watex.datasets.make_ves#

watex.datasets.make_ves(*, samples=31, min_rho=10.0, max_rho=1000.0, max_depth=100.0, order='-', as_frame=False, seed=None, iorder=3)[source]#

Generate Vertical Electrical Sounding (VES) data from pseudo-depth measurements.

For a large pseudo-depth measurements, one can change the number of samples to a large values. The default samples presumed collected is samples=31 measurements in deeper.

Parameters:

  • samples (int, default=42) – number of measurements depth AB/2 in meters.

  • max_rho (float, default=1e3) – maximum resistivity value expected in deeeper on the survey area in \(\\Omega.m\)

  • min_rho (float, default=1e1) – minimum resistivity value expected in deeper on the survey area in \(\\Omega.m\)

  • order (str , default='-') – Direction of the projection line. By default the projected line is in ascending order i.e. from SW to NE with angle r set to 45 degrees. Could be - for descending order. Any other value should be in ascending order.

  • max_depth (float, default=100) – Value of the measurement in deeper expected to reach by AB/2 in meters.

  • as_frame (bool, default=False,) – if True, outputs the data into as a pandas dataframe, Boxspace object otherwise.

  • seed (int, Optional,) – It allows reproducing the same data. If value is passed, it reproduces the same data at that sample points.

  • iorder (float, default=3) – Inflexion order. If None should compute using the length of extrema (local + global). Must be lower as possible to let the fitting VES curve more realistic.

Return type:

(pd.Dataframe | Boxspace )

Notes

when returning the Boxspace object, each columns of ‘VES’ data can be retrieved as an attributes. Check the examples below

Examples

>>> from watex.datasets.gdata import make_ves
>>> b = make_ves (samples =50 , order ='+') # 50 measurements in deeper
>>> b.resistivity [:-7]
Out[314]:
array([429.873 , 434.255 , 438.5707, 442.8203, 447.0042, 451.1228,
       457.5775])
>>> b.frame.head(3)
Out[315]:
    AB   MN  resistivity
0  1.0  0.6   429.872999
1  2.0  0.6   434.255018
2  3.0  0.6   438.570675