Metadata-Version: 1.1
Name: multihist
Version: 0.3.0
Summary: Convenience wrappers around numpy histograms
Home-page: https://github.com/jelleaalbers/multihist
Author: Jelle Aalbers
Author-email: j.aalbers@uva.nl
License: MIT
Description: multihist
        ===========
        
        .. image:: https://travis-ci.org/JelleAalbers/multihist.svg?branch=master
            :target: https://travis-ci.org/JelleAalbers/multihist
        
        `https://github.com/JelleAalbers/multihist`
        
        Thin wrapper around numpy's histogram and histogramdd.
        
        Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class
        with methods for adding new data to existing histograms, take averages, projecting, etc.
        
        For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std).
        For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging.
        
        Synopsis::
        
            # Create histograms just like from numpy...
            m = Hist1d([0, 3, 1, 6, 2, 9], bins=3)
        
            # ...or add data incrementally:
            m = Hist1d(bins=100, range=(-3, 4))
            m.add(np.random.normal(0, 0.5, 10**4))
            m.add(np.random.normal(2, 0.2, 10**3))
        
            # Get the data back out:
            print(m.histogram, m.bin_edges)
        
            # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std
            plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", linestyle='steps')
            plt.plot(m.bin_centers, m.density, label="Empirical PDF", linestyle='steps')
            plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", linestyle='steps')
            plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std))
            plt.legend(loc='best')
            plt.show()
        
            # Slicing and arithmetic behave just like ordinary ndarrays
            print("The fourth bin has %d entries" % m[3])
            m[1:4] += 4 + 2 * m[-27:-24]
            print("Now it has %d entries" % m[3])
        
            # Of course I couldn't resist adding a canned plotting function:
            m.plot()
            plt.show()
        
            # Create and show a 2d histogram. Axis names are optional.
            m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y'])
            m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6))
            m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6))
            m2.plot()
            plt.show()
        
            # x and y projections return Hist1d objects
            m2.projection('x').plot(label='x projection')
            m2.projection(1).plot(label='y projection')
            plt.legend()
            plt.show()
        
        
        Alternatives
        ------------
        Of course, the easiest alternative is just to use np.histogram without any wrappers.
        
        If you're looking for a more fancy histogram class, and don't mind installing a big framework,
        you might like the particle physics workhorse ROOT (`https://root.cern.ch/root/html/TH1.html`) and one of its python bindings (pyROOT, rootpy).
        
        If you do come from a ROOT background, you might appreciate pyhistogram (`https://github.com/cbourjau/pyhistogram`) instead,
        which has a much more ROOT-like interface.
        
        Another python histogram package oriented towards physics is `http://docs.danse.us/histogram/0.2.1/intro.html`. This has support for physical units in histograms and error propagation, but the interface is further removed from numpy. 
        
        
        
        
        History
        -------
        
        ------------------
        0.3.0 (2015-09-28)
        ------------------
        * Several new histdd functions: cumulate, normalize, percentile...
        * Python 2 compatibility
        
        
        ------------------
        0.2.1 (2015-08-18)
        ------------------
        * Histdd functions sum, slice, average now also work
        
        ----------------
        0.2 (2015-08-06)
        ----------------
        * Multidimensional histograms
        * Axes naming
        
        --------------------
        0.1.1-4 (2015-08-04)
        --------------------
        Correct various rookie mistakes in packaging...
        Hey, it's my first pypi package!
        
        ----------------
        0.1 (2015-08-04)
        ----------------
        Initial release
        
        * Hist1d, Hist2d
        * Basic test suite
        * Basic readme
Keywords: multihist,histogram
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
