.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_truncated_unimodal_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_truncated_unimodal_kde_1d.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_truncated_unimodal_kde_1d.py:


1D truncated unimodal example
==============================

This example shows how to use ``lightkde.kde_1d`` with and without a limit and
how it compares to ``scipy.stats.gaussian_kde`` for a truncated unimodal distribution.

.. GENERATED FROM PYTHON SOURCE LINES 9-10

Import packages

.. GENERATED FROM PYTHON SOURCE LINES 10-16

.. code-block:: default

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

    from lightkde import kde_1d








.. GENERATED FROM PYTHON SOURCE LINES 17-18

Generate synthetic data from a univariate normal distribution and truncate it:

.. GENERATED FROM PYTHON SOURCE LINES 18-22

.. code-block:: default

    np.random.seed(42)
    sample = norm.rvs(size=2_000)
    sample = sample[sample > 0]








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Estimate kernel density using ``lightkde``:

.. GENERATED FROM PYTHON SOURCE LINES 24-27

.. code-block:: default

    density_vec_without_x_min, x_vec_without_x_min = kde_1d(sample_vec=sample)
    density_vec_with_x_min, x_vec_with_x_min = kde_1d(sample_vec=sample, x_min=0)








.. GENERATED FROM PYTHON SOURCE LINES 28-29

Estimate kernel density using ``scipy``:

.. GENERATED FROM PYTHON SOURCE LINES 29-32

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 33-34

Plot the data against the kernel density estimates:

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.. code-block:: default

    fig, ax = plt.subplots(figsize=(6, 4))
    ax.plot(x_vec_with_x_min, density_vec_with_x_min, "--r", label="lightkde; with x_min=0")
    ax.plot(
        x_vec_without_x_min,
        density_vec_without_x_min,
        ":r",
        label="lightkde; without x_min",
    )
    ax.plot(
        x_vec_without_x_min, scipy_density_vec, zorder=1, label="scipy.stats.gaussian_kde"
    )
    ax.hist(sample, bins=30, density=True, alpha=0.5, label="data")
    ax.legend()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_plot_truncated_unimodal_kde_1d_001.png
   :alt: plot truncated unimodal kde 1d
   :srcset: /auto_examples/images/sphx_glr_plot_truncated_unimodal_kde_1d_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 50-53

When ``x_min=0`` is used, ``lightkde`` approximates well the histogram of the data.
Without this option it behaves similarly to the ``scipy`` method, both extend to a
region without data.


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

   **Total running time of the script:** ( 0 minutes  1.908 seconds)


.. _sphx_glr_download_auto_examples_plot_truncated_unimodal_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_truncated_unimodal_kde_1d.py <plot_truncated_unimodal_kde_1d.py>`

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

      :download:`Download Jupyter notebook: plot_truncated_unimodal_kde_1d.ipynb <plot_truncated_unimodal_kde_1d.ipynb>`