cadences.append(cadence * 2) # two feet!
log.info("DATA START TIME %s UTC" % datetime.datetime.utcfromtimestamp(start_t).strftime("%Y-%m-%d %H:%M:%S"))
num_samples = len(ts)
log.info("NUM DATA SAMPLES %s" % num_samples)
if len(ts) != int(ts[-1]):
log.warning("%s != %s" % (util.format_time(len(ts)), util.format_time(ts[-1])))
log.info("DURATION %s" % (util.format_time(ts[-1])))
log.info("CONVERTING AND SAMPLING")
# clean data
cadences = science.filter_deviations(cadences, positive_only=True)
# heartrates = science.filter_deviations(heartrates)
# normalize data
cadences_norm = science.normalize(cadences)
heartrates_norm = science.normalize(heartrates)
# show
ctx = drawing.Context(2000, 250, relative=True, flip=True)
ctx.line([(float(i) / num_samples, cadences_norm[i]) for i in range(num_samples)], stroke=(0, 0, 255), thickness=2)
ctx.line([(float(i) / num_samples, heartrates_norm[i]) for i in range(num_samples)], stroke=(255, 0, 0), thickness=2)
ctx.show()
ctx.image.save("cadence_heartrate.png", 'PNG')
def rate_to_pulse(signal, sample_rate=60):
""" Given a rate signal, convert into a list of pulse times. Sample rates in hz. """
""" Assuming the initial signal is 1hz """
sample_rate = float(sample_rate)
elapsed_time = 0.0
log.info("AUDIO SAMPLE RATE %s" % sample_rate)
log.info("AUDIO LENGTH (samples) %s" % len(data))
seconds = float(len(data)) / sample_rate
log.info("AUDIO DURATION %s" % util.format_time(seconds))
skip = video_start_t - audio_start_t
log.info("AUDIO SKIP %s%s" % ('-' if skip < 0 else '', util.format_time(abs(skip))))
# downsample to 60hz
target_sample_rate = 60.0
signal = science.downsample(data, int(sample_rate / target_sample_rate))
log.info("NEW LENGTH (samples) %s" % len(signal))
average = np.average(signal)
reduced = signal - average
reduced = [x if x >= 0 else 0 for x in reduced]
reduced = science.smooth(reduced, window_len=50)
reduced = science.normalize(reduced)
signal = science.normalize(signal)
log.info("DETECTING PEAKS")
# the lookahead is key. dont want to see two peaks, but not too small
# in this case, a breath a second?
max_peaks, min_peaks = science.detect_peaks(reduced, lookahead=60)
breaths = []
for peak in max_peaks:
sample, y = peak
t = sample / target_sample_rate
t -= skip
if t < 0:
continue
if t > MEDIA_LENGTH:
continue
