pyPDAF.PDAF.omi_weights_l

pyPDAF.PDAF.omi_weights_l()

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

Parameters:

  • verbose (int) – Verbosity flag

  • locweight (int) –

    Types of localization function:

    1. unit weight;

    2. exponential;

    3. 5-th order polynomial;

    4. 5-th order polynomial with regulatioin using mean variance;

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

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

    Vector of localization cut-off radii; observation weight=0 if distance > cradius

    The array dimension nobs_l is Number of local observations

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

    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].

    The array dimension nobs_l is Number of local observations

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

    input matrix

    The 1st-th dimension nobs_l is Number of local observations The 2nd-th dimension ncols is the 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]