Metadata-Version: 2.1
Name: pygram11
Version: 0.11.1
Summary: Fast histogramming in Python built on pybind11 and OpenMP.
Home-page: https://github.com/douglasdavis/pygram11
Author: Doug Davis
Author-email: ddavis@ddavis.io
Maintainer: Doug Davis
Maintainer-email: ddavis@ddavis.io
License: MIT License
Description: # pygram11
        
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        Simple and fast histogramming in Python accelerated with
        [OpenMP](https://www.openmp.org/) with help from
        [pybind11](https://github.com/pybind/pybind11).
        
        `pygram11` provides functions for very fast histogram calculations
        (and the variance in each bin) in one and two dimensions. The API is
        very simple; documentation can be [found
        here](https://pygram11.readthedocs.io/) (you'll also find [some
        benchmarks](https://pygram11.readthedocs.io/en/stable/bench.html)
        there).
        
        ## Installing
        
        ### From PyPI
        
        Binary wheels are provided for Linux and macOS. They can be installed
        from [PyPI](https://pypi.org/project/pygram11/) via pip:
        
        ```
        pip install pygram11
        ```
        
        ### From conda-forge
        
        For installation via the `conda` package manager [pygram11 is part of
        conda-forge](https://anaconda.org/conda-forge/pygram11).
        
        ```none
        conda install pygram11 -c conda-forge
        ```
        
        Please note that on macOS the OpenMP libraries from LLVM (`libomp`)
        and Intel (`libiomp`) may clash if your `conda` environment includes
        the Intel Math Kernel Library (MKL) package distributed by
        Anaconda. You may need to install the `nomkl` package to prevent the
        clash (Intel MKL accelerates many linear algebra operations, but does
        not impact pygram11):
        
        ```none
        conda install nomkl ## sometimes necessary fix (macOS only)
        ```
        
        ### From Source
        
        You need is a C++14 compiler and OpenMP. If you are using a relatively
        modern GCC release on Linux then you probably don't have to worry
        about the OpenMP dependency. If you are on macOS, you can install
        `libomp` from Homebrew (pygram11 does compile on Apple Silicon devices
        with Python version 3.9 and `libomp` installed from Homebrew). With
        those dependencies met, simply run:
        
        ```none
        git clone https://github.com/douglasdavis/pygram11.git --recurse-submodules
        cd pygram11
        pip install .
        ```
        
        Or let pip handle the cloning procedure:
        
        ```none
        pip install git+https://github.com/douglasdavis/pygram11.git@main
        ```
        
        Tests are run on Python versions 3.6 through 3.9 (binary wheels are
        provided for those versions); an earlier version of Python 3 might
        work, but this is not guaranteed (and you will have to manually remove
        the `>= 3.6` requirement in the `setup.cfg` file).
        
        ## In Action
        
        A histogram (with fixed bin width) of weighted data in one dimension:
        
        ```python
        >>> rng = np.random.default_rng(123)
        >>> x = rng.standard_normal(10000)
        >>> w = rng.uniform(0.8, 1.2, x.shape[0])
        >>> h, err = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w)
        ```
        
        A histogram with fixed bin width which saves the under and overflow in
        the first and last bins:
        
        ```python
        >>> x = rng.standard_normal(1000000)
        >>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True)
        ```
        
        where we've used `__` to catch the `None` returned when weights are
        absent. A histogram in two dimensions with variable width bins:
        
        ```python
        >>> x = rng.standard_normal(1000)
        >>> y = rng.standard_normal(1000)
        >>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0, 3.1]
        >>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0, 2.8]
        >>> h, err = pygram11.histogram2d(x, y, bins=[xbins, ybins])
        ```
        
        Histogramming multiple weight variations for the same data, then
        putting the result in a DataFrame (the input pandas DataFrame will be
        interpreted as a NumPy array):
        
        ```python
        >>> N = 10000
        >>> weights = pd.DataFrame({"weight_a": np.abs(rng.standard_normal(N)),
        ...                         "weight_b": rng.uniform(0.5, 0.8, N),
        ...                         "weight_c": rng.uniform(0.0, 1.0, N)})
        >>> data = rng.standard_normal(N)
        >>> count, err = pygram11.histogram(data, bins=20, range=(-3, 3), weights=weights, flow=True)
        >>> count_df = pd.DataFrame(count, columns=weights.columns)
        >>> err_df = pd.DataFrame(err, columns=weights.columns)
        ```
        
        I also wrote a [blog
        post](https://ddavis.io/posts/introducing-pygram11/) with some simple
        examples.
        
        ## Other Libraries
        
        - [boost-histogram](https://github.com/scikit-hep/boost-histogram)
          provides Pythonic object oriented histograms.
        - Simple and fast histogramming in Python using the NumPy C API:
          [fast-histogram](https://github.com/astrofrog/fast-histogram) (no
          variance or overflow support).
        - If you want to calculate histograms on a GPU in Python, check out
          [cupy.histogram](https://docs-cupy.chainer.org/en/stable/reference/generated/cupy.histogram.html#cupy.histogram).
          They only have 1D histograms (no weights or overflow).
        
        ---
        
        If there is something you'd like to see in pygram11, please open an
        issue or pull request.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: C++
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
