def __init__(self, _variables):
''' Loading output variables of the model, their values will be stored in h5 and plotted
argv[0]: file with variable names from the model
'''
self.outputs= OutputModelVar(_variables)
self.outputs.load_varList()
print self.outputs.get_varList()
self.measurements= []
class Validation():
''' class for validation, things to do
TODO: pass the data read to the era method (both simulation and measurement)
TODO: save the result in the same .h5 file of the simulation
'''
def __init__(self, _variables):
''' Loading output variables of the model, their values will be stored in h5 and plotted
argv[0]: file with variable names from the model
'''
self.outputs= OutputModelVar(_variables)
self.outputs.load_varList()
print self.outputs.get_varList()
self.measurements= []
def load_sourcesCSV(self, _sourceCSV, _component, _signalComplex):
'''
_sourceCSV: .csv file, i.e. ./res/File_8.csv
_model: name of the model , for retrieving the h5.group
_component: name of the component, for retrieving the h5.dataset
_signalComplex: array with pair name of signals [meas, angle] that refer to a complex signal
'''
if (_sourceCSV != ''):
self.iocsv= InputCSVStream(_sourceCSV, ',')
''' select the signals according to variables '''
''' name is the representation of the measurement
meas signals/variables that name the signal of a measurement
i.e: name KTHLAB:EMLAB; meas KTHLAB:EMLAB:Magnitude,KTHLAB:EMLAB:Angle
i.e: name bus1.V; meas bus1.v,bus1.angle '''
self.iocsv.load_csvValues(_component, _signalComplex[0], _signalComplex[1])
print 'PMU Signal ', self.iocsv.get_senyal(_component).__str__()
def load_sourcesH5(self, _sourceH5, _model, _component):
'''
_sourceH5: .h5 file, i.e. './res/PMUdata_Bus1VA2VALoad9PQ.h5'
_model: name of the model , for retrieving the h5.group
_component: name of the component, for retrieving the h5.dataset
'''
if (_sourceH5 != ''):
self.ioh5= InputH5Stream(_sourceH5)
self.ioh5.open_h5()
self.ioh5.load_h5(_model, _component)
print 'Simulation Signal ', self.ioh5.get_senyal(_component).__str__()
def load_pandaSource(self, _sourceCSV, _sourceH5, _modelName, _component, _variable):
'''
_sourceCSV: something like './res/PMUdata_Bus1VA2VALoad9PQ.csv'
_sourceH5: something like 'PMUdata_Bus1VA2Venam1.h5'
'''
csvData = pd.read_csv(_sourceCSV,sep=",",usecols=(1,2,3,4,5,6))
csvData.to_hdf(_sourceH5,'df', complib='zlib', complevel=9)
self.ioh5.open_exth5(_sourceH5)
self.ioh5.open_load_h5(_modelName, _component, _variable)
# return self.iocsv.get_senyal('KTHLAB:EMLAB'), self.ioh5.get_senyal('block0')
# return (csvData, self.ioh5.get_senyal('block0'))
def get_sources(self, _measurement, _component):
''' _measurement> 'KTHLAB:EMLAB'
_component> 'block0'
'''
return [self.iocsv.get_senyal(_measurement), self.ioh5.get_senyal(_component)]
def method_ME(self, _measSignal, _simSignal, _order):
''' TODO: create a subset of signals from original signal in object senyal
call mode estimation method with each subset, inside a loop '''
meEngine= ModeEstimation()
meEngine.set_order(_order)
''' 1) mode Estimation with PMU signal '''
if _measSignal!= None:
meEngine.modeEstimationMat('C:/Users/fragom/PhD_CIM/PYTHON/ScriptMAE/lib/mes.jar',_measSignal)
''' 2) mode Estimation with simulation signal '''
if _simSignal!= None:
meEngine.modeEstimationPY(_simSignal.get_signalReal())
''' TODO: pass the whole signal to Vedran mode estimation '''
# print 'Model Frequency ', meEngine.get_modeFrequency()
# print 'Model Damping ', meEngine.get_modeDamping()
def method_ERA(self, _measSignal, _simSignal):
'''
_measSignal as output
_simSignal as input
'''
'''TODO: match sampletime from meas with sim '''
# if (_measSignal.get_sampleTime()!= _simSignal.get_sampleTime()):
timeSignal= _measSignal.get_sampleTime()
if _measSignal!= None and _simSignal!= None:
outSignal= _measSignal.get_signalMag()
inSignal= _simSignal.get_signalReal()
else:
if _measSignal!= None:
outSignal= _measSignal.get_signalMag()
inSignal= _measSignal.get_signalMag()
if _simSignal!= None:
outSignal= _simSignal.get_signalReal()
inSignal= _simSignal.get_signalReal()
num_states = 2
# a,b,c = mr.compute_ERA_model([timeSignal,outSignal,inSignal], num_states)
a,b,c = mr.compute_ERA_model(numpy.array(outSignal[0:1000]), num_states)
print 'Measurements: '
print 'A= ', a
print 'B= ', b
print 'C= ', c
a,b,c = mr.compute_ERA_model(numpy.array(inSignal), num_states)
print 'Simulation: '
print 'A= ', a
print 'B= ', b
print 'C= ', c