← Back to catalog

ODAT-SE

issp-center-dev / ODAT-SE

Optimization and inverse-problem framework for experimental data analysis (formerly 2DMAT)

Machine learningOthersPythonMPL-2.0
17 1 19 Open issues / PRsUpdated: 2026-10-11

Overview

(No description provided)

Typical use cases

Target users

Experimental researchersComputational-science software developersGraduate students

Fields

inverse-problemoptimization

Quick Start

This software provides a Quickstart / tutorial.

README

ODAT-SE -- an open framework for data analysis

Open Data Analysis Tool for Science and Engineering (ODAT-SE) is a framework for applying a search algorithm to a direct problem solver to find the optimal solution. It has been developed by the name 2DMAT, and since version 3.0, it is organized as an open platform for data analysis by modularizing direct problem solvers and search algorithms.

As the standard direct problem solver, the experimental data analysis software for two-dimensional material structure analysis is prepared. The direct problem solver gives the deviation between the experimental data and the calculated data obtained under the given parameters such as atomic positions as a loss function used in the inverse problem. The optimal parameters are estimated by minimizing the loss function using a search algorithm. For further use, the original direct problem solver or the search algorithm can be defined by users. ODAT-SE offers wrappers of direct problem solvers for some of quantum beam diffraction experiments such as the total-reflection high-energy positron diffraction (TRHEPD) experiment. As algorithms, it offers some minimizers such as the Nelder-Mead method and some samplers such as the population annealing Monte Carlo method.

In the future, we plan to add other direct problem solvers and search algorithms in ODAT-SE.

Branch Build status Documentation
main (latest, stable) main doc_en doc_ja

odatse

odatse is a python framework library for solving inverse problems. It also offers a driver script to solve the problem with predefined optimization algorithms and direct problem solvers. (odatse also stands for the name of the script.)

Prerequisites
  • Required
    • python >= 3.9
    • numpy >= 1.14
    • matplotlib >= 3
    • tomli >= 1.2 (for Python < 3.11)
  • Optional
    • scipy
      • for minsearch and global_search algorithms
    • mpi4py >= 3.1
      • for MPI-parallel algorithms such as exchange and pamc
    • physbo >= 2.0
      • for bayes algorithm
    • tqdm
      • for progress display
Install
  • From PyPI (Recommended)
    • python3 -m pip install -U ODAT-SE
      • If you install them locally, use --user option like python3 -m pip install -U --user
      • To install all the optional dependencies at once, use python3 -m pip install -U "ODAT-SE[all]"
  • From Source (For developers)
    1. update pip >= 19 by python3 -m pip install -U pip
    2. python3 -m pip install ODAT_SE_ROOT_DIRECTORY to install odatse package and odatse command
      • ODAT_SE_ROOT_DIRECTORY means the directory including this README.md file.
Simple Usage
  • odatse input.toml (use the installed script)
  • python3 src/odatse_main.py input.toml (use the raw script)
  • For details of the input file, see the document.

Files and directories of ODAT-SE

  • src/
    • source codes
  • sample/
    • sample usages
  • doc/
    • source codes of documents (manuals)
  • tests/
    • for automatic test
  • README.md
    • this file
  • pyproject.toml
    • metadata for ODAT-SE

License

This package is distributed under Mozilla Public License v2.0 (MPL-2.0).

We hope that you cite the following references when you publish the results using 2DMAT / ODAT-SE: "Data-analysis software framework 2DMAT and its application to experimental measurements for two-dimensional material structures", Y. Motoyama, K. Yoshimi, I. Mochizuki, H. Iwamoto, H. Ichinose, and T. Hoshi, Computer Physics Communications 280, 108465 (2022).

Bibtex:

@article{MOTOYAMA2022108465,
  title = {Data-analysis software framework 2DMAT and its application to experimental measurements for two-dimensional material structures},
  journal = {Computer Physics Communications},
  volume = {280},
  pages = {108465},
  year = {2022},
  issn = {0010-4655},
  doi = {https://doi.org/10.1016/j.cpc.2022.108465},
  url = {https://www.sciencedirect.com/science/article/pii/S0010465522001849},
  author = {Yuichi Motoyama and Kazuyoshi Yoshimi and Izumi Mochizuki and Harumichi Iwamoto and Hayato Ichinose and Takeo Hoshi}
}

© 2020- The University of Tokyo. All rights reserved. This software was developed with the support of "Project for advancement of software usability in materials science" of The Institute for Solid State Physics, The University of Tokyo.

Source: https://github.com/issp-center-dev/ODAT-SE README

Richness

Active · 386 commits/yrLast updated: 2026-10-11 ()

Richness = how many of docs / examples / tests are in place, shown as 0–3 stars (1 star = 1 item; the checklist below is the breakdown). Software listed by hand (without repository data) is not assessed and is left out when filtering by a richness of 1 or more.

Information

Primary language
Python
Languages
Python, Shell, CMake
License
MPL-2.0
Provider
github
DOI
10.1016/j.cpc.2022.108465
Documentation
https://www.pasums.issp.u-tokyo.ac.jp/odat-se/

Citation

Y. Motoyama et al., Comput. Phys. Commun. 280, 108465 (2022) (published as 2DMAT).

DOI: 10.1016/j.cpc.2022.108465

Contact / Contribution