Metadata-Version: 1.1
Name: dmdd
Version: 0.1.1
Summary: Enables simple simulation and Bayesian posterior analysis of recoil-event data from dark-matter direct-detection experiments under a wide variety of scattering theories. 
Home-page: https://github.com/veragluscevic/dmdd_2014
Author: V. Gluscevic, S. D. McDermott
Author-email: verag@ias.edu
License: UNKNOWN
Description: dmdd
        =========
        
        A python package that enables simple simulation and Bayesian posterior analysis
        of nuclear-recoil data from dark matter direct detection experiments 
        for a wide variety of theories of dark matter-nucleon interactions.    
        
        ``dmdd`` has the following features:
        
        * Calculation of the nuclear-recoil rates for various non-standard momentum-, velocity-, and spin-dependent scattering models. 
         
        * Calculation of the appropriate nuclear response functions triggered by the chosen scattering model.
          
        * Inclusion of natural abundances of isotopes for a variety of target elements: Xe, Ge, Ar, F, I, Na.
        
        * Simple simulation of data (where data is a list of nuclear recoil energies, including Poisson noise) under different models. 
        
        * Bayesian analysis (parameter estimation and model selection) of data using ``MultiNest``.
        
        All rate and response functions directly implement the calculations of `Anand et al. (2013) <http://arxiv.org/abs/1308.6288>`_ and `Fitzpatrick et al. (2013) <https://inspirehep.net/record/1094068?ln=en>`_ (for non-relativistic operators, in ``rate_genNR`` and ``rate_NR``), and `Gresham & Zurek (2014) <http://arxiv.org/abs/1401.3739>`_ (for UV-motivated scattering models in ``rate_UV``). Simulations follow the prescription from `Gluscevic & Peter (2014) <http://adsabs.harvard.edu/abs/2014JCAP...09..040G>`_ and `Gluscevic et al. (2015) <http://arxiv.org/abs/1506.04454>`_.
         
        
        Dependencies
        ------------
        
        All of the package dependencies (listed below) are contained within the `Anaconda python distribution <http://continuum.io/downloads>`_, except for ``MultiNest`` and ``PyMultinest``. 
        
        For simulations, you will need:
        
        * basic python scientific packages (``numpy``, ``scipy``, ``matplotlib``)
        
        * ``cython``
        
        To do posterior analysis, you will also need:
        
        * ``MultiNest``
        
        * ``PyMultiNest``
        
        To install these two, follow the instructions `here <http://astrobetter.com/wiki/MultiNest+Installation+Notes>`_.
        
        
        Installation
        ------------
        
        Install ``dmdd`` either using pip::
        
            pip install dmdd
        
        or by cloning the repository::
        
            git clone https://github.com/veragluscevic/dmdd.git
            cd dmdd
            python setup.py install
        
        Usage
        ------
        
        For a quick tour of usage, check out the `tutorial notebook <https://github.com/veragluscevic/dmdd/blob/master/dmdd_tutorial.ipynb>`_; for more complete documentation, `read the docs <http://dmdd.rtfd.org>`_; and for the most important formulas and definitions regarding the ``rate_NR`` and ``rate_genNR`` modules, see also `here <https://github.com/veragluscevic/dmdd/blob/master/rate_calculators.pdf>`_.
        
        Attribution
        -----------
        
        This package was originally developed for Gluscevic et al (2015). If you use this code in your research, please cite this ASCL reference [pending], and the following publications: `Gluscevic et al (2015) <http://arxiv.org/abs/1506.04454>`_, `Anand et al. (2013) <http://arxiv.org/abs/1308.6288>`_, `Fitzpatrick et al. (2013) <https://inspirehep.net/record/1094068?ln=en>`_, and `Gresham & Zurek (2014) <http://arxiv.org/abs/1401.3739>`_. 
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
