Skip to main content

Light-sheet Dataset EXploration and Processing

Project description

fishcolorproj

dexp | Light-sheet Dataset EXploration and Processing

dexp is a napari, CuPy, Zarr, and DASK based library for managing, processing and visualizing light-sheet microscopy datasets. It consists in light-sheet specialised image processing functions (equalisation, denoising, dehazing, registration, fusion, stabilization, deskewing, deconvolution), visualization functions (napari-based viewing, 2D/3D rendering, video compositing and resizing, mp4 generation), as well as dataset management functions (copy, crop, concatenation, tiff conversion). Almost all functions are GPU accelerated via CuPy but also have a numpy-based fallback option for testing on small datasets. In addition to a functional API, DEXP offers a command line interface that makes it very easy for non-coders to pipeline large processing jobs all the way from a large multi-terabyte raw dataset to fully processed and rendered video in MP4 format.

How to install dexp

Prerequisites:

dexp works on OSX and Windows, but it is recomended to use the latest version of Ubuntu. We recommend a machine with a top-of-the-line NVIDIA graphics card (min 12G to be confortable).

First, make sure to have a working python installation Second, make sure to have a compatible and functional CUDA installation

Once these prerequisites are satified, you can install dexp.

Installation:

dexp can simply be installed with:

To installs dexp with GPU support (CUDA 11.2) do:

pip install dexp[cuda112]

Other available CUDA versions (from CuPy) are: cuda111, cuda110, cuda102, cuda101, cuda100.

If instead you do not wish to add CUDA support, you can instead do:

pip install dexp

Quick one-line environment setup and installation:

The following line will delete any existing dexp environment, recreate it, and install dexp with support for CUDA 11.2:

conda deactivate; conda env remove --name dexp; conda create -y --name dexp python=3.8; conda activate dexp; pip install dexp[cuda112]

Leveraging extra CUDA libraries for faster processing:

If you want you dexp installation to be even faster, you can install additional libraries such as CUDNN and CUTENSOR with the following command:

python setup.py cudalibs --cuda 11.2

Change the version accordingly...

How to use dexp ?

First you need a dataset aqquired on a light-sheet microscope, see here for supported microscopes and formats.

Second, you can use any of the commands here to process your data. The list of commands can be found by :

dexp --help

Example usage

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

dexp-2021.4.7rc0.tar.gz (178.3 kB view details)

Uploaded Source

Built Distribution

dexp-2021.4.7rc0-py2.py3-none-any.whl (325.9 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file dexp-2021.4.7rc0.tar.gz.

File metadata

  • Download URL: dexp-2021.4.7rc0.tar.gz
  • Upload date:
  • Size: 178.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.5

File hashes

Hashes for dexp-2021.4.7rc0.tar.gz
Algorithm Hash digest
SHA256 8143aadcd43ae9c1fe2721b8f574b0f22fe9f0f096d512847651055283fcf7a7
MD5 758806608743a930c90159ee2992fce2
BLAKE2b-256 2a944e54990e51811a2883d6198655fbcd05653b91fe1656e5d27050d972faf8

See more details on using hashes here.

File details

Details for the file dexp-2021.4.7rc0-py2.py3-none-any.whl.

File metadata

  • Download URL: dexp-2021.4.7rc0-py2.py3-none-any.whl
  • Upload date:
  • Size: 325.9 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.5

File hashes

Hashes for dexp-2021.4.7rc0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 a4c728862b701fa3acbbb89a548fdfeebabc83e6c200261478ade40e26e55b27
MD5 34bd8df722b19b383dab5f7b2f3e6724
BLAKE2b-256 8b55de12494fa7c1db369ac625acd412c353b56cb3c7300663b5d00576fc390b

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