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 __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 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 __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