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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_taylor_diagram.py:


===========================
Plot Taylor Diagram
===========================

This example uses fake data and fitted model prediction to plot taylor diagram

The R package open-air has provided a nice explanation on what taylor diagram is:
     https://bookdown.org/david_carslaw/openair/sections/model-evaluation/taylor-diagram.html (Figure 20.2)

.. GENERATED FROM PYTHON SOURCE LINES 11-49



.. image-sg:: /auto_examples/images/sphx_glr_plot_taylor_diagram_001.png
   :alt: plot taylor diagram
   :srcset: /auto_examples/images/sphx_glr_plot_taylor_diagram_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/cgeniepy/envs/stable/lib/python3.12/site-packages/cgeniepy/skill.py:422: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.
      self.ax.scatter(np.arccos(correlation), std,*args, **kwargs)






|

.. code-block:: Python

    import numpy as np
    from scipy.optimize import curve_fit
    from cgeniepy.skill import ArrComparison, TaylorDiagram
    import matplotlib.pyplot as plt

    def generate_data(x, a, b, c, noise=0.5):
        y = a * np.exp(-b * x) + c  # Exponential function
        np.random.seed(90148)
        noise = np.random.normal(0, noise, size=len(x))
        return y + noise

    def exp_func(x, a, b, c):
        return a * np.exp(-b * x) + c

    def linear_func(x, a, b):
        return a * x + b

    # Generate random data
    x = np.linspace(0, 10, 50)
    y = generate_data(x, 5, 0.3, 2)

    ## linear model
    popt, pcov = curve_fit(linear_func, x, y)
    fit1 = linear_func(x, *popt)

    ## exp model
    popt2, pcov2 = curve_fit(exp_func, x, y)
    fit2 = exp_func(x, *popt2)

    ## Create Comparison instance
    ac1 = ArrComparison(y, fit1, 'linear')
    ac2 = ArrComparison(y, fit2, 'exponential')

    ## Create TaylorDiagram instance
    diagram = TaylorDiagram([ac1, ac2])
    diagram.setup_ax(crmse_contour=True)
    diagram.plot(s=20)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.170 seconds)


.. _sphx_glr_download_auto_examples_plot_taylor_diagram.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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