Metadata-Version: 2.1
Name: dexp
Version: 2021.4.7rc0
Summary: Light-sheet Dataset EXploration and Processing
Home-page: https://github.com/royerlab/dexp
Author: Jordao Bragantini, Ahmet Can Solak, Loic A Royer
Author-email: jordao.bragantini@czbiohub.org, ahmetcan.solak@czbiohub.org, loic.royer@czbiohub.org
License: MIT
Description: 
        ![fishcolorproj](https://user-images.githubusercontent.com/1870994/113943035-b61b0c00-97b6-11eb-8cfd-ac78e2976ebb.png)
        # **dexp** | Light-sheet Dataset EXploration and Processing 
        
        **dexp** is a [napari](https://napari.org/), [CuPy](https://cupy.dev/), [Zarr](https://zarr.readthedocs.io/en/stable/), and [DASK](https://dask.org/) 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](https://cupy.dev/) but also have a [numpy](https://numpy.org/)-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](https://github.com/royerlab/dexp/wiki/install_python) 
        Second, make sure to have a compatible and functional [CUDA installation](https://github.com/royerlab/dexp/wiki/install_cuda)
        
        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](https://cupy.dev/)) 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](https://github.com/royerlab/dexp/wiki/dexp_datasets) for supported microscopes and formats.
        
        Second, you can use any of the commands [here](https://github.com/royerlab/dexp/wiki/dexp_commands) to process your data.
        The list of commands can be found by :
        
        ```
        dexp --help
        ```
        
        
        ### Example usage
        
        
        
        
        
        
        
          
         
        
        
        
        
          
          
        
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Environment :: GPU :: NVIDIA CUDA :: 11.1
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
Provides-Extra: cuda112
Provides-Extra: cuda111
Provides-Extra: cuda110
Provides-Extra: cuda102
Provides-Extra: cuda101
Provides-Extra: cuda100
