def main(event, structure, recompute, normalize):
trial_numbers = sorted(settings.keys())
plot_dir = utils.mkdir(
os.path.join(PATHS['figures_dir'], 'identification-results',
'-'.join(event.lower().split(' ')),
'-'.join(structure.split(' '))))
for trial_number in trial_numbers:
msg = 'Identifying {} controller from {} for trial #{}'
msg = msg.format(structure, event, trial_number)
print('=' * len(msg))
print(msg)
print('=' * len(msg))
trial = utils.Trial(trial_number)
if recompute:
trial.remove_precomputed_data()
trial.identify_controller(event, structure)
fig, axes = trial.plot_joint_isolated_gains(event,
structure,
normalize=normalize)
solver = trial.control_solvers[event][structure]
id_num_steps = solver.identification_data.shape[0]
title = """\
{} Scheduled Gains Identified from {} Gait Cycles in Trial {}
Nominal Speed: {} m/s, Gender: {}
"""
fig.suptitle(
title.format(structure.capitalize(), id_num_steps, trial_number,
trial.meta_data['trial']['nominal-speed'],
trial.meta_data['subject']['gender']))
fig.set_size_inches((14.0, 14.0))
plt.tight_layout()
plt.subplots_adjust(top=0.85)
fig_path = os.path.join(plot_dir, 'gains-' + trial_number + '.png')
fig.savefig(fig_path, dpi=300)
print('Gain plot saved to {}'.format(fig_path))
plt.close(fig)
fig, axes = trial.plot_validation(event, structure)
fig_path = os.path.join(plot_dir,
'validation-' + trial_number + '.png')
fig.savefig(fig_path, dpi=300)
print('Validation plot saved to {}'.format(fig_path))
plt.close(fig)
def main(event, force):
PATHS = utils.config_paths()
trial_numbers = sorted(settings.keys())
event_fname = '-'.join(event.lower().split(' '))
hist_dir = utils.mkdir(
os.path.join(PATHS['figures_dir'], 'gait-cycle-histograms'))
grf_dir = utils.mkdir(os.path.join(PATHS['figures_dir'], 'vertical-grfs'))
for trial_number in trial_numbers:
msg = 'Cleaning {} event data for trial #{}'.format(
event, trial_number)
print('=' * len(msg))
print(msg)
print('=' * len(msg))
trial = utils.Trial(trial_number)
if force:
trial.remove_precomputed_data()
trial._write_event_data_frame_to_disk(event)
trial._write_inverse_dynamics_to_disk(event)
trial._section_into_gait_cycles(event)
cleansed_gait_cycles, _ = trial._remove_bad_gait_cycles(event)
gait_data = trial.gait_data_objs[event]
axes = gait_data.gait_cycle_stats.hist()
fig = plt.gcf()
fig.savefig(os.path.join(hist_dir,
trial_number + '-' + event_fname + '.png'),
dpi=300)
plt.close(fig)
axes = plot_gait_cycles(gait_data.gait_cycles, 'FP2.ForY')
fig = plt.gcf()
fig.savefig(os.path.join(
grf_dir, trial_number + '-' + event_fname + '-before.png'),
dpi=300)
plt.close(fig)
axes = plot_gait_cycles(cleansed_gait_cycles, 'FP2.ForY')
fig = plt.gcf()
fig.savefig(os.path.join(
grf_dir, trial_number + '-' + event_fname + '-after.png'),
dpi=300)
plt.close(fig)
del trial, fig, axes, cleansed_gait_cycles, gait_data
from scipy.integrate import odeint
from scipy.interpolate import interp1d
from pydy.codegen.code import generate_ode_function
from pydy.viz import Scene
import pygait2d
from pygait2d import derive, simulate
from dtk import process
# local
import utils
import simulation
from grf_landmark_settings import settings
# load the data and find a controller
trial_number = '068'
trial = utils.Trial(trial_number)
trial._write_event_data_frame_to_disk('Longitudinal Perturbation')
event_data_frame = trial.event_data_frames['Longitudinal Perturbation']
event_data_frame = simulation.estimate_trunk_somersault_angle(event_data_frame)
# TODO : will likely need to low pass filter the two time derivatives
event_data_frame['Trunk.Somersault.Rate'] = \
process.derivative(event_data_frame.index.values.astype(float),
event_data_frame['Trunk.Somersault.Angle'],
method='combination')
event_data_frame['RGTRO.VelY'] = \
process.derivative(event_data_frame.index.values.astype(float),
event_data_frame['RGTRO.PosY'], method='combination')