def computeBtnEvent(self):
# load selected signals from selected files
self.signals = []
self.outputBaseName = None
ismat = False
dic = self.ui.methodWidget.getArgumentsAsDictionary()
if not(dic.has_key("ignoreInputSignals") and dic["ignoreInputSignals"]):
for f in self.filesSelected:
if f.endswith('.mat'):
matfile = loadmat(f)
ismat = True
for s in self.signalsSelected:
if not ismat:
if not self.filesHasHeaders:
self.signals.append(ExtractSignalFromCSV(str(f), separator=self.columnSeparator, unit='ms', columns=s, header=False, headerValues=self.signalsHeader))
else:
self.signals.append(ExtractSignalFromCSV(str(f), separator=self.columnSeparator, unit='ms', columns=[str(self.headerMap[s])]))
else:
ci = self.signalsHeader.index(s)
cn = str(self.signalsHeader[ci])
self.signals.append(ExtractSignalFromMAT(str(f), columns_index=ci, columns_wanted_names=[cn], matfile=matfile))
self.ui.methodWidget.compute(self.signals, self.getOutputBasename())
self.ui.stopPushButton.setEnabled(True)
self.ui.startPushButton.setEnabled(False)
self.ui.toolBox.setEnabled(False)
self.showStatus("Computing...")
def loadSignal(self, file, name):
signal = None
ismat = False
if file.endswith('.mat'):
matfile = loadmat(file)
ismat = True
if not ismat:
if not self.filesHasHeaders:
signal = ExtractSignalFromCSV(str(file),
separator=self.columnSeparator,
unit='ms',
columns=name,
header=False,
headerValues=self.signalsHeader)
else:
signal = ExtractSignalFromCSV(
str(file),
separator=self.columnSeparator,
unit='ms',
columns=[str(self.headerMap[name])])
else:
ci = self.signalsHeader.index(name)
cn = str(self.signalsHeader[ci])
signal = ExtractSignalFromMAT(str(file),
columns_index=ci,
columns_wanted_names=[cn],
matfile=matfile)
return signal
def exportSelectedSignals(self):
table = self.ui.inputSignalsWidget
selectedSignalsHeader = []
# give filename
self.getSelectedFiles()
firstTime = True
fileNumber = 1
for index in range(table.rowCount()):
item = table.item(index, 0)
ismat = False
for f in self.getSelectedFiles():
if f.endswith('.mat'):
matfile = loadmat(f)
ismat = True
if item.checkState() == QtCore.Qt.Checked:
s = str(item.text())
if not ismat:
if not self.filesHasHeaders:
signal = ExtractSignalFromCSV(str(f), separator=self.columnSeparator, unit='ms', columns=s, header=False,
headerValues=self.signalsHeader)
else:
signal = ExtractSignalFromCSV(str(f), separator=self.columnSeparator, unit='ms', columns=[str(self.headerMap[s])])
else:
ci = self.signalsHeader.index(s)
cn = str(self.signalsHeader[ci])
signal = ExtractSignalFromMAT(str(f), columns_index=ci, columns_wanted_names=[cn], matfile=matfile)
if firstTime:
seletedSignals = pd.DataFrame(index=signal.index)
firstTime = False
#seletedSignals.insert(col,s,signal)
#col += 1
seletedSignals[s+str(fileNumber)] = signal
fileNumber += 1
fileName = ''
fileName = QtGui.QFileDialog.getSaveFileName(self)
if not fileName:
print "Error export signals: No filename provided"
return
else:
if not fileName.endsWith('.csv'):
fileName = fileName+'.csv'
seletedSignals.to_csv(path_or_buf=fileName,header=True,sep=';')
def computeBtnEvent(self):
# load selected signals from selected files
self.signals = []
ismat = False
for f in self.filesSelected:
if f.endswith('.mat'):
matfile = loadmat(f)
ismat = True
for s in self.signalsSelected:
if not ismat:
if not self.filesHasHeaders:
self.signals.append(
ExtractSignalFromCSV(
str(f),
separator=self.columnSeparator,
unit='ms',
columns=s,
header=False,
headerValues=self.signalsHeader))
else:
self.signals.append(
ExtractSignalFromCSV(
str(f),
separator=self.columnSeparator,
unit='ms',
columns=[str(self.headerMap[s])]))
else:
ci = self.signalsHeader.index(s)
cn = str(self.signalsHeader[ci])
self.signals.append(
ExtractSignalFromMAT(str(f),
columns_index=ci,
columns_wanted_names=[cn],
matfile=matfile))
self.ui.methodWidget.compute(self.signals)
self.ui.stopPushButton.setEnabled(True)
self.ui.startPushButton.setEnabled(False)
self.ui.toolBox.setEnabled(False)
self.showStatus("Computing...")
""" Define signals in pd.dataFrame format """
# preparing the input signals
N = 20 # number of samples
f = 1.0 # sinewave frequency (Hz)
Fs = 200 # sampling frequency (Hz)
n = np.arange(0,N) # number of samples
# input signals
x = pd.DataFrame({'X':np.sin(2*3.14*f*n/Fs)})
y = pd.DataFrame({'Y':np.sin(2*3.14*2*f*n/Fs)})
'''
"""OR"""
""" Import signals from a .csv file """
filename = 'data_examples/2Persons_Monovariate_Continuous_data_2.csv'
filename = 'data_examples/2PersonsMonoContData2Rsp500ms.csv'
x = ExtractSignalFromCSV(filename, columns = ['x'], unit = 's')
y = ExtractSignalFromCSV(filename, columns = ['y'], unit = 's')
# Resample and Interpolate data to have constant frequency
#x = ResampleAndInterpolate(x, rule='500ms', limit=5)
#y = ResampleAndInterpolate(y, rule='500ms', limit=5)
#filenameOut = 'data_examples/2PersonsMonoContData2Rsp500ms.csv'
#out = pd.DataFrame({'x': x.iloc[:,0], 'y': y.iloc[:,0]}).reset_index()
#out.drop('Time (s)', 1, inplace=True)
#out.to_csv(filenameOut,index_label =['Time','x','y'])
'''
"""OR"""
""" Import signals from a .mat file """
from Methods.utils.ExtractSignal import ExtractSignalFromCSV
'''
""" Define signals in pd.dataFrame format """
# preparing the input time series
N = 20 # number of samples
f = 1.0 # sinewave frequency (Hz)
Fs = 200 # sampling frequency (Hz)
n = np.arange(0,N) # number of samples
# input time series
x = pd.DataFrame({'X':np.sin(2*3.14*f*n/Fs)})
y = pd.DataFrame({'Y':np.sin(2*3.14*2*f*n/Fs)})
'''
"""OR"""
""" Import signal from a .csv file """
filename = 'data_examples/2Persons_Multivariate_Continous_data.csv'
x = ExtractSignalFromCSV(filename, columns=['Upper body mq'])
y = ExtractSignalFromCSV(filename, columns=['Upper body mq.1'])
z = ExtractSignalFromCSV(filename, columns=['Left Hand mq'])
a = ExtractSignalFromCSV(filename, columns=['Left Hand mq.1'])
'''
"""OR"""
""" Import signal from a .mat file """
filename = 'data_examples/data_example_MAT.mat'
x = ExtractSignalFromMAT(filename, columns_index =[0,2], columns_wanted_names=['Time', 'GlobalBodyActivity0'])
y = ExtractSignalFromMAT(filename, columns_index =[10], columns_wanted_names=['GlobalBodyActivity1'])
'''
signals = [x, y, z, a]
N = signals[0].shape[0]
n = np.arange(0, N)
'''
""" Define signals in pd.dataFrame format """
# preparing the input time series
N = 1000 # number of samples
f = 1.0 # sinewave frequency (Hz)
Fs = 200 # sampling frequency (Hz)
n = np.arange(0,N) # number of samples
# input time series
x = pd.DataFrame({'X':np.sin(2*3.14*f*n/Fs)}, np.arange(0,N) )
y = pd.DataFrame(2.0*np.random.rand(N,1),np.arange(0,N))
'''
"""OR"""
""" Import signal from a .csv file """
filename = 'data_examples/2Persons_Multivariate_Continous_data.csv'
x = ExtractSignalFromCSV(filename, columns=['Upper body mq'])
y = ExtractSignalFromCSV(filename, columns=['Upper body mq.1'])
'''
"""OR"""
""" Import signal from a .mat file """
filename = 'data_examples/data_example_MAT.mat'
z = ExtractSignalFromMAT(filename, columns_index =[0,2], columns_wanted_names=['Time', 'GlobalBodyActivity0'])
t = ExtractSignalFromMAT(filename, columns_index =[10], columns_wanted_names=['GlobalBodyActivity1'])
'''
signals = [x, y]
N = signals[0].shape[0]
n = np.arange(0, N)
""" Plot input signals """