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        "author": "Charles Jekel",
        "author_email": "cjekel@gmail.com",
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        "description": "About\n=====\n\nA library for fitting continuous piecewise linear functions to data.\nJust specify the number of line segments you desire and provide the\ndata.\n\n|Downloads a month| |pwlf ci| |codecov| |PyPI version| |Conda|\n\nCheck out the\n`documentation <https://jekel.me/piecewise_linear_fit_py>`__!\n\nRead the `blog\npost <http://jekel.me/2017/Fit-a-piecewise-linear-function-to-data/>`__.\n\n.. figure:: https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/examplePiecewiseFit.png\n   :alt: Example of a continuous piecewise linear fit to data.\n\n   Example of a continuous piecewise linear fit to data.\n\n.. figure:: https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/sinWaveFit.png\n   :alt: Example of a continuous piecewise linear fit to a sine wave.\n\n   Example of a continuous piecewise linear fit to a sine wave.\n\nNow you can perform segmented constant fitting and piecewise\npolynomials! |Example of multiple degree fits to a sine wave.|\n\nFeatures\n========\n\nFor a specified number of line segments, you can determine (and predict\nfrom) the optimal continuous piecewise linear function f(x). See `this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments.py>`__.\n\nYou can fit and predict a continuous piecewise linear function f(x) if\nyou know the specific x locations where the line segments terminate. See\n`this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitWithKnownLineSegmentLocations.py>`__.\n\nIf you want to pass different keywords for the SciPy differential\nevolution algorithm see `this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments_passDiffEvoKeywords.py>`__.\n\nYou can use a different optimization algorithm to find the optimal\nlocation for line segments by using the objective function that\nminimizes the sum of square of residuals. See `this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/useCustomOptimizationRoutine.py>`__.\n\nInstead of using differential evolution, you can now use a multi-start\ngradient optimization with fitfast() function. You can specify the\nnumber of starting points to use. The default is 2. This means that a\nlatin hyper cube sampling (space filling DOE) of 2 is used to run 2\nL-BFGS-B optimizations. See `this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/sineWave_time_compare.py>`__\nwhich runs fit() function, then runs the fitfast() to compare the\nruntime differences!\n\nInstallation\n============\n\nPython Package Index (PyPI)\n---------------------------\n\nYou can now install with pip.\n\n::\n\n   python -m pip install pwlf\n\nConda\n-----\n\nIf you have conda, you can also install from conda-forge.\n\n::\n\n   conda install -c conda-forge pwlf\n\nFrom source\n-----------\n\nOr clone the repo\n\n::\n\n   git clone https://github.com/cjekel/piecewise_linear_fit_py.git\n\nthen install with pip\n\n::\n\n   python -m pip install ./piecewise_linear_fit_py\n\nHow it works\n============\n\nThis\n`paper <https://github.com/cjekel/piecewise_linear_fit_py/raw/master/paper/pwlf_Jekel_Venter_v2.pdf>`__\nexplains how this library works in detail.\n\nThis is based on a formulation of a piecewise linear least squares fit,\nwhere the user must specify the location of break points. See `this\npost <http://jekel.me/2018/Continous-piecewise-linear-regression/>`__\nwhich goes through the derivation of a least squares regression problem\nif the break point locations are known. Alternatively check out\n`Golovchenko\n(2004) <http://golovchenko.org/docs/ContinuousPiecewiseLinearFit.pdf>`__.\n\nGlobal optimization is used to find the best location for the user\ndefined number of line segments. I specifically use the `differential\nevolution <https://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.optimize.differential_evolution.html>`__\nalgorithm in SciPy. I default the differential evolution algorithm to be\naggressive, and it is probably overkill for your problem. So feel free\nto pass your own differential evolution keywords to the library. See\n`this\nexample <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/fitForSpecifiedNumberOfLineSegments_passDiffEvoKeywords.py>`__.\n\nChangelog\n=========\n\nAll changes now stored in\n`CHANGELOG.md <https://github.com/cjekel/piecewise_linear_fit_py/blob/master/CHANGELOG.md>`__\n\nNew ``weights=`` keyword allows you to perform weighted pwlf fits!\nRemoved TensorFlow code which can now be found\n`here <https://github.com/cjekel/piecewise_linear_fit_py_tf>`__.\n\nRequirements\n============\n\nPython 2.7+\n\nNumPy >= 1.14.0\n\nSciPy >= 1.2.0\n\npyDOE >= 0.3.8\n\nLicense\n=======\n\nMIT License\n\nCitation\n========\n\n.. code:: bibtex\n\n   @Manual{pwlf,\n   author = {Jekel, Charles F. and Venter, Gerhard},\n   title = {{pwlf:} A Python Library for Fitting 1D Continuous Piecewise Linear Functions},\n   year = {2019},\n   url = {https://github.com/cjekel/piecewise_linear_fit_py}\n   }\n\n.. |Downloads a month| image:: https://img.shields.io/pypi/dm/pwlf.svg\n.. |pwlf ci| image:: https://github.com/cjekel/piecewise_linear_fit_py/workflows/pwlf%20ci/badge.svg\n.. |codecov| image:: https://codecov.io/gh/cjekel/piecewise_linear_fit_py/branch/master/graph/badge.svg?token=AgeDFEQXed\n   :target: https://codecov.io/gh/cjekel/piecewise_linear_fit_py\n.. |PyPI version| image:: https://img.shields.io/pypi/v/pwlf\n.. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/pwlf\n   :target: https://anaconda.org/conda-forge/pwlf\n.. |Example of multiple degree fits to a sine wave.| image:: https://raw.githubusercontent.com/cjekel/piecewise_linear_fit_py/master/examples/figs/multi_degree.png\n\n\n",
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