Linopy

Linopy: Linear optimization with n-dimensional labeled variables - Published in JOSS (2023)

https://github.com/pypsa/linopy

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

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Found 8 DOI reference(s) in README and JOSS metadata
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7 of 49 committers (14.3%) from academic institutions
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

cbc cplex glpk gurobi linear-optimisation linear-optimization optimisation optimization python xarray xpress

Keywords from Contributors

energy-system solar renewable-energy wind renewable-timeseries reanalysis pv potentials heat-pump era5

Scientific Fields

Earth and Environmental Sciences Physical Sciences - 62% confidence

Engineering Computer Science - 60% confidence

Last synced: 4 months ago · JSON representation

Repository

Linear optimization with N-D labeled arrays in Python

Basic Info

Host: GitHub
Owner: PyPSA
License: mit
Language: Python
Default Branch: master
Homepage: https://linopy.readthedocs.io
Size: 3.38 MB

Statistics

Stars: 208
Watchers: 5
Forks: 64
Open Issues: 61
Releases: 46

Topics

cbc cplex glpk gurobi linear-optimisation linear-optimization optimisation optimization python xarray xpress

Created almost 5 years ago · Last pushed 4 months ago

Metadata Files

Readme Contributing License

linopy: Optimization with array-like variables and constraints

Linear\ Integer\ Non-linear\ Optimization in\ PYthon

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.

Benchmarks

linopy is designed to be fast and efficient. The following benchmark compares the performance of linopy with the alternative popular optimization packages.

Performance Benchmark

Main features

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

Define (arrays of) continuous 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

bash pip install linopy

or on conda-forge

bash conda install -c conda-forge linopy

In a Nutshell

Linopy aims to make optimization programs transparent and flexible. To illustrate its usage, let's consider a scenario where we aim to minimize the cost of buying apples and bananas over a week, subject to daily and weekly vitamin intake constraints.

```python

import pandas as pd import linopy

m = linopy.Model()

days = pd.Index(["Mon", "Tue", "Wed", "Thu", "Fri"], name="day") apples = m.addvariables(lower=0, name="apples", coords=[days]) bananas = m.addvariables(lower=0, name="bananas", coords=[days]) apples

Variable (day: 5)

[Mon]: apples[Mon] ∈ [0, inf] [Tue]: apples[Tue] ∈ [0, inf] [Wed]: apples[Wed] ∈ [0, inf] [Thu]: apples[Thu] ∈ [0, inf] [Fri]: apples[Fri] ∈ [0, inf] ```

Add daily vitamin constraints

```python

m.addconstraints(3 * apples + 2 * bananas >= 8, name="dailyvitamins")

Constraint daily_vitamins (day: 5):

[Mon]: +3 apples[Mon] + 2 bananas[Mon] ≥ 8 [Tue]: +3 apples[Tue] + 2 bananas[Tue] ≥ 8 [Wed]: +3 apples[Wed] + 2 bananas[Wed] ≥ 8 [Thu]: +3 apples[Thu] + 2 bananas[Thu] ≥ 8 [Fri]: +3 apples[Fri] + 2 bananas[Fri] ≥ 8 ```

Add weekly vitamin constraint

```python

m.addconstraints((3 * apples + 2 * bananas).sum() >= 50, name="weeklyvitamins")

Constraint weekly_vitamins

+3 apples[Mon] + 2 bananas[Mon] + 3 apples[Tue] ... +2 bananas[Thu] + 3 apples[Fri] + 2 bananas[Fri] ≥ 50 ```

Define the prices of apples and bananas and the objective function

```python

appleprice = [1, 1.5, 1, 2, 1] bananaprice = [1, 1, 0.5, 1, 0.5] m.objective = appleprice * apples + bananaprice * bananas ```

Finally, we can solve the problem and get the optimal solution:

```python

m.solve() m.objective.value 17.166 ```

... and display the solution as a pandas DataFrame ```python

m.solution.to_pandas() apples bananas day Mon 2.667 0 Tue 0 4 Wed 0 9 Thu 0 4 Fri 0 4 ```

Supported solvers

linopy supports the following solvers

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

Development Setup

To set up a local development environment for linopy and to run the same tests that are run in the CI, you can run:

sh python -m venv venv source venv/bin/activate pip install uv uv pip install -e .[dev,solvers] pytest

The -e flag of the install command installs the linopy package in editable mode, which means that the virtualenv (and thus the tests) will run the code from your local checkout.

Citing Linopy

If you use Linopy in your research, please cite the following paper:

Hofmann, F., (2023). Linopy: Linear optimization with n-dimensional labeled variables. Journal of Open Source Software, 8(84), 4823, https://doi.org/10.21105/joss.04823

A BibTeX entry for LaTeX users is

latex @article{Hofmann2023, doi = {10.21105/joss.04823}, url = {https://doi.org/10.21105/joss.04823}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {84}, pages = {4823}, author = {Fabian Hofmann}, title = {Linopy: Linear optimization with n-dimensional labeled variables}, journal = {Journal of Open Source Software} }

License

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

Owner

Name: PyPSA
Login: PyPSA
Kind: organization

Website: www.pypsa.org
Repositories: 29
Profile: https://github.com/PyPSA

Python for Power System Analysis

JOSS Publication

Linopy: Linear optimization with n-dimensional labeled variables

Published

April 22, 2023

DOI

10.21105/joss.04823

Volume 8, Issue 84, Page 4823

Authors

Fabian Hofmann

Department of Digital Transformation in Energy Systems, Technical University of Berlin

Editor

Fei Tao

GitHub Events

Total

Create event: 55
Release event: 12
Issues event: 55
Watch event: 38
Delete event: 30
Member event: 1
Issue comment event: 143
Push event: 158
Pull request review comment event: 33
Pull request event: 135
Pull request review event: 68
Fork event: 13

Last Year

Create event: 61
Release event: 12
Issues event: 56
Watch event: 38
Delete event: 34
Member event: 1
Issue comment event: 146
Push event: 170
Pull request review comment event: 33
Pull request event: 143
Pull request review event: 70
Fork event: 13

Committers

Last synced: 5 months ago

All Time

Total Commits: 938
Total Committers: 49
Avg Commits per committer: 19.143
Development Distribution Score (DDS): 0.319

Past Year

Commits: 99
Committers: 19
Avg Commits per committer: 5.211
Development Distribution Score (DDS): 0.657

Top Committers

Name	Email	Commits
Fabian	f**f@g**e	639
pre-commit-ci[bot]	6****]	79
Lukas Trippe	l**p@p**e	32
Jonas Hoersch	j**h@c**g	26
Heinz-Alexander Fuetterer	f**h@p**e	25
ulfw	u**e@m**m	19
Fabian Neumann	f**n@o**e	17
Siddharth Krishna	s****a	8
Henning Blunck	m**l@h**e	7
fabulous	f**s@G**x	7
Irieo	i**n@g**m	6
Dan Allford	d**d@b**m	5
Florian Maurer	m**r@f**e	5
Daniel Müller	a**r@m**g	4
Felix Hennings	f**x@d**l	4
Fridolin Glatter	g**r@i**t	4
Michał Marszal	m**l@i**l	4
pz-max	m**n@e**k	4
Oscar Dowson	o****w	4
ollie-bell	5****l	3
Maciej Krakowiak	m**k@i**l	2
FBumann	1****n	2
Daniel Rüdt	1****t	2
Daniel Castro Uriegas	d**s@h**m	2
dependabot[bot]	4****]	2
lumbric	l**c@g**m	2
qheuristics	g**r@g**m	2
Victor	v**8@g**m	1
Zlatan Kadragić	a**e@g**m	1
aszekMosek	m**k@m**m	1
and 19 more...

Committer Domains (Top 20 + Academic)

mosek.com: 2 idea.edu.pl: 2 gmx.de: 1 pm.me: 1 climateanalytics.org: 1 posteo.de: 1 outlook.de: 1 henningblunck.de: 1 godulas-imac-5.fritz.box: 1 bp.com: 1 fh-aachen.de: 1 mailbox.org: 1 doingthemath.nl: 1 iiasa.ac.at: 1 ed.ac.uk: 1 cellophil.de: 1 zooplus.com: 1 itpau.com.au: 1 ise.fraunhofer.de: 1 cthulhu.energynautics.com: 1 vt.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 150
Total pull requests: 450
Average time to close issues: 3 months
Average time to close pull requests: 9 days
Total issue authors: 66
Total pull request authors: 50
Average comments per issue: 2.0
Average comments per pull request: 1.35
Merged pull requests: 379
Bot issues: 0
Bot pull requests: 71

Past Year

Issues: 44
Pull requests: 165
Average time to close issues: about 1 month
Average time to close pull requests: 7 days
Issue authors: 31
Pull request authors: 26
Average comments per issue: 1.14
Average comments per pull request: 0.88
Merged pull requests: 122
Bot issues: 0
Bot pull requests: 20

View more stats

Top Authors

Issue Authors

FabianHofmann (17)
FBumann (8)
fneum (7)
willu47 (6)
aurelije (6)
loongmxbt (6)
dannyopts (5)
apfelix (5)
Cellophil (4)
KanaBaradei (4)
tburandt (4)
leuchtum (4)
thomgeo (3)
p-glaum (3)
BuiMCanmet (2)

Pull Request Authors

FabianHofmann (177)
pre-commit-ci[bot] (66)
lkstrp (32)
siddharth-krishna (16)
afuetterer (15)
coroa (13)
fneum (9)
leuchtum (8)
RobbieKiwi (6)
ollie-bell (6)
odow (5)
dannyopts (5)
ulfworsoe (5)
qheuristics (4)
PeterKlein11 (4)

Top Labels

Issue Labels

bug (14) enhancement (7) solver interface (7) documentation (6) model formulation (4) question (1) discussion (1) data-model (1)

Pull Request Labels

solver interface (5) dependencies (4) enhancement (2)

Packages

Total packages: 2
Total downloads:
- pypi 53,820 last-month
Total docker downloads: 71

Total dependent packages: 5
(may contain duplicates)
Total dependent repositories: 8
(may contain duplicates)
Total versions: 56
Total maintainers: 2

pypi.org: linopy

Linear optimization with N-D labeled arrays in Python

Homepage: https://github.com/PyPSA/linopy
Documentation: https://linopy.readthedocs.io/
License: MIT License
Latest release: 0.5.6
published 4 months ago

Versions: 55
Dependent Packages: 4
Dependent Repositories: 7
Downloads: 53,820 Last month
Docker Downloads: 71

Rankings

Dependent packages count: 3.3%

Downloads: 3.7%

Average: 5.5%

Dependent repos count: 5.7%

Stargazers count: 7.4%

Forks count: 7.7%

Maintainers (2)

fabhof lkstrp

Last synced: 4 months ago

conda-forge.org: linopy

Homepage: https://github.com/PyPSA/linopy
License: MIT
Latest release: 0.0.14
published about 3 years ago

Versions: 1
Dependent Packages: 1
Dependent Repositories: 1

Rankings

Dependent repos count: 24.1%

Dependent packages count: 28.9%

Average: 33.5%

Stargazers count: 38.7%

Forks count: 42.4%

Last synced: 4 months ago

Dependencies

setup.py pypi

bottleneck *
dask >=0.18.0
deprecation *
numexpr *
numpy *
scipy *
toolz *
tqdm *
xarray *

.github/workflows/CI.yaml actions

actions/checkout v2 composite
actions/setup-python v1 composite
codecov/codecov-action v2 composite
crazy-max/ghaction-chocolatey v1 composite

.github/workflows/deploy.yml actions

actions/checkout master composite
actions/setup-python v1 composite
pypa/gh-action-pypi-publish master composite

benchmark/environment.yaml pypi

ortools >=9.5

Linopy

Science Score: 95.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

linopy: Optimization with array-like variables and constraints

Benchmarks

Main features

Installation

In a Nutshell

Variable (day: 5)

Constraint daily_vitamins (day: 5):

Constraint weekly_vitamins

Supported solvers

Development Setup

Citing Linopy

License

Owner

JOSS Publication

Linopy: Linear optimization with n-dimensional labeled variables

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: linopy

Rankings

Maintainers (2)

conda-forge.org: linopy

Rankings

Dependencies

Constraint `daily_vitamins` (day: 5):

Constraint `weekly_vitamins`