def __init__( self, genprog, num_edits):
DD.__init__( self )
self.genprog = genprog
self.assume_axioms_hold = False
self.num_edits = num_edits
if options.disable_cache:
self.cache_outcomes = 0
def __init__(self, debuggee, args_1, args_2, version):
DD.__init__(self)
self.debugcase1 = args_1
self.debugcase2 = args_2
self.gdb = StateGDB(debuggee)
self.gdb2 = StateGDB(debuggee)
self.conn = sqlite3.connect("abc/"+version+".db")
print "abc/"+version+".db"
self.count_all = self.conn.execute("select count(*) from testcase").fetchall()[0][0]
self.count_fail = self.conn.execute("select count(*) from testcase where pass=0").fetchall()[0][0]
cur = self.conn.execute("select para, pass from testcase where name='%s'"%args_1).fetchall()
assert cur[0][1] == 1
self.args_1 = cur[0][0]
self.gdb2.question("set args " + self.args_1)
cur = self.conn.execute("select para, pass from testcase where name='%s'"%args_2).fetchall()
assert cur[0][1] == 0
self.args_2 = cur[0][0]
cur = self.conn.execute("select lineNumber from breakpoint").fetchall()
self.syncpoints = []
for cur2 in cur:
self.syncpoints.append(cur2[0])
self.gdb.break_at(cur2[0])
self.gdb2.break_at(cur2[0])
print self.syncpoints
self.check = check.CheckResult(RightVersion, self.syncpoints)
def __init__( self, genprog, builder, deltas ):
DD.__init__( self )
self.builder = builder
self.genprog = genprog
infomsg( "INFO: computing optimized energy usage" )
self.optimized = self.get_fitness( deltas )
self.mean = numpy.mean( self.optimized )
def __init__( self, genprog, builder, deltas ):
DD.__init__( self )
self.builder = builder
self.genprog = genprog
if options.disable_cache:
self.cache_outcomes = 0
self.duration = 0
infomsg( "INFO: computing optimized energy usage" )
self.optimized = np.array( self.get_fitness( deltas ) )
self.mean = np.mean( self.optimized, axis = 0 )
assert np.all( self.mean > 0 ), "'optimized' variant has 0 fitness!"
def wiener_filtering(clean_signal, filename, audio_sr):
"""
Performs Wiener Filtering on a file located at filepath
:param clean_signal: 1D numpy array containing the signal of a clean audio file
:param filename: string of the audio file name
"""
if len(clean_signal) > MAX_SIGNAL_LENGTH:
clean_signal = clean_signal[:MAX_SIGNAL_LENGTH]
write_name = filename.split(".")[0]
if '+' not in filename:
noisy_signal = generate_noise(clean_signal, sr=audio_sr)
new_path = "audio/test_audio_noisy/" + write_name + "_noisy.wav"
wavwrite(new_path, noisy_signal, audio_sr)
else:
noisy_signal = clean_signal.copy()
stft_noisy, DD_gains, noise_est = DD(noisy_signal)
TSNR_sig, TSNR_gains = TSNR(stft_noisy, DD_gains, noise_est)
signal_est = HRNR(stft_noisy, TSNR_sig, TSNR_gains, noise_est)
signal_est = highpass(signal_est, audio_sr)
new_path = "audio/test_audio_results/" + write_name + "_reduced.wav"
wavwrite(new_path, signal_est, audio_sr)
if '+' not in filename:
print("Noisy Segmented SNR: " + snr(noisy_signal, clean_signal))
print("Denoisned Segmented SNR: " + snr(signal_est, noisy_signal))
def __init__(self, path):
self.CD = CD(path)
self.DD = DD(path)
self.DO = DO(path)
self.GD = GD(path)
self.GO = GO(path)
self.ID = ID(path)
self.OO = OO(path)
def noise_filter(signal, sample_rate):
stft_noisy, DD_gains, noise_est = DD(signal)
TSNR_sig, TSNR_gains = TSNR(stft_noisy, DD_gains, noise_est)
new_signal = HRNR(stft_noisy, TSNR_sig, TSNR_gains, noise_est)
new_signal = highpass(new_signal, sample_rate)
return new_signal
class CodeBook:
def __init__(self, path):
self.CD = CD(path)
self.DD = DD(path)
self.DO = DO(path)
self.GD = GD(path)
self.GO = GO(path)
self.ID = ID(path)
self.OO = OO(path)
def decode(self, code, encoding, year):
if encoding == 'CD':
return self.CD.decode(code, year)
elif encoding == 'DD':
return self.DD.decode(code, year)
elif encoding == 'DO':
return self.DO.decode(code, year)
elif encoding == 'GD':
return self.GD.decode(code, year)
elif encoding == 'GO':
return self.GO.decode(code, year)
elif encoding == 'ID':
return self.ID.decode(code, year)
elif encoding == 'OO':
return self.OO.decode(code, year)
else:
print 'Error: encoding do not found'
exit(-1)
def decode_file(self, filename, encoding, year):
data = []
f = open(filename)
for line in f.readlines():
code = self.decode(line[:-1], encoding, year)
data.append(code)
f.close()
return data
def _dd( self, c, n ):
assert self.test(c) == self.FAIL
return DD._dd( self, c, n )
