API reference#
This page provides an auto-generated summary of linopy’s API.
Creating a model#
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Linear optimization model. |
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Assign a new, possibly multi-dimensional array of variables to the model. |
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Assign a new, possibly multi-dimensional array of constraints to the model. |
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Add a linear objective function to the model. |
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Create a linopy.LinearExpression from argument list. |
Remove all constraints stored under reference name name from the model. |
Classes under the hook#
Variable#
Variable
is a subclass of xarray.DataArray
and contains all labels referring to a multi-dimensional variable.
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Variable container for storing variable labels. |
Get the lower bounds of the variables. |
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Get the upper bounds of the variables. |
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Sum the variables over all or a subset of dimensions. |
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Filter variables based on a condition. |
Sanitize variable by ensuring int dtype with fill value of -1. |
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A variables container used for storing multiple variable arrays. |
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A scalar variable container. |
Variables#
Variables
is a container for multiple N-D labeled variables. It is automatically added to a Model
instance when initialized.
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A variables container used for storing multiple variable arrays. |
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Add a variable to the variables container. |
Remove variable name from the variables. |
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Get all continuous variables. |
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Get all integers variables. |
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Get all binary variables. |
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Get all integers variables. |
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Convert all variables to a single pandas Dataframe. |
LinearExpressions#
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A linear expression consisting of terms of coefficients and variables. |
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Sum the expression over all or a subset of dimensions. |
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Filter variables based on a condition. |
Returns a LinearExpressionGroupBy object for performing grouped operations. |
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Rolling window object. |
Create a linear expression by using tuples of coefficients and variables. |
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Merge multiple expression together. |
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A scalar linear expression container. |
Constraint#
Constraint
is a subclass of xarray.DataArray
and contains all labels referring to a multi-dimensional constraint.
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Projection to a single constraint in a model. |
Get the left-hand-side coefficients of the constraint. |
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Get the left-hand-side variables of the constraint. |
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Get the left-hand-side linear expression of the constraint. |
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Get the signs of the constraint. |
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Get the right hand side constants of the constraint. |
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Convert the constraint to a pandas DataFrame. |
Constraints#
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A constraint container used for storing multiple constraint arrays. |
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Add a constraint to the constraints constrainer. |
Remove constraint name from the constraints. |
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Coefficient range of the constraint. |
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Get the subset of constraints which are purely inequalities. |
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Get the subset of constraints which are purely equalities. |
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Set constraints labels to -1 where all variables in the lhs are missing. |
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Convert all constraint to a single pandas Dataframe. |
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Construct a constraint matrix in sparse format. |
IO functions#
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Get a fresh created problem file if problem file is None. |
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Get a fresh created solution file if solution file is None. |
Write out a model to a lp or mps file. |
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Write out the model to a netcdf file. |
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Read in a model from a netcdf file. |
Solvers#
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Solve a linear problem using the cbc solver. |
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Solve a linear problem using the glpk solver. |
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Highs solver function. |
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Solve a linear problem using the cplex solver. |
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Solve a linear problem using the gurobi solver. |
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Solve a linear problem using the xpress solver. |
Solving#
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Solve the model with possibly different solvers. |
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Solver status. |
Termination condition of the solver. |
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Status and termination condition of the solver. |
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Solution returned by the solver. |
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Result of the optimization. |