You can download this example as a Jupyter notebook or start it in interactive mode.

Testing with Linopy#

In some cases you want to make sure the objects you create with linopy are correct and behave as expected. For this purpose, linopy provides a small testing framework, available at linopy.testing. In the following, we will show how to use it.

import pandas as pd
import linopy
from linopy.testing import assert_varequal, assert_linequal, assert_conequal
/tmp/ipykernel_1955/ DeprecationWarning:
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(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
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  import pandas as pd
m = linopy.Model()

Check the equality of two variables#

The most basic test is to check the equality of two variables. This can be done with the assert_varequal function. If the two variables are not equal, an AssertionError is raised.

x = m.add_variables(coords=[pd.RangeIndex(10, name="first")], name="x")

assert_varequal(x, m.variables.x)
# or
assert_varequal(x, m.variables["x"])

Check the equality of two expressions#

Analogeous to the assert_varequal function, the assert_expr_equal function can be used to check the equality of two expressions.

y = m.add_variables(coords=[pd.RangeIndex(10, name="first")], name="y")

expr = 2 * x + y
assert_linequal(expr, 2 * x + y)

Check the equality of two constraints#

And finally, the assert_constraint_equal function can be used to check the equality of two constraints.

con = m.add_constraints(expr >= 3, name='con')

assert_conequal(con, m.constraints.con)

Note that we cannot compare the con object with the original unassigned constraint expr >= 3. This is because a constraint object gets labels as soon as we add it to model. However we can make a workaround, and compare a new unassigned constraint, derived from con with the original constraint.


assert_conequal(expr >= 3, con.lhs >= con.rhs)