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Split data output into average and individual files for each biometric, moved gyro averaging into its own class

This commit is contained in:
NateSchoolfield 2021-01-31 18:02:26 -08:00
parent 093e7a5006
commit 4dfd194483
4 changed files with 91 additions and 54 deletions
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21
2021_01_31_avg Normal file
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@ -0,0 +1,21 @@
time,heartrate_2,heartrate_5,heartrate_10,heartrate_15,movement_10,movement_30,movement_60
2021-01-31 16:04:05.010676,nan,nan,nan,nan,48,48,48
2021-01-31 16:04:35.476006,79,79,79,79,16,16,17
2021-01-31 16:04:36.241533,79,79,79,79,16,16,17
2021-01-31 16:04:37.005988,79,79,79,79,16,16,17
2021-01-31 16:04:37.816041,nan,79,79,79,16,16,17
2021-01-31 16:04:38.625689,nan,79,79,79,16,16,18
2021-01-31 16:04:39.436024,nan,79,79,79,16,16,17
2021-01-31 16:04:40.246131,nan,nan,79,79,16,16,17
2021-01-31 16:04:41.011184,nan,nan,79,79,16,16,17
2021-01-31 16:04:41.821067,nan,nan,79,79,16,16,17
2021-01-31 16:04:42.631347,nan,nan,79,79,16,16,17
2021-01-31 16:04:43.441469,nan,nan,79,79,16,16,17
2021-01-31 16:04:44.251060,nan,nan,79,79,16,16,17
2021-01-31 16:04:45.016751,nan,nan,79,79,16,16,17
2021-01-31 16:04:45.825831,nan,nan,nan,79,16,16,17
2021-01-31 16:04:46.680977,nan,nan,nan,79,16,16,17
2021-01-31 16:04:47.446140,nan,nan,nan,79,15,16,17
2021-01-31 16:04:48.210846,nan,nan,nan,79,15,16,17
2021-01-31 16:04:49.021353,nan,nan,nan,79,15,16,17
2021-01-31 16:04:49.831218,nan,nan,nan,79,15,16,17

1
2021_01_31_raw Normal file
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@ -0,0 +1 @@
time,heartrate_2,heartrate_5,heartrate_10,heartrate_15,movement_10,movement_30,movement_60

6
bluesleep.py
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@ -114,8 +114,6 @@ def vibrate_random(duration):
band.vibrate(vibrate_ms)
time.sleep(vibro_delay)
def sleep_monitor_callback(data):
tick_time = time.time()
if not sleepdata.last_tick_time:
@ -123,7 +121,7 @@ def sleep_monitor_callback(data):
process_data(data, tick_time)
average_data(tick_time)
def connect(mac_filename, auth_key_filename):
def connect():
global band
success = False
timeout = 3
@ -179,7 +177,7 @@ def vibrate_rolling():
band.vibrate(x)
if __name__ == "__main__":
connect(mac_filename, auth_key_filename)
connect()
threading.Thread(target=start_data_pull).start()
threading.Thread(target=timed_buzzing, args=([buzz_delay, 15])).start()
#sleepdata.init_graph()

117
sleepdata.py
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@ -1,14 +1,10 @@
from datetime import datetime
from os import path
import csv
import matplotlib.pyplot as plt
import matplotlib.animation as animation
#Todo: separate graph animation from data averaging
#Todo: log raw data separately from average data
sleep_data = {
'heartrate': {
@ -34,14 +30,9 @@ 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))
datestamp = datetime.now().strftime("%Y_%m_%d")
csv_header_name_format = '{}_{}'
csv_filename_format = '{}_{}.csv'
plt.style.use('dark_background')
graph_figure = plt.figure()
@ -49,17 +40,52 @@ graph_axes = graph_figure.add_subplot(1, 1, 1)
graph_data = {}
def write_csv(data):
global fieldnames
global csv_filename
class Average_Gyro_Data():
gyro_last_x = 0
gyro_last_y = 0
gyro_last_z = 0
# 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.
def process(self, gyro_data):
gyro_movement = 0
for gyro_datum in gyro_data:
gyro_delta_x = abs(gyro_datum['x'] - self.gyro_last_x)
self.gyro_last_x = gyro_datum['x']
gyro_delta_y = abs(gyro_datum['y'] - self.gyro_last_y)
self.gyro_last_y = gyro_datum['y']
gyro_delta_z = abs(gyro_datum['z'] - self.gyro_last_z)
self.gyro_last_z = gyro_datum['z']
gyro_delta_sum = gyro_delta_x + gyro_delta_y + gyro_delta_z
gyro_movement += gyro_delta_sum
return gyro_movement
def write_csv(data, name):
fieldnames = ['time']
for fieldname in data[0]:
if fieldname != 'time':
fieldnames.append(fieldname)
if name == 'raw':
name = '{}_{}'.format(name, fieldname)
csv_filename = csv_filename_format.format(datestamp, name)
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)
open_handle = 'w'
else:
with open(csv_filename, 'a', newline='') as csvfile:
csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
open_handle = 'a'
with open(csv_filename, open_handle, newline='') as csvfile:
csv_writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
if open_handle == 'w':
csv_writer.writeheader()
if type(data) is list:
for row in data:
csv_writer.writerow(row)
else:
csv_writer.writerow(data)
@ -69,13 +95,18 @@ def flush_old_raw_data(tick_time):
periods = s_data['periods']
cleaned_raw_data = []
old_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)
else:
old_raw_data.append(raw_datum)
s_data['raw_data'] = cleaned_raw_data
if old_raw_data:
write_csv(old_raw_data, 'raw')
def average_raw_data(tick_time):
global last_heartrate
@ -101,8 +132,6 @@ def average_raw_data(tick_time):
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:
@ -110,38 +139,19 @@ def average_raw_data(tick_time):
period_averages_dict[period_seconds] = zero_to_nan(period_data_average)
csv_out[data_type + str(period_seconds)] = zero_to_nan(period_data_average)
csv_header_field_name = csv_header_name_format.format(data_type, period_seconds)
csv_out[csv_header_field_name] = zero_to_nan(period_data_average)
s_data['averaged_data'].append(period_averages_dict)
write_csv(csv_out)
write_csv([csv_out], 'avg')
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
gyro_movement = average_gyro_data.process(gyro_data)
print("Gyro: {}".format(gyro_movement))
sleep_move['raw_data'].append({
'time': tick_time,
value_name: gyro_movement
@ -157,11 +167,13 @@ def process_heartrate_data(heartrate_data, 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
@ -175,7 +187,6 @@ def update_graph_data():
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:
@ -184,6 +195,7 @@ def update_graph_data():
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']
@ -225,6 +237,11 @@ def graph_animation(i):
if plotflag:
plt.legend()
def init_graph():
ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
plt.show()
if __name__ == 'sleepdata':
average_gyro_data = Average_Gyro_Data()