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'])