sga

HydroErr.HydroErr.sga(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the Spectral Gradient Angle (SGA).

../_images/SGA.png

Range: -π/2 ≤ SID < π/2, closer to 0 is better.

Notes: The spectral gradient angle measures the angle between the two vectors in hyperspace. It indicates how well the shape of the two series match – not magnitude. SG is the gradient of the simulated or observed time series.

Parameters:
  • simulated_array (one dimensional ndarray) – An array of simulated data from the time series.
  • observed_array (one dimensional ndarray) – An array of observed data from the time series.
  • replace_nan (float, optional) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • replace_inf (float, optional) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • remove_neg (boolean, optional) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
  • remove_zero (boolean, optional) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
Returns:

The Spectral Gradient Angle.
Return type:

float

Examples

>>> import HydroErr as he
>>> import numpy as np

>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.sga(sim, obs)
0.26764286472739834

References

  • Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE, pp. 163-166.