0 for inputs and 1 for results.
"""
backend_model.__calliope_run_config = AttrDict.from_yaml_string(model_data.attrs['run_config'])
if backend_model.__calliope_run_config['mode'] != 'plan':
raise exceptions.ModelError(
'Cannot rerun the backend in {} run mode. Only `plan` mode is '
'possible.'.format(backend_model.__calliope_run_config['mode'])
)
timings = {}
log_time(logger, timings, 'model_creation')
results, backend_model = backend_run.run_plan(
model_data, timings, run_pyomo,
build_only=False, backend_rerun=backend_model
)
for k, v in timings.items():
results.attrs['timings.' + k] = v
exceptions.ModelWarning(
'model.results will only be updated on running the model from '
'`model.run()`. We provide results of this rerun as a standalone xarray '
'Dataset'
)
results.attrs.update(model_data.attrs)
for key, var in results.data_vars.items():
var.attrs['is_result'] = 1
inputs = access_pyomo_model_inputs(backend_model)
for key, var in inputs.data_vars.items():
var.attrs['is_result'] = 0
results.update(inputs)
run_data = results
return run_data
def rerun_pyomo_model(model_data, backend_model):
"""
Rerun the Pyomo backend, perhaps after updating a parameter value,
(de)activating a constraint/objective or updating run options in the model
model_data object (e.g. `run.solver`).
Returns
-------
run_data : xarray.Dataset
Raw data from this rerun, including both inputs and results.
to filter inputs/results, use `run_data.filter_by_attrs(is_result=...)`
with 0 for inputs and 1 for results.
"""
if model_data.attrs['run.mode'] != 'plan':
raise exceptions.ModelError(
'Cannot rerun the backend in {} run mode. Only `plan` mode is '
'possible.'.format(model_data.attrs['run.mode'])
)
timings = {}
log_time(timings, 'model_creation')
results, backend_model = backend_run.run_plan(
model_data, timings, run_pyomo,
build_only=False, backend_rerun=backend_model
)
for k, v in timings.items():
results.attrs['timings.' + k] = v
exceptions.ModelWarning(
'model.results will only be updated on running the model from '
'`model.run()`. We provide results of this rerun as a standalone xarray '
'Dataset'
)
results.attrs.update(model_data.attrs)
for key, var in results.data_vars.items():
var.attrs['is_result'] = 1
inputs = access_pyomo_model_inputs(backend_model)
for key, var in inputs.data_vars.items():
var.attrs['is_result'] = 0
results.update(inputs)
run_data = results
return run_data
def rerun_pyomo_model(model_data, run_config, backend_model):
"""
Rerun the Pyomo backend, perhaps after updating a parameter value,
(de)activating a constraint/objective or updating run options in the model
model_data object (e.g. `run.solver`).
Returns
-------
new_model : calliope.Model
New calliope model, including both inputs and results, but no backend interface.
"""
backend_model.__calliope_run_config = run_config
if run_config["mode"] != "plan":
raise exceptions.ModelError(
"Cannot rerun the backend in {} run mode. Only `plan` mode is "
"possible.".format(run_config["mode"]))
timings = {}
log_time(logger, timings, "model_creation")
results, backend_model = backend_run.run_plan(
model_data,
run_config,
timings,
run_pyomo,
build_only=False,
backend_rerun=backend_model,
)
inputs = access_pyomo_model_inputs(backend_model)
# Add additional post-processed result variables to results
if results.attrs.get("termination_condition",
None) in ["optimal", "feasible"]:
results = postprocess_model_results(results,
model_data.reindex(results.coords),
timings)
for key, var in results.data_vars.items():
var.attrs["is_result"] = 1
for key, var in inputs.data_vars.items():
var.attrs["is_result"] = 0
new_model_data = xr.merge((results, inputs))
new_model_data.attrs.update(model_data.attrs)
new_model_data.attrs.update(results.attrs)
# Only add coordinates from the original model_data that don't already exist
new_coords = [
i for i in model_data.coords.keys()
if i not in new_model_data.coords.keys()
]
new_model_data = new_model_data.update(model_data[new_coords])
# Reorganise the coordinates so that model data and new model data share
# the same order of items in each dimension
new_model_data = new_model_data.reindex(model_data.coords)
exceptions.warn(
"The results of rerunning the backend model are only available within "
"the Calliope model returned by this function call.")
new_calliope_model = calliope.Model(config=None, model_data=new_model_data)
new_calliope_model._timings = timings
return new_calliope_model