# spacepy.data_assimilation.ensemble

class spacepy.data_assimilation.ensemble(ensembles=50)

  Ensemble-based data assimilation subroutines for the Radiation Belt Model

  `EnKF`(A, Psi, Inn, HAp) | analysis subroutine after code example in Evensen 2003 this will take the prepared matrices and calculate the analysis most efficiently, A will be returned
  `EnKF_oneobs`(A, Psi, Inn, HAp) | analysis subroutine for a single observations with the EnKF.
  `add_model_error`(model, A, PSDdata) | this routine will add a standard error to the ensemble states
  `add_model_error_obs`(model, A, Lobs, y) | this routine will add a standard error to the ensemble states
  `getHA`(model, Lobs, A) | compute HA provided L vector of observations and ensemble matrix A
  `getHAprime`(HA) | calculate ensemble perturbation of HA HA' = HA-HA_mean
  `getHPH`(Lobs, Pfxx) | compute HPH
  `getInnovation`(y, Psi, HA) | compute innovation ensemble D'
  `getperturb`(model, y) | compute perturbations of observational vector

  EnKF(A, Psi, Inn, HAp)

    analysis subroutine after code example in Evensen 2003 this will take the prepared matrices and calculate the analysis most efficiently, A will be returned

    Parameters:
      A
      Psi
      Inn
      HAp
    Returns:
      out
  EnKF_oneobs(A, Psi, Inn, HAp)

    analysis subroutine for a single observations with the EnKF. This is a special case.

    Parameters:
      A
      Psi
      Inn
      HAp
    Returns:
      out
  add_model_error(model, A, PSDdata)

    this routine will add a standard error to the ensemble states

    Parameters:
      model
      A
      PSDdata
    Returns:
      out
  add_model_error_obs(model, A, Lobs, y)

    this routine will add a standard error to the ensemble states

    Parameters:
      model
      A
      Lobs
      y
    Returns:
      out
  getHA(model, Lobs, A)

    compute HA provided L vector of observations and ensemble matrix A

    Parameters:
      model
      Lobs
      A
    Returns:
      out
  getHAprime(HA)

    calculate ensemble perturbation of HA HA’ = HA-HA_mean

    Parameters:
      HA
    Returns:
      out
  getHPH(Lobs, Pfxx)

    compute HPH

    Parameters:
      Lobs
      Pfxx
    Returns:
      out
  getInnovation(y, Psi, HA)

    compute innovation ensemble D’

    Parameters:
      y
      Psi
      HA
    Returns:
      out
  getperturb(model, y)

    compute perturbations of observational vector

    Parameters:
      model
      y
    Returns:
      out