diptest 0.2.2

Hartigan's diptest.

diptest

A Python/C(++) implementation of Hartigan & Hartigan's dip test for unimodality.

The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference. Other than unimodality, it makes no further assumptions about the form of the null distribution.

Dependencies

• numpy
• [Optional] OpenMP

Parallelisation of the p-value computation using bootstrapping is offered using OpenMP. OpenMP is disabled by default but can be enabled, see installation section below. Multi-threading can be turned off by setting the number of threads equal to 1. See the docstring of diptest for details.

Installation

diptest can be installed from PyPi using:

    pip install diptest

Wheels containing the pre-compiled extension are available for:

• Windows x84-64 - CPython 3.7 - 3.10
• Linux x84-64 - CPython 3.7 - 3.10
• MacOS x84-64 - CPython 3.7 - 3.10
• MacOS ARM-64 - CPython 3.8 - 3.10

If you have a C/C++ compiler available it is advised to install without the wheel as this enables architecture specific optimisations.

    pip install diptest --no-binary diptest

Compatible compilers through Pybind11:

• Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer)
• GCC 4.8 or newer
• Microsoft Visual Studio 2015 Update 3 or newer
• Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI)
• Cygwin/GCC (previously tested on 2.5.1)
• NVCC (CUDA 11.0 tested in CI)
• NVIDIA PGI (20.9 tested in CI)

Enable OpenMP

To enable OpenMP use:

    CMAKE_ARGS="-DDIPTEST_ENABLE_OPENMP=ON" pip install diptest --no-binary diptest

Debug installation

To enable a debug build use:

    CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Debug" pip install diptest --no-binary diptest

Debug printing

To enable the debug print statements use:

    CMAKE_ARGS="-DDIPTEST_ENABLE_DEBUG=ON" pip install diptest --no-binary diptest

then call the function with debug argument set to a value greater than zero:

    diptest(x, debug=1)

Usage

This library provides two functions:

• dipstat
• diptest

The first only computes Hartigan's dip statistic. diptest computes both the statistic and the p-value. The p-value can be computed using interpolation of a critical value table (default) or by bootstrapping the null hypothesis. Note that for larger samples (N > 1e5) this is quite compute and memory intensive.

    import numpy as np
    import diptest

    # generate some bimodal random draws
    N = 1000
    hN = N // 2
    x = np.empty(N, dtype=np.float64)
    x[:hN] = np.random.normal(0.4, 1.0, hN)
    x[hN:] = np.random.normal(-0.4, 1.0, hN)

    # only the dip statistic
    dip = diptest.dipstat(x)
    
    # both the dip statistic and p-value
    dip, pval = diptest.diptest(x)

References

Hartigan, J. A., & Hartigan, P. M. (1985). The Dip Test of Unimodality. The Annals of Statistics.

Hartigan, P. M. (1985). Computation of the Dip Statistic to Test for Unimodality. Journal of the Royal Statistical Society. Series C (Applied Statistics), 34(3), 320-325.

Acknowledgement

diptest is just a Python port of Martin Maechler's R module of the same name. The package wrapping the C implementation was originally written by Alistair Muldal. The fork is an update with a number of changes:

• Fixes a buffer overrun issue in _dip.c by reverting to the original C implementation
• Python bindings using Pybind11 (C++) instead of Cython
• P-value computation using bootstrapping has been moved down to C++ with optional parallelisation support through OpenMP
• Removed overhead caused by debug branching statements by placing them under a compile-time definition
• Added tests and wheel support

License

diptest is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.