python-bumps 0.7.12-1 source package in Ubuntu

Changelog

python-bumps (0.7.12-1) unstable; urgency=medium

  * New upstream release.
  * Standards-Version: 4.4.0
  * debhelper compatibility level 12
    - use pybuild build_dir to build docs
  * debian patch docs_no_caller_name.patch drops caller_name from list
    of functions in docs (it is not provided by bumps)
  * drop Build and Test dependencies on python-sklearn to help the
    Python2 removal transition. sklearn is used by
    bumps/dream/entropy.py but dream tests are disabled because of an
    error in bounds handlers test. Closes: #934941.
  * Build-Depends: python3-tk, python-tk. Also Test Depends.

 -- Drew Parsons <email address hidden>  Mon, 19 Aug 2019 12:01:09 +0800

Upload details

Uploaded by:
Debian Science Team
Uploaded to:
Sid
Original maintainer:
Debian Science Team
Architectures:
any-amd64 any-i386 all powerpc
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

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Builds

Eoan: [FULLYBUILT] amd64 [FULLYBUILT] i386

Downloads

File Size SHA-256 Checksum
python-bumps_0.7.12-1.dsc 2.9 KiB 334916ea78e555265f150990283fab63924e8e9740f00b365b1af3b08197fa7e
python-bumps_0.7.12.orig.tar.gz 3.2 MiB cfdd98b0c4074591551858554dc9afe330bda1646019e6ed753569345679465d
python-bumps_0.7.12-1.debian.tar.xz 12.6 KiB 0129f029d91a333a33ca6f5cfba0ea24d03a7389bf0f1431475c51bb8a3e8827

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bumps-private-libs: No summary available for bumps-private-libs in ubuntu eoan.

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bumps-private-libs-dbgsym: debug symbols for bumps-private-libs
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python-bumps-doc: data fitting and Bayesian uncertainty modeling for inverse problems (docs)

 Bumps is a set of routines for curve fitting and uncertainty analysis
 from a Bayesian perspective. In addition to traditional optimizers
 which search for the best minimum they can find in the search space,
 bumps provides uncertainty analysis which explores all viable minima
 and finds confidence intervals on the parameters based on uncertainty
 in the measured values. Bumps has been used for systems of up to 100
 parameters with tight constraints on the parameters. Full uncertainty
 analysis requires hundreds of thousands of function evaluations,
 which is only feasible for cheap functions, systems with many
 processors, or lots of patience.
 .
 Bumps includes several traditional local optimizers such as
 Nelder-Mead simplex, BFGS and differential evolution. Bumps
 uncertainty analysis uses Markov chain Monte Carlo to explore the
 parameter space. Although it was created for curve fitting problems,
 Bumps can explore any probability density function, such as those
 defined by PyMC. In particular, the bumps uncertainty analysis works
 well with correlated parameters.
 .
 Bumps can be used as a library within your own applications, or as a
 framework for fitting, complete with a graphical user interface to
 manage your models.
 .
 This is the common documentation package.

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