watex.property.BagoueNotes

class watex.property.BagoueNotes

A contest class about the Bagoue dataset.

The dataset comes from Bagoue region, located in WestAfrica and lies between longitudes 6° and 7° W and latitudes 9° and 11° N in the north of Cote d’Ivoire.

The average FR observed in this area fluctuates between 1 and 3 m3/h. Refer to the link of case story paper in the repository docs to visualize the location map of the study area with the geographical distribution of the various boreholes in the region. The geophysical data and boreholesdata were collected from National Office of Drinking Water(ONEP) and West-Africa International Drilling Company (FORACO-CI) during the Presidential Emergency Program (PPU) in 2012-2013 and the National Drinking Water Supply Program (PNAEP) in 2014. The data are firstly composed of Electrical resistivity profile (ERP) data collected from geophysical survey lines with various arrays such as Schlumberger, gradient rectangle and Wenner \(\alpha\) or \(\beta\) and the Vertical electricalsounding (VES) data carried out on the selected anomalies. The configuration used during the ERP is Schlumberger with distance of \(AB = 200m \quad \text{and} \quad MN =20m\).

The class gives some details about the test dataset used throughout the `WATex`_ packages. It is a guidance for the user to get any details about the data preprocessed in order to wuick implement or testing the method. Some examples to fetching infos and data are illustrated below:

Examples

>>> from watex.datasets import fetch_data
>>> bag_records = fetch_data('original').get('DESCR')
... 'https://doi.org/10.5281/zenodo.5571534: bagoue-original'
>>> data_contests =fetch_data('original').get('dataset-contest')
... {'__documentation:': '`watex.property.BagoueNotes.__doc__`',
...     '__area': 'https://en.wikipedia.org/wiki/Ivory_Coast',
...     '__casehistory': 'https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR031623',
...     '__wikipages': 'https://github.com/WEgeophysics/watex/wiki',
...     '__citations': ('https://doi.org/10.1029/2021wr031623',
...      ' https://doi.org/10.5281/zenodo.5529368')}
>>> #-->  fetching X, y dat
>>> # get the list of tags before
>>> tags=fetch_data('original').get('tags')
... ('Bagoue original', ...,'Bagoue prepared sets', 'Bagoue untouched test sets')
>>> len(tags)
... 11
>>> --> fetch the preprocessing sets of data
>>> X, y = fetch_data('preprocessing')
>>> X.shape , y.shape
... ((344, 8), (344,))
>>> list(X.columns)
... ['power', 'magnitude', 'sfi', 'ohmS', 'lwi', 'shape', 'type', 'geol']
>>> X, y = fetch_data('prepared') # data are vectorized and onehotencoded
... ((344, 18), (344,))
>>> X, y = fetch_data('test sets')
>>> X.shape , y.shape
... ((87, 12), (87,))