Metadata-Version: 2.4
Name: bumps
Version: 1.0.2
Summary: Data fitting with bayesian uncertainty analysis
Author-email: Paul Kienzle <paul.kienzle@nist.gov>
License: Bumps is in the public domain.
        
        Code in individual files has copyright and license set by individual authors.
        
        bumps.gui, bumps.quasinewton
        ----------------------------
        
        Copyright (C) 2006-2011, University of Maryland
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/ or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in
        all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
        THE SOFTWARE.
        
        
        DREAM
        -----
        
        Copyright (c) 2008, Los Alamos National Security, LLC
        All rights reserved.
        
        Copyright 2008. Los Alamos National Security, LLC. This software was produced under U.S.
        Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is
        operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S.
        Government has rights to use, reproduce, and distribute this software.
        
        NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES A NY WARRANTY, EXPRESS OR
        IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE.  If software is modified to
        produce derivative works, such modified software should be clearly marked, so as not to
        confuse it with the version available from LANL.
        
        Additionally, redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        * Redistributions of source code must retain the above copyright notice, this list of
          conditions and the following disclaimer.
        * Redistributions in binary form must reproduce the above copyright notice, this list of
          conditions and the following disclaimer in the documentation and/or other materials
          provided with the distribution.
        * Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL
          the U.S. Government, nor the names of its contributors may be used to endorse or promote
          products derived from this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS "AS IS" AND
        ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
        OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS
        ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
        SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
        SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
        HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
        (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
        EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
        Random123
        ---------
        
        Copyright 2010-2012, D. E. Shaw Research.
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are
        met:
        
        * Redistributions of source code must retain the above copyright
          notice, this list of conditions, and the following disclaimer.
        
        * Redistributions in binary form must reproduce the above copyright
          notice, this list of conditions, and the following disclaimer in the
          documentation and/or other materials provided with the distribution.
        
        * Neither the name of D. E. Shaw Research nor the names of its
          contributors may be used to endorse or promote products derived from
          this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
        "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
        LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
        A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
        OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
        SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
        LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
        DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
        THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
        (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
        
        MPFit
        -----
        
        The original version of this software, called LMFIT, was written in FORTRAN
        as part of the MINPACK-1 package by XXX.
        
        Craig Markwardt converted the FORTRAN code to IDL.  The information for the
        IDL version is:
        
             Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770
             craigm@lheamail.gsfc.nasa.gov
             UPDATED VERSIONs can be found on my WEB PAGE:
        
                http://cow.physics.wisc.edu/~craigm/idl/idl.html
        
        Mark Rivers created this Python version from Craig's IDL version.
            Mark Rivers, University of Chicago
            Building 434A, Argonne National Laboratory
            9700 South Cass Avenue, Argonne, IL 60439
            rivers@cars.uchicago.edu
            Updated versions can be found at http://cars.uchicago.edu/software
        
        bumps.simplex
        -------------
        
        ::
        
            # ******NOTICE***************
            # From optimize.py module by Travis E. Oliphant
            #
            # You may copy and use this module as you see fit with no
            # guarantee implied provided you keep this notice in all copies.
            # *****END NOTICE************
        
        bumps.cli.warn_with_traceback
        -----------------------------
        
        From http://stackoverflow.com/questions/22373927/get-traceback-of-warnings
        answered by mgab (2014-03-13)
        edited by Gareth Rees (2015-11-28)
        
        
        bumps.dream.entropy.Timer
        -------------------------
        
        Based on: Eli Bendersky https://stackoverflow.com/a/5849861
        Extended with tic/toc by Paul Kienzle
        
        bumps.dream.entropy.MultivariateT.rvs
        -------------------------------------
        
        From farhawa on stack overflow
        https://stackoverflow.com/questions/29798795/multivariate-student-t-distribution-with-python
        
        
        bumps.lsqerror.comb
        -------------------
        
        From dheerosaur
        https://stackoverflow.com/questions/4941753/is-there-a-math-ncr-function-in-python/4941932#4941932
        
Project-URL: documentation, https://bumps.readthedocs.io
Project-URL: homepage, https://bumps.readthedocs.io
Project-URL: repository, https://github.com/bumps/bumps
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: Public Domain
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: h5py
Requires-Dist: dill
Requires-Dist: matplotlib
Requires-Dist: blinker
Requires-Dist: aiohttp
Requires-Dist: python-socketio
Requires-Dist: plotly
Requires-Dist: mpld3
Requires-Dist: graphlib_backport; python_version < "3.9"
Provides-Extra: dev
Requires-Dist: build; extra == "dev"
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: wheel; extra == "dev"
Requires-Dist: setuptools; extra == "dev"
Requires-Dist: sphinx; extra == "dev"
Requires-Dist: versioningit; extra == "dev"
Dynamic: license-file

==============================================
Bumps: data fitting and uncertainty estimation
==============================================

Bumps provides data fitting and Bayesian uncertainty modeling for inverse
problems.  It has a variety of optimization algorithms available for locating
the most like value for function parameters given data, and for exploring
the uncertainty around the minimum.

Installation is with the usual python installation command::

    pip install bumps

Once the system is installed, you can verify that it is working with::

    bumps doc/examples/peaks/model.py --chisq
    bumps -h

To start the GUI use::

    bumps

Documentation is available at `readthedocs <http://bumps.readthedocs.org>`_. See
`CHANGES.rst <https://github.com/bumps/bumps/blob/master/CHANGES.rst>`_
for details on recent changes.

If a compiler is available, then significant speedup is possible for DREAM using::

    python -m bumps.dream.build_compiled

If you have installed from source, you must first check out the random123 library::

    git clone --branch v1.14.0 https://github.com/DEShawResearch/random123.git bumps/dream/random123
    python -m bumps.dream.build_compiled

|CI| |RTD| |DOI|

.. |CI| image:: https://github.com/bumps/bumps/actions/workflows/test-publish.yml/badge.svg
   :alt: Build status
   :target: https://github.com/bumps/bumps/actions/workflows/test-publish.yml

.. |DOI| image:: https://zenodo.org/badge/18489/bumps/bumps.svg
   :alt: DOI tag
   :target: https://zenodo.org/badge/latestdoi/18489/bumps/bumps

.. |RTD| image:: https://readthedocs.org/projects/bumps/badge/?version=latest
   :alt: Documentation status
   :target: https://bumps.readthedocs.io/en/latest/?badge=latest
