def pick_orientation(scaled, timestamps, orientation):
""" Sends picks on a single orientation, either 'n', 'e', or 'z'. """
# ---------------------------------------------------------------
# CREATE AGENTS AND STREAMS
# ---------------------------------------------------------------
# 1. DECIMATE SCALED DATA.
# Window of size DECIMATION is decimated to its average.
decimated = Stream('decimated')
map_window(lambda v: sum(v) / float(len(v)), scaled, decimated,
window_size=self.decimation, step_size=self.decimation)
# 2. DECIMATE TIMESTAMPS.
# Window of size DECIMATION is decimated to its last value.
decimated_timestamps = Stream('decimated_timestamps')
map_window(lambda window: window[-1],
timestamps, decimated_timestamps,
window_size=self.decimation, step_size=self.decimation)
# 3. DEMEAN (subtract mean from) DECIMATED STREAM.
# Subtract mean of window from the window's last value.
# Move sliding window forward by 1 step.
demeaned = Stream('demeaned', initial_value=[0.0] * (LTA_count - 1))
map_window(lambda window: window[-1] - sum(window) / float(len(window)),
decimated, demeaned,
window_size=LTA_count, step_size=1)
# 4. MERGE TIMESTAMPS WITH DEMEANED ACCELERATIONS.
# Merges decimated_timestamps and demeaned to get timestamped_data.
timestamped_data = Stream('timestamped_data')
zip_streams(in_streams=[decimated_timestamps, demeaned], out_stream=timestamped_data)
# 5. DETECT PICKS.
# Output a pick if the value part of the time_value (t_v) exceeds threshold.
picks = Stream('picks')
filter_element(lambda t_v: abs(t_v[1]) > self.pick_threshold, timestamped_data, picks)
# 6. QUENCH PICKS.
# An element is a (timestamp, value).
# Start a new quench when timestamp > QUENCH_PERIOD + last_quench.
# Update the last quench when a new quench is initiated.
# Initially the last_quench (i.e. state) is 0.
quenched_picks = Stream('quenched_picks')
# f is the filtering function
def f(timestamped_value, last_quench, QUENCH_PERIOD):
timestamp, value = timestamped_value
new_quench = timestamp > QUENCH_PERIOD + last_quench
last_quench = timestamp if new_quench else last_quench
# return filter condition (new_quench) and next state (last_quench)
return new_quench, last_quench
filter_element(f, picks, quenched_picks, state=0, QUENCH_PERIOD=2)
# 7. SEND QUENCHED PICKS.
self.send_event(quenched_picks)
def compute_func(in_streams, out_streams):
def f(x):
return x < 5
check_list = filter(f, source_list)
t = Stream()
filter_element(func=f, in_stream=in_streams[0], out_stream=t)
check_correctness_of_output(in_stream=t, check_list=check_list)
stream_to_file(
in_stream=t,
filename='single_process_single_source_filter_example_1.dat')
def compute_func(in_streams, out_streams):
def less_than_n(v, n):
return v <= n, n + 1
check_list = [1, 3, 5, 7, 9]
t = Stream()
filter_element(func=less_than_n,
in_stream=in_streams[0],
out_stream=t,
state=0)
check_correctness_of_output(in_stream=t, check_list=check_list)
stream_to_file(in_stream=t, filename='filter_element_example_1.dat')
from stream import _no_value, _multivalue
from check_agent_parameter_types import *
from recent_values import recent_values
from op import filter_element
scheduler = Stream.scheduler
# In the following, x is a stream that must be declared
# before the functions are called.
x = Stream('x')
#----------------------------------------------------------------
# Filter to only have odd numbers
#----------------------------------------------------------------
def is_odd_number(v):
return not v%2
odd = Stream()
filter_element(func=is_odd_number, in_stream=x, out_stream=odd)
# Example: If x = [0, 1, 2,.... ] then y is [0, 2, 4, ...]
#----------------------------------------------------------------
# Filter to only have even numbers
#----------------------------------------------------------------
def is_even_number(v):
return v%2
even = Stream()
filter_element(func=is_even_number, in_stream=x, out_stream=even)
#----------------------------------------------------------------
# Filter to only have positive numbers
#----------------------------------------------------------------
# Test filtering
def positive(v): return v < 0
def examples_filter_element():
x = Stream('x')
#----------------------------------------------------------------
# Filter to only have even numbers
#----------------------------------------------------------------
even = Stream()
filter_element(func=lambda v: not v % 2, in_stream=x, out_stream=even)
# Example: If x = [0, 1, 2, 3, ... ] then y is [0, 2, 4, ...]
#----------------------------------------------------------------
# Filter to only have odd numbers
#----------------------------------------------------------------
odd = Stream()
filter_element(func=lambda v: v % 2, in_stream=x, out_stream=odd)
#----------------------------------------------------------------
# Filter to only have negative numbers
#----------------------------------------------------------------
neg = Stream('negative')
filter_element(func=lambda v: v < 0, in_stream=x, out_stream=neg)
#----------------------------------------------------------------
# Filter to only have non_negativenumbers
#----------------------------------------------------------------
non_neg = Stream('non_negative')
filter_element(func=lambda v: v >= 0, in_stream=x, out_stream=non_neg)
#----------------------------------------------------------------
# filter_element with state and no additional arguments
#----------------------------------------------------------------
def less_than_n(v, state):
next_output_element = (v <= state)
next_state = state + 1
return next_output_element, next_state
y = Stream('y')
less = Stream()
filter_element(func=less_than_n, in_stream=y, out_stream=less, state=0)
# State on j-th step is j.
# less_than_n(v, state) returns (v < j) on the j-th step.
# less filters out all elements v for which v > j
# So if y is [1, 5, 0, 2, 6, 3] then since states are [ 0, 1, 2, 3, 4,..]
# then since not(y[0] <= 0), not(y[1] <= 1),
# y[2] <= 2, y[3] <=3, .... the sequence of outputs of the function
# less_than_v are [(False, 0), (False, 1), (True, 2), (True, 3), ...]. So
# the output stream contains y[2], y[3], ... or [0, 2, ...]
#----------------------------------------------------------------
# filter_element with state and with additional keyword arguments
#----------------------------------------------------------------
# The keyword argument is addend.
def less_than_n_plus_addend(v, state, addend):
# return pair: boolean filter, next state
return v <= state + addend, state + 1
z = Stream('z')
less_addend = Stream()
filter_element(func=less_than_n_plus_addend,
in_stream=z,
out_stream=less_addend,
state=0,
addend=3)
# State on j-th step is j.
# Stream less contains z[j] if and only if z[j] <= j+3
# For example, if z = [2, 3, 3, 4, 10, 15, 7, .....] then the
# output stream is [2, 3, 3, 4, 7, ...]
#----------------------------------------------------------------
# filter out numbers above the threshold
#----------------------------------------------------------------
def threshold(v, threshold):
return v > threshold
above_threshold = Stream('above threshold')
filter_element(func=threshold,
in_stream=x,
out_stream=above_threshold,
threshold=0)
# Put data into input streams and run.
DATA_x = list(range(-5, 5, 1))
x.extend(DATA_x)
DATA_y = [1, 5, 0, 2, 6, 3]
y.extend(DATA_y)
DATA_z = [2, 3, 3, 4, 10, 15, 7]
z.extend(DATA_z)
run()
# Inspect output
assert recent_values(even) == [-4, -2, 0, 2, 4]
assert recent_values(odd) == [-5, -3, -1, 1, 3]
assert recent_values(non_neg) == [0, 1, 2, 3, 4]
assert recent_values(neg) == [-5, -4, -3, -2, -1]
assert recent_values(less) == [0, 2, 3]
assert recent_values(less_addend) == [2, 3, 3, 4, 7]
assert recent_values(above_threshold) == [1, 2, 3, 4]
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 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')
def test_pick_orientation_with_verbose_output():
PHIDGETS_ACCELERATION_TO_G = 1.0 / 3.0
DECIMATION = 2
LTA_count = 2
PICK_THRESHOLD = 0.5
# ---------------------------------------------------------------
# Input streams
# raw is the stream of raw acceleration data along one axis.
# timestamps is the stream of timestamps
scaled = Stream('scaled')
timestamps = Stream('timestamps')
# Decimate acceleration.
# Window of size DECIMATION is decimated to its average.
# Input = scaled
# Output = decimated.
decimated = Stream('decimated')
map_window(lambda v: sum(v) / float(len(v)),
scaled,
decimated,
window_size=DECIMATION,
step_size=DECIMATION)
# Decimate timestamps.
# Window of size DECIMATION is decimated to its last value.
# Input = timestamps
# Output = decimated_timestamps.
decimated_timestamps = Stream('decimated_timestamps')
map_window(lambda window: window[-1],
timestamps,
decimated_timestamps,
window_size=DECIMATION,
step_size=DECIMATION)
# Demean (subtract mean) from decimated stream.
# Subtract mean of window from the window's last value.
# Move sliding window forward by 1 step.
# Input = decimated
# Output = demeaned
demeaned = Stream('demeaned', initial_value=[0.0] * (LTA_count - 1))
map_window(lambda window: window[-1] - sum(window) / float(len(window)),
decimated,
demeaned,
window_size=LTA_count,
step_size=1)
# Add timestamps to demeaned accelerations.
# Merges decimated_timestamps and demeaned to get timestamped_data.
# Inputs = decimated_timestamps, demeaned
# Outputs = timestamped_data
timestamped_data = Stream('timestamped_data')
zip_streams(in_streams=[decimated_timestamps, demeaned],
out_stream=timestamped_data)
# Detect picks.
# Output a pick if the value part of the time_value (t_v) exceeds threshold.
# Input = timestamped_data
# Output = picks
picks = Stream('picks')
filter_element(lambda t_v: abs(t_v[1]) > PICK_THRESHOLD, timestamped_data,
picks)
# Quench picks.
# An element is a (timestamp, value).
# Start a new quench when timestamp > QUENCH_PERIOD + last_quench.
# Update the last quench when a new quench is initiated.
# Initially the last_quench (i.e. state) is 0.
# Input = picks
# Output = quenched_picks
quenched_picks = Stream('quenched_picks')
# f is the filtering function
def f(timestamped_value, last_quench, QUENCH_PERIOD):
timestamp, value = timestamped_value
new_quench = timestamp > QUENCH_PERIOD + last_quench
last_quench = timestamp if new_quench else last_quench
# return filter condition (new_quench) and next state (last_quench)
return new_quench, last_quench
filter_element(f, picks, quenched_picks, state=0, QUENCH_PERIOD=2)
# Send quenched picks.
send_event(quenched_picks, orientation='n')
# ---------------------------------------------------------------
# ---------------------------------------------------------------
# Drive test
print_stream(timestamps, 'timestamps')
print_stream(scaled, 'scaled')
print_stream(decimated, 'decimated')
print_stream(decimated_timestamps, 'decimated_timestamps')
print_stream(demeaned, 'demeaned')
print_stream(timestamped_data, 'timestamped_data')
print_stream(picks, 'picks')
scaled.extend([1.0, 1.0, 2.0, 4.0, 4.0, 20.0, 8.0, 8.0, 4.0, 6.0])
timestamps.extend(list(range(12)))
run()