| Name | mlgidGUI JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | A GUI program for GIWAXS images analysis and annotation |
| upload_time | 2025-08-17 17:02:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12.0 |
| license | None |
| keywords |
xray
python
giwaxs
scientific-analysis
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# _mlgidGUI_
## Project summary
_mlgidGUI_ is a graphical tool for the analysis and annotation of 2D scattering data e.g. Grazing-Incidence Wide-Angle X-ray Scattering (GIWAXS).
The resulting datasets can be used for training and testing ML models or further manual analysis.
_mlgidGUI_ is well suited for the annotation of 2D diffraction images with radial symmetry.
In particular, it focuses on grazing-incidence wide-angle scattering data analysis and its specific needs.
## Installation
### Recommended: Precompiled AppImage or EXE
Readily compiled packages for the x64 architecture with Windows and Unix are available at the releases page:
[https://github.com/mlgid-project/mlgidGUI/releases](https://github.com/mlgid-project/mlgidGUI/releases)
To run the program on Windows simply double click on the file and ignore the security warnings.
Follow these instructions to run the AppImage: [https://docs.appimage.org/introduction/quickstart.html](https://docs.appimage.org/introduction/quickstart.html)
### Alternative: Installation with conda
* Install miniconda
[https://www.anaconda.com/download/success#miniconda](https://www.anaconda.com/download/success#miniconda)
* Create environment
`conda create -n mlgidGUI python=3.12 pip`
* Activate environment
`conda activate mlgidGUI`
Clone with git:
`git clone https://github.com/mlgid-project/mlgidGUI.git`
`cd ./mlgidGUI`
`pip install ./`
`python3 main.py`
### Alternative: Installation from PyPI
`pip install mlgidGUI`
* Apply a Patch to pyqtgraph
`pip install git+https://github.com/pyqtgraph/pyqtgraph.git@6eb0bf1a928c3e8fef332bbebe8a9da3be0ab19a`
## Usage
Import images or HDF5 files into the program, select an image in the Project Manager and begin labeling.
To add annotations, hold `Ctrl + Alt`, then click, hold, and drag the mouse over the image, similar to using a shape-drawing tool.
The key combination `Ctrl + H` can be used to hide the annotations. The annotated data can be exported as PASCAL-VOC
dataset or as an HDF5 file.
- We added a CIF file, a GIWAXS image, and an HDF5 file in the `docs\example_files` folder to provide the user with examples.
- For a short demonstration of the program usage, please refer to the [Workflow section](./docs/WORKFLOW.md).
- For a detailed guidance, please refer to the [Documentation section](./docs/DOCUMENTATION.md)
## Papers
This project is part of our broader efforts to improve and automate GIWAXS analysis. Below is a list of related papers.
### ML-based peak detection and structure refinement
_Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data_
V. Starostin, V. Munteanu, A. Greco, E. Kneschaurek, A. Pleli, F. Bertram, A. Gerlach, A. Hinderhofer, and F. Schreiber. npj Comput Mater 8, 101 (2022) [https://doi.org/10.1038/s41524-022-00778-8](https://doi.org/10.1038/s41524-022-00778-8)
### Deployment at synchrotron facilities for real-time analysis
_End-to-end deep learning pipeline for real-time processing of
surface scattering data at synchrotron facilities_
V. Starostin, L. Pithan, A. Greco, V. Munteanu, A. Gerlach, A. Hinderhofer, and F. Schreiber. Synchrotron Radiation News, 35:4, 21-27 (2022) [https://doi.org/10.1080/08940886.2022.2112499](https://doi.org/10.1080/08940886.2022.2112499)
### Benchmarking peak detection
_Benchmarking deep learning for automated peak detection on GIWAXS data_
C. Völter, V. Starostin, D. Lapkin, V. Munteanu, M. Romodin, M. Hylinski, A. Gerlach, A. Hinderhofer, F. Schreiber. Journal of Applied Crystallography (2025) accepted [https://doi.org/10.1107/S1600576725000974](https://doi.org/10.1107/S1600576725000974)
Raw data
{
"_id": null,
"home_page": null,
"name": "mlgidGUI",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.12.0",
"maintainer_email": null,
"keywords": "xray, python, giwaxs, scientific-analysis",
"author": null,
"author_email": "Constantin V\u00f6lter <constantin.voelter@gmail.com>, Vladimir Starostin <v.starostin.m@gmail.com>",
"download_url": null,
"platform": null,
"description": "# _mlgidGUI_\n## Project summary\n\n_mlgidGUI_ is a graphical tool for the analysis and annotation of 2D scattering data e.g. Grazing-Incidence Wide-Angle X-ray Scattering (GIWAXS).\nThe resulting datasets can be used for training and testing ML models or further manual analysis. \n_mlgidGUI_ is well suited for the annotation of 2D diffraction images with radial symmetry.\nIn particular, it focuses on grazing-incidence wide-angle scattering data analysis and its specific needs.\n\n## Installation\n\n### Recommended: Precompiled AppImage or EXE\nReadily compiled packages for the x64 architecture with Windows and Unix are available at the releases page:\n[https://github.com/mlgid-project/mlgidGUI/releases](https://github.com/mlgid-project/mlgidGUI/releases)\n\nTo run the program on Windows simply double click on the file and ignore the security warnings.\n\n\nFollow these instructions to run the AppImage: [https://docs.appimage.org/introduction/quickstart.html](https://docs.appimage.org/introduction/quickstart.html)\n\n### Alternative: Installation with conda\n* Install miniconda\n[https://www.anaconda.com/download/success#miniconda](https://www.anaconda.com/download/success#miniconda)\n* Create environment\n`conda create -n mlgidGUI python=3.12 pip`\n* Activate environment\n`conda activate mlgidGUI`\n\nClone with git:\n\n`git clone https://github.com/mlgid-project/mlgidGUI.git`\n\n`cd ./mlgidGUI`\n\n`pip install ./`\n\n`python3 main.py`\n\n\n### Alternative: Installation from PyPI\n\n`pip install mlgidGUI`\n* Apply a Patch to pyqtgraph\n\n`pip install git+https://github.com/pyqtgraph/pyqtgraph.git@6eb0bf1a928c3e8fef332bbebe8a9da3be0ab19a`\n\n## Usage\n\nImport images or HDF5 files into the program, select an image in the Project Manager and begin labeling.\nTo add annotations, hold `Ctrl + Alt`, then click, hold, and drag the mouse over the image, similar to using a shape-drawing tool.\nThe key combination `Ctrl + H` can be used to hide the annotations. The annotated data can be exported as PASCAL-VOC \ndataset or as an HDF5 file.\n\n- We added a CIF file, a GIWAXS image, and an HDF5 file in the `docs\\example_files` folder to provide the user with examples.\n- For a short demonstration of the program usage, please refer to the [Workflow section](./docs/WORKFLOW.md).\n- For a detailed guidance, please refer to the [Documentation section](./docs/DOCUMENTATION.md)\n\n\n## Papers\n\nThis project is part of our broader efforts to improve and automate GIWAXS analysis. Below is a list of related papers.\n\n### ML-based peak detection and structure refinement\n\n_Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data_\n\nV. Starostin, V. Munteanu, A. Greco, E. Kneschaurek, A. Pleli, F. Bertram, A. Gerlach, A. Hinderhofer, and F. Schreiber. npj Comput Mater 8, 101 (2022) [https://doi.org/10.1038/s41524-022-00778-8](https://doi.org/10.1038/s41524-022-00778-8)\n\n### Deployment at synchrotron facilities for real-time analysis\n\n_End-to-end deep learning pipeline for real-time processing of\nsurface scattering data at synchrotron facilities_\n\nV. Starostin, L. Pithan, A. Greco, V. Munteanu, A. Gerlach, A. Hinderhofer, and F. Schreiber. Synchrotron Radiation News, 35:4, 21-27 (2022) [https://doi.org/10.1080/08940886.2022.2112499](https://doi.org/10.1080/08940886.2022.2112499)\n\n### Benchmarking peak detection\n\n_Benchmarking deep learning for automated peak detection on GIWAXS data_\n\nC. V\u00f6lter, V. Starostin, D. Lapkin, V. Munteanu, M. Romodin, M. Hylinski, A. Gerlach, A. Hinderhofer, F. Schreiber. Journal of Applied Crystallography (2025) accepted [https://doi.org/10.1107/S1600576725000974](https://doi.org/10.1107/S1600576725000974)\n\n",
"bugtrack_url": null,
"license": null,
"summary": "A GUI program for GIWAXS images analysis and annotation",
"version": "0.5.0",
"project_urls": {
"Homepage": "https://github.com/mlgid-project/mlgidGUI"
},
"split_keywords": [
"xray",
" python",
" giwaxs",
" scientific-analysis"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "714c04f8210c895c32a781a0409856758dcb5c23dc5a1c94fe949dc7cc28a91a",
"md5": "8de16f83ca590b91b85e4915c79fd186",
"sha256": "014bfd4d3b2b72bb000e053e822d374f20694bbe8c8fa615434db2f238b61825"
},
"downloads": -1,
"filename": "mlgidgui-0.5.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "8de16f83ca590b91b85e4915c79fd186",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.12.0",
"size": 1741097,
"upload_time": "2025-08-17T17:02:04",
"upload_time_iso_8601": "2025-08-17T17:02:04.194406Z",
"url": "https://files.pythonhosted.org/packages/71/4c/04f8210c895c32a781a0409856758dcb5c23dc5a1c94fe949dc7cc28a91a/mlgidgui-0.5.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-17 17:02:04",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mlgid-project",
"github_project": "mlgidGUI",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mlgidgui"
}
