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