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#!/usr/bin/env python3
from bluepy import btle
from bluepy.btle import BTLEDisconnectError
from miband import miband
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import csv
import random
from os import path
import threading
import re
import subprocess
import time
from datetime import datetime
sleep_data = {
'heartrate': {
'value_name': 'bpm',
'periods': [2, 5, 10, 15],
'raw_data': [],
'averaged_data': [],
},
'movement':{
'value_name': 'movement',
'periods': [10, 30, 60],
'raw_data': [],
'averaged_data': [],
'workspace': {
'gyro_last_x' : 0,
'gyro_last_y' : 0,
'gyro_last_z' : 0
}
}
}
auth_key_filename = 'auth_key.txt'
mac_filename = 'mac.txt'
csv_filename = "sleep_data.csv"
plt.style.use('dark_background')
graph_figure = plt.figure()
graph_axes = graph_figure.add_subplot(1, 1, 1)
graph_data = {}
last_tick_time = None
tick_seconds = 0.5
fieldnames = ['time']
for data_type in sleep_data:
periods = sleep_data[data_type]['periods']
for period in periods:
fieldnames.append(data_type + str(period))
#-------------------------------------------------------------------------#
def write_csv(data):
global fieldnames
global csv_filename
if not path.exists(csv_filename):
with open(csv_filename, 'w', newline='') as csvfile:
csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
csv_writer.writeheader()
csv_writer.writerow(data)
else:
with open(csv_filename, 'a', newline='') as csvfile:
csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
csv_writer.writerow(data)
def get_mac_address(filename):
mac_regex_pattern = re.compile(r'([0-9a-fA-F]{2}(?::[0-9a-fA-F]{2}){5})')
try:
with open(filename, "r") as f:
hwaddr_search = re.search(mac_regex_pattern, f.read().strip())
if hwaddr_search:
MAC_ADDR = hwaddr_search[0]
else:
print ("No valid MAC address found in {}".format(filename))
exit(1)
except FileNotFoundError:
print ("MAC file not found: {}".format(filename))
exit(1)
return MAC_ADDR
def get_auth_key(filename):
authkey_regex_pattern = re.compile(r'([0-9a-fA-F]){32}')
try:
with open(filename, "r") as f:
key_search = re.search(authkey_regex_pattern, f.read().strip())
if key_search:
AUTH_KEY = bytes.fromhex(key_search[0])
else:
print ("No valid auth key found in {}".format(filename))
exit(1)
except FileNotFoundError:
print ("Auth key file not found: {}".format(filename))
exit(1)
return AUTH_KEY
def process_heartrate_data(heartrate_data, tick_time):
print("BPM: " + str(heartrate_data))
if heartrate_data > 0:
value_name = sleep_data['heartrate']['value_name']
sleep_data['heartrate']['raw_data'].append({
'time': tick_time,
value_name: heartrate_data
} )
def process_gyro_data(gyro_data, tick_time):
# Each gyro reading from miband4 comes over as a group of three,
# each containing x,y,z values. This function summarizes the
# values into a single consolidated movement value.
global sleep_data
sleep_move = sleep_data['movement']
sleep_workspace = sleep_move['workspace']
gyro_last_x = sleep_workspace['gyro_last_x']
gyro_last_y = sleep_workspace['gyro_last_y']
gyro_last_z = sleep_workspace['gyro_last_z']
value_name = sleep_move['value_name']
gyro_movement = 0
for gyro_datum in gyro_data:
gyro_delta_x = abs(gyro_datum['x'] - gyro_last_x)
gyro_last_x = gyro_datum['x']
gyro_delta_y = abs(gyro_datum['y'] - gyro_last_y)
gyro_last_y = gyro_datum['y']
gyro_delta_z = abs(gyro_datum['z'] - gyro_last_z)
gyro_last_z = gyro_datum['z']
gyro_delta_sum = gyro_delta_x + gyro_delta_y + gyro_delta_z
gyro_movement += gyro_delta_sum
sleep_workspace['gyro_last_x'] = gyro_last_x
sleep_workspace['gyro_last_y'] = gyro_last_y
sleep_workspace['gyro_last_z'] = gyro_last_z
sleep_move['raw_data'].append({
'time': tick_time,
value_name: gyro_movement
})
def flush_old_raw_data(tick_time):
global sleep_data
for data_type in sleep_data:
s_datum = sleep_data[data_type]
periods = s_datum['periods']
cleaned_raw_data = []
for raw_datum in s_datum['raw_data']:
datum_age = tick_time - raw_datum['time']
if datum_age < max(periods):
cleaned_raw_data.append(raw_datum)
s_datum['raw_data'] = cleaned_raw_data
def average_raw_data(tick_time):
global sleep_data
timestamp = datetime.fromtimestamp(tick_time)
csv_out = {'time': timestamp }
for data_type in sleep_data:
s_datum = sleep_data[data_type]
period_averages_dict = {'time': timestamp}
periods = s_datum['periods']
value_name = s_datum['value_name']
flush_old_raw_data(tick_time)
for period_seconds in periods:
period_data = []
period_averages_dict[period_seconds] = 0
for raw_datum in s_datum['raw_data']:
datum_age_seconds = tick_time - raw_datum['time']
if datum_age_seconds < period_seconds:
period_data.append(raw_datum[value_name])
if len(period_data) > 0:
period_data_average = sum(period_data) / len(period_data)
else:
print("({}) Period data empty: {}".format(data_type,
period_seconds))
period_data_average = 0
period_averages_dict[period_seconds] = zero_to_nan(period_data_average)
csv_out[data_type + str(period_seconds)] = zero_to_nan(period_data_average)
s_datum['averaged_data'].append(period_averages_dict)
write_csv(csv_out)
def zero_to_nan(value):
if value == 0:
return (float('nan'))
return int(value)
def sleep_monitor_callback(data):
global sleep_data
global last_tick_time
tick_time = time.time()
if not last_tick_time:
last_tick_time = time.time()
if data[0] == "GYRO":
process_gyro_data(data[1], tick_time)
elif data[0] == "HR":
process_heartrate_data(data[1], tick_time)
if (tick_time - last_tick_time) >= tick_seconds:
average_raw_data(tick_time)
last_tick_time = time.time()
def init_graph_data():
for data_type in sleep_data:
data_periods = sleep_data[data_type]['periods']
graph_data[data_type] = {
'time': [],
'data': {}
}
for period in data_periods:
graph_data[data_type]['data'][period] = []
def update_graph_data():
global sleep_data
global graph_data
for data_type in sleep_data:
s_datum = sleep_data[data_type] # Re-referenced to shorten name
avg_datum = s_datum['averaged_data']
if len(avg_datum) > 1:
g_datum = graph_data[data_type] # Re-referenced to short name
data_periods = s_datum['periods']
starting_index = max([(len(g_dataum['time']) - 1), 0])
ending_index = len(avg_datum) - 1
# Re-referenced to shorten name
sleep_data_range = avg_datum[starting_index:ending_index]
for sleep_datum in sleep_data_range:
g_datum['time'].append(sleep_datum['time'])
for period in data_periods:
if g_datum['data'][period] != 'nan':
g_datum['data'][period].append(sleep_datum[period])
def graph_animation(i):
global sleep_data
global graph_axes
global graph_data
plotflag = False
if len(graph_data) == 0:
init_graph_data()
update_graph_data()
for data_type in graph_data:
if len(graph_data[data_type]['time']) > 0:
graph_axes.clear()
break
for data_type in sleep_data:
s_datum = sleep_data[data_type]
g_datum = graph_data[data_type]
if len(g_datum['time']) > 0:
plotflag = True
data_periods = sleep_data[data_type]['periods']
for period in data_periods:
axis_label = "{} {} sec".format(s_datum['value_name'], period)
graph_axes.plot(g_datum['time'],
g_datum['data'][period],
label=axis_label)
if plotflag:
plt.legend()
def connect():
global band
global mac_filename
global auth_key_filename
success = False
timeout = 3
msg = 'Connection to the MIBand failed. Trying again in {} seconds'
MAC_ADDR = get_mac_address(mac_filename)
AUTH_KEY = get_auth_key(auth_key_filename)
while not success:
try:
band = miband(MAC_ADDR, AUTH_KEY, debug=True)
success = band.initialize()
except BTLEDisconnectError:
print(msg.format(timeout))
time.sleep(timeout)
except KeyboardInterrupt:
print("\nExit.")
exit()
def start_data_pull():
global band
while True:
try:
band.start_heart_and_gyro(callback=sleep_monitor_callback)
except BTLEDisconnectError:
band.gyro_started_flag = False
connect()
if __name__ == "__main__":
connect()
data_gather_thread = threading.Thread(target=start_data_pull)
data_gather_thread.start()
ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
plt.show()
#import simpleaudio as sa
# comfort_wav = 'comfort.wav'
# wave_obj = sa.WaveObject.from_wave_file(comfort_wav)
# comfort_delay = 30
# comfort_lasttime = time.time()