watex.methods.EMAP#

class watex.methods.EMAP(window_size=5, component='xy', mode='same', method='slinear', out='srho', c=2, **kws)[source]#

Base processing of EMAP data.

Fast process of EMAP ( for short periods). Tools are used for data sanitizing, removing noises and filtering.

Parameters

data (Path-like object or list of :class:watex.edi.Edi` or pycsamt.core.edi.Edi objects) – Collections of EDI-objects
freqs (array-like, shape (N)) – Frequency array. It should be the complete frequency used during the survey area. It can be get using the :func:`getfullfrequency ` No need if ediObjs is provided.
window_size (int) – the length of the window. Must be greater than 1 and preferably an odd integer number. Default is 5
component (str) – field tensors direction. It can be xx, xy,``yx``, yy. If arr2d` is provided, no need to give an argument. It become useful when a collection of EDI-objects is provided. If don’t specify, the resistivity and phase value at component xy should be fetched for correction by default. Change the component value to get the appropriate data for correction. Default is xy.
mode (str) – mode of the border trimming. Should be ‘valid’ or ‘same’.’valid’ is used for regular trimimg whereas the ‘same’ is used for appending the first and last value of resistivity. Any other argument except ‘valid’ should be considered as ‘same’ argument. Default is same.
method (str, default slinear) – Interpolation technique to use. Can also be nearest. Refer to the documentation of interpolate2d().
out (str) – Value to export. Can be sfactor, tensor for corrections factor and impedance tensor. Any other values will export the static corrected resistivity.
c (int,) – A window-width expansion factor that must be input to the filter adaptation process to control the roll-off characteristics of the applied Hanning window. It is recommended to select c between 1 and 4. Default is 2.

Examples

>>> import matplotlib.pyplot as plt
>>> from watex.methods.em import Processing
>>> edipath = 'data/edis'
>>> p = Processing().fit(edipath)
>>> p.window_size =2
>>> p.component ='yx'
>>> rc= p.tma()
>>> # get the resistivy value of the third frequency  at all stations
>>> p.res2d_[3, :]
... array([ 447.05423001, 1016.54352954, 1415.90992189,  536.54293994,
       1307.84456036,   65.44806698,   86.66817791,  241.76592273,
       ...
        248.29077039,  247.71452712,   17.03888414])
>>>  # get the resistivity value corrected at the third frequency
>>> rc [3, :]
... array([ 447.05423001,  763.92416768,  929.33837349,  881.49992091,
        404.93382163,  190.58264151,  160.71917654,  163.30034875,
        394.2727092 ,  679.71542811,  953.2796567 , 1212.42883944,
        ...
        164.58282866,   96.60082159,   17.03888414])
>>> plt.semilogy (np.arange (p.res2d_.shape[1] ), p.res2d_[3, :], '--',
                  np.arange (p.res2d_.shape[1] ), rc[3, :], 'ok--')

References

1: http://www.zonge.com/legacy/PDF_DatPro/Astatic.pdf