def run_operate(model_data, timings, backend, build_only):
"""
For use when mode is 'operate', to allow the model to be built, edited, and
iteratively run within Pyomo.
"""
log_time(logger,
timings,
'run_start',
comment='Backend: starting model run in operational mode')
defaults = AttrDict.from_yaml_string(model_data.attrs['defaults'])
run_config = AttrDict.from_yaml_string(model_data.attrs['run_config'])
operate_params = ['purchased'] + [
i.replace('_max', '') for i in defaults if i[-4:] == '_max'
]
# Capacity results (from plan mode) can be used as the input to operate mode
if (any(model_data.filter_by_attrs(is_result=1).data_vars)
and run_config.get('operation.use_cap_results', False)):
# Anything with is_result = 1 will be ignored in the Pyomo model
for varname, varvals in model_data.data_vars.items():
if varname in operate_params:
varvals.attrs['is_result'] = 1
varvals.attrs['operate_param'] = 1
else:
cap_max = xr.merge([
v.rename(k.replace('_max', ''))
for k, v in model_data.data_vars.items() if '_max' in k
])
cap_equals = xr.merge([
v.rename(k.replace('_equals', ''))
for k, v in model_data.data_vars.items() if '_equals' in k
])
caps = cap_max.update(cap_equals)
for cap in caps.data_vars.values():
cap.attrs['is_result'] = 1
cap.attrs['operate_param'] = 1
model_data.update(caps)
# Storage initial is carried over between iterations, so must be defined along with storage
if ('loc_techs_store' in model_data.dims.keys()
and 'storage_initial' not in model_data.data_vars.keys()):
model_data['storage_initial'] = (xr.DataArray(
[0 for loc_tech in model_data.loc_techs_store.values],
dims='loc_techs_store'))
model_data['storage_initial'].attrs['is_result'] = 0
exceptions.warn(
'Initial stored energy not defined, set to zero for all '
'loc::techs in loc_techs_store, for use in iterative optimisation')
# Operated units is carried over between iterations, so must be defined in a milp model
if ('loc_techs_milp' in model_data.dims.keys()
and 'operated_units' not in model_data.data_vars.keys()):
model_data['operated_units'] = (xr.DataArray(
[0 for loc_tech in model_data.loc_techs_milp.values],
dims='loc_techs_milp'))
model_data['operated_units'].attrs['is_result'] = 1
model_data['operated_units'].attrs['operate_param'] = 1
exceptions.warn(
'daily operated units not defined, set to zero for all '
'loc::techs in loc_techs_milp, for use in iterative optimisation')
comments, warnings, errors = checks.check_operate_params(model_data)
exceptions.print_warnings_and_raise_errors(warnings=warnings,
errors=errors)
# Initialize our variables
solver = run_config['solver']
solver_io = run_config.get('solver_io', None)
solver_options = run_config.get('solver_options', None)
save_logs = run_config.get('save_logs', None)
window = run_config['operation']['window']
horizon = run_config['operation']['horizon']
window_to_horizon = horizon - window
# get the cumulative sum of timestep resolution, to find where we hit our window and horizon
timestep_cumsum = model_data.timestep_resolution.cumsum(
'timesteps').to_pandas()
# get the timesteps at which we start and end our windows
window_ends = timestep_cumsum.where((timestep_cumsum % window == 0) | (
timestep_cumsum == timestep_cumsum[-1]))
window_starts = timestep_cumsum.where((~np.isnan(window_ends.shift(1))) | (
timestep_cumsum == timestep_cumsum[0])).dropna()
window_ends = window_ends.dropna()
horizon_ends = timestep_cumsum[timestep_cumsum.isin(window_ends.values +
window_to_horizon)]
if not any(window_starts):
raise exceptions.ModelError(
'Not enough timesteps or incorrect timestep resolution to run in '
'operational mode with an optimisation window of {}'.format(
window))
# We will only update timseries parameters
timeseries_data_vars = [
k for k, v in model_data.data_vars.items()
if 'timesteps' in v.dims and v.attrs['is_result'] == 0
]
# Loop through each window, solve over the horizon length, and add result to
# result_array we only go as far as the end of the last horizon, which may
# clip the last bit of data
result_array = []
# track whether each iteration finds an optimal solution or not
terminations = []
if build_only:
iterations = [0]
else:
iterations = range(len(window_starts))
for i in iterations:
start_timestep = window_starts.index[i]
# Build full model in first instance
if i == 0:
warmstart = False
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(logger,
timings,
'model_gen_1',
comment='Backend: generating initial model')
backend_model = backend.generate_model(window_model_data)
# Build the full model in the last instance(s),
# where number of timesteps is less than the horizon length
elif i > len(horizon_ends) - 1:
warmstart = False
end_timestep = window_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
logger,
timings,
'model_gen_{}'.format(i + 1),
comment=(
'Backend: iteration {}: generating new model for '
'end of timeseries, with horizon = {} timesteps'.format(
i + 1, window_ends[i] - window_starts[i])))
backend_model = backend.generate_model(window_model_data)
# Update relevent Pyomo Params in intermediate instances
else:
warmstart = True
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
logger,
timings,
'model_gen_{}'.format(i + 1),
comment='Backend: iteration {}: updating model parameters'.
format(i + 1))
# Pyomo model sees the same timestamps each time, we just change the
# values associated with those timestamps
for var in timeseries_data_vars:
# New values
var_series = window_model_data[var].to_series().dropna(
).replace('inf', np.inf)
# Same timestamps
var_series.index = backend_model.__calliope_model_data['data'][
var].keys()
var_dict = var_series.to_dict()
# Update pyomo Param with new dictionary
getattr(backend_model, var).store_values(var_dict)
if not build_only:
log_time(logger,
timings,
'model_run_{}'.format(i + 1),
time_since_run_start=True,
comment='Backend: iteration {}: sending model to solver'.
format(i + 1))
# After iteration 1, warmstart = True, which should speed up the process
# Note: Warmstart isn't possible with GLPK (dealt with later on)
_results = backend.solve_model(
backend_model,
solver=solver,
solver_io=solver_io,
solver_options=solver_options,
save_logs=save_logs,
warmstart=warmstart,
)
log_time(logger,
timings,
'run_solver_exit_{}'.format(i + 1),
time_since_run_start=True,
comment='Backend: iteration {}: solver finished running'.
format(i + 1))
# xarray dataset is built for each iteration
_termination = backend.load_results(backend_model, _results)
terminations.append(_termination)
_results = backend.get_result_array(backend_model, model_data)
# We give back the actual timesteps for this iteration and take a slice
# equal to the window length
_results['timesteps'] = window_model_data.timesteps.copy()
# We always save the window data. Until the last window(s) this will crop
# the window_to_horizon timesteps. In the last window(s), optimistion will
# only be occurring over a window length anyway
_results = _results.loc[dict(
timesteps=slice(None, window_ends.index[i]))]
result_array.append(_results)
# Set up initial storage for the next iteration
if 'loc_techs_store' in model_data.dims.keys():
storage_initial = _results.storage.loc[{
'timesteps':
window_ends.index[i]
}].drop('timesteps')
model_data['storage_initial'].loc[
storage_initial.coords] = storage_initial.values
backend_model.storage_initial.store_values(
storage_initial.to_series().dropna().to_dict())
# Set up total operated units for the next iteration
if 'loc_techs_milp' in model_data.dims.keys():
operated_units = _results.operating_units.sum(
'timesteps').astype(np.int)
model_data['operated_units'].loc[{}] += operated_units.values
backend_model.operated_units.store_values(
operated_units.to_series().dropna().to_dict())
log_time(logger,
timings,
'run_solver_exit_{}'.format(i + 1),
time_since_run_start=True,
comment='Backend: iteration {}: generated solution array'.
format(i + 1))
if build_only:
results = xr.Dataset()
else:
# Concatenate results over the timestep dimension to get a single
# xarray Dataset of interest
results = xr.concat(result_array, dim='timesteps')
if all(i == 'optimal' for i in terminations):
results.attrs['termination_condition'] = 'optimal'
elif all(i in ['optimal', 'feasible'] for i in terminations):
results.attrs['termination_condition'] = 'feasible'
else:
results.attrs['termination_condition'] = ','.join(terminations)
log_time(logger,
timings,
'run_solution_returned',
time_since_run_start=True,
comment='Backend: generated full solution array')
return results, backend_model
def run_operate(model_data, timings, backend, build_only):
"""
For use when mode is 'operate', to allow the model to be built, edited, and
iteratively run within Pyomo.
"""
log_time(
logger,
timings,
"run_start",
comment="Backend: starting model run in operational mode",
)
defaults = UpdateObserverDict(
initial_yaml_string=model_data.attrs["defaults"],
name="defaults",
observer=model_data,
)
run_config = UpdateObserverDict(
initial_yaml_string=model_data.attrs["run_config"],
name="run_config",
observer=model_data,
)
# New param defaults = old maximum param defaults (e.g. energy_cap gets default from energy_cap_max)
operate_params = {
k.replace("_max", ""): v
for k, v in defaults.items() if k.endswith("_max")
}
operate_params[
"purchased"] = 0 # no _max to work from here, so we hardcode a default
defaults.update(operate_params)
# Capacity results (from plan mode) can be used as the input to operate mode
if any(model_data.filter_by_attrs(
is_result=1).data_vars) and run_config.get(
"operation.use_cap_results", False):
# Anything with is_result = 1 will be ignored in the Pyomo model
for varname, varvals in model_data.data_vars.items():
if varname in operate_params.keys():
varvals.attrs["is_result"] = 1
varvals.attrs["operate_param"] = 1
else:
cap_max = xr.merge([
v.rename(k.replace("_max", ""))
for k, v in model_data.data_vars.items() if "_max" in k
])
cap_equals = xr.merge([
v.rename(k.replace("_equals", ""))
for k, v in model_data.data_vars.items() if "_equals" in k
])
caps = cap_max.update(cap_equals)
for cap in caps.data_vars.values():
cap.attrs["is_result"] = 1
cap.attrs["operate_param"] = 1
model_data.update(caps)
comments, warnings, errors = checks.check_operate_params(model_data)
exceptions.print_warnings_and_raise_errors(warnings=warnings,
errors=errors)
# Initialize our variables
solver = run_config["solver"]
solver_io = run_config.get("solver_io", None)
solver_options = run_config.get("solver_options", None)
save_logs = run_config.get("save_logs", None)
window = run_config["operation"]["window"]
horizon = run_config["operation"]["horizon"]
window_to_horizon = horizon - window
# get the cumulative sum of timestep resolution, to find where we hit our window and horizon
timestep_cumsum = model_data.timestep_resolution.cumsum(
"timesteps").to_pandas()
# get the timesteps at which we start and end our windows
window_ends = timestep_cumsum.where((timestep_cumsum % window == 0) | (
timestep_cumsum == timestep_cumsum[-1]))
window_starts = timestep_cumsum.where((~np.isnan(window_ends.shift(1))) | (
timestep_cumsum == timestep_cumsum[0])).dropna()
window_ends = window_ends.dropna()
horizon_ends = timestep_cumsum[timestep_cumsum.isin(window_ends.values +
window_to_horizon)]
if not any(window_starts):
raise exceptions.ModelError(
"Not enough timesteps or incorrect timestep resolution to run in "
"operational mode with an optimisation window of {}".format(
window))
# We will only update timseries parameters
timeseries_data_vars = [
k for k, v in model_data.data_vars.items()
if "timesteps" in v.dims and v.attrs["is_result"] == 0
]
# Loop through each window, solve over the horizon length, and add result to
# result_array we only go as far as the end of the last horizon, which may
# clip the last bit of data
result_array = []
# track whether each iteration finds an optimal solution or not
terminations = []
if build_only:
iterations = [0]
else:
iterations = range(len(window_starts))
for i in iterations:
start_timestep = window_starts.index[i]
# Build full model in first instance
if i == 0:
warmstart = False
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
logger,
timings,
"model_gen_1",
comment="Backend: generating initial model",
)
backend_model = backend.generate_model(window_model_data)
# Build the full model in the last instance(s),
# where number of timesteps is less than the horizon length
elif i > len(horizon_ends) - 1:
warmstart = False
end_timestep = window_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
logger,
timings,
"model_gen_{}".format(i + 1),
comment=(
"Backend: iteration {}: generating new model for "
"end of timeseries, with horizon = {} timesteps".format(
i + 1, window_ends[i] - window_starts[i])),
)
backend_model = backend.generate_model(window_model_data)
# Update relevent Pyomo Params in intermediate instances
else:
warmstart = True
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
logger,
timings,
"model_gen_{}".format(i + 1),
comment="Backend: iteration {}: updating model parameters".
format(i + 1),
)
# Pyomo model sees the same timestamps each time, we just change the
# values associated with those timestamps
for var in timeseries_data_vars:
# New values
var_series = (
window_model_data[var].to_series().dropna().replace(
"inf", np.inf))
# Same timestamps
var_series.index = backend_model.__calliope_model_data["data"][
var].keys()
var_dict = var_series.to_dict()
# Update pyomo Param with new dictionary
getattr(backend_model, var).store_values(var_dict)
if not build_only:
log_time(
logger,
timings,
"model_run_{}".format(i + 1),
time_since_run_start=True,
comment="Backend: iteration {}: sending model to solver".
format(i + 1),
)
# After iteration 1, warmstart = True, which should speed up the process
# Note: Warmstart isn't possible with GLPK (dealt with later on)
_results = backend.solve_model(
backend_model,
solver=solver,
solver_io=solver_io,
solver_options=solver_options,
save_logs=save_logs,
warmstart=warmstart,
)
log_time(
logger,
timings,
"run_solver_exit_{}".format(i + 1),
time_since_run_start=True,
comment="Backend: iteration {}: solver finished running".
format(i + 1),
)
# xarray dataset is built for each iteration
_termination = backend.load_results(backend_model, _results)
terminations.append(_termination)
_results = backend.get_result_array(backend_model, model_data)
# We give back the actual timesteps for this iteration and take a slice
# equal to the window length
_results["timesteps"] = window_model_data.timesteps.copy()
# We always save the window data. Until the last window(s) this will crop
# the window_to_horizon timesteps. In the last window(s), optimistion will
# only be occurring over a window length anyway
_results = _results.loc[dict(
timesteps=slice(None, window_ends.index[i]))]
result_array.append(_results)
# Set up initial storage for the next iteration
if "loc_techs_store" in model_data.dims.keys():
storage_initial = _results.storage.loc[{
"timesteps":
window_ends.index[i]
}].drop("timesteps")
model_data["storage_initial"].loc[
storage_initial.coords] = storage_initial.values
backend_model.storage_initial.store_values(
storage_initial.to_series().dropna().to_dict())
# Set up total operated units for the next iteration
if "loc_techs_milp" in model_data.dims.keys():
operated_units = _results.operating_units.sum(
"timesteps").astype(np.int)
model_data["operated_units"].loc[{}] += operated_units.values
backend_model.operated_units.store_values(
operated_units.to_series().dropna().to_dict())
log_time(
logger,
timings,
"run_solver_exit_{}".format(i + 1),
time_since_run_start=True,
comment="Backend: iteration {}: generated solution array".
format(i + 1),
)
if build_only:
results = xr.Dataset()
else:
# Concatenate results over the timestep dimension to get a single
# xarray Dataset of interest
results = xr.concat(result_array, dim="timesteps")
if all(i == "optimal" for i in terminations):
results.attrs["termination_condition"] = "optimal"
elif all(i in ["optimal", "feasible"] for i in terminations):
results.attrs["termination_condition"] = "feasible"
else:
results.attrs["termination_condition"] = ",".join(terminations)
log_time(
logger,
timings,
"run_solution_returned",
time_since_run_start=True,
comment="Backend: generated full solution array",
)
return results, backend_model
def run_operate(model_data, timings, backend, build_only):
"""
For use when mode is 'operate', to allow the model to be built, edited, and
iteratively run within Pyomo.
"""
log_time(timings, 'run_start',
comment='Backend: starting model run in operational mode')
defaults = ruamel.yaml.load(model_data.attrs['defaults'], Loader=ruamel.yaml.Loader)
operate_params = ['purchased'] + [
i.replace('_max', '') for i in defaults if i[-4:] == '_max'
]
# Capacity results (from plan mode) can be used as the input to operate mode
if (any(model_data.filter_by_attrs(is_result=1).data_vars) and
model_data.attrs.get('run.operation.use_cap_results', False)):
# Anything with is_result = 1 will be ignored in the Pyomo model
for varname, varvals in model_data.data_vars.items():
if varname in operate_params:
varvals.attrs['is_result'] = 1
varvals.attrs['operate_param'] = 1
else:
cap_max = xr.merge([
v.rename(k.replace('_max', ''))
for k, v in model_data.data_vars.items() if '_max' in k
])
cap_equals = xr.merge([
v.rename(k.replace('_equals', ''))
for k, v in model_data.data_vars.items() if '_equals' in k
])
caps = cap_max.update(cap_equals)
for cap in caps.data_vars.values():
cap.attrs['is_result'] = 1
cap.attrs['operate_param'] = 1
model_data.update(caps)
# Storage initial is carried over between iterations, so must be defined along with storage
if ('loc_techs_store' in model_data.dims.keys() and
'storage_initial' not in model_data.data_vars.keys()):
model_data['storage_initial'] = (
xr.DataArray([0 for loc_tech in model_data.loc_techs_store.values],
dims='loc_techs_store')
)
model_data['storage_initial'].attrs['is_result'] = 0
exceptions.ModelWarning(
'Initial stored energy not defined, set to zero for all '
'loc::techs in loc_techs_store, for use in iterative optimisation'
)
# Operated units is carried over between iterations, so must be defined in a milp model
if ('loc_techs_milp' in model_data.dims.keys() and
'operated_units' not in model_data.data_vars.keys()):
model_data['operated_units'] = (
xr.DataArray([0 for loc_tech in model_data.loc_techs_milp.values],
dims='loc_techs_milp')
)
model_data['operated_units'].attrs['is_result'] = 1
model_data['operated_units'].attrs['operate_param'] = 1
exceptions.ModelWarning(
'daily operated units not defined, set to zero for all '
'loc::techs in loc_techs_milp, for use in iterative optimisation'
)
comments, warnings, errors = checks.check_operate_params(model_data)
exceptions.print_warnings_and_raise_errors(warnings=warnings, errors=errors)
# Initialize our variables
solver = model_data.attrs['run.solver']
solver_io = model_data.attrs.get('run.solver_io', None)
solver_options = model_data.attrs.get('run.solver_options', None)
save_logs = model_data.attrs.get('run.save_logs', None)
window = model_data.attrs['run.operation.window']
horizon = model_data.attrs['run.operation.horizon']
window_to_horizon = horizon - window
# get the cumulative sum of timestep resolution, to find where we hit our window and horizon
timestep_cumsum = model_data.timestep_resolution.cumsum('timesteps').to_pandas()
# get the timesteps at which we start and end our windows
window_ends = timestep_cumsum.where(
(timestep_cumsum % window == 0) | (timestep_cumsum == timestep_cumsum[-1])
)
window_starts = timestep_cumsum.where(
(~np.isnan(window_ends.shift(1))) | (timestep_cumsum == timestep_cumsum[0])
).dropna()
window_ends = window_ends.dropna()
horizon_ends = timestep_cumsum[timestep_cumsum.isin(window_ends.values + window_to_horizon)]
if not any(window_starts):
raise exceptions.ModelError(
'Not enough timesteps or incorrect timestep resolution to run in '
'operational mode with an optimisation window of {}'.format(window)
)
# We will only update timseries parameters
timeseries_data_vars = [
k for k, v in model_data.data_vars.items() if 'timesteps' in v.dims
and v.attrs['is_result'] == 0
]
# Loop through each window, solve over the horizon length, and add result to
# result_array we only go as far as the end of the last horizon, which may
# clip the last bit of data
result_array = []
# track whether each iteration finds an optimal solution or not
terminations = []
if build_only:
iterations = [0]
else:
iterations = range(len(window_starts))
for i in iterations:
start_timestep = window_starts.index[i]
# Build full model in first instance
if i == 0:
warmstart = False
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
timings, 'model_gen_1',
comment='Backend: generating initial model'
)
backend_model = backend.generate_model(window_model_data)
# Build the full model in the last instance(s),
# where number of timesteps is less than the horizon length
elif i > len(horizon_ends) - 1:
warmstart = False
end_timestep = window_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
timings, 'model_gen_{}'.format(i + 1),
comment=(
'Backend: iteration {}: generating new model for '
'end of timeseries, with horizon = {} timesteps'
.format(i + 1, window_ends[i] - window_starts[i])
)
)
backend_model = backend.generate_model(window_model_data)
# Update relevent Pyomo Params in intermediate instances
else:
warmstart = True
end_timestep = horizon_ends.index[i]
timesteps = slice(start_timestep, end_timestep)
window_model_data = model_data.loc[dict(timesteps=timesteps)]
log_time(
timings, 'model_gen_{}'.format(i + 1),
comment='Backend: iteration {}: updating model parameters'.format(i + 1)
)
# Pyomo model sees the same timestamps each time, we just change the
# values associated with those timestamps
for var in timeseries_data_vars:
# New values
var_series = window_model_data[var].to_series().dropna().replace('inf', np.inf)
# Same timestamps
var_series.index = backend_model.__calliope_model_data__['data'][var].keys()
var_dict = var_series.to_dict()
# Update pyomo Param with new dictionary
for k, v in getattr(backend_model, var).items():
if k in var_dict:
v.set_value(var_dict[k])
if not build_only:
log_time(
timings, 'model_run_{}'.format(i + 1), time_since_start=True,
comment='Backend: iteration {}: sending model to solver'.format(i + 1)
)
# After iteration 1, warmstart = True, which should speed up the process
# Note: Warmstart isn't possible with GLPK (dealt with later on)
_results = backend.solve_model(
backend_model, solver=solver, solver_io=solver_io,
solver_options=solver_options, save_logs=save_logs, warmstart=warmstart,
)
log_time(
timings, 'run_solver_exit_{}'.format(i + 1), time_since_start=True,
comment='Backend: iteration {}: solver finished running'.format(i + 1)
)
# xarray dataset is built for each iteration
_termination = backend.load_results(backend_model, _results)
terminations.append(_termination)
_results = backend.get_result_array(backend_model, model_data)
# We give back the actual timesteps for this iteration and take a slice
# equal to the window length
_results['timesteps'] = window_model_data.timesteps.copy()
# We always save the window data. Until the last window(s) this will crop
# the window_to_horizon timesteps. In the last window(s), optimistion will
# only be occurring over a window length anyway
_results = _results.loc[dict(timesteps=slice(None, window_ends.index[i]))]
result_array.append(_results)
# Set up initial storage for the next iteration
if 'loc_techs_store' in model_data.dims.keys():
storage_initial = _results.storage.loc[{'timesteps': window_ends.index[i]}].drop('timesteps')
model_data['storage_initial'].loc[storage_initial.coords] = storage_initial.values
for k, v in backend_model.storage_initial.items():
v.set_value(storage_initial.to_series().dropna().to_dict()[k])
# Set up total operated units for the next iteration
if 'loc_techs_milp' in model_data.dims.keys():
operated_units = _results.operating_units.sum('timesteps').astype(np.int)
model_data['operated_units'].loc[{}] += operated_units.values
for k, v in backend_model.operated_units.items():
v.set_value(operated_units.to_series().dropna().to_dict()[k])
log_time(
timings, 'run_solver_exit_{}'.format(i + 1), time_since_start=True,
comment='Backend: iteration {}: generated solution array'.format(i + 1)
)
if build_only:
results = xr.Dataset()
else:
# Concatenate results over the timestep dimension to get a single
# xarray Dataset of interest
results = xr.concat(result_array, dim='timesteps')
if all(i == 'optimal' for i in terminations):
results.attrs['termination_condition'] = 'optimal'
else:
results.attrs['termination_condition'] = ','.join(terminations)
log_time(
timings, 'run_solution_returned', time_since_start=True,
comment='Backend: generated full solution array'
)
return results, backend_model