def impulse(samplingRate=None, fftDegree=None):
"""
Generates a normalized impulse signal at time zero,
with zeros to fill the time length
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
numSamples = 2**fftDegree
impulseSignal = np.zeros(numSamples)
impulseSignal[0] = 1.0
impulseSignal = SignalObj(signalArray=impulseSignal,
domain='time',
samplingRate=samplingRate,
signalType='energy')
impulseSignal.creation_name = creation_name
return impulseSignal
def merge(signal1, *signalObjects):
"""Gather all channels of the signalObjs given as input arguments into a single SignalObj."""
j = 1
freqMin = cp.deepcopy(signal1.freqMin)
freqMax = cp.deepcopy(signal1.freqMax)
comment = cp.deepcopy(signal1.comment)
channels = cp.deepcopy(signal1.channels)
timeSignal = cp.deepcopy(signal1.timeSignal)
for inObj in signalObjects:
if signal1.samplingRate != inObj.samplingRate:
message = '\
\n To merge signals they must have the same sampling rate!\
\n SignalObj 1 and ' + str(j +
1) + ' have different sampling rates.'
raise AttributeError(message)
if signal1.numSamples != inObj.numSamples:
message = '\
\n To merge signals they must have the same length!\
\n SignalObj 1 and ' + str(j + 1) + ' have different lengths.'
raise AttributeError(message)
comment = comment + ' / ' + inObj.comment
for ch in inObj.channels._channels:
channels.append(ch)
timeSignal = np.hstack((timeSignal, inObj.timeSignal))
j += 1
newSignal = SignalObj(timeSignal,
domain='time',
samplingRate=signal1.samplingRate,
freqMin=freqMin,
freqMax=freqMax,
comment=comment)
channels.conform_to()
newSignal.channels = channels
return newSignal
def filter(self, sigobj):
"""
Filter the signal object.
For each channel inside the input signalObj, will be generated a new
SignalObj with the channel filtered signal.
Args:
sigobj: SignalObj
Return:
output: List
A list containing one SignalObj with the filtered data for each
channel in the original signalObj.
"""
if self.samplingRate != sigobj.samplingRate:
raise ValueError(
"SignalObj must have same sampling rate of filter to be filtered."
)
n = self.sos.shape[2]
output = []
chl = []
for ch in range(sigobj.numChannels):
sobj = sigobj[ch]
filtered = np.zeros((sobj.numSamples, n))
for k in range(n):
cContigousArray = sobj.timeSignal[:].copy(order='C')
filtered[:, k] = ss.sosfilt(self.sos[:, :, k].copy(order='C'),
cContigousArray,
axis=0).T
chl.append(copy(sobj.channels[sobj.channels.mapping[0]]))
chl[-1].num = k + 1
chl[-1].name = f'Band {k+1}'
chl[-1].code = f'B{k+1}'
signalDict = {
'signalArray': filtered,
'domain': 'time',
'samplingRate': self.samplingRate,
'freqMin': sigobj.freqMin,
'freqMax': sigobj.freqMax,
}
out = SignalObj(**signalDict)
out.channels = ChannelsList(chl)
# out.timeSignal = out.timeSignal * out.channels.CFlist()
output.append(out)
chl.clear()
return output
def resample(signal, newSamplingRate):
"""
Resample the timeSignal of the input SignalObj to the
given sample rate using the scipy.signal.resample() function
"""
newSignalSize = np.int(signal.timeLength * newSamplingRate)
resampled = ss.resample(signal.timeSignal[:], newSignalSize)
newSignal = SignalObj(resampled, "time", newSamplingRate)
return newSignal
def read_wav(fileName):
"""Read a wave file into a SignalObj."""
samplingRate, data = wf.read(fileName)
if data.dtype == 'int16':
data = data / (2**15)
if data.dtype == 'int32':
data = data / (2**31)
signal = SignalObj(data, 'time', samplingRate=samplingRate)
return signal
def impulse(samplingRate=None, fftDegree=None):
"""
Generates a normalized impulse signal at time zero,
with zeros to fill the time length
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames is ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
numSamples = 2**fftDegree
# FIXME: I don't know why you created this way. I guess it would be better
# to just create a vector of zeros and then substitute the first sample by
# 1.
# =========================================================================
# impulseSignal = (numSamples / samplingRate) \
# * np.ones(numSamples) + 1j * np.random.randn(numSamples)
# impulseSignal = np.real(np.fft.ifft(impulseSignal))
# impulseSignal = impulseSignal / max(impulseSignal)
# =========================================================================
impulseSignal = np.zeros(numSamples)
impulseSignal[0] = 1.0
impulseSignal = SignalObj(signalArray=impulseSignal,
domain='time',
samplingRate=samplingRate,
signalType='energy')
impulseSignal.creation_name = creation_name
return impulseSignal
def fft_convolve(signal1, signal2):
"""
Use scipy.signal.fftconvolve() to convolve two time domain signals.
>>> convolution = pytta.fft_convolve(signal1,signal2)
"""
# Fs = signal1.Fs
conv = ss.fftconvolve(signal1.timeSignal, signal2.timeSignal)
signal = SignalObj(conv, 'time', signal1.samplingRate)
return signal
def filter(self, signalObj):
if self.samplingRate != signalObj.samplingRate:
raise ValueError("SignalObj must have same sampling\
rate of filter to be filtered.")
output = []
for ch in range(signalObj.numChannels):
filtered = np.zeros((signalObj.numSamples))
filtered[:] = ss.sosfilt(self.sos[:, :],
signalObj.timeSignal[:, ch],
axis=0).T
signalDict = {
'signalArray': filtered,
'domain': 'time',
'samplingRate': self.samplingRate,
'freqMin': self.band[0],
'freqMax': self.band[1]
}
output.append(SignalObj(**signalDict))
else:
return output
def sin(Arms=0.5,
freq=1000,
timeLength=1,
phase=2 * np.pi,
samplingRate=default.samplingRate,
fftDegree=None):
"""
Generates a sine signal with the traditional parameters plus some PyTTa
options.
Creation parameters:
--------------------
* Arms (float) (optional):
The signal's RMS amplitude.
>>> Apeak = Arms*sqrt(2);
* freq (float) (optional):
Nothing to say;
* timeLength (float) (optional):
Sine timeLength in seconds;
* fftDegree (int) (optional);
2**fftDegree signal's number of samples;
* phase (float) (optional):
Sine phase in radians;
* samplingRate (int) (optional):
Nothing to say;
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
if fftDegree is not None:
timeLength = 2**(fftDegree) / samplingRate
t = np.linspace(0, timeLength - (1 / samplingRate),
int(samplingRate * timeLength))
sin = Arms * (2**(1 / 2)) * np.sin(2 * np.pi * freq * t + phase)
sinSigObj = SignalObj(sin,
domain='time',
samplingRate=samplingRate,
freqMin=default.freqMin,
freqMax=default.freqMax)
sinSigObj.creation_name = creation_name
return sinSigObj
def random_noise(kind='white',
samplingRate=None,
fftDegree=None,
startMargin=None,
stopMargin=None,
windowing='hann'):
"""
Generates a noise of kind White, Pink (TO DO) or Blue (TO DO), with a
silence at the beginning and ending of the signal, plus a fade in to avoid
abrupt speaker excursion. All noises have normalized amplitude.
White noise is generated using numpy.randn between [[1];[-1]];
Pink noise is still in progress;
Blue noise is still in progress;
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
# It was done like this because a function default argument is a value
# assigned at import time, and PyTTa have a default object that handles
# default values for all functions and all classes across all submodules.
# In order to it work as expected, the values should be reassigned at
# every function call to get updated default values. Otherwise, despite
# how the default has it's properties values changed, it won't change
# for the function calls.
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
if startMargin is None:
startMargin = default.startMargin
if stopMargin is None:
stopMargin = default.stopMargin
# [samples] initial silence number of samples
stopSamples = round(stopMargin * samplingRate)
# [samples] ending silence number of samples
startSamples = round(startMargin * samplingRate)
# [samples] total silence number of samples
marginSamples = startSamples + stopSamples
# [samples] full signal number of samples
numSamples = 2**fftDegree
# [samples] Actual noise number of samples
noiseSamples = int(numSamples - marginSamples)
if kind.upper() in ['WHITE', 'FLAT']:
noiseSignal = np.random.randn(noiseSamples)
# elif kind.upper() == 'PINK: # TODO
# noiseSignal = np.randn(Nnoise)
# noiseSignal = noiseSignal/max(abs(noiseSignal))
# noiseSignal = __do_pink_filtering(noiseSignal)
# elif kind.upper() == 'BLUE: # TODO
# noiseSignal = np.randn(Nnoise)
# noiseSignal = noiseSignal/max(abs(noiseSignal))
# noiseSignal = __do_blue_filtering(noiseSignal)
noiseSignal = __do_noise_windowing(noiseSignal, noiseSamples, windowing)
noiseSignal = noiseSignal / max(abs(noiseSignal))
noiseSignal = np.concatenate(
(np.zeros(int(startSamples)), noiseSignal, np.zeros(int(stopSamples))))
noiseSignal = SignalObj(signalArray=noiseSignal,
domain='time',
freqMin=default.freqMin,
freqMax=default.freqMax,
samplingRate=samplingRate)
noiseSignal.creation_name = creation_name
return noiseSignal
def sweep(freqMin=None,
freqMax=None,
samplingRate=None,
fftDegree=None,
startMargin=None,
stopMargin=None,
method='logarithmic',
windowing='hann'):
"""
Generates a chirp signal defined by the "method" input, windowed, with
silence interval at the beggining and end of the signal, plus a hanning
fade in and fade out.
>>> x = pytta.generate.sweep()
>>> x.plot_time()
Return a signalObj containing a logarithmic chirp signal from 17.8 Hz
to 22050 Hz, with a fade in beginning at 17.8 Hz time instant and ending at
the 20 Hz time instant; plus a fade out beginning at 20000 Hz time instant
and ending at 22050 Hz time instant.
The fade in and the fade out are made with half hanning window. First half
for the fade in and last half for the fade out. Different number of points
are used for each fade, so the number of time samples during each frequency
is respected.
Input arguments (default), (type):
------------------------
* freqMin (20), (float)
* freqMax (20), (float)
* samplingRate (44100), (int)
* fftDegree (18), (float)
* startMargin (0.3), (float)
* stopMargin (0.7), (float)
* method (logarithmic'), (string)
* windowing ('hann'), (string)
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
# It was done like this because a function default argument is a value
# assigned at import time, and PyTTa have a default object that handles
# default values for all functions and all classes across all submodules.
# In order to it work as expected, the values should be reassigned at
# every function call to get updated default values. Otherwise, despite
# how the default has it's properties values changed, it won't change
# for the function calls.
if freqMin is None:
freqMin = default.freqMin
if freqMax is None:
freqMax = default.freqMax
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
if startMargin is None:
startMargin = default.startMargin
if stopMargin is None:
stopMargin = default.stopMargin
# frequency limits [Hz]
freqLimits = {
'freqMin': freqMin / (2**(1 / 6)),
'freqMax': min(freqMax * (2**(1 / 6)), samplingRate / 2)
}
samplingTime = 1 / samplingRate # [s] sampling period
stopSamples = stopMargin * samplingRate
# [samples] initial silence number of samples
startSamples = startMargin * samplingRate
# [samples] ending silence number of samples
marginSamples = startSamples + stopSamples
# [samples] total silence number of samples
numSamples = 2**fftDegree # [samples] full signal number of samples
sweepSamples = numSamples - marginSamples + 1
# [samples] actual sweep number of samples
if sweepSamples < samplingRate / 10:
raise Exception('Too small resultant sweep. For such big margins you' +
' must increase your fftDegree.')
sweepTime = sweepSamples / samplingRate # [s] sweep's time length
timeVecSweep = np.arange(0, sweepTime, samplingTime) # [s] time vector
if timeVecSweep.size > sweepSamples:
timeVecSweep = timeVecSweep[0:int(sweepSamples)] # adjust length
sweep = 0.95 * ss.chirp(timeVecSweep,
freqLimits['freqMin'],
sweepTime,
freqLimits['freqMax'],
'logarithmic',
phi=-90) # sweep, time domain
sweep = __do_sweep_windowing(sweep, timeVecSweep, freqLimits, freqMin,
freqMax, windowing) # fade in and fade out
# add initial and ending slices
timeSignal = np.concatenate(
(np.zeros(int(startSamples)), sweep, np.zeros(int(stopSamples))))
if timeSignal.size != numSamples:
timeSignal = timeSignal[0:int(numSamples)] # adjust length
# transforms into a pytta signalObj and sets the correct name
sweepSignal = SignalObj(signalArray=timeSignal,
domain='time',
samplingRate=samplingRate,
**freqLimits)
sweepSignal.creation_name = creation_name
return sweepSignal
def noise(kind='white',
samplingRate=None,
fftDegree=None,
startMargin=None,
stopMargin=None,
windowing='hann'):
"""
Generates a noise of kind White, Pink (TO DO) or Blue (TO DO), with a
silence at the beginning and ending of the signal, plus a fade in to avoid
abrupt speaker excursioning. All noises have normalized amplitude.
White noise is generated using numpy.randn between [[1];[-1]];
# FIXME: This looks incorrect because the signal has normal
# distribution, so no limits but an average and standard deviation.
Pink noise is still in progress;
Blue noise is still in progress;
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames is ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
if startMargin is None:
startMargin = default.startMargin
if stopMargin is None:
stopMargin = default.stopMargin
# [samples] initial silence number of samples
stopSamples = round(stopMargin * samplingRate)
# [samples] ending silence number of samples
startSamples = round(startMargin * samplingRate)
# [samples] total silence number of samples
marginSamples = startSamples + stopSamples
# [samples] full signal number of samples
numSamples = 2**fftDegree
# [samples] Actual noise number of samples
noiseSamples = int(numSamples - marginSamples)
if kind.upper() in ['WHITE', 'FLAT']:
noiseSignal = np.random.randn(noiseSamples)
# elif kind.upper() == 'PINK: # TODO
# noiseSignal = np.randn(Nnoise)
# noiseSignal = noiseSignal/max(abs(noiseSignal))
# noiseSignal = __do_pink_filtering(noiseSignal)
# elif kind.upper() == 'BLUE: # TODO
# noiseSignal = np.randn(Nnoise)
# noiseSignal = noiseSignal/max(abs(noiseSignal))
# noiseSignal = __do_blue_filtering(noiseSignal)
noiseSignal = __do_noise_windowing(noiseSignal, noiseSamples, windowing)
noiseSignal = noiseSignal / max(abs(noiseSignal))
noiseSignal = np.concatenate(
(np.zeros(int(startSamples)), noiseSignal, np.zeros(int(stopSamples))))
noiseSignal = SignalObj(signalArray=noiseSignal,
domain='time',
freqMin=default.freqMin,
freqMax=default.freqMax,
samplingRate=samplingRate)
noiseSignal.creation_name = creation_name
return noiseSignal
def __parse_load(className):
name = className.split('.')[0]
jsonFile = open(className, 'r')
openJson = json.load(jsonFile)
if name == 'SignalObj':
openMat = sio.loadmat(openJson['timeSignalAddress'])
out = SignalObj(openMat['timeSignal'],
domain=openJson['lengthDomain'],
samplingRate=openJson['samplingRate'],
freqMin=openJson['freqLims'][0],
freqMax=openJson['freqLims'][1],
comment=openJson['comment'])
out.channels = __parse_channels(openJson['channels'], out.channels)
os.remove(openJson['timeSignalAddress'])
elif name == 'ImpulsiveResponse':
ir = pytta_load(openJson['SignalAddress']['ir'])
out = ImpulsiveResponse(ir=ir, **openJson['methodInfo'])
os.remove(openJson['SignalAddress']['ir'])
elif name == 'RecMeasure':
inch = list(np.arange(len(openJson['inChannels'])))
out = RecMeasure(device=openJson['device'],
inChannels=inch,
lengthDomain='samples',
fftDegree=openJson['fftDegree'])
out.inChannels = __parse_channels(openJson['inChannels'],
out.inChannels)
elif name == 'PlayRecMeasure':
inch = list(1 + np.arange(len(openJson['inChannels'])))
excit = pytta_load(openJson['excitationAddress'])
out = PlayRecMeasure(excitation=excit,
device=openJson['device'],
inChannels=inch)
out.inChannels = __parse_channels(openJson['inChannels'],
out.inChannels)
os.remove(openJson['excitationAddress'])
elif name == 'FRFMeasure':
inch = list(1 + np.arange(len(openJson['inChannels'])))
excit = pytta_load(openJson['excitationAddress'])
out = FRFMeasure(excitation=excit,
device=openJson['device'],
inChannels=inch)
out.inChannels = __parse_channels(openJson['inChannels'],
out.inChannels)
os.remove(openJson['excitationAddress'])
elif name == 'Meta':
out = []
for val in openJson.values():
out.append(pytta_load(val))
os.remove(val)
os.remove(className)
jsonFile.close()
return out
def __h5_unpack(objH5Group):
"""
Unpack an HDF5 group into its respective PyTTa object
"""
if objH5Group.attrs['class'] == 'SignalObj':
# PyTTaObj attrs unpacking
samplingRate = objH5Group.attrs['samplingRate']
freqMin = _h5.none_parser(objH5Group.attrs['freqMin'])
freqMax = _h5.none_parser(objH5Group.attrs['freqMax'])
lengthDomain = objH5Group.attrs['lengthDomain']
comment = objH5Group.attrs['comment']
# SignalObj attr unpacking
channels = eval(objH5Group.attrs['channels'])
# Added with an if for compatibilitie issues
if 'signalType' in objH5Group.attrs:
signalType = _h5.attr_parser(objH5Group.attrs['signalType'])
else:
signalType = 'power'
# Creating and conforming SignalObj
SigObj = SignalObj(signalArray=np.array(objH5Group['timeSignal']),
domain='time',
signalType=signalType,
samplingRate=samplingRate,
freqMin=freqMin,
freqMax=freqMax,
comment=comment)
SigObj.channels = channels
SigObj.lengthDomain = lengthDomain
return SigObj
elif objH5Group.attrs['class'] == 'ImpulsiveResponse':
systemSignal = __h5_unpack(objH5Group['systemSignal'])
method = objH5Group.attrs['method']
winType = objH5Group.attrs['winType']
winSize = objH5Group.attrs['winSize']
overlap = objH5Group.attrs['overlap']
IR = ImpulsiveResponse(method=method,
winType=winType,
winSize=winSize,
overlap=overlap,
ir=systemSignal)
return IR
elif objH5Group.attrs['class'] == 'RecMeasure':
# PyTTaObj attrs unpacking
samplingRate = objH5Group.attrs['samplingRate']
freqMin = _h5.none_parser(objH5Group.attrs['freqMin'])
freqMax = _h5.none_parser(objH5Group.attrs['freqMax'])
comment = objH5Group.attrs['comment']
lengthDomain = objH5Group.attrs['lengthDomain']
fftDegree = objH5Group.attrs['fftDegree']
timeLength = objH5Group.attrs['timeLength']
# Measurement attrs unpacking
device = _h5.list_w_int_parser(objH5Group.attrs['device'])
inChannels = eval(objH5Group.attrs['inChannels'])
blocking = objH5Group.attrs['blocking']
# Recreating the object
rObj = measurement(kind='rec',
device=device,
inChannels=inChannels,
blocking=blocking,
samplingRate=samplingRate,
freqMin=freqMin,
freqMax=freqMax,
comment=comment,
lengthDomain=lengthDomain,
fftDegree=fftDegree,
timeLength=timeLength)
return rObj
elif objH5Group.attrs['class'] == 'PlayRecMeasure':
# PyTTaObj attrs unpacking
samplingRate = objH5Group.attrs['samplingRate']
freqMin = _h5.none_parser(objH5Group.attrs['freqMin'])
freqMax = _h5.none_parser(objH5Group.attrs['freqMax'])
comment = objH5Group.attrs['comment']
lengthDomain = objH5Group.attrs['lengthDomain']
fftDegree = objH5Group.attrs['fftDegree']
timeLength = objH5Group.attrs['timeLength']
# Measurement attrs unpacking
device = _h5.list_w_int_parser(objH5Group.attrs['device'])
inChannels = eval(objH5Group.attrs['inChannels'])
outChannels = eval(objH5Group.attrs['outChannels'])
blocking = objH5Group.attrs['blocking']
# PlayRecMeasure attrs unpacking
excitation = __h5_unpack(objH5Group['excitation'])
outputAmplification = objH5Group.attrs['outputAmplification']
# Recreating the object
prObj = measurement(kind='playrec',
excitation=excitation,
outputAmplification=outputAmplification,
device=device,
inChannels=inChannels,
outChannels=outChannels,
blocking=blocking,
samplingRate=samplingRate,
freqMin=freqMin,
freqMax=freqMax,
comment=comment)
return prObj
elif objH5Group.attrs['class'] == 'FRFMeasure':
# PyTTaObj attrs unpacking
samplingRate = objH5Group.attrs['samplingRate']
freqMin = _h5.none_parser(objH5Group.attrs['freqMin'])
freqMax = _h5.none_parser(objH5Group.attrs['freqMax'])
comment = objH5Group.attrs['comment']
lengthDomain = objH5Group.attrs['lengthDomain']
fftDegree = objH5Group.attrs['fftDegree']
timeLength = objH5Group.attrs['timeLength']
# Measurement attrs unpacking
device = _h5.list_w_int_parser(objH5Group.attrs['device'])
inChannels = eval(objH5Group.attrs['inChannels'])
outChannels = eval(objH5Group.attrs['outChannels'])
blocking = objH5Group.attrs['blocking']
# PlayRecMeasure attrs unpacking
excitation = __h5_unpack(objH5Group['excitation'])
outputAmplification = objH5Group.attrs['outputAmplification']
# FRFMeasure attrs unpacking
method = _h5.none_parser(objH5Group.attrs['method'])
winType = _h5.none_parser(objH5Group.attrs['winType'])
winSize = _h5.none_parser(objH5Group.attrs['winSize'])
overlap = _h5.none_parser(objH5Group.attrs['overlap'])
# Recreating the object
frfObj = measurement(kind='frf',
method=method,
winType=winType,
winSize=winSize,
overlap=overlap,
excitation=excitation,
outputAmplification=outputAmplification,
device=device,
inChannels=inChannels,
outChannels=outChannels,
blocking=blocking,
samplingRate=samplingRate,
freqMin=freqMin,
freqMax=freqMax,
comment=comment)
return frfObj
elif objH5Group.attrs['class'] == 'Analysis':
# Analysis attrs unpacking
anType = _h5.attr_parser(objH5Group.attrs['anType'])
nthOct = _h5.attr_parser(objH5Group.attrs['nthOct'])
minBand = _h5.attr_parser(objH5Group.attrs['minBand'])
maxBand = _h5.attr_parser(objH5Group.attrs['maxBand'])
comment = _h5.attr_parser(objH5Group.attrs['comment'])
title = _h5.attr_parser(objH5Group.attrs['title'])
dataLabel = _h5.attr_parser(objH5Group.attrs['dataLabel'])
errorLabel = _h5.attr_parser(objH5Group.attrs['errorLabel'])
xLabel = _h5.attr_parser(objH5Group.attrs['xLabel'])
yLabel = _h5.attr_parser(objH5Group.attrs['yLabel'])
# Analysis data unpacking
data = np.array(objH5Group['data'])
# If error in save moment was None no group was created for it
if 'error' in objH5Group:
error = np.array(objH5Group['error'])
else:
error = None
# Recreating the object
anObject = Analysis(anType=anType,
nthOct=nthOct,
minBand=minBand,
maxBand=maxBand,
data=data,
dataLabel=dataLabel,
error=error,
errorLabel=errorLabel,
comment=comment,
xLabel=xLabel,
yLabel=yLabel,
title=title)
return anObject
elif objH5Group.attrs['class'] == 'dict':
dictObj = {}
for PyTTaObjName, PyTTaObjH5Group in objH5Group.items():
dictObj[PyTTaObjName] = __h5_unpack(PyTTaObjH5Group)
return dictObj
elif objH5Group.attrs['class'] == 'list':
dictObj = {}
for idx, PyTTaObjH5Group in objH5Group.items():
dictObj[int(idx)] = __h5_unpack(PyTTaObjH5Group)
idxs = [int(item) for item in list(dictObj.keys())]
maxIdx = max(idxs)
listObj = []
for idx in range(maxIdx + 1):
listObj.append(dictObj[idx])
return listObj
else:
raise NotImplementedError
def colored_noise(color: str or int = 'white',
samplingRate: int = None,
fftDegree: int = None,
numChannels: int = None,
startMargin: float = None,
stopMargin: float = None,
windowing: str = 'hann'):
"""
Power law noise generator.
Based on the algorithm in:
Timmer, J. and Koenig, M.:
On generating power law noise.
Astron. Astrophys. 300, 707-710 (1995)
Generate random noise with respect to the `(1/f)**B` rate. `f` stands for
frequency and `B` is an integer power.
The colors and its spectrum characteristics:
* Purple | Differentiated:
* +6.02 dB/octave | +20 dB/decade | B = -2;
* color: 'purple', 'diff', 'differentiated';
* Blue | Azure:
* +3.01 dB/octave | +10 dB/decade | B = -1;
* color: 'blue', 'azure'
* White | Flat:
* +0.00 dB/octave | +0 dB/decade | B = 0;
* color: 'white', 'flat';
* Pink | Flicker:
* -3.01 dB/octave | -10 dB/decade | B = 1;
* color: 'pink', 'flicker', '1/f';
* Red | Brownian:
* -6.02 dB/octave | -20 dB/decade | B = 2;
* color: 'red', 'brown', 'brownian';
The output signal will have `startMargin` silence at the beginning of the
waveform, and `stopMargin` silence at the end.
There is a fade-in between the starting silence and the noise itself that
occurs during 5% of the total noise duration.
@author: Chum4k3r
"""
# Code snippet to guarantee that generated object name is
# the declared at global scope
# for frame, line in traceback.walk_stack(None):
for framenline in traceback.walk_stack(None):
# varnames = frame.f_code.co_varnames
varnames = framenline[0].f_code.co_varnames
if varnames == ():
break
# creation_file, creation_line, creation_function, \
# creation_text = \
extracted_text = \
traceback.extract_stack(framenline[0], 1)[0]
# traceback.extract_stack(frame, 1)[0]
# creation_name = creation_text.split("=")[0].strip()
creation_name = extracted_text[3].split("=")[0].strip()
# It was done like this because a function default argument is a value
# assigned at import time, and PyTTa have a default object that handles
# default values for all functions and all classes across all submodules.
# In order to it work as expected, the values should be reassigned at
# every function call to get updated default values. Otherwise, despite
# how the default has it's properties values changed, it won't change
# for the function calls.
if samplingRate is None:
samplingRate = default.samplingRate
if fftDegree is None:
fftDegree = default.fftDegree
if numChannels is None:
numChannels = len(default.outChannel)
if startMargin is None:
startMargin = default.startMargin
if stopMargin is None:
stopMargin = default.stopMargin
# [samples] initial silence number of samples
stopSamples = round(stopMargin * samplingRate)
# [samples] ending silence number of samples
startSamples = round(startMargin * samplingRate)
# [samples] total silence number of samples
marginSamples = startSamples + stopSamples
# [samples] full signal number of samples
numSamples = 2**fftDegree
# [samples] Actual noise number of samples
noiseSamples = int(numSamples - marginSamples)
if type(color) == int:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, color,
samplingRate)
elif type(color) == str:
if color.upper() in ['PURPLE', 'DIFF', 'DIFFERENTIATED']:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, -2,
samplingRate)
elif color.upper() in ['BLUE', 'AZURE']:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, -1,
samplingRate)
elif color.upper() in ['WHITE', 'FLAT']:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, 0,
samplingRate)
elif color.upper() in ['PINK', 'FLICKER', '1/F']:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, 1,
samplingRate)
elif color.upper() in ['RED', 'BROWN', 'BROWNIAN']:
noiseSignal = _powerlaw_noise(noiseSamples, numChannels, 2,
samplingRate)
else:
raise ValueError("Unknow noise color.")
else:
raise TypeError("`color` must be int or str.")
noiseSignal = __do_noise_windowing(noiseSignal, noiseSamples, windowing)
# noiseSignal = noiseSignal / max(abs(noiseSignal))
noiseSignal = np.concatenate((np.zeros(
(int(startSamples), numChannels)), noiseSignal,
np.zeros((int(stopSamples), numChannels))))
noiseSignal = SignalObj(signalArray=noiseSignal,
domain='time',
freqMin=default.freqMin,
freqMax=default.freqMax,
samplingRate=samplingRate)
noiseSignal.creation_name = creation_name
return noiseSignal
