denss 0.0.1+20200710gac8923a-2 source package in Ubuntu

Changelog

denss (0.0.1+20200710gac8923a-2) unstable; urgency=medium

  * Source only upload for migration to testing

 -- Sebastien Delafond <email address hidden>  Mon, 30 Nov 2020 09:51:32 +0100

Upload details

Uploaded by:
Debian Science Team
Uploaded to:
Sid
Original maintainer:
Debian Science Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe misc
Noble release universe misc
Mantic release universe misc
Lunar release universe misc
Jammy release universe misc

Builds

Hirsute: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
denss_0.0.1+20200710gac8923a-2.dsc 1.7 KiB 8ef2ae4c1141e0533d6a1a48aa12cd32bcf22ed23889c8d852daba5679cbb8c2
denss_0.0.1+20200710gac8923a.orig.tar.xz 90.1 KiB 3c1314356c8850eb6f018016860e3eb10b41e790e65e0e0bc2326613ff94692b
denss_0.0.1+20200710gac8923a-2.debian.tar.xz 1.8 KiB 956fbd0060eae5ff46b5ff343211d0b278b9996669399159cf43eb21365b6a4f

No changes file available.

Binary packages built by this source

python3-denss: 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.