Metadata-Version: 1.1
Name: latbin
Version: 0.1.4
Summary: Python lattice binning package for large data
Home-page: https://github.com/astrodsg/latbin
Author: Tim Anderton, Dylan Gregersen
Author-email: <quidditymaster@gmail.com>;<dylan.gregersen@utah.edu>
License: 3-clause BSD style license
Description: LATBIN
        ======
        
        This python package is used for binning onto lattices in multiple
        dimensions.
        
        Why Use Lattices
        ----------------
        
        For "regular" 2D histogramming you implicitly use a Z2 lattice! But the
        errors involved with quantizing to a Z2 lattices is larger than A2.
        
        insert plot here showing proper comparison of Z2 and A2 give name of
        file which creates Z2-A2 comparison
        
        Basic Example
        -------------
        
        This example shows how to take a normal data set and histogram the data
        onto an A2 (aka honeycomb) lattice using latbin.
        
        .. code:: python
        
            import latbin
            import numpy as np
        
            # create some fake data with shape (npts,ndim)
            npts,ndim = 60000,2
            data = np.random.normal(size=(npts, ndim))*4.0
        
            # create an A2 lattice (honeycomb binning)
            a2 = latbin.ALattice(2)
        
            # histogram the data onto A2 Lattice
            h = a2.histogram(data)
        
            # get the lattice points in the data space
            centers = h.centers()
        
            # show the result
            import matplotlib.pylab as plt
            plt.title("Honeycomb binning (A2 Lattice)")
            plt.scatter(centers[:,0],centers[:,1],c=h.values(), s=70)
            plt.show()
        
        Installation
        ------------
        
        In the terminal you can install this in the usual way.
        
        .. code:: bash
        
            python setup.py install
        
        
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
