watex.cases.features.GeoFeatures#

class watex.cases.features.GeoFeatures(**kws)[source]#

Features class. Deals with Electrical Resistivity profile (VES), Vertical electrical Sounding (VES), Geological (Geol) data and Borehole data(Boreh). Set all features values of differents investigation sites. Features class is composed of:

  • erp class get from watex.methods.erp.ERP_colection

  • geol obtained from watex.geology.geology.Geology

  • boreh get from watex.geology.geology.Borehole

Parameters

  • *features_fn* (str , Path_like) – File to geoelectical features files.

  • *ErpColObjs* (object) – Collection object from erp survey lines.

  • *vesObjs* (object,) – Collection object from vertical electrical sounding (VES) curves.

  • *geoObjs* (object,) – Collection object from geol class. See watex.geology.geology.Geology.

  • *boreholeObjs* (object) – Collection of boreholes of all investigation sites. Refer to watex.geology.geology.Borehole

Holds on others optionals infos in kwargs arguments:

Attributes

Type

Description

df

pd.core.DataFrame

Container of all features composed of featureLabels

site_ids

array_like

ID of each survey locations.

site_names

array_like

Survey locations names.

gFname

str

Filename of features_fn.

ErpColObjs

obj

ERP erp class object.

vesObjs

obj

VES ves class object.

geoObjs

obj

Geology geol class object.

borehObjs

obj

Borehole boreh class obj.

Notes

Be sure to not miss any coordinates files. Indeed, each selected anomaly should have a borehole performed at that place for supervising learing. That means, each selected anomaly referenced by location coordinates and id on erp must have it own ves, geol and boreh data. For furher details about classes object , please refer to the classes documentation aforementionned.

Examples

>>> from watex.cases.features import GeoFeatures
>>> data ='data/geodata/main.bagciv.data.csv'
>>> featObj =GeoFeatures().fit(data )
>>> featObj.id_
Out[114]:
array(['e0000001', 'e0000002', 'e0000003', 'e0000004', 'e0000005',
       'e0000006', 'e0000007'], dtype='<U8')
>>> featObj.site_names_
>>> featObj.site_names_[:7]
Out[115]: array(['b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7'], dtype=object)