Skip to main content

napari plugin to deal with charging artifacts in tomography electron microscopy data

Project description

okapi-em

https://github.com/rosalindfranklininstitute/okapi-em

A napari plugin for processing serial-FIB-SEM data.

Powered by chafer and quoll.

A full description of this software is presented in biorXiv preprint paper:

https://doi.org/10.1101/2022.12.15.520541

This napari plugin contains the following tools:

  • slice alignment using constrained SIFT
  • two charge artifact suppression filters
    • directional fourier bandapass filter
    • line-by-line filter function optimiser and subtraction (requires charge artifact labels) - uses chafer
  • fourier ring correlation (FRC) resolution estimation - uses quoll

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

You can install okapi-em via pip:

>pip install okapi-em

or using napari's plugin installation engine Plugins->Install/Uninstall Plugins... and filter for Okapi-EM.

For installing in development mode , clone this package then navigate to the cloned okapi-em folder and run:

>pip install -e .

Okapi-EM is a napari plugin. Launching napari is therefore required.

>napari

and then navigate Menu->Plugins->Okapi-EM

Note that to launch napari in older versions of python (<=3.7) you will need to use the command:

>python -m napari

Computing requirements

Okapi-EM does not require powerful computers to run. None of the tools use GPU accelaration.

The minimum recommended RAM depends on the size of the data being used in napari. For a full image stack of 1Gb, it is recommended that user ensure that 3Gb of RAM is available or can be used. Modern OS's can extend physical RAM using swap memory (Linux) or cache (in Windows and also known as virtual memory), but processing can be significantly slower.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the Apache Software License 2.0 license, "okapi-em" is free and open source software

Citing

Please cite usage using the following reference.

Perdigão, L. M. A. et al. Okapi-EM – a napari plugin for processing and analysing cryogenic serial FIB/SEM images. 2022.12.15.520541 Preprint at https://doi.org/10.1101/2022.12.15.520541 (2022).

Issues

There is currently a known issue with napari running in Linux machines, that it does not find the OpenGL driver correctly. This will hopefully be resolved in the near future. If you bump into this issue we recommend trying to downgrade the python version. This is not an Okapi-EM problem.

If you encounter any problems, please file an issue along with a detailed description.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

okapi-em-0.0.10.tar.gz (43.9 kB view details)

Uploaded Source

Built Distribution

okapi_em-0.0.10-py3-none-any.whl (45.2 kB view details)

Uploaded Python 3

File details

Details for the file okapi-em-0.0.10.tar.gz.

File metadata

  • Download URL: okapi-em-0.0.10.tar.gz
  • Upload date:
  • Size: 43.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for okapi-em-0.0.10.tar.gz
Algorithm Hash digest
SHA256 b38468f8b5ab9c6259f24761e633660514943872bb77b61f3d2cdf5f7f5cae7b
MD5 e14e175dc97c6d79f6b14af0f87629cb
BLAKE2b-256 e9b7141a07d8ad30c11d46e980ddfb15eee617198dc8ad7fe5f973a734fb27e9

See more details on using hashes here.

File details

Details for the file okapi_em-0.0.10-py3-none-any.whl.

File metadata

  • Download URL: okapi_em-0.0.10-py3-none-any.whl
  • Upload date:
  • Size: 45.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for okapi_em-0.0.10-py3-none-any.whl
Algorithm Hash digest
SHA256 6652865c246e0833bd4d9e0dc5a388fdaffe1121f9b8a9ca86715cff1bdc1504
MD5 8cd20e1ff890bf51310bc17024044813
BLAKE2b-256 d78d173ea41381485a40f3f1baa2df8189c83aa318255f00a4c7a82c4c940ed1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page