Moved data handling outside of main script into sleepdata module
This commit is contained in:
Nate Schoolfield
parent
8435b2bd69
commit
7bf0e8539d
285
bluesleep.py
285
bluesleep.py
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@ -4,12 +4,7 @@ from bluepy import btle
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from bluepy.btle import BTLEDisconnectError
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from miband import miband
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import csv
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import random
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from os import path
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import sleepdata
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import threading
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import re
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@ -18,69 +13,23 @@ import subprocess
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import time
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from datetime import datetime
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sleep_data = {
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'heartrate': {
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'value_name': 'bpm',
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'periods': [2, 5, 10, 15],
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'raw_data': [],
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'averaged_data': [],
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},
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'movement':{
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'value_name': 'movement',
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'periods': [10, 30, 60],
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'raw_data': [],
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'averaged_data': [],
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'workspace': {
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'gyro_last_x' : 0,
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'gyro_last_y' : 0,
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'gyro_last_z' : 0
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}
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}
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}
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auth_key_filename = 'auth_key.txt'
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mac_filename = 'mac.txt'
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csv_filename = "sleep_data.csv"
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plt.style.use('dark_background')
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graph_figure = plt.figure()
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graph_axes = graph_figure.add_subplot(1, 1, 1)
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graph_data = {}
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last_heartrate = 0
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last_tick_time = None
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tick_seconds = 0.5
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fieldnames = ['time']
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for data_type in sleep_data:
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periods = sleep_data[data_type]['periods']
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for period in periods:
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fieldnames.append(data_type + str(period))
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band = None
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#-------------------------------------------------------------------------#
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def write_csv(data):
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global fieldnames
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global csv_filename
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if not path.exists(csv_filename):
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with open(csv_filename, 'w', newline='') as csvfile:
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csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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csv_writer.writeheader()
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csv_writer.writerow(data)
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else:
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with open(csv_filename, 'a', newline='') as csvfile:
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csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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csv_writer.writerow(data)
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class regex_patterns():
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mac_regex_pattern = re.compile(r'([0-9a-fA-F]{2}(?::[0-9a-fA-F]{2}){5})')
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authkey_regex_pattern = re.compile(r'([0-9a-fA-F]){32}')
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def get_mac_address(filename):
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mac_regex_pattern = re.compile(r'([0-9a-fA-F]{2}(?::[0-9a-fA-F]{2}){5})')
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try:
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with open(filename, "r") as f:
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hwaddr_search = re.search(mac_regex_pattern, f.read().strip())
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hwaddr_search = re.search(regex_patterns.mac_regex_pattern, f.read().strip())
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if hwaddr_search:
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MAC_ADDR = hwaddr_search[0]
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else:
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@ -93,10 +42,9 @@ def get_mac_address(filename):
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def get_auth_key(filename):
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authkey_regex_pattern = re.compile(r'([0-9a-fA-F]){32}')
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try:
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with open(filename, "r") as f:
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key_search = re.search(authkey_regex_pattern, f.read().strip())
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key_search = re.search(regex_patterns.authkey_regex_pattern, f.read().strip())
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if key_search:
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AUTH_KEY = bytes.fromhex(key_search[0])
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else:
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@ -107,210 +55,28 @@ def get_auth_key(filename):
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exit(1)
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return AUTH_KEY
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def process_heartrate_data(heartrate_data, tick_time):
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print("BPM: " + str(heartrate_data))
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if heartrate_data > 0:
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value_name = sleep_data['heartrate']['value_name']
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sleep_data['heartrate']['raw_data'].append({
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'time': tick_time,
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value_name: heartrate_data
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} )
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def process_gyro_data(gyro_data, tick_time):
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# Each gyro reading from miband4 comes over as a group of three,
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# each containing x,y,z values. This function summarizes the
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# values into a single consolidated movement value.
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global sleep_data
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sleep_move = sleep_data['movement']
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sleep_workspace = sleep_move['workspace']
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gyro_last_x = sleep_workspace['gyro_last_x']
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gyro_last_y = sleep_workspace['gyro_last_y']
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gyro_last_z = sleep_workspace['gyro_last_z']
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value_name = sleep_move['value_name']
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gyro_movement = 0
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for gyro_datum in gyro_data:
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gyro_delta_x = abs(gyro_datum['x'] - gyro_last_x)
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gyro_last_x = gyro_datum['x']
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gyro_delta_y = abs(gyro_datum['y'] - gyro_last_y)
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gyro_last_y = gyro_datum['y']
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gyro_delta_z = abs(gyro_datum['z'] - gyro_last_z)
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gyro_last_z = gyro_datum['z']
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gyro_delta_sum = gyro_delta_x + gyro_delta_y + gyro_delta_z
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gyro_movement += gyro_delta_sum
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sleep_workspace['gyro_last_x'] = gyro_last_x
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sleep_workspace['gyro_last_y'] = gyro_last_y
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sleep_workspace['gyro_last_z'] = gyro_last_z
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sleep_move['raw_data'].append({
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'time': tick_time,
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value_name: gyro_movement
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})
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def flush_old_raw_data(tick_time):
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global sleep_data
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for data_type in sleep_data:
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s_data = sleep_data[data_type]
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periods = s_data['periods']
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cleaned_raw_data = []
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for raw_datum in s_data['raw_data']:
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datum_age = tick_time - raw_datum['time']
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if datum_age < max(periods):
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cleaned_raw_data.append(raw_datum)
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s_data['raw_data'] = cleaned_raw_data
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def average_raw_data(tick_time):
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global sleep_data
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global last_heartrate
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timestamp = datetime.fromtimestamp(tick_time)
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csv_out = {'time': timestamp }
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for data_type in sleep_data:
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s_data = sleep_data[data_type]
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period_averages_dict = {'time': timestamp}
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periods = s_data['periods']
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value_name = s_data['value_name']
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flush_old_raw_data(tick_time)
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for period_seconds in periods:
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period_data = []
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period_averages_dict[period_seconds] = 0
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for raw_datum in s_data['raw_data']:
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datum_age_seconds = tick_time - raw_datum['time']
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if datum_age_seconds < period_seconds:
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period_data.append(raw_datum[value_name])
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if len(period_data) > 0:
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period_data_average = sum(period_data) / len(period_data)
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else:
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print("({}) Period data empty: {}".format(data_type,
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period_seconds))
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if data_type == "heartrate" and period_seconds == min(periods):
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period_data_average = last_heartrate
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else:
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period_data_average = 0
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period_averages_dict[period_seconds] = zero_to_nan(period_data_average)
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csv_out[data_type + str(period_seconds)] = zero_to_nan(period_data_average)
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s_data['averaged_data'].append(period_averages_dict)
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write_csv(csv_out)
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def zero_to_nan(value):
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if value == 0:
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return (float('nan'))
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return int(value)
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def process_data(data, tick_time):
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if data[0] == "GYRO":
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sleepdata.process_gyro_data(data[1], tick_time)
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elif data[0] == "HR":
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sleepdata.process_heartrate_data(data[1], tick_time)
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def sleep_monitor_callback(data):
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global sleep_data
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global last_tick_time
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tick_time = time.time()
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if not last_tick_time:
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last_tick_time = time.time()
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if not sleepdata.last_tick_time:
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sleepdata.last_tick_time = time.time()
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if data[0] == "GYRO":
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process_gyro_data(data[1], tick_time)
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elif data[0] == "HR":
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process_heartrate_data(data[1], tick_time)
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process_data(data, tick_time)
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if (tick_time - last_tick_time) >= tick_seconds:
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average_raw_data(tick_time)
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last_tick_time = time.time()
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if (tick_time - sleepdata.last_tick_time) >= sleepdata.tick_seconds:
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sleepdata.average_raw_data(tick_time)
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sleepdata.last_tick_time = time.time()
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def init_graph_data():
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for data_type in sleep_data:
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data_periods = sleep_data[data_type]['periods']
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graph_data[data_type] = {
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'time': [],
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'data': {}
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}
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for period in data_periods:
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graph_data[data_type]['data'][period] = []
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def update_graph_data():
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global sleep_data
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global graph_data
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for data_type in sleep_data:
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s_data = sleep_data[data_type] # Re-referenced to shorten name
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avg_data = s_data['averaged_data']
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if len(avg_data) > 1:
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g_data = graph_data[data_type] # Re-referenced to short name
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data_periods = s_data['periods']
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starting_index = max([(len(g_data['time']) - 1), 0])
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ending_index = len(avg_data) - 1
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# Re-referenced to shorten name
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sleep_data_range = avg_data[starting_index:ending_index]
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for sleep_datum in sleep_data_range:
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g_data['time'].append(sleep_datum['time'])
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for period in data_periods:
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if g_data['data'][period] != 'nan':
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g_data['data'][period].append(sleep_datum[period])
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def graph_animation(i):
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global sleep_data
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global graph_axes
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global graph_data
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plotflag = False
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if len(graph_data) == 0:
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init_graph_data()
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update_graph_data()
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for data_type in graph_data:
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if len(graph_data[data_type]['time']) > 0:
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graph_axes.clear()
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break
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for data_type in sleep_data:
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s_data = sleep_data[data_type]
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g_data = graph_data[data_type]
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if len(g_data['time']) > 0:
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plotflag = True
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data_periods = sleep_data[data_type]['periods']
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for period in data_periods:
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axis_label = "{} {} sec".format(s_data['value_name'], period)
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graph_axes.plot(g_data['time'],
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g_data['data'][period],
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label=axis_label)
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if plotflag:
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plt.legend()
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def connect():
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def connect(mac_filename, auth_key_filename):
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global band
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global mac_filename
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global auth_key_filename
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success = False
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timeout = 3
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msg = 'Connection to the MIBand failed. Trying again in {} seconds'
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msg = 'Connection to the band failed. Trying again in {} seconds'
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MAC_ADDR = get_mac_address(mac_filename)
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AUTH_KEY = get_auth_key(auth_key_filename)
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@ -326,10 +92,7 @@ def connect():
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print("\nExit.")
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exit()
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def start_data_pull():
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global band
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while True:
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try:
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band.start_heart_and_gyro(sensitivity=1, callback=sleep_monitor_callback)
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@ -357,6 +120,7 @@ def vibrate_pattern(duration):
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time.sleep(vibro_delay)
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def vibrate_rolling():
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print("Sending rolling vibration...")
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for x in range(10):
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for x in range(20, 40, 1):
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band.vibrate(x)
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@ -364,11 +128,12 @@ def vibrate_rolling():
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band.vibrate(x)
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if __name__ == "__main__":
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connect()
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connect(mac_filename, auth_key_filename)
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#vibrate_pattern(10)
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data_gather_thread = threading.Thread(target=start_data_pull)
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data_gather_thread.start()
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ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
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plt.show()
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sleepdata.init_graph()
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#import simpleaudio as sa
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@ -0,0 +1,45 @@
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class miband4():
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class bytepatterns():
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vibration = 'ff{:02x}00000001'
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gyro_start = '01{:02x}19'
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start = '0100'
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stop = '0000'
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heart_measure_keepalive = '16'
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stop_heart_measure_continues = '150100'
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start_heart_measure_continues = '150101'
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stop_heart_measure_manual = '150200'
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fetch_begin = '100101'
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fetch_error = '100104'
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fetch_continue = '100201'
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fetch_complete = '100204'
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auth_ok = '100301'
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request_random_number = '0200'
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auth_key_prefix = '0300'
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def vibration(duration):
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byte_pattern = miband4.bytepatterns.vibration
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return bytes.fromhex(byte_pattern.format(duration))
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def gyro_start(sensitivity):
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byte_pattern = miband4.bytepatterns.gyro_start
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return bytes.fromhex(byte_pattern.format(sensitivity))
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start = bytes.fromhex(bytepatterns.start)
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stop = bytes.fromhex(bytepatterns.stop)
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heart_measure_keepalive = bytes.fromhex(bytepatterns.heart_measure_keepalive)
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stop_heart_measure_continues = bytes.fromhex(bytepatterns.stop_heart_measure_continues)
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start_heart_measure_continues = bytes.fromhex(bytepatterns.start_heart_measure_continues)
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stop_heart_measure_manual = bytes.fromhex(bytepatterns.stop_heart_measure_manual)
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fetch_begin = bytes.fromhex(bytepatterns.fetch_begin)
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fetch_error = bytes.fromhex(bytepatterns.fetch_error)
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fetch_continue = bytes.fromhex(bytepatterns.fetch_continue)
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fetch_complete = bytes.fromhex(bytepatterns.fetch_complete)
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auth_ok = bytes.fromhex(bytepatterns.auth_ok)
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request_random_number = bytes.fromhex(bytepatterns.request_random_number)
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auth_key_prefix = bytes.fromhex(bytepatterns.auth_key_prefix)
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29
miband.py
29
miband.py
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@ -3,6 +3,8 @@ import logging
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import struct
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import binascii
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from bytepatterns import miband4 as bytepattern
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from bluepy.btle import (
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Peripheral, DefaultDelegate,
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ADDR_TYPE_RANDOM, ADDR_TYPE_PUBLIC,
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@ -55,33 +57,6 @@ class Delegate(DefaultDelegate):
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print ("Unhandled handle: " + str(hnd) + " | Data: " + str(data))
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class bytepattern():
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def vibration(duration):
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byte_pattern = 'ff{:02x}00000001'
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return bytes.fromhex(byte_pattern.format(duration))
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def gyro_start(sensitivity):
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byte_pattern = '01{:02x}19'
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return bytes.fromhex(byte_pattern.format(sensitivity))
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start = bytes.fromhex('0100')
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stop = bytes.fromhex('0000')
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heart_measure_keepalive = bytes.fromhex('16')
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stop_heart_measure_continues = bytes.fromhex('150100')
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start_heart_measure_continues = bytes.fromhex('150101')
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stop_heart_measure_manual = bytes.fromhex('150200')
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fetch_begin = bytes.fromhex('100101')
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fetch_error = bytes.fromhex('100104')
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fetch_continue = bytes.fromhex('100201')
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fetch_complete = bytes.fromhex('100204')
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auth_ok = bytes.fromhex('100301')
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request_random_number = bytes.fromhex('0200')
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auth_key_prefix = bytes.fromhex('0300')
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252
sleepdata.py
252
sleepdata.py
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@ -1,24 +1,236 @@
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class Sleep_Data(object):
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def __init__(self):
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print("init")
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from datetime import datetime
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from os import path
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import csv
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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#Todo: separate graph animation from data averaging
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#Todo: log raw data separately from average data
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sleep_data = {
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'heartrate': {
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'value_name': 'bpm',
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'periods': [2, 5, 10, 15],
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'raw_data': [],
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'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
|
||||
}
|
||||
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
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
last_heartrate = 0
|
||||
last_tick_time = None
|
||||
tick_seconds = 0.5
|
||||
|
||||
csv_filename = "sleep_data.csv"
|
||||
|
||||
fieldnames = ['time']
|
||||
for data_type in sleep_data:
|
||||
periods = sleep_data[data_type]['periods']
|
||||
for period in periods:
|
||||
fieldnames.append(data_type + str(period))
|
||||
|
||||
|
||||
plt.style.use('dark_background')
|
||||
graph_figure = plt.figure()
|
||||
graph_axes = graph_figure.add_subplot(1, 1, 1)
|
||||
graph_data = {}
|
||||
|
||||
|
||||
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 flush_old_raw_data(tick_time):
|
||||
for data_type in sleep_data:
|
||||
s_data = sleep_data[data_type]
|
||||
periods = s_data['periods']
|
||||
|
||||
cleaned_raw_data = []
|
||||
|
||||
for raw_datum in s_data['raw_data']:
|
||||
datum_age = tick_time - raw_datum['time']
|
||||
if datum_age < max(periods):
|
||||
cleaned_raw_data.append(raw_datum)
|
||||
|
||||
s_data['raw_data'] = cleaned_raw_data
|
||||
|
||||
def write_csv(data):
|
||||
a = ''
|
||||
|
||||
|
||||
def average_raw_data(tick_time):
|
||||
global last_heartrate
|
||||
timestamp = datetime.fromtimestamp(tick_time)
|
||||
csv_out = {'time': timestamp }
|
||||
|
||||
for data_type in sleep_data:
|
||||
s_data = sleep_data[data_type]
|
||||
period_averages_dict = {'time': timestamp}
|
||||
periods = s_data['periods']
|
||||
value_name = s_data['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_data['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))
|
||||
if data_type == "heartrate" and period_seconds == min(periods):
|
||||
period_data_average = last_heartrate
|
||||
else:
|
||||
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_data['averaged_data'].append(period_averages_dict)
|
||||
write_csv(csv_out)
|
||||
|
||||
|
||||
|
||||
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.
|
||||
|
||||
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 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 zero_to_nan(value):
|
||||
if value == 0:
|
||||
return (float('nan'))
|
||||
return int(value)
|
||||
|
||||
def update_graph_data():
|
||||
for data_type in sleep_data:
|
||||
s_data = sleep_data[data_type] # Re-referenced to shorten name
|
||||
avg_data = s_data['averaged_data']
|
||||
|
||||
if len(avg_data) > 1:
|
||||
|
||||
g_data = graph_data[data_type] # Re-referenced to short name
|
||||
data_periods = s_data['periods']
|
||||
|
||||
starting_index = max([(len(g_data['time']) - 1), 0])
|
||||
ending_index = len(avg_data) - 1
|
||||
|
||||
# Re-referenced to shorten name
|
||||
sleep_data_range = avg_data[starting_index:ending_index]
|
||||
|
||||
for sleep_datum in sleep_data_range:
|
||||
g_data['time'].append(sleep_datum['time'])
|
||||
for period in data_periods:
|
||||
if g_data['data'][period] != 'nan':
|
||||
g_data['data'][period].append(sleep_datum[period])
|
||||
|
||||
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 graph_animation(i):
|
||||
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_data = sleep_data[data_type]
|
||||
g_data = graph_data[data_type]
|
||||
if len(g_data['time']) > 0:
|
||||
plotflag = True
|
||||
data_periods = sleep_data[data_type]['periods']
|
||||
for period in data_periods:
|
||||
axis_label = "{} {} sec".format(s_data['value_name'], period)
|
||||
graph_axes.plot(g_data['time'],
|
||||
g_data['data'][period],
|
||||
label=axis_label)
|
||||
|
||||
if plotflag:
|
||||
plt.legend()
|
||||
|
||||
def init_graph():
|
||||
ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
|
||||
plt.show()
|
||||
|
|
|
|||
Loading…
Reference in New Issue