mosaictools
A Python package for surrogate modeling of modal properties using the Mode-Shape-Adapted Input parameter domain Cutting (MOSAIC) method.
- Source code: https://github.com/blazkurent/mosaictools
- Bug reports: https://github.com/blazkurent/mosaictools/issues
Overview
The package is designed for structural dynamics workflows where mode degeneration effects (for example mode crossing, veering, or coalescence) make direct surrogate modeling difficult. MOSAIC addresses this by splitting the parameter domain into modal subdomains and fitting local generalized Polynomial Chaos Expansion (gPCE) models.
Reference preprint: MOSAIC method
Installation
Install from PyPI:
pip install mosaictools
Features
- Global multi-mode surrogate model through
Mosaic. - Automatic mode-wise subdomain discovery from eigenvector similarity (MAC-based clustering).
- Local frequency and eigenvector approximation in each subdomain.
- Built-in classifier support:
classification_method='svc'(scikit-learnSVC)classification_method='custom'(any compatible classifier instance)
- K-fold cross-validation helper and modal error metrics.
- Model persistence to
.msicfiles.
Demo Notebook and Data
A complete demonstration is included in:
The corresponding example arrays are in:
License
This project is licensed under the MIT License. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for mosaictools 0.2.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mosaictools-0.2.2.tar.gz | 13.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mosaictools-0.2.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 29.7 kB
Release files / mosaictools-0.2.2.tar.gz
| Download URL | mosaictools-0.2.2.tar.gz |
|---|---|
| Size | 13.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
afe4d62b6167ea1ca5151ddeca3cae327cf4dfd46352d73421d5f0a162e7208a
|
|
BLAKE2b-256 checksum How to use checksums |
fc7c73b72b478750a77cb21c7ffe1249ca565e015a2172b3f712b77ede0d4596
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 27, 2026.
Transparency logRelease files / mosaictools-0.2.2-py3-none-any.whl
| Download URL | mosaictools-0.2.2-py3-none-any.whl |
|---|---|
| Size | 16.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5a991c6d048861aa819bdefd732cbd1fc1fd7d5e6605749022fd80d505b79310
|
|
BLAKE2b-256 checksum How to use checksums |
3786446cb5c0f2cd3c857a028117eb065cbde48bd289a8ff9ed9cf290d695c3c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 27, 2026.
Transparency log
