linopy.model.Model.add_expressions

Model.add_expressions(data, name=None, mask=None)

Assign a new, possibly multi-dimensional array of expressions to the model.

Parameters:

• data (Variable, LinearExpression, QuadraticExpression, or Sequence of (constant, variable) tuples) – The expression(s) to add. This can be a Variable or LinearExpression, or a sequence of (constant, variable) tuples which will be summed up.

• coords (list/xarray.Coordinates, optional) – The coords of the expression array. The default is None.

• name (str, optional) – Reference name of the added expressions. The default None results in a name like “expr1”, “expr2” etc.

• mask (array_like, optional) – Boolean mask with False values for expressions which are skipped. The shape of the mask has to match the shape the added expressions. Default is None.

Raises:

ValueError – If neither lower bound and upper bound have coordinates, nor coords are directly given.

Returns:

linopy.LinearExpression | linopy.QuadraticExpression – Expression which was added to the model.

Examples

>>> from linopy import Model
>>> import pandas as pd
>>> m = Model()
>>> time = pd.RangeIndex(10, name="Time")
>>> x = m.add_variables(lower=0, coords=[time], name="x")
>>> expr = m.add_expressions(x + 1, name="expr")