watex.datasets.load_gbalo#

watex.datasets.load_gbalo(*, kind='erp', as_frame=True, index_rhoa=0, tag=None, data_names=None, **kws)[source]#

Load Gbalo ERP and VES datasets.

The dataset is composed of DC-Electrical resistivity profiling (ERP) and Vertical sounding (VES) data collected from Gbalo locality. To change the kind of dataset, pass erp or ves to parameter key. The data is collected during the National Drinking Water Supply Program (PNAEP) occurs in 2012-2014 in Cote d’Ivoire. Refer to erp_doc and ves_doc for the data arrangement illustration.

Parameters:

kind (str , ['ves'|'erp'], default is {'erp'}) – the kind of DC data to retrieve. If kind`is set to ``ves` and VES data is fetched and ERP otherwise.
as_frame (bool, default=False) –
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If as_frame is False, then returning a Boxspace dictionary-like object, with the following attributes: data : {ndarray, dataframe} of shape (33, 6) and (45, 4) for VES and ERP

The data matrix. If as_frame=True, data will be a pandas DataFrame.

resistivity: {array-like} of shape (33,) and (45,) for VES and ERP
The resistivity of the sounding point.

station: {array-like} of shape (33,) and (45,) for VES and ERP
The motion distance of each station that increasing in meters. can be considered as the station point for data collection.

northing: {array-like} of shape (33,) and (45,) for VES and ERP
The northing coordinates in UTM in meters at each station where the data is collected.

easting: {array-like} of shape (33,) and (45,) for VES and ERP
The easting coordinates in UTM in meters at each station where the data is collected.

latitude: {array-like} of shape (33,) and (45,) for VES and ERP
The latitude coordinates in degree decimals or ‘DD:MM.SS’ at each station where the data is collected.

longitude: {array-like} of shape (33,) and (45,) for VES and ERP
The longitude coordinates in degree decimals or ‘DD:MM.SS’ at each station where the data is collected.

DESCR: str
The full description of the dataset.

filename: str
The path to the location of the data.
(tag (None) – Always None for API consistency
data_names) (None) – Always None for API consistency
kws (dict,) – Keywords arguments pass to _is_readable() function for parsing data.

Returns:

data – Dictionary-like object, with the following attributes. data : {ndarray, dataframe}

The data matrix. If as_frame=True, data will be a pandas DataFrame.

Return type:

Boxspace

Example

>>> from watex.datasets import load_gbalo
>>> b= load_gbalo (as_frame =False , kind ='erp')
>>> b.station  # retreive the station position
... array([  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100.,
       110., 120., 130., 140., 150., 160., 170., 180., 190., 200., 210.,
       220., 230., 240., 250., 260., 270., 280., 290., 300., 310., 320.,
       330., 340., 350., 360., 370., 380., 390., 400., 410., 420., 430.,
       440.])

Notes

The array configuration is schlumberger and the max depth investigation is 100 meters for \(AB/2\) (current electrodes). The profiling step \(AB\) is fixed to 100 meters whereas \(MN/2\) also fixed to (potential electrodes) to 10 meters. The total number of station data collected is 45 while the sounding points is estimated to 33. station , easting and northing are in meters and rho columns are in ohm.meters as apparent resistivity values. Furthermore, the total number of soundings performed with the prefix ‘SE’ is 4.