.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_bimodal_kde_1d.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_bimodal_kde_1d.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_bimodal_kde_1d.py:


1D bimodal example
====================

This example shows how to use ``lightkde.kde_1d`` and how it compares to
``scipy.stats.gaussian_kde`` for a bimodal univariate case.

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Import packages:

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.. code-block:: default

    import matplotlib.pyplot as plt
    import numpy as np
    from scipy.stats import gaussian_kde, norm

    from lightkde import kde_1d








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Generate synthetic data from two univariate normal distributions:

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.. code-block:: default

    np.random.seed(42)
    sample = np.hstack((norm.rvs(size=2_000), 0.3 * norm.rvs(size=1_000) + 5))








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Estimate kernel density using ``lightkde``:

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.. code-block:: default

    density_vec, x_vec = kde_1d(sample_vec=sample)








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Estimate kernel density using ``scipy``:

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.. code-block:: default

    gkde = gaussian_kde(dataset=sample)
    scipy_density_vec = gkde.evaluate(x_vec)








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Plot the data against the kernel density estimates:

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.. code-block:: default

    plt.plot(x_vec, density_vec, "--r", label="lightkde")
    plt.plot(x_vec, scipy_density_vec, label="scipy.stats.gaussian_kde")
    plt.hist(sample, bins=100, density=True, alpha=0.5, label="data")
    plt.legend()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_plot_bimodal_kde_1d_001.png
   :alt: plot bimodal kde 1d
   :srcset: /auto_examples/images/sphx_glr_plot_bimodal_kde_1d_001.png
   :class: sphx-glr-single-img





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The ``scipy`` method oversmooths the kernel density and it is far
from the histogram of the data that it is expected to follow.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  1.833 seconds)


.. _sphx_glr_download_auto_examples_plot_bimodal_kde_1d.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_bimodal_kde_1d.py <plot_bimodal_kde_1d.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_bimodal_kde_1d.ipynb <plot_bimodal_kde_1d.ipynb>`