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postprocess.py
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postprocess.py
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import os
import sys
##############################################################
# once per second (independent on the window size used for calculation)
PROCESSING_WINDOW_SIZE = 20
NUM_TIME_HISTOGRAM_BINS = 16
NUM_FREQUENCY_HISTOGRAM_BINS = 9
N = 0
AXIS = ["x", "y", "z"]
##############################################################
def create_subs(names):
r = {}
for n in names:
r[n] = [""] * N
return r
def axis_conversion_function(lines, window_size, data_window_size):
subs = create_subs(AXIS)
line_counter = 0 if window_size == 1 else data_window_size - 1
axis = -1
for line in lines:
if "axis" in line:
axis += 1
line_counter = 0 if window_size == 1 else data_window_size - 1
continue
if line_counter >= N:
print("line counter out of range:", line_counter)
break
subs[AXIS[axis]][line_counter] = line
line_counter += window_size
return subs
def axis_fft_conversion_function(lines, window_size, data_window_size):
subs = {}
for a in AXIS:
for bin in range(data_window_size // 2 + 1):
name = a + "_" + str(bin)
subs[name] = [""] * N
line_counter = 0 if window_size == 1 else data_window_size - 1
axis = -1
frequency = 0
num_frequencies = data_window_size // 2 + 1
for line in lines:
if "axis" in line:
axis += 1
line_counter = 0 if window_size == 1 else data_window_size - 1
frequency = 0
continue
if line_counter >= N:
print("line counter out of range:", line_counter)
break
name = AXIS[axis] + "_" + str(frequency)
subs[name][line_counter] = line
frequency += 1
if frequency >= num_frequencies: # this is the number of samples
# start from DC again
frequency = 0
line_counter += window_size
return subs
def simple_conversion_function(lines, window_size, data_window_size):
subs = create_subs([""])
line_counter = 0 if window_size == 1 else data_window_size - 1
for line in lines:
if line_counter >= N:
print("line counter out of range:", line_counter)
break
subs[""][line_counter] = line
line_counter += window_size
return subs
def histogram_conversion_function(lines, window_size, num_bins, data_window_size):
subs = {}
for a in AXIS:
for bin in range(num_bins):
name = a + "_" + str(bin)
subs[name] = [""] * N
line_counter = 0 if window_size == 1 else data_window_size - 1
axis = -1
for line in lines:
if "axis" in line:
axis += 1
line_counter = 0 if window_size == 1 else data_window_size - 1
continue
bins = line.split()
for bin in range(num_bins):
name = AXIS[axis] + "_" + str(bin)
subs[name][line_counter] = bins[bin]
line_counter += window_size
return subs
##############################################################
def time_axis_conversion_function(lines, data_window_size):
return axis_conversion_function(lines, 1, data_window_size)
def time_simple_conversion_function(lines, data_window_size):
return simple_conversion_function(lines, 1, data_window_size)
def time_axis_periodic_conversion_function(lines, data_window_size):
return axis_conversion_function(lines, PROCESSING_WINDOW_SIZE, data_window_size)
def time_simple_periodic_conversion_function(lines, data_window_size):
return simple_conversion_function(lines, PROCESSING_WINDOW_SIZE, data_window_size)
def time_histogram_conversion_function(lines, data_window_size):
return histogram_conversion_function(lines, PROCESSING_WINDOW_SIZE, NUM_TIME_HISTOGRAM_BINS, data_window_size)
##############################################################
def frequency_axis_conversion_function(lines, data_window_size):
return axis_conversion_function(lines, 1, data_window_size)
def frequency_axis_fft_conversion_function(lines, data_window_size):
return axis_fft_conversion_function(lines, PROCESSING_WINDOW_SIZE, data_window_size)
def frequency_simple_conversion_function(lines, data_window_size):
return simple_conversion_function(lines, 1, data_window_size)
def frequency_axis_periodic_conversion_function(lines, data_window_size):
return axis_conversion_function(lines, PROCESSING_WINDOW_SIZE, data_window_size)
def frequency_simple_periodic_conversion_function(lines, data_window_size):
return simple_conversion_function(lines, PROCESSING_WINDOW_SIZE, data_window_size)
def frequency_histogram_conversion_function(lines, data_window_size):
return histogram_conversion_function(lines, PROCESSING_WINDOW_SIZE, NUM_FREQUENCY_HISTOGRAM_BINS, data_window_size)
##############################################################
class Feature:
def __init__(self, name, conversion_function):
self.name = name
self.conversion_function = conversion_function
self.lines = []
self.subs = {"" : []}
def add_line(self, line):
self.lines.append(line.strip())
def process(self, data_window_size):
print("Convert", self.name)
self.subs = self.conversion_function(self.lines, data_window_size)
def get_subnames(self):
return map(lambda x: self.name + " " + x, sorted(self.subs.keys()))
def get_values(self, i):
row = []
for sname in sorted(self.subs.keys()):
row.append(self.subs[sname][i])
return row
##############################################################
features = []
def add_feature(f):
features.append(f)
def get_feature(name):
for f in features:
if f.name == name: return f
return None
def initialize():
global features
features = []
if True:
# nonperiodic features
add_feature(Feature("mean (f)", time_axis_conversion_function))
add_feature(Feature("mean (i)", time_axis_conversion_function))
add_feature(Feature("quartile_25 (i)", time_axis_conversion_function))
add_feature(Feature("median (i)", time_axis_conversion_function))
add_feature(Feature("quartile_75 (i)", time_axis_conversion_function))
add_feature(Feature("min (i)", time_axis_conversion_function))
add_feature(Feature("max (i)", time_axis_conversion_function))
add_feature(Feature("variance (i)", time_axis_conversion_function))
add_feature(Feature("std (f)", time_axis_conversion_function))
add_feature(Feature("0-crossings (i)", time_axis_conversion_function))
add_feature(Feature("entropy (f)", time_axis_conversion_function))
if True:
# periodic features + magnitude
add_feature(Feature("mean (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("mean (f, p)", time_axis_periodic_conversion_function))
add_feature(Feature("quartile_25 (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("median (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("quartile_75 (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("min (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("max (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("variance (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("std (f, p)", time_axis_periodic_conversion_function))
add_feature(Feature("0-crossings (i, p)", time_axis_periodic_conversion_function))
add_feature(Feature("entropy (f, p)", time_axis_periodic_conversion_function))
add_feature(Feature("histogram (i, p)", time_histogram_conversion_function))
add_feature(Feature("magnitude (f)", time_simple_conversion_function))
add_feature(Feature("magnitude^2 (i)", time_simple_conversion_function))
if True:
# Note: all of these are periodic in the sense that FFT / integer FFT is done once per window size
add_feature(Feature("spectral density (i)", frequency_axis_fft_conversion_function))
add_feature(Feature("spectral density (f)", frequency_axis_fft_conversion_function))
add_feature(Feature("spectral maxima (i)", frequency_axis_periodic_conversion_function))
add_feature(Feature("spectral maxima (f)", frequency_axis_periodic_conversion_function))
add_feature(Feature("spectral centroid (f)", frequency_axis_periodic_conversion_function))
add_feature(Feature("spectral flux (f)", frequency_axis_periodic_conversion_function))
add_feature(Feature("spectral entropy (f)", frequency_axis_periodic_conversion_function))
add_feature(Feature("spectral magn. area (f)", frequency_simple_periodic_conversion_function))
add_feature(Feature("spectral magn. area^2 (i)", frequency_simple_periodic_conversion_function))
add_feature(Feature("spectral histogram (i)", frequency_histogram_conversion_function))
add_feature(Feature("spectral histogram (f)", frequency_histogram_conversion_function))
def build_csv(sdata, tdata, csvfilename, rawfilename, data_window_size):
global N
N = len(sdata)
initialize()
current_feature = None
with open(rawfilename, "r") as infile:
for line in infile:
if "Start feature:" in line:
# start of a feature block
fname = line[14:].strip()
current_feature = get_feature(fname)
continue
if "Feature:" in line:
# end of a feature block
current_feature = None
continue
if current_feature:
current_feature.add_line(line)
# the column titles
names = ["t", "x", "y", "z"]
for f in features:
f.process(data_window_size)
names += f.get_subnames()
with open(csvfilename, "w") as outfile:
# write the first line: the column titles
outfile.write("\t".join(names) + "\n")
for i in range(N):
#for s, t in zip(sdata, tdata):
s = sdata[i]
t = tdata[i]
row = [t, s[0], s[1], s[2]]
for f in features:
row += f.get_values(i)
outfile.write("\t".join(map(str, row)) + "\n")