Metadata-Version: 1.1
Name: puq
Version: 2.4.0
Summary: PUQ Uncertainty Quantification Tool
Home-page: https://github.com/c-PRIMED/puq
Author: Martin Hunt
Author-email: mmh@purdue.edu
License: mit
Description: ************
        Introduction
        ************
        
        :Version: 2.2.9
        :Authors: Martin Hunt
        :Web site: https://github.com/c-PRIMED/puq
        :Documentation: http://c-primed.github.io/puq/
        :Copyright: This document has been placed in the public domain.
        :License: MIT License.
        
        Purpose
        =======
        
        PUQ is a framework for building response surfaces and performing Uncertainty
        Quantification (UQ) and sensitivity analysis. It was created with the goal of
        making an easy to use framework that could be easily integrated and extended.
        
        Features
        ========
        
        * Implemented as a Python library but can be used from the command line
          with a minimum of Python knowledge.
        
        * Collects all results into a single HDF5 file.
        
        * Implements Monte Carlo and Latin Hypercube sampling.
        
        * For better scalability, includes a Smolyak sparse grid method.
        
        * Builds response surfaces from sample points.
        
        * Includes GUIs to visualize and compare PDFs and response surfaces.
        
        * Can use PyMC to perform Bayesian calibration on input parameters.
        
        Dependencies
        ============
        
        PUQ is tested to work under Python 2.6+. Python 3 is not yet supported.
        
        To build, you will need a working C/C++ compiler.
        PUQ requires the following Python modules:
        
        - numpy >= 1.6
        - scipy >= 0.8
        - matplotlib >= 1.1
        - sympy >= 0.7.1
        - h5py >= 1.3
        - jsonpickle
        - poster
        - pytest
        
        
        Install
        =======
        
        This package uses distutils, which is the default way of installing
        python modules. To install in your home directory, use::
        
          python setup.py install --user
        
        To install for all users on Unix/Linux or Mac::
        
          python setup.py build
          sudo python setup.py install
        
        
        History
        =======
        
        PUQ is based upon work supported by the Department of Energy [National Nuclear Security Administration]
        under Award Number DE-FC52-08NA28617.”
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Topic :: Scientific/Engineering :: Information Analysis
