def compute_func(in_streams, out_streams):
# This is a simple example of a composed agent consisting
# of two component agents where the composed agent has two
# input streams and no output stream.
# The first component agent zips the two input streams and puts
# the result on its output stream t which is internal to the
# network.
# The second component agent puts values in its input stream t
# on a file called output.dat.
from sink import stream_to_file
# t is an internal stream of the network
t = Stream()
zip_stream(in_streams=in_streams, out_stream=t)
stream_to_file(in_stream=t, filename='output.dat')
def compute_3(in_streams, out_streams):
t = Stream()
zip_stream(in_streams, t)
stream_to_file(t, 'result_2.dat')
def f(in_streams, out_streams):
"""
Compute Function for Sensor Reader
Parameters
----------
in_streams: list of input Streams - acceleration reordered to conform SAF standard of [N, E, Z]
in_streams[0] - acceleration N
in_streams[1] - acceleration E
in_streams[2] - acceleration Z
in_streams[3] - timestamp
out_streams: list of Streams
out_streams[0] - acceleration N (averaged and picked)
out_streams[1] - acceleration E (averaged and picked)
out_streams[2] - acceleration Z (averaged and picked)
out_streams[3] - timestamp
"""
n_acc = len(in_streams) - 1
# DECLARE STREAMS
scaled_acc = [Stream('scaled_acc_' + str(i)) for i in range(n_acc)]
inverted_acc = Stream('inverted_acc') # stream for inverted acceleration E
averaged_acc = [Stream('averaged_acc_' + str(i)) for i in range(n_acc)]
acc_timestamp = Stream(
'acc_timestamp') # timestamp corresponding to the averaged_acc
acc_merged = Stream(
'acc_merged') # acceleration stream merged with timestamp stream
acc_picked = Stream(
'acc_picked') # stream of acc data picked according to timestamp
# CREATE AGENTS
# 1. SCALE ACCELERATION
# our special order CSN Phidgets are scaled to +/- 2g instead of +/- 6g
def scale_g(v):
return PHIDGETS_ACCELERATION_TO_G * v
for i in range(n_acc):
map_element(func=scale_g,
in_stream=in_streams[i],
out_stream=scaled_acc[i])
# TODO: CHECK AND REPORT MISSING SAMPLES
# if self.last_phidgets_timestamp:
# sample_increment = int(
# round((phidgets_timestamp - self.last_phidgets_timestamp) / PHIDGETS_NOMINAL_DATA_INTERVAL))
# if sample_increment > 4 * self.decimation:
# logging.warn('Missing >3 samples: last sample %s current sample %s missing samples %s', \
# self.last_phidgets_timestamp, phidgets_timestamp, sample_increment)
# elif sample_increment == 0:
# logging.warn('Excess samples: last sample %s current sample %s equiv samples %s', \
# self.last_phidgets_timestamp, phidgets_timestamp, sample_increment)
# 2. INVERT ACCELERATION E
# invert channel 1 (E-W) - this results in +1g being reported when the sensor is resting on its E side
def invert_channel(v):
return -1 * v
map_element(func=invert_channel,
in_stream=scaled_acc[1],
out_stream=inverted_acc)
# 3. AVERAGE WINDOW
def average_samples(window):
return sum(window) / float(len(window))
# average for inverted channel
map_window(func=average_samples,
in_stream=inverted_acc,
out_stream=averaged_acc[1],
window_size=PHIDGETS_DECIMATION,
step_size=PHIDGETS_DECIMATION)
for i in [0, 2]:
map_window(func=average_samples,
in_stream=scaled_acc[i],
out_stream=averaged_acc[i],
window_size=PHIDGETS_DECIMATION,
step_size=PHIDGETS_DECIMATION)
# 4. OBTAIN CORRESPONDING TIMESTAMP
def get_timestamp(window):
return window[-1]
map_window(func=get_timestamp,
in_stream=in_streams[3],
out_stream=acc_timestamp,
window_size=PHIDGETS_DECIMATION,
step_size=PHIDGETS_DECIMATION)
# 5. ZIP ACCELERATION AND TIMESTAMP STREAMS
zip_stream(in_streams=averaged_acc + [acc_timestamp],
out_stream=acc_merged)
# 6. QUENCH SENSOR READING
def timestamp_picker(v, state):
if v[3] - state > PICKER_INTERVAL: # generate output
return False, v[3]
else:
return True, state
filter_element(func=timestamp_picker,
in_stream=acc_merged,
out_stream=acc_picked,
state=0)
# 7. UNZIP STREAM - to pass streams to other processes
unzip(in_stream=acc_picked, out_streams=out_streams)
def compute_func_3(in_streams, out_streams):
t = Stream()
zip_stream(in_streams, out_stream=t)
stream_to_file(in_stream=t, filename='result.dat')
def g(in_streams, out_streams):
"""
Compute Function for Picker
Parameters
----------
in_streams: list of input Streams passed from sensor reader process (f)
in_streams[0] - acceleration N
in_streams[1] - acceleration E
in_streams[2] - acceleration Z
in_streams[3] - timestamp
"""
# DECLARE STREAMS
adjusted_acc = [
Stream('adjusted_acc_{}'.format(i))
for i in range(len(in_streams) - 1)
]
adjusted_timestamp = Stream('adjusted_timestamp')
merged_acc = Stream('acc_merged')
filtered_acc = Stream('filtered_acc')
quenched_acc = Stream('quenched_acc')
# DEFINE AGENTS
# 1. ADJUST LTA - subtract long-term-average from sample data
def adjust_lta(window):
return abs(window[-1] - sum(window) / len(window))
for i in range(len(in_streams) - 1):
map_window(func=adjust_lta,
in_stream=in_streams[i],
out_stream=adjusted_acc[i],
window_size=LTA_COUNT,
step_size=1)
# 2. ADJUST TIMESTAMP - obtain timestamp corresponding to each window
def adjust_timestamp(window):
return window[-1]
map_window(func=adjust_timestamp,
in_stream=in_streams[-1],
out_stream=adjusted_timestamp,
window_size=LTA_COUNT,
step_size=1)
# 3. ZIP STREAM - zip acceleration and timestamp streams
zip_stream(in_streams=adjusted_acc + [adjusted_timestamp],
out_stream=merged_acc)
# 4. DETECT ANOMALY - filter out small magnitude to report only large acceleration
def detect_anomaly(v):
return any(map(lambda x: x > PICKER_THRESHOLD, v[:-1]))
filter_element(func=detect_anomaly,
in_stream=merged_acc,
out_stream=filtered_acc)
# 5. QUENCH PICKER
def quench_picker(v, state):
timestamp = v[3]
if timestamp - state < MINIMUM_REPICK_INTERVAL_SECONDS:
return _no_value, state
else:
state = timestamp
return v, state
map_element(func=quench_picker,
in_stream=filtered_acc,
out_stream=quenched_acc,
state=0)
# 6. STREAM RESULTS TO FILE - for test purposes
stream_to_file(quenched_acc, './phidget_data.txt')
