lts_classes

Documentation for the data processing classes. These classes are used internally by ltsva.

class lts_array.classes.lts_classes.OLSEstimator(data)[source]

Class for ordinary least squares beamforming.

calculate_co_array(data)

Calculate the co-array coordinates (x, y) for the array.

correlate(data)

Cross correlate the time series data.

solve(data)[source]

Calculate trace velocity, back-azimuth, MdCCM, and confidence intervals.

Parameters:data (DataBin) – The DataBin object.
class lts_array.classes.lts_classes.LTSEstimator(data)[source]

Class for least trimmed squares (LTS) beamforming.

calculate_co_array(data)

Calculate the co-array coordinates (x, y) for the array.

correlate(data)

Cross correlate the time series data.

h_calc(data)[source]

Generate the h-value, the number of points to fit.

Parameters:
  • ALPHA (float) – The decimal percentage of points to keep. Default is 0.75.
  • n (int) – The total number of points.
  • p (int) – The number of parameters.
Returns:

h: The number of points to fit.
Return type:

(int)
solve(data)[source]

Apply the FAST_LTS algorithm to calculate a least trimmed squares solution for trace velocity, back-azimuth, MdCCM, and confidence intervals.

Parameters:data (DataBin) – The DataBin object.