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

Recovers NSAMT tensors from sample of EDI files. 
"""
# Author: L.Kouadio 
# Licence: BSD-3-clause

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

#%%
# 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:
    
# >>> TPlot().fit(<<raw_data>>).plot_multiple_recovery (sites =['E12', 'E27'])
#%%
# Refer to :doc:`EM method <methods.html#em-em>` for the output 
 
#%%
# 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: 
edi_corrected =EMAP (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).

# %%
# 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: 
    
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) 
#%%
# Let visualize the raw-tensor and compared to the filtered tensors

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

#%%
# Let visualize the filtered tensors pass to parameter `ffilter`: 
#
# * Triming moving average (TMA) ( ``tma`` is the default filter)

TPlot(**plot_kws ).fit(edi_data). plot_ctensor2d (to_log10=True)
#%%
# * Fixed-length-dipole (FLMA) 

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

#%%
# * Fixed-length-dipole (FLMA) 


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