Metadata-Version: 1.1
Name: g2tools
Version: 1.4
Summary: Utilities for muon g-2 analyses in lattice QCD.
Home-page: https://github.com/gplepage/g2tools.git
Author: G. Peter Lepage, Cornell University
Author-email: g.p.lepage@cornell.edu
License: GPLv3+
Description: g2tools
        -------
        
        This module contains a small number of tools useful for analyzing
        contributions to the muon's magnetic moment from (lattice) QCD vacuum
        polarization. The functions or classes include:
        
            **moments(G)**
                Compute moments of *jj* correlator *G*.
        
            **mom2taylor(mom)**
                Convert moments ``mom`` into Taylor coefficients for *q*\ :sup:`2`-expansion.
        
            **taylor2mom(tayl)**
                Convert Taylor expansion coefficients ``tayl`` into moments.
        
            **vacpol(mom)**
                Create a Pade approximant for the subtracted
                vacuum polarization (``PI-hat``) from the *jj* correlator
                whose moments (or Taylor coefficients) are in *mom*.
        
            **fourier_vacpol(G)**
                Create subtracted vacuum polarization (``PI-hat``) by
                Fourier transforming *jj* correlator ``G(t)``.
        
            **a_mu(pihat, Q)**
                Compute the contribution to the muon's *g-2*
                anomaly from function pihat (usually built by vacpol).
        
            **R2G(E, R)**
                Compute the Euclidean G(t) corresponding to data 
                for Re+e-.
        
            **R2a_mu(E, R)**
                Compute the leading-order contribution to the 
                muon's *g-2* anomaly corresponding to data 
                for Re+e-.
        
            **TanhWin(t0, t1, dt)**
                Create a filter for applying a t-window in 
                ``monents(...)`` or ``fourier_vacpol(...)``.
        
            **pade_gvar(f, m, n)**
                General-purpose code for determining Pade approximants
                to a power series whose coefficients are ``GVar``\s (ie,
                Gaussian random variables, for error propagation).
        
            **pade_svd(f, m, n)**
                General-purpose code for determining Pade approximants
                for a power series whose coefficients are floats.
                Uses *svd* regularization to stabilize results when
                the input data are noisy.
        
        Information on how to install the module is in the file INSTALLATION.
        
        To test the module try ``make tests``.
        
        Documentation is in the doc directory: open doc/html/index.html
        or look online at <https://g2tools.readthedocs.io>.
        
        The examples directory has a complete example, showing how to go from Monte
        Carlo data for a *jj* correlator to a contribution to the muon's magnetic
        moment anomaly *a*\ :sub:`µ`. See also the introduction in the documentation.
        
        The general technique that underlies this module is described in
        Chakraborty *et al*, Phys.Rev. D89 (2014) no.11, 114501. Google
        ``arXiv:1403.1778`` to find a preprint on the web.
        
        | Created by G. Peter Lepage (Cornell University) on on 2014-09-13.
        | Copyright (c) 20014-22 G. Peter Lepage.
        
        .. image:: https://zenodo.org/badge/66222496.svg
           :target: https://zenodo.org/badge/latestdoi/66222496
        
        
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Physics
Requires: numpy (>=1.7)
Requires: gvar (>=7.3)
Requires: scipy
Requires: lsqfit
