| Name | psf-generator JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | None |
| Summary | PSF Generator: a PyTorch-based library to simulate point spread functions for microscopies. |
| upload_time | 2025-02-05 14:09:14 |
| maintainer | Jonathan Dong |
| docs_url | None |
| author | Jonathan Dong, Jonathan Chuah, Daniel Sage |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2024 Biomedical Imaging Group, EPFL
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
point-spread-function
pytorch
deep learning
microscopies
imaging
optical system
fluorescene
psf engineering
light propagation
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
furo
humanize
matplotlib
numpy
pytest
scikit-image
scipy
sphinx
myst-nb
pydata-sphinx-theme
torch
tqdm
zernikepy
notebook
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PSF-Generator
***
Welcome to the psf-generator library!
This library implements various physical models that compute the point spread function (PSF) for microscopes.
PSF characterizes the response of an imaging system to a point source of light and is crucial for tasks such as
deconvolution, correction of aberrations, and characterization of the system.
We classify these models based on their physical property (scalar or vectorial) and numerical property (computed on a
Cartesian or spherical coordinate system) and implement them as the following four
_propagators_
| Name of propagator | Other names |
|--------------------------------|:---------------------------:|
| `ScalarCartesianPropagator` | simple/scalar Fourier model |
| `ScalarSphericalPropagator` | Kirchhoff model |
| `VectorialCartesianPropagator` | vectorial Fourier model |
| `VectorialSphericalPropagator` | Richards-Wolf model |
All of them can be derived from the Richards-Wolf integral under certain parameterization and conditions.
For details on the theory, please kindly refer to our paper
[Revisiting PSF models: unifying framework and high-performance implementation](todo:addlink) or the documentation: TO ADD LINK.
# Installation
## Basic Installation
```
pip install psf-generator
```
That's it for the basic intallation; you're ready to go!
## Developer Installation
If you're interested in experimenting with the code base, please clone the repository and install it using the following commands:
```
git clone git@github.com:Biomedical-Imaging-Group/psf_generator.git
cd psf_generator
pip install -e .
```
# Demos
Jupyter Notebook demos can be found under `demos/`.
# Napari Plugin
You can find our Napari plugin [here](https://github.com/Biomedical-Imaging-Group/napari-psfgenerator).
# Documentation
Documentation can be found here: TO ADD LINK
# Cite Us
TODO
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"description": "# PSF-Generator\n***\nWelcome to the psf-generator library!\n\nThis library implements various physical models that compute the point spread function (PSF) for microscopes. \nPSF characterizes the response of an imaging system to a point source of light and is crucial for tasks such as \ndeconvolution, correction of aberrations, and characterization of the system.\n\nWe classify these models based on their physical property (scalar or vectorial) and numerical property (computed on a \nCartesian or spherical coordinate system) and implement them as the following four\n_propagators_\n\n| Name of propagator | Other names |\n|--------------------------------|:---------------------------:|\n| `ScalarCartesianPropagator` | simple/scalar Fourier model |\n| `ScalarSphericalPropagator` | Kirchhoff model |\n| `VectorialCartesianPropagator` | vectorial Fourier model |\n| `VectorialSphericalPropagator` | Richards-Wolf model |\n\nAll of them can be derived from the Richards-Wolf integral under certain parameterization and conditions.\nFor details on the theory, please kindly refer to our paper\n[Revisiting PSF models: unifying framework and high-performance implementation](todo:addlink) or the documentation: TO ADD LINK.\n\n# Installation\n\n## Basic Installation\n\n```\npip install psf-generator\n```\n\nThat's it for the basic intallation; you're ready to go!\n\n## Developer Installation\n\nIf you're interested in experimenting with the code base, please clone the repository and install it using the following commands:\n```\ngit clone git@github.com:Biomedical-Imaging-Group/psf_generator.git\ncd psf_generator\npip install -e .\n```\n\n# Demos\n\nJupyter Notebook demos can be found under `demos/`.\n\n# Napari Plugin\nYou can find our Napari plugin [here](https://github.com/Biomedical-Imaging-Group/napari-psfgenerator).\n\n# Documentation\nDocumentation can be found here: TO ADD LINK\n\n# Cite Us\n\nTODO\n",
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