Moved data handling outside of main script into sleepdata module

bluesleep.py

@@ -4,12 +4,7 @@ from bluepy import btle
 from bluepy.btle import BTLEDisconnectError
 
 from miband import miband
-
+import sleepdata
-import matplotlib.pyplot as plt
-import matplotlib.animation as animation
-import csv
-import random
-from os import path
 
 import threading
 import re
@@ -18,69 +13,23 @@ 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')
+band = None
-graph_figure = plt.figure()
-graph_axes = graph_figure.add_subplot(1, 1, 1)
-graph_data = {}
-
-last_heartrate = 0
-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))
-
 
 #-------------------------------------------------------------------------#
 
-
+class regex_patterns():
-def write_csv(data):
+    mac_regex_pattern = re.compile(r'([0-9a-fA-F]{2}(?::[0-9a-fA-F]{2}){5})')
-    global fieldnames
+    authkey_regex_pattern = re.compile(r'([0-9a-fA-F]){32}')
-    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())
+            hwaddr_search = re.search(regex_patterns.mac_regex_pattern, f.read().strip())
-
             if hwaddr_search:
                 MAC_ADDR = hwaddr_search[0]
             else:
@@ -93,10 +42,9 @@ def get_mac_address(filename):
 
 
 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())
+            key_search = re.search(regex_patterns.authkey_regex_pattern, f.read().strip())
             if key_search:
                 AUTH_KEY = bytes.fromhex(key_search[0])
             else:
@@ -107,210 +55,28 @@ def get_auth_key(filename):
             exit(1)
     return AUTH_KEY
 
-
+def process_data(data, tick_time):
-def process_heartrate_data(heartrate_data, tick_time):
+    if data[0] == "GYRO":
-    print("BPM: " + str(heartrate_data))
+        sleepdata.process_gyro_data(data[1], tick_time)
-    if heartrate_data > 0:
+    elif data[0] == "HR":
-        value_name = sleep_data['heartrate']['value_name']
+        sleepdata.process_heartrate_data(data[1], tick_time)
-        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_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 average_raw_data(tick_time):
-    global sleep_data
-    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 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:
+    if not sleepdata.last_tick_time:
-        last_tick_time = time.time()
+        sleepdata.last_tick_time = time.time()
     
-    if data[0] == "GYRO":
+    process_data(data, tick_time)
-        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:
+    if (tick_time - sleepdata.last_tick_time) >= sleepdata.tick_seconds:
-        average_raw_data(tick_time)
+        sleepdata.average_raw_data(tick_time)
-        last_tick_time = time.time()
+        sleepdata.last_tick_time = time.time()
 
-
+def connect(mac_filename, auth_key_filename):
-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_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 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_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 connect():
     global band
-    global mac_filename
-    global auth_key_filename
-
     success = False
     timeout = 3
-    msg = 'Connection to the MIBand failed. Trying again in {} seconds'
+    msg = 'Connection to the band failed. Trying again in {} seconds'
 
     MAC_ADDR = get_mac_address(mac_filename)
     AUTH_KEY = get_auth_key(auth_key_filename)
@@ -326,10 +92,7 @@ def connect():
             print("\nExit.")
             exit()
 
-
 def start_data_pull():
-    global band
-
     while True:
         try:
             band.start_heart_and_gyro(sensitivity=1, callback=sleep_monitor_callback)
@@ -357,6 +120,7 @@ def vibrate_pattern(duration):
                 time.sleep(vibro_delay)
 
 def vibrate_rolling():
+    print("Sending rolling vibration...")
     for x in range(10):
         for x in range(20, 40, 1):
             band.vibrate(x)
@@ -364,11 +128,12 @@ def vibrate_rolling():
             band.vibrate(x)
 
 if __name__ == "__main__":
-    connect()
+    connect(mac_filename, auth_key_filename)
+    #vibrate_pattern(10)
     data_gather_thread = threading.Thread(target=start_data_pull)
     data_gather_thread.start()
-    ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
+    sleepdata.init_graph()
-    plt.show()
+
 
 
 #import simpleaudio as sa

bytepatterns.py

@@ -0,0 +1,45 @@
+
+class miband4():
+
+        class bytepatterns():
+            vibration = 'ff{:02x}00000001'
+            gyro_start = '01{:02x}19'
+            start = '0100'
+            stop = '0000'
+            heart_measure_keepalive = '16'
+            stop_heart_measure_continues = '150100'
+            start_heart_measure_continues = '150101'
+            stop_heart_measure_manual = '150200'
+            fetch_begin = '100101'
+            fetch_error = '100104'
+            fetch_continue = '100201'
+            fetch_complete = '100204'
+            auth_ok = '100301'
+            request_random_number = '0200'
+            auth_key_prefix = '0300'
+
+        def vibration(duration):
+            byte_pattern = miband4.bytepatterns.vibration
+            return bytes.fromhex(byte_pattern.format(duration))
+
+        def gyro_start(sensitivity):
+            byte_pattern = miband4.bytepatterns.gyro_start
+            return bytes.fromhex(byte_pattern.format(sensitivity))
+
+        start = bytes.fromhex(bytepatterns.start)
+        stop = bytes.fromhex(bytepatterns.stop)
+
+        heart_measure_keepalive = bytes.fromhex(bytepatterns.heart_measure_keepalive)
+        stop_heart_measure_continues = bytes.fromhex(bytepatterns.stop_heart_measure_continues)
+        start_heart_measure_continues = bytes.fromhex(bytepatterns.start_heart_measure_continues)
+        stop_heart_measure_manual = bytes.fromhex(bytepatterns.stop_heart_measure_manual)
+
+        fetch_begin = bytes.fromhex(bytepatterns.fetch_begin)
+        fetch_error = bytes.fromhex(bytepatterns.fetch_error)
+        fetch_continue = bytes.fromhex(bytepatterns.fetch_continue)
+        fetch_complete = bytes.fromhex(bytepatterns.fetch_complete)
+
+        auth_ok = bytes.fromhex(bytepatterns.auth_ok)
+        request_random_number = bytes.fromhex(bytepatterns.request_random_number)
+        auth_key_prefix = bytes.fromhex(bytepatterns.auth_key_prefix)
+        

miband.py

@@ -3,6 +3,8 @@ import logging
 import struct
 import binascii
 
+from bytepatterns import miband4 as bytepattern
+
 from bluepy.btle import (
     Peripheral, DefaultDelegate, 
     ADDR_TYPE_RANDOM, ADDR_TYPE_PUBLIC,
@@ -55,33 +57,6 @@ class Delegate(DefaultDelegate):
             print ("Unhandled handle: " + str(hnd) + " | Data: " + str(data))
 
 
-class bytepattern():
-    def vibration(duration):
-        byte_pattern = 'ff{:02x}00000001'
-        return bytes.fromhex(byte_pattern.format(duration))
-
-    def gyro_start(sensitivity):
-        byte_pattern = '01{:02x}19'
-        return bytes.fromhex(byte_pattern.format(sensitivity))
-
-    start = bytes.fromhex('0100')
-    stop = bytes.fromhex('0000')
-
-    heart_measure_keepalive = bytes.fromhex('16')
-    stop_heart_measure_continues = bytes.fromhex('150100')
-    start_heart_measure_continues = bytes.fromhex('150101')
-    stop_heart_measure_manual = bytes.fromhex('150200')
-
-    fetch_begin = bytes.fromhex('100101')
-    fetch_error = bytes.fromhex('100104')
-
-    fetch_continue = bytes.fromhex('100201')
-    fetch_complete = bytes.fromhex('100204')
-
-    auth_ok = bytes.fromhex('100301')
-
-    request_random_number = bytes.fromhex('0200')
-    auth_key_prefix = bytes.fromhex('0300')
 
 
 

sleepdata.py

@@ -1,6 +1,13 @@
-class Sleep_Data(object):
+from datetime import datetime
-    def __init__(self):
+from os import path
-        print("init")
+
+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 = { 
@@ -22,3 +29,208 @@ class Sleep_Data(object):
                     }
                 } 
             }
+
+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()