Source code for calliope.backend.pyomo.interface

import xarray as xr
import pyomo.core as po  # pylint: disable=import-error

from calliope.backend.pyomo.util import get_var
from calliope.backend import run as backend_run
from calliope.backend.pyomo import model as run_pyomo

from calliope.core.util.dataset import reorganise_dataset_dimensions
from calliope.core.util.logging import log_time
from calliope import exceptions

def access_pyomo_model_inputs(backend_model):
    If the user wishes to inspect the parameter values used as inputs in the backend
    model, they can access a new Dataset of all the backend model inputs, including
    defaults applied where the user did not specify anything for a loc::tech

    all_params = { get_var(backend_model,, sparse=True)
        for i in backend_model.component_objects()
        if isinstance(i, po.base.param.IndexedParam)

    return reorganise_dataset_dimensions(xr.Dataset(all_params))

def update_pyomo_param(backend_model, param, index, value):
    A Pyomo Param value can be updated without the user directly accessing the
    backend model.

    param : str
        Name of the parameter to update
    index : tuple of strings
        Tuple of dimension indeces, in the order given in model.inputs for the
        reciprocal parameter
    value : int, float, bool, or str
        Value to assign to the Pyomo Param at the given index

    Value will be updated in-place, requiring the user to run the model again to
    see the effect on results.

    if not hasattr(backend_model, param):
        raise exceptions.ModelError(
            'Parameter {} not in the Pyomo Backend. Check that the string '
            'matches the corresponding constraint/cost in the model.inputs '
            'xarray Dataset'.format(param)
    elif not isinstance(getattr(backend_model, param), po.base.param.IndexedParam):
        raise exceptions.ModelError(
            '{} not a Parameter in the Pyomo Backend. Sets and decision variables '
            'cannot be updated by the user'.format(param)
    elif index not in getattr(backend_model, param):
        raise exceptions.ModelError(
            'index {} not in the Pyomo Parameter {}. call '
            'model.access_backend_model_inputs to see the indeces of the Pyomo '
            'Parameters'.format(index, param)
            'Warning: we currently do not check that the updated value is the '
            'correct data type for this Pyomo Parameter, this is your '
            'responsibility to check!'
        getattr(backend_model, param)[index] = value

def activate_pyomo_constraint(backend_model, constraint, active=True):
    Takes a constraint or objective name, finds it in the backend model and sets
    its status to either active or deactive.

    constraint : str
        Name of the constraint/objective to activate/deactivate
        Built-in constraints include '_constraint'
    active: bool, default=True
        status to set the constraint/objective
    if not hasattr(backend_model, constraint):
        raise exceptions.ModelError(
            'constraint/objective {} not in the Pyomo Backend.'.format(constraint)
    elif not isinstance(getattr(backend_model, constraint), po.base.Constraint):
        raise exceptions.ModelError(
            '{} not a constraint in the Pyomo Backend.'.format(constraint)
    elif active:
        getattr(backend_model, constraint).activate()
    elif not active:
        getattr(backend_model, constraint).deactivate()
        raise ValueError('Argument `active` must be True or False')

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`).

    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 '

    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

        'model.results will only be updated on running the model from '
        '``. We provide results of this rerun as a standalone xarray '

    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

    run_data = results

    return run_data

[docs]class BackendInterfaceMethods: def __init__(self, model): self._backend = model._backend_model self._model_data = model._model_data
[docs] def access_model_inputs(self): return access_pyomo_model_inputs(self._backend)
access_model_inputs.__doc__ = access_pyomo_model_inputs.__doc__
[docs] def update_param(self, *args, **kwargs): return update_pyomo_param(self._backend, *args, **kwargs)
update_param.__doc__ = update_pyomo_param.__doc__
[docs] def activate_constraint(self, *args, **kwargs): return activate_pyomo_constraint(self._backend, *args, **kwargs)
activate_constraint.__doc__ = activate_pyomo_constraint.__doc__
[docs] def rerun(self, *args, **kwargs): return rerun_pyomo_model(self._model_data, self._backend, *args, **kwargs)
rerun.__doc__ = rerun_pyomo_model.__doc__