watex.methods.erp.DCMagic.summary#

DCMagic.summary(*, coerce=False, force=False, return_table=True, keep_params=False, like=Ellipsis)[source]#

Retrieve sites details and aggregate the table to compose unique DC features.

Parameters

  • coerce (bool, default=True) – If coordinates data of sites are missing in a profile/site, setting coerce to True will use the Electrical Resistivity Profiling coordinates to fit each Vertical Electrical Sounding sites by default or vice-versa. To avoid an unexpected behavior, it is strongly recommended to provide the same sounding point coordinates used for the expecting drilling point passed in attribute sves_ in term:DC profiles.

  • force (bool, default=False) – In principle, number of profiles should be equals to number of sites where the drilling operations is perfomed. Force allows to aggregate the dataframe even this condition is not met, otherwise, an error raises.

  • return_table (bool, default=True,) – Returns DC-features in a pandas dataframe rather than DCMagic object.

  • keep_params (bool, default=False,) – If True , keeps only the predicted parameters in the summary table, otherwise returns all main DC-resistivity details of the site.

  • like (str, Optional) –

    Can be [‘ERP’ | ‘VES’]. When one of DC-methods such as VES or ERP is not supplied, summary method of DCMagic returns an DCError because DCMagic expects each sounding point to have its profiling data with expected drilling point coordinates ( passed in attributes sves_) explicity specified . However to constraint the DCMagic works like DCSounding or DCProfiling in order to return the table of VES or ERP, the parameter like can be turn to ERP or VES.

    Changed in version 0.2.2: Deprecated parameter work_as. like parameter operates simmilary as work_as did.

Returns

self or table_ – Returns DCMagic object or DataFrame of sites details.

Return type

DCMagic or DataFrame

Examples

>>> import watex as wx
>>> data = wx.make_erp (seed =42 , n_stations =12, as_frame =True )
>>> ro= wx.DCProfiling ().fit(data)
>>> ro.summary()
       dipole   longitude  latitude  ...  shape  type       sfi
line1      10  110.486111  26.05174  ...      C    EC  1.141844
>>> data_no_xy = wx.make_ves ( seed=0 , as_frame =True)
>>> vo = wx.methods.VerticalSounding (
    xycoords = (110.486111,   26.05174)).fit(data_no_xy).summary()
>>> vo.table_
         AB    MN   arrangememt  ... nareas   longitude  latitude
area                             ...
None  200.0  20.0  schlumberger  ...      1  110.486111  26.05174
>>> dm = wx.methods.DCMagic ().fit(vo, ro )
>>> dm.summary ()
   dipole  longitude  latitude  ...  max_depth  ohmic_area  nareas
0      10  110.48611  26.05174  ...      109.0  690.063003       1
>>> dm.summary (keep_params =True )
   longitude  latitude shape  ...       sfi  sves_resistivity  ohmic_area
0  110.48611  26.05174     C  ...  1.141844               1.0  690.063003
>>> list( dm.table_.columns )
['longitude',
 'latitude',
 'shape',
 'type',
 'magnitude',
 'power',
 'sfi',
 'sves_resistivity',
 'ohmic_area']