Skip to main content

Python package for tomographic data processing and reconstruction

Project description

NeuTomPy toolbox

NeuTomPy toolbox is a Python package for tomographic data processing and reconstruction. Such toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative reconstruction methods as well as the Filtered Back Projection algorithm. The NeuTomPy toolbox was conceived primarily for Neutron Tomography and developed to support the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data-processing workflow for their data.

Features

  • Readers and writers for TIFF and FITS files and stack of images
  • Data normalization with dose correction, correction of the rotation axis tilt, ring-filters, outlier removals, beam-hardening correction
  • A wide range of reconstruction algorithms powered by ASTRA toolbox: FBP, SIRT, SART, ART, CGLS, NN-FBP, MR-FBP
  • Image quality assessment with several metrics

Installation

NeuTomPy toolbox supports Linux, Windows and Mac OS 64-bit operating systems.

First of all, install a conda python environment with Python 3.5 or 3.6.

It is required to install some dependencies, hence run the following inside a conda environment:

$ conda install -c simpleitk simpleitk
$ conda install -c astra-toolbox astra-toolbox
$ conda install -c conda-forge ipython numpy numexpr matplotlib astropy tifffile opencv scikit-image read-roi mkl_fft scipy six tqdm pywavelets

Then install NeuTomPy toolbox via pip:

$ pip install neutompy

NB: If a segmentation fault occurs when importing NeuTomPy, install PyQt5 via pip:

$ pip install PyQt5

Update

To update a NeuTomPy installation to the latest version run:

$ pip install neutompy --upgrade

Documentation

Complete documentation can be found on Read the Docs: https://neutompy-toolbox.readthedocs.io.

Tutorials and code examples of typical usage can be found in the folder examples.

A sample dataset for testing purpose can be found here. This dataset includes neutron radiographs of a phantom sample acquired at the IMAT beamline, ISIS neutron spallation source, UK.

Reference

If you use the NeuTomPy toolbox for your research, please cite the following paper:

D. Micieli, T. Minniti, G. Gorini, “NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction”, SoftwareX, Volume 9 (2019), pp. 260-264, https://doi.org/10.1016/j.softx.2019.01.005.

License

The project is licensed under the GPLv3 license.

Contact

If you want to contact us for any reasons, please send an email to: neutompy@gmail.com

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

neutompy-1.0.9.tar.gz (41.2 kB view details)

Uploaded Source

Built Distribution

neutompy-1.0.9-py3-none-any.whl (60.6 kB view details)

Uploaded Python 3

File details

Details for the file neutompy-1.0.9.tar.gz.

File metadata

  • Download URL: neutompy-1.0.9.tar.gz
  • Upload date:
  • Size: 41.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.3 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.8

File hashes

Hashes for neutompy-1.0.9.tar.gz
Algorithm Hash digest
SHA256 f229d4ed34d1e096493fa8efa037ba8dcd5761252fc63910e0c28d01b0913a17
MD5 37407c49cd4d7b7d1f83123a8dd67b33
BLAKE2b-256 9c2b009a007a2012b76c05d7165e3f24d48eb219e1b52fa37d840fb7758f54ad

See more details on using hashes here.

File details

Details for the file neutompy-1.0.9-py3-none-any.whl.

File metadata

  • Download URL: neutompy-1.0.9-py3-none-any.whl
  • Upload date:
  • Size: 60.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.7.3 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.8

File hashes

Hashes for neutompy-1.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 dcd4273464338e5b985fbbf5f58499cf4e3354e502604bb85d3ab8e41d768e2e
MD5 b0a8de64af9a9494ad4c634f2b67d145
BLAKE2b-256 8289906934359b86b331ea866fba4d672562b562907bf08abe8249ed4b44f47e

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