watex.utils.scalePosition#

watex.utils.scalePosition(ydata, xdata=None, func=None, c_order=0, show=False, **kws)[source]#

Correct data location or position and return new corrected location

Parameters:

  • ydata (array_like, series or dataframe) – The dependent data, a length M array - nominally f(xdata, ...).

  • xdata (array_like or object) – The independent variable where the data is measured. Should usually be an M-length sequence or an (k,M)-shaped array for functions with k predictors, but can actually be any object. If None, xdata is generated by default using the length of the given ydata.

  • func (callable) – The model function, f(x, ...). It must take the independent variable as the first argument and the parameters to fit as separate remaining arguments. The default func is linear function i.e for f(x)= ax +b. where a is slope and b is the intercept value. Setting your own function for better fitting is recommended.

  • c_order (int or str) – The index or the column name if ydata is given as a dataframe to select the right column for scaling.

  • show (bool) – Quick visualization of data distribution.

  • kws (dict) – Additional keyword argument from scipy.optimize_curvefit parameters. Refer to scipy.optimize.curve_fit.

Returns:

  • - ydata - array -like - Data scaled

  • - popt - array-like Optimal values for the parameters so that the sum of

  • the squared residuals of f(xdata, \*popt) - ydata is minimized.

  • - pcov - array like The estimated covariance of popt. The diagonals provide

  • the variance of the parameter estimate. To compute one standard deviation

  • errors on the parameters use perr = np.sqrt(np.diag(pcov)). How the

  • sigma parameter affects the estimated covariance depends on absolute_sigma

  • argument, as described above. If the Jacobian matrix at the solution

  • doesn’t have a full rank, then ‘lm’ method returns a matrix filled with

  • np.inf, on the other hand ‘trf’ and ‘dogbox’ methods use Moore-Penrose

  • pseudoinverse to compute the covariance matrix.

Examples

>>> from watex.utils import erpSelector, scalePosition
>>> df = erpSelector('data/erp/l10_gbalo.xlsx')
>>> df.columns
... Index(['station', 'resistivity', 'longitude', 'latitude', 'easting',
'northing'],
dtype='object')
>>> # correcting northing coordinates from easting data
>>> northing_corrected, popt, pcov = scalePosition(ydata =df.northing ,
xdata = df.easting, show=True)
>>> len(df.northing.values) , len(northing_corrected)
... (20, 20)
>>> popt  # by default popt =(slope:a ,intercept: b)
...  array([1.01151734e+00, 2.93731377e+05])
>>> # corrected easting coordinates using the default x.
>>> easting_corrected, *_= scalePosition(ydata =df.easting , show=True)
>>> df.easting.values
... array([790284, 790281, 790277, 790270, 790265, 790260, 790254, 790248,
...       790243, 790237, 790231, 790224, 790218, 790211, 790206, 790200,
...       790194, 790187, 790181, 790175], dtype=int64)
>>> easting_corrected
... array([790288.18571705, 790282.30300999, 790276.42030293, 790270.53759587,
...       790264.6548888 , 790258.77218174, 790252.88947468, 790247.00676762,
...       790241.12406056, 790235.2413535 , 790229.35864644, 790223.47593938,
...       790217.59323232, 790211.71052526, 790205.8278182 , 790199.94511114,
...       790194.06240407, 790188.17969701, 790182.29698995, 790176.41428289])
.. _Bagoue region: https://en.wikipedia.org/wiki/Bagou%C3%A9

. _Cote d’Ivoire: https://en.wikipedia.org/wiki/Ivory_Coast