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miband5_de
Author | SHA1 | Date |
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NateSchoolfield | a94b699bbd |
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c04bc1eb2efaea8efc20882624b9a0f6
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@ -15,7 +15,7 @@ mac_filename = 'mac.txt'
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maximize_graph = False
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vibration_settings = {
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'interval_minutes': 0.1,
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'interval_minutes': 45,
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'duration_seconds': 5,
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'type': 'random'
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}
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import pyaudio, time, threading
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import numpy as np
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from matplotlib import pyplot as plt
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import matplotlib.animation as animation
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CHUNKSIZE = 1024 # fixed chunk size
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# initialize portaudio
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p = pyaudio.PyAudio()
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info = p.get_host_api_info_by_index(0)
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numdevices = info.get('deviceCount')
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for i in range(0, numdevices):
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if (p.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
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print("Input Device id ", i, " - ", p.get_device_info_by_host_api_device_index(0, i).get('name'))
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stream = p.open(
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format=pyaudio.paInt16,
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channels=1,
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rate=44100,
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input=True,
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frames_per_buffer=CHUNKSIZE,
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input_device_index=18
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)
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plt.style.use('dark_background')
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graph_figure = plt.figure()
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graph_figure.canvas.set_window_title('blesleep')
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graph_axes = graph_figure.add_subplot(1, 1, 1)
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graph_data = {}
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def graph_animation(i):
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graph_axes.clear()
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graph_axes.plot(numpydata)
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ani = animation.FuncAnimation(graph_figure, graph_animation, interval=1000)
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def get_audio_data():
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global numpydata
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while True:
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data = stream.read(CHUNKSIZE)
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numpydata = np.frombuffer(data, dtype=np.int16)
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time.sleep(1)
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threading.Thread(target=get_audio_data).start()
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plt.show()
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# close stream
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stream.stop_stream()
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stream.close()
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p.terminate()
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@ -0,0 +1,76 @@
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import time, os, csv
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from datetime import datetime
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from matplotlib import pyplot as plt
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datapath = '/home/daddy/Projects/miband/data/2021_02_07/'
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window_min = 30
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figure_height = 9
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figure_width = 18
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fullscreen = False
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files = os.listdir(datapath)
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wavs = []
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csvs = []
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for file in files:
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if 'wav' in file:
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wavs.append(file)
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elif 'csv' in file and 'raw' in file:
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csvs.append(file)
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event_data_list = []
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for wav in wavs:
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event_dict = {
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'name': wav,
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'data': {
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'mov_x': [],
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'mov_y': [],
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'bpm_x': [],
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'bpm_y': []
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}
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}
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wavtime = datetime.strptime(wav, '%Y_%m_%d__%H_%M_%S.wav')
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wavestamp = wavtime.timestamp()
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plotdata_mov_x = []
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plotdata_mov_y = []
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plotdata_bpm_x = []
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plotdata_bpm_y = []
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for mycsv in csvs:
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with open((datapath + mycsv), newline='') as csvfile:
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csvreader = csv.DictReader(csvfile, delimiter=',')
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for row in csvreader:
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if abs((float(wavestamp) - float(row['time']))) <= (window_min * 60):
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if 'bpm' in row:
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event_dict['data']['bpm_x'].append( int(row['bpm']) )
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event_dict['data']['bpm_y'].append( datetime.fromtimestamp(float(row['time'])) )
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elif 'movement' in row:
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event_dict['data']['mov_x'].append( int(row['movement']) )
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event_dict['data']['mov_y'].append( datetime.fromtimestamp(float(row['time'])) )
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event_data_list.append(event_dict)
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for event_data in event_data_list:
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event_name = event_data['name'].rsplit('.', 1)[0]
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output_png_filename = '{}{}'.format(event_name, ".png")
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data = event_data['data']
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plt.close("all")
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fig, ax = plt.subplots()
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ax2 = ax.twinx()
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ax.plot(data['bpm_y'], data['bpm_x'], color="red")
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ax2.plot(data['mov_y'], data['mov_x'], color="blue")
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fig.set_figheight(figure_height)
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fig.set_figwidth(figure_width)
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fig.autofmt_xdate()
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if fullscreen:
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plt.get_current_fig_manager().full_screen_toggle()
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plt.title(event_name)
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if data['bpm_y']:
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plt.savefig(datapath + output_png_filename)
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plt.show()
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@ -0,0 +1 @@
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EB:DD:13:97:9C:0D
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@ -0,0 +1,19 @@
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.arange(0, 10, 0.1)
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y1 = 0.05 * x**2
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y2 = -1 *y1
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fig, ax1 = plt.subplots()
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ax2 = ax1.twinx()
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figures=[manager.canvas.figure
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for manager in plt._pylab_helpers.Gcf.get_all_fig_managers()]
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print(figures)
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ax1.plot(x, y1, 'g-')
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ax2.plot(x, y2, 'b-')
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plt.show()
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