import multiprocessing
import os
from pathlib import Path
import pandas as pd
from utils.l1_trend_filter import l1_trend_filter
from utils.constants import movement_dir, questionnaire_dir, patient_dir
from utils.data_handling import load_all_files, get_data, get_data_from_observation
import numpy as np
data_path = '../preprocessed/'
quest_path = data_path + '/questionnaire/'
Path(quest_path).mkdir(parents=True, exist_ok=True)
mov_path = data_path + '/movement/'
Path(mov_path).mkdir(parents=True, exist_ok=True)
def preprocess_movement(df, overwrite=False):
id = df['subject_id'][0]
data, channels = get_data_from_observation(movement_dir, df)
channels_sorted = []
# Sort by the following pattern
for task in ['Relaxed1', 'Relaxed2', 'RelaxedTask1', 'RelaxedTask2', 'StretchHold', 'LiftHold', 'HoldWeight',
'PointFinger', 'DrinkGlas', 'CrossArms', 'TouchIndex', 'TouchNose', 'Entrainment1', 'Entrainment2']:
for wrist in ['LeftWrist', 'RightWrist']:
for sensor in ['Time', 'Accelerometer', 'Gyroscope']:
if sensor == 'Time':
channel_name = '_'.join([task, wrist, sensor])
channels_sorted.append(channel_name)
else:
for axis in ['X', 'Y', 'Z']:
channel_name = '_'.join([task, wrist, sensor, axis])
channels_sorted.append(channel_name)
sorting_indices = [channels.index(channel_name) for channel_name in channels_sorted]
data = data[sorting_indices]
channels = np.array(channels)[sorting_indices]
to_remove = 'Time|LiftHold|PointFinger|TouchIndex'
keep_mask = ~pd.Series(channels).str.contains(to_remove)
channels = channels[keep_mask]
to_process = 'Accelerometer'
process_mask = pd.Series(channels).str.contains(to_process)
# Check if file already exists
if not overwrite:
all_files = os.listdir(mov_path)
all_files = list(filter(lambda f: f.endswith('.bin'), all_files))
if f'{id}_ml.bin' in all_files:
return
# Remove assessment steps
data = data[keep_mask]
# Remove gravitational offset
data[process_mask, :] = np.apply_along_axis(lambda x: x - l1_trend_filter(x, vlambda=50, verbose=False), 1,
data[process_mask, :])
# Remove first half second of the signal (vibration notification)
data = data[:, 48:]
data.tofile(f'{mov_path}{id}_ml.bin')
if __name__ == '__main__':
# Store file list for ml project
df = pd.concat(load_all_files(patient_dir))
df['label'] = df['condition']
df.replace({'label': {'Healthy': 0,
"Parkinson's": 1,
'Other Movement Disorders': 2,
'Essential Tremor': 2,
'Multiple Sclerosis': 2,
'Atypical Parkinsonism': 2}},
inplace=True)
df.to_csv(f'{data_path}file_list.csv', index=False, sep=',')
# Store all questionnaire data for ml project
data, channels = get_data(questionnaire_dir)
for idx, data_sample in enumerate(data):
data_sample.tofile(f'{quest_path}{idx + 1:03d}_ml.bin')
# Store file list for ml project
df_list = load_all_files(movement_dir)
# Run in parallel
with multiprocessing.Pool() as pool:
# call the function for each item in parallel
pool.map(preprocess_movement, df_list)