linopy 0.2

Linear optimization with N-D labeled arrays in Python

linopy: Linear optimization with N-dimensional labeled variables

linopy is an open-source python package that facilitates optimization with real world data. It builds a bridge between data analysis packages like xarray & pandas and problem solvers like cbc, gurobi (see the full list below). Linopy supports Linear, Integer, Mixed-Integer and Quadratic Programming while aiming to make linear programming in Python easy, highly-flexible and performant.

Main features

linopy is heavily based on xarray which allows for many flexible data-handling features:

• Define (arrays of) contnuous or binary variables with coordinates, e.g. time, consumers, etc.
• Apply arithmetic operations on the variables like adding, substracting, multiplying with all the broadcasting potentials of xarray
• Apply arithmetic operations on the linear expressions (combination of variables)
• Group terms of a linear expression by coordinates
• Get insight into the clear and transparent data model
• Modify and delete assigned variables and constraints on the fly
• Use lazy operations for large linear programs with dask
• Choose from different commercial and non-commercial solvers
• Fast import and export a linear model using xarray's netcdf IO

Installation

So far linopy is available on the PyPI repository

pip install linopy

or on conda-forge

conda install -c conda-forge linopy

Supported solvers

linopy supports the following solvers

• Cbc
• GLPK
• HiGHS
• Gurobi
• Xpress
• Cplex

Note that these do have to be installed by the user separately.

License

Copyright 2021 Fabian Hofmann

This package is published under MIT license. See LICENSE.txt for details.