Light-sheet Dataset EXploration and Processing
Project description
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), colored console output, and napari support do:
pip install dexp[color, cuda112, napari]
Other available CUDA versions (from CuPy) are: cuda111, cuda110, cuda102, cuda101, cuda100. We recommend using the most recent CUDA version that your system supports, and avoiding versions below 10.0
If instead you do not wish to add CUDA support, you can instead do:
pip install dexp
For OSX users: Before installating dexp, you will first need to install cairo:
brew install cairo
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[color,cuda112, napari]
Leveraging extra CUDA libraries for faster processing:
If you want you dexp CUDA-based processing to be even faster, you can install additional libraries such as CUDNN and CUTENSOR with the following command:
install cudalibs 11.2
Change the CUDA version accordingly...
Versions
The list of released versions can be found here. The version format is: YYYY.MM.DD.M where YYYY is the year, MM the month, dd the day, and M is the number of elapsed minutes of the day. Git tags are automatically set to link pipy versions to github tagged versions so that the corresponding code can be inspected on github, probably the most important feature. This is a very simple and semantically clear versionning scheme that accomodates for a rapid rate of updates.
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
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