cosgen
- Ebuilds: 1
Description:
COSgen (Contrast optimized stimulus generator) is a Python package
for the optimization of stimulus sequences in stimulus evoked
fMRI experiments. It is fully compatible with the stimulus
sequence delivery solution COSplay.
Homepage:https://github.com/IBT-FMI/COSgen License: GPL-3
libxc
- Ebuilds: 1, Testing: 4.3.4 Description:
Libxc is a library of exchange-correlation functionals for density-functional theory.
The aim is to provide a portable, well tested and reliable set of exchange and correlation
functionals that can be used by all the ETSF codes and also other codes.
In Libxc you can find different types of functionals: LDA, GGA, hybrids, and mGGA (experimental).
These functionals depend on local information, in the sense that the value of the potential
at a given point depends only on the values of the density -- and the gradient of the density
and the kinetic energy density, for the GGA and mGGA cases.
It can calculate the functional itself and its derivative; for some functionals,
higher-order derivatives are available.
Libxc is written in C and has Fortran bindings. It is released under the MPL2 license.
Contributions are welcome.
Homepage:http://octopus-code.org/wiki/Libxc License: MPL-2.0
simpleitk
- Ebuilds: 3, Testing: 1.2.4 Description:
SimpleITK is an image analysis toolkit with a large number of components
supporting general filtering operations, image segmentation and registration.
It is built on top of the Insight Segmentation and Registration Toolkit ITK
with the intent of providing a simplified interface to ITK. SimpleITK itself
is written in C++ but is available for a large number of programming
languages.
Homepage:https://simpleitk.org/ License: Apache-2.0
torchio
- Ebuilds: 1, Testing: 0.17.42 Description:
TorchIO is a Python package containing a set of tools to efficiently read,
preprocess, sample, augment, and write 3D medical images in deep learning
applications written in PyTorch, including intensity and spatial transforms
for data augmentation and preprocessing. Transforms include typical computer
vision operations such as random affine transformations and also
domain-specific ones such as simulation of intensity artifacts due to MRI
magnetic field inhomogeneity or k-space motion artifacts.
Homepage:https://torchio.readthedocs.io/ License: Apache-2.0