{ "info": { "author": "V. Gluscevic, S. D. McDermott", "author_email": "verag@ias.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Physics" ], "description": "dmdd\n=========\n\nA python package that enables simple simulation and Bayesian posterior analysis\nof nuclear-recoil data from dark matter direct detection experiments \nfor a wide variety of theories of dark matter-nucleon interactions. \n\n``dmdd`` has the following features:\n\n* Calculation of the nuclear-recoil rates for various non-standard momentum-, velocity-, and spin-dependent scattering models. \n \n* Calculation of the appropriate nuclear response functions triggered by the chosen scattering model.\n \n* Inclusion of natural abundances of isotopes for a variety of target elements: Xe, Ge, Ar, F, I, Na.\n\n* Simple simulation of data (where data is a list of nuclear recoil energies, including Poisson noise) under different models. \n\n* Bayesian analysis (parameter estimation and model selection) of data using ``MultiNest``.\n\nAll rate and response functions directly implement the calculations of `Anand et al. (2013) `_ and `Fitzpatrick et al. (2013) `_ (for non-relativistic operators, in ``rate_genNR`` and ``rate_NR``), and `Gresham & Zurek (2014) `_ (for UV-motivated scattering models in ``rate_UV``). Simulations follow the prescription from `Gluscevic & Peter (2014) `_ and `Gluscevic et al. (2015) `_.\n \n\nDependencies\n------------\n\nAll of the package dependencies (listed below) are contained within the `Anaconda python distribution `_, except for ``MultiNest`` and ``PyMultinest``. \n\nFor simulations, you will need:\n\n* basic python scientific packages (``numpy``, ``scipy``, ``matplotlib``)\n\n* ``cython``\n\nTo do posterior analysis, you will also need:\n\n* ``MultiNest``\n\n* ``PyMultiNest``\n\nTo install these two, follow the instructions `here `_.\n\n\nInstallation\n------------\n\nInstall ``dmdd`` either using pip::\n\n pip install dmdd\n\nor by cloning the repository::\n\n git clone https://github.com/veragluscevic/dmdd.git\n cd dmdd\n python setup.py install\n \nNote that if you do not set the ``DMDD_MAIN_PATH`` environment variable, then importing ``dmdd`` will create ``~/.dmdd`` and use that location to store simulations and posterior samples.\n\nUsage\n------\n\nFor a quick tour of usage, check out the `tutorial notebook `_; for more complete documentation, `read the docs `_; and for the most important formulas and definitions regarding the ``rate_NR`` and ``rate_genNR`` modules, see also `here `_.\n\nAttribution\n-----------\n\nThis package was originally developed for `Gluscevic et al (2015) `_. If you use this code in your research, please cite `this ASCL reference `_, and the following publications: `Gluscevic et al (2015) `_, `Anand et al. (2013) `_, `Fitzpatrick et al. 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