<gh_stars>0
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""Created on Tue Jan 01 16:00:00 2019

@author: gsutanto
"""

import numpy as np
import os
import sys
import copy
import matplotlib.pyplot as plt
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../dmp_param/"))
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../dmp_base/"))
sys.path.append(
    os.path.join(os.path.dirname(__file__), "../../../dmp_discrete/"))
sys.path.append(
    os.path.join(os.path.dirname(__file__), "../../../cart_dmp/quat_dmp"))
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../utilities/"))
from TauSystem import *
from QuaternionDMPUnrollInitParams import *
from CanonicalSystemDiscrete import *
from QuaternionDMP import *
from utilities import *

plt.close("all")


def quat_dmp_multi_traj_training_test(
    dmp_home_dir_path="../../../../",
    canonical_order=2,
    time_reproduce_max=2.0,
    tau_reproduce=2.0,
    unroll_qtraj_save_dir_path="",
    unroll_qtraj_save_filename="",
    is_smoothing_training_traj_before_learning=False,
    percentage_padding=None,
    percentage_smoothing_points=None,
    smoothing_mode=None,
    smoothing_cutoff_frequency=None,
    unroll_learned_params_save_dir_path="",
    unroll_learned_params_save_filename="",
    is_plotting=False):
  task_servo_rate = 300.0
  dt = 1.0 / task_servo_rate
  model_size = 25
  tau = MIN_TAU

  assert ((canonical_order >= 1) and (canonical_order <= 2))
  assert (time_reproduce_max >= 0.0)
  assert (tau_reproduce >= 0.0)

  tau_sys = TauSystem(dt, tau)
  canonical_sys_discr = CanonicalSystemDiscrete(tau_sys, canonical_order)
  quat_dmp = QuaternionDMP(model_size, canonical_sys_discr)
  tau = tau_reproduce

  unroll_qtraj_list = list()
  unroll_learned_params_list = list()

  for k in range(2):
    if (k == 0):
      sub_quat_dmp_training_path = "/data/cart_dmp/quat_dmp/multi_traj_training/"
    elif (k == 1):
      #            sub_quat_dmp_training_path = "/data/dmp_coupling/learn_tactile_feedback/scraping_wo_tool/human_baseline/prim02/"
      # this one is a more challenging case, because Quaternions -Q and Q both represent the same orientation
      # (this is to see if the low-pass filtering on such Quaternion trajectory is done right;
      #  if NOT low-pass filtering will just make things
      #  (especially the QuaternionDMP fitting) so much worse and WRONG):
      sub_quat_dmp_training_path = "/data/cart_dmp/quat_dmp_unscrewing/prim02/"

    set_qtraj_input = quat_dmp.extractSetTrajectories(
        dmp_home_dir_path + sub_quat_dmp_training_path,
        start_column_idx=10,
        time_column_idx=0)

    [
        critical_states_learn, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV,
        PSI
    ] = quat_dmp.learnFromSetTrajectories(
        set_qtraj_input,
        task_servo_rate,
        is_smoothing_training_traj_before_learning=is_smoothing_training_traj_before_learning,
        percentage_padding=percentage_padding,
        percentage_smoothing_points=percentage_smoothing_points,
        smoothing_mode=smoothing_mode,
        smoothing_cutoff_frequency=smoothing_cutoff_frequency)

    ## Reproduce
    qdmp_unroll = quat_dmp.unroll(critical_states_learn, tau,
                                  time_reproduce_max, dt)
    unroll_qtraj_time = qdmp_unroll.time.T - dt
    unroll_qtraj_Q = qdmp_unroll.X.T
    unroll_qtraj = np.hstack([unroll_qtraj_time, unroll_qtraj_Q])
    unroll_qtraj_list.append(copy.deepcopy(unroll_qtraj))

    unroll_learned_params = np.zeros((model_size + 1 + 2, 5))
    unroll_learned_params[0:model_size, 0:3] = W.T
    unroll_learned_params[model_size:model_size + 1, 0:3] = mean_A_learn.T
    unroll_learned_params[model_size, 3] = mean_tau
    unroll_learned_params[model_size + 1:model_size + 2,
                          0:4] = quat_dmp.mean_start_position.T
    unroll_learned_params[model_size + 2:model_size + 3,
                          0:4] = quat_dmp.mean_goal_position.T
    unroll_learned_params_list.append(copy.deepcopy(unroll_learned_params))

  unroll_qtraj_concatenated = np.vstack(unroll_qtraj_list)
  unroll_learned_params_concatenated = np.vstack(unroll_learned_params_list)

  if (os.path.isdir(unroll_qtraj_save_dir_path)):
    np.savetxt(unroll_qtraj_save_dir_path + "/" + unroll_qtraj_save_filename,
               unroll_qtraj_concatenated)

  if (os.path.isdir(unroll_learned_params_save_dir_path)):
    np.savetxt(
        unroll_learned_params_save_dir_path + "/" +
        unroll_learned_params_save_filename, unroll_learned_params_concatenated)

  if (is_plotting):
    quat_dmp.plotDemosVsUnroll(set_qtraj_input, qdmp_unroll)

  return unroll_qtraj_concatenated, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV, PSI


if __name__ == "__main__":
  unroll_qtraj_concatenated, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV, PSI = quat_dmp_multi_traj_training_test(
      time_reproduce_max=5.98734,  #1.9976,
      tau_reproduce=5.98734,  #1.9976,
      is_smoothing_training_traj_before_learning=False,  #True,
      #                                                                                                                       percentage_padding=1.5, percentage_smoothing_points=3.0,
      #                                                                                                                       smoothing_mode=3, smoothing_cutoff_frequency=5.0,
      is_plotting=True)

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""Created on Tue Jan 01 16:00:00 2019

@author: gsutanto
"""

import numpy as np
import os
import sys
import copy
import matplotlib.pyplot as plt
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../dmp_param/"))
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../dmp_base/"))
sys.path.append(
    os.path.join(os.path.dirname(__file__), "../../../dmp_discrete/"))
sys.path.append(
    os.path.join(os.path.dirname(__file__), "../../../cart_dmp/quat_dmp"))
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../utilities/"))
from TauSystem import *
from QuaternionDMPUnrollInitParams import *
from CanonicalSystemDiscrete import *
from QuaternionDMP import *
from utilities import *

plt.close("all")


def quat_dmp_multi_traj_training_test(
    dmp_home_dir_path="../../../../",
    canonical_order=2,
    time_reproduce_max=2.0,
    tau_reproduce=2.0,
    unroll_qtraj_save_dir_path="",
    unroll_qtraj_save_filename="",
    is_smoothing_training_traj_before_learning=False,
    percentage_padding=None,
    percentage_smoothing_points=None,
    smoothing_mode=None,
    smoothing_cutoff_frequency=None,
    unroll_learned_params_save_dir_path="",
    unroll_learned_params_save_filename="",
    is_plotting=False):
  task_servo_rate = 300.0
  dt = 1.0 / task_servo_rate
  model_size = 25
  tau = MIN_TAU

  assert ((canonical_order >= 1) and (canonical_order <= 2))
  assert (time_reproduce_max >= 0.0)
  assert (tau_reproduce >= 0.0)

  tau_sys = TauSystem(dt, tau)
  canonical_sys_discr = CanonicalSystemDiscrete(tau_sys, canonical_order)
  quat_dmp = QuaternionDMP(model_size, canonical_sys_discr)
  tau = tau_reproduce

  unroll_qtraj_list = list()
  unroll_learned_params_list = list()

  for k in range(2):
    if (k == 0):
      sub_quat_dmp_training_path = "/data/cart_dmp/quat_dmp/multi_traj_training/"
    elif (k == 1):
      #            sub_quat_dmp_training_path = "/data/dmp_coupling/learn_tactile_feedback/scraping_wo_tool/human_baseline/prim02/"
      # this one is a more challenging case, because Quaternions -Q and Q both represent the same orientation
      # (this is to see if the low-pass filtering on such Quaternion trajectory is done right;
      #  if NOT low-pass filtering will just make things
      #  (especially the QuaternionDMP fitting) so much worse and WRONG):
      sub_quat_dmp_training_path = "/data/cart_dmp/quat_dmp_unscrewing/prim02/"

    set_qtraj_input = quat_dmp.extractSetTrajectories(
        dmp_home_dir_path + sub_quat_dmp_training_path,
        start_column_idx=10,
        time_column_idx=0)

    [
        critical_states_learn, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV,
        PSI
    ] = quat_dmp.learnFromSetTrajectories(
        set_qtraj_input,
        task_servo_rate,
        is_smoothing_training_traj_before_learning=is_smoothing_training_traj_before_learning,
        percentage_padding=percentage_padding,
        percentage_smoothing_points=percentage_smoothing_points,
        smoothing_mode=smoothing_mode,
        smoothing_cutoff_frequency=smoothing_cutoff_frequency)

    ## Reproduce
    qdmp_unroll = quat_dmp.unroll(critical_states_learn, tau,
                                  time_reproduce_max, dt)
    unroll_qtraj_time = qdmp_unroll.time.T - dt
    unroll_qtraj_Q = qdmp_unroll.X.T
    unroll_qtraj = np.hstack([unroll_qtraj_time, unroll_qtraj_Q])
    unroll_qtraj_list.append(copy.deepcopy(unroll_qtraj))

    unroll_learned_params = np.zeros((model_size + 1 + 2, 5))
    unroll_learned_params[0:model_size, 0:3] = W.T
    unroll_learned_params[model_size:model_size + 1, 0:3] = mean_A_learn.T
    unroll_learned_params[model_size, 3] = mean_tau
    unroll_learned_params[model_size + 1:model_size + 2,
                          0:4] = quat_dmp.mean_start_position.T
    unroll_learned_params[model_size + 2:model_size + 3,
                          0:4] = quat_dmp.mean_goal_position.T
    unroll_learned_params_list.append(copy.deepcopy(unroll_learned_params))

  unroll_qtraj_concatenated = np.vstack(unroll_qtraj_list)
  unroll_learned_params_concatenated = np.vstack(unroll_learned_params_list)

  if (os.path.isdir(unroll_qtraj_save_dir_path)):
    np.savetxt(unroll_qtraj_save_dir_path + "/" + unroll_qtraj_save_filename,
               unroll_qtraj_concatenated)

  if (os.path.isdir(unroll_learned_params_save_dir_path)):
    np.savetxt(
        unroll_learned_params_save_dir_path + "/" +
        unroll_learned_params_save_filename, unroll_learned_params_concatenated)

  if (is_plotting):
    quat_dmp.plotDemosVsUnroll(set_qtraj_input, qdmp_unroll)

  return unroll_qtraj_concatenated, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV, PSI


if __name__ == "__main__":
  unroll_qtraj_concatenated, W, mean_A_learn, mean_tau, Ft, Fp, QgT, cX, cV, PSI = quat_dmp_multi_traj_training_test(
      time_reproduce_max=5.98734,  #1.9976,
      tau_reproduce=5.98734,  #1.9976,
      is_smoothing_training_traj_before_learning=False,  #True,
      #                                                                                                                       percentage_padding=1.5, percentage_smoothing_points=3.0,
      #                                                                                                                       smoothing_mode=3, smoothing_cutoff_frequency=5.0,
      is_plotting=True)
#!/usr/bin/env python2 # -*- coding: utf-8 -*- Created on Tue Jan 01 16:00:00 2019  @author: gsutanto #            sub_quat_dmp_training_path = "/data/dmp_coupling/learn_tactile_feedback/scraping_wo_tool/human_baseline/prim02/" # this one is a more challenging case, because Quaternions -Q and Q both represent the same orientation # (this is to see if the low-pass filtering on such Quaternion trajectory is done right; #  if NOT low-pass filtering will just make things #  (especially the QuaternionDMP fitting) so much worse and WRONG): ## Reproduce #1.9976, #1.9976, #True, #                                                                                                                       percentage_padding=1.5, percentage_smoothing_points=3.0, #                                                                                                                       smoothing_mode=3, smoothing_cutoff_frequency=5.0,