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This project forked from jacob720/load-h5
High Throughput TOmography pipeline for reconstruction
License: BSD 3-Clause "New" or "Revised" License
Python 68.55%
Shell 29.26%
Dockerfile 2.19%
htto's Introduction
HTTO (High Throughput TOmography pipeline)
- A Python tool for parallel read of h5 tomographic data using MPI protocols
- The data can be re-chunked, saved and re-loaded (e.g. projection or sinogram-wise)
- The data is then can be processed by any tomographic packages, e.g. TomoPy, ASTRA
Setup a Development Environment:
Using VScode Dev Containers
- Clone the repository from GitHub using
git clone [email protected]:dkazanc/HTTO.git
- Open the directory in
VSCode and follow the prompts in the bottom right
- Clone the repository from GitHub using
git clone [email protected]:dkazanc/HTTO.git
- Install dependencies from the environment file
conda env create htto --file conda/environment.yml
- Activate the environment with
conda activate htto
- Install the enviroment in development mode with
python setup.py develop
Install as a Python module
- Ensure all nessacary dependencies are present in the environment (you may wish to refer to the Using Conda directions above)
- Install the module with
python setup.py install
- Execute
docker build . --tag htto
- Install the module as described in
- Simply execute the python module with
python -m htto <args>
- For help with the command line interface simply execute
python -m htto --help
- Build the container as described in Building the Container
- Run the container with
docker run htto <args>
- For help with the command line interface simply execute
docker run htto python -m htto --help
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