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denss 0.0.1%2B20200710gac8923a-2
 `12345678910111213141516171819202122232425262728293031323334353637383940` ``````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. ``````