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tasks.py
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tasks.py
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import os
import time
import numpy as np
import pylab as pl
import pandas as pd
import pdb
import matplotlib.pyplot as plt
plt.rcParams['mathtext.fontset'] = 'cm'
plt.rcParams['mathtext.rm'] = 'serif'
import osimpipeline as osp
from osimpipeline import utilities as util
from osimpipeline import postprocessing as pp
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
class working_directory():
"""Use this to temporarily run code with some directory as a working
directory and to then return to the original working directory::
with working_directory('<dir>'):
pass
"""
def __init__(self, path):
self.path = path
self.original_working_dir = os.getcwd()
def __enter__(self):
os.chdir(self.path)
def __exit__(self, *exc_info):
os.chdir(self.original_working_dir)
class TaskCopyMotionCaptureData(osp.TaskCopyMotionCaptureData):
REGISTRY = []
def __init__(self, study, walk100=None, walk125=None, walk150=None,
walk175=None, run200=None, run300=None, run400=None, run500=None):
regex_replacements = list()
default_args = dict()
default_args['walk100'] = walk100
default_args['walk125'] = walk125
default_args['walk150'] = walk150
default_args['walk175'] = walk175
default_args['run200'] = run200
default_args['run300'] = run300
default_args['run400'] = run400
default_args['run500'] = run500
for subject in study.subjects:
cond_args = subject.cond_args
if 'walk100' in cond_args: walk100 = cond_args['walk100']
else: walk100 = default_args['walk100']
if 'walk125' in cond_args: walk125 = cond_args['walk125']
else: walk125 = default_args['walk125']
if 'walk150' in cond_args: walk150 = cond_args['walk150']
else: walk150 = default_args['walk150']
if 'walk175' in cond_args: walk175 = cond_args['walk175']
else: walk175 = default_args['walk175']
if 'run200' in cond_args: run200 = cond_args['run200']
else: run200 = default_args['run200']
if 'run300' in cond_args: run300 = cond_args['run300']
else: run300 = default_args['run300']
if 'run400' in cond_args: run400 = cond_args['run400']
else: run400 = default_args['run400']
if 'run500' in cond_args: run500 = cond_args['run500']
else: run500 = default_args['run500']
for datastr, condname, arg in [
('Walk_100', 'walk1', walk100),
('Walk_125', 'walk2', walk125),
('Walk_150', 'walk3', walk150),
('Walk_175', 'walk4', walk175),
('Run_200', 'run1', run200),
('Run_300', 'run2', run300),
('Run_400', 'run3', run400),
('Run_500', 'run4', run500)]:
# Marker trajectories.
regex_replacements.append(
(
os.path.join(subject.name, 'Data',
'%s %02i.trc' % (datastr, arg[0])).replace('\\',
'\\\\'),
os.path.join('experiments',
subject.name, condname, 'expdata',
'marker_trajectories.trc').replace('\\','\\\\')
))
# Ground reaction.
regex_replacements.append(
(
os.path.join(subject.name, 'Data',
'%s %02i%s.mot' % (datastr, arg[0],arg[1])).replace(
'\\','\\\\'),
os.path.join('experiments', subject.name, condname,
'expdata','ground_reaction_orig.mot').replace(
'\\','\\\\')
))
# EMG
regex_replacements.append(
(
os.path.join(subject.name, 'Results', datastr,
'%s%02i_gait_controls.sto' % (datastr, arg[0])
).replace('\\','\\\\'),
os.path.join('experiments', subject.name,
condname, 'expdata', 'emg_with_headers.sto'
).replace('\\','\\\\')
))
regex_replacements.append((
os.path.join(subject.name, 'Data',
'Static_FJC.trc').replace('\\','\\\\'),
os.path.join('experiments', subject.name, 'static',
'expdata',
'marker_trajectories.trc').replace('\\','\\\\')
))
super(TaskCopyMotionCaptureData, self).__init__(study,
regex_replacements)
class TaskRemoveEMGFileHeaders(osp.StudyTask):
REGISTRY = []
def __init__(self, study):
super(TaskRemoveEMGFileHeaders, self).__init__(study)
self.name = '%s_remove_emg_file_headers' % study.name
self.doc = 'Remove headers from EMG data files.'
file_dep = list()
target = list()
for subject in study.subjects:
for cond in subject.conditions:
if cond.name == 'static': continue
file_dep += [os.path.join(subject.results_exp_path,
cond.name, 'expdata', 'emg_with_headers.sto')]
target += [os.path.join(subject.results_exp_path,
cond.name, 'expdata', 'emg.sto')]
self.add_action(file_dep, target, self.remove_file_headers)
def remove_file_headers(self, file_dep, target):
for i, fpath in enumerate(file_dep):
infile = open(fpath, 'r').readlines()
if os.path.isfile(target[i]):
print 'File ' + target[i] + ' already exists. Deleting...'
os.remove(target[i])
print 'Writing to ' + target[i]
with open(target[i], 'w') as outfile:
prev_line = ''
writing = False
for index, line in enumerate(infile):
if 'endheader' in prev_line:
writing = True
if writing:
outfile.write(line)
prev_line = line
class TaskUpdateGroundReactionColumnLabels(osp.TrialTask):
REGISTRY = []
def __init__(self, trial):
super(TaskUpdateGroundReactionColumnLabels, self).__init__(trial)
self.name = trial.id + '_update_grf_column_labels'
self.add_action(
[os.path.join(trial.expdata_path, 'ground_reaction_orig.mot')],
[trial.ground_reaction_fpath],
self.dispatch)
def dispatch(self, file_dep, target):
from perimysium.dataman import storage2numpy, ndarray2storage
import re
data = storage2numpy(file_dep[0])
new_names = list()
for name in data.dtype.names:
if name == 'time':
new_name = name
elif name.endswith('_1'):
new_name = re.sub('ground_(.*)_(.*)_1', 'ground_\\1_l_\\2',
name)
else:
new_name = re.sub('ground_(.*)_(.*)', 'ground_\\1_r_\\2',
name)
new_names.append(new_name)
data.dtype.names = new_names
ndarray2storage(data, target[0])
class TaskScaleMuscleMaxIsometricForce(osp.SubjectTask):
REGISTRY = []
def __init__(self, subject):
super(TaskScaleMuscleMaxIsometricForce, self).__init__(subject)
self.subject = subject
self.name = '%s_scale_max_force' % self.subject.name
self.doc = 'Scale subject muscle Fmax parameters from Handsfield2014'
self.generic_model_fpath = self.study.source_generic_model_fpath
self.subject_model_fpath = os.path.join(self.subject.results_exp_path,
'%s.osim' % self.subject.name)
self.scaled_param_model_fpath = os.path.join(
self.subject.results_exp_path,
'%s_scaled_Fmax.osim' % self.subject.name)
self.add_action([self.generic_model_fpath, self.subject_model_fpath],
[self.scaled_param_model_fpath],
self.scale_model_parameters)
def scale_model_parameters(self, file_dep, target):
"""From Handsfields 2014 figure 5a and from Apoorva's muscle properties
spreadsheet.
v: volume fraction
V: total volume
F: max isometric force
l: optimal fiber length
F = v * sigma * V / l
*_g: generic model.
*_s: subject-specific model.
F_g = v * sigma * V_g / l_g
F_s = v * sigma * V_s / l_s
F_s = (F_g * l_g / V_g) * V_s / l_s
= F_g * (V_s / V_g) * (l_g / l_s)
Author: Chris Dembia
Borrowed from mrsdeviceopt GitHub repo:
https://github.com/chrisdembia/mrsdeviceopt
"""
print("Muscle force scaling: "
"total muscle volume and optimal fiber length.")
def total_muscle_volume_regression(mass):
return 91.0*mass + 588.0
generic_TMV = total_muscle_volume_regression(75.337)
subj_TMV = total_muscle_volume_regression(self.subject.mass)
import opensim as osm
generic_model = osm.Model(file_dep[0])
subj_model = osm.Model(file_dep[1])
generic_mset = generic_model.getMuscles()
subj_mset = subj_model.getMuscles()
for im in range(subj_mset.getSize()):
muscle_name = subj_mset.get(im).getName()
generic_muscle = generic_mset.get(muscle_name)
subj_muscle = subj_mset.get(muscle_name)
generic_OFL = generic_muscle.get_optimal_fiber_length()
subj_OFL = subj_muscle.get_optimal_fiber_length()
scale_factor = (subj_TMV / generic_TMV) * (generic_OFL / subj_OFL)
print("Scaling '%s' muscle force by %f." % (muscle_name,
scale_factor))
generic_force = generic_muscle.getMaxIsometricForce()
scaled_force = generic_force * scale_factor
subj_muscle.setMaxIsometricForce(scaled_force)
subj_model.printToXML(target[0])
class TaskAdjustScaledModelMarkers(osp.SubjectTask):
REGISTRY = []
def __init__(self, subject, marker_adjustments):
super(TaskAdjustScaledModelMarkers, self).__init__(subject)
self.subject = subject
self.name = '%s_adjust_scaled_model_markers' % self.subject.name
self.doc = 'Make adjustments to model marker post-scale'
self.scaled_param_model_fpath = os.path.join(
self.subject.results_exp_path,
'%s_scaled_Fmax.osim' % self.subject.name)
self.final_model_fpath = os.path.join(
self.subject.results_exp_path,
'%s_final.osim' % self.subject.name)
self.marker_adjustments = marker_adjustments
self.add_action([self.scaled_param_model_fpath],
[self.final_model_fpath],
self.adjust_model_markers)
def adjust_model_markers(self, file_dep, target):
print("Adjusting model markers... ")
import opensim as osm
scaled_model = osm.Model(file_dep[0])
markerSet = scaled_model.updMarkerSet()
for name, adj in self.marker_adjustments.iteritems():
marker = markerSet.get(name)
loc = marker.getOffset()
loc.set(adj[0], adj[1])
marker.setOffset(loc)
scaled_model.printToXML(target[0])
class TaskBodyKinematicsSetup(osp.SetupTask):
REGISTRY = []
def __init__(self, trial, ik_setup_task, **kwargs):
super(TaskBodyKinematicsSetup, self).__init__('body_kinematics',
trial, **kwargs)
self.doc = 'Create a setup file for a BodyKinematics analysis.'
self.ik_setup_task = ik_setup_task
self.rel_kinematics_fpath = os.path.relpath(
ik_setup_task.solution_fpath, self.path)
self.solution_fpath = os.path.join(
self.path, 'results',
'%s_%s_body_kinematics_BodyKinematics_pos_global.sto' % (
self.study.name, trial.id))
# Fill out setup.xml template and write to results directory
self.create_setup_action()
def fill_setup_template(self, file_dep, target,
init_time=None, final_time=None):
with open(file_dep[0]) as ft:
content = ft.read()
content = content.replace('@STUDYNAME@', self.study.name)
content = content.replace('@NAME@', self.tricycle.id)
content = content.replace('@MODEL@',
os.path.relpath(self.subject.scaled_model_fpath, self.path))
content = content.replace('@COORDINATES_FILE@',
self.rel_kinematics_fpath)
content = content.replace('@INIT_TIME@', '%.4f' % init_time)
content = content.replace('@FINAL_TIME@', '%.4f' % final_time)
with open(target[0], 'w') as f:
f.write(content)
class TaskBodyKinematics(osp.ToolTask):
REGISTRY = []
def __init__(self, trial, bodykin_setup_task, **kwargs):
super(TaskBodyKinematics, self).__init__(bodykin_setup_task,
trial, exec_name='analyze', **kwargs)
self.doc = "Run an OpenSim BodyKinematics analysis."
self.ik_setup_task = bodykin_setup_task.ik_setup_task
self.file_dep += [
self.subject.scaled_model_fpath,
bodykin_setup_task.results_setup_fpath,
self.ik_setup_task.solution_fpath
]
self.targets += [
bodykin_setup_task.solution_fpath
]
class TaskValidateMarkerErrors(osp.StudyTask):
REGISTRY = []
def __init__(self, study, cond_names=['walk1','walk2','walk3','walk4']):
super(TaskValidateMarkerErrors, self).__init__(study)
suffix = ''
self.costdir = ''
if not (study.costFunction == 'Default'):
suffix += '_%s' % study.costFunction
self.costdir = study.costFunction
self.name = 'validate_marker_errors%s' % suffix
self.doc = 'Compute marker errors across subjects and conditions.'
self.results_path = study.config['results_path']
self.validate_path = os.path.join(study.config['validate_path'],
'marker_errors')
self.cond_names = cond_names
self.subjects = study.subjects
errors_fpaths = list()
for cond_name in cond_names:
for subject in study.subjects:
errors_fpaths.append(os.path.join(self.results_path,
'experiments', subject.name, cond_name, 'ik',
'marker_error.csv'))
val_fname = os.path.join(self.validate_path, 'marker_errors.txt')
self.add_action(errors_fpaths, [val_fname],
self.validate_marker_errors)
def validate_marker_errors(self, file_dep, target):
if not os.path.isdir(self.validate_path): os.mkdir(self.validate_path)
all_errors = pd.DataFrame()
for file in file_dep:
df = pd.read_csv(file)
df = df.drop(columns=['Unnamed: 0', 'time'])
all_errors = pd.concat([all_errors, df])
numMarkers = all_errors.shape[1]
all_errors_sq = all_errors.pow(2)
all_errors_sq_sum_norm = all_errors_sq.sum(axis=1) / float(numMarkers)
rms_errors = all_errors_sq_sum_norm.pow(0.5)
peak_rms = rms_errors.max()
mean_rms = rms_errors.mean()
with open(target[0],"w") as f:
f.write('subjects: ')
for isubj, subject in enumerate(self.subjects):
if isubj:
f.write(', %s' % subject.name)
else:
f.write('%s' % subject.name)
f.write('\n')
f.write('conditions: ')
for icond, cond_name in enumerate(self.cond_names):
if icond:
f.write(', %s' % subject)
else:
f.write('%s' % cond_name)
f.write('\n')
f.write('Peak RMS marker error: %1.3f cm \n' % (100*peak_rms))
f.write('Mean RMS marker error: %1.3f cm \n' % (100*mean_rms))
class TaskValidateKinetics(osp.StudyTask):
REGISTRY = []
def __init__(self, study, cond_name='walk2',
gen_forces=['hip_flexion_r_moment',
'knee_angle_r_moment',
'ankle_angle_r_moment'],
residual_moments=['pelvis_tilt_moment',
'pelvis_list_moment',
'pelvis_rotation_moment'],
residual_forces=['pelvis_tx_force',
'pelvis_ty_force',
'pelvis_tz_force'],
grfs=['ground_force_r_vx',
'ground_force_r_vy',
'ground_force_r_vz']):
super(TaskValidateKinetics, self).__init__(study)
suffix = ''
self.costdir = ''
if not (study.costFunction == 'Default'):
suffix += '_%s' % study.costFunction
self.costdir = study.costFunction
self.name = 'validate_kinetics%s' % suffix
self.doc = 'Validate joint moments and residuals across subjects and conditions.'
self.results_path = study.config['results_path']
self.validate_path = os.path.join(study.config['validate_path'],
'kinetics')
self.figure_path = os.path.join(study.config['figures_path'],
'figureS1')
self.cond_name = cond_name
self.subjects = study.subjects
self.gen_forces = gen_forces
if not len(residual_moments) == 3:
raise Exception('There must be 3 residual moment components.')
self.residual_moments = residual_moments
if not len(residual_forces) == 3:
raise Exception('There must be 3 residual force components.')
self.residual_forces = residual_forces
if not len(grfs) == 3:
raise Exception('There must be 3 ground reaction force components.')
self.grfs = grfs
colors = plt.cm.jet(np.linspace(0, 1, len(study.subjects)))
masses = list()
for subject in study.subjects:
masses.append(subject.mass)
moments_fpaths_all = list()
bodykin_fpaths_all = list()
gait_events_all = list()
isubjs_all = list()
grf_fpaths_all = list()
moments_fpaths = list()
gait_events = list()
isubjs = list()
for isubj, subject in enumerate(study.subjects):
fpath = os.path.join(self.results_path, 'experiments',
subject.name, cond_name, 'id', 'results',
'%s_%s_%s_id_solution.sto' % (study.name, subject.name,
cond_name))
moments_fpaths.append(fpath)
moments_fpaths_all.append(fpath)
bodykin_fpath = os.path.join(self.results_path, 'experiments',
subject.name, cond_name, 'body_kinematics', 'results',
'%s_%s_%s_body_kinematics_BodyKinematics_pos_global.sto' % (
study.name, subject.name, cond_name))
bodykin_fpaths_all.append(bodykin_fpath)
condition = subject.get_condition(cond_name)
trial = condition.get_trial(1)
cycles = trial.get_cycles()
gait_events_this_cond = list()
for cycle in cycles:
gait_events_this_cond.append((cycle.start, cycle.end))
gait_events.append(gait_events_this_cond)
gait_events_all.append(gait_events_this_cond)
isubjs.append(isubj)
isubjs_all.append(isubj)
grf_fpath = os.path.join(self.results_path, 'experiments',
subject.name, cond_name, 'expdata', 'ground_reaction.mot')
grf_fpaths_all.append(grf_fpath)
joint_moments_fname = os.path.join(self.validate_path,
'joint_moments_%s.pdf' % cond_name)
joint_moments_figname = os.path.join(self.figure_path,
'figureS1.pdf')
self.add_action(moments_fpaths,
[joint_moments_fname, joint_moments_figname],
self.validate_joint_moments, gait_events, isubjs,
colors, masses)
residuals_fname = os.path.join(self.validate_path, 'residuals.txt')
moments_fname = os.path.join(self.validate_path, 'moments.csv')
forces_fname = os.path.join(self.validate_path, 'forces.csv')
self.add_action(moments_fpaths_all,
[residuals_fname, moments_fname, forces_fname],
self.validate_residuals, gait_events_all, grf_fpaths_all,
bodykin_fpaths_all)
def validate_joint_moments(self, file_dep, target, gait_events, isubjs,
colors, masses):
if not os.path.isdir(self.validate_path): os.mkdir(self.validate_path)
if not os.path.isdir(self.figure_path): os.mkdir(self.figure_path)
nice_gen_force_names = {'hip_flexion_r_moment': 'hip flexion moment',
'knee_angle_r_moment': 'knee flexion moment',
'ankle_angle_r_moment': 'ankle plantarflexion moment'}
y_lim_dict = {'hip_flexion_r_moment': (-1.2, 1.2),
'knee_angle_r_moment': (-1.2, 1.2),
'ankle_angle_r_moment': (-0.5, 2.0)}
subject_labels = ['subject 1', 'subject 2', 'subject 3', 'subject 4',
'subject 5']
import matplotlib
fig = pl.figure(figsize=(6, 8))
ind_axes = list()
ind_handles = list()
mean_axes = list()
dfs = list()
for iforce, gen_force in enumerate(self.gen_forces):
idx = iforce + 1
ind_axes.append(fig.add_subplot(len(self.gen_forces), 2, 2*idx-1))
mean_axes.append(fig.add_subplot(len(self.gen_forces), 2, 2*idx))
dfs.append(pd.DataFrame())
for file, gait_events_cond, isubj in zip(file_dep, gait_events, isubjs):
id = pp.storage2numpy(file)
time = id['time']
for ige, gait_event in enumerate(gait_events_cond):
start = np.argmin(abs(time-gait_event[0]))
end = np.argmin(abs(time-gait_event[1]))
new_time = np.linspace(time[start], time[end], 101)
pgc = np.linspace(0, 100, 101)
for iforce, gen_force in enumerate(self.gen_forces):
force = id[gen_force][start:end] / masses[isubj]
force_interp = np.interp(new_time, time[start:end], force)
dfs[iforce] = pd.concat([dfs[iforce],
pd.DataFrame(force_interp)], axis=1)
sign = -1 if gen_force=='ankle_angle_r_moment' else 1
h, = ind_axes[iforce].plot(pgc, sign*force_interp, color=colors[isubj])
if not ige and not iforce:
ind_handles.append(h)
ind_axes[iforce].set_ylabel(
'%s (N-m/kg)' % nice_gen_force_names[gen_force])
ind_axes[iforce].set_ylim(y_lim_dict[gen_force])
ind_axes[iforce].set_xlim((0, 100))
ind_axes[iforce].axhline(ls='--', color='lightgray', zorder=0)
ind_axes[iforce].spines['top'].set_visible(False)
ind_axes[iforce].spines['right'].set_visible(False)
ind_axes[iforce].tick_params(direction='in')
ind_axes[0].legend(ind_handles, subject_labels, fancybox=False,
frameon=False, prop={'size': 8}, loc=2)
for iforce, gen_force in enumerate(self.gen_forces):
sign = -1 if gen_force=='ankle_angle_r_moment' else 1
force_mean = sign*dfs[iforce].mean(axis=1)
force_std = dfs[iforce].std(axis=1)
mean_axes[iforce].fill_between(pgc, force_mean-force_std,
force_mean+force_std, color='black', alpha=0.2)
std_h = matplotlib.patches.Patch(color='black', alpha=0.2)
mean_h, = mean_axes[iforce].plot(pgc, force_mean, color='black')
mean_axes[iforce].set_ylim(y_lim_dict[gen_force])
mean_axes[iforce].set_xlim((0, 100))
mean_axes[iforce].axhline(ls='--', color='lightgray', zorder=0)
mean_axes[iforce].spines['top'].set_visible(False)
mean_axes[iforce].spines['right'].set_visible(False)
mean_axes[iforce].tick_params(direction='in')
mean_axes[iforce].set_yticklabels([])
if iforce == len(self.gen_forces)-1:
mean_axes[iforce].set_xlabel('gait cycle (%)')
ind_axes[iforce].set_xlabel('gait cycle (%)')
else:
ind_axes[iforce].set_xticklabels([])
mean_axes[iforce].set_xticklabels([])
mean_axes[0].legend([mean_h, std_h], ['mean', 'std'], fancybox=False,
frameon=False, prop={'size': 8}, loc=2)
fig.tight_layout()
fig.savefig(target[0])
fig.savefig(target[0].replace('.pdf', '.png'), dpi=600)
fig.savefig(target[1])
fig.savefig(target[1].replace('.pdf', '.png'), dpi=600)
pl.close(fig)
def validate_residuals(self, file_dep, target, gait_events, grf_fpaths,
bodykin_fpaths):
if not os.path.isdir(self.validate_path): os.mkdir(self.validate_path)
df_moments = pd.DataFrame()
df_forces = pd.DataFrame()
df_grfs = pd.DataFrame()
df_ycom = pd.DataFrame()
for file, gait_events_cond, grf_fpath, bodykin_fpath in zip(file_dep,
gait_events, grf_fpaths, bodykin_fpaths):
id = pp.storage2numpy(file)
time = id['time']
grfs = pp.storage2numpy(grf_fpath)
grf_time = grfs['time']
bodykin = pp.storage2numpy(bodykin_fpath)
bodykin_time = bodykin['time']
for gait_event in gait_events_cond:
start = np.argmin(abs(time-gait_event[0]))
end = np.argmin(abs(time-gait_event[1]))
new_time = np.linspace(time[start], time[end], 101)
for residual_moment in self.residual_moments:
moment = id[residual_moment][start:end]
moment_interp = np.interp(new_time, time[start:end], moment)
df_moments = pd.concat([df_moments,
pd.DataFrame(moment_interp)], axis=1)
for residual_force in self.residual_forces:
force = id[residual_force][start:end]
force_interp = np.interp(new_time, time[start:end], force)
df_forces = pd.concat([df_forces,
pd.DataFrame(force_interp)], axis=1)
grf_start = np.argmin(abs(grf_time-gait_event[0]))
grf_end = np.argmin(abs(grf_time-gait_event[1]))
new_grf_time = np.linspace(grf_time[grf_start],
grf_time[grf_end], 101)
for grf in self.grfs:
reaction = grfs[grf][grf_start:grf_end]
reaction_interp = np.interp(new_grf_time,
grf_time[grf_start:grf_end], reaction)
df_grfs = pd.concat([df_grfs, pd.DataFrame(reaction_interp)],
axis=1)
bodykin_start = np.argmin(abs(bodykin_time-gait_event[0]))
bodykin_end = np.argmin(abs(bodykin_time-gait_event[1]))
new_bodykin_time = np.linspace(bodykin_time[bodykin_start],
bodykin_time[bodykin_end], 101)
ycom = bodykin['center_of_mass_Y'][bodykin_start:bodykin_end]
ycom_interp = np.interp(new_bodykin_time,
bodykin_time[bodykin_start:bodykin_end], ycom)
df_ycom = pd.concat([df_ycom, pd.DataFrame(ycom_interp)], axis=1)
df_moments = pd.DataFrame(np.vstack(np.split(df_moments, 3*len(file_dep),
axis=1)))
df_forces = pd.DataFrame(np.vstack(np.split(df_forces, 3*len(file_dep),
axis=1)))
df_grfs = pd.DataFrame(np.vstack(np.split(df_grfs, 3*len(file_dep),
axis=1)))
df_ycom = pd.DataFrame(np.vstack(np.split(df_ycom, 3*len(file_dep),
axis=1)))
mag_moments = np.linalg.norm(df_moments, axis=1)
mag_forces = np.linalg.norm(df_forces, axis=1)
mag_grfs = np.linalg.norm(df_grfs, axis=1)
peak_grfs = mag_grfs.max()
avg_ycom = df_ycom.mean()[0]
peak_moments = 100*mag_moments.max() / (peak_grfs * avg_ycom)
rms_moments = 100*np.sqrt(np.mean(np.square(mag_moments))) / (
peak_grfs * avg_ycom)
peak_forces = 100*mag_forces.max() / peak_grfs
rms_forces = 100*np.sqrt(np.mean(np.square(mag_forces))) / peak_grfs
with open(target[0],"w") as f:
f.write('subjects: ')
for isubj, subject in enumerate(self.subjects):
if isubj:
f.write(', %s' % subject.name)
else:
f.write('%s' % subject.name)
f.write('\n')
f.write('condition: %s', self.cond_name)
f.write('\n')
f.write('Peak residual moments (%% GRF): %1.3f \n' % peak_moments)
f.write('RMS residual moments (%% GRF): %1.3f \n' % rms_moments)
f.write('Peak residual forces (%% GRF): %1.3f \n' % peak_forces)
f.write('RMS residual forces (%% GRF): %1.3f \n' % rms_forces)
target_dir = os.path.dirname(target[1])
if not os.path.exists(target_dir):
os.makedirs(target_dir)
with open(target[1], 'w') as f:
f.write('residual moments\n')
df_moments.to_csv(f, line_terminator='\n')
target_dir = os.path.dirname(target[2])
if not os.path.exists(target_dir):
os.makedirs(target_dir)
with open(target[2], 'w') as f:
f.write('residual forces\n')
df_forces.to_csv(f, line_terminator='\n')
class TaskValidateKinematics(osp.StudyTask):
REGISTRY = []
def __init__(self, study, cond_name='walk2',
joint_angles=['hip_flexion_r',
'knee_angle_r',
'ankle_angle_r']):
super(TaskValidateKinematics, self).__init__(study)
suffix = ''
self.costdir = ''
if not (study.costFunction == 'Default'):
suffix += '_%s' % study.costFunction
self.costdir = study.costFunction
self.name = 'validate_kinematics%s' % suffix
self.doc = 'Validate joint angles across subjects and conditions.'
self.results_path = study.config['results_path']
self.validate_path = os.path.join(study.config['validate_path'],
'kinematics')
self.figure_path = os.path.join(study.config['figures_path'],
'figureS2')
self.cond_name = cond_name
self.subjects = study.subjects
self.joint_angles = joint_angles
colors = plt.cm.jet(np.linspace(0, 1, len(study.subjects)))
angles_fpaths = list()
gait_events = list()
isubjs = list()
for isubj, subject in enumerate(study.subjects):
fpath = os.path.join(self.results_path, 'experiments',
subject.name, cond_name, 'ik',
'%s_%s_%s_ik_solution.mot' % (study.name, subject.name,
cond_name))
angles_fpaths.append(fpath)
condition = subject.get_condition(cond_name)
trial = condition.get_trial(1)
cycles = trial.get_cycles()
gait_events_this_cond = list()
for cycle in cycles:
gait_events_this_cond.append((cycle.start, cycle.end))
gait_events.append(gait_events_this_cond)
isubjs.append(isubj)
joint_angles_fname = os.path.join(self.validate_path,
'joint_angles_%s.pdf' % cond_name)
joint_angles_figname = os.path.join(self.figure_path,
'figureS2.pdf')
self.add_action(angles_fpaths,
[joint_angles_fname, joint_angles_figname],
self.validate_joint_angles, gait_events, isubjs,
colors)
def validate_joint_angles(self, file_dep, target, gait_events, isubjs,
colors):
if not os.path.isdir(self.validate_path): os.mkdir(self.validate_path)
if not os.path.isdir(self.figure_path): os.mkdir(self.figure_path)
nice_joint_angle_names = {'hip_flexion_r': 'hip flexion angle',
'knee_angle_r': 'knee flexion angle',
'ankle_angle_r': 'ankle plantarflexion angle'}
y_lim_dict = {'hip_flexion_r': (-40, 40),
'knee_angle_r': (-10, 80),
'ankle_angle_r': (-30, 30)}
subject_labels = ['subject 1', 'subject 2', 'subject 3', 'subject 4',
'subject 5']
import matplotlib
fig = pl.figure(figsize=(6, 8))
ind_axes = list()
ind_handles = list()
mean_axes = list()
dfs = list()
for iangle, joint_angle in enumerate(self.joint_angles):
idx = iangle + 1
ind_axes.append(fig.add_subplot(len(self.joint_angles), 2, 2*idx-1))
mean_axes.append(fig.add_subplot(len(self.joint_angles), 2, 2*idx))
dfs.append(pd.DataFrame())
for file, gait_events_cond, isubj in zip(file_dep, gait_events, isubjs):
ik = pp.storage2numpy(file)
time = ik['time']
for ige, gait_event in enumerate(gait_events_cond):
start = np.argmin(abs(time-gait_event[0]))
end = np.argmin(abs(time-gait_event[1]))
new_time = np.linspace(time[start], time[end], 101)
pgc = np.linspace(0, 100, 101)
for iangle, joint_angle in enumerate(self.joint_angles):
angle = ik[joint_angle][start:end]
angle_interp = np.interp(new_time, time[start:end], angle)
dfs[iangle] = pd.concat([dfs[iangle],
pd.DataFrame(angle_interp)], axis=1)
h, = ind_axes[iangle].plot(pgc, angle_interp, color=colors[isubj])
if not ige and not iangle:
ind_handles.append(h)
ind_axes[iangle].set_ylabel(
'%s (degrees)' % nice_joint_angle_names[joint_angle])
ind_axes[iangle].set_ylim(y_lim_dict[joint_angle])
ind_axes[iangle].set_xlim((0, 100))
ind_axes[iangle].axhline(ls='--', color='lightgray', zorder=0)
ind_axes[iangle].spines['top'].set_visible(False)
ind_axes[iangle].spines['right'].set_visible(False)
ind_axes[iangle].tick_params(direction='in')
ind_axes[0].legend(ind_handles, subject_labels, fancybox=False,
frameon=False, prop={'size': 8}, loc=9)
for iangle, joint_angle in enumerate(self.joint_angles):
angle_mean = dfs[iangle].mean(axis=1)
angle_std = dfs[iangle].std(axis=1)
mean_axes[iangle].fill_between(pgc, angle_mean-angle_std,
angle_mean+angle_std, color='black', alpha=0.2)
std_h = matplotlib.patches.Patch(color='black', alpha=0.2)
mean_h, = mean_axes[iangle].plot(pgc, angle_mean, color='black')
mean_axes[iangle].set_ylim(y_lim_dict[joint_angle])
mean_axes[iangle].set_xlim((0, 100))
mean_axes[iangle].axhline(ls='--', color='lightgray', zorder=0)
mean_axes[iangle].spines['top'].set_visible(False)
mean_axes[iangle].spines['right'].set_visible(False)
mean_axes[iangle].tick_params(direction='in')
mean_axes[iangle].set_yticklabels([])
if iangle == len(self.joint_angles)-1:
mean_axes[iangle].set_xlabel('gait cycle (%)')
ind_axes[iangle].set_xlabel('gait cycle (%)')
else:
ind_axes[iangle].set_xticklabels([])
mean_axes[iangle].set_xticklabels([])
mean_axes[0].legend([mean_h, std_h], ['mean', 'std'], fancybox=False,
frameon=False, prop={'size': 8}, loc=9)
fig.tight_layout()
fig.savefig(target[0])
fig.savefig(target[0].replace('.pdf', '.png'), dpi=600)
fig.savefig(target[1])
fig.savefig(target[1].replace('.pdf', '.png'), dpi=600)
pl.close(fig)
def get_muscle_parameters_as_list(fpath):
import h5py
hdf_output = h5py.File(fpath, 'r')
params = hdf_output['OptInfo']['paramCal']
lMo = list()
lTs = list()
alf = list()
e0 = list()
musc_names = list()
for musc in params:
musc_names.append(musc)
for param in params[musc]:
if param == 'optimal_fiber_length':
lMo.append(params[musc][param][0][0])
elif param == 'tendon_slack_length':
lTs.append(params[musc][param][0][0])
elif param == 'pennation_angle':
alf.append(params[musc][param][0][0])
elif param == 'muscle_strain':
e0.append(params[musc][param][0][0])
return lMo, lTs, alf, e0, musc_names
def get_muscle_parameters_as_dict(fpath):
import h5py
hdf_output = h5py.File(fpath, 'r')
params = hdf_output['OptInfo']['paramCal']
lMo = dict()
lTs = dict()
alf = dict()
e0 = dict()
musc_names = list()
for musc in params:
musc_names.append(musc)
for param in params[musc]:
if param == 'optimal_fiber_length':
lMo[musc] = params[musc][param][0][0]
elif param == 'tendon_slack_length':
lTs[musc] = params[musc][param][0][0]
elif param == 'pennation_angle':
alf[musc] = params[musc][param][0][0]
elif param == 'muscle_strain':
e0[musc] = params[musc][param][0][0]
return lMo, lTs, alf, e0, musc_names
class TaskCalibrateParametersMultiPhaseSetup(osp.SetupTask):
REGISTRY = []
# TODO: the specific trial is ignored in this task, since this is
# really a subject task.
def __init__(self, trial, conditions, cycles, param_dict, cost_dict,
passive_precalibrate=False, **kwargs):
super(TaskCalibrateParametersMultiPhaseSetup, self).__init__(
'calibrate_multiphase', trial, **kwargs)
self.doc = "Create a setup file for a mulit-phase parameter " \
"calibration tool."
self.path = os.path.join(self.subject.results_exp_path, 'calibrate')
self.name = '%s_calibrate_multiphase_setup' % self.subject.name
self.kinematics_files = list()
self.rel_kinematics_files = list()
self.kinetics_files = list()
self.rel_kinetics_files = list()
self.emg_files = list()
self.rel_emg_files = list()
self.init_times = list()
self.final_times = list()
self.cycle_ids = list()
self.trial_results_exp_paths = list()
for cond in self.subject.conditions:
if not (cond.name in conditions): continue
# Assume only trial per condition
trial = cond.trials[0]
for cycle in trial.cycles:
if not(cycle.name in cycles): continue
kinematics_file = os.path.join(trial.results_exp_path, 'ik',
'%s_%s_ik_solution.mot' % (self.study.name, trial.id))
self.kinematics_files.append(kinematics_file)
self.rel_kinematics_files.append(
os.path.relpath(kinematics_file, self.path)
)
kinetics_file = os.path.join(trial.results_exp_path, 'id',
'results', '%s_%s_id_solution.sto' % (self.study.name,
trial.id))
self.kinetics_files.append(kinetics_file)
self.rel_kinetics_files.append(
os.path.relpath(kinetics_file, self.path)
)
emg_file = os.path.join(trial.results_exp_path, 'expdata',
'emg.sto')
self.emg_files.append(emg_file)
self.rel_emg_files.append(
os.path.relpath(emg_file, self.path)
)
self.init_times.append(cycle.start)
self.final_times.append(cycle.end)
self.cycle_ids.append(cycle.id)
self.trial_results_exp_paths.append(trial.results_exp_path)
self.results_setup_fpath = os.path.join(self.path, 'setup.m')
self.results_output_fpath = os.path.join(self.path,
'%s_%s_calibrate.mat' % (self.study.name, self.subject.name))
self.param_dict = param_dict
self.cost_dict = cost_dict
self.passive_precalibrate = passive_precalibrate
# Fill out setup.m template and write to results directory
self.create_setup_action()
def create_setup_action(self):
if not os.path.exists(self.path): os.makedirs(self.path)
self.add_action(
['templates/%s/setup.m' % self.tool],
[self.results_setup_fpath],
self.fill_setup_template,
)
def fill_setup_template(self, file_dep, target):
with open(file_dep[0]) as ft:
content = ft.read()
possible_params = ['optimal_fiber_length', 'tendon_slack_length',
'pennation_angle', 'muscle_strain']
paramstr = ''
for param in possible_params:
if param in self.param_dict:
paramstr += param + ' = true;\n'
else:
paramstr += param + ' = false;\n'
possible_costs = ['emg']
coststr = ''
for cost in possible_costs:
if cost in self.cost_dict:
coststr += cost + ' = true;\n'
else:
coststr += cost + ' = false;\n'
pass_cal = ''