Linear optimization with N-D labeled arrays in Python
linopy: Linear optimization with N-D labeled variables
linopy is an open-source python package that facilitates linear or mixed-integer optimisation with real world data. It builds a bridge between data analysis packages like xarray & pandas and linear problem solvers like cbc, gurobi (see the full list below). The project aims to make linear programming in python easy, highly-flexible and performant.
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
So far linopy is available on the PyPI repository
pip install linopy
linopy supports the following solvers
Note that these do have to be installed by the user separately.
Copyright 2021 Fabian Hofmann
This package is published under license GNU Public License GPLv3
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