{
    "info": {
        "author": "Doug Davis",
        "author_email": "ddavis@ddavis.io",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Unix",
            "Programming Language :: C++",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: 3 :: Only"
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        "description": "# pygram11\n\n[![Build Status](https://dev.azure.com/ddavis0485/pygram11/_apis/build/status/douglasdavis.pygram11?branchName=master)](https://dev.azure.com/ddavis0485/pygram11/_build/latest?definitionId=4&branchName=master)\n[![builds.sr.ht status](https://builds.sr.ht/~ddavis/pygram11.svg)](https://builds.sr.ht/~ddavis/pygram11?)\n[![Documentation Status](https://readthedocs.org/projects/pygram11/badge/?version=latest)](https://pygram11.readthedocs.io/en/latest/?badge=latest)\n![](https://img.shields.io/pypi/pyversions/pygram11.svg?colorB=blue&style=flat)\n![PyPI - Wheel](https://img.shields.io/pypi/wheel/pygram11?color=blue)\n[![PyPI version](https://img.shields.io/pypi/v/pygram11.svg?colorB=486b87&style=flat)](https://pypi.org/project/pygram11/)\n[![Conda Forge](https://img.shields.io/conda/vn/conda-forge/pygram11.svg?colorB=486b87&style=flat)](https://anaconda.org/conda-forge/pygram11)\n\nSimple and fast histogramming in Python accelerated with\n[OpenMP](https://www.openmp.org/) (with help from\n[pybind11](https://github.com/pybind/pybind11)).\n\n`pygram11` provides fast functions for calculating histograms (and\ntheir statistical uncertainties). The API is very simple;\ndocumentation can be [found here](https://pygram11.readthedocs.io/) (you'll\nalso find [some\nbenchmarks](https://pygram11.readthedocs.io/en/stable/purpose.html#some-benchmarks)\nthere).\n\n**Note**: the last version of pygram11 supporting Python 2 is\n[0.5.2](https://github.com/douglasdavis/pygram11/releases/tag/0.5.2).\n\n## Installing\n\npygram11 only requires [NumPy](https://www.numpy.org/). To build from\nsource you'll need a C++ compiler with C++11 support.\n\n### From PyPI\n\nBinary wheels are provided for Linux (starting with version 0.5.0) and\nmacOS (starting with version 0.5.1), they can be installed from\n[PyPI](https://pypi.org/project/pygram11/) via pip.\n\n```\npip install pygram11\n```\n\n### From conda-forge\n\nFor a simple installation process via the `conda` package manager\n[pygram11 is part of\nconda-forge](https://anaconda.org/conda-forge/pygram11).\n\n```none\nconda install pygram11 -c conda-forge\n```\n\nPlease note that on macOS the OpenMP libraries from LLVM (`libomp`)\nand Intel (`libiomp`) can clash if your `conda` environment includes\nthe Intel Math Kernel Library (MKL) package distributed by\nAnaconda. You may need to install the `nomkl` package to prevent the\nclash (Intel MKL accelerates many linear algebra operations, but does\nnot impact pygram11):\n\n```none\nconda install nomkl ## sometimes necessary fix (macOS only)\n```\n\n### From Source\n\n```none\npip install git+https://github.com/douglasdavis/pygram11.git@master\n```\n\nTo ensure OpenMP acceleration in a build from source, read the OpenMP\nsection of the docs. If you have a modern GCC verion on Linux, you\nprobably don't have to worry about anything. If you are on macOS,\nyou'll probably want to install `libomp` from Homebrew.\n\n**Note**: For releases older than v0.5, when building from source or\nPyPI, `pybind11` was required to be explicitly installed before\n`pygram11` (because `setup.py` used `pybind11` to determine include\ndirectories). Starting with v0.5 `pybind11` is bundled as a git\nsubmodule for installations from source.\n\n## In Action\n\nA histogram (with fixed bin width) of weighted data in one dimension,\naccelerated with OpenMP:\n\n```python\n>>> x = np.random.randn(10000)\n>>> w = np.random.uniform(0.8, 1.2, 10000)\n>>> h, staterr = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w, omp=True)\n```\n\nA histogram with fixed bin width which saves the under and overflow in\nthe first and last bins (using `__` to catch the `None` returned due\nto the absence of weights):\n\n```python\n>>> x = np.random.randn(1000000)\n>>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True, omp=True)\n```\n\nA histogram in two dimensions with variable width bins:\n\n```python\n>>> x = np.random.randn(10000)\n>>> y = np.random.randn(10000)\n>>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0]\n>>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0]\n>>> h, __ = pygram11.histogram2d(x, y, bins=[xbins, ybins])\n```\n\nHistogramming multiple weight variations for the same data, then\nputting the result in a DataFrame (the input pandas DataFrame will be\ninterpreted as a NumPy array):\n\n```python\n>>> weights = pd.DataFrame({\"weight_a\" : np.abs(np.random.randn(10000)),\n...                         \"weight_b\" : np.random.uniform(0.5, 0.8, 10000),\n...                         \"weight_c\" : np.random.rand(10000)})\n>>> data = np.random.randn(10000)\n>>> count, err = pygram11.histogram(data, bins=20, range=(-3, 3),\n...                                 weights=weights, flow=True, omp=True)\n>>> count_df = pd.DataFrame(count, columns=weights.columns)\n>>> err_df = pd.DataFrame(err, columns=weights.columns)\n```\n\nI also wrote a [blog\npost](https://ddavis.io/posts/introducing-pygram11/) with some simple\nexamples.\n\n## Other Libraries\n\n- There is an effort to develop an object oriented histogramming\n  library for Python called\n  [boost-histogram](https://indico.cern.ch/event/803122/contributions/3339214/attachments/1830213/2997039/bhandhist.pdf). This\n  library will be feature complete w.r.t. everything a physicist needs\n  with histograms.\n- Simple and fast histogramming in Python using the NumPy C API:\n  [fast-histogram](https://github.com/astrofrog/fast-histogram). No\n  weights or overflow).\n- If you want to calculate histograms on a GPU in Python, check out\n  [cupy.histogram](https://docs-cupy.chainer.org/en/stable/reference/generated/cupy.histogram.html#cupy.histogram). They\n  only have 1D histograms (no weights or overflow).\n\n---\n\nIf there is something you'd like to see in pygram11, please open an\nissue or pull request.\n\n\n",
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