===========
Quick Start
===========

To use the various functions in pybaselines, simply input the measured
data and any required parameters. All baseline correction functions in pybaselines
will output two items: a numpy array of the calculated baseline and a
dictionary of potentially useful parameters.

The main interface for all baseline correction algorithms in pybaselines is through
the :class:`~.Baseline` object for one dimensional data and :class:`~.Baseline2D` for
two dimensional data.

A simple example is shown below.

.. plot::
   :align: center
   :context: reset
   :include-source: True

    import matplotlib.pyplot as plt
    import numpy as np
    from pybaselines import Baseline, utils

    x = np.linspace(1, 1000, 1000)
    # a measured signal containing several Gaussian peaks
    signal = (
        utils.gaussian(x, 4, 120, 5)
        + utils.gaussian(x, 5, 220, 12)
        + utils.gaussian(x, 5, 350, 10)
        + utils.gaussian(x, 7, 400, 8)
        + utils.gaussian(x, 4, 550, 6)
        + utils.gaussian(x, 5, 680, 14)
        + utils.gaussian(x, 4, 750, 12)
        + utils.gaussian(x, 5, 880, 8)
    )
    # exponentially decaying baseline
    true_baseline = 2 + 10 * np.exp(-x / 400)
    noise = np.random.default_rng(1).normal(0, 0.2, x.size)

    y = signal + true_baseline + noise

    baseline_fitter = Baseline(x_data=x)

    bkg_1, params_1 = baseline_fitter.modpoly(y, poly_order=3)
    bkg_2, params_2 = baseline_fitter.asls(y, lam=1e7, p=0.02)
    bkg_3, params_3 = baseline_fitter.mor(y, half_window=30)
    bkg_4, params_4 = baseline_fitter.snip(
        y, max_half_window=40, decreasing=True, smooth_half_window=3
    )

    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='mor')
    plt.plot(x, bkg_4, '--', label='snip')

    plt.legend()
    plt.show()