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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_derivative.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_plot_derivative.py:


Computing derivative
======================

Examples showing the calculation of derivative of 1D and 2D arrays.
Note that 1) the speed is enhanced, 2) the edge issue during the convolution is resolved, and 3) the NaN values are considered.

.. GENERATED FROM PYTHON SOURCE LINES 8-85



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_derivative_001.png
          :alt: 1D Array
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_plot_derivative_002.png
          :alt: 2D Array, dZ/dx (kkpy), dZ/dx (slower)
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    kkpy : 575 ms (22.4x faster)
    slow : 12872 ms
    Is equal? :  True






|

.. code-block:: default
   :lineno-start: 8

    import numpy as np
    import matplotlib.pyplot as plt
    import datetime
    import kkpy

    def ddx_slow(var, dx):
        res = np.full(var.shape, np.nan)
        for i in np.arange(var.shape[0]):
            for j in np.arange(var.shape[1]):
                if j <= int(dx/2):
                    res[i,j] = (arr2d[i,j+int(dx/2)]-arr2d[i,0]) / (j+int(dx/2))
                elif j+int(dx/2) > var.shape[1]-1:
                    res[i,j] = (arr2d[i,-1]-arr2d[i,j-int(dx/2)]) / (var.shape[1]-j+int(dx/2)-1)
                else:
                    res[i,j] = (arr2d[i,j+int(dx/2)]-arr2d[i,j-int(dx/2)]) / (dx-1)
        return res

    winsize = 5

    # 1D Array
    xarr = np.linspace(0, 2*np.pi, 1000)
    yarr = np.sin(xarr)
    deriv1d = kkpy.util.derivative(yarr, winsize, pixelsize=xarr[1]-xarr[0])

    fig = plt.figure(figsize=(4,3), dpi=200)
    plt.plot(xarr, yarr, label='f(x)')
    plt.plot(xarr, deriv1d, label='Derivative of f(x)')
    kkpy.plot.tickint(ax=plt.gca(), major=np.pi/4)
    plt.gca().set_xticks(np.arange(0, 2*np.pi+0.01, np.pi/4))
    plt.gca().set_yticks(np.arange(-1, 1.01, 0.25))
    labels = ['$0$', r'$\pi/4$', r'$\pi/2$', r'$3\pi/4$', r'$\pi$',
              r'$5\pi/4$', r'$3\pi/2$', r'$7\pi/4$', r'$2\pi$']
    plt.gca().set_xticklabels(labels)
    plt.legend()
    plt.grid()
    plt.title('1D Array')
    plt.show()

    # 2D Array
    arr2d = np.sqrt(np.arange(100)**2/10 + np.arange(50)[:,np.newaxis]**2)
    arr2d[30:40,50:60] = np.nan
    arr2d[5:7,78:90] = np.nan

    time1 = datetime.datetime.now()
    deriv2d_kkpy = kkpy.util.derivative(arr2d, winsize, axis=1)
    elapsed_kkpy = datetime.datetime.now()-time1

    time2 = datetime.datetime.now()
    deriv2d_slow = ddx_slow(arr2d, winsize)
    elapsed_slow = datetime.datetime.now()-time2

    titles = ['2D Array', 'dZ/dx (kkpy)', 'dZ/dx (slower)']
    minmax = 0.4

    fig = plt.figure(figsize=(11,3), dpi=200)
    gs = fig.add_gridspec(1,3)
    ax = plt.subplot(gs[0])
    pm = ax.pcolormesh(arr2d, cmap=plt.cm.turbo)
    kkpy.plot.tickint(ax=ax, major=20, minor=10, which='xaxis')
    kkpy.plot.tickint(ax=ax, major=10, minor=5, which='yaxis')
    plt.colorbar(pm, ax=ax)
    ax.set_title(titles[0])
    plt.grid()
    for i_v, variable in enumerate([deriv2d_kkpy, deriv2d_slow]):
        ax = plt.subplot(gs[i_v+1])
        pm = ax.pcolormesh(variable, vmin=-minmax, vmax=minmax, cmap=plt.cm.bwr)
        kkpy.plot.tickint(ax=ax, major=20, minor=10, which='xaxis')
        kkpy.plot.tickint(ax=ax, major=10, minor=5, which='yaxis')
        plt.colorbar(pm, ax=ax)
        ax.set_title(titles[i_v+1])
        plt.grid()
    plt.show()

    print(f'kkpy : {elapsed_kkpy.microseconds} ms ({elapsed_slow.microseconds/elapsed_kkpy.microseconds:.1f}x faster)')
    print(f'slow : {elapsed_slow.microseconds} ms')
    print('Is equal? : ', np.array_equal(deriv2d_kkpy, deriv2d_slow, equal_nan=True))



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

   **Total running time of the script:** ( 0 minutes  5.582 seconds)

**Estimated memory usage:**  26 MB


.. _sphx_glr_download_auto_examples_plot_derivative.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/Kwonil-Kim/kkpy/gh-pages?filepath=notebooks/auto_examples/plot_derivative.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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