#!/usr/bin/env python
import sys
import json
import h5py
import numpy as np
import filterfcn
import regularization

def main():
  try:
    f = open('input/usrparam.json')
  except IOError:
    raise ImportError(
      'must have a json file named "usrparam.json" within the input ')
  param = json.load(f)
  f.close()

  try:
    from usrfcn import slip_basis
  except ImportError:
    raise ImportError(
      'must have a module named "usrfcn.py" within the input '
      'directory and it must contain the function slip_basis')

  try:
    from usrfcn import fluidity_basis
  except ImportError:
    raise ImportError(
      'must have a module named usrfcn.py within the input '
      'directory and it must contain the function fluidity_basis')

  try:
    f = h5py.File('input/greens_functions.h5','r')
  except IOError:
    raise IOError(
      'must have a h5py file named greens_functions.h5 within the '
      'input directory')

  try:
    F = f['elastic'][...]
    G = f['viscoelastic'][...]
    x = f['metadata/position'][...]
  except KeyError:
    raise KeyError

  f.close()
  try:
    f = h5py.File('input/data.h5','r')
  except IOError:
    raise IOError(
      'must have a h5py file named data.h5 within the input directory')

  alpha = 1.0
  u = f['data'][...]
  Cd = f['covariance'][...]
  x2 = f['metadata/position'][...]
  t = f['metadata/time'][...]

  # check for consistency between input
  Ns,Ds,Nv,Nx,Dx = np.shape(G)
  Nt = len(t)
  assert np.all(np.isclose(x,x2))
  assert np.shape(u) == (Nt,Nx,Dx)
  assert np.shape(Cd) == (Nt,Nx,Dx,Dx)
  assert np.shape(F) == (Ns,Ds,Nx,Dx)
  assert np.shape(G) == (Ns,Ds,Nv,Nx,Dx)

  p = filterfcn.state_parser(Ns,Ds,Nv,Nx,Dx)

  Xprior,Cprior = regularization.priors(p)

  reg = regularization.regularization_matrix(param,p)

  kalman = inverse.KalmanFilter(Xprior,Xprior,
                                filterfcn.transition,
                                filterfcn.observation,
                                filterfcn.process_covariance)
  for i in range(Nt):
    flat_ui = filterfcn.flat_data(u[i,...])
    flat_Cdi = filterfcn.flat_covariance(Cd[i,...])
    kalman.update(flat_ui,flat_Cdi,
                  observation_args=(t[i],F,G,p),
                  solver_kwargs={'regularization':reg})
    if i != (Nt - 1):
      kalman.predict(transition_args=(t[i+1],t[i],p),
                     process_covariance_args=(t[i+1],t[i],alpha,p))   

  kalman.smooth()
  return



ae80dc6267b3df9b6e72d3ad8ac700fd402a0670
<mask token>

<mask token>


def main():
    try:
        f = open('input/usrparam.json')
    except IOError:
        raise ImportError(
            'must have a json file named "usrparam.json" within the input ')
    param = json.load(f)
    f.close()
    try:
        from usrfcn import slip_basis
    except ImportError:
        raise ImportError(
            'must have a module named "usrfcn.py" within the input directory and it must contain the function slip_basis'
            )
    try:
        from usrfcn import fluidity_basis
    except ImportError:
        raise ImportError(
            'must have a module named usrfcn.py within the input directory and it must contain the function fluidity_basis'
            )
    try:
        f = h5py.File('input/greens_functions.h5', 'r')
    except IOError:
        raise IOError(
            'must have a h5py file named greens_functions.h5 within the input directory'
            )
    try:
        F = f['elastic'][...]
        G = f['viscoelastic'][...]
        x = f['metadata/position'][...]
    except KeyError:
        raise KeyError
    f.close()
    try:
        f = h5py.File('input/data.h5', 'r')
    except IOError:
        raise IOError(
            'must have a h5py file named data.h5 within the input directory')
    alpha = 1.0
    u = f['data'][...]
    Cd = f['covariance'][...]
    x2 = f['metadata/position'][...]
    t = f['metadata/time'][...]
    Ns, Ds, Nv, Nx, Dx = np.shape(G)
    Nt = len(t)
    assert np.all(np.isclose(x, x2))
    assert np.shape(u) == (Nt, Nx, Dx)
    assert np.shape(Cd) == (Nt, Nx, Dx, Dx)
    assert np.shape(F) == (Ns, Ds, Nx, Dx)
    assert np.shape(G) == (Ns, Ds, Nv, Nx, Dx)
    p = filterfcn.state_parser(Ns, Ds, Nv, Nx, Dx)
    Xprior, Cprior = regularization.priors(p)
    reg = regularization.regularization_matrix(param, p)
    kalman = inverse.KalmanFilter(Xprior, Xprior, filterfcn.transition,
        filterfcn.observation, filterfcn.process_covariance)
    for i in range(Nt):
        flat_ui = filterfcn.flat_data(u[i, ...])
        flat_Cdi = filterfcn.flat_covariance(Cd[i, ...])
        kalman.update(flat_ui, flat_Cdi, observation_args=(t[i], F, G, p),
            solver_kwargs={'regularization': reg})
        if i != Nt - 1:
            kalman.predict(transition_args=(t[i + 1], t[i], p),
                process_covariance_args=(t[i + 1], t[i], alpha, p))
    kalman.smooth()
    return

import sys
import json
import h5py
import numpy as np
import filterfcn
import regularization


def main():
    try:
        f = open('input/usrparam.json')
    except IOError:
        raise ImportError(
            'must have a json file named "usrparam.json" within the input ')
    param = json.load(f)
    f.close()
    try:
        from usrfcn import slip_basis
    except ImportError:
        raise ImportError(
            'must have a module named "usrfcn.py" within the input directory and it must contain the function slip_basis'
            )
    try:
        from usrfcn import fluidity_basis
    except ImportError:
        raise ImportError(
            'must have a module named usrfcn.py within the input directory and it must contain the function fluidity_basis'
            )
    try:
        f = h5py.File('input/greens_functions.h5', 'r')
    except IOError:
        raise IOError(
            'must have a h5py file named greens_functions.h5 within the input directory'
            )
    try:
        F = f['elastic'][...]
        G = f['viscoelastic'][...]
        x = f['metadata/position'][...]
    except KeyError:
        raise KeyError
    f.close()
    try:
        f = h5py.File('input/data.h5', 'r')
    except IOError:
        raise IOError(
            'must have a h5py file named data.h5 within the input directory')
    alpha = 1.0
    u = f['data'][...]
    Cd = f['covariance'][...]
    x2 = f['metadata/position'][...]
    t = f['metadata/time'][...]
    Ns, Ds, Nv, Nx, Dx = np.shape(G)
    Nt = len(t)
    assert np.all(np.isclose(x, x2))
    assert np.shape(u) == (Nt, Nx, Dx)
    assert np.shape(Cd) == (Nt, Nx, Dx, Dx)
    assert np.shape(F) == (Ns, Ds, Nx, Dx)
    assert np.shape(G) == (Ns, Ds, Nv, Nx, Dx)
    p = filterfcn.state_parser(Ns, Ds, Nv, Nx, Dx)
    Xprior, Cprior = regularization.priors(p)
    reg = regularization.regularization_matrix(param, p)
    kalman = inverse.KalmanFilter(Xprior, Xprior, filterfcn.transition,
        filterfcn.observation, filterfcn.process_covariance)
    for i in range(Nt):
        flat_ui = filterfcn.flat_data(u[i, ...])
        flat_Cdi = filterfcn.flat_covariance(Cd[i, ...])
        kalman.update(flat_ui, flat_Cdi, observation_args=(t[i], F, G, p),
            solver_kwargs={'regularization': reg})
        if i != Nt - 1:
            kalman.predict(transition_args=(t[i + 1], t[i], p),
                process_covariance_args=(t[i + 1], t[i], alpha, p))
    kalman.smooth()
    return

#!/usr/bin/env python
import sys
import json
import h5py
import numpy as np
import filterfcn
import regularization

def main():
  try:
    f = open('input/usrparam.json')
  except IOError:
    raise ImportError(
      'must have a json file named "usrparam.json" within the input ')
  param = json.load(f)
  f.close()

  try:
    from usrfcn import slip_basis
  except ImportError:
    raise ImportError(
      'must have a module named "usrfcn.py" within the input '
      'directory and it must contain the function slip_basis')

  try:
    from usrfcn import fluidity_basis
  except ImportError:
    raise ImportError(
      'must have a module named usrfcn.py within the input '
      'directory and it must contain the function fluidity_basis')

  try:
    f = h5py.File('input/greens_functions.h5','r')
  except IOError:
    raise IOError(
      'must have a h5py file named greens_functions.h5 within the '
      'input directory')

  try:
    F = f['elastic'][...]
    G = f['viscoelastic'][...]
    x = f['metadata/position'][...]
  except KeyError:
    raise KeyError

  f.close()
  try:
    f = h5py.File('input/data.h5','r')
  except IOError:
    raise IOError(
      'must have a h5py file named data.h5 within the input directory')

  alpha = 1.0
  u = f['data'][...]
  Cd = f['covariance'][...]
  x2 = f['metadata/position'][...]
  t = f['metadata/time'][...]

  # check for consistency between input
  Ns,Ds,Nv,Nx,Dx = np.shape(G)
  Nt = len(t)
  assert np.all(np.isclose(x,x2))
  assert np.shape(u) == (Nt,Nx,Dx)
  assert np.shape(Cd) == (Nt,Nx,Dx,Dx)
  assert np.shape(F) == (Ns,Ds,Nx,Dx)
  assert np.shape(G) == (Ns,Ds,Nv,Nx,Dx)

  p = filterfcn.state_parser(Ns,Ds,Nv,Nx,Dx)

  Xprior,Cprior = regularization.priors(p)

  reg = regularization.regularization_matrix(param,p)

  kalman = inverse.KalmanFilter(Xprior,Xprior,
                                filterfcn.transition,
                                filterfcn.observation,
                                filterfcn.process_covariance)
  for i in range(Nt):
    flat_ui = filterfcn.flat_data(u[i,...])
    flat_Cdi = filterfcn.flat_covariance(Cd[i,...])
    kalman.update(flat_ui,flat_Cdi,
                  observation_args=(t[i],F,G,p),
                  solver_kwargs={'regularization':reg})
    if i != (Nt - 1):
      kalman.predict(transition_args=(t[i+1],t[i],p),
                     process_covariance_args=(t[i+1],t[i],alpha,p))   

  kalman.smooth()
  return


