| Name | waveorder JSON |
| Version |
2.2.0
JSON |
| download |
| home_page | None |
| Summary | Wave-optical simulations and deconvolution of optical properties |
| upload_time | 2024-12-22 04:20:05 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD 3-Clause License Copyright (c) 2019, Chan Zuckerberg Biohub Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
simulation
optics
phase
scattering
polarization
label-free
permittivity
reconstruction-algorithm
qlipp
mipolscope
permittivity-tensor-imaging
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# waveorder
[](https://pypi.org/project/waveorder)
[](https://pypistats.org/packages/waveorder)
[](https://pepy.tech/project/waveorder)
[](https://github.com/mehta-lab/waveorder/graphs/contributors)
This computational imaging library enables wave-optical simulation and reconstruction of optical properties that report microscopic architectural order.
## Computational label-agnostic imaging
https://github.com/user-attachments/assets/4f9969e5-94ce-4e08-9f30-68314a905db6
`waveorder` enables simulations and reconstructions of label-agnostic microscopy data as described in the following [preprint](https://arxiv.org/abs/2412.09775)
<details>
<summary> Chandler et al. 2024 </summary>
<pre><code>
@article{chandler_2024,
author = {Chandler, Talon and Hirata-Miyasaki, Eduardo and Ivanov, Ivan E. and Liu, Ziwen and Sundarraman, Deepika and Ryan, Allyson Quinn and Jacobo, Adrian and Balla, Keir and Mehta, Shalin B.},
title = {waveOrder: generalist framework for label-agnostic computational microscopy},
journal = {arXiv},
year = {2024},
month = dec,
eprint = {2412.09775},
doi = {10.48550/arXiv.2412.09775}
}
</code></pre>
</details>
Specifically, `waveorder` enables simulation and reconstruction of 2D or 3D:
1. __phase, projected retardance, and in-plane orientation__ from a polarization-diverse volumetric brightfield acquisition ([QLIPP](https://elifesciences.org/articles/55502)),
2. __phase__ from a volumetric brightfield acquisition ([2D phase](https://www.osapublishing.org/ao/abstract.cfm?uri=ao-54-28-8566)/[3D phase](https://www.osapublishing.org/ao/abstract.cfm?uri=ao-57-1-a205)),
3. __phase__ from an illumination-diverse volumetric acquisition ([2D](https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-23-9-11394&id=315599)/[3D](https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-7-10-3940&id=349951) differential phase contrast),
4. __fluorescence density__ from a widefield volumetric fluorescence acquisition (fluorescence deconvolution).
The [examples](https://github.com/mehta-lab/waveorder/tree/main/examples) demonstrate simulations and reconstruction for 2D QLIPP, 3D PODT, 3D fluorescence deconvolution, and 2D/3D PTI methods.
If you are interested in deploying QLIPP, phase from brightfield, or fluorescence deconvolution for label-agnostic imaging at scale, checkout our [napari plugin](https://www.napari-hub.org/plugins/recOrder-napari), [`recOrder-napari`](https://github.com/mehta-lab/recOrder).
## Permittivity tensor imaging
Additionally, `waveorder` enabled the development of a new label-free imaging method, __permittivity tensor imaging (PTI)__, that measures density and 3D orientation of biomolecules with diffraction-limited resolution. These measurements are reconstructed from polarization-resolved images acquired with a sequence of oblique illuminations.
The acquisition, calibration, background correction, reconstruction, and applications of PTI are described in the following [paper](https://doi.org/10.1101/2020.12.15.422951) published in Nature Methods:
<details>
<summary> Yeh et al. 2024 </summary>
<pre><code>
@article{yeh_2024,
author = {Yeh, Li-Hao and Ivanov, Ivan E. and Chandler, Talon and Byrum, Janie R. and Chhun, Bryant B. and Guo, Syuan-Ming and Foltz, Cameron and Hashemi, Ezzat and Perez-Bermejo, Juan A. and Wang, Huijun and Yu, Yanhao and Kazansky, Peter G. and Conklin, Bruce R. and Han, May H. and Mehta, Shalin B.},
title = {Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution},
journal = {Nature Methods},
volume = {21},
number = {7},
pages = {1257--1274},
year = {2024},
month = jul,
issn = {1548-7105},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-024-02291-w}
}
</code></pre>
</details>
PTI provides volumetric reconstructions of mean permittivity ($\propto$ material density), differential permittivity ($\propto$ material anisotropy), 3D orientation, and optic sign. The following figure summarizes PTI acquisition and reconstruction with a small optical section of the mouse brain tissue:
## Examples
The [examples](https://github.com/mehta-lab/waveorder/tree/main/examples) illustrate simulations and reconstruction for 2D QLIPP, 3D phase from brightfield, and 2D/3D PTI methods.
If you are interested in deploying QLIPP or phase from brightbrield, or fluorescence deconvolution for label-agnostic imaging at scale, checkout our [napari plugin](https://www.napari-hub.org/plugins/recOrder-napari), [`recOrder-napari`](https://github.com/mehta-lab/recOrder).
## Citation
Please cite this repository, along with the relevant preprint or paper, if you use or adapt this code. The citation information can be found by clicking "Cite this repository" button in the About section in the right sidebar.
## Installation
Create a virtual environment:
```sh
conda create -y -n waveorder python=3.10
conda activate waveorder
```
Install `waveorder` from PyPI:
```sh
pip install waveorder
```
Use `waveorder` in your scripts:
```sh
python
>>> import waveorder
```
(Optional) Install example dependencies, clone the repository, and run an example script:
```sh
pip install waveorder[examples]
git clone https://github.com/mehta-lab/waveorder.git
python waveorder/examples/models/phase_thick_3d.py
```
(M1 users) `pytorch` has [incomplete GPU support](https://github.com/pytorch/pytorch/issues/77764),
so please use `export PYTORCH_ENABLE_MPS_FALLBACK=1`
to allow some operators to fallback to CPU
if you plan to use GPU acceleration for polarization reconstruction.
Raw data
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"description": "# waveorder\n\n[](https://pypi.org/project/waveorder)\n[](https://pypistats.org/packages/waveorder)\n[](https://pepy.tech/project/waveorder)\n[](https://github.com/mehta-lab/waveorder/graphs/contributors)\n\n\n\n\n\nThis computational imaging library enables wave-optical simulation and reconstruction of optical properties that report microscopic architectural order.\n\n## Computational label-agnostic imaging\n\nhttps://github.com/user-attachments/assets/4f9969e5-94ce-4e08-9f30-68314a905db6\n\n`waveorder` enables simulations and reconstructions of label-agnostic microscopy data as described in the following [preprint](https://arxiv.org/abs/2412.09775)\n<details>\t\n<summary> Chandler et al. 2024 </summary>\n<pre><code>\n@article{chandler_2024,\n author = {Chandler, Talon and Hirata-Miyasaki, Eduardo and Ivanov, Ivan E. and Liu, Ziwen and Sundarraman, Deepika and Ryan, Allyson Quinn and Jacobo, Adrian and Balla, Keir and Mehta, Shalin B.},\n\ttitle = {waveOrder: generalist framework for label-agnostic computational microscopy},\n\tjournal = {arXiv},\n\tyear = {2024},\n\tmonth = dec,\n\teprint = {2412.09775},\n\tdoi = {10.48550/arXiv.2412.09775}\n}\n</code></pre>\n</details>\n\nSpecifically, `waveorder` enables simulation and reconstruction of 2D or 3D:\n\n1. __phase, projected retardance, and in-plane orientation__ from a polarization-diverse volumetric brightfield acquisition ([QLIPP](https://elifesciences.org/articles/55502)),\n\n2. __phase__ from a volumetric brightfield acquisition ([2D phase](https://www.osapublishing.org/ao/abstract.cfm?uri=ao-54-28-8566)/[3D phase](https://www.osapublishing.org/ao/abstract.cfm?uri=ao-57-1-a205)),\n\n3. __phase__ from an illumination-diverse volumetric acquisition ([2D](https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-23-9-11394&id=315599)/[3D](https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-7-10-3940&id=349951) differential phase contrast),\n\n4. __fluorescence density__ from a widefield volumetric fluorescence acquisition (fluorescence deconvolution). \n\nThe [examples](https://github.com/mehta-lab/waveorder/tree/main/examples) demonstrate simulations and reconstruction for 2D QLIPP, 3D PODT, 3D fluorescence deconvolution, and 2D/3D PTI methods.\n\nIf you are interested in deploying QLIPP, phase from brightfield, or fluorescence deconvolution for label-agnostic imaging at scale, checkout our [napari plugin](https://www.napari-hub.org/plugins/recOrder-napari), [`recOrder-napari`](https://github.com/mehta-lab/recOrder).\n\n## Permittivity tensor imaging \n\nAdditionally, `waveorder` enabled the development of a new label-free imaging method, __permittivity tensor imaging (PTI)__, that measures density and 3D orientation of biomolecules with diffraction-limited resolution. These measurements are reconstructed from polarization-resolved images acquired with a sequence of oblique illuminations.\n\nThe acquisition, calibration, background correction, reconstruction, and applications of PTI are described in the following [paper](https://doi.org/10.1101/2020.12.15.422951) published in Nature Methods:\n\n<details>\n<summary> Yeh et al. 2024 </summary>\n<pre><code>\n@article{yeh_2024,\n\tauthor = {Yeh, Li-Hao and Ivanov, Ivan E. and Chandler, Talon and Byrum, Janie R. and Chhun, Bryant B. and Guo, Syuan-Ming and Foltz, Cameron and Hashemi, Ezzat and Perez-Bermejo, Juan A. and Wang, Huijun and Yu, Yanhao and Kazansky, Peter G. and Conklin, Bruce R. and Han, May H. and Mehta, Shalin B.},\n\ttitle = {Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution},\n\tjournal = {Nature Methods},\n\tvolume = {21},\n\tnumber = {7},\n\tpages = {1257--1274},\n\tyear = {2024},\n\tmonth = jul,\n\tissn = {1548-7105},\n\tpublisher = {Nature Publishing Group},\n\tdoi = {10.1038/s41592-024-02291-w}\n}\n</code></pre>\n</details>\n\nPTI provides volumetric reconstructions of mean permittivity ($\\propto$ material density), differential permittivity ($\\propto$ material anisotropy), 3D orientation, and optic sign. The following figure summarizes PTI acquisition and reconstruction with a small optical section of the mouse brain tissue:\n\n\n\n## Examples\nThe [examples](https://github.com/mehta-lab/waveorder/tree/main/examples) illustrate simulations and reconstruction for 2D QLIPP, 3D phase from brightfield, and 2D/3D PTI methods.\n\nIf you are interested in deploying QLIPP or phase from brightbrield, or fluorescence deconvolution for label-agnostic imaging at scale, checkout our [napari plugin](https://www.napari-hub.org/plugins/recOrder-napari), [`recOrder-napari`](https://github.com/mehta-lab/recOrder).\n\n## Citation\n\nPlease cite this repository, along with the relevant preprint or paper, if you use or adapt this code. The citation information can be found by clicking \"Cite this repository\" button in the About section in the right sidebar.\n\n## Installation\n\nCreate a virtual environment:\n\n```sh\nconda create -y -n waveorder python=3.10\nconda activate waveorder\n```\n\nInstall `waveorder` from PyPI:\n\n```sh\npip install waveorder\n```\n\nUse `waveorder` in your scripts:\n\n```sh\npython\n>>> import waveorder\n```\n\n(Optional) Install example dependencies, clone the repository, and run an example script:\n```sh\npip install waveorder[examples]\ngit clone https://github.com/mehta-lab/waveorder.git\npython waveorder/examples/models/phase_thick_3d.py\n```\n\n(M1 users) `pytorch` has [incomplete GPU support](https://github.com/pytorch/pytorch/issues/77764),\nso please use `export PYTORCH_ENABLE_MPS_FALLBACK=1`\nto allow some operators to fallback to CPU\nif you plan to use GPU acceleration for polarization reconstruction. \n",
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