.. 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_2d.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_2d.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_truncated_unimodal_kde_2d.py:


2D truncated unimodal example
==============================

This example shows how to use ``lightkde.kde_2d`` with and without a limit and
how it compares to ``scipy.stats.gaussian_kde`` for a truncated unimodal bivariate
distribution.

.. GENERATED FROM PYTHON SOURCE LINES 10-11

Import packages

.. GENERATED FROM PYTHON SOURCE LINES 11-17

.. code-block:: default

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

    from lightkde import kde_2d








.. GENERATED FROM PYTHON SOURCE LINES 18-19

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

.. GENERATED FROM PYTHON SOURCE LINES 19-23

.. code-block:: default

    np.random.seed(42)
    sample = multivariate_normal.rvs(mean=[0, 0], size=8000)
    sample = sample[sample[:, 0] > 0]








.. GENERATED FROM PYTHON SOURCE LINES 24-25

Estimate kernel density using ``lightkde``:

.. GENERATED FROM PYTHON SOURCE LINES 25-32

.. code-block:: default

    density_mx_without_x_min, x_mx_without_x_min, y_mx_without_x_min = kde_2d(
        sample_mx=sample
    )
    density_mx_with_x_min, x_mx_with_x_min, y_mx_with_x_min = kde_2d(
        sample_mx=sample, xy_min=[0, -5]
    )








.. GENERATED FROM PYTHON SOURCE LINES 33-34

Estimate kernel density using ``scipy``:

.. GENERATED FROM PYTHON SOURCE LINES 34-40

.. code-block:: default

    gkde = gaussian_kde(dataset=sample.T)
    xy_mx = np.hstack(
        (x_mx_without_x_min.reshape(-1, 1), y_mx_without_x_min.reshape(-1, 1))
    )
    scipy_density_mx = gkde.evaluate(xy_mx.T).reshape(x_mx_without_x_min.shape)








.. GENERATED FROM PYTHON SOURCE LINES 41-42

Plot the data against the kernel density estimates:

.. GENERATED FROM PYTHON SOURCE LINES 42-111

.. code-block:: default

    bins = (30, 30)
    data_density_mx, xedges, yedges = np.histogram2d(
        sample[:, 0], sample[:, 1], bins=bins, density=True
    )
    z_min = 0
    z_max = np.max(data_density_mx)
    x_min, x_max = -0.5, max(xedges)
    y_min, y_max = min(yedges), max(yedges)

    # plot
    cmap = "afmhot_r"
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
        2, 2, figsize=(8, 7), sharex="all", sharey="all"
    )

    # data
    h = ax1.hist2d(
        sample[:, 0],
        sample[:, 1],
        bins=bins,
        density=True,
        vmin=z_min,
        vmax=z_max,
        cmap=cmap,
    )[-1]
    ax1.set_title("data")

    # scipy
    ax2.contourf(
        x_mx_without_x_min,
        y_mx_without_x_min,
        scipy_density_mx,
        levels=50,
        vmin=z_min,
        vmax=z_max,
        cmap=cmap,
    )
    ax2.set_xlim(x_min, x_max)
    ax3.set_ylim(y_min, y_max)
    ax2.set_title("scipy.stats.gaussian_kde")

    # lightkde
    ax3.contourf(
        x_mx_with_x_min,
        y_mx_with_x_min,
        density_mx_with_x_min,
        levels=50,
        vmin=z_min,
        vmax=z_max,
        cmap=cmap,
    )
    ax3.set_title("lightkde; with x_min=0")

    ax4.contourf(
        x_mx_without_x_min,
        y_mx_without_x_min,
        density_mx_without_x_min,
        levels=50,
        vmin=z_min,
        vmax=z_max,
        cmap=cmap,
    )
    ax4.set_title("lightkde; without x_min")

    fig.subplots_adjust(right=0.80)
    cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7], aspect=50)
    fig.colorbar(h, cax=cbar_ax, label="density")
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_plot_truncated_unimodal_kde_2d_001.png
   :alt: data, scipy.stats.gaussian_kde, lightkde; with x_min=0, lightkde; without x_min
   :srcset: /auto_examples/images/sphx_glr_plot_truncated_unimodal_kde_2d_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 112-113

When ``x_min=0`` is used, ``lightkde`` approximates well the histogram of the data.


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

   **Total running time of the script:** ( 0 minutes  6.270 seconds)


.. _sphx_glr_download_auto_examples_plot_truncated_unimodal_kde_2d.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_2d.py <plot_truncated_unimodal_kde_2d.py>`

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

      :download:`Download Jupyter notebook: plot_truncated_unimodal_kde_2d.ipynb <plot_truncated_unimodal_kde_2d.ipynb>`