pyPDAF.PDAF.omi_weights_l_sgnl

pyPDAF.PDAF.omi_weights_l_sgnl()

This function computes a weight vector according to the distances of observations from the local analysis domain with given localisation radius.

Parameters:

• verbose (int) – Verbosity flag

• locweight (int) –

  Types of localization function

  0. unit weight;

  1. exponential;

  2. 5-th order polynomial;

  3. 5-th order polynomial with regulatioin using mean variance;

  4. 5-th order polynomial with regulatioin using variance of single observation point;

• cradius (float) – Vector of localization cut-off radii; observation weight=0 if distance > cradius

• sradius (float) – Vector of support radii of localization function. It has no impact if locweight=0; weight = exp(-d / sradius) if locweight=1; weight = 0 if d >= sradius else f(sradius, distance) if locweight in [2,3,4].

• matA (ndarray[tuple[nobs_l, ncols], np.float64]) –

  input matrix

  The1st-th dimension nobs_l is Number of local observations; the2nd-th dimension ncols is number of columns.

• ivar_obs_l (ndarray[tuple[nobs_l], np.float64]) –

  Local vector of inverse obs. variances (nobs_l)

  The array dimension nobs_l is Number of local observations.

• dist_l (ndarray[tuple[nobs_l], np.float64]) –

  Local vector of obs. distances (nobs_l)

  The array dimension nobs_l is Number of local observations.

Returns:

weight_l – Output: vector of weights

The array dimension nobs_l is Number of local observations

Return type:

ndarray[tuple[nobs_l], np.float64]