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.. "glr_examples/applications/plot_nsamt_tensor_recovery.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_applications_plot_nsamt_tensor_recovery.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_applications_plot_nsamt_tensor_recovery.py:


============================
NSAMT tensors recovery 
============================

Recovers NSAMT tensors from sample of EDI files. 

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.. code-block:: default

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








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Tensor recovery is necessary when dealing with 
NSAMT data. The code below is an example to recover the weak and missing 
frequency signal using .em processing methods. The tensor recovery and the 
data quality control are ensured by the methods :meth:`~watex.methods.Processing.zrestore` and 
:meth:`~watex.methods.Processing.qc` respectively. The :mod:`~watex.view.TPlot`
module from .view is used for the visualization. For a demonstration,
I collect twelve samples of EDI objects stored in the software as:

.. GENERATED FROM PYTHON SOURCE LINES 19-25

.. code-block:: default

    from watex.datasets import load_edis 
    from watex.methods import Processing
    from watex.view import TPlot
    edi_data = load_edis (return_data =True, samples =12 ) 
    new_Z =Processing().fit(edi_data ).zrestore ()








.. GENERATED FROM PYTHON SOURCE LINES 26-34

The method :meth:`~watex.methods.Processing.exportedis` can be used to export 
the new tensor (new_Z) ready for modeling. 
In the example below (umcommented), I use raw non-preprocessed EDI data 
as ``raw_data`` that includes missing tensor 
and weak frequency signals. The complete case data history data can be 
available upon request. Thus the recovered resistivity tensor from 
randomly sites E12 and E27 can be visualized by feeding the “raw_data”  
to the fit  method  of :mod:`~watex.view.TPlot` as follow:

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.. code-block:: default

    
    # >>> TPlot().fit(<<raw_data>>).plot_multiple_recovery (sites =['E12', 'E27'])







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Refer to :doc:`EM method <methods.html#em-em>` for the output 

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After recovering the signal, the latter exhibits a field strength amplitude for 
the next processing step like filtering. A simple filtering like adaptative moving average 
(AMA) proposed by Torres-verdìn and Bostick, (1992) can be used by simply calling: 

.. GENERATED FROM PYTHON SOURCE LINES 43-47

.. code-block:: default

    edi_corrected =Processing (window_size =5, c =2 ).fit(edi_data ).ama () 
    # where 'c' is a window-width expansion factor inputted to the filter adaptation process to control 
    # the roll-off characteristics of the Hanning window (Torres-verdìn and Bostick, 1992).








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Note that, like all the :mod:`~watex.view` plotting classes, :class:`~watex.view.TPlot` inherits from a global 
abstract base class parameters  :class:`~watex.utils.box.BasePlot`. Thus, each plot 
can flexibly be customized according 
to the user's desire. For instance, to visualize the corrected 2D tensors, one 
can customize its plot as: 

.. GENERATED FROM PYTHON SOURCE LINES 53-60

.. code-block:: default

    
    plot_kws = dict(
    ylabel = '$Log_{10}Frequency [Hz]$', 
    xlabel = '$Distance(m)$', 
    cb_label = '$Log_{10}Rhoa[\Omega.m]$', 
    fig_size =(6, 3), 
    font_size =7) 







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Let visualize the raw-tensor and compared to the filtered tensors

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.. code-block:: default


    TPlot(**plot_kws).fit(edi_data).plot_tensor2d(to_log10 =True)




.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_001.png
   :alt: plot nsamt tensor recovery
   :srcset: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <AxesSubplot:xlabel='$Distance(m)$', ylabel='$Log_{10}Frequency [Hz]$'>



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Let visualize the filtered tensors pass to parameter `ffilter`: 

* Triming moving average (TMA) ( ``tma`` is the default filter)

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.. code-block:: default


    TPlot(**plot_kws ).fit(edi_data). plot_ctensor2d (to_log10=True)



.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_002.png
   :alt: plot nsamt tensor recovery
   :srcset: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <AxesSubplot:xlabel='$Distance(m)$', ylabel='$Log_{10}Frequency [Hz]$'>



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* Fixed-length-dipole (FLMA) 

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.. code-block:: default


    TPlot(**plot_kws ).fit(edi_data).plot_ctensor2d(to_log10 =True, ffilter ='flma')




.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_003.png
   :alt: plot nsamt tensor recovery
   :srcset: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <AxesSubplot:xlabel='$Distance(m)$', ylabel='$Log_{10}Frequency [Hz]$'>



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* Fixed-length-dipole (FLMA) 

.. GENERATED FROM PYTHON SOURCE LINES 78-80

.. code-block:: default



    TPlot(**plot_kws ).fit(edi_data).plot_ctensor2d(to_log10 =True, ffilter ='ama')


.. image-sg:: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_004.png
   :alt: plot nsamt tensor recovery
   :srcset: /glr_examples/applications/images/sphx_glr_plot_nsamt_tensor_recovery_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <AxesSubplot:xlabel='$Distance(m)$', ylabel='$Log_{10}Frequency [Hz]$'>




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

   **Total running time of the script:** ( 0 minutes  3.374 seconds)


.. _sphx_glr_download_glr_examples_applications_plot_nsamt_tensor_recovery.py:

.. only:: html

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


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        :alt: Launch binder
        :width: 150 px



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

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