def draw():
t_now = util.timestamp(ms=True)
ctx.translate(-1., -0.85, -1.5)
ctx.rotate(*rotation_x)
ctx.rotate(*rotation_y)
colors = (1., 1., 1., 1.), (.7, 1., 1., 1.), (1., .7, .7, 1.),
for s, sensor in enumerate(list(sensor_data)):
samples = sensor_data[sensor]
if len(samples):
# x = [((t_now - sample[0]) / 10.0, (sample[1][0] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# y = [((t_now - sample[0]) / 10.0, (sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# z = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
ts = [(t_now - sample[0]) / 10.0 for sample in samples]
ys = [(sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0]) for sample in samples]
zs = [(sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0]) - 0.5 for sample in samples]
# ys = list(sp.smooth(sp.remove_shots(ys)))
# zs = list(sp.smooth(sp.remove_shots(zs)))
# ys = list(sp.remove_shots(ys))
# zs = list(sp.remove_shots(zs))
ys = (np.array(ys) * 2.0) - 0.5
zs = (np.array(zs) * 2.0) - 0.5
ys = sp.smooth(ys, 20)
zs = sp.smooth(zs, 20)
# combo_yz = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0]), ((sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) - 0.5) for sample in list(samples)]
combo_yz = [(ts[i], ys[i], zs[i]) for i in range(0, len(ys))]
ctx.lines3D(combo_yz, color=colors[s], thickness=2.0)
def optimal_gauss_kernel_size(train, optimize_steps, progress=None):
""" Return the optimal kernel size for a spike density estimation
of a spike train for a gaussian kernel. This function takes a single
spike train, which can be a superposition of multiple spike trains
(created with :func:`collapsed_spike_trains`) that should be included
in a spike density estimation.
Implements the algorithm from
(Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).
:param train: The spike train for which the kernel
size should be optimized.
:type train: :class:`neo.core.SpikeTrain`
:param optimize_steps: Array of kernel sizes to try (the best of
these sizes will be returned).
:type optimize_steps: Quantity 1D
:param progress: Set this parameter to report progress. Will be
advanced by len(`optimize_steps`) steps.
:type progress: :class:`.progress_indicator.ProgressIndicator`
:returns: Best of the given kernel sizes
:rtype: Quantity scalar
"""
if not progress:
progress = ProgressIndicator()
x = train.rescale(optimize_steps.units)
N = len(train)
C = {}
sampling_rate = 1024.0 / (x.t_stop - x.t_start)
dt = float(1.0 / sampling_rate)
y_hist = tools.bin_spike_trains({0: [x]}, sampling_rate)[0][0][0]
y_hist = sp.asfarray(y_hist) / N / dt
for step in optimize_steps:
s = float(step)
yh = sigproc.smooth(y_hist,
sigproc.GaussianKernel(2 * step),
sampling_rate,
num_bins=2048,
ensure_unit_area=True) * optimize_steps.units
# Equation from Matlab code, 7/2012
c = (sp.sum(yh**2) * dt - 2 * sp.sum(yh * y_hist) * dt +
2 * 1 / sp.sqrt(2 * sp.pi) / s / N)
C[s] = c * N * N
progress.step()
# Return kernel size with smallest cost
return min(C, key=C.get) * optimize_steps.units
def optimal_gauss_kernel_size(train, optimize_steps, progress=None):
""" Return the optimal kernel size for a spike density estimation
of a spike train for a gaussian kernel. This function takes a single
spike train, which can be a superposition of multiple spike trains
(created with :func:`collapsed_spike_trains`) that should be included
in a spike density estimation.
Implements the algorithm from
(Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).
:param train: The spike train for which the kernel
size should be optimized.
:type train: :class:`neo.core.SpikeTrain`
:param optimize_steps: Array of kernel sizes to try (the best of
these sizes will be returned).
:type optimize_steps: Quantity 1D
:param progress: Set this parameter to report progress. Will be
advanced by len(`optimize_steps`) steps.
:type progress: :class:`.progress_indicator.ProgressIndicator`
:returns: Best of the given kernel sizes
:rtype: Quantity scalar
"""
if not progress:
progress = ProgressIndicator()
x = train.rescale(optimize_steps.units)
N = len(train)
C = {}
sampling_rate = 1024.0 / (x.t_stop - x.t_start)
dt = float(1.0 / sampling_rate)
y_hist = tools.bin_spike_trains({0: [x]}, sampling_rate)[0][0][0]
y_hist = sp.asfarray(y_hist) / N / dt
for step in optimize_steps:
s = float(step)
yh = sigproc.smooth(
y_hist, sigproc.GaussianKernel(2 * step), sampling_rate, num_bins=2048,
ensure_unit_area=True) * optimize_steps.units
# Equation from Matlab code, 7/2012
c = (sp.sum(yh ** 2) * dt -
2 * sp.sum(yh * y_hist) * dt +
2 * 1 / sp.sqrt(2 * sp.pi) / s / N)
C[s] = c * N * N
progress.step()
# Return kernel size with smallest cost
return min(C, key=C.get) * optimize_steps.units
signals = []
labels = list(streams.keys())
log.info("LABELS %s" % labels)
for label in labels:
log.info(label)
ts = tses[label]
ts = [t_min] + ts + [t_max]
values = [d[label] if label in d else None for d in streams[label]]
values = [values[0]] + values + [values[-1]]
values = sp.remove_shots(values, nones=True) # repair missing values
signal = sp.resample(ts, values)
num_samples = len(signal)
sample_rate = num_samples / duration
signal = sp.normalize(signal)
signal = sp.smooth(signal, 15)
signals.append(signal)
log.info("Drawing...")
ctx = drawing.Context(1200, 500, margin=20, hsv=True)
for b in range(12):
ctx.line(b / 12, 0, b / 12, 1, stroke=(0.5, 0.5, 0.5, 0.5), thickness=0.5)
ctx.line(1, 0, 1, 1, stroke=(0.5, 0.5, 0.5, 0.5), thickness=0.5)
for i, signal in enumerate(signals):
color = i / (len(signals) + 4) + .1, 1., .8, 1.
ctx.plot(signal, stroke=color, thickness=1.5)
ctx.line(10 / ctx.width, 1 - ((10 + (i * 10)) / ctx.height), 30 / ctx.width, 1 - ((10 + (i * 10)) / ctx.height), stroke=color, thickness=2)
ctx.label(35 / ctx.width, 1 - ((13 + (i * 10)) / ctx.height), labels[i].upper(), size=8)
ctx.output("charts/")
def generate():
# load data into t and count arrays per species
species = OrderedDict()
start_t = util.timestamp(util.parse_date(str(config['start'])))
end_t = util.timestamp(util.parse_date(str(config['end'])))
max_count = 0
with open("data.csv") as f:
data = csv.reader(f)
for r, row in enumerate(data):
if r == 0:
continue
plot = row[1]
name = row[2]
if len(config['species_list']
) and name not in config['species_list']:
continue
dt = datetime.datetime(int(row[3]), 1,
1) + datetime.timedelta(int(row[4]) - 1)
t = util.timestamp(dt)
if t < start_t or t > end_t:
continue
count = 0 if row[5] == "NA" else int(row[5])
if count > max_count:
max_count = count
if name not in species:
species[name] = {'ts': [start_t, t - 1], 'counts': [0, 0]}
species[name]['ts'].append(t)
species[name]['counts'].append(count)
species = OrderedDict(sorted(species.items()))
print("--> loaded")
# add a zero count at the start and end of every year
yts = [
util.timestamp(datetime.datetime(y, 1, 1)) for y in range(1974, 2017)
]
for name in species:
ts = species[name]['ts']
for yt in yts:
i = 0
while i < len(ts) and ts[i] < yt:
i += 1
if i > 0:
end_season_t = ts[i - 1]
if i < len(ts):
start_season_t = ts[i]
ts.insert(i, start_season_t - config['tail'])
species[name]['counts'].insert(i, 0)
ts.insert(i, end_season_t + config['tail'])
species[name]['counts'].insert(i, 0)
species[name]['ts'].append(end_t)
species[name]['counts'].append(0)
print("--> onsets added")
# create and draw signals
signals = []
names = []
i = 0
for name, data in species.items():
print("Processing %s..." % name)
# create signal from bloom counts
signal = sp.resample(data['ts'], data['counts'])
if config['normalize']:
signal = sp.normalize(signal)
else:
signal = sp.normalize(signal, 0, max_count)
signal = sp.smooth(signal, size=8)
signal = sp.limit(
signal,
max(signal)) # get rid of noise below 0 for onset detection
# add spikes for peaks
if config['peak_spikes']:
peaks, valleys = sp.detect_peaks(signal, lookahead=50)
peak_signal = np.zeros(len(signal))
for peak in peaks:
peak_signal[peak[0]] = 1.0
signal += peak_signal
# add spikes for onsets
if config['onset_spikes']:
onsets = sp.detect_onsets(signal)
onset_signal = np.zeros(len(signal))
for onset in onsets:
onset_signal[onset] = 0.5
onset_signal[onset + 1] = 0.4
onset_signal[onset + 2] = 0.25
signal += onset_signal
# limit
signal = sp.limit(signal, 1.0)
signal *= 0.9 # hack, just controlling gain
signals.append(signal)
names.append(name)
i += 1
return signals, names
def process_walk(walk_id, force=False):
if not model.process_check(walk_id):
log.error("Walk %s already processed" % walk_id)
if force:
log.info("--> forcing...")
model.remove_sequence(walk_id)
else:
return
log.info("Processing walk %s" % walk_id)
# fetch data
data = model.fetch_accels(walk_id)
data = [(reading['t'], reading['x'], reading['y'], reading['z'])
for reading in data]
# let's sample every millisecond, so the time of the last reading is how many samples we need
data = np.array(data)
ts = data[:, 0]
total_samples = int(ts[-1])
# need at least 10s of data
# add 2000 for trimming at nd
if total_samples < 10000 + 2000:
log.info("No footsteps detected (too short)")
model.hide(walk_id)
return
# resample the values
xs = sp.resample(ts, data[:, 1], total_samples)
ys = sp.resample(ts, data[:, 2], total_samples)
zs = sp.resample(ts, data[:, 3], total_samples)
# skip for accelerometer startup and for phone out of pocket at end
skipin, skipout = 0, 2000
xs = xs[skipin:-skipout]
ys = ys[skipin:-skipout]
zs = zs[skipin:-skipout]
total_samples -= (skipin + skipout)
log.info("TOTAL SAMPLES %s (%fs)" % (total_samples,
(total_samples / 1000.0)))
# get 3d magnitude (not RMS) -- orientation shouldnt matter
ds = np.sqrt(np.power(xs, 2) + np.power(ys, 2) + np.power(zs, 2))
# prep the raw values for display
# normalize the values to a given range (this is Gs)
MIN = -10.0
MAX = 10.0
xs = (xs - MIN) / (MAX - MIN)
ys = (ys - MIN) / (MAX - MIN)
zs = (zs - MIN) / (MAX - MIN)
# smooth them
xs = sp.smooth(xs, 300)
ys = sp.smooth(ys, 300)
zs = sp.smooth(zs, 300)
# process the magnitude signal
ds = sp.smooth(ds, 500)
ds = np.clip(ds, -10.0, 10.0) # limit the signal to +-10 Gs
ds = sp.normalize(ds)
ds = 1 - ds
ds = sp.compress(ds, 3.0)
ds = sp.normalize(ds)
# detect peaks
peaks, valleys = sp.detect_peaks(ds, lookahead=50, delta=0.10)
peaks = np.array(peaks)
valleys = np.array(valleys)
log.info("PEAKS %s" % len(peaks))
if not len(peaks):
log.info("No footsteps detected")
model.hide(walk_id)
return
# get foot separator line
fxs = [int(peak[0]) for peak in peaks]
fys = [peak[1] for peak in peaks]
avs = np.average([peak[1] for peak in peaks])
fys[0] = avs # it's going to start with a peak, so we need to bring it up or down accordingly
fxs.append(total_samples - 1)
fys.append(avs)
fs = sp.resample(fxs, fys, total_samples)
fs = sp.smooth(fs, 3000)
# print out
log.info("Saving sequence (%s)..." % walk_id)
sequence = []
for p, peak in enumerate(peaks):
foot = 'right' if peak[1] > fs[int(peak[0])] else 'left'
t = peak[0]
t += 250 # turns out the peak hits just before the step
sequence.append((t, foot))
# fix triples
for i in range(len(sequence) - 2):
if sequence[i][1] == sequence[i + 1][1] == sequence[i + 2][1]:
sequence[i + 1] = (sequence[i + 1][0],
'right') if sequence[i + 1][1] == 'left' else (
sequence[i + 1][0], 'left')
model.insert_sequence(walk_id, sequence)
plot(walk_id, xs, ys, zs, ds, peaks, total_samples, fs)
def generate():
# load data into t and count arrays per species
species = OrderedDict()
start_t = util.timestamp(util.parse_date(str(config['start'])))
end_t = util.timestamp(util.parse_date(str(config['end'])))
max_count = 0
with open("data.csv") as f:
data = csv.reader(f)
for r, row in enumerate(data):
if r == 0:
continue
plot = row[1]
name = row[2]
if len(config['species_list']) and name not in config['species_list']:
continue
dt = datetime.datetime(int(row[3]), 1, 1) + datetime.timedelta(int(row[4]) - 1)
t = util.timestamp(dt)
if t < start_t or t > end_t:
continue
count = 0 if row[5] == "NA" else int(row[5])
if count > max_count:
max_count = count
if name not in species:
species[name] = {'ts': [start_t, t - 1], 'counts': [0, 0]}
species[name]['ts'].append(t)
species[name]['counts'].append(count)
species = OrderedDict(sorted(species.items()))
print("--> loaded")
# add a zero count at the start and end of every year
yts = [util.timestamp(datetime.datetime(y, 1, 1)) for y in range(1974, 2017)]
for name in species:
ts = species[name]['ts']
for yt in yts:
i = 0
while i < len(ts) and ts[i] < yt:
i += 1
if i > 0:
end_season_t = ts[i-1]
if i < len(ts):
start_season_t = ts[i]
ts.insert(i, start_season_t - config['tail'])
species[name]['counts'].insert(i, 0)
ts.insert(i, end_season_t + config['tail'])
species[name]['counts'].insert(i, 0)
species[name]['ts'].append(end_t)
species[name]['counts'].append(0)
print("--> onsets added")
# create and draw signals
signals = []
names = []
i = 0
for name, data in species.items():
print("Processing %s..." % name)
# create signal from bloom counts
signal = sp.resample(data['ts'], data['counts'])
if config['normalize']:
signal = sp.normalize(signal)
else:
signal = sp.normalize(signal, 0, max_count)
signal = sp.smooth(signal, size=8)
signal = sp.limit(signal, max(signal)) # get rid of noise below 0 for onset detection
# add spikes for peaks
if config['peak_spikes']:
peaks, valleys = sp.detect_peaks(signal, lookahead=50)
peak_signal = np.zeros(len(signal))
for peak in peaks:
peak_signal[peak[0]] = 1.0
signal += peak_signal
# add spikes for onsets
if config['onset_spikes']:
onsets = sp.detect_onsets(signal)
onset_signal = np.zeros(len(signal))
for onset in onsets:
onset_signal[onset] = 0.5
onset_signal[onset+1] = 0.4
onset_signal[onset+2] = 0.25
signal += onset_signal
# limit
signal = sp.limit(signal, 1.0)
signal *= 0.9 # hack, just controlling gain
signals.append(signal)
names.append(name)
i += 1
return signals, names
def get_speed_series(trajectories, slices):
return [
np.insert(sigproc.smooth(get_trajectory_speed(trajectories[s, 0:2])),
0, 0) for s in slices
]
def draw():
t_now = util.timestamp(ms=True)
# ctx.translate(0, 0, -1)
# ctx.translate(-1.5, -0.5, -1)
# ctx.translate(-1.3, -0.85, -2)
ctx.translate(-1., -0.85, -1.5)
ctx.rotate(*rotation_x)
ctx.rotate(*rotation_y)
# axes
# ctx.line3D(-.25, 0, 0, .25, 0, 0, color=(1., 1., 0., 1.))
# ctx.line3D(0, -.25, 0, 0, .25, 0, color=(0., 1., 1., 1.))
# ctx.line3D(0, 0, -.25, 0, 0, .25, color=(1., 0., 1., 1.))
# for (start_t, stop_t) in sessions:
# if stop_t is None:
# stop_t = t_now
# if t_now - stop_t > 10.0:
# continue
# # ctx.line((t_now - stop_t) / 10.0, .99, (t_now - start_t) / 10.0, .99, color=(1., 0., 0., .2), thickness=10.0)
# x1 = (t_now - stop_t) / 10.0
# x2 = (t_now - start_t) / 10.0
# ctx.rect(x1, 0.0, x2 - x1, 1.0, color=(1., 0., 0., 0.25))
# for s, (sensor, (t, rssi)) in enumerate(sensor_rssi.items()):
# if t_now - t > 3:
# bar = 0.01
# else:
# bar = 1.0 - (max(abs(rssi) - 25, 0) / 100)
# x = (20 + (s * 20)) / ctx.width
# ctx.line(x, .1, x, (bar * 0.9) + .1, color=(0., 0., 0., 0.5), thickness=10)
# if sensor not in labels:
# print("Adding label for sensor %s" % sensor)
# labels.append(sensor)
# ctx.label(x, .05, str(sensor), font="Monaco", size=10, width=10, center=True)
colors = (1., 1., 1., 1.), (.7, 1., 1., 1.), (1., .7, .7, 1.),
for s, sensor in enumerate(list(sensor_data)):
samples = sensor_data[sensor]
if len(samples):
# x = [((t_now - sample[0]) / 10.0, (sample[1][0] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# y = [((t_now - sample[0]) / 10.0, (sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# z = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
ts = [(t_now - sample[0]) / 10.0 for sample in samples]
ys = [(sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0]) for sample in samples]
zs = [(sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0]) - 0.5 for sample in samples]
# ys = list(sp.smooth(sp.remove_shots(ys)))
# zs = list(sp.smooth(sp.remove_shots(zs)))
# ys = list(sp.remove_shots(ys))
# zs = list(sp.remove_shots(zs))
ys = (np.array(ys) * 2.0) - 0.5
zs = (np.array(zs) * 2.0) - 0.5
ys = sp.smooth(ys, 20)
zs = sp.smooth(zs, 20)
# combo_yz = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0]), ((sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) - 0.5) for sample in list(samples)]
combo_yz = [(ts[i], ys[i], zs[i]) for i in range(0, len(ys))]
ctx.lines3D(combo_yz, color=colors[s], thickness=2.0)
def process_walk(walk_id, force=False):
if not model.process_check(walk_id):
log.error("Walk %s already processed" % walk_id)
if force:
log.info("--> forcing...")
model.remove_sequence(walk_id)
else:
return
log.info("Processing walk %s" % walk_id)
# fetch data
data = model.fetch_accels(walk_id)
data = [(reading['t'], reading['x'], reading['y'], reading['z']) for reading in data]
# let's sample every millisecond, so the time of the last reading is how many samples we need
data = np.array(data)
ts = data[:,0]
total_samples = int(ts[-1])
# need at least 10s of data
# add 2000 for trimming at nd
if total_samples < 10000 + 2000:
log.info("No footsteps detected (too short)")
model.hide(walk_id)
return
# resample the values
xs = sp.resample(ts, data[:,1], total_samples)
ys = sp.resample(ts, data[:,2], total_samples)
zs = sp.resample(ts, data[:,3], total_samples)
# skip for accelerometer startup and for phone out of pocket at end
skipin, skipout = 0, 2000
xs = xs[skipin:-skipout]
ys = ys[skipin:-skipout]
zs = zs[skipin:-skipout]
total_samples -= (skipin + skipout)
log.info("TOTAL SAMPLES %s (%fs)" % (total_samples, (total_samples / 1000.0)))
# get 3d magnitude (not RMS) -- orientation shouldnt matter
ds = np.sqrt(np.power(xs, 2) + np.power(ys, 2) + np.power(zs, 2))
# prep the raw values for display
# normalize the values to a given range (this is Gs)
MIN = -10.0
MAX = 10.0
xs = (xs - MIN) / (MAX - MIN)
ys = (ys - MIN) / (MAX - MIN)
zs = (zs - MIN) / (MAX - MIN)
# smooth them
xs = sp.smooth(xs, 300)
ys = sp.smooth(ys, 300)
zs = sp.smooth(zs, 300)
# process the magnitude signal
ds = sp.smooth(ds, 500)
ds = np.clip(ds, -10.0, 10.0) # limit the signal to +-10 Gs
ds = sp.normalize(ds)
ds = 1 - ds
ds = sp.compress(ds, 3.0)
ds = sp.normalize(ds)
# detect peaks
peaks, valleys = sp.detect_peaks(ds, lookahead=50, delta=0.10)
peaks = np.array(peaks)
valleys = np.array(valleys)
log.info("PEAKS %s" % len(peaks))
if not len(peaks):
log.info("No footsteps detected")
model.hide(walk_id)
return
# get foot separator line
fxs = [int(peak[0]) for peak in peaks]
fys = [peak[1] for peak in peaks]
avs = np.average([peak[1] for peak in peaks])
fys[0] = avs # it's going to start with a peak, so we need to bring it up or down accordingly
fxs.append(total_samples-1)
fys.append(avs)
fs = sp.resample(fxs, fys, total_samples)
fs = sp.smooth(fs, 3000)
# print out
log.info("Saving sequence (%s)..." % walk_id)
sequence = []
for p, peak in enumerate(peaks):
foot = 'right' if peak[1] > fs[int(peak[0])] else 'left'
t = peak[0]
t += 250 # turns out the peak hits just before the step
sequence.append((t, foot))
# fix triples
for i in range(len(sequence) - 2):
if sequence[i][1] == sequence[i+1][1] == sequence[i+2][1]:
sequence[i+1] = (sequence[i+1][0], 'right') if sequence[i+1][1] == 'left' else (sequence[i+1][0], 'left')
model.insert_sequence(walk_id, sequence)
plot(walk_id, xs, ys, zs, ds, peaks, total_samples, fs)
def sample(draw=False):
log.info("START SAMPLE")
# get the time
# dt = timeutil.get_dt(tz=config['tz'])
# dt -= datetime.timedelta(days=300) # time adjustment if necessary for testing
# t_utc = timeutil.t_utc(dt)
t_utc = timeutil.t_utc()
dt = timeutil.get_dt(t_utc, tz=config['tz'])
log.info("CURRENT TIME %s" % timeutil.get_string(t_utc, tz=config['tz']))
# pull the last 24 hours worth -- we're going to normalize over that to set our dynamic levels
log.info(config['sites'][config['sample']])
# # this is the real-time last 24 hours
# query = {'site': config['sample'], 't_utc': {'$gt': t_utc - 86400, '$lt': t_utc}}
# log.info(query)
# results = db.entries.find(query)
# this is the last 24 hours we have
# assume updating every 15 minutes, last 24 hours is the last 96 results
results = db.entries.find({
'site': config['sample']
}).sort([('t_utc', DESCENDING)]).limit(96)
results = list(results)
results.reverse()
log.info("%s results" % len(results)) # should be 96
log.info(json.dumps(results[-1], indent=4,
default=lambda d: str(d))) # show the last one
# resample signals for each
ts = [d['t_utc'] for d in results]
duration = ts[-1] - ts[0]
log.info("DURATION %s %s" % (duration, timeutil.format_seconds(duration)))
signals = []
rates = []
labels = list(config['labels'].values())
labels.sort()
for i, label in enumerate(labels):
# log.debug(label)
try:
values = [d[label] if label in d else None for d in results]
values = sp.remove_shots(values,
nones=True) # repair missing values
signal = sp.resample(ts, values)
num_samples = len(signal)
sample_rate = num_samples / duration
rates.append(sample_rate)
signal = sp.normalize(signal)
signal = sp.smooth(signal, 15)
signals.append(signal)
except KeyError as e:
log.error(log.exc(e))
log.error(values)
# draw if desired
if draw:
from housepy import drawing
ctx = drawing.Context(1200, 500, margin=20, hsv=True)
for i, label in enumerate(labels):
color = i / len(labels), .8, .8, 1.
signal = signals[i]
ctx.plot(signal, stroke=color, thickness=2)
ctx.output("charts/")
# collapse into n-dimensional points
points = []
for i in range(len(signals[0])):
point = [signal[i] for signal in signals]
points.append(point)
# PCA to 4D -- this takes whatever data we've got and maximizes variation for our four panels
points = np.array(points)
# log.debug("INPUT: %s POINTS, %s DIMENSIONS" % points.shape)
points = decomposition.PCA(n_components=4).fit_transform(points)
# log.debug("OUTPUT: %s POINTS, %s DIMENSIONS" % points.shape)
# normalize each dimension independently, again amplifying dynamics
points = np.column_stack((sp.normalize(points[:, 0], np.min(points[:, 0]),
np.max(points[:, 0])),
sp.normalize(points[:, 1], np.min(points[:, 1]),
np.max(points[:, 1])),
sp.normalize(points[:, 1], np.min(points[:, 1]),
np.max(points[:, 2])),
sp.normalize(points[:, 2], np.min(points[:, 3]),
np.max(points[:, 3]))))
# now, for each time this is queried we want to return an interpolation between the last two points
# this essentially implements a delay that closes in on the most recent query
# ...hopefully to be refreshed with a new USGS reading when it gets there
# if that reading doesnt come, it's ok, it just hovers there until we proceed
# aaandd actually we want a couple of hours delay, because these come in at bulk every 1-4 hours
# we know we have 96 points. four hours back is 16 points
# interpolating between points -17 and -16 should give the most recent guaranteed smooth transitions
# transduction takes time, pues
point_a = points[-17]
point_b = points[-16]
# log.debug(point_a)
# log.debug(point_b)
# linear interpolation over 15 minutes
position = (((dt.minute % 15) * 60) + dt.second) / (15 * 60)
# log.debug(position)
point = [(point_a[i] * (1.0 - position)) + (point_b[i] * position)
for i in range(len(point_a))]
log.info("RESULT: %s" % point)
return point
print("DURATION %s %s" % (duration, strings.format_time(duration)))
signals = []
rates = []
labels = list(config['labels'].values())
labels.sort()
for i, label in enumerate(labels):
log.info(label)
try:
values = [d[label] if label in d else None for d in results]
values = sp.remove_shots(values, nones=True) # repair missing values
signal = sp.resample(ts, values)
num_samples = len(signal)
sample_rate = num_samples / duration
rates.append(sample_rate)
signal = sp.normalize(signal)
signal = sp.smooth(signal, 15)
signals.append(signal)
# color = colors[i]
color = i / len(labels), .8, .8, 1.
ctx.plot(signal, stroke=color, thickness=2)
ctx.line(10 / ctx.width, 1 - ((10 + (i * 10)) / ctx.height), 30 / ctx.width, 1 - ((10 + (i * 10)) / ctx.height), stroke=color, thickness=2)
ctx.label(35 / ctx.width, 1 - ((13 + (i * 10)) / ctx.height), label.upper(), size=8)
except KeyError as e:
log.error(log.exc(e))
log.error(values)
ctx.output("charts/")
points = []
for i in range(len(signals[0])):
point = [signal[i] for signal in signals]
def main(session_id):
result = db.branches.find({'session': session_id}).sort([('t', ASCENDING)])
if not result.count():
print("NO DATA!")
exit()
log.info("Start processing...")
result = list(result)
ts = [r['t'] for r in result]
rms = [r['sample'][3] for r in result]
duration = ts[-1] - ts[0]
SAMPLING_RATE = 60 # hz
log.info("DURATION %fs" % duration)
signal = sp.resample(ts, rms, duration * SAMPLING_RATE)
signal = sp.remove_shots(signal)
signal = sp.normalize(signal)
signal = sp.smooth(signal, 15)
# this number should match some lower frequency bound. ie, put this in hz.
# the smaller the number, the more it will affect small motion
# so this should be higher than the slowest motion we care about
# ie, dont care about motion over 0.5hz, which is 120 samples
trend = sp.smooth(signal, 120)
signal -= trend
signal += 0.5
atrend = sp.smooth(signal, 500)
## autocorrelation
auto = sp.autocorrelate(signal)
# this should be small -- if 60hz, fastest gesture would reasonably be half of that, so 30
peaks, valleys = sp.detect_peaks(auto, 10)
peaks = [peak for peak in peaks[1:] if peak[1] > 0.5]
partials = []
for peak in peaks:
frequency = SAMPLING_RATE / peak[0]
partial = frequency * 1000
partials.append([partial, float(peak[1])])
log.info("%d samps\t%fhz\t%f magnitude\t%f map" % (peak[0], frequency, peak[1], partial))
log.info(partials)
ctx = drawing.Context(2000, 750)
ctx.plot(auto, stroke=(0.0, 0.0, 0.0, 1.0), thickness=2.0)
for peak in peaks:
x = peak[0] / len(auto)
ctx.line(x, 0.0, x, peak[1], stroke=(1.0, 0.0, 0.0, 1.0))
ctx.output("graphs")
## audio
audio_signal = sp.make_audio(signal)
spectrum(audio_signal, SAMPLING_RATE)
AUDIO_RATE = 11025
filename = "%s.wav" % util.timestamp()
sound.write_audio(audio_signal, filename, AUDIO_RATE)
subprocess.call(["open", filename])
log.info("AUDIO DURATION %fs" % (duration / (AUDIO_RATE / SAMPLING_RATE)))
ctx = drawing.Context(2000, 750)
ctx.plot(signal, stroke=(0.0, 0.0, 0.0, 1.0), thickness=2.0)
ctx.plot(trend, stroke=(1.0, 0.0, 0.0, 1.0), thickness=2.0)
ctx.plot(atrend, stroke=(0.0, 0.0, 1.0, 1.0), thickness=2.0)
ctx.output("graphs")
log.info("--> done") # around 300ms
def draw():
t_now = util.timestamp(ms=True)
# ctx.translate(0, 0, -1)
# ctx.translate(-1.5, -0.5, -1)
# ctx.translate(-1.3, -0.85, -2)
ctx.translate(-1., -0.85, -1.5)
ctx.rotate(*rotation_x)
ctx.rotate(*rotation_y)
# axes
# ctx.line3D(-.25, 0, 0, .25, 0, 0, color=(1., 1., 0., 1.))
# ctx.line3D(0, -.25, 0, 0, .25, 0, color=(0., 1., 1., 1.))
# ctx.line3D(0, 0, -.25, 0, 0, .25, color=(1., 0., 1., 1.))
# for (start_t, stop_t) in sessions:
# if stop_t is None:
# stop_t = t_now
# if t_now - stop_t > 10.0:
# continue
# # ctx.line((t_now - stop_t) / 10.0, .99, (t_now - start_t) / 10.0, .99, color=(1., 0., 0., .2), thickness=10.0)
# x1 = (t_now - stop_t) / 10.0
# x2 = (t_now - start_t) / 10.0
# ctx.rect(x1, 0.0, x2 - x1, 1.0, color=(1., 0., 0., 0.25))
# for s, (sensor, (t, rssi)) in enumerate(sensor_rssi.items()):
# if t_now - t > 3:
# bar = 0.01
# else:
# bar = 1.0 - (max(abs(rssi) - 25, 0) / 100)
# x = (20 + (s * 20)) / ctx.width
# ctx.line(x, .1, x, (bar * 0.9) + .1, color=(0., 0., 0., 0.5), thickness=10)
# if sensor not in labels:
# print("Adding label for sensor %s" % sensor)
# labels.append(sensor)
# ctx.label(x, .05, str(sensor), font="Monaco", size=10, width=10, center=True)
colors = (1., 1., 1., 1.), (.7, 1., 1., 1.), (1., .7, .7, 1.),
for s, sensor in enumerate(list(sensor_data)):
samples = sensor_data[sensor]
if len(samples):
# x = [((t_now - sample[0]) / 10.0, (sample[1][0] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# y = [((t_now - sample[0]) / 10.0, (sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
# z = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0])) for sample in list(samples)]
ts = [(t_now - sample[0]) / 10.0 for sample in samples]
ys = [(sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0])
for sample in samples]
zs = [(sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0]) - 0.5
for sample in samples]
# ys = list(sp.smooth(sp.remove_shots(ys)))
# zs = list(sp.smooth(sp.remove_shots(zs)))
# ys = list(sp.remove_shots(ys))
# zs = list(sp.remove_shots(zs))
ys = (np.array(ys) * 2.0) - 0.5
zs = (np.array(zs) * 2.0) - 0.5
ys = sp.smooth(ys, 20)
zs = sp.smooth(zs, 20)
# combo_yz = [((t_now - sample[0]) / 10.0, (sample[1][2] - RANGE[0]) / (RANGE[1] - RANGE[0]), ((sample[1][1] - RANGE[0]) / (RANGE[1] - RANGE[0])) - 0.5) for sample in list(samples)]
combo_yz = [(ts[i], ys[i], zs[i]) for i in range(0, len(ys))]
ctx.lines3D(combo_yz, color=colors[s], thickness=2.0)
log.info("Processing...")
total_time = ts[-1] - ts[0]
total_samples = int(total_time / 60) # once per minute
log.debug("last_t %s" % ts[-1])
log.debug("total_time %s" % total_time)
log.debug("total_time_f %s" % strings.format_time(total_time))
log.debug("total_samples %s" % total_samples)
sample_length = total_time / total_samples
log.debug("sample_length %s" % sample_length)
signal = sp.resample(ts, hrs, total_samples)
signal = sp.normalize(signal)
signal = signal - sp.smooth(signal, size=100) # flatten it out a bit
threshold = np.average(signal) + (2 * np.std(signal)) # threshold is average plus 2 std deviation
smoothed_signal = sp.smooth(signal, size=10)
peaks, valleys = sp.detect_peaks(smoothed_signal, lookahead=10, delta=.001)
max_peak = max(peaks, key=lambda p: p[1])
log.info("max_peak %s" % max_peak)
peaks = [peak for peak in peaks if peak[1] > threshold]
def draw():
from housepy import drawing
log.info("--> done")
log.info("Plotting...")
ctx = drawing.Context()
ctx.plot(signal)
ctx.plot(smoothed_signal, stroke=(100, 0, 0))
for peak in peaks:
def process_walk(walk_id, force=False):
if not model.process_check(walk_id):
log.error("Walk %s already processed" % walk_id)
if force:
log.info("--> forcing...")
model.remove_sequence(walk_id)
else:
return
log.info("Processing walk %s" % walk_id)
data = model.fetch_accels(walk_id)
data = [(reading['t'], reading['x'], reading['y'], reading['z']) for reading in data]
# log.debug(data)
# let's sample every millisecond, so the time of the last reading is how many samples we need
data = np.array(data)
# log.debug(data)
ts = data[:,0]
total_samples = ts[-1]
log.info("TOTAL SAMPLES %s (%fs)" % (total_samples, (total_samples / 1000.0)))
# resample the values
xs = sp.resample(ts, data[:,1], total_samples)
ys = sp.resample(ts, data[:,2], total_samples)
zs = sp.resample(ts, data[:,3], total_samples)
# skip 0.5s for intro
skip = 500
xs = xs[skip:]
ys = ys[skip:]
zs = zs[skip:]
total_samples -= skip
# # for testing
# log.debug(total_samples)
# xs = xs[(30 * 1000):(50 * 1000)]
# ys = ys[(30 * 1000):(50 * 1000)]
# zs = zs[(30 * 1000):(50 * 1000)]
# total_samples = len(xs)
# log.debug(total_samples)
# get 3d vector
ds = np.sqrt(np.power(xs, 2) + np.power(ys, 2) + np.power(zs, 2))
# normalize the values to a given range (this is gs, I believe)
MIN = -20.0
MAX = 20.0
xs = (xs - MIN) / (MAX - MIN)
ys = (ys - MIN) / (MAX - MIN)
zs = (zs - MIN) / (MAX - MIN)
ds = (ds - MIN) / (MAX - MIN)
# low-pass filter
ds = sp.smooth(ds, 300)
# ds = sp.normalize(ds)
# av = np.average(ds)
# detect peaks
# lookahead should be the minimum time of a step, maybe .3s, 300ms
peaks, valleys = sp.detect_peaks(ds, lookahead=150, delta=0.10)
if len(peaks) and peaks[0][0] == 0:
peaks = peaks[1:]
peaks = np.array(peaks)
valleys = np.array(valleys)
log.info("PEAKS %s" % len(peaks))
log.info("VALLEYS %s" % len(valleys))
if not (len(peaks) and len(valleys)):
log.info("No footsteps detected")
return
peaks = valleys
# start = np.min((np.min(peaks[:,0]), np.min(valleys[:,0])))
start = np.min(peaks[:,0])
log.debug("START %s" % start)
xs = xs[start:]
ys = ys[start:]
zs = zs[start:]
ds = ds[start:]
peaks = [(peak[0] - start, peak[1]) for peak in peaks]
valleys = [(valley[0] - start, valley[1]) for valley in valleys]
total_samples -= start
# get foot separator line
fxs = [peak[0] for peak in peaks]
fys = [peak[1] for peak in peaks]
avs = np.average([peak[1] for peak in peaks])
fxs.append(total_samples-1)
fys.append(avs)
fs = sp.resample(fxs, fys, total_samples)
fs = sp.smooth(fs, 3000)
# print out
log.info("Saving sequence (%s)..." % walk_id)
sequence = []
for p, peak in enumerate(peaks):
foot = 'right' if peak[1] > fs[peak[0]] else 'left'
sequence.append((peak[0], foot))
model.insert_sequence(walk_id, sequence)
plot(walk_id, xs, ys, zs, ds, peaks, valleys, total_samples, fs)