Merge pull request #1 from rmackinnon/master
Code review and PEP8 clean-ups
This commit is contained in:
NateSchoolfield
GitHub
commit
c23c3a4c81
137
bluesleep.py
137
bluesleep.py
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@ -51,7 +51,7 @@ 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.items():
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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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@ -78,14 +78,15 @@ 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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regex_match_from_file = re.search(mac_regex_pattern, f.read().strip())
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if regex_match_from_file:
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MAC_ADDR = regex_match_from_file[0]
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hwaddr_search = re.search(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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print ("No valid MAC address found in " + str(filename))
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print ("No valid MAC address found in {}".format(filename))
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exit(1)
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except FileNotFoundError:
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print ("MAC file not found: " + filename)
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print ("MAC file not found: {}".format(filename))
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exit(1)
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return MAC_ADDR
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@ -94,14 +95,14 @@ 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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regex_match_from_file = re.search(authkey_regex_pattern, f.read().strip())
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if regex_match_from_file:
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AUTH_KEY = bytes.fromhex(regex_match_from_file[0])
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key_search = re.search(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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print ("No valid auth key found in " + str(filename))
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print ("No valid auth key found in {}".format(filename))
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exit(1)
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except FileNotFoundError:
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print ("Auth key file not found: " + filename)
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print ("Auth key file not found: {}".format(filename))
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exit(1)
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return AUTH_KEY
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@ -110,19 +111,26 @@ 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({ 'time': tick_time, value_name: heartrate_data } )
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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, each containing x,y,z values
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# This function summarizes the values into a single consolidated movement value
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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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gyro_last_x = sleep_data['movement']['workspace']['gyro_last_x']
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gyro_last_y = sleep_data['movement']['workspace']['gyro_last_y']
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gyro_last_z = sleep_data['movement']['workspace']['gyro_last_z']
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value_name = sleep_data['movement']['value_name']
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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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@ -134,27 +142,31 @@ def process_gyro_data(gyro_data, tick_time):
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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_data['movement']['workspace']['gyro_last_x'] = gyro_last_x
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sleep_data['movement']['workspace']['gyro_last_y'] = gyro_last_y
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sleep_data['movement']['workspace']['gyro_last_z'] = gyro_last_z
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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_data['movement']['raw_data'].append({ 'time': tick_time, value_name: gyro_movement } )
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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.items():
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periods = sleep_data[data_type]['periods']
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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_datum['periods']
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cleaned_raw_data = []
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for raw_datum in sleep_data[data_type]['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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sleep_data[data_type]['raw_data'] = cleaned_raw_data
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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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@ -163,18 +175,18 @@ def average_raw_data(tick_time):
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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.items():
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period_averages_dict = {}
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period_averages_dict['time'] = timestamp
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periods = sleep_data[data_type]['periods']
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value_name = sleep_data[data_type]['value_name']
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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 sleep_data[data_type]['raw_data']:
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for raw_datum in s_datum['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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@ -182,21 +194,21 @@ def average_raw_data(tick_time):
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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 ("(" + data_type + ") Period data empty: " + str(period_seconds))
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print("({}) Period data empty: {}".format(data_type,
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period_seconds))
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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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sleep_data[data_type]['averaged_data'].append(period_averages_dict)
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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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else:
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return int(value)
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@ -210,8 +222,7 @@ def sleep_monitor_callback(data):
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if data[0] == "GYRO":
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process_gyro_data(data[1], tick_time)
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if data[0] == "HR":
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elif data[0] == "HR":
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process_heartrate_data(data[1], tick_time)
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if (tick_time - last_tick_time) >= tick_seconds:
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@ -220,7 +231,7 @@ def sleep_monitor_callback(data):
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def init_graph_data():
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for data_type, _ in sleep_data.items():
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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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@ -229,23 +240,31 @@ def init_graph_data():
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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.items():
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if len(sleep_data[data_type]['averaged_data']) > 1:
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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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data_periods = sleep_data[data_type]['periods']
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if len(avg_data) > 1:
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starting_index = max([(len(graph_data[data_type]['time']) - 1), 0])
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ending_index = len(sleep_data[data_type]['averaged_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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for sleep_datum in sleep_data[data_type]['averaged_data'][starting_index:ending_index]:
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graph_data[data_type]['time'].append(sleep_datum['time'])
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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 graph_data[data_type]['data'][period] != 'nan':
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graph_data[data_type]['data'][period].append(sleep_datum[period])
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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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@ -259,28 +278,35 @@ def graph_animation(i):
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update_graph_data()
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for data_type, _ in graph_data.items():
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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.items():
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if len(graph_data[data_type]['time']) > 0:
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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_datum['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 = sleep_data[data_type]['value_name'] + " " + str(period) + "sec"
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graph_axes.plot(graph_data[data_type]['time'], graph_data[data_type]['data'][period], label=axis_label)
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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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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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MAC_ADDR = get_mac_address(mac_filename)
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AUTH_KEY = get_auth_key(auth_key_filename)
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@ -289,15 +315,14 @@ def connect():
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try:
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band = miband(MAC_ADDR, AUTH_KEY, debug=True)
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success = band.initialize()
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break
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except BTLEDisconnectError:
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print('Connection to the MIBand failed. Trying out again in 3 seconds')
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time.sleep(3)
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continue
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print(msg.format(timeout))
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time.sleep(timeout)
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except KeyboardInterrupt:
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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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14
miband.py
14
miband.py
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@ -1,13 +1,20 @@
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import sys, os, time
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import logging
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from bluepy.btle import Peripheral, DefaultDelegate, ADDR_TYPE_RANDOM,ADDR_TYPE_PUBLIC, BTLEException, BTLEDisconnectError
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from constants import UUIDS, AUTH_STATES, ALERT_TYPES, QUEUE_TYPES, MUSICSTATE
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import struct
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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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BTLEException, BTLEDisconnectError
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)
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from datetime import datetime, timedelta
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from Crypto.Cipher import AES
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from datetime import datetime
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from constants import (
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UUIDS, AUTH_STATES, ALERT_TYPES, QUEUE_TYPES, MUSICSTATE
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)
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try:
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from Queue import Queue, Empty
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except ImportError:
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@ -96,6 +103,7 @@ class Delegate(DefaultDelegate):
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else:
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print ("Unhandled handle: " + str(hnd) + " | Data: " + str(data))
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class miband(Peripheral):
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_send_rnd_cmd = struct.pack('<2s', b'\x02\x00')
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_send_enc_key = struct.pack('<2s', b'\x03\x00')
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