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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_glr_examples_methods_plot_noise_removal.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_glr_examples_methods_plot_noise_removal.py:


=================================
Remove noises  
=================================

Filtered data to remove outliers, artifacts and existing noises in  
AMT data  

.. GENERATED FROM PYTHON SOURCE LINES 9-12

.. code-block:: Python

    # Author: L.Kouadio 
    # Licence: BSD-3-clause








.. GENERATED FROM PYTHON SOURCE LINES 13-14

Import required modules 

.. GENERATED FROM PYTHON SOURCE LINES 14-18

.. code-block:: Python

    import matplotlib.pyplot as plt 
    import watex as wx 
    from watex.methods.em import EMAP, filter_noises 








.. GENERATED FROM PYTHON SOURCE LINES 19-22

Fetch EDI data for the tests and load EM EDI objects 
using the Processing class. 
The demonstration will focus on the first station ``S00``

.. GENERATED FROM PYTHON SOURCE LINES 22-25

.. code-block:: Python


    edi_data = wx.fetch_data ('edis', samples =25 , return_data =True ) 
    p= EMAP ( ).fit(edi_data)







.. GENERATED FROM PYTHON SOURCE LINES 26-28

Print 
print seven values of resistivity and phase of the first station 

.. GENERATED FROM PYTHON SOURCE LINES 28-35

.. code-block:: Python

    station_index =4  
    print(p.ediObjs_[station_index].Z.resistivity[:, 0, 1][:7])  # resistivity
    # phase 
    print(p.ediObjs_[station_index].Z.phase[:, 0, 1][:7]) #  phase
    # Impedance values 
    print(p.ediObjs_[station_index].Z.z[:, 0, 1][:7])  # impedance tensor 





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

 .. code-block:: none

    [ 810.3093 1961.8073 3286.6644 4534.5832 4894.6956 3744.1404 2527.8144]
    [34.1924 35.7665 34.2575 32.2367 33.6213 31.8762 29.0344]
    [12766.91 +8673.923j 18348.04+13216.7j   22777.28+15512.85j
     25780.15+16257.67j  24827.42+16508.58j  20849.86+12965.86j
     16608.03 +9219.031j]




.. GENERATED FROM PYTHON SOURCE LINES 36-40

Compute resistivity and phase from filters 
* base smooth filter with `method` set to ``base`` ( default)
* Torres-Verdin filter  with `method` set to ``tv``
* Adaptive moving average  filter with `method` set to ``ama` 

.. GENERATED FROM PYTHON SOURCE LINES 40-48

.. code-block:: Python


    res_b, phase_b = filter_noises (
        p, component='xy', return_z= False, )
    res_t, phase_t = filter_noises (
        p, component='xy', return_z= False, method ='torres')
    res_a, phase_a = filter_noises (
        p, component='xy', return_z= False, method ='ama')








.. GENERATED FROM PYTHON SOURCE LINES 49-50

Plot filtered data at station S00 

.. GENERATED FROM PYTHON SOURCE LINES 50-80

.. code-block:: Python


    fig, ax = plt.subplots(2,1, figsize =(10, 6))
    ax[0].plot(p.freqs_, p.ediObjs_[station_index].Z.resistivity[:, 0, 1],
                   'b-', label='Original Data')
    ax[0].plot(p.freqs_, res_b[:, station_index], '-ok', 
                   label='Smoothed Resistivity')
    ax[0].plot(p.freqs_, res_t[:, station_index], '-sg', 
                   label='Torres-Verdin Resistivity')
    ax[0].plot(p.freqs_, res_a[:, station_index], '-vr', 
                   label='AMA Resistivity ')
    ax[0].set_xlabel('Frequency (Hz)')
    ax[0].set_ylabel('Resistivity( $\Omega$.m)')
    ax[0].set_title(f'Filtered data at Station S{station_index}')
    ax[0].set_yscale ('log') 
    ax[0].set_xscale ('log')
    ax[0].legend()
    ax[0].grid(True)
    ax[1].plot(p.freqs_, p.ediObjs_[station_index].Z.phase[:, 0, 1], 
                   'b-', label='Original Phase Data')
    ax[1].plot(p.freqs_, phase_b[:, station_index], '-ok', 
                   label='Smoothed Phase')
    ax[1].plot(p.freqs_, phase_t[:, station_index], '-sg', 
                   label='Torres-Verdin phase')
    ax[1].plot(p.freqs_, phase_a[:, station_index], '-vr', 
                   label='AMA phase')
    ax[1].set_xlabel('Frequency (Hz)')
    ax[1].set_ylabel('phase( degrees)')
    ax[1].set_xscale ('log')
    ax[1].legend()
    ax[1].grid(True)
    plt.show()


.. image-sg:: /glr_examples/methods/images/sphx_glr_plot_noise_removal_001.png
   :alt: Filtered data at Station S4
   :srcset: /glr_examples/methods/images/sphx_glr_plot_noise_removal_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.587 seconds)


.. _sphx_glr_download_glr_examples_methods_plot_noise_removal.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/watex/watex/0.3.X?urlpath=lab/tree/notebooks/glr_examples/methods/plot_noise_removal.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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