Metadata-Version: 2.1
Name: pybaselines
Version: 0.4.1
Summary: A collection of algorithms for estimating the baseline of experimental data
Home-page: https://github.com/derb12/pybaselines
Author: Donald Erb
Author-email: donnie.erb@gmail.com
License: BSD-3-Clause
Project-URL: Source Code, https://github.com/derb12/pybaselines
Project-URL: Documentation, https://pybaselines.readthedocs.io
Description: ===========
        pybaselines
        ===========
        
        .. image:: https://img.shields.io/pypi/v/pybaselines.svg
            :target: https://pypi.python.org/pypi/pybaselines
            :alt: Most Recent Version
        
        .. image:: https://readthedocs.org/projects/pybaselines/badge/?version=latest
            :target: https://pybaselines.readthedocs.io
            :alt: Documentation Status
        
        .. image:: https://img.shields.io/pypi/pyversions/pybaselines.svg
            :target: https://pypi.python.org/pypi/pybaselines
            :alt: Supported Python versions
        
        .. image:: https://img.shields.io/badge/license-BSD%203--Clause-blue.svg
            :target: https://github.com/derb12/pybaselines/tree/main/LICENSE.txt
            :alt: BSD 3-clause license
        
        
        pybaselines is a collection of algorithms for estimating the baseline of experimental data.
        
        * For Python 3.6+
        * Open Source: BSD 3-Clause License
        * Source Code: https://github.com/derb12/pybaselines
        * Documentation: https://pybaselines.readthedocs.io.
        
        
        .. contents:: **Contents**
            :depth: 1
        
        
        Introduction
        ------------
        
        pybaselines provides many different algorithms for fitting baselines to data from
        experimental techniques such as Raman, FTIR, NMR, XRD, PIXE, etc. The aim of
        the project is to provide a semi-unified API to allow quickly testing and comparing
        multiple baseline algorithms to find the best one for a set of data.
        
        pybaselines has 25+ baseline algorithms. Baseline fitting techniques are grouped
        accordingly (note: when a method is labelled as 'improved', that is the method's
        name, not editorialization):
        
        a) Polynomial (pybaselines.polynomial)
        
            1) poly (Regular Polynomial)
            2) modpoly (Modified Polynomial)
            3) imodpoly (Improved Modified Polynomial)
            4) penalized_poly (Penalized Polynomial)
            5) loess (Locally Estimated Scatterplot Smoothing)
        
        b) Whittaker-smoothing-based techniques (pybaselines.whittaker)
        
            1) asls (Asymmetric Least Squares)
            2) iasls (Improved Asymmetric Least Squares)
            3) airpls (Adaptive Iteratively Reweighted Penalized Least Squares)
            4) arpls (Asymmetrically Reweighted Penalized Least Squares)
            5) drpls (Doubly Reweighted Penalized Least Squares)
            6) iarpls (Improved Asymmetrically Reweighted Penalized Least Squares)
            7) aspls (Adaptive Smoothness Penalized Least Squares)
            8) psalsa (Peaked Signal's Asymmetric Least Squares Algorithm)
        
        c) Morphological (pybaselines.morphological)
        
            1) mpls (Morphological Penalized Least Squares)
            2) mor (Morphological)
            3) imor (Improved Morphological)
            4) mormol (Morphological and Mollified Baseline)
            5) amormol (Averaging Morphological and Mollified Baseline)
            6) rolling_ball (Rolling Ball Baseline)
        
        d) Window-based (pybaselines.window)
        
            1) noise_median (Noise Median method)
            2) snip (Statistics-sensitive Non-linear Iterative Peak-clipping)
            3) swima (Small-Window Moving Average)
        
        e) Optimizers (pybaselines.optimizers)
        
            1) collab_pls (Collaborative Penalized Least Squares)
            2) optimize_extended_range
            3) adaptive_minmax (Adaptive MinMax)
        
        f) Miscellaneous methods (pybaselines.misc)
        
            1) interp_pts (Interpolation between points)
        
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        pybaselines requires `Python <https://python.org>`_ version 3.6 or later
        and the following libraries:
        
        * `NumPy <https://numpy.org>`_ (>= 1.14)
        * `SciPy <https://www.scipy.org/scipylib/index.html>`_ (>= 0.11)
        
        
        All of the required libraries should be automatically installed when
        installing pybaselines using either of the two installation methods below.
        
        Additionally, pybaselines has the following optional dependencies:
        
        * `pentapy <https://github.com/GeoStat-Framework/pentapy>`_ (>= 1.0):
          provides a faster solver for Whittaker-smoothing-based methods
        
        
        Stable Release
        ~~~~~~~~~~~~~~
        
        pybaselines is easily installed from `pypi <https://pypi.org/project/pybaselines>`_
        using `pip <https://pip.pypa.io>`_, by running the following command in your terminal:
        
        .. code-block:: console
        
            pip install --upgrade pybaselines
        
        This is the preferred method to install pybaselines, as it will always install the
        most recent stable release.
        
        To also install the optional dependencies when installing pybaselines, do:
        
        .. code-block:: console
        
            pip install --upgrade pybaselines[full]
        
        
        Development Version
        ~~~~~~~~~~~~~~~~~~~
        
        The sources for pybaselines can be downloaded from the `Github repo`_.
        
        The public repository can be cloned using:
        
        .. code-block:: console
        
            git clone https://github.com/derb12/pybaselines.git
        
        
        Once the repository is downloaded, it can be installed with:
        
        .. code-block:: console
        
            cd pybaselines
            pip install .
        
        
        .. _Github repo: https://github.com/derb12/pybaselines
        
        
        Quick Start
        -----------
        
        To use the various functions in pybaselines, simply input the measured
        data and any required parameters. All baseline functions in pybaselines
        will output two items: a numpy array of the calculated baseline and a
        dictionary of parameters that can be helpful for reusing the functions.
        
        For more details on each baseline algorithm, refer to the `algorithms section`_ of
        pybaselines's documentation.
        
        .. _algorithms section: https://pybaselines.readthedocs.io/en/latest/algorithms/index.html
        
        
        A simple example is shown below.
        
        .. code-block:: python
        
            import matplotlib.pyplot as plt
            import numpy as np
            import pybaselines
            from pybaselines import utils
        
            x = np.linspace(100, 4200, 1000)
            # a measured signal containing several Gaussian peaks
            signal = (
                utils.gaussian(x, 2, 700, 50)
                + utils.gaussian(x, 3, 1200, 150)
                + utils.gaussian(x, 5, 1600, 100)
                + utils.gaussian(x, 4, 2500, 50)
                + utils.gaussian(x, 7, 3300, 100)
            )
            # baseline is a polynomial plus a broad gaussian
            true_baseline = (
                10 + 0.001 * x
                + utils.gaussian(x, 6, 2000, 2000)
            )
            noise = np.random.default_rng(1).normal(0, 0.2, x.size)
        
            y = signal + true_baseline + noise
        
            bkg_1 = pybaselines.polynomial.modpoly(y, x, poly_order=3)[0]
            bkg_2 = pybaselines.whittaker.asls(y, lam=1e7, p=0.01)[0]
            bkg_3 = pybaselines.morphological.imor(y, half_window=25)[0]
            bkg_4 = pybaselines.window.snip(
                y, max_half_window=40, decreasing=True, smooth_half_window=1
            )[0]
        
            plt.plot(x, y, label='raw data', lw=1.5)
            plt.plot(x, true_baseline, lw=3, label='true baseline')
            plt.plot(x, bkg_1, '--', label='modpoly')
            plt.plot(x, bkg_2, '--', label='asls')
            plt.plot(x, bkg_3, '--', label='imor')
            plt.plot(x, bkg_4, '--', label='snip')
        
            plt.legend()
            plt.show()
        
        
        The above code will produce the image shown below.
        
        .. image:: https://github.com/derb12/pybaselines/raw/main/docs/images/quickstart.jpg
           :align: center
           :alt: various baselines
        
        
        Contributing
        ------------
        
        Contributions are welcomed and greatly appreciated. For information on
        submitting bug reports, pull requests, or general feedback, please refer
        to the `contributing guide`_.
        
        .. _contributing guide: https://github.com/derb12/pybaselines/tree/main/docs/contributing.rst
        
        
        Changelog
        ---------
        
        Refer to the changelog_ for information on pybaselines's changes.
        
        .. _changelog: https://github.com/derb12/pybaselines/tree/main/CHANGELOG.rst
        
        
        License
        -------
        
        pybaselines is open source and freely available under the BSD 3-clause license.
        For more information, refer to the license_.
        
        .. _license: https://github.com/derb12/pybaselines/tree/main/LICENSE.txt
        
        
        Author
        ------
        
        * Donald Erb <donnie.erb@gmail.com>
        
Keywords: materials characterization,materials science,baseline,background,baseline subtraction,background subtraction
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
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: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: full
