motif-learn: machine learning in scanning transmission electron microscopy
Welcome to motif-learn, a Python package designed to apply machine learning techniques to scanning transmission electron microscopy (STEM) data. This tool enables researchers to identify and analyze structural motifs in atomic resolution images efficiently, offering a powerful way to explore materials with defects.
Installation🛠️
pip install motif-learn
To install the latest development version directly from GitHub:
pip install git+https://github.com/jiadongdan/motif-learn.git
How to use motif-learn👨🏫
- 📘Introduction to Zernike polynimials.
- 🔧How to use
ZPs? - 🔷How to extract symmetry information using
zmoments? - 🧩How to automatically estimate patch size from image?
License⚖️
motif-learn is licensed under the MIT License. For more details, see the LICENSE file.
Citation📜
If you find this project useful, please cite:
Dan, Jiadong, Xiaoxu Zhao, Shoucong Ning, Jiong Lu, Kian Ping Loh, Qian He, N. Duane Loh, and Stephen J. Pennycook. "Learning motifs and their hierarchies in atomic resolution microscopy." Science Advances 8, no. 15 (2022): eabk1005. 📄[paper]
Dan, Jiadong, Cheng Zhang, Xiaoxu Zhao and N. Duane Loh. " Symmetry quantification and segmentation in STEM imaging through Zernike moments." Chinese Physics B, (2024). 📄[paper]
1 maintainer
Metadata
1 maintainer
Release files for motif-learn 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| motif_learn-0.1.2.tar.gz | 78.2 kB | Without provenance | Details for motif_learn-0.1.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| motif_learn-0.1.2-py3-none-any.whl | Python 3 | none | any | Details for motif_learn-0.1.2-py3-none-any.whl |
Total release size: 177.0 kB
Release files / motif_learn-0.1.2.tar.gz
| Download URL | motif_learn-0.1.2.tar.gz |
|---|---|
| Size | 78.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ea4027f520326642e1d572b9fc31d0ffa1c6ffcda48fecb9d4e0e1b49b25eab3
|
|
BLAKE2b-256 checksum How to use checksums |
cd1858d98ad17cf2aebca72c80e4427e342af21c5dccdc61da72820162b76aa7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Release files / motif_learn-0.1.2-py3-none-any.whl
| Download URL | motif_learn-0.1.2-py3-none-any.whl |
|---|---|
| Size | 98.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ade67fb4a2747cac383cb81af1f924ce710607bf407d263eeb1653fe6c250252
|
|
BLAKE2b-256 checksum How to use checksums |
96e4e58f337ac9eb74f5f3bcb2edb7d9d185f1798e65be67ab676f053e56f1de
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Security for motif-learn 0.1.2
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.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
