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        "author": "Jelle Aalbers",
        "author_email": "j.aalbers@uva.nl",
        "bugtrack_url": null,
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            "Development Status :: 3 - Alpha",
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        "description": "multihist\n===========\n\n.. image:: https://travis-ci.org/JelleAalbers/multihist.svg?branch=master\n    :target: https://travis-ci.org/JelleAalbers/multihist\n\n`https://github.com/JelleAalbers/multihist`\n\nThin wrapper around numpy's histogram and histogramdd.\n\nNumpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class\nwith methods for adding new data to existing histograms, take averages, projecting, etc.\n\nFor 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std).\nFor d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging.\n\nSynopsis::\n\n    # Create histograms just like from numpy...\n    m = Hist1d([0, 3, 1, 6, 2, 9], bins=3)\n\n    # ...or add data incrementally:\n    m = Hist1d(bins=100, range=(-3, 4))\n    m.add(np.random.normal(0, 0.5, 10**4))\n    m.add(np.random.normal(2, 0.2, 10**3))\n\n    # Get the data back out:\n    print(m.histogram, m.bin_edges)\n\n    # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std\n    plt.plot(m.bin_centers, m.normalized_histogram, label=\"Normalized histogram\", linestyle='steps')\n    plt.plot(m.bin_centers, m.density, label=\"Empirical PDF\", linestyle='steps')\n    plt.plot(m.bin_centers, m.cumulative_density, label=\"Empirical CDF\", linestyle='steps')\n    plt.title(\"Estimated mean %0.2f, estimated std %0.2f\" % (m.mean, m.std))\n    plt.legend(loc='best')\n    plt.show()\n\n    # Slicing and arithmetic behave just like ordinary ndarrays\n    print(\"The fourth bin has %d entries\" % m[3])\n    m[1:4] += 4 + 2 * m[-27:-24]\n    print(\"Now it has %d entries\" % m[3])\n\n    # Of course I couldn't resist adding a canned plotting function:\n    m.plot()\n    plt.show()\n\n    # Create and show a 2d histogram. Axis names are optional.\n    m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y'])\n    m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6))\n    m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6))\n    m2.plot()\n    plt.show()\n\n    # x and y projections return Hist1d objects\n    m2.projection('x').plot(label='x projection')\n    m2.projection(1).plot(label='y projection')\n    plt.legend()\n    plt.show()\n\n\nAlternatives\n------------\nOf course, the easiest alternative is just to use np.histogram without any wrappers.\n\nIf you're looking for a more fancy histogram class, and don't mind installing a big framework,\nyou might like the particle physics workhorse ROOT (`https://root.cern.ch/root/html/TH1.html`) and one of its python bindings (pyROOT, rootpy).\n\nIf you do come from a ROOT background, you might appreciate pyhistogram (`https://github.com/cbourjau/pyhistogram`) instead,\nwhich has a much more ROOT-like interface.\n\nAnother 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. \n\n\n\n\nHistory\n-------\n\n\n------------------\n0.6.0 (2019-06-30)\n------------------\n* Correct step plotting at edges, other plotting fixes\n* Histogram numpy structured arrays\n* Fix deprecation warnings (#6)\n* `lookup_hist`\n* `.max()` and `.min()` methods\n* percentile support for higher-dimensional histograms\n* Improve Hist1d.get_random (also randomize in bin)\n\n------------------\n0.5.4 (2017-09-20)\n------------------\n* Fix issue with input from dask\n\n------------------\n0.5.3 (2017-09-18)\n------------------\n* Fix python 2 support\n\n------------------\n0.5.2 (2017-08-08)\n------------------\n* Fix colorbar arguments to Histdd.plot (#4)\n* percentile for Hist1d\n* rebin method for Histdd (experimental)\n\n------------------\n0.5.1 (2017-03-22)\n------------------\n* get_random for Histdd no longer just returns bin centers (Hist1d does stil...)\n* lookup for Hist1d. When will I finally merge the classes...\n\n------------------\n0.5.0 (2016-10-07)\n------------------\n* pandas.DataFrame and dask.dataframe support\n* dimensions option to Histdd to init axis_names and bin_centers at once\n\n------------------\n0.4.3 (2016-10-03)\n------------------\n* Remove matplotlib requirement (still required for plotting features)\n\n------------------\n0.4.2 (2016-08-10)\n------------------\n* Fix small bug for >=3 d histograms\n\n------------------\n0.4.1 (2016-17-14)\n------------------\n* get_random and lookup for Histdd. Not really tested yet.\n\n------------------\n0.4.0 (2016-02-05)\n------------------\n* .std function for Histdd\n* Fix off-by-one errors\n\n------------------\n0.3.0 (2015-09-28)\n------------------\n* Several new histdd functions: cumulate, normalize, percentile...\n* Python 2 compatibility\n\n------------------\n0.2.1 (2015-08-18)\n------------------\n* Histdd functions sum, slice, average now also work\n\n----------------\n0.2 (2015-08-06)\n----------------\n* Multidimensional histograms\n* Axes naming\n\n--------------------\n0.1.1-4 (2015-08-04)\n--------------------\nCorrect various rookie mistakes in packaging...\nHey, it's my first pypi package!\n\n----------------\n0.1 (2015-08-04)\n----------------\nInitial release\n\n* Hist1d, Hist2d\n* Basic test suite\n* Basic readme",
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