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distance.py
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167 lines (144 loc) · 5.96 KB
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import os
import pathlib
import sys
import numpy as np
import pandas as pd
from datetime import datetime
from datetime import timezone
import matplotlib.pyplot as plt
import warnings
from scipy import signal
from scipy.integrate import simpson
## arg data: pass in the returned cleaned data from combine
def custom_filter(data):
for i in range(0,len(data)):
b, a = signal.butter(3, 0.35, btype='lowpass', analog=False)
low_passed_butter = signal.filtfilt(b, a, data[i]['gFx'])
data[i]['filtered_gFx'] = low_passed_butter
data[i]['filtered_gFx'] = data[i]['filtered_gFx'].abs()
b, a = signal.butter(3, 0.25, btype='lowpass', analog=False)
low_passed_butter = signal.filtfilt(b, a, data[i]['gFy'])
data[i]['filtered_gFy'] = low_passed_butter
data[i]['filtered_gFy'] = data[i]['filtered_gFy'].abs()
b, a = signal.butter(3, 0.35, btype='lowpass', analog=False)
low_passed_butter = signal.filtfilt(b, a, data[i]['gFz'])
data[i]['filtered_gFz'] = low_passed_butter
data[i]['filtered_gFz'] = data[i]['filtered_gFz'].abs()
data[i] = data[i].drop(['timestamp2', 'timestamp_y', 'cadence', 'fractional_cadence'],axis=1)
return data
def parseAccl(data, splits):
data['time'] = pd.to_datetime(data['time'], format='%Y-%m-%d %H:%M:%S')
data['time'] = data['time'] - data['time'][0]
# data['time'] = pd.to_datetime(data['time'],format= '%H:%M:%S' ).dt.time
# data['time'] = data['time'].values.astype(np.int64) // 10 ** 6
data['timestamp'] = data['time'].round('50ms')
data = data.groupby(['timestamp'], as_index=False).mean()
# print(data)
splitted_data = []
for split in splits:
# print(split)
index = data.loc[data['timestamp'] == split].index[0]
splitted_data.append(data.iloc[index:index+2400,]) ## 120s * 10
return splitted_data
def parseSplits(data):
data['start_time'] = data['start_time'] - data['start_time'][0]
data['start_time'] = data['start_time']*1000
data['start_time'] = pd.to_datetime(data['start_time'], unit='ms')
data['start_time'] = data['start_time'] - data['start_time'][0]
data = data.iloc[1: , :]
return data['start_time'].iloc[::2,]
def parseActivity(data, splits):
data['timestamp'] = data['timestamp'] - data['timestamp'][0]
data['timestamp'] = data['timestamp']*1000
data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')
data['timestamp'] = data['timestamp'] - data['timestamp'][0]
splitted_data = []
for split in splits:
# print(split)
index = data.loc[data['timestamp'] == split].index[0]
splitted_data.append(data.iloc[index:index+120,])
return splitted_data
def combine(activity_data, accl_data):
data = []
for i in range(len(activity_data)):
acc = accl_data[i]
act = activity_data[i]
acc['timestamp2'] = acc['timestamp'].dt.floor('s')
acc = pd.merge(acc, act, left_on='timestamp2', right_on='timestamp', how='left')
acc = acc.fillna(method='ffill')
data.append(acc)
return data
def split_waves(data):
wave = []
first_dip = 0
dip_flag = False
# print(data)
for i in range(1, len(data)-1):
if ((data['filtered_gFy'][i-1] > data['filtered_gFy'][i]) and (data['filtered_gFy'][i] < data['filtered_gFy'][i+1])):
first_dip = i
break
# first_dip = 1280
for i in range(first_dip+1, len(data)-1):
# for i in range(1280, 1380):
if ((data['filtered_gFy'][i-1] > data['filtered_gFy'][i]) and (data['filtered_gFy'][i] < data['filtered_gFy'][i+1])):
if (not dip_flag):
dip_flag = True
else:
if ((i-first_dip+1) > 6):
wave.append(data.loc[first_dip:i])
# plt.plot(range(i-first_dip+1),data.loc[first_dip:i]['filtered_gFy'])
first_dip = i
dip_flag = False
# data.loc[1350:1400]['filtered_gFx'].to_csv("x_subsetData", index=False)
# plt.show()
# distance = avg velocity * time
# avg velocity = area under curve of acceleration
distance = []
for i in range(0, len(wave)):
area = simpson(wave[i]['filtered_gFy'], dx=5)
cur_distance = round(area * len(wave[i]))
distance.append(cur_distance)
# print(distance)
# plt.hist(distance, bins=25)
# plt.show()
return distance
def main():
warnings.filterwarnings("ignore")
input_directory_tread = pathlib.Path(sys.argv[1])
input_directory_out = pathlib.Path(sys.argv[1])
## treadmill data
accl = pd.read_csv(input_directory_tread / 'accl.csv', index_col=False)
splits = pd.read_csv(input_directory_tread / 'splits.csv')
activity = pd.read_csv(input_directory_tread / 'activity.csv')
splits = parseSplits(splits)
accl = parseAccl(accl, splits)
activity = parseActivity(activity, splits)
combined = combine(activity, accl)
filtered_combined = custom_filter(combined)
# print(filtered_combined)
distance = []
for sub_data in filtered_combined:
distance += split_waves(sub_data)
plt.hist(distance, bins=50)
# plt.show()
# save file
pd.DataFrame({"distance":distance}).to_csv("treadmill_distance.csv", index=False)
## outside data
accl = pd.read_csv(input_directory_out / 'accl.csv', index_col=False)
splits = pd.read_csv(input_directory_out / 'splits.csv')
activity = pd.read_csv(input_directory_out / 'activity.csv')
splits = parseSplits(splits)
accl = parseAccl(accl, splits)
activity = parseActivity(activity, splits)
combined = combine(activity, accl)
filtered_combined = custom_filter(combined)
# print(filtered_combined)
distance = []
for sub_data in filtered_combined:
distance += split_waves(sub_data)
plt.hist(distance, bins=50)
# plt.show()
# save file
pd.DataFrame({"distance":distance}).to_csv("outside_distance.csv", index=False)
if __name__ == "__main__":
main()