Commonly used tomography data processing methods at DLS.

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  • License: BSD License (BSD-3-Clause)
  • Author: Daniil Kazantsev
  • Requires: Python >=3.10
  • Provides-Extra: dev
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Project description

HTTomolibGPU is a collection of image processing methods in Python for computed tomography. The methods are GPU-accelerated with the open-source Python library CuPy. Most of the methods migrated from TomoPy and Savu software packages. Some of the methods also have been optimised to ensure higher computational efficiency, before ported to CuPy.

The purpose of HTTomolibGPU

Although HTTomolibGPU can be used as a stand-alone library, it has been specifically developed to work together with the HTTomo package as its backend for data processing. HTTomo is a user interface (UI) written in Python for fast big tomographic data processing using MPI protocols or as well serially.

Installation

HTTomolibGPU is available on PyPI, so it can be installed into either a virtual environment or a conda environment.

Virtual environment

$ python -m venv httomolibgpu
$ source httomolibgpu/bin/activate
$ pip install httomolibgpu

Conda environment

$ conda create --name httomolibgpu # create a fresh conda environment
$ conda activate httomolibgpu # activate the environment
$ conda install conda-forge::cupy==12.3.0
$ pip install httomolibgpu

Setup the development environment:

$ git clone git@github.com:DiamondLightSource/httomolibgpu.git # clone the repo
$ conda env create --name httomolibgpu -c conda-forge cupy==12.3.0 # install dependencies
$ conda activate httomolibgpu # activate the environment
$ pip install -e ./httomolibgpu[dev] # editable/development mode

Project details

Verified details

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Avatar for dkazanc from gravatar.com dkazanc

Unverified details

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  • License: BSD License (BSD-3-Clause)
  • Author: Daniil Kazantsev
  • Requires: Python >=3.10
  • Provides-Extra: dev
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