https://github.com/sandialabs/mapit
MAPIT (Material Accountancy Performance Indicator Toolkit) is a Python package designed to aid in safeguards analysis of bulk materials.
https://github.com/sandialabs/mapit
scr-2523 snl-applications snl-visualization
Last synced: 3 months ago
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MAPIT (Material Accountancy Performance Indicator Toolkit) is a Python package designed to aid in safeguards analysis of bulk materials.
- Host: GitHub
- URL: https://github.com/sandialabs/mapit
- Owner: sandialabs
- License: gpl-3.0
- Created: 2021-05-04T18:33:41.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-02-14T15:03:45.000Z (4 months ago)
- Last Synced: 2025-02-14T16:22:22.287Z (4 months ago)
- Topics: scr-2523, snl-applications, snl-visualization
- Language: Python
- Homepage: https://sandialabs.github.io/MAPIT/
- Size: 64.6 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
](https://www.gnu.org/licenses/gpl-3.0.en.html)
---
MAPIT (Material Accountancy Performance Indicator Toolkit) is a Python package designed to aid in safeguards analysis of bulk materials. The inherent flexibility is designed to allow safeguards practitioners ask the "what if?" questions while providing transparency into commonly employed statistical tests.
MAPIT provides both a graphical user interface (GUI) and an application program interface (API). The API can be used with other popular Python libraries to extend functionality and integrate with other analytical workflows.
> :warning: The MAPIT API has breaking changes in v1.40.0-beta. The API was modified to accommodate a parallel processing capability. See [API documentation](https://sandialabs.github.io/MAPIT/apidocs.html) for more details.
* [Features](#features)
* [Development](#devlopment)
* [Installation](#installation)
* [Getting Started](#getting-started)
* [Documentation](#documentation)
* [Contact](#contact)
* [Data Privacy](#data-privacy)
* [License](#license)
## Features
* Flexible I/O tools to allow analysis on a wide range of datasets
* Permits analysis for datasets collected with non-uniform sample times
* .csv or .mat file types
* Approachable graphical interface
* Accessibility features for improved readability
* Light/dark themes
* Tutorial dataset generated from a Separations and Safeguards Performance Model ([1](https://www.osti.gov/biblio/1375573-integration-sspm-stage-mpact-virtual-facility-distributed-test-bed),[2](https://www.osti.gov/biblio/1646073-separations-safeguards-performance-model-sspm-capabilities-application-integration),[3](https://www.osti.gov/biblio/1476138-bulk-handling-facility-modeling-simulation-safeguards-analysis)) fuel fabrication model based on IAEA STR-150 ([4](https://inis.iaea.org/search/search.aspx?orig_q=RN:17037216))
* Automated error propagation
* Automated calculation of common safeguards statistical tests
* Material Unaccounted For (MUF) / Inventory Difference (ID)
* Standard Error of MUF (sigma MUF) / Standard Error of ID (SEID)
* Cumulative MUF (CUMUF)
* Standardized Independent Transformed MUF (SITMUF)
* Page's Trend Test on SITMUF
* Built in threshold optimization tools
* Visualization tools
* Data export capabilities
* Standalone Python-based API
## Devlopment
> :construction: **MAPIT IS IN BETA** :construction: \
> \
> You may notice various bugs and errors. Help contribute to this project by reporting them through our issue page.
We aim for biannual release updates for MAPIT, but if you have immediate needs that need to be addressed, please open an issue or contact us directly.
## Installation
MAPIT is not hosted on PyPI and must be installed via source or conda. For installing from source, this can be done by `pip install git+https://github.com/MAPIT/MAPIT.git`. Alternatively, it can be installed from a local download using `pip install .`.
:::{important}
The recommended install method is via conda; `conda install mapit -c conda-forge`.
:::
To streamline this process for users new to Python, we have included both Windows-based and Unix-based installers for those new to Python. These can be found in the `windows_scripts` and `unix_scripts` folder respectively.
## Getting Started
After installation, the MAPIT GUI can be launched using `MAPIT` from terminal after installation. Examples of the API can be found in the API_examples folder. We have included an exemplar dataset (located in a separate [MAPIT-tools](https://github.com/sandialabs/MAPIT-tools) repository) and several tutorial exercises to help you get started, which can be found [here](https://sandialabs.github.io/MAPIT/guided_exercises.html)
## Documentation
We have included documentation for several guided exercises as well as the API, both can be found [here](https://sandialabs.github.io/MAPIT/)
## Contact
* MAPIT developers, Sandia National Laboratories, [email protected]
## Data Privacy
MAPIT is an entirely offline tool; we do not collect any data from MAPIT unless expressly provided by end users through contact with us.
## License
* [License](LICENSE)
* [Third-Party Notice](NOTICE.md)
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.
## Acknowledgements
This work is supported by the DOE/NE Office of Materials and Chemical Technologies (NE-43) - Materials Protection, Accounting, and Control Technologies (MPACT) program.