Source: denss
Section: python
Priority: optional
Maintainer: Debian Science Maintainers
Uploaders: Sebastien Delafond
Build-Depends:
debhelper (>= 12),
dh-python,
python3-all,
python3-numpy,
python3-scipy,
python3-setuptools
Standards-Version: 4.5.0
Homepage: https://github.com/tdgrant1/denss
Vcs-Browser: https://salsa.debian.org/science-team/denss
Vcs-Git: https://salsa.debian.org/science-team/denss.git
Package: python3-denss
Architecture: all
Depends: ${misc:Depends}, ${shlibs:Depends}, ${python3:Depends}
Description: calculate electron density from a solution scattering profile
DENSS is an algorithm used for calculating ab initio electron density
maps directly from solution scattering data. DENSS implements a novel
iterative structure factor retrieval algorithm to cycle between real
space density and reciprocal space structure factors, applying
appropriate restraints in each domain to obtain a set of structure
factors whose intensities are consistent with experimental data and
whose electron density is consistent with expected real space
properties of particles.
.
DENSS utilizes the NumPy Fast Fourier Transform for moving between
real and reciprocal space domains. Each domain is represented by a
grid of points (Cartesian), N x N x N. N is determined by the size of
the system and the desired resolution. The real space size of the box
is determined by the maximum dimension of the particle, D, and the
desired sampling ratio. Larger sampling ratio results in a larger
real space box and therefore a higher sampling in reciprocal space
(i.e. distance between data points in q). Smaller voxel size in real
space corresponds to higher spatial resolution and therefore to
larger q values in reciprocal space.