def operator_overload(self, another_stream, func):
from merge import zip_map
output_stream = Stream()
zip_map(func=func,
in_streams=[self, another_stream],
out_stream=output_stream)
return output_stream
def echo_func(self, in_streams, out_streams):
# IDENTIFY INPUT AND OUTPUT STREAMS.
# We give names for the input and output streams
# so that the code is easier to read; this is
# merely a convenience.
# The input and output streams are:
original_sound = in_streams[0]
echo = out_streams[0]
echo.extend([0]*self.delay)
# CREATE INTERNAL STREAMS.
original_sound_plus_echo = Stream(
name='original sound plus echo')
# CREATE AGENTS
# Make the agent that sums the input
# streams --- which are echo and original
# sound --- to get the original sound plus the echo.
zip_map(
func=sum,
in_streams=[original_sound, echo],
out_stream=original_sound_plus_echo)
# Make the agent that creates the echo by
# echoing the original sound plus the echo.
window_dot_product(
in_stream=original_sound_plus_echo,
out_stream=echo,
multiplicand_vector=self.attenuation_vector)
# Agents that store sounds in files
stream_to_file(in_stream=echo,
filename=self.echo_name + '.txt')
stream_to_file(in_stream=original_sound_plus_echo,
filename='original_sound_plus_' + self.echo_name + '.txt')
def compute(in_streams):
def subtract_mean(window):
return window[-1] - sum(window) / float(len(window))
def magnitude_of_vector(coordinates):
return math.sqrt(sum([v * v for v in coordinates]))
def simple_anomaly(value, threshold):
if value > threshold: return 1.0
else: return 0.0
zero_mean_streams = [
Stream('zero mean e'),
Stream('zero mean n'),
Stream('zero mean z')
]
magnitude_stream = Stream('magnitude')
anomaly_stream = Stream('anomalies')
filenames = ['zero_mean_e.txt', 'zero_mean_n.txt', 'zero_mean_z.txt']
for i in range(3):
map_window(func=subtract_mean,
in_stream=in_streams[i],
out_stream=zero_mean_streams[i],
window_size=8000,
step_size=1)
zip_map(func=magnitude_of_vector,
in_streams=zero_mean_streams,
out_stream=magnitude_stream)
map_element(func=simple_anomaly,
in_stream=magnitude_stream,
out_stream=anomaly_stream,
threshold=0.1)
stream_to_file(in_stream=magnitude_stream, filename='magnitude.txt')
stream_to_file(in_stream=anomaly_stream, filename='anomaly.txt')
def aggregate(self, in_streams, out_streams):
# IDENTIFY INPUT AND OUTPUT STREAMS.
# original_sound = in_streams[0]
# echoes = in_streams[1:]
# original_stream_copy = out_streams[0]
# heard_sound = out_streams[1]
# CREATE INTERNAL STREAMS.
# This agent has no internal streams.
# CREATE AGENTS
# Create agent that creates heard sound by summing
# original sound and and all echoes.
zip_map(sum, in_streams, out_streams[1])
# Copy original stream to an output stream.
copy_stream(in_streams[0], out_streams[0])
# Create agents that store sounds in files.
stream_to_file(out_streams[1], self.output_file_name)
stream_to_file(in_streams[0], 'original_sound.txt')
def compute_func(in_streams, out_streams):
## F1 - Amplitude Adjustment
def delay_and_attenuate(in_streams):
tmp = deepcopy(in_streams)
for i in range(len(tmp)):
tmp[i] *= alpha
#print("Length of tmp==tmp1 --",len(tmp)==len(tmp1))
return tmp
t = Stream(initial_value=[0] * delay)
w = Stream()
map_list(func=delay_and_attenuate,
in_stream=in_streams[0],
out_stream=t)
zip_map(sum, [t, in_streams[0]], w)
stream_to_file(in_stream=w, filename='Reverb.dat')
def compute_func(in_streams, out_streams):
# Name external streams for convenience
original_sound = in_streams[0]
heard_sound = out_streams[0]
# Define internal streams
echo = Stream(name='echo', initial_value=[0]*delay)
# Create agents
# Agent that creates heard sound from original sound and echo
zip_map(func=sum,
in_streams=[original_sound, echo],
out_stream=heard_sound)
# Agent that creates the echo from the heard sound.
window_dot_product(
in_stream=heard_sound, out_stream=echo,
multiplicand_vector=attenuation_vector)
# Agents that store sounds in files
stream_to_file(in_stream=heard_sound, filename='heard.txt')
stream_to_file(in_stream=echo, filename='echo.txt')
stream_to_file(in_stream=original_sound, filename='original_sound.txt')
def g(in_streams, out_stream, **kwargs):
return zip_map(func, in_streams, out_stream, **kwargs)
y_stream = Stream('y')
# magnitude_stream is the stream of magnitudes.
magnitude_stream = Stream('magnitudes')
# input_stream is the same as raw_data_stream
# except that it starts after window_size.
# This allows for an appropriate comparison
# with zero_mean_stream
input_stream = Stream('input data')
# CREATE AGENTS
# Create x_stream
stream_to_file(in_stream=x_stream, filename='x_data_file.txt')
# Create y_stream
stream_to_file(in_stream=y_stream, filename='y_data_file.txt')
# Create magnitude_stream
zip_map(func=magnitude_of_vector,
in_streams=[x_stream, y_stream],
out_stream=magnitude_stream)
# Save magnitude_stream to file
stream_to_file(in_stream=magnitude_stream, filename='magnitude_file.txt')
# Put sin values into x_stream and
# cosin values into y_stream
t = np.arange(0.0, 8.0, 0.05)
x_data = np.sin(2 * np.pi * t)
y_data = np.cos(2 * np.pi * t)
x_stream.extend(list(x_data))
y_stream.extend(list(y_data))
Stream.scheduler.step()
# Use matplotlib to plot the data
def f(in_streams, out_streams):
"""
Parameters
----------
in_streams: list of Stream
in_streams is usually a list of 3 streams indicating
measurements in x, y and z directions or equivalently
in e, n, and z (for east, north, vertical) directions.
Each triaxial sensor generates x, y and z streams.
out_streams: list of Stream
out_streams has only one element, which is a
Stream of int. An element of this stream is either
1.0 or 0.0. An element is 1.0 to indicate that an
anomaly was detected and is 0.0 otherwise.
"""
# DECLARE STREAMS
# 1. zero_means
# Array of stream with one stream for each stream in in_streams
# zero_means[0, 1, 2] usually represent streams in the x, y, z direction generated by a single triaxial sensor.
zero_means = [
Stream('zero_means_' + str(i)) for i in range(len(in_streams))
]
# magnitudes is a stream of magnitudes of a vector from its x, y, z values
magnitudes = Stream('magnitudes')
# CREATE AGENTS
# 1. subtract_mean agent
# Define the terminating function
def subtract_mean(window):
return window[-1] - sum(window) / float(len(window))
# Wrap the terminating function to create an agent
for i in range(len(in_streams)):
map_window(func=subtract_mean,
in_stream=in_streams[i],
out_stream=zero_means[i],
window_size=500,
step_size=1,
initial_value=0.0)
# 2. magnitude agent
# Define the terminating function
def magnitude_of_vector(coordinates):
return math.sqrt(sum([v * v for v in coordinates]))
# Wrap the terminating function to create an agent
zip_map(func=magnitude_of_vector,
in_streams=zero_means,
out_stream=magnitudes)
# 3. local anomaly agent
# Define the terminating function
def simple_anomaly(value):
if value > ANOMALY_THRESHOLD:
return 1.0
else:
return 0.0
# Wrap the terminating function to create an agent
map_element(func=simple_anomaly,
in_stream=magnitudes,
out_stream=out_streams[0])
