Package: yotse
YOTSE - Your Optimization Tool for Scientific ExperimentsYOTSE is a powerful tool designed to enable computational experiments using any software, with a particular emphasis on NetSquid in the context of the Quantum Internet Alliance. This library offers a set of predefined functions for performing parameter explorations and optimizations in a scalable and efficient manner.Documentation can be foundhere.Note: If you use this package for research purposes, consider checking out the underlying optimization libraries and citing them.Table of ContentsInstallationUsageContributingTestingLicenseInstallationPython PackageTo install this project, simply:pipinstallyotseManualIf you want to contribute to the project, then clone the repository as follows: (Make sure you have the necessary permissions.)gitclonehttps://github.com/SURFQuantum/yotse.gitOnce the repository is cloned, navigate to the project's root directory, with:cdyotseIf you do not havepoetryalready installed you need will need  toinstallit first.
Then install yotse using:poetryinstallTo verify your installation you can now do:poetryruntests&&poetryrunexamplesUsageGeneralYOTSE is versatile, catering to a wide range of software. While it has a special focus on NetSquid, it is flexible enough to accommodate any software you wish to use for your computational experiments.Our library offers predefined functions for parameter exploration and optimization. We base our tools on the QCG-PilotJob project, a job manager executable both locally and in HPC centers. You can find more information about QCG-PilotJob in theirofficial docsor in theoriginal paper.The optimization component of YOTSE involves a base class that can be used with any external optimization library. This flexibility allows you to tailor your optimization process to your specific needs and preferences.Detailed examples of usage are provided in the/examplesdirectory.---
title: Workflow
---
flowchart LR
    p("`user_pre
    (Experiment)`")
    a(analysis script)
    s(user script)
    c{{"`csv output
     input params, cost
     (Core)`"}}
    S[[Best Solution]]
    q["`QcgPilotJob
    (Executor)`"]
    G["`GenericOptimization
    (Optimizer)`"]

    p --> q -- schedules jobs of --> s --> c --> G --  generates new datapoints for --> q
    G --> S
    s -.-> a -.-> cUsage with SLURMIn order to make usage with HPC resources andSLURMas smooth and simple as possible, yotse can generate its own SLURM files ready for execution.Simply specify your SLURM parameters (such as required nodes or time) and modules to be imported in theSystemSetupand then executepython<your_yotse_script.py>--slurmThis will generate a SLURM script for your (default name:slurm.job) which you can then submit by:sbatchslurm.jobIn order to keep dependencies as clean as possible, we suggest using two virtual environments for SLURM execution:A virtual environment that has all dependencies of your own script installed and can execute your owns script without errors. Pass this asSystemSetup.venv.A virtual environment that hasyotseinstalled. Pass this asSystemSetup.slurm_venvsuch that it is activated before SLURM executes the yotse commands.Class structure---
title: Class diagram
---
flowchart TB
  E["Executor"]
  c["cost function"]
  O["Optimizer"]
  G["GenericOptimization"]
  D[("database_from_csv")]
  Ex["Experiment"]
  E --> Ex --> O
  E -- generates --> O -- contains --> G -- contains --> D
  c -- replaced by --x D
  D -- fitness fun in --> GContributingWe appreciate contributions. To contribute:Fork the projectCreate your feature branch (git checkout -b feature/MyNewFeature)Commit your changes (git commit -am 'Add some feature')Push to the branch (git push origin feature/MyNewFeature)Executemake verifyto check if your changes pass the required checksOpen a Pull RequestTestingTo run tests on the project, navigate to the project's root directory and:poetryruntestsIf you want to open the coverage report do:poetryrunshow-covand the coverage report should open in your default browser.To run the examples, in the root directory executepoetryrunexamplesLicenseThis project is licensed under the terms of the Apache License 2.0. For more details, refer to the LICENSE file in the project root.