def objective_step_detection_single_side_lhs(p):
s = create_sd_class_for_obj_functions()
s.normalize_norm()
s.select_specific_samples(p['sample_ids'])
s.step_detection_single_side(side='lhs',
signal_to_use='norm',
vert_win=None,
use_single_max_min_for_all_samples=True,
smoothing='mva',
mva_win=p['mva_win'],
peak_min_thr=p['peak_min_thr'],
peak_min_dist=p['peak_min_dist'],
verbose=p['verbose'],
do_normalization=False)
s.add_gait_metrics(verbose=False,
max_dist_from_apdm=p['max_dist_from_apdm'])
return get_obj_function_results(s,
'lhs',
p['metric'],
verbose=p['verbose'])
def step_detection_fusion_high_level_union_two_stages(p):
s = create_sd_class_for_obj_functions()
s.normalize_norm()
s.select_specific_samples(p['sample_ids'])
s.step_detection_fusion_high_level(signal_to_use='norm',
vert_win=None,
use_single_max_min_for_all_samples=True,
smoothing='mva',
mva_win=p['mva_win'],
peak_min_thr=p['peak_min_thr'],
peak_min_dist=p['peak_min_dist'],
fusion_type='union_two_stages',
intersect_win=p['intersect_win'],
union_min_dist=p['union_min_dist'],
union_min_thresh=p['union_min_thresh'],
verbose=p['verbose'],
do_normalization=False)
s.add_gait_metrics(verbose=False,
max_dist_from_apdm=p['max_dist_from_apdm'])
return get_obj_function_results(s,
'fusion_high_level_union_two_stages',
p['metric'],
verbose=p['verbose'])