watex.utils.get2dtensor#

watex.utils.get2dtensor(z_or_edis_obj_list, /, tensor='z', component='xy', kind='modulus', return_freqs=False, **kws)[source]#

Make tensor into two dimensional array from a collection of Impedance tensors Z.

Out 2D resistivity, phase-error and tensor matrix from a collection of EDI-objects.

Matrix depends of the number of frequency times number of sites. The function asserts whether all data from all frequencies are available. The missing values should be filled by NaN. Note that each element of z is (nfreq, 2, 2) dimension for:

xx ( 0, 0) ------- xy ( 0, 1)
yx ( 1, 0) ------- yy ( 1, 1)
Parameters:

  • z_or_edis_obj_list (list of watex.edi.Edi or watex.externals.z.Z) – A collection of EDI- or Impedances tensors objects.

  • tensor (str, default='z') – Tensor name. Can be [ resistivity|phase|z|frequency]

  • component (str, default='xy' (TE mode)) – EM mode. Can be [‘xx’, ‘xy’, ‘yx’, ‘yy’]

  • out (str) – kind of data to output. Be sure to provide the component to retrieve the attribute from the collection object. Except the error and frequency attribute, the missing component to the attribute will raise an error. for instance resxy for xy component. Default is resxy.

  • kind (str , default='modulus') – focuses on the tensor output. Note that the tensor is a complex number of ndarray (nfreq, 2,2 ). If set to``modulus`, the modulus of the complex tensor should be outputted. If real or``imag``, it returns only the specific one. Default is complex.

  • return_freqs (Arraylike ,) – If True , returns also the full frequency ranges.

  • kws (dict) – Additional keywords arguments from :meth:`~EM.getfullfrequency `.

Returns:

mat2d – the matrix of number of frequency and number of Edi-collectes which correspond to the number of the stations/sites.

Return type:

arraylike2d

Examples

>>> from watex.datasets import load_huayuan
>>> from watex.methods import get2dtensor
>>> box= load_huayuan ( key ='raw', clear_cache = True, samples =7)
>>> data = box.data
>>> phase_yx = get2dtensor ( data, tensor ='phase', component ='yx')
>>> phase_yx.shape
(56, 7)
>>> phase_yx [0, :]
array([        nan,         nan,         nan,         nan, 18.73244951,
       35.00516522, 59.91093054])
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