.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_ch2\ch2_5.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_ch2_ch2_5.py>`
        to download the full example code.

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

.. _sphx_glr_auto_ch2_ch2_5.py:


=============================================
2.3.2 Applying maps to kernels
=============================================
In this experiment, we show how applying different maps car ruin your results. 

.. GENERATED FROM PYTHON SOURCE LINES 7-22

.. code-block:: Python


    # Importing necessary modules
    import os
    import sys

    curr_f = os.path.join(os.getcwd(), "codpy-book", "utils")
    sys.path.insert(0, curr_f)


    import matplotlib.pyplot as plt
    import numpy as np

    from codpy import core
    from codpy.kernel import Kernel








.. GENERATED FROM PYTHON SOURCE LINES 23-26

import CodPy's core module and Kernel class
from codpy.plotting import plot1D
Lets import multi_plot function from codpy utils

.. GENERATED FROM PYTHON SOURCE LINES 26-158

.. code-block:: Python

    from codpy.plot_utils import multi_plot, plot1D


    # Define the sinusoidal function
    def periodic_fun(x):
        """
        A sinusoidal function that generates a sum of sines based on the input ``x``.
        """
        from math import pi

        sinss = np.cos(2 * x * pi)
        if x.ndim == 1:
            sinss = np.prod(sinss, axis=0)
            ress = np.sum(x, axis=0)
        else:
            sinss = np.prod(sinss, axis=1)
            ress = np.sum(x, axis=1)
        return ress + sinss


    def codpy_model(
        x, fx, z, kernel_name="gaussian", map={}, order=0, rescale=True
    ):
        kernel = Kernel(
            set_kernel=core.kernel_setter(kernel_name, map,order),
            x=x,
            fx=fx,
            order = order
        )
        if rescale:
            kernel.rescale()
        out= kernel(z)
        return out


    # Lets define helper function to plot 3D projection of the function
    def plot_trisurf(xfx, ax, legend="", elev=90, azim=-100, **kwargs):
        from matplotlib import cm

        """
        Helper function to plot a 3D surface using a trisurf plot.

        Parameters:
        - xfx: A tuple containing the x-coordinates (2D points) and their 
          corresponding function values.
        - ax: The matplotlib axis object for plotting.
        - legend: The legend/title for the plot.
        - elev, azim: Elevation and azimuth angles for the 3D view.
        - kwargs: Additional keyword arguments for further customization.
        """

        xp, fxp = xfx[0], xfx[1]
        x, fx = xp, fxp

        X, Y = x[:, 0], x[:, 1]
        Z = fx.flatten()
        ax.plot_trisurf(X, Y, Z, antialiased=False, cmap=cm.jet)
        ax.view_init(azim=azim, elev=elev)
        ax.title.set_text(legend)


    def generate2Ddata(sizes_x):
        data_x = np.random.uniform(-1, 1, (sizes_x, 2))
        data_z = np.random.uniform(-1, 1, (sizes_x, 2))
        fx = periodic_fun(data_x).reshape(-1, 1)
        fz = periodic_fun(data_z).reshape(-1, 1)

        legend = ["Ground truth"]

        kernel_list = [
            "gaussian",
            "matern",
        ]

        results = [(data_z, fz)]
        # Prepare the results for plotting each kernel
        results += [
            (data_z, codpy_model(data_x, fx, data_z, kernel_name,"standardmean"))
            for kernel_name in kernel_list
        ]

        # Legends for each kernel in the plot
        legends = legend + kernel_list

        # Plot all kernels using multi_plot in a 4x4 grid
        multi_plot(
            results,
            plot_trisurf,
            f_names=legends,
            mp_nrows=1,
            mp_ncols=3,
            mp_figsize=(12, 16),
            projection="3d",
        )


    core.KerInterface.set_verbose()
    # generate2Ddata(400)
    # plt.show()


    def generate1Ddata(sizes_x):
        data_x = np.random.uniform(-1, 1, (sizes_x, 1))
        data_z = np.random.uniform(-1.5, 1.5, (sizes_x, 1))
        fx = periodic_fun(data_x).reshape(-1, 1)
        fz = periodic_fun(data_z).reshape(-1, 1)

        kernel_list = ["gaussianper", "matern", "gaussian"]
        order_list = [2, 2, 0]
        map_list = [{}, "standardmean", "mindistance"]

        # Prepare the results for plotting each kernel
        results = [
            (data_z, codpy_model(data_x, fx, data_z, kernel_name, map=map, order=order))
            for kernel_name,order,map in zip(kernel_list,order_list,map_list)
        ]

        # Legends for each kernel in the plot
        legends = kernel_list

        # Plot all kernels using multi_plot in a 4x4 grid
        multi_plot(
            results,
            plot1D,
            f_names=legends,
            mp_nrows=1,
            mp_ncols=3,
            mp_figsize=(12, 3),
        )

    generate1Ddata(400)
    plt.show()
    pass


.. image-sg:: /auto_ch2/images/sphx_glr_ch2_5_001.png
   :alt: gaussianper, matern, gaussian
   :srcset: /auto_ch2/images/sphx_glr_ch2_5_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.296 seconds)


.. _sphx_glr_download_auto_ch2_ch2_5.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: ch2_5.ipynb <ch2_5.ipynb>`

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

      :download:`Download Python source code: ch2_5.py <ch2_5.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: ch2_5.zip <ch2_5.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_