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linopy.model.Model.add_variables

linopy.model.Model.add_variables#

Model.add_variables(lower=-inf, upper=inf, coords=None, name=None, mask=None, binary=False, integer=False, **kwargs)#

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

Variables may be added with lower and/or upper bounds. Unless a coords argument is provided, the shape of the lower and upper bounds define the number of variables which will be added to the model under the name name.

Parameters:

  • lower (float/array_like, optional) – Lower bound of the variable(s). Ignored if binary is True. The default is -inf.

  • upper (TYPE, optional) – Upper bound of the variable(s). Ignored if binary is True. The default is inf.

  • coords (list/xarray.Coordinates, optional) – The coords of the variable array. These are directly passed to the DataArray creation of lower and upper. For every single combination of coordinates a optimization variable is added to the model. The default is None.

  • name (str, optional) – Reference name of the added variables. The default None results in a name like “var1”, “var2” etc.

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

  • binary (bool) – Whether the new variable is a binary variable which are used for Mixed-Integer problems.

  • integer (bool) – Whether the new variable is a integer variable which are used for Mixed-Integer problems.

  • **kwargs – Additional keyword arguments are passed to the DataArray creation.

Raises:

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

Returns:

linopy.Variable – Variable which was added to the model.

Examples

>>> from linopy import Model
>>> import pandas as pd
>>> m = Model()
>>> time = pd.RangeIndex(10, name="Time")
>>> m.add_variables(lower=0, coords=[time], name="x")
Variable (Time: 10)
-------------------
[0]: x[0] ∈ [0, inf]
[1]: x[1] ∈ [0, inf]
[2]: x[2] ∈ [0, inf]
[3]: x[3] ∈ [0, inf]
[4]: x[4] ∈ [0, inf]
[5]: x[5] ∈ [0, inf]
[6]: x[6] ∈ [0, inf]
[7]: x[7] ∈ [0, inf]
[8]: x[8] ∈ [0, inf]
[9]: x[9] ∈ [0, inf]
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