237 lines
7.3 KiB
Python
237 lines
7.3 KiB
Python
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': [],
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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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last_heartrate = 0
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last_tick_time = None
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tick_seconds = 0.5
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csv_filename = "sleep_data.csv"
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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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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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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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def flush_old_raw_data(tick_time):
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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 write_csv(data):
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a = ''
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def average_raw_data(tick_time):
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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 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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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 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 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 update_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 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 graph_animation(i):
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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 init_graph():
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ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
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plt.show()
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